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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]

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!!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.

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!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).

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!

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).'

!

!

!

!

!

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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).'

!!!!!!!!!!!!!!!!!!!!!

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!

! 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.! !!

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!!

! 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!!

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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

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Technologies in Banten, Indonesia, which will employ roughly 500 workers locally, plus a support staff in our home base of Southeastern Ohio.