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The opinions expressed in this paper do not necessarily reflect the position ofFondazione Eni Enrico Mattei

Cities and CulturesGianmarco I.P. Ottaviano and Giovanni Peri

NOTA DI LAVORO 92.2004

MAY 2004KTHC – Knowledge, Technology, Human Capital

Gianmarco I.P. Ottaviano, Università di Bologna, Fondazione Eni Enrico Mattei and CEPRGiovanni Peri, UC Davis and CESifo

Cities and Cultures

SummaryWe investigate the existence of wage premium due to cultural diversity across UScities. Using census data from 1970 to 1990, we find that at the urban level richerdiversity is systematically associated with higher average nominal wages for whiteUS-born males. We measure cultural diversity in a city using the variety of languagesspoken by city-residents. While the positive correlation between wages and diversitysurvives a battery of robustness checks, it seems to be larger once foreign cultureshave been assimilated. Finally, instrumental variable estimation hints at causationgoing from diversity to wages. Comparing real and nominal wages across cities, weinterpret these results as evidence that diversity enhances productivity.

Keywords: Cultural diversity, Productivity, Wages, Metropolitan areas

JEL Classification: O4, R0, F1

We are grateful to Alberto Alesina, Ed Glaeser, Eliana LaFerrara, Dino Pinelli,Vernon Henderson, and workshop participants at FEEM Milan and UBC Vancouverfor helpful discussions. We thank Elena Bellini for outstanding research assistance.Financial support from Bocconi University and FEEM is gratefully acknowledged.

Address for correspondence:

Gianmarco I.P. OttavianoDepartment of EconomicsUniversity of BolognaStrada Maggiore 4540126 BolognaItalyPhone: +39 051 2092602Email: ottavian@economia.unibo.it

1 Introduction

“Global civilization could never be anything other than the coalition at global levels of cul-

tures, each of them retaining its originality.” (Claude Levi-Strauss)

Recent world developments are bringing the issue of cultural diversity to the forefront. Indeed, as argued

by Alesina and La Ferrara (2003): “In a more integrated world, the question of how different people can

peacefully interact is the critical problem for the next many decades” (p.29).

From the economic point of view, the question is whether a culturally homogeneous society is more

efficient than a culturally diversified one. The answer is not obvious. On the one hand, cultural diversity

generates potential costs as it may entail conflicts of preferences, not to mention racism and prejudices,

leading to a suboptimal provision of private and public goods (Alesina, Baqir, and Easterly, 1999; Alesina,

Baqir, and Hoxby, 2004). On the other hand, cultural diversity creates potential benefits by increasing the

variety of goods, services and skills available for consumption and production (Lazear, 1999a,b; O’Reilly,

Williams and Barsade, 1998). By bringing together complementary skills and alternative approaches to

problem solving, it may also boost innovation (Berliant and Fujita, 2003).

The aim of the present article is to investigate the impact of cultural diversity on the economic life of

US cities. Specifically, we tackle the following questions: Is there such a thing as a diversity wage premium

in US cities? Do identical workers earn higher or lower wages in urban environments that are identical to

others in all respects except their richer cultural diversity? Is the diversity premium associated with different

productivity or different consumption amenities?

In so doing, we use data from 160 metropolitan areas for three census years, 1970, 1980 and 1990. US

metropolitan areas represent a natural laboratory to investigate cultural diversity under many respects.

First, the US have a long standing tradition as a favorite immigrant destination from both developed and

developing countries. During the period of observation most immigrants have targeted urban rather than

rural areas. This has made US cities a ‘melting pot’ of different cultures within a rather homogeneous

institutional framework. Second, the US is arguably the most advanced market economy and the largest

fully integrated marketplace. In the US prices reflect preferences and costs better than in any other place.

In particular, in the US people are quite mobile. For instance, census data reveal that 36 per cent of the

population moved from one state to another between 1985 and 1990. As people respond to changes in

the local working and living environment, we may expect them to ‘vote with their feet’ thus seizing (and

revealing) consumption and wage gains wherever they arise (Blanchard and Katz, 1992). Last but not least,

the availability and quality of data are better in the US than anywhere else.

We focus on linguistic groups as carriers of cultural identity and on US-born white individuals as our

2

reference group. In other words, we investigate whether and how linguistic diversity affects the wage of US-

born white males aged between 40 and 50 years. This has two advantages. First, it avoids composition effects

in our dependent variable. Second, it highlights the effects of immigrants on the well-being of the dominant

and most integrated group of natives. We choose linguistic diversity as our central explanatory variable

because it captures not only the place of birth but also its associated traditions. In addition, language allows

us to capture cultural identity beyond the first generation of immigrants. Other dimensions of diversity, such

as race and skills, are used as controls.

The effect of diversity on aggregate economic performance has been mainly studied by means of growth

regressions using racial fragmentation as the key explanatory variable. At the cross country level, Easterly

and Levine (1997) find that, ceteris paribus, in countries characterized by more racial fragmentation income

grows less than in more homogeneous ones. Collier and Gunning (1999) explain such behavior in terms of

mutual distrust among ethnic groups, which makes it difficult to build social capital and to share productive

public goods. However, when comparing countries, institutions do play a role. Collier (2001) finds that

democracies are better at coping with ethnic diversity. More generally, Easterly (2001) stresses the impor-

tance of good institutions in mitigating the negative impact of diversity on growth. Growth regressions have

also been used at the city level, using population growth rather than income growth as dependent variable.

The reason is that people are much more mobile across cities in the same country than across countries

so that migration tends to arbitrage out income differences. This approach is due to Glaeser, Scheinkman

and Shleifer (1995) who find that racial fragmentation has a positive impact on population growth only

when matched by segregation. According to Alesina and La Ferrara (2003), that may be due to the fact

that segregation makes it possible to enjoy the benefits of diversity in production without paying its costs

in terms of social conflicts over public goods provision. Florida (2002a,b) argues that segregation is not

the only way to fence off social unrest as a consequence of diversity. He shows that tolerant cities, where

tolerance is instrumented by the presence of artists, bohemians, and other creative people, are the most

active in human capital accumulation and innovation. Finally, several contributions focus on the issue of

new immigrants into the US and their effect on native workers (see, e.g. Borjas, 1994 and 1995; Card, 1990

and 2001) analyze the effects of new immigrants on the locations and wages of native workers. These works

reveal a small negative impact of new immigrants on the wages of low-skilled natives.

A different empirical approach to the study of diversity in cities is adopted by Ottaviano and Peri (2004).

Following Roback (1982), they develop a model of a multicultural system of open cities that allows them to

use the observed variations of wages and rents of US-born workers to identify the nature of the externalities

associated with cultural diversity. Their main finding is that, on average, US-born citizens attribute a

dominant production amenity value to cultural diversity. The present paper complements our previous work

3

under two respects. First, we propose a more detailed model of the production side of the economy. In

so doing, we adapt the set-up by Alesina, Spolaore, and Wacziarg (2000) to design a theoretical model

of aggregate production where mobile workers belonging to different cultural groups contribute different

services but exchanges among them are hampered by cultural diversity. We find that in such model total

factor productivity is positively related to cultural diversity and negatively on the average cultural distance

between groups. Thus, differently from Alesina and La Ferrara (2003), we stress the costs of communication

among different groups rather than suboptimal public goods provision as the main cost of diversity. Second,

we follow Glaeser and Mare (2001) in netting out and interpreting the impact of diversity on wages in the

presence of mobile and heterogeneous workers.

We find evidence in favour of a diversity wage premium: richer diversity is associated with higher white

wages, while keeping other factors - such as average schooling and employment - constant. This association,

which is both economically and statistically significant, can be interpreted in terms of higher productivity.

As such finding seem to clash with the literature cited above, we investigate in greater detail such superficial

incompatibility. This allows us to qualify our results under two respects. On the one hand, we emphasize the

different effects of diversity on the private and the public sectors, by showing that the impact of diversity on

the provision of public goods is indeed negative as in Alesina, Baqir, and Easterly (1999) as well as Alesina,

Baqir, and Hoxby (2004). On the other hand, we highlight the importance of the adoption of a core of

shared norms (‘assimilation’) for fruitful multicultural interactions, by showing that the impact of recent

immigrants on productivity is negative as in Borjas (1994, 1995), while overall diversity has a positive effect.

The paper is organized in five sections after the introduction. Section 2 presents the dataset and some

descriptive statistics. Section 3 derives the theoretical model that will be used to guide the empirical analysis.

This is implemented in section 4, which describes the main regression and a battery of robustness checks.

Section 5 compares our findings with related studies focusing on the provision of public goods and the impact

of recent immigrants. Section 6 concludes.

2 Descriptive Statistics: Cultures in US Cities

We start our analysis of the effect of cultural diversity on wages in US cities by presenting the dataset and

some descriptive statistics.

2.1 Data on US Cities

Our unit of observation is the Standard Metropolitan Statistical Area (SMSA). Data at SMSA level for the

US are available from different sources. We use mostly the Census Public Use Microdata Sample (PUMS)

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data (1971, 1981 and 1991) that allow for the most detailed analysis when calculating average values and

shares across groups. We also include data from the ‘County and City Data Book’ from several years in

order to obtain some aggregate variables such as employment, income, population, spending for local public

goods, and some indices of cultural composition. We consider 160 SMSAs that are identified in each of the

three census years considered. We have around 1,200,000 individual observations for 1991, 900,000 for 1981,

and 500,000 for 1971. We use them to construct aggregate variables and indices at the SMSA level. The

reason for focusing on SMSAs is twofold. First, SMSAs constitute closely connected economic units within

which interactions are intense. Thus, they seem to fit the theoretical model presented in section 3 in which

local services are used to produce the final output. Second, they exhibit a higher degree of linguistic diversity

than the rest of the country as new immigrants and their offsprings traditionally settle down in larger cities.

