Identity, Beliefs, and Political Conflict∗

Giampaolo Bonomi†, Nicola Gennaioli‡and Guido Tabellini§ First version: October 2018 This version: July 2020

Abstract

We present a theory of identity politics that builds on two ideas. First, when policy conflict renders a certain social divide (economic of cultural) salient, a voter’s beliefs are tainted by the stereotype of the group he identifies with. Second, culture influences beliefs across many domains. The theory yields two main implications: i) voters’beliefs are polarized along the distinctive features of salient groups; ii) cultural change and spe- cific economic shocks can render cultural groups salient, inducing large and non standard changes in beliefs and policies. In particular, shocks concentrating economic losses on social conservative groups (e.g., globalization) can induce voters to switch from class to cultural identity, dampening their demand for redistribution and exacerbating conflict in other social dimensions. We show that U.S. survey evidence is broadly consistent with these implications.

∗We are grateful to Roland Benabou, Ben Enke, Ed Glaeser, Elhanan Helpman, Nathan Nunn, Raffella Piccarreta, Giacomo Ponzetto, Jesse Shapiro, Andrei Shleifer, Marco Tabellini, Pierre Yared and to partic- ipants in workshops at ASREC, Bocconi, the Bundesbank, CEPR, CIFAR, Harvard, the IMF, Honk Kong Baptist University, National Taiwan University, National University of Singapore, CEIBS, the LSE ERINN conference, the NBER, Princeton, UPF, Università Statale di Milano, the University of Namur, and to Viola Corradini, Daniele d’Arienzo, Carlo Medici, Francesca Miserocchi and Giulia Travaglini for outstanding re- search assistance. Tabellini thanks the ERC grant 741643 and Gennaioli the ERC (GA 647782) and MIUR (FARE grant) for financial support. †Department of Economics, UC San Diego. E-mail: [email protected] ‡Department of Finance and IGIER, Università Bocconi. E-mail: [email protected] §Department of Economics and IGIER, Università Bocconi; CEPR; CESifo. E-mail: [email protected]

1 1 Introduction

During the last decades the political systems of advanced democracies have witnessed mo- mentous change. The letf-right divide on redistribution has weakened, and new dimensions of conflict such as immigration and civil rights have emerged. During this period income in- equality has increased, so one would have expected the demand for redistribution to soar. By contrast, many poor and less educated voters have moved to the right, while richer and more educated voters have moved to the left (Piketty 2017). The recent populist wave has added another puzzle: these trends are accentuated by adverse economic shocks. In the US, Autor et al. (2017) show that areas exposed to import competition have become more conservative. In Europe, Colantone and Stanig (2017, 2018 a,b) and Anelli et al. (2018) show that areas hurt by free trade or skilled biased technical change have moved toward nationalistic and socially conservative positions. Leading theories of changing political conflict rest on supply mechanisms. In one variant, the driver of change is growing extremism of party platforms, which makes them more distin- guishable on a large set of issues (Fiorina and Abrams 2008, Levendusky 2009). This aspect surely plays a role, but it cannot explain changes in voters’ preferences, in particular why many lower class voters demand less redistribution despite increasing inequality. In another variant, politicians attract voters by catering to their cultural views, and then persuade them on issues they are less interested in or informed about (Zaller 1992, Shleifer and Murphy 2004). This helps clarify persuasion in some domains, but it cannot account why voters hit by adverse economic shocks become more conservative and anti-redistribution. Moreover, supply mechanisms are often country specific, while these political trends have occurred in several countries with very different political systems. This paper offers a demand side theory that emphasizes voters’ social identity and its effect on beliefs. We build on two pillars. First, following the social psychology of identity (Tajfel and Turner 1979, Turner et al. 1987), we assume that when policy conflict renders a certain social partition salient, voters’ beliefs are tainted by the stereotype of the group they identify with. In line with social psychology, we formalize salient groups to be those that face the strongest policy conflict in society, and formalize stereotypes as exaggerating the distinctive features of a group (Bordalo et al. 2016). In this framework, during periods of strong economic conflict voters identify with their income class. Lower class voters become too pessimistic about social mobility and upper class voters too optimistic, which enhances class conflict. When instead cultural conflict between social progressives and social conservatives is strong, voters identify with their cultural group. Cultural views become more extreme, which feeds back into stronger cultural conflict. The second pillar of our analysis is the notion that culture influences beliefs across many domains. Haidt (2012) and Enke et al. (2019b) show that this is true with respect to moral

2 universalism, which captures the extent to which people trust socially distant others and apply their value system to them. Moral universalists are progressive with regard to civil rights or immigration, but also in economic domains: they trust the government more and (hence) are more pro-redistribution. Similarly, Stancheva (2020), shows that cultural traits such as trust in government and fairness norms shape preferences over tax policy. Critically, in our model the effect of these deep seated cultural values is time varying: it is subdued during times of class identity and becomes strong during times of cultural identity. Our analysis shows that the political realignments described at the beginning can be un- derstood as the result of a shift to cultural identity, favored by the increased relevance of issues like civil rights and immigration, but also by economic shocks such as globalization and technology that hit social conservative groups (because they are less educated or live in declining regions). By enhancing the similarity between people with similar culture, and obfuscating differences in income or status, these shocks render salient the conflict between progressive/cosmopolitan voters and conservative/nationalist ones, while dampening tradi- tional redistributive conflict between rich and poor. As voters realign culturally, they form two camps that disagree on almost everything. More generally, by shaping beliefs, endogenous social identities alter the effects of economic and social change. We develop our analysis in three steps. In Section 2 we discuss the psychological founda- tions of identity and present our model of beliefs. We show that cultural identity amplifies actual and perceived polarization across cultural groups. Section 3 endogeneizes voters’identities in a setting with both economic and cultural con- flict. Voters can identify with their income class (Upper vs Lower) or their cultural group (Progressive vs Conservative). Shocks that increase: i) the welfare relevance of cultural con- flict, for instance a large inflow of immigrants, or ii) the intensity of cultural conflict, such as socially progressive voters becoming more extreme, or iii) economic shocks that increase the correlation between income and social progressiveness, can induce a switch from class to cultural identity. When this occurs, voters’beliefs change in many domains, which influences the policy platforms offered by two opportunistic candidates. The switch to cultural identity causes disagreement to rise over cultural issues but possibly to fall over redistribution, with ambiguous net effects. But there are two key additional effects. First, cultural identity increases the coherence of polarization, boosting the correlation of beliefs across issues. A group of voters is progressive, both socially and economically, another group is conservative in both domains. Under class identity, instead, lower class voters could be socially conservative but still support redistribution, and viceversa for upper class voters. Second, a switch from economic to cultural identity amplifies conflict between cultural groups (eg. religious vs secular), but it may dampen conflict between economic groups (eg. upper vs lower classes). The reason is that cultural identity cuts across economic classes:

3 some poor individuals are conservative, others are progressive. Identification with their class emphasizes what they have in common, being poor, and amplifies conflicts with the rich. Cultural identity has the opposite effect: it enhances differences within the working class, dampening redistributive conflict with the upper class. In Section 5 we show that both predictions are supported by time patterns in the ANES survey data. Section 4 adds a third dimension of heterogeneity, exposure to international trade, besides income and culture. Here individuals can also identify as cosmopolitan or nationalist. We show that trade shocks hitting social conservative strata can drive identity away from class. Indeed, such shocks render cultural differences salient when thinking also about trade conflict. Thus, losers from trade become more socially conservative and demand less redistribution, and viceversa for winners from trade. In Section 5 we show that this prediction too is consistent with the data. Exploiting the CCES survey and the methodology of Autor et al. (2017), we show that residents of commuter zones exposed to imports from China became more anti redistribution and averse to immigrants, especially if religious. A long tradition in the social sciences argues that group identity and group-tainted beliefs play a key role in political change. According to Marx and Engels, individual workers should identify with the proletariat, viewing themselves as part of a historical class struggle, rather than as carriers of specific cultural or regional traits. For nationalists like Mazzini or Herder individuals should instead downplay economic or religious differences and view themselves as part of the broader national community. Lipset and Rokkan (1960) describe the evolution of Western party systems as reflecting shifting identities across salient groups such as income classes, religious versus secular groups, center versus periphery, etc. Akerlof and Kranton (2000) develop the first economic model where identity changes the payoffs of certain actions. They do not consider beliefs. The important paper by Shayo (2009) has introduced identity into political economy models, and Helpman and Grossman (2018) have adapted that model to trade policy. In these papers, voters obtain positive utility from the status of their group, causing them to internalize the welfare of the latter. Hence, identity shifts produce changes in voters’demands. In our model identification is not related to status, so a voter is not penalized from identifying with the lower class. Instead, identity affects policy demands through beliefs. Both Shayo (2009) and Grossman and Helpman (2018) contrast identification with a narrow group (class) vs a broad one (the nation). We instead allow social groups to be heterogeneous, so that identity switches divide previously cohesive groups, realigning voters in new ones. This yields new predictions that are supported by the data. Despite these differences, these papers highlight complementary mechanisms. One advantage of our approach is that it sheds light on belief distortions.1

1 Fryer and Jackson (2008) study the implications of categorization for discrimination and identities. We instead emphasize stereotypes (Bordalo et al. 2016) and focus on different applications. Diermeyer and Li (2018) study a model of electoral competition where voters pay more attention to the policies of the candidate

4 There is growing evidence of systematic and persistent distortions in voters’beliefs (Flynn et al. 2017, Achen and Bartels 2016, Johnston et al. 2017, Stancheva 2020). In our approach, as in Shleifer and Murphy (2004), beliefs are a social phenomenon, and reflect the distinctive features of the group one identifies with. We discuss in context how our model helps explain recent findings on voters’beliefs. Benabou and Tirole (2011), (2016) offer a different approach in which identity reflects beliefs about one-self, and beliefs adjust to improve self-image and own welfare, taking anticipatory utility into account. Relative to these papers, our model helps explain group stereotypes, group-stereotyped beliefs about self, and the notion that the beliefs of certain voters sometimes seem to run counter their own interests. A long standing tradition in political science, started by Key (1955), has studied electoral realignments - see Sundquist (1983) and Mayhew (2004). This work mostly focuses on the US and seeks to explain lasting changes in party positions and in the composition of party supporters. Our theory endogeneizes voters’ socioeconomic realignments, abstracting from parties and the influence of political leaders. Incorporating the interaction between voters’ identities and the political supply side is an important topic for future work. Finally, our work is related to a growing literature that seeks to explain the rise of nation- alism and populism as a reaction to economic distress and economic insecurity (see Guriev and Papaioannou 2020 for a recent survey). Like Norris and Inglehart (2019), we emphasize the importance of the cultural divide. But we explain why this divide can be created and amplified by specific economic shocks due to trade or technology.

2 The Social Psychology of Identity and Stereotypes

The "Social Identity Perspective", the leading theory of identity and intergroup relations, combines Social Identity Theory (SIT, Tajfel and Tuner, 1979), and Self Categorization Theory (SCT, Turner et al. 1987, Hogg and Abrams 1998). According to this approach, when a social cleavage becomes salient, individuals identify with one of the relevant groups. Identity then anchors beliefs about self, others, and behavior. Experiments using the "minimal group paradigm" found that even arbitrary groups exert this effect provided they are made salient (Tajfel, Billig, et al. 1971). We now discuss the psychology of this mechanism and introduce our formalization of distorted beliefs. they identify with. This can induce polarization even if candidates are opportunistic. Some papers also provide evidence supporting the implications of social identities, for private consumption (Atkin et al. 2019) or for political behavior (Grosfeld et al. 2013).

5 2.1 Identification

According to SCT, identity is a form of self-categorization in society. An individual belongs to many groups: income class, religion, nation, etc, but he does not identify with all of them at the same time. Identity and behavior depend on which social partition is salient (e.g., Hogg et al. 1998). For instance, at a football match one’sown team is the salient group: it captures the relevant cleavage, and so it affects behavior more than other groups. The importance of social cleavages gives rise to the “meta contrast ratio”: a person identi- fies with a group of people who are: i) similar to him, but also and crucially ii) highly dissimilar from/in conflict with, the outgroup (Oakes, 1987). At a football match, the group of "team supporters" is more salient than that of "soccer lovers" because it reflects the cleavage of the moment.2 This idea naturally travels to politics. A shock indicative of a conflict, say a police offi cer killing a black person, renders a social partition salient, say that between those who believe that racial discrimination is significant and those who believe it is not. Many people identify themselves as members of either group, affecting beliefs and behavior.

2.2 Stereotyped Beliefs and Depersonalization

How does identity affect beliefs and behavior? According to SIT and SCT, identity causes individual perceptions of self and others to be tainted by group features (see also Sherif 1936, Festinger 1950). Experiments on groups of objects by Tajfel and Wilkes (1963) and subsequent work in social settings (Haslam and Turner 1992) show that this occurs via two effects. First, people stereotype groups by exaggerating differences between ingroups and out- groups. In the previous example, those who believe that the police is biased against mi- norities stereotype the other group as "racist", while the skeptics stereotype the others as "radicals". Society is divided in "us versus them". Second, identity causes an individual to "depersonalize", namely to move his beliefs and perceptions closer to the stereotypical group member. McGarty et al. (1992) define the stereotype as the group’s most representative type: "...the less a person differs from ingroup members and the more he or she differs from out group members, the more representative he or she is of the in-group." The stereotype is not necessarily the modal trait, it must be distinctive relative to the out-groups. Thus, depersonalization heightens polarization. Moderate people slant their beliefs toward leftwing radicalism or racism, at least to some extent, enhancing conflict.3 SIT holds that, beyond affecting beliefs, identity can also yield positive self-esteem if one

2 The importance of cross-group differences in creating within group similarity is reminiscent of the early Tajfel and Wilkes (1963) experiments, and plays a key role in theories of categorization, starting from Rosch (1978). See also Tversky (1977) for a close notion of contextual similarity. 3 Many experiments document such "group polarization", namely the tendency for group members to hold a more extreme position on an issue than their individually expressed positions (e.g., Mackie 1986).

6 identifies with a high status group. Some experiments document ingroup favoritism, which can be viewed as enhancing group status. Existing political economy models of identity are centrally built on this ingredient (e.g., Shayo 2009). To highlight the new implications of our mechanism we abstract from this effect. Status cannot evidently be the sole driving force, because individuals often identify with underdog groups.4

2.3 Formalizing Distorted Beliefs

We present our model of belief distortions and analyze its key properties. For now we take group identity as given. We endogenize it in Section 3. As an illustration, consider a voter who is uncertain about the benefit ψ˜ of a socially progressive policy, say more immigration or an extension of civil rights. ψ˜ is a Gaussian random variable with pdf z ψ˜ ψ , where ψ denotes the mean of ψ˜. Different values of ψ˜ | represent disparate pieces of data (what is the share of honest immigrants/racist policemen?), or value judgments (how important is cultural diversity?). The voter’s core beliefs average such knowledge and are captured by ψ. We think of z ψ˜ ψ as being stored in the voter’s | memory, but subject to selective retrieval.   A voter with higher ψ is more progressive. ψ is distributed in society with mean zero and cdf H (ψ). There are two groups: the social conservatives, having ψ < ψ, and the social progressives, having ψ > ψ. The groups are pre-defined: threshold ψ is historically given. Identity cues the voter to retrieve from memory facts or value judgments thatb are consistent with his group’sstereotype,b while downplaying the rest. As a result,b a voter’sposition partly reflects core beliefs ψ and party reflects stereotypical group beliefs. We formalize this process following the BCGS (2016) stereotypes model: priming a person with his group G induces him to retrieve and overweight the group’s representative trait, defined as the trait that is relatively more frequent in G compared to outgroup G. This process captures Kahneman and Tversky’s representativeness heuristic. It also captures the notion, used in SCT, that stereotypes must be distinctive of G compared to G.5 Denote by zθ ψ˜ ψ, G the distorted beliefs of a voter with core beliefs ψ when he identifies | with group G. Following  BCGS, identity distorts retrieval of z ψ˜ ψ as follows: |   4 There is also considerable debate on whether it is always the case that group identity leads to ingroup bias (e.g., Hinkle and Brown 1990). The predominant view now is that ingroup bias arises when discrimination is a group norm but not otherwise. A belief based mechanism like ours can arguably generate such effect. 5 BCGS (2016) show that this model accounts for observed belief distortions in social domains, but also for beliefs in macro and finance (Gennaioli and Shleifer 2018). Closer to the current setting, BCGS (2018) show that gender stereotypes influence self confidence. When assessing own ability, people are too opti- mistic/pessimistic in domains of knowledge where their gender has a competitive advantage/disadvantage.

7 χ θ ˜ z ψ ψG,G zθ ψ˜ ψ, G z ψ˜ ψ , (1) | ∝ | zθ ψ˜ ψ , G     G    where ψG E [ψ G] denotes the core belief of the average group member and parameter ≡ | χ 0 captures the strength of the memory distortion. The key term is the likelihood ratio in ≥ Equation (1): it leads the individual to overweight facts or opinions ψ˜ that are more frequently held by the average member of his group G compared to the average outgroup G.6 Relative to BCGS (2016) Equation (1) makes two innovations. First, stereotypes affect own beliefs, not just beliefs about others. Thus, beliefs emerge as a fixed point: own beliefs are distorted by group stereotypes, but the latter in turn depend on the distorted beliefs of group members. Formally, the likelihood ratio in Equation (1) depends on distorted beliefs, which are those observed in society. Second, the group G through which social reality is perceived depends on identity, as we will see when the latter is endogenized. The fixed point problem in Equation (1) proves tractable under Gaussianity of z ψ˜ ψ . | In particular, provided distortions χ are not too strong the fixed point exists, is stable, and can be characterized as follows. All proofs are in Appendix 1.

χ Proposition 1 Suppose that χ < 1/2. Then, defined θ 1 2χ > 0, identification with group ≡ − G = SC,SP distorts the beliefs of voter ψ as follows:

ψθ = ψ + θ ψ ψ . (2) G G − G
Holding core beliefs ψ fixed, identification with the social conservatives cues the voter to overweight the risks of progressive policies, inducing him to become even more conservative. Identification with the social progressives cues the voter to think of the benefits of progressive policies, inducing him to become even more progressive. These effects are due to the so called "kernel of truth" property: stereotypes exaggerate underlying group differences, captured by the mean difference in core beliefs ψ ψ across ingroups and outgroups. G − G A growing body of work shows the importance of belief distortions in politics. Relative to rightwing respondents, leftwing respondents estimate lower social mobility (Alesina et al. 2018a), larger inequality (Gimpelson and Treisman 2018), a lower number of immigrants (Grigorieff et al. 2018), and they are more likely to believe in the anthropogenic nature of global warming (Kahan 2014). Stantcheva (2020) shows that beliefs about taxes depend on

6 A mechanical application of the BCGS model implies that the likelihood ratio should be defined using the distributions of ψ in groups G and G. These are obtained by aggregating a family of individual distributions zθ ψ ψ, G , which renders the analysis intractable. To simplify, we capture each group using the belief | e distribution  for its average type. This preserves the idea that stereotypes magnify group (average) differences, ande yields convenient closed form solutions.

8 partisanship, but also on socioeconomic traits such as gender and culture, in particular fairness principles and trust in government. Another regularity is that voter positions do not reflect stable core beliefs: they change when certain political or socioeconomic groups are primed (e.g., Janky 2018, Han and Wackman 2017). Even subtle cues can exert large effects: eliciting the number of immigrants reduces subjects’demand for redistribution (Alesina et al. 2018b).7 Our model offers a way to rationalize these facts. When forming an opinion, the voter recruits from memory facts or value judgments typical of his group. Priming a group G shapes beliefs by changing the salient social partition which, by Equation (1), affects which thoughts come to mind. This mechanism can work through partisanship: politicized voters take cues from the typical party position (Zaller 1992). Our model is not inconsistent with partisan identity. Here, however, we focus on social groups. This helps capture seismic changes, in which pre-existing partisan identities weaken. When a new social conflict becomes salient, it reaches voters due to grassroot discontent, the media, or even messages by political outsiders, cueing them to think about politics in a new way. This occurs also for non-politically affi liated voters, who identify with the economic or cultural group they are most similar to. One key implication of Equation (2) concerns group polarization.

Corollary 1 When all voters identify with groups G and G, disagreement among average group members is amplified relative to disagreement in rational beliefs. Formally,

θ θ ψ ψ = ψ ψ (1 + 2θ) > ψ ψ . G − G G − G G − G
Group identity boosts polarization due to the activation of stereotypes. This occurs for all voters, not just partisans, and it is enhanced by social influence. If politicians or media such as Facebook or Instagram disseminate stereotypes, these more readily come to mind - θ is larger - which leads to more polarization. Edogenizing θ to account for media or political supply effects is an interesting avenue for future work. A second implication, due to Equation (1), is that perceived polarization is even greater than actual polarization. Consider the beliefs held in society about the average member of G

(i.e. the second order beliefs about ψG). When thinking about this voter, people do not fully θ ˜ ˜ retrieve his true first order distorted beliefs z ψ ψG,G . Rather, they overweight traits ψ that are stereotypical of the group, G, to which the voter belongs. Overweighting is again performed using the likelihood ratio in (1).

θ θ Corollary 2 Denote by ψG and ψG the perceived mean positions of the average group mem- 7 Effects typically attributed to information may also be partly due to identity priming. In Alesina et al. (2018a), demand for redistributionb b increases when subjects are provided pessimistic information about mobility, but this effect is only present for left wing respondents. Kuziemko et al. (2015) shows that informing low income individuals about their income rank in society increases their demand for inheritance taxes.

