William R. Kerr* Files...While informative, citation patterns do not quantify the extent to which following countries realize economic ben-eﬁts from better access to U.S. innovations

ETHNIC SCIENTIFIC COMMUNITIES AND INTERNATIONALTECHNOLOGY DIFFUSION

William R. Kerr*

Abstract—This study explores the role of U.S. ethnic scientific andentrepreneurial communities for international technology transfer to theirhome countries. U.S. ethnic researchers are quantified through an ethnic-name database and individual patent records. International patent citationsconfirm knowledge diffuses through ethnic networks, and manufacturingoutput in foreign countries increases with an elasticity of 0.1–0.3 tostronger scientific integration with the U.S. frontier. Specifications ex-ploiting exogenous changes in U.S. immigration quotas address reverse-causality concerns. Exercises further differentiate responses by develop-ment stages in home countries. Ethnic technology transfers areparticularly strong in high-tech industries and among Chinese economies.

I. Introduction

THE adoption of new technologies and innovations is aprimary engine for economic growth, improving

worker productivity and spurring higher standards of living.Invention, however, is concentrated in advanced economies.OECD countries account for 83% of the world’s R&Dexpenditure and 98% of its patenting (OECD, 2004). Evenwithin the OECD, a disproportionate share of R&D isundertaken in the United States. Diffusion of new innova-tions from technologically leading nations to followingeconomies is thus necessary for the economic developmentof poorer regions and the achievement of global prosperity.

Economic models often describe a worldwide technologyfrontier, where new ideas and innovations travel quickly toall countries.1 Rapid diffusion may be a good approximationfor industrialized economies, but many advances are eithernot available or not adopted in poorer countries. Casestudies in the business sociology and economic historyliteratures suggest this poor adoption may result from inad-equate access to the informal or practical knowledge thatcomplements the codified details of new innovations. Be it

between two people or two countries, knowledge transfer ismuch more complicated than sharing blueprints, processdesigns, or journal articles. Intellectual spillovers are oftenthought to be important for the formation of cities andhigh-tech clusters, and perhaps heterogeneous access to thecodified and tacit knowledge associated with new innova-tions shapes the effective technology sets of followingcountries.2

Recent research stresses the importance of ethnic scien-tific communities in frontier countries for conveying newtechnologies to their home countries. In surveys of SiliconValley, 82% of Chinese and Indian immigrant scientists andengineers report exchanging technical information withtheir respective nations; 18% further invest in businesspartnerships (Saxenian, 2002a, 2002b). Studies of softwareoffshoring suggest 30% of India’s systems workforce ro-tates through the United States to obtain the tacit knowledgenecessary for their work (Piore, 2004). Moreover, someobservers believe the success of India versus Mexico andother countries in this field derives in part from India’sstrong U.S. entrepreneurial community. More generally,explorations of knowledge diffusion find countries with acommon language have larger R&D spillovers and interna-tional patent citation rates (for example, Keller, 2002b; Jaffe& Trajtenberg, 1999).

Ethnicity thus offers an observable channel for exploringthe extent to which international networks transmit thecodified and tacit knowledge of new inventions. This studyexamines whether a larger ethnic research community in theUnited States improves technology diffusion to foreigncountries of the same ethnicity. U.S. ethnic research com-munities are quantified by applying an ethnic-name data-base to individual U.S. patent records (for example, identi-fies inventors with Chinese versus Hispanic names). Thesematched records describe the ethnic composition of U.S.scientists and engineers with previously unavailable detail.These trends are joined with industry-level manufacturingdata for foreign countries (for example, Chinese computerresearch in the United States is paired with China’s com-puter industry) in an econometric framework that isolatesthe role of scientific integration by exploiting within-industry variation.

Received for publication August 12, 2006. Revision accepted for pub-lication March 8, 2007.

* Harvard Business School and MIT.Comments are appreciated and can be sent to [email protected]. I am

especially grateful to Daron Acemoglu for advice on this project; to AjayAgrawal, George-Marios Angeletos, Josh Angrist, Pol Antras, DavidAutor, Nick Bloom, Ricardo Caballero, Richard Freeman, Xavier Gabaix,Francesco Giavazzi, Michael Greenstone, Tarun Khanna, Josh Lerner,Ashley Lester, Guido Lorenzoni, Byron Lutz, Megan MacGarvie, RamanaNanda, Philip Oreopoulos, Sari Pekkala, Michael Piore, Julio Rotemberg,John Van Reenen, and Ivan Werning, and four referees for insightfulthoughts; to seminar participants at Boston University, Clemson, FederalReserve Board of Governors, Goldman Sachs, Harvard, LSE, MIT, andthe NBER for comments; and to Dan Johnson and Brian Silverman fordata assistance. This research is supported by the National ScienceFoundation, the MIT George Schultz Fund, and HBS Research. This paperwas first circulated in August 2004; a previous version was released asHBS working paper 06-022.

1 For example, Mankiw, Romer, and Weil (1992) and Heckscher-Ohlintrade theory. Recent descriptions of multiple technology frontiers build ongeographic distances to major R&D nations (for example, Keller, 2002b),the innovations of trading partners (such as, Grossman & Helpman, 1991;Coe & Helpman, 1995; Coe, Helpman, & Hoffmaister, 1997), or interna-tional patenting decisions (for example, Eaton & Kortum, 1999). Keller(2004) reviews the technology transfer literature.

2 Marshall (1890) and Jacobs (1970) describe the forces contributing tospatial agglomeration, while Rosenthal and Strange (2003) and Ellison,Glaeser, and Kerr (2007) provide more recent empirical tests. Othercountry-specific differences that inhibit adoption include barriers to tech-nological investment, capital-labor or human capital disparities, differ-ences in the organization of production, and the appropriateness oftechnology. Representative papers in this literature are Parente and Pres-cott (1994), Atkinson and Stiglitz (1969), Nelson and Phelps (1966),Banerjee and Newman (1993), and Acemoglu and Zilibotti (2001), re-spectively.

The Review of Economics and Statistics, August 2008, 90(3): 518–537© 2008 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology

To clarify this empirical methodology, the next sectiondevelops a theoretical model where a technology followerdepends on the imitation of frontier innovations for techni-cal progress in its manufacturing sector. In order to imitatethese frontier technologies, however, scientists in the fol-lowing country require codified and tacit knowledge withrespect to the frontier inventions. This knowledge is ac-quired and transferred through the scientists of the follow-ing country’s ethnicity who work in the frontier economy.The model thereby relates the technology follower’s man-ufacturing output and productivity growth to its scientificintegration with the leader. The primary estimating equa-tions employed in this study are determined within thisframework.

Section III then describes the ethnic patenting data setconstructed, and a first characterization of ethnicity’s role ininternational knowledge transfer is undertaken through ci-tation patterns. Foreign researchers are found to cite U.S.researchers of their own ethnicity 30%–50% more fre-quently than researchers of other ethnicities, even aftercontrolling for detailed technology classes. A further exam-ination divides the sample into different time lags from thefiling dates of the cited U.S. patents to the dates of the citingforeign patents. This analysis reveals that the own-ethnicityeffect is most important during the first five years of thediffusion process. After peaking in the fifth year, the higherethnic citation rates decline to the tenth year.

While informative, citation patterns do not quantify theextent to which following countries realize economic ben-efits from better access to U.S. innovations. To characterizeforeign output and productivity realizations, the U.S. ethnicpatenting data are combined with industry-level manufac-turing data for foreign countries in section IV. Ethnic re-search communities are quantified at the industry-year levelby aggregating individual patent records. Panel estimationsthen test whether output increases in foreign countries astheir respective ethnic research communities in the UnitedStates develop. The specifications only exploit within-industry variation. The results suggest growth in U.S. ethnicscientific communities increases foreign output with elas-ticities of 0.1–0.3 depending upon how the data areweighted. These parameter estimates are economically andstatistically significant, and the output expansion is decom-posed into employment and labor productivity gains.