We measure average labor productivity as hourly wage. This is calculated as yearly salary divided by

weeks worked in the year, and then by hours worked in the week. Such measure is not contaminated by the

variations in labor supply such as the length of the working week. We select working individuals between 16

and 65 years of age as our universe. In order to identify an average city-specific level of wage and estimate its

dependence on cultural diversity, we try to minimize the composition effects. Accordingly, we consider a large

and homogeneous group within each city, namely the one consisting of white US-born male non-agricultural

workers aged between 40 and 50 who are heads of households. The average hourly wage constructed using

this procedure for city c, call it wc with c = 1, ..., 160, is neither affected by composition effects nor distorted

by potential discriminatory factors (across cultures). In particular, the construction of wc is not affected by

the degree of diversity of a city, as it considers only the wage of one (the largest) group.

Cultural diversity is a multidimensional concept. It could stem from different ethnic or linguistic groups

but also from different skills or US regional origins. Nonetheless, ‘cultural diversity’ as it is commonly referred

to, mostly involves ethno-linguistic differences. Ethnicity and language, together with religion as a possible

third candidate, are probably the most important characteristics for the identification of a sub-group (or

sub-culture) within the US. Indeed, especially in the US, most debates about diversity are strongly related

to issues such as the ‘Latino identity’ or the ‘Chinese-American Community’ (i.e., linguistic grouping). For

these reasons we choose measures of linguistic diversity as our favorite empirical correlates of the broader

concept of cultural diversity. Measures of diversity in skills, race and regional origins will be used as controls

in order to verify the robustness of our results.

The theoretical model of Section 3 suggests a specific index of diversity, stemming from the ‘taste of

variety’ embedded in the aggregate production function (6): the sum of the population shares of the different

groups raised to the power of α. Since in (6) α represents the labor share of aggregate income, we follow

common practice by setting α = 0.66 for the US economy. Formally, we define the our own index of linguistic

5

diversity of city c in year t as:

divLangct =Xj

(lcj)0.66t (1)

where Lang labels the the variable ‘language’ and (lcj)t is the share of the group speaking language j at

home in the total population of workers of city c in year t. The index has its minimum value at 1, if all city

residents are from the same linguistic group. The more equal the distribution of citizens across groups, the

larger is the index.

In order to check that our results do not depend too strongly on the particular form chosen for the

diversity indices, however, we also consider a more standard measure of diversity, namely, the so called

‘index of fractionalization’. Such index, popularized in cross-country studies by Mauro (1995) and largely

used thereafter, captures the probability that two individuals, taken at random from a universe made of

different groups, belong to the same group. The index of fractionalization is calculated as 1 minus the

Herfindal index of concentration across groups. Formally, we define the fractionalization index of linguistic

diversity. of city c in year t as:

frac(Langc,t) = 1−Xj

(lcj)2t (2)

This index reaches its maximum value 1 when each individual is in a different group, and its minimum

value 0 when all individuals belong to the same group. In addition to its usefulness for robustness checks,

fractionalization also allows us to get a feeling of the extent of diversity of US cities by comparing their

linguistic diversity with those calculated in cross-country studies. Note that the correlation between the two

indices, divLangct and frac(Langc,t), across the 160 SMSA’s is about 0.85, which confirms that the two

indices are indeed capturing the same features of linguistic diversity across cities.

2.2 Diversity in US Cities

Table 1 reports the summary statistics on the shares of the five main linguistic groups after merging all

other groups together. Column 1 and 3 report the averages in 1970 and 1990 for each share across the

160 metropolitan areas, column 2 and 4 report the standard deviation in 1970 and 1990. A linguistic

group is defined as speaking prevalently a certain language at home. For parsimony, the table includes only

very few specific linguistic groups even though other groups are important especially in some cities. The

fractionalization index reported in the last row of the table is calculated, instead, using all the (29) groups

that are listed in the Data Appendix. Besides English speakers we report the average shares of German,

Spanish, and Italian speakers (corresponding to the most largely represented European languages), as well as

Chinese speakers. Table 1 displays a tendency of linguistic diversity to decrease between 1970 and 1990: the

6

index of fractionalization decreases significantly. In particular, while the share of English speakers remains

stable, other European Languages become less relevant, while Spanish turns into the second linguistic group

in the country. The share of Chinese speakers slightly increases as well. The diversity of European languages,

still present to a certain extent in 1970, gave way to a single large Spanish speaking minority in 1990.

Table 2 reports the fractionalization index of some representative SMSAs in 1990. The two largest

metropolis, New York and Los Angeles, are the most diverse cities along the linguistic dimension. Both

cities have a very large Spanish speaking community (in L.A. it represents 30% of the population, while in

N.Y it reaches 17%) and a non negligible Chinese speaking group. The third most diverse city in our group

is San Francisco. Cities in the mid-west such as Cincinnati and Indianapolis rank very low in diversity. The

fact that, in general, larger cities are associated with more diversity implies that we will have to control

for some measure of city size when analyzing the impact of diversity on productivity. To put into context

the extent of diversity in US cities, their linguistic fractionalization can be compared with the cross-country

values reported by the Atlas Narodov Mira and published in Taylor and Hudson (1972) for year 1960. Those

values have been largely used in the growth literature (see, e.g., Easterly and Levine, 1997, and Collier,

2001). A diversified city such as San Francisco has a linguistic fractionalization equal to 0.62, which is the

level of Malawi or Pakistan. Afghanistan, well known for hosting many different ethnicities, reaches a value

of 0.66 that is only slightly higher. More homogenous cities such as Cincinnati and Pittsburgh have a level

of fractionalization equal to 0.08, which is the same as that of very homogenous Sweden. Between these

extremes US cities span a range of linguistic diversity that is about two thirds of the range spanned by

countries in the world.

Finally, Figure 1 presents a partial scatterplot of the nominal average wage for year 1990 against the index

of linguistic diversity, divLangc1990 . We use the (zero-mean) residuals of nominal wages after controlling

for population in the city as city-density could be correlated both with wages and diversity. Figure 1 reveals

the presence of a diversity premium: nominal wages are higher in more diverse cities. In particular, the

estimated slope of the fitted line is both positive (equal to 5.5) and significant (with a t-statistic of 4.4). To

check the robustness of such correlation and to understand its determinants is the objective of the following

sections.

3 A Model of Multicultural Production

We consider an open system of a large number C of non-overlapping cities, indexed by c = 1, ..., C. There

is one factor of production, labor. There are L workers and they are perfectly mobile between and within

cities and we call Lc the number of workers located in city c. We assume that intercity commuting costs

7

are prohibitive so that for any worker the cities of work and residence coincide. We also ignore intra-city

commuting costs, which allows us to focus on the intercity allocations of workers. Workers are differentiated

by ‘culture’ across M groups with Lci measuring the number of residents of city c in group i = 1, ...,M .

Following Glaeser and Mare (2001), workers belonging to different groups may also differ in terms of their

endowments of efficiency units of labor.

3.1 Labor Supply

Workers’ utility is homothetic and defined over the consumption of a freely tradable good Y as well as over

a set of non-tradables produced under constant returns to scale and perfect competition using land as their

only input. Landowners are absentee. Since workers are freely mobile, in equilibrium each of them expects

to earn the same real wage wherever located:

wciP c

=wc

0i

P c0, ∀c, c0 = 1, ..., C (3)

where wci is the nominal wage of a typical worker of group i in city c, and Pc is the local price index inclusive

of traded and non-traded goods.

Groups may differ in terms of efficiency units and we call φci the endowment of efficiency units of a typical

worker of group i in city c. Accordingly, the average wage in city c may reflect two effects: a group-wise

composition effect and a cost-of-living effect. Indeed, if one defines W ci as the wage per efficiency unit of

group i in city c, i.e. wci =Wci φ

ci , the average of the nominal wage can be decomposed as:

ewc = fW c + eφc (4)

where ewc ≡PMi=1 lnw

ci/L

c is a geometric mean. Similar definitions apply to the other variables in (4).

Noticing that (3) also implies the equalization of real wages per efficiency unit allows us to write:

fW c −fW c0 = lnP c

P c0

which, together with (4), gives:

ewc − ewc0 = eφc − eφc0 + lnµ P cP c0

¶(5)

for all c, c0 = 1, ..., C. Thus, the free mobility of workers implies that differences in nominal wages across

cities are driven by different compositions across groups or different costs of living or both.

To check the relative importance of these two effects we compare the scatterplot of nominal wages in

8

Figure 1 with the analogous plot of real wages in Figure 2. In each city the corresponding real wage

is calculated as the ratio of the nominal wage to the cost of living in 1990, as published quarterly by the

American Chamber of Commerce Research Association (ACCRA). As mentioned above, Figure 1 reveals the

presence of a diversity premium: nominal wages are higher in more diverse cities. Figure 2, however, shows

that, once we control for the local cost of living, the wage premium disappears: nominal wage differences are

exactly matched by local price differences. In fact, a slightly (non significant) negative correlation appears.

