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Is Europe an optimal political area?

ABSTRACT Employing a wide range of individual-level surveys, we study the extent of cultural and institutional heterogeneity within the European Union and how this changed between 1980 and 2009. We present several novel empirical regularities that paint a complex picture. Although Europe has experienced both systematic economic convergence and an increased coordination across national and subnational business cycles since 1980, this has not been accompanied by cultural or institutional convergence. Such persistent heterogeneity does not necessarily spell doom for further political integration, however. Compared with observed heterogeneity within EU member states themselves, or in well-functioning federations such as the United States, cultural diversity across EU members is of a similar order of magnitude. The main stumbling block on the road to further political integration may not be heterogeneity in fundamental cultural traits, but other cleavages, such as national identities.

The European Union is facing hard challenges. Throughout the EU, many citizens have become less trusting of EU institutions and less tolerant of supranational interference with domestic policies. As a result, the process of European integration is struggling--and, for the first time, has even reversed direction with Brexit. Populist parties, which blame the EU for everything that is wrong in their own countries, have gained electoral support. Animosity between countries and, particularly, a North--South cleavage are evident. (1) Is this just a (temporary) by-product of the recent financial crisis, or are the recent tensions a manifestation of preexisting and deeper cleavages? Was the project of a federal Europe too ambitious, because Europeans are too heterogeneous in their economic interests, beliefs, and cultural values, or are the current difficulties mainly due to inadequate supranational institutions? The answers are not simple, and we uncover forces pushing in opposite directions.

We follow Alberto Alesina and Enrico Spolaore (1997, 2003) and Robert Barro (1991) in thinking of the optimal size of a political union as emerging from the trade-off between the benefits of integration in terms of economies of scale and scope, and the cost due to heterogeneity in preferences. There are economies of scale in Europe. To begin with, Europe has a large market with free trade. In addition, environmental protection, control of immigration, defense against terrorism, foreign policy, a common army, research, and innovation may all be best addressed at the European rather than at the national level, and more so today than 30 years ago. Europeans are aware of these advantages. In the 2016 Eurobarometer survey (European Commission 2016), a very large fraction of respondents favored more EU-level decisionmaking in areas such as fighting terrorism (80 percent in favor), promoting peace and democracy (80 percent), protecting the environment (77 percent), dealing with migration from outside the EU (71 percent), and securing energy supplies (69 percent).

Is there sufficient commonality of views among Europeans to make it possible to reap the benefits of these economies of scale? Specifically, how different are Europeans in fundamental cultural traits? And during the last 30 years, have they become more similar in terms of economic, institutional, and cultural fundamentals? To address these questions, we study the EU-15 countries plus Norway between 1980 and 2009. Thus, we do not investigate Central and Eastern Europe, nor do we study the consequences of the recent financial crisis. (2)

We begin by documenting a deep process of economic integration in goods, services, and financial markets. The first phase of this process, approximately between 1980 and the late 1990s, was also accompanied by rapid economic convergence, with poorer European countries growing faster than richer ones. Convergence continued, although at a lower speed, until the 2008-09 global financial crisis. We also show increased comovement across EU economies (a relevant condition for optimal currency unions, if not for political ones), both at the national and subnational levels (using the NUTS3 regions; (3) see subsection I.C below). In addition, and contrary to the United States, overall after-tax income inequality has not increased within this group of countries since 1980.

One would expect economic integration and convergence to be accompanied by increased homogeneity in attitudes between citizens of different countries. Increasingly shared values were among the anticipated benefits posited by the founding fathers of the EU. (4) We find no evidence of this. On the contrary, between 1980 and 2009 Europeans became slightly more different in their attitudes toward trust, values such as appreciation of hard work or obedience, gender roles, sexual morality, religiosity, ideology, the state's role in the economy, and related economic issues. We show that these traits evolved over time and are not immutable national characteristics. Both Northern and Southern European countries became more secular, but the former at a faster rate than the latter, so cross-country differences increased.

European integration also deliberately attempted to harmonize institutions and policies in several areas, establishing common benchmarks and targets for institutional improvement. Did this lead to institutional convergence? We find mixed evidence: In some institutional areas, European countries became more similar, but in others the opposite happened. In particular, the quality of public administration and of legal systems did not converge, with Southern Europe falling further behind Northern Europe.

Does this mean that the project of a political union in Europe is doomed? Not so fast. In the second part of this paper, we show that preference heterogeneity and cultural diversity are about 10 times as large within each EU country in our sample than between them. This finding applies not only to individual data but also to regional averages. Within-country differences in regional averages are sometimes larger than differences between the average traits of regions belonging to different countries (think of Northern Italy versus Southern Germany, and Northern Italy versus Southern Italy). If the fully functioning democracies in Europe can handle a substantial amount of within-country cultural diversity, why could the EU not handle a similar level of heterogeneity between individuals in different countries?

A comparison with the United States leads to similar conclusions. Europeans are not more different from each other than Americans, who, incidentally are also becoming more different from each other. If the United States can handle these differences relatively well, what prevents Europe from also doing so? Relatively small cultural differences in Europe are probably vastly amplified by other cleavages, such as national identity and language. Cooperation and conflict resolution are much easier if individuals share a common history, centuries of nation building, and a common language, as in the United States. Thus, the critical issue for the future of European integration is not so much that Europeans are still too different from each other in terms of culture, policy preferences, or national interests. The important question is the evolution of national identities versus a European identity.

Our paper is related to several recent contributions. Spolaore (2013) adopts the same conceptual approach as our paper, emphasizing the benefit of scale and the cost of heterogeneity. He discusses Jean Monnet's theory, according to which any additional move toward integration in Europe cannot be reversed. On this point, Luigi Guiso, Paola Sapienza, and Luigi Zingales (2016) argue that the EU is stuck in the "middle of the river"--gone far enough to be very costly to abandon, but subject to too many forces pulling in a centrifugal direction. Guiso, Helios Herrera, and Massimo Morelli (2016) emphasize the German/Greek cultural divide during the sovereign debt crisis. Our more systematic evidence provides a different view, in terms of similarity of "cultural fundamentals." Markus Brunnermeier, Harold James, and Jean-Pierre Landau (2016) highlight how different economic ideas, especially between the French and the Germans, are a crucial impediment to further economic integration. These differences are clearly there, and in our analysis we confirm that cultural attitudes in France are quite different from those in Germany. However, we focus on deep cultural traits that we think are more important for the long-run viability of a political union, compared with possibly contingent ideas about the appropriate macroeconomic policy framework.

The paper is organized as follows. Section I discusses economic convergence in Europe. Sections II and III consider cultural and institutional convergence. Section IV compares cultural heterogeneity within and across the EU countries. Section V compares the EU countries with the U.S. states, and section VI concludes.

I. Economic Convergence

One of the purposes of the EU has been to foster greater economic integration among its members. This goal has been vastly achieved. How did this affect economic convergence between European countries and regions? A large body of literature has addressed this question, with mixed results that depend on the sample of countries, time period, method of analysis, and type of convergence. Existing studies generally find evidence of economic convergence in GDP per capita in the long run, due to the catch-up in growth of the poorer countries (Greece, Ireland, Portugal, and Spain in the earlier period, and Eastern Europe more recently). (5)

An equally large body of literature asks whether trade and financial integration make business cycles more or less synchronized. A priori, the effect can go either way, because trade integration may lead to specialization and hence divergence, or complementarity in production and convergence. Likewise, financial integration could amplify the domestic effects of idiosyncratic shocks or increase the international transmission of such shocks, with ambiguous effects on synchronization. The evidence is mixed, although the prevailing view is that business fluctuations have become more synchronized within Europe, particularly in the eurozone. (6)

In this section, we revisit and complement the analysis of economic convergence and output comovement for the EU-15 countries plus Norway in the period 1980-2009. This is the same sample of countries and same period covered by the analysis of cultural convergence in section II. The data sources for the variables used in this section are described in table A.l in the online appendix. (7)

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I.A. Trends in Average Per Capita Income

We start with long-run convergence in GDP per capita. The source is Penn World Table 9 (Feenstra, Inklaar, and Timmer 2015). (8) Figure 1 depicts the standard deviation of real GDP per capita among the 16 countries in our sample. Barro and Xavier Sala-i-Martin (1992) pioneered this type of analysis, which they call sigma convergence. After an initial drop in the 1980s and 1990s, the dispersion in real GDP per capita remained roughly stable between the late 1990s and 2009.

