Information and Gender Differences in Individual Preferences for Protection
Alexandra Guisinger
[email protected]
Conflict Processes Panel 21-30, Public Opinion and IR
September 1, 2011
APSA
Men and women consistently differ in their preference for trade protection, even after controlling
for individual economic characteristics. Differences in information about trade could explain the
gap in at least two ways. First, men and women possess different political and economic
knowledge – particularly knowledge about trading partners – leading to divergent expectations
about the benefits of trade. Second, men and women process and evaluate information about
trade differently. To explore the role of information, I conducted a survey of 1,500 potential
voters in the 2010 United States Congressional Election. The survey contained an experiment
that isolated the effects of participants’ perceptions of the United States’ primary trading
partners, and a second experiment that randomly varied exposure to a positive frame on the
benefits of such trade. I find evidence on behalf of both roles for information in the formation of
trade preferences. Men and women possess different priors on the trade behavior of the United
States and reacted differently to the experimental stimuli. I find that women are more likely to
erroneously identify the top US trading partner as China and be more protectionist, but – in
contrast to men’s behavior - women's beliefs about the top trading partner do not appear to cause
their preference for protection. In the second experiment, positive information about the role of
trade in the US economy increased the gender gap: while both men and women hearing a
positive message similarly updated on the benefits of trade for US employment, men were far
more influenced by the positive message than women in terms of individual employment
benefits.
1
Over the past decade, international political economy scholars have focused great attention on
the determinants of individual preferences for trade protection. The use of survey data, both from
the United States and internationally, has become commonplace, particularly as researchers try to
close the continuing theoretical divide on whether individual preferences are determined by a
Stolper-Samuelson based factor classification or Ricardo-Viner based sectoral (industrial)
identification1. A positive externality of such work has been growing knowledge about nonindividual and non-economic sources of trade protection such as socio-tropic factors (Mansfield
and Mutz, 2009), neighborhood attachment (Ceccoli, Gelleny, and Kaltenthaler, 2004; Rodrik
and Mayda. 2005), national pride (Rodrik and Mayda. 2005), nationalism, and chauvinism
(O’Rourke and Sinnot, 2001); and values and ideology (Wolfe and Mendlesohn, 2005). Across
such surveys, a single, theoretically unexplained finding persists: ceteris paribus, women are
more trade protectionist than men (see for example, Hiscox and Burgoon, m.s.; Ceccoli, Gelleny,
Kaltenthaler 2004; Scheve and Slaughter, 2001; O’Rourke, Kevin and Richard Sinnott, 2001).
Given that gender-clustered differences remain even after controlling for observed economic
circumstances, this paper explores the gender gap via differences in information possession and
processing, specifically in gendered differences in economic and political knowledge and in the
response to economic evidence.
The literature on American politics has long noted the political knowledge gap between men and
women and debated its effects (see Mondak and Anderson 2004 for a review). Trade policy is no
exception. In multiple surveys, women are more likely to express “no opinion” when asked about
trade policy. When asked knowledge questions about specific policies, women are also more
1
Baker 2005; Beaulieu et al. 2005; Ceccoli et al. 2004; Mayda and Rodrik 2005; Mayda et al. 2006; O’Rourke and
Sinnott 2001; Scheve and Slaughter 2001.
2
likely to state “Don’t Know” or provide incorrect answers (see for example, Guisinger 2009).
This gap in knowledge may be one reason women differ from men in their preferences. If this is
the case, ensuring both groups possess similar information to both groups should lead to similar
preferences, all else equal. In contrast, if due to unobservable criteria, men and women assess the
applicability of trade-related information differently, then dissemination of similar information
may serve to exacerbate gender-based disparities in stated preferences for trade protection. To
assess the validity of these hypotheses, I undertake a two-part survey experiment in which 1,500
subjects receive treatments to alter their beliefs about their country’s main trading partner and the
economic effects of trade on the country.
Review of Literature
Although the finding of a gender gap in trade preferences is widely noted, only two published
papers to date directly address the gap. In a twist on typical production-based models of trade
policy preferences, Hall, Kao, and Nelson (1998) argue that women are more likely to be defined
by their role as a consumer and thus more likely to support lower tariffs on imported goods. They
find a temporal correlation between increased women’s suffrage and lower US tariff rates.
However, this macro relationship is at odds with micro-level empirical studies which have time
and time again found individual women less likely to favor of policies which increase
international trade. Why this is so remains up for debate.
Breaking from consumer and producer typologies, Gidengil (1995) instead characterizes the
gender divide through differences in values and beliefs: “economic men” are assumed to be more
concerned with individual material concerns and less concerned with egalitarianism and “social
3
women” are assumed to be more influenced by social concerns, particularly a commitment to
welfare state.
2
Analyzing survey data concerning a proposed Canada-U.S. Free Trade
Agreement, Gilengil finds that differences in material circumstances compose only a small
portion of the 16 percentage point difference between men’s support (60%) and women’s
support (44%) for the agreement. Using a regression decomposition technique to separate the
contribution of differences in levels and in intensity (saliency) of effect, Gilengil finds empirical
support for the contention that women were less likely to perceive the economic benefits of free
trade but also that such economic considerations were less likely to impact their assessment of
the agreement. However, the observational nature of the study prevents exploration of the source
of the differences in saliency and in particular the role of that knowledge may play these
differences.
A pair of unpublished studies, one by Burgoon and Hiscox (2008) and one by Lee (2011), focus
specifically on the role of economic literacy and exposure to orthodox economic theory as the
cause of the preference gap. This work is a gendered outgrowth of previous work from the
economics field which assesses the impact of economic knowledge on public opinions on
economic policy issues in general (see for example, Walstad and Rebeck 2002) and international
trade specifically (Hainmueller and Hiscox 2006). Burgoon and Hiscox draw upon observational
survey data of 1500 respondents to show that the gender gap in trade preferences cannot be
explained away by controlling for skills and job characteristics or risk sensitivity linked to
2
Gidengil directly acknowledges Gilligran’s In a Different Voice (1982) for providing this characterization. Sapiro
(2002) notes weakly supportive evidence of such a characterization from analysis of the “most import problems
facing this country” question on the American National Election Studies (NES) surveys in 1992, 1994, 1996, and
1998: men were slightly, but statistically significantly, more likely to name economic issues and women were more
likely to name social issues, although in general there was wide agreement across both genders and the size of the
gap varied across survey years.
