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Salvation by Good Works?: Offshoring, Corporate Philanthropy, and Public Attitudes Toward Trade Policy
Andrew Kerner University of Michigan Jane Lawrence Sumner University of Minnesota
Abstract: We explore the relationship between offshoring, corporate image, and attitudes towards free trade. Offshore production is an especially unpopular aspect of globalization, and we hypothesize that priming Americans to think about American firms’ offshore production will dampen their enthusiasm for another, more popular aspect of globalization: free trade. We also test the idea that aversion to offshoring is tied to the idea that offshoring firms violate social norms by acting without considering the local communities that depend on them (Brunk 2010). If this is the case, we expect that the effect of being primed to consider offshoring should be mitigated if the offshoring firm effectively demonstrates is commitment to its local community through a corporate social responsibility (CSR) initiative. We test these hypotheses using an online survey experiment in which individuals rate a corporate press release announcing a new (offshore-produced) fitness tracker, and are asked about their trade policy preferences. We find support for our hypotheses. Prior exposure to our offshore-mentioning press release depresses enthusiasm for free trade. However, that effect is substantially mitigated if the press release also mentions the producing firm’s commitment to local philanthropy.
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Individuals’ trade preferences come from a variety of sources. The traditional focus in the
economics and political science literatures has been on material drivers, typically relying
on one or the other of the Ricardo-Viner (specific factors) or Heckscher-Ohlin (mobile
factors) models of trade (for example, Magee 1978, Magee, Brock, and Young 1989,
Grossman and Helpman 1994, Mayda and Rodrik 2005, Hiscox 2001, Milner and Kobuta
2005, Rogowski 1989, Scheve and Slaughter 2001). While material interests are an
intuitive place to start, the explanatory power of purely material theories is sometimes
quite low (Mansfield and Mutz 2009), and often suggests a reality marked by substantial
deviations from what trade theory suggests individuals’ preferences should be (See Kuo
and Naoi 2015 for a review.) A second wave of research in this area has focused on
ideational and affective factors that drive individuals’ trade preferences. These studies
have focused on the impact on trade preferences of patriotism and chauvinism (O’Rourke
and Sinnot 2001), out-group anxieties and racism (Sabet 2014; Mansfield and Mutz 2009,
Guisinger 2014, Margalit 2012), and acculturation through higher education (Hainmueller
and Hiscox 2006). To paraphrase Sabet (2014), individual trade preferences are as much
about “feelings” as anything else.
Overlooked in the literature’s focus on the affective drivers of individuals’ trade
preferences is the potential role of individuals’ feelings towards firms, particularly those
that move production offshore.1 This hole in the literature is notable, and should be filled,
for several reasons. First, we can hardly consider what “free trade” means in a modern,
American context without considering its tight relationship with globalized production
chains. Ruhl (2015) and Lanz and Miordout (2011: 16) report that roughly 30 percent of
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!1 We use the term “offshoring to refer to American firms that shift jobs abroad. We do not
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American exports, and 45 percent of American imports occur within global supply chains
(see also Moran and Oldenski 2015). Recent American trade agreements are at least as
focused on securing a policy environment conducive to global supply chains as they are to
reducing tariffs. Second, Americans’ distaste for offshoring is well established (Mansfield
and Mutz 2013;!Jensen!and Lindstädt 2013), and that is distaste often used as a focal point
for opposition to the liberal trading environment in which offshoring occurs. This has been
especially notable during the current (2016) presidential primary campaign season, in
which presidential candidates from both parties have repeatedly invoked offshoring, often
with references to specific companies, as a reason to reapply larger tariff walls and
abandon further trade liberalization.2
This article begins to fill this hole in the literature. We ask: How do individuals’
attitudes towards offshoring firms affect their attitudes towards trade? To the extent that
an offshoring “frame” for free trade depresses enthusiasm for free trade, to what extent is
that effect subject to an individuals’ affective relationship to the firm doing the
offshoring? In other words, to what extent are popular reactions to offshoring about, or at
least subject to, “feelings?”
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!2 Bernie Sanders, for example, noted in an April 29th, 2015 Guardian op-ed that “Trade deals … have been abysmal failures: they allowed corporations to shut down operations in the US and move work to low-wage countries.” Republican presidential candidate Donald Trump has repeatedly linked offshoring to his critique of American trade policy and his calls for a higher tariff wall between the United States and Mexico. Trump suggested that his substantially more protectionist policies would prompt the CEO of Ford Motor Company to say "Mr. President, we've decided to move the plant back to the United States, and we're not going to build it in Mexico." He has also gone after the Carrier Corporation specifically. Former Michigan Governor and current executive at the Hillary Clinton-supporting political action committee “Correct The Record” Jennifer Granholm says of Secretary Clinton’s opposition to the Trans-Pacific partnership that, “She doesn’t want to be party to the continual offshoring of American jobs” (McMorris 2015).
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In answering these questions, we link the literature on trade preferences with the
(substantial) literature on corporate brand-management, and make contributions to both.
Firms occasionally engage in unpopular business practices, and when they do, they often
use branding campaigns to project a countervailing, “good” public image, often by
invoking the firm’s corporate social responsibility (CSR). Oil companies often make
public commitments to environmental stewardship (de Vries et al. 2013; Du and Viera Jr.
2012; Muralidharan et al., 2011; Laufer 2003); companies known for poor labor practices
often invest in labor-related CSR initiatives (see Kytle and Ruggie 2005: 14-15 on Nike’s
efforts). CSR-based branding campaigns are typically judged on their capacity to restore a
positive brand image, help the firm sell products, earn revenue, or increase share price (see
for example, Torelli, Monga, and Kaikati 2012; Hainmueller and Hiscox 2012, Curran and
Moran 2007). While the! literature’s focus on firm-level effects is sensible, it is also, we
argue, limited. Corporate behaviors impacts on public perception often extend beyond the
firm and into the policy context in which that action occurs. If public distaste for “bad”
corporate behavior can spill over into attitudes towards the broader policy context, can
compensatory demonstrations of “good” corporate behavior have countervailing effects on
public policy attitudes? Are otherwise politically salient corporate misbehaviors made less
so if the firm is better liked? Put in the context of this paper, can effective brand
management shape offshoring’s political implications?!
We answer these questions through a survey experiment. Individuals in our
experiment complete two, ostensibly unrelated, tasks. The first is to rate a new product
line (of fitness trackers) on the basis of a corporate press release. The press release
mentions that they are to be made in Indonesia by an American firm acting with a local
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partner. The press release states the location of production as a fact, but makes no attempt
to highlight any policy or political implications of that fact; its primary purpose is
(naturally) to highlight the product’s features and positive press related to it. Nonetheless,
manipulation checks reveal that a substantial majority of our respondents recalled where
the product was manufactured. Participants’ second task is to take a survey of their
attitudes towards trade policy.
There are two experimental manipulations in our research design. The first is the
order in which survey takers complete their tasks. Some survey takers state their trade
policy preferences before being exposed to the press releases, others do so afterwards. A
second manipulation is in the press release itself. Participants are randomly assigned into
one of three press releases: a baseline press release that describes the product (including
the location of its production), one that adds additional text stressing the low consumer
prices and corporate efficiency that (this particular instance of) offshore production makes
possible, and another that adds language to the baseline text emphasizing the firm’s
philanthropic investments near their domestic (Ohio) headquarters. As we note in more
detail below, our CSR manipulation “ successfully generated the perception that the firm
was “good for the community” and paints the firm in a “positive light.”
Introducing the concepts of offshoring and CSR through corporate press releases
serves two purposes. First, corporate press releases allow us to mimic corporate
communication around CSR and corporate civic engagement, while offering a more
realistic platform than advertising to introduce the offshore location of production.
Second, the press release allows us to introduce offshoring in an apolitical context,
without any external indication that the location of our product’s production is more
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important than its price, its features, or any other fact about it. It is entirely up to the
survey taker to invest political salience in that fact, or not. That subtlety creates an
especially conservative test of offshoring’s impact on trade policy preferences.