This pattern is confirmed by the analysis of beta convergence (again using Barro and Sala-i-Martin's terminology). In figure 2, we illustrate a crosscountry regression plot, where we estimate a linear regression of the growth of real GDP per capita between 1980 and 2009 against the initial level of real GDP per capita in 1980 (in logs) in the same sample of countries. The slope of the regression line is negative and statistically different from zero, indicating that throughout this period average growth was higher for the initially poorer countries. The evidence of beta convergence is much weaker from the late 1990s onward, consistent with sigma convergence, but this is largely due to the strong performance of Norway (a high-income country), which benefited from the rise in oil prices in more recent years. The sample includes both those countries that belong to the Economic and Monetary Union (EMU) and those that do not, but the pattern is similar if we confine our attention to the EMU.

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I.B. Income Inequality

We now turn to the dispersion of individual income within Europe. Income (which is highly correlated with education and occupational status) is a key determinant of cultural traits (Inglehart 1997). Anthony Atkinson (2015) and Thomas Piketty (2014), among others, document that inequality has increased in some (but not all) advanced countries. At the same time, there was convergence in average per capita income between countries in Europe. The net effect of these two forces is uncertain. How did overall income inequality evolve in Europe between the early 1980s and 2010?

To answer, we rely on micro data from the Luxembourg Income Study (LIS), which are obtained from independent income surveys and are ex post harmonized. The data are available for only a subset of countries, namely, Denmark, Finland, France, Germany, Italy, Luxembourg, the Netherlands, Spain, and the United Kingdom. Income is measured as total disposable household income, net of taxes and transfers. It is converted to individual income using weighted household size by country, and to 2010 purchasing power parity-adjusted dollars for all years. We pool together all households in our sample, irrespective of nationality, and compute a yearly Gini coefficient. (9) The evolution of the after-tax Gini coefficient is roughly flat between 1985 and 2010 (see figure A.1 in the online appendix). The forces of economic convergence and the within-country dynamics of increased inequality appear to cancel out. Thus, in Europe as a whole (for the countries for which we have LIS data), inequality did not increase, contrary to what happened in the United States (Piketty and Saez 2003).

I.C. Correlation in Yearly Growth Rates

Next, we consider the issue of economic convergence within the EU at the business cycle frequency. The unit of analysis is the NUTS3 region, and the data are from Cambridge Econometrics. We split the sample into two subperiods, preceding and following the inception of the single currency: 1980-98 and 1999-2009. For each subperiod, we estimate a matrix of pair-wise linear correlation coefficients, [[rho].sub.ijt], of the yearly growth rate of GDP between all regions in the sample, where i and j denote regions and t=1,2 denotes subperiods. We then compute the change in these correlation coefficients over the two subperiods, [[delta].sub.ij] = [[rho].sub.ij2] - [[rho].sub.ij1]. Figure 3 illustrates the kernel density of these changes--the distribution of [[delta].sub.ij]--for (i, j) pairs of regions belonging to the same country (dotted line) and to different countries (solid line). (10) Although the same-country distribution centers approximately on zero, the distribution for regions belonging to different countries is clearly shifted to the right (the median and mean of the kernel density are positive). Thus, the introduction of the euro is associated with an increase in the correlation of yearly output growth for (t, j) pairs belonging to different countries, while within-country correlations have not changed substantially on average. In other words, since the euro began to be used, there has been increased synchronization of regional output across European countries at the yearly frequency, but not within countries.

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This result also holds when focusing only on (i, j) pairs of regions with a sum of log populations (measured in 1980) above the median or above the 75th percentile, and also for regional pairs with geographic distance of the regions' centroids above the median or above the 75th percentile. Thus, increased output comovement does not come solely for tiny or very close pairs of regions, but holds across all of Europe, and is not only due to the catching up of small regions. We have also disaggregated output by sector, and the result of enhanced comovement between regions belonging to different countries holds for all sectors, with the exception of agriculture. (11)

Finally, notice that while our estimates of [[rho].sub.ijt] are likely carrying noise due to sampling variability, this particular issue should not affect the relative position of the distributions that we report--barring nonintuitive changes in sampling variability over time.

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Is this enhanced correlation in yearly growth rates just a consequence of sharing a common monetary policy and a common currency, or does it reflect more general tendencies, such as commercial and financial integration? To address this question, we consider the change in correlation coefficients, [[delta].sub.ij], between different groups of regions. Figure 4 depicts the distribution of [[delta].sub.ij] within the EMU, outside the EMU, and between regions both inside and outside the EMU. The shift to the right is most pronounced for regions within the EMU, but the change in correlation between EMU and non-EMU pairs also has a large density mass above zero, suggesting that the increased output synchronization is not just due to sharing a common monetary policy.

We next focus only on the EMU countries. We repeat the same exercise as figure 4, but for three groups of regions: (i, j) pairs within the core set of countries in the eurozone, pairs within the periphery only, and pairs between the core and periphery. The core countries are defined as Austria, Belgium, Finland, France, Germany, Luxembourg, and the Netherlands. The periphery consists of Greece, Ireland, Italy, Portugal, and Spain. There has been increased comovement in all three groups of regions, but it has been most pronounced within the core and between the core and periphery, suggesting that the shocks that have hit the periphery have remained more idiosyncratic (recall that the second subperiod ends in 2009, so the analysis does not include the European sovereign debt crisis). Figure A.2 in the online appendix shows the results.

I.D. Cluster Analysis

Finally, we consider cluster analysis, which imposes less structure on the data, to look at comovements in regional output. Here, too, the raw data are yearly growth rates in regional real GDP, for the same two subperiods, 1980-98 and 1999-2009. We employ two methods of analysis. The first is a dimensionality reduction method--principal component analysis (PCA). (12) The second method is a partitioning cluster analysis--spectral clustering (SC). Dimensionality reduction methods aim to reduce a multidimensional problem into a lower dimensional one. For us this is equivalent to saying: Although the output dynamics of Europe at the regional level in our sample can be described by 966 different output time series (one for each NUTS3 region), we can do equally well by concentrating on only one or two main dimensions. This would be a valid approximation, for instance, if there were one or two groups of regions in Europe following nearly identical growth trajectories within each cluster. Spectral clustering is a subtler method, and aims not only to reduce the dimensionality of the problem but also to truly classify observations (regions) into groups of connected regions ("connected" meaning that i and j covary in terms of output in the graph represented by the adjacency matrix [GAMMA] = {[[rho].sub.ijt]}). (13)

Figure 5 illustrates the results for the EU-15 countries. The left panel depicts the PCA approach and produces the scree plot profile of eigenvalues for the subperiods 1980-98 and 1999-2009. The scree plot "elbow" clearly has a sharper angle in the second period, indicating the possibility of representing the correlations among regions as a lower dimensional space. The graph shows how regional output growth within Europe is almost one-dimensional in the 1999-2009 period. (14)

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The right panel depicts the SC approach. Finding the number of connected components of [GAMMA] is equivalent to estimating the rank of [GAMMA] (Trebbi and Weese 2015). Let us indicate such rank as J and [[lambda].sub.k] as the k-largest eigenvalue of [GAMMA]. Asymptotically, the first Jof these eigenvalues will be positive and bounded away from zero, while the remaining N - J will hover around zero. We report the lowest eigenvalues of [GAMMA] (that is, we try to visualize [[lambda].sub.k] for k[less than or equal to]N-J). Such a statistic has the same intuition of standard scree plots. A reduction in the number of estimated clusters is evident, because in the 1999-2009 period the curve moves away from zero faster than in the 1980-98 period, indicating fewer clusters in 1999-2009.