Then (5) implies that compositional differences are negligible: the diversity premium is likely not to be

the result of omitted ability bias. In what follows, we use this result to simplify the model by neglecting

cross-group differences in efficiency units. In particular, we normalize the number of efficiency units per

worker to one.

3.2 Labor Demand

The production side is modeled by adapting the multi-regional trade model by Alesina, Spolaore, and

Wacziarg (2000). The tradable good Y is produced using an array of differentiated intermediate inputs

X. Both are produced under perfect competition. Each group i = 1, ...,M supplies one and only one

intermediate input using its group-specific endowment of labor (‘culture’). Due to our normalization of

efficiency units, each group member contributes one unit of labor and the i-group’s resource yields Li units

of the corresponding intermediate good. Thus, M is also the number of intermediate inputs (‘services’)

available and Li is both the amount of intermediate input supplied by and the number of workers belonging

to group i.

Final production by group i in city c is given by:

Y ci = A (Kci )1−α

SXj=1

¡Xcji

¢α, 0 < α < 1 (6)

where Xcji denotes the amount of intermediate input produced by group j and used by group i, K

ci is physical

capital used by group i and A is total factor productivity. Capital is freely mobile within and between cities

at rental rate r. Notice that (6) exhibits ‘love of variety’ in terms of intermediates: it is more productive

to spread a given total amount of intermediate consumption across all available inputs than to concentrate

that same amount on a single input. The more so the lower the value of α (i.e. the lower the elasticity of

substitution between intermediates ε = 1/(1− α)).

The final good is freely traded both within and between cities. Intermediates are only traded within cities

and such trade incurs iceberg transaction costs. Of one unit shipped between groups only a fraction (1− τ c)reaches destination. This can be widely interpreted as the resource cost of interaction (‘communication’)

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between groups. These costs may depend on the exact identities of the groups living in the city: interactions

are likely to be more difficult when the groups of citizens are very different (e.g., Japanese and Italians) than

when they are rather homogenous (e.g., Italians and Spanish). The costs of interaction may also depend on

the time from arrival of the different cohorts of immigrants: interactions are likely to be easier when groups

have had enough time to assimilate a common set of norms and habits from the host society. We will come

back to these points in the empirical analysis.

Let PY be the price of the final good and let this good be the numeraire (hence PY = 1). Call P cji

the price per unit shipped (‘mill price’) of an intermediate produced by group j and sold to group i in city

c. Recalling that, when Zcji units are shipped, only (1 − τc)Zcji units arrive at destination, we have that

the amount of intermediate available for final production is Xcji = (1 − τ c)Zcji. Then, given (6), pricing at

marginal cost implies that the mill price of one unit of intermediate shipped from group j to group i in city

c is:

A (Kci )1−α (1− τ c)αα

¡Zcji¢α−1

= P cji for i 6= j (7)

A¡Kcj

¢1−α(1− τ c)αα

¡Zcjj¢α−1

= P cjj for i = j (8)

Since the mill price of an intermediate has to be the same whatever the buyer, we have P cji = Pcjj . Thus,

by (7) and (8), the ratio of within-group to between-group shipments can be written as:

ZcjiZcjj

=Kci

Kcj

(9)

This result can be used to substitute for Zcji in the resource constraint for group j’s specific resource:

MXi=1

Zcji = Lcj (10)

which can then be solved for within-group shipments:

Zcjj = Lcj

Kcj

Kc(11)

where Kc =PMi=1K

ci is the total capital stock in the city. By (9), the associated between-groups shipments

are then:

Zcji = Lcj

Kci

Kc(12)

Recalling that Xcji = (1 − τ c)Zcji and plugging (11) as well as (12) into (6) yields the group i’s final

10

output:

Y ci = KciA(1− τc)α (Kc)−α

MXj=1

¡Lcj¢α

(13)

By summing up across all groups, we get the total output of the city Y c =PMj=1 Y

cj :

Y c = A(1− τ c)α (Kc)1−αMXj=1

¡Lcj¢α

Profit maximization then implies that the average wage in city c equals:

wc = A1α (1− τ c)α(1− α)

1−αα r−

1−αα

MXj=1

¡λcj¢α 1

α

(14)

where wc ≡PSj=1wjcλjc and λj ≡ Lcj/

PMj=1 L

cj is the share of citizens belonging to group j. Equation (14)

can be taken in logarithm to yield:

lnwc = constant + ln(1− τ c) +1

αln

MXj=1

¡λcj¢α

(15)

This is our estimating equation, which implies that, due to love of variety in final production, the wage

should be higher in cities that, ceteris paribus, have: (i) a more balanced distribution across groups; (ii)

lower costs of interaction.

Note thatPMj=1

¡λcj¢αis the theoretical measure of diversity underpinning the use of (1) as explanatory

variable. As pointed out by Ottaviano and Peri (2004) in the wake of Roback (1982), the sign of its estimated

coefficient in single-equation regressions such as (15) should be interpreted with great care. The reason is

that a positive coefficient is compatible with two very different stories. On the one hand, it could reflect a

positive impact of diversity on productivity: workers are more productive in multi-cultural environments.

On the other hand, it could reflect a negative impact of diversity on utility: workers dislike multi-cultural

environments, so they ask for monetary compensation if they have to cope. Nonetheless, Figure 1 and 2

make us favor the first story. The reason is that the variations in the ACCRA cost of living across cities

are essentially driven by the price differentials of non-trade goods, most notably land rents. Figure 1 shows

that nominal wages are higher in more diverse cities. Figure 2 shows that the costs of living are equally

higher in more diverse cities. Higher wages and higher prices of non-tradables are compatible with a positive

correlation between diversity and productivity but incompatible with a negative correlation between diversity

and utility: nobody would ever pay more in order to reside in a place she happens to dislike. Hence, when

estimating various versions of (15), we will interpret the eventual finding of positive and significant coefficients

11

for our diversity measures as evidence of positive association between diversity and urban productivity.

4 Effects of Diversity in US Cities

We now turn to the estimation of the effects of diversity on cities’ average wages using panel regression

techniques. In so doing, we will address several econometric issues. In particular, we will check that our

results are robust to potential omitted variable bias as well as to potential endogeneity bias. Finally we will

qualify and compare our findings with reference to the existing literature.

4.1 Basic Regressions

Our basic regression analyzes the impact of diversity on the average wage using a panel of the 160 SMSAs

in three census years (1970, 1980 and 1990). We control for 160 city fixed effects, αc, and for year dummies,

βt. Therefore, we identify the effect of diversity on productivity by exploiting only the within-city variation

over time. In each specification we also control for the average level of schooling of workers, the share of

White, and the share of college-educated workers in the city. Based on the theoretical model presented in

section 3, our basic estimating equation is:

ln(wc,t) = αc + βt + γ1 (sc,t) + γ2 (whitec,t) + γ3(collegec,t) (16)

+γ4(Lang diversityc,t)

where, on the left hand side, the dependent variable ln(wc,t) is the log of the average hourly wage of white

US born males between 40 and 50 years of age (see Section 2.1 for details). On the right hand side, sc,t

is average schooling of white US born males 40 to 50 years old in city c in year t, whitec,t and collegec,t

are the share of white workers and the share of college-educated workers in the city, Lang diversityc,t is

the index of linguistic diversity measured as either (1) or (2). The city fixed effects control for permanent

differences across cities (such as size, location, proximity to the coast and to the US borders), while the time

effects control for common national trends. The average years of schooling, sc,t, control for returns to human

capital while the shares of white and college-educated workers control for composition characteristics of the

city.

We estimate few variations of equation (16). The results are reported in Table 3. Columns I to III

report the results obtaining by using the diversity index divLangct as the measure of linguistic diversity;

Column IV to VI report the results of otherwise identical specifications that uses the fractionalization index

12

frac(Langc,t) instead. Our very first specification (Column I) shows the estimates from a pooled OLS

regression without city fixed effects. This specification simply shows that, even after controlling for average

schooling, for the share of white workers, and for the share of college graduates, the average wage levels of

white US-born workers are still positively correlated with linguistic diversity across cities. The coefficient is

both significant and large. As the standard deviation of the linguistic diversity index is 0.30, the estimated

coefficient implies that two cities with a difference of one standard deviation in their linguistic diversity have

a 15 per cent difference in their average wages. Controlling for city fixed effects, in Column II, and for

city-size, as captured by log(employment), in Column III, does not change much the estimated coefficient,

which remains close to 0.4. Similarly, using the linguistic fractionalization index as measure of linguistic

diversity, we still get a positive and significant coefficient. As the standard deviation of frac(Langc,t) in

the sample is about 0.20, the quantitative effect of increasing fractionalization by one standard deviation is

also a 15-to-20 per cent increase in the average wage of our reference group. The other variables included

have the expected impacts. Returns to schooling are estimated around 6 per cent and the share of white

population has a positive and significant effect. Nevertheless, once we control for average schooling, the

share of college-graduates does not have any significant effect on the average wage.

4.2 Other forms of Diversity

The regressions in Table 4 test the robustness of the effect of linguistic diversity when other measures of

diversity are introduced. Cultural diversity may simply be a proxy of skill diversity, or may be correlated

with diversity of races so that the correlation found above may be the result of an omitted variable.

