The
Great
Plains
Sociologist
The Official Journal of the Great Plains Sociological Association
Volume 21, 2010
Great Plains Sociologist
Editors
Daphne E. Pedersen, University of North Dakota
Laura Colmenero-Chilberg, Black Hills State University
The Great Plains Sociological Association publishes the Great Plains Sociologist (GPS) as a
general sociological journal. We endeavor to publish articles of general interest to sociologists
in the region and beyond. The organization primarily seeks to serve sociologists from the Great
Plains; however, that does not limit material published in the journal by author or subject.
The following guidelines are offered to authors who wish to submit work to the GPS for
publication.
1. Topics should be of interest to a wide audience of sociologists. This should not be read as
implying that a majority must agree with the findings or discussions. Popularity of findings
plays absolutely no role in the decision to publish. The decision to publish is based on quality of
work.
2. Manuscripts should be in the range of 15 to 25 journal pages, including tables, charts, etc.
3. GPS will consider many types of manuscripts for publication. We publish traditional research
articles that empirically test hypotheses derived from social theory, thought pieces explicating
ideas or investigating specific topics, and articles that focus on teaching techniques or
experiences to mention but a few.
4. Articles submitted for review should be saved as Word documents and emailed to the editor.
Upon acceptance, full contact information for each contributing author, including a brief
biographical sketch, must be submitted.
Contact information for submission of articles:
Daphne E. Pedersen
[email protected]
(701) 777-4247
5. Manuscripts submitted for publication are emailed to the journal editor and then sent to at
least two reviewers for comment and recommendation. The journal editor also reads
submissions and has responsibility for the final decision to publish or not. The editor and
reviewers may request that a manuscript be rejected, or rewritten and resubmitted.
6. To preserve anonymity, please attach a cover page to the manuscript that contains
authorship, address, and institutional affiliation. The next page should contain the title of the
article only. The authorship page will be removed and retained by the editor to assure
anonymity. Please omit author citations in the reference page and text.
7. Manuscript format should follow American Sociological Review (ASA) standards, including intext and bibliographic references.
2
About the Authors
Donald E. Arwood
is Professor of Sociology at South Dakota State University.
The main focus of Professor Arwood's research has been
the elaboration of the structural symbolic interactionist
perspective, particularly as it relates to the self as both a
social product and a social force.
W. Trevor Brooks
is a first-year professor at Coastal Carolina Community
College. His scholarly areas of interest are demography,
the family, and statistics.
Pamela Carriveau
is an associate professor of political science at Black Hills
State University. Her current research focuses on social
policy issues ranging from abortion policy at the state level
to education policy in an international context.
Laura Colmenero-Chilberg
is an associate professor of sociology at Black Hills State
University. Her current research interests include
investigations into the changing definitions of self related
to attitudes towards abortion and images of gender in
popular culture, more specifically popular genre fiction.
Matti Grotte
is a 2010 graduate of the University of North Dakota,
where she majored in Sociology and minored in
Psychology. She is considering graduate school to become
a
high
school
guidance
counselor.
Brittney Hansen
earned her undergraduate degree in Biology from the
University of North Dakota in 2010. She also completed a
minor in Sociology, having an interest in medical sociology.
Krista Lynn Minnotte
is an Assistant Professor of Sociology at the University of
North Dakota. Her research program centers on workfamily issues along with gender and workplace
organizations.
Joshua J. Turner
is a research associate at the National Strategic Planning
and Analysis Research Center at Mississippi State
University. His research interests include demography,
labor market trends, and survey design.
3
Index of Articles
Page Number
Title
Author(s)
5
Structural Conditions and Migration in the
Dakotas
Joshua J. Turner
W. Trevor Brooks
Donald E. Arwood
28
Understanding Workday Housework Participation:
Testing Three Theories
Krista Lynn Minnotte
Matti Grotte
51
Voters Reframe the Abortion Policy Debate: A
Theoretical Analysis of Abortion Attitudes in
South Dakota
Pamela Carriveau
Laura ColmeneroChilberg
94
Fat Matters: From Sociology to Science
Brittney Hansen
97
Debating Sex and Gender
Laura ColmeneroChilberg
Book Reviews
4
Structural Conditions and Migration in the Dakotas
Joshua J. Turner
Abstract
W. Trevor Brooks
Donald E. Arwood
This study examines the influence of selected structural conditions on the
county-level net-migration trends of North Dakota and South Dakota. Key
principles from Lee’s Theory of Migration (1966) and Wallerstein’s World Systems
model (1974) were integrated to explain how geographic context, economic
dependency, and pace of economic development combine to serve as the main
catalysts behind the migration patterns in these two states. Results indicate that
commuting patterns, the percentage of workers employed in extractive
industries, the percentage of workers employed in manufacturing, and job
change rates were significant predictors of county migration patterns.
INTRODUCTION
Migration is not a random occurrence, it is a selective process influenced by a variety of
factors. One set of factors that cannot be ignored includes structural conditions like natural
amenities (Cromartie 1998; Johnson and Beale 2002), interstate access (Lichter and Fuguitt
1980), and adjacency to metropolitan areas. These conditions play a crucial role in determining
whether an area is likely to attract new populations or lose existing ones.
The purpose of this study is to explore the relationship between structural conditions
and the county-level net-migration rates of North Dakota and South Dakota. Net-migration is
an indicator of the movement of populations (both domestic and international) into or out of an
area. For a county to have experienced a positive rate of net-migration more people would

Joshua J. Turner, Mississippi State University, 203 Robert Louis Jones Circle, Mississippi State, MS
39762; e-mail: jturner@ nsparc.msstate.edu
5
have moved into it rather than moved out. Conversely, a negative rate of net-migration is the
result of the number of out-migrants exceeding the number of in-migrants (Weeks 2008).
Figure 1
Distribution of Non-Metropolitan vs. Metropolitan Counties in North Dakota and
South Dakota, 1990-2000
The counties of these two states were selected for several reasons. First, they comprise
a sparsely populated region, heavily isolated from the major urban centers of the country. Nonmetropolitan counties account for over 90 percent of the counties in these states (U.S. Census
Bureau 2000; see Figure 1). This provides researchers with a setting to test the possibility of a
core-periphery relationship, a concept that will be defined later in the study.
Second, the
pattern of net-migration among these non-metropolitan counties runs counter to national trends
between 1990 and 2000, a period in which 71 percent of non-metropolitan counties recorded
positive population change (Johnson 1999). During this same decennial census period, over 80
percent of the non-metropolitan counties in North Dakota and South Dakota recorded negative
6
rates of net-migration (U.S. Census 2000 Bureau; see Figure 1). Finally, this study presents an
opportunity to add to the limited amount of research on the migration patterns of these two
states. Up to this point, much of the research focusing on county-level migration in Great Plains
states has been largely descriptive (Albrecht 1993; Kulcsar and Bolender 2006; Rathge 2005;
2008). Less emphasis has been placed on the possible theoretical frameworks that could help
explain the role that certain structural conditions play in influencing migration rates in this
region. Adding a theoretical approach to the literature will help researchers better understand
the role that structural conditions, such as commuting patterns and job growth, have played in
influencing the migration patterns of the Dakotas.
This article begins with a review of past research identifying the relationships between
migration, geographic context, and economic development. Key principles from Lee’s Theory of
Migration (1966) and Wallerstein’s World Systems model (1974) are used to explain these
relationships. From these theoretical frameworks, hypotheses are developed and tested in an
attempt to show the viability of these theories in explaining the county-level net-migration in
the Dakotas.
REVIEW OF LITERATURE
Historically, the non-metropolitan counties of the Great Plains have been susceptible to
out-migration, even in times of overall growth for non-metropolitan counties on a national scale
(Albrecht 1993).
In the Dakotas, over 80 percent of non-metropolitan counties recorded
negative rates of net migration between 1990 and 2000 (U.S. Census Bureau 2000). This runs
counter to the “rural rebound” of the 1990s, a period in which 71 percent of non-metropolitan
counties recorded gains in population (Johnson 1999).
Geographic context has long been effective in predicting a region’s potential for
7
population growth and economic development (Lee 1966). Isolation from core areas or areas
possessing greater concentrations of capital and higher levels of economic development has left
many non-metropolitan-or peripheral-areas in a state of uneven development relative to their
metropolitan counterparts (O’Hare and Mather 2008). Lack of development brings with it a lack
of economic diversity and opportunity, as well as increased levels of social isolation for areas
already dealing with high levels of geographic isolation (O’Hare and Mather 2008; Tickamyer
and Duncan 1990).
In addition to being isolated, the Great Plains is heavily dependent on extractive
industries, particularly agriculture (White 1998). Data on the county typology of North and
South Dakota show that 70 percent of counties are classified as being farming or mining
dependent1.
The average net-migration rate for these counties was recorded at - 6.04
migrants/1,000 population between 1990 and 2000 (U.S. Census Bureau 2000; USDA-Economic
Research Service 2004).
The reduced demand for labor due to technological advancements in agriculture has
resulted in a decrease in occupational opportunity in agriculturally-dependent counties that
have failed to develop employment opportunities in alternative industries (Rowley 1998). This
situation has contributed to the redistribution of population in states located in the Great Plains
(Albrecht 1993; Bowers 1998; Cromartie 1998; Davidson 1996; Rathge 2005; Rathge and
Highman 1998). In this situation, metropolitan counties located in Great Plains states are likely
to receive in-migrants from their non-metropolitan counterparts (White 1998).
Indeed,
between 1990 and 2000, metropolitan counties in the states of North and South Dakota
1
A county is classified as farming-dependent when farm earnings account for at least 15 percent or more
of total county earnings or when farming occupations account for 15 percent or more of all occupations
in a county’s workforce. Similarly, a county is classified as mining-dependent when at least 15 percent of
total county earnings are derived from mining related occupations (USDA-ERS 2004).
8
averaged a net-migration rate of 8.82 migrants/1,000 population compared to a negative
average of - 5.56 migrants/1,000 population for non-metropolitan counties (U.S. Census Bureau
2000; USDA-Economic Research Service 2004).
Metropolitan counties provide opportunities in industries that call for specific levels of
education and training. They also possess infrastructures that allow them to attract industries
associated with amenity-based development such as manufacturing, retail sales, and
entertainment and recreation services (Nord and Cromartie 2000). The migrants that do move
to disadvantaged counties tend to work in unskilled labor and have lower educational
attainment (Domina 2006; Nord 1998).
Access and proximity to urban centers through commuting has influenced in-migration
to some non-metropolitan counties (Johnson and Beale 1994). The ability to commute to work
attracts younger adults with families who are allowed access to urban centers while being able
to raise children in a more rural environment (Johnson and Fuguitt 2000).
These smaller
communities adjacent to more metropolitan areas are attractive to those who desire less
expensive housing and the possibilities of maintaining family ties (Nitschke 2004).
More convenient access to interstate highways has helped to increase the ability of
people to commute and gain access to urban centers (Lichter and Fuguitt 1980). Access to
interstate highways has also been viewed as a potential force behind population gain,
population redistribution patterns, and job increase (Lichter and Fuguitt 1980; Smith 1971; Voss
and Chi 2006). Some support can be found for these statements when looking at the migration
trends of the Dakotas. Metropolitan counties with interstate access were among the fastest
growing counties in the Great Plains region. For example, Lincoln County, South Dakota, was
one of the top 60 fastest-growing counties in the nation, with a population change rate of 56.4
percent between 1990 and 2000 (U.S. Census Bureau 2001).
9
The Sioux Falls metropolitan
statistical area- of which Lincoln County is a part and where South Dakota’s two interstate
highways intersect- was also among the fastest growing small metropolitan areas during this
period (U.S. Census Bureau 2001).
THEORETICAL FRAMEWORK
This study integrates key principles from Lee’s Theory of Migration (1966) and
Wallerstein’s World Systems model (1974) to explain the migration patterns of North and South
Dakota. The consolidation of these two frameworks serves as an example of theory integration.
This occurs when relevant parts from at least two theories are integrated to more effectively
explain what neither theory can sufficiently do alone (Wagner and Berger 1985).
Lee (1966) argues that populations can be influenced to leave a place of origin if more
favorable opportunities are perceived to exist in a new destination. In Lee’s model, negative
features influencing out-migration are seen as “push” factors, while positive features influencing
in-migration are seen as “pull” factors.
Many of Lee’s key theoretical statements are
appropriate for explaining the migration trends of the Dakotas.
For instance, Lee (1966:52) highlights how new and more diverse opportunities can
affect volumes of migration by arguing, “The volume of migration within a given territory varies
with the degree of diversity of areas included in that territory,” and, “new opportunities are
continually created in places to which workers must be drawn, and old enterprises are ruthlessly
abandoned when they are no longer profitable.”
In another set of statements, Lee (1966:54) points to the role that economic
development can play in the migration process by stating, “The volume and rate of migration
vary with the state of progress in a country or area,” and “higher rates of progress can lead to
the creation of populations that respond quickly to new opportunities and react swiftly to
diminishing opportunities.”
10
These statements made by Lee relate well to the migration trends observed in the
Dakotas, where economic opportunities are more abundant in counties with access to a core
city. Conversely, North and South Dakota’s most remote counties continue to lose economic
activity or fail to attract new development, which may push existing residents to migrate.
According to Lee both of these situations can contribute to out-migration.
The addition of Wallerstein’s model (1974) helps explain why origins and destinations
exist. This model is often used to explain the exploitive relationship between less developed
countries and the multinational corporations of core countries (Massey, Arango, Hugo,
Kouaouci, Pellingro, and Taylor 1993). However, it is also useful in explaining the exploitive
relationship between the economic centers and peripheral hinterlands within countries (Galtung
1971). In this scheme, the penetration of capitalist economic relations into the rural hinterland
displaces workers, creating a mobile population that is prone to migrate to centers of more
diverse economic activity (Rogers, Korsching, and Donnemeyer 1988).
A World Systems perspective puts metropolitan counties in the core areas and centers of
diverse economic activity, while placing the non-metropolitan, agriculturally dependent counties,
into the periphery (Krugman 1991; White 1998). This industrial dominance leads to the further
dependence and spatial inequality of non-metropolitan counties that lack access to basic
amenities and the necessary networks for competing with urban economies.
Here the justification of integrating key ideas from both Lee and Wallerstein is
reinforced, as parallels can be drawn between their explanations of migration patterns. While
Lee describes the characteristics of areas most likely to lose or gain population through
migration, the addition of Wallerstein’s concepts of “core” and “periphery” places the counties
of the Dakotas into an appropriate geographic context to show the interdependent relationship
that exists between the metropolitan and non-metropolitan counties of the two states. The
11
map in Figure 1 illustrates the spatial concentration of metropolitan counties in these two
states, while also displaying the lack of access some non-metropolitan counties have to
metropolitan areas.
The general proposition of this study focuses on the influence of selected structural
conditions on the migration patterns of North and South Dakota. From this general proposition
several hypotheses can be deduced. These hypotheses, which are listed below, relate well to
the arguments of Lee and Wallerstein and to the main goal of this study for a number of
reasons.
First, they test the very “push” and “pull” factors that Lee argues influence the
migration process. Second, using levels of rurality and commuting patterns as independent
variables strengthens the possibility of displaying a core- periphery relationship described by
Wallerstein. Finally, a focus on a county’s share of employment in specific industries and rates
of job change is an effective way to test the frameworks of the two selected theorists, as both
approaches place an emphasis on the relationship between economic development and
migration. From these general propositions this study proposes the following hypotheses:
H 1:
There is a negative relationship between higher levels of rurality and
net-migration.
H 2:
There is a positive relationship between the percentage of workers commuting
out of their home county for work and net-migration.
H 3:
There is a positive relationship between interstate access and net- migration.
H 4:
There is a negative relationship between the percentage of workers employed in
extractive industries and net-migration.
H 5:
There is a positive relationship between the percentage of workers employed in
manufacturing and net-migration.
H 6:
There is a positive relationship between the percentage of workers employed in
retail services and net-migration.
H 7:
There is a positive relationship between job change rate and net-migration.
12
METHODOLOGY
Data and Units of Analysis
Data were collected from the U.S. Census Bureau, the U.S. Department of Agriculture’s
Economic Research Service, the U.S. Department of Transportation, and the Bureau of
Economic Analysis. Counties were selected over other possible units of analysis because they
have defined political boundaries in which decisions are made (Lichter and Johnson 2006). All
counties (n=119) were included in the sample, regardless of total population, and combined
into one analysis.
Operationalization of Concepts
Net-migration rates. The dependent variable for this study was the county-level netmigration rates recorded between the years of 1990 and 2000.
These rates measure the
number of in- and out- migrants (both domestic and international) per 1,000 population (Tarver
1961). The mean net-migration rate for all counties was recorded at - 4.23; that is, for every
1,000 people living in a county, 4.23 more people migrated out between the years of 1990 and
2000 (U.S. Census Bureau 2000).
Geographic context.
Three variables were used to measure a county’s geographic
context: (1) Rural-Urban Continuum Codes, (2) the percentage of residents commuting outside
of their home county for work, and (3) interstate access. Rural-Urban Continuum Codes classify
counties based on population size, adjacency to metropolitan areas, and levels of rurality
(USDA-Economic Research Service 2004).
These codes range from “1” to “9.”
Counties
assigned codes ranging from “1” to “3” are classified as metropolitan while those coded “4”
through “9” are classified as non-metropolitan. A total of eleven counties (9.2 percent) were
classified as metropolitan counties. These counties were all assigned a code of “3,” meaning
they were located in metropolitan areas of fewer than 250,000 residents.
13
The majority of
counties (53.7 percent) were assigned a Rural-Urban Continuum Code of “9.” These counties
are classified as being non-adjacent to metropolitan areas and either completely rural or home
to an urban population of fewer than 2,500 residents (USDA-Economic Research Service 2004).
The second geographic context variable focused on the relationship between commuting
and net-migration. This was achieved by utilizing U.S. Census Bureau data that measured the
percentage of workers commuting outside of their home county for work. In 2000, the average
percentage of workers commuting outside of their home county for work in North and South
Dakota was recorded at 20.27 percent (U.S Census Bureau 2000; See Table 1).
