Home-Biased Gravity:

The Role of Migrant Tastes in International Trade

Penglong Zhang ∗

December, 2018

Abstract

Immigrants tend to buy products from their home countries. As a result, the moreimmigrants of a given ethnicity a country has, the more it will tend to import fromthose immigrants’ country of origin. This effect of migrant heterogeneity is ignoredby the standard gravity literature that assumes homogeneous preferences among resi-dent national consumers. This paper embeds that observed regularity into a structuralgravity model. Gravity derived from the Almost Ideal Demand System generates bi-lateral trade shares with three distinct components: ethnic composition of residentpopulation, bilateral trade cost, and per capita income. Using international trade andtransnational migration data among 40 countries, this paper estimates the home biasof each ethnic group in tastes. The results show that consumers’ tastes for productsfrom their country of origin deviate from unbiased levels by 35 percent on average.Ethnic taste bias is found to explain half of the home bias in trade. Counterfactu-als suggest that anti-immigration policy significantly impedes trade with countries oforigin.

Keywords: Home bias, Tastes, Migration.JEL Classification: D10, F11, F14, F22.

∗School of Public Policy and Management, Tsinghua University, Beijing 100086 (email: zhangpeng-long@tsinghua.edu.cn). I am deeply grateful to James Anderson for his guidance throughout my graduatestudy at Boston College, and to my committee Arthur Lewbel, Zhijie Xiao, and Ben Li for their invaluablefeedback, which greatly improved the paper. Pol Antras, James Rauch, David Atkin, Roberto Rigobon,Felix Tintelnot, Pablo Fajgelbaum, and Amit Khandelwal provided very helpful comments. I also benefitedfrom discussions with Susanto Basu, Fabio Schiantarelli, Rosen Valchev, Jaromir Nosal, Michael Grubb,Maurizio Zanardi, Juanjuan Zhang, and the seminar participants at Boston College, MIT Sloan School ofManagement, Tsinghua University, the meetings of the Midwest International Economics Group, as well asthe European Trade Study Group. All errors are mine.

1 Introduction

The world is still far from flat today. An extensive literature finds that international tradeflows are too small relative to intra-national trade to be explained by observable tradecosts—the “border puzzle” or the “home-bias-in-trade puzzle”.1 Figure 1 plots the sharesof inter- and intra-national trade in total expenditures on manufacturing goods acrosscountries, as well as their native population shares in 2010. First, the home expenditureshares (blue bars) are much larger than the levels under hypothetical frictionless trades(dash bars) which are equal to countries’ production shares.2 This implies a large homebias in trade (the difference between blue and dash bars). Second, the import (home) shareis positively correlated to the immigrant (native) population share, implying a importantrole of migrant heterogeneity in international trade.

Consumers have home-biased preferences.3 Consumers bring their idiosyncratic pref-erences with them when they migrate. Thus immigrants tend to buy products from theirhome countries. This effect of migrant heterogeneity is ignored by the standard trade lit-erature that assumes homogeneous preferences among resident national consumers.4 Thehome bias in taste on home goods is believed to contribute to the home bias in trade, butare empirically indistinguishable from trade costs in gravity model (see Anderson, 2011).Ignoring home-biased taste would lead us to overestimate the extent to which trade lib-eralization can foster market integration. Failure to account for preference heterogeneitycauses problems in welfare gain across consumers (see Lewbel and Pendakur, 2017).

This paper relaxes the assumption of representative consumer to heterogeneous ethnicconsumers in terms of taste biases. More specifically, any given consumer has a taste bi-ased towards the good produced by her country of origin wherever she currently resides

1The border puzzle (home-bias-in-trade puzzle) is a widely discussed problem in macroeconomics andinternational finance, first documented by McCallum (1995). He finds that the U.S.-Canadian border led to1988 trade between Canadian provinces that is a factor 22 times trade between U.S. states and Canadianprovinces. Obstfeld and Rogoff (2001) identify this as one of the six major puzzles in international eco-nomics. Anderson and van Wincoop (2003) develop the structural gravity model to explain the puzzle withmultilateral resistances that are the average trade resistance between a country and its all trading partners.But even after controlling for multilateral resistances, there is still a border barrier that is implausibly highto be explicable by directly measured trade cost—tariffs and freight costs (See Anderson and van Wincoop,2004).

2In a standard benchmark with homogeneous consumer preference, if foreign products were just asaccessible and desirable as domestic ones, then under complete specialization each country would consumeits expenditure share of every other country’s production.

3Empirical work using micro-level data provides much evidence. See rice in Morey (2016) and car inCosar et al. (2018).

4Atkin (2013) and Bronnenberg et al. (2012) use domestic migration to explore the regional taste differ-ences. Di Comite et al. (2014) propose a new model where consumer preferences are heterogeneous acrosscountries but there are no migrations.

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in the world. Thus consumers are heterogeneous in terms of how large their taste biasesare. I extend the structural gravity model by building and estimating a structural compo-nent of home-biased preferences. The gravity model generates bilateral trade shares withthree distinct components: ethnic composition of resident population, bilateral trade cost,and per capita income. Market taste depends in part on the ethnic origin of consumers.When ethnicities are home-biased in tastes, migration promotes trade with countries oforigin. Using international trade and transnational migration data among 40 countries,this paper estimates the home bias of each ethnic group in tastes. The results show thatconsumers’ tastes for products from their country of origin deviate from unbiased levelsby 35 percent on average. Ethnic taste bias is found to explain half of the home bias intrade.

Similar to Atkin (2013), I define the tastes for goods as pure budget share shifters inexpenditure-share equations, which measure how much a consumer likes the goods whenthe qualities and prices are exactly the same. In the Armington setup,5 a consumer withunbiased tastes (a hypothetical metropolitan) spends her income equally on goods fromdifferent countries,6 while a consumer with fully biased tastes (a hypothetical parochial)spends all her income only on home good given all else equal. Then any arbitrary con-sumer is in the middle between those two. Then I define the taste bias of a consumeras relative distance between the unbiased and fully-biased tastes. The taste bias is zerofor unbiased consumers and one for fully-biased consumers. Thus consumers are het-erogeneous in terms of how large their taste biases are. In order to distinguish the tastedifferences from income differences acting through non-homotheticity, I adopt the AlmostIdeal Demand System (AIDS) constructed by Deaton and Muellbauer (1980).7 AIDS is asecond-order approximation to any demand system and it is flexible enough to imposeany taste, price or income elasticity structures.

The key assumption in this paper is that a consumer’s tastes only depend on her coun-try of origin, unrelated to where she currently lives. Thus the consumers from the sameethnic group share the common tastes regardless wherever they are geographically lo-cated.8 One viable explanation is the habit formation in tastes proposed by Atkin (2013),

5Each country specializes in the production of a distinct variety.6Actually I allow for asymmetric tastes for the hypothetical metropolitan in both model and empirical

part. The simplification here is just for the intuition discussion.7Researchers typically regard products as differentiated by taste (horizontal differentiation) and quality

(vertical differentiation). Recent literature usually interprets the high income-elastic goods in the model ashigh-quality goods and the low income-elastic goods as low-quality goods. Trade papers by Hallak (2006),Fieler (2011), Caron et al. (2014), Feenstra and Romalis (2014), and Fajgelbaum and Khandelwal (2016) findthat richer countries export goods with higher income elasticities.

8I also allow a taste difference between ethnic consumers at home and abroad in the extension part.

2

which argues that adult taste favors the foods consumed as a child and the preferencesgained in childhood persist into adulthood.9 Thus, consumers are biased towards thegoods with the same national origin of themselves all else equal regardless wherever theyare located. For example, a Chinese immigrant in Canada is biased towards Chinese foodproducts, an Italian immigrant in U.K. has a biased taste towards Italian clothing prod-ucts, and a Japanese immigrant in the U.S. prefers Japanese electronic products, althoughthey have left their home countries for years. This assumption on the preference yieldsan aggregate demand system that is feasible to estimate.

With the taste structure and the ethnic-preference assumption, I derive the home-biasedgravity equation by aggregating expenditure shares across ethnic groups within eachcountry. In addition to trade costs and income effects, the gravity also captures the impactof the trade promoter that stimulates bilateral trade share via immigration. The intuitionis that the biased immigrants spend larger shares on their home products than the na-tives do, increasing the import shares from their home countries to their host countries.For example, the higher the Mexican immigrant share in the U.S. population, the higherimport share of Mexican products in the U.S. market. The marginal effect of this promoteris stronger when ethnic Mexicans’ taste bias is larger. In additional to multilateral resis-tances, the home-biased gravity also gives multilateral promoters which are the averageterms of bilateral promoters (immigrant shares) across all trade partners.

Next, I obtain the symmetric home-biased gravity by assuming that both productsand taste biases are symmetric. By comparing this simplified gravity with CES structuralgravity, I provide structural components to the unobservable bilateral market tastes thatare usually taken as error terms in literature. The key implication of the home-biasedgravity equation is that the bilateral taste is determined by the bilateral migration, and itsmarginal effect is determined by the taste bias.

The home-biased gravity equation gives three implications on intra-national trade.First, non-immigration countries or those with more biased consumers have a higherhome bias in trade. Second, countries with lower domestic trade barriers, or producingmore price-elastic goods have a higher home bias in trade. Third, rich countries pro-ducing high-quality goods, or poor countries producing low-quality goods have a higherhome bias in trade than the others. The last two implications are straightforward. Theintuition of the first one is that immigration countries, like the U.S., import more thannon-immigration ones, like Japan, because large shares of immigrants spend more onproducts imported from their homes, resulting in a smaller domestic share of the host

9He terms this process habit formation and provides ample evidence in the psychology and nutritionliterature. See Bronnenberg et al. (2012) for more discussion.

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country. The magnitude of this effect is determined by the native population share andtheir taste bias for home good relative to global ones.

In the empirical part of the paper, I start with estimating the home-bias gravity model.The taste shifters are the ethnicity-specific coefficients of immigrant shares. With interna-tional manufacturing trade and transnational migration data among 40 countries, I findthat the import share is significantly increasing in immigrant share for most ethnic groupswith other standard gravity variables as controls. The four ethnic groups with the highesttaste shifters are Japanese, Chinese, Brazilians, and Americans, while the four with low-est taste shifters are Luxembourgers, Maltese, Belgians, and Dutchmen. On average, thetaste shifters is 0.35. This paper also estimates sectoral gravity in agricultural and servicesectors, final and intermediate goods, as well as 14 manufacturing industries. It is foundthat consumers have larger taste bias in agricultural products, while almost unbiasedon services. Import share is more sensitive to immigrant share for final goods than forintermediate goods. Sectoral estimation makes my results applicable to the micro levelliterature on home preference.

I also address two potential econometric issues by estimating the symmetric home-biased gravity equation. First, this trade-creating effect by immigration could also workthrough the trade cost channel in which migration network reduces information barriersand enhances contract enforcement. I add two variables measuring network effect intothe regression. First one is the probability that, if we select an individual at randomfrom each country, they will have a connection defined as common national origin. Thismeasure extends the one in Rauch and Trindade (2002) that only captures the ethnic-Chinese Network. The second one is emigration share that also contributes to the networkbut has no effect on trade through preference channel. The results show that the networkeffect channel is not significant to aggregate manufacturing import share. Second, theestimated coefficient is subject to potential endogeneity issues. In order to address thereverse causality from migration to trade, I instrument the stock of immigrants in 2010by the stock in 2000, similarly to Card (2001). I also estimate the gravity equation bytaking first-order differences between two cross-sections to eliminate any potential time-invariant omitted variables. Both strategies leave the main results unchanged.

In robustness checks, I examine asymmetry on the unbiased tastes and the price elas-ticities. By combining results of unbiased tastes and taste biases, I find that both worldand American consumers favor American products. Germans and British are less biasedin their taste but world consumers favor their products. By combining results of price andincome elasticities, I find that American, German, and Italian products are high qualityand more price-elastic, while Indonesian, Indian, and Chinese products are low quality

4

and less price-elastic.Using the estimated parameters, I conduct two counterfactual experiments. First, I

decompose the contribution of the taste effect, trade cost effect, and income effect to thehome bias in trade. About half of the trade bias is explained by the trade cost, the otherhalf is explained by the taste bias, very little is explained by the income effect. Second, Icheck how migration policy affects home bias in trade. The U.S. will decrease its openness(import share) by 6 percent with an immigration ban.

Lastly, I extent the model by introducing the “local bias” in addition to the “homebias”. On one hand, a consumer has a biased taste towards the good produced by herhome country (where she is originally from). On the other hand, her taste is also biasedtoward to the good produced locally (where she is living). To estimate the local biasparameters, I impose the constraint that local bias is proportional to native populationshare which implies that immigrants are more likely to assimilate in a country with morenatives. The results show that consumers located at countries with higher native shareare more likely to locally bias in their tastes. After controlling for local bias in gravity,the home taste shifters estimated are mostly smaller but close to the baseline results. Thissuggests that the immigrants’ taste bias is robust even with assimilation.

This paper contributes to the gravity literature in international trade in two aspects.First, it introduces consumer preference heterogeneity into gravity models, which is ad-ditional to the gravity with firm heterogeneity (Chaney, 2008; Helpman et al., 2008), thenon-homothetic gravity with consumer income heterogeneity (Fajgelbaum and Khandel-wal, 2016), and the gravity with consumer geography heterogeneity (?). Second, it allowshome-biased tastes when discussing trade with AIDS preference, which is supplementaryto translog gravity (Novy, 2013), trade includes entry of new goods (Feenstra, 2010), priceand income of international comparison (Neary, 2004), and so on.

My paper helps explain the “border puzzle” or “home-bias-in-trade puzzle” by home-biased preferences, which is supplementary to other explanations, e.g., trade cost (Obst-feld and Rogoff, 2001), multilateral resistance (Anderson and van Wincoop, 2003), non-homothetic preference (Caron et al., 2014), multi-stage production (Yi, 2010), contractualenforcement (Anderson and Marcouiller, 2002), and scale effect (Ramondo et al., 2016).Chaney (2014, 2016) and Allen (2014) discuss the resistances from information barrier.Head and Mayer (2013) summarize the sources of resistance as imperfect information,very localized tastes, and distribution networks.

There is a large literature that examines the effect of international migration on inter-national trade. Some papers have explored the effect of immigration on bilateral tradeflows, typically finding that immigration and trade are complements. In particular, im-

5

migrants are found to reduce barriers to exports by facilitating communication betweenfirms and reducing setup costs in the destination country, which is called “network ef-fect” by Rauch and Trindade (2002, 2003). This trade cost channel of migration on trade isexplored also in Combes et al. (2005), Iranzo and Peri (2009), and Felbermayr et al. (2015).This paper is along this line by providing a different perspective on preference channel.

More broadly, my work is also related to the rich literature on the effect of migrationon economic outcomes, as well as work examining the importance of ethnicity and eth-nic diversity. Hanson (2010) discusses more consequences of international migration inadditional to trade. Literature focuses on migration effects on labor market (Ottavianoet al., 2013; Foged and Peri, 2016), on GDP per worker (Fulford et al., 2017), on invest(Burchardi et al., 2017), on income (Ortega and Peri, 2014), and on wealth redistribution(Luttmer and Singhal, 2011).

The rest of this paper is organized as follows. In Section 2, I present the model andderive the home-biased gravity. In Section 3, I estimate the gravity to get the estimatesof the taste biases and all the other theoretical parameters. The robustness checks areconducted in Section 4. Section 5 gives counterfactual experiments and Section 6 extendsthe model to include local bias. I conclude and discuss future directions in Section 7.

2 Model

I present how to incorporate home-biased preferences into the international trade frame-work in Section 2.1, and derive the home-biased gravity equation in Section 2.2. Section2.3 discusses the gravity’s implications for the home-biased trade.

2.1 Preferences

The world consists of N countries, indexed by i as exporter and n as importer. Eachcountry specializes in the production of a distinct variety. Let pni be the price in country nof the good imported from country i. The iceberg trade cost of exporting from i to n is tni.No-arbitrage condition implies that pni = piitni. There are N ethnic groups of consumers,indexed by h as the national origin. Each ethnic group is globally distributed and thuseach country is populated by N ethnic groups. Note that n, i, h = 1, 2, ..., N. Let snh be theimmigrant population share in country n from country h if h 6= n, and snn be the nativepopulation share in country n. Then ∑N

h=1 snh = 1 holds for all n.Consumers have the Almost Ideal Demand System (AIDS) preference introduced by

Deaton and Muellbauer (1980), which can be rationalized as a non-homothetic second-

6

order approximation to an arbitrary expenditure system. Specifically, any consumer whois from country h and resides at country n has expenditure functions given in logarithmicform as

ln ehn = ln Qh

n + uhn

N

∏i=1

(pni)φi , (1)

where ehn is the minimum expenditure at which the consumer can obtain utility uh

n givenprices pni. The price index ln Qh

n is given in logarithmic form as

ln Qhn =

N

∑i=1

αhi ln pni +

12

N

∑i=1

N

∑i′=1

γii′ ln pni ln pni′ . (2)

Applying the Shephard’s lemma and differentiating the expenditure function with re-spect to log price pni generates the expenditure share in good (produced by country) i forconsumer (of ethnic group) h at country n equal to

whni = αh

i +N

∑i′=1

γii′ ln pni′ + φi ln

(eh

nQh

n

), (3)

for i = 1, ..., N. The shares must be non-negative.These expenditure shares have some nice features. First, αh

i is a taste parameter ofconsumer h for the good i which shifts the expenditure share independently from theprices and income. Second, γii′ is the cross-price elasticity for good i when i′ 6= i andthe own-price elasticity for good i when i′ = i. Third, φi is the income elasticity whichcaptures the non-homothetic component of the preference. Positive φi implies luxurygoods (with high quality) while negative φi implies necessary goods (with low quality). 10

I refer to ehn/Qh

n as adjusted “real” income (expenditure) by individual price index whichvaries by the individual consumption basket compositions. To satisfy homogeneity ofdegree one, the parameters are constrained by αh

i ∈ [0, 1], ∑Ni=1 αh

i = 1 for all h, ∑Ni=1 γii′ =

0 for all i′, and ∑Ni=1 φi = 0. Symmetry is imposed to satisfy the Young’s Theorem, γii′ =

γi′i. Concavity is imposed by the requirement that γii′ is negative semi-definite. Whenφi = 0 for all i, AIDS becomes the homothetic translog preference. When γii′ = 0 for alli, i′ and φi = 0 for all i, AIDS becomes the Cobb-Douglas preference.