I.E. Discussion

The early phase of European integration in the 1980s and 1990s, which coincided with the development of the Single Market, saw economic convergence and catch-up growth by the poorer countries. This convergence slowed down in the second phase of European integration, from the late 1990s until 2008, which coincided with the single currency. Conversely, the single currency period was associated with increased comovement in regional output growth at the yearly frequency, especially between the EMU's core countries, but also between its core and periphery countries, and between regions both inside and outside it. Finally, overall income inequality remained stable between the mid-1980s and the onset of the 2008-09 global financial crisis.

II. Cultural Divergence

Europeans have not become culturally more similar during the last three decades. Several arguments would lead us to expect cultural convergence from 1980 onward. First, as argued above, this was a period of economic integration, with more mobility of goods, capital, and people within Europe. Increased economic exchange should strengthen mutual adaptation and understanding. (15) Second, economic convergence should lead to convergence of cultural traits. Third, the single currency led to correlated economic shocks (of a monetary nature) and policy coordination in Europe. This may also reinforce cultural similarities, as national media and public debates devote more attention to common European issues. Fourth, this period was not associated with an increase in income inequality, which could have bred cultural divergence. Conversely, there are also subtler reasons to expect divergence. Trade integration changes relative prices and the structure of production, leading different countries to specialize in different sectors, and in some cases this can push countries toward cultural divergence (Olivier, Thoenig, and Verdier 2008). Moreover, sharing common economic policies can increase conflicts and antagonize public opinion (Feldstein 1997).

We consider a broad range of questions in waves 1 through 4 of the European Values Survey (EVS), which are approximately 10 years apart, with the first one in 1980-81 and the last one in 2008-09. We have data for the same EU-15 countries plus Norway considered in the previous section, although for a few countries the first two waves are missing. (16) We selected several longitudinally harmonized questions asked in all waves, which capture attitudes toward five sets of issues extensively studied in the literature. (17) Because, in section V below, we compare Europe and the United States, a criterion for selecting questions was also the availability of comparable questions in the General Social Survey for the United States.

The issues are (i) religiosity, which includes questions that seek to capture the strength of religious beliefs and principles (including acceptance of euthanasia and suicide) and adherence to religious practices; (ii) sexual morality, such as attitudes toward homosexuality, divorce, and abortion; (iii) gender equality, concerning the role of women in the workplace and in the family; (iv) the role of the state, which includes questions eliciting beliefs about the role of the state vis-a-vis the market, the desirability of redistribution, the respondent's left/right ideology, and whether success in life reflects effort or luck; and (v) cultural capital, which includes questions eliciting general social values and attitudes toward others, for example, generalized trust or specific virtues appreciated in children, such as obedience, hard work, and unselfishness. Note that these questions relate to deep cultural beliefs, some of which evolve relatively slowly over time, and which are not particularly sensitive to business cycle fluctuations. (18) They seek to capture fundamental cultural traits and values that may be considered as prerequisites for sharing common political institutions and identities. The full set of questions is listed in table A.2 in the online appendix. (19)

We purposely consider a broad set of cultural traits above and beyond economic issues. We are not discussing here the formation of, say, a free trade area, but a full political union. In order to survive, a "nation" needs a certain amount of commonality of fundamental views above and beyond mere economic philosophies. (20) In any event, in the online appendix we show robustness to the selection of cultural traits considered by solely limiting the subsets of cultural traits to the role of the state and cultural capital.

We also consider a set of individual socioeconomic covariates--such as age, education, and occupation--that are likely determinants of cultural traits (these are listed in table A.3 of the online appendix). They are all coded as binary variables. For computational simplicity, we only consider a random subsample of 250 respondents per country and for each wave (each survey has about 1,500 respondents on average); but the results are robust to including 500 respondents per country-wave. The computational issues will become evident in the construction of the pairwise individual distance measures described in the following subsection.

II.A. Cultural Difference

Here we only consider the questions and countries that were included in all four waves. (21) Because we have 250 individuals for each country-wave, our sample consists of 2,750 individuals per wave. (22) Each individual corresponds to a vector in the TV-dimensional space of cultural attitudes and of socioeconomic characteristics. Let [Y.sub.is] denote the entire TV x 1 vector of cultural dimensions for individual i in wave s, with elements [y.sub.is], and [X.sub.is] be the vector of K socioeconomic features, with elements [x.sub.is]. [X.sub.is] and [Y.sub.is] summarize the answers to the questions. We can construct a measure of cultural distance between individuals i and j in wave s based on the Gaussian kernel as [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], where [theta] is the kernel width and [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] is the Euclidean distance. Socioeconomic distance [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII](s) between individuals is similarly defined. (23) We can compute pairwise distances [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] for each pair of individuals per wave, giving 3,779,875 = (2,750 x 2,749)/2 total (i, j) pairs for each (Y, X) and each s. It is then clear why we impose a balanced number of individuals (250) for each country, as much of our analysis will evolve around generating distributions of pairwise individual distances [d.sub.ij](s).

A natural conjecture is that, as socioeconomic distance [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII](s) between two individuals increases, so does cultural distance [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII](s). To remove the effect of socioeconomic distance, we can compute the conditional cultural distance between any two individuals, by conditioning each element of vector [Y.sub.is] on vector [X.sub.is] (by taking the residuals of a set of regressions of each component [y.sub.is] on the entire vector [X.sub.is], then computing the distance between these residuals for any two individuals).

We can then nonparametrically estimate the distribution of cultural distances between all individuals in our sample at different points in time. In particular, we can estimate the distribution of cultural distances between citizens of the same and of different countries in waves s = 1, 4. Comparing these two waves tells us how the distribution of cultural distances has evolved during the last 30 years.

These distributions are given in figure 6. The densities are estimated using the Epanechnikov kernel function. The dotted line refers to wave 1 (about 1980), and the solid line refers to wave 4 (about 2009). The left-side panels refer to unconditional distances, and the right-side panels refer to conditional distances. The bottom two panels refer to within-country cultural distances (that is, using distances generated by (i,j) belonging to the same country), and the top two panels refer to distances among individuals of different countries. The more recent (s = 4) distribution is shifted to the right, both unconditionally and conditionally, and by approximately the same amount within and between countries. On average, Europeans have become more dissimilar, both within and between countries.

This result, in part, may depend on the distance metric used. The Gaussian kernel function is a quadratic function and gives more weight to the dimensions across which the individuals appear most dissimilar. Estimating the same distribution of distances using the cosine distance, [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII](s) = [Y.sub.is] x [Y.sub.js]/||[Y.sub.is]|| x ||[Y.sub.js]||, which does not place as much weight on large differences across specific cultural dimensions, gives two almost-overlapping distributions in waves 1 and 4, both unconditionally and conditionally, and both within and between countries. (24) Thus, we can conclude that during the last 30 years, there is virtually no evidence of cultural convergence, either within or between countries. If anything, we see cultural divergence.

Although figure 6 illustrates the overall distribution of cultural distance for all countries in our sample, we can also consider each country in isolation, focusing for simplicity on average cultural distance, rather than on the entire distribution of distances. This is done in table A.4 in the online appendix. For each country, we report the change in average cultural distance between waves 1 and 4, within each country, and between the citizens of a country and European citizens from all other countries, unconditionally and conditionally, on socioeconomic covariates. The last row of table A.4 reports the change in average distance, within and between all countries in the sample. All countries became more different from the others; also, within countries, cultural distance increased over time by about the same amount. In wave 1, average cultural distance within and across countries is about 0.55 with our standardized measures. Thus, on average cultural distance between two random individuals increased by about 10 percent both between and within countries between 1980 and 2009 (the average change is slightly larger across than within countries). The change is also highly statistically significant for all countries. The increase is particularly pronounced for Italy and Ireland, but there is no pattern concerning core versus periphery, or inside versus outside the EMU. Finally, note that wave 4 dates to 2008-09, before the sovereign debt crisis that plunged Southern Europe into a deep recession. In fact, some divergence could already be observed in comparing wave 1 with wave 3 (sampled in 1999-2000).

II.B. Specific Cultural Traits

We now consider changes in specific cultural traits and include all 16 countries and all questions. For each of the five broad issue categories--religiosity, sexual morality, gender equality, the role of the state, and cultural capital--we extract the first principal component of the specific survey answers referring to that issue in the overall sample, which pools together answers on all questions for all countries and all waves. The specific questions within each broad issue are generally highly correlated with the respective first principal components, as shown in table A.5 in the online appendix, except for the question on altruism, which we therefore omit from this part of the analysis. We focus only on country means.