9 bers. Perceived polarization among them is larger than their actual belief polarization:

θ θ θ θ 1 + 4θ ψ ψ = ψ ψ . G − G G − G 1 + 2θ     When thinking about theb socialb progressives, beliefs overweight their stereotypical open attitudes, when thinking about the conservatives, beliefs overweight their closed attitudes. Perceived disagreement exceeds actual disagreement. The amplification of perceived polariza- tion is a consequence of stereotypical thinking. It is not necessarily linked to identification with one group or the other. Even a non-identified voter exaggerates actual polarization, if he is susceptible to stereotypical thinking. There is a large literature measuring perceived and affective polarization among US parties. Westfall et al. (2015) and BCGS (2016) show that US voters exaggerate differences in the policy views of Democrats and Republicans. These exaggerations are held by all voters, including the non politically affi liated ones, which is consistent with Corollary 2. Bordalo et al. (2019) offer intriguing new evidence: the gap between perceived and actual polarization is especially large in policy issues that are more salient and in which there is more actual disagreement. This is a direct implication of our model when identity is endogenized.8 In the current example, identity distorts beliefs and value judgments regarding the general effects of a policy, say immigration. We also allow identity to distort a voter’s perception of his own situation, such as his income rank in society or exposure to foreign competition. When forming beliefs about these aspects, the voter distorts objective uncertainty toward the opinions that are more frequently held in his group compared to the outgroup, as in Equation (1). A voter identified with the lower class retrieves his group’sdistinctive beliefs of economic backwardness, so he becomes too pessimistic about his future income.9 Similarly, a voter identifying with the losers from trade retrieves examples of workers similar to him who lost their job due to foreign competition, increasing his demand for trade protection. We now apply these ideas to politics. When a sudden shock or drastic social change render a certain policy conflict salient, voters think about the consequences of the policy in

8 Gentzkow (2017) shows that in the US perceived polarization and distrust of political rivals (so called affective polarization) has increased more than actual divergence in policy views. A contributing factor may be a rise in θ, fueled by stronger stereotyping in traditional or social media. Ahler (2018) shows that correcting misperceptions about the out-party also reduces affective polarization. Druckman et al. (2019) show that affective polarization is boosted by retrieval of extreme party stereotypes, again consistent with our model. 9 Cruces et al. (2009) document that Buenos Aires residents misperceive their current income on the basis of local conditions. Voters from rich neighborhoods underestimate their income rank relative to voters from poor neighborhood, as if each voter uses the local income distribution as a reference. Misperception of current income and social mobility may be due to many factors, including local conditions. Our point is that when class conflict becomes salient (or social classes are primed), individuals identify with their class and misperceptions become class-based. Consistent with this view, Stantcheva (2020) shows that US Republicans (Democrats) overestimate (underestimate) their income rank in society.

10 the context of the social group they feel similar to. This brings to mind stereotypical attitudes and traits to which beliefs are anchored. When the salient issue changes, the voter’sidentity can change, altering beliefs across several domains.

3 Identity and Political Conflict

We study a simple model with conflict over redistribution and cultural policy. We first derive the rational policy preferences. Next, we allow voters to identify either with their economic class or cultural group. Salience of cultural conflict fosters cultural identity, and viceversa for economic conflict. We then show that identity distorts policy preferences and that a switch from economic to cultural identity amplifies conflict among cultural groups, dampening it across classes. This is reflected in the equilibrium policies chosen under electoral competition. We close the section by highlighting empirical predictions of the theory.

3.1 The Model

There are two policies. The first is a proportional income tax τ 0 that finances a public good. ≥ It entails quadratic distortions, ϕ τ 2, ϕ > 0, which reduce aggregate income. The second − 2 is cultural policy q, with larger q indicating a more liberal stance (e.g., more extensive civil rights or immigration). We think of cultural policies as those mainly based on values, rather than on material interests, such as abortion, civil rights, race relations and immigration.10 There is a measure one of individuals who differ in their expected relative income, ε, and their cultural traits, ψ. A voter type is summarized by vector (ψ, ε), which we assume to be distributed in society according to the normal cdf H (ψ, ε) , with mean (0, 0), unit standard deviations and correlation coeffi cient ρ. We assume that income and social progressiveness are non-negatively correlated, 1 > ρ 0, owing for instance to education. ≥ A voter’spolicy evaluation depends on three sources of uncertainty. The first is about the 2 effects of q. Preferences over q follow the quadratic loss 1 q ψ˜ , where ψ˜ is the voter’s 2 − preferred policy, Gaussian with individual-specific mean ψ. Recall that higher ψ stands for more socially progressive culture and hence higher preferred q. Second, voters are uncertain about their tax burden because their income 1+˜ε is stochastic. ˜ε is Gaussian with voter-specific mean ε. Thus, a rational agent of type ε expects to earn 1+ε. Beliefs about ˜ε capture believed social mobility, and higher ε implies a higher tax burden.

10 Our conclusions also hold if we allow preferences over immigration to depend on income, provided the dependence is weaker than that on culture. Card, Dustmann and Preston (2012) study attitudes toward immigration in the European Social Survey. They argue that the bulk of attitudes is attributable to preferences over "compositional amenities" such as cultural homogeneity, religion, language, especially across more and less educated natives. Economic factors such as the impact of immigration on wages plays a small role.

11 Third, voters are uncertain about the benefit of the public good g. Its marginal utility ν˜ is also Gaussian with expected value ν + ψ. Since ψ has zero mean in the population, the average marginal value of g is ν > 1. More socially progressive individuals (with higher ψ) are also more in favor of redistribution, because of their trust in government or fairness norms, consistent with Stancheva (2020), or their moral universalism (Enke et al. 2019a). The distribution of ν differs from that of preferences over cultural policy ψ˜, because these beliefs refer to different, possibly unrelated, policies. Nevertheless, the voter’s culture ψ has implications for both.e11 Since ε has zero mean in the population, aggregate income is 1. Thus, the quantity of g is equal to the tax rate τ. The expected utility of rational voter (ε, ψ) is:12 ϕ κ W εψ (τ, q) = (1 + ε) (1 τ) τ 2 + (ν + ψ)τ (q ψ)2 , (3) − − 2 − 2 − where superscript εψ denotes the type and κ > 0 captures the weight attached to policy q. Neglecting non-negativity constraints, the rational bliss point of type (ε, ψ) is:

(ν + ψ) (1 + ε) τ εψ = − , qεψ = ψ. (4) ϕ

More socially progressive voters demand more redistribution and a more liberal cultural policy. Richer voters demand less redistribution because of their greater tax burden. The socially optimal policy maximizes aggregate rational welfare:

W εψ (τ, q) dH(ε, ψ)dεdψ. Z Given linear private and public consumption, and given the assumed normal cdf H(ε, ψ), the socially optimal policies are: ν 1 τ 0 = − , q0 = 0. (5) ϕ Equation (4) shows that policy disagreement combines two underlying socioeconomic con- flicts. One is the rich vs poor conflict over taxes, captured by expected relative income ε. The second is cultural conflict, captured by ψ, which affects the evaluation of both redistributive and cultural policies. This formalization is consistent with the idea that there is a key dimen- sions of cultural heterogeneity, moral universalism, that shapes policy evaluation in several domains, economic and non-economic. More universalistic agents apply their value system to socially more distant individuals (cf. Tabellini 2008, Enke et al. 2019a). As a result, they are

11 It would be equivalent to assume that ν˜ is positively correlated with ψ. We however prefer to use the formalism of correlations only for the carriers of identity, income and culture. 2 12 e We omit for simplicity the additive constant kV ar ψ˜ that obtains when computing E q ψ . −     e 12 more open and progressive with regard to civil rights or immigration, but also more trusting of government and more pro-redistribution (cf. Enke et al. 2019b, Haidt 2012). The economic situation of an individual, instead, only affects his economic policy preferences - although it can be correlated with culture. As emphasized by Sundquist (1983), economics and culture have divided American voters throughout history, and major political realignments have occurred when the main parties shifted their positions on these divides. We endogenize these realignments. When economic shocks or social change cause voters to identify with their class, economic conflict prevails, when they cause voters to identify with their culture, cultural conflict prevails. Realignments occur as heterogeneous voters sort into identifying with different groups.

3.2 Endogenous Identity

3.2.1 Groups

Social groups are defined on income ε and culture ψ. As discussed in Section 2, with respect to culture a voter can be either socially conservative, SC ψ ψ < ψ , or progressive, ≡ | SP ψ ψ ψ , where ψ is historically given. With respect ton income, voterso belong to the ≡ | ≥ b uppern class U oε ε ε or to the lower class L ε ε < ε , where ε is again historically ≡ { | ≥ } ≡ { | } given. We assumeb that culturalb groups and income classes are alternative partitions of society: a voter can identify eitherb economically or culturally, but notb both. Forb instance, a type who is poor (ε < 0) and socially conservative (ψ < 0) can identify either with the lower class, or with the social conservatives. The assumption that groups are unidimensional simplifies the analysis but is realistic. Lipset and Rokkan (1967) argue that much political change in recent history can be accounted for by switching identities across a few, broad, and historically determined groups such as "rich/elite vs. poor/masses", "traditional/religious vs. progressive/secular" or "center vs. periphery". In our setting, groups are naturally defined along the class and cultural divides.13 A fuller understanding of the structure of groups, and the role of politicians in creating them, is an important problem for future work.

Each group G is summarized by its average member (εG, ψG). If ψ and ε are correlated, then groups differ in both dimensions, and not just in the dimension along which they are

13 Class identity has been central during the 20th century, the most extreme example being communist class struggle. But even in the US, Campbell et al. (1966) show that during the New Deal many working class voters turned Democratic because they identified with this party as it represented "the common man". Cultural identity, for instance with respect to racial or religious groups, has also played a key role.

13 defined. In particular, bivariate normality of H(ε, ψ) implies:

εSP εSC = ρ ψ ψ , (6) − SP − SC ψU ψL = ρ (εU εL) .
(7) − − If ρ > 0, the social progressives are richer than the conservatives, and the upper classes are more progressive than the lower classes. Of course, since ρ < 1, cultural groups mostly differ along culture, ψ ψ > εSP εSC and economic classes along income, εU εL > ψ ψ . SP − SC − − U − L We make the following reasonable assumption:

ψ ψ 1 ρ < SP − SC < , (A1) εU εL ρ  −  which implies that income differences are larger between classes than between cultural groups, 14 εU εL > εSP εSC , and viceversa for cultural differences, ψ ψ > ψ ψ . − − SP − SC U − L

3.2.2 Metacontrast and Identification

How does a voter choose his identity? As discussed in Section 2, identity maximizes: i) similarity between oneself and the in-groups, and ii) contrast between the in-groups and the out-groups. The latter criterion is key because it shapes the extent to which a certain social partition is salient for all voters, be it economic or cultural. To highlight the role of group salience, we only consider criterion ii), subject to the con- straint that one can only identify with his own income class or cultural group, because he is naturally more similar to them than to the outgroups. As a result, everyone identifies along the contrast-maximizing dimension. As shown in a previous version, our main results are similar if identification reflects a full tradeoff between criteria (i) and (ii) above, except that in this case some voters identify based on culture, others on class.15 Since we study political conflicts, policy preferences shape group contrast. We measure contrast between G and G by the welfare loss that the average member of G experiences when moving from his ideal policy τ G, qG to that of the average outgroup, (τ G, qG):16

14 In what follows, we discuss the implications
of whether ψ ψ εU εL. Given that H(ψ, ε) is SP − SC ≶ − normal with mean 0 and unit variances, it can be shown that, if ˆε and ψˆ have the same sign, then

ψ ψ εU εL as ψˆ ˆε SP − SC T − | | T | | See Schivardi et al. (2020) for a proof in a different context. 15 The formal analysis of that model can be found in a prior draft, available as CEPR Discussion paper 13390 16 Consistent with the idea that social categories and their representations are historically given, the average group members do not depend on which voters identify with them.

14 C G, G = W G τ G, qG W G τ G, qG . (8) − The definition uses rational bliss points,
but little
changes if we use stereotyped bliss points. To make the model tractable, we consider a second order approximation of Equation (8):17

2 2 C G, G (εG ε ) + (1 + κ)(ψ ψ ) 2(εG ε )(ψ ψ ). (9) ' − G G − G − − G G − G The first term captures
economic conflict, the second term cultural conflict, where the latter is b weighted by the relative importance κ κϕ of cultural policy (κ is the welfare weight attached ≡ to q). κ increases in tax distortions ϕ because they render redistribution less appealing for everybody, increasing the relative importanceb of q. If contrastb between economic classes is larger than that between cultural groups, C (U, L) > C (SP,SC), then each voter (ε, ψ) identifies with his economic class. Otherwise he identifies with his cultural group. Define:

2 2 ψ ψ (1 ρ) [1 SP − SC ] εU εL − − − α 2 . (10) ≡ ψ ψ   SP − SC ρ2 εU εL − − b   We then prove:

Proposition 2 Everyone identifies with his cultural group (economic class) if κ > α (if 2 ψ ψ ∂α ψ ψ κ < α). Furthermore: (i) α is strictly decreasing in SP − SC . (ii) ρ SP − SC . εU εL ∂ρ ∝ εU εL − − −b b b   b Identityb is shaped by threeb forces. First, cultural identity is more likely to prevail when differences among cultural groups are high relative to those among economic groups, namely ψ ψ ψ ψ when SP − SC is high. Note that culture is favored relative to class: if SP − SC 1 cultural εU εL εU εL − − ≥ identity surely prevails because α 0. The reason is that culture is a more effi cient summary ≤ of policy disagreement: cultural groups disagree on all issues, economic classes mainly on redistribution. Thus, class identityb requires stark economic inequality, while cultural identity is possible even if cultural differences are not so pronounced.

More generally, higher inequality εU εL among classes raises redistributive conflict and − increases α, making class identity more likely to prevail. Stronger cultural divisions ψ ψ SP − SC have the opposite effect: they reduce class cohesion and favor cultural identity. In a 1893 letter, Friedrichb Engels argued that class struggle proved diffi cult in the US owing to deep ethnic cleavages within the US working class. Similarly, expansion of higher level education may

17 As shown in the Appendix, Equation (9) is obtained by taking a second order approximation of G G G W τ , q with respect to mean income and social progressiveness in G, (εG, ψG), at the point (εG, ψG).  

15 enhance the difference ψ ψ between progressive elites and the rest, making cultural SP − SC identity more likely (Fukuyama 2018). Second, cultural identity is more likely to prevail when the relative importance of cultural policy κ increases. Higher κ means that q is more salient. A large inflow of immigrant or episodes of discrimination against minorities bring cultural issues top of mind, raising κ (via higher welfareb weight κ). Similarly,b financial globalization can raise tax distortions ϕ through tax competition, increasing the salience of cultural issues, favoring cultural identity. b Third, higher correlation ρ between income and culture favors identification along the dimension where groups are relatively more divided. When ρ is higher, cultural groups are more economically homogeneous, and economic groups more culturally homogeneous. As a result, they are both more effi cient carriers of conflict. However, the partition with more acute ψ ψ group conflict is even more so. If cultural divisions SP − SC are suffi ciently large, cultural εU εL − identity is favored. If economic divisions are suffi ciently large, class identity is favored. As an example, consider skill biased technological change. It has impoverished less educated and hence more socially conservative workers (Autor 2019), akin to higher ρ. Proposition 2 says that this economic force can lead to cultural identity provided cultural conflict is suffi ciently pronounced, and despite the fact that income inequality may have increased.

3.3 Beliefs and Policy Preferences

Consider the effects of identity switches. Identity distorts beliefs through stereotypes but, unlike in Section 2, beliefs now vary both along income prospects, ˜ε, and cultural views ψ˜. This, in turn, affects preferences over both τ and q. θ θ Let (εG, ψG) denote the expected income and culture of type (ε, ψ) if he identifies with εψ εψ group G. Similarly, let τ G and qG denote his bliss points if he identifies with G. Repeating the steps that led to Proposition 1 in Section 2, and using (4), we have:

Proposition 3 A type (ε, ψ) identified with group G = SC, SP, U, L perceives his future expected income and his cultural trait to be:

θ ε = ε + θ (εG ε ) , (11) G − G ψθ = ψ + θ ψ ψ . (12) G G − G
The same voter’s ideal policies are equal to:

ψ ψ (εG ε ) τ εψ = τ εψ + θ G − G − − G , (13) G ϕ
qεψ = qεψ + θ ψ ψ . (14) G G − G
16 A voter identified with G distorts his perceived social mobility and his cultural views by the corresponding difference between his group and the outgroup. These belief distortions cause the voter’s bliss points to depart from their rational values (τ εψ, qεψ). The perceived tax burden is enhanced (dampened) if the voter identifies with a group whose members are on average economically dominant (backward). The benefits from open cultural policies and universalistic redistribution is enhanced (dampened) if the voter identifies with a group whose members are on average more socially progressive (conservative). As an example, consider a lower class and social conservative type (ε, ψ). By Proposition 3, (6) and (7), if he identifies with his class his policy preferences are:

εψ εψ θ(1 ρ)(εU εL) εψ εψ τ = τ + − − , q = q ρθ(εU εL) (15) L ϕ L − −

εψ εψ The voter exaggerates his demand for redistribution, τ L > τ , the more so the greater is class inequality in income. When he thinks about social mobility, many stereotypically poor lower class fellows come to mind, making him pessimistic about himself too. Like the Marxist proletarian, he thinks of himself as the member of an oppressed class, which increases his demand for redistribution. If income and culture are uncorrelated, ρ = 0, the voter’s his preferred q is non distorted. If instead ρ > 0, social conservatism is a typical feature of the εψ εψ lower class, which enhances his conservative stance qL < q . This also reduces the voter’s trust in government, but since ρ < 1 his demand for redistribution is still excessive. Now suppose that cultural policy becomes salient. If this voter switches to socially con- servative identity, his bliss points become:

θ(1 ρ)(ψ ψ ) τ εψ = τ εψ − SP − SC , qεψ = qεψ θ(ψ ψ ) (16) SC − ϕ SC − SP − SC

εψ εψ Naturally, he is even more opposed to liberal policies, qSC < qL . Crucially, he demands εψ εψ less redistribution than before, τ SC < τ L . Two forces work in the same direction. First, having abandoned class identification, he is more optimistic about his social mobility. Second, because he is more conservative, his trust in government drops.18 This drastic change may be caused by a shock that is only in part or wholly unrelated to redistribution. A salient conflict on abortion may induce the voter to switch to religious identity. Because such identity also primes the voter to distrust the government, it reduces

18 In the model these effects are so strong that the voter now demands less than rational redistribution εψ εψ (τ SC < τ ). This occurs because ε and ψ are assumed to have commensurate yet opposite effects on preferences over τ. If income was a suffi ciently more important determinant of preferences over taxes than culture, the identity switch would dampen - yet not flip - the distortion relative to rational preferences. Even in this case, however, shocks that induce identity switches from class to culture reduce the demand for redistribution of poor voters and make them more socially conservative.

17 his perceived benefit of redistribution. A similar effect obtains if there is a large inflow of immigrants from a different culture or religion. The latter weakens class cohesion (especially if many immigrants are lower class) and reinforces the voter’s cultural identity, reducing his demand for redistribution. Alesina et al. (2018) find that making people think about immi- grants reduces their support for redistribution, particularly if the respondent is less educated and right wing. They argue that respondents are unwilling to redistribute toward strangers. An alternative interpretation is that the treatment primes cultural identity in conservative and anti-immigrant subjects.

3.3.1 Policy Disagreement and Coherence of Polarization

There is a lively debate on whether over time US voters have become more polarized or not, along some issues or overall (Abramovitz and Saunders 2008, Fiorina and Abrams 2008). Interestingly, Gentzkow (2016) shows that polarization has become more coherent: disagree- ment among Republicans and Democrats has become more correlated across policy issues. Our model does not feature political parties. Yet, a switch from class to cultural identity offers a fruitful way for thinking about changes in overall polarization and conflict. εψ εψ To see this, let τ d , qd denote the bliss points of type (ε, ψ) if he identifies on dimension d, for d = ˜ε, ψ.˜

Proposition 4 Suppose that identity switches from class to culture. Then, if ρ is low enough: 1) the variance of ideal cultural policies increases while the variance of ideal tax rates can increase or decrease: var qεψ > var qεψ , var τ εψ var υεψ . ψ˜ ˜ε ψ˜ T ˜ε 2) the covariance between  a voter’s preferences  over redistribution  and cultural policy in- creases: cov τ εψ, qεψ > cov τ εψ, qεψ . Under cultural identity such covariance is positive, ψ˜ ψ˜ ˜ε ˜ε but it may be negative under class identity: cov τ εψ, qεψ > 0 cov τ εψ, qεψ . ψ˜ ψ˜ T ˜ε ˜ε     When identity switches from class to culture, disagreement over q heightens because cul- tural belief extremism increases. Disagreement over τ instead changes in an ambiguous way: weaker class conflict reduces disagreement over the tax burden but cultural polarization in- creases it over the benefit of universal transfers. Average disagreement issue by issue may go up or down, but cultural policies become a locus of relatively stronger conflict. Stronger disagreement over q is not distinctive of a switch to cultural identity. In Matejka and Tabellini (2018), rational voters are more attentive to issues where stakes are higher. Also in their model, then, higher κ shifts voters’attention to q, amplifying conflict over it. A switch to cultural identity does more than that: it causes voters to overweight culture wherever it is relevant. A refugee crisis canb increase conflict over abortion or gay rights despite the fact that these issues are very remote from the identity trigger.

18 This spillover is at the heart of result 2). When identity switches from class to culture, a voter’ssocial progressiveness becomes a potent common factor driving his views on both q and τ. Conservative voters align against immigration, abortion and universalistic welfare, even if poor. Socially progressive voters align in the opposite direction. The correlation among policy views increases, and —even if disagreement over any individual issue may not have increased massively —voters are divided into two camps that disagree on many issues. The increased coherence of polarization in the US is typically explained by stronger par- tisan sorting. The Democratic Party has become more liberal, the Republican Party more conservative, and partisan voters —who take cues from party positions —exhibit extreme and correlated policy views (cf. Klein 2020). In our model cultural identity does something dif- ferent: it increases overall extremism on some issues but not others, and crucially it tends to affect all voters, not only partisans, because identity lies with a social group.

3.3.2 Group Disagreement

Since identification is based on socio-economic traits, the model also has predictions for dis- agreement among average members of different socioeconomic groups. Switches in identity amplify disagreement between some groups and dampen it for others. Consider the benchmark of rational beliefs, and let τ G, qG denote the average rational bliss points of members of group G. Thus, τ G = τ εψdH(ε, ψ G) and qG = qεψdH(ε, ψ G). | | τ q Under rational beliefs, by (4), conflict betweenRR opposite economic classes overRR and is:

L U (1 ρ)(εU εL) L U τ τ = − − > 0, q q = ρ(εL εU ), (17) − ϕ − − while rational conflict between opposite cultural groups is:

(1 ρ)(ψ ψ ) τ SP τ SC = − SP − SC > 0, qSP qSC = ψ ψ > 0. (18) − ϕ − SP − SC
How are these cleavages affected by social identities? To answer this question, we first analyze conflict among the groups with which people identify.