We administered our survey to 812 participants using Amazon’s MTurk during the
last week of December 2015. The main features of our results are as follows. Prior
exposure to the idea of offshoring (via the baseline, or “offshoring” press release) reduced
enthusiasm for free trade significantly: compared to the control group, exposure to the
offshoring press release reduced the predicted probability of supporting free trade by
roughly 13%, while increasing opposition to free trade by about 8% and neutrality to free
to trade by 5%. However, the effect of that exposure is mitigated entirely if the respondent
is also exposed to information about the offshoring firm’s philanthropic efforts. Survey
takers exposed to our “CSR” press release espouse trade views that are nearly identical to
those of survey takers in the control group. These findings are consistent with the idea that
emphasizing a firm’s civic engagement in its home community can prevent its offshoring
from becoming political salient, and with Brunk’s findings that opposition to offshoring is
rooted to distaste for the offshoring firm.
We find no statistically significant evidence that pairing information about
offshoring with information about efficiency and reduced consumer prices has any
mitigating effects. Survey takers in the “low price” treatment group espoused trade policy
preferences that were statistically indistinguishable from survey takers in the offshoring
treatment group. This (non)finding is notable for what it says about the roots of
offshoring’s political salience, but also for its contrast with Rho and Tomz’s (2015)
finding that efficiency-based frames increase respondents’ enthusiasm for free trade.
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Without overstating the comparability of the two findings – the Rho and Tomz experiment
differs from our on a variety of dimensions – the contrast suggests that, despite their being
intertwined in an economic sense, the politics of “offshoring” and “trade” are actually
quite different, and that the latter is more easily seen as more of an economic issue than
the former. We also explore conditional relationships, and find that the treatment effects
that we do find are more pronounced among survey takers living in counties that have
experienced recent job losses, and among respondents without a four-year college degree,
the latter of which is in keeping with previous work on framing effects and trade
preferences (Ardanez, Murilo and Pinto 2013; Hiscox 2006).
Taken as a whole, our findings make two important points: First, Americans link
their distaste for offshoring to their attitudes towards trade. That distaste for offshoring
bleeds into trade policy preferences is not an especially surprising finding – political
campaigns appear to be operating under this assumption - but the finding that simply
mentioning offshoring in an apolitical context can depress enthusiasm for free trade is
both new and notable. The second important message from our experiment is that
messaging campaigns that paint offshoring firms in a positive light can mitigate
offshoring’s effect on trade politics. This finding helps clarify the causal process by which
offshoring becomes politically salient, which, evidently, is substantially influenced by
individuals’ beliefs about the offshoring firm. It also makes a more general and typically
overlooked point: corporate communications meant primarily for the purpose of brand
management can affect the context in which public policies are popularly understood.
While not the primary purpose of this article, future research would do well to further
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explore and clarify the relationship between corporate brand management and the political
zeitgeist.
The remainder of this paper is as follows. Section II describes our survey
experiment and the data collected. Part III describe our results. Part IV concludes.
Part II – Our experiment
Our experiment’s aim is to randomly assign exposure to treatments that prime people to
think about offshoring in different contexts, and to evaluate whether doing so affects their
stated attitudes towards trade policy.12 Our experiment fits within a broader set of works
on “priming” and “issue framing” and trade policy preferences (e.g. Naoi and Kume 2011;
Rho and Tomz 2015; Hermann et al 2001; Hiscox 2006; Ardanez, Murillo and Pinto 2013;
Slothus and de Vreese 2010; Margalit 2012). This literature consistently finds that trade
attitudes vary according to the context in which the idea of free trade is presented. For
example, soliciting individuals’ trade preferences in the context of “pro-trade” frames
stressing job creation, efficiency and lower prices tends to increase support for trade;
soliciting trade preferences in the context of “anti-trade” frames that stress job losses and
unfair competition decrease it (e.g. Hiscox 2006; Rho and Tomz 2015; Ardanez, Murillo
and Pinto, 2013). Others have noted that more subtle “primes” can also be used to explore
the determinants of trade policy preferences. In priming experiments, the experimenter
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!12 Our use of an experimental method to characterize the relationship between attitudes towards offshoring, trade, and firms reflects our belief that these relationships would be a difficult if not impossible to identify using observational data. While attitudes towards offshoring and firms plausibly inform attitudes towards trade, the reverse is also likely true, and both are likely informed by hard-to-measure third factors.
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exposes respondents to ostensibly unrelated material prior to giving their trade views. The
primes are meant test the relationship to increase, at least temporarily, the salience of
certain ideas about culture, ideology, etc. at the moment the respondent answers trade-
related questions, so that those concepts’ relationship to trade policy preferences can be
studied. Margalit (2012), for example, shows that priming Americans to think about
American culture depresses their enthusiasm towards free trade.
Our experiment explores the effects of “offshoring” and “CSR” primes on trade
policy attitudes. Our experiment includes two parts: a survey that asks respondents to rate
the effectiveness of a corporate press release for a company’s a new product line, and a
“political” survey asking respondents about their attitudes towards free trade. Our political
survey focused on attitudes towards the negotiations over the Trans-Pacific Partnership
(TPP), which was the most topical trade-related issue at the time. Our experiment is, for
the most part, not deceptive. Survey takers are told they are participating in academic
research, and are made aware of who is conducting that research. However, survey takers
are told “The purpose of this study is academic research for projects in both business and
political science”. While technically accurate, this statement is intended to give the
impression that the two surveys (one very clearly about business, the other very clearly
about politics) are unrelated, but packaged together out of convenience. This is not true, as
randomly assigned variation in one survey acts as a treatment for the other.
Upon accessing the survey, respondents are randomly assigned into the control
group or into one of three different treatment groups. Survey takers in the control group
are asked about their opinions on trade before seeing the corporate press release and, thus,
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without being primed to think about offshoring. We focus our attention on the survey’s
first question, which asked about support for free trade in a general sense. We asked:
Do you support or oppose free trade (e.g., the elimination of tariffs and quotas)?
Responses include: “Strongly Oppose”, “Oppose”, “Neutral”, “Support” and “Strongly
Support.”16
Importantly, this was the first question that we asked, preceding any mention of the TPP.
There was no opportunity for the TPP-theme of the survey to influence survey taker’s
answers.17
All of the treatment groups are shown the corporate press release before the trade
survey. The press release announces a new a fitness tracker (“the blurge”), describes its
many features, and indicates that it is the product of a partnership between an American
firm (Applied Technologies) and an Indonesian firm (Indico). Survey takers are reminded
that the press release is “hypothetical” – making the firms in question’s lack of a web
presence unsurprising to the curious. The press release is written from the perspective of
Applied Technologies. The full text of that press release is available in the appendix. After
reading the press release, survey takers are asked a battery of consumer-related questions
about whether they have ever used a fitness tracker, the likelihood of their buying this
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!16 In the analysis we collapse these into three categories (oppose, neutral, support), but the substantive interpretation remains the same if the five-category response is used. 17 Our analysis of framing effects on attitudes towards the TPP reveals similar patterns to what is described below, though the effects interact with partisanship in ways that fall outside of this paper’s theoretical purview. The results are discussed in earlier versions of the paper - see Kerner and Sumner 2016 – and are available from the authors on request.
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fitness tracker, their feelings about the price, and so forth. There were ten such questions
in total. The questions referring to the product have no direct purpose in our experiment;
their indirect purpose is to be filler, there to make the “business” portion of the survey
appear realistic, and to obscure the surveys’ true intent.
The experimental manipulation is in how the press release concludes. Respondents
in the “offshoring” treatment group are shown a press release that concludes with the
following text:
The blurge will be proudly manufactured at a new state-of-the-art manufacturing facility operated jointly by Indico and Applied Technologies in Banten, Indonesia, which will employ roughly 1,000 workers locally, plus a support staff in our home base of Southeastern Ohio.
The text is meant to describe the joint venture in positive terms (it is a corporate-issued
press release, after all) but in terms that are as descriptive and neutral as one can be while
still reinforcing that this product will be manufactured abroad.
The “low prices” treatment group is given a press release that appends the
following text highlighting the idea that low consumer products and corporate efficiency
are among the fruits of multinational production:
"This new factory is an example of our vision in action--- it highlights our commitment to providing high-quality goods for consumers at an affordable price," George Dennis, CEO of Applied Technologies, said. "It also utilizes key synergies between the interests of the consumer and the design and manufacturing expertise that Applied Technologies and Indico are bringing to this joint venture."