The inclusion of multiple countries and industries affordsa more structured characterization of ethnicity’s role fortechnology diffusion and economic growth than case-basedor survey-based research. This platform also allows us totest the robustness of the results to other explanatory factorsand to assess the extent to which the well-documentedtransfers of Asian high-tech enclaves in Silicon Valleygeneralize to other settings. The measured elasticities aremoderately robust to further incorporating human capitaland physical capital developments abroad, general countrytrends, and so on. Performance in these tests is weakened by

the less variation in growth of U.S. research communitiesthat exists across industries within an ethnicity than acrossethnicities. Sample decompositions further find that theoutcomes are especially strong in high-tech sectors andwithin the Chinese ethnicity. While measurable growtheffects are present in the broader sample, they are substan-tially weaker than the showcase examples often discussed.

Reverse causality is a prominent concern in these types ofspecifications, where human capital developments in theforeign country could simultaneously result in higher outputgrowth and more ethnic researchers emigrating to theUnited States. Section V returns to the theoretical model tohighlight how immigration quotas offer a foothold foraddressing these issues. The resulting reduced-form strategyis applied in the context of the Immigration Act of 1990, amajor revision of the U.S. quotas system, that led to a surgein the immigration of scientists and engineers from previ-ously constrained countries. The immigration quotas exer-cise suggests that growth in U.S. ethnic research communi-ties increases foreign output with elasticities of 0.3–0.4.While the immigration experiment cannot resolve omittedvariable biases, the qualitative findings of this exercisesupport the results found with the ethnic patenting approach.

Finally, the diverse set of countries studied affords addi-tional insights regarding how the benefits accruing to tech-nology followers differ by development stage. An extensionto the theoretical model allows sector reallocation fromagriculture to manufacturing. After a transition point to fullemployment in the manufacturing sector, greater technologytransfer raises labor productivity and output levels withconstant employment. This is the steady-state descriptiondeveloped in section II. Prior to this transition, however, thefollowing country responds with growth in manufacturingemployment as well as labor productivity gains. Consistentwith these predictions, interactions with development stageshow labor productivity growth is mostly concentrated ineconomies that have transitioned to full manufacturing em-ployment (for example, the Asian tiger economies); coun-tries with large agricultural sectors instead increase industryoutput through higher employment levels (for example,Mainland China, India).

The results of this project suggest poor access to thecodified and tacit knowledge regarding new innovationsdoes contribute to slow technology diffusion. Ethnic scien-tific and entrepreneurial channels are important for thetransfer of this practical or informal information, and thusdifferences in ethnic research communities in frontier econ-omies are partly responsible for the heterogeneous technol-ogy opportunities of developing or emerging countries. Inaddition to characterizing technology diffusion, a betterunderstanding of these ethnic linkages provides an impor-tant contribution to the “brain drain” versus “brain circula-tion” debate. While a full cost-benefit analysis (such asKapur & McHale, 2005) is beyond this paper’s scope, the

ETHNIC SCIENTIFIC COMMUNITIES AND INTERNATIONAL TECHNOLOGY DIFFUSION 519

technology transfer results highlight a potential benefit fromhigh-skilled immigration to advanced countries.

II. Theoretical Framework

This section outlines a simple leader-follower model oftechnology transfer. Both economies feature a manufactur-ing sector characterized by an expanding-product-varietyproduction function where technological progress occursthrough the adoption of new intermediate products used inproduction of final goods. Entrepreneurial scientists livingin each country supply these new technologies for profit,and they can either invent the intermediate products them-selves or imitate foreign innovations. Spillovers from pastinnovations increase the research productivity of currentscientists for invention and generate endogenous growth.3

Knowledge is local, however, in that a country’s researcherproductivity for invention builds only on its own pastresearch. That is, the capabilities of the two nations toinvent evolve separately.

Researchers can alternatively imitate foreign inventionsfor use in their own country. Their effectiveness in doing so,however, depends upon their codified and tacit knowledgewith respect to the foreign country’s innovations. In prepa-ration for the empirical analysis, ethnicity is incorporatedinto the framework to model this knowledge network. Spe-cifically, the following country is of homogeneous ethnicity;the frontier country is primarily of another ethnicity but ishome to some researchers of the following country’s eth-nicity. These frontier expatriates acquire and transmit theknowledge necessary for effective imitation in the followingcountry.

Variables for the leader’s economy are denoted by a tilde(for example, Y), while the follower’s variables are in plainfont (for example, Y). Superscripts and subscripts furtherdistinguish ethnicity and sector as required. The first sectionoutlines the core elements of the follower’s economy, fol-lowed by differences in the leader’s economy. The steady-state outcome is then characterized.4

A. Follower’s Economy

The technology follower’s economy contains L workersof homogeneous ethnicity F employed in manufacturingand research. Its labor market is competitive, such thatworkers are free to move between the two sectors and arepaid their marginal products of labor in each. Denote theworkers employed in manufacturing and research by LM andLR, respectively. The behavior of the manufacturing sector isfirst described, followed by the research sector and consum-ers.

The competitive manufacturing sector produces finalgoods YM that can be consumed or used to make interme-diate manufacturing goods. The price of final goods isnormalized to 1. Production for a representative firm i thatemploys labor LMi

and nondurable intermediates Xij of typej takes the form

YMi � ALMi

1��j�1

N

�Xij��. (1)

� is the elasticity of output with respect to intermediateinputs (0 � � � 1), A is a common manufacturing produc-tivity parameter, and N is the number of intermediate prod-uct varieties currently available in the follower. In equilib-rium, firms employ equal amounts of all intermediate inputs(Xij � Xi @j) and equation (1) can be simplified to YMi

�ALMi

1��Xi�N � ALMi

1��(NXi)�N1��. Thus, the production func-tion exhibits constant returns to scale in labor and totalintermediate inputs NXi, but a larger number N of interme-diate goods increases output by distributing the total inter-mediate inputs over more goods and thereby raising themarginal product of each.

Technical progress takes the form of increases in N, eitherthrough inventions I or imitations M of the leader’s inven-tions (N � I � M). Entrepreneurial research firms choosebetween invention and imitation by comparing the produc-tivity of the two techniques. The research productivity forinvention in the follower is determined by the existing stockof the follower’s inventions, or �I/�t � I � LR. There are nointernational knowledge spillovers in the sense that re-searchers in the follower cannot build on the the leader’sstock of inventions directly in innovation. The follower’sresearchers can alternatively imitate the leader’s inventionsat a rate

�M

�t� � I�M

I � �HF�� LR, (2)

where I is the leader’s invention stock and HF is thefollower’s human capital stock with respect to the leader’sinventions. A larger stock of frontier inventions affords alarger pool of technologies that can be imitated, thus raisingthe imitation productivity for the follower’s researchers.The imitation of products exhausts the available pool, how-ever, and the function decreases with the ratio of imitatedproducts to the available frontier stock, � � 0. [1] � 0when all available products have been imitated, and [0] issufficiently large to ensure some imitation occurs withhuman capital for foreign technologies. The (HF) spec-ification models that tacit knowledge of frontier inven-tions is necessary for successfully adopting them in thefollower. This human capital stock depreciates at a rate �,and the population of follower’s ethnic researchers in theleader undertaking inventive activity adds to it: �HF/�t ��HF � LR

F.

3 For example, Romer (1990), Rivera-Batiz and Romer (1991), andBarro and Sala-i-Martin (1995).

4 Section IV discusses transitional dynamics to this steady state whenlabor reallocation from an agricultural sector is introduced. Technologyflows are the only interactions between the two countries. The modelabstracts from trade, and immigration is restricted in the base scenario.