Figure A.4 in the online appendix depicts the EU average (the solid line) and each country average (the dots) of each of these first principal components. The figures refer to unconditional responses, but our results are very similar when repeating the exercise on first principal components constructed by conditioning on socioeconomic covariates. Some change clearly took place in almost all cultural dimensions: Religiosity decreased on average, sexual morality and gender equality became less "traditional," and attitudes turned in favor of state intervention. Moreover, for all these dimensions except the role of the state, the dispersion between country averages appears to have increased over time or remained constant. This is generally visible from the figures, and is confirmed by the analysis of standard deviations across countries (limiting the sample of countries to only those that are sampled in waves 1 through 4).

Finally, we find that in four out of five cases, the divergence is due to several Northern European countries accentuating their differences relative to the EU average in the more recent waves, and likewise to several Southern European countries (notably Greece, Italy, and Portugal) moving in the opposite direction relative to the EU average. In other words, and in the terminology of Ronald Inglehart (1997), while Northern Europe has been becoming more "modern" at a faster pace than the EU average, Southern Europe (with the exception of Spain) has been following the general trend, but is increasingly lagging behind. These results are displayed in figures A.5 through A.9 in the online appendix.

II.C. Discussion

The evidence discussed above suggests that European citizens have not become more similar to one another during the last 30 years. The lack of cultural convergence also cannot be attributed to persistence in cultural traits. Individual traits have changed: All of Europe has become more secular, less traditional, and more tolerant, and also more inclined to accept a larger role for the state in risk sharing and redistribution. Moreover, the lack of cultural convergence cannot be blamed on an increase in inequality.

III. Institutional Divergence

A priori, one would expect to see institutional convergence in Europe. Harmonization of policies and institutions was an explicit goal of the process of European integration in several areas, such as product and financial market regulation. Even where EU member states retained unconstrained sovereignty, Europe often provided benchmarks and incentives for harmonization and to diffuse best practices, particularly with the so-called Lisbon Strategy. (25) Conversely, deeper integration may have also set in motion countervailing forces pushing toward institutional divergence. As trade barriers fall, countries are led to specialize in different tradable goods sectors. Moreover, the single currency led to a real exchange rate appreciation in Southern versus Northern Europe. This, in turn, shifted resources toward the nontradable sectors in Southern Europe, while the opposite happened in some Northern European countries. These opposite changes in the structure of production may have altered government incentives and policies, leading to institutional divergence. (26)

We consider a wide range of institutional outcomes in four specific policy areas. The first is the quality of government and public administration. Here we extract the first principal component from three sets of variables, which aggregate information about the quality and timeliness of the information provided by public administrations, the extent to which the executive can be held accountable to voters, the effectiveness and quality of the bureaucracy, and the absence of corruption in public administration and in the political system. (27) Relatedly, a governance indicator is constructed as the principal component from a number of World Bank Worldwide Governance Indicators, similar to those measured by the first index for the quality of government.

The second policy area is the quality of legal institutions. This variable aggregates a variety of indicators based on perceptions about the quality of the legal system, such as the protection of property rights, judicial independence, impartiality of courts, the rule of law, and civil liberties. The primary sources are institutional classifications compiled by the Fraser Institute, the World Bank, the Heritage Foundation, PRS Group, and Freedom House.

The third area is education. Here we use the first principal component of Programme for International Student Assessment (PISA) test scores for mathematics, science, and reading comprehension.

The fourth area is regulatory environment. Here we use the product market regulation variable in the Organization for Economic Cooperation and Development's (OECD's) database, a summary indicator of the regulatory environment in a broad range of areas, including state control and involvement, barriers to entrepreneurship, and barriers to trade and investment. A full list of the variables for each of these areas, with the corresponding sources and periods of availability, is given in table A.6 of the online appendix.

We start by asking whether we observe convergence or divergence in these institutional outcomes between countries by examining sigma convergence plots. Figure 7 plots the standard deviations across countries for each of the four broad indicators over time. (28) The quality of public administration converged between countries in the 1980s and 1990s, but since 2000 it has diverged sharply, and by 2010 dispersion was above its initial point. The same pattern emerges from the governance indicators, which are only available starting in the late 1990s. The quality of legal institutions is also only available starting in 1990. Here, too, we observe divergence, particularly since 2000. (29) PISA scores converged, although the data are available only every three years between 2000 and 2012. Product market regulation converged (data are available every five years, starting in 1998), which was an explicit EU policy goal. Conditioning on per capita income does not change the picture much. (30)

As with culture, the divergence in quality of government and legal institutions is largely driven by Southern Europe (mainly Italy, Greece, and Portugal) deteriorating relative to the European average, and some of the Nordic European countries improving relative to the average. In the two areas where there has been convergence, education and regulation, the process seems uniform, with most countries converging, from above or from below the European average. Figures A.10 through A.13 in the online appendix highlight these patterns.

III.A. Discussion

The observed convergence in product market regulation was a deliberate policy goal. The observed convergence in PISA scores is less obvious. The divergence in the quality of institutions is surprising. A conjecture is that trade integration and the single currency affected European countries' structures of production and allocations of resources. EU member states that enjoyed an institutional comparative advantage accentuated their specialization in sectors where these advantages were relevant for productivity. Those with a comparative disadvantage moved in the opposite direction. The single currency reinforced this tendency, because it led to exchange rate appreciation in Southern Europe, pushing more resources in the nontradable sectors (where institutions are less important determinants of aggregate productivity). These changes, in turn, could have altered political incentives in opposite directions in these two groups of countries. (31)

IV. Cultural Heterogeneity within the European Union

The previous sections showed that Europeans have not become more similar in their cultural traits. Does this mean that Europeans cannot form a political union? The answer to this question depends on the level of heterogeneity, and not just on whether it is decreasing or increasing over time. In this section, we compare the level of heterogeneity within and between countries. Consider an individual country in Europe, say, France. This country is a well-functioning democracy and manages to accommodate a certain cultural heterogeneity among the French. How much larger is heterogeneity between citizens of different EU countries, compared with what we observe within each country? If Europe as a whole is not much more heterogeneous compared with each country in isolation, then what prevents further political integration in the EU is not cultural differences per se. Throughout this section, we use all the cultural variables described in table A. 1 in the online appendix, focusing on wave 4 only.

IV.A. Cultural Distance between Europeans

Figure 8 shows the distribution of cultural distance between pairs (i,j) of individuals sampled within the same country (dotted line) and in any pair of different countries (solid line). The left panel highlights that there is a slightly lower average and median distance within a country than between countries, but the differences are quantitatively small. The right panel shows the same result using the residuals of the regression of cultural distances on socioeconomic distances. There is only a slightly larger uniformity between countries.

[FIGURE 8 OMITTED]

These results are consistent, although in a different context, with those of Klaus Desmet, Ignacio Ortuno-Ortin, and Romain Wacziarg (forthcoming), who find that for ethnic groups in the 76 countries that they study, "within-group variation in culture trumps between-group variation." They suggest that even relatively small differences between countries' cultural attitudes may become important precisely because they are associated with a feeling of belonging to separate entities (ethnic groups in their case, countries in ours).

Could these results be driven by measurement error, as pairwise distances are the result of aggregation over many noisy answers at the individual level? If within-country cultural distance is observed with noise, the observed within-country variance would be inflated. In the appendix at the end of this paper, we formalize this assessment. We show that, in order to produce a within-country variance that is misleadingly larger than the cross-country variance of the country means, the variance of the individual measurement errors must be more than 9 times larger (about an order of magnitude) than the true cross-country variance in the country means. In essence, saying that this result is driven by measurement error is equivalent to implying that the individual EVSs are essentially uninformative (roughly, a 1/10 signal-to-noise ratio), which seems implausible.