4
maternity, or gender differences in political values. Rather, they determine that much of the gap
can be explained by gender differences in economic knowledge as proxied by knowledge of
NAFTA participants and the name of the Secretary of State. What Burgoon and Hiscox can’t tell
us is whether the knowledge itself is important or whether knowledge is serving as a proxy for
other differences between genders. To bypass the limitations of an observational study, Lee
(m.s.) undertakes a survey experiment of 190 undergraduates. Among the population studied,
Lee finds little empirical support for a link between trade preferences and economic literacy (as
measured by economics courses taken) but instead, through a series of survey experiments, finds
support for the argument that women’s greater concern for those facing hardships causes their
relatively greater support for protectionist policies. A goal of this paper is to draw on both
designs by expanding a survey design technique to a larger, more nationally representational
sample.
Gender and trade policy
Discussion of the gender divide in policy preferences has a longer history in other - non-trade
related - policy realms (see review in Wolbrecht 2005). As described in Shapiro and Mahajan
(1986), gender differences in responses on public opinion surveys first began to be widely
recognized in the 1970s and early 1980s by Hazel Erkstine, William Schneider, Tom Smith, and
the editors of Public Opinion. Early scholarship found distinct differences in preferences over
policies concerning force and violence (Smith 1984), social welfare issues (Cook 1979), but
surprisingly not on women’s rights issues (editors of Public Opinion). In their own analysis of
public opinion surveys from the 1960s to 1980s, Shaprio and Mahajan found persistent
differences in opinions on the use of force and violence, regulation and protective policies, what
5
they term “compassion” issues to do with social welfare, but again not on women’s issues other
than abortion.
Explanations for gender-related attitudinal differences initially fell into two broad camps:
essentialist arguments derived from assumptions about biological differences and socioconstructivist arguments focused on the culturally created expectations about gender-specific
behavior. (Andersen 1997). More recently, the debate has focused primarily within the socioconstructivist realm, with numerous over-lapping conceptual explanations for differences (see
for example, Carroll 1988; Schlesinger and Heldmen 2001; Howell and Day 2000) divided
between structural and situational explanations and socio-psychological differences (Gidengil et
al m.s.) and including socialization of different norms and expectations, the impact of differences
in education, and differences in work and family roles and responsibilities (Sapiro 2002). This
paper focuses on three proposed explanations which interact extant but non-gendered theories
about trade policy preferences with concepts from the gender gap literature: the political and
economic knowledge gap between men and women, the observed and unobserved differences in
objective and subjective individual economic roles and conditions (see for example Eagly and
Diekman 2006), and gendered differences in the saliency of individual versus societal conditions,
what has been traditionally deemed women’s greater compassion for the vulnerable (see for
example, Shaprio and Mahajan 1986; Welch and Hibbing 1992).
Trade policy preferences and knowledge
Non-trade related studies demonstrate that “fully-informed” respondents have different stated
policy preferences than the less informed respondents and that disparities in policy-specific
6
information can be particularly influential in preference formation (Gilens 2001). Trade policy
preference formation experiences knowledge gaps at both the general and specific level. Thus,
one potential source for the gender gap in trade policy preferences is the gender gap in trade
policy knowledge.
Numerous studies have found that men test better than women on questions about politics. For
example, analysis of results from a ten question “knowledge battery” incorporated in the 1990
Citizens’ Participation Study (CPS) found the gap between men and women scores equaled
almost a full question or .93 on the 10 point scale (Verba, Burns, and Schlozman 1997). While
similar initial estimates of a considerable political knowledge gap between men and women have
been halved by more recent analysis correcting for the greater propensity of men to guess, the
new estimates still find the divide between genders to be on par with other constructs such as
interest in politics and political efficacy (Mondak and Anderson 2004). Specific to economic
knowledge, Walstad and Rebeck (2002) calculated that men scored 7.6 to 11.9 percentage points
higher on tests of macroeconomic knowledge, after controlling for age, education, income, party
identification, and even economic education.
In terms of specific knowledge, survey results have shown that women as a group tend to be less
knowledgeable or less certain about trade-related policies. Burgoon and Hiscox (m.s.) found
women 27-30% less likely than men to name all three signatories of NAFTA correctly.
Guisinger (2009) using data from the CCES 2008 election survey found that compared to
women, men were significantly less likely to answer “Don’t Know” and more likely to correctly
identify their Senator’s policy position on trade (operationalized by knowledge of their Senator’s
7
roll call vote on the ratification of a new free trade agreement between the US and countries in
Central America, CAFTA). Respondents of both genders were far less familiar with trade policy
specifics than other policies: when asked about their Senator’s behavior on a series of roll call
votes, respondents were 48% more likely to provide a wrong answer and 51% more likely to
select “Don’t Know” on CAFTA compared to the average for other roll call votes during the
same Congressional session (banning “late-term” abortion, federal funding for stem cell
research, a timetable to withdraw from Iraq, citizenship for illegal immigrants, increasing the
federal minimum wage, and extending capital gains tax cuts).
To the extent that preferences over trade-related policies depend on knowledge about the effects
of trade and thus the types of trade, confusion about US trading partners and the role of trade in
the economy could manifest in different preferences. If so, correcting and providing information
about trade-related policies should remove a source of the gender gap in trade preferences.
Individual and societal concerns and trade policy preferences
A second explanation for the gender gaps may lie not in knowledge about trade but in beliefs
about the effect of trade on one’s employment, beliefs which are in turn dependent on how
individuals internalize information about trade and its effects. Men and women’s differing
expectations about individually benefiting from the upside of trade or potentially paying the
costs of trade-related economic volatility may create different preferences even among
economically similar individuals.