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Finally, the “CSR” treatment group is given a press release that replaces the “low prices”
text with the following text that highlights Applied Technologies’ commitment to its local
community.
Applied Technologies remains firmly committed to serving the Ohioan communities that it has historically worked with for the past 50 years. For that reason, a portion of the revenues that we earn through this new product line will continue to be reinvested in local, community based-services. One such initiative that we are particularly excited about is aimed at combatting child obesity. According to the CDC, the percentage of adolescents aged 12–19 years who are obese increased from 5% to nearly 21% between 1980 and 2012. This is a serious problem, and Applied Technologies will be funding in-school initiatives to help counter that trend through nutritional and fitness education in local schools, and through after-school sports programs. Moreover, in our programs aimed at high-school aged kids, Applied Technologies will be donating one blurge for each participant to help them create and achieve their fitness and health goals.
We are interested in two relationships. The first is the difference, if any, between trade
policy attitudes among respondents whose trade attitudes were solicited with any
prompting to think about offshoring, and respondents who were previously exposed to our
“offshoring” press release. To the extent that individuals connect their (typically negative)
feelings towards offshoring to their attitudes towards trade, respondents who are primed to
consider offshoring should be less supportive of trade than those who are not.
The second set of relationships that we consider is between the trade attitudes among
respondents that were exposed to our “offshoring” press release, and the trade attitudes of
respondents that were exposed to our “CSR” and “low price” press releases. To the extent
that our CSR text is able to generate positive feelings towards our firm and its
commitment to its home community, we expect that respondents who are exposed to it
will be more prone to support free trade than respondents who are only exposed to
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information about offshore production. Importantly, our CSR campaign is not
compensatory in any meaningful sense. It suggests that the firm is attentive to the social
conditions of local communities, and that the firm sees itself as an enduring part of that
community. It plausibly undermines the idea that the offshoring firm acts without
considering its local community. But our CSR initiative does not create new jobs, or
provide a social good – education, job re-training – that is linked in any meaningful way to
lost jobs. To the extent that our CSR message is relevant to whatever political salience
individuals find in offshoring, it suggests the relevance of individuals’ affective
relationship with firms to that process, rather than their beliefs about material
compensation.
We have fewer ex ante expectations about the impact of exposure to our “low
price” press release. On one hand, we found no evidence in prior surveys that our low
price press release painted our firm or its decision to produce offshore in a more positive
light. This is not surprising: while low prices benefit consumers, they do virtually nothing
to compensate American communities for the loss of work, and do not provide evidence of
the firm’s attentiveness to that community or its high ethical standard. To the extent that
the causal link between beliefs about offshoring and beliefs about trade policy runs
through individuals’ affective relationship with the offshoring firm, we would not expect
our low price frame to have any impact. On the other hand, stressing the corporate
efficiency of offshore production is policy relevant to the extent that commercial
globalization is justified on the basis of its effects on American consumers. Rho and Tomz
(2015) find that “efficiency cues” concerning trade’s effect on the macroeconomy
substantially reduced support for protectionism. On a purely economic basis it seems
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reasonable to believe that reminding people that offshore production typically lowers
consumer prices might mitigate, if not reverse, whatever protectionist effect that
mentioning offshoring has. Though as Mansfield and Mutz (2013) make clear, Americans
are quite capable of resenting offshoring while supporting trade with foreign firms, despite
their rough economic equivalence.
Finally, we expect that any treatment effect could be conditioned by demographic
variables. Individuals who live in areas in which employment prospects are dwindling
(i.e., jobs have been lost) may be more responsive to threat of offshoring, or towards the
idea of corporate investments in local communities. We also expect any treatment effects
should decline in education. College-educated respondents may be more set in their
attitudes towards trade, and thus less affected by priming. Such a finding would be
consistent with previous findings in Hiscox (2006) and Ardanez, Murilo and Pinto (2013).
Our consideration of these conditional relationships is partly to more accurately
characterize the conditions under which offshoring- and CSR-based primes might be most
operative, but also because our findings are more credible to the extent that they show the
same conditional relationships that have been found elsewhere.
Our survey includes several features that help us screen out respondents who
completed, but did not pay attention to, our survey. First, many of our demographic
variables (gender, race, state where respondent grew up) are asked via open-response text
box, allowing us to screen out respondents who are not paying attention and are quickly
clicking through the survey. Often in our data this manifests by respondents entering
repeated pairs of letters (“twtwtw”), numbers when prompted for text (“47” when asked
for gender), or giving responses that are nonsensical in context (“notebook” when asked in
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which state they were raised). All of these observations are removed.19 Second, we also
included two manipulation check questions, both of which asked respondents to recall
factual information from the prompt (where the company is located in the US and in
which country they are producing abroad). 68% of respondents properly identified that
Applied Technologies is based in Ohio, and 65% of respondents properly identified that
they are offshoring production to Indonesia. The second most frequent responses were
Indiana and China, respectively. 53% of respondents correctly answered both questions.
We report our models using the full sample of respondents (excluding those who failed to
provide sensible answers to open-ended responses), as well as models that use the more
restricted sample of people who answered the manipulation check questions correctly.
Setting, Sample, and External Validity
We conduct our survey experiment using Amazon’s MTurk, restricting recruitment to
U.S. residents over the age of 18. MTurk yields a more representative sample than
standard samples of convenience, and at a lower cost than other recruitment methods
(Buhrmester, Kwang, and Gosling 2011; Berinsky, Huber, and Lenz 2012). For our
purposes, MTurk is more than a sample of convenience --- it is drawn from the subset of
the population - frequent internet users, a group to which U.S.-based Turkers undoubtedly
belong - that is especially likely to be exposed to CSR advertising (Du, Bhattacharya, and
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!19 There are, in practice, very few of these observations, and their removal has no substantive impact on our results.
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Sen 2010).32 With respect to its applicability to experimental studies of trade, Huff and
Tingley (2015:5) argue that MTurk respondents hold occupations and come from
geographic locations that are comparable to samples used in professional polling. An
additional benefit of using MTurk is that it allows us to reasonably make standard SUTVA
assumptions: because the treatment is assigned uniformly to all units via a computer, there
are no variations in treatment within groups (e.g., concerns about whether a respondent
‘takes’ the treatment), and because respondents are recruited using the internet and are
thus unlikely to know or encounter other respondents with whom they could discuss the
experiment,33 the treatment of one individual should not influence the treatment of
another.
Our survey was conducted in the final week of 2015. 943 respondents began the
survey and 812 completed and submitted it. Payment was contingent upon submission of
the survey and entry of a code into MTurk.34 Removing observations with nonsensical
answers to open-ended questions left us with 807 respondents. The survey was hosted by
Qualtrics, which randomly assigned each respondent into the control group or into one of
the three treatment groups, with the aim of assigning roughly equal numbers of
respondents to each of the four groups. Individual random assignment ensures that other
characteristics of the respondents are uncorrelated with the assignment and should be
balanced across the groups. Of the completed survey, there were 209 in the control group,
186 in the offshoring group, 211 in the CSR group, and 201 in the low prices group.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!32 Other MTurk-based experiments relating to CSR include Burbano (2016), Chernev and Blair (2015) and Frank and Smith (2014). 33 We are aware of the MTurk sub-Reddit in which Internet users discuss MTurk surveys but think it is sufficiently unlikely ours will be discussed, as it is not especially lucrative. 34 Twelve respondents completed the survey but did not enter the code for payment.
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Our randomization appears to produce balanced samples. Table 1 shows the
results of a series of linear probability models aimed at predicting assignment into one of
the experimental categories. As Table 1 shows, apart from categories including very few
people (e.g., Jehovah’s Witnesses), no demographic category is a statistically significant
predictor of assignment to any treatment group; all of the variables, together, explain
virtually none of the variance in assignment (R2 ≈ 0). Additional balance information for
major demographic variables across our four groups is available in Appendix A.