[FIGURE 9 OMITTED]

To check that this methodology can capture differences between countries, we repeat this exercise focusing on Turkey, a possible candidate EU member state, but one with a religious, economic, and historical background that is substantially different from those of many EU countries. In figure 9, the left panel displays the distribution of cultural distances between Turkey and the EU (solid line) and within Turkey (dotted line). In the right panel, we show the same for the distribution of cultural residuals. (32) This graph looks starkly different from figure 8, and here we clearly observe much more heterogeneity between Turkish citizens and EU respondents than within Turkey. Taking into account socioeconomic characteristics does not reduce the between-country distance by much.

IV.B. Cultural, Socioeconomic, and Geographic Distance

If culturally different regions are also at opposite geographic borders of the political area, political integration is more difficult. Similar considerations apply to socioeconomic distance. To address these questions, we estimate the following linear regression:

(1) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

[FIGURE 10 OMITTED]

where [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] indicates cultural distance between individuals i and j (in wave 4), [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] their socioeconomic distance, [u.sub.ij] is an unobserved error term, and i and j can belong to the same or to different countries, depending on the sample specification. Below, we also estimate equation 1; but on the right-hand side, we replace [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] with geographic distance, [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], based on the NUTS3 region of residence of the respondents. (33)

SOCIOECONOMIC DISTANCE Figure 10 plots the regression line, with [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] referring to socioeconomic distance, for individuals in the same country (the dashed line) and in different countries (the solid line). (34) Cultural distance is positively related to socioeconomic distance, and the slope coefficient [beta] is about the same within and across countries. Although [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] and [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] are roughly of the same size, the magnitude of the estimated intercept a is about 10 times larger than the slope coefficient [beta]. The intercept a of this regression gives us the average cultural distance for two individuals of the same socioeconomic status, [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] = 0 (belonging to the same or to different countries, depending on the sample). Two individuals who are socio-economically identical that come from the same country differ, on average, by 0.52 units in their cultural traits, whereas two socioeconomically identical individuals from two different countries differ by approximately 0.58 units on average. This confirms two properties of the data. First, socioeconomic distance explains only a small portion of cultural distance. Second, different countries do differ in cultural traits, but this difference is small compared with the average within-country distance.

Estimating the same regression line for citizens of different pairs of countries, or for the same country, we can estimate average bilateral distances between countries or within each country. This is what we show in table 1, which reports the estimated values of the intercept a for all countries in our sample and for the EU as a whole (we omit standard errors, but the estimates are all highly significant). The diagonal elements restrict the sample to individuals i and j belonging to the same country. The off-diagonal elements are estimated for i in the row country and j in the column country. Thus, the first row in the table displays the average distance between two Austrians with the same socioeconomic features, then between an Austrian and a Belgian with the same features, and so on. Average distances between countries vary between 0.52 and 0.64. The average distances of individuals in the same country (on the diagonal) vary between 0.5 and 0.6, and are not much smaller than the off-diagonal elements. In addition, looking at the diagonal entries, we do not see some countries that are much more homogeneous than others (the Scandinavian countries tend to be more homogeneous, but the patterns are not very precise). (35)

GEOGRAPHIC DISTANCE Next, we estimate the same regression line from equation 1, but replace [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] with geographic distance [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII](again, for individuals belonging to the same or to different countries, and with two-way clustered standard errors). Figure 11 displays the estimated regression lines. Again, the slope is positive and significant (and of about the same size as for the between-countries regression), but its value is negligible compared with the intercept (that is, compared with average distance among individuals living in the same region)--note that the order of magnitude of [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] and [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] is about the same. Two individuals from regions in different countries that are very far apart geographically differ by not more than 0.02 cultural distance unit. Thus geographic distance, like socioeconomic distance, is positively correlated with cultural distance, but it does not explain much of the observed cultural heterogeneity (the [R.sup.2]s of the regressions are small). (36)

[FIGURE 11 OMITTED]

IV.C. The Cultural Center of Europe

Knowing the region of residence of each respondent, we can compute the cultural distance of each region from the average cultural traits in Europe as a whole. In other words, we can locate the cultural core of Europe and its cultural periphery. Here we use wave 4 only, and we sample 500 individuals per country.

[FIGURE 12 OMITTED]

Consider the N x 1 vector Y: of cultural attitudes for individual i denned in section II. We use the notion of the geometric center, or centroid, of a set of points. The centroid of a set of vectors is their vector mean, [bar.Y]. The vector mean is computed as the solution to the following problem:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII],

where || || is the Euclidean distance. The vector [bar.Y] can be thought of as the "cultural center" of Europe. We can compute the distance of any individual i from the vector [bar.Y] in the same way as described in section II--as [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. Because we know the region of residence of each respondent i, we can compute the average cultural distance of each region from the centroid [bar.Y]. We illustrate our findings in figure 12. Lighter colors denote smaller cultural distances from the cultural center. The countries closest to the centroid are Germany and Austria. But Belgium, the Netherlands, and some regions of Spain and Portugal, are also relatively close. Much more distant are France, Italy (particularly Southern Italy), Greece, and Ireland. The sharp distance of France from the centroid (and from Germany) is consistent with Brunnermeier, James, and Landau's (2016) argument. Figure 12 also shows much regional variation within countries. For instance, Northern Italy is much closer to the centroid than Southern Italy. There is much heterogeneity in the United Kingdom as well, which is consistent with the vast regional variation in the 2016 vote on Brexit.

Are those individuals who are closer to the cultural center of Europe more in favor of European integration? To address this issue, we exploit a question in the EVS that asks whether the respondent is afraid of possibly adverse consequences of European integration in a number of policy areas. (37) We extract the first principal components of all these fears and regress them on cultural distance from the centroid of Europe in the full sample of our individuals, controlling for socioeconomic covariates. The results are displayed in table 2. Standard errors are clustered by region. To facilitate the interpretation, the dependent variable (fear of European integration) is normalized to lie between 0 and 1. Distance from the cultural centroid is always highly significant (also when controlling for individual socioeconomic covariates and regional or country fixed effects) and with the expected sign: Being more afraid of European integration is positively correlated with distance from the cultural centroid. Nevertheless, the magnitude of the estimated coefficient is not large. The estimated value of 0.0711 in column 4 implies that reducing cultural distance from its average value of about 0.62 to its minimum of about 0.26 would reduce fear of European integration by about 6 percent of its average value--recall that fear of integration has been normalized to lie between 0 and 1. Thus, not only are Europeans very similar to each other, but cultural heterogeneity also does not seem to be so important for attitudes in favor or against integration. This is a further indication that cultural heterogeneity per se does not seem to be the main stumbling block preventing further integration.

IV.D. Discussion

Within-country heterogeneity in cultural differences swamps crosscountry heterogeneity. Cultural heterogeneity is also related to geographic and socioeconomic dimensions, but most of it is unexplained. The European countries we consider are well-functioning democracies, despite the large internal variance in cultural traits we highlight. These findings thus suggest that the extent of cultural differences across European citizens living in different countries should not be an obstacle to further European political integration. This inference is further reinforced by the finding that cultural distance, although correlated with attitudes against European integration, only explains a small fraction of these attitudes.

V. Comparing the United States and the European Union

In this section, we compare the degree of heterogeneity of views within the European Union to that within the United States.

V.A. Data

For the United States, we use the General Social Survey (GSS). In line with Winston Churchill's conception of the "United States of Europe," one could roughly equate U.S. states with EU member states, but the available data from the GSS are not sufficiently rich, and small states have too few respondents. Therefore, we consider only nine large states for which we have enough observations: California, Florida, Illinois, Michigan, New York, North Carolina, Ohio, Pennsylvania, and Texas. (38) As an alternative, we also aggregated all states into five macro regions, and all our results were very similar.

A second problem is that the questions asked in the GSS are not identical to (and are fewer than) those in the EVS. In the online appendix, we describe exactly how we did the matching between the GSS and EVS. The GSS questions we use are listed in table A.7 in the online appendix and are a subset of the questions used for Europe. These questions cover the same five sets of issues included in the analysis of Europe, although in some cases fewer questions are included under some topics. In the static analysis of within-state versus between-state heterogeneity, and where we compare the United States with the European Union, a total of 15 questions are available. (39) An asterisk denotes the 6 questions that were not available in wave 1, and that thus are not used in the analysis of cultural convergence. (40) Finally, table A.3 in the online appendix lists the socioeconomic covariates we use in the analysis of GSS data.