THE REVIEW OF ECONOMICS AND STATISTICS520

Regardless of how new products are acquired, the entre-preneurial research firms gain perpetual monopoly rightsover the production and sale of new intermediate goods inthe follower. The present discounted value of these rents fora good j at time t is V(t) � �

t (Pj � Cj) Xje�r�(s,t) � (s�t)ds,

where Pj is the selling price and Cj is the cost of producingthe intermediate good. The average interest rate betweentimes t and s is r� (s,t), which is constant in equilibrium. Cj �1 for research firms as one unit of YM is required to produceone unit of intermediate input.

Monopoly rights afford research firms the power to set Pj

in each period to maximize (Pj � 1)Xj. As price takers, themanufacturing firms equate the marginal product of anintermediate good, �YMi

/�Xij in equation (1), with its price Pj

for a demand of Xij � (A�/Pj)1/(1��)LMi. Substituting this

demand function into the research firm’s maximizationproblem, summing across final-goods producers, and takingthe derivative with respect to Pj yields the monopoly pricePj � ��1. Thus, research firms charge the same price (Pj �P) and face similar aggregate demands of X � A1/(1��)

�2/(1��)LM. The constant interest rate, price, and aggregatedemand relationships simplify the value of inventing orimitating a new technology V(t) to

V � �1 � �

�� A1/�1��2/�1��

1

rLM. (3)

Constant intermediate demand functions also simplify thefollower’s aggregate output,

YM � A1/�1��2�/�1��LMN. (4)

On the consumer side, households maximize a linearlifetime utility function U � �

0 c(t) � e��tdt, where � is the

rate of time preference. Consumers earn wage w and receivethe interest rate r on savings. In equilibrium, � � r.

B. Leader’s Economy

Before the equilibrium for the follower’s economy can bedetermined, the frontier economy must be described. Theleader’s economy is identical to the follower’s except in itsethnically heterogeneous labor force and in its invention ofnew intermediate goods. Workers of both the leader’s andfollower’s ethnicity live in the leader. Workers of the lead-er’s ethnicity move between the manufacturing and researchsectors, but the follower’s expatriates work only in theresearch sector (LM � LM

L , LR � LRL � LR

F). The follower’sethnic population in the leader is small enough to ensuresome scientists of the leader’s ethnicity are always required.The aggregate populations of the two countries are equal(L � L).

Researchers of both ethnicities contribute to and utilizethe existing frontier invention stock I in developing newintermediate products: �IF/�t � I � LR

F and �IL /�t � I � LRL,

where I � IF � IL. This research specification again high-

lights the role of past inventions I in making current re-searchers more productive, and assumes inventions made inthe follower do not contribute to the leader’s researcherproductivity for invention. More subtly, ethnicity does notmatter for invention in the leader—both types of scientistsare symmetric with respect to the frontier invention stock.Finally, frontier researchers of the follower’s ethnicity canimitate products made in the follower with a productivityanalogous to equation (2).5

C. Steady-State Description: Leader Invents, FollowerImitates

This case determines the core estimating equation for thisstudy. Without invention in the follower, the frontier econ-omy operates in isolation, and imitation does not occur (N �I). The leader’s research sector is competitive with respectto labor markets, and scientists earn the marginal product oftheir innovative efforts. Denote by V the present discountedvalue of making a new invention in the leader. As research-ers invent I new products each period (that is, (�I/�t)/LR �I), the wage paid to scientists is V � I. Likewise, wages in themanufacturing sector are equal to the marginal product oflabor (1 � �)YM/LM. Labor mobility between sectors re-quires that these wages be equal, V � I � (1 � �)YM/LM.Substituting into this free-entry condition the leader’s ver-sions of the value of innovations (3) and aggregate output(4), and noting r � �, the steady-state allocation of laborin the frontier economy is found to be LM � �/� and LR �L � �/�. Thus, the growth rate of both the stock offrontier intermediate technologies and manufacturingoutput is L � �/�.

Returning to the follower’s economy, all intermediateproducts come through imitation of the leader (N � M).Labor mobility again requires wage equality for the fol-lower, V � (I[M/I](HF)) � (1 � �)YM/LM. Substituting inthe value of new intermediates V from equation (3) andaggregate output YM from equation (4),

r �I

M�M

I � �HF��LM. (5)

With identical preferences and aggregate populations, thefollower’s interest rate and allocations of labor to manufac-turing and research are the same as the leader.6 Equation (5)further shows the steady-state ratio of the follower’s imi-tated products to available frontier products M/I is constantand increases with the follower’s human capital stock withrespect to the leader’s technologies (� � 0). Stronger

5 The potential crowding out of U.S. workers and students from scienceand engineering fields by immigrants is often debated (for example,Borjas, 2005; Freeman, 2005). This model incorporates a crowding-outeffect for analytical convenience only.

6 These conditions hold for more general utility functions. As Barro andSala-i-Martin (1995) note, technological diffusion can equalize rates ofreturn without other interactions between economies.


knowledge transfer improves researcher productivity forimitation in the follower and closes the steady-state gap tothe frontier.

Simplifying equation (5) for economies of equal sizerelates the follower’s imitated technology stock to the tech-nology frontier and the follower’s knowledge for frontierinnovations, M � I[M/I](HF). Substituting this relation-ship into the follower’s manufacturing output (4), takinglogs, and collapsing time-invariant terms into a constant �,the follower’s manufacturing output depends upon its hu-man capital stock with respect to frontier research withelasticity : ln(YM) � � � ln(I) � ln(HF). The humancapital stock is ��1LR

F in steady state, so that

ln�YM� � � � ln�I� � ln�LRF�, (6)

where ��1 is absorbed into the constant. Equation (6) is thebasis for the estimating equations employed in sections IVand V. The statistical framework will return to the intricaciesof empirically estimating this relationship, but the outlook ispromising that the relationship will be directly identified ifthis scenario holds.

The follower’s imitation-versus-invention decision deter-mines the condition required for this steady-state descrip-tion. Specifically, the productivity of the follower’s re-searchers for invention must be less than the researcherproductivity for imitating frontier innovations in equilib-rium,

I � I�M

I � �HF�. (7)

The assumption I � 0 requires that equation (7) holdforever; without a knowledge stock on which to build, a firstinvention is impossible. While this may describe extremelypoor regions, the more interesting implication for develop-ing or emerging countries is that, even with a small inven-tion stock, the comparative benefit to imitation can besustained so long as access to the codified and tacit knowl-edge for a growing stock of frontier innovations is main-tained. Section V discusses the case where equation (7) nolonger holds.7

III. Ethnic Patenting and InternationalCitations Analysis

The above model is applied to technology transfer fromthe United States through ethnic networks. Estimation of the parameter requires quantifying each ethnicity’s humancapital stock with respect to U.S. research. This sectionoutlines the data set built for this exercise, and presents ananalysis of knowledge flows using international patent ci-tation records. The ethnic patenting data are then joined

with foreign output metrics in section IV to evaluate equa-tion (6) directly.

A. Ethnic Patenting Records

Ethnic technology development in the United States isquantified through the NBER Patent Data File originallycompiled by Hall, Jaffe, and Trajtenberg (2001). This dataset offers detailed records for all patents granted by the U.S.Patent and Trademark Office (USPTO) from January 1975to December 1999. Each patent record provides informationabout the invention (for example, technology classification,citations of prior art) and the inventors submitting theapplication (name, city, and so on). To estimate ethnicities,a commercial database of ethnic first names and surnames ismapped into the inventor records. Kerr (2007c) documentsthe name-matching algorithms, lists frequent ethnic names,and provides extensive descriptive statistics. The match rateis 98% for U.S. patent records, and the process affords thedistinction of nine ethnicities: Chinese, English, European,Hispanic, Indian, Japanese, Korean, Russian, and Vietnam-ese.

Table 1 describes the 1985–1997 U.S. sample, whilefigure 1 illustrates the evolving ethnic contribution to U.S.technology development as a percentage of patents grantedby the USPTO. The trends demonstrate a growing ethniccontribution to U.S. technological development, especiallyamong Chinese and Indian scientists. Ethnic inventors aremore concentrated in high-tech industries like computersand pharmaceuticals and in gateway cities relatively closerto their home countries (for example, Chinese in San Fran-cisco, European in New York, and Hispanic in Miami). Thefinal three rows demonstrate a close correspondence of theestimated ethnic composition to the country-of-birth com-position of the U.S. science and engineering workforce inthe 1990 Census.