Proposition 5 Relative to rational beliefs, class identity amplifies conflict among classes, cultural identity amplifies conflict among cultural groups:

τ L τ U = (τ L τ U )(1 + 2θ), qL qU = (qL qU )(1 + 2θ). ˜ε − ˜ε − ˜ε − ˜ε − τ SP τ SC = (τ SP τ SC )(1 + 2θ), qSP qSC = (qSP qSC )(1 + 2θ). ψ˜ − ψ˜ − ψ˜ − ψ˜ −

19 Class identity polarizes the upper and lower class over beliefs about mobility, so their conflict over τ is enhanced. If income and culture are correlated, conflict also affects q (if ρ = 0, then qL = qU and there is no amplification). Under cultural conflict disagreement among the average social progressive and conservative is amplified on q and, if ρ > 0, on τ. Suppose now that identity switches from class to culture. What happens to disagreement among voters sorted by their class? And what happens to disagreement among voters sorted by their cultural groups?

Proposition 6 If identity switches from class to culture, then: (i) conflict between cultural groups (SP vs SC) is amplified over q, it is amplified or reverses direction over τ:

qSP qSC > qSP qSC > 0, (19) ψ˜ − ψ˜ ˜ε − ˜ε τ SP τ SC > τ SP τ SC , (20) ψ˜ − ψ˜ ˜ε − ˜ε (ii) conflict between economic classes (L vs U) can be dampened or enhanced over q, while it is dampened or reverses direction over τ:

qL qU qL qU , (21) ψ˜ − ψ˜ S ˜ε − ˜ε τ L τ U < τ L τ U . (22) ψ˜ − ψ˜ ˜ε − ˜ε Disagreement among cultural groups changes in an intuitive way. As voters abandon class identity, they feel more socially conservative or progressive, depending on their affi liation. This enhances conflicts over q between them. With regard to taxes, as class conflict weakens and the influence of culture rises, social progressives experience a strong increase in their preference for redistribution and social conservatives experience the reverse. Hence, the demand for τ by the average progressive relative to the average conservative certainly increases. The result is more surprising for income classes. Consider disagreement over q. Under class identity, when ρ > 0, lower class members display a common conservative bias and upper class members display a common progressive bias. As identity switches to culture, income classes lose cohesion. Some lower class members identify as social conservatives, others as progressives. There are two effects. On the one hand, the conservative majority of the lower class becomes even more conservative, which increases disagreement with the upper class. On the other hand, the progressive lower class members become even more progressive, which creates a countervailing force. The same happens, in reverse, for the upper class. On net, disagreement over q by voters sorted according to their income class may rise or fall. Consider disagreement over taxes. Under class identity, all lower class members are pes- simistic about social mobility, so they demand large redistribution. As identity switches to culture, these beliefs become less pessimistic, which reduces demand for τ, dampening conflict

20 with the upper class. In addition, the socially conservative majority of the lower class now strongly distrusts the government, which further reduces τ. The opposite occurs for the upper class. Thus, disagreement over redistribution by income classes drops or reverses in sign. This result is due to an important feature of our model: group heterogeneity. An increase in the salience of cultural conflict does not only polarize opposite cultural groups. If it leads to a shift in identity, it culturally divides the lower class, dampening class conflict.

3.4 Equilibrium Policy

We now study how identity affects equilibrium policy. As in standard models of probabilistic voting, we assume that two candidates commit to policy platforms ahead of the elections in order to maximize the probability of winning (cf. Persson and Tabellini 2000). To isolate the role of beliefs, we assume that all voters have the same degree of mobility across parties. In this εψ case, the equilibrium policy maximizes perceived utilitarian welfare. Specifically, let Wd (τ, q) denote the perceived expected utility of agent (ε, ψ) if he identifies based on dimension d, for d = ˜ε, ψ.˜ Then the equilibrium policy is defined by:

εψ (τ ∗, q∗) = arg max W (τ, q) dH (ψ, ε) , for d = ˜ε, ψ˜ (23) τ,q d Z Suppose that everyone identifies based on class. Then, exploiting Proposition 3, the first order conditions of the problem imply:

θ (εU εL) (1 ρ)(πL πU ) τ ∗ = τ ◦ + − − − , (24) ϕ

q∗ = q◦ θρ (εU εL)(πL πU ), (25) − − − where τ ◦ and q◦ denote the socially optimal policies, and πL, πU denote the size of the upper and the lower class, respectively. Suppose that the lower class is the larger group, πL > πU . Then equilibrium taxes are above the social optimum and, if ρ > 0, cultural policy is too conservative. These distortions are stronger the larger is actual class conflict, (εU εL). − In Marxian theory, class identity benefits the largest group and class consciousness is necessary for class struggle. Something similar occurs with class identity here: it leads lower class voters to be more radical in their demands. Opportunistic politicians then accommodate these demands because the lower class is bigger. If the lower class is also more conservative, then cultural policy is also too restrictive, for the same reason.

21 Suppose instead that everyone identifies based on culture. Then we have:

¯ ¯ (1 ρ) ψSP ψSC (πSC πSP ) τ ∗ = τ ◦ θ − − − , ϕ −
¯ ¯ q∗ = q◦ θ ψ ψ (πSC πSP ). − SP − SC −
Here too, the equilibrium is distorted in the direction of the stereotypes of the larger group.

If the social conservative group is bigger, πSC > πSP , then cultural policy is too conservative

(q∗ < q◦) while taxes are too low (τ ∗ < τ ◦). Identity switches have a qualitatively more significant effect on equilibrium redistribution than on cultural policy: the size of government drops, while the effect on q is ambiguous since it depends on group size and on ρ. We summarize this discussion in the following:

Proposition 7 Suppose that πL > πU and πSC > πSP .

i) If all voters identify along class, then τ ∗ > τ ◦ and q∗ q◦, with strict inequality if ρ > 0. ≤ ii) If all voters identify along culture, then τ ∗ < τ ◦ and q∗ < q◦.

iii) A switch from class to cultural identity reduces τ ∗ and has ambiguous effect on q∗.

3.5 Discussion

Our model captures a chain reaction whereby, by influencing beliefs, changes in identity alter the effects of economic shocks:

economic and social change/salient policy group identities beliefs policies. ⇒ ⇒ ⇒ This chain reaction can explain why the growing importance of cultural conflict in the US and other Western democracies has occurred in tandem with a drop in the demand for redis- tribution, despite increasing income inequality. Modern societies have become more liberal and permissive than in the 1950s and 1960s, partly due to the influence of an increasing mass of educated and progressive voters (cf. Fukuyama 2018, Goodhart 2017). More traditional social strata did not alter their value system to the same extent, and resent the new status quo. Rapidly increasing immigration added further strains. These changes can be viewed as having increased the relative importance of cultural policy, κ, as well as the contrast between opposite cultural groups. In recent years, other contributing factors may have been an in- creased mobility of capital, which raised tax distortions ϕ alsob increasing κ, and skill biased technical change, that increased the correlation ρ between income and education (and hence between ε and ψ). In our model, all these transformations encourage culturalb identification. Frank (2004) beautifully describes the "cultural backlash" in Kansas, a state that was firmly Democratic in the past, where socially conservative and low income voters have become ob-

22 sessed with civil rights, leading them to see politics through issues such as immigration or race, and away from redistribution, consistent with the chain reaction above. Even though identity is often not measured, an identity switch from class to culture has a number of specific predictions that we outlined in the previous propositions, namely:

Enhanced disagreement over cultural policies (Proposition 4). • Stronger correlation of policy views across issues (Proposition 4) • Enhanced cultural and economic conflict between cultural groups (Proposition 6) • Dampened class disagreement over redistribution (Proposition 6). • Each of these predictions can be separately obtained also if voters are rational, for specific changes in the relevant parameters. But a rational version of our model would not deliver the entire range of implications. Specifically, by (4), (17) and (18): (i) An increase in κ neither affects the covariance of policy opinions nor group conflict or individual disagreements. (ii) An increase in ϕ diminishes conflict over τ for all groups (class and culture based), it has no effect on conflict over q, and it always drives the covariance between q and τ closer to zero. (iii) An increase in ρ also reduces the covariance of policy preferences over q and τ, and reduces conflict over τ between both cultural and economic groups. (iv) A rise in cultural differences, ψ ψ , amplifies rational conflict between cultural groups, but not between economic SP − SC groups; it also has no effect on the covariance of policy preferences (unless it is associated with an increase in the variance of culture ψ). The above predictions could also obtain as a result of party identity. If party positions become more extreme (say because of the increased influence of cultural extremists within each party), loyal voters become more extreme, too. Relative to this account, our approach has two advantages. First, it can account for why voters belonging to different socio-economic groups realign in different parties and change their beliefs. Second, it can explain why even many non politically affi liated voters become more extreme, as we show in Section 5.19 Our discussion has focused on the consequences of a shift from class to cultural identity, and in section 5 we present recent corroborating evidence. But other important episodes in US political history can be interpreted as identity shifts in the opposite direction: from culture to class. A prominent example is the political realignment that took place in the late 1930s and

19 Note that our model of social identities can also accommodate partisan politicians. In such a model, a shift in the dimension of identification from class to culture would lead to a reshuffl ing in the composition of voters across parties, even if party positions remained fixed. Socially conservative and lower class voters, that when identified with their class voted for the left, would now be attracted by the more conservative right wing party. Similarly, culturally progressive and upper class voters would be attracted by the more progressive left wing party. As discussed by Piketty (2018), this sorting of voters across parties according to their cultural views is exactly what has taken place in the US, UK and France.

23 1940s, and that pushed the Democratic Party to abandon its support for racial discrimination. As emphasized by Schickler (2016), this was the result of a grassroot movement initiated by the core groups that supported the New Deal. Industrial unions, African Americans and urban liberals in the North-East pushed for a fusion of class and race, in a joint defense of labor rights and civil rights. They did so out of the ideological left-wing conviction that racial division undermined class consciousness, but also because of expediency: the inflow of black immigrants from the South undermined the threat of strikes as black workers could be used as replacement in the workplace. Incorporating the blacks in the working class would remove this threat and strengthen the labor movement. In the words of union leader John Brophy, quoted by Schickler (2018), "Behind every lynching is the figure of the labor exploiter, the man in the corporation who would deny labor its fundamental rights". The vote of African Americans was also pivotal in several key districts in the North East, and this too induced the Democratic Party to recognize African Americans as full right members of the working class. In line with this, Calderon et al. (2019) study US counties between 1940-1970, and show that where the inflow of black immigrants from the South was larger, the Democratic Party gained more votes and grassroot activism was strengthened. Group leaders and local politicians clearly played an important role in this and other episodes of political realignment. In our model, their role in this episode could be viewed as increasing the relative salience of class conflict, akin to reducing κ. Our current analysis does not explicitly allow parties or group leaders to affect the perceived salience of an issue. Extending the model in this direction is an important task for future research.b

4 Globalization

Nationalism and protectionism are important components of populist platforms in the US and Europe. It is perhaps less obvious why they have been coupled with identity politics based on race or cultural conservatism, and why they have been embraced by rightwing parties, traditionally in favor of free trade. We now show that in our model this pattern arises when the losers from trade are disproportionately social conservative, because they are less educated or live in areas with more traditional culture. As economic insecurity hurts a culturally homogeneous group, its conservative culture becomes salient. This leads to a bundling of belief distortions in trade, culture, and a fall in their demand for redistribution. This prediction is broadly consistent with evidence that cultural conservatism is systematically correlated with adverse economic shocks, particularly in trade (Autor et al. 2017, Colantone and Stanig 2017). Section 5 offers more specific evidence in its support.

24 4.1 The Model

A small open economy has a continuum of individuals of size 1 and two traded goods: one exported, the other imported. The rationally expected utility of a generic voter is: κ w = x + U(m) + (ν + ψ)g (q ψ)2, (26) − 2 − where x and m denote his consumption of the exported and imported good respectively and U(.) is a concave quadratic utility function. g denotes public consumption, q is a cultural policy, and ψ is the voter’s culture, which continues to influence also his preferences over public spending. The price of the exported good is 1, the price of the imported good is p. Denoted by Y the voter’sincome, his budget constraint is:

Y = x + pm.

Income is uncertain, and its variation is absorbed by the numeraire x. There are two sources of income risk. The first one, denoted by (1 + ˜ε), is a taxable income source earned in the export sector, with voter specific average (1 + ε). The second one is non-taxable and earned from a specific factor that can be employed in either sector with probabilities that are ex-ante uncertain. The income is 1 if employed in the export sector, and p if employed in the import competing sector. The probability of employment in the import sector is σ(1 η˜), − where σ (0, 1) captures exposure to foreign competition. η˜ is a random variable with ∈ individual specific mean η. An individual with higher η is more employable in the export sector, perhaps because he is more mobile or skilled. Higher η corresponds to higher benefits from globalization.20 In sum, voters differ in: expected income ε, benefits from trade η, and culture ψ. A voter type is the triple (ε, η, ψ). There are three policies. As in Section 3, one is cultural policy q, another the distortionary income tax τ used to finance spending g. In addition, there is an ad valorem tariff t, so that

the domestic price of imports is p = (1 + t)p∗, where p∗ is the world price. The revenue from the tariff is distributed as a lump sum transfer T to voters. The expected income of voter (ε, ψ, η) is equal to: e 2 εη τ Y (τ, t) = (1 + ε)(1 τ) + σ(1 η)[p∗(1 + t) 1] + 1 ϕ + T . − − − − 2 The first term is net taxable income. The remaining terms denote expected incomee from the specific input, tax distortions and the lump sum transfer from the tariff. The income tax

20 To rely on normality, which yields the exact distortion in beliefs of Equation (2), we assume that the variance is suffi ciently low that σ(1 η) is between zero and one with very large probability. We could also specify a bounded distribution for η−, such as a beta, and obtain very similar results. e e 25 τ redistributes between individuals with high vs low expected income ε, the tariff between individuals with high vs low benefits from trade, η. If p < 1, increased exposure to imports (higher σ) causes the income of this voter to drop, the more so the lower is η. Because individuals differ only in their expected income and they are risk neutral with respect to the numeraire, their indirect utility function is separable in ε, η and ψ. Thus tax rate τ εψ and cultural policy qεψ rationally preferred by types (ε, ψ) is still given by (4). The rationally optimal tariff tη decreases in export sector employability η. Identity can still be class based, with groups L ε ε < ε and U ε ε > ε , or culture- ≡ { | } ≡ { | } based, with groups SC ψ ψ < ψ and SP ψ ψ > ψ . There are also two new groups ≡ | ≡ | that voters can identify with:n the nationalistso N n η η < ηobwho are highly exposedb to trade, ≡ { | } and the cosmopolitans C η η >bη who are little exposedb to it. ≡ { | } All voters identify along the dimension (income, culture,b or trade) that maximizes group contrast. We prove that now (approximated)b group contrast is equal to:

2 2 2 C(G, G) (εG ε ) + (1 + κ)(ψ ψ ) 2(εG ε )(ψ ψ ) + σ(η η ) , (27) ' − G G − G − − G G − G G − G

which is Equation (9) plus an extrab trade conflict term (η η ) associatedb with the import G − G tariff. The weight σ σ2ϕ captures the relative importance of trade versus taxes. When the ≡ probability of import sector employment σ is higher, everyone is more exposed to trade so conflict over tariffsb is more welfare important, σ is higher. The distribution of voters (ε, ψ, η) is Gaussian around mean (0, 0, 0) and with unit vari- ances. We assume that the benefits from tradeb(η) are positively correlated with progressive culture (ψ), and denote with 1 > a > 0 the correlation coeffi cient. Culturally open people may be geographically more mobile, so their employability η is higher. Alternatively, the import competing sector may be concentrated in culturally conservative areas, so that many individuals with high exposure to trade, low η, have also low ψ. To simplify the presentation, we assume that the remaining elements of the correlation matrix are 0. Appendix 3 shows that the main results generalize to positive correlation between all elements, provided that corr (ψ, η) > corr (ε, η) . This assumption is supported by the evidence discussed in section 5. Using data from trade exposure, we show that residents of commuter zones more exposed to import competition tend to be less educated and more religious, but on average have the same income as in other commuter zones. As in the two dimensional case, we assume that direct differences are larger than indirect ones. Specifically, in addition to assumption A1 we impose that cultural differences are larger among cultural groups than among cosmopolitans and nationalists. Conversely, trade conflict

26 is larger in the latter than in the former groups. Formally, this means that:

ψ ψ 1 a < SP − SC < . (A2) η η a C − N 4.2 Identification and Policy Demands 2 εU εL To study identification, denote the squared ratio between group differences as ∆εψ − , ≡ ψSP ψSC 2 2 − εU εL ψSP ψSC   ∆εη η −η and ∆ψη η −η . We then find: ≡ C − N ≡ C − N     Proposition 8 Class identity prevails when the welfare importance of culture and trade are 2 ∆εη 1 relatively low, κ < min ∆εψ a σ, 2 2 σ 1. Cultural identity prevails when the welfare − a − a − 2 h i 2 1 a ∆ψη importance of culture is high relative to trade, κ max ∆εψ a σ, − 2 σ 1. Finally, ∆ψη a b b b ≥ − − − trade identity prevails when the welfare importance ofh trade is high relative toi culture, σ 2 1 1 ∆ψη a ≥ max 2 2 (1 + κ) , 2− (1 + κ) . b b b a ∆εψ a 1 a ∆ψη − − h   i b Identities follow theb salient policies,b as captured by the weights κ and σ. Increasing exposure to trade σ eventually leads to a trade-focused identity, with conflict between the cosmopolitans and the nationalists. Note however that the correlationba > 0 betweenb social conservatism and tradeb exposure implies that moderate increases in σ may trigger a switch from class identity to cultural, and not trade, identity. b Corollary 3 Suppose that identity is class based, and trade exposure σ increases until there is an identity switch. If a > 0, there is a threshold κ∗ such that identity switches to culture for κ > κ∗ and to trade otherwise. b b If theb voters more vulnerable to trade are also socially conservative, a > 0, growing integra- tion into the world economy may favor cultural identity provided cultural policy is suffi ciently salient to begin with. Brewing culture wars can be fueled by a trend in globalization. The intuition is that, when a > 0, the cultural cleavage effi ciently encodes the trade cleavage too. Globalization renders such cultural clash even more salient, favoring identification toward it. If then σ increases further, so that trade exposure becomes very high, conflict switches from culture to trade, as shown in Proposition 8. Regardlessb of whether higher exposure to trade induces a trade-based or cultural identity, it causes similar belief distortions. When identity switches to trade, nationalists naturally exaggerate their exposure to it. By contrast, cosmopolitans become more in favor of open- ness. Crucially, a > 0 implies that the conflict between nationalists and cosmopolitans also enhances the cultural conservatism of losers from trade and reduces class conflict, because the stereotypical loser from trade is socially conservative and middle class. The opposite is true

27 for the cosmopolitans, whose stereotypical member is socially progressive. Similar effects occur if the trade shock causes a switch to cultural identity. In this case, the polarization along cultural issues is stronger, but there is also an effect on trade preferences due to a > 0. These belief adjustments imply that an economic change such as globalization may generate a far reaching change in policy demands.

Proposition 9 An increase in trade exposure σ that causes identity to switch from class to trade or from class to culture increases the covariances between the demands for: (i) redistri- bution; (ii) free trade; and (iii) socially progressiveb policies.

When hit by the adverse trade shock, nationalists (social conservatives) demand higher tariffs, but also less redistribution and less liberal policies. By contrast, the cosmopolitans (social progressives) demand lower tariffs, more redistribution, and more liberal policies. So- ciety is partitioned into two highly conflictual blocks. Due to the identity switch, the demand for redistribution drops among the economic losers and rises among the winners. In many countries, trade and technology shocks have contributed to stagnant wages and increasing inequality. One may have expected economic losers to swell the ranks of left wing parties, but this has not occurred. Our model offers an explanation: these shocks have reduced the cohesiveness of the lower class. Socially conservative poor voters, who traditionally iden- tified with their income class, are now attracted by nationalism or by culturally conservative platforms because they appeal to both their trade preferences and their cultural views. This identity shift also reduces their taste for redistribution. The opposite occurs for voters with opposite political features. Greater income inequality between nationalists and cosmopoli- tans, or between socially conservatives and socially progressives, renders this effect stronger by making these alternative cleavages even more effi cient carriers of conflict.

5 Some Evidence

We explore the empirical validity of some of our predictions using US survey data. We consider the effect of higher salience of cultural conflict (parameter κˆ), and of trade conflict (parameter σˆ). These broad themes have already been studied. Since Hunter (1992), there is debate over the growing importance of cultural conflict in the US. Autor et al (2017) show that trade shocks fostered political conservatism in the US. Here we look at these patterns in a new way, focusing on the specific implications of our theory. With respect to cultural conflict, we show that our theory can rationalize patterns in survey data, without attempting to address causality. For trade conflict, we build on the IV strategy of Autor et al. (2017).

28 5.1 Cultural Conflict

Figure 1 plots the share of respondents in a repeated Pew survey that report a certain issue as one of the three most important ones facing the US, between 2001 and 2018. The importance of race and immigration has soared after 2012, well ahead of Trump’sentry into politics.21

Figure 1

As the salience κˆ of cultural conflict on immigration and race increases, our theory predicts that voters switch to cultural identity, and this exerts three effects. First, it divides voters into opposing clusters that exhibit strong cultural conflict. Second, it changes the associa- tion between different policy views, increasing the correlation between economic and cultural progressiveness. Third, these changes should be systematically related to demographics that capture cultural attitudes. Policy disagreement among cultural groups should increase in all domains, while disagreement over redistribution among voters belonging to different economic classes should dampen. To assess these predictions, we use survey data from the American National Elections Studies (ANES) for five waves during 2000-2016 (one every four years).

5.1.1 Changing Dimensions of Political Conflict

We start by asking whether the increasing importance of immigration and race enhances cultural conflict relative to economic conflict. Based on Figure 1, we divide the sample in two subperiods: 2000-2008 and 2012-2016. For each subperiod, we assign respondents to one of two clusters using the K-means algorithm: the two groups are formed so that the within-group variance of voters’opinions on race&immigration and on redistribution is minimized. Clusters identify the prevailing social cleavage, as captured by a crude partition of society into its two most cohesive groups. We measure voters’views on immigration and race, q in our model, using the first poly- choric principal component of opinions on immigration and race relations. We measure voter’s views on redistribution, τ, by the first polychoric principal component of two questions on the desired size of government spending and redistributive policies. To remove the effect of party positions and demographics, we perform this exercise on the residuals of each of the two princi- pal components by conditioning (over the whole period) on wave fixed effects, the respondent’s race, gender, age and age squared, and on dummy variables for whether he identifies with the

21 To highlight long term trends, we omit "the state of the economy" and "unemployment", which folow the business cycle (esp the Great Recession), and national security. Cultural issues were prominent also in the early 2000s. As emphasized by Abramowitz (2019), this is likely to reflect a continuing decline of the share of white voters over the eligible population, due to immigration from Asia and Latin America and a higher fertility of non-white. According to ANES data, (in 2016) nonwhites made up 39% of eligible voters under the age of 30, compared with only 17% of eligible voters over seventy" - Abramowitz (2019, chp 1).