[Table 1 Here]
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!!Additional Sources of Data
To test for geography-based conditional effects we incorporate data on job gains and
losses measured at the county level, using data from the U.S. Census’ County Business
Patterns database. We use the proportion of jobs in 2007 that were lost or gained by 2013
(the most recent year in which data are available) as a gauge for the economic context that
the respondent lives in. Positive values indicate job losses over this time period, negative
numbers indicate job gains.
Part III – Results
Does CSR change how people feel about Applied Technologies?
Our first set of findings speaks to the effect that our CSR treatments has on feelings about
our fictional company, Applied Technologies. While not related to trade attitudes per se, it
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speaks to the extent that our manipulation actually manipulated something. We assessed
the strength of our manipulation through separate field-testing exercises, in which we
randomly assigned MTurkers into receiving different versions of the press release, and
then had them rate Applied Technologies on a scale of 0-10 on three measures: whether
they believed Applied Technologies was a good company, whether Applied Technologies
was good for its community, and whether they felt the press release they read portrayed
Applied Technologies in a positive light. We conducted this exercise twice, once before
our experiment (December 2015) and once afterwards (June 2016), and came to nearly
identical results both times.
Figure 1 characterizes the results of those exercises. These data contrast firm
ratings from a random sample of respondents that were exposed to offshoring text, and a
random sample of respondents that were exposed to the CSR text. The top row shows the
results of the first (2015) test, and the bottom row shows the results of the second (2016)
test. Consistent with our expectations, respondents in the CSR treatment routinely rate the
company as being higher on the scale of "good for its community" than those who
received the offshoring press release.35 In the December 2015 iteration, the average
respondent in the CSR group rated Applied Technologies as 8.77 in this category, while
the average respondent in the offshoring treatment group rated Applied Technologies 8.22.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!35 The effectiveness of this particular campaign is not accidental, as it hews as closely as possible to the criteria for effective CSR set forth in Du, Bhattarchaya and Sen (2010). These criteria include demonstrating a commitment to a social cause, stressing the intrinsic motivation, rather than profit motivation, and communicating the congruence between the social issue and the firms’ business. We also found, through trial and error that focusing our CSR on philanthropy in the US was more effective than focusing on philanthropy geared toward foreign recipients. The data and analysis behind our determination that this particular CSR language was suitable for our purposes are available from the authors on request.
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These differences are statistically significant at the 0.018 level. The June 2016 iteration
suggests a larger effect. The average respondent in the CSR group rated Applied
Technologies as 8.52 in this category, while the average respondent in the offshoring
treatment group rated Applied Technologies 6.70. These differences are statistically
significant at far below the .001 level.
[Figure 1 Here]
Our CSR campaign appears to have had a smaller and less consistent effect on
respondents' beliefs about the company as a whole. While the second iteration of our field
test suggests that CSR painted Applied Technologies in a positive light (9.35 average
rating in the CSR group, vs. 8.97 in the offshoring group, with the difference statistically
significant at the .05 level) and gave the impression that it was a good company (8.63
average rating in the CSR group, vs. 7.79 in the offshoring group, with the difference
statistically significant at the .001 level), these differences are not evident in the first
iteration. Even when statistically significant, however, differences along these dimensions
are substantially smaller than with respect to whether or not the firm is good for the
community. In light of Brunk’s (2010: 259-260) argument that opposition to offshoring is
substantially rooted in a judgment that offshoring firms violate social norms by
undermining their home communities, we expect that exposure to our CSR prompt will
mitigate, at least to some extent, whatever depressing effect the offshoring language
would otherwise have on trade preferences. To the extent that it does not have a mitigating
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effect, that finding would seem more likely a failure of the theory than of our ability to
manipulate opinions about whether our firm had forsaken its local community.
In unreported field tests, we found nearly identical results from a similar CSR
campaign focused on anti-smoking efforts in local (Ohio) schools. CSR campaigns
directed at Indonesian causes were found to be ineffective. As noted above, we also found
that exposure to the low price treatment did not suggest to respondents that Applied
Technologies was good for the community, or a good company, or that the press release
portrayed the company in a positive light.
Does exposure to offshoring reduce support for free trade?
Our first cut at analyzing the results of our experiment is to compare trade preferences
among respondents in our control group with trade preferences among respondents in our
offshoring treatment group. The bar chart in Figure 2 illustrates these differences
graphically. As shown, prior exposure to information about offshoring reduces support for
free trade and increases opposition to it. 52.2% of control group respondents either support
or strongly support free trade, compared to 46.6% among those exposed to the offshoring
treatment; 12.9% of respondents in the control group either oppose or strongly oppose free
trade, compared to 20.6% of those exposed to the offshoring treatment. Both of those
pairwise differences are statistically significant at the .05 level.36 In line with expectations,
exposure to the idea of offshoring reduces enthusiasm for free trade, even when it is
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!36 A chi-squared test of independence between the two groups – essentially a test of whether the two distributions, rather than specific pairwise differences, are systematically distinct - narrowly avoids rejecting the null of no difference at conventional confidence levels, with a p-value of .11.
! 21!
presented in a politically neutral context without any overt connection made between it
and free trade.
[Figure 2 Here]
We next explore – again, via simple tests of means - whether exposure to out CSR
or low prices treatments mitigates the anti-trade effects that follow from exposure to our
offshoring prime. These effects are noted graphically in Figure 3. The left side of Figure 3
compares trade attitudes between respondents in the offshoring treatment group and the
CSR treatment group; the right side of Figure 3 compares trade attitudes between
respondents in the offshoring treatment group and the low price treatment group.
The left side of Figure 3 plainly shows that respondents who received a positive
message about the offshoring firm - the CSR treatment group - were more likely to
express support for free trade than respondents in the offshoring treatment group. 54.0%
of respondents who received the CSR treatment supported free trade while just 46.6% in
the offshoring treatment group did. 14.6% of respondents in the CSR treatment group
opposed free trade while 20.6% in the offshoring treatment group did. Both of these
differences are statistically significant at the .05 level.37 The right panel of Figure 3 shows
that this treatment effect is less pronounced among those who received the low price
treatment. 49.3% of respondent in the low price group supported free trade, and 16.9%
opposed it. While this represents a small drop in opposition relative to the offshoring
group, that drop is mostly made up for with an increase in neutrality, with no
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!37 A chi-squared test fails to reject the null of no differences across the distribution, likely on the basis of the similarity in the neutral groups.
! 22!
commensurate increase in support for free trade. None of the distinctions with respondents
in the offshoring group are statistically significant.
[Figure 3 Here]
It is important to point out that even while the CSR treatment “works” to increase
acceptance of free trade, respondents in this group are no more acceptant of free trade than
respondents in the control group of survey takers whose trade opinions were solicited
without having been first prompted to consider offshoring. 52.2% of the untreated
respondents either support or strongly support free trade, compared to 54.0% of
respondents who received the CSR treatment; 12.9% of the untreated respondents either
oppose or strongly oppose free trade, compared to 14.6% of respondents who received the
CSR treatment. Neither of these differences is statistically significant.
In sum, simple tests of means support the main arguments in this paper: being
primed to consider offshoring depresses attitudes towards free trade, but that depressive
effect is mitigated if respondents are simultaneously presented with information about the
offshoring firm’s engagement in its local community. This is consistent with the idea that
offshoring is a powerful frame, which works substantially through Americans’ negative
views of offshoring firms.38
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!38 While we interpret this “cancelling out” as meaningful, it could be that the goodwill that firms generate through CSR campaign creates an independent, positive effect on support for free trade that offsets the negative effect brought about by exposure to the idea offshoring. That the two cancel out in this case could be a coincidence based on the apparently similar strengths of our two offsetting treatments. While our instinct is to suspect the former scenario, our experimental design was not set up to distinguish between the two and we leave resolution of that question to future research.
! 23!