8
A standard explanation for policy preference differences between men and women is the gender
differences in the distribution of economic roles. Scholars have noted that in the US during the
20th century, gender was a stronger determinant of employment allocation than race (Padavic and
Reskin, 2002). For trade policy, the effect of gendered distributions of economic roles could be
particularly acute since the two primary theoretical explanations for an individual’s preference
depend on a characterization of the individual by class and by industry sector. Both explanations
start with Heckscher-Ohlin based assumptions of international trade and thus assume that a
country will export goods that intensively use a country’s abundant resources and import those
goods that intensively use a country’s scare resources. In the process of determining individuals’
responses to such trade, the two major models differ in their assumptions about the mobility of
the factors of production (often characterized as land, labor, and capital but now more frequently
described as skilled and unskilled labor). Trade preference models referencing StolperSamuelson assume perfect mobility within factors of production and thus characterize
individuals very generally according to their ownership of factors of production. By changing the
relative supply of each factor, trade is assumed to change the prices each factor receives:
increasing prices for the abundant factors and decreasing prices for the scarce factor. In
developed countries like the US, such models predict that due to direct individual economic
concerns, holders of capital and high-skilled labor will be more likely to support international
trade while labor, especially low-skilled labor, will be more likely to favor limits on international
trade. Trade preference models referencing Ricardo-Viner assume limited factor mobility across
different industries and thus characterize individuals primarily according to their industry rather
than economic class. Again trade can affect the price each individual receives for their
contribution to production: increasing prices in export competing industries and decreasing
9
prices in import-competing industries. Preference formation thus crosses factor lines: capitalists,
high-skilled labor, and low-skilled labor all share similar interests when the ability to move to a
different industry is limited. The centrality of job types, industry, and skill-levels as the source of
differences in individual trade preferences means that few studies have failed to control for these
types of observable socio-economic indicators. Additionally, the expectations provided by such
models do not neatly fit with the empirical finding on high support for protection among women.
To the extent that international trade allows for growth in US services industries, traditional
gender segregation trends in the US disadvantage American men not women (Anker 1998) and
would suggest greater support for protection by low-skilled men in particular.
Focusing on individuals’ current economic roles fails to capture individuals’ perception of
vulnerability or insecurity in their economic status. A corollary to the orthodox trade theory’s
expectation that increased liberalization generates increased growth is that increased
liberalization additionally increases uncertainty, volatility, and risk. If women as a group
perceive themselves to be less likely to benefit from the potential upside of increased trade and
more likely to pay the cost of any downside because of disadvantages in recruitment processes
(Padavic and Reskin 2002) and retention practices (Hall 1972; Diebold et al 1998)3; if women
are less supportive of the idea that people should move to regions where employment is more
available (Gidengil et al. m.s.); or if women as primary caregivers even in dual-income families
(Presser 1994) are more concerned with jobs compatible to family life (Darian 1975; Glass and
3
Hall (1982) estimated that in 1978, men were 30 percent more likely than women to reach five or more years of
employment tenure, a much greater retention divide than he calculated between whites and blacks. Ureta (1992,
cited in Dielbold 1998, corrected Hall’s calculation to arrive at the lower but still substantial figure of 22%
difference between men and women. Sheeran (1975-1976) noted that while the application of Title VII in the United
States increased employment opportunities for women and minorities, it did nothing to adjust workers’ seniority
status which had been built on a foundation of discrimination and thus left women and minorities more vulnerable
during economic downturns.
10
Camarigg 1992), then their responses may differ significantly from men’s even when provided
the same information. Perceptions of vulnerability rather than current circumstances have been
found to effect attitudes towards general domestic policies (Rusciano 1992) as well economic
policies (Gidengil 1995; Welch and Hibbing 1992); thus they may similarly explain differences
in trade preferences.
In contrast with the knowledge gap hypothesis, the provision of information will do little to
alleviate the gender gap and may perhaps enlarge the gap if men and women update their
perceptions differently even when presented the same factual information.
A third source of the gender gap may arise from gender-related differences in the salience of
societal economic concerns relatively individual self-interests. Mansfield and Mutz (2009) have
found that an individual’s perception of whether trade benefits or hurts the country has a stronger
effect on an individual’s trade preferences than his or her own economic circumstances or
perceptions about trade’s effects on those circumstances. This new model of trade preferences
meshes with long-standing arguments that women are more likely to support “altruistic” policies
which benefit society in general and disadvantaged groups specifically. Some scholars have
argued that women’s relative vulnerability increases identification with disadvantaged groups,
enhancing women’s altruistic instincts (for review, see Schlesinger and Heldman 2001, 61-62).
Previous research has found women more supportive towards public assistance in general
(Edlund 1999; Blekesaune, Morten, and Jill Quadagno 2003) and to disadvantaged groups
specifically (Shapiro and Mahajan 1986). Trade protection offers just this type of assistance to
groups that would be disadvantaged by competition from imports. If altruism explains a portion
11
of trade preferences and women tend to be more altruistic, gendered differences in sociotropic
concerns then serve as a source or magnifier of gender differences in trade preferences.
Research Strategy
To test the information-based explanations for the gender divide in trade protection preferences, I
designed a pair of survey experiments: one that isolated the effect of participants’ perceptions of
the United States’ primary trading partners, and a second experiment that randomly varied
exposure to a positive frame on the benefits of such trade. Unlike observational studies, survey
experiments can independently vary individual circumstances, allowing for more careful testing
of causal mechanisms: here, the effect of information on trade preferences. In both experiments,
only truthful – albeit carefully chosen and selectively applied – information was provided in
order to see the changes that could be created by information correction and provision. The
survey also collected data on standard individual characteristics, such as age, gender, income,
education, and political views. As such, the analysis combines the broad sample of traditional
observational data with the inferential power of experimental data.
A few works on international trade and related policies have undertaken an experimental survey
framework (Hermann et al 2001; Hiscox and Hainmueller 2010). In these studies, the researchers
provided treatment descriptions with very generic nomenclature and hypothetical scenarios.
Even if one is willing to make the assumptions that survey respondents have meaningful
opinions about abstract scenarios, such a research design is inappropriate for a study focused on
knowledge. Thus, I use real names of countries and current data from government sources to
describe the US’s relationship with these actual countries.
12
Use of experimental survey data requires some degree of caution. Experimental economists and
psychologists claim women’s preferences are more situationally specific and their social
preferences are more malleable than those of men (Croson and Gnezzy 2009). The initial
expectation is that women should be more responsive to cues within the survey than men. Also,
without panel data it may be difficult to determine how long treatment effects last. However,
these limitations are outweighed by the benefits of the ability to rigorously test the constructs of
interest.