10Note that γii′ and φi are semi-elasticities since they relate expenditure shares to logs of prices andincome, but I refer to them as elasticities to save notation. Actually the price elasticities are −1 + γii′/wh

ni +

whni, and the income elasticities are 1 + φi/wh

ni.

7

Following Anderson and Zhang (2018),11 I specialize the general γii′ to

γii′ = γβi(−δii′ + βi′), (4)

where the Kronecker delta δii′ is 1 when i = i′ and 0 otherwise, βi ∈ [0, 1], and ∑Ni=1 βi = 1.

Specialization (4) satisfies all the general restrictions but imposes a tight restriction on thecross-effects. In particular, complementarity is ruled out, all off-diagonal terms of thesubstitution effects matrix are non-negative. βi = 1/N is the special case proposed byFeenstra (2003).12 The variation of βi allows for asymmetric demand responses to pricechanges. This gives AIDS preference CES-like components because the price terms inequation (3) become −γβi ln (pni/ pn) which captures cross-effects in substitution withthe log of a ratio of own price to an average price ln pn = ∑N

i′=1 βi′ ln pni′ .

Home-Biased Tastes

In equation (3), αhi is a taste parameter for good i but heterogeneous across consumers.

It is unvarying to where consumers consume but varying to where they are originallyfrom. One viable explanation is the habit formation in tastes proposed by Atkin (2013),which argues that adult taste favors the foods consumed as a child and the preferencesgained in childhood persist into adulthood.13 Therefore, consumers from the same ethnicgroup share the common tastes regardless wherever they live. Different to Atkin (2013),the tastes in this paper are assumed to be exogenous.14 Another important feature ofthe differences in consumer tastes is home bias. A large amount of empirical work fromdisaggregate level data provides evidence for its existence. For any consumer, she hasa biased taste towards the good produced by her home country (where she is originallyfrom) whatever her host country (where she resides and consumes) is. In other word,consumers are biased towards the goods with same national origin of themselves all elseequal regardless wherever they are located. For example, an Italian immigrant in theU.S. has a biased taste towards Italian products even though he has resided in the U.S.for years. An American living in the U.S. is biased towards American products sincethey have the same national origin. More structure on the home bias yields an aggregatedemand system that is feasible to estimate.

First, I assume a hypothetical world unbiased consumer with tastes for good 1, 2, ..., N

11They use AIDS preference to obtain the Almost Ideal gravity to study international zero trade flows.12This special case is followed by Novy (2013) and Fajgelbaum and Khandelwal (2016).13He terms this process habit formation and provides ample evidence in the psychology and nutrition

literature.14Lan and Li (2015) discuss how economic openness affects nationalism.

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as α1, α2, ..., αN, and ∑Ni=1 αi = 1. Then I specialize ethnic consumer h’s tastes as

αhi = (1− θh)αi + θhδh

i , (5)

where the Kronecker delta δhi is 1 when i = h meaning home goods, and 0 otherwise,

and θh ∈ [0, 1] is the taste shifter which measures how biased ethnic consumer h’s taste istowards her home good h. This specialization satisfies conditions ∑N

i=1 αhi = 1 for all h. For

any consumer h, she raises her taste for home good h by a shifter θh, while reduces thosefor all the other goods by 1− θh (as well as the home good to make sure taste aggregatesequal to 1). When θh = 0, ethnic consumer h are unbiased, exactly like the hypotheticalworld consumer. When θh = 1, the consumers from ethnic group h have fully biasedtastes, i.e. 1 for home good while 0 for all the others. Thus the set of taste shifters θhN

h=1is the measurement of the taste biases across ethnicities, and it is the key set of parametersin this paper. Now I rewrite the expenditure equations (3) as

whni = (1− θh)αi + θhδh

i − γβi ln(

pni

pn

)+ φi ln

(eh

nQh

n

), (6)

where average price in market n is ln pn = ∑Ni=1 βi ln pni. And −γβi are the semi price

elasticities and φi are the semi income elasticities.A different way to interpret the taste shifter θh is the fraction of fully biased consumers

in ethnic group h. In this case, I assume there are two types of consumers in each ethnicgroup. One is a cosmopolitan who is unbiased exactly like the hypothetical world con-sumer, and the other one is a parochial who spends all on home product while zero onothers given all else equal. If their population shares are 1− θh and θh respectively, thenthe average taste of ethnic group h for home good is (1− θh)αh + θh ∗ 1, and tastes forthe other goods is (1− θh)αi + θh ∗ 0, which are exactly the same as equation (5). ThusθhN

i=1 could be interpreted as the parochial population shares of each ethnic group, andthey measure how ethnicities are biased. When θh = 0, ethnic group h are all cosmopoli-tans. When θh = 1, ethnic group h are all parochial.

A byproduct of this taste structure is within-country consumer heterogeneity. Givengood i, only consumer i raises her taste by a shifter θi, while all the others reduce theirtastes by different levels, 1− θh, in terms of their own taste shifters. For any good, con-sumers in one market thus have heterogeneous tastes for it due to their heterogeneoustaste shifters. When θh = 0 for all h = 1, 2, ..., N, all consumers anywhere in the world arehomogeneous in preference, which is exactly the same to the case with a representativeconsumer.

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2.2 Home-Biased Gravity

In this part, I first aggregate individual expenditure shares across all ethnic groups withineach market (country), and then derive the gravity equation for each country pair thatdepends on aggregate data and the demand parameters.

I assume that ethnic groups within a country share the same income, i.e. ehn = en.15

Aggregating individual expenditure shares across ethnic groups gives the market importshare of country n from country i by wni = ∑N

h=1 snhwhni, i.e.,

wni = (1− θn)αi + θisni − γβi ln(

pni

pn

)+ φi ln

(en

Qn

), (7)

where the average taste bias in market n

θn =N

∑h=1

snhθh, (8)

and the average price index in market n by aggregating individual price index ln Qhn,

ln Qn =N

∑h=1

snh ln Qhn. (9)

Equivalently, equation (7) could be taken as the expenditure shares of a “representa-tive” consumer in market n who is a “mixed-blood” with all ethnic origins. Since ethniccompositions differ, the representative consumers in different markets have different de-mand.

Next, I let Xni be the value of imports of importer n from exporter i, and let En be thetotal expenditure of the importer. Then

wni =Xni

En(10)

is the share of aggregate expenditures in country n devoted to goods from country i. Thenequation (7) could be rewritten as

Xni

En= [αi − γβi ln

(pii

p

)] + (θisni − αi θn)− γβi ln

(tni

Pn

)+ φi ln

(en

Qn

), (11)

15On the absence of data, I skip within-country cross-ethnicity income difference, but keep cross-countryincome difference. Lewbel (1991) finds E[eh ln eh]/Eeh = ln Eeh + Σ with U.S. data and later literature, likeFajgelbaum and Khandelwal (2016), follow his idea to control for the index Σ with country fixed effects.

10

where p = ∑Ni=1 βi pii and ln Pn = ∑N

i=1 βi ln tni. Income of each exporter i equals the sumof sales to every country,

Yi =N

∑n=1

Xni, (12)

where Yi measures exporter i’s total income. Substitute equation (11) into equation (12)and divide through by Y which is the total world income. Subtract the resulting ex-pression from equation (11) and simplify by canceling term [αi − γβi ln

(piip

)]. Denote

ln rn = ln (en/Qn) as the real expenditure, then I term the resulting expression as thehome-biased gravity

Xni

En− Yi

Y= (θisni − θiΠs

i − αiPsn)− γβi ln

(tni

ΠiPn

)+ φi ln

(rn

r

), (13)

where

ln Πi =N

∑n=1

(En

Y

)ln tni, ln Pn =

N

∑i=1

βi ln(

tni

Πi

), (14)

and

Πsi =

N

∑n=1

(En

Y

)sni, Ps

n =N

∑i=1

θi(sni −Πsi ), (15)

and

ln r =N

∑n=1

(En

Y

)ln rn. (16)

On the left hand side, XniEn− Yi

Y is the deviation of bilateral trade per unit of n’s ex-penditure from its frictionless level Yi

Y . There are three terms on the right hand side,which capture the taste effect, price effect, and income effect, respectively. The second term,−γβi ln

(tni

ΠiPn

), is the effect of relative bilateral trade resistance from origin i to destina-

tion n, where ln Πi and ln Pn are the outward and inward multilateral resistances in log,respectively. This term is very similar to the CES structural gravity in Anderson and vanWincoop (2003). The last term, φi ln

( rnr), is the non-homothetic component of the gravity

equation and captures the effect of relative income per capita of market n where ln r is theaverage world income per capita in log. This term is very similar to the non-homotheticcomponent of the gravity equation in Fajgelbaum and Khandelwal (2016).

The first term, θisni− θiΠsi − αiPs

n, is the one of the most important novelties in this pa-per. It captures the effect of relative trade “promoter” that stimulates bilateral trade viabilateral migration from origin i to destination n. The intuition is that immigrants raisethe market average taste for their home country’s products. This is because immigrantswith home-biased tastes spend larger shares on home-produced products, increasing the

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import shares of the host country from their home countries. The magnitude of the pro-moter is increasing in how large the bilateral immigrant share, sni, is, as well as how largetheir taste shifter, θi, is. I refer to Πs

i and Psn respectively as the outward and inward “mul-

tilateral promoters” that summarize the average trade promoters between a country andits trading partners. When everybody is unbiased, i.e. θi = 0 for all i, the trade promot-ers are shut down, and, together with a symmetry assumption βi = 1/N, this gravityequation corresponds to that in Fajgelbaum and Khandelwal (2016).

Symmetric Home-Biased Gravity

In order to compare the home-biased gravity with the CES gravity in Anderson and vanWincoop (2003), I impose symmetry conditions, i.e. θi = θ, αi = 1/N, βi = 1/N andφi = 0 for all i, denote γ′ = γ/N, then I get the symmetric home-biased gravity

Xni

En− Yi

Y= θ(sni −Πs

i − Psn)− γ′ ln

(tni

ΠiPn

), (17)

where

ln Πi =N

∑n=1

(En

Y

)ln tni, ln Pn =

1N

N

∑i=1

ln(

tni

Πi

), (18)

and

Πsi =

N

∑n=1

(En

Y

)sni, Ps

n =1N

N

∑i=1

(sni −Πsi ) . (19)

When θ = 0, the taste effect is shut down and the symmetric home-biased gravity isidentical to the translog gravity in Novy (2013). Recall the CES gravity is

lnXni

En− ln

Yi

Y= Ωni + (1− σ) ln

(tni

Π∗i P∗n

), (20)

where

(Π∗i )1−σ =

N

∑n=1

(En

Y

)(tni

P∗n

)1−σ

, (P∗n )1−σ =

N

∑i=1

(Yn

Y

)(tni

Π∗i

)1−σ

. (21)

Comparing equation (17) and (20) gives the the difference of the functional form on theleft hand side: a difference in shares vs. a difference in log shares. The own price effect inthe two gravity equations is essentially the same while the cross price effects are capturedby price index terms that differ, but would ordinarily be econometrically controlled forwith origin and destination fixed effects. What’s more important is that the unobservablebilateral taste effect Ωni is offered a micro fundamental structure in this paper while taken

12

as part of the errors in traditional gravity literature. Since the multilateral promotersare absorbed by origin and destination fixed effects, econometrically the difference boilsdown to an additional term, the immigrant share. The coefficient of immigrant shares isjust the taste shifter.

2.3 Home-Biased Trade

Since we are interested in home bias in trade, I can write down the home share deviationbased on the home-biased gravity equation (13) as

HBn ≡Xnn

En− Yn

Y= (θnsnn − θnΠs

n − αnPsn)− γβn ln

(tnn

ΠnPn

)+ φn ln

(rn

r

), (22)

where HBn is defined as country n’s home bias in trade, or trade bias, that measures thedeviation of home expenditure share from its frictionless level. Equation (22) shows thathome bias in trade could be explained by three effects separately: taste effect, price (tradecost) effect, and income effect. Regarding the three effects, the following implications areobtained.

Implication 1. Non-immigration countries have a larger trade bias than immigration ones. Homecountries of more biased consumers have a larger trade bias.

Implication 2. Countries with lower internal trade barriers have a larger trade bias. Countriesproducing more price-elastic goods have a larger trade bias.

Implication 3. Rich countries producing luxury goods or poor countries producing necessarygoods have a larger trade bias than the others.

Implication 1 holds because ∂HBnn∂snn

= θn > 0 and the intuition is that immigrationcountries (with smaller snn, like the U.S.) import more than non-immigration countries(with larger snn, like Japan) since large amount of immigrants spend more on productsproduced by their home countries, resulting in less domestic expenditure share of the hostcountry. The magnitude of this effect is determined by the natives’ taste shifter for homegood relative to that for the global good, which are controlled by Πs

n and Psn. Implication

2 is about the trade cost effect that higher internal cost reduces domestic consumptionrelative to imports, which is consistent with the structural gravity literature Andersonand van Wincoop (2003) and Anderson and Yotov (2010).16 Implication 3 implies that the

16In Anderson and Yotov (2010), they propose and estimate a similar index constructed home bias (CHB)

by CES structural gravity: CHBn = Xni/YnYi/Y =

(tnn

Πn Pn

)1−σ.

13

income effect increases trade bias for rich countries above average (ln( rn

r)> 0) which

produce positively semi-income-elastic goods (φi > 0, luxury goods), like Germany, orpoor countries below average (ln

( rnr)< 0) which produce negatively semi-income-elastic

goods (φi < 0, necessary goods), like China. This result of income effect is consistent withCaron et al. (2014).

3 Estimation

In this section, I estimate the home-biased gravity derived in Section 2. Section 3.1 de-scribes the data, and Section 3.2 presents the main estimation results. I also discuss someother econometric issues in Section 3.3.

3.1 Data

To estimate the home-biased gravity, I merge datasets on bilateral migration, trade, andgravity respectively. First, the migration data are from United Nation International Mi-grant Stock Dataset that presents estimates of total immigrant stock at mid-year by originand destination for year of 2000 and 2010, and are available for all countries and areasof the world. The estimates are based on official statistics on the foreign-born or the for-eign population. I use the data in year of 2010 as the baseline, and do robustness checkswith the data in year of 2000. Second, trade flows are from World Input-Output Database(WIOD) that records bilateral trade flows and production data for 40 countries (27 Eu-ropean countries and 13 other large countries) across 35 sectors that cover food, manu-facturing, and service. The data also record total expenditures by sector and country oforigin, as well as final consumption. Third, I obtain bilateral distance, common language,border, and tariff information from CEPII’s Gravity database. Price levels, adjusted forcross-country quality variation, are obtained from Feenstra and Romalis (2014). GDP,GDP per capita, and population are from the Penn World Tables.

Recall the home-biased gravity equation

Xni

En=

Yi

Y+ (θisni − θiΠs

i − αiPsn)− γβi ln

(tni

ΠiPn

)+ φi ln

(rn

r

),

where there are a large number of parameters to estimate. Actually only the taste shiftersθiN

i=1 are the focus of this paper. Thus I can reduce the number of estimated parametersby imposing some restrictions. First, let βi = 1/N for all i, which leads constant own price

14

elasticity following Feenstra (2003).17 Second, income elasticities and exporter income areassumed to satisfy φi = ψc + ψ ln ri for all i, similar to Feenstra and Romalis (2014) andFajgelbaum and Khandelwal (2016). The theoretical restriction ∑N

i=1 φi = 0 implies ψc =

−ψ(1/N)∑Ni=1 ln ri, transforming this linear relationship to φi = ψ(ln ri − ln r) where

ln r = (1/N)∑Ni′=1 ln ri′ , and reducing the number of income elasticity parameters to be

estimated from N to one. For the tastes of the hypothetical unbiased world consumer, Iproxy αi = EMi/ ∑i′ EMi′ where EMi is the extensive margin in global trade estimated byHummels and Klenow (2005).18 I will also go through a number of robustness checks toensure that my results do not solely depend on this particular extensive margin measure.Then the home-biased gravity equation becomes

Xni

En= θisni − γ′ ln tni + ψ ln rni + λnαi + fi + gn + εni, (23)

where ln rni = ln rn ln ri, fi = Yi/Y − θiΠsi + γ′ ln Πi + ψ ln ri ln r, and gn = γ′ ln Pn +

ψ ln rn ln r + ψ ln r ln r.I proxy bilateral trade costs with bilateral observables. Specifically,

ln tni = ρ ln dni + ρbborderni + ρl langni + ρttari f fni + ρiinternalni + εni, (24)

where dni is the bilateral distance between origin i and destination n. Parameter ρ re-flects the elasticity between distance and trade costs, borderni and langni are commonborder and common language, tari f fni is bilateral tariff level, and εni is idiosyncratic er-rors for trade costs. Importantly, internalni is 0 for import and 1 for internal trade similarto Ramondo et al. (2016) and Anderson and Yotov (2017). Then the specification of thehome-biased gravity is

Xni

En= θisni + b ln dni + bbborderni + bl langni + bttari f fni + biinternalni + ψ ln rni + λnEMi

+ fi + gn + εni. (25)

And fi, gn could be controlled by fixed effects of origins and destinations. There are 86parameters to be estimated b, bb, bl, bt, bi, ψ, θi40

i=1, λi40i=1. Actually λi40

i=1 are usedto control the effects of multilateral promoters, and thus not the coefficients of interest.

17I allow asymmetric price elasticities in robustness check.18They construct a measure of the extensive margin across countries based on shipments in more than

5000 six-digit product categories from 126 exporting countries to 59 importing countries for the year 1995.The extensive margin is measured by weighting categories of goods by their overall importance in exports.They document that the extensive margin tends to be larger for big countries.