Our second cut at analyzing these data is to take a parametric approach, using an
ordered logit to model the trichotomous outcome variable (“oppose free trade”, “neutral”,
“support free trade”) as a function of treatment group and demographic variables. Our
models use the offshoring treatment group as the reference category. The coefficient on
the “Control” indicator variable therefore compares trade attitudes of respondents in the
offshoring group with the trade attitudes of respondents in the control group. We expect a
positive coefficient for this variable, suggesting that exposure to the offshoring treatment
reduces enthusiasm for free trade (or put in terms more consonant with the coefficient, that
not exposing respondents to the offshoring treatment should increase support for free trade
relative to a baseline of exposure). The coefficients on the “CSR” and “low price”
indicator variables similarly compare the trade attitudes of respondents who were treated
with the CSR and low price corporate press releases with the trade attitudes of respondents
exposed to the offshoring press release. Positive coefficients on these variables indicate
that respondents exposed to those prompts were more pro-trade than respondents in the
offshoring group. Positive coefficients on both the control and the CSR/low price indicator
variables would indicate that exposure to the offshoring press release reduced support for
free trade, but that the additional CSR/low price texts recouped some of the lost support.
The relative magnitude of the coefficients indicate how much of the effect was recouped:
identical coefficients would indicate that entire effect was mitigated, smaller coefficients
on the CSR and low price variables would indicate that only a fraction of the effect had
been mitigated.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
! 24!
Our first set of models is listed in Table 2. We report our models with and without
control variables (age, income, gender, educational attainment, partisan identification, and
the extent to which their county has suffered job losses between 2007 and 2013), and with
and without dropping observations from respondents who failed the manipulation checks
(Figures 2 and 3 are based on the full sample, without consideration of manipulation check
questions). Across all four models, we find that the coefficient on the control treatment is
positive and statistically significant, indicating that exposure to the offshoring treatment
reduced support for free trade.
[Table 2 Here]
Figure 4 depicts predicted probabilities graphically for a hypothetical respondent
whose age and county-level job losses are held at the sample mean, and for whom all
categorical variables (income, college, gender, and partisan orientation) are set to
reference categories or zero. We compute predicted probabilities based on the results of
model 3. The black square represents the predicted probability of this hypothetical
respondent opposing/being neutral towards/supporting free trade when in the control
group; the end point of the red arrow indicates the predicted probability of this
hypothetical respondent opposing/being neutral towards/supporting free trade when in the
offshoring treatment group. Exposing respondents to the offshoring treatment reduces
support, and increases neutrality and opposition towards free trade.39 Exposure to the
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!39 The intercept estimates indicate that these models can discriminate between opposition and neutrality, but struggle to discriminate between neutrality and support. In a sense, then, these models tell us the difference between opposition and non-opposition. As a
! 25!
offshoring prompt reduces the predicted probability of support by about 13% (from 52.7%
to 34.4%), while increasing opposition by about 8% and neutrality by about 5%.
[Figure 4 Here]
The models reported in Table 2 also estimate positive and statistically significant
coefficients for the CSR treatment indicator, indicating that the trade-depressing effect of
exposure to the offshoring prompt is offset by the inclusion of the CSR message in the
corporate press release. Notably, the estimated coefficients on the “CSR group” indicator
variable are almost identical to the coefficients estimated for the “control group” indicator.
All of the damage done to free trade by exposure to the offshoring prompt is undone if that
exposure is coupled with the CSR text. Also notable is that this offsetting effect is as
evident in the subset of people who correctly answered all of the attention check questions
as it is in the sample overall. The appearance of offsetting effects is therefore not likely
due to respondents given the longer texts not paying sufficient attention to the offshoring
part of the script. Even respondents who were perfectly able to recall the offshoring-
related facts of the press release found those facts less relevant to their trade preferences
when they were coupled with the CSR message. In contrast, these regression results
provide no evidence that exposure to our low prices text affected our respondents reported
trade attitudes. The relevant coefficients are positive, as expected, but very small, and do
not approach statistical significance.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!robustness check, we also ran these models as logit models with three separate DVs (opposition, neutrality, support). The substantive interpretation changes little. We report these models in Appendix B.
! 26!
[Figure 5 Here]
Figure 5 illustrates the results relevant to exposure to our CSR treatment for the
same hypothetical respondent noted above. The red arrows with black endpoints are
identical to those in Figure 1. The additional blue arrows with red endpoints compare the
predicted probability that the same representative respondent supports/opposes/is neutral
toward free trade, when placed in the offshoring treatment group (the red endpoints) and
when they are placed in the CSR treatment group (the blue arrows). As the figure
illustrates, receiving the CSR treatment almost perfectly undoes the damage wrought by
the offshoring treatment.
Tables 3 and 4 report conditional versions of our main model estimates.40 The
models reported in Table 3 focus on the interaction between our treatments and whether a
respondent is college educated. Previous work suggests that issue framing affects college-
educated respondents’ trade attitudes less than it does non-college educated respondents.
We explore this interaction by estimating split sample regressions on our college educated
and the non-college educated.41 Models 1 and 3 show that the same effects noted in the
main models is present in the subsample of non-college graduates, though in a slightly
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!40 Bar charts analogous to Figure 2 and 3, but geared towards the conditional relationship are available in the appendix. 41 We also ran this model using a traditional interactive framework, but prefer the split sample regressions for their presentational clarity given the binary nature of our interacting variable. The results of the fully interactive model are essentially the same, and available from the authors on request. Another benefit of the split sample research design is that it allows our control variables’ coefficient estimates to vary according to the respondent’s status as a college graduate.
! 27!
larger and more statistically significant form.42 Models 2 and 4 show that treatment
effects are entirely absent from college-educated respondents. The associated predicted
probabilities (based on models 3 and 4 in Table 3) are shown in Figure 6. The left side of
figure 6 illustrates the differences in the predicted probability that our representative
respondent supports/opposes/is neutral toward free trade, based on whether they are in the
offshoring treatment group or the control group. The inverted triangles represent the
predicted probabilities when the respondent is in the control group, and the conventionally
oriented triangles indicate the probability when they are in the offshoring treatment group.
These predicted probabilities are also disaggregated by whether or not the respondent has
(at least) a four-year college degree (empty) or not (shaded). As the figure illustrates, the
effect of exposure to our offshoring prompt is greater for non-college degree holders than
for college degree holders. Although college degree holders are slightly less favorable
toward trade than non-college degree holders in the control group, their support does not
wane nearly as much when exposed to the offshoring treatment. While support for trade
among those without college degrees drops 14.9% (from 53.2% to 38.3%), the drop
among those with college degrees is only about 6% (from 51.2% to 45.2%).
[Figure 6 Here]
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!42!It is worth noting that the samples used to estimate the models in table 3 include all respondents, regardless of whether they answered the attention check questions correctly. Previously reported findings suggest that this should not bias our results towards accepting our hypotheses. We find weaker results than what is reported above using the restricted samples, which follows from the smaller sample size. This is especially true with regards to the conditional effect of placement in control group.!
! 28!
The right side of Figure 6 illustrates the difference between respondents in the offshoring
treatment group and respondents in the CSR treatment group. The CSR treatment had a
much stronger effect among those without college degrees, whose probability of
supporting free trade jumped 18.5 percent (from 38.3% to 56.8%) when placed in the CSR
treatment group from the baseline established in the offshoring treatment group. College
educated respondents exposed to the CSR prompt displayed only a 4.7 percent increase in
the probability of supporting free trade (from 45.2% to 50%). The predicted probability of
opposition to free trade among non-college degree holders similarly fell by about 10.3%
under the CSR treatment (from 22.1% to 11.8%) while it fell only 2.9% among degree
holders (from 20.6% To 17.7%).
Threre is susbtantial suport in these data for the idea that college educated people are less
succeptible to trade-policy issue priming.
We also considered the possibility that respondents living in areas that have
experienced more job loss should be especially sensitive to reminders of offshoring. We
explore that possibility in our data using the job loss variable described above. The
distribution of these data are illustrated in Figure 7. This distibution of job losses is
roughly normal, with a mean slightly larger than zero (meaning the most counties lost jobs
over this time period). The hardest hit in our data is Saluda County, South Carolina, which
lost nearly 2/3 of its 2007 jobs during this period, largely as the result of closing textile
factories.43 Jobs in Saluda County dropped from 3389 jobs in 2007 to 1191 jobs in 2013,
resulting in a total job loss proportion of 0.649. On the other end of the spectrum,
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!43 Richard Fausset, “Immigration through S.C. voters’ eyes”. Los Angeles Times: 18 Jan 2008.