Table 1
Correlation Values for Independent Variables and Net-Migration Rates
Independent Variables
N
Rural-Urban Continuum Code
Percentage Commuting Out of County for Work, 2000
Interstate Runs Through County (0=no, 1=yes)†
Percentage Employed in Extractive Industries, 2000
Percentage Employed in Manufacturing, 2000
Percentage Employed in Retail, 2000
Job Change Rate, 1990-2000
*p= .05; **p= < .01; ***p= < .001
†
Eta utilized for this variable
119
119
119
119
119
119
119
County
Mean
-20.27
-18.02
7.30
10.22
13.36
r-Value
-.474***
.388***
.301***
-.578***
.420***
.342***
.569***
Presence of an interstate highway was the third variable used to measure the
relationship between geographic context and net-migration.
These counties were identified
using data from the U.S. Department of Transportation (2007). To measure this relationship a
dummy variable was created.
Counties with an interstate highway running within its
boundaries were assigned a code of “1” (n= 36) while those without an interstate highway
were assigned a code of “0” (n= 83).
14
Economic dependency. Three variables were used to examine the relationship between
economic dependency and net-migration.
These variables were the percentage of workers
employed in industries related to resource extraction (most notably agriculture), manufacturing,
and retail services. Figures in Table 1 show that on average 18.01 percent of workers in these
counties were employed in extractive industries in 2000, compared to 7.30 percent in
manufacturing and 10.22 percent in retail services (U.S. Census Bureau 2000). Though these
variables only test the relationship between economic dependency and net-migration at one
point in time, they are effective in displaying how a county’s share of employment in a specific
industry can help to predict migration trends and whether certain industries are associated with
a positive or negative rate of net-migration.
Economic development. Job change rates provided by the Bureau of Economic Analysis
(2000) were used to examine the relationship between county-level economic development and
rates of net-migration. These rates measure the percent change in total employment, while
also serving as an indicator of job creation, a chief indicator of economic development. The
average job change rate for the counties under analysis was recorded at 13.36 percent between
the years of 1990 and 2000. This was lower than the nation as a whole, which recorded a job
change rate of 19.54 percent (Bureau of Economic Analysis 2000; See Table 1).
Modeling Strategy
Hypotheses were tested through bivariate correlations and an Ordinary Least Squares
(OLS) regression analysis.
Bivariate correlations were used initially to display individual
relationships between the selected independent variables and net-migration. An Ordinary Least
Squares regression model was then utilized to illustrate the combined influence these variables
have on the strength and direction of county-level net-migration and as the deciding factor in
accepting or rejecting the research hypotheses.
15
RESULTS
Results from the bivariate correlations show initial empirical support for all research
hypotheses. The percentage of workers employed in extractive industries (r = - .578; p < .001)
and job change rates (r = .569; p < .001) show the strongest relationships with net-migration
rates. All other independent variables are moderately associated with the dependent variable
(See Table 1).
When combined into one regression model the seven independent variables account for
61.1 percent of the variance in the dependent variable. Support is found for four of the original
seven research hypotheses, while three are found to be statistically insignificant. The results of
the bivariate analysis and the regression model as they relate to the hypotheses are discussed
in greater detail in the following sections.
H1: There is a negative relationship between higher levels of rurality and net-migration.
Rural-Urban Continuum Codes and rates of net-migration were utilized to test the association
between rurality and net-migration.
As seen in Table 1, the strength of the bivariate
relationship is negative, moderate (r = - .474), and statistically significant (p < .001).1 Results
from the regression analysis (see Table 2) reveal that the strength of the relationship between
Rural-Urban Continuum Codes and rates of net-migration becomes statistically insignificant
when other variables are controlled for (β = -.072; p < .374).
1
Although there is no consensus on the verbal interpretation of values of r, this study applies the
following scale: .01 to .25 = weak; .26 to .50 = moderate; .51 to .75 = strong; .76 to 1.00 = very
strong.
16
Table 2
OLS Regression Analysis of Net Migration Rates for the Counties of North and
South Dakota, 1990-2000 (N= 119).
b
Constant
Rural-Urban Continuum Code
Percentage Commuting Out of County for
Work, 2000
Interstate Runs Through County (0=no,
1=yes)
Percentage Employed in Extractive
Industries, 2000
Percentage Employed in Manufacturing, 2000
Percentage Employed in Retail, 2000
Job Change Rate, 1990-2000
R2 = .611
S.E.
Beta
t-Value
pValue
.0570
.3740
-10.776
-.384
5.602
.431
-10.776
-.072
-1.924
-.893
.274
.047
.371
5.882
-1.409
1.471
-.066
-.958
.3400
-.293
.091
-.295
-3.220
.0020
.242
.560
.163
.109
.284
.039
.146
.146
.300
2.225
1.973
4.164
.0280
.0510
< .0001
<.0001
H2: There is a positive relationship between the percentage of workers commuting out
of their home county for work and net-migration. This relationship is moderate, positive (r =
.388) and statistically significant (p < .001) in the bivariate analysis. The relationship remains
statistically significant when included in the regression analysis (β = .371; p < .0001) and it is
also the strongest relationship in the analysis.
H3: There is a positive relationship between interstate access and net-migration.
Though found to be positively and significantly associated with net-migration in the bivariate
analysis (r = .301; p < .001), this is not the case in the regression model. In fact, when
included with other factors, access to an interstate highway becomes a negative (β = -.066)
and statistically insignificant predictor (p < .340) of net-migration.
H4: There is a negative relationship between the percentage of workers employed in
extractive industries and net-migration.
Findings from the bivariate analysis support the
argument that greater dependence on extractive-related industries is associated with negative
17
net-migration (r = -.578; p < .001). Indeed, there is a strong, negative association. This
relationship remains statistically significant in the regression model (β = - .295; p < .0020).
H5: There is a positive relationship between the percentage of workers employed in
manufacturing and net-migration.
The bivariate analysis reveals a moderate positive
association between the percentage of workers employed in manufacturing and rates of netmigration (r = .420; p < .001). The results from the regression model do not discount this
relationship; even when controlling for the relationships between all of the independent
variables with net-migration, the relationship between employment in manufacturing and netmigration remains positive (β = .146) and statistically significant (p < .028).
H6: There is a positive relationship between the percentage of workers employed in
retail services and net-migration.
Though it displays a moderate statistically significant
association in the bivariate analysis (r = .342; p < .001), the percentage of workers employed
in retail services does not produce a statistically significant relationship in the regression model
(β = .146; p < .051).
H7: There is a positive relationship between job change rate and net-migration. The
relationship between job change rate and rates of net-migration produces the second strongest
association of the relationships in the bivariate analyses (r = .569; p < .001). When combined
with other variables in the full regression model job change rate remains as the second
strongest predictor of county-level net-migration (β = .300; p < .0001).
To summarize these tests, the bivariate analyses provided support for all of the
hypotheses. However, when put to the strain of statistical control in the regression analyses,
higher levels of rurality, interstate access, and the percentage of workers employed in retail
trade were found not to be statistically associated with net-migration, leading to the rejection of
Hypotheses 1, 3, and 6. Final results show that the percentage of workers commuting out of
18
their home county for work, the percentage of workers employed in extractive industries, the
percentage of workers employed in manufacturing, and job change rates were found to be
statistically associated with net-migration, which lead to the acceptance of Hypotheses 2, 4, 5
and 7.
DISCUSSION
Though some of the hypotheses were not supported by the regression analysis, this
study reveals a number of findings which support themes from the theories of both Lee (1966)
and Wallerstein (1974).
When tested individually, all indicators of geographic context,
economic dependency, and economic development are significantly associated with netmigration.
When controlling for other factors, four variables are found to be statistically
significant in the regression analysis: percentage of workers commuting outside of their home
county for work, the percentage of workers employed in extractive industries, the percentage of
workers employed in manufacturing, and rate of job change.
The percentage of workers commuting out of their home county for work is the
strongest predictor of county-level net-migration. The positive association between these two
variables supports findings in the literature and suggests that regardless of a county’s
metropolitan or non-metropolitan status, greater access to labor markets may act as a pullfactor, influencing migration into a county.
The percentage of workers employed in extractive industries, especially in agriculture, is
the only variable to be negatively and statistically related with net-migration. If a relatively high
percentage of workers employed in extractive industries is a reflection of a lack of economic
diversity, and researchers we reviewed imply that it is (Rogers et al. 1988; Rowley 1998), this
negative relationship lends support to the ideas of both Lee (1966) and Wallerstein (1974) who
make note of how locations failing to develop industrial diversity face the threat of population
19
loss through out-migration. More substantial support will come if subsequent research projects
find that this negative relationship holds up when changes in employment and net-migration
are studied over time.
The percentage of workers employed in manufacturing and job rate change may reflect
a more diverse economy; if they do, they serve as examples of how greater economic diversity
or a greater pace of economic development can lead to positive net-migration.
This is an
argument made several times by Lee (1966) and Wallerstein (1974) as well as those who have
used the key principles of these theorists to explain migration (Rogers et al. 1988). The rate of
job change, (recorded at 13.36 percent for the Dakotas between 1990 and 2000), most likely
the better indicator of economic diversity, may represent new economic opportunities, a pullfactor known to spark in-migration.
In general, counties with relatively higher percentages of persons commuting out of the
county to work, relatively higher percentages employed in manufacturing but lower percentages
employed in extractive industries, and relatively higher rates of job growth experienced
relatively higher rates of positive net-migration. As seen in a comparison of maps in Figures 1
and 2, many but certainly not all of these counties are metropolitan or near metropolitan
counties.
In theoretical terms, and maybe in actual terms, these counties possess greater
access to valued resources and opportunities. Conversely, the Dakotas’ most isolated counties
may lack the assets to attract new development, which may lead to an increased dependency
on core and metropolitan areas, leading to further complications for populations that may
already be aging, isolated, or economically disadvantaged.
20
Figure 1
Net-Migration Rates in North Dakota and South Dakota 1990-2000 (Positive vs.
Negative)
Having an Interstate highway running through these counties may not be the best way
to alleviate these problems. An Interstate may simply make it easier for people and business to
pass right through them. Decreasing the dependency of peripheral counties may take a multifaceted approach. As Whitener and Parker (2007) have noted, addressing these discrepancies
may require unique policy options that entail local, state, and national governmental action to
stimulate peripheral counties' economies and living conditions by enhancing web-based
economic activities, luring economic activities that add value to agricultural products,
strengthening schools and other public services, and building up and improving access to
recreational activities. Taking these measures may help improve the quality of life in peripheral
counties; whether they influence higher levels of in-migration is a potential topic for another
study.
21
CONCLUSION
The objective of this study was to explain the relationship between structural conditions
and the county-level migration patterns in North and South Dakota.
While past research
involving the Dakotas focused more on describing the population issues of the Great Plains
region as a whole (Albrecht 1993; Rathge 2005; 2008) this study introduced an alternative for
explaining migration patterns in two states where research is limited. Final results indicate that
the prevalence of commuting, the percentage of workers employed in extractive industries, the
percentage of workers employed in manufacturing, and rate of job change are all statistically
significant predictors of county-level net-migration.
In the future, research could be expanded by including more states from the Great
Plains region to determine whether results from this study are found to be unique to certain
states or consistent across the entire region. It will also be important for future studies to
analyze the impact technology has had on the Dakotas, by investigating whether technology
has displaced workers in non-metropolitan areas, thus further supporting dependency themes.
Finally, it will be vital to look at the impact the current economic decline has had on the
peripheral counties of these two states and whether they will continue to experience outmigration influenced by economic hardship and spatial inequality.
22
REFERENCES
Albrecht, Don E. 1993. “The Renewal of Population Loss in the Nonmetropolitan Great Plains.”
Rural Sociology 58(2):233-246.
Bowers, Douglas E. 1998. “Contents and Editor’s Notebook.” Rural Development Perspectives
13(1):1.
Cromartie, John B. 1998. “Net Migration in the Great Plains Increasingly Linked to Natural
Amenities and Suburbanization.” Rural Development Perspectives 13(1):27-34.
Davidson, Osha Gray. 1996. Broken Heartland: The Rise of America’s Rural Ghetto. Expanded
Edition. University of Iowa Press, Iowa City, Iowa.
Domina, Thurston. 2006. “What Clean Break? Education and Nonmetropolitan Migration
Patterns, 1989-2004.” Rural Sociology 71(3):373-398.
Fuguitt, Glenn V., Calvin L. Beale, and Stephen J. Tordella. 2002. “Recent Trends In Older
Population Change and Migration for Nonmetro Areas, 1970-2000.” Rural America
17(3):11-19.
Galtung, Johan. 1971. “A Structural Theory of Imperialism.” Journal of Peace Research 8(2):81117.
Garasky, Steven. 2002. “Where are they going?: A Comparison of Urban and Rural Youths’
Locational Choices After Leaving the Parental Home.” The Journal of Human Resources
30(3):460-471.
Horton, Hayword Derrick. 1999. “Critical Demography: Paradigm of the Future?” Sociological
Forum 14(3):363-367.
Johnson, Kenneth M. 1999. “Reports on America: The Rural Rebound.” Population Reference
Bureau 1(3):1-19.
Johnson, Kenneth M. and Calvin L. Beale. 1994. “The Recent Revival of Widespread Population
23
Growth in Nonmetropolitan Areas of the United States.” Rural Sociology 59(4):655-667.
Johnson, Kenneth M. and Calvin L. Beale. 2002. “Nonmetro Recreation Counties: Their
Identification and Rapid Growth.” Rural America 17(4):12-19
Johnson, Kenneth M. and Glen V. Fuguitt. 2000. “Continuity and Change in Rural Migration
Patterns, 1950-1995.” Rural Sociology 65(1):27-49.
Krugman, Paul. 1991. “Increasing Returns and Economic Geography.” The Journal of Political
Economy 99(3):483-499.
Kulcsár, Laszlo J. and Benjamin C. Bolender. 2006. "Home on the Range: Aging in Place in Rural
Kansas", Online Journal of Rural Research and Policy Issue 2006.3. Retrieved July 2010
(http://www.ojrrp.org/issues/2006/03/index.html)
Lee, Everett S. 1966. “A Theory of Migration.” Demography 3(1):47-57.
Lichter, Daniel T. and Glenn V. Fuguitt. 1980. “Demographic Responses to Transportation
Innovation: The Case of the Interstate Highway.” Social Forces 59(2):492-512.
Lichter, Daniel and Kenneth Johnson. 2006. "Emerging Rural Settlement Patterns and the
Geographical Redistribution of America's New Immigrants." Rural Sociology 71(1):109131.
Massey, Douglas S., Joaquin Arango, Graeme Hugo, Ali Kouaouci, Adela Pellingro, and J.
Edward Taylor. 1993. “Theories of International Migration: A Review and Appraisal.”
Population and Development Review 19(3):431-466.
Nitschke, L. 2004. “The Toll on Rural Commuters in the U.S.” Population Reference Bureau.
Retrieved July 2010
(http://www.prb.org/Articles/2004/TheTollonRuralCommutersintheUS.aspx).
Nord, Mark M. 1998. “Poor People on the Move: County-to-County Migration and the Spatial
Concentration of Poverty.” Journal of Regional Science 38(2):329-351.
24
Nord, Mark and John B. Cromartie. 2000. “Migration in the Nonmetropolitan South.” Southern
Rural Sociology 16:175-205.
North Dakota State Data Center. 2009. Net-Migration in North Dakota by County: 1950-1960 to
2000-2009. Retrieved July 2010
(http://www.ndsu.edu/sdc/publications/preparedtables.htm#tablesmig).
O’Hare, William and Mark Mather. 2008. “Child Poverty is Highest in Rural Counties in U.S.”
Population Reference Bureau. Retrieved July 2010
(http://www.prb.org/Articles/2008/childpoverty.aspx).
Rathge, Richard W. 2005. “The Changing Population Profile of the Great Plains.” Great Plains
Sociologist 17(2):82-99.
Rathge, Richard W. 2008. “Future Population Shifts in the Great Plains and Their Implications.”
Great Plains Sociologist 19:109-126.
Rathge, Richard W. and Paula Highman. 1998. “Population Change in the Great Plains: A
History of Prolonged Decline.” Rural Development Perspectives 13(1):19-26.
Rieger, Jon H. 1972. “Geographic Mobility and the Occupational Attainment of Rural Youth: A
Longitudinal Evaluation.” Rural Sociology 37(2):189-207.
Rogers, Everett M., Rabel J. Burdge, Peter F. Korsching, and Joseph F. Donnermeyer. 1988.
Social Change in Rural Societies. Englewood Cliffs, New Jersey: Prentice Hall.
Rowley, Thomas D. 1998. “Sustaining the Great Plains.” Rural Development Perspectives
13(1):2-6.
Smith, David M. 1971. Industrial Location. New York: Wiley.
Tarver, James D. 1961. “Predicting Migration.” Social Forces 39(3):207-213.
Tickamyer, Ann R. and Cynthia M. Duncan. 1990. “Poverty and Opportunity Structure in Rural
America.” Annual Review of Sociology 16:67-86.
25
U.S. Bureau of Economic Analysis. 2000. Regional Economic Information System. Retrieved
July 2010 (http://www.bea.gov/regional/docs/reis2006dvd.cfm).
U.S. Census Bureau. 1990. 1990 Census Summary Tape File 1. Retrieved July 2010
(http://www.census.gov/main/www/cen1990.html).
U.S. Census Bureau. 2000. Census 2000 Summary File 3. Retrieved July 2010
(http://www.census.gov/Press-Release/www/2002/sumfile3.html).
U.S. Census Bureau. 2000. Census 2000 County and County Equivalent Areas Cartographic
Boundary Files. Retrieved July 2010
(http://www.census.gov/geo/www/cob/co2000.html)
U.S. Census Bureau. 2001. Census 2000 PHC-T-3. Ranking Tables for Metropolitan Areas: 1990
and 2000. Retrieved July 2010 (http://census.gov/population/www/cen2000/briefs/phct3/index.html).
U.S. Census Bureau. 2001. Census 2000 Redistricting Data (P.L. 94-171) Summary File.
Retrieved July 2010 (http://www/census.gov/population/www/cen2000/briefs/phct4/index.html).
U.S. Census Bureau. 2004. Population Estimates Program. Retrieved July 2010
(http://www.census.gov/popest/estimates.php).