15

Recall the real expenditure per capita is defined as ln rn = ln (en/Qn), where en, nominalexpenditures per capita, are observable too. Aggregate price index ln Qn can be proxiedby a Stone index following literature,19 that is

ln Qn =N

∑i=1

wni ln(piidρni), (26)

where pii are the quality-adjusted prices estimated by Feenstra and Romalis (2014) andthe value of ρ will be discussed later. This proxy of the aggregate index is identical to theaggregate proxy of the individual indices due to nice property of the Stone index.20

Because we do not directly observe trade costs, I cannot separately identify ρ andγβ. Following Fajgelbaum and Khandelwal (2016) and Novy (2013), I set ρ = 0.177.Then the parameters of interest θiN

i=1 could be estimated directly and other theoreticalparameters φiN

i=1 and γ are identified by φi = ψ(ln ri − (1/N)∑Ni′=1 ln ri′), and γ =

Nb/ρ.To investigate more variation of the taste biases, I also estimate sectoral (and indus-

trial) gravity equations across 14 manufacturing industries, final and intermediate goodsectors, as well as food and service sectors using more disaggregate level data. Disaggre-gate estimation makes my results able to speak to many micro level literature on homepreferences. More specifically, I estimate

Xkni

Ekn= θk

i sni + bk ln dni + bkbborderni + bkl langni + bkttari f fni + bkiinternalni +ψk ln rkni +λk

nEMi

+ f ki + gk

n + εkni, (27)

where all variables with a superscript k is defined in the same way to those without anysuperscript but in good class k.21 I run the regression separately with corresponding dataand obtain the taste shifter estimates across all ethnicities in all good classes.

19Deaton and Muellbauer (1980) first time use a Stone index to proxy the AIDS price index. The tradeliterature, like Atkin (2013) and Fajgelbaum and Khandelwal (2016), follow this approximation.

20If I instead proxy individual price index ln Qhn = ∑N

i=1 whni ln(piid

ρni), the aggregate of individ-

ual index, followed by equation (9), becomes ln Qn = ∑Nh=1 snh ln Qh

n = ∑Nh=1 ∑N

i=1 snhwhni ln(piid

ρni) =

∑Ni=1 wni ln(piid

ρni) which is exactly the same to equation (26).

21Good classes could be final and intermediate goods, or food, manufacturing and services, or differentindustrial goods.

16

3.2 Main Results

The baseline results are reported in column (1) in Table 1. It reports the estimates of 40ethnicity-specific coefficients on Immigrant share, and coefficients on variable Distance,Border, Language, Tariff, Internal, as well as that on income per capita product, whileestimates of the fixed effects and other coefficients are dropped since they are not theparameters of interest.

The estimate of distance elasticity is -0.009, significantly negative, suggesting that bi-lateral distance reduces trade flows between countries, which is consistent with literature.Under the assumption that ρ = 0.177, the estimate implies γ = 2.03. The additional tradecosts—common border, common language, tariff, internal trade—also have the intuitivesigns. The estimate of the income elasticity parameter, ψ, is 0.006, significantly differentfrom zero. This means that richer countries are more likely to spend on products fromricher countries, conditional on trade costs and tastes, consistent with literature.

The table also reports the estimates of the 40 θi parameters, one corresponding toeach ethnic group, in the subsequent rows. The four ethnic groups with highest θ’s areJapanese, Chinese, Brazilians and Americans, with taste shifters above 0.5. The four eth-nic groups with lowest θ’s are Luxembourgers, Maltese, Belgians, and Dutchmen, withtaste shifters not different from 0. This means the former ethnicities are highly home-biased consumers whereas the latter are almost unbiased. On average, the taste bias mea-sured by the ethnicity-specific immigrant coefficients is 0.352. My results above providethe evidence of taste bias’ existence and estimate the magnitude of the bias for every eth-nic group. But what causes the biases and why they differ among ethnicities are verycomplicated. Thus they are not the key issues in this paper.

To investigate more variation of the taste biases, I also estimate sectoral gravity inequation (27) across manufacturing, agricultural and service sectors, as well as final andintermediate goods using more disaggregate level data. First I estimate the taste bias inthree sectors in Figure 2 (See full results in Table 2). Compared to manufacturing sector(the baseline estimates) in column (1), agricultural sector in column (2) tends to be morefavored by home consumers, while service sector in column (3) is much less biased. Inother word, consumers have larger taste bias (0.448 on average) when they are facing agri-cultural products, while almost unbiased (0.053 on average) on services. This makes sensebased on the taste habit formation assumption that food tastes are even more persistent.Note Indians are the most biased ethnic group in agricultural sector. This is consistentwith what we usually observe that Indian immigrants prefer Indian food although theytend to be far more highly educated and. Next I report the results for two types of goodsin Table 3. Import share is more sensitive to immigrant share for final good in column

17

(2) than for intermediate good in column (3) on average. This is very consistent to theconsumer taste theory.

The industrial estimates are obtained in Table A.1-A.2 in Appendix. Table 4 displaysthe consumers’ average taste bias on different industrial goods. Their tastes are morebiased for goods from Textiles, Leather products, transportation products, and food, bev-erages, and tobacco, while much less biased in non-metallic minerals, rubber and plastics,and electrical and optical equipment. Table 5 displays the most biased ethnic groups indifferent industries. For examples, Australians and Americans are biased when buyingfood, beverage, and tobacco, Japanese and Koreans are biased when buying transporta-tion products, and Canadians are biased when buying wood products.

3.3 Other Results

3.3.1 Symmetric Bias

To compare the estimates with literature, I also estimate the symmetric home-biased grav-ity. The econometric specification of equation (17) is

Xni

En= θsni + b ln dni + bbborderni + bl langni + fi + gn + vni, (28)

where fi =YiY − θPs + γ′ ln Πi and gn = γ′ ln Pn − θΠs

i . And fi, gn could be controlled byfixed effects of origins and destinations. To make the estimates comparable with litera-ture, I only include the foreign trade. Thus the tariff term is captured by the importer’sfixed effect. Internal trade dummy is also dropped since there are no internal trade obser-vations any more.

The results are reported in Table 6. Column (1) reports the estimates of coefficients onvariable Immigrant share, Distance, Common border, and Common language, while theestimates of fixed effects are dropped since they are not the parameters of interest. Theestimate of distance elasticity is negatively significant suggesting that bilateral distancereduces trade flow between countries, consistent with literature. Coefficients on othergravity variables are also significant. Common border and common language increasebilateral trade flow, while tariff reduces bilateral trade flow significantly. The coefficienton immigrant share is estimated to be 0.405, significantly different from zero, which con-firms the existence of consumer’s taste bias. An interpretation of its implication is 41percent of consumers in the world are parochial and they will spend all on home goodsgiven all else equal, while the other 59 percent are cosmopolitans and they are indifferentamong goods with different national origins. This result is also close to the average taste

18

shifter estimates, 0.352, in the baseline regression.I also estimate equation (28) for a different cross-section, the year of 2000, for which

migration data are available. One of the reasons is to check that my results are not the ar-tifact of a particular time period. The second reason is to check whether there is a changein the immigrant share coefficient. Column (2) reports the estimates. The taste bias es-timate is 0.467 with little change compared to that in the year of 2010, which confirmsthe robustness of the results. This is consistent with my basic assumption that the con-sumer tastes are persistent and the estimate is a good measure for consumers’ taste bias.Column (3) tests the gravity shutting down the taste term, i.e. translog gravity, and theresults are very similar to Novy (2013). Together with column (1), it reveals that the co-efficients of traditional gravity variables change very little with or without the additionalmigration variable. This implies that migration share is not correlated to traditional grav-ity variables, and it does explain a new part of the variance of trade shares that traditionalgravity variables do not capture. Column (4) estimates the standard CES structural grav-ity. Since the dependent variable is in log, the elasticity estimated is different to that inother columns.

3.3.2 Endogeneity

There are some potential concerns related to the estimation. The first concern is that im-migrant share may be correlated with trade shocks vni. When this is the case, the OLS es-timation leads to inconsistent results. Such endogeneity bias can arise from two sources.The first is reverse causality. It is possible that some positive shock on the value of bilateraltrade between two countries leads to more migration between the two places. For exam-ple, more people migrate internationally to do business associate to more internationaltrade. The second is omitted variables. Migration may be correlated with unobservedfactors that also affect trade, such as the trading partners’ cultural similarity or bilateraleconomic policies.

In order to address the reverse causality from migration to trade, I instrument the stockof immigrant in 2010 by that in 2000 similarly to Hanson (2010).22 Then I run a Two-StageLeast Square regression for equation (28) to test whether my estimation suffers from abig endogeneity problem. I also estimate the gravity equation by taking first differencesbetween the above two years to eliminate any potential time-invariant omitted variables.

22Card (2001) firstly suggest thats supply-push component of the immigrant inflows to a particular city,which is based on historical settlement patterns and the total number of newly arriving immigrants fromdifferent source countries, is a potential candidate for such an instrumental variable for migration.

19

More specifically, I estimate

∆Xni

En= θ∆sni + f ′i + g′n + v′ni, (29)

to test whether θ is significant or not.The results are reported in Table 7. First, I check whether the lagged immigrant share

in the year of 2000 is a valid instrument for current immigrant share in the year of 2010.column (2) shows the instrument’s coefficient is significant and the F-test value is 41.2,which implies that the explanatory power of instruments is high. Then I do a 2SLS regres-sion. The immigration’s coefficient is 0.321 still significant reported in column (3). It is0.08 smaller than, very close to, the OLS estimate in column (1). The potential endogene-ity problem causes an under-estimation of the taste bias but in a negligible magnitude.One of the reasons is that immigrant stock share is a state variable which is accumulativefor a very long period while import share is a flow variable which is annual in my dataset.Thus there is very weak causality relationship on the opposite direction. Another reasonis that a large portion of the international migration is caused by non-economic reasons,e.g., conflict, climate, education, religion, or economic reasons but unrelated to trade,e.g., job opportunity. In addition, I also do a long-difference estimation to equation (29)and obtain the result in column (4). The migration coefficient is 0.782, still significantlydifferent from zero, and is robust with the gravity variables as controls in column (5).Therefore, I am confident that endogeneity issues is not big concern in my estimation.

3.3.3 Alternative Channel

The second concern related to my estimation is the coefficient of immigrant shares maycapture the stimulating effect more than just taste bias. The taste shifters may be over-estimated because migration increases trade not only through the preference channel butthrough the trade cost channel as well. As Rauch (2001) argues, migration builds up busi-ness and social networks cross national borders can help to alleviate problems of contractenforcement and provide information about trading opportunities. Rauch and Trindade(2002) find that ethnic Chinese networks, proxied by the product of ethnic Chinese pop-ulation shares, increased bilateral trade significantly, and more for differentiated than forhomogeneous products. In order to check the network channel, I construct a new variable

Networkni =N

∑j=1

snjsij. (30)

20

This variable gives the probability that, if we select an individual at random from eachcountry, they will have a connection defined as common national origin. By construction,it is symmetric for country pairs. When j = CHN, it’s an indirect link identical to that inRauch and Trindade (2002), and when j = n, i, it’s a summation of two direct links similarto that in Combes et al. (2005). But my measure extends them to a summation of allindirect and two direct links, and thus captures the network effect as much as possible.Then I re-specify bilateral trade cost by adding a new variable, Network, and rerun theregression of equation (28) to control the possible network channel.

The results in Table 8 show that the coefficients on network is insignificant with migra-tion in column (2) and without in (4). In column (3) and (5), Emigration share is added butits coefficient is not significant either. It is straightforward because emigrants contributeto bilateral network but not preference effect. column (6) checks the network effect in CESgravity and the coefficient on network is 13.504 significantly positive, which is consistentwith Rauch and Trindade (2002), which implies that my measure for network is valid. Allthose evidence tell that the network channel is negligible for import share of the aggregatemanufacturing products.

4 Robustness

In this section, I do robustness check with different specifications. Unbiased tastes areproxied by alternative measures in Section 4.1 and price elasticities are asymmetric inSection 4.2.

4.1 Alternative Measure of Unbiased Tastes

In the baseline estimation, I proxy αi = EMi/ ∑i′ EMi′ where EMi is the extensive marginin global trade. Now I will replace it with other measures to ensure that my results do notsolely depend on this particular extensive margin measure.. The first one is exporter’spopulation in log, and the second is exporter’s GDP in log. Table 9 reports the estimatesin column (2) and column (3) respectively. The results are very close to the baseline esti-mation in column (1). I even also use 1/N to replace EM to impose symmetric unbiasedtastes. It is reported in column (4). All results are robust.

Figure 4 plots the unbiased tastes and taste biases. Because unbiased tastes αi measureto what level global consumers favor the good produced by country i, and θi measureshow ethnicity i’s taste on their home product i deviates from that level. Then I can con-clude from the figure that both world and home consumers favor American products, and

21

neither favor Luxembourg products. Also note that Germans and British are less biasedin their taste but world consumers favor their products a lot.

4.2 Asymmetric Price Elasticity

In the baseline estimation, it is assumed the consumers have symmetric price elasticity foreach country’s product. Now let’s relax this symmetry restriction to more general case.23

Specifically, βiNi=1 are asymmetric. Then the home-biased gravity is specified as

Xni

En= θisni − γβi ln tni + ψ ln rni + λnαi + fi + gn + γβi ln Pn + εni, (31)

where

ln Pn =N

∑i=1

βi ln(

tni

Πi

). (32)

And ln rni = ln rn ln ri, fi = Yi/Y − θiΠsi + γ′ ln Πi + ψ ln ri ln r, and gn = ψ ln rn ln r +

ψ ln r ln r. As seen from equation (32), the inward multilateral resistances are unobserv-able because they have inside parameters βiN

i=1 to be estimated. If I take unobservablesas parameters to be estimated, the interactive terms will cause nonlinearity in regres-sion. Bai (2009) extends the argument in Chamberlain (1984) to models with interac-tive effects, and shows that more consistent estimates are obtained with a projection ofthe interactive term onto an average of regressors when interest is centered on coeffi-cients of non-interactive terms. I follow his idea to project the unobservables such thatln Pn = η ln Pn + εn where

ln Pn =N

∑i=1

µi ln tni, (33)

and I let µi = 1/N following Mundlak (1978). I also test the different sets of values of µ’s.Therefore the econometric specification of the home-biased gravity equation becomes

Xni

En= θisni + bi ln dni + bbborderni + bl langni + bttari f fni + biinternalni + ψ ln rni + λnEMi

+ ηiPdn + fi + gn + vni. (34)

Note that the coefficient on distance is exporter-specific. And fi, gn could be controlledby fixed effects of origins and destinations. vni = εni + γβiεni, and ln Pd

n are calculated by

23Anderson and Zhang (2018) estimate the asymmetric price elasticities by projecting elasticities on ex-porter’s export sophistication.

22

equation (33) but with ln dni instead of ln tni.24 Then I can use the General Least Squaremethod to estimate the equation.

Table 10 reports the estimates in column (2). Exporter-specific distance elasticity is notreported since it is not the parameters of interest. The results are very close to the baselineestimation in column (1) and the average is 0.412. I also try different weights, Yi/YN

i=1,to project the multilateral promoters in column (3). All results are robust. Then the the-oretical parameters γ and βi are identified by γ = −∑N

i=1 bi/ρ, and βi = bi/ ∑Ni′=1 bi′ .

Figure 5 plots the price elasticity ργβi and income elasticity φi. American, German, andItalian products are luxury goods because they have positive semi income elasticities,and thus their income elasticities are larger than one. These products are also price elasticwhich means consumers are very sensitive to their price changes. Indian, Indonesian,and Chinese products are necessary goods (with negative semi income elasticities), andprice inelastic. Different to the estimates in Fajgelbaum and Khandelwal (2016), I allowβiN

I=1 varying to exporters. This variation of price elasticities also reduces the variationof income elasticities estimated in their paper.25

5 Counterfactuals

In this section, I do counterfactual experiments with my baseline estimates. Section 5.1studies to what extent the home bias in trade can be explained by taste effect, trade costeffect, and income effect, respectively. In Section 5.2, I conduct migration policy counter-factual experiment to test its impact on international trade.

5.1 Variation Decomposition

First, I use these estimates to assess the role of each in contributing to the home bias intrade. Recall the formula implied by home-biased gravity equation

HBn = (θnsnn − θnΠsn − αnPs

n)︸ ︷︷ ︸Gs

n

−γβn ln(

tnn

ΠnPn

)︸ ︷︷ ︸

Gtn

+ φn ln(rn

r

)︸ ︷︷ ︸

Gin

.

Specifically, I term the taste effect, trade cost effect, and income effect as Gsn, Gt

n, and Gin

respectively. Then I calculate the contribution of each effect on trade bias of each country24Here I impose asymmetry only on distance elasticities. If all other standard gravity variables have

heterogeneous coefficients, too many variables in regression would cause degrees of freedom problem.25Fajgelbaum and Khandelwal (2016) obtain the estimates of income elasticities with a range of -0.048 to

0.052, whereas the range in this paper is -0.006 to 0.004.

23

byContributionl

n = Gln/HBn (35)

where l = s, t, i.First column in Table 11 lists the trade biases for 40 countries, which are the deviations

of home shares from their frictionless levels in manufacturing sector. The average bias isas high as 54%. The country with the largest bias, 86%, is Brazil and the one with thelowest, 10%, is Luxembourg. China and the U.S. both have modest level of home biasaround 60% because they have large production shares in the world market which meanshigh frictionless level of intra-national trade. Column (2)-(4) report the percentage contri-bution of the three effects to home bias in trade. On average, 43.2% of the market homebias could be explained by trade cost, 56.3% explained by home-biased taste, only 0.5%explained by income effect. In other word, trade cost and home-biased taste are the maincauses of home bias in trade, while income effect is negligible. Taste effect larger thantrade cost effect for most of the economies. China, Japan, and the U.S., have the largesttaste effect above 70% because their ethnic consumers are most biased. Head and Mayer(2013) show that between 50% to 85% of the distance effects on trade flows are due to in-direct trade costs (that they call as “dark trade costs”). Another paper by Hou et al. (2017)find the contribution of distance effect through dyadic preferences is about 96%. Com-pared to their work, my results show that the contribution of taste effect (correspondingto dark trade costs in their paper), about 56.3%, is reasonable and consistent.