B. International Patent Citation Analysis

The ethnic-name database is also applied to foreignpatent records registered in the United States. Inventionsoriginating outside the U.S. account for just under half ofUSPTO patents, with applications from Japan comprising45% of this foreign total. Kerr (2007c) presents the matchedcharacteristics for countries grouped to the ethnicities iden-tifiable with the database. From a quality-assurance perspec-tive, the results are very encouraging. First, the ethnic-namedatabase assigns ethnicities to 98% of foreign records.Second, the estimated inventor compositions are quite rea-sonable, with the own-ethnicity contributions in all but threeregions being greater than 80% (for example, 89% ofinventors filing from Chinese countries and regions areclassified as ethnically Chinese). Like the United States,own-ethnicity shares should be less than 100% because offoreign researchers.

In addition to serving as a quality-assurance check, pat-ents registered with the USPTO by foreign inventors afford

7 Immigration is restricted in this framework. Moreover, the follower’sworkers would prefer to emigrate to the leader as the frontier wage rate ishigher ceteris paribus because of the larger stock of intermediate goods.


an initial characterization of international knowledge flowsthrough ethnic scientific networks. Each patent record in-cludes citations of prior inventions on which the currentpatent builds, and the pattern of these citations can beinformative about communication channels between re-searchers.8 This first exercise simply compares the ethniccomposition of cited U.S. inventors across different foreigninventor ethnicities. That is, do Chinese inventors livingoutside of the United States tend to cite more Chineseinventors living in the United States than their technologyfield would suggest?

Inventor names are only included with patents grantedfrom 1975 to 1999, and the data are cut in two ways to forma uniform sample. First, only the citations of foreign patentapplications to the USPTO from 1985 to 1997 are consid-ered. Second, the application year of the cited U.S. patentmust be within ten years of the application date of the citingforeign patent. That is, citations of 1975–1984 U.S.domestic patents are considered for foreign patents ap-plied for in 1985, while 1976–1985 is the appropriateten-year window for 1986 patents. In addition, all within-company citations and patents with inventors in multiplecountries are excluded.9

From this sample, citation counts are developed by cellsthat contain four dimensions: (i) the ethnicity of the citingforeign inventor, (ii) the ethnicity of the cited U.S. inventor,(iii) the technology class of the citing foreign inventor, and(iv) the technology class of the cited U.S. inventor. Thelatter two dimensions are necessary for isolating ethnicity’srole since patents cite other patents within their technologyfield far more frequently than those outside of their field. Ifethnicities concentrate in different industries in the UnitedStates and abroad, measured ethnic flows could be merelycapturing that technologies build upon prior art in their owndiscipline.

Almost 100,000 cells are formed with this organiza-tion, and many cells contain zero values. The zero valuesare due to both the small sizes of some ethnicities (suchas, Vietnamese inventors outside of the United States)and that researchers in a given field simply do not cite theuniverse of technologies in their work. Count data con-taining zero values can be appropriately handled with anegative-binomial model. The counts are regressed on anindicator variable for whether the citing foreign ethnicityand cited U.S. ethnicity are the same, as well as vectorsof fixed effects for each of the four dimensions on whichcells are formed. These fixed effects remove basic levelsdifferences between the series (for example, English inthe United States receiving uniformly more citations,Vietnamese researchers abroad making uniformly fewerinventions and citations). An indicator variable is alsoincluded for whether the cited and citing technologycategories are the same.

The coefficient on the indicator variable for same-ethnicity is transformed into an incidence rate ratio thatgives the higher rate of citations within an ethnic group. The

8 Jaffe, Trajtenberg, and Fogarty (2000) and Duguet and MacGarvie(2005) discuss using patent citations to study knowledge transfer. Jaffe,Trajtenberg, and Henderson (1993), Peri (2005), Hu and Jaffe (2004),Agrawal, Cockburne, and McHale (2006), and MacGarvie (2006) areexamples of applications in an international distance context.

9 Patents may have multiple inventors with different ethnicities. Thereported regressions only consider citations for which a dominate ethnicitycan be assigned to both patents (that is, a single ethnicity accounts forstrictly more than 50% of multiple inventors). English-ethnicity inventorsabroad are excluded. These restrictions are required for the cells con-structed for the citations estimations and are not carried forward into theoutput and productivity analyses. The results are robust to alternativetechniques like Thompson (2006) below.

TABLE 1.—DESCRIPTIVE STATISTICS FOR INVENTORS RESIDING IN THE UNITED STATES

Ethnicity of Inventor (Percent Distribution)

English Chinese European Hispanic Indian Japanese Korean Russian Vietnamese

A. Ethnic Inventor Shares Estimated from U.S. Inventor Records

1985–1990 Share 79.7 3.7 7.3 3.3 2.9 0.8 0.7 1.5 0.21990–1997 Share 76.4 5.4 6.9 3.7 3.7 0.9 0.8 1.7 0.4Chemicals 74.4 6.5 7.5 3.6 4.3 0.9 0.9 1.6 0.3Computers 75.2 6.4 6.2 3.5 4.7 0.9 0.8 1.7 0.7Pharmaceuticals 75.5 5.2 7.5 4.1 3.8 1.1 1.0 1.6 0.3Electrical 75.0 6.3 7.0 3.6 3.7 1.0 0.9 1.9 0.5Mechanical 81.9 2.5 7.2 3.2 2.4 0.6 0.5 1.5 0.2Miscellaneous 82.6 2.4 7.0 3.5 2.0 0.5 0.5 1.3 0.2Top MSAs as a

percentage of MSAspatents

KC (89) SF (12) NYC (11) MIA (17) NYC (6) LA (2) BAL (3) BOS (3) AUS (2)WS (89) LA (7) NOR (11) SD (8) BUF (6) SD (2) COL (2) NYC (3) LA (1)

MEM (86) NYC (7) STL (11) WPB (6) AUS (6) SF (2) SF (2) PRO (3) SF (1)

B. Ethnic Scientist and Engineer Shares Estimated from 1990 U.S. Census Records

Bachelor’s share 87.6 2.7 2.3 2.4 2.3 0.6 0.5 0.4 1.2Master’s share 78.9 6.7 3.4 2.2 5.4 0.9 0.7 0.8 1.0Doctorate share 71.2 13.2 4.0 1.7 6.5 0.9 1.5 0.5 0.4

Notes: Metropolitan statistical areas (MSAs)—AUS (Austin), BAL (Baltimore), BOS (Boston), BUF (Buffalo), COL (Columbus), HRT (Hartford), KC (Kansas City), LA (Los Angeles), MEM (Memphis), MIA(Miami), NOR (New Orleans), NYC (New York City), PRO (Providence), SA (San Antonio), SD (San Diego), SF (San Francisco), STL (St. Louis), WPB (West Palm Beach), and WS (Winston-Salem). MSAs areidentified from inventors’ city names using city lists collected from the Office of Social and Economic Data Analysis at the University of Missouri, with a matching rate of 98%. Manual coding further ensures allpatents with more than 100 citations and all city names with more than 100 patents are identified. 1990 Census statistics are calculated by country of birth using the country-ethnicity groupings listed in table 2;English provides a residual in the Census statistics.


incidence rate ratio for all citations is 1.496 with a standarderror of 0.052. This coefficient is statistically different from1, the level where own-ethnicity citations have the samefrequency as citations of other ethnicities, and suggests amoderate effect that own-ethnicity citations are 50% higherthan citations to other ethnicities once the basic levels andindustry effects are removed. This ethnic differential is acouple of orders of magnitude less than the within-technology field effect, and Kerr (2007a) shows that tightertechnology controls by disaggregating the sample can

weaken the own-ethnicity differential to 20%–30%. Thetighter specifications, however, remain economically andstatistically important. To further study the time path ofthese knowledge flows, the negative-binomial regressionsare performed separately for each citation lag of one to tenyears, rather than collapsing the data into a single regres-sion. The coefficients from these regressions and theirconfidence bands (two standard deviations) are presentedgraphically in figure 2. Common ethnicity appears mostimportant for international technology diffusion in the first

FIGURE 1.—U.S. ETHNIC PATENTING

Ethnic inventor shares are for granted USPTO patents grouped by application years. Only inventors residing in the United States at the time of patent application are included. Additional ethnic groups, includingthe English majority, are omitted from the figure for visual clarity. Ethnicity is determined through inventor names on patents as described in the text and Kerr (2007c).