To test that linguistic diversity has a direct correlation to average wages, we control for fractionalization

along the dimensions of skills, state of birth, and race. Columns I and IV include a fractionalization index

calculated on four skill groups identified in terms of schooling achievements. The groups are high school

dropouts, high school graduates, college dropouts and college graduates. While skill diversity seem to have a

negative impact on the wage, the impact of linguistic diversity is still positive and significant. The negative

impact of skills dispersion after controlling for average schooling can be interpreted as a sign of decreasing

returns to skills: it is better to have workers at the mean level of skills than dispersed skill levels around it.

Columns II and V add a measure of fractionalization based on the state of birth of urban workers. If

the benefit to productivity simply came from having workers from different origins, or if it were caused by a

reverse connection between high productivity and immigration in the city from other states, the coefficient of

state-of-birth fractionalization should be positive. However, it turns out to be insignificant and very small.

Finally, we add an index of racial fractionalization in Columns III and VI.1 This index is introduced

1The index is constructed including the following ‘ethnic groups’: White, Black, Native American, Japanese, Chinese,

13

mainly to check whether cities actually benefit from cultural diversity per se rather than from some sort of

generic ‘tolerance’ for diversity. This last hypothesis is advanced in a series of paper by Florida 2002 (a,b)

and focuses on cultural tolerance as a catalyst for innovation and creativity. While linguistic diversity is

still significant after including racial diversity, this last variable has itself a positive impact on the wage.

Recall, however, that we are controlling for the share of white people, so higher diversity essentially means

a smaller black community relative to the other minorities. Therefore, the positive effect of racial diversity

may be simply capturing the negative effect of a larger share of black citizens. For our purposes, however,

what matters is that the effect of linguistic diversity remains significant and positive also after adding this

control.

4.3 Robustness Checks

Table 5 presents several robustness check of our previous results. The table includes only the estimated

coefficients on linguistic diversity in several possible specifications. To ease comparison, specification (1)

reports the coefficient estimate on diversity in the basic regression. Specification (2) uses total income per

capita, rather than wages, as dependent variable. The reason is that our theoretical model implies that the

aggregate output of a city Y c, and not only its average wage, should positively depend on diversity. Using

personal income, the impact of diversity, while somewhat smaller, is still positive and significant. This could

be due to the fact that personal income is a noisier measure of local economic conditions as it also includes

capital income possibly earned from investment anywhere in the country.

Specification (3) considers a larger definition of cultural identity obtained by grouping the languages

associated with similar cultures. This generates the following classification: Neo-Latin, Slavic, Anglo-Saxon,

South-Asian, East-Asian, African, and Native languages. The objective is to capture the fact that, say,

Spanish and Chinese are two cultures that are more different than Spanish and Italian. Thus, diversity

between the former pair may bring higher benefits from diversity as well as higher communication costs.

The estimated coefficient for linguistic diversity measured using the above large groups is both positive and

highly significant. The exact value (0.53) is somewhat higher than the one in the basic specification: diversity

across large groups seem to matter more.

Specification (4) controls for a ‘weaker’ form of cultural belonging, that is, the ancestry of individuals.

People may have different relevant characteristics not only because they are from different countries but also

because their ancestors and traditions are. Including the ancestry control does not change the impact of

linguistic diversity. Moreover, the ancestral diversity coefficient (not reported) is not significant. Specification

(5) simply controls for city population rather than employment as a better measure of density. No relevant

Filipino, Hawaiian, Korean, Others.

14

differences arise.

The last Specification (6) in Table 5 addresses the issue of the potential endogeneity of linguistic diversity:

richer linguistic diversity may be the consequence rather than the cause of higher average wage. Indeed, fast

growing cities may attract more people from outside, among them more foreign-born, so they end up with

larger linguistic diversity. Short of a randomized experiment, it is hard to rule out this channel completely.

One way to reduce the endogeneity bias is to resort to instrumental variable estimation. Specifically, we

consider growth in wages as our dependent variable and growth in linguistic diversity as our key explanatory

variable. Then we instrument the latter using the distance of each city from the international borders, from

the coast, as well as from New York and Los Angeles as the main ports of entry into the US. The underlying

idea is that, during the period from 1970 to 1990, the US have experienced a large increase in immigrants

for reasons that are exogenous to any particular city characteristics. Just for a geographic accident, cities

closer to the coast, the border or the main ports of entries have received a larger inflow of those immigrants.

Presumably, these distances have no direct impact on productivity. Being exogenous to the productivity

shocks of the period of observation, they can be used as instrumental variables. The full set of instruments

explains about 20 per cent of the variation in linguistic diversity. Specification (6) shows that the positive

correlation between linguistic diversity and wages survives the IV procedure.

To sum up, the positive correlation between wages and diversity across US cities survives a whole battery

of robustness checks. Instrumental variable estimation support the view that the direction of causation goes

from diversity to wages. The theoretical model allows us to describe such effect as cultural diversity boosting

productivity.

5 Public Goods and Recent Immigrants

The last two subsections of this empirical analysis are devoted to clarifying and qualifying our previous

results. In particular, we begin by reconciling our findings of a positive effect of linguistic diversity on

productivity with previous studies that found a negative association of ethnic diversity with other measures

of economic performance such as the provision of public goods. We then show that our findings are not

incompatible with studies that identify a small negative impact of new immigrants on the wages of natives.

5.1 Public Goods

A recently developed strand of research analyzes the effect of racial heterogeneity on local policies, partic-

ularly policies that involve redistribution (see, e.g., Alesina and La Ferrara, 2003, for a survey). The idea

is that communities with a higher degree of ethnic fragmentation are less willing to pool their resources

15

for public goods provision. Intuitively, in the presence of higher fragmentation each ethnic group cares less

about the benefit to the other ethnic groups. Such problem materializes in the underprovision of public

goods because individuals do not pay the marginal cost of a service. In the case of well defined markets,

where people pay the marginal cost of the service they use, there is no harm in having heterogeneous agents.

This is why we did not consider such an effect in our model. Here, however, we want to make sure that our

data are consistent with previous works showing ethnic fragmentation to be harmful for the local provision

of public goods, especially education (see, Alesina, Baqir, and Easterly, 1999; Alesina, Baqir, and Hoxby,

2004).

Table 6 considers whether the racial diversity of a city reduces its per capita spending for local public

services after we controlling for the local level of income, the local level of education, and the city size. Again,

we consider a panel estimation with city and time fixed-effects. Column I shows the impacts of linguistic

and racial diversities on local spending per capita. Consistently with the existing literature, racial diversity

has a negative impact. Linguistic diversity, however, has no impact whatsoever. Column II and III report

the impacts of diversity on the provision of local public goods. In particular, they show that racial diversity

decreases the expenditures in public education, thus confirming the findings of Alesina, Baqir, and Easterly

(1999). Racial diversity also increases the expenditures in police and security, which supports the idea that

ethnic diversity may generate social unrest and more need for policing. As to linguistic diversity, its effect on

both variables is not significant. Thus, linguistic diversity seems to maintain its overall positive productive

impact with no discernible effect on public good provision.

5.2 Recent Immigrants

Our study reveals a positive effect of linguistic diversity on the average wage at the city level. However,

existing studies on immigration to the US find a moderate negative effect of inflows of immigrants on the

wages of the natives (Borjas, 1994 and 1995). Such studies mostly concentrate on low-skilled wages, whereas

we consider the average wage of US born workers. Nevertheless, our positive effect may seem at odds with

this literature.

Those apparently conflicting findings can be reconciled. Our definition of linguistic diversity is based

on the language people speak at home. That may comprise not only new immigrants but also long-time

foreign-born residents and second generation immigrants as long as linguistic identity is maintained (which

is quite common for some groups). Under this respect, our theoretical model can be used to explain the

different results one gets when only new immigrants are considered. In particular, as discussed in Section 3,

interactions are likely to be easier when groups have had enough time to assimilate a common set of norms

16

and habits from the host society. This implies that the costs of interaction (τ) between the groups of city

dwellers may depend on the arrival time of the different cohorts of immigrants. Accordingly, in each city

the gradual assimilation of old cohorts and the arrival of new ones change its τ through time, which is not

captured by the city fixed effects. If such an ‘assimilation effect’ were relevant, we should observe a negative

impact of the share of new immigrants in a city on its average wage, while linguistic diversity (due to both

first and older generation of immigrants) should maintain its positive impact.

In each year we define as ‘new immigrants’ the foreign-born immigrated into the US within the previous

five years, the reason being that the census PUMS in 1970 and 1990 report the place of residence of individuals

five years earlier. The share of new immigrants in each city is then included in our regressions as additional

control. The corresponding results are reported in Table7. Column I shows the OLS estimates in differences

(1970-1990) whereas Columns II and III report the instrumental variable estimates. As in the previous

section, our instruments are the distances to the border, to the coast, and to N.Y. and L.A.. We use them

as proxies of the changes in both new immigrants and linguistic diversity. Since we consider a twenty-year

interval, the changes in linguistic diversity not controlled for by the changes in new immigrants are due

to the changes in the number of ‘old immigrants’ (i.e. those who have entered the US for more than five

years earlier). While linguistic diversity has still a significant and positive effect on wages, the share of new

immigrants has an insignificant (almost significant in one instrumental variable estimation) negative effect

on wages. Overall, our findings support the ‘assimilation effect’ hypothesis. Cities may face some initial

costs in coping with cultural diversity: the effect of new immigrants on local wages is small and possibly

slightly negative. However, once the initial costs of assimilation are incurred, the benefits of diversity for

productivity materialize.