5.2 Policy Implications

In this part, I discuss how migration policy affects the home bias in trade with the pa-rameter values estimated in the baseline regression. If the bilateral immigrant share snn

changes, this has a direct effect on the corresponding home bias HBn. But the change insnn also has an indirect effect on home bias through a change in multilateral promoters,which is similar to indirect trade cost effect on trade through multilateral resistances inAnderson and van Wincoop (2003). I refer to the direct and indirect effects together as ageneral equilibrium effect following Novy (2013). Specifically,

∆HBn = θn∆snn︸ ︷︷ ︸direct effect

−θn∆Πsn − αn∆Ps

n︸ ︷︷ ︸indirect effect

(36)

The taste bias θn only captures the direct effect of a change in snn on HBn. The indirecteffect could be captured by multilateral promoters −θn∆Πs

n and −αn∆Psn.

Consider an extreme migration policy, like migration ban, for country n, to raise native

24

population share to 1. How does its home bias response to this shock? Since transnationalmigration promotes international trade, we expect an increase in the home market share.Using the equation above, I can calculate the percentage change of home bias ∆HBn/HBn

for each country. The results are reported in column (2) in Table 12. It is found that,the increase in home bias is a small number on average, only 3.7%, probably becauseinternational migration is at a very low level comparing to the total world population.But for immigrant countries, the increase is much larger. Australia will increase its homebias by 12.8 percent and Canada will increase by 10.5 percent. The U.S. will increaseintra-national trade by 6.6 percent with an immigration ban.

On the opposite, if we remove all the migration barrier at country n, and allow con-sumers freely migrant across its border, the country will end up with an ethnic compo-sition exactly the same as the world population. Instead we expect a decrease in homemarket share as well as the home bias. column (3) reports the results. The home biasdecreases by 56 percent on average, much larger than that in the case of the migrant ban.China’s home biases will go down by the most because they have very small immigrantpopulation and their consumers are highly biased in taste.

6 Extension

In this section, I introduce the “local bias” in addition to “home bias” into the model toallow immigrants to assimilate. I redefine the taste parameter in equation (3). On onehand, a consumer has a biased taste towards the good produced by her home country(where she is originally from). On the other hand, her taste is also biased toward to thegood produced locally (where she is living).26 Then I specialize ethnic consumer (ethnicgroup) h’s tastes at location (country) n as

αhni = (1− θh −Θn)αi + θhδh

i + Θnδni, (37)

where the Kronecker delta δni is 1 when i = n meaning local goods, and 0 otherwise,and Θn ∈ [0, 1] is the local shifter which measures how consumers at location n’s taste istowards to local good n. This specialization still satisfies conditions ∑N

i=1 αhni = 1 for all

h and all n. For any consumer h at location n, she raises her taste for home good h by ashifter θh, for local good n by a shifter Θn, while reduces those for all the other goods by

26Bronnenberg et al. (2012) use micro-level data to infer that approximately 40 percent of the geographicvariation in market shares is attributable to persistent brand preferences. They also show that their dataalso strongly reject the hypothesis that all that matters is where consumers lived in childhood: consumerswho move after age 25 still eventually converge to the consumption patterns of their new state of residence.

25

1− θh −Θn. Thus the set of local shifters ΘhNh=1 is the measurement of the local biases

across locations. Now I rewrite the expenditure equations (3) as

whni = (1− θh −Θn)αi + θhδh

i + Θnδni − γβi ln(

pni

pn

)+ φi ln

(eh

nQh

n

). (38)

Aggregating individual expenditure shares across ethnic groups gives the market importshare of country n from country i by wni = ∑N

h=1 snhwhni, i.e.,

wni = (1− θn −Θn)αi + θisni + Θnδni − γβi ln(

pni

pn

)+ φi ln

(en

Qn

). (39)

Then equation (39) could be rewritten as

Xni

En= [αi − γβi ln

(pii

p

)] + (θisni − αi(θn + Θn)) + Θnδni − γβi ln

(tni

Pn

)+ φi ln

(en

Qn

).

(40)Substitute equation (40) into equation (12) and divide through by Y which is the totalworld income. Subtract the resulting expression from equation (40) and simplify by can-celing term [αi − γβi ln

(piip

)]. Then the home-biased gravity with local bias becomes

Xni

En− Yi

Y= (θisni− θiΠs

i − αiPsn)− γβi ln

(tni

ΠiPn

)+ φi ln

(rn

r

)+

(Θnδni −Θi

Ei

Y

), (41)

where ln Πi, ln Pn, and Πsi are the same as the baseline model, while

Psn =

N

∑i=1

θi(sni −Πsi ) + (Θn −

N

∑n′=1

(En′

Y

)Θn′). (42)

On the left hand side, XniEn− Yi

Y is the deviation of bilateral trade per unit of n’s expen-diture from its frictionless level Yi

Y . There are a fourth term on the right hand side now,which capture the local effect, in additional to taste effect, price effect, and income effectin the baseline mode. It captures the effect of relative trade “promoter” that stimulatesinternal (local) trade due to the local bias in taste. When consumers everywhere has nonlocal bias, i.e. Θn = 0 for all n, the internal trade promoter is shut down, and this gravityequation corresponds to that in the baseline model.

To estimate the gravity equation (41), I follow the methodology in the baseline esti-mation. Note that ΘiEi/Y is captured by exporter-specific fixed effect. δni is a dummyfor internal trade. In addition, I assume local shifters to satisfy Θn = µsnn for all n. This

26

is reasonable because immigrants are more likely to assimilate in a country with biggernative population share. Then the number of local bias parameters to be estimated isreduced from forty to one. Thus the specification of the gravity equation (41) is

Xni

En= θisni + b ln dni + bbborderni + bl langni + bttari f fni + bisnn× internalni +ψ ln rni +λnαi

+ fi + gn + εni. (43)

In contrast to (25), the dummy internal now interacts with native population share. Theidea behind this variable is to capture the variation of border barriers across locations.27

The results are reported in column (2) in Table 13. The coefficient estimates of distance,common border, common language, tariff, and income are exactly the same as the baselineresults in column (1). The coefficient of the interaction term of internal trade and nativepopulation share is positively significant, which suggests consumers located at countrieswith higher native share are more likely to locally bias in their tastes. The table also re-ports the estimates of the 40 θi parameters, one corresponding to each ethnic group, in thesubsequent rows. After controlling local bias in gravity, the home taste shifters estimatedare mostly smaller. On average, the taste bias measured by the ethnicity-specific immi-grant coefficients is 0.302, which is lower than 0.352 in the symmetric estimation. Nowthe four ethnic groups with highest θ’s are Japanese, Australian, Chinese, and Ameri-cans. The four ethnic groups with lowest θ’s are Maltese, Belgians, Luxembourgers andDutchmen, with taste shifters not different from 0.

7 Conclusion

Trade models usually have identical preferences across consumers. Differences in de-mand across countries, such as home-biased tastes, are often understood by unobservable“trade costs” which are indistinguishable. It is important to quantitatively assess the twobecause declines in trade costs and homogenization of tastes have different implicationsfor trade policies and welfare changes. This paper provides a structural component ofthe home-biased preferences which is feasible to estimate. The bilateral market taste isdetermined by bilateral migration and their taste bias. I estimate taste bias for each eth-nic group and find that taste bias explains the trade bias more than trade cost does. Thedifference in taste bias explains the large variation of home bias across countries.

27Anderson et al. (2015) first time use the interaction terms of internal trade and a series of country-specific characteristics to estimate their impacts on international borders in the presence of a complete setof exporter-time and importer-time fixed effects within a structural gravity model.

27

The home-biased gravity model here suggests several extensions. (i) Endogenousmigration can be added into the model to provide more policy implications. (ii) An-other potential extension is to allow for within-country income differences to investigatemore variations of taste biases. In empirical there are some other possible extensions.(i) More information on immigrants, like education, age, or religion, can be included totest whether the taste biases differ within ethnicity. (ii) By looking at different migrationgenerations, check whether the taste bias decays over time. More challenging, perhapsleading outside the scope of the present model, is finding the deep causes of the tastebias formation. This is related to other research on how culture and institution persistor change across ethnic groups. I believe that these are all fruitful directions for futureresearch.

28

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32

Tables and Figures

0.8

0.82

0.84

0.86

0.88

0.9

0.92

0.94

0.96

0.98

1

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

China Japan Brazil India Korea Russia UnitedStates

Italy France Germany UnitedKingdom

Estonia Belgium

Production Share Home Share Import Share Native Population Share

Figure 1: Home Bias in Trade and Native Population Share

Notes: Import (home) share is the share of a country’s imports (domestic goods) in its total expenditures.Native population share is the share of a country’s natives in its total population, which equals to one minusthe foreign-born population share. Production share is the share of a country’s production in world totalproduction. Data source: WIOD and UNdata.

33

Table 1: Home-Biased Gravity Estimates: Baseline

Import shareDistance -0.009∗∗∗ (0.001)Border 0.023∗∗∗ (0.002)Language 0.014∗∗∗ (0.002)Tariff -0.008∗∗∗ (0.001)Income 0.006∗∗∗ (0.001)Internal 0.141∗∗ (0.071)JPN X Immigrant 0.670∗∗∗ (0.075)CHN X Immigrant 0.657∗∗∗ (0.073)BRA X Immigrant 0.586∗∗∗ (0.074)USA X Immigrant 0.583∗∗∗ (0.080)IDN X Immigrant 0.576∗∗∗ (0.074)AUS X Immigrant 0.556∗∗∗ (0.087)TUR X Immigrant 0.545∗∗∗ (0.075)IND X Immigrant 0.542∗∗∗ (0.074)KOR X Immigrant 0.529∗∗∗ (0.074)ITA X Immigrant 0.517∗∗∗ (0.076)RUS X Immigrant 0.503∗∗∗ (0.073)ESP X Immigrant 0.495∗∗∗ (0.078)CAN X Immigrant 0.491∗∗∗ (0.085)FIN X Immigrant 0.444∗∗∗ (0.076)IRL X Immigrant 0.437∗∗∗ (0.082)FRA X Immigrant 0.425∗∗∗ (0.077)ROM X Immigrant 0.417∗∗∗ (0.074)GRC X Immigrant 0.393∗∗∗ (0.075)BGR X Immigrant 0.389∗∗∗ (0.074)PRT X Immigrant 0.366∗∗∗ (0.075)SWE X Immigrant 0.341∗∗∗ (0.079)POL X Immigrant 0.340∗∗∗ (0.074)MEX X Immigrant 0.334∗∗∗ (0.074)TWN X Immigrant 0.333∗∗∗ (0.075)LTU X Immigrant 0.327∗∗∗ (0.076)DEU X Immigrant 0.304∗∗∗ (0.080)CZE X Immigrant 0.259∗∗∗ (0.075)GBR X Immigrant 0.254∗∗∗ (0.078)AUT X Immigrant 0.252∗∗∗ (0.081)CYP X Immigrant 0.240∗∗∗ (0.083)LVA X Immigrant 0.207∗∗ (0.082)HUN X Immigrant 0.191∗∗ (0.076)SVK X Immigrant 0.181∗∗ (0.075)SVN X Immigrant 0.159∗∗ (0.074)EST X Immigrant 0.127 (0.085)DNK X Immigrant 0.116 (0.077)NLD X Immigrant 0.107 (0.078)BEL X Immigrant 0.049 (0.080)MLT X Immigrant -0.013 (0.079)LUX X Immigrant -0.156 (0.104)Observations 1600Adjusted R2 0.973Mean 0.352Std

Notes. Table reports the estimates of the home-biased gravity equation. Robust standard errorsin parentheses. Significance * .10, ** .05, *** .01.34

Table 2: Home-Biased Gravity Estimates: by Sector

(1) (2) (3)Manufacturing (Baseline) Agricultural Service

Distance -0.009∗∗∗ (0.001) -0.005∗∗∗ (0.001) -0.001∗∗∗ (0.000)Border 0.023∗∗∗ (0.002) 0.022∗∗∗ (0.003) 0.001 (0.001)Language 0.014∗∗∗ (0.002) 0.010∗∗∗ (0.003) 0.002∗∗ (0.001)Tariff -0.008∗∗∗ (0.001) -0.000 (0.000) -0.006 (0.007)Income 0.006∗∗∗ (0.001) 0.001∗∗ (0.000) 0.002∗∗∗ (0.000)Internal 0.141∗∗ (0.071) 0.253∗∗ (0.104) 0.807∗∗∗ (0.080)JPN X Immigrant 0.670∗∗∗ (0.075) 0.409∗∗∗ (0.109) 0.094∗∗ (0.037)CHN X Immigrant 0.657∗∗∗ (0.073) 0.637∗∗∗ (0.108) 0.118∗∗∗ (0.033)BRA X Immigrant 0.586∗∗∗ (0.074) 0.665∗∗∗ (0.108) 0.092∗∗∗ (0.032)USA X Immigrant 0.583∗∗∗ (0.080) 0.606∗∗∗ (0.117) 0.149∗∗ (0.060)IDN X Immigrant 0.576∗∗∗ (0.074) 0.678∗∗∗ (0.108) 0.108∗∗∗ (0.030)AUS X Immigrant 0.556∗∗∗ (0.087) 0.776∗∗∗ (0.127) 0.167∗∗ (0.074)TUR X Immigrant 0.545∗∗∗ (0.075) 0.684∗∗∗ (0.109) 0.018 (0.100)IND X Immigrant 0.542∗∗∗ (0.074) 0.694∗∗∗ (0.108) -0.071 (0.218)KOR X Immigrant 0.529∗∗∗ (0.074) 0.266∗∗ (0.109) -0.059 (0.146)ITA X Immigrant 0.517∗∗∗ (0.076) 0.353∗∗∗ (0.111) 0.109∗∗∗ (0.031)RUS X Immigrant 0.503∗∗∗ (0.073) 0.626∗∗∗ (0.106) 0.103∗∗∗ (0.029)ESP X Immigrant 0.495∗∗∗ (0.078) 0.466∗∗∗ (0.114) 0.105∗∗∗ (0.032)CAN X Immigrant 0.491∗∗∗ (0.085) 0.586∗∗∗ (0.124) 0.087∗∗∗ (0.033)FIN X Immigrant 0.444∗∗∗ (0.076) 0.352∗∗∗ (0.111) 0.056∗ (0.031)IRL X Immigrant 0.437∗∗∗ (0.082) 0.545∗∗∗ (0.120) -0.216∗∗∗ (0.034)FRA X Immigrant 0.425∗∗∗ (0.077) 0.532∗∗∗ (0.112) 0.116∗∗∗ (0.031)ROM X Immigrant 0.417∗∗∗ (0.074) 0.617∗∗∗ (0.108) 0.095∗∗∗ (0.030)GRC X Immigrant 0.393∗∗∗ (0.075) 0.510∗∗∗ (0.109) 0.082∗∗∗ (0.031)BGR X Immigrant 0.389∗∗∗ (0.074) 0.393∗∗∗ (0.108) 0.078∗∗∗ (0.030)PRT X Immigrant 0.366∗∗∗ (0.075) 0.486∗∗∗ (0.110) 0.096∗∗∗ (0.031)SWE X Immigrant 0.341∗∗∗ (0.079) 0.406∗∗∗ (0.115) 0.064∗∗ (0.032)POL X Immigrant 0.340∗∗∗ (0.074) 0.514∗∗∗ (0.108) 0.086∗∗∗ (0.030)MEX X Immigrant 0.334∗∗∗ (0.074) 0.639∗∗∗ (0.108) 0.105∗∗∗ (0.029)TWN X Immigrant 0.333∗∗∗ (0.075) 0.253∗∗ (0.110) 0.061∗∗ (0.029)LTU X Immigrant 0.327∗∗∗ (0.076) 0.126 (0.111) 0.054∗ (0.031)DEU X Immigrant 0.304∗∗∗ (0.080) 0.291∗∗ (0.117) 0.102∗∗∗ (0.033)CZE X Immigrant 0.259∗∗∗ (0.075) 0.462∗∗∗ (0.109) 0.070∗∗ (0.031)GBR X Immigrant 0.254∗∗∗ (0.078) 0.487∗∗∗ (0.114) 0.091∗∗∗ (0.032)AUT X Immigrant 0.252∗∗∗ (0.081) 0.390∗∗∗ (0.118) 0.075∗∗ (0.033)CYP X Immigrant 0.240∗∗∗ (0.083) 0.331∗∗∗ (0.121) 0.078∗∗ (0.034)LVA X Immigrant 0.207∗∗ (0.082) 0.489∗∗∗ (0.120) 0.095∗∗∗ (0.034)HUN X Immigrant 0.191∗∗ (0.076) 0.346∗∗∗ (0.111) 0.022 (0.031)SVK X Immigrant 0.181∗∗ (0.075) 0.341∗∗∗ (0.110) 0.070∗∗ (0.031)SVN X Immigrant 0.159∗∗ (0.074) 0.482∗∗∗ (0.109) 0.093∗∗∗ (0.031)EST X Immigrant 0.127 (0.085) 0.598∗∗∗ (0.124) 0.084∗∗ (0.035)DNK X Immigrant 0.116 (0.077) 0.545∗∗∗ (0.112) 0.032 (0.032)NLD X Immigrant 0.107 (0.078) -0.012 (0.114) 0.027 (0.032)BEL X Immigrant 0.049 (0.080) -0.161 (0.117) 0.027 (0.033)MLT X Immigrant -0.013 (0.079) 0.485∗∗∗ (0.114) 0.013 (0.032)LUX X Immigrant -0.156 (0.104) 0.018 (0.151) -0.450∗∗∗ (0.043)Observations 1600 1600 1600Adjusted R2 0.973 0.963 0.998Mean 0.352 0.448 0.053Std

Notes: Table reports the estimates of home-biased gravity equation with sectoral trade flow data.Robust standard errors in parentheses. Significance * .10, ** .05, *** .01.35