Data
The data comes from the 2010 Cooperative Congressional Election Survey conducted by
YouGov/Polimetrix both before and after the 2010 US Midterm election. From an opt-in panel of
respondents, YouGov/Polimetrix used a matched random sample technique to select a nationally
representative, stratified sample of 30,000 registered and unregistered US adults 4. The full
sample received a common set of questions and a randomly selected sub-sample of 1,500
respondents received a smaller module of additional questions. Data collection occurred in two
waves, pre- and post- election, with approximately a 15% respondent drop off between the two
waves.
All respondents answered a battery of questions concerning their individual characteristics
(including income, education, gender, and age) and political views (creating a 7 point political
4
The sample included three strata: Registered and Unregistered voters, State Size, and Competitive and
Uncompetitive Congressional Districts. Because of the common research interest in voters, YouGov/Polimetrix
purposefully oversampled registered voters.
13
identification scale). Respondents in the sub-sample received two sets of questions concerning
trade and trade-related policies.
In one phase of the study, respondents were asked to identify “Who do you think ranks as the
United States’ top trading partner?” from a randomly rotated list of five countries: Canada,
China, the European Union, Japan, and Mexico. The first experiment5 followed immediately
afterwards; a randomly selected half of those who selected China were informed that Canada was
in fact the US’s largest trading partner and half of those who selected Canada were told that
China and Mexico were the next two largest trading partners. All respondents then received a
battery of questions concerning trade related policies. Specifically, they were asked to select
“Agree”, “Somewhat agree”, “Somewhat disagree”, “Disagree”, “Don't know” on whether the
government should increase limits on imported goods and services (Limits on Imports), increase
subsidies to affected industries (Subsidies), do more to stabilize the dollar (Stabilize Dollar),
increase unemployment benefits and job retraining programs (Increase Benefits), and ensure its
trading partners protect basic rights of workers (Worker’s Rights) 6. Answers were coded to a 5
point scale (-2 to 2) from “Disagree” to “Agree” with “Don’t knows” coded as 0s.
The treatment highlights two potential shifts: first, whether shared information leads to
convergence between gendered groupings and second, whether correcting information leads to
convergence within gendered groupings. If a lack of information, uncertainty of information, or
poor information is driving differences then shared, correct information should eradicate these
differences. In contrast, if genders process the information differently then preference gaps
5
Although the respondents were the same, the two experiments occurred on different dates: the data for experiment
1 was collected after the US Congressional election and the data for experiment 2 was collected in the weeks prior to
the election.
6
Due to drop-off between the pre- and post-surveys, approximately 1,240 individuals completed this set of
questions.
14
between genders should stay stable or even increase whereas differences within gender groups
should decline.
In a separate wave of the survey, the second experiment started with a random assignment of two
different prompts prior to a series of questions about trade’s effect on employment. A randomly
selected half of the participants received a “treatment” in the form of factual, but positive,
information about the rank of the US as the largest trade country and the link between exports
and jobs in both the service and the manufacturing sectors: “The US continues to rank as the
largest trading country in the world. In the service sector, the US dominates the international
market, exporting more than the next two countries combined. US exports in services more
than doubled between 1998 and 2008 and service exports are estimated to support at least
3.5 million US jobs. Even in the manufacturing sector, where the US faces stronger
competition, more than 1 in 5 jobs are dependent on exports.” Then all participants were
informed that “The US government continues to expand opportunities to trade through bi-lateral
and multi-lateral agreements with foreign countries” before being asked to answer a set of four
questions concerning how international trade effected “Your employment”, “Employment of
friends and family”, Employment in your region”, and “Employment in the United States” with
the choices of “Benefit Greatly”, “Benefit Slightly”, “No Difference”, “Hurt Slightly”, “Hurt
Greatly”, and “Don’t know”. Again, answers were coded on to a -2 to 2 scale (“Disagree” to
“Agree”) with “No Difference” and “Don’t know” comprising the center score of 0.
Furthermore, respondents were then asked to select why they thought trade affected employment
in the way they did, selecting among provided answers or providing their own response. (See
Appendix A for full text). By varying the information provided to respondents, the treatment
allows for analysis of whether men and women respond differently to the type of positive
15
framing often provided by economists, politicians, and others supporting increased trade
liberalization.
Analysis of Lack of Political Knowledge: Does “Wrong” Information Lead to a Gender Gap in
Trade Policy Preferences
The 2010 CCES survey results corroborate prior findings of a divide between men and women in
their support for trade-related policies. For a set of trade related policy questions about whether
the US government should increase limits on imports (LIMITS ON IMPORTS), increase subsidies to
affected industries (SUBSIDIES), do more to stabilize the dollar (STABILIZE DOLLAR), increase
unemployment benefits and job retraining programs (INCREASE BENEFITS) and ensure its trading
partners protect basic rights of workers (WORKER’S RIGHTS), the left-side of Figure 1 displays
the average responses of the control group coded answers (-2 Disagree to 2 Agree) by gender.
On each issue, the average response by women (denoted by a square marker) was more
supportive than the average response by men (denoted by a diamond marker). Additionally, the
same survey responses suggest that knowledge or perceptions about who ranks as the US’s top
trading partner also matters in the formation of preferences over trade policy. The right-side of
Figure 1 breaks down the same responses by “top trading partner” response. Respondents
selected among one of 5 choices: Canada, China, the European Union, Japan, and Mexico. The
20% who selected the correct answer of “Canada” on average were less supportive of trade
ameliorating policies than the almost 80% who erroneously selected China, the European Union,
Japan, or Mexico. The full 60% of the sample who identified China as the US’s top partner were
16
equally or more supportive of the policies than those who selected the European Union, Japan, or
Mexico7.
As presaged by previous studies of economic knowledge, the probability of a respondent
correctly answering the top trading partner question varies according to gender category. As
depicted in Figure 2, women were as a group less likely than men to correctly identify Canada as
primary (14% of women to 29% of men) and more likely to incorrectly identify China as primary
(65% of women to 54% of men). A simple chi-squared test shows these differences between
men’s and women’s responses are significant (Pr = 0.000)8.