U.S. Department of Agriculture, Economic Research Service. 2004. “Measuring Rurality: 2003
Rural-Urban Continuum Codes.” Retrieved July 2010
(http://www.ers.usda.gov/Briefing/Rurality/RuralurbCon/)
U.S. Department of Agriculture, Economic Research Service. 2004. “Measuring Rurality: 2003
Urban Influence Codes.” Retrieved July 2010
(http://www.ers.usda.gov/Briefing/Rurality/UrbanInf/)
U.S. Department of Agriculture, Economic Research Service. 2004. “Measuring Rurality: 2004
26
County Typology Codes Methods, Data Sources, and Documentation.” Retrieved July
2010 (http://www.ers.usda.gov/Briefing/Rurality/Typology/Methods/).
U.S. Department of Agriculture, Economic Research Service. 2005. Profiles of America:
Demographic Data and Graphic Builder. Retrieved July 2010
(http://maps.ers.usda.gov/Profiles/index.aspx)
U.S. Department of Transportation, Federal Highway Administration. 2007. State
Transportation Websites. Retrieved July 2010
(http://www.fhwa.dot.gov/planning/nhs//).
U.S. Department of Commerce, Bureau of Economic Analysis. 2000. Regional Economic
Accounts. Retrieved July 2010 (http://www.bea.gov/regional/reis/).
Voss, Paul R. and Guangqing Chi. 2006. “Highways and Population Change.” Rural Sociology
71(1):33-58.
Wagner, David G. and Joseph Berger. 1985. “Do Sociological Theories Grow?” The American
Journal of Sociology 90 (4):697-728.
Wallerstein, Immanuel. 1974. The Modern World System: Capitalist Agriculture And the Origins
of the European World-Economy in the 16th Century. New York: Academic Press.
Weeks, John R. 2008. Population: An Introduction to Concepts and Issues. 10th Edition.
Thomson and Wadsworth, Belmont, California.
White, Stephen E. 1998. “Migration Trends in the Kansas Ogallala Region and the Internal
Colonial Dependency Model.” Rural Sociology 63(2):253-271.
Whitener, Leslie A. and Tim Parker. 2007. “Policy Options for a Changing Rural America.”
AmberWaves Retrieved July 2010
(http://www.ers.usda.gov/AmberWaves/May07SpecialIssue/Features/Policy.htm).
27
Understanding Workday Housework Participation:
Testing Three Theories
Krista Lynn Minnotte
Abstract
Matti Grotte
In this study we examine the usefulness of three theories (time availability
theory, gender ideology theory, and relative resources) in predicting workday
housework performance among partnered and married mothers and fathers. In
doing so, we incorporate an extended version of time availability theory that
considers the use of flexible scheduling and the presence of nonstandard work
hours in addition to number of hours worked. Our hypotheses are addressed
with data from a nationally representative sample of employed adults. Our
results show that none of the theories are useful in predicting fathers’ workday
housework performance; however, two theories are beneficial for predicting
mothers’ performance. Specifically, mothers with more traditional gender
ideologies (in support of gender ideology theory) along with mothers who work
fewer hours than their partners (in support of time availability theory) perform
more workday housework. Additionally, and in support of an extended version
of time availability theory, we find the use of flexible scheduling among mothers
is associated with higher levels of workday housework performance.
Implications of the study are discussed.
INTRODUCTION
The large scale entrance of women into the paid labor force has served to challenge a
“traditional” division of labor in which women care for the family domain and men attend to
breadwinning (Bianchi, Milkie, Sayer, and Robinson 2000; Deutsch 1999; Hochschild 1989).
Women have succeeded in joining the ranks of men in the workforce, but the concomitant
change of men increasing their participation in the family work, including housework, has been
slow and uneven (Coltrane 2000; Hochschild 1989). The gender gap evident in the housework
performance of husbands and wives creates tension and stress for married and partnered

Krista Lynn Minnotte, Ph.D., University of North Dakota, Department of Sociology, Gillette Hall Room
202, 225 Centennial Drive Stop 7136, Grand Forks, ND 58202-7136; e-mail: [email protected]
28
women (Hochschild 1989; Milkie, Raley, and Bianchi 2010). Indeed, the unequal division of
housework labor is often implicated in conflict between partners and has been linked to
reductions in relationship quality (Kluwer, Heesink, and Van de Vliert 1996; Stevens, Kiger, and
Riley 2001). Considering that the division of labor becomes increasingly traditional following
the transition to parenthood (Baxter, Hewitt, and Haynes 2008; Nomaguchi and Milkie 2003),
these tensions may be heightened among partners who have children.
For these reasons,
understanding the housework participation of married or partnered individuals with children in
the home is a particularly worthwhile project.
Scholars have noted that while men’s relative housework hours have increased that
much of this is explained by women cutting back on their housework time (Press and Townsley
1998).
As such, scholars over the past few decades have sought to elucidate mechanisms
explaining participation in housework, with research consistently finding that gender remains
one the most important predictors of this variable (Coltrane 2000; Shelton and John 1996). As
scholars have sought to explain the gendered division of household labor, three key theories
have been the most influential: relative resources theory, time availability theory, and gender
ideology theory.
This paper examines how well each of these three theories explains
housework participation, while also extending time availability theory to include other workplace
factors that may serve to expand or contract the time available to do housework. In particular,
we consider the role of nonstandard work hours and the use of flexible scheduling (Noonan,
Estes, and Glass 2007; Presser, 1994; 2003). We devote our attention to housework performed
on work days—days that are likely to be the most stressful and difficult to manage, especially
among households with dependent children.
We contribute to the understanding of the
gendered division of household labor by focusing on workday housework, which is largely
unexplored in the literature, along with our incorporation of an expanded version of time
29
availability theory.
To address our research questions we use data from a nationally
representative sample of employed adults in the United States, with our attention restricted to
those who are married or partnered with children under the age of 18 living in the home.
Analyses are conducted separately by gender to explore gendered processes that may come
into play in explaining workday housework participation.
Predicting Housework Performance
As mentioned earlier, three primary theories have been used by scholars to explain
participation in housework: relative resources theory, gender ideology theory, and time
availability theory. In this section of the paper, we review key literature pertaining to each of
the theories, and then propose the hypotheses that will guide our analyses.
Relative Resources. According to relative resources theory, individuals use their
resources, typically in the form of earnings, to bargain for reduced housework performance
(Coverman 1989; Shelton and John 1996). Operating from the assumption that housework is
unpleasant, individuals are viewed as using income to buy their way out of this form of labor.
Along these lines, in married or partnered relationships housework performance is tied to
earnings, with those who earn higher incomes performing less housework. Hence, as women’s
earnings increase relative to their partners, their partner’s housework contributions should
increase; and when women’s earnings are low compared to their partners, women’s housework
contributions will remain high.
Studies that have examined the linear relationship between
relative income and housework have found that men tend to perform more housework when
their wives make greater contributions to household income (Bianchi et al. 2000; Ross 1987).
Here we consider whether the proportion of household income contributed by the respondent
(compared to his or her partner or spouse) is related to the respondent’s housework
30
performance. We propose the following hypothesis based on the central argument of relative
resources theory:
Hypothesis 1:
Proportional income will be negatively related to workday housework
performance.
Gender Ideology. Scholars have also proposed that individuals’ beliefs and attitudes
regarding gender are central to the division of household labor, with gender ideologies playing
an especially important role (Hochschild 1989). Gender ideology is defined as “how a person
identifies herself or himself with regard to marital and family roles that are traditionally linked to
gender” (Greenstein 1996:586). Gender ideology theory suggests that women who hold
traditional gender ideologies will regard the home as their province, and hence will perform
more housework than egalitarian women who are oriented to both work and home; whereas
men who are more traditional will perform less housework than more egalitarian men because
traditional men will view housework as “women’s work” (Coverman 1989; Fuwa 2004; Shelton
and John 1996).
We argue that gender ideologies may be especially important in predicting workday
housework performance, as traditional women may feel pressure to perform housework on such
days to ensure they are behaving in accordance with their gender ideologies despite their paid
labor force participation. Egalitarian women, on the other hand, may feel no such need to
demonstrate their dedication to traditional beliefs by performing housework on work days.
Previous scholarship has demonstrated that men with more egalitarian gender ideologies tend
to perform more housework than more traditional men, and women with more egalitarian
gender ideologies spend less time performing housework than more traditional women, even
when employment and parental status are controlled for (Bianchi et al. 2000; Fuwa 2004;
31
Gazco-Windle and McMullin 2003; Presser 1994; Ross 1987). Gender ideology theory leads us
to propose the following hypotheses:
Hypothesis 2:
Mothers with more traditional gender ideologies will perform more
housework on workdays than more egalitarian mothers.
Hypothesis 3:
Fathers with more traditional gender ideologies will perform less
housework on workdays than more egalitarian fathers.
Time Availability. Time availability theory contends that among partnered individuals
that whichever partner has fewer time commitments will perform more housework, with time
commitments typically determined by considering labor force hours (Coverman 1989; Shelton
and John 1996). As such, individuals who work more hours compared to their partners should
perform less housework and those individuals who work fewer hours than their partners should
perform more housework. Two reviews of the housework literature have concluded that work
hours are negatively related to the housework performance of both men and women (Coltrane
2000; Shelton and John 1996).
A few scholars have expanded time availability to consider the use of flexible scheduling
in predicting housework, as the use of flexible scheduling may lead to individuals adjusting their
work schedules to meet their family needs (Noonan et al. 2007; Silver and Goldscheider 1994;
Wharton 1994). Indeed, findings from previous studies indicate that some women deliberately
choose to work in jobs characterized by flexibility in the scheduling of hours in the hopes of
being more available to take care of needs emerging from the family domain, such as
housework (Hilbrect, Shaw, Johnson, and Andrey 2008; Wharton 1994).
Although a relationship between the use of flexible scheduling and enhanced housework
performance is often assumed to exist, very little research has examined the relationship
between these two variables, with existing research tending to focus on only women (e.g.,
32
Silver and Goldscheider; Wharton 1994). For instance, Silver and Goldscheider (1994) in their
examination of the availability of flexible fringe benefits and women’s housework performance
found that these benefits were associated with increased housework performance among
mature women but not among younger women. A potential limitation of their research is its
focus on the availability of policies, as we know from previous scholarship that the provision of
flexible scheduling benefits does not necessarily result in their use due to fear of career
repercussions (Blair-Loy and Wharton 2002; Secret 2000).
Hence, we cannot know if the
women in Silver and Goldscheider’s study actually used the flexible fringe benefits that were
available to them, and presumably it is the use of the policies that allows workers to expand
their time to attend to family needs. This means that it is especially important to examine how
the actual use of such policies is related to housework performance, and only a few studies
have done so. One such study using a Midwestern sample of employed parents found that
wives who use flexible scheduling, in contradiction to their predictions, actually performed less
housework (Noonan et al. 2007).
Despite this contradictory finding, we expect flexible
scheduling to be associated with higher levels of workday housework performance because it
allows workers the ability to arrange their work hours so as to better take care of family-related
needs, including housework on the days when they are working (Wharton 1994).
Nonstandard work hours are another mechanism that may serve to expand the time an
individual has available to perform housework on days when they are working. Presser (1994;
2003) has been at the forefront of calling attention to both the increasing prevalence of
nonstandard work hours in our 24/7 economy and the ramifications of this important change for
a variety of marital and family outcomes, including the division of domestic labor. Presser’s
(2003) work demonstrated that among couples with children that wives spend more time on
female-typed household tasks when they work nonstandard shifts, and at least one study has
33
found that shift work is associated with a general increase in the housework participation of
women (Silver and Goldscheider 1994). Moreover, Presser’s research (1994; 2003) indicated
that when husbands and wives work different schedules (usually due to one partner working a
nonstandard schedule) that this tends to enhance men’s housework participation.
In the
present study, we are unable to consider the role of the spouse’s schedule, but we do examine
whether there is a relationship between the nonstandard work hours of the respondent and the
respondent’s workday housework performance. We argue that nonstandard hours are likely to
enhance housework performance because a respondent who works nonstandard hours most
likely works a different shift than his or her partner, which leaves the respondent primarily
responsible for household tasks that arise when he or she is not at work.
Regarding the
extended version of time availability theory, we put forth the following hypotheses:
Hypothesis 4:
Relative work hours will be negatively related to workday housework
performance.
Hypothesis 5: The use of flexible scheduling will be positively related to workday
housework performance.
Hypothesis 6: Nonstandard work schedules will be positively related to workday
housework performance.
Demographic Control Variables. We also take into account a number of demographic
control variables in our analyses, including age, race, education, and the presence of children
under the age of 6 in the household.
Age is important to consider, as previous research
indicates that housework performance may differ among younger versus older respondents
(e.g., Silver and Goldscheider 1994). For instance, Bianchi and colleagues (2000) found that
age was negatively related to the housework performance of husbands. Previous research has
also indicated that the division of household labor may be more traditional among certain ethnic
34
groups, such as Hispanics (Sayer and Fine 2010), whereas it tends to be egalitarian among
other groups, such as African-Americans (John and Shelton 1997), leading us to include race in
our analyses. Studies have shown that children, especially the presence of children under the
age of 6, are related to increased housework performance among men and women and to a
larger gender gap in housework performance (e.g., Bianchi et al. 2000; Perkins and DeMeis
1996). For that reason, we include the presence of children under age 6 as one of the variables
in our study.
METHOD
To address our proposed hypotheses we use data from the 2002 National Study of the
Changing Workforce (NSCW). A questionnaire regarding work and family life was developed by
The Families and Work Institute (Bond, Thompson, Galinsky, and Prottas 2003), which was
then used by Harris Interactive to collect the data. The data is a nationally representative
sample of employed adults who were interviewed during an eight month time period.
The
sample was generated using random-digit dialing, with interviewers determining eligibility at the
time of the telephone call. To be eligible for participation an individual had to be at least 18
years of age and employed in the paid labor force.
After eligibility was determined, a
computer-assisted telephone interviewing system was utilized to obtain the data with the
interviews lasting approximately 45 minutes.
The resulting dataset for the 2002 NSCW
contained 2,810 employees, including 1,640 women and 1,170 men. For the purposes of this
study, analysis was restricted to those respondents who were married or partnered, whose
spouse or partner was employed in the paid labor force, who reported having at least one child
under the age of 18 living in their home, and who were not missing data on any of the study
variables (N = 613). Please note that although our data contain some measures regarding the
35
spouse or partner of the respondent that we do not have couple data; the mothers and fathers
in the sample are not married or partnered to each other.
Measures
Dependent variable. Workday housework performance was measured by asking the
respondents “on average, on days when you're working, about how much time do YOU spend
on home chores -- things like cooking, cleaning, repairs, shopping, yardwork, and keeping track
of money and bills?” Respondents gave their answers either in minutes or hours, and for the
purposes of this analysis all responses were converted to hours.
Independent variables.
The variable relative work hours was created by taking the
respondent’s report of how many hours he or she worked on average per week at all jobs and
subtracting the respondent’s report of his or her partner’s average work hours per week at all
jobs. Positive scores on the variable indicate that the respondent reports working more hours
than his or her partner, negative scores indicate the partner works more hours than the
respondent (according to the respondent), and a score of zero indicates that the respondent
works the same number of hours as the partner (according to the respondent). Use of flexible
scheduling was measured by one item that asked respondents the extent to which they used
flexible scheduling options that were available at their place of work. Responses ranged from
“a lot” (coded as a 1) to “not at all” (coded as a 4). The scores were then reverse coded such
that higher scores reflect greater use of available flexible scheduling. Additionally, respondents
who reported that their workplace did not have flexible scheduling options available were
assigned a code of 0 on this variable. Hence, the final range of scores for this variable is from 0
= no flexible scheduling available to 4 = uses a lot.
To measure nonstandard work hours
respondents were asked to characterize the schedule they worked at their main job. Response
categories that respondents could select from included a regular daytime shift, a regular
36
evening shift, a regular night shift, a rotating shift, a split shift, a variable schedule with no set
hours, or some other schedule. Respondents who indicated working a regular daytime shift
were coded 0 on nonstandard work hours and all other respondents were coded 1 (indicating
they worked a nonstandard work schedule).
Gender ideology was measured by asking respondents to indicate their extent of
agreement with the following statement: “it is much better for everyone involved if the man
earns the money and the woman takes care of the home and children?” Available response
categories ranged from 1 = strongly agree to 4 = strongly disagree.
The responses were
reverse coded such that higher scores are indicative of more traditional gender ideologies. To
examine the effects of differential income we included a measure of proportion of income that
indicates the proportion of the total household income that was contributed by the respondent
(respondent’s income divided by the respondent’s income plus the partner’s income).
Demographic control variables. Age was measured in years. Race was entered as a
series of dummy variables (African-American, Hispanic, and other race) with White used as the
reference group. Education was also a series of dummy variables (less than high school, some
college, college graduate, and postgraduate degree) with high school education serving as the
comparison group. Lastly, the presence of children under 6 was a dummy variable that was
coded 1 for the presence of such children in the household and 0 if no such children were
present.
Analytic Strategy. To address our hypotheses separate Ordinary Least Squares (OLS)
regression equations were estimated for fathers and mothers in order to address gendered
processes that may come into play. Each OLS regression equation contains the demographic
control variables (age, race, education, and presence of children under 6) along with the
37
independent variables (relative work hours, use of flexible scheduling, nonstandard work hours,
gender ideology and proportion of income) in predicting workday housework performance.
RESULTS
The descriptive statistics for the study variables are displayed in Table 1, along with the
results of t tests pertaining to all non-dummy variables.
It is noteworthy that there is a
statistically significant difference between the means of mothers (M = 3.11, SD = 2.16) and
fathers (M = 1.94, SD = 1.50) on workday housework performance, with mothers performing
significantly more workday housework than fathers. Among fathers we find that roughly 12%
self-report as Hispanic, 10% self-report as Black, 74% self-report as White, and 5% self-report
as some other race. For mothers we find that approximately 12% self-report as Hispanic, 7%
self-report as Black, 78% self-report as White, and 3% self-report as some other race. Among
mothers we find that roughly 8% have less than a high school education, 28% have a high
school diploma, 31% have some college, 24% have a four year college degree, and 8% have a
postgraduate degree as their highest level of education.
Among fathers we find that
approximately 15% have less than a high school education, 30% have a high school diploma,
26% have some college, 20% have a four year college degree, and 10% have a postgraduate
degree as their highest level of educational attainment.