29 Democratic or the Republican party interacted with wave fixed effects. Thus, any change in the data occurs on top of changing party positions that uniformly influence party members. The resulting variables have been normalized so that they all have unit variance during the whole period 2000-2016. The Appendix provides more detail. Figure 2 reports the results. The left hand panel refers to 2000-2008, the right hand panel to 2012-2016. The vertical axis measures attitudes towards immigration and race (higher val- ues correspond to more progressive attitudes), the horizontal axis attitudes on redistribution (higher values correspond to more desired spending). Each dot is an individual.

Figure 2

In the first subperiod, clusters are defined by economic conflict. There is a blue (light) cluster of voters supportive of large government, and a red (dark) cluster of voters supportive of small government. Within each cluster there is large variation of cultural views. The change in 2012 is stark. Relative to the first subperiod, the line demarcating the two groups has rotated counter-clockwise by 45 degrees. The blue group still prefers a larger government but is now much more culturally progressive than the red group, as it takes on higher values along the vertical axis relative to the latter. Cultural conflict has become a key cleavage, intertwined with redistributive conflict.22 By Proposition 9, an identity switch from class to culture is accompanied by sharper con- flict over several issues, not just immigration and race. Is there evidence of this in the data? To answer this question, Table 1 looks at the average cluster positions on abortion (an alter- native measure of q), trade policy, t, and trust in government (an indicator of universalism). We also report the average cluster position on the policy issues used to estimate the clusters (race, immigration and redistribution), as well as political affi liation.

Table 1

Two patterns stand out. First, as expected given Figure 2, conflict between the red and blue clusters increases in immigration and race, while it mitigates on redistribution. Second, and importantly, the red cluster, which is more "rightwing", becomes much more anti-abortion and less trusting in the government. The opposite occurs in the blue cluster. In trade, there is a striking reversal: the blue cluster switches from being less favorable to being more favorable to

22 This finding speaks to a longstanding debate on US politics. Starting from the late 1990s, several scholars argued that cultural conflict had become the marker of divisions between Republicans and Democrats. Other scholars, while admitting the growing importance of culture, showed that economic conflict was still predom- inant (e.g., Ansohlabere et al. 2006). Figure 2 shows that both views are partly right. Despite the growing importance of cultural conflict, until 2008 the leading source of disagreement was economic. Starting from 2012, however, things have changed: cultural conflict has become key.

30 free trade than the red cluster. Cultural conservatism and nationalism are associated. Because we control for partisanship, there is no tangible difference in political affi liation across the two clusters. Thus, the clusters capture broad divisions in the population that are not simply accounted for by partisanship. The red cluster has also become smaller in size. In our theory, changes in the clusters are produced by changes in the socioeconomic compo- sition of their members. Under class identity, conflicting groups are defined by their economic features (rich vs poor). Under cultural identity, they are defined by their cultural traits (pro- gressive vs conservative). To explore whether this is the case, we ask which individual feature, economics or culture, best predicts how voters are assigned to each cluster. We measure a voter’seconomic traits by extracting the first polychoric principal component from a respon- dent’s income group and his self defined social class - we call it Economics. We measure a voter’s culture by extracting the first polychoric principal component from educational at- tainment and an indicator of religiosity (Enke et al. 2019a documents that more religious individuals are less universalistic) - we call the resulting measure Culture. The Appendix shows that each principal component is strongly correlated with the variables from which they are extracted. Higher values indicate richer and higher class (resp. more educated and secular) individuals. Both variables have been standardized by demeaning and dividing by their standard deviation. We then use these two first principal components to predict cluster membership. The results are reported in Table 2 (estimation is by probit).23

Table 2

During 2000-2008, "blue membership" is correlated with Economics: "blue" respondents are more likely to be poor, and there are no tangible difference in cultural traits. During 2012- 2016, blue membership is correlated with Culture: it goes to voters who are more secular and educated. Cluster membership has remained similar in terms of political affi liations, so these changes do not reflect changing party compositions. The predictive power of these variables is small, however. The estimated marginal effect of Economics on the blue cluster in the first period is 6 ppt, that of Culture in the second period is 8 ppt - recall that both variables have a standard deviation of 1 over the whole sample period. This evidence does not prove that there has been an identity switch but it detects two key symptoms of it, namely: (i) the formation of opposing clusters that more starkly disagree on several cultural and trade related issues and (ii) a (weak) redefinition of the membership criteria of opposing groups, from economic to cultural.

23 Results are similar if, rather than the principal components Economics and Culture, we use all the variables from which they are extracted, except that collinearity between some of these variables makes it more diffi cult to interpret some estimated coeffi cients.

31 5.1.2 Coherence of Conflict

Consider next the changing association between policy views. Our theory builds on an under- lying positive correlation, created by progressive culture, between liberal views on civil rights, openness to immigration and trade, and taste for redistribution. The model then predicts that, when cultural conflict becomes salient, such cultural factor becomes a stronger determinant of policy preferences, increasing the positive association in measured opinions. To assess whether this is the case, we implement factor analysis on policy views for 2000- 2008 and 2012-2016 separately. Factor analysis seeks to explain the co-movement of a set of correlated variables by modelling them as combinations of few orthogonal latent components (plus an error term). The first factor accounts for the largest share of common variation of policy views. The second factor best accounts for the remaining variation, conditional on being orthogonal to the first one. Again, policy preferences are the estimated residuals after conditioning on race, gender, age and age squared, wave fixed effects alone and interacted with party affi liations. Table 3 reports the variance accounted by each of the two factors and their loadings, namely the magnitude and direction with which each factor predicts policy views.24

Table 3

There is a similar structure across periods. One factor, which we call "F ", loads negatively on redistribution, positively on immigration and trade openness, with negligible effect on race and abortion. This factor resembles standard economic conflict: richer voters stand for trade openness, immigration and oppose redistribution, and viceversa for poor voters. The other factor, which we call "CF " (or Correlated Factor), loads positively on all policy views. It resembles cultural conflict in our model: socially progressive voters stand for trade openness, immigration, redistribution and civil rights, and viceversa for socially conservative voters.25 Crucially, in the period 2012-16, the potency of the CF factor strongly increases: it explains

24 The factor extraction method that we adopt (iterated principal axis factoring) relies on spectral decom- position of the policy views correlation matrix, modified to net out the idiosyncratic variability. Loadings are proportional to the matrix eigenvectors, and the share of variance explained by each factor is proportional to the corresponding eigenvalues. We see a clear inflection between the second and third largest eigenvalues, and therefore retain only the first two factors. 25 These factor loadings are consistent with the predictions of the model in section 4 if preferences over immigration also depend on individual income ε (eg., because immigrants compete less with productive people), albeit to a small extent. This feature, which mimics our assumptions for trade openness in Section 4, implies that the rich oppose redistributon but support immigration and trade, and viceversa for the poor. The factor representation holds even if ε and ψ are correlated, ρ > 0. In this case, the factor with lower explanatory power captures the orthogonal component of the less important "structural driver". This rep- resentation implies that when identity switches to culture, the loadings of CF should increase and those of F should become smaller in magnitude, which is consistent with Table 3. Our model admits an exact linear factor structure under the added assumption that the strength of belief distortions linearly increases for voters with more extreme ε and ψ.

32 a larger share of the variance in policy views, and its loads increase for almost all policy issues. The effect is particularly dramatic in the case of redistribution. Our model explains these facts through a switch from class to cultural identity, which increases the importance of the "cultural factor" for explaining policy views across domains. To see whether this interpretation has bite, we check whether factors can have an eco- nomic vs culture interpretation. To do so, we regress the factor scores F and CF on the principal components Economics and Culture described above. Consistent with capturing cultural preferences, CF is explained almost exclusively by the variable Culture, extracted from education and religiosity, especially in the second period. Conversely, factor F is ex- plained primarily by Economics, extracted from the respondent’s income and social class. To ease interpretation we standardize factors and independent variables in each of the two periods. In 2012-2016, a one-standard deviation increase in Economics is associated with a 28% increase in the standard deviation of F , and the effect of Culture on CF has about the same magnitude. Estimates are fairly stable across periods.

Table 4

This evidence is thus consistent with a switch from class to cultural identity. Gentzkow (2016) documents that individual positions of self declared Democrats and Republicans have become more correlated across issues. We show that this trend holds generally, also controlling for party affi liation, and that it may be due to growing importance of cultural values.

5.1.3 Group Conflict

Finally, consider the implications of our theory for policy disagreements among different socio- economic groups. As identity switches from class to culture, the lower class reduces its demand for redistribution and the upper class increases it, reducing class conflict over it. On the other hand, conflict between cultural groups rises: the social progressives demand more civil rights, immigration, free trade and redistribution, and trust the government more, compared to the social conservatives. To assess these predictions, we sort respondents according to their: a) social class (upper and upper-middle vs low), b) education (graduate vs the rest), and c) religiosity (secular vs religious). Division a) stands for traditional economic groups. Divisions b) and c) are alternative cultural partitions. Figure 3 reports the changing preferences of these groups - each panel corresponds to a social group - over: (i) redistribution, measured by the first polychoric principal component of the questions already used in the cluster analysis; (ii) globalization, measured by the first polychoric principal component of views on immigration and free trade (higher values corre-

33 sponding to more favorable views); (iii) civil rights, measured by the first polychoric principal component of views on abortion and race (higher values are more progressive); (iv) low trust in government, measured by a dummy variable for whether the respondent is in the first quin- tile of the overall distribution of trust in government for that year. We analyze immigration and trade together in (ii) because these policies have tangible and similar economic effects, whereas categories (iii) and (iv) are by and large cultural. Once again, these variables are the estimated residuals after conditioning (over the whole period) on wave fixed effects, the respondent’s race, gender, age and age squared, and on dummy variables for whether he identifies with the Democratic or the Republican party in- teracted with wave fixed effects. Figure 3

The patterns are broadly consistent with our model. Traditional redistributive conflict dampens. The lower class reduces its demand for redistribution, while demand by the upper class increases, as expected. The fraction of individuals who distrust the government increases in the lower class, but not in the upper classes. Class conflict over civil rights increases, that over globalization does not. These ambiguous effects are consistent with Proposition 6. By contrast, conflict between cultural groups increases in all dimensions. The social pro- gressives (be they secular or graduates), increase their demand for redistribution, conservatives reduce it. Fight over redistribution has switched from the economic to the cultural domain. Accordingly, cultural groups diverge also in terms of their distrust in government. Conflict over civil rights and globalization also increases. Common explanations of growing cultural divisions and increased correlation in policy views stress the role of parties. In one view, extremists increasingly sort into parties over time (e.g. Fiorina and Abrams 2008). In another view, party elites emphasize cultural issues to attract certain voters who strongly care about them, and then these voters align with party positions on economic issues, which they are little informed about (eg. Achen and Bartel 2016, Klein 2020). These views are not easy to reconcile with our evidence. Sorting explains growing extremism among partisan voters, but does not explain the population-wide changes in preferences we document here. Party persuasion explains why conservatives (progressives) may follow the Republican (Democratic) party’slead on economic issues, but does not explain why their cultural preferences become more extreme. The persuasion argument is also hard to reconcile with the growingly progressive attitudes of highly educated voters, who are arguably more informed than the less educated ones, and yet become much more progressive over time in their preferences across all issues. More broadly, political supply effects or even effects based on party identity have a hard time explaining why these patterns do not depend on party affi liation. Recall that in all our exercises we control for time varying effects of party affi liation, and Figure A1 in the

34 Appendix shows that the results are similar when we only look at non-politically identified voters. A second broader challenge faced by supply side stories is that similar patterns are observed in other advanced democracies, with very different institutions and party systems. Evans and Mellon (2016) document how, in the British Social Attitudes survey of 2015, the UK working class has become more similar to other income groups with regard to views on inequality and redistribution, but it is predominantly more conservative on immigration, the death penalty, homosexuality and morality. Identity-switches induce belief distortions that can instead explain these patterns.

5.2 Exposure to Import Competition

We documented increasing correlation of policy views and changing disagreement among socio- economic groups, without linking these patterns to sources of identity switch. Here we estab- lish such link with shocks induced by globalization. We consider the effects of exposure to Chinese imports, measured as in Autor et al. (2017). Relative to them, we separately look at policy views in different domains and consider more specific predictions of our theory.26 We measure policy views using the CCES survey, an online US survey conducted between 2006 and 2016, with 36,000 respondents per year on average. One advantage over ANES is that for a subset of 9,500 respondents there is a panel dimension over 2010-14, so we can check whether the opinions of the same respondent changed over time. We look at the two policy indicators available both in the cross section and the panel: preferences for redistribution and, as indicator of social progressiveness, preferences over immigration. Both variables are measured by extracting the first principal component from two questions on government spending and taxation and on border control and illegal immigrants, respectively (see the Appendix). Unfortunately the CCES survey does not ask about trade policy. As in Autor et al. (2017), import exposure is measured as the change in imports from China.27 This variable varies at the commuter zone (CZ) level, and we have about 60 respon-

26 The main outcome variables in Autor et al. (2017) are different voting variables (in the elections and in the US Congress). They also look at indicators of conservative ideology from surevy data, obtained by aggregating 10 questions in the Pew (2014) survey; the Pew survey lacks the panel dimension and has about 25 respondents per CZ. 27 Autor et al. (2013), which introduces the general methodology, measure the change in import exposure in each CZ by the average change in Chinese import penetration in the CZ’s industries, weighted by each industry’sshare in the CZ initial employment. Thus, the change in export exposure in CZ c is defined as:

Lm,c,00 Im,t2 Im,00 ∆IPc = − (28) Lc,00 × Ym,91 + Im,91 Xm,91 m M X∈ − where the first term in summation is the share of manufacturing industry m in total employment of CZ c, while the second term is the increase in US imports from China of products typical of m between 2000 and year t2, standardized by m’smarket size in 1991 (i.e, prior to the boom in China’sexports). Since the change in penetration is likely to be endogenous, imports are instrumented as in Acemoglu et al (2016), in a way

35 dents per CZ and year on average (15 in the panel analysis). US imports from China grew fast before the start of our sample. Thus, in the repeated cross section we measure the change in import exposure between 2000 and 2016. This amounts to assuming that the full effect of imports on identity entails a lag. As in Autor et al. (2017), we instrument the CZ change in Chinese imports with the change in Chinese imports to Europe over the same period. In our model, exposure to Chinese imports proxies for average employability in the import competing sector in the CZ, the variable σ (1 η) in Section 4. By construction, CZs that have − become more exposed to Chinese imports ("more exposed CZs" henceforth) have increased the share of employment in import competing industries (the probability σ of being employed in these industries is higher for everybody). In addition, in more exposed CZs many voters are specialized in import competing activities and hence they are losers from trade (η is low). By the analysis of Section 4, the shift to cultural or trade-identity should be more pro- nounced in more exposed CZ (since σ has increased more in these CZ than elsewhere).28 The first prediction of our model, then, is that the average resident in a CZ that has be- come more exposed should become more nationalist, because he is a loser from trade and - if trade exposure is more positively correlated with culture than with income - also more socially conservative. As class identity is abandoned, redistributive conflict should dampen: the poor demand less redistribution, the rich become less opposed to it. If the upper class are a minority, the first effect should prevail, and on average the CZ should demand less redistribution. We first verify that, in line with Section 4, people who are more exposed to import compe- tition (σ (1 η) is higher) are also more conservative, but not systematically richer or poorer. − The CCES data support this assumption: respondents in more exposed CZs are on average less educated and more religious, but their income is uncorrelated with imports exposure. Thus, the "China shock" qualifies as a possible driver of a switch to cultural or trade identity.29 We test our predictions by estimating two sets of regressions. In the repeated cross sections individual attitudes are sampled in the first (2006-7) and last (2016) year of our sample - the

EU EU similar to Author et al (2013). The instrument is obtained by replacing (Im,t2 Im,00) with (Im,t2 Im,00), namely the increase of Chinese imports in eight European countries over the same− period, and all the− other terms in (1) with their values in 1988. 28 The average CZ is composed by a mix of export oriented communities, for which globalization is also salient, and of communities that are little affected by trade where globalization is not salient. Thus, in CZs that have become more exposed, globalization has become more salient than in the average community. 29 We explore conditional correlations with a regression at the CZ level. The dependent variable is the increase in Chinese imports, ∆IP00 16. The covariates, measured at the beginning of the CCES sample period, are the CZ’s share of respondents− who have some college education, college education or more, who are secular, and the respondent’saverage income. The results are (standard error in parenthesis, asterisks denoting significance levels):

∆IP00 16 = 1.533∗∗∗ 0.915∗∗∗somecollege06 0.789∗∗∗collegemore06 0.618∗∗secular06 + 0.002income06. − (0.283) − (0.249) − (0.293) − (0.267) (0.003)

36 initial year varies by question. We use the same specification as in Autor et al. (2017), namely:

yi,c,t = β ∆IPc d2 + X0 (β + β d2) + Z0β d2 + β d2 + αc + ui,c,t, 0 ∗ i,c,t 1 2 ∗ c 3 ∗ 4

where i denotes the respondent, c the CZ and t = 1, 2 the year, yi,c,t measures individual

attitudes in year t, ∆IPc is the instrumented increase in import exposure in the CZ, d2 is a

dummy variable that equals 1 in the second period. The coeffi cient of interest is β0, which measures the change of average attitudes in CZs that have become more exposed . We control

for individual covariates Xi,c,t (gender, race, educational attainments, age, age squared and 30 interest in politics), for CZ covariates Zc in year 2000, and CZ fixed effects (αc). In the panel data, we estimate the following specification:

∆yi,c = β0∆IPc + Xi,c,0 1 β1 + Zc0β2 + ui,c,

where ∆yi,c measures the change in attitudes between 2010 and 2014. Again, the coeffi cient of

interest is β0, which measures the change in opinions of the average resident in more exposed

CZs. We again control for respondent demographics and on his initial attitudes in 2010 Xi,c,1 ,

for CZ characteristics Zc (which do not include mean reversion controls, they are already

captured by Xi,c,1), plus a dummy variable for those who changed CZ between 2010 and 2014,

as well as the interaction of this dummy with ∆IPc. Results (available upon request) are robust to also controlling for initial party affi liation, religiosity, and income of respondents. Note that variation in attitudes is measured over a relatively short period (five years). Since identities and opinions are likely to change slowly over time, this is a demanding exercise. Table 5 reports the estimates for the two specifications, with and without covariates for the CZ. Estimation is by 2SLS and standard errors are clustered at the CZ level. The first two columns refer to repeated cross sections, the others to panel data.

Table 5

Both in the cross section and the panel, residents of more exposed CZs become less fa- vorable to redistribution and more averse to immigration. The effect is not always significant at conventional levels, but it is always directionally as predicted. In line with our model, in- creased cultural conservatism on immigration goes hand in hand with increased conservatism

30 As in Autor et al. (2017), the vector Zc includes the manufacturing share of employment, the offshorability and routine task indexes of Autor and Dorn (2013) and the interaction between the county vote share for GW Bush in the presidential election an a dummy for Republican victory in that county, all variables meaured in 2000. Inclusion of these variables is important for identification, given the nature of the instrument defined in the previous footnote. As in Autor et al. (2017), Zc also includes the CZ average of the dependent variable measured at t = 1, y¯c,1, to allow for mean reversion of attitudes over time.

37 on public spending and taxation.31 Our theory additionally predicts changing disagreement within CZs across different groups. Compared to communities in which globalization is less salient, in CZs that have become more exposed: (i) disagreement of income classes over redistribution should dampen, because the lower class demand less redistribution, the upper class accept more of it, and (ii) the social conservatives become more extreme in their views on all issues: as they identify with their cultural group (or based on the correlated trait of nationalism), they become more averse to immigrants and to redistribution.32 Unfortunately, we cannot measure individual level trade exposure, so we cannot test the model’spredictions for different trade groups. To test these predictions, we estimate the same regressions of Table 5, but interact the import shocks with dummy variables for different socio-economic groups (and include these dummy variables as separate regressors). Lower-class is measured by a dummy that equals 1 if the respondent is in the bottom 33% of the (national) income distribution of the CCES sample. For cultural groups, we use a dummy that equals 1 if the respondent is secular rather than religious. Both dummy variables are measured at the beginning of the sample period (2010). We only estimate these interacted regressions in the panel, which allows us to see how a voter’s beliefs change depending on his initial socio-economic status. Of course these are demanding regressions, since we only have an average of 15 respondents per CZ. Table 6 reports the estimates. In columns (1)-(4), the dependent variable is preferences for redistribution. In columns (5)-(6), it is aversion to immigrants. Consider first columns (1) and (2). As predicted, in the CZs that have become more exposed, demand for redistribution by poor voters has decreased more compared to the middle and upper classes, and the difference is economically significant: a change in exposure by one standard deviation implies a reduction of the gap in demand for redistribution between the bottom 33% and the rest of the population by about 50% of its mean level across CZs in 2010. The estimated coeffi cient on import exposure non-interacted (i.e. for middle and upper class respondents) is negative and significant at the 10% level in column 2, suggesting that these groups too on average demand less redistribution if hit by the trade shock, although the effect is half that on the bottom income group. A possible interpretation is that trade exposure causes even middle and upper class voters identify with

31 According to our panel estimates, an acceleration in import penetration by one standard deviation reduces the willingness to redistribute by 22.5% and increases aversion to immigrants by 15.5%, both relative to the standand deviation of the change of mean attitudes across CZs between 2010 and 2014. The magnitude of the estimated effects in the repeated cross sections is smaller: a one-standard-deviation increase in imports exposure changes attitudes by about 8%-13% of a standard deviation of their mean change across CZs. 32 The prediction of how social progressives change their views in more exposed communities is not clear-cut. First, in these communities many progressives may also be tied to the import competing sector (recall that the average η is low). As identity switches to trade, they may become nationalists, so their belief move in opposite direction to those of social progressives not tied to the import competing sector. Second, identity may be partly geographic, and an increase in imports in the CZ may induce even winners from trade to identify with their losing local community, which would also dampen disagreement between cultural groups.