6 Conclusions

We have investigated whether immigrants into the US contribute to the economic prosperity of their host

cities through the cultural diversity they bring. In particular, we have studied the effect of cultural diversity

on the wages of the native population. We started with no obvious a priori. On the production side, if

different cultures contribute different skills and expertise in producing goods and services, cultural diversity

may enhance productivity. However, difficulties in integration and communication across different groups

of citizens may harm aggregate productivity. On the consumption side, cultural diversity may increase the

variety of available goods and services. At the same time, however, heterogenous preferences may trigger

social conflicts on the provision of public goods.

By studying 160 US SMAs in the period 1970-1990, we have found a significant and robust positive

17

correlation between cultural diversity increases and the wages of white US-born workers. By comparing the

distributions of wages and costs of living across US cities, we have argued that such correlation is compat-

ible only with a dominant positive correlation between productivity and diversity. Moreover, instrumental

variable estimation supports the idea of causation going from the latter to the former. To the best of our

knowledge, these results are new.

We have finally qualified our findings under two respects. First, our data agree with previous studies in

that ethnic diversity is found to be bad for the provision of local public goods as more diverse societies are

less willing to pool resources for collective purposes. Second, our analysis points out that the benefits from

immigrants who have integrated (i.e. have been in the US for a longer period of time) are larger than those

from new immigrants. This suggests that integration and assimilation may be prerequisites for reaping the

gains of cultural diversity.

18

References

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partment of Economics, mimeo.

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Economics 114, 1243-84

[3] Alesina A., Baqir R. and C. Hoxby (2004) Political Jurisdictions in Heterogenous Communities, Journal

of Political Economy, forthcoming.

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Literature 37, 64-111.

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Journal of Economics 112, 1203-1250.

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19

[16] Florida R. (2002b) The Economic Geography of Talent, Annals of the Association of Economic Geog-

raphers 92, 743-755.

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of Monetary Economics 36, 117-143.

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[20] Lazear E. (1999b) Culture and language, Journal of Political Economy, Supplement, 95-125.

[21] Mauro P. (1995) Corruption and Growth, Quarterly Journal of Economics 110, 681-712.

[22] O’Reilly C., K. Williams and S. Barsade (1998) Group Demography and Innovation: Does Diversity

Help?, in Research on Managing Groups and Teams, D. Gruenfeld et al. editors, JAI Press.

[23] Ottaviano G.I.P. and G. Peri (2004) The Value of Cultural Diversity: Evidence from US cities, Centre

for Economic Policy Research, Discussion Paper n.4233.

[24] Roback J. (1982) Wages, rents and the quality of life, Journal of Political Economy 90, 1257-78.

20

7

Data Appendix

The data on ethnic and linguistic composition of Cities have been obtained from the 1970-1990 Public Use

Microdata Sample of the US Census. We selected all people in working age (16-65) in each year and we

identified the city where they lived using the SMSA code for 1980 and 1990, while in 1970 we used the county

group code to identify the metropolitan area. We used the variable ”Language Spoken in the Home” in order

to identify the linguistic identity of the person. We construct groups that can be kept homogenous across

census years. The linguistic groups that we identify are the following: English, Scandinavian, Dutch, French,

Celtic, German, Polish, Czech, Slovac, African language, Russian, Rumanian, Indo-European, Hungarian,

Yiddish, Greek, Italian, Spanish, Portuguese, Chinese, Arabic, Albanian, Persian, Hindi, Hebrew, East-

Southeast Asian, Filipino, American Indian, Other languages. Once we have grouped people in racial we use

the shares of each group within a city in our sample as measure of the share of the population in that city

belonging to that group. In Table 4 we use the following racial groups to construct racial fractionalization:

white, black, native American, Japanese, Chinese, Filipino, Hawaiian, Korean, others. In table 5 we use the

following 15 groups of Ancestry for white people: Dutch, English, French, German, Greek, Hungarian, Irish,

Italian, Norwegian, Polish, Portuguese, Russian, Scottish, Swedish, Ukrainian.

We use the Variable ”Salary and Wage” to measure the yearly wage income and we divide that by the

number of weeks worked in a year and then by the number of hours worker in a week in order to obtain the

hourly wage. We transform the wage in real terms by deflating it for the GDP deflator. The data on total

city employment are from the ”County and City Databook” and measure the total non-farm employment in

the metropolitan area.

The list of metropolitan areas used in our study is reported in the following table.

21

Name and state of the cities used

Abilene, TX Dayton-Springfield, OH Lexington, KY Rockford, IL

Akron, OH Decatur, IL Lima, OH Sacramento, CA

Albany-Schenectady-Troy, NY

Denver, CO Lincoln, NE Saginaw-Bay City-Midland, MI

Albuquerque, NM Des Moines, IA Little Rock-North Little Rock, AR

St. Louis, MO-IL

Allentown-Bethlehem-Easton, PA

Detroit, MI Los Angeles-Long Beach, CA Salem, OR

Altoona, PA Duluth-Superior, MN-WI Louisville, KY-IN Salinas, CA

Amarillo, TX El Paso, TX Lubbock, TX Salt Lake City-Ogden, UT

Appleton-Oshkosh-Neenah, WI

Erie, PA Macon, GA San Antonio, TX

Atlanta, GA Eugene-Springfield, OR Madison, WI San Diego, CA

Atlantic-Cape May, NJ Fayetteville, NC Mansfield, OH San Francisco, CA

Augusta-Aiken, GA-SC Flint, MI Memphis, TN-AR-MS San Jose, CA

Austin-San Marcos, TX Fort Lauderdale, FL Miami, FL Santa Barbara-Santa Maria-Lompoc, CA

Bakersfield, CA Fort Wayne, IN Milwaukee-Waukesha, WI Santa Rosa, CA

Baltimore, MD Fresno, CA Minneapolis-St. Paul, MN-WI Seattle-Bellevue-Everett, WA

Baton Rouge, LA Gainesville, FL Modesto, CA Shreveport-Bossier City, LA

Beaumont-Port Arthur, TX Gary, IN Monroe, LA South Bend, IN

Billings, MT Grand Rapids-Muskegon-Holland, MI

Montgomery, AL Spokane, WA

Biloxi-Gulfport-Pascagoula, MS

Green Bay, WI Muncie, IN Springfield, MO

Binghamton, NY Greensboro--Winston-Salem--High Point, NC

Nashville, TN Stockton-Lodi, CA

Birmingham, AL Greenville-Spartanburg-Anderson, SC

New Orleans, LA Syracuse, NY

Bloomington-Normal, IL Hamilton-Middletown, OH New York, NY Tacoma, WA

Boise City, ID Harrisburg-Lebanon-Carlisle, PA

Newark, NJ Tampa-St. Petersburg-Clearwater, FL

Brownsville-Harlingen-San Benito, TX

Honolulu, HI Norfolk-Virginia Beach-Newport News, VA-NC

Terre Haute, IN

Buffalo-Niagara Falls, NY Houston, TX Odessa-Midland, TX Toledo, OH

Canton-Massillon, OH Huntington-Ashland, WV-KY-OH

Oklahoma City, OK Trenton, NJ

Cedar Rapids, IA Indianapolis, IN Omaha, NE-IA Tucson, AZ

Champaign-Urbana, IL Jackson, MI Orlando, FL Tulsa, OK

Charleston-North Charleston, SC

Jackson, MS Pensacola, FL Tuscaloosa, AL

Charlotte-Gastonia-Rock Hill, NC-SC

Jacksonville, FL Peoria-Pekin, IL Tyler, TX

Chattanooga, TN-GA Jersey City, NJ Philadelphia, PA-NJ Utica-Rome, NY

Chicago, IL Johnstown, PA Phoenix-Mesa, AZ Vallejo-Fairfield-Napa, CA

Cincinnati, OH-KY-IN Kalamazoo-Battle Creek, MI Pittsburgh, PA Waco, TX

Cleveland-Lorain-Elyria, OH Kansas City, MO-KS Portland-Vancouver, OR-WA Washington, DC-MD-VA-WV

Colorado Springs, CO Kenosha, WI Raleigh-Durham-Chapel Hill, NC

Waterloo-Cedar Falls, IA

Columbia, MO Knoxville, TN Reading, PA West Palm Beach-Boca Raton, FL

Columbia, SC Lafayette, LA Reno, NV Wichita, KS

Columbus, OH Lafayette, IN Richmond-Petersburg, VA Wilmington-Newark, DE-MD

Corpus Christi, TX Lancaster, PA Riverside-San Bernardino, CA

Wilmington, NC

Dallas, TX Lansing-East Lansing, MI Roanoke, VA York, PA

Davenport-Moline-Rock Island, IA-IL

Las Vegas, NV-AZ Rochester, NY Youngstown-Warren, OH

22

23

Tables and Figures

Table 1

Main Linguistic Shares in 160 U.S. Metropolitan areas

Table 2 Linguistic Shares in some Metropolitan areas, 1990.

language shares Average 1970

Std. Deviation 1970

Average 1990

Std. Deviation 1990

English 0.800 0.123 0.789 0.120 German 0.033 0.032 0.006 0.004 Italian 0.017 0.024 0.006 0.004 Spanish 0.039 0.091 0.132 0.110 Chinese 0.001 0.013 0.015 0.010 Other 0.129 0.071 0.052 0.020 Fractionalization Index of Language