On average, the fathers are
significantly older (M = 39.58, SD = 8.39) than the mothers (M = 38.45, SD = 8.74) in our
sample. Roughly 47% of the fathers and 38% of the mothers report the presence of at least
one child under the age of 6 within the home. Mothers and fathers report fairly similar gender
ideologies (M = 2.44, SD = 1.10, M = 2.36, SD = 1.13, respectively), and similar levels of
working a nonstandard work schedule (29% of fathers and 26% of mothers). On average,
mothers, who contribute about 40% of their household income, also earn significantly less than
their spouses or partners (measured proportionately) compared to the fathers, who contribute
38
about 67% of their household income. Mothers, on average, also work significantly fewer hours
than their spouses or partners compared to fathers. Mothers average approximately 9.89 hours
less work hours per week (SD = 18.76) than their partners or spouses, compared fathers who
on average work about 12.77 hours more per week (SD = 18.13) than their partners or
spouses. Mothers, on average, are also significantly more likely to use flexible scheduling (M =
2.20, SD = 1.32) than the fathers in the sample (M = 1.90, SD = 1.29).
Table 1
Descriptive Statistics (N = 308 fathers and 305 mothers)
Fathers
Variables
Age
Hispanic
African American
White
Other race
Less than high school
High school
Some college
College degree
Postgraduate degree
Presence of children under 6
Gender ideology
Proportion of income
Relative work hours
Use of flexible scheduling
Nonstandard work hours
Workday housework performance
M
SD
39.58*
.12
.10
.77
.05
.15
.30
.26
.20
.09
.47
2.44
.67*
12.77*
1.90*
.29
1.94*
8.39
.32
.30
.42
.21
.35
.46
.44
.40
.29
.50
1.10
.19
18.13
1.29
.45
1.50
Mothers
M
SD
38.45*
.12
.07
.82
.03
.08
.28
.31
.24
.08
.38
2.36
.40*
-9.89*
2.20*
.26
3.11*
8.74
.33
.38
.38
.17
.27
.45
.46
.43
.27
.49
1.13
.19
18.76
1.32
.44
2.16
*Indicates a t test of the difference between the means was significant at the .05 level or
higher. Note that t tests were not performed on the dummy variables. aThe comparison group
is Whites. bHigh school degree is the reference category. cHigher scores indicate a more
traditional gender ideology. dThe higher the score, the greater the proportion of income
contributed by the respondent. eThe higher the score, the more hours the respondent works
relative to his or her partner.
The results of the OLS regression models are shown in Table 2. First, we note that the
OLS regression model fails to achieve statistical significance for the fathers, and none of the
39
variables are significant in predicting fathers’ workday housework performance. That is, neither
relative resources theory, gender ideology theory, nor time availability theory are predictive of
men’s workday housework.
Next, we briefly describe the findings pertaining to the control
variables for mothers. The results indicate that Hispanic mothers report performing significantly
more workday housework than White mothers do. In terms of education, mothers with less
than high school education and mothers with college degrees report significantly higher levels
of workday housework than mothers with high school degrees.
Next, we address whether or not the findings support the hypotheses for mothers.
Hypothesis 1 states that proportional income will be negatively related to workday housework
performance, and the results fail to support this hypothesis for mothers.
Hypothesis 2
concerning the relationship between mothers’ gender ideologies and mothers’ workday
housework performance is supported.
The results indicate that more traditional gender
ideologies are positively related to workday housework performance. Hypothesis 3 regarding
fathers’ gender ideologies is not supported. Hypothesis 4 predicts that relative work hours will
be negatively related to workday housework performance, and this hypothesis is supported for
mothers. The findings show that the more hours a mother works relative to her partner or
spouse the less workday housework she reports performing. Hypothesis 5 concerning the use
of flexible scheduling is supported for mothers, as higher scores on the use of flexible
scheduling are associated with greater levels of workday housework.
regarding nonstandard work hours is not supported for mothers.
40
The last hypothesis
Table 2
Summary of Regression Analyses for Variables Predicting Workday Housework
Performance (N = 308 men and 305 women)
Fathers
Variables
B
SE B
Mothers
Β
B
SE B
β
-.01
Control variables
Age
-.01
.02
-.05
-.004
.02
Hispanica
-.30
.32
-.06
.89
.44
African Americana
-.04
.38
.08
-.15
.47
-.02
Other racea
-.04
.38
-.01
.18
.61
.02
Less than high schoolb
-.16
.41
-.03
-1.03
.50
-.13*
Some collegeb
-.13
.23
-.04
-.35
.34
-.07
College degreeb
-.38
.26
-.11
-.71
.34
-.15*
Postgraduate degreeb
Presence of children
under 6
-.39
.34
-.08
-.79
.48
-.11
.25
.23
.08
-.53
.30
-.12
Gender ideologyc
.10
.09
.08
.23
.12
.12*
Proportion of incomed
-.41
.57
-.05
-.22
.77
-.02
Relative work hourse
Use of flexible
scheduling
-.002
.01
-.02
-.02
.01
-.17**
-.02
.07
-.01
.28
.10
.17**
Nonstandard work hours
-.10
.21
-.03
.44
.30
.09
.12*
Independent variables
Adjusted R2
-.002f
.09
*p <.05. **p <.01. ***p <.001. aThe comparison group is Whites.
b
High school degree is the
reference category. cHigher scores indicate a more traditional gender ideology. dThe higher the
score, the greater the proportion of income contributed by the respondent. eThe higher the
score, the more hours the respondent works relative to his or her partner. fNote that the OLS
regression model is not significant for men.
41
DISCUSSION
The present study sought to examine the utility of three traditional housework theories
(relative resources, gender ideology, and time availability) in predicting workday housework
hours among partnered mothers and fathers using data from a nationally representative sample
of working adults.
We narrowed our examination to consider the mechanisms predicting
workday housework performance among married or partnered workers with at least one child
under age 18 living in the home. In particular, we followed other scholars in extending the time
availability perspective to consider not only relative hours worked, but also the use of flexible
scheduling (Noonan et al. 2007; Silver and Goldscheider 1994; Wharton 1994) and nonstandard
work hours (Presser 1994; 2003). We focused on the performance of housework on workdays
because we view these housework hours as especially burdensome, and hence they may be
more likely to cause stress and conflict among working parents who are married or partnered.
It may also be the case that increasing men’s contributions to workday housework is especially
difficult.
The results from the present study suggest that important gendered processes may be
at play in the performance of workday housework hours. Indeed, our study suggests that none
of the traditional theories are useful in predicting fathers’ performance of workday housework
hours.
Not only are none of the variables significant in predicting such fathers’ workday
housework hours, but the model itself is not significant—suggesting that these variables, taken
together, play little, if any, role in shaping fathers’ workday housework hours. The existing
scholarship has tended to predict men’s housework in general rather than men’s housework on
workdays, which may help explain why little variation in fathers’ housework is explained by our
study (e.g., Baxter et al. 2008; Bianchi et al. 2000; Gazso-Windle and McMullin 2003; John and
Shelton 1997; Presser 1994). Future research would benefit from identifying factors that do
42
make a difference in predicting the workday housework performance of partnered men with
dependent children. For example, in keeping with an extended time availability perspective,
scholars could consider the role of work-to-family conflict in predicting workday housework
performance. In contrast to the lack of significant predictors of housework for fathers, among
mothers support is found for the extended time availability theory and gender ideology theory,
suggesting these theories are salient for explaining variation in mothers’ workday housework
performance.
In terms of the extended time availability theory, we find that partnered mothers’ use of
flexible scheduling is associated with increased workday housework performance. In this way,
it does appear that flexible scheduling policies enhance working mothers’ ability to attend to
family needs, such as the performance of household chores, on workdays. However, we find
no evidence of such a relationship for working partnered fathers, which is in accordance with
the findings from a study conducted by Noonan and colleagues (2007). Together these findings
suggest that workplace flexibility, while it may be beneficial for fathers in other ways, does not
appear to enhance their performance of household chores on days that they are working. We
encourage future research to consider whether the use of flexible scheduling is predictive of
other forms of family work, such as emotion-work performance (Minnotte, Stevens, Minnotte,
and Kiger 2007; Minnotte, Pedersen, Mannon, and Kiger 2010) or child care.
Our findings differ from those of Noonan and colleagues (2007), in that they found a
negative relationship between use of flexible scheduling among mothers and their housework
performance, whereas we found a positive relationship. We believe a potential explanation for
the contradictory nature of our finding is that their study focused on a Midwestern sample,
while our study considered a nationally representative sample of employed fathers and
mothers. It could be that the predictors of housework performance are potentially shaped by
43
regional differences.
Moreover, given that their study focused on households with children
around age 7 the results may be also more salient to families with young children. Regardless,
the results from our study suggest that the use of flexible scheduling may expand the time
mothers have available to perform housework on workdays.
Our study finds tentative support for gender ideology theory in predicting mothers’
workday housework performance, with mothers adhering to more traditional gender ideologies
reporting greater performance. We find no evidence of gender ideology shaping the workday
housework performance of working fathers. We think part of the reason we failed to find a
relationship is that we were unable to take into account the gender ideology of the fathers’
partners. Indeed, previous research suggests that partners’ gender ideologies work in concert
to affect the domestic labor performance of men, with egalitarian men married to egalitarian
women being especially likely to have high levels of housework performance (Greenstein 1996).
The present study is characterized by four primary limitations that should be taken into
consideration.
First, although we were able to examine the role of two key partner
characteristics (income and work hours), a full examination of how the dynamics of the couple
come into play in shaping the division of domestic labor is not possible with the present data.
In order to fully address the couple-level characteristics that undoubtedly shape the negotiation
of housework on workdays data from couples is essential.
For instance, the present study
failed to find a significant relationship between nonstandard hours and workday housework
performance, which might be explained by our inability to directly take into account the
partner’s work schedule.
We assume that most of the time if the respondent works a
nonstandard shift that his or her partner probably works a standard shift, especially since the
strategy of rotating shifts is sometimes undertaken by couples to avoid non-family childcare or
to save money on childcare (Deutsch 1999).
44
We also know from previous research (e.g.,
Presser 1994) that when husbands work hours that differ from those of their spouse this tends
to increase the amount of housework performed by such husbands. Hence, our failure to find
such a relationship may stem from our inability to take the partner’s work schedule into
account.
A second limitation of our study concerns the measurement of variables concerning the
respondent’s partner or spouse. Even though we do include some couple level mechanisms in
the present study (relative work hours and proportion of income), we should be mindful that all
data regarding the respondent’s spouse were gathered from the respondent, and may not
accurately represent either the partner’s true work hours or the partner’s actual income.
Third, the use of a one-item measure of gender ideology is not ideal, especially since people’s
gender ideologies are often complex and dynamic. However, we note that this one item has
traditionally been used in many indexes of gender ideology (Davis and Greenstein 2009). Lastly,
we must be mindful that our operationalization of housework, like other telephone survey
measures of housework, is generally not considered as reliable as other measures, such as time
diaries or interviews conducted in person (Bryant, Kang, Zick, and Chan 2004).
Hence,
telephone surveys, such as the one used by the present study, may lead to overestimations of
time spent on housework (Press and Townsley 1998).
For these reasons, we must remain
cautious in the interpretation of the results of the present study.
In conclusion, we have addressed the relative utility of three theories in predicting the
workday housework performance of partnered mothers and fathers with children under the age
of 18 living in the home. Our results point to the salience of the use of flexible scheduling in
predicting mothers’ workday housework performance, with such use associated with increased
housework among the partnered mothers of dependent children in our sample.
No such
relationship was found among the men in the sample. Altogether, our results suggest that
45
future research consider expanded versions of time availability theory rather than just
examining the sheer number of hours worked. Additionally, our work points to the importance
of considering dyadic level mechanisms in predicting workday housework performance among
partnered and married parents. We trust that future research will continue to elucidate the
mechanisms surrounding the gendered division of household labor.
46
REFERENCES
Baxter, Janeen, Belinda Hewitt, and Michele Haynes. 2008. “Life Course Transitions
and Housework: Marriage, Parenthood, and Time on Housework.” Journal of
Marriage and Family 70:259-272.
Bianchi, Suzanne M., Melissa A. Milkie, Liana C. Sayer, and John P. Robinson. 2000.
“Is Anyone Doing the Housework? Trends in the Gender Division of Household
Labor.” Social Forces 79:191-229.
Blair-Loy, Mary and Amy S. Wharton. 2002. “Employees’ Use of Work-Family Policies
and the Workplace Social Context.” Social Forces 80:813-845.
Bond, James T., Cindy Thompson, Ellen Galinsky, and David Prottas. 2003. The 2002 National
Study of the Changing Workforce. New York: Families and Work Institute.
Bryant, W. Keith, Hyojin Kang, Cathleen D. Zick, and Anna Y. Chan. 2004.
“Measuring Housework in Time Use Surveys.” Review of Economics of the
Household 2:23-47.
Coltrane, Scott. 2000. “Research on Household Labor: Modeling and Measuring the
Social Embeddedness of Routine Family Work.” Journal of Marriage and the Family
62:1208-1233.
Coverman, Shelley. 1989. “Women’s Work is Never Done: The Division of Domestic
Labor. Pp. 356-367 in Women: A Feminist Perspective, 4th ed., edited by J.
Freeman. Mountain View, CA: Mayfield Publishing Co.
Davis, Shannon N. and Theodore N. Greenstein. 2009. “Gender Ideology: Components,
Predictors, and Consequences.” Annual Review of Sociology 35:87-105.
Deutsch, Francine M. 1999. Halving It All: How Equally Shared Parenting Works.
Cambridge, MA: Harvard University Press.
47
Fuwa, Makiko. 2004. “Macro-Level Gender Inequality and the Division of Household
Labor in 22 countries.” American Sociological Review 69: 751-767.
Gazso-Windle, Amber and Julie Ann McMullin. 2003. “Doing Domestic Labour:
Strategising in a Gendered Domain.” Canadian Journal of Sociology 28:341-366.
Greenstein, Theodore N. 1996. “Husbands’ Participation in Domestic Labor: Interactive
Effects of Wives’ and Husbands’ Gender Ideologies.” Journal of Marriage and the
Family 58:585-595.
Hersch, Joni and Leslie S. Stratton. 1994. “Housework, Wages, and the Division of
Housework Time for Employed Spouses.” American Economic Review 84:120-125.
Hilbrecht, Margo, Susan M. Shaw, Laura C. Johnson, and Jean Andrey. 2008. “‘I’m
Home for the Kids’: Contradictory Implications for Work-Life Balance among
Teleworking Mothers.” Gender, Work, and Organization 15:454-476.
Hochschild, Arlie R., with Machung, A. 1989. The Second Shift: Working Parents and
the Revolution at Home. New York: Viking Press.
John, Daphne and Beth Anne Shelton. 1997. “The Production of Gender among Black
and White Women and Men: The Case of Household Labor.” Sex Roles 36:171193.
Kluwer, Ester S., José A.M. Heesink, and Evert Van de Vliert. 1996. “Marital Conflict
About the Division of Household Labor and Paid Work.” Journal of Marriage
and Family 58:958-969.
Milkie, Melissa A., Sara B. Raley, and Suzanne M. Bianchi. 2009. “Taking on the
Second Shift: Time Allocations and Time Pressures of U.S. Parents with
Preschoolers.” Social Forces 88:487-518.
Minnotte, Krista Lynn, Daphne E. Pedersen, Susan E. Mannon, and Gary Kiger. 2010.
48
“Tending to the Emotions of Children: Predicting Parental Performance of
Emotion Work with Children.” Marriage & Family Review 46:224-241.
Minnotte, Krista Lynn, Daphne Pedersen Stevens, Michael C. Minnotte, and Gary Kiger.
2007. “Emotion-Work Performance among Dual-Earner Couples: Testing Four
Theoretical Perspectives.” Journal of Family Issues 28:773-793.
Nomaguchi, Kei M. and Melissa Milkie. 2003. “Costs and Rewards of Children: The
Effects of Becoming a Parent on Adults’ Lives.” Journal of Marriage and
Family 65:356-374.
Noonan, Mary C., Sarah Beth Estes, and Jennifer L. Glass. 2007. “Do Workplace
Flexibility Policies Influence Time Spent in Domestic Labor?” Journal of Family
Issues 28:263-288.
Perkins, H. Wesley and Debra K. DeMeis. 1996. “Gender and Family Effects on the
Second-Shift Domestic Activity of College-Educated Young Adults.” Gender &
Society 10:78-93.
Press, Julie E. and Eleanor Townsley. 1998. “Wives’ and Husbands’ Housework
Reporting: Gender, Class, and Social Desirability.” Gender and Society 12:188218.
Presser, Harriet B. 1994. “Employment Schedules among Dual-Earner Spouses and the
Division of Household Labor by Gender.” American Sociological Review 59:348364.
Presser, Harriet B. 2003. Working in a 24/7 Economy: Challenges for American
Families. New York: Russell Sage Foundation.
Ross, Catherine E. 1987. “The Division of Labor at Home.” Social Forces 65:816-833.
Sayer, Liana C. and Leah Fine. Forthcoming. “Racial-Ethnic Differences in U.S.
49
Married Women’s and Men’s Housework.” Social Indicators Research.
http://www.springerlink.com/content/wu2605783451p588/ downloaded
November 12, 2010.
Secret, Mary. 2000. “Identifying the Family, Job, and Workplace Characteristics of
Employees Who Use Work-Family Benefits.” Family Relations 49:217-225.
Shelton, Beth Anne and Daphne John. 1996. “The Division of Household Labor.”
Annual Review of Sociology 22:299-322.
Silver, Hilary and Frances Goldscheider. 1994. “Flexible Work and Housework: Work
and Family Constraints on Women’s Domestic Labor.” Social Forces 72:11031119.
Stevens, Daphne, Gary Kiger, and Pamela J. Riley. 2001. “Working Hard and Hardly
Working: Domestic Labor and Marital Satisfaction among Dual-Earner Couples.”
Journal of Marriage and Family 63:514-526.
Wharton, Carol S. 1994. “Finding Time for the Second Shift: The Impact of Flexible
Work Schedules on Women’s Double Days.” Gender & Society 8:189-205.