Table 3: Home-Biased Gravity Estimates: by Good

(1) (2) (3)Aggregate (Baseline) Final Intermediate

Distance -0.009∗∗∗ (0.001) -0.008∗∗∗ (0.001) -0.009∗∗∗ (0.001)Border 0.023∗∗∗ (0.002) 0.023∗∗∗ (0.002) 0.023∗∗∗ (0.002)Language 0.014∗∗∗ (0.002) 0.015∗∗∗ (0.002) 0.013∗∗∗ (0.002)Tariff -0.008∗∗∗ (0.001) -0.006∗∗∗ (0.001) -0.008∗∗∗ (0.001)Income 0.006∗∗∗ (0.001) 0.004∗∗∗ (0.001) 0.002∗∗ (0.001)Internal 0.141∗∗ (0.071) -0.012 (0.075) 0.263∗∗∗ (0.072)JPN X Immigrant 0.670∗∗∗ (0.075) 0.770∗∗∗ (0.079) 0.578∗∗∗ (0.076)CHN X Immigrant 0.657∗∗∗ (0.073) 0.831∗∗∗ (0.078) 0.553∗∗∗ (0.075)BRA X Immigrant 0.586∗∗∗ (0.074) 0.828∗∗∗ (0.078) 0.451∗∗∗ (0.076)USA X Immigrant 0.583∗∗∗ (0.080) 0.748∗∗∗ (0.084) 0.496∗∗∗ (0.082)IDN X Immigrant 0.576∗∗∗ (0.074) 0.866∗∗∗ (0.078) 0.411∗∗∗ (0.075)AUS X Immigrant 0.556∗∗∗ (0.087) 0.733∗∗∗ (0.092) 0.498∗∗∗ (0.089)TUR X Immigrant 0.545∗∗∗ (0.075) 0.741∗∗∗ (0.079) 0.436∗∗∗ (0.076)IND X Immigrant 0.542∗∗∗ (0.074) 0.810∗∗∗ (0.079) 0.411∗∗∗ (0.076)KOR X Immigrant 0.529∗∗∗ (0.074) 0.695∗∗∗ (0.079) 0.429∗∗∗ (0.076)ITA X Immigrant 0.517∗∗∗ (0.076) 0.656∗∗∗ (0.080) 0.422∗∗∗ (0.078)RUS X Immigrant 0.503∗∗∗ (0.073) 0.535∗∗∗ (0.077) 0.497∗∗∗ (0.074)ESP X Immigrant 0.495∗∗∗ (0.078) 0.631∗∗∗ (0.083) 0.419∗∗∗ (0.080)CAN X Immigrant 0.491∗∗∗ (0.085) 0.703∗∗∗ (0.090) 0.418∗∗∗ (0.087)FIN X Immigrant 0.444∗∗∗ (0.076) 0.529∗∗∗ (0.080) 0.375∗∗∗ (0.078)IRL X Immigrant 0.437∗∗∗ (0.082) 0.525∗∗∗ (0.087) 0.349∗∗∗ (0.084)FRA X Immigrant 0.425∗∗∗ (0.077) 0.625∗∗∗ (0.081) 0.322∗∗∗ (0.078)ROM X Immigrant 0.417∗∗∗ (0.074) 0.689∗∗∗ (0.078) 0.259∗∗∗ (0.075)GRC X Immigrant 0.393∗∗∗ (0.075) 0.596∗∗∗ (0.079) 0.287∗∗∗ (0.076)BGR X Immigrant 0.389∗∗∗ (0.074) 0.547∗∗∗ (0.078) 0.320∗∗∗ (0.076)PRT X Immigrant 0.366∗∗∗ (0.075) 0.500∗∗∗ (0.080) 0.316∗∗∗ (0.077)SWE X Immigrant 0.341∗∗∗ (0.079) 0.476∗∗∗ (0.083) 0.251∗∗∗ (0.080)POL X Immigrant 0.340∗∗∗ (0.074) 0.573∗∗∗ (0.078) 0.225∗∗∗ (0.076)MEX X Immigrant 0.334∗∗∗ (0.074) 0.697∗∗∗ (0.078) 0.094 (0.075)TWN X Immigrant 0.333∗∗∗ (0.075) 0.572∗∗∗ (0.079) 0.242∗∗∗ (0.077)LTU X Immigrant 0.327∗∗∗ (0.076) 0.554∗∗∗ (0.081) 0.150∗ (0.078)DEU X Immigrant 0.304∗∗∗ (0.080) 0.530∗∗∗ (0.084) 0.179∗∗ (0.082)CZE X Immigrant 0.259∗∗∗ (0.075) 0.473∗∗∗ (0.079) 0.131∗ (0.076)GBR X Immigrant 0.254∗∗∗ (0.078) 0.375∗∗∗ (0.082) 0.174∗∗ (0.080)AUT X Immigrant 0.252∗∗∗ (0.081) 0.372∗∗∗ (0.085) 0.156∗ (0.082)CYP X Immigrant 0.240∗∗∗ (0.083) 0.347∗∗∗ (0.088) 0.221∗∗∗ (0.085)LVA X Immigrant 0.207∗∗ (0.082) 0.378∗∗∗ (0.087) 0.120 (0.084)HUN X Immigrant 0.191∗∗ (0.076) 0.478∗∗∗ (0.080) 0.040 (0.078)SVK X Immigrant 0.181∗∗ (0.075) 0.384∗∗∗ (0.080) 0.049 (0.077)SVN X Immigrant 0.159∗∗ (0.074) 0.257∗∗∗ (0.079) 0.089 (0.076)EST X Immigrant 0.127 (0.085) 0.319∗∗∗ (0.089) -0.003 (0.086)DNK X Immigrant 0.116 (0.077) 0.179∗∗ (0.081) 0.068 (0.079)NLD X Immigrant 0.107 (0.078) 0.205∗∗ (0.083) 0.018 (0.080)BEL X Immigrant 0.049 (0.080) 0.191∗∗ (0.084) -0.055 (0.082)MLT X Immigrant -0.013 (0.079) 0.172∗∗ (0.083) -0.174∗∗ (0.080)LUX X Immigrant -0.156 (0.104) 0.011 (0.110) -0.281∗∗∗ (0.107)Observations 1600 1600 1600Adjusted R2 0.973 0.972 0.974Mean 0.352 0.528 0.248Std

Notes: Table reports the estimates of home-biased gravity equation with intermediate andfinal trade flow data. Robust standard errors in parentheses. Significance * .10, ** .05, *** .01.36

−.5

0.5

1T

aste

Bia

s (

θi)

0 10 20 30 40Ethnicity ID

Manufacturing (Baseline) Agricultural

Service

Figure 2: Taste Shifters θi by Sector

Figure plots ethnicity i’ taste bias toward home products in different sectors.

−.2

0.2

.4.6

.8T

aste

Bia

s (

θi)

0 10 20 30 40Ethnicity ID

Aggregate (Baseline) Final

Intermediate

Figure 3: Taste Shifters θi by Good

Figure plots ethnicity i’ taste bias toward home products on different types of goods.

37

Table 4: Home-Biased Gravity Estimates: by Industry

Industry Mean Standard DeviationAggregate 0.352 0.189

Textiles 0.893 0.258Leather and footwear 0.783 0.349Transport equipment 0.479 0.266Food, beverages, and tobacco 0.454 0.207Machinery 0.408 0.240Chemical products 0.375 0.276Manufacturing, nec 0.349 0.230Basic metals and fabricated metal 0.311 0.198Wood products 0.222 0.191Printing and publishing 0.196 0.180Electrical and optical equipment 0.192 0.214Rubber and plastics 0.050 0.256Other non-metallic minerals -0.001 0.145

Notes: Table reports the estimates of home-biased gravity equation withindustrial trade flow data. Only mean and stardard deviation of tastebias estimates are reported. Robust standard errors in parentheses. Sig-nificance * .10, ** .05, *** .01.

38

Table 5: The Most Biased Ethnic Groups by Industry

Notes: Table reports the most biased ethnic groups by industry.

39

Table 6: Symmetric Home-Biased Gravity Estimates

(1) (2) (3) (4)Share Share 2000 Share log Flow

Immigrant share 0.405∗∗ 0.467∗∗

(0.204) (0.205)

Distance -0.010∗∗∗ -0.010∗∗∗ -0.010∗∗∗ -1.305∗∗∗

(0.001) (0.001) (0.001) (0.045)

Common border 0.024∗∗∗ 0.023∗∗∗ 0.026∗∗∗ 0.304∗∗∗

(0.004) (0.005) (0.005) (0.118)

Common language 0.012∗∗∗ 0.013∗∗∗ 0.014∗∗∗ 0.379∗∗∗

(0.004) (0.005) (0.004) (0.140)

Constant 0.106∗∗∗ 0.108∗∗∗ 0.110∗∗∗ 19.393∗∗∗

(0.013) (0.014) (0.013) (0.528)Observations 1560 1560 1560 1560Adjusted R2 0.598 0.538 0.590 0.915

Notes: Table reports the estimates of symmetric home-biased gravity equa-tion with manufacturing trade flow data. Robust standard errors in paren-theses. Significance * .10, ** .05, *** .01.

40

Table 7: Symmetric Home-Biased Gravity Estimates: Endogeneity

(1) (2) (3) (4) (5)OLS 1SLS 2SLS ∆ Share ∆ Share

Immigrant share 0.405∗∗ 0.321∗

(0.204) (0.169)

Distance -0.010∗∗∗ -0.000∗∗∗ -0.010∗∗∗ -0.000(0.001) (0.000) (0.001) (0.000)

Common border 0.024∗∗∗ -0.000 0.024∗∗∗ 0.002(0.004) (0.000) (0.004) (0.001)

Common language 0.012∗∗∗ 0.001∗ 0.013∗∗∗ -0.004∗∗∗

(0.004) (0.000) (0.004) (0.001)

lagged Immigrant share (Instrument) 0.972∗∗∗

(0.038)

∆ Immigrant share 0.782∗∗∗ 0.839∗∗∗

(0.214) (0.215)

Constant 0.106∗∗∗ 0.003∗∗∗ 0.107∗∗∗ 0.000∗ 0.002(0.013) (0.001) (0.013) (0.000) (0.002)

F 41.208Observations 1560 1560 1560 1560 1560R2 0.619 0.964 0.618 0.009 0.016

Notes: Table reports the estimates of symmetric home-biased gravity equation with instrumental vari-ables. Robust standard errors in parentheses. Significance * .10, ** .05, *** .01.

41

Table 8: Symmetric Home-Biased Gravity Estimates: Network Effect

(1) (2) (3) (4) (5) (6)Share Share Share Share Share log Flow

Immigrant share 0.405∗∗ 0.604∗∗ 0.398∗∗

(0.204) (0.242) (0.202)

Distance -0.010∗∗∗ -0.010∗∗∗ -0.010∗∗∗ -0.010∗∗∗ -0.010∗∗∗ -1.291∗∗∗

(0.001) (0.001) (0.001) (0.001) (0.001) (0.045)

Common border 0.024∗∗∗ 0.025∗∗∗ 0.025∗∗∗ 0.025∗∗∗ 0.027∗∗∗ 0.147(0.004) (0.005) (0.005) (0.005) (0.005) (0.120)

Common language 0.012∗∗∗ 0.013∗∗∗ 0.013∗∗∗ 0.013∗∗∗ 0.014∗∗∗ 0.296∗∗

(0.004) (0.004) (0.004) (0.004) (0.005) (0.134)

Network -0.212 0.097 13.504∗∗∗

(0.136) (0.096) (2.761)

Emigrant share -0.192 -0.207(0.130) (0.134)

Constant 0.106∗∗∗ 0.108∗∗∗ 0.108∗∗∗ 0.108∗∗∗ 0.111∗∗∗ 19.160∗∗∗

(0.013) (0.013) (0.013) (0.013) (0.013) (0.529)Observations 1560 1560 1560 1560 1560 1560Adjusted R2 0.598 0.599 0.599 0.591 0.592 0.916

Notes: Table reports the estimates of symmetric home-biased gravity equation with controls ofmigration network effect. Significance * .10, ** .05, *** .01.

42

Table 9: Robustness: Alternative Measure of Unbiased Tastes

(1) (2) (3) (4)EM ln POP ln GDP Symmetric

Distance -0.009∗∗∗ (0.001) -0.009∗∗∗ (0.001) -0.009∗∗∗ (0.001) -0.007∗∗∗ (0.001)Border 0.023∗∗∗ (0.002) 0.023∗∗∗ (0.002) 0.023∗∗∗ (0.002) 0.025∗∗∗ (0.002)Language 0.014∗∗∗ (0.002) 0.013∗∗∗ (0.002) 0.013∗∗∗ (0.002) 0.014∗∗∗ (0.002)Tariff -0.008∗∗∗ (0.001) -0.008∗∗∗ (0.001) -0.007∗∗∗ (0.001) -0.003∗∗∗ (0.001)Income 0.006∗∗∗ (0.001) 0.003∗ (0.002) 0.008∗∗ (0.003) 0.001 (0.001)Internal 0.141∗∗ (0.071) 0.131∗ (0.071) 0.161∗∗ (0.071) 0.150∗∗ (0.073)JPN X Immigrant 0.670∗∗∗ (0.075) 0.683∗∗∗ (0.075) 0.658∗∗∗ (0.075) 0.669∗∗∗ (0.077)CHN X Immigrant 0.657∗∗∗ (0.073) 0.666∗∗∗ (0.074) 0.638∗∗∗ (0.074) 0.652∗∗∗ (0.076)BRA X Immigrant 0.586∗∗∗ (0.074) 0.590∗∗∗ (0.074) 0.569∗∗∗ (0.074) 0.637∗∗∗ (0.076)USA X Immigrant 0.583∗∗∗ (0.080) 0.606∗∗∗ (0.080) 0.565∗∗∗ (0.080) 0.575∗∗∗ (0.083)IDN X Immigrant 0.576∗∗∗ (0.074) 0.584∗∗∗ (0.074) 0.557∗∗∗ (0.074) 0.592∗∗∗ (0.076)AUS X Immigrant 0.556∗∗∗ (0.087) 0.569∗∗∗ (0.087) 0.533∗∗∗ (0.087) 0.573∗∗∗ (0.090)TUR X Immigrant 0.545∗∗∗ (0.075) 0.553∗∗∗ (0.075) 0.526∗∗∗ (0.075) 0.559∗∗∗ (0.077)IND X Immigrant 0.542∗∗∗ (0.074) 0.546∗∗∗ (0.075) 0.528∗∗∗ (0.075) 0.607∗∗∗ (0.077)KOR X Immigrant 0.529∗∗∗ (0.074) 0.540∗∗∗ (0.075) 0.515∗∗∗ (0.075) 0.559∗∗∗ (0.077)ITA X Immigrant 0.517∗∗∗ (0.076) 0.529∗∗∗ (0.076) 0.498∗∗∗ (0.076) 0.510∗∗∗ (0.079)RUS X Immigrant 0.503∗∗∗ (0.073) 0.511∗∗∗ (0.073) 0.488∗∗∗ (0.073) 0.532∗∗∗ (0.075)ESP X Immigrant 0.495∗∗∗ (0.078) 0.505∗∗∗ (0.078) 0.474∗∗∗ (0.078) 0.487∗∗∗ (0.081)CAN X Immigrant 0.491∗∗∗ (0.085) 0.505∗∗∗ (0.085) 0.471∗∗∗ (0.085) 0.501∗∗∗ (0.088)FIN X Immigrant 0.444∗∗∗ (0.076) 0.454∗∗∗ (0.076) 0.424∗∗∗ (0.076) 0.435∗∗∗ (0.079)IRL X Immigrant 0.437∗∗∗ (0.082) 0.448∗∗∗ (0.083) 0.419∗∗∗ (0.083) 0.428∗∗∗ (0.085)FRA X Immigrant 0.425∗∗∗ (0.077) 0.437∗∗∗ (0.077) 0.408∗∗∗ (0.077) 0.421∗∗∗ (0.079)ROM X Immigrant 0.417∗∗∗ (0.074) 0.428∗∗∗ (0.074) 0.398∗∗∗ (0.074) 0.409∗∗∗ (0.076)GRC X Immigrant 0.393∗∗∗ (0.075) 0.405∗∗∗ (0.075) 0.373∗∗∗ (0.075) 0.385∗∗∗ (0.077)BGR X Immigrant 0.389∗∗∗ (0.074) 0.400∗∗∗ (0.074) 0.370∗∗∗ (0.074) 0.383∗∗∗ (0.077)PRT X Immigrant 0.366∗∗∗ (0.075) 0.374∗∗∗ (0.076) 0.344∗∗∗ (0.075) 0.358∗∗∗ (0.078)SWE X Immigrant 0.341∗∗∗ (0.079) 0.350∗∗∗ (0.079) 0.319∗∗∗ (0.079) 0.331∗∗∗ (0.082)POL X Immigrant 0.340∗∗∗ (0.074) 0.349∗∗∗ (0.074) 0.320∗∗∗ (0.074) 0.334∗∗∗ (0.077)MEX X Immigrant 0.334∗∗∗ (0.074) 0.345∗∗∗ (0.074) 0.314∗∗∗ (0.074) 0.358∗∗∗ (0.076)TWN X Immigrant 0.333∗∗∗ (0.075) 0.342∗∗∗ (0.075) 0.311∗∗∗ (0.075) 0.355∗∗∗ (0.078)LTU X Immigrant 0.327∗∗∗ (0.076) 0.339∗∗∗ (0.077) 0.304∗∗∗ (0.077) 0.319∗∗∗ (0.079)DEU X Immigrant 0.304∗∗∗ (0.080) 0.316∗∗∗ (0.080) 0.288∗∗∗ (0.080) 0.304∗∗∗ (0.083)CZE X Immigrant 0.259∗∗∗ (0.075) 0.270∗∗∗ (0.075) 0.238∗∗∗ (0.075) 0.248∗∗∗ (0.077)GBR X Immigrant 0.254∗∗∗ (0.078) 0.263∗∗∗ (0.078) 0.232∗∗∗ (0.078) 0.250∗∗∗ (0.081)AUT X Immigrant 0.252∗∗∗ (0.081) 0.261∗∗∗ (0.081) 0.228∗∗∗ (0.081) 0.241∗∗∗ (0.084)CYP X Immigrant 0.240∗∗∗ (0.083) 0.251∗∗∗ (0.084) 0.217∗∗∗ (0.084) 0.230∗∗∗ (0.086)LVA X Immigrant 0.207∗∗ (0.082) 0.218∗∗∗ (0.083) 0.182∗∗ (0.083) 0.205∗∗ (0.085)HUN X Immigrant 0.191∗∗ (0.076) 0.199∗∗∗ (0.076) 0.170∗∗ (0.076) 0.181∗∗ (0.079)SVK X Immigrant 0.181∗∗ (0.075) 0.191∗∗ (0.076) 0.160∗∗ (0.076) 0.168∗∗ (0.078)SVN X Immigrant 0.159∗∗ (0.074) 0.166∗∗ (0.075) 0.138∗ (0.075) 0.144∗ (0.077)EST X Immigrant 0.127 (0.085) 0.141∗ (0.085) 0.107 (0.085) 0.120 (0.088)DNK X Immigrant 0.116 (0.077) 0.126 (0.077) 0.092 (0.077) 0.106 (0.080)NLD X Immigrant 0.107 (0.078) 0.115 (0.078) 0.084 (0.078) 0.100 (0.081)BEL X Immigrant 0.049 (0.080) 0.059 (0.080) 0.027 (0.080) 0.040 (0.083)MLT X Immigrant -0.013 (0.079) -0.006 (0.079) -0.029 (0.079) -0.032 (0.081)LUX X Immigrant -0.156 (0.104) -0.133 (0.105) -0.193∗ (0.105) -0.173 (0.108)Observations 1600 1600 1600 1600Adjusted R2 0.973 0.974 0.974 0.971Mean 0.352 0.362 0.332 0.353Std

Notes: Table reports the estimates of home-biased gravity equation with alternative measures for unbiasedtastes. Robust standard errors in parentheses. Significance * .10, ** .05, *** .01.43

AUS

AUT

BEL

BGR

BRA

CAN

CHN

CYPCZE

DEU

DNK

ESP

EST

FINFRA

GBR

GRC

HUN

IDNIND

IRL

ITA

JPN

KOR

LTU

LUX

LVA

MEX

MLT

NLD

POLPRT

ROM

RUS

SVKSVN

SWE

TUR

TWN

USA

−.2

0.2

.4.6

Ho

me

ta

ste

s (

θi)

0 .01 .02 .03 .04 .05World tastes (αi)

Figure 4: World and Home Tastes

Figure plots world consumers’ unbiased tastes αi on goods produced by country i, and ethnicity i con-sumers’ taste shifter (bias) toward their home product.