0.333 0.150 0.180 0.133

city English German Italian Spanish Chinese Other Fractionalization Atlanta, GA 0.934 0.005 0.001 0.026 0.004 0.030 0.137 Chicago, IL 0.804 0.009 0.008 0.094 0.006 0.079 0.355 Cincinnati, OH-KY-IN 0.962 0.008 0.002 0.009 0.001 0.018 0.080 Dallas, TX 0.850 0.004 0.001 0.112 0.005 0.029 0.265 El Paso, TX 0.322 0.009 0.001 0.656 0.002 0.011 0.473 Indianapolis, IN 0.963 0.005 0.001 0.014 0.001 0.016 0.070 Los Angeles, CA 0.570 0.005 0.003 0.300 0.025 0.097 0.591 New York, NY 0.645 0.007 0.028 0.177 0.032 0.111 0.550 Philadelphia, PA-NJ 0.922 0.007 0.009 0.026 0.003 0.034 0.148 Pittsburgh, PA 0.958 0.005 0.008 0.008 0.002 0.020 0.080 San Francisco, CA 0.679 0.009 0.006 0.107 0.087 0.112 0.620 Washington, DC-MD-VA-WV 0.857 0.007 0.003 0.053 0.009 0.071 0.278

24

Table 3 Basic Panel Estimation

Specification I

Pooled OLS

II Fixed Effects

III Fixed Effects

IV Pooled OLS

V Fixed Effects

VI Fixed Effects

Index of Linguistic Diversity Used

Div(Langc,t) FracLangc,t

Average Schooling 0.06* (0.01)

0.06* (0.01)

0.06* (0.01)

0.07* (0.01)

0.06* (0.01)

0.06* (0.01)

Share of White 0.36* (0.06)

0.49* (0.20)

0.57* (0.20)

0.30* (0.06)

0.57* (0.20)

0.52* (0.20)

Share of College 0.28 (0.27)

0.14 (0.20)

0.14 (0.20)

0.25 (0.20)

0.10 (0.20)

0.05 (0.20)

Linguistic Diversity 0.54* (0.04)

0.38* (0.10)

0.36* (0.10)

0.74* (0.11)

1.00* (0.17)

0.95* (0.17)

ln(Employment) 0.06 (0.04)

0.04 (0.03)

City Effects No Yes Yes No Yes Yes R2 0.97 0.99 0.99 0.97 0.99 0.99 Observations 480 480 480 480 480 480

Dependent Variable: natural logarithm of average hourly wage of white males 40-50 years in 1990 U.S. $. All Regressions include time dummies, in parenthesis Heteroskedasticity-Robust Standard Errors *significant at 5% Specification I: Basic Specification, Method of Estimation, Pooled OLS, including fixed time effects. Diversity Index

used: DivLang =∑ j tj

cl66.0)(

Specification II: Basic Specification, Method of Estimation, Panel estimation with Fixed Effects, including fixed time

effects. Diversity Index used: DivLang =∑ j tj

cl66.0)(

Specification III: Basic Specification, Method of Estimation, Panel estimation with Fixed Effects, including fixed

time effects and ln(Employment) as controls. Diversity Index used: DivLang =∑ j tj

cl66.0)(

Specification IV: Basic Specification, Method of Estimation, Pooled OLS, including fixed time effects. Diversity

Index used: FracLang = ∑− j tj

cl2)(1

Specification V: Basic Specification, Method of Estimation, Panel estimation with Fixed Effects, including fixed time

effects. Diversity Index used: FracLang = ∑− j tj

cl2)(1

Specification VI: Basic Specification, Method of Estimation, Panel estimation with Fixed Effects, including fixed

time effects and ln(Employment) as controls. Diversity Index used FracLang = ∑− j tj

cl2)(1

25

Table 4

Adding Measures of “Diversity” as Controls Specification I II III IV V VI Index of Linguistic Diversity Used

Div(Langc,t) FracLangc,t

Average Schooling

0.06* (0.01)

0.06* (0.01)

0.06* (0.01)

0.06* (0.01)

0.06* (0.01)

0.06* (0.01)

Share of White 0.63* (0.31)

0.63* (0.31)

0.56* (0.19)

0.63* (0.31)

0.63* (0.31)

0.32 (0.33)

Share of College 0.49 (0.34)

0.49 (0.34)

0.77* (0.33)

0.49 (0.34)

0.49 (0.34)

0.59 (0.32)

Linguistic Diversity

0.33* (0.10)

0.34* (0.10)

0.49* (0.11)

0.94* (0.18)

0.94* (0.18)

0.95* (0.18)

Skills Fractionalization

-0.71 (0.38)

-0.72 (0.38)

-1.11* (0.38)

-0.83* (0.36)

-0.84* (0.33)

-0.90* (0.37)

State of Birth Fractionalization

-0.005 (0.01)

-0.004 (0.01)

-0.003 (0.01)

-0.002 (0.01)

Racial Fractionalization

0.50* (0.13)

0.26* (0.12)

ln(Employment) 0.06 (0.04)

0.07 (0.04)

0.03 (0.04)

0.06 (0.04)

0.06 (0.04)

0.03 (0.04)

City Effects Yes Yes Yes Yes Yes Yes R2 0.99 0.99 0.99 0.99 0.99 0.99 Observations 480 480 480 480 480 480

Dependent Variable: natural logarithm of average hourly wage of white males 40-50 years in 1990 U.S. $. All Regressions include time dummies, in parenthesis Heteroskedasticity-Robust Standard Errors *significant at 5% Specification I: Method of Estimation, Pooled OLS, including fixed time effects and fractionalization index for education groups. We use four education groups (High School Dropout, High School Graduates, College Dropouts and College Graduates) as skill groups. Specification II: Method of Estimation, Pooled OLS, including fixed time effects and fractionalization index for education groups and State of birth. We use the fifty state of births as groups. Specification III: Method of Estimation, Pooled OLS, including fixed time effects and fractionalization index for education groups, State of birth and Race. We use white, black, native American, Japanese, Chinese, Filipino, Hawaiian, Korean, others as racial groups.

Specification IV : As specification I, using FracLang = ∑− j tj

cl2)(1 as index of linguistic diversity

Specification V: As specification II, using FracLang = ∑− j tj

cl2)(1 as index of linguistic diversity

Specification VI: As specification III, using FracLang = ∑− j tj

cl2)(1 as index of linguistic diversity

26

Table 5 Robustness Checks

Specification Coefficient of Linguistic

Diversity: Div(Langc,t) (1) Basic 0.36*

(0.10) (2) Personal Income as Dependent Variable

0.28* (0.06)

(3) Large Linguistic Groups

0.53* (0.14)

(4) Controlling for Ancestry Diversity (1970 dropped)

0.50* (0.22)

(5) Controlling for Population

0.37* (0.10)

(6) Instrumental Variables Estimation IV: Distance from Ports of Entry

0.50* (0.11)

All Regressions include time dummies, city fixed effects average schooling, share of whites, share of college graduates and ln(Employment) as controls. Dependent Variable : ln average hourly wage of white males 40-50 years in 1990 U.S. $. Reported is the coefficient on the Diversity index Div(Lang). In parenthesis Heteroskedasticity-Robust Standard Errors * significant at 5%.

(1) Basic Regression, as in Column III of Table 3 (2) Using ln(personal income) in 1990 US $ as dependent variable rather than ln(wage) (3) Grouping linguistic groups into larger “cultural groups” based on the proximity of the countries of origin of the language. Groups are listed in the Appendix (4) Including the diversity of origin of ancestors (parents). This variable is available for 1980 and 1990 only (5) Including ln(Population) as a control, rather than ln(Employment). (6) Using instrumental variable for linguistic diversity. The instruments used are distance from the coast, distance from the border and distance from New York and Los Angeles, the largest port of entries in the U.S. In the first stage regression (not reported) the IV explain 20% of the variance of linguistic diversity

27

Table 6

Effects of Ethnic and Linguistic Diversity on the Provision of Public Goods

Specification I

Total Local Spending per Capita

II Local Spending in Education

III Local Spending in Police-Security

Ln(Income) 0.44* (0.06)

0.56* (0.12)

0.57* (0.13

Linguistic Diversity 0.14 (0.10)

-0.04 (0.09)

0.01 (0.11)

Racial Diversity -0.22* (0.11)

-0.19* (0.10)

0.34* (0.14)

Ln(Employment) 0.10* (0.04)

-0.16* (0.05)

-0.21* (0.05)

City Effects Yes Yes Yes R2 0.98 0.97 0.98 Observations 480 480 480

Dependent Variable: natural logarithm of real public spending per capita of local administration in 1990 U.S. $. Regressions include time dummies and city fixed effects ** significant at 5%, * significant at 10% Specification I: Dependent Variable is natural logarithm of real total public spending per capita of local administration in 1990 U.S. $. Method of estimation is OLS. Specification II: Dependent Variable is natural logarithm of real public spending per capita of local administration for School and Education in 1990 U.S. $. Method of estimation is OLS. Specification III: Dependent Variable is natural logarithm of real public spending per capita of local administration for Police and Security in 1990 U.S. $. Method of estimation is OLS.