50
Voters Reframe the Abortion Policy Debate:
A Theoretical Analysis of Abortion Attitudes in
South Dakota
Pamela Carriveau
Abstract
Laura Colmenero-Chilberg
Since the Supreme Court’s announced its decision in Roe v. Wade
(1973), individuals and groups opposed to legalized abortion in
the United States have battled to reverse the ruling. Using
established political processes, incremental steps such as parental
consent requirements and/or twenty-four hour waiting periods
have been strategically advanced as they charted the path to their
real goal – an all-out abortion ban. Contemporary South Dakota
Pro-Life activists abandoned this incremental approach in 2006, in
the belief that the time was ripe for a voter-supported broadbased abortion ban that could be used to challenge the Supreme
Court decision. They failed, however, to find the right
combination of policy components to appeal to a majority of South
Dakota voters. The November 2006 election resulted in 56% of
South Dakota voter opposing the ban compared to 44% who
supported it. The November 2008 election results were extremely
close – 55% to 45%. However, analysis of individual attitudes
towards abortion using 2006 GSS data suggested that a much
wider margin of opposition and support exists in the voting public.
When we compare the individual responses to abortion questions
to the severe requirements of the proposed ban, we find that as
many as 92% of the voters should have opposed the law. In this
article, we attempt to explain the discrepancy between attitude
preference and voting behavior by showing how the debate on
abortion, in South Dakota specifically but also across the nation,
has been reconceptualized not as a rational calculation of
preferences about abortion but perhaps as a much broader
referendum on family values.

Pamela Carriveau, Ph.D., Black Hills State University, Black Hills State University, Political Science and
Sociology, 1200 University St., Spearfish, SD 57799; e-mail: [email protected]
51
INTRODUCTION
Abortion has been a relentlessly controversial issue in contemporary American society.
Historically, however, this has not always been the case. Prior to the 19th century, abortion was
merely one of the many choices in the reproductive lives of women (Hull and Hoffer 2001).
Women chose abortion for a variety of reasons including an inability to provide economically for
their children, unwed pregnancies, pregnancies due to rape, and a fear for their personal
physical or mental health. Attitudes about abortion were considered private affairs and not
openly discussed, and abortions were often self performed with or without the help of herbalists
and midwives, doctors, family and/or friends. Products used were locally produced with herbal
knowledge widespread.
During the 19th century, many of the products that were used as
abortifacients were fairly easily obtained at local businesses; they were often indirectly but
publicly marketed to women as products that could restart or regulate menstruation with
common knowledge understanding the true purpose of the remedy (Hull and Hoffer 2001;
Myrsiades 2002). It was during the latter half of this century that abortion was criminalized in
the United States (Hull and Hoffer 2001).
While abortion continued to be illegal in the United States for most of the 20th century,
by the 1960s it was a fairly noncontroversial issue in American society as a whole and seen as a
“humanitarian medical issue under the control and supervision of physicians” (McDonagh
2007:188). The largest dissenting group was Roman Catholics. In 1973 when Roe v Wade
reestablished the legality of abortion for American women, the arguments in favor were framed
as a protection of women, returning the decision whether to terminate a pregnancy to the
woman who was pregnant. Soon after the Supreme Court decision, however, concern with the
morality of abortion resurfaced as a central moral concern for not just Roman Catholics but also
increasingly for American Evangelical Protestants. Since the 1970s the number of citizens at
52
the ideological extremes of the abortion issue has increased, but the number of people found in
the middle has remained fairly stable (Sullins 1999).
This ideological extremism both solidified and became increasingly part of the public
dialogue during the 1980s and the 1990s, driving a cultural wedge in American attitudes
between those who increasingly identified themselves as either Pro-Life or Pro-Choice (Hoffman
and Johnson 2005; Joffee 2005). The issue of abortion was assaulted directly with attacks on
women’s health clinics and the doctors and nurses who participated in or performed abortions.
Other more indirect battles were also fought over issues such as RU486, stem cell research and
partial birth abortions.
From the passage of Roe v Wade, the public battle on abortion would lead us to believe
there have been two and only two clearly delineated and contradictory positions on this issue –
Pro-Life and Pro-Choice. Traditionally, identification of membership in particular demographic
categories such as religion, political party, level of education, etc., have provided placement into
one of these two groups, and voting behaviors were thought to be clear (Adams 1997;
Bohlzendahl and Myers 2004; Craig and O’Brien 1993; Evans 1997, 2002; Hoffman and Miller
1998; Jelen and Wilcox 2003) Those identified as Pro-Life would vote to ban abortion, seeing it
as an issue about the right to and sanctity of life. Those identified as Pro-Choice would vote to
retain it, seeing it as an issue about the limitation of individual rights.
More recently the boundaries began to blur somewhat between the two categories with
identification of differing attitudes towards traumatic and convenience abortions.
Traumatic
reasons, such as danger to a woman’s life and health or in the case of rape or incest, have
tended to elicit much stronger support for abortion from the general public. Elective (or what
have been called convenience) abortions, such as for reasons of financial hardship, a desire for
no more children, or if the mother is unmarried, have found much lower levels of support
53
(Hoffman and Johnson 2005).
This idea that there are women and circumstances that are
“worthy” of abortion and those that are “unworthy” is somewhat reminiscent of past sociological
discussions on the worthy and unworthy poor (Gans 1972). Even as advocates of both the ProLife and Pro-Choice movements have used “absolutist rhetoric” (Strickler and Danigelis
2002:189) to battle it out in the media, contemporary Americans have proved somewhat
ambivalent in their attitudes towards abortion, and the extremist definitions of the two
categories have not necessarily matched this ambivalence. Most Americans support abortion
remaining legal (Joffee 2005), but the blurring of the boundary between the two opposing
positions has raised the idea that the deciding element for many centers on the reason
motivating the wish for the abortion (Hoffman and Johnson 2005; Shaw 2003).
WHAT VOTER CHARACTERISTICS SEEM TO MAKE A DIFFERENCE?
Several variables commonly have been investigated by researchers attempting to
identify American attitudes towards abortion. The first three -- education, gender, and race -have been found to have inconsistent predictive abilities for attitudes about abortion. Three
other variables – political affiliation, church affiliation, and church attendance -- have proven to
be somewhat more explanatory about American attitudes on abortion than any of the others.
Education
In the past, education was identified as a strong indicator of a person’s stance on
abortion (Chafetz and Ebaugh 1983; Granberg 1991; Harris and Mills 1985; Jones and Westoff
1978; Petersen and Mauss 1976). The higher the level of education a person had, the more
likely they would be to support abortion, even within religious traditions (Evans 1997). There
has been a decline, however, in this correlation between education and support for abortion,
regardless of religious affiliation (Petersen 2001). Active affiliation (and the key here is level of
54
church attendance, not just congregational membership) appears to reduce the liberalizing
effects of education (Jelen and Wilcox 2003).
Gender
Gender is another commonly believed correlate with pro-abortion attitudes.
In
particular, liberal gender attitudes have been linked to higher levels of support for the ProChoice position.
There has been a clear liberalization of gender attitudes in American society
(Bolzendahl and Myers 2004), but while past studies may have found that gender can be
explanatory of liberal gender ideology (Craig and O’Brien 1993; Luker 1984), more recent
research would indicate that a that this does not necessarily provide a link to higher levels of
support for abortion (Jelen and Wilcox 2003; Kaufmann 2002; Strickler and Danigelis 2002).
Many areas that were one time perceived as feminist (right to equal pay, job opportunities, and
education) have gained such broad cultural acceptance that they are just commonly held by a
large portion of the population and are no longer identified with feminism. This change has not
occurred in attitudes towards abortion (Bolzendahl 2004).
Some research indicates that
attitudes about abortion actually have become more divided over time related to this one issue
(DiMaggio, Evans, and Bryson 1996).
Race
In general, abortion attitudes are not strongly affected by race and ethnicity, although
Black Americans and Hispanics traditionally have been identified as more Pro-Life than White
Americans (Evans 2002; Hall and Ferree 1986). Strickler and Danigelis (2002) found that by
the middle 1990s, there was a greater proportion of African American adults who were ProChoice than there were White adults. Black Americans have increasingly diverged on the issue
of abortion from both White evangelicals and Roman Catholics (Evans 2002).
Hispanic
Americans, even with their overwhelmingly Roman Catholic tradition, are shown to have the
55
same attitudes about abortion as the general population, although there is a greater inclination
for them to be Pro-Life (Bolks et al. 2000; Roberts 2007). In the case of both Hispanics and
Black Americans, race seems to trump religion.
Political Affiliation
The Republican Party today is the usual political affiliation of Pro-Life proponents and
the Democratic Party for Pro-Choice advocates. It is interesting to note that this is a relatively
recent situation.
In the 1968 election between Humphrey and Nixon, Pro-Lifers voted
predominantly for Humphrey and Pro-Choicers for Nixon (Adams 1997).
Distinctly different
positions on abortion are found beginning with the 1984 Democratic and Republic National
Platforms (Carmines and Woods 2002).
As recently as 2008, the Republican National Platform
clearly indicated “At its core, abortion is a fundamental assault on the sanctity of innocent
human life” (p. 59), while the Democratic National Platform stated “The Democratic Party
strongly and unequivocally supports Roe v Wade and a woman’s right to choose a safe and
legal abortion … and we oppose any and all efforts to weaken or undermine that right” (p. 52).
This switch in focus has been perceived to be as a result of the guiding hand of party elites
(Adams 1997; Carmines and Wood 2002; Roh and Haider-Markel 2003). Some researchers
have proposed the abortion issue can actually serve as a tactical tool for political elites to
position themselves in the nominating process (Jelen and Wilcox 2003; Layman 2001).
Religious Affiliation
Roman Catholics were the group that began the Pro-Life crusade. After Roe v Wade,
Evangelical Protestants joined the battle, and today the Pro-Life position is usually presented
through the lens of a fundamentalist religious orientation identified as the Christian Right.
American Christian religious affiliation can be divided into three major groups:
56
Roman
Catholics, Evangelical Protestants, and Mainline Protestants1. The first two groups have been
seen as overwhelmingly Pro-Life with the last one Pro-Choice. There have been recent changes
in this positioning.
Roman Catholics have become increasingly polarized over the issue of
abortion (Evans 2002; Hoffman and Johnson 2005), even while the official position of the
church is Pro-Life, and this “within-group” diversity has increased over the past 30 years (Evans
2002; Hoffmann and Miller 1998).
Hoffmann and Miller (1998) looked at attitudes among
different religious groups to abortion from 1972 to 1994 and found that over time, both
Catholics and Protestants have become slightly less unified regarding abortion. Protestants,
primarily Lutherans and Methodists, have diverged on the issues of abortion.
According to
Sullins (1999), over time younger Roman Catholics have become slightly more permissive in
their attitudes towards abortion, while younger Protestants have become slightly less
permissive. Even some Evangelical Protestants seem to be moving in a less divisive direction.
“[A] younger generation of evangelical pastors – including the widely emulated preachers Rick
Warren and Bill Hybels – are pushing the movement and its theology in new directions”
(Kirkpatrick 2007:40) including a renewed focus on saving souls, spiritual growth, social justice
and a movement away from the strident anti-abortion and anti-same sex marriage arguments
of the past. Alliances between the Christian Right and Roman Catholics also seem unlikely to
broaden beyond the common attitude towards abortion. Bendyna et al. (2001) identify strong
differences on issues such as the death penalty, Creationism, and social welfare.
Church Attendance
Church attendance has proven to be one of the strongest indicators of attitudes towards
abortion, trumping all of the others characteristics, including religious affiliation.
Younger
Protestants overall attend religious services more, and they have become less Pro-Choice.
1
There is some disagreement (both popular and academic) about how Latter Day Saints (LDS) should be
categorized, but the GSS clearly places the LDS in the Protestant classification along with the Jehovah’s
Witnesses and Christian Scientists (Smith 1986/87).
57
Church attendance has dropped among young Roman Catholics since 1972, and they have
become more Pro-Choice in their attitudes (Jelen and Wilcox 2003; Sullins 1999).
Even
frequent attendees of denominations that voice a stronger Pro-Choice message are more ProLife in their attitudes (Jelen and Wilcox 2003). Overwhelmingly, it seems that those individuals
who have active participation in their religious organization are more strongly against abortion
then those with lesser levels.
SOUTH DAKOTA AND ABORTION IN 2006 AND 2008
In 2006 South Dakota took center stage in the first legislative attempt to set up a direct
legal challenge to Roe v Wade. In the past, political action only had been taken to chip away at
the decision (parental consent, etc.). South Dakota was an interesting state to select for this
attack on reproductive rights. With a population under 800,000 people and predominantly rural
in geography, there was actually only a single abortion provider in the state, located in its
largest city (Sioux Falls) in the far southeast corner. South Dakota already had one of the most
restrictive climates in the nation for abortion: parental consent for minors, a 24-hour waiting
period for all women seeking abortion, pharmacies allowed to refuse to provide contraception
(Halloran 2006), and, most recently, a 2008 requirement that doctors providing a woman with
an abortion ask if she wished to see the sonogram of the fetus. South Dakota is also one of
only six states in the nation with a “trigger” law on the books that would automatically make
abortion illegal in the state if federal policy allows it (Vestal 2006).
The South Dakota state legislature successfully passed HB 1215 in 2006, which would
have made all abortions in the state a felony except in the case of threat to the pregnant
woman’s life. The bill failed to include the traumatic exceptions often highlighted as necessary
to draw the backing of moderates – rape and incest (Hoffman and Johnson 2005), but it was
signed into law by Governor Mike Rounds who had openly proclaimed his support for the
58
legislation: “I am Pro-Life and I do know that my personal belief is that the best way to
approach elimination of abortion is one step at a time. And I do think that this court will
ultimately take apart Roe v Wade one-step at a time” (“South Dakota legislature attacks Roe v
Wade” 2006). The law was subsequently referred to South Dakota voters prior to its effective
date where it was defeated 56% to 44% (Kafka 2007).
Commonly held reasons for its defeat were varied. Its failure to include the traumatic
exceptions for rape and incest made it unappealing to many voters, including many
conservatives: “The fact that this bill didn’t have exceptions for the mother’s health was what
concerned a lot of women, even conservative women” (Sunshower 2006:28).
It was also
perceived as the government forcing its will on individual decision makers: “People in South
Dakota don’t like government telling them what to do” (Bravin 2006).
For some Native
American women, the issue was seen as opposing traditional cultural values. Theresa Two
Bulls, a one-time vice president of the Ogala Sioux Tribe and present state senator, explained,
“In our tradition, the woman is the backbone of the family. It’s up to her to decide when and
where to have and raise children” (Sunshower 2006:27). Native Americans are 8.5% of South
Dakota’s population (U.S. Census Bureau 2008).
The 2008 South Dakota abortion ban bill was not brought to the legislature directly.
Preceded by HB 1193 which stated, “No facility that performs abortions may perform an
abortion on a pregnant woman without first offering the pregnant woman an opportunity to
view a sonogram of her unborn child,” Initiated Measure 11 targeted the perceived failure of
the 2006 legislation to address voter dissatisfaction with its lack of exceptions for traumatic
reasons. The 2008 legislation included exceptions for the life and/or health of the mother, or in
the case of rape or incest. While these exceptions seemed to provide much broader latitude to
allow abortion, in reality they were highly regulated exceptions with the health of the mother
59
related to “the serious risk of substantial and irreversible impairment of a major bodily organ or
system of the woman” (Initiated Measure 11 Initiative Petition 2008). Framed as a method to
protect women and children, Initiated Measure 11 included rigorous ties to the criminal justice
system through required biological sampling for forensic DNA testing of both the mother and
the aborted fetus as part of its intention to “deter fraudulent claims” (Ballot Pamphlet 2008) and
severe penalties for doctors who didn’t meet the requirements of the law including being
charged with a Class 4 felony with punishment of up to 10 years in jail and a fine of up to
$20,000 (Initiated Measure 11 Initiative Petition 2008).
came from both ends of the ideological spectrum.
Resistance to Initiated Measure 11
Many religious conservatives found
themselves unable to vote for a ban that provided exceptions for any reason. To try to deal
with this moral conflict for Roman Catholics, dioceses in South Dakota urged congregants to
vote for the measure. In an open letter to his diocese, Bishop Blase Cupich of the Diocese of
Rapid City said, “The ultimate and preferred goal is to defend the right to life for all the unborn
against the violence of abortion.
However, a gradualist approach is also a responsible and
justifiable way of proceeding” (Cupich 2008). At the other end of the spectrum, ideological
liberals could not support the idea of any limitations on the ability of a woman to have an
abortion. Regardless of the included exceptions, the 2008 abortion ban was defeated, this time
with a 55% to 45% vote.
THEORETICAL ANALYSIS – CHANGING SYMBOLS
Symbolic Interactionism can provide a lens through which we can try to interpret the
failure of the 2006 and 2008 attempts to ban all forms of abortion in South Dakota. Blumer
explained, “Humans act toward a thing on the basis of the meaning they assign to the thing”
(Blumer 1969:2). One key to understanding human behavior like voting to support or not
support specific policy positions (for this study, abortion) lies in this very process of social
60
interaction.
Abstract representations, symbols, can have different meanings for different
individuals. The meanings attached to symbols arise out of the context of individuals’ social
interactions and their interpretation of the situations within which the symbols exist (Blumer
1969).
Meaning is not inherent but rather is constructed as action takes place within this
interpreted social environment. Words or gestures that evoke common meanings among users
and others in their social groups are called significant symbols (Mead 1934). They are important
in social interaction because they allow people to communicate and “enable people to anticipate
how others are likely to act in a situation” permitting coordination of action (Sandstrom, Martin,
and Fine 2006:4). The meanings people give to things are not static but can change as the
individual’s interpretation of the interactive process changes (Blumer 1969), and “…people
create, negotiate, and change social meanings through the process of interactions” (Sandstrom,
Martin, and Fine 2006:1). When we try to understand the narrow margins by which the South
Dakota 2006 and 2008 abortion legislation failed there are two important significant symbols to
consider – the symbol of family and that of abortion itself.
Abortion and family are symbols that have had different meanings for different people at
different points in history. People perceive their social environment as composed of divergent
social categories that have values, beliefs and significant symbols attached to them, and these
are held to be important by individuals that identify themselves as members of the social
category (Miller and Hoffman 1999). There are, of course, many social category groupings in
each individual’s life. Individuals essentially choose “sides” and then coordinate their actions
based on the unique value-set they perceive to be held by members of that social category.
Once an individual has self-identified with a particular social category, the norms of that
category provide a polarizing vehicle, which leads to a differentiation of others into in-groups or
out-groups, impacting attitudes and behaviors of the individuals in both sets of groups. The
61
decision in Roe v Wade (1973) and the formation of Jerry Falwell’s Moral Majority and Pat
Robertson’s Christian Coalition as political forces in the 1970s and 1980s led to the solidification
of two clearly delineated and meaningful oppositional positions about the meaning of abortion
and the family, and by extension to two recognized social categories – Pro-Life and Pro-Choice.