FIGURE 2.—OWN-ETHNICITY CITATION RATE

Negative binomial regressions test whether foreign inventors cite U.S. inventors of their own ethnicity more frequently than their technology fields would suggest. Cells group U.S. international patent citationsby the ethnicity of the citing foreign inventor, the ethnicity of the cited U.S. inventor, the technology class of the citing foreign inventor, and the technology class of the cited U.S. inventor. Citations are furtherseparated by diffusion lags measured as years between the applications of the citing and cited patents. Estimations are unweighted and performed separately for each diffusion lag. Estimations include an indicatorvariable for same ethnicity, an indicator variable for same technology, and vectors of fixed effects for the four dimensions on which cells are constructed. Coefficients on the same-ethnicity indicator variable aretransformed into the reported incident rate ratios. The solid line represents coefficient estimates and the dashed lines show the confidence bands (two standard deviations).


few years after an invention, peaking in a citation lag of fourto five years.10

C. Codified and Tacit Knowledge Transfer

The international patent citation exercises confirm thatknowledge diffusion occurs at an uneven rate across coun-tries and further suggest that ethnic scientific networks areimportant for short-run technology transfer from the UnitedStates. The declining importance to common ethnic tiesover time in many respects resembles the declining impor-tance of geographic distance in knowledge diffusion overtime (for example, Keller, 2002b). While the citation regres-sions explicitly measure inventor-to-inventor knowledgeflows, the short-term differentials are more generally repre-sentative of the transfer of codified and tacit knowledge.

The heightened transfer of codified knowledge can arisefrom several factors. Most simply, ethnic networks aidawareness of new technologies that are developed. Evenwith modern communications advances, information con-tinues to diffuse through professional networks and word ofmouth. Second, ethnic business networks can aid trust andinformal contracts where traditional legal enforcement isuncertain. Ethnic diasporas have facilitated trade flows forcenturies (for example, Rauch, 2001; Rauch & Trindade,2002), and frontier ethnic expatriates can serve as reputationintermediaries for the transfer of new technologies, too.These transfers are understandably cautious given weakerinternational intellectual property protections. Kapur (2001)notes that U.S. ethnic scientists and entrepreneurs are likelyto play a greater role as reputation intermediaries in indus-tries where tacit knowledge is important with respect toquality. U.S. Indian entrepreneurs have substantially en-hanced the brand reputation of India’s programmers.

The transfer of the practical knowledge necessary forusing or adapting new innovations is also aided by frontierexpatriates. This tacit knowledge applies to both the specifictechnologies developed and the broader context of innova-tion. Often times, the technology diffusion encouraged bycross-border ethnic transfers encourages the formation ofnew firms seeking to integrate into industries characterizedby decentralized production and cross-firm collaborations.In these environments, informal knowledge regarding com-ponent integration and the industry’s future direction arecritical; these insights can moreover illuminate pitfalls toavoid. The importance of this tacit knowledge cannot beoverestimated. Lester and Piore (2004) describe how aJapanese communications equipment manufacturer with-drew from the U.S. market after being excluded fromstandards hearings held by the Federal Communications

Commission (FCC), despite the fact that the FCC publishedthe transcripts of its sessions! The Japanese vendor felt itwould not understand adequately the unspoken or implicitdecisions being made.11

Finally, this study has interesting parallels to two recentpapers regarding knowledge diffusion through Indian entre-preneurial and scientific networks. In a study of India’ssoftware industry, Nanda and Khanna (2006) find that en-trepreneurs outside of software hubs rely more on the Indiandiaspora than those working within centers like Bangalore.These findings suggest that diaspora networks may serve assubstitutes for local institutions and technology opportuni-ties. Looking within a single economy, Agrawal, Kapur, andMcHale (2007) jointly examine knowledge diffusionthrough co-location and co-ethnicity using domestic patentcitations made by Indian inventors living in the UnitedStates. While being in the same city or the same ethnicityboth encourage knowledge diffusion, their estimations sug-gest that the marginal benefit of co-location is four timeslarger for inventors of different ethnicities. This substitut-ability between social and geographic proximity can createdifferences between a social planner’s optimal distributionof ethnic members and what the inventors themselves wouldchoose.12

IV. Output and Productivity Analysis

This section turns to the next question of whether thisgreater transfer of knowledge for U.S. innovations throughethnic networks produces measurable economic improve-ments for foreign countries. The U.S. ethnic patentingtrends are joined with data on foreign manufacturing indus-tries, and an empirical extension of specification (6) isdeveloped and estimated.

A. Foreign Manufacturing Data

The benefit of knowledge integration for foreign devel-opment is evaluated through the Industrial Statistics Data-base of the United Nations Industrial Development Organi-zation (UNIDO). The UNIDO collects industry-levelmanufacturing statistics for the International Yearbook ofIndustrial Statistics and specialized publications on topicslike development and competition. Researchers at theUNIDO supplement the data resources of the OECD with

10 Kerr (2007a) tabulates these negative-binomial regression results.This unpublished appendix also contains results using the data set andtechniques developed by Thompson (2006). After assigning ethnicities toinventors in Thompson’s data set, estimations using Thompson’s tech-nique yield a quantitatively similar role for own-ethnicity in internationalcitations of 40%–60% depending upon the specification.

11 A second intuitive example is the construction of an atomic bomb.While the basic designs are available on the Internet, efforts to stemnuclear weapons proliferation focus extensively on the scientists with thetacit knowledge necessary for implementation. Other examples are drawnfrom Amsden (2001), Feinstein and Howe (1997), Kim (1997), Lim(1999), and Saxenian (2006). Polanyi (1958, 1966) introduces tacit knowl-edge; Granovetter (1973) highlights the strength of weak ties.

12 Technology diffusion is also facilitated by foreign direct investmentand multinational enterprises (for example, Branstetter, 2006; Singh,2004). Foley and Kerr (2007) find growth in the U.S.-based ethnicresearchers within U.S. multinational firms is correlated with larger FDIinto countries of the researchers’ ethnicity. Moreover, the organizationalform of the FDI shifts toward more direct entry versus joint ventures.


national records for non-OECD members, creating a uniqueglobal resource. The UNIDO’s stated objective is the com-pilation of internationally comparable and internally consis-tent series (such as, variable definitions, accounting units,collection procedures).