28

Table 7

Cultural Diversity and New Immigrants

Dependent Variable: natural logarithm of average hourly wage of white males 40-50 years in 1990 U.S. $. All Regressions include time dummies, in parenthesis Heteroskedasticity-Robust Standard Errors *significant at 5% Specification I: OLS estimates on differenced variables 1970-1990. Differencing eliminates the fixed level effect and the coefficients are identified on 160 changes. Specification II: Instrumental Variables estimation on differenced variables 1970-1990.Endogenous Variables are Div(Lang) and Share of New Immigrants Instruments are: Distance from Coast, Distance from Border, Distance from Los Angeles and Distance from New York. They explain 18% of the variation of Linguistic diversity and 16% of the variation of share of new immigrants. Specification III: Instrumental Variables estimation on differenced variables 1970-1990.Endogenous Variables are Div(Lang) and Share of New Immigrants Instruments are: Distance from Coast, Distance from Border. They explain 16% of the variation of Linguistic diversity and 10% of the variation of share of new immigrants.

Specification I OLS on 70-90 differences

II Instrumental Variables Estimation (a)

III Instrumental Variables Estimation (b)

Average Schooling 0.05* (0.01)

0.05* (0.01)

0.05* (0.02)

Linguistic Diversity Div(Lang)

0.15* (0.04)

0.33* (0.12)

0.57* (0.13)

Share of New Immigrants (5 years)

-0.25 (0.52)

-0.31 (1.02)

-2.03 (1.10)

Ln(Employment) 0.04* (0.02)

0.01 (0.02)

0.01 (0.02)

R2 0.15 0.10 0.11 Observations 160 160 160

29

Figure 1 – Nominal Wages and Diversity in US Cities, 1990

Partial Correlation between Nominal Wage and Diversity in 1990no

min

al w

age

linguistic diversity index1.14492 1.91263

-7.09642

10.6672

AbileneAkron

Albany-S

Albuquer

Allentow

Altoona

AmarilloAppleton

Atlanta

Atlantic

Augusta-Austin-S

Bakersfi

Baltimor

Baton RoBeaumont

Billings

Biloxi-G

Binghamt

Birmingh

Blooming

Boise Ci

Brownsvi

Buffalo-Canton-M

Cedar Ra

Champaig

Charlest

CharlottChattano

ChicagoCincinna

ClevelanColorado

Columbia

Columbia

Columbus

Corpus C

DallasDavenpor

Dayton-S

Decatur

Denver

Des Moin

DetroitDuluth-S

El Paso

ErieEugene-S

Fayettev

Flint

Fort Lau

Fort Way

Fresno

Gainesvi

GaryGrand Ra

Green Ba

Greensbo

Greenvil

Hamilton

Harrisbu

Honolulu

Houston

Huntingt

IndianapJackson

Jackson

Jacksonv

Jersey C

Johnstow

KalamazoKansas C

Kenosha

Knoxvill

Lafayett

Lafayett

Lancaste

Lansing-

Las VegaLexingtoLimaLincoln

Little R Los Ange

Louisvil LubbockMacon

Madison

MansfielMemphis

Miami

MilwaukeMinneapo

Modesto

Monroe

MontgomeMuncie

Nashvill

New Orle

New York

Newark

Norfolk-

Odessa-M

Oklahoma

Omaha

OrlandoPensacol

Peoria-P

Philadel

Phoenix-

Pittsbur

Portland

Raleigh-

Reading

Reno

Richmond

Riversid

Roanoke

RochesteRockford

SacramenSaginaw-St. Loui

Salem

Salinas

Salt LakSan Anto

San Dieg

San Fran

San JoseSanta BaSanta Ro

Seattle-

Shrevepo

South Be

SpokaneSpringfiStockton

SyracuseTacoma

Tampa-St

Terre HaToledo

Trenton

Tucson

TulsaTuscaloo

Tyler

Utica-Ro

Vallejo-

Waco

Washingt

Waterloo

West Pal

Wichita

Wilmingt

Wilmingt

York

Youngsto

slope: 5.5 (s.e. 1.2) t-stat= 4.4

Figure 2 – Real Wages and Diversity in US Cities, 1990

Partial Correlation between Real Wage and Diversity in 1990

rea

l wag

e

linguistic diversity index1.14492 1.91263

-1.04347

1.16332

Abilene

AkronAlbany-S

Albuquer

AllentowAltoona Amarillo

Appleton

Atlanta

Atlant ic

Augusta-

Aust in-S

Bakersfi

Baltimor

Baton RoBeaumont

Billings

Biloxi-G

BinghamtBirmingh

Blooming

Boise Ci

Brownsvi

Buffalo-Canton-M

Cedar Ra

Champaig

Charlest

Charlott

ChattanoChicago

Cincinna

Clevelan

Colorado

Columbia

ColumbiaColumbus

Corpus C

Dallas

Davenpor

Dayton-S

DecaturDenver

Des Moin

Detroit

Duluth-S

El Paso

Erie

Eugene-S

Fayettev

Flint

Fort LauFort Way

Fresno

Gainesvi

GaryGrand Ra

Green BaGreensbo

GreenvilHamilton

Harrisbu

Honolulu

Houston

Huntingt

Indianap

Jackson

Jackson

Jacksonv

Jersey C

Johnstow

Kalamazo

Kansas C

KenoshaKnoxvill

Lafayett

Lafayett

Lancaste

Lansing- Las Vega

Lexingto

Lima Lincoln

Litt le R

Los Ange

Louisvil

Lubbock

Macon

Madison

MansfielMemphis

Miami

MilwaukeMinneapo

ModestoMonroe

MontgomeMuncie Nashvill

New Orle

New York

Newark

Norfolk-

Odessa-M

Oklahoma

Omaha

Orlando

Pensacol

Peoria-P

Philadel

Phoenix-

PittsburPortland

Raleigh-

Reading

RenoRichmond

Riversid

Roanoke

Rocheste

Rockford

Sacramen

Saginaw-

St. Loui

Salem Salinas

Salt LakSan Anto San Dieg

San Fran

San JoseSanta Ba

Santa Ro

Seattle-

Shrevepo

South BeSpokaneSpringfi

Stockton

Syracuse

TacomaTampa-St

Terre Ha

Toledo

Trenton

Tucson

Tulsa

Tuscaloo

Tyler

Utica-RoVallejo-

Waco

WashingtWaterloo

West Pal

Wichita

Wilmingt

Wilmingt

York

Youngsto

slope: -1.15 (s.e. 1.14) t-stat= -1.04

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Multi-Ethnicity in Rome. Reading Governance in a Local Context KTHC 37.2004 Kristine CRANE (lxviii): Governing Migration: Immigrant Groups’ Strategies in Three Italian Cities – Rome,

Naples and Bari KTHC 38.2004 Kiflemariam HAMDE (lxviii): Mind in Africa, Body in Europe: The Struggle for Maintaining and Transforming

Cultural Identity - A Note from the Experience of Eritrean Immigrants in Stockholm ETA 39.2004 Alberto CAVALIERE: Price Competition with Information Disparities in a Vertically Differentiated Duopoly PRA 40.2004 Andrea BIGANO and Stef PROOST: The Opening of the European Electricity Market and Environmental Policy:

Does the Degree of Competition Matter? CCMP 41.2004 Micheal FINUS (lxix): International Cooperation to Resolve International Pollution Problems

KTHC 42.2004 Francesco CRESPI: Notes on the Determinants of Innovation: A Multi-Perspective Analysis CTN 43.2004 Sergio CURRARINI and Marco MARINI: Coalition Formation in Games without Synergies CTN 44.2004 Marc ESCRIHUELA-VILLAR: Cartel Sustainability and Cartel Stability NRM 45.2004 Sebastian BERVOETS and Nicolas GRAVEL (lxvi): Appraising Diversity with an Ordinal Notion of Similarity:

An Axiomatic Approach NRM 46.2004 Signe ANTHON and Bo JELLESMARK THORSEN (lxvi): Optimal Afforestation Contracts with Asymmetric

Information on Private Environmental Benefits NRM 47.2004 John MBURU (lxvi): Wildlife Conservation and Management in Kenya: Towards a Co-management Approach NRM 48.2004 Ekin BIROL, Ágnes GYOVAI and Melinda SMALE (lxvi): Using a Choice Experiment to Value Agricultural

Biodiversity on Hungarian Small Farms: Agri-Environmental Policies in a Transition al Economy CCMP 49.2004 Gernot KLEPPER and Sonja PETERSON: The EU Emissions Trading Scheme. Allowance Prices, Trade Flows,

Competitiveness Effects GG 50.2004 Scott BARRETT and Michael HOEL: Optimal Disease Eradication CTN 51.2004 Dinko DIMITROV, Peter BORM, Ruud HENDRICKX and Shao CHIN SUNG: Simple Priorities and Core

Stability in Hedonic Games SIEV 52.2004 Francesco RICCI: Channels of Transmission of Environmental Policy to Economic Growth: A Survey of the

Theory SIEV 53.2004 Anna ALBERINI, Maureen CROPPER, Alan KRUPNICK and Nathalie B. SIMON: Willingness to Pay for

Mortality Risk Reductions: Does Latency Matter? NRM 54.2004 Ingo BRÄUER and Rainer MARGGRAF (lxvi): Valuation of Ecosystem Services Provided by Biodiversity