For the Pro-Life social category, a significant symbol has been that of abortion. This
very same significant symbol is influential for the Pro-Choice social category. How can both
sides use the same symbol?
Although the symbol itself is the same, the definition of that
symbol and its meaning is widely different, and the difference in the symbol itself revolves
around two contradictory but equally powerful definitions of family.
It is these two
contradictory definitions of family and their symbolic meaning that we find in the contemporary
abortion dispute – the traditional family structure vs. the contemporary family structure.
Advocates of the Pro-Life position have identified with the definition of the traditional
family based on a clearly identified set of family values articulated by Talcott Parsons in the
mid-20th century. He branded it as the only family structure leading to societal equilibrium. This
explanation of the structure of the family has been adopted (although falsely) as the historical
norm (Coontz 1992, 2005). To have traditional family values has included a very specific set of
criteria. One of the most visible contemporary definers and proponents of this traditional family
values set has been Dr. James Dobson with his Focus on the Family franchise. On the Focus on
the Family website (http://www.focusonthefamily.com), the value set of the traditional family
are clearly articulated: heterosexual marriage and nuclear family structure originated in the
plans of the Christian God; distinctly divided gender roles and responsibilities with the husband
clearly identified as breadwinner and the wife as homemaker; the permanence of marriage;
sexual relations only within marriage, and the sanctity of life.
The opposite characteristics
(definitional of the contemporary family values structure) are seen as harmful to the family, and
62
ultimately to society as a whole. They include non-traditional family structures and roles, and
the acceptance of divorce, cohabitation and premarital sex, and abortion. The traditional family
values position holds that failure to comply with the characteristics of the traditional family
endangers the institution of the family, society as a whole, and the United States as a nation.
Advocates of the Pro-Choice position have argued the opposing position on the health
and structure of the family: the family is not deteriorating; in fact, it might even be stronger
than in the past. Researchers such as Amato (Amato 2004), Coontz (1992; 2005) and others
(Sandberg and Hofferth 2001) would indicate that families are undergoing change, but that
they are resilient and even happier today than they have been in the past. Families are not
defined by a single structure, but may have multiple forms and instead are defined by whether
they undertake a certain set of core responsibilities: physical maintenance and care of
members; addition of new members to the family unit through procreation or adoption;
engagement in the socialization of new generations; social control of the family unit,
production, distribution and consumption of goods and services necessary to the family; and
maintenance of the family and motivation of its members using love (Cheal 2002).
Radical Pro-Life and Pro-Choice individuals from the adoption of Roe v Wade have
defined the meaning of the abortion symbol in a consistent manner: Pro-Life extremists have
seen abortion as a serious social problem undermining the traditional family, the symbol of the
ideal America, and the glue holding society together.
Pro-Choice extremists have not seen
abortion as harmful to the social system and consider it may even be beneficial to families,
since the opportunity to exercise reproductive choice was and is an exercise of individual civil
and human rights leading to a more egalitarian and democratic society as a whole.
The
definition of the abortion symbol from the position of the extremists is clear and not in question,
but what about those individuals who do not hold these same extremist positions? How do they
63
interpret the situation and define the abortion symbol and the institution of the family? What
impact do they see abortion having on traditional American society and its bedrock, the
traditional family?
Some Pro-Choice voters may have redefined the significant symbol of abortion. These
individuals may still label themselves as Pro-Choice, but where abortion was once for them the
symbol of individual freedom and reproductive rights, it is now perceived to be a symbol of
destruction, a serious social problem eroding the foundations of the American family and by
extension American society as a whole. For them, the two symbols – abortion and family – are
inextricably intertwined, and in contemporary American society, the changing face of the
American family and its perceived fragility has huge emotional currency. This identification of
the family as a significant symbol with a traditional family values definition as a concern for
some who might otherwise define themselves as Pro-Choice may help explain the unexpected
results associated with the 2006 and 2008 South Dakota failed abortion initiatives.
Where do we see evidence that members of the Pro-Choice social category are
concerned about the fitness of the American family? If this explanation is viable, then there
should be an indication of a changing meaning of the symbols of family and abortion for some
who have defined themselves as Pro-Choice. In South Dakota, the organization that holds the
Pro-Choice position is meaningfully named South Dakota Campaign for Healthy Families. In the
literature they provided for canvassing purposes during the 2008 election period, the family and
the effects on it are also clearly highlighted:
“The decision to have an abortion is profoundly difficult for a
woman and her family. This complex, personal decision should be
between a woman, her family, her God and her doctor. No woman
makes this decision without consulting with family and after deep
soul-searching. Sometimes a woman decides that ending a
pregnancy is the best decision for her and her family”
(http://sdhealthy.3cdn.net/423df35d50e0f58a5a_w4m6bpntq.pdf)
.
64
A series of television advertisements aired during summer and fall of 2008 from this
group advocated the idea that the proposed abortion would give Government (which level is not
indicated) undeserved power “over certain personal and private medical decisions of South
Dakota families”
(http://www.sdhealthyfamilies.org/news/entry/new_ad_highlights_unprecedented_government
_intrusion_of_measure_11/).
An example of this on the national level was the clear inclusion of the traditional family symbol
in both the 2008 Republican and Democratic Platforms: “It’s time we just stop talking about
family values and start pursuing policies that truly value families” (p. 15) and “We support the
appointment of judges who respect the traditional family values and dignity of human life” (p.
52).
Commentary on the fast-approaching destruction of the traditional American family is
found in both the scholarly and popular presses. In the scholarly press, research like that done
by Gallagher (1996:184) blames the deterioration of the family on women’s failure to seek out
suitable breadwinners freeing them to be homemakers who can “devote their talents and
education and energy to the rearing of their children, the nurturing of family relationships, and
the building of community and neighborhood.” Ten years later, Thistle (2006:7) continued to
support this idea highlighting the harms suffered through the loss of the “traditional” male and
female division of labor resulting from an increased presence of women in the workplace:
“While emphasis on women’s role as mothers, for example, once blocked their opportunities
outside the home, the withdrawal of support for women’s domestic labor in recent years is
creating new hardships.”
This paper argues that some South Dakotans, in particular some of those who have
identified themselves as Pro-Choice, may have changed their voting behaviors on abortion after
65
redefining the meaning of abortion and the family, adopting new symbolic meanings for them.
Critical for the analysis in this study is the idea that social categories are not cemented in
meaning, and over time they can be and are reconstructed based on the changing interactions
and definitions assigned to the significant symbols that represent the social categories. Using
the lens of Symbolic Interactionism we can investigate the possibility that the changing
meanings of the significant symbols abortion and the family may provide an explanation for
voting behaviors in middle-ground voters in the Pro-Choice social category.
HYPOTHESES
American citizens had never had the opportunity to vote directly on the issue of
abortion. The 2006 South Dakota abortion ban legislation, referred to the voters through the
referendum process, provided this opportunity. Using attitudinal survey data we attempted to
make sense of the 2006 abortion ban vote. It quickly became clear that the established causal
variables used to explain attitudes toward legalized abortion were not capable of explaining the
results of this election. The initiative driven 2008 South Dakota abortion ban election results
provided another opportunity to collect data related to this issue.
This situation, an election in 2006, an attitudinal survey from 2006, and an election in
2008, presented a unique opportunity for the study of abortion attitudes and votes in a quasiexperimental design.
We could use public opinion survey data to measure support for the
abortion ban and then compare the survey data to the actual vote on the proposed abortion
bans recorded in both the November 2006 and November 2008 elections. Initially, we expected
that the percentage of voters supporting/opposing the abortion ban in 2006 would be different
than the percentage in 2008. The 2008 abortion ban was the less restrictive of the two, thus
we expected there would be more public support for the 2008 ban. Additionally, we expected
that the 2006 attitudinal data would predict the voting behavior for both elections; people who
66
expressed agreement/disagreement with abortion in different situations would vote accordingly
in both 2006 and 2008.
However, analysis of the actual election returns in conjunction to the attitudinal survey
data made clear that neither of these expectations were supported. More people voted for the
abortion ban than would be expected based on their expressed attitudes about abortion. Also,
the for and against vote distributions in 2006 were practically identical to the vote distributions
in 2008 even though there were significant differences between the 2006 and 2008 abortion
ban policies. If attitudes about abortion were not guiding the vote decisions, what was?
Based on language used by both sides of the abortion ban campaigns, we decided to
investigate the influence of attitudes about family values on expressed abortion attitudes.
Using Blumer’s symbolic interactionist lens with the opposing symbols of traditional family
values and contemporary family values, we developed a new composite independent variable
which served as an indicator of commitment to “family values” and used it model attitudes
toward abortion. The use of this variable in our model provided one possible explanation for
the discrepancy between the attitudinal survey data and the actual vote outcome in South
Dakota.
Hypothesis 1: Voters with traditional family values will be more likely to support the
abortion ban.
Hypothesis 2: Family values attitudes can be used to explain why the vote results in
2006 and 2008 are so similar.
METHOD
In order to assess the level of support for the proposed South Dakota abortion ban with
exceptions for the life/health of the mother and victims of rape/incest, we utilized the 2006
General Social Survey from the National Opinion Research Center. This raised the question of
67
whether national survey data could be disaggregated in such a way as to analyze regional or
state attitudes. A number of researchers have used aggregated individual responses in national
surveys to develop indicators of state level attitudes on issues (Brace, Sims-Butler, Arceneaux,
and Johnson 2002; Erikson, Wright, and McIver 1993; Griffin 2006; Jones and Norrander 1996;
Norrander 2001; Norrander and Wilcox 1999). Specifically, Brace et al. (2002) analyzed the use
of national GSS data to study state level attitudes on a number of issues, including abortion.
They found, “while the sampling strategy used to collect the data was not designed to produce
representative state samples, these samples appear to be representative” (177).
However,
sample size can be a significant problem when national data is broken down by state. In order
to use GSS data specific only to South Dakota and attain a sample size large enough for
statistical analysis, we would have needed to pool survey responses over a long period of time.
For example, Brace et al. (2002) pool state specific GSS data from 1974 to 1998. Although
Brace et al. (2002) find high reliability and validity, especially with abortion attitudes, using this
method, our theory argued that the causes of abortion attitudes had changed in the last three
decades. Consequently, we were unwilling to use the pooled state-specific data over such a
long period of time. As an alternative, we decided to analyze attitudinal data only from the
2006 GSS survey but expanded our sample to the regional level to increase our sample size.
The West North Central region is comprised of Minnesota, Iowa, Missouri, North Dakota, South
Dakota, Nebraska, and Kansas.
We hypothesized that attitudes about family values influence abortion attitudes, thus,
we first operationalized family values and then conducted reliability testing on this composite
measure to ascertain its usefulness. Next we mapped the frequency distributions of abortion
attitudes for the West North Central (which contains South Dakota) as well as the other regions
in the United States. These frequencies were categorized by the conditions under which the
68
abortion would occur: any reason, not married, married but doesn’t want more children, low
income, strong chance of a serious birth defect, pregnant as a result of rape, and in the case
that the woman’s own health was seriously endangered by the pregnancy. These categories
corresponded to the exceptions (or lack thereof) included in the 2006 and 2008 abortion bans,
which allowed us to speculate how the votes on the ban should have distributed had people
voted solely on their attitudes about abortion. Given the difference between the expected vote
distribution and the actual vote distribution, we attempted to construct a better predictive
model for abortion attitudes using OLS regression. Specifically we compared a model including
a family values variable to one without this variable to show that the family values variable
exhibits a strong relationship to abortion attitudes. Finally, we employed bar charts to better
illustrate the influence of family values attitudes on abortion attitudes. These charts provided
some support for our second hypothesis, that family values attitudes explain why the vote
distributions in the 2006 and 2008 abortion ban votes were so similar even though the
proposed policies were significantly different.
Dependent Variables
The GSS asked seven questions regarding legalized abortion. Respondents answered
either “yes” (1) or “no” (2).
“Please tell me whether or not you think is should be possible for a pregnant woman to obtain a
legal abortion…
If
If
If
If
If
If
If
she wants an abortion for any reason.
she is not married and does not want to marry the man?
she is married and does not want any more children?
the family has a very low income and cannot afford any more children?
there is a strong chance of serious defect in the baby?
she became pregnant as a result of rape?
the woman’s own health is seriously endangered by the pregnancy?
69
These questions were considered separately as well as in a simple additive scale which ranges
from approval in all cases (7) to disapproval in all cases (14). The first four questions listed
above have been described in some of the literature as “elective” reasons (some
groups/research refer to these questions as the “convenience” rationales for abortion) while the
final three questions are referred to as the “traumatic” reasons for legalized abortion (Hoffman
and Johnson 2005).
Independent Variables
A number of independent variables are used to explain levels of support (or lack thereof) for
an abortion ban in general and for the proposed South Dakota initiative. Arguably, level of
support for legalized abortion varies across the country. The GSS breaks the fifty states and
District of Columbia into nine different regions. See Table 1.
(1) New England = Maine, Vermont, New Hampshire, Massachusetts, Connecticut, Rhode
Island
(2) Middle Atlantic = New York, New Jersey, Pennsylvania
(3) East North Central = Wisconsin, Illinois, Indiana, Michigan, Ohio
(4) West North Central = Minnesota, Iowa, Missouri, North Dakota, South Dakota,
Nebraska, Kansas
(5) South Atlantic = Delaware, Maryland, West Virginia, North Carolina, South Carolina,
Georgia, Florida, District of Columbia
(6) East South Central = Kentucky, Tennessee, Alabama, Mississippi
(7) West South Central = Arkansas, Oklahoma, Louisiana, Texas
(8) Mountain = Montana, Idaho, Wyoming, Nevada, Utah, Colorado, Arizona, New Mexico
(9) Pacific = Washington, Oregon, California, Alaska, Hawaii
South Dakota is located in the West North Central Region. The West North Central region
includes an interesting combination of states covering a large area of the country. Politically,
Minnesota is moderately liberal while South and North Dakota, Nebraska, and Kansas are more
conservative (and the western sides of these states also have strong libertarian elements.) This
raised the question of whether data from the West North Central region would accurately reflect
the attitudes of citizens of South Dakota. But just as this West North Central region includes
liberal and conservative communities, so does the state of South Dakota. Within the state,
70
people often refer to “east river” and “west river” when discussing politics – the Missouri River
splits the state nearly down the middle. “East river” includes the Sioux Falls area as well as four
of the state’s public universities and is reliably more liberal especially compared to “west river.”
Table 1
Percentage of “Yes” Responses for Each Abortion Question for Each Region
Woman’s health
seriously endangered
Pregnant as a result of
rape
Strong chance of
serious defect
Low income – can’t
afford more children
Married – wants no
more children
Not married
Abortion if woman
wants for any reason
GSS Abortion Questions
Middle Atlantic
46.0% 42.4% 47.2% 47.3% 77.8% 82.2% 89.3%
East North
Central
West North
Central
South Atlantic
42.1% 40.8% 41.6% 41.5% 72.3% 75.9% 84.1%
East South
Central
West South
Central
Mountain
30.0% 29.0% 30.6% 35.1% 64.3% 70.1% 77.1%
Pacific
51.2% 51.9% 54.3% 55.8% 80.5% 82.8% 92.2%
All Regions
Combined
40.4% 40.1% 42.5% 42.6% 74.2% 77.6% 87.9%
35.5% 38.8% 36.1% 37.7% 74.6% 76.9% 90.8%
35.9% 37.0% 38.3% 37.4% 71.2% 75.0% 87.9%
28.3% 25.9% 33.5% 31.3% 65.0% 71.1% 85.6%
45.9% 45.3% 47.4% 48.1% 84.3% 85.4% 93.7%
71
In addition to studying abortion attitudes based on region, we also modeled abortion
attitudes using a number of independent variables.
As mentioned earlier: gender, race,
education level, income level, political ideology, and religion have been used in the past to
predict abortion attitudes. The GSS allowed us to model these variables as well:
Female:
0 = Male; 1 = Female.
White:
0 = Black and Other; 1 = White.
Education:
0
1
2
3
4
=
=
=
=
=
Less than high school;
high school;
junior college;
bachelor;
graduate
Family Income 2006:
1 = Under $1,000;
2 = $1,000 to $2,999;
3 = $3,000 to $3,999;
4 = $4,000 to $4,999;
5 = $5,000 to $5,999;
6 = $6,000 to $6,999;
7 = $7,000 to $7,999;
8 = $8,000 to $9,999;
9 = $10,000 to $12,499;
10 = $12,500 to $14,999;
11 = $15,000 to $17,499;
12 = $17,500 to $19,999;
13 = $20,000 to $22,499;
14 = $22,500 to $24,999;
15 = $25,000 to $29,999;
16 = $30,000 to $34,999;
17 = $35,000 to $39,999;
18 = $40,000 to $49,999;
19 = $50,000 to $59,999;
20 = $60,000 to $74,999;
21 = $75,000 to $89,999;
22 = $90,000 to $109,999;
23 = $110,000 to $129,999;
24 = $130,000 to $149,999;
25 = $150,000 or over.
Political Views (Ideology):
1 = extremely liberal;
2 = liberal;
72
3
4
5
6
7
=
=
=
=
=
slightly liberal;
moderate;
slightly conservative;
conservative;
extremely conservative.
The GSS accessed respondents’ religious choices in a number of ways, including
affiliation and level of fundamentalism, but church attendance has been one of the most reliable
predictors of attitudes toward abortion in earlier research.
Church attendance: “How often do you attend religious services?”
0 = Never
1 = Less than once a year
2 = Once a year
3 = Several times a year
4 = Once a month
5 = Two to three times a month
6 = Nearly every week
7 = Every week
8 = More than once a week
As the literature review above notes, past research has had mixed success with these
standard predictors of abortion attitude. Upon study of the rhetoric used in the 2006 and 2008
abortion ban campaigns (by both those who supported and opposed the ban), we identified a
concept that we believe will be particularly effective in understanding how someone feels about
the abortion issue: family values. As discussed in the theory section above, traditional family
values include heterosexual marriage and nuclear family structure originating in the Christian
God’s plan; distinctly divided gender roles and responsibilities with the husband clearly
identified as breadwinner and the wife as homemaker; the permanence of marriage; sexual
relations only within marriage; and the sanctity of life. Contemporary family values include
support of non-traditional family structures and roles, divorce, cohabitation and premarital sex,
and abortion. To operationalize this concept we created an index using questions asked in the
GSS related to these aspects of family values. These questions include:
1. Bible as word of God
73
“Which of these statements comes closest to describing your feelings about the
Bible?
a. The Bible is the actual word of God and is to be taken literally, word
for word.
b. The Bible is the inspired word of God but not everything in it should be
taken literally, word for word.
c. The Bible is an ancient book of fables, legends, history, and moral
precepts recorded by men.”