! 29!
Richmond County, Virginia added 565 jobs to a 2007 base of 1002, for a job loss
proportion of -0.564.44
[Figure 7 Here]
Given the continuous nature of our jobs loss variable, we test for possible
interactive effects in the traditionally way by directly interacting that variable with out
treatment group indicators. The models reported in Table 4 show that our CSR treatment’s
effect is strongly conditioned by the loss of local jobs. The interaction term is positive and
statistically significant across all four models, indicating that the CSR treatment had a
stronger effect on respondents living in counties that have suffered more recent job loss.
The estimated coefficients on the constituent CSR variable remain consistently positive,
but are only marginally statistically significant, suggesting that the strong effects noted in
our main models are concentrated among respondents from counties with at least average
levels of job loss. These dissimilar effects across local economic contexts lend themselves
to a straightforward, though admittedly ex post, explanation. The community support
promised by the CSR press release is likely an especially appealing gesture in a county
that is suffering economically. By contrast, respondents from counties that are not
suffering economically may not be as moved by the corporate involvement in childhood
health, or may find the involvement intrusive and unwanted. The accuracy of this ex post
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!44 Interestingly, Richmond County added jobs, but actually lost 41 firms during this period, while Saluda County only lost 25 firms. Saluda County actually had more firms in 2013, after its loss, than Richmond County had in 2007, before its loss (250 and 217, respectively).
! 30!
explanation is not obviously testable with the data we have collected, and we leave that
possibility to future research.
The evidence in these models of an interaction between our control group indicator
and county job loss is less convincing: the interaction terms are consistently positive, but
never approach statistical significance, and their magnitude varies substantially across
models. While not directly within the scope of this paper’s theoretical claims, the failure
to find firmer evidence of an interaction between local job losses and the impact of
exposure to the offshoring treatment is at least somewhat surprising. In contrast to our
expectations, these results suggest that reminders of offshoring affect Americans’ trade
policy preferences similarly, regardless of their local economic context.
Figure 8 shows graphically how the difference between the trade attitudes of
respondents in the offshoring and the CSR treatment groups vary by local job loss. The
triangles indicate the relevant predicted probabilities for our representative respondent in
the offshoring treatment group, while the circle represent relevant predicted probabilities
for our representative respondent in the CSR treatment group. The shaded figures
represent predicted probabilities for a representative respondent living in a county with job
losses one standard deviation below the mean, the white figures indicate the same for a
respondent living in a county with job losses at the mean, and the hatched figures
represent relevant predicted probabilities for a representative respondent living in a county
with job losses one standard deviation above the mean. As figure 8 shows, the effect of the
additional CSR text is far greater among respondents who have experienced more job
losses in their county. These effects are especially notable with respect to support for free
trade, but are present in opposition to free trade as well. Interestingly, that large effect
! 31!
among respondents in high-job-loss countries is best understood as homogenizing trade
attitudes across respondents from counties with different degrees of job loss. Respondents
from counties with more job loss in the offshore treatment group (and the control group)
are much less likely to support free trade, and more likely to oppose it. Exposure to the
CSR treatment brings their trade policy preferences closer to respondents in counties with
low or average levels of job loss.
[Table 4 Here]
[Figure 8 Here]
Part V- Conclusion
Trade and offshoring are deeply intertwined as economic phenomena and, increasingly,
also as political phenomena. While we know from previous research that Americans
generally like trade and dislike offshoring, we know very little (nothing, as far as the
authors know) about whether attitudes towards offshoring affect attitudes toward trade,
and how. This is important omission from the literature for a variety of reasons, the most
obvious of which is that the “offshoring” frame is a dominant theme of trade politics
discourse. A less obvious, but no less important, reason is that considering these
relationships provides a platform to explore the ways that corporate brand management
and messaging affect the political environment.
This paper sought to fill that hole in our knowledge through a randomized survey
experiment in which respondents are primed to think about offshoring, with and without
countervailing messages about corporate social responsibility. The main results of our
! 32!
survey experiment are clear: priming people to think about a firm’s offshore production
reduces enthusiasm for free trade. That is not a tremendously surprising finding, but the
fact that we found this to be true even in an experimental context in which the offshoring
prime was delivered subtly, and in an entirely apolitical context, is notable. In the real
world, of course, politicians and other interested parties are quite willing to connect those
dots. Our research design is conservative in that respect, and the results of our experiment
suggest that the effects of more overt and overtly political utilizations of the “offshoring”
frame are likely substantial. It also suggests that even the routine, non-politicized ways in
which American are exposed to reminders of offshoring – most frequently through
country of origin labeling on consumer products – may have political consequences.
The second notable result in our experiment is that priming people to also think
about the offshoring firm’s corporate philanthropy – even corporate philanthropy with no
material benefit for those whose jobs are vulnerable to offshoring - mitigates the
depressive effect that exposure to the firm’s offshoring otherwise has on acceptance of
free trade. This finding suggests two things that are worth considering for future research.
First, it corroborates Mansfield and Mutz (2013) and Jensen and Lindstädt’s (2013)
finding that distaste for offshoring has more to do with its emotional resonance than its
material consequences. More specifically, these experimental findings support the
testimonial evidence presented by Brunk (2010): individuals’ distaste for offshoring
appears here to be deeply tied to perceptions that the offshoring firm does so at the
expense of, and without considering the implications for, their home communities.
This finding also suggests that corporate branding and corporate popularity can
have real political consequences. According to the YouGov BrandIndex for 2015, 6 of the
! 33!
7 top brands in the United States are technology companies or their subsidiaries: Amazon,
Netflix, YouTube, Google, Apple, and Samsung.46 The only non-technology company that
competes is Cancer Treatment Centers of America, which sits at number 5, between
Google and Apple. All of these brands are substantially more popular than leading firms in
other industries. While the scores for these firms range from 31.2 (Amazon) to 21.5
(Samsung), the most popular oil company (Shell) only registers a 5.2, and the most
popular bank (Capital One Bank) scored a 4.8. (Ford, the most popular carmaker,
registered a more tech-company-like 19.5.) The point of noting these disparities is that all
of these firms – both the popular and the unpopular - operate in regulatory and policy
environments, have an interest in maintaining those environments in ways that redound to
the firm’s benefit, and actively try to influence government to that end. And many of the
most popular brands avail themselves of practices - global supply chains utilizing cheaper
labor abroad, making use of byzantine corporate structures to avoid paying US corporate
taxes – that are intrinsically unpopular. The moral of this paper is that the tenability of the
(permissive) policy environment in which these firms operate is likely linked to popular
perceptions of the firms themselves. A world in which Google, Facebook and Apple are
less beloved changes the politics surrounding trade, IP protection, and corporate taxation.
That is not to say that policy outcomes would necessarily change – clearly, unpopular
firms are able to influence policies to their benefit – but it would alter the landscape in
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!46 According to the website brandindex.com, “These brands were rated using YouGov BrandIndex’s Buzz score which asks respondents, “If you've heard anything about the brand in the last two weeks, through advertising, news or word of mouth, was it positive or negative?”
! 34!
which that influence is wielded, potentially including the instruments used to wield
corporate power and their efficacy.
For all of that, there are limitations to our experimental finding’s mapping to real-
world phenomena that are worth noting. First, our CSR campaign “worked”, which is to
say that our field-testing indicated that it caused survey takers to describe our firm as
“doing good.” While the focus here has been on the political effects of a successful CSR
campaign, it stands to reason that an unsuccessful CSR campaign would have different
(and, likely, null) effects on political attitudes. Similarly, not every exposure to offshoring
should be assumed to elicit as strongly negative reactions as ours did. Our product was
made in Indonesia, which in addition to anxieties related to job loss, may invoke racial and
religious-based out-group anxieties. Perhaps being prompted to think about offshoring to a
different country would generate a different response. All of that is to say that this article
raises as many new questions as it answers. But it does provide a powerful proof of
concept to suggest that those questions are worth pursuing.!
! 35!
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!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!Figure'1:'Effect'of'treatments'on'opinions'about'Applied'Technology.'Top'row:'first'iteration'of'surveys.'Bottom'row:'
second'iteration'of'surveys.