44

Table 10: Robustness: Asymmetric Distant Elasticity

(1) (2) (3)Symmetric 1/N Incomeshare

Distance -0.009∗∗∗ (0.001)Border 0.023∗∗∗ (0.002) 0.010∗∗∗ (0.002) 0.015∗∗∗ (0.002)Language 0.014∗∗∗ (0.002) 0.012∗∗∗ (0.002) 0.012∗∗∗ (0.002)Tariff -0.008∗∗∗ (0.001) -0.005∗∗∗ (0.001) 0.000 (0.001)Income 0.006∗∗∗ (0.001) 0.003∗∗ (0.001) 0.001 (0.001)Internal 0.141∗∗ (0.071) 0.099∗ (0.058) 0.162∗∗∗ (0.062)JPN X Immigrant 0.670∗∗∗ (0.075) 0.707∗∗∗ (0.062) 0.649∗∗∗ (0.067)CHN X Immigrant 0.657∗∗∗ (0.073) 0.726∗∗∗ (0.061) 0.694∗∗∗ (0.065)BRA X Immigrant 0.586∗∗∗ (0.074) 0.675∗∗∗ (0.066) 0.669∗∗∗ (0.069)USA X Immigrant 0.583∗∗∗ (0.080) 0.509∗∗∗ (0.066) 0.446∗∗∗ (0.071)IDN X Immigrant 0.576∗∗∗ (0.074) 0.700∗∗∗ (0.062) 0.670∗∗∗ (0.067)AUS X Immigrant 0.556∗∗∗ (0.087) 0.682∗∗∗ (0.075) 0.649∗∗∗ (0.080)TUR X Immigrant 0.545∗∗∗ (0.075) 0.626∗∗∗ (0.061) 0.576∗∗∗ (0.066)IND X Immigrant 0.542∗∗∗ (0.074) 0.685∗∗∗ (0.065) 0.696∗∗∗ (0.070)KOR X Immigrant 0.529∗∗∗ (0.074) 0.642∗∗∗ (0.062) 0.620∗∗∗ (0.067)ITA X Immigrant 0.517∗∗∗ (0.076) 0.485∗∗∗ (0.062) 0.475∗∗∗ (0.067)RUS X Immigrant 0.503∗∗∗ (0.073) 0.617∗∗∗ (0.063) 0.566∗∗∗ (0.066)ESP X Immigrant 0.495∗∗∗ (0.078) 0.470∗∗∗ (0.064) 0.452∗∗∗ (0.069)CAN X Immigrant 0.491∗∗∗ (0.085) 0.597∗∗∗ (0.070) 0.507∗∗∗ (0.075)FIN X Immigrant 0.444∗∗∗ (0.076) 0.461∗∗∗ (0.063) 0.412∗∗∗ (0.067)IRL X Immigrant 0.437∗∗∗ (0.082) 0.521∗∗∗ (0.069) 0.427∗∗∗ (0.073)FRA X Immigrant 0.425∗∗∗ (0.077) 0.456∗∗∗ (0.063) 0.397∗∗∗ (0.068)ROM X Immigrant 0.417∗∗∗ (0.074) 0.488∗∗∗ (0.061) 0.414∗∗∗ (0.065)GRC X Immigrant 0.393∗∗∗ (0.075) 0.452∗∗∗ (0.062) 0.383∗∗∗ (0.066)BGR X Immigrant 0.389∗∗∗ (0.074) 0.476∗∗∗ (0.061) 0.392∗∗∗ (0.065)PRT X Immigrant 0.366∗∗∗ (0.075) 0.436∗∗∗ (0.062) 0.360∗∗∗ (0.067)SWE X Immigrant 0.341∗∗∗ (0.079) 0.346∗∗∗ (0.065) 0.310∗∗∗ (0.069)POL X Immigrant 0.340∗∗∗ (0.074) 0.357∗∗∗ (0.060) 0.317∗∗∗ (0.065)MEX X Immigrant 0.334∗∗∗ (0.074) 0.427∗∗∗ (0.064) 0.368∗∗∗ (0.069)TWN X Immigrant 0.333∗∗∗ (0.075) 0.458∗∗∗ (0.063) 0.427∗∗∗ (0.068)LTU X Immigrant 0.327∗∗∗ (0.076) 0.359∗∗∗ (0.063) 0.310∗∗∗ (0.067)DEU X Immigrant 0.304∗∗∗ (0.080) 0.251∗∗∗ (0.065) 0.202∗∗∗ (0.070)CZE X Immigrant 0.259∗∗∗ (0.075) 0.297∗∗∗ (0.062) 0.245∗∗∗ (0.066)GBR X Immigrant 0.254∗∗∗ (0.078) 0.269∗∗∗ (0.064) 0.216∗∗∗ (0.069)AUT X Immigrant 0.252∗∗∗ (0.081) 0.300∗∗∗ (0.066) 0.241∗∗∗ (0.071)CYP X Immigrant 0.240∗∗∗ (0.083) 0.372∗∗∗ (0.071) 0.244∗∗∗ (0.074)LVA X Immigrant 0.207∗∗ (0.082) 0.284∗∗∗ (0.067) 0.204∗∗∗ (0.072)HUN X Immigrant 0.191∗∗ (0.076) 0.254∗∗∗ (0.062) 0.183∗∗∗ (0.067)SVK X Immigrant 0.181∗∗ (0.075) 0.244∗∗∗ (0.062) 0.175∗∗∗ (0.066)SVN X Immigrant 0.159∗∗ (0.074) 0.241∗∗∗ (0.062) 0.159∗∗ (0.065)EST X Immigrant 0.127 (0.085) 0.195∗∗∗ (0.070) 0.116 (0.074)DNK X Immigrant 0.116 (0.077) 0.163∗∗ (0.064) 0.105 (0.068)NLD X Immigrant 0.107 (0.078) 0.123∗ (0.064) 0.070 (0.069)BEL X Immigrant 0.049 (0.080) 0.030 (0.066) 0.000 (0.070)MLT X Immigrant -0.013 (0.079) 0.102 (0.074) -0.023 (0.071)LUX X Immigrant -0.156 (0.104) -0.000 (0.087) -0.133 (0.092)Observations 1600 1600 1600Adjusted R2 0.973 0.985 0.982Mean 0.352 0.412 0.355Std

Notes: Table reports the estimates of home-biased gravity equation with asymmetric dis-tant elasticity. Robust standard errors in parentheses. Significance * .10, ** .05, *** .01.45

AUS AUT BEL

BGRBRA

CAN

CHN

CYP

CZE

DEUDNK

ESP

EST

FINFRAGBR

GRC

HUN

IDN

IND

IRL

ITAJPNKOR

LTU

LUX

LVA

MEX

MLT

NLD

POL

PRT

ROM

RUS

SVKSVN

SWE

TUR

TWN

USA

−.0

06

−.0

04

−.0

02

0.0

02

.00

4In

co

me

ela

sticitie

s (

φi)

0 .1 .2 .3 .4 .5Price elasticities (γβi)

Figure 5: Price and Income Elasticity

Figure plots consumers’ price and income elsticity on goods produced by each country i.

46

Table 11: Counterfactuals: Contribution Decomposition

Share Contribution of (%)home bias trade cost taste income

Brazil 86.3 34.5 65.2 0.3India 84.1 35.9 62.1 2.1Indonesia 81.5 29.7 69.4 1.0Japan 79.7 25.6 74.3 0.1Korea 77.1 34.7 65.2 0.1Turkey 75.5 30.2 69.8 0.1Russia 74.2 34.0 66.0 0.0Australia 70.4 33.6 66.2 0.2Italy 68.5 29.4 70.4 0.1Spain 65.3 29.9 70.1 0.1China 62.9 24.9 74.3 0.8Finland 62.8 31.5 68.3 0.2Canada 62.4 33.3 66.5 0.2United States 61.3 26.8 72.9 0.4Romania 60.9 31.6 68.3 0.1Ireland 59.1 33.5 66.1 0.4Greece 58.6 33.8 66.2 0.0France 58.6 32.2 67.6 0.2Bulgaria 58.5 33.4 66.5 0.1Taiwan 58.4 43.6 56.1 0.3Mexico 58.2 42.9 56.8 0.3Portugal 56.1 36.7 63.3 0.0Poland 53.5 36.6 63.4 0.0Sweden 51.8 37.9 61.8 0.3Lithuania 51.6 38.6 61.4 0.0Germany 49.3 44.3 55.4 0.3Czech 45.0 42.5 57.5 0.0United Kingdom 43.8 44.4 55.3 0.2Cyprus 42.9 49.8 50.0 0.2Austria 42.2 44.0 55.5 0.5Latvia 39.6 52.2 47.7 0.0Hungary 38.5 50.3 49.7 0.0Slovakia 37.2 51.2 48.8 0.0Slovenia 35.7 55.0 45.0 0.0Estonia 32.1 64.8 35.2 0.0Denmark 31.9 62.7 36.7 0.6Netherlands 31.8 64.7 34.5 0.8Belgium 25.4 77.7 21.5 0.7Malta 20.9 99.7 0.2 0.1Luxembourg 10.3 89.6 0.9 9.5Mean 54.1 43.2 56.3 0.5St.d. 18.0 16.9 17.6 1.5

Notes: Table reports the contribution decomposition of the homebias in trade.

47

Table 12: Counterfactuals: Migration Policy Implication

Shares % ∆ shares withCountry Home Bias zero migration full migrationBrazil 86.3 -0.6 -66.1India 84.1 -0.6 -62.8Indonesia 81.5 0.9 -68.7Japan 79.7 -5.0 -80.7Korea 77.1 0.2 -66.0Turkey 75.5 2.1 -69.0Russia 74.2 0.4 -65.9Australia 70.4 12.8 -65.1Italy 68.5 2.0 -71.0Spain 65.3 4.9 -69.5China 62.9 -26.6 -101.9Finland 62.8 4.2 -66.5Canada 62.4 10.5 -66.2United States 61.3 6.3 -85.4Romania 60.9 2.3 -66.4Ireland 59.1 9.9 -64.3Greece 58.6 3.2 -64.2France 58.6 2.7 -67.7Bulgaria 58.5 2.7 -64.4Taiwan 58.4 3.0 -54.5Mexico 58.2 1.7 -55.6Portugal 56.1 4.5 -61.3Poland 53.5 2.5 -61.4Sweden 51.8 6.6 -59.7Lithuania 51.6 5.0 -59.3Germany 49.3 4.4 -55.3Czech 45.0 3.7 -55.1United Kingdom 43.8 5.2 -53.2Cyprus 42.9 9.6 -48.2Austria 42.2 8.3 -52.9Latvia 39.6 9.0 -45.4Hungary 38.5 5.1 -46.8Slovakia 37.2 4.9 -46.1Slovenia 35.7 4.3 -42.8Estonia 32.1 9.7 -33.0Denmark 31.9 5.9 -33.7Netherlands 31.8 6.1 -30.7Belgium 25.4 7.0 -17.1Malta 20.9 6.8 0.3Luxembourg 10.3 15.5 2.4Mean 54.1 3.7 -56.0St.d. 18.0 6.5 20.1

Notes: Table reports the policy implication with the home-biased gravity.

48

Table 13: Home-Biased Gravity Estimates: Local Bias

(1) (2)Import share Import share

Distance -0.009∗∗∗ (0.001) -0.009∗∗∗ (0.001)Border 0.023∗∗∗ (0.002) 0.023∗∗∗ (0.002)Language 0.014∗∗∗ (0.002) 0.014∗∗∗ (0.002)Tariff -0.008∗∗∗ (0.001) -0.008∗∗∗ (0.001)Income 0.006∗∗∗ (0.001) 0.006∗∗∗ (0.001)Internal 0.141∗∗ (0.071)Internal × Native share 0.231∗∗∗ (0.077)JPN X Immigrant 0.670∗∗∗ (0.075) 0.590∗∗∗ (0.076)CHN X Immigrant 0.657∗∗∗ (0.073) 0.568∗∗∗ (0.079)BRA X Immigrant 0.586∗∗∗ (0.074) 0.498∗∗∗ (0.079)USA X Immigrant 0.583∗∗∗ (0.080) 0.549∗∗∗ (0.065)IDN X Immigrant 0.576∗∗∗ (0.074) 0.488∗∗∗ (0.078)AUS X Immigrant 0.556∗∗∗ (0.087) 0.587∗∗∗ (0.049)TUR X Immigrant 0.545∗∗∗ (0.075) 0.467∗∗∗ (0.075)IND X Immigrant 0.542∗∗∗ (0.074) 0.453∗∗∗ (0.080)KOR X Immigrant 0.529∗∗∗ (0.074) 0.448∗∗∗ (0.076)ITA X Immigrant 0.517∗∗∗ (0.076) 0.450∗∗∗ (0.073)RUS X Immigrant 0.503∗∗∗ (0.073) 0.417∗∗∗ (0.077)ESP X Immigrant 0.495∗∗∗ (0.078) 0.446∗∗∗ (0.068)CAN X Immigrant 0.491∗∗∗ (0.085) 0.505∗∗∗ (0.053)FIN X Immigrant 0.444∗∗∗ (0.076) 0.376∗∗∗ (0.073)IRL X Immigrant 0.437∗∗∗ (0.082) 0.427∗∗∗ (0.059)FRA X Immigrant 0.425∗∗∗ (0.077) 0.365∗∗∗ (0.071)ROM X Immigrant 0.417∗∗∗ (0.074) 0.329∗∗∗ (0.078)GRC X Immigrant 0.393∗∗∗ (0.075) 0.314∗∗∗ (0.076)BGR X Immigrant 0.389∗∗∗ (0.074) 0.303∗∗∗ (0.078)PRT X Immigrant 0.366∗∗∗ (0.075) 0.300∗∗∗ (0.072)SWE X Immigrant 0.341∗∗∗ (0.079) 0.299∗∗∗ (0.067)POL X Immigrant 0.340∗∗∗ (0.074) 0.255∗∗∗ (0.077)MEX X Immigrant 0.334∗∗∗ (0.074) 0.248∗∗∗ (0.077)TWN X Immigrant 0.333∗∗∗ (0.075) 0.258∗∗∗ (0.075)LTU X Immigrant 0.327∗∗∗ (0.076) 0.263∗∗∗ (0.072)DEU X Immigrant 0.304∗∗∗ (0.080) 0.272∗∗∗ (0.065)CZE X Immigrant 0.259∗∗∗ (0.075) 0.180∗∗ (0.076)GBR X Immigrant 0.254∗∗∗ (0.078) 0.206∗∗∗ (0.068)AUT X Immigrant 0.252∗∗∗ (0.081) 0.228∗∗∗ (0.062)CYP X Immigrant 0.240∗∗∗ (0.083) 0.236∗∗∗ (0.058)LVA X Immigrant 0.207∗∗ (0.082) 0.195∗∗∗ (0.059)HUN X Immigrant 0.191∗∗ (0.076) 0.125∗ (0.073)SVK X Immigrant 0.181∗∗ (0.075) 0.109 (0.074)SVN X Immigrant 0.159∗∗ (0.074) 0.076 (0.077)EST X Immigrant 0.127 (0.085) 0.138∗∗ (0.054)DNK X Immigrant 0.116 (0.077) 0.057 (0.071)NLD X Immigrant 0.107 (0.078) 0.057 (0.069)BEL X Immigrant 0.049 (0.080) 0.017 (0.064)MLT X Immigrant -0.013 (0.079) -0.062 (0.069)LUX X Immigrant -0.156 (0.104) 0.025 (0.025)Observations 1600 1600Adjusted R2 0.973 0.973Mean 0.352 0.302Std

Notes. Table reports the estimates of the home-biased gravity equationafter controlling local bias. Robust standard errors in parentheses. Signif-icance * .10, ** .05, *** .01.