V.B. Economic and Cultural Convergence in the United States

Let us begin with economic convergence. Barro and Sala-i-Martin (1992) study a long-term panel on personal income that goes back to 1840. They show that some beta convergence across U.S. states took place. As Peter Ganong and Daniel Shoag (2012) note, in 1940 average per capita income was 4.37 times larger in Connecticut than in Mississippi. This ratio declined to 2.28 in 1960, and to 1.76 in 1980. During the same period, the authors also show evidence of sigma convergence, except for some temporary shocks (for example, the Civil War). During the last 30 years, the convergence process has slowed down. The slope of the convergence relationship has fallen by more than 50 percent if one compares the subperiods 1940-60 and 1990-2010 (Ganong and Shoag 2012). The Connecticut-Mississippi income ratio in 2012 was 1.77, the same as in 1980.41 From the work of many scholars (for example, Piketty and Saez 2003; Piketty 2014) we also know that income inequality in the United States has increased significantly in the last few decades (contrary to our findings for the EU countries reviewed above).

As in Europe, we find that between 1980 and 2010, cultural diversity increased both across and within U.S. states, both in absolute terms and conditioning on socioeconomic status. These results are shown in figure A.14 and table A.8 in the online appendix. Distance did not increase in all cultural dimensions. Dispersion increased in attitudes toward the role of the state, sexual morality, and gender equality. Individuals seem to have become more similar in their religious beliefs and cultural capital. (42)

Notice that even if our results for economic and cultural convergence are similar for the European Union and the United States, the underlying mechanisms need not be the same. In the United States, the increase in cultural dispersion is consistent with the increase in political polarization among voters and political parties (McCarty, Poole, and Rosenthal 2016), which, in turn, may be related to the increase in income inequality. In the EU, the explanations may be related to specialization and institutional divergence. Further research on this point is warranted.

V.C. Cultural Distance within and across US. States

We now compute cultural distance within and across U.S. states and compare these with the EU, using the latest waves of the GSS and EVS. For the United States, we now use all the available questions. When directly comparing the European Union with the United States, we use the subset of questions in the EVS corresponding to those available in the GSS. The top-left panel of figure 13, which is the analog of figure 8, shows the distribution of distance between pairs of individuals in the United States within and across states. The top-right panel reproduces the same picture for the distance in the residuals of culture on a set of socioeconomic controls identical to the ones used for Europe. These two panels do not show any difference in the distribution within and across states. Thus, unlike in Europe, there is no more heterogeneity between states compared with that within states. As shown below, however, this is because there is more heterogeneity within U.S. states than within individual EU member states. The between-state differences are about the same in Europe and the United States.

The middle and bottom panels of figure 13 compare the distribution of cultural distances in the United States and Europe. The middle left panel depicts the distribution of unconditional cultural distance between individuals living in the same U.S. state (solid line) and the same European country (the dotted line). The middle right panel does the same for the distributions of distances in the residuals (that is, conditioning on socioeconomic covariates). The bottom panels refer to the distribution of cultural distances for individuals living in different U.S. states and European countries (the solid and dotted lines, respectively). There is more diversity within a U.S. state than within a EU country--the U.S. distribution of cultural distance is shifted to the right compared with the European distribution. However, we do not observe more diversity across U.S. states than across EU countries (the average distance between U.S. states is about the same as between EU countries). Europe as a whole is not less culturally heterogeneous than the United States.

V.D. Cultural, Socioeconomic, and Geographic Distance: The United States versus Europe

SOCIOECONOMIC DISTANCE As was done for Europe, we regress cultural distance [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] on socioeconomic distance [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], following equation 1. Figure A. 15 in the online appendix depicts the regression lines for individuals living in the same U.S. state and in two different ones. The two regression lines almost overlap, in accordance with the finding in the previous subsection that the distribution of cultural distance is the same within and between states. As in Europe (figure 10), the slope is positive, but small relative to the intercept (recall, however, that in Europe we found small but significant differences in the intercepts). Cultural distance is related to socioeconomic distance (within and across states), but most of the cultural distance between individuals is unexplained by their observed socioeconomic status.

As is done in table 1 for Europe, we have estimated this same regression for individuals belonging to different pairs of U.S. states. The intercepts are shown in table A.9 in the online appendix, which reports the average cultural distance between pairs of individuals of identical socioeconomic level, coming one from the row state and the other from the column state. First, the average distance between individuals of the same socioeconomic level does not vary much across pairs of states (from a minimum of 0.54 to a maximum of 0.63 across different states, a similar order of magnitude as between EU countries). Second, individuals in New York and California are on average more similar to each other than when compared with individuals in other states. This highlights the cultural similarity between two states on the opposite coasts.

GEOGRAPHIC DISTANCE In Europe, geographic distance, [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], contributes only slightly to explaining cultural distance, [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. But this is not the case in the United States. We find no correlation between geographic distance and cultural distance within the United States, as shown in figure A.16 in the online appendix. In the United States, geography does not explain cultural distance, in contrast to Europe. The reason may be greater mobility of people within the United States than within Europe. As noted with reference to table A.9 in the online appendix, this may also be due to greater similarity between the two U.S. coasts than between each coast and the central states. This geographic pattern may facilitate political integration compared with Europe, where we see a North/South divide in economics, institutional quality, and, to a smaller extent, in culture.

V.E. Discussion

A comparison between the European Union and the United States suggests that the fundamental cultural differences among Americans are not bigger than those among Europeans. Along this dimension, if Americans can share a well-functioning union of states under one federal system, so could Europeans. Needless to say, the United States has had 250 years of nation building and 150 years have gone by since the Civil War. Europe has had a much shorter common history, and only 70 years have gone by since the last inter-European war. Americans share a common language, and geographic mobility in the United States has been much higher than within Europe, or even within individual European countries. Mobility helped create a melting pot and thus a common identity, but apparently did not dampen cultural heterogeneity.

VI. Concluding Remarks

Europe is at a crossroads. As emphasized by the European Commission (2017), EU citizens are becoming impatient with their institutions, and major decisions need to be made. The European Commission believes that the European project either needs to be scaled down to the Single Market and a free trade agreement, or pushed toward deeper integration. Muddling through the current difficulties might be the easier solution in the short run, but it risks aggravating the EU's long-run prospects and further alienating European citizens who perceive the current situation as unsatisfactory.

But does Europe have the required fundamentals to become a viable political union? If the perceived benefits of integration are high, and cultural heterogeneity is relatively small and plays only a minor role, what prevents the EU from taking further steps toward a political union? We think the answer is the heritage of nationalism. Europeans retain strong national identities, amplified by different languages, and the memories of their past violent conflicts are still too strong and recent to overcome mutual distrust (Guiso, Sapienza, and Zingales 2009). Nationalist sentiments are on the rise, and this was true even before the financial crisis, which probably reinforced this tendency.

Although there is much variation among countries, between 1980 and 2009 most Europeans became prouder of their national identities; on average, the percentage of respondents who are proud of their nationality increased from 37 percent in the early 1980s to almost 50 percent in 2008-09, as shown in table A. 10 in the online appendix. Nationalism probably increased further after the financial crisis, in line with past episodes. (43)

If Europe wants to proceed further along the road of political integration, an important challenge is to reinforce a common European identity and to reduce mutual distrust between different nationals. According to Euro-barometer surveys, Europeans seem ready to accept a transfer of sovereignty to the center in the provision of some global public goods, such as security, border control, and environmental protection. But a political union should also be resilient to economic shocks like the recent financial crisis, and this presupposes agreement on a possibly minimalist set of principles of risk sharing and solidarity. This in turn requires sufficiently strong feelings of mutual identification and of belonging to a recognized and legitimate political community. This prerequisite for political integration is not out of reach. Despite the rise of nationalism, European identity has not weakened. According to Eurobarometer surveys reported by Jacques Nancy (2016), 51 percent of respondents say they felt both national and European in 2016, against 39 percent who felt only national. These numbers are not very different from those in the distant past. Thus, despite recent difficulties, the European project is still popular, although struggling. Restoring economic growth and avoiding prolonged stagnation would certainly contribute to further improvement in its popularity.