Since women are more likely to believe that China is the US’s top trading partner and since those
who believe that China is the US’s top trading partner are significantly more protectionist, then
the next step is to test whether women are more protectionist than men because they hold
erroneous information about the US’s major trading partner. To test this proposition that gender
differences in economic knowledge lead to the differences in stated preferences on trade policy, I
extend the basic observational analysis in two ways – first entering in gender and other
characteristics to check that the statistical significance of estimates on trade partner response are
not due to omitted variable bias and second entering an interaction on gender and response to
ensure that the finding on economic knowledge in the form of country response is not in fact
hiding its own gender divide. If the results are simply driven by differences in economic
7
Regression analyses undertaken on data collected from respondents in the control group (those whose perceptions
were not adjusted through a specific primer described below) supports the descriptive presentation in Figure 1.
Recoded responses (-2 Disagree to 2 Agree) for each policy area (LIMITS ON IMPORTS; SUBSIDIES; STABILIZE
DOLLAR; INCREASE BENEFITS; WORKER’S RIGHTS) were regressed on respondents’ answer to the question of whom
ranks as the US’s top trading partner through use of a set of dummy variables denoting each possible answer (the
excluded category was Canada). With one exception, those who had ranked China as the US’s top trading partner
were substantially and significantly more supportive of protectionist policies than those who answered Canada. The
size of the increase in support ranged from .19 to .35, a 5 to 9% swing on the 4 point scale.
8
Analysis utilizing Mlogit and thus allowing for the inclusion of controls for age, partisanship, and skill-level,
provides evidence that women are significantly less likely to answer “Canada”, the “correct” answer.
17
knowledge regardless of gender issues, entering gender should not remove the significance of the
results. If gender plays a special role not only independently but also in conjunction with
knowledge (in this case, top partner response), then an interaction should be significant.
Second, I analyze the results including observations from the survey experiment in which half of
those who responded “China” received the correction that Canada was actually the United
States’ largest trading partner and half of those who responded “Canada” received the
information about China and Mexico being the next two largest trading partners. Since priors on
trading partner strongly effect preferences, there should be a strong treatment effect if the lack of
shared information is the source of the disagreement. In particular women who receive the
treatment should have effect sizes that counterbalance the observational findings on women in
general.
Quantitative analysis can more closely parse the relationship between the persistent gap and
gender by incrementally incorporating the potential effects of personal characteristics and the
treatment itself into the basic regression model. Table 2 presents the results of detailed analysis
of this observational and experimental survey data on both the support for increasing limits on
imports (Limits on Imports) and the support for ensuring trading partners protect basic rights of
workers (Worker’s Rights). The first is the most commonly conceived type of trade protection
and the latter is often deemed a specific concern of women. Models 1 to 3 use only observations
from the control set (with no attempt to manipulate beliefs about the top trading country);
whereas models 3 through 4 include also data from the survey experiment in which half of the
China respondents received the additional information that Canada was in fact the US’s top
18
trading country. Model 1 repeats the results of the base analysis presented in Table 1 and
discussed previously. Model 2 includes individual characteristics that may determine trade
protection preferences: a seven point party-identification measure (PID7), age (AGE), gender
(FEMALE), and skill-level (SKILLED) with skilled defined as some education after high school.9
Of these individual-characteristics, skill-level has received the most theoretical and empirical
attention. Heckscher-Ohlin based assumptions of international trade generate the expectation that
countries will export goods which use intensively abundant resources and import those which
use scare resources. Thus in a developed country such as the US, high-skilled workers will
benefit from the ability to export, while low-skilled workers will find themselves facing
increased competition from imports. Model 3 further incorporates an interaction term combining
trade partner response and gender which allows for independent varying effects of top trading
partner priors by gender.
For both “LIMITS
ON IMPORTS”
and “WORKER’S RIGHTS”, a similar pattern emerges: adding
individual characteristics and thus allowing for an independent estimate of each gender’s
propensity to support protectionist measures initially diminishes the size and wipes out the
significance of “RESPOND CHINA” suggesting that it is gender, not trade partner knowledge that
is the source of differences in trade preferences. However, model 3 presents a fascinating gender
difference: for “LIMITS
ON IMPORTS”,
the coefficients on “RESPOND CHINA”, “FEMALE” and
“RESPONDED CHINA & FEMALE” were all significantly different from the base category of male
participants answering “Canada”. In particular, the interaction between “RESPOND CHINA and
“FEMALE” is large, significant, and negative which in conjunction witha positive estimate on
“RESPOND CHINA” in model 3 suggests that women responding “China” are in fact no more (or
9
The smaller number of observations prevented the inclusion of employment status in models 1-3.
19
less) protectionist than women in general. However, men who respond “China” are in fact more
protectionist than other men. Thus it is men, not women, who are reacting to their belief about
the identity of the US’s primary trading partner. The analysis on “WORKERS’ RIGHTS” shows the
same pattern although the significance on the coefficient for “RESPOND CHINA” is not robust
(although the estimated coefficient is itself fairly stable). Thus, observational data disputes the
hypothesis that it is women’s lack of knowledge about trade that is driving the difference in men
and women’s preferred levels of protectionism. Clearly a subset of those responding China are
far more protectionist, but that subset is men. Men appear far more affected by country-type than
women.
Including data from the survey experiment in the analysis (Models 4 to 6) further confirms the
findings from the observational data. Providing shared information on the nature of the US top
trading partner does little to change the gender gap between men and women: the treatment
effect by itself (Model 4), interacted by gender (Model 5), and interacted by skill-level do not
substantially or significantly diminish the gap between men and women on their trade policy
preferences and the coefficient on “FEMALE” remains stable once priors on trade country are
interacted with gender (Models 3-6). Even if the interaction of treatment with gender were
significant, at the current estimate, “knowledge” would account for less than 17% of the gender
gap. Priors about trading partners matter – for a subset of individuals – but not in a way that
explains higher levels of support for protectionist policies by women.