49

A Appendix

50

Tabl

eA

.1:H

ome-

Bias

edG

ravi

tyEs

tim

ates

:by

Indu

stry

I

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

reg0

reg3

reg4

reg5

reg6

reg7

reg8

reg9

Dis

tanc

e-0

.009∗∗∗

(0.0

01)

-0.0

07∗∗∗

(0.0

01)

-0.0

06∗∗∗

(0.0

02)

-0.0

08∗∗∗

(0.0

03)

-0.0

07∗∗∗

(0.0

01)

-0.0

04∗∗∗

(0.0

01)

-0.0

21∗∗∗

(0.0

03)

-0.0

12∗∗∗

(0.0

01)

Bord

er0.

023∗∗∗

(0.0

02)

0.02

2∗∗∗

(0.0

02)

0.02

2∗∗∗

(0.0

04)

0.02

7∗∗∗

(0.0

05)

0.02

1∗∗∗

(0.0

02)

0.02

1∗∗∗

(0.0

02)

0.04

2∗∗∗

(0.0

06)

0.02

4∗∗∗

(0.0

03)

Lang

uage

0.01

4∗∗∗

(0.0

02)

0.01

3∗∗∗

(0.0

02)

0.01

6∗∗∗

(0.0

04)

0.01

3∗∗∗

(0.0

05)

0.00

9∗∗∗

(0.0

02)

0.01

2∗∗∗

(0.0

02)

0.01

3∗∗

(0.0

06)

0.01

7∗∗∗

(0.0

03)

Tari

ff-0

.008∗∗∗

(0.0

01)

-0.0

04∗∗∗

(0.0

01)

-0.0

10∗∗∗

(0.0

02)

-0.0

05∗∗

(0.0

02)

-0.0

02∗∗

(0.0

01)

-0.0

07∗∗∗

(0.0

01)

0.00

1(0

.002

)-0

.011∗∗∗

(0.0

01)

Inco

me

0.00

6∗∗∗

(0.0

01)

0.00

4∗∗∗

(0.0

01)

0.00

2(0

.002

)0.

003∗∗

(0.0

01)

0.00

1∗(0

.001

)0.

002∗∗∗

(0.0

01)

0.00

2(0

.001

)0.

005∗∗∗

(0.0

01)

Inte

rnal

0.14

1∗∗

(0.0

71)

0.24

0∗∗∗

(0.0

69)

-0.5

41∗∗∗

(0.1

25)

-0.5

19∗∗∗

(0.1

65)

0.49

6∗∗∗

(0.0

67)

0.49

3∗∗∗

(0.0

64)

-0.5

67∗∗∗

(0.1

82)

-0.0

59(0

.091

)JP

NX

Imm

igra

nt0.

670∗∗∗

(0.0

75)

0.63

7∗∗∗

(0.0

73)

1.11

2∗∗∗

(0.1

31)

0.86

8∗∗∗

(0.1

73)

0.32

1∗∗∗

(0.0

70)

0.41

5∗∗∗

(0.0

67)

1.41

4∗∗∗

(0.1

91)

0.84

4∗∗∗

(0.0

95)

CH

NX

Imm

igra

nt0.

657∗∗∗

(0.0

73)

0.68

4∗∗∗

(0.0

72)

1.28

3∗∗∗

(0.1

29)

1.27

1∗∗∗

(0.1

71)

0.43

5∗∗∗

(0.0

69)

0.39

8∗∗∗

(0.0

66)

1.45

9∗∗∗

(0.1

88)

0.84

1∗∗∗

(0.0

94)

BRA

XIm

mig

rant

0.58

6∗∗∗

(0.0

74)

0.63

6∗∗∗

(0.0

72)

1.30

3∗∗∗

(0.1

30)

1.36

7∗∗∗

(0.1

72)

0.41

6∗∗∗

(0.0

70)

0.33

5∗∗∗

(0.0

67)

1.42

9∗∗∗

(0.1

90)

0.67

4∗∗∗

(0.0

95)

USA

XIm

mig

rant

0.58

3∗∗∗

(0.0

80)

0.71

3∗∗∗

(0.0

78)

1.00

6∗∗∗

(0.1

41)

0.53

8∗∗∗

(0.1

86)

0.32

2∗∗∗

(0.0

76)

0.39

6∗∗∗

(0.0

72)

1.50

5∗∗∗

(0.2

05)

0.80

2∗∗∗

(0.1

02)

IDN

XIm

mig

rant

0.57

6∗∗∗

(0.0

74)

0.65

7∗∗∗

(0.0

72)

1.17

8∗∗∗

(0.1

30)

1.25

6∗∗∗

(0.1

71)

0.43

3∗∗∗

(0.0

70)

0.33

6∗∗∗

(0.0

66)

1.22

0∗∗∗

(0.1

89)

0.76

3∗∗∗

(0.0

94)

AU

SX

Imm

igra

nt0.

556∗∗∗

(0.0

87)

0.72

3∗∗∗

(0.0

85)

1.01

5∗∗∗

(0.1

53)

1.30

3∗∗∗

(0.2

02)

0.43

1∗∗∗

(0.0

82)

0.39

8∗∗∗

(0.0

78)

1.51

5∗∗∗

(0.2

23)

0.63

4∗∗∗

(0.1

11)

TUR

XIm

mig

rant

0.54

5∗∗∗

(0.0

75)

0.67

8∗∗∗

(0.0

73)

1.19

6∗∗∗

(0.1

31)

1.38

4∗∗∗

(0.1

73)

0.32

6∗∗∗

(0.0

71)

0.38

2∗∗∗

(0.0

67)

1.36

0∗∗∗

(0.1

92)

0.87

0∗∗∗

(0.0

95)

IND

XIm

mig

rant

0.54

2∗∗∗

(0.0

74)

0.60

3∗∗∗

(0.0

73)

1.31

1∗∗∗

(0.1

31)

1.37

4∗∗∗

(0.1

73)

0.38

6∗∗∗

(0.0

70)

0.27

8∗∗∗

(0.0

67)

1.49

5∗∗∗

(0.1

91)

0.66

6∗∗∗

(0.0

95)

KO

RX

Imm

igra

nt0.

529∗∗∗

(0.0

74)

0.56

3∗∗∗

(0.0

73)

1.21

6∗∗∗

(0.1

31)

1.05

0∗∗∗

(0.1

73)

0.31

0∗∗∗

(0.0

70)

0.28

9∗∗∗

(0.0

67)

1.37

5∗∗∗

(0.1

91)

0.63

8∗∗∗

(0.0

95)

ITA

XIm

mig

rant

0.51

7∗∗∗

(0.0

76)

0.55

7∗∗∗

(0.0

75)

1.25

2∗∗∗

(0.1

34)

1.14

3∗∗∗

(0.1

77)

0.28

5∗∗∗

(0.0

72)

0.31

9∗∗∗

(0.0

68)

1.37

5∗∗∗

(0.1

95)

0.52

3∗∗∗

(0.0

97)

RU

SX

Imm

igra

nt0.

503∗∗∗

(0.0

73)

0.59

1∗∗∗

(0.0

71)

0.54

4∗∗∗

(0.1

28)

0.51

6∗∗∗

(0.1

69)

0.34

1∗∗∗

(0.0

69)

0.29

1∗∗∗

(0.0

65)

1.49

1∗∗∗

(0.1

86)

0.56

4∗∗∗

(0.0

93)

ESP

XIm

mig

rant

0.49

5∗∗∗

(0.0

78)

0.62

2∗∗∗

(0.0

76)

0.80

4∗∗∗

(0.1

38)

0.92

3∗∗∗

(0.1

82)

0.33

5∗∗∗

(0.0

74)

0.33

0∗∗∗

(0.0

70)

1.24

9∗∗∗

(0.2

00)

0.51

3∗∗∗

(0.1

00)

CA

NX

Imm

igra

nt0.

491∗∗∗

(0.0

85)

0.58

2∗∗∗

(0.0

83)

1.03

8∗∗∗

(0.1

49)

0.55

5∗∗∗

(0.1

97)

0.44

9∗∗∗

(0.0

80)

0.29

1∗∗∗

(0.0

76)

1.47

1∗∗∗

(0.2

17)

0.63

4∗∗∗

(0.1

08)

FIN

XIm

mig

rant

0.44

4∗∗∗

(0.0

76)

0.53

5∗∗∗

(0.0

74)

0.70

4∗∗∗

(0.1

34)

0.57

4∗∗∗

(0.1

77)

0.34

0∗∗∗

(0.0

72)

0.35

4∗∗∗

(0.0

68)

1.15

1∗∗∗

(0.1

95)

0.33

3∗∗∗

(0.0

97)

IRL

XIm

mig

rant

0.43

7∗∗∗

(0.0

82)

0.33

5∗∗∗

(0.0

81)

0.69

3∗∗∗

(0.1

45)

0.60

0∗∗∗

(0.1

92)

0.24

5∗∗∗

(0.0

78)

0.37

5∗∗∗

(0.0

74)

0.88

1∗∗∗

(0.2

12)

0.60

6∗∗∗

(0.1

05)

FRA

XIm

mig

rant

0.42

5∗∗∗

(0.0

77)

0.53

6∗∗∗

(0.0

75)

0.83

7∗∗∗

(0.1

35)

0.44

7∗∗

(0.1

78)

0.23

8∗∗∗

(0.0

72)

0.26

8∗∗∗

(0.0

69)

1.25

8∗∗∗

(0.1

96)

0.39

0∗∗∗

(0.0

98)

RO

MX

Imm

igra

nt0.

417∗∗∗

(0.0

74)

0.59

2∗∗∗

(0.0

72)

0.83

8∗∗∗

(0.1

30)

0.44

8∗∗∗

(0.1

71)

0.28

3∗∗∗

(0.0

70)

0.23

3∗∗∗

(0.0

66)

1.18

8∗∗∗

(0.1

89)

0.30

7∗∗∗

(0.0

94)

GR

CX

Imm

igra

nt0.

393∗∗∗

(0.0

75)

0.52

0∗∗∗

(0.0

73)

1.05

0∗∗∗

(0.1

32)

1.10

7∗∗∗

(0.1

74)

0.14

6∗∗

(0.0

71)

0.24

5∗∗∗

(0.0

67)

1.30

2∗∗∗

(0.1

92)

0.28

2∗∗∗

(0.0

96)

BGR

XIm

mig

rant

0.38

9∗∗∗

(0.0

74)

0.36

7∗∗∗

(0.0

72)

1.33

5∗∗∗

(0.1

30)

1.16

4∗∗∗

(0.1

72)

0.24

0∗∗∗

(0.0

70)

0.11

1∗(0

.067

)1.

316∗∗∗

(0.1

90)

0.27

3∗∗∗

(0.0

95)

PRT

XIm

mig

rant

0.36

6∗∗∗

(0.0

75)

0.42

0∗∗∗

(0.0

74)

1.13

8∗∗∗

(0.1

33)

1.04

4∗∗∗

(0.1

75)

0.35

4∗∗∗

(0.0

71)

0.15

3∗∗

(0.0

68)

1.25

0∗∗∗

(0.1

93)

0.36

9∗∗∗

(0.0

96)

SWE

XIm

mig

rant

0.34

1∗∗∗

(0.0

79)

0.45

2∗∗∗

(0.0

77)

0.55

0∗∗∗

(0.1

39)

0.00

0(.)

0.35

2∗∗∗

(0.0

74)

0.31

6∗∗∗

(0.0

71)

0.50

6∗∗

(0.2

02)

0.23

0∗∗

(0.1

01)

POL

XIm

mig

rant

0.34

0∗∗∗

(0.0

74)

0.56

2∗∗∗

(0.0

73)

0.67

1∗∗∗

(0.1

30)

0.75

3∗∗∗

(0.1

72)

0.32

2∗∗∗

(0.0

70)

0.17

7∗∗∗

(0.0

67)

1.30

4∗∗∗

(0.1

90)

0.34

9∗∗∗

(0.0

95)

MEX

XIm

mig

rant

0.33

4∗∗∗

(0.0

74)

0.61

2∗∗∗

(0.0

72)

1.06

3∗∗∗

(0.1

30)

1.31

7∗∗∗

(0.1

71)

0.25

7∗∗∗

(0.0

70)

0.14

5∗∗

(0.0

66)

1.10

3∗∗∗

(0.1

89)

0.50

1∗∗∗

(0.0

94)

TWN

XIm

mig

rant

0.33

3∗∗∗

(0.0

75)

0.52

8∗∗∗

(0.0

74)

1.11

1∗∗∗

(0.1

32)

0.40

4∗∗

(0.1

75)

-0.0

95(0

.071

)0.

106

(0.0

68)

1.31

1∗∗∗

(0.1

93)

0.55

3∗∗∗

(0.0

96)

LTU

XIm

mig

rant

0.32

7∗∗∗

(0.0

76)

0.43

0∗∗∗

(0.0

75)

0.95

2∗∗∗

(0.1

35)

1.24

7∗∗∗

(0.1

78)

0.17

5∗∗

(0.0

72)

0.09

6(0

.069

)0.

993∗∗∗

(0.1

96)

0.62

6∗∗∗

(0.0

98)

DEU

XIm

mig

rant

0.30

4∗∗∗

(0.0

80)

0.42

0∗∗∗

(0.0

78)

0.48

3∗∗∗

(0.1

41)

0.44

0∗∗

(0.1

86)

0.18

9∗∗

(0.0

76)

0.10

2(0

.072

)1.

247∗∗∗

(0.2

05)

0.09

5(0

.102

)C

ZE

XIm

mig

rant

0.25

9∗∗∗

(0.0

75)

0.45

4∗∗∗

(0.0

73)

0.75

6∗∗∗

(0.1

32)

0.48

2∗∗∗

(0.1

74)

0.30

8∗∗∗

(0.0

71)

0.05

9(0

.067

)1.

083∗∗∗

(0.1

92)

0.15

3(0

.096

)G

BRX

Imm

igra

nt0.

254∗∗∗

(0.0

78)

0.37

5∗∗∗

(0.0

76)

0.70

1∗∗∗

(0.1

37)

0.49

8∗∗∗

(0.1

81)

0.19

5∗∗∗

(0.0

74)

0.27

9∗∗∗

(0.0

70)

0.92

4∗∗∗

(0.2

00)

0.08

6(0

.100

)A

UT

XIm

mig

rant

0.25

2∗∗∗

(0.0

81)

0.42

7∗∗∗

(0.0

79)

0.66

4∗∗∗

(0.1

42)

0.52

9∗∗∗

(0.1

88)

0.26

1∗∗∗

(0.0

76)

0.13

6∗(0

.073

)0.

793∗∗∗

(0.2

07)

0.19

3∗(0

.103

)C

YP

XIm

mig

rant

0.24

0∗∗∗

(0.0

83)

0.39

8∗∗∗

(0.0

82)

0.61

3∗∗∗

(0.1

47)

0.66

4∗∗∗

(0.1

94)

0.29

5∗∗∗

(0.0

79)

0.19

6∗∗∗

(0.0

75)

0.00

0(.)

-0.0

23(0

.107

)LV

AX

Imm

igra

nt0.

207∗∗

(0.0

82)

0.32

4∗∗∗

(0.0

80)

0.86

1∗∗∗

(0.1

45)

0.72

2∗∗∗

(0.1

91)

0.39

3∗∗∗

(0.0

78)

0.10

5(0

.074

)0.

000

(.)0.

028

(0.1

05)

HU

NX

Imm

igra

nt0.

191∗∗

(0.0

76)

0.44

9∗∗∗

(0.0

74)

0.77

3∗∗∗

(0.1

34)

0.65

8∗∗∗

(0.1

77)

0.08

4(0

.072

)0.

123∗

(0.0

68)

1.18

0∗∗∗

(0.1

95)

0.15

7(0

.097

)SV

KX

Imm

igra

nt0.

181∗∗

(0.0

75)

0.30

7∗∗∗

(0.0

74)

0.56

4∗∗∗

(0.1

33)

0.50

4∗∗∗

(0.1

75)

0.26

3∗∗∗

(0.0

71)

0.01

0(0

.068

)1.

211∗∗∗

(0.1

94)

0.12

4(0

.096

)SV

NX

Imm

igra

nt0.

159∗∗

(0.0

74)

0.31

8∗∗∗

(0.0

73)

0.91

9∗∗∗

(0.1

31)

0.79

9∗∗∗

(0.1

73)

-0.0

67(0

.070

)0.

142∗∗

(0.0

67)

0.47

0∗∗

(0.1

91)

0.09

2(0

.095

)ES

TX

Imm

igra

nt0.

127

(0.0

85)

0.33

0∗∗∗

(0.0

83)

0.62

1∗∗∗

(0.1

49)

0.56

6∗∗∗

(0.1

97)

0.17

8∗∗

(0.0

80)

0.04

6(0

.076

)0.

539∗∗

(0.2

17)

0.01

6(0

.108

)D

NK

XIm

mig

rant

0.11

6(0

.077

)0.

195∗∗∗

(0.0

75)

0.51

3∗∗∗

(0.1

36)

0.47

8∗∗∗

(0.1

79)

-0.0

63(0

.073

)0.

156∗∗

(0.0

69)

0.72

2∗∗∗

(0.1

98)

0.19

0∗(0

.098

)N

LDX

Imm

igra

nt0.

107

(0.0

78)

0.22

7∗∗∗

(0.0

76)

0.55

1∗∗∗

(0.1

38)

0.54

1∗∗∗

(0.1

82)

-0.1

07(0

.074

)0.

115

(0.0

70)

0.72

5∗∗∗

(0.2

00)

0.09

5(0

.100

)BE

LX

Imm

igra

nt0.

049

(0.0

80)

0.13

9∗(0

.078

)0.