In the long run, mutual distrust among Europeans can be reduced by expanding European educational initiatives. In the history of nation building, public education has always played a major role (Alesina, Giuliano, and Reich 2017). The Erasmus Programme of student exchange works well, but the evidence suggests that it did not have a large impact on shaping European identities, probably because its self-selected participants are already very pro-Europe (Sigalas 2010; Wilson 2011; Mitchell 2014). If one agrees that further political integration would be a good idea, then this program could be expanded to reach more young people in high school or in technical institutions, and not just primarily university students. Moreover, school programs could be designed to include a more extensive curriculum covering European institutions and citizenship.

The feasibility of European political integration also depends on how it is achieved. The institutional foundations of the transfer of sovereignty have important implications for citizens' national versus European identification. Intergovernmental decisionmaking in the European Council inevitably increases perceived international conflicts and breeds mistrust, because national political delegation forces politicians to show to their respective constituencies that they have "won" and brought home a good deal. Instead, having a European policymaking institution in charge that is accountable to all European citizens, either directly or indirectly through the European Parliament, would be more likely to encourage compromise. It could also accelerate the formation of European identities and the emergence of a European (as opposed to national) public forum, where European policy issues would be discussed with a European perspective. But transferring political power from the European Council to European institutions requires the consent of national governments, which may be jealous of their own prerogatives and may not accept the emergence of powerful European political actors. Exploring these institutional aspects of how to achieve further European integration is an important challenge for future analysis and policy discussion.

ACKNOWLEDGMENTS We are grateful to our editor, James Stock; to our discussants, Markus Brunnermeier and Elias Papaioannou; and to many conference participants for detailed comments and suggestions. We also thank Matilde Bombardini, Nicola Gennaioli, Francesco Giavazzi, Luigi Guiso, Francesco Passarelli, Eric Weese, and Luigi Zingales for useful conversations. We thank Bocconi University, the Canadian Institute for Advanced Research, and the European Research Council for financial support; and Igor Cerasa, Clemence Idoux, Matteo Ferroni, Armando Miano, and Giorgio Saponaro for excellent research assistance.

APPENDIX

Measurement Error

Let [y.sub.ic] be the observed cultural measure in country c for individual (or pair of individuals) i. Let the observed [y.sub.ic] be a mismeasured proxy for the true latent cultural measure [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. Particularly, assume the presence of idiosyncratic measurement error [[epsilon].sub.ic] and country-specific mismeasurement [v.sub.c]. We posit

(2) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

with [[epsilon].sub.ic] independent and identically distributed classic measurement errors orthogonal to [v.sub.c], which is also independent and identically distributed with a mean of zero. Let us derive the mean and variance of [y.subic] within country c based on equation 2--so taken relative to individuals ;' in country c, hence the subscript [E.sub.i] [V.sub.i] used below. We obtain

(3) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII],

and

(4) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII].

We can further compute the variance of country-specific means across different cs:

(5) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII].

Ad absurdum, let us take the extreme case in which the measurement error is so large to potentially mask a within-country true variance of the latent cultural measure [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] that is less than or equal to the observed cross-country variance in country means [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], or

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

Then, consider that the measured within-country variance has to satisfy

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII].

Rearranging this inequality yields

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII],

which implies that

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII].

From our empirical estimates, we know that [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. Hence, [V.sub.i]([[epsilon].sub.ic]) >> 9 x [V.sub.c]([E.sub.i]([y.sub.ic])). Notice that [V.sub.i]([[epsilon].sub.ic])/[V.sub.c]([E.sub.i].([y.sub.ic])) can be read as the noise-to-signal ratio of the individual country survey relative to the benchmark of the (arguably better measured) cross-country dispersion of the culture measure [V.sub.c]([E.sub.(y.sub.iC]))- Therefore, [V.sub.i][E.sub.i][y.sub.ic]/[V.sub.c]([E.sub.i]([y.sub.ic])) >> 9.

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ALBERTO ALESINA

Harvard University

GUIDO TABELLINI

Bocconi University

FRANCESCO TREBBI

University of British Columbia

Conflict of Interest Disclosure: Guido Tabellini and Francesco Trebbi received financial support for this research from the Canadian Institute for Advanced Research. Alberto Alesina and Tabellini are also affiliated with the Innocenzo Gasparini Institute for Economic Research at Bocconi University. With the exception of the aforementioned affiliations, the authors did not receive financial support from any firm or person for this paper or from any firm or person with a financial or political interest in this paper. They are currently not officers, directors, or board members of any organization with an interest in this paper.

(1.) For an extensive discussion of the political difficulties facing the EU, including the rise of populist parties, see Beck and Underhill (2017).

(2.) The countries considered are Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, Sweden, and the United Kingdom.

(3.) NUTS stands for nomenclature des unites territoriales statistiques, or nomenclature of territorial units for statistics.

(4.) See, for example, the Schuman Declaration of May 9, 1950 (https://europa.eu/european-union/about-eu/symbols/europe-day/schuman-declaration_en).

(5.) Several studies document how, up until the onset of the financial crisis in 2008, the various phases of EU deepening have led to greater trade integration (Gil-Pareja, Llorca-Vivero, and Martinez-Serrano 2008), more financial integration (Jappelli and Pagano 2010), and more labor mobility (Portes 2015; European Central Bank 2015) between EU member states. Economic convergence has been studied, for instance, by Mackowiak and others (2008), Kutan and Yigit (2009), Boldrin and Canova (2001), and Villaverde and Maza (2008).

(6.) See, for instance, Frankel and Rose (1998); Kalemli-Ozcan, Papaioannou, Peydro (2013); Gogas (2013); and Backus, Kehoe, and Kydland (1992).

(7.) The online appendixes for this and all other papers in this volume may be found at the Brookings Papers web page, www.brookings.edu/bpea, under "Past BPEA Editions."

(8.) Our result also holds using GDP data from Cambridge Econometrics. The main difference between the two sources is that Cambridge Econometrics does not adjust for deviations of market exchange rates from purchasing power parity.

(9.) See Brandolini (2009) for the issues that arise in computing a supernational measure of income inequality.

(10.) The distribution has been fitted with the Epanechnikov kernel, with a bandwidth of 0.0466.

(11.) We also explored comovement in regional employment, with the same method. On average, the correlation coefficients of the yearly growth of employment have gone down for regions belonging to the same country, whereas they have remained stable for regions belonging to different EU countries. In other words, in the more recent period there has been less comovement in employment within countries, but not across countries. Given the patterns described above for GDP growth, this is the mirror image of divergent productivity growth within (but not across) countries.

(12.) In an earlier version of this paper, we also considered multidimensional scaling as an alternative dimensionality reduction approach.

(13.) More precisely, spectral clustering levers on the spectral properties of the graph that is associated with the similarity matrix of the problem, which for us is the matrix of real GDP correlation coefficients among regions, r. Think of each correlation coefficient as telling us the strength of the link between two regions. The correlation matrix is essentially equivalent to the adjacency matrix of a weighted undirected graph, where nodes are regions and the link weights are given by the correlation coefficients. It turns out that counting clusters in this network is the same as trying to find the number of connected components of the graph (visually, the bundles of nodes are tight to each other, but far away from other bundles). Trebbi and Weese (2015) offer additional discussion of some of these methodologies.

(14.) Virtually identical results are obtained if we restrict ourselves to EMU countries.

(15.) See Norris and Inglehart (2009) for a qualitative discussion.

(16.) The first wave is missing for Austria, Greece, Luxembourg, Portugal, and Finland. Moreover, the first wave was asked only for West Germany. The second wave is missing for Greece and Luxembourg.

(17.) See, for instance, Alesina and Giuliano (2014, 2015), Guiso, Sapienza, and Zingales (2015), and Tabellini (2008).

(18.) See Giavazzi, Petrov, and Schiantarelli (2014) on this point, and see Alesina and Giuliano (2015) for a broader discussion of the evolution of cultural values in relation to institutional changes.