To illustrate the persistence of the gender gap, Figure 3 displays for the control group (“no
treatment”) and treatment groups (“China/Mexico treatment” and “Canada treatment”, the
20
average response by gender group and by trade partner response to the question “The US
government should increase limits on imported goods and services”. The gap between women’s
and men’s responses across treatment conditions is striking. Treatments did shift average
responses. Those who responded Canada but were informed about China and Mexico’s rank
supported limits on imports more than those who did not receive the treatment. Those who
responded China and received the correction that Canada was in fact the US’s top trading partner
supported limits on imports less than those who did not receive the treatment. Yet, the
information provision only marginally diminished the gap between men’s responses and
women’s responses, suggesting a durability of gender differences which is unaffected by
knowledge about trading partners.
Analysis of Different Processing of Political Knowledge
The fact that men’s trade preferences varied so greatly depending on their perceptions of trading
partner, suggests that men and women may process political knowledge differently. In the
second experiment, prior to questions concerning the effect of trade on their own employment,
employment of their friends and family, employment in their region, and employment in the
United States, all participants were informed of continuous government efforts to expand
international trade. In addition, a randomly selected half received a positive framework which
highlighted the importance of trade to the United States’ economy, the US’ world dominance in
the service sector, the recent growth in US exports in services, and the importance of the export
trade not only in service jobs but also manufacturing jobs.
21
The descriptive data provided by the control group (those not provided a positive framework
before being asked to state their beliefs on the effect of international trade on their and other’s
employment) suggests only small differences between individual types (low skilled, high skilled,
male, female) but dramatic differences between individuals’ beliefs about the effects of trade on
their own employment and on US employment. Figure 4 displays a breakdown of responses both
by question and by individual type. On average only 25% of respondents believe that trade hurts
their own employment, but 62% of all respondents believe that trade hurts employment in the
United States. Disaggregating the data by groups results in only minimal variations: compared to
high-skilled workers, low-skilled workers are slightly more likely to think that trade hurts
employment, more likely to think it makes a difference, and less likely to think it helps,
regardless of which type of employment is under discussion; compared to women, men are more
likely to think trade helps their own and US employment; compared to men, women are slightly
more likely to think that trade makes no difference to their own or US employment prospects.
These differences are minor however to the overwhelming difference between individuals’
expectations for themselves and for others.
On its face, the descriptive data provides support for not only socio-tropic explanations of trade
preferences but also gendered differences. Since the majority of individuals do not think trade
affects them, it is unsurprising that individual trade preferences appear more linked to sociotropic concerns than individualistic ones. That women are slightly less likely to think that trade
hurts their own employment and more likely to think that trade hurts others provides evidence
for the hypothesis that women’s altruism underpins the gender divide in trade preferences.
22
Analysis of the survey experiment results complicates such a characterization of the causal link
between gender and differences in trade preferences. To see whether women’s concerns about
general unemployment are the source of the gender differences in trade once other factors are
considered, I analyze data collected from both the control group and the treatment group,
including the dummy variable “TREATMENT” and the interaction of the treatment dummy
variable with “FEMALE” to allow for the estimation of the difference created by a positive
treatment and to allow for the potential of a heterogeneous treatment effect.
Not surprisingly, a positive framing of trade dramatically increased respondent perceptions about
the benefit of trade on employment and particularly employment in the region and in the United
States as a whole. Figure 5 presents a comparison between the control group and treatment group
for all four questions on employment (“Your employment “, “Employments of friends and
family”, “Employment in you region”, and “Employment in the United States”. Again the square
marker denotes the average response by women and a diamond marker denotes the average
response by men with the line between the two demarking the difference for both the control
group and the treatment group. Surprising, the positive message dramatically increased the
average differences between men and women: increasing a .02 to .12 difference between genders
to a .25 to .34 difference between genders. Across the board, men responded far more positively
to the treatment than women. Such findings run counter to the expectation of experimental
economists and psychologists that women’s preferences are more situationally specific and their
social preferences are more malleable than those of men (Croson and Gnezzy 2009).
23
Table 3 presents regression analysis which allows for additional variable of interest to also be
included: as before a seven point party-identification measure (PID7), age (AGE), gender
(FEMALE), and skill-level (SKILLED). With the exception of party-identification, all are
significant. As theoretically expected, skilled workers are less concerned about the effect of trade
on their own employment and this difference carries across the board to beliefs about the effect
of trade on others. Women, with a few exceptions, appear less certain of the benefits of trade on
their own employment and this difference from men increases as they consider employment
more generally.
The average treatment effect has the expected positive effect on individuals’ perception of
trade’s effects on employment. Comparing across Model 1’s, the treatment effect on individual
benefits appears somewhat smaller than those for societal benefits; however, this is only when
treatment effects are assumed to be homogenous. Allowing for a heterogeneous treatment effect
through the inclusion of the interaction between “TREATMENT” and “FEMALE” (Model 1) finds
significant gender differences in the reaction to the treatment: for own employment and
employment of friends and family women, the treatment effect is substantially smaller on men,
about 2/3 smaller (comparable in size to the difference between high and low skilled workers.).
The gendered treatment effect is smaller and not significant for the more general societal
concerns.
This evidence suggests that it may be women’s concerns about their individual risk rather than
socio-tropic concerns that lead to the difference in trade preferences - a reverse expectation to
that presented by the literature to date.
24
While the experiment does not directly explore why men reacted more positively to the positive
trade information, insight is provided by responses to follow-up questions. Individuals were
prompted to explain why they thought that trade benefitted or hurt their own employment and
US employment and were provided both set answers and the opportunity to provide an open
answer. In comparing men’s and women’s responses both from the control and treatment group,
a few patterns emerge. In the control group, men were far more likely than women to explain
their individual employment benefits in terms of their own skills (45% versus 13%); women
were far more likely to explain the benefit of trade in terms of the success of their company
internationally and their ability to change jobs. In the treatment group, differences between men
and women were in fact minimized as many more men expressed benefits in terms of
international competitiveness. In comparison, men and women’s explanations for why trade hurt
their employment were very similar in the control group and diverged in the treatment group: in
particular, the treatment group had almost half the percentage of men who thought that trade hurt
employment (26% control, 12% treatment) but those who continued to think trade hurt
overwhelmingly expressed concerns about outsourcing. Women’s concerns about outsourcing
varied only minimally. Also, in both the control group and the treatment group, men were less
likely to express concerns about moving for work than women were.