2. Premarital sex
“If a man and woman have sex relations before marriage, do you think it is
always wrong, almost always wrong, wrong only sometimes, or not wrong at
all?”
3. Extramarital sex
“What is your opinion about a married person having sexual relations with
someone other than the marriage partner -- is it always wrong, almost always
wrong, wrong only sometimes, or not wrong at all?”
4. Homosexual sex
“What about sexual relations between two adults of the same sex -- do you think
it is always wrong, almost always wrong, wrong only sometimes, or not wrong at
all?”
5. Divorce law
“Should divorce in this country be easier or more difficult to obtain than it is
now?”
6. Better for a man to work, woman to tend the home
“It is much better for everyone involved if the man is the achiever outside the
home and the woman takes care of the home and family.”
7. Mother working does not hurt children.
“A working mother can establish just as warm and secure a relationship with her
children as a mother who does not work.”
Once coded appropriately, we created a simple additive index of these variables resulting in a
variable we call Family Values Index, which ranges from contemporary family values to
traditional family values with an alpha of .681 when we looked at the nation as whole and an
alpha of .697 for the West North Central region (which includes South Dakota). Given the
exploratory nature of this research, these levels were acceptable even though they fell just shy
of the standard .7 alpha criteria for an adequate scale. Our theory provided strong justification
74
for the inclusion of all of these items in the Family Values Index based on face validity. Item
analysis also supported the inclusion of each item, although some more than others. See Table
2. The items related to premarital sex, homosexual sex, women staying at home, and their
interpretation of the Bible strongly correlate with one another. The questions regarding women
working (R2 = .174) and divorce laws (R2 = .227) were the weakest correlates. We elected to
retain both of these questions even though they stand out as the least productive items in the
index because opposition to divorce and beliefs regarding the effect on the well-being of the
children of the mother working are fundamental to the traditional family values position.
Table 2
Item Analysis of Family Values Index
All Regions
West North Central
Squared
Multiple
Correlation
Alpha If Item
Deleted
Squared
Multiple
Correlation
Alpha If Item
Deleted
.174
.681
.213
.696
Mother Working
Hurts Children
Man Work, Woman
Home
Premarital Sex
.255
.640
.315
.644
.362
.598
.381
.643
Extramarital Sex
.121
.664
.285
.663
Homosexual Sex
.399
.584
.480
.573
Bible
.244
.638
.217
.673
Divorce
.070
.685
.137
.705
Family Values
Index
All Seven Items
Cronbach’s Alpha = .681
75
Cronbach’s Alpha = .697
RESULTS
The 2006 South Dakota abortion ban (Referred Law 6) included exceptions that would
allow a woman to get an abortion only in the event that her life was in danger. This ban was
rejected by the voters by a 55.57% to 44.43% margin. Activists supporting an abortion ban
modified the exclusions in 2008 to be slightly more expansive in hopes of attracting enough
voter support to pass the ban. Thus, the 2008 South Dakota abortion ban (Initiated Measure
11) included exceptions for the health of the mother and in the case of rape or incest. In an
interesting turn of events, once again the voters in South Dakota rejected the ban by a very
similar margin with 55.21% voting against the ban and 44.79% voting for it (South Dakota
Secretary of State Election Results 2008). This was a surprising result. Why were the vote
margins practically the same given the differences between the two abortion bans?
To begin to answer this question we first turned to public opinion survey data on
abortion attitudes.
Using the 2006 GSS, we looked first at the frequency distribution of
responses in the abortion scale for respondents living in the West North Central region, which
includes South Dakota, illustrating the differing levels of support for legalized abortion. See
Table 3. Looking at the extremes of the scale, 33% of the respondents supported legalized
abortion for any reason while only 8.5% supported a complete abortion ban by responding “no”
to legalized abortion for all of the reasons offered in the survey. The breaking point between
the elective and traumatic reasons for legalized abortion is also telling; 47.2% of the
respondents indicated “yes” to at least one of the elective reasons. However, relating this to
the November 2008 vote on the South Dakota ban, the initiative did not provide exceptions for
all of the traumatic reasons listed, only in the case of rape/incents and when the health of the
mother was seriously threatened.
Thus, this data shows that without the inclusion of an
exception for “a strong chance of a serious birth defect,” only 28.3% of the respondents should
76
Table 3
Percentages Abortion Attitudes Scale
West North Central
Region
All Regions
Percent
Cumulative
Percent
Percent
Cumulative
Percent
Yes in every case.
33.0
33.0
35.4
35.4
Yes in every case, yes to not married.
4.7
37.7
4.9
40.3
Yes in every case, yes to not married,
yes to married.
6.6
44.3
4.6
45.0
Yes in every case, yes to not married,
yes to married, yes to low income.
2.8
47.2
5.4
50.4
21.1
71.5
Elective Reasons Above – Traumatic Reasons Below
Yes in every case, yes to not married,
yes to married, yes to low income, yes
to serious birth defects.
24.5
71.7
South Dakota Abortion Ban 2008 – Below
Yes in every case, yes to not married,
yes to married, yes to low income, yes
to serious birth defects, yes to rape.
8.5
80.2
9.8
81.2
Yes in every case, yes to not married,
yes to married, yes to low income, yes
to serious birth defects, yes to rape,
yes to health of the mother.
11.3
91.5
9.0
90.2
9.8
100.0
South Dakota Abortion Ban 2006 - Below
No in every case
8.5
N=106
77
100.0
N=1764
have supported an abortion ban like the one proposed in South Dakota in 2008 and only 8.5%
of the voters should have supported an abortion ban like the one proposed in South Dakota in
2006.1
Instead of answering the question we posed above, this analysis of attitudinal data
raised additional questions. We not only lacked an explanation for why the 2006 and 2008
votes were so similar, even though the abortion bans were different, now we had to ask why
the actual vote distributions were roughly 55% to 45% for both years instead of the 72% to
28% for the 2008 ban and 91% to 9% for the 2006 ban indicated by the attitudinal data. In
the context of the 2008 election, why did roughly 18% of the voters (the difference between
45% and 28%) vote for a ban that did not include exceptions they personally supported as
valid? For the 2006 election, that number increases from 18% to 36% of the electorate voting
against their preferences. Given the lack of exit polling data that might more directly address
this discrepancy, we turned again to the GSS available attitudinal data and decided to specify a
predictive model of abortion attitudes.
Surveys of previous research had identified a number of independent variables used to
predict abortion attitudes.
These included gender, race/ethnicity, education level, income,
political ideology, and religion. Using OLS regression we modeled the relationship between our
1
Because we were attempting to evaluate actual voting behavior rather than just attitudes, it could be
more accurate to map the attitudes of likely voters rather than those of the general adult population. We
ran this analysis, selecting for people who had voted in the 2000 and 2004 presidential election, and
found that likely voters appeared to be slightly less supportive of an abortion ban than the adult
population in general. However, a t-test comparison of all respondents to likely voters showed that the
means were not significantly different between the two groups. The focus on likely voters dropped our
sample size for the West North Central region from N=106 with all respondents to N=82 with just the
likely voters. Small samples sizes affect generalizability as well as tests of statistical significance.
Additionally, elections with abortion bans attract a lot of voter attention. According to the South Dakota
Secretary of State’s Office – Voter Statistics, the voter turnout in the 2006 election, an off-year election
that did not have even a South Dakota Senate race to draw voter attention but did include the more
restrictive abortion ban initiative, the voter turnout of registered voters was 67.3% (58% of the voting
age public). This was a remarkably high voter turnout for an off-year election; only Minnesota had
higher turnout that election. The 2008 election included the presidential race as well as the revised
abortion ban, drawing high voter interest. For these reasons the rest of this analysis continued to include
all respondents rather than limit itself to only likely voters.
78
abortion index and these standard independent variables for both the West North Central region
and for the entire United States. See Table 4. Education level, political ideology, and church
attendance were statistically significant predictors of abortion attitudes for both the West North
Central region and for the nation as a whole. As education increased, so did abortion tolerance;
conservatives and frequent church attendees were less tolerant of abortion. Gender, race, and
income were not significant predictors of abortion attitudes in either model.
These results
corresponded to those found in most of the earlier research on this topic. Unfortunately, these
results did not seem to clearly address the questions we identified regarding the difference
between expressed attitude and voting behavior in South Dakota.
Table 4
OLS Regression of Abortion Attitudes Index Without Family Values
West North Central
Constant
Female
White
Education
Family Income
Political Ideology
Church
Attendance
N
B
(Std. Error)
8.019
(1.001)
.370
(.427)
-.721
(.643)
-.394*
(.192)
-.019
(.019)
.461**
(.160)
.394***
(.080)
101
Beta
.074
-.100
-.178
-.094
.255
.426
All Regions
B
(Std. Error)
7.792
(.214)
.021
(.108)
-.169
(.124)
-.439***
(.045)
.002
(.003)
.405***
(.038)
.321***
(.020)
1716
R
.598
.500
R-Square
.358
.250
p<=.000***; p<=.01**; p<=.05*
79
Beta
.004
-.029
-.208
.015
.230
.354
In the 2008 election, the campaigns both for and against the abortion ban appealed to
voters’ notions of family values. As noted above, the campaigns’ marketing research found this
concept to be especially salient to the abortion issue for voters in South Dakota. We decided to
operationalize the concept of family values in an index and then construct a model that would
include this independent variable. See Table 5.
Table 5
OLS Regression of Abortion Attitudes Index With Family Values
West North Central
Constant
Family Values
Index
Female
White
Education
Family Income
Political Ideology
Church
Attendance
B
(Std. Error)
5.401
(1.860)
.188*
(.082)
-.188
(.599)
-.476
(.957)
-.404
(.270)
-.039
(.027)
.484*
(.223)
.324*
(.122)
Beta
.294
-.037
-.064
-.190
-.186
.249
.350
All Regions
B
(Std. Error)
4.664
(.440)
.250***
(.023)
-.179
(.151)
-.032
(.175)
-.191**
(.067)
-.006
(.005)
.174**
(.056)
.178***
(.030)
N
49
761
R
.706
.587
R-Square
.499
.344
p<=.000***; p<=.01**; p<=.05*
80
Beta
.397
-.036
-.005
-.091
-.037
.103
.200
Looking at the OLS regression model of abortion attitudes, the family values index
predicts attitudes about abortion in the expected way; as you move away from contemporary
family values to traditional family values, opposition to legal abortion increases (b=.188* for
West North Central and b=.250*** for all regions). Of interest to us was the observation that
among all independent variables included in the model, family values seemed to be one of the
strongest predictors of abortion attitudes (Beta = .294 for West North Central and Beta = .397
for all regions). In the West North Central model, only church attendance was a stronger
predictor (Beta = .350) and in the model for All Regions, family values is the strongest predictor
of abortion attitudes. Additionally, the general predictive power of the model increased with the
inclusion of the family values variable. In the West North Central model without family values,
R = .598, while the model including family values has an R of .706. In the All Regions model
without family values, R = .500, while the model including family values has an R of .587.1
The family values variable appears to be a valuable addition to the study of abortion
attitudes, providing strength and clarity to the abortion attitude model. Additionally, we argued
that it provided an explanation for the puzzling questions we have raised regarding the vote
margins in the 2006 and 2008 elections as well as the apparent difference between voting
behavior and attitudinal data on abortion.
Simple bar charts presenting the relationship
between family values and abortion attitudes lend support to this argument. See Figures 1 and
22 .
Looking at the chart presenting this relationship for all regions, among people who
supported banning abortions for “elective” reasons, people with traditional family values
outnumbered those with contemporary family values. Among people who supported abortion
1
It is also interesting to note that including the family values variable affected the performance of one of
the other independent variables in the models. In the second model for the West North Central region
only family values and political ideology were significant, education level was no longer significant.
2
For both Graphs 1 and 2 the contemporary/traditional family values continuum was roughly divided in
half with those scoring in the lower half labeled “contemporary” and those scoring in the upper half
labeled “traditional.”
81
for at least one of the “elective” reasons, people with contemporary family values outnumbered
those with traditional family values.
Figure 1 shows that traditional family values were
prevalent among those surveyed from the West North Central region.
As shown by our
regression models, the family values variable was the strongest predictor of abortion attitude.
Figure 1
West North Central Region: Abortion Attitudes by Family Values
82
Figure 2
All Regions: Abortion Attitudes by Family Values
Although we do not have the exit polling data necessary to test this directly, we argued
that individuals who ascribed to traditional family values may be more likely to vote to support
83
the abortion bans even if they also believed that there should be additional categorical
exceptions to the ban. For example, because of his or her commitment to traditional family
values someone who believed there should be exceptions to the ban in the case of severe birth
defects may have still voted for the ban because he or she believed the threat abortion makes
to traditional family values outweighed his or her belief in a birth defect exception. In the
language of our theory, this person may have voted against his or her abortion symbol in favor
of his or her family values symbol.
Thus, the addition of exceptions to the abortion ban
between 2006 and 2008 did not make a difference in the final vote margin because the vote
decision was not about the included exceptions; it was about family values. However, the
percentage of the electorate adhering to traditional family values arguably would not have
changed between 2006 and 2008, thus providing a plausible explanation for the vote results in
2006 and 2008 being almost identical though the proposed policies were significantly different.
DISCUSSION AND CONCLUSION
Although the abortion issue has been receiving attention for decades, the vote in South
Dakota in 2006 was the first public referendum on the issue. This provided the first opportunity
we have had to compare public opinion polling data with an actual voting event and as a result
we became aware of this very interesting discrepancy between expressed attitudes about
legalized abortion and an actual vote on an abortion ban. Changes in the exceptions to the
abortion ban between 2006 and 2008 did not result in a meaningful change in voter response to
the bans. It appears people did not vote in accordance with their expressed opinion about the
necessity of legal abortion in the cases of birth defects, rape/incest, and a threat to the health
of the mother.
As a result, the inadequacy of the standard predictors of abortion attitudes, especially in
relation to voting behavior, was exposed. In the context of the South Dakota elections we
84
conceptualized, operationalized, and modeled a new variable: family values. The addition of
this variable has increased the strength of our abortion attitude model. It also provided one
plausible, although one we admittedly could not test directly, explanation for the discrepancy
between expressed abortion attitude and actual vote choice on the abortion issue.
There are some real limitations to what we were able to accomplish with this research.
We could not directly connect the attitudinal data on family values or abortion to choices made
by voters about the abortion bans. This means we were unable to directly test either one of
our hypotheses. Instead we relied on logical inference to draw comparisons between our model
of abortion attitudes and actual voting behavior.
This is why we offer this research as a
theoretical analysis of abortion attitudes in South Dakota. Additionally, while attitudes about
family values may strongly influence attitudes about abortion, other attitudes may have also
influenced the actual abortion ban vote choice. Future research should investigate the effect of
other symbols on the abortion vote as well as the possibility that the family values symbol may
resonate more with certain types of voters than with others.
Some might argue the theoretical and operational integrity of the family values index,
suggesting that we simply took a number of issues strongly correlated with abortion, stuck
them in an index, and then used them to predict abortion attitudes. In response, we offer that
“family values” is a symbol that has meaning in society.
It is used by both those on the
traditional and on the contemporary side of the issue. As was shown above in our theory,
“family values” has been used somewhat casually by sociologists but used frequently by the
general public. What was surprising was that we could not find any examples from the relevant
literature of ways to operationalize this concept.
Scholars talk about “family values” but
apparently have not attempted to model it quantitatively. Consequently we were obliged to
create our own operationalization using items available to us in the GSS. However, we do
85
recognize that an index is only as valid as its constituent parts. We were somewhat limited by
the questions asked by the GSS in 2006.
On past surveys the GSS has asked different
questions, collected different data, which might better fit the family values theoretical concept.
Next steps include further refining the operationalization of this concept and then testing its
utility in other contexts. Experimental research designs should also be utilized to draw clear
connections between the family values symbol and the abortion issue. We also plan a more
extensive analysis of relevant content to clearly support our argument that “family values” as a
symbol has been employed by all sides in the South Dakota abortion ban campaigns.
An obvious next step in this research project would be to collect attitudinal and vote
choice data that is specific to South Dakota. This would address a number of the weaknesses
with the study we present here. First of all, we relied on a national survey to study attitudes in
South Dakota. Although we could study the specific region (West North Central) in which South
Dakota is placed by the GSS, we could not confirm that the attitudes of this wider region
matched those of respondents specific to South Dakota.
Secondly, we could not directly
connect a voter’s attitudes about abortion and their vote choice with our current data. We
theorize that, logically, the attitudes expressed in the GSS survey should correspond to the
choices voters made in the 2006 and 2008 elections but that is a theory we cannot actually test
with our current data. A survey of South Dakota voters would address both of these problems.
However, we used the vote in South Dakota merely to identify a possible problem with
using attitudinal data to study the abortion issue in general. Abortion attitude does not appear
to be the same as abortion vote choice. Based on this observation we identified a different
theoretical concept to be used to analyze abortion attitudes, family values. We applied this
concept to the GSS region that includes South Dakota but also analyzed its utility when applied
to abortion attitudes across the country. Compared to the standard variables used to predict
86
attitudes about abortion, our analysis suggested that this concept may have real predictive
power. People seeking to understand public opinion on abortion could benefit from including
attitudes about family values in their analysis.
87
REFERENCES
The Abrams Report on MSNBC. 2006. “South Dakota Legislature Attacks Roe v Wade: State
Prepares to Pass Bill Banning All Abortions Except to Save Mom's Life.” Retrieved March
14, 2008 (http://www.msnbc.msn.com/id/11542260/).
Adams, Greg D. 1997. “Abortion: Evidence of an Issue Evolution.” American Journal of
Political Science 41:718-737.
Amato, Paul. 2004. “The Future of Marriage.” In Vision 2004: What is the Future of Marriage?
Eds. Paul Amato and N. Gonzalez. Minneapolis, MN: National Council on Family
Relations.
Ballot Pamphlet. 2008. Compiled by the Office of the Secretary of State of South Dakota Chris
Nelson.