Table 2 describes the sample and lists the three-digit ISICindustries. The panels include all country-industry observa-tions surveyed at least four times from 1985 to 1997 thatcorrespond to non-English ethnicities identifiable with theethnic-name database (for example, Canada, the UnitedKingdom, Africa, and the Middle East are excluded). Threeindustry characteristics are considered: output, employment,and labor productivity measured as output per employee.Table 2 aggregates the annual industry-level data to describethe country-level manufacturing sectors. While direct com-parisons across countries are limited with an unbalancedpanel, the output and labor productivity differences betweenindustrialized countries (such as, Japan) and developingnations are clearly evident. The underlying industry-levelmetrics also agree with published UNIDO and World Bankstatistics.13

B. Output and Productivity Estimation Framework

The combined data set affords an industry-level analysisof technology transfer with multiple countries and ethnici-ties. Extending equation (6) to industry i and country c ofethnicity e,

ln�Yci� � �ci � ln�Ii� � ln�LR,ei�, (8)

where LR,ei is the size of the U.S. research community ofethnicity e in industry i. While analytically convenient, thissteady-state description must be adapted for the empiricalexercises. The ethnic human capital stocks for U.S. tech-nologies change over the 1985–1997 period—the source ofidentification for the parameter. The citation regressionsin figure 2 highlight that ethnic ties have an important lagstructure, especially for the first five years of knowledgedissemination. Rewriting equation (8) in discrete time tomodel this five-year dependency,

ln�Ycit� � �ci � ln�Iit� � ln��s�1

5

LR,ei,t�s�. (9)

Ethnic patenting data provide an empirical foothold forestimating these U.S. ethnic scientific research communi-ties. Rewriting the U.S. researcher productivity functioninto a discrete-time form for industry i and ethnicity e,

13 Kerr (2007a) documents additional descriptive statistics for this sam-ple, the data set development process, and alternative UNIDO panelsconsidered. The appendix also describes the mapping of USPTO technol-ogy classifications to ISIC industries, building on Johnson (1999) andSilverman (1999).

TABLE 2.—UNIDO INDUSTRY SAMPLE

Country1980 Agr.

ShareUNIDO3

PanelOutput

Country1980 Agr.

ShareUNIDO3

PanelOutput

Level Growth Level Growth

Single Ethnic Mappings: Chinese Economies:

India 70% 85–97 117,950 6% China, Mainland 74% 85–97 327,173 11%Japan 11% 85–97 2,053,048 7% Hong Kong 1% 85–97 30,520 3%Russia 16% 93–97 109,729 12% Macao 6% 85–97 1,209 8%South Korea 37% 85–97 230,942 14% Singapore 2% 85–97 37,830 16%Soviet Union 16% 85–89 1,087,914 7% Taiwan 8% 85–96 145,055 11%

European Economies: Hispanic Economies:

Austria 10% 85–97 73,524 5% Argentina 13% 85–90, 93–96 66,160 11%Belgium 3% 85–92, 95–97 31,958 5% Bolivia 53% 85–97 1,474 7%Denmark 7% 85–91 38,198 9% Brazil 37% 90, 92–95 127,807 11%Finland 12% 85–97 52,510 4% Chile 21% 85–97 20,604 10%France 8% 85–96 517,276 8% Colombia 40% 85–97 20,099 5%Germany 7% 91–97 870,625 7% Costa Rica 35% 85–97 3,264 5%Germany, East 85–92 233,905 12% Cuba 24% 85–89 10,531 �1%Germany, West 85–89 734,523 12% Ecuador 40% 85–97 4,372 3%Italy 13% 85–94, 96–97 390,266 7% Honduras 57% 90–95 989 8%Luxembourg 5% 85–97 2,952 3% Mexico 36% 85–97 61,612 4%Netherlands 6% 85–97 117,868 6% Panama 29% 85–94, 96–97 1,468 4%Norway 8% 85–97 37,467 4% Peru 40% 85–92, 94–96 13,944 8%Poland 30% 90–97 54,895 6% Philippines 52% 85–97 23,238 11%Sweden 6% 85–97 93,727 6% Portugal 26% 85–97 36,365 8%Switzerland 6% 86–96 37,827 7% Spain 18% 85–97 201,951 8%

Uruguay 17% 85–97 4,648 6%Venezuela 15% 85–97 24,174 1%

Notes: Output values are expressed in millions of 1987 U.S. dollars. Levels and growth rates are unweighted averages of yearly country-level aggregates derived from the industry data used in the UNIDO3 panel.See Kerr (2007a) for additional descriptive statistics and data preparation steps. ISIC rev. 2 industries: Food products (311), Beverages (313), Tobacco (314), Textiles (321), Wearing apparel, except footwear (322),Leather products (323), Footwear, except rubber or plastic (324), Wood products, except furniture (331), Furniture, except metal (332), Paper and products (341), Printing and publishing (342), Industrial chemicals(351), Other chemicals (352), Petroleum refineries (353), Misc. petroleum and coal products (354), Rubber products (355), Plastic products (356), Pottery, china, earthenware (361), Glass and products (362), Othernonmetallic mineral products (369), Iron and steel (371), Nonferrous metals (372), Fabricated metal products (381), Machinery, except electrical (382), Machinery, electric (383), Transport equipment (384),Professional & scientific equipment (385), and Other manufactured products (390). Industry 390 is excluded.


IeitFlow � Iit � LR,eit. The measured patenting of ethnicity e in

year t again depends upon the overall stock of U.S. knowl-edge and the size of the ethnic research group in the UnitedStates (measured at the beginning of the year). By abstract-ing from the endogenous growth stimulus, the researcherproductivity becomes time-invariant: Iit � Iit0

. Thus, theU.S. ethnic research community can be inferred from thepatent flow divided by the constant researcher productivity(LR,eit � Iit0

�1 � IeitFlow). Substituting this simplified form into

equation (9), the time-invariant researcher productivity I it0�1

is separated from the patent sum and incorporated withln (Iit) into an industry-year fixed effect �it. Likewise, thebase productivity constants �ci are extended into country-industry fixed effects.

To keep the exposition simple, define PATeitUS to be the

five-year sum of recent U.S. ethnic patenting in an industry.The core estimating equation becomes

ln�Ycit� � � � ln�PATeitUS� � �ci � �it � εcit, (10)

where �ci and �it are the vectors of country-industry andindustry-year fixed effects, respectively. These fixed effectswarrant careful discussion. First, the country-industry ef-fects �ci remove levels differences between series. Without�ci, a positive would be found if output in China’scomputer industry and U.S. Chinese research in the com-puter industry are higher than average. Incorporating �ci

instead requires the output growth in China’s computerindustry be above average if the U.S. Chinese computerresearch growth is above average. Focusing on relativegrowth rates removes time-invariant factors that potentiallyconfound the analysis (for example, the productivity param-eters A, ethnicity size).

The derivation of equation (10) highlights two impor-tant roles for the industry-year fixed effects �it. First, �it

extract the overall growth in the U.S. knowledge stockfor an industry (for example, the strong increase incomputer and pharmaceutical research vis-a-vis mechan-ical research). Second, �it control for the invention pro-ductivity of researchers, so that ethnic patenting flows areviable proxies for ethnic research in the United States.More generally, the industry-year effects remove allindustry-level trends common to the countries (such as,demand shifts, price changes) and fluctuations in patentstatistics due to changes in USPTO resources (for exam-ple, Griliches, 1990).

These fixed effects are crucial for the interpretation of the parameter. This project does not estimate the effect ofU.S. patenting on foreign output and productivity; indeed,isolating that specific channel from other knowledge flowsbetween countries is not feasible with industry-level out-comes. Moreover, the substantial increase in the number ofpatents granted by the USPTO over the last two decades isdifficult to interpret. Instead, equation (10) forces variationto be within industries, isolating the size of ethnic commu-nities from aggregate industry trends. A positive coeffi-

cient requires that higher relative growth of Chinese com-puter research compared to Indian computer research in theUnited States correlate with higher relative output growth inChina’s computer industry compared to India’s computerindustry.

C. Ethnic Patenting Estimator

The five-year patent sums PATeitUS are developed for each

ethnicity-industry from the patent database. The matchedUSPTO records describe the ethnic composition of U.S.scientists and engineers with previously unavailable detail:incorporating the major ethnicities working in the U.S.scientific community; separating out detailed technologiesand manufacturing industries; and providing annual metrics.The panel econometrics (10) require this level of cross-sectional and longitudinal variation to estimate generalelasticities. The procedure does, however, have three poten-tial limitations that should be discussed before presentingthe results.