! 39!
!Figure!2 Support For Free Trade Across Respondents in the Control and the Offshoring Treatment Group
! 40!
!Figure'3:'Support'for'free'trade'in'CSR'and'offshoring'treatments.'
! 41!
!
Figure'4:'Effect'of'offshoring'treatment'on'predicted'probabilities'of'supporting'free'trade.'Age'and'job'loss'held'at'mean,'categorical'variables'set'to'reference'categories'or'zero.'Predicted'probabilities'based'on'model'(3).
! 42!
!
Figure!5:!Effect!of!CSR!on!predicted!probabilities!of!supporting! free! trade.!Age!and!job! loss! held! at! mean,! categorical! variables! set! to! reference! categories! or! zero.!Predicted!probabilities!based!on!model!(3).
!
! 43!
!!
!Effect' of' the' offshoring' prompt' on' support' for' trade,'disaggregated'bywhether'respondent'has'at'least'a'fourJyear'college'degree.'Based'on'model'(3).'
!Effect' of' the' CSR' prompt' on' support' for' trade,'disaggregated' by' whether' respondent' has' at' least' a'fourJyear'college'degree.'Based'on'model'(3).'
Figure'6'!!!!!!
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!
Figure'7:'Proportion'of'jobs'lost'during'the'recession'(2013'data'are'most'recent'available).'
!
!
!
!
!
! 45!
!!
Figure'8:'Predicted'probabilities'of'supporting'free'trade.'Triangles'indicate'predictions'under'offshoring'treatment,'and'circles'indicate'support'under'CSR'treatment,'while'shading'indicates'level'of'job'losses.'The'effect'of'CSR,'when'compared'with'the'offshoring'treatment,'is'especially'large'among'those'that'have'experienced'the'
greatest'job'losses.'Coefficients'from'jobs'interaction'model'(3).'
!!!!!!!!!!!!!!!!!!!!!
! 46!
!
! Dependent'variable:'Treatment'Assignment'! !! Control! Offshoring! Low!Prices! CSR!
! (1)! (2)! (3)! (4)!!College! U0.036! 0.008! 0.061*! U0.033!
! (0.033)! (0.032)! (0.033)! (0.033)!Female! U0.010! U0.012! U0.009! 0.030!
! (0.032)! (0.031)! (0.032)! (0.032)!$30,000U59,999! 0.030! U0.022! U0.019! 0.010!
! (0.039)! (0.038)! (0.039)! (0.040)!$60,000U89,999! 0.024! 0.066*! U0.091**! 0.001!
! (0.048)! (0.045)! (0.047)! (0.048)!$90,000+! 0.137***! U0.048! U0.060! U0.030!
! (0.052)! (0.050)! (0.051)! (0.052)!Age! U0.001! 0.0004! U0.00000! 0.001!
! (0.001)! (0.001)! (0.001)! (0.001)!Job!Loss! U0.033! 0.401**! U0.164! U0.204!
! (0.166)! (0.159)! (0.164)! (0.166)!Constant! 0.296***! 0.206***! 0.261***! 0.236***!
! (0.057)! (0.054)! (0.056)! (0.057)!!Observations! 767! 767! 767! 767!
R2! 0.011! 0.017! 0.010! 0.005!Adjusted!R2! 0.002! 0.007! 0.001! U0.004!Residual!Std.!Error!(df!=!759)! 0.439! 0.419! 0.433! 0.439!
F!Statistic!(df!=!7;!759)! 1.222! 1.823*! 1.084! 0.568!!Note:! *p<0.10!**p<0.05!***p<0.01!
Table!1:!Treatment!Balance.!Reference!category!for!income!is!“<$30,000”.!“Female”!is!selfUreported!via!text!box,!allowing!respondents!to!accurately!express!their!gender!identity!in!their!own!words,!and!the!reference!category!is!“nonUfemaleUidentifying”.!
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!! Dependent'variable:!! !! Support!for!Free!Trade!(three!ordered!categories)!
! (1)! (2)! (3)! (4)!Control! 0.339**! 0.446**! 0.418**! 0.538**!
! (0.191)! (0.261)! (0.198)! (0.269)!CSR! 0.391**! 0.503**! 0.492**! 0.575**!
! (0.192)! (0.254)! (0.200)! (0.264)!Low!Prices! 0.181! 0.292! 0.195! 0.344!
! (0.193)! (0.268)! (0.198)! (0.276)!College! ! ! 0.063! U0.014!
! ! ! (0.147)! (0.203)!$30,000U59,999! ! ! 0.137! 0.144!
! ! ! (0.171)! (0.240)!$60,000U89,999! ! ! 0.216! 0.150!
! ! ! (0.210)! (0.283)!$90,000+! ! ! 0.218! 0.305!
! ! ! (0.230)! (0.308)!Age! ! ! U0.008! 0.001!
! ! ! (0.006)! (0.008)!Female! ! ! U0.187! U0.162!
! ! ! (0.140)! (0.193)!Democrat! ! ! 0.025! 0.085!
! ! ! (0.141)! (0.192)!Job!Loss! ! ! U0.800! U1.258!
! ! ! (0.747)! (1.082)!Intercepts:! ! ! ! !
Oppose|Neutral! U1.421***! U1.166***! U1.161***! U1.098***!
! (0.152)! (0.200)! (0.290)! (0.400)!Neutral|Support! 0.215**! 0.330**! 0.007! 0.408!
! (0.141)! (0.191)! (0.283)! (0.396)!Observations! 807! 431! 767! 412!Attention!Checks! N! Y! N! Y!Note:! *p<0.10!**p<0.05!***p<0.01!
Table!2:!Ordered!logit!models.! !!
! 48!
!!
! Dependent'variable:!! !! Support!for!Free!Trade!(three!ordered!categories)!
! (1)! (2)! (3)! (4)!!Control! 0.617**! 0.120! 0.604**! 0.240!
! (0.281)! (0.263)! (0.287)! (0.277)!CSR! 0.761***! 0.081! 0.751**! 0.190!
! (0.281)! (0.266)! (0.293)! (0.281)!Low!Prices! 0.284! 0.086! 0.256! 0.101!
! (0.290)! (0.259)! (0.296)! (0.272)!$30,000U59,999! ! ! U0.062! 0.409*!
! ! ! (0.229)! (0.261)!$60,000U89,999! ! ! 0.361! 0.237!
! ! ! (0.334)! (0.286)!$90,000+! ! ! U0.304! 0.554**!
! ! ! (0.393)! (0.302)!Age! ! ! U0.015**! U0.002!
! ! ! (0.008)! (0.008)!Female! ! ! U0.054! U0.290*!
! ! ! (0.204)! (0.197)!Democrat! ! ! 0.045! 0.009!
! ! ! (0.207)! (0.194)!Job!Loss! ! ! U0.473! U1.034!
! ! ! (1.145)! (0.993)!Intercepts:! ! ! ! !Oppose|Neutral! U1.238***! U1.571***! U1.837***! U1.443***!
! (0.223)! (0.209)! (0.404)! (0.435)!Neutral|Support! 0.522***! U0.029! U0.101! 0.098!
! (0.212)! (0.191)! (0.390)! (0.427)!!Observations! 375! 432! 363! 404!
College!degree! N! Y! N! Y!!Note:! *p<0.10!**p<0.05!***p<0.01!
Table!3:!Split!sample!ordered!logit!models.!All!models!include!the!full!sample!of!respondents,!without!accounting!for!the!number!of!comprehension!questions!they!
answered!correctly.!! !
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!! Dependent'variable:!! !! Support!for!Free!Trade!(three!ordered!categories)!
! (1)! (2)! (3)! (4)!Control! 0.333*! 0.380! 0.369**! 0.409*!
! (0.216)! (0.306)! (0.219)! (0.311)!CSR! 0.276*! 0.324! 0.340*! 0.367!
! (0.212)! (0.292)! (0.216)! (0.298)!Low!Prices! 0.034! 0.136! 0.051! 0.195!
! (0.211)! (0.306)! (0.214)! (0.314)!Job!Loss!! U3.288**! U3.922*! U3.007**! U3.987*!