Bendyna, RSM, Mary E., John C. Green, Mark J. Rozell, and Clyde Wilcox. 2001. “Uneasy
Alliance: Conservative Catholics and the Christian Right.” Sociology of Religion 62:5164.
Blumer, Herbert. 1969. Symbolic Interactionism. Englewood Cliffs, NJ: Prentice Hall.
Bolks, Sean M., Diana Evans, J.L. Polinard, and Robert D. Wrinkle. 2000. “Core Beliefs and
Abortion Attitudes: A Look at Latinos.” Social Science Quarterly 81:253-260.
Bolzendahl, Catherine I. and Daniel J. Myers. 2004. “Feminist Attitudes and Support for
Gender Equality: Opinion Change in Women and Men, 1974-1998.” Social Forces
83:759-790.
Brace, Paul, Kellie Sims-Butler, Kevin Arceneaux, and Martin Johnson. 2002. “Public Opinion in
the American States: New Perspectives Using National Survey Data.” American Journal
of Political Science 46:173-189.
Bravin, Jess. 2006. “Election 2006: Abortion Foes Face Pair of Big Tests; South Dakota
88
Referendum Could Recast Debate as Supreme Court Prepares to Weigh In.” Wall Street
Journal, November 8, p. A4.
Carmines, Edward G. and James Woods. 2002. “The Role of Party Activists in the Evolution of
the Abortion Issue.” Political Behavior 24:361-377.
Chafetz, Janet Saltzman and Helen Rose Fuchs Ebaugh. 1983. “Growing Conservatism in the
United States? An examination of trends in political opinion between 1972 and 1980.”
Sociological Perspectives 26:275-298.
Coontz, Stephanie. 1992. The Way We Never Were. New York: Basic Books
---------- . 2005. Marriage, a History. New York: Penguin Group.
Craig, Barbara and David O’Brien. 1993. Abortion and American Politics. Chatham, NJ:
Chatham House Publishers.
Cupich, Bishop Blase. October 4, 2008. “One Nation under God: A Message on Exercising
Faithful Citizenship.” Retrieved November 11, 2008
(www.rapidcitydiocese.org/Bishop/BishopPage.htm).
Davis, James Allan and Tom W. Smith. General Social Surveys, 1972-2006. [machine-readable
data file]. Principal Investigator, James A. Davis; Director and Co-Principal Investigator,
Tom W. Smith; Co-Principal Investigator, Peter V. Marsden, NORC ed. Chicago: National
Opinion Research Center, producer, 2005; Storrs, CT: The Roper Center for Public
Opinion Research, University of Connecticut, distributor. 1 data file (51,020 logical
records) and 1 codebook (2,571 pp).
Democratic Platform. 2008. Retrieved October 11, 2009
(http://www.democrats.org/a/party/platform.html).
DiMaggio, Paul, John Evans, and Bethany Bryson. 1996. “Have Americans’ Social Attitudes
become More Polarized?” American Journal of Sociology 102:690-755.
89
Erikson, Robert S., Gerald C. Wright, and John P. McIver. 1993. Statehouse Democracy: Public
Opinion and Policy in the American States. Cambridge: Cambridge University Press.
Evans, John H. 1997. “Worldviews or Social Groups as the Source of Moral Value Attitudes:
Implications for Culture Wars Thesis.” Sociological Forum 12:371-404.
----------. 2002. “Polarization in Abortion Attitudes in U.S. Religious Traditions, 1972-1998.”
Sociological Forum 17:397-422.
Focus on the Family website. Retrieved November 11, 2008
(http://www.focusonthefamily.com/).
Gallagher, Maggie. 1996. The Abolition of Marriage: How We Destroy Lasting Love.
Washington, DC: Regnery.
Gans, Herbert. 1972. “Positive Functions of Poverty.” American Journal of Sociology 78:275-89.
Granberg, Donald. 1991. “Conformity to Religious Norms Regarding Abortion.” Sociological
Quarterly 32:267-75.
Griffin, John D. 2006. “Senate Apportionment as a Source of Political Inequality.” Legislative
Studies Quarterly 31:405-432.
Hall, Elaine J. and Myra Marx Ferree. “Race Differences in Abortion Attitudes.” Public Opinion
Quarterly 50:193-207.
Halloran, Liz. 2006. “A Bitter War’s Latest Front; South Dakota Voters Will Pass Judgment on
the Nation’s Toughest Abortion law. U.S. News & World Report 141:36-41.
Harris, Richard J. and Edgar W. Mills. 1985. “Religion, Values, and Attitudes Toward Abortion.
Journal for the Scientific Study of Religion 24:137-154.
Hoffmann, John P. and Sherrie Mills Johnson. 2005. “Attitudes toward Abortion among
Religious Traditions in the United States: Change or Continuity?” Sociology of Religion
66:161-182.
90
Hoffmann, John P. and Alan S. Miller. 1998. “Denominational Influences on Sociology Divisive
Issues: Polarization or Continuity.” Journal for the Scientific Study of Religion 37:528546.
Hull, N.E.H. and Peter Charler Hoffer. 2001. Roe v Wade: The Abortion Rights Controversy in
American History. Lawrence, KS: The University Press of Kansas.
Initiated Measure 11 Initiative Petition. 2008. Retrieved November 11, 2008
(http://www.sdsos.gov/electionsvoteregistration/electvoterpdfs/2008/2008regulateperfor
manceofabortions.pdf).
Jelen, Ted G. and Clyde Wilcox. 2003. “Causes and Consequences of Public Attitudes toward
Abortion: A Review and Research Agenda.” Political Research Quarterly 56:489-500.
Joffee, Carole. Winter 2005. “It’s not Just Abortion, Stupid.” Dissent 52:91-96.
Jones, Bradford S. and Barbara Norrander. 1996. “The Reliability of Aggregated Public Opinion
Measures.” American Journal of Political Science 40:295-309.
Jones, Elisa F. and Charles F. Westoff. 1978. “How Attitudes towards Abortion Are Changing.”
Population and Environment 1:5-21.
Kafka, Joe. 2007. “Abortion: Petition Effort Organized by Districts to Show Results to
Lawmakers. Rapid City Journal, October 12, pp. 1A, 7A.
Kaufmann, Karen M. 2002. “Culture Wars, Secular Realignment, and the Gender Gap in Party
Identification.” Political Behavior 24:283-307.
Kirkpatrick, David D. 2007. “The Evangelical Crackup.” New York Times, October 28.
Retrieved February 09, 2008
(http://www.nytimes.com/2007/10/28/magazine/28Evangelicals-t.html).
Layman, Geoffrey C. 2001. The Great Divide: Religious and Cultural Conflict in American
Political Parties. New York: Columbia University Press.
91
Luker, Kristin. 1984. Abortion and the Politics of Motherhood. Berkley: University of California
Press.
McDonagh, Eileen. Fall 2007. “The Case of the Missing Frame: Abortion as Self-Defense.”
Historical Methods 40:187-191.
Mead, George Herbert. 1934. Mind, Self, and Society. Chicago: University of Chicago Press.
Miller, Allan and John Hoffman. 1999. “The Growing Divisiveness: Culture Wars or a War of
Words.” Social Forces 78:721-752.
Myrsiades, Linda. 2002. Splitting the Baby: The Culture of Abortion in Literature and Law,
Rhetoric and Cartoons. New York: Peter Lang.
Norrander, Barbara. 2001. “Measuring State Public Opinion with the Senate National Election
Study.” State Politics and Policy Quarterly 1:111-125.
Norrander, Barbara, and Clyde Wilcox. 1999. “Public Opinion and Policymaking in the States:
The Case of Post-Roe Abortion Policy.” Policy Studies Journal 27:707-722.
Petersen, Larry R. 2001. “Religion, Plausibility Structures and Education’s Effect on Attitudes
toward Elective Abortion.” Journal for the Scientific Study of Religion 40:187-203.
Petersen, Larry and Armand L. Mauss. 1976. “Religion and the ‘Right to Life’: Correlates of
Opposition to Abortion.” Sociological Analysis 37:243-54.
Roberts, Tom. Nov. 23, 2007. “ ’08 Election Brings Major Shift in Religious-Political
Alignments.” National Catholic Reporter 44:5-6.
Roh, Jongho and Donald P. Haider-Markel. 2003. “All Politics Is Not Local: National Forces in
State Abortion Initiatives.” Social Science Quarterly 84:15-31
Sandberg, John F. and Sandra L. Hofferth. 2001. “Changes in Children’s Times with Parents,
U.S. 1981-1997.” Demography 38:423-36.
Sandstrom, Kent L., Daniel D. Martin, and Gary Alan Fine. 2006. Symbols, Selves, and Social
92
Reality. Los Angeles: Roxbury Publishing.
Shaw, Greg M. 2003. “The Polls – Trends: Abortion.” Public Opinion Quarterly 67:407-429.
South Dakota Healthy Family website. Retrieved November 11, 2008
(http://www.sdhealthyfamilies.org/).
South Dakota Secretary of State. “Official General Election Results, Statewide Ballot Questions,
November 4, 2008. Retrieved March 16, 2009
(http://www.sdsos.gov/electionsvoteregistration/pastelections_electioninfo08_generalbq
.shtm).
Strickler, Jennifer and Nicholas L. Danigelis. 2002. “Changing Frameworks in Attitudes toward
Abortion.” Sociological Forum 17:187-201.
Sullins, D. Paul. 1999. “Catholic/Protestant Trends on Abortion: Convergence and Polarity.”
Journal for the Scientific Study of Religion 38:354-369.
Sunshower, Suzanne. 2006. “Women Warriors Help Stem the Tide in South Dakota.” Off Our
Backs 36:27-29.
Thistle, Susan. 2006/2007. From Marriage to the Market: The Transformation of Women’s
Lives and Work. Berkley and Los Angeles, CA: University of California Press.
U.S. Census Bureau. “State and County Quick Facts.” Retrieved March 17, 2008
(http://quickfacts.census.gov/qfd/states/46000.html).
U.S. Republican Party. Republican Platform. 2008. Retrieved November 11, 2008
(http://www.gop.com/2008Platform/).
Vestal, Christine. June 22, 2006. “States Probe Limits of Abortion Policy.” Retrieved November
11, 2008 (http://www.stateline.org/live/printable/story?contentId=121780).
93
Book Review
Fat Matters: From Sociology to Science
Edited by Gina Tsichlia and Alexandra M. Johnstone
Reviewed by:
Brittney Hansen
Tsichlia, Gina and Alexandra M. Johnstone (Eds.). Fat Matters: From Sociology to Science. M &
K Update Ltd., 2010, 144 pp., $45.00 paperback.
Fat Matters is multi-faceted book written by a range of experts that examines the
underlying factors behind the ‘Obesity Epidemic’.
The aim of the book is to review in a
systematic way different aspects of this growing issue from a variety of angles, from sociology,
through medicine to technology. Each chapter attempts to define, explain, or present ways to
remedy some aspect of the current obesity epidemic plaguing the United Kingdom and the
United States. An underlying concept that is mentioned frequently throughout the text is the
influence of the media on what society feels is acceptable in terms of how an individual feels
about the way they look.
In this text there is not a direct thesis question to be answered, but the goal of
reviewing the ‘Obesity Epidemic’ is obtained. Not only does the text define and explain obesity
from a sociological and scientific perspective, it presents new and upcoming research that can
further our understanding of obesity and how to curb its detrimental effects for the individual
and society as a whole. The book also presents an introduction into a new field of research by
arguing that obesity is not only a sociological phenomenon but also a psychological one. This

Brittney Hansen; e-mail: [email protected]
94
research is relatively new and through further study can provide important insights into obesity
that are currently unknown. In addition to current and upcoming research the text delves into
the current public debate regarding obesity and who is to blame. The debate focuses on
whether or not obesity is to be blamed on the individual themselves or the society that
influences that individual and enables them to become obese. Much research has shown that
obesity is a social construct and is greatly influenced by society.
Although the text is a summary of many separate endeavors within the topic of obesity,
I found it to be put together well. The key points of the book were well supported; each point
that was emphasized was backed with valid research. Media and cultural influence on obesity
seemed to be a central theme throughout the text and it was validated through a variety of
studies. An average person without a medical or sociological background could read the text
and understand the research and views presented.
Fat Matters had both its strengths and weaknesses. I felt its main strength was the fact
that it was relatively easy to read and understand.
Another is the presentation of obesity
through many different lenses to illustrate the complexity of the issue; there are many
underlying, contributing factors in addition to those that are more obvious. The main weakness
of the text is the difference in writing styles from chapter to chapter. Although the topics fit
together well, I found it difficult sometimes to transition from one author’s writing style to the
next. In addition, many topics were covered in the book relatively quickly and could have been
presented in greater depth. I felt that much more information could have been presented that
would have be not only interesting, but informative to the reader.
Fat Matters does not necessarily make a great contribution to the field of sociology or
medical sociology, but it is an adequate introduction into one of the subfields in medical
sociology. It is ideal for individuals interested in understanding a sociological perspective of a
95
common medical condition. A Medical Sociology professor could use this text as a resource for
his/her students to see how a common medical issue is viewed through a sociological lens.
Fat Matters is a compilation of literary works on the subject of obesity in the UK and the
US; its point is to review the obesity epidemic and the multiple factors that tie into it. I feel it
succeeds in this mission. It covers a lot of ground in 10 relatively short chapters and provides a
decent introduction into obesity as a social phenomenon.
96
Book Review
Debating Sex and Gender
By Georgia Warnke
Reviewed by:
Laura Colmenero-Chilberg
Warnke, Georgia. Debating Sex and Gender. Oxford University Press, 2011, 144 pp., $19.95
paperback.
Georgia Warnke’s Debating Sex and Gender, the fifth offering in Oxford University
Press’s Fundamentals of Philosophy Series1, takes as its central focus a continuing concern in
feminist scholarship -- the definitional problem between sex and gender.
The first four chapters of this text review the historical debates that have waged on this
topic, reviewing the core questions that have arisen in the past fifty years of scholarship in this
area.
In Chapter One the key question is, “What are the relationships and/or differences
between the biologically based and the culturally based gender?” Using the work of Worthman
on hormones and Fausto-Sterling on bone density, Warnke ends the chapter by pointing out
that recent research supports a “reciprocal conception of the causal process” (p. 28) where sex
may lead to gender but the reverse may also be true.

Laura Colmenero-Chilberg, Ph.D.; Department of History and Social Science, Sociology Program, Black
Hills State University, 1200 University, Unit 9120, Spearfish, SD 57799-9120; e-mail:
[email protected]
1
Other texts in this series include Biomedical Ethics by Walter Glanno;, Mind: A Brief introduction by
John R. Searle; A Contemporary Introduction to Free Will by Robert Kane, and Political Philosophy by A.
John Simmons.
97
Chapter Two begins with the stark binary attitude most western cultures have about
gender and then moves onto discussing how intersexed individuals have fit into these social
systems as well as how other cultures have created social space for a variety of different
gendered identities including berdache, hijras and American transgendered people.
Warnke
suggests, “Perhaps, however, we would do better to conceive of both sex and gender as bell
curves rather than as absolutes” (p. 51).
Chapter Three focuses on the idea of gender as both performance and performative,
beginning with the work of Harold Garfinkel and the “Agnes” research and moving onto more
contemporary theorists such as Judith Butler and her discussion of the necessity to move
beyond the heteronormativity of much of the modern investigations into gender.
Chapter Four finishes the review of past scholarship on gender by discussing the
attempt to broaden feminist inquiry beyond its initial white, middle class base into gender’s
intersections with race, class, etc. Warnke looks to the work of scholars such as Mohanty and
Minh-ha who point out our continued habit of perceiving gender as if it were separate from
these other statuses, our failure to understand that “[r]ace is classed and gendered; class is
raced and gendered; age is raced, gendered and classed; and so on” (p. 98-99).
In Chapter Five, Warnke pulls together the disparate threads of this issue and considers
a broader theory of identity asking us to consider that perhaps “sex and gender are simply ways
of understanding who we and others are and, as such, that they must conform to the
conditions of understanding” where “our understandings of meaning and context reciprocally
constitute each other” (p. 119).
Coherent and well-written, this relatively short text is a useful summary of the key
points that are involved in this debate.
Warnke’s writing style is easily understandable.
98
Debating Sex and Gender will serve as an excellent supplementary text in undergraduate
Sociology of Gender and Women’s Studies courses.
99
Are you interested in a graduate program that permits students to engage in focused study of
the problems of crime, crime control, and the criminal justice system while simultaneously
developing a strong foundation in related areas of criminological theory, research methods, and
administration? North Dakota State University offers both a Master of Science in Criminal
Justice Administration and a Doctor of Philosophy degree in Criminal Justice, designed to train
graduate students in this increasingly marketable field.
North Dakota State University
Graduate Studies in Criminal Justice ~ (Ph.D. and M.S. degree programs)
www.ndsu.edu/cjps/






Masters program offers specialized curriculums ~ Criminology track and Applied track
Doctoral program with an emphasis on criminological theory/methods, policing and corrections
2:1 Student/Faculty ratio
Funding opportunities in the form of teaching/research assistantships and tuition waivers
Travel support to attend regional and national conferences
Solid relationship with local, state and regional criminal justice agencies
The Faculty

Kevin Thompson ~ Professor and Department Head (Ph.D., University of Arizona, 1986)
Delinquency; Quantitative Methods; Alcohol and Drugs; Juvenile Drug Courts

Thomas McDonald ~ Professor (Ph.D., Southern Illinois-Carbondale, 1972
Criminal Justice; Deviant Behavior; Treatment and Reintegration; Evaluation Research

Carol A. Archbold ~ Associate Professor/C.J. Graduate Director (Ph.D., University of
Nebraska-Omaha, 2002
Police Studies; Race, Gender and the Criminal Justice System; Qualitative Research Methods

Amy Stichman ~ Assistant Professor (Ph.D., University of Cincinnati, 2003)
Institutional Corrections; Correctional Program Effectiveness; Sex Offender Laws and Treatment

Sarah Browning ~ Assistant Professor (Ph.D., University of Toronto, 2007
Violence; Causal risk Factors for Substance Abuse; Drug Courts; Quantitative Methods

Courtney Waid ~ Assistant Professor (Ph.D., Florida State University, 2010
Offender Treatment and Reintegration; Penal Control; Fear of Crime; Juvenile Justice Policy
100