38 their local community, making them more conservative compared to other CZs. There is no evidence that the shock affects differentially the demand for redistribution of secular vs religious respondents (columns 3 and 4 of Table 6), although the estimated effect for religious people is more negative than for the whole population. However, trade exposure amplifies the aversion to immigrants of religious people, whereas it does not affect the aversion of secular respondents (the two estimated coeffi cients in columns 5 and 6 roughly cancel out). Thus, in more exposed CZs conflict over immigration between religious and secular is amplified. This is a prediction of our theory if the shock induces the social conservatives to identify with their cultural group or with the nationalists. Also in this case, the effect is relatively large: an acceleration in import penetration by one standard deviation causes an increase in polarization between secular and religious people by 20% of its CZ mean level in 2010. All the results are robust (in terms of size and statistical significance) if we study heterogeneity by running different regressions on the subsamples coorresponding to each of the cultural and income groups, instead of using interactions. Thus, as predicted by our model, demand for redistribution by the lower class drops relative to the upper and middle class, while socially conservative traits are amplified by exposure to the shock. Table 6

A possible concern with these estimates is that the period over which we measure policy attitudes (2006-2016 in the cross section, 2010-14 in the panel) overlaps with another major economic shock, the 2007-2008 financial crisis, which could act as confound. As a first step, note that in our theory not all economic shocks should induce a switch to cultural or trade identity. Trade shocks do so because they hit social conservative groups, but the financial crisis also hit urban financial centers, so it is not obvious that it could have the same effect.33 Neverthless, Tables A5 and A6 in the Appendix show that the results of Table 5 are robust to also controlling for the intensity of the financial crisis, measured as the change in net worth in a CZ (Mian and Sufi 2014). The magnitude and direction of the estimates of Table 6 are also robust, but we lose statistical significance. This may be due to the smaller sample, since this shock is missing for the county of residence of roughly 1500 panel participants. Overall, the results are broadly consistent with the predictions of our model. An adverse shock that, like trade, is correlated with an existing cultural cleavage causes voters to abandon class identity in favor of cultural (or trade based) identity. As a result, class conflict dampens and culture becomes a more important driver of political preferences.

33 Consistent with this notion, the increase in housing net worth (i.e. the reverse of the financial shock) is positively correlated with our imports shock variable, although the correlation coeffi cients are not large (about 0.15 in both the panel and the cross section). This is reassuring: the financial crisis was if anything less severe in the CZs more adversely affected by globalization.

39 6 Concluding Remarks

Identity theory provides a rich and promising framework. We have shown how it can ex- plain systematic distortions in political beliefs, actual and perceived polarization, causes and consequences of changing political cleavages, the effects of trade or technology shocks. The general idea is that political conflict builds on a set of latent social groupings, char- acterized along economic and cultural traits, and representing demands that are more or less correlated across different issues. A well known grouping, of course, is income or wealth based. But ascriptive groups have also played a role historically, for instance emphasizing culture, geography, or race. As political cleavages change, voter switch their identification from their income class, to their cultural, geographical, or racial group. Crucially, while the switch may be driven by the political issue of the day, it influences beliefs across the board, because different social groups cut society in clusters of interests along many domains. Here we explored some key implications of this approach, but much more remains to be done. An important set of issues concerns the role of media or politicians in rendering socioeconomic identities salient. New digital media, such as Twitter or Facebook, may enhance stereotypical thinking because they focus attention on simple and forceful messages at the expenses of more nuanced policy debates. Disintermediation of the traditional media may favor leaders that reach out to voters with emotional and symbolic messages that appeal to their identities. At the same time, identities also form in spontaneous interactions with neighbors or friends sharing similar problems. Exploring the mechanisms for the diffusion of identities is an important topic for future empirical research. Consider next the political supply side. One natural question is about persuasion. Which dimensions of conflict ought to be emphasized by vote maximizing politicians? More impor- tantly, how can voters be induced to identify with leaders, and which features make them more appealing to voters? Marxist thinkers such as Gramsci stressed the role of the commu- nist party and of intellectuals in fostering class awareness. Nation builders such as Bismarck used nationalism to mobilize support, and the Catholic Church promoted identity politics on the basis of religious values. Sometimes political leaders may even create new, party-based identities that supersede traditional social groups. Glaeser (2005) analyses how a leader can mobilize voters by spreading messages of hatred against a minority group, while Gleaser et al. (2015) discuss how a party can energize its supporters by taking more extreme positions. We think that our demand side approach may be useful to understand these and other supply side aspects. For instance, our results suggest that it may be easier to identify with politicians that impersonate group stereotypes. This implies that, when polarization is strong, the most successful politician is likely to come from the tails, not the middle. This may help explain why successful populist politicians often look similar to the unskilled and unexperi- enced labor market outsiders that voted them in offi ce (see in particular Dal Bo et al. 2018).

40 A final set of questions concern the evolution of party systems. Under what circumstances does a party represents a single identity group, and when does it instead act as an ensemble of heterogeneous social identities? The US Republican party seems to represent those on the right that identify along the income dimension, and the social conservatives that identify on culture, while the Democrats get the opposite groups in each dimension of identification. But this has changed at the time of major political realignments. How do party realignments interact with social identities, and how do political and social identities influence each other? More generally, what is the role of political leaders in this process? We believe that exploring the interaction between demand and supply factors within the framework of identity theory opens up a new and exciting research agenda.

7 References

Abramowitz, Alan I., and Kyle L. Saunders (2008). "Is polarization a myth?." The Journal of Politics 70.2: 542-555. Abramowitz, Alan I. (2019), "The Great Alignment - Race, Party Transformation and the Rise of Donald Trump", Yale University Press, New Haven Acemoglu, Daron, David Autor, David Dorn, Gordon H. Hanson, and Brendan Price (2016), “Import Competition and the Great US Employment Sag of the 2000s”, Journal of Labor Economics, 34(S1), S141-S198. Achen, Christopher H., and Larry M. Bartels (2016), Democracy for Realists: Why Elec- tions Do Not Produce Responsive Government, Princeton University Press. Ahler, Douglas (2018) "The Group Theory of Parties: Identity Politics, Party Stereotypes , and Polarization in the 21st Century", The Forum, 16 (1): 3-22. Akerlof, George , and A. Rachel E. Kranton (2000), “Economics and Identity”, The Quar- terly Journal of Economics, Volume 115, Issue 3, 715—753. Alesina, Alberto and Edward Glaeser (2005), Fighting Poverty in the US and Europe: A World of Difference, Oxford UP, Oxford. Alesina, Alberto, Elie Murard and Hillel Rapoport (2019) "Immigration and Preferences for Redistribution in Europe" Harvard, mimeo Alesina, Alberto, Stefanie Stantcheva, and Armando Miano (2018a), “Immigration and Redistribution”, mimeo. Alesina, Alberto, Stefanie Stantcheva, and Edoardo Teso (2018b), “Intergenerational Mo- bility and Preferences for Redistribution”,American Economic Review 108.2 (2018): 521-54 Anelli, Massimo, Italo Colantone, and Piero Stanig (2018), “We Were The Robots: Au- tomation and Voting Behavior in Western Europe”, mimeo.

41 Ansolabehere, Stephen, Jonathan Rodden, and James M. Snyder Jr. "Purple america." Journal of Economic Perspectives 20.2 (2006): 97-118. Atkin, David, Eve Colson-Sihra, and Moses Shayo. How do we choose our identity? a revealed preference approach using food consumption. No. w25693. National Bureau of Economic Research, 2019. Autor, David (2019) "Work of the Past, Work of the Furture", American Economic Review Papers and Proceedings. 109: 1-32 Autor, David, David Dorn and Gordon Hanson (2013), “The China Syndrome: Local Labor Market Effects of Import Competition in the United States”, American Economic Review, 103(6), 2121-68. Autor, David, David Dorn, Gordon Hanson and Kaveh Majlesi (2017), “Importing Political Polarization? The Electoral Consequences of Rising Trade Exposure”, MIT Working Paper. Bénabou, Roland, and Jean Tirole (2011), “Identity, Morals, and Taboos: Beliefs as As- sets”, The Quarterly Journal of Economics, Volume 126, Issue 2, 805—855. Bénabou, Roland, and Jean Tirole (2016), “Mindful Economics: The Production, Con- sumption, and Value of Beliefs”, Journal of Economic Perspectives, 30(3), 141-164. Bordalo, Pedro, Katherine Coffman, Nicola Gennaioli, and Andrei Shleifer (2016), “Stereo- types”, The Quarterly Journal of Economics, 1753—1794. Bordalo, Pedro, Katherine Coffman, Nicola Gennaioli, and Andrei Shleifer (2018), “Beliefs about Gender”, American Economic Review, forthcoming. Bordalo, Pedro, Marco Tabellini and David Yang (2019) "Stereotypes and Politics", mimeo, Harvard Calderon, Alvaro, Vasiliki Fouka and Marco Tabellini (2019), "Racial Diversity, Electoral Preferences, and the Supply of Policy: the Great Migration and Civil Rights", Harvard Uni- versity, mimeo Campbell, Angus, Philip E. Converse, Warren E. Miller, and Donald E. Stokes (1960), The American Voter, The University of Chicago Press. Card, David, Christian Dustmann, and Preston Ian (2012), "Immigration, Wages, and Compositional Amenities", Journal of the European Economic Association, 10, 78-119. Colantone, Italo and Piero Stanig (2017) "The Trade Origins of Economic Nationalism: Import Competition and Voting Behavior in Western Europe" , American Journal of Political Science, forthcoming Colantone, Italo, and Piero Stanig (2018a), “The Economic Roots of the Cultural Backlash: Global Competition and Attitudes in Western Europe”, mimeo Colantone, Italo, and Piero Stanig (2018b), “Global Competition and Brexit”, American Political Science Review, 112(2). Cruces, Guillermo, Ricardo Perez-Truglia, and Martin Tetaz (2013), "Biased Perceptions of

42 Income Distribution and Preferences for Redistribution: Evidence from a Survey Experiment", Journal of Public Economics, 98, 100-112. Dal Bó, Ernesto, Frederico Finan, Olle Folke, Torsten Persson, and Johanna Rickne (2018), “Economic Losers and Political Winners: Sweden’sRadical Right”, mimeo. Diermeier, Daniel, and Christopher Li (2018), “Partisan Affect and Elite Polarization”, American Political Science Review, 1-5. Druckman, James N., and Matthew S. Levendusky. "What do we measure when we measure affective polarization?." Public Opinion Quarterly 83.1 (2019): 114-122. Enke, Ben (2018), “Moral Values and Voting”, mimeo. Enke, Ben (2019), “Kinship, Cooperation, and the Evolution of Moral System”, Quarterly Journal of Economics, forthcoming. Enke, Ben, Ricardo Rodriguez- Padilla and Florian Zimmerman (2019a), "Moral Univer- salism:Measurement and Heterogeneity", Harvard University, mimeo Enke, Ben, Ricardo Rodriguez- Padilla and Florian Zimmerman (2019b), "Moral Univer- salism and the Structure of Ideology", Harvard University, mimeo Evans, Geoffrey and Jonathan Mellon (2016), "Social Class - Identity, awareness and political attitudes: why are we still working class", British Social Attitudes 33, NatCen Social Research 33rd Annual Report Festinger, Leo (1950), “Informal Social Communication”, Psychological Review, 57, pp. 271- 282. Fiorina, Morris P., and Samuel J. Abrams. "Political polarization in the American public." Annu. Rev. Polit. Sci. 11 (2008): 563-588. Flynn, D. J., Brendan Nyhan and Jason Reifler (2017) "The Nature and Origins of Misper- ceptions: Understainding False and Unsupported Beliefs about Politics", Advances in Political Psychology, 38, 1, pp. 127-150 Frank, Thomas, "What’sthe Matter with Kansas? - How Conservatives Won the Heart of America", Picador, 2007 Fryer, Roland, and Matthew Jackson (2008), “A Categorical Model of Cognition and Biased Decision-Making”, B.E. Journal of Theoretical Economics, 8 (2008), 1—42 Fukuyama, Francis (2018), "Identity - The Demand for Dignity and the Politics of Resent- ment", Farrar, Strauss and Giroux, New York Gennaioli, Nicola, and Andrei Shleifer (2018), A Crisis of Beliefs (Investor Psychology and Financial Fragility), Princeton University Press. Gentzkow, Matthew (2016), “Polarization in 2016”, mimeo. Gidron, Norman, and Peter A. Hall (2017), “The politics of social status: economic and cultural roots of the populist right”, British Journal of Sociology, 68 (S1), S57.S84. Gimpelson, Vladimir, and Daniel Treisman (2018), “Misperceiving inequality”, Economics

43 &Politics, Vol. 30 (1), 27-54. Glaeser, Edward L., Giacomo A. M. Ponzetto, and Jesse M. Shapiro (2005) “Strategic Extremism: Why Republicans and Democrats Divide on Religious Values,”Quarterly Journal of Economics, 120:4, 1283—330 Goodhart, David (2017), "The Road to Somwhere - The New Tribes Shaping British Pol- itics", Penguin Books Graham, Jesse, Brian Nosek, and Jonathan Haidt (2009), “Liberals and Conservatives Rely on Different Sets of Moral Foundations,”Journal of Personality and Social Psychology, 96 (5), 1029. Grigorieff, Alexis, Christopher Roth, and Diego Ubfal. "Does information change attitudes towards immigrants? Representative evidence from survey experiments." Representative Evi- dence from Survey Experiments (2018). Grosfeld, Irena, Alexander Rodnyansky, and Ekaterina Zhuravskaya. "Persistent anti- market culture: a legacy of the pale of settlement after the holocaust." American Economic Journal: Economic Policy 5.3 (2013): 189-226. Guiso, Luigi, Helios Herrera, Massimo Morelli, and Tommaso Sonno (2017), “Demand and Supply of Populism”, EIEF Working Papers Series 1703, Einaudi Institute for Economics and Finance (EIEF). Grossman, Gene M., and Elhanan Helpman (2018), “Identity Politics and Trade Policy”, mimeo. Haidt, Jonathan. The righteous mind: Why good people are divided by politics and religion. Vintage, 2012. Han, J. and Wackman, D. B. (2017), "Partisan Self-Stereotyping: Testing the Salience Hypothesis in a Prediction of Political Polarization", International Journal of Communication, 11, 603—625. Haslam, Alexander S., and John C. Turner (1992), "Context-Dependent Variation in So- cial Sterotyping 2: the Relationship between Frame of Reference, Self-Categorization and Accentuation", European Journal of Social Psychology, 22(3). Hinkle, Steve, and Rupert Brown (1990), “Intergroup Comparisons and Social Identity: Some Links and Lacunae”. In Abrans, D., and M. Hogg (Eds.), Social Identity Theory: Constructive and Critical Advances. New York: Springer Verlag. Hogg, Michael A., and Dominic Abrams (1998), Social Identification —A social psychology of intergroup relations and group processes, Routledge, London. Howe, Irving (1977), "Socialism and America", Harcourt Brace. Hunter, James Davison. Culture wars: The struggle to control the family, art, education, law, and politics in America. Avalon Publishing, 1992. Janky, Bela, (2018), "Salience of Political Identity and Social Attitudes", mimeo, Center

44 for Social Sciences, Hungarian Academy of Sciences. Johnston, Christopher D., Howard G. Lavine, Christopher M. Federico (2017), Open versus Closed: Personality, Identity, and the Politics of Redistribution, Cambridge University Press Kahan, Dan M. (2014), “Climate-Science Communication and the Measurement Problem”, Advances in Pol. Psych., 36, 1-43. Key, V.O. (1955), "A theory of critical elections" Journal of Politics 17:3-18 Klein, Ezra (2020), "Why we Are Polarized", Avid Reader pr Kuziemko, Ilyana, Michael Norton, Emmanuel Saez, and Stefanie Stantcheva (2015), “How Elastic are Preferences for Redistribution? Evidence from Randomized Survey Experiments”, American Economic Review, 105, 4, 1478-1508. Levendusky, Matthew S. (2009). “The Microfoundations of Mass Polarization.”Political Analysis 17 (2): 162—76 Lipset, Seymour Martin and Stein Rokkan (1967) Cleavage structures, party systems, and voter alignments: an introduction, in Lipset S M & Rokkan S, eds. Party systems and voter alignments: cross-national perspectives. Free Press, New York. p. 1-64 Mackie, Diane M. (1986), “Social Identification Effects in Group Polarization”, Journal of Personality and Social Psychology, 84, 932-943. Matejka, Filip, and Guido Tabellini. "Electoral competition with rationally inattentive voters." Available at SSRN 3070204 (2017). Mayhew, David (2004), "Electoral realignments: A Critique of An American Genre", Yale University Press, New Heaven McGarty, Craig, John C. Turner, Micheal A. Hogg, Barbara David, and Margaret S. Wetherell (1992), "Group Polarization as Conformity to the Prototypical Group Member", British Journal of Social Pyschology, 31(1). Mian, Atif, and Amir Sufi (2014), “What Explains the 2007â09 Drop in Employment?”, Econometrica, 82: 2197-2223. Murphy, Kevin M., and Andrei Shleifer (2004), "Persuasion in Politics", American Eco- nomic Review Papers and Proceedings, 94(2), 435-439. Oakes, Penelope J. (1987), “The Salience of Social Categories”, In Turner, J., M Hogg, P. Oakes, S. Reicher, and M Wetherell, 1987, Rediscovering the Social Group: A Self Categoriza- tion Theory, pp. 117-141, Oxford UK: Blackwell. Persson, Torsten, and Guido Tabellini (2000), Political economics —Explaining economic policy, MIT Press. Piketty, Thomas (2018), “Brahmin Left vs Merchant Right: Rising Inequality and the Changing Structure of Political Conflict. Evidence from France, Britain and the US 1948- 2017”, WID.world WP 2018/7. Pierce, Justin R., and Peter Schott (2012), “A concordance between ten-digit US Harmo-

45 nized System codes and SIC/NAICS product classes and industries”, Journal of Economic and Social Measurement, 37, 1-2, 61-96. Rosch, Eleanor (1978), “Principles of Categorization”,In E. Rosch, and B.B. Lloyd (Eds.), Cognition and Categorization, Hillsdale, NJ: Erlbaum, 27-48. Shayo, Moses (2009), “A model of social identity with an application to political economy: Nation, class, and redistribution”, American Political Science Review, 103 (2), 147-174.ù Schickler, Eric (2016), "Racial Realignment: The Transformation of American Liberalism, 1932-1965", Princeton University Press, Princeton Schickler, Eric (2018), "Debunking the Myth that "Identity Politics" is Bad for the De- mocratic Party", Vox, www.vox.com, April 21, 2018 Sherif, Muzafer (1936), The Psychology of Social Norms, New York, Harper. Stancheva Stefanie (2020), "Understanding Tax Policy: How do People Reason?", mimeo Harvard University Sundquist James L. (1983), "Dynamics of the Party System - Alignment and Realignment of Political Parties in the United States ", The Brookings Institution, Washington DC Tabellini, Guido. "The scope of cooperation: Values and incentives." The Quarterly Jour- nal of Economics 123.3 (2008): 905-950. Tabellini, Marco (2018), "Gifts of the Immigrants, Woes of the Natives: Lessons from the Age of Mass Migration", Harvard Business School, mimeo Tajfel, Henri, and John Turner (1979), “An Integrative Theory of Intergroup Conflict”, in W.G. Austin & S. Worcel (Eds.), The Social Psychology of Intergroup Relations, pp. 33-47, Monterey CA: Brooks/Cole. Tajfel, Henri, and A.L. Wilkes (1963), “Classification and Quantitative Judgment”, British Journal of Psychology, 54, pp. 101-114. Tajfel, Henri, M. Billig, R. Bundy, and Claude Flament (1971), “Social Categorization and Intergroup Behavior”, European Journal of Social Psychology, 1, 149-178. Turner, John, Michael A. Hogg, Penelope Oakes, Steven D. Reicher, and Margareth S. Wetherell (1987), Rediscovering the Social Group: A Self Categorization Theory, New York, Blackwell. Tversky, Amos (1977), “Features of Similarity”, Psychological Review, 84, 327-352. Westfall , Jacob, Leaf Van Boven, John R. Chambers, and Charles M. Judd, Perceiving Political Polarization in the United States: Party Identity Strength and Attitude Extremity Exacerbate the Perceived Partisan Divide, Perspectives on Psychological Science 2015, Vol. 10(2) 145—158. Zaller, John R. The nature and origins of mass opinion. Cambridge university press, 1992.