674∗∗∗

(0.1

41)

0.59

6∗∗∗

(0.1

86)

-0.1

57∗∗

(0.0

76)

0.00

5(0

.072

)0.

936∗∗∗

(0.2

05)

0.04

1(0

.102

)M

LTX

Imm

igra

nt-0

.013

(0.0

79)

0.05

8(0

.077

)0.

742∗∗∗

(0.1

39)

0.50

1∗∗∗

(0.1

83)

-0.2

41∗∗∗

(0.0

74)

-0.0

31(0

.071

)0.

425∗∗

(0.2

02)

0.00

9(0

.101

)LU

XX

Imm

igra

nt-0

.156

(0.1

04)

-0.3

80∗∗∗

(0.1

02)

1.09

5∗∗∗

(0.1

84)

0.66

5∗∗∗

(0.2

42)

-0.2

90∗∗∗

(0.0

99)

-0.6

23∗∗∗

(0.0

94)

0.58

8∗∗

(0.2

68)

-0.0

32(0

.133

)O

bser

vati

ons

1600

1600

1600

1560

1600

1600

1520

1600

Adj

uste

dR

20.

973

0.98

40.

899

0.84

20.

986

0.98

60.

882

0.94

4M

ean

0.35

20.

454

0.89

30.

783

0.22

20.

196

1.06

90.

375

Std

Not

es:

Tabl

ere

port

sth

ees

tim

ates

ofho

me-

bias

edgr

avit

yeq

uati

onw

ith

indu

stri

altr

ade

flow

data

.R

obus

tsta

ndar

der

rors

inpa

rent

hese

s.Si

gnifi

canc

e*

.10,

**.0

5,**

*.0

1.

51

Tabl

eA

.2:H

ome-

Bias

edG

ravi

tyEs

tim

ates

:by

Indu

stry

II

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

reg0

reg1

0re

g11

reg1

2re

g13

reg1

4re

g15

reg1

6D

ista

nce

-0.0

09∗∗∗

(0.0

01)

-0.0

11∗∗∗

(0.0

01)

-0.0

08∗∗∗

(0.0

01)

-0.0

10∗∗∗

(0.0

01)

-0.0

08∗∗∗

(0.0

02)

-0.0

11∗∗∗

(0.0

02)

-0.0

04∗∗

(0.0

02)

-0.0

10∗∗∗

(0.0

02)

Bord

er0.

023∗∗∗

(0.0

02)

0.02

9∗∗∗

(0.0

03)

0.01

7∗∗∗

(0.0

02)

0.02

6∗∗∗

(0.0

03)

0.02

4∗∗∗

(0.0

03)

0.01

3∗∗∗

(0.0

03)

0.02

2∗∗∗

(0.0

04)

0.02

2∗∗∗

(0.0

03)

Lang

uage

0.01

4∗∗∗

(0.0

02)

0.01

4∗∗∗

(0.0

03)

0.00

6∗∗∗

(0.0

02)

0.01

2∗∗∗

(0.0

03)

0.01

4∗∗∗

(0.0

03)

0.01

2∗∗∗

(0.0

03)

0.02

0∗∗∗

(0.0

04)

0.02

0∗∗∗

(0.0

03)

Tari

ff-0

.008∗∗∗

(0.0

01)

-0.0

11∗∗∗

(0.0

01)

-0.0

02∗∗

(0.0

01)

-0.0

08∗∗∗

(0.0

01)

-0.0

15∗∗∗

(0.0

01)

-0.0

10∗∗∗

(0.0

01)

-0.0

16∗∗∗

(0.0

02)

-0.0

04∗∗

(0.0

02)

Inco

me

0.00

6∗∗∗

(0.0

01)

0.00

2∗(0

.001

)0.

001∗

(0.0

01)

0.00

0(0

.001

)0.

001

(0.0

01)

0.00

1(0

.001

)0.

003∗∗∗

(0.0

01)

0.00

3∗∗

(0.0

02)

Inte

rnal

0.14

1∗∗

(0.0

71)

0.36

8∗∗∗

(0.0

97)

0.72

8∗∗∗

(0.0

58)

0.21

7∗∗

(0.0

85)

-0.1

08(0

.106

)0.

054

(0.1

09)

-0.1

78(0

.123

)0.

115

(0.1

14)

JPN

XIm

mig

rant

0.67

0∗∗∗

(0.0

75)

0.43

7∗∗∗

(0.1

02)

0.16

7∗∗∗

(0.0

61)

0.61

9∗∗∗

(0.0

89)

0.88

5∗∗∗

(0.1

12)

0.60

4∗∗∗

(0.1

15)

1.00

6∗∗∗

(0.1

29)

0.58

7∗∗∗

(0.1

20)

CH

NX

Imm

igra

nt0.

657∗∗∗

(0.0

73)

0.44

5∗∗∗

(0.1

00)

0.21

2∗∗∗

(0.0

60)

0.63

4∗∗∗

(0.0

88)

0.80

7∗∗∗

(0.1

10)

0.50

8∗∗∗

(0.1

13)

0.93

6∗∗∗

(0.1

27)

0.56

0∗∗∗

(0.1

19)

BRA

XIm

mig

rant

0.58

6∗∗∗

(0.0

74)

0.32

5∗∗∗

(0.1

01)

0.17

5∗∗∗

(0.0

61)

0.51

4∗∗∗

(0.0

88)

0.62

1∗∗∗

(0.1

11)

0.43

1∗∗∗

(0.1

14)

0.78

9∗∗∗

(0.1

28)

0.71

6∗∗∗

(0.1

19)

USA

XIm

mig

rant

0.58

3∗∗∗

(0.0

80)

0.37

3∗∗∗

(0.1

09)

0.07

5(0

.066

)0.

556∗∗∗

(0.0

95)

0.69

9∗∗∗

(0.1

20)

0.34

4∗∗∗

(0.1

23)

0.78

1∗∗∗

(0.1

38)

0.36

5∗∗∗

(0.1

29)

IDN

XIm

mig

rant

0.57

6∗∗∗

(0.0

74)

0.27

8∗∗∗

(0.1

01)

0.17

5∗∗∗

(0.0

61)

-0.1

17(0

.088

)0.

176

(0.1

10)

0.53

0∗∗∗

(0.1

13)

0.91

3∗∗∗

(0.1

27)

0.41

5∗∗∗

(0.1

19)

AU

SX

Imm

igra

nt0.

556∗∗∗

(0.0

87)

0.18

9(0

.119

)0.

182∗∗

(0.0

71)

0.62

1∗∗∗

(0.1

04)

0.52

5∗∗∗

(0.1

30)

0.07

2(0

.134

)0.

752∗∗∗

(0.1

50)

0.61

1∗∗∗

(0.1

40)

TUR

XIm

mig

rant

0.54

5∗∗∗

(0.0

75)

0.29

8∗∗∗

(0.1

02)

0.01

1(0

.062

)0.

360∗∗∗

(0.0

89)

0.73

5∗∗∗

(0.1

12)

0.47

8∗∗∗

(0.1

15)

0.24

3∗(0

.129

)0.

460∗∗∗

(0.1

20)

IND

XIm

mig

rant

0.54

2∗∗∗

(0.0

74)

0.31

7∗∗∗

(0.1

02)

0.11

7∗(0

.061

)0.

529∗∗∗

(0.0

89)

0.63

4∗∗∗

(0.1

11)

0.42

3∗∗∗

(0.1

15)

0.77

7∗∗∗

(0.1

29)

0.00

9(0

.120

)K

OR

XIm

mig

rant

0.52

9∗∗∗

(0.0

74)

0.30

9∗∗∗

(0.1

02)

0.05

8(0

.061

)0.

489∗∗∗

(0.0

89)

0.59

8∗∗∗

(0.1

12)

0.46

6∗∗∗

(0.1

15)

0.84

5∗∗∗

(0.1

29)

0.57

0∗∗∗

(0.1

20)

ITA

XIm

mig

rant

0.51

7∗∗∗

(0.0

76)

0.28

6∗∗∗

(0.1

04)

0.12

6∗∗

(0.0

63)

0.49

4∗∗∗

(0.0

91)

0.73

0∗∗∗

(0.1

14)

0.40

3∗∗∗

(0.1

17)

0.56

2∗∗∗

(0.1

32)

0.63

0∗∗∗

(0.1

23)

RU

SX

Imm

igra

nt0.

503∗∗∗

(0.0

73)

0.41

9∗∗∗

(0.0

99)

0.17

2∗∗∗

(0.0

60)

0.59

3∗∗∗

(0.0

87)

0.52

7∗∗∗

(0.1

09)

0.49

2∗∗∗

(0.1

12)

0.53

3∗∗∗

(0.1

25)

0.61

7∗∗∗

(0.1

17)

ESP

XIm

mig

rant

0.49

5∗∗∗

(0.0

78)

0.26

2∗∗

(0.1

07)

0.15

6∗∗

(0.0

64)

0.52

6∗∗∗

(0.0

93)

0.60

4∗∗∗

(0.1

17)

0.23

2∗(0

.120

)0.

552∗∗∗

(0.1

35)

0.60

5∗∗∗

(0.1

26)

CA

NX

Imm

igra

nt0.

491∗∗∗

(0.0

85)

0.25

3∗∗

(0.1

16)

-0.0

13(0

.070

)0.

472∗∗∗

(0.1

01)

0.52

5∗∗∗

(0.1

27)

0.06

9(0

.131

)0.

814∗∗∗

(0.1

47)

0.47

5∗∗∗

(0.1

37)

FIN

XIm

mig

rant

0.44

4∗∗∗

(0.0

76)

0.13

3(0

.104

)0.

066

(0.0

63)

0.36

0∗∗∗

(0.0

91)

0.73

2∗∗∗

(0.1

14)

0.45

1∗∗∗

(0.1

17)

0.27

9∗∗

(0.1

31)

0.42

8∗∗∗

(0.1

23)

IRL

XIm

mig

rant

0.43

7∗∗∗

(0.0

82)

-0.0

96(0

.113

)-0

.020

(0.0

68)

0.21

8∗∗

(0.0

99)

0.15

3(0

.124

)0.

650∗∗∗

(0.1

27)

0.31

8∗∗

(0.1

43)

-0.1

01(0

.133

)FR

AX

Imm

igra

nt0.

425∗∗∗

(0.0

77)

0.20

9∗∗

(0.1

05)

0.03

2(0

.063

)0.

474∗∗∗

(0.0

92)

0.61

5∗∗∗

(0.1

15)

0.21

8∗(0

.118

)0.

653∗∗∗

(0.1

32)

0.41

6∗∗∗

(0.1

24)

RO

MX

Imm

igra

nt0.

417∗∗∗

(0.0

74)

0.14

2(0

.101

)0.

110∗

(0.0

61)

0.35

2∗∗∗

(0.0

88)

0.32

9∗∗∗

(0.1

11)

0.03

5(0

.114

)0.

711∗∗∗

(0.1

28)

0.49

6∗∗∗

(0.1

19)

GR

CX

Imm

igra

nt0.

393∗∗∗

(0.0

75)

0.14

2(0

.102

)0.

065

(0.0

62)

0.45

5∗∗∗

(0.0

89)

0.22

9∗∗

(0.1

12)

0.19

5∗(0

.115

)0.

346∗∗∗

(0.1

29)

0.43

7∗∗∗

(0.1

21)

BGR

XIm

mig

rant

0.38

9∗∗∗

(0.0

74)

0.02

2(0

.101

)0.

026

(0.0

61)

0.47

2∗∗∗

(0.0

88)

0.35

5∗∗∗

(0.1

11)

0.11

6(0

.114

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173

(0.1

28)

0.46

4∗∗∗

(0.1

19)

PRT

XIm

mig

rant

0.36

6∗∗∗

(0.0

75)

0.07

9(0

.103

)0.

079

(0.0

62)

0.31

9∗∗∗

(0.0

90)

0.38

9∗∗∗

(0.1

13)

0.31

0∗∗∗

(0.1

16)

0.33

1∗∗

(0.1

30)

0.50

4∗∗∗

(0.1

22)

SWE

XIm

mig

rant

0.34

1∗∗∗

(0.0

79)

-0.1

40(0

.108

)-0

.031

(0.0

65)

0.32

8∗∗∗

(0.0

94)

0.52

5∗∗∗

(0.1

18)

0.13

9(0

.121

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795∗∗∗

(0.1

36)

0.19

1(0

.127

)PO

LX

Imm

igra

nt0.

340∗∗∗

(0.0

74)

0.10

7(0

.101

)0.

060

(0.0

61)

0.25

6∗∗∗

(0.0

88)

0.32

8∗∗∗

(0.1

11)

0.08

6(0

.114

)0.

352∗∗∗

(0.1

28)

0.41

9∗∗∗

(0.1

19)

MEX

XIm

mig

rant

0.33

4∗∗∗

(0.0

74)

-0.1

44(0

.101

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101∗

(0.0

61)

0.20

5∗∗

(0.0

88)

0.09

1(0

.111

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.014

(0.1

14)

0.42

8∗∗∗

(0.1

28)

0.65

5∗∗∗

(0.1

19)

TWN

XIm

mig

rant

0.33

3∗∗∗

(0.0

75)

-0.0

13(0

.103

)-0

.025

(0.0

62)

0.36

9∗∗∗

(0.0

90)

0.47

7∗∗∗

(0.1

13)

-0.0

38(0

.116

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638∗∗∗

(0.1

30)

-0.1

30(0

.121

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UX

Imm

igra

nt0.

327∗∗∗

(0.0

76)

-0.1

02(0

.104

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.168∗∗∗

(0.0

63)

0.00

9(0

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084

(0.1

14)

0.01

8(0

.118

)0.

482∗∗∗

(0.1

32)

0.55

0∗∗∗

(0.1

23)

DEU

XIm

mig

rant

0.30

4∗∗∗

(0.0

80)

0.01

1(0

.109

)-0

.047

(0.0

66)

0.32

5∗∗∗

(0.0

95)

0.62

0∗∗∗

(0.1

20)

0.13

9(0

.123

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588∗∗∗

(0.1

38)

0.29

2∗∗

(0.1

29)

CZ

EX

Imm

igra

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259∗∗∗

(0.0

75)

0.10

9(0

.102

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.033

(0.0

62)

0.22

9∗∗

(0.0

89)

0.41

4∗∗∗

(0.1

12)

0.09

8(0

.115

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655∗∗∗

(0.1

29)

0.31

6∗∗∗

(0.1

21)

GBR

XIm

mig

rant

0.25

4∗∗∗

(0.0

78)

0.18

8∗(0

.107

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012

(0.0

64)

0.29

4∗∗∗

(0.0

93)

0.50

9∗∗∗

(0.1

17)

-0.0

07(0

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414∗∗∗

(0.1

35)

0.29

0∗∗

(0.1

26)

AU

TX

Imm

igra

nt0.

252∗∗∗

(0.0

81)

-0.1

80(0

.110

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.017

(0.0

66)

0.31

6∗∗∗

(0.0

96)

0.47

7∗∗∗

(0.1

21)

0.11

9(0

.124

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171

(0.1

39)

0.39

1∗∗∗

(0.1

30)

CY

PX

Imm

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240∗∗∗

(0.0

83)

-0.0

49(0

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109

(0.0

69)

0.25

3∗∗

(0.1

00)

0.15

9(0

.125

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(0.1

29)

0.15

4(0

.144

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355∗∗∗

(0.1

35)

LVA

XIm

mig

rant

0.20

7∗∗

(0.0

82)

-0.1

96∗

(0.1

12)

-0.1

84∗∗∗

(0.0

68)

0.06

7(0

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061

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23)

-0.0

34(0

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125

(0.1

42)

0.27

3∗∗

(0.1

32)

HU

NX

Imm

igra

nt0.

191∗∗

(0.0

76)

-0.0

93(0

.104

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.156∗∗

(0.0

63)

0.06

8(0

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564∗∗∗

(0.1

14)

-0.0

29(0

.117

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366∗∗∗

(0.1

31)

0.27

2∗∗

(0.1

23)

SVK

XIm

mig

rant

0.18

1∗∗

(0.0

75)

0.02

4(0

.103

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.015

(0.0

62)

0.20

8∗∗

(0.0

90)

0.17

1(0

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097

(0.1

16)

0.45

0∗∗∗

(0.1

30)

0.09

5(0

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NX

Imm

igra

nt0.

159∗∗

(0.0

74)

0.03

7(0

.102

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.120∗

(0.0

61)

0.17

9∗∗

(0.0

89)

0.17

3(0

.112

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115

(0.1

15)

0.17

0(0

.129

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334∗∗∗

(0.1

20)

EST

XIm

mig

rant

0.12

7(0

.085

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.431∗∗∗

(0.1

16)

-0.2

92∗∗∗

(0.0

70)

0.11

7(0

.101

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113

(0.1

27)

-0.0

48(0

.130

)0.

252∗

(0.1

46)

0.14

7(0

.136

)D

NK

XIm

mig

rant

0.11

6(0

.077

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.236∗∗

(0.1

05)

-0.1

58∗∗

(0.0

63)

0.10

1(0

.092

)0.

225∗

(0.1

15)

0.17

7(0

.119

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123

(0.1

33)

0.00

4(0

.124

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LDX

Imm

igra

nt0.

107

(0.0

78)

-0.4

18∗∗∗

(0.1

07)

-0.1

78∗∗∗

(0.0

64)

0.09

0(0

.093

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201∗

(0.1

17)

0.12

7(0

.120

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160

(0.1

35)

0.23

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LX

Imm

igra

nt0.

049

(0.0

80)

-0.3

97∗∗∗

(0.1

09)

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76∗∗∗

(0.0

66)

0.06

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124

(0.1

20)

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51(0

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168

(0.1

38)

-0.0

22(0

.129

)M

LTX

Imm

igra

nt-0

.013

(0.0

79)

-0.3

64∗∗∗

(0.1

08)

-0.2

62∗∗∗

(0.0

65)

0.05

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.094

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034

(0.1

18)

0.00

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151

(0.1

36)

0.24

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nt-0

.156

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04)

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24∗∗∗

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(0.0

86)

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25(0

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094

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56)

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64(0

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1600

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Adj

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Std

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52