First, the approach does not distinguish foreign-bornethnic researchers in the United States from later genera-tions working as scientists and engineers, especially for theEuropean contribution. While research on social and busi-ness networks finds the strength of ties to home countriesdeclines for later generations, the ethnic patenting approachcan only estimate total ethnic scientific populations. Thepanel econometrics employed for the output and productiv-ity analyses, however, identify off of relative changes inthese community sizes. Census and Immigration and Natu-ralization Service (INS) records confirm these changes areprimarily due to new immigration for the period studied,substantially weakening this overall concern. Moreover, theimmigration reform exercises in section V yield similarresults when focusing specifically on new arrivals throughU.S. quotas changes.

On a related topic, recent surveys of ethnic technologytransfer from the United States to China and India suggeststechnical exchanges are particularly aided by the circularlabor movements of U.S.-trained researchers and entrepre-neurs (for example, Saxenian, 2006; Nanda & Khanna,2006). The ethnic patenting technique cannot quantify themagnitudes of reverse migration and circular migrationflows, instead being restricted to net growth in U.S. ethnicresearcher populations. In this metric’s defense, the scien-tific integration it captures likely embodies circular flowstoo, and the 1985–1997 period predates most large-scalereturn migration decisions. If anything, the extent to whichreturn migrations are important should lead to finding anegative coefficient in the estimations. Return migrationand circular movements are rapidly growing in importance,however, and it is hoped that future research will illuminatethese issues further.

Finally, the name-matching technique does not distin-guish finer ethnic and linguistic divisions within the nine


major ethnic groupings. It would be advantageous to sepa-rate Mexican from Chilean scientists within the Hispanicethnicity, to distinguish Chinese engineers with ties toTaipei versus Beijing versus Shanghai, and so on. Thesedistinctions are not possible for this study’s large-scaleanalysis, and several countries will map into the Chinese,European, and Hispanic ethnicities for the output and pro-ductivity analyses below. The empirical analysis accountsfor this multiplicity by conservatively clustering standarderrors at the ethnicity-industry level; this cross-sectionalclustering further addresses the serial-correlation concernsof Bertrand, Duflo, and Mullainathan (2004). Despite theclustering, measurement error from the broader ethnic divi-sions may still bias the estimated coefficients downward.The positive elasticities evident will nevertheless supportthe conclusion that technology following countries experi-ence economic growth due to stronger technology transferfrom the United States.

D. Basic Output and Productivity Regressions

As a final preparation step, the levels specification (10) isfirst-differenced for estimation,

� ln�Ycit� � � � � ln�PATeitUS� � �it � εcit, (11)

where εcit � εcit � εcit�1.14 Table 3 reports the primaryresults. The top row finds that output rises with strongscientific integration to the United States. As both vari-ables are in logs, the 0.091 coefficient in the upper-leftcorner finds a 10% increase in U.S. ethnic research isassociated with a 1% increase in foreign output. Industryoutput expansion can come through both labor produc-tivity gains and expansion in employment. Disaggregat-ing the output regression, panels B and C find laborproductivity growth facilitates most of the manufacturingdevelopment captured in this sample.

Three weighting schemes are tested: no weights,weighted by the 1985–1987 industry-level patenting inthe United States, and weighted by the 1985–1987 size ofthe foreign manufacturing industry. The coefficients inthe weighted regressions are larger than the unweightedspecification, measuring an output elasticity of approxi-mately 0.3. The patent weights emphasize high-tech in-dustries and the strong interactions of the Chinese andIndian research communities with their home countries.The output weights instead focus on the largest industriesand offer a sense of the average treatment effect forindustries. Coefficient estimates tend to be marginally

smaller with the output weights than the patent weightsbecause of the output weights’ greater emphasis on tra-ditional economic sectors (such as food products, tex-tiles). Both approaches, however, yield more consistentresults than the unweighted regressions by focusing at-tention on larger countries and industries and reducingmeasurement error in the ethnic patenting estimator. Theweighted estimations are the preferred specifications ofthis study.15

The basic estimations reported in table 3 are consistentwith technology following countries realizing economicgains from stronger scientific integration with the UnitedStates. These benefits appear to extend beyond the inventor-to-inventor flows evident with the citations analysis, asthese U.S. ethnic research communities facilitate broadermanufacturing output growth through superior access to theU.S. technology set. The remainder of this section furthertests this finding by incorporating country-level controls,examining sector reallocations, and so on.16

E. Foreign Country Development Controls

The industry-year fixed effects create an empirical envi-ronment where U.S. ethnic patenting serves as a viablemetric for the strength of ethnic research communities.

14 The efficiency of this first-differences form versus the levels specifi-cation turns on whether the error term εcit is autoregressive. If autoregres-sive deviations are substantial, the first-differences form is preferred; aunit root error is fully corrected. If there is no serial correlation, however,first-differencing introduces a moving-average error component. Estima-tions of the autoregressive parameter in the levels specification for thisstudy find serial correlations of 0.5–0.6, while �0.1 is evident in thefirst-differences form.

15 The elasticities are larger and more uniform in the levels estimation(10). The unweighted output elasticity is 0.241 (0.126), while the patent-and output-weighted elasticities are 0.420 (0.228) and 0.400 (0.147),respectively. Kerr (2007a) documents equivalent results using the levelsspecifications for all of the tables presented below.

16 The differential technology transfer explains 1%–2% of the sample’soutput and productivity growth variation after removing aggregate indus-try trends. The percentage accounted for rises to 3%–5% with the sectorreallocation specifications studied below. These percentages provide order-of-magnitude estimates for the total growth accounted for, althoughcalculations after removing industry-year effects likely understate the totalimpact due to technology transfer. These technology gains in turn producecomparative advantages for trade (Kerr, 2007b).

TABLE 3.—UNIDO FIRST-DIFFERENCES SPECIFICATIONS

NoWeights

PatentWeights

OutputWeights

(1) (2) (3)

A. � Log Foreign Output

� log U.S. ethnicresearch community

0.091(0.056)

0.340(0.133)

0.285(0.074)

Observations 8,736 8,736 8,736

B. � Log Foreign Labor Productivity


0.087(0.049)

0.214(0.114)

0.217(0.072)


C. � Log Foreign Employment


0.003(0.036)

0.127(0.084)

0.068(0.047)


Notes: Row titles document the dependent variable studied; column titles document the weightingscheme employed. Panel estimations consider country-industry-year observations taken from the 1985–1997 UNIDO manufacturing database. Log U.S. ethnic research communities are estimated at theethnicity-industry-year level from the U.S. ethnic patenting data set. Regressions include industry-yearfixed effects. Standard errors are conservatively clustered at the ethnicity-industry level.


Moreover, the focus on within-industry variation circum-vents many problems in interpretation that could arise fromdifferent industry trends (such as, rapid high-tech growth).As the constructed panel includes multiple industries withina country, additional tests can be performed that furthercontrol for countrywide development. Table 4 undertakesfour such tests, with panel A simply replicating the basefirst-differences regressions for foreign output from table 3.

An immediate concern is whether the results are captur-ing only foreign human capital development, which couldreasonably lead to an expansion in foreign manufacturingand the emigration of researchers to the United States. TheNSF collects annual data on the U.S. PhD science andengineering graduates by country of birth. As an initialrobustness check on the general human capital developmentstory, panel B adds the log trend in these graduates as anadditional covariate. The role of the U.S. ethnic scientificcommunity remains strong and significant. (The immigra-tion analysis in section V returns to these PhD trends and thereverse-causality concern.)

Panel C next explores the role of physical capital devel-opment in explaining the output growth. The theory insection II only models nondurable intermediate inputs, asimplification that removes the need to track two statevariables. Labor productivity and output growth occur withcapital deepening as well as technology adoption, however.