46 Figure 1: Most Important Problem Facing The Country

Notes: The graph shows the share of respondents mentioning the selected issues among the top three most important problems facing the US. Source: Pew Research Center public data. Figure 2: Changing Dimension of Political Conflict: Cluster Analysis

2000-2008 2012-2016 3.5 3.5 2.5 2.5 1.5 1.5 .5 .5 -.5 -.5 Immigration and Race Immigration and Race -1.5 -1.5 -2.5 -2.5 -3.5 -3.5 -3.5 -2.5 -1.5 -.5 .5 1.5 2.5 3.5 -3.5 -2.5 -1.5 -.5 .5 1.5 2.5 3.5 Redistribution Redistribution

Blue Cluster Red Cluster Blue Cluster Red Cluster

Notes: The vertical axes measure attitudes towards immigration and race (higher values correspond to more open attitudes), the horizontal axes attitudes on redistribution (higher values correspond to more desired redistribution), for samples representative of the US adult population in 2000-2008 and 2012-2016. These measures were constructed by first extracting the first polychoric principal component from two sets of questions, one set for each of these two dimensions of political conflict, and then estimating the residuals after conditioning on gender, race, age, age squared an wave fixed effects, alone and interacted with respondents’ party identity. Each marker corresponds to an individual. The colors indicate how respondents were split between two clusters, individuated applying K-Means method on the above-mentioned residuals, for the two periods separately. As initial guess for the group means we use centers obtained applying Ward’s method on the same data. Dashed lines represent group means. See the appendix for the list of questions, the data sources and more information on how the variables were treated before the analysis. Source: ANES Figure 3: Trends in Group Conflict .2 .6 .1 .4 0 .2 -.1 0 -.2 -.2 -.3 2000 2005 2010 2015 2000 2005 2010 2015 Redistribution Globalization L Class U-M/U Class L Class U-M/U Class .6 .2 .1 .4 0 .2 0 -.1 -.2 -.2 2000 2005 2010 2015 2000 2005 2010 2015 Civil Rights Low Trust in Government L Class U-M/U Class L Class U-M/U Class

(a) Class .3 .15 .2 .1 .1 .05 0 0 -.1 -.05 2000 2005 2010 2015 2000 2005 2010 2015 Redistribution Globalization Secular Religious Secular Religious .4 .05 .2 0 0 -.05 -.2 -.1 2000 2005 2010 2015 2000 2005 2010 2015 Civil Rights Low Trust in Government Secular Religious Secular Religious

(b) Religion eiist,adpnl()rprsmasi eetdeuainlgop sm olg n rdae.Suc:ANES by Source: means graduate). reports and (b) college panel (some class), groups middle/upper panel educational upper of variable, selected and quintile squared, each in class For age first means (lower identity. age, the reports class party a in (c) respondents’ social on is panel by with conditioning respondent and interacted means after religiosity, the and in values residuals if alone trends prin- higher are one effects, reports variables measures, polycoric to fixed (a) All three first wave equal government. these and the dummy principal in For race is a trust polychoric gender, is Rights policy. of first Distrust Civil distribution abortion the yearly levels; attitudes. and and the is immigration liberal relations spending Globalization more desired race government to towards e standards; correspond on attitudes policy living questions of trade and two component on jobs cipal of questions citizens’ two component to of principal seeing component polychoric in first role the government’s is Redistribution Notes:

-.2 0 .2 .4 -.3 -.2 -.1 0 .1 2000 2000 Graduate Graduate 2005 2005 Redistribution Civil Rights 2010 2010 Some College Some College (c) 2015 2015 Education

-.3 -.2 -.1 0 .1 -.2 0 .2 .4 .6 .8 2000 2000 Low TrustinGovernment Graduate Graduate 2005 2005 Globalization 2010 2010 Some College Some College 2015 2015 Table 1: Cluster Description

2000-2008 2012-2016 Blue Red Blue Red (Light) (Dark) (Light) (Dark)

Immigration 0.14 0.01 0.43 -0.44 Race Relations 0.10 -0.08 0.43 -0.47 Redistribution 0.77 -0.80 0.48 -0.65 Abortion 0.09 -0.03 0.18 -0.10 Trade -0.08 0.09 0.09 -0.14

Trust in Gvt 0.03 -0.01 0.12 -0.23

Independent 42.6 40.2 40.7 42.4 Republican 24.7 27.6 28.3 27.7 Democrat 32.7 32.2 31.0 29.9

Population share 50.0 50.0 56.3 43.7

Notes: Immigration, Race Relations, Redistribution, Abor- tion, Trade and Trust in Government are cluster means of the residuals obtained after conditioning on age, age squared, race, wave fixed effects, alone and interacted with respon- dents’ party identity. Parties are the share of cluster members identified with each political group. Population share is the share of respondents classified in each cluster. Source: ANES

Table 2: Cluster Membership and Core Demographics

Blue (Light) Cluster 2000-2008 2012-2016

Economics -0.156*** -0.00530 (0.0404) (0.0460) Culture 0.0431 0.203*** (0.0395) (0.0436)

Observations 1,621 1,537

Notes: *** p < 0.01, ** p < 0.05, * p < 0.1. All specifications include controls for age, age squared, gender, race, wave fixed effects, alone and interacted with respondents’ party identity. Estimation by probit. Robust stan- dard errors in parentheses. Source: ANES. Table 3: Factors

2000-2008 2012-2016 F CF F CF

Redistr. Spending -0.63 0.08 -0.53 0.35 Redistr. Assist -0.49 0.13 -0.44 0.25 Immigration 0.13 0.57 0.31 0.73 Race Relations 0.00 0.31 0.08 0.25 Trade 0.18 0.27 0.19 0.22 Abortion -0.08 0.11 0.03 0.19

Share Variance 0.57 0.43 0.42 0.58

Notes: For each of the two periods, we extract two factors from respondents’ attitudes and policy prefer- ences, using the (iterated) principal axis method. At- titudes and preferences are residuals after condition- ing on respondents’ age, age squared, gender, race, and wave fixed effects, alone and interacted with re- spondents’ party identity. Source: ANES.

Table 4: Factors and Core Demographics

2000-2008 2008-2016 F CF F CF

Economics 0.238*** 0.0646* 0.279*** 0.0675* (0.0341) (0.0338) (0.0387) (0.0371) Culture -0.00766 0.275*** 0.0612* 0.289*** (0.0338) (0.0331) (0.0369) (0.0366)

Observations 1,336 1,336 1,197 1,197 R-squared 0.056 0.096 0.096 0.108

Notes: Notes: *** p < 0.01, ** p < 0.05, * p < 0.1. All specifica- tions include controls for age, age squared, gender, race and wave fixed effects, alone and interacted with respondents’ party iden- tity. Estimation is by OLS. Robust standard errors in parentheses. Source: ANES. Table 5: Exposure to Imports from China and Attitudes

Cross Section Panel A. Redistribution ∆ IP ∗ Second Period -0.019 -0.053** (0.018) (0.026) ∆ IP -0.034 -0.166** (0.037) (0.070)

Observations 66,308 66,308 7,244 7,244 F on Excluded Instruments 67.05 26.84 56.50 39.90 B. Immigration ∆ IP ∗ Second Period -0.024** -0.035 (0.012) (0.025) ∆ IP -0.083*** -0.126** (0.031) (0.058)

Observations 71,649 71,649 9,408 9,408 F on Excluded Instruments 74.66 30.74 52.92 37.47 CZ Controls NO YES NO YES Notes: *** p < 0.01, ** p < 0.05, * p < 0.1. For each commuter zone (CZ), the change in penetration refers to the period between 2000 and 2016 in the cross section and between 2004 and 2014 in the panel. In the cross section, the dependent variables are measured in the following pairs of years: 2006 and 2016 (Panel A); 2007 and 2016 (Panel B). In the panel, all dependent variables are first differenced and all regressions include a control for the level of the dependent variable in 2010 (the specification is mathematically equivalent if we use the level in 2014 as dependent variable). All specifications include demographic controls for gender, a quadratic of age, educational attainment, race, interest in politics (in the cross section they are interacted with the second period dummy, while in the panel they refer to the first period; interest in politics is not available in 2006); the cross section also includes the CZ mean of the dependent variable in the initial period interacted with a dummy variable for the second period; the panel also includes a dummy variable for those who changed CZ between 2010 and 2014, alone and interacted with the change in imports exposure. Fixed effects for CZ and for the second period (referred to as “Second Period” in the table) are included in the cross sections. CZ controls refer to year 2000 and include the manufacturing share in CZ employment, the offshorability and routine task indexes of Autor and Dorn (2013), and the county-level republican vote share interacted with a dummy for Repubblican victory in that county. Standard errors are clustered at CZ level. Estimation is by 2SLS. Source: CCES. Table 6: Heterogeneous Effects

Redistribution Immigration (1) (2) (3) (4) (5) (6)

∆ IP 0.000460 -0.139* -0.0330 -0.184*** -0.113*** -0.177*** (0.0458) (0.0765) (0.0353) (0.0677) (0.0298) (0.0555) ∆ IP ∗ Bottom 33% -0.132** -0.133** (0.0643) (0.0652) ∆ IP ∗ Secular 0.0207 0.0371 0.152** 0.151** (0.0778) (0.0767) (0.0662) (0.0660)

CZ Controls NO YES NO YES NO YES Observations 6,520 6,520 7,244 7,244 9,408 9,408 F on Excluded Instruments 19.10 21.19 22.16 19.91 22.64 18.73

Notes: *** p < 0.01, ** p < 0.05, * p < 0.1. For each commuter zone (CZ), the change in penetration refers to the period between 2004 and 2014. All dependent variables are first differenced and all regressions include a control for the level of the dependent variable in 2010 (the specification is mathematically equivalent if we use the level in 2014 as dependent variable). Income class and religiosity refer to 2010. All specifications include demographic controls for gender, a quadratic of age, educational attainment, race, interest in politics, all measured in 2010; we also include a dummy variable for those who changed CZ between 2010 and 2014, alone and interacted with the change in imports exposure. CZ controls refer to year 2000 and include the manufacturing share in CZ employment, the offshorability and routine task indexes of Autor and Dorn (2013), and the county-level republican vote share interacted with a dummy for Republican victory in that county. Standard errors are clustered at CZ level. Estimation is by 2SLS. Source: CCES. Table A1: Correlation of Demographics with Indexes

Economics Culture

Economics 1 Culture 0.39 1 Class 0.87 0.32 Income 0.86 0.38 Religion -0.08 -0.86 Education 0.48 0.84

Notes: Polychoric Correlations on 2000-2016. Source: ANES. Figure A1: Trends in Group Conflict: Politically Unidentified .2 .8 .6 0 .4 -.2 .2 0 -.4 -.2 -.6 2000 2005 2010 2015 2000 2005 2010 2015 Redistribution Globalization L Class U-M/U Class L Class U-M/U Class .6 .2 .1 .4 0 .2 -.1 0 -.2 -.2 -.3 2000 2005 2010 2015 2000 2005 2010 2015 Civil Rights Low Trust in Government L Class U-M/U Class L Class U-M/U Class

(a) Class .2 .3 .2 .1 .1 0 0 -.1 -.2 -.1 2000 2005 2010 2015 2000 2005 2010 2015 Redistribution Globalization Secular Religious Secular Religious .4 .1 .05 .2 0 0 -.05 -.2 -.1 2000 2005 2010 2015 2000 2005 2010 2015 Civil Rights Low Trust in Government Secular Religious Secular Religious

(b) Religion dctoa rus(oecleeadgaut) h apei etitdt l niiul h ontietf as identify (lower not class do social who individuals by all selected means in to in means restricted reports trends is ANES (c) squared, sample reports Source: of panel The age and quintile (a) Republicans. religiosity, age, graduate). first panel or by and on the means Democrats variable, college conditioning in reports each (some (b) is after For panel groups respondent residuals class), educational the are values middle/upper effects. if variables prin- higher upper fixed one All and measures, polycoric wave to class three government. first equal and in these the dummy principal race For trust is a polychoric gender, of is Rights policy. first Distrust distribution Civil abortion the yearly levels; attitudes. and and the is immigration liberal relations spending Globalization more desired race government to towards e standards; correspond on attitudes policy living questions of trade and two component on jobs cipal of questions citizens’ two component to of principal seeing component polychoric in first role the government’s is Redistribution Notes:

0 .2 .4 .6 -.3 -.2 -.1 0 .1 2000 2000 Graduate Graduate 2005 2005 Redistribution Civil Rights 2010 2010 Some College Some College (c) 2015 2015 Education

-.2 -.1 0 .1 .2 0 .5 1 2000 2000 Low TrustinGovernment Graduate Graduate 2005 2005 Globalization 2010 2010 Some College Some College 2015 2015 Table A2: Cluster Membership and Core Demographics

Blue (Light) Cluster 2000-2008 2012-2016

M Class -0.0444 0.156 (0.0826) (0.101) U-M/U Class -0.317** 0.185 (0.125) (0.142) Income: 17-33% -0.124 0.292* (0.137) (0.159) Income: 34-67% -0.202 -0.0965 (0.128) (0.138) Income: 68-95% -0.310** -0.0846 (0.136) (0.155) Income: Top 5% -0.360** -0.268 (0.178) (0.241) Religious -0.0586 -0.285*** (0.0829) (0.0954) Some College -0.0501 0.127 (0.0888) (0.111) College 0.0546 0.159 (0.106) (0.122) Graduate 0.142 0.580*** (0.131) (0.150)

Observations 1,621 1,537

Notes: *** p < 0.01, ** p < 0.05, * p < 0.1. All specifications include controls for age, age squared, gender, race, and wave fixed effects, alone and interacted with respondents’ party identity. Estimation by probit. Robust stan- dard errors in parentheses. Source: ANES. Table A3: Factors and Core Demographics

2000-2008 2012-2016 F CF F CF

Middle Class 0.123* 0.123* 0.152* 0.0894 (0.0688) (0.0672) (0.0856) (0.0824) Upper Class 0.398*** 0.0821 0.280** 0.120 (0.110) (0.0999) (0.112) (0.116) Income: 17-33% 0.0207 -0.288** -0.0326 0.190* (0.120) (0.119) (0.143) (0.111) Income: 33-66% 0.256*** -0.230** 0.195* -0.0118 (0.0986) (0.100) (0.116) (0.0973) Income: 66-95% 0.356*** -0.149 0.385*** 0.0803 (0.108) (0.111) (0.121) (0.118) Income: Top 5% 0.453*** -0.199 0.678*** 0.0517 (0.137) (0.142) (0.175) (0.168) Religious 0.142** -0.150** 0.0633 -0.293*** (0.0710) (0.0671) (0.0776) (0.0775) Some College 0.137* 0.253*** 0.0579 0.120 (0.0754) (0.0741) (0.0940) (0.0890) College 0.165* 0.709*** 0.312*** 0.422*** (0.0861) (0.0816) (0.0944) (0.104) Graduate 0.0533 0.837*** 0.251** 0.812*** (0.105) (0.110) (0.109) (0.120)

Observations 1,336 1,336 1,197 1,197 R-squared 0.068 0.130 0.114 0.127

Notes: *** p < 0.01, ** p < 0.05, * p < 0.1. All specifications include controls for age, gender, race and wave fixed effects, alone and interacted with respondents’ party identity. Estimation is by OLS. Robust standard errors in parentheses. Source: ANES. Table A4: Correlation Between Shocks

∆ HNW06−09

∆ IP00−16 0.1563 0.0000 ∆ IP04−14 0.1504 0.0000

Notes: Correlations are com- puted at county level.

Table A5: Trade Shocks vs Net Worth Shocks

Redistribution Immigration Cross Section Panel Cross Section Panel

∆ IP ∗ Second Period -0.0812** -0.0461 (0.0320) (0.0322) ∆ IP -0.210** -0.156*** (0.0846) (0.0596)

∆ HNW ∗ Second Period -0.119 -0.0678 (0.100) (0.113) ∆ HNW 0.0868 0.0858 (0.108) (0.101)

Observations 55,878 6,095 60,931 7,874 F on Excluded Instruments 18.63 33.11 21.15 32.15

Notes: *** p < 0.01, ** p < 0.05, * p < 0.1. For each commuter zone (CZ), the change in penetration refers to the period between 2000 and 2016 in the cross section and between 2004 and 2014 in the panel. For each county, the change in net worth is measured between 2006 and 2009. In the cross section, attitudes towards redistribution are measured in 2006 and 2016; attitudes toward immigration are measured in 2007 and 2016. In the panel, all dependent variables are first differenced and all regressions include a control for the level of the dependent variable in 2010 (the specification is mathematically equivalent if we use the level in 2014 as dependent variable). All specifications include the full set of individual controls, CZ controls, and FE described in Table 5. Standard errors are clustered at CZ level. Estimation is by 2SLS. Source: CCES. Table A6: Trade Shocks vs Net Worth Shocks: Heterogeneity

Redistribution Immigration

∆ IP -0.181* -0.219*** -0.191*** (0.0941) (0.0810) (0.0580) ∆ IP ∗ Bottom 33% -0.131 (0.0832) ∆ IP ∗ Secular 0.0154 0.0988 (0.0826) (0.0696)

∆ HNW 0.0647 0.116 0.130 (0.118) (0.103) (0.0948)

Observations 5,472 6,095 7,874 F on Excluded Instruments 17.47 16.44 16.04

Notes: *** p < 0.01, ** p < 0.05, * p < 0.1. For each commuter zone (CZ), the change in penetration refers to the period between 2004 and 2014. For each county, the change in net worth is measured between 2006 and 2009. All dependent variables are first differenced and all regressions include a control for the level of the dependent variable in 2010 (the specification is mathematically equivalent if we use the level in 2014 as dependent variable). All specifications include the full set of individual controls, CZ controls, and FE described in Table 5. Standard errors are clustered at CZ level. Estimation is by 2SLS. Source: CCES. 8 Appendix Appendix 1: Proofs Proof of Proposition 1. Using Equation (1) we can write the distrorted likelihood ratio between the average group members as:

2χ θ ˜ ˜ θ ˜ z ψ ψG,G z ψ ψG z ψ ψG,G = , (29) θ ˜  ˜   θ ˜  z ψ ψG, G z ψ ψG z ψ ψG, G        which defines a mapping x = f (x) for which we look for an positive fixed point. There is only one such fixed point, which is stable provided f (x) is concave. This is ensured by 2χ < 1, in which case: 1 1 2χ θ ˜ ˜ − z ψ ψG,G z ψ ψG = . θ ˜   ˜  z ψ ψG, G z ψ ψG      As a result, Equation (1) becomes:

χ 1 2χ ˜ − z ψ ψG zθ ψ˜ ψ, G z ψ˜ ψ , | ∝ | z ψ˜ ψ      G    χ which is the BCGS (2016) equation with diagnosticity θ 1 2χ . With Gaussian distribu- ≡ − tions this yields: ψθ ψz˜ θ ψ˜ ψ, G dψ˜ = ψ + θ ψ ψ . G ≡ | G − G Z  
Proof of Corollaries 1 and 2. The disagreement among average group types is equal to:

θ θ ψ ψ = ψ + θ ψ ψ ψ θ ψ ψ = ψ ψ (1 + 2θ) , SP − SC SP SP − SC − SC − SP − SC SP − SC


˜ θ which proves Corollary 1. Denote by ψG the random variable capturing the distorted beliefs of the average member of group G. Using again Equation (1), the distorted perception of such average member G is equal to:

χ θ ˜ z ψ ψG,G θ ˜ θ θ ˜ z ψG = z ψ ψG,G ,  θ ˜  z ψ ψG, G        so that, given that again all distributtions are Gaussian (see BCGS 2016), the perceived

47 position of the average member of G is equal to:

θ θ θ θ θ ψ = ψ + ψ ψ , SP SP 1 + 2θ SP − SC     b where we exploited the definition χ θ . This immediately implies that: ≡ 1+2θ

θ θ θ θ 2θ ψ ψ = ψ ψ 1 + SP − SC SP − SC 1 + 2θ     θ θ 1 + 4θ b b = ψ ψ . SP − SC 1 + 2θ    

Proof for the Approximate Contrast Function of Section 3. We derive the approxi-

G (v+ψG) (1+εG) mation of C(G, G) in (9) from Equation (8). Recall that τ = T (εG, ψG) = ϕ− and G G G G G G G q = ψ Q(ψ ), so that W τ , q = W T (εG, ψ ),Q ψ . We take a second order ≡ G G G G G approximation of W τ , q with respect
to mean income and

social progressiveness in G,

(εG, ψG), at the point(εG, ψG) in which the two groups face no conflict. In doing so, recall G G G G G G G that by (??) Wτq = 0, that the optimality conditions imply Wτ τ , q = Wq τ , q = 0, and that Tεψ = 0. This implies that:

C(G, G) W G τ G, qG W G τ G, qG ≡ − 1 G G
G 2  2 W τ , q (Tε) (εG ε ) (30) ' −2 ττ − G 1 G G G
2 G G G 2 2 W τ , q (Tψ) + W τ , q (Qψ) (ψ ψ ) (31) −2 ττ qq G − G G G G W τ , q Tψ
Tε(εG ε )(ψ ψ
)  (32) − ττ − G G − G
Note that Tε = 1/ϕ < 0, Tψ = 1/ϕ and Qψ = 1. From Equation (??) we also have that: − W G = ϕ and W G = κ. Thus, the above expression can be rewritten as: ττ − qq −

1 1 2 1 1 2 C(G, G) ϕ− (εG ε ) + ϕ− + κ (ψ ψ ) ' 2 − G 2 G − G 1 ϕ− (εG ε )(ψ ψ ).
(33) − − G G − G Given that proportionality constants do not change the nature of the expression we further simplify as:

2 2 C(G, G) (εG ε ) + (1 + κϕ)(ψ ψ ) 2(εG ε )(ψ ψ ), ' − G G − G − − G G − G which is Equation (9) under the definition κ κϕ. ≡

b 48 Proof of Proposition 2. All voters (ε, ψ) identify with their cultural group if and only if group contrast among cultural groups is larger than among income groups, namely when:

C (SP,SC) C (U, L) ≥ ⇔ 2 2 (εSP εSC ) + (1 + κ)(ψ ψ ) 2(εSP εSC )(ψ ψ ) − SP − SC − − SP − SC ≥ 2 2 (εU εL) + (1 + κ)(ψU ψL) 2(εU εL)(ψU ψL). − b − − − − Exploiting the structure of correlation,b this condition becomes:

2 2 2 2 2 (ψ ψ ) ρ + 1 + κ 2ρ (εU εL) 1 + ρ + κρ 2ρ , SP − SC − ≥ − −

which is equivalent to: b b

2 ψ ψ SP − SC (1 ρ)2 + κ (1 ρ)2 + κρ2 , εU εL − ≥ −  −      or: b b 2 2 ψ ψ ψ ψ κ SP − SC ρ2 (1 ρ)2 1 SP − SC . εU εL − ≥ − − εU εL " −  # "  −  # 2 b ψ ψ 2 we know from A1 that the left hand side is positive, namely SP − SC > ρ . As a result, εU εL − cultural identity prevails provided:  

2 2 ψ ψ (1 ρ) 1 SP − SC − − εU εL κ α  − .  2  ≥ ≡ ψ ψ SP − SC ρ2 εU εL − − b b    Properties i) and ii) are immediate upon inspection. θ Proof of Proposition 3. We already know from Section 2 the expression for ψG. Given a gaussian income distribution f (˜ε ε) we can define, in line with Equation (1), the distorted | beliefs over income by a voter identified with group G as:

χ θ f (˜ε εG,G) f θ (˜ε ε, G) = f (˜ε ε) | , θ | | "f ˜ε εG, G #
which, repeating the logic of the fixed point yields:

θ ε = ε + θ (εG ε ) . G − G

49 The policy demands of a voter (ε, ψ) identified with group G are then given by:

v + ψθ εθ τ εψ = G − G , G ϕ εψ θ qG = ψG,

which, by replacing the explicit expressions for beliefs, immediately yield the result of the proposition. εψ εψ Proof of Proposition 4. Denote by τ d , qd as the policy demands of voter (ε, ψ) when identification occurs along dimension d = ˜ε, ψ˜. Then, under class identity these demands are equal to (net of irrelevant constant terms):

εψ ψ ε (1 ρ)(εU εL) τ = − 2θIU − − , (34) ˜ε ϕ − ϕ εψ q = ψ + 2IU θρ (εU εL) , (35) ˜ε −

where IU is the indicator of upper class membership. As a result, the variance covariance matrix of policy preferences is equal to:

2 εψ εψ 2 1 ρ var τ = var τ + 4θ (1 + θ) πU (1 πU )(εU εL) − , ˜ε − − ϕ    εψ εψ
2 2 var q = var q + 4θ (1 + θ) πU (1 πU )(εU εL) ρ , ˜ε − − εψ εψ εψ
εψ 2 1 ρ cov τ , q = cov τ , q 4θ (1 + θ) πU (1 πU )(εU εL) ρ − , ˜ε ˜ε − − − ϕ    
where πU = Pr (ε U). ∈ Under cultural identity, the policy demands by voter (ε, ψ) are equal to (net of irrelevant constant terms):

ψ ε ψ ψ (εSP εSC ) τ εψ = − + 2θI SP − SC − − , (36) ψ˜ ϕ SP ϕ
εψ q = ψ + 2ISP θ ψ ψ , (37) ψ˜ SP − SC
Where ISP is the indicator of social progressive membership. As a result, the variance covari-

50 ance matrix of policy preferences is equal to:

2 εψ εψ 2 1 ρ var τ = var τ + 4θ (1 + θ) πSP (1 πSP ) ψ ψ − ψ˜ − SP − SC ϕ    εψ εψ

2 var q = var q + 4θ (1 + θ) πSP (1 πSP ) ψ ψ ψ˜ − SP − SC εψ εψ εψ
εψ
2 1 ρ cov τ , q = cov τ , q + 4θ (1 + θ) πSP (1 πSP ) ψ ψ − ψ˜ ψ˜ − SP − SC ϕ    

Compare now the outcomes as identity switches from class to culture. In so doing, suppose that identity changes due to an increase in κ. The advantage of this shock is that it leaves all rational policy demands unaffected. We later discuss what happens with other shocks. Consider first what happens to the variance of beliefs about redistribution. In moving from class based to cultural identity, this variance increases if an only if:

var τ εψ > var τ εψ ψ˜ ˜ε ⇐⇒  2   2 πSP (1 πSP ) ψ ψ > πU (1 πU )(εU εL) . − SP − SC − −
This inequality is ambiguous because it depends on the ratio of cultural versus income conflict

ψSP ψSC − , even if the size of the different groups is the same (πU = πSP ). εU εL − Consider the variance of beliefs about culture. This variance increases as identity switches from class to culture provided:

var qεψ > var qεψ ψ˜ ˜ε ⇐⇒  2   2 2 πSP (1 πSP ) ψ ψ > πU (1 πU )(εU εL) ρ . − SP − SC − −

2
2 2 Note that by A1 ψ ψ > (εU εL) ρ , so this variance increases if groups are similar SP − SC − in size. More generally, this
condition is met provided the correlation ρ is low enough. Finally, consider the covariance between preferences over redistribution and over cultural policy.

cov τ εψ, qεψ > cov τ εψ, qεψ ψ˜ ψ˜ ˜ε ˜ε ⇐⇒ 2 1 ρ    2 1 ρ πSP (1 πSP ) ψ ψ − > πU (1 πU )(εU εL) ρ − , − SP − SC ϕ − − − ϕ    
which is always fulfilled for ρ < 1. It is immediate to see that cov τ εψ, qεψ > 0 (for the ψ˜ ψ˜ εψ εψ   covariance is positive in the rational world), while cov τ ˜ε , q˜ε can be negative provided θ is large enough.  