Conclusion
Why do men and women differ in their preferences for trade protection? Although women and
men differ in tests of economic knowledge, particularly in questions pertaining to trade, these
25
informational disparities do not appear to drive preference differences. Instead men and women
appear to respond differently to positive messages about trade. Contrary to those who suggest
that women’s altruism drives their greater desire for protectionist policies, positive messages
appear to mainly affect individual’s beliefs about employment, suggesting that individual
concerns may be the primary source of preference differences between the sexes.
26
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Figure 1: Support for trade related policies by gender and by perception of top trading
partner
30
Figure 2: Respondents answer to the question “Who is the United States’s top trading
partner?” by gender
Canada
China
Women
European Union
Men
Japan
Mexico
0%
10%
20%
30%
40%
50%
31
60%
70%
Figure 3: Protectionist stance conditioned on gender, perception of major trading partner,
and experimental treatment providing information on top trading partner(s)
Average Response by Gender Type
(-2 Disagree to 2 Agree)
"The US government should increase limits on
imported goods and services"
Women
0.50
0.45
0.40
Women
0.30
0.20
0.10
Women
0.11
0.10
-0.10
Men
-0.12
Men
-0.20
-0.40
0.10
Men
0.00
-0.30
Women
0.33
0.29
-0.28
Men
Canada
(Control)
Canada
(China/Mexico
treatment)
China
(Control)
China
(Canada treatment)
"Top Trading Partner" response and treatment
32
Figure 4: Perception of the benefits of group by gender and skill-types
"What do you believe has been the effect of
trade on..."
100%
9%
3%
11%
11%
7%
75%
50%
16%
22%
66%
69%
66%
63%
62%
28%
24%
26%
19%
20%
21%
19%
60%
61%
24%
13%
25%
69%
25%
25%
7%
69%
63%
No Difference
24%
Hurts
0%
All
Low High Male Female
(794) Skilled Skilled (370) (424)
(179) (615)
Your Employment
Helps
All
Low High Male Female
(793) Skilled Skilled (371) (422)
(178) (615)
Employment in the United States
33
Figure 5:
Average Response by Gender Type
(-2 Hurts Greatly to 2 Benefits Greatly)
Positive messages and the gender gap:
"What do you believe has been the effect of trade
on..."
Female
Male
0.20
0.07
0.00
-0.08
-0.20
-0.25
-0.27
-0.18
-0.26
-0.40
-0.36
-0.42
-0.55
-0.61
-0.60
-0.49
-0.80
-1.00
-0.60
-0.65
-0.72
-0.82
-0.94
ControlTreatment
Group Group
ControlTreatment
Group Group
Your Employment
Employment of
Friends and Family
34
ControlTreatment
Group Group
Employment in
Your Region
ControlTreatment
Group Group
Employment in
the United States
Table 1: Do trade partner perceptions matter?
VARIABLES
(Dep var: recoded support)
Limits on Subsidies
Imports
Respond China
0.27
0.15
0.64
0.27
Respond EU
Respond Japan
Respond Mexico
Constant
Observations
R-squared
-0.01
0.22
0.55
0.32
0.03
0.13
633
0.02
*
0.35
0.15
** 0.82
0.26
*
0.22
0.22
0.03
0.32
0.33
0.13
640
0.02
Stabilize
Dollar
**
0.24
0.10
*** 0.15
0.19
**
Increase
benefits
Workers'
rights
0.24
0.10
0.15
0.19
0.04
0.16
0.15
0.23
0.04
0.16
0.15
0.23
0.19
0.13
0.20
0.22
0.10
0.19
0.42
0.27
*** 1.11
0.09
635
0.01
*** 1.11
0.09
635
0.01
*** 0.75
0.11
638
0.01
Standard errors below coefficients, in italics. *** p<0.01, ** p<0.05, * p<0.1
35
**
***
Table 2:
Limits on Imports
Observational &
Observational Only
Experimental
Workers' rightsObservational &
Observational Only
Experimental
VARIABLES
(Dep var: recoded
support)
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Respond China
0.27 *
0.19
0.41 ** 0.46 ** 0.42 ** 0.41 ** 0.19
0.06
0.22
0.26 *
0.23
0.22
0.15
0.15
0.19
0.13
0.12
0.16
0.15
0.16
0.16
0.64 ** 0.50 *
0.82 ** 0.80 ** 0.80 ** 0.80 ** 0.20
0.00
0.47
0.46
0.46
0.46
0.27
0.27
0.41
0.40
0.40
0.40
0.22
0.22
0.33
0.33
0.33
0.33
-0.01
-0.08
0.31
0.26
0.26
0.24
-0.10
-0.13
0.34
0.32
0.32
0.30
0.22
0.23
0.31
0.31
0.31
0.31
0.19
0.19
0.26
0.26
0.26
0.26
0.55 *
0.34
-0.14
-0.11
-0.11
-0.12
0.42
0.17
0.33
0.35
0.35
0.35
0.32
0.32
0.52
0.51
0.51
0.51
0.27
0.26
0.43
0.43
0.43
0.43
-0.40
-0.40
0.25
0.25
Respond EU
Respond Japan
Respond Mexico
0.18
0.19
Respond China & Female
-0.57 *
0.30
0.28
0.29
0.29
Respond EU & Female
-0.72
-0.62
-0.62
-0.64
0.54
0.53
Respond Japan & Female
-0.90 ** -0.78 *
Respond Mexico & Female
7 point Party ID
-0.58 ** -0.51 *
0.19
0.53
-0.78 *
-0.52 *
-0.44 *
0.25
0.03
Female
0.53
0.44
-0.78 *
0.44
0.44
0.46
0.45
0.45
0.45
0.38
0.38
0.38
0.38
0.52
0.58
0.58
0.55
-0.45
-0.40
-0.40
-0.42
0.66
0.65
0.65
0.65
0.55
0.55
0.55
0.55
0.03
0.02
0.02
0.02
0.02
0.02
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.02
0.00
0.02
-0.16 *** -0.16 *** -0.16 *** -0.16 *** -0.16 ***
0.01 ** 0.01 ** 0.01 *** 0.01 *** 0.01 ***
0.12
0.00
0.25
0.25
-0.31 ** -0.31 ** -0.18
-0.18
0.14
0.26
0.14
Unemployed
0.02
0.00
0.11
0.11
0.33 *** 0.77 *** 0.74 *** 0.74 *** 0.74 ***
0.25
0.10
-0.27 **
0.21
0.21
-0.27 ** -0.28 ** -0.17 *
0.14
0.11
0.11
0.43 *** 0.43 *** 0.43 ***