This investment in physical machinery and structures isclearly endogenous to technology transfer from the frontiereconomy, because of both the larger available technologyset and the general equilibrium economic developmentexperienced. Nevertheless, additional confidence for therole of frontier scientific communities can be establishedthrough joint tests with this factor input. The output-weighted coefficient retains most of its economic magnitudeand statistical strength; the patent-weighted coefficient re-tains 80% of its original economic importance but is nolonger statistically significant.17

More generally, panels D and E incorporate into equation(11) linear country time trends and nonparametric country-year fixed effects, respectively. These additional controlsremove trends common to the industries within a country,including the overall growth in each ethnicity’s U.S. re-search community (for example, the strong increases inChinese and Indian patenting in the United States). Forforeign output, the country effects extract national businesscycles, trend manufacturing gains, trade agreements, and soon. A positive coefficient in these estimations requires thathigher relative growth of Chinese computer research toChinese pharmaceutical research in the United States bepartially correlated with higher relative output growth inChina’s computer industry to its pharmaceutical industry(after worldwide industry trends are removed).

The inclusion of both country-year and industry-yearfixed effects in a first-differenced specification is a verystringent test, and much of the variation is removed from thesample. While the positive correlations are preserved inthree of the four weighted regressions, only one coefficientis statistically significant. Moreover, the correlations are 0or negative in the unweighted specifications. These declinesin coefficient magnitudes are partly explained by the rela-tively uniform growth (versus levels) in each ethnicity’sU.S. research communities across industries in a log expan-sion. That is, much greater variation exists across ethnicitiesthan across industries within an ethnicity (Kerr, 2007c). Tothe extent that this uniform growth is what is being capturedby the country-year fixed effects, the core estimations cor-rectly measure the general elasticity. This study cannotreject, however, that the base elasticities are upward biaseddue to presence of an omitted variable operating at thecountry-year level too.

F. Sample Decompositions

The core objective of these empirical exercises is quan-tifying the mean output gains from U.S. technology transferthrough ethnic networks across a diverse group of countries

17 The UNIDO data unfortunately lack capital records for almost half ofthe sample. Moreover, the available capital stocks are measured withsubstantial error, downward biasing the capital coefficients. Kerr (2007a)details the construction of the capital stocks and provides additional tests.These results can be extended to include labor in a production functionestimation, although the employment response is even more endogenousto technology transfer as discussed below.

TABLE 4.—UNIDO COUNTRY CONTROLS SPECIFICATIONS

NoWeights

PatentWeights

OutputWeights

(1) (2) (3)

A. Base Foreign Output Regression


0.091(0.056)

0.340(0.133)

0.285(0.074)


B. Including Foreign PhD Students in the United States


0.061(0.035)

0.313(0.073)

0.210(0.065)

� log foreign PhDstudents in UnitedStates

0.038(0.068)

0.050(0.081)

0.053(0.073)


C. Including Foreign Physical Capital Stocks


0.026(0.069)

0.275(0.173)

0.209(0.091)

� log foreign capitalstock

0.069(0.030)

0.112(0.047)

0.059(0.034)


D. Including Country Time Trends


0.000(0.061)

0.130(0.102)

0.153(0.068)


E. Including Country-Year Effects


�0.092(0.048)

0.149(0.107)

�0.022(0.059)


Notes: See table 3. Panel A replicates the foreign country-industry output regressions from table 3.Panels B through E incorporate the country controls indicated by the row titles. All regressions maintainindustry-year fixed effects and the clustering of standard errors.


and industries. It is informative, however, to identify whichobservations are most responsible for the aggregate find-ings. Table 5 investigates this question through severalsample decompositions.

Case studies of successful technology diffusion oftenfocus on the computer and pharmaceutical industries, andthe exceptional outcomes of Asian scientific communities inSilicon Valley are widely noted. While the industry-yeareffects control for the overall growth in each industry’sresearch and output (for example, Griliches, 1994), ethnicdifferences in high-tech industries alone could still be re-sponsible for the positive correlations. To some extent, thestronger coefficients in the patent-weighted regressions sug-gest this is true, and panel B begins by directly excludingthe computer and pharmaceutical industries from the sam-ple. The results are mixed. On one hand, both the un-weighted and patent-weighted coefficients decline substan-tially in economic magnitude. On the other hand, the patent-weighted coefficient does remain statistically significant andthe output-weighted elasticity is broadly robust. Thesemixed results suggest the gains are concentrated in high-tech sectors, but that they are not entirely exclusive to them.

Chinese economies, more often than not, are also thecenterpieces of technology transfer stories. The U.S. Chi-nese research community experiences strong growth duringthe sample period, and Mainland China has exceptional

manufacturing gains too. When excluding Mainland Chinain panel C, the unweighted elasticity loses a third of itsmagnitude and its statistical significance, but the weightedregressions deliver fairly similar results. Unreported regres-sions further find that the weighted parameter estimates donot depend significantly on the inclusion of any one countryin the sample. Panel D of table 5 demonstrates, however,that excluding the full Chinese ethnicity can be importanteven for the weighted estimations. Given that the Chinesegrouping includes three of the four Asian tiger economies(Hong Kong, Singapore, and Taiwan) and Mainland China,it is not too surprising that the effect is sensitive to theirinclusion. Further tests find that the decline in the coefficientsize is mostly linked to dropping the computer and drugindustries for the Chinese economies. This cautions that thewell-documented outcomes for Silicon Valley are in somesense special even for the Chinese, with the benefits ofscientific collaboration for manufacturing being weaker inmost other contexts.18

The UNIDO sample also includes several industrializedeconomies that are undertaking extensive R&D themselves.For example, Japanese inventors living in the United States,who are well identified with the ethnic-name database,patented less than 10,000 inventions from 1985 to 1997;almost 300,000 USPTO patents were awarded to Japaneseinventors living outside of the United States during thisperiod.19 Positive correlations of foreign country growth toU.S. ethnic research may simply be capturing reverse tech-nology flows, intracompany patenting, or defensive patent-ing from these advanced economies. Exploring this issue,panel E excludes Japan, European countries, and Russia andfinds similar results to the full sample. Likewise, the lastrow drops the large bloc of Hispanic countries and findssimilar coefficients in the weighted regressions.

To summarize, the unweighted elasticities are clearlysensitive to the sample composition, while the weightedelasticities are more robust across sample compositions. Theethnic technology transfer mechanism is especially strongfor high-tech and Chinese outcomes, reflective of the dis-proportionate number of case studies written. The weightedspecifications suggest, however, that some transfer benefitsextend beyond these special outcomes to other ethnicitiesand more traditional industries. The next section refines themain effects to characterize further differences in outcomesby development stage.

G. Sector Reallocation

The theoretical framework presented in section II buildson the assumption of full employment in the technology

18 Dropping only the computer and drug industries for Chinese econo-mies yields coefficients slightly larger than those in panel D that arestatistically significant in the two weighted regressions.

19 The estimates are sums over inventor ethnicity percentages at thepatent level. Japanese inventors are associated with more patents becauseof multiple inventors.

TABLE 5.—UNIDO SAMPLE DECOMPOSITIONS

NoWeights

PatentWeights

OutputWeights

(1) (2) (3)



0.091(0.056)

0.340(0.133)

0.285(0.074)


B. Excluding Computers and Drugs


0.058(0.054)

0.126(0.076)

0.207(0.063)


C. Excluding Mainland China


0.059(0.061)

0.308(0.166)

0.258(0.086)


D. Excluding All Chinese Economies


0.059(0.058)

0.195(0.131)

0.238(0.082)


E. Excluding All Advanced Economies


0.117(0.080)

0.386(0.116)

0.255(0.079)


F. Excluding All Hispanic Economies


0.055(0.071)

0.334(0.162)

0.243(0.091)


Notes: See table 3. Panel A replicates the foreign country-industry output regressions from table 3.Panels B through F exclude the observations indicated by the row titles. All regressions maintainindustry-year fixed effects and the clustering of standard errors.


follower’s manufacturing and research sectors. While theestimating equation (6) relates the follower’s output to itsresearch presence in the leader, the same elasticity wouldhold for labor productivity specifications. With full employ-ment, output gains can only come through labor productiv-ity enhancements. Many developing economies have largeagricultural sectors, however, and the migration from agri-culture to manufacturing is important for characterizingeconomic development (for