Conservation: An Integrated Hydrological and Economic Model to Value the Enhanced Nitrogen Retention in Renaturated Streams

NRM 55.2004 Timo GOESCHL and Tun LIN (lxvi): Biodiversity Conservation on Private Lands: Information Problems and Regulatory Choices

NRM 56.2004 Tom DEDEURWAERDERE (lxvi): Bioprospection: From the Economics of Contracts to Reflexive Governance CCMP 57.2004 Katrin REHDANZ and David MADDISON: The Amenity Value of Climate to German Households CCMP 58.2004 Koen SMEKENS and Bob VAN DER ZWAAN: Environmental Externalities of Geological Carbon Sequestration

Effects on Energy Scenarios NRM 59.2004 Valentina BOSETTI, Mariaester CASSINELLI and Alessandro LANZA (lxvii): Using Data Envelopment

Analysis to Evaluate Environmentally Conscious Tourism Management NRM 60.2004 Timo GOESCHL and Danilo CAMARGO IGLIORI (lxvi):Property Rights Conservation and Development: An

Analysis of Extractive Reserves in the Brazilian Amazon CCMP 61.2004 Barbara BUCHNER and Carlo CARRARO: Economic and Environmental Effectiveness of a

Technology-based Climate Protocol NRM 62.2004 Elissaios PAPYRAKIS and Reyer GERLAGH: Resource-Abundance and Economic Growth in the U.S. NRM 63.2004 Györgyi BELA, György PATAKI, Melinda SMALE and Mariann HAJDÚ (lxvi): Conserving Crop Genetic

Resources on Smallholder Farms in Hungary: Institutional Analysis NRM 64.2004 E.C.M. RUIJGROK and E.E.M. NILLESEN (lxvi): The Socio-Economic Value of Natural Riverbanks in the

Netherlands NRM 65.2004 E.C.M. RUIJGROK (lxvi): Reducing Acidification: The Benefits of Increased Nature Quality. Investigating the

Possibilities of the Contingent Valuation Method ETA 66.2004 Giannis VARDAS and Anastasios XEPAPADEAS: Uncertainty Aversion, Robust Control and Asset Holdings GG 67.2004 Anastasios XEPAPADEAS and Constadina PASSA: Participation in and Compliance with Public Voluntary

Environmental Programs: An Evolutionary Approach GG 68.2004 Michael FINUS: Modesty Pays: Sometimes! NRM 69.2004 Trond BJØRNDAL and Ana BRASÃO: The Northern Atlantic Bluefin Tuna Fisheries: Management and Policy

Implications CTN 70.2004 Alejandro CAPARRÓS, Abdelhakim HAMMOUDI and Tarik TAZDAÏT: On Coalition Formation with

Heterogeneous Agents IEM 71.2004 Massimo GIOVANNINI, Margherita GRASSO, Alessandro LANZA and Matteo MANERA: Conditional

Correlations in the Returns on Oil Companies Stock Prices and Their Determinants IEM 72.2004 Alessandro LANZA, Matteo MANERA and Michael MCALEER: Modelling Dynamic Conditional Correlations

in WTI Oil Forward and Futures Returns SIEV 73.2004 Margarita GENIUS and Elisabetta STRAZZERA: The Copula Approach to Sample Selection Modelling:

An Application to the Recreational Value of Forests CCMP 74.2004 Rob DELLINK and Ekko van IERLAND: Pollution Abatement in the Netherlands: A Dynamic Applied General

Equilibrium Assessment ETA 75.2004 Rosella LEVAGGI and Michele MORETTO: Investment in Hospital Care Technology under Different

Purchasing Rules: A Real Option Approach CTN 76.2004 Salvador BARBERÀ and Matthew O. JACKSON (lxx): On the Weights of Nations: Assigning Voting Weights in

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REDONDO (lxx): Optimal Information Transmission in Organizations: Search and Congestion CTN 78.2004 Francis BLOCH and Armando GOMES (lxx): Contracting with Externalities and Outside Options

CTN 79.2004 Rabah AMIR, Effrosyni DIAMANTOUDI and Licun XUE (lxx): Merger Performance under Uncertain Efficiency Gains

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and Coalition Government Formation CTN 83.2004 Kfir ELIAZ, Debraj RAY and Ronny RAZIN (lxx): Group Decision-Making in the Shadow of Disagreement CTN 84.2004 Sanjeev GOYAL, Marco van der LEIJ and José Luis MORAGA-GONZÁLEZ (lxx): Economics: An Emerging

Small World? CTN 85.2004 Edward CARTWRIGHT (lxx): Learning to Play Approximate Nash Equilibria in Games with Many Players IEM 86.2004 Finn R. FØRSUND and Michael HOEL: Properties of a Non-Competitive Electricity Market Dominated by

Hydroelectric Power KTHC 87.2004 Elissaios PAPYRAKIS and Reyer GERLAGH: Natural Resources, Investment and Long-Term Income CCMP 88.2004 Marzio GALEOTTI and Claudia KEMFERT: Interactions between Climate and Trade Policies: A Survey IEM 89.2004 A. MARKANDYA, S. PEDROSO and D. STREIMIKIENE: Energy Efficiency in Transition Economies: Is There

Convergence Towards the EU Average? GG 90.2004 Rolf GOLOMBEK and Michael HOEL : Climate Agreements and Technology Policy PRA 91.2004 Sergei IZMALKOV: Multi-Unit Open Ascending Price Efficient Auction KTHC 92.2004 Gianmarco I.P. OTTAVIANO and Giovanni PERI: Cities and Cultures

(lix) This paper was presented at the ENGIME Workshop on “Mapping Diversity”, Leuven, May 16-17, 2002 (lx) This paper was presented at the EuroConference on “Auctions and Market Design: Theory, Evidence and Applications”, organised by the Fondazione Eni Enrico Mattei, Milan, September 26-28, 2002 (lxi) This paper was presented at the Eighth Meeting of the Coalition Theory Network organised by the GREQAM, Aix-en-Provence, France, January 24-25, 2003 (lxii) This paper was presented at the ENGIME Workshop on “Communication across Cultures in Multicultural Cities”, The Hague, November 7-8, 2002 (lxiii) This paper was presented at the ENGIME Workshop on “Social dynamics and conflicts in multicultural cities”, Milan, March 20-21, 2003 (lxiv) This paper was presented at the International Conference on “Theoretical Topics in Ecological Economics”, organised by the Abdus Salam International Centre for Theoretical Physics - ICTP, the Beijer International Institute of Ecological Economics, and Fondazione Eni Enrico Mattei – FEEM Trieste, February 10-21, 2003 (lxv) This paper was presented at the EuroConference on “Auctions and Market Design: Theory, Evidence and Applications” organised by Fondazione Eni Enrico Mattei and sponsored by the EU, Milan, September 25-27, 2003 (lxvi) This paper has been presented at the 4th BioEcon Workshop on “Economic Analysis of Policies for Biodiversity Conservation” organised on behalf of the BIOECON Network by Fondazione Eni Enrico Mattei, Venice International University (VIU) and University College London (UCL) , Venice, August 28-29, 2003 (lxvii) This paper has been presented at the international conference on “Tourism and Sustainable Economic Development – Macro and Micro Economic Issues” jointly organised by CRENoS (Università di Cagliari e Sassari, Italy) and Fondazione Eni Enrico Mattei, and supported by the World Bank, Sardinia, September 19-20, 2003 (lxviii) This paper was presented at the ENGIME Workshop on “Governance and Policies in Multicultural Cities”, Rome, June 5-6, 2003 (lxix) This paper was presented at the Fourth EEP Plenary Workshop and EEP Conference “The Future of Climate Policy”, Cagliari, Italy, 27-28 March 2003 (lxx) This paper was presented at the 9th Coalition Theory Workshop on "Collective Decisions andInstitutional Design" organised by the Universitat Autònoma de Barcelona and held in Barcelona,Spain, January 30-31, 2004

2003 SERIES

CLIM Climate Change Modelling and Policy (Editor: Marzio Galeotti )

GG Global Governance (Editor: Carlo Carraro)

SIEV Sustainability Indicators and Environmental Valuation (Editor: Anna Alberini)

NRM Natural Resources Management (Editor: Carlo Giupponi)

KNOW Knowledge, Technology, Human Capital (Editor: Gianmarco Ottaviano)

IEM International Energy Markets (Editor: Anil Markandya)

CSRM Corporate Social Responsibility and Management (Editor: Sabina Ratti)

PRIV Privatisation, Regulation, Antitrust (Editor: Bernardo Bortolotti)

ETA Economic Theory and Applications (Editor: Carlo Carraro)

CTN Coalition Theory Network

2004 SERIES

CCMP Climate Change Modelling and Policy (Editor: Marzio Galeotti )

GG Global Governance (Editor: Carlo Carraro)

SIEV Sustainability Indicators and Environmental Valuation (Editor: Anna Alberini)

NRM Natural Resources Management (Editor: Carlo Giupponi)

KTHC Knowledge, Technology, Human Capital (Editor: Gianmarco Ottaviano)

IEM International Energy Markets (Editor: Anil Markandya)

CSRM Corporate Social Responsibility and Management (Editor: Sabina Ratti)

PRA Privatisation, Regulation, Antitrust (Editor: Bernardo Bortolotti)

ETA Economic Theory and Applications (Editor: Carlo Carraro)

CTN Coalition Theory Network