(19.) As in any multicountry survey, it is possible that the same question asked in a different language may lead to some measurement error because the questions may not be interpreted identically in every country. Below and in the appendix, we discuss issues of measurement error that relate also to this point.

(20.) See Brunnermeier, James, and Landau (2016) for a discussion of these economic differences in the EU.

(21.) They are Belgium, Denmark, France, Germany, Ireland, Italy, the Netherlands, Norway, Spain, Sweden, and the United Kingdom, and the included questions are those without an asterisk in appendix table A.2.

(22.) Note that different individuals are sampled in each wave and we do not have a panel of survey participants.

(23.) The parameter 9 of the Gaussian kernel is [theta] = 1/2[[sigma].sup.2], where [sigma] controls the width of the neighborhoods over which individuals are compared. For small [sigma], [theta] is large, implying that two individuals that are minimally different in their answers are deemed very far apart already. For large [sigma], [theta] is small, implying that distance away from a point increases at a slower rate. Note that this [sigma] parameter is not the same as the variance of the answer to the questions in the population (which is normalized to 1 in all answers and dimensions here). [sigma] is a parameter regulating the definition of distance in the answer space. We calibrate [sigma], that is, the kernel bandwidth, to the number of dimensions following Hainmueller and Hazlett (2914).

(24.) These results are available upon request.

(25.) Learning from other European countries also became more salient in the policy debates, and this too may have led to institutional convergence, as suggested by Buera, Monge-Naranjo, and Primiceri (2011).

(26.) Levchenko (2007) and Nunn (2007) study institutions as a source of comparative advantage, while Tabellini (2008) shows how culture too can be a source of comparative advantage. These papers treat institutions (or culture) as exogenous. Do and Levchenko (2009) study a theoretical model where a reduction in trade costs can lead to institutional deterioration.

(27.) Some of the underlying components of the original variables are coded on the basis of hard information, and others are based on surveys and report perceptions about the quality of government or the absence of corruption. The correlation coefficients between the extracted first principal component and the three underlying variables is always very high, ranging from .8 to .9.

(28.) In the quality of government panel, Germany and Luxembourg are omitted because data are available for only some years. In the PISA scores panel, the year 2003 is missing for the United Kingdom.

(29.) These results are consistent with, and complement, those of Papaioannou (2016).

(30.) Specifically, we regressed each variable on the log of real per capita GDP from the Penn World Tables, and where necessary we extracted the first principal component from the residuals of each variable. The first period of convergence in the quality of government is much dampened, but the divergence since 2000 remains pronounced. For the quality of legal institutions and for the PISA scores, conditioning on per capita income does not change the results illustrated above. Convergence in product market regulation is not evident anymore, however.

(31.) Work by Calligaris and others (2016) highlights that a similar phenomenon may have occurred even within countries. In Italy, for instance, the effect of the common currency increased the difference between modern sectors and firms that took advantage of European integration and others, which fell further behind. The difference is quantitatively striking. See also Gopinath and others (2015).

(32.) Because of data availability, the individual observations used for Turkey are much less than for the other countries, but still we get a reasonable amount of pairs of Turkish with non-Turkish individuals. In total, there are more than 7,000 pairs of individuals in which one is Turkish.

(33.) Geographic distances are computed using the haversine formula.

(34.) Confidence intervals are adjusted for two-way clustering at the country of residence of each individual.

(35.) We also compared the standard deviations of the within- and cross-country distributions of bilateral distances, and they are approximately of the same order of magnitude, suggesting that the dispersion in cultural distances is similar within and across countries.

(36.) Fazio and Lavecchia (2013) also show that generalized trust is spatially correlated, also for regions belonging to different countries.

(37.) The fears associated with the building of the EU listed in the questions are loss of social security, loss of national identity, our country paying more to the EU, a loss of power in the world, and the loss of jobs.

(38.) The nine states we selected reach 60 observations in most of the waves. In a few cases, they do not (the lower bound is Illinois in wave 2, which has 39 surveyed individuals who replied to all the questions).

(39.) In the GSS, questions about approval of abortion, approval of homosexuality, feeling of control over one's own life, and belief in God are asked in subsamples of individuals for whom other questions are not available. We thus exclude them from the analysis.

(40.) The GSS is conducted every other year. To match the EVS waves, we thus grouped GSS data as follows: The surveys of 1984 and 1986 correspond to wave 1; those of 1990, 1991, and 1993 to wave 2; those of 1998 and 2000 to wave 3; and those of 2006, 2008, and 2010 to wave 4.

(41.) Ganong and Shoag (2012) argue that labor mobility played a central role in income convergence. During the period of strongest convergence, until 1980, population flowed from poor to rich states, and initial income could well predict changes in population. At present, this pattern has largely disappeared.

(42.) These results are available upon request. Also in table A.8 of the online appendix we show the same exercise performed in table A.4. Average distance between individuals in different countries increased between wave 1 and wave 4 in a statistically significant way, both conditionally and unconditionally on socioeconomic covariates, by about 10 percent, approximately the same magnitude as for Europe.

(43.) Funke, Schularick, and Trebesch (2016) show that support for extreme right-wing parties generally increases after financial crises.
Table 1. Average Cultural Distance between Individuals of Identical
Socioeconomic Status, 2009 (a)


    AT    BE    DE    DK    ES    FI    FR    GB    GR    IE    IT

AT  0.54
BE  0.59  0.57
DE  0.55  0.59  0.54
DK  0.60  0.60  0.60  0.47
ES  0.58  0.58  0.55  0.55  0.52
FI  0.57  0.59  0.57  0.55  0.55  0.53
FR  0.60  0.59  0.60  0.57  0.57  0.57  0.57
GB  0.59  0.60  0.59  0.57  0.61  0.59  0.59  0.56
GR  0.54  0.60  0.56  0.61  0.60  0.58  0.60  0.60  0.48
IE  0.60  0.62  6.00  0.61  0.63  0.58  0.61  0.60  0.58  0.60
IT  0.59  0.61  0.60  0.62  0.65  0.61  0.61  0.59  0.54  0.60  0.53
LU  0.61  0.61  0.62  0.61  0.61  0.60  0.60  0.60  0.61  0.62  0.61
NL  0.58  0.59  0.57  0.53  0.57  0.56  0.59  0.56  0.60  0.60  0.61
NO  0.58  0.57  0.56  0.54  0.53  0.53  0.55  0.58  0.59  0.60  0.62
PT  0.56  0.56  0.54  0.60  0.59  0.56  0.57  0.56  0.52  0.57  0.54
SE  0.61  0.61  0.59  0.52  0.55  0.56  0.57  0.59  0.65  0.63  0.66

                                        All
    IT    LU    NL    NO    PT    SE    EU

AT                                      0.58
BE                                      0.59
DE                                      0.57
DK                                      0.56
ES                                      0.58
FI                                      0.56
FR                                      0.58
GB                                      0.59
GR                                      0.58
IE                                      0.61
IT  0.53                                0.61
LU  0.61  0.61                          0.61
NL  0.61  0.60  0.50                    0.56
NO  0.62  0.60  0.55  0.51              0.55
PT  0.54  0.59  0.57  0.57  0.49        0.57
SE  0.66  0.62  0.56  0.54  0.60  0.52  0.57

Sources: European Values Survey, wave 4; authors' calculations.
(a.) For the country name abbreviations, see the notes to figure 2.

Table 2. Cultural Distance and Fear of the European Union (a)

                   Fear of the European Union
                   (1)           (2)           (3)

Cultural distance
from EU centroid    0.0992 (**)   0.0844 (**)   0.0794 (**)
                   (0.039)       (0.036)       (0.035)
Controls             No            Yes           Yes
Fixed effects        No            No          Country
No. of
observations        6,810         6,810         6,810
[R.sup.2]           0.002         0.096         0.156

                   Fear of the European Union
                   (4)

Cultural distance
from EU centroid    0.0711 (**)
                   (0.034)
Controls             Yes
Fixed effects      Region
No. of
observations        6,810
[R.sup.2]           0.209

Sources: European Values Survey, wave 4; authors' calculations.
(a.) Standard errors are clustered by region. Statistical significance
is indicated at the (**) 5 percent level.
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