! (1.486)! (2.385)! (1.516)! (2.496)!College! ! ! 0.060! U0.027!
! ! ! (0.148)! (0.204)!$30,000U59,999! ! ! 0.133! 0.129!
! ! ! (0.171)! (0.241)!$60,000U89,999! ! ! 0.180! 0.087!
! ! ! (0.211)! (0.287)!$90,000+! ! ! 0.191! 0.271!
! ! ! (0.231)! (0.309)!Age! ! ! U0.007! 0.001!
!
! ! (0.006)! (0.008)!Female! ! ! U0.198! U0.169!
! ! ! (0.141)! (0.193)!Democrat! ! ! 0.030! 0.071!
! ! ! (0.141)! (0.193)!Control!×!Job!Loss! 0.461! 1.765! 0.455! 2.084!
! (2.213)! (3.350)! (2.236)! (3.446)!CSR!×!Job!Loss! 4.115**! 5.261*! 3.948*! 5.238*!
! (2.067)! (3.074)! (2.087)! (3.159)!Low!Prices!×!Job!Loss! 3.616*! 2.592! 3.581*! 2.556!
! (1.968)! (3.062)! (1.979)! (3.139)!
Intercepts:! ! ! ! !
Oppose|Neutral! U1.543***! U1.361***! U1.729***! U1.277***!
!(0.173)! (0.244)! (0.299)! (0.424)!
Neutral|Support! 0.099! 0.161! U0.100! 0.239!! (0.162)! (0.233)! (0.291)! 0.419!
Observations! 786! 423! 767! 412!Attention!Checks! N! Y! N! Y!
!Note:! *p<0.10!**p<0.05!***p<0.01!Table!4:!Ordered!logit!models!with!job!loss!interaction.!
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!Appendix A: Balance Across Groups Table 1: Educational Attainment by Group (Percentages)
Control Offshoring Low Prices CSR All
Less than High School 0.48% 0.53% 0.00% 0.94% 0.49%
High School / GED 12.44% 11.64% 8.46% 9.86% 10.59%
Some College 25.36% 19.05% 25.87% 30.05% 25.25%
2-year College Degree 9.57% 13.76% 7.46% 9.39% 9.98%
4-year College Degree 38.76% 39.15% 41.29% 34.27% 38.30%
Masters Degree 10.53% 13.23% 14.93% 12.68% 12.81%
Professional Degree (JD, MD) 0.96% 1.06% 1.00% 0.94% 0.99%
Doctoral Degree 1.91% 1.59% 1.00% 1.88% 1.60% Table 2: Gender Identification by Group (Percentages)
Control Offshoring Low Prices CSR All
Male 46.63% 48.37% 45.96% 42.31% 45.74%
Female 53.37% 51.63% 54.04% 57.69% 54.26% Table3: Reported Household Income by Group (Percentages)
Control Offshoring Low Prices CSR All
Less than 30,000 28.71% 31.75% 35.32% 31.92% 31.90%
30,000 – 39,999 14.35% 12.70% 15.42% 16.43% 14.78%
40,000 – 49,999 10.05% 9.52% 9.45% 9.39% 9.61%
50,000 – 59,999 9.09% 7.94% 10.45% 8.45% 8.99%
60,000 – 69,999 7.66% 8.99% 8.46% 7.98% 8.25%
70,000 – 79,999 6.22% 10.05% 3.98% 7.51% 6.90%
80,000 – 89,999 3.35% 6.35% 2.99% 3.76% 4.06%
90,000 – 99,999 6.70% 4.23% 2.49% 4.69% 4.56%
100,000 or more 11.96% 7.41% 10.45% 7.98% 9.48%
Do Not Know 1.91% 1.06% 1.00% 1.88% 1.48%
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Appendix B: Alternative Model Specification (Logit)
!! Dependent'variable:!! !! oppose! neither! support!
! (1)! (2)! (3)! (4)! (5)! (6)!!Control! U0.581**! U0.646**! 0.071! 0.014! 0.259! 0.350*!
! (0.274)! (0.285)! (0.213)! (0.223)! (0.202)! (0.211)!CSR! U0.470*! U0.472*! U0.094! U0.248! 0.353*! 0.493**!
! (0.267)! (0.274)! (0.215)! (0.227)! (0.202)! (0.211)!Low!Prices! U0.265! U0.256! 0.022! 0.001! 0.143! 0.165!
! (0.260)! (0.268)! (0.215)! (0.225)! (0.204)! (0.213)!Job!Loss! ! 1.827*! ! U0.666! ! U0.441!
! ! (1.031)! ! (0.820)! ! (0.774)!College! ! U0.010! ! U0.070! ! 0.070!
! ! (0.209)! ! (0.165)! ! (0.154)!$30,000U59,999!! 0.150! ! U0.364*! ! 0.254!
! ! (0.250)! ! (0.191)! ! (0.182)!$60,000U89,999!! 0.130! ! U0.428*! ! 0.332!
! ! (0.298)! ! (0.235)! ! (0.220)!$90,000+! ! 0.208! ! U0.537**! ! 0.367!
! ! (0.327)! ! (0.261)! ! (0.241)!Age! ! 0.019**! ! U0.010! ! U0.003!
! ! (0.008)! ! (0.007)! ! (0.006)!Female! ! U0.042! ! 0.330**! ! U0.264*!
! ! (0.200)! ! (0.159)! ! (0.148)!Democrat! ! U0.065! ! 0.034! ! 0.004!
! ! (0.202)! ! (0.158)! ! (0.149)!Constant! U1.327***! U2.153***! U0.693***! U0.170! U0.172! U0.195!
! (0.180)! (0.400)! (0.156)! (0.321)! (0.147)! (0.302)!!Observations! 807! 767! 807! 767! 807! 767!
Log!Likelihood! U353.589!U333.128! U513.335! U475.732! U557.608! U523.749!Akaike!Inf.!Crit.! 715.177! 690.255! 1,034.669! 975.464! 1,123.216!1,071.498!
!Note:! *p**p***p<0.01!!
! 52!
Appendix C: Full Script of Offshoring Release
Applied Technologies and Indico are thrilled to be partnering in a technological breakthrough that will revolutionize the way you monitor your exercise and maximize your fitness. This revolutionary new product – the blurge - is the next generation in activity trackers, combing Applied Technologies’ expertise in fitness with Indico’s path breaking work in wearable technology. Fitness trackers have helped millions make the small changes in their daily lifestyle that help them meet their fitness and health goals. But even the most popular fitness trackers have been plagued by their limited functionalities, uncomfortable wristbands, and high price. The blurge is going to change all that, and revolutionize the fitness tracker market. The blurge combines all of the simplicity and functionality you have come to expect from an Applied Technologies fitness tracker, with new features such as DYNGOAL - a dynamic, constantly updating goal-setting program that automatically updates your daily goals to help you meet your weekly and monthly benchmarks. Even better, through our new partnership with Indico, the world leader in wearable technology, the blurge technology comes to you in the lightest, most comfortable, and innovative designs available in any fitness tracker on the market. Activity Tracker Magazine calls the blurge “remarkable…there is nothing else quite like it on the market…you barely know you are wearing anything…wow.” Fitness monthly calls DYNGOAL technology a "game changer" and "the best personal trainer that you can wear on your wrist". With the blurge you will be able to: · Automatically and accurately track steps taken, distance traveled, and calories burned · Easily input calories consumed, for optimized total fitness monitoring. · Automatically updates nutrition and exercise targets. Never let a night out stop you from meeting your goals! · Monitor resting and active heart rates. · Easily access easy-to-understand, comprehensive data calculated over the day, the week or the month. And now, thanks to our partnership with Indico, these features are being offered to you with: · The brightest LED display on the market · The lightest, sleekest design on the market · Waterproof and fully functional up to 5m of water. · Up to 14 days of battery life And all of this is being packaged at prices that begin at $195 for the blurge plus a three-year, no-questions-asked warranty. The blurge will be proudly manufactured at a new state-of-the-art manufacturing facility operated jointly by Indico and Applied
! 53!
Technologies in Banten, Indonesia, which will employ roughly 500 workers locally, plus a support staff in our home base of Southeastern Ohio.
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