Treatment (1)
0.09
0.21
-0.17 *
0.09
0.21
-0.26 **
0.11
0.29 ** 0.29 ** 0.28 **
0.16
0.16
0.16
0.14
0.14
0.14
-0.06
0.02
-0.20
-0.02
0.03
-0.17
0.09
0.10
Treatment * Female (1)
0.16
0.24
0.13
0.20
-0.14
-0.12
-0.09
-0.07
0.21
0.21
0.18
0.18
Treatment * Skilled (1)
0.27
0.24
0.23
Constant
0.44
-1.06 *** -0.98 ** -0.98 ** -0.97 **
0.44 *** 0.89 *** 0.84 *** 0.84 *** 0.84 ***
Skilled
0.23
-0.96 ** -0.89 ** -0.89 ** -0.91 **
-0.09 *** -0.09 *** -0.08 *** -0.08 *** -0.08 ***
Age
-0.44 *
0.03
-0.04
-0.19
-0.42
-0.41
-0.32
0.19
0.75 *** 1.60 *** 1.47 *** 1.25 *** 1.26 *** 1.34 ***
0.13
0.32
0.33
0.28
0.28
0.28
0.11
0.26
0.27
0.23
0.23
Observations
633
621
621
967
967
967
638
626
626
973
973
973
R-squared
0.02
0.07
0.08
0.07
0.07
0.07
0.01
0.12
0.13
0.12
0.12
0.12
* For China respondents only
36
0.24
Table 3: Analysis of respondents’ perceptions of the benefit of trade on employment
Dependent Variables (-2 to 2 =
Hurt Greatly to Benefit Greatly)
Treatment
Treatment * Female
7 point Party ID
Age
Age Squared
Female
Skilled
Constant
Observations
R-squared
Your
Employment
Model 1 Model 2
0.19 *** 0.30 ***
0.05
0.07
-0.20 **
0.09
-0.01
-0.01
0.01
0.01
-0.04 *** -0.04 ***
0.01
0.01
0.00 *** 0.00 ***
0.00
0.00
-0.13 *** -0.04
0.05
0.06
0.15 *** 0.15 ***
0.06
0.06
0.65 *** 0.57 **
0.24
0.24
1456
1456
0.04
0.04
Employment of
friends and family
Model 1 Model 2
0.32 *** 0.44 ***
0.06
0.08
-0.22 *
0.11
0.00
0.00
0.01
0.01
-0.06 *** -0.06 ***
0.01
0.01
0.00 *** 0.00 ***
0.00
0.00
-0.19 *** -0.09
0.06
0.08
0.25 *** 0.25 ***
0.07
0.07
0.83 *** 0.75 **
0.30
0.30
1461
1461
0.06
0.06
37
Employment in your
region
Model 1 Model 2
0.32 *** 0.36 ***
0.06
0.09
-0.09
0.13
-0.02
-0.02
0.01
0.01
-0.05 *** -0.05 ***
0.01
0.01
0.00 *** 0.00 ***
0.00
0.00
-0.17 *** -0.12
0.06
0.09
0.31 *** 0.31 ***
0.08
0.08
0.60 *
0.56 *
0.33
0.33
1458
1458
0.05
0.05
Employment in the
United States
Model 1 Model 2
0.42 ***
0.44 ***
0.07
0.10
-0.05
0.14
-0.02
-0.02
0.02
0.02
-0.06 *** -0.06 ***
0.01
0.01
0.00 ***
0.00 ***
0.00
0.00
-0.23 *** -0.20 **
0.07
0.09
0.24 ***
0.24 ***
0.08
0.08
0.86 **
0.84 **
0.35
0.35
1462
1462
0.06
0.06
Appedix A
Experiment 1: Trade Partner Manipulation
Initial prompt: “We would now like to ask you about international trade.
Who do you think ranks as the United States’ top trading partner?” Constrained answer set of Canada, China, The European
Union, Japan, and Mexico randomly presented.
Half of those answering China receive following treatment:
 “Actually, CANADA is the top trading partner of the United States.”
Half of those answering Canada receive the following treatment:
 “Correct. In 2009, our three largest trading partners are Canada ($430 Billion), China ($366 Billion), and Mexico ($305
Billion).”
Common Question Grid:
Now we would like your opinion on the role of the US government in responding to international economic and security issues.
Row (questions)
 The US government should be more active in international economic organizations such as the World Trade
Organization.
 The US government should increase limits on imported goods and services.
 The US government should increase subsidies to industries competing against imports from foreign countries.
 The US government should do more to stabilize the value of the US dollar.
 The US government should increase unemployment benefits and job retraining programs.
 The US government should spend more securing its borders.
 The US government should spend more on military defense programs.
 The US government should ensure its trading partners protect the basic rights of workers.
Column (answers)
<1> Agree <2> Somewhat Agree <3> Somewhat Disagree <4> Disagree <5> Don’t Know
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Experiment 2: Altruism Manipulation
Control group prompt: “The US government continues to expand opportunities to trade through bi-lateral and multi-lateral
agreements with foreign countries. What do you believe has been the effect of trade on the following:”
Treatment group prompt: “The US continues to rank as the largest trading country in the world. In the service sector, the US
dominates the international market, exporting more than the next two countries combined. US exports in services more than
doubled between 1998 and 2008 and service exports are estimated to support at least 3.5 million US jobs. Even in the
manufacturing sector, where the US faces stronger competition, more than 1 in 5 jobs are dependent on exports.
The US government continues to expand opportunities to trade through bi-lateral and multi-lateral agreements with foreign
countries. What do you believe has been the effect of trade on the following:”
Rows (questions)




Your employment
Employment of friends and family
Employment in your region
Employment in the United States
Column (answers)
<1> Benefit Greatly <2> Benefit Slightly <3> No Difference <4> Hurt Slight <5> Hurt Greatly
<6> Don’t know
39