51 Consider now other shocks that may switch identity from class to culture. Keeping every- (ψSP ψSC ) thing else, an increase in contrast − still leaves rational demands unchanged. It is (εU εL) − still true that that the variance of τ is ambiguous and that for low ρ the variance of q in- creases. It is always true that the covariance between these instruments increases. Keeping everything else constant, if identity switches from class to culture, an increase in correlation ρ reduces the rational variance of τ, so the effect of the identity switch on this dimension remains ambiguous, it is still the case that the variance of q inceases provided the initial ρ was low enough. When ρ increases the rational covariance between τ and q falls, so the identity switch causes the distorted covariance to rise if and only if the increase in ρ is not too high. The same is true for the increase in tax distortions ϕ. G G Proof of Proposition 5. Denote by τ d , qd the distorted policy preferences of the average ˜ member of group G when he is identified along
d = ˜ε, ψ. Then, Equations (4), (13) and (14) imply:

τ G = τ G + θ τ G τ G , d − qG = qG + θ qG qG . d −   These in turn imply that policy disagreement among groups of current identification is equal to:

τ G τ G = τ G τ G (1 + 2θ) , d − d − qG qG = qG qG (1 + 2θ) , d − d −   which immediately yields the proposition. Proof of Proposition 6. Consider cultural groups. Under class identity, the preferred tax rates and cultural policies of the average social progressive and social conservative are equal to:

SP SP (1 ρ)(εU εL) (1 ρ)(εU εL) τ ˜ε = τ θ − − πU SP + θ − − πL SP , − ϕ | ϕ | SP SP q˜ε = q + θρ (εU εL) πU SP θρ (εU εL) πL SP , − | − − | SC SC (1 ρ)(εU εL) (1 ρ)(εU εL) τ ˜ε = τ θ − − πU SC + θ − − πL SC , − ϕ | ϕ | SC SC q˜ε = q + θρ (εU εL) πU SC θρ (εU εL) πL SC , − | − − |

where πX Y is the share of members of Y that belong to X. So, disagreement among cultural |

52 groups under class identity is:

SP SC SP SC (1 ρ)(εU εL) τ ˜ε τ ˜ε = τ τ 2θ − − πU SP πU SC − − − ϕ | − | SP SC SP SC q˜ε q˜ε = q q
+ 2θρ (εU εL) πU SP πU SC .
− − − | − |

By Proposition 5, under cultural identity disagreement among cultural groups in both dimen- sions is (1 + 2θ) times the rational disagreement. As a result:

τ SP τ SC > τ SP τ SC ψ˜ − ψ˜ ˜ε − ˜ε ⇔ SP SC (1 ρ)(εU εL) τ τ > − − πU SP πU SC , − − ϕ | − |

SP SC which is always true because when ρ > 0, τ > τ and πU SP > πU SC . Their disagreement | | over q increases provided:

qSP qSC > qSP qSC ψ˜ − ψ˜ ˜ε − ˜ε ⇔ SP SC q q ψSP ψSC > ρ (εU εL) πU SP πU SC , − ≡ − − | − |


which is true by A1. Consider now economic groups. Under class identity, the disagreement over tax rates and cultural policies between the average upper and lower class voters are again (1+2θ) times their rational disagreement. Consider now disagreement under cultural identity. Policy preferences by average class members are:

U U (1 ρ) ψSP ψSC (1 ρ) ψSP ψSC τ ˜ = τ + θ − − πSP U θ − − πSC U , ψ ϕ | ϕ |
−
U U q˜ = q + θ ψSP ψSC πSP U θ ψSP ψSC πSC U , ψ − | − − | L L (1 ρ) ψSP
ψSC (1 ρ
) ψSP ψSC τ ˜ = τ + θ − − πSP L θ − − πSC L, ψ ϕ | ϕ |
−
L SC q˜ = q + θ ψSP ψSC πSP L θ ψSP ψSC πSC L, ψ − | − − |

where πX Y is the share of members of Y that belong to X. So, disagreement among economic | classes under cultural identity is:

L U L U (1 ρ) ψSP ψSC τ ˜ τ ˜ = τ τ 2θ − − πSP U πSP L ψ ψ ϕ | | − − −
− L U L U q˜ q˜ = q q
2θ ψSP ψSC πSP U πSP L .
ψ − ψ − − − | − |

53 As a result:

qL qU > qL qU ψ˜ − ψ˜ ˜ε − ˜ε ⇔ U L q q > ψSP ψSC πSP U πSP L − − | − | ⇔ ρ(εU εL
) > ψSP ψSC
πSP U πSP L
, − − | − |

which is ambiguous. We also have:

τ L τ U < τ L τ U ψ˜ − ψ˜ ˜ε − ˜ε ⇔ (1 ρ) ψSP ψSC L U − − πSP U πSP L < τ τ , ϕ | | −
− −

which is always true. Proof of Proposition 7. Immediate after inspecting (24), (25), (??), (??). Trade Policy Model: Solving for Optimal Policies and Approximating the Contrast Function. Assume that the utility function U(m) takes the form:

1 U(m) = (ω m)2 −2 − with ω > 0 and ω large so that U(m) is concave. Since income effects are absorbed by consumption of the export good, every individual consumes the same amount of the imported good, namely: m = ω p M(p). − ≡ By the linear production function, domestic production of the import good by an individual b consumer is σ (1 η˜) which, aggregated across all consumer yields σ 1 ηdH(η) . Be- − − cause the average value of η is zero, aggregate output in the import competingZ sector is σ. 34 This implies that the revenue of the tariff is equal to T = tp∗(m σ). Let −

S(t) = U(m) (1 + te)p∗m, b −

denote the consumer surplus from the importedb good. Thenb we can write the expected indirect utility function of type (ε, η, ψ) as κ W εηψ(τ, t, q) = Y εη(τ, t) + S(t) + (ν + ψ)τ (q ψ)2 (38) − 2 −

The indirect utility function is separable in ε, ψ and η, so the tax rate preferred by types

34 We implicitly restrict parameters so that in equilibrium there are indeed imports, namely m > σ.

54 b (ε, ψ), τ εψ, is still given by (4) in the previous section. Exploiting the envelope theorem and simplifying, the tariff preferred by individual η is:

η t = ησ/p∗ B(η). − ≡ Intuitively, higher benefit from trade η means lower preferred tariff. Taking a quadratic approximation of the welfare function as above, we get:

1 G G G G 2 2 C(G, G) W τ , q , t (Tε) (εG ε ) ' −2 ττ − G 1 G G G G
2 G G G G 2 2 W τ , q , t (Tψ) + W τ , q , t (Qψ) (ψ ψ ) (39) −2 ττ qq G − G G G G G W τ , q , t Tψ
Tε(εG ε )(ψ ψ )
 (40) − ττ − G G − G 1 G G G G 2 2 W τ , q , t
(Bη) (η η ) (41) −2 tt G − G
εηψ εηψ 2 εηψ In this model we have: W = ϕ, W = (p∗) ,W = κ, Tε = 1/ϕ, Tψ = 1/ϕ, ττ − tt − qq − − Bη = σ/p∗, and Qψ = 1. or: −

1 1 2 1 1 2 C(G, G) ϕ− (εG ε ) + ϕ− + κ (ψ ψ ) ' 2 − G 2 G − G 1 1
2 2 ϕ− (εG ε )(ψ ψ ) + σ (η η ) (42) − − G G − G 2 G − G which can be further simplified to:

2 2 2 2 C(G, G) (εG ε ) + (1 + κϕ)(ψ ψ ) 2(εG ε )(ψ ψ ) + ϕσ (η η ) , ' − G G − G − − G G − G G − G which is the one in the text after defining κ = κϕ and σ ϕσ2. ≡ Proof of Proposition 8 and of Corollary 3. In case of class based identity, group contrast is equal to: b b

2 2 2 2 C(U, L) (εU εL) (1 ρ) + κρ + σγ ' − − ηε   where σ is the salience of trade (compared to taxes whichb are parameterizedb by 1/ϕ, which is

embedded in ), κ is that of culture (again relative to taxes), and γηε is the regression coeffi cient of η onbε. In case of cultural identity, group contrast is equal to: b C(SP,SC) (ψ ψ )2 (1 ρ)2 + κ + σγ2 , ' SP − SC − ηψ   where γηψ is the regression coeffi cient of η on ψ. In case of tradeb b identity, group contrast is

55 equal to: C(C,N) (η η )2 γ γ 2 + κγ2 + σ , ' C − N εη − ψη ψη where γ is the regression coeffi cient of ε onhη and γ
is that of ψ oni η. Suppose now that εη ψη b b all three dimensions have the same variance σ2 and, as discussed in the text:

η = aψ + u where a [0, 1] and ρ = 0. Then we can rewrite contrast as: ∈

2 C(U, L) (εU εL) , ' − C(SP,SC) (ψ ψ )2 1 + κ + σa2 , ' SP − SC 2 2 C(C,N) (ηC ηN ) a (1 + κ) + σ , ' − b b 2 2   2 ψSP ψSC ηC ηN ψSP ψSC Denote by ∆ψε − , by ∆ηε − and by ∆b ψη b − . Furthemore, εU εL εU εL η η ≡ − ≡ − ≡ C − N denote z 1 + κ, we have that:     ≡

2 b b C(SP,SC) C(U, L) ∆ψεz + ∆ψεa σ 1, ≥ ⇔ ≥

2 2 C(SP,SC) C(C,N) ∆ψη a bz 1 ab ∆ψη σ 0, ≥ ⇔ − − − ≥ 2 C(C,N) C(U, L) ∆ηεa
z + ∆ ηεσ 1.
≥ ⇔ b ≥ b In the case of no correlation, when a = 0 the conditions become: b b

C(SP,SC) C(U, L) ∆ψεz 1, ≥ ⇔ ≥

C(SP,SC) C(C,N) ∆ψηz b σ 0, ≥ ⇔ − ≥

C(C,N) C(U, L) ∆ηεσ 1. ≥ ⇔ b ≥b

In this case, class identity prevails for (z, σ): z [1, 1/∆ψε] , σ [1, 1/∆ηε]. Cul- ∈b ∈ tural identity prevails for z > max [1/∆ψε, σ/∆ψη], and trade identity prevails when σ ≥ max [1/∆ηε, ∆ψηz]. In this case, it is clear thatb b higherb trade exposure bσ always eases transi- tion to trade identity whileb it makes the case ofb both class and cultural identity more diffi cult.b Consider nowb the case with positive correlations a > 0. The locus ofb points of indifference between cultural and trade identity is identified by:

2 1 a ∆ψη z = − 2 σ, ∆ψη a  −  which is upward sloping owing to conditionb A.2. The locusb of points of indifference between

56 cultural and economic identity is identified by the downward sloping curve:

1 z a2σ, ≥ ∆ψε − which has intercept 1/∆ψε on the verticalb axis. Noteb that now higher importance of trade σ favors cultural identification. Finally, the locus of indifference between cultural and trade based identity is given by the downward sloping curve: b 1 1 z = 2 2 σ. ∆ηεa − a

b b 2 The interctep of this curve on the vertical axis is 1/∆ηεa , which is higher than the other

intercept 1/∆ψε. Furthermore, the three curves all meet at the same point (z∗, σ∗). Consider in fact the condition for indiffeerence among all groups: b b 2 ∆ψε z + a σ = 1,

2
2 ∆ψη z + a σb ba z + σ = 0, − ∆ a2
z +σ = 1.
b ηε b b b If the first and the last conditions are met, then
also the middle one is met because, by b b replacing the first and the last, it becomes:

∆ 1 1 1 ψη = 0 = 0. ∆ψε − ∆ηε ⇐⇒ ∆ηε − ∆ηε

This means that, taking into account that κ = z 1 identity is determined according to the − following three regions: b b 1 1 1 class κ < min a2σ, σ 1, ∆ − ∆ a2 − a2 −  ψε ηε  1 1 a2∆ b b 2 bψη culture κ max a σ, − 2 σ 1, ≥ ∆ψε − ∆ψη a −   −   1 1 a2∆ b 2 b ψη b trade a σ 1 κ < − 2 σ 1. ∆ψε − − ≤ ∆ψη a −  −  b b b Proof of Proposition 9. Consider the model with trade. For simplicy, capture the trade policy by a linearization of the inverse of the tariff oθ = σηθ, where o stands for "openness".

b

57 The policy demands by voter (ε, ψ, η) are equal to (net of irrelevant constant terms):

εψ ψ ε (1 ρ)(εU εL) τ = − 2θIU − − , (43) ˜ε ϕ − ϕ εψ q = ψ + 2θIU ρ (εU εL) . (44) ˜ε − θ o = ση + 2σθIU (aρ + b)(εU εL) , (45) − where IU is the upper class membershipb indicator.b As a result, the variance covariance matrix of policy preferences is equal to:

2 εψη εψη 2 1 ρ var τ = var τ + 4θ (1 + θ) πU (1 πU )(εU εL) − , ˜ε − − ϕ    εψη εψη
2 2 var q = var q + 4θ (1 + θ) πU (1 πU )(εU εL) ρ , ˜ε − −  εψη εψη
var o˜ε = var o

εψ εψ εψη
εψη 2 1 ρ cov τ , q = cov τ , q 4θ (1 + θ) πU (1 πU )(εU εL) ρ − , ˜ε ˜ε − − − ϕ    εψη εψ εψη εψη
cov o˜ε , q˜ε = cov o , q  εψη εψ εψη εψη
cov o˜ε , τ ˜ε = cov o , τ  
where πU = Pr (ε U) Under cultural identity, the policy demands by voter (ε, ψ) are equal ∈ to (net of irrelevant constant terms):

ψ ε ψ ψ (εSP εSC ) τ εψ = − + 2θI SP − SC − − , (46) ψ˜ ϕ SP ϕ
εψ q = ψ + 2ISP θ ψ ψ . (47) ψ˜ SP − SC εψη o = ση + 2σθISP (a + bρ) ψ
ψ , (48) ψ˜ SP − SC
where ISP is the social progressiveb groupb membership indicator.

58 As a result, the variance covariance matrix of policy preferences is equal to:

2 εψ εψ 2 1 ρ var τ = var τ + 4θ (1 + θ) πSP (1 πSP ) ψ ψ − ψ˜ − SP − SC ϕ    εψ εψ

2 var q = var q + 4θ (1 + θ) πSP (1 πSP ) ψ ψ ψ˜ − SP − SC  εψη εψη 2 2 2 var o = var o
+ 4σ θ (1 + θ) πSP (1 πSP ) a ψ
ψ ψ˜ − SP − SC εψ εψ εψ
εψ 1 ρ
2 cov τ , q = cov τ , q + 4θ (1 + θ) πSP (1 πSP ) − ψ ψ ψ˜ ψ˜ b − ϕ SP − SC    εψ εψ εψ εψ
2
cov o , q = cov o , q + 4σθ (1 + θ) πSP (1 πSP ) a ψ ψ ψ˜ ψ˜ − SP − SC  εψ εψ εψ εψ
1 ρ
2 cov τ , o = cov τ , o + 4σθ (1 + θ) πSP (1 πSP ) a − ψ ψ ψ˜ ψ˜ b − ϕ SP − SC    

b Consider an increase in σ that switches identity to culture. The patterns concerning var τ εψ , ψ˜ εψ εψ εψ   var q˜ and cov τ ˜ , q˜ remain the same as we studied in Section 3. Specifically, the ψ ψ b ψ variance  of preferences overτ changes in an ambiguous way, the variance of preferences over q increases if ρ is low enough, and the covariance between τ and q increases. Consider the new terms involving trade policy. Artificially keeping σ constant, polarization over trade increases provided: b εψ εψ 2 2 var o > var o πSP (1 πSP ) a ψ ψ > 0, ψ˜ ˜ε ⇐⇒ − SP − SC    
which always holds, and the increase in σ further increases var oεψ , reinforcing the effect. ψ˜ Artificially keeping σ constant, the covariance between trade openness and cultural policy increases provided: b b εψ εψ εψη εψ 2 cov o , q > cov o , q πSP (1 πSP ) a ψ ψ > 0, ψ˜ ψ˜ ˜ε ˜ε ⇐⇒ − SP − SC    
which always holds, and the increase in σ further reinforces the effect. Finally, and again keeping σ artificially constant, the covariance between trade and redis- tribution increases unambiguously provided:b b εψ εψ εψη εψ 1 ρ 2 cov τ , o > cov o , τ 4θ (1 + θ) πSP (1 πSP ) a − ψ ψ > 0, ψ˜ ψ˜ ˜ε ˜ε ⇐⇒ − ϕ SP − SC      
which is again verified and reinforced by the increase in σ. If identity switches to trade, the effect is very similar to that of switching to culture (the conditions are only flipped between these two policies and ψ ψ is replaced with the correspondingb term in η). SP − SC

59 Appendix 2. Trade Identity: The General Case Consider identification in the general tri-dimensional model with a rich structure of corre- lations. Recall that the general contrast functions are:

2 2 2 2 C(U, L) (εU εL) (1 ρ) + κρ + σγ , ' − − ηε 2 2 2 C(SP,SC) (ψSP ψSC) (1 ρ) + κ + σγηψ , ' − − 2 b 2 b 2 C(C,N) (ηC ηN ) γεη γψη + κγψη +σ , ' − − b b h
i where σ and κ and the regression coeffi cients are defined as before.b Supposeb now that all three dimensions have the same variance of 1 and that: b b η = aψ + bε + u

where a, b > 0 with a2 + b2 1. Then we can rewrite contrast as: ≤

2 2 2 2 C(U, L) (εU εL) (1 ρ) + κρ + σ (aρ + b) , ' − −   C(SP,SC) (ψ ψ )2 (1 ρ)b2 + κ +b σ (a + bρ)2 , ' SP − SC − C(C,N) (η η )2 (a b)2 (1 ρ)2 + κ (a + bρ)2 + σ . ' C − N − − b b 2 x x   Use again the notation ∆xy y−y . In the spirit of A.1b (which we continueb to impose) ≡ − and of A.2 assume that trade contrast  is largest among the trade groups, cultural contrast among the cultural groups and income contrast among the incoe groups. In addition to A.1 this means imposing:

1 2 (a + bρ) < ∆ < (A.3) ψη a + bρ   p 1 2 (aρ + b) < ∆ < ηε aρ + b   p Consider now the three inequality. Wthen have that:

C(SP,SC) C(U, L) ≥ ⇔ 2 2 2 2 κ ∆ψε ρ + σ ∆ψε (a + bρ) (aρ + b) (1 ρ) (1 ∆ψε) . − − ≥ − −

2  By assumptionb A.1 ∆ψε > ρb . The coeffi cient on σ could be positive or negative. We then

b

60 have:

C(SP,SC) C(C,N) ≥ ⇔ 2 2 2 2 κ ∆ψη (a + bρ) + σ ∆ψη (a + bρ) 1 (a b) ∆ψη (1 ρ) . − − ≥ − − −       Note thatb by A.3 the coeffi cientsb in front of κ is positive and that in front ot σ is negative. Finally, we have: b b C(C,N) C(U, L) ≥ ⇔ 2 2 2 2 2 κ ∆ηε (a + bρ) ρ + σ ∆ηε (aρ + b) (1 ρ) 1 ∆ηε (a b) . − − ≥ − − −       Note thatb by A.3 the coeffi cient ofb σ is positive, while that on κ could be positive or negative. On the basis of this preliminary analysis, we can state the following result. b b 2 Proposition 10 If 1 > max ∆ηε (a b) , ∆ψε , then: i) if κ and σ are suffi ciently low, then − class identity prevails, ii) if κ is suffi ciently high then cultural identity prevails, and iii) if σ is suffi ciently high, then trade identity prevails. b b b b This follows immediately from the fact that an increase in κ unambiguously increases the appeal of cutlure via a vis the others and an increase in σ does the same with respect to trade. The condition in the proposition makes sure that the constant termsb in C (U, L) C(SP,SC) ≥ and of C (U, L) C(C,N) are positive, so that when κ andb σ are small, class identity prevails. ≥ Suppose now that we are in a class identity regime and there is an increase in trade exposure σ. We know that such increase, if large enoughb willb eventually lead to trade identity. But can such increase lead to a switch toward cultural identity? A necessary condition for this to occurb is that:

2 aρ + b 2 ∆ψε ∆ ρ , 1 , (Z.1) ≥ ψε ≡ a + bρ ∈   which implies that higher trade exposure σ favors cultural identity
vis a vis class identity (higher exposure to trade can never favor cultural identity vis a vis trade identity). This condition is easier to fulfil when a is largeb relative to b and when ρ is low. For b = 0 the condition is implied by A.1. Intuitively, the stronger the correlation between culture and trade, and the less the cor- relation between income and trade, the more a trade shock can favor a cultural identity over class identity. Under condition (Z.1), the locus of points such that C(SP,SC) = C(U, L) is negatively sloped in the plane (σ, κ) with positive vertical intercept κi,ψε at σ = 0. On the other hand, the locus of points C(SP,SC) = C(C,N) is positively sloped in (σ, κ) with positive or negative b b b b 61 b b vertical intercept κi,ψη at σ = 0. Thus, cultural identity is chosen for values of (σ, κ) that lie above both loci. Thus, a necessary condition for observing a switch from class to culture when σ increases isb to havebκi,ψη κi,ψε which is easy to check that it is always fulfilledb b given ≤ A.1, A.3 and a > b. Finally,b to observe a switchb fromb class to culture when σ increases, we also need to make sure that there is a region in the space in which the parameter sets for which class and culture are equilibria are adjacent. To see this, consider the locusb of indifference between trade and 2 2 class C(C,N) C(U, L). If the coeffi cient on κ is positive, namely ∆ηε (a + bρ) ρ , this is ≥ ≥ a negatively sloped curve with positive vertical intercept κi,ηε at σ = 0. In this case, there are values of (κ, σ) for which higher σ causes societyb to transitions from class to culture provided: b b 2 2 2 b (1 ρ) 1b ∆ηε (a b) (1 ρ) (1 ∆ψε) κi,ηε = − − − > κi,ψε = − − . 2 2 2 ∆ηε (a + bρ) ρ (∆ψε ρ )  −  − b   b By A.3, the denominator of κi,ηε is always smaller than that of κi,ψε. As a result, the condition

is surely met if the numerator of κi,ηε is larger than the numerator of κi,ψε, namely: b b 2 b ∆ψη > (a b) .b (Z.2) −

If cultral contrast is non smaller than trade contrast, ∆ψη 1, the condition is always fulfilled. ≥ Consider then the case in which the indifference locus C(C,N) = C(U, L) features a 2 2 negative coeffi cient on κ, namely ∆ηε (a + bρ) < ρ . In this case, the indifference line is

positively sloped, with a negative vertical intercept κi,ηε,1 at σ = 0. In this case, there is always a region in whichb higher σ causes society to transitions from class to culture provided. To summarize this discussion, we can state the followingb result.b b 2 Corollary 4 If ∆ψε ∆ and ∆ψη > (a b) , there is a region in which an increase in ≥ ψε − trade exposure triggers a transition from to class culture based identity.

One may wonder whether there is always an equilibrium identification pattern. This outcome is achieved when there are no preference cycles. Given the previous analysis, this is

guaranteed provided the unique intersection point (κ∗, σ∗) between the C(SP,SC) = C(U, L)

and C(SP,SC) = C(C,N) curves is such hat, when evaluated at κ∗, the locus C(C,N) =

C(U, L) requires σ σ∗. In this case, there is nob regionb in which C(SP,SC) < C(U, L), ≥ C(SP,SC) > C(C,N) and yet C(C,N) > C(U, L). There are parameterb conditions that ensure that this isb the case,b but we do not make them explicit here. Note, in concluding, that

higher σ can generate a transition from class to culture when κ > κ∗, which confirms the role of high cultural importance identified in the simplified model of the main text. b b b

62 Appendix 3. Empirical Analysis

Trends in Political Conflict: PEW and ANES

The data used to create Figure 1 are publicly available on the Pew Research Center website. Specifically, we use data from the following surveys: June 2018 Political Survey, January 2017 Political Survey, December 2015 Political Survey, December 2014 Political Survey, March 2012 Political Survey, December 2011 Political Survey, February 2010 Political Survey, February 2009 Political and Economic Survey, January 2008 Political Survey, September 2007 Political Survey, January 2006 News Interest Index, January 2005 News Interest Index, July 2004 Foreign Policy and Party Images, April 2003 Iraq Poll, February 2001 News Interest Index. All such surveys are conducted on nationally representative samples of US adults aged 18 or more, with size ranging from 1303 individuals in 2010 to 2009 individuals in 2004. Survey weights are used to enhance representativeness at national level. For the analysis of the most important problem we rely on the following question: “What do you think is the most important problem facing the country today? [Record up to three responses, in order of mention]”. The question is open-ended, but in the public release of the datasets answers have been classified in roughly 55 macro categories, with only minor changes in classification over time. We further aggregate the categories “Abortion" and “Rights of Women Under Attack/Rolling Back" in the macro category “Abortion and Women Rights". To create the trends, we consider for each of the selected issues the share of respondents including such issue among their first three mentions. Sections 5.1.1-5.1.3 use data from the American National Election Studies (ANES), and in particular of the surveys carried out in 2000, 2004, 2008, 2012 and 2016. We use the version of the variables available in the Cumulative Dataset of December 2018, and complement such information with data from the yearly releases when required. Following standard practice of dynamic analyses on ANES, we restrict the analysis to the Face-to-Face sample. Results are robust if we add the WEB sample, available for years 2012 and 2016. Given that the target population of the analysis consists of all adult individuals living in the US, in computing aggregates and running regressions we use individual survey weights, which ensure that the sample is representative of the US adult population at national level. All individual weights are rescaled so that each wave/year has a cumulative weight of one. Yearly sample sizes range from roughly 1200 individuals in 2004 to about 2322 individuals in 2008. Below we describe the questions and variables used in the analysis. To measure policy preferences we rely on the following questions: Redistr. Spending “Some people think the government should provide fewer services, even in areas such as health and education, in order to reduce spending. Other people feel

63 that it is important for the government to provide many more services even if it means an increase in spending. Where would you place yourself on this scale?”Answers are given on a seven-point scale, and recoded so that the variable is increasing in respondents’desired size of government. Redistr. Assist “Some people feel that the government in Washington should see to it that every person has a job and a good standard of living. Others think the government should just let each person get ahead on his/their own. Where would you place yourself on this scale?” Answers are given on a seven-point scale, and recoded so that the variable is increasing in respondents’desired government assistance. Immigration “Do you think the number of immigrants from foreign countries who are permitted to come to the United States to live should be [1. increased a lot; 2. increased a little; 3 left the same as it is now; 4 decreased a little; 5. decreased a lot]?” Answers are in a scale from 1 to 5, following the order in which they appear in the question. We reverse the scale so that higher values correspond to more liberal views. Race Relations Index constructed from the following two questions (Group Thermome- ter): “Still using the thermometer, how would you rate the following group: Blacks.”; “Still using the thermometer, how would you rate the following group: Whites.”. Answers are collected on a 0-100 scale, and answers higher than 97 are coded as 97. 0 represents the “coldest”(most averse) feelings, while 100 is “warmest”feelings. Our index of race relations is simply the difference between the rating given to black people and the one given to white people. Abortion “There has been some discussion about abortion during recent years. Which one of the opinions on this page best agrees with your view? You can just tell me the number of the opinion you choose. [1. By law, abortion should never be permitted; 2. The law should permit abortion only in case of rape, incest, or when the woman’slife is in danger; 3. The law should permit abortion for reasons other than rape, incest, or danger to the woman’slife, but only after the need for the abortion has been clearly established; 4. By law, a woman should always be able to obtain an abortion as a matter of personal choice]” Trade “Some people have suggested placing new limits on foreign imports in order to protect American jobs. Others say that such limits would raise consumer prices and hurt American exports. Do you favor or oppose placing new limits on imports, or haven’t you thought much about this? [1. Favor; 3. Haven’tthough much about this; 5. Oppose]” Trust in Government Index contained in the ANES cumulative file. It is constructed aggregating the following five variables: “Trust the Federal Government To Do What is Right”, “Federal Government Run by Few Interests or for the Benefit of All”, “How Much Does the Federal Government Waste Tax Money”,and “How Many Government Offi cials Are Crooked”. The final index ranges between 0 and 100.

64 Low Trust In Government Indicator variable equal to 1 if the respondent scores in the lowest quintile of the wave-specific distribution of “Trust in Government”. Using the questions described above, we construct the following indexes, based on the polychoric correlation matrix computed pooling the five waves together. Redistribution First polychoric principal component extracted from “Redistr. Spend- ing”and “Redistr. Assist”. It correlates positively with both measures. Immigration and Race First polychoric principal component extracted from “Immigra- tion”and “Race Relations”. It correlates positively with both measures. Globalization First polychoric principal component extracted from “Immigration”and “Trade”. It correlates positively with both measures. Civil Rights First polychoric principal component extracted from “Race Relations”and “Abortion”. It correlates positively with both measures. Each of the twelve measures described above are then regressed on individual characteris- tics and wave fixed effects using the following regression, estimated with OLS,

2 yi = α + αt + β1agei + β2agei + β3whitei + β4womani + γgtpartyg + i g D,R ∈{X }

where yi is the attitude/preference of individual i, αt are wave fixed effects, partyg are group dummy variables for people identifying as Democrats or Republicans (the omitted

category being Independents); the coeffi cients of these group dummies, γgt, are wave specific; age, white and woman are individual demographics, described below. We use standardized residuals from these regressions as our final measures of individual attitudes/preferences in all the analyses carried out on ANES data, except for Figure A1. In Figure A1 the sample is restricted to people who identify as Independents and therefore we do not condition on political affi liation. All residuals are standardized to have mean 0 and standard deviation 1 on the pooled sample. The other variables used in the analysis are the following: Social Class “There’s been some talk these days about different social classes. Most people say they belong either to the middle class or the working class. Do you ever think of yourself as belonging in one of these classes?”;Depending on the answer, the following follow- up questions are asked: (i) “Well, if you had to make a choice, would you call yourself middle class or working class?”;(ii) “Well, if you had to make a choice, would you call yourself middle class or working class?”;(iii) “Would you say that you are about average middle/working class or that you are in the upper part of the middle/ working class? ”We aggregate answers “Lower Class (Volunteered)”, “Average Working ”, “Working”and “Upper Working”in the macro

65 category “Lower Class”;answers “Lower Middle”and “Average Middle”in the macro category “Middle Class”; and “Upper Middle”and “Upper (Volunteered)”in “Upper Middle/Upper Class”. Income Self-reported family income in the year before the survey. It should include income from all sources, including salaries, wages, pensions, social security, dividends, interest, and all other income. The variable contained in the Cumulative Data File classifies observations in 5 year-specific quantiles, reported in Tables A2 and A3. Religion “Do you consider Religion to be an important part of your life? Answers are binary, and we code “Yes”as 1, “No”as 0. Education Highest degree obtained. In the regression analysis, education is accounted for by four dummy variables for whether the respondent attained a graduate degree, college degree, some college, and high school or less. White Self-identified race. An indicator for whether the respondent identifies as white. Age Self-reported age, in years. The square of age is included in the regressions in order to account for non-linear relation typically found when dealing with subjective dependent variables. Woman Self-identified gender. Dummy equal to 1 if the respondent identifies as female. Party “Generally speaking, do you usually think of yourself as a Republican, a Democrat, an Independent, or what?”. A small share of respondents answers “None/No preference”. we code Independents and these respondents in the same category. Figure 2 is constructed as follows: (i) We estimate the residuals of “Immigration and Race”and “Redistribution”and standardize them on the full pooled sample, as described earlier in this section. (ii) We then cluster individuals based on these residuals, using the K-means algorithm with K = 2, with initial guesses the group means obtained by stopping at two groups the hierarchical partition obtained applying Ward’s method on the same residuals. The clustering exercise is performed on 2000-2008 and 2012-2016 separately. (iii) For each of the two periods, we plot respondents in the two-dimensional space identified by the residuals, with two different colors based on cluster membership. We use blue markers for the cluster which is on average more in favor of redistribution, red markers for the one more averse to redistribution. Dashed lines indicate cluster means. In Table 2 and Table A2 the dependent variable is a dummy variable equal to 1 (0) if the individual is classified in the blue (red) cluster. Table 3 is obtained as follows: (i) We estimate the residuals of “Redistr. Spending”, “Redistr. Assist”, “Immigration”, “Race Relations”, “Trade”, and “Abortion”, as described earlier in this section. (ii) We then extract two factors from the correlation matrices of these residuals over the periods 2000-2008 and 2012-2016 separately (results are substantally the same if use the covariance matrix instead). The method of factor extraction is Principal

66 Axis Factoring: to obtain factor loadings, spectral (eigenvalue) decomposition is performed on a modified correlation matrix, where the ones on the diagonal have been replaced by an initial estimate of the attitudes’communalities (the squared multiple correlation coeffi cients) and all other entries are the same of a standard correlation matrix. After the extraction, the initial estimate of the common variance (communality) is updated based on the output of the decomposition. The process is iterated until convergence. (iii) When we perform the analysis leaving the number of factors free, the output suggests the presence of only two main factors. As required by the iterated nature of the communality estimation, we perform the analysis again, specifying that at most two factors need to be retained (the results from the constrained and uncontrained estimation are very similar, in terms of factor loadings and relative importance of the two factor extracted). (vi) Finally, we report in the table the factor loadings and the share of common variance explained by each the first two factors thus obtained. In Table 4 and Table A3, the dependent variables are factor scores (i.e. estimates of the level of the two factors for each individual), obtained using the regression method.

Exposure to Import Competition: CCES

Data In the analysis of the effects of import shocks from China performed in section 5.2, we combine data from multiple datasets. All individual level variables are from the Cooperative Congressional Election Study (CCES), a series of surveys with questions on political attitudes, vote choices and individual demographic characteristics. The surveys are administered online on a opt-in basis, but sample matching is employed to assure representativeness of the target population, namely US individuals aged 18 or more. The cross-sectional study has been carried out yearly starting in 2006. Between 2010 and 2014 the CCES also had a longitudinal component, with questions similar to the ones administered in the cross section. We exploit both datasets. In the repeated cross-section analysis, for each outcome variable of interest, we use the first and the last wave for which comparable questions on that outcome are asked. Following this logic, for preferences for redistribution and immigration, we use as first years 2006 and 2007 respectively. For each of the four outcomes, the second year of measurement is 2016. In our panel analysis we rely on the data collected in 2010 and 2014. Autor et al. (2013) measure the change in import exposure in each commuter zone (CZ) by the average change in Chinese import penetration in the CZ’sindustries, weighted by each industry’sshare in the CZ initial employment. Thus, the change in export exposure in CZ c is defined as: Lm,c,00 Im,t2 Im,00 ∆IPc = − (49) Lc,00 × Ym,91 + Im,91 Xm,91 m M X∈ −

67 where the first term in summation is the share of manufacturing industry m in total employ- ment of CZ c, while the second term is the increase in US imports from China of products typical of m between 2000 and year t2, standardized by m’s market size in 1991 (i.e, prior to the boom in China’sexports). Since the change in penetration is likely to be endogenous, imports are instrumented as in Acemoglu et al (2016), in a way similar to Author et al (2013). EU EU The instrument is obtained by replacing (Im,t Im,00) with (I I ), namely the increase 2 − m,t2 − m,00 of Chinese imports in eight European countries over the same period, and all the other terms in (1) with their values in 1988. Data on bilateral imports are downloaded from the UN Comtrade database in HS-6 product classification. In particular, we obtain data on imports from China for the US as well as for eight European countries, namely Australia, Denmark, Finland, Germany, Japan, New Zealand, Spain and Switzerland. Such data are treated following a procedure similar to Autor et al. (2013), Acemoglu et al. (2016) and Autor et al. (2017). In particular, to obtain industry- level imports, we apply the crosswalk developed by Pierce and Schott (2012), which maps each HS-6 product into a single SIC industry. In the cross-section analysis we consider changes in imports between 2000 and 2016 (the last year of measurement of our outcome variables). For consistency with the cross section, also in analyzing the panel we consider shocks starting 6 years before the first year of measurement of attitudes, and therefore consider changes in imports between 2004 and 2014. Trade flows are made comparable across time by deflating them with the PCE index. Import shocks are weighted using data on employment by county and industry contained in the County Business Patterns (CBS). As these employment figures are often reported in brackets, we use the fixed-point methodology developed by Autor et al. (2013) to make them continuous. We also map the counties in commuting zones (CZ), as in Acemoglu et al. (2016). The CZ-level and county-level controls are obtained form two sources. The first is the online public database of the American University, from which we downloaded the data on the 2000 presidential elections, broken down by county. The second is the dataset built by Autor and Dorn (2013), which contains the offshorability and routine task intensity indexes that we include among the CZ controls in our models. After combining data from all these sources we obtain a final pooled estimation sample of more than 66,000-72,000 individuals for the outcomes studied in the cross section. This amounts to roughly 60 individuals per CZ and year. The sample size of the panel samples is between 7,250 and 9,408 in-between, at 8300, roughly 15 individuals per CZ (and year) on average. The unit of variation of import shocks are CZs, and the CCES micro data do not include survey weights that ensure representativeness at CZ or county level. All analyses are therefore unweighted. The sample size of the cross section, considerably larger than the one of found in similar studies, and the balanced longitudinal structure of the 2010-2014 panel

68 survey limit between-year sampling variability. To perform the robustness checks presented in Table A5 an Table A6, we use the housing net worth shocks between 2006 and 2009, taken from the county-level dataset by Mian and Sufi (2014). Below, we describe the main dependent variables and the individual controls used in our analysis, all coming from the CCES. The other variables are described in more detail in the sources indicated above. Redistribution First principal component of the following two questions: “If your state were to have a budget deficit this year it would have to raise taxes on income and sales or cut spending, such as on education, health care, welfare, and road construction. What would you prefer more, raising taxes or cutting spending? Choose a point along the scale from 0 to 100”; “If the state had to raise taxes, what share of the tax increase should come from increased income taxes and what share from increased sales taxes? Choose a point along the scale from 0 to 100.”. The component correlates positively with willingness to raise taxes instead of cutting spending and with higher desired share of tax revenues from income tax (and these types of answers are positively correlated). Hence the index captures willingness to redistribute. Immigration. We extract the first polychoric principal component from two questions: “What do you think the U.S. government should do about immigration? Grant legal status to all illegal immigrants who have held jobs and paid taxes for at least 3 years, and not been convicted of any felony crimes. [1. Yes; 2. No]”and “What do you think the U.S. government should do about immigration? Increase the number of border patrols on the US-Mexican border. [1. Yes; 2. No]”. “Immigrantion”is the resulting first principal component, recoded so that higher values capture more liberal views on immigration. Both dependent variables are demeaned and divided by their standard deviation computed on the two periods pooled together. The regression and correlation analysis also makes use of the following individual controls: Education Self-reported highest educational level achieved. Based on this question we create dummy variables for three education levels (less than college, some college, college or more). White Self-identified race. Dummy equal to 1 if the respondent identifies as white. Age Self reported age. We also include its square in order to account for non-linear relations often found when dealing with subjective dependent variables. Woman Self-reported gender. Dummy equal to 1 if the respondent reports being a female. Secular “How important is religion in your life? [1. Very important; 2. Somewhat important; 3. Not too important; 4. Not Important]”. Indicator variable equal to 1 if the respondent answers “Not too important”or “Not important”.

69 Family Income Self-reported annual family income, in 12 income brackets. Made con- tinuous by coding each bracket as its midpoint. Income Bottom 33% Indicator variable equal to 1 if the respondent falls in the bottom third of the wave-specific family income distribution. Interest in Politics “Some people seem to follow what’s going on in government and public affairs most of the time, whether there’s an election going on or not. Others aren’t that interested. Would you say you follow what’sgoing on in government and public affairs? [1. Most of the time; 2. Some of the time; 3. Only now and then; 4. Hardly at all]”. Based on this question we construct a binary variable (grouping answer 1 and 2 in one category, 3 and 4 in the other). The question is not asked in the 2006 cross section, and therefore is not included in the analysis on the 2006-2016 cross section. CZ Mover Dummy equal to 1 if the commuting zone of residence of the respondent changed between 2010 and 2014.

Heterogeneity Analysis: Specification In order to test the heterogeneity of the effect of import shocks on different social groups, we rely on the following specification,

∆yi,c = α + β ∆IPc + β ∆IPc Gi + β Gi + X0 β + Z0β + ui,c, 0 1 ∗ 2 i,c,1 3 c 4

where ∆yi,c measures the change in individual i’sattitudes between 2010 and 2014; ∆IPc is the

change in import penetration in CZ c, between 2004 and 2014; Gi is a dummy variable equal to 1 if i belongs to the social group for which we want to study the heterogeneous effect (people

in the bottom third of the income distribution in 2010 or people who are secular in 2010). Xi,c includes a set of individual covariates (gender, race, educational attainements, age and age squared, interest in politics) measured in 2010, plus i’s initial attitudes in 2010 to allow for differential trends (e.g. mean reversion). As in the baseline specification described in section 5.2 of the paper, the vector also includes an indicator variable for those who changed CZ between 2010 and 2014, alone and interacted with the shocks; here these latter two variables

are also interacted with Gi, to correctly identify the heterogeneous effects of the shocks on

members of G and G¯ who lived in the CZ throughout the five years. Zc is the vector of 35 covariates referring to the CZ in the year 2000. ∆IPc and its interactions are instrumented using the usual instrument (and the corresponding interactions).

35 As in Autor et al. (2017), the vector Zc includes the manufacturing share of employment, the offshorability and routine task indexes of Autor and Dorn (2013) and the interaction between the county vote share for GW Bush in the presidential election an a dummy for Republican victory in that county, all variables meaured in 2000. Inclusion of these variables is important for identification, given the nature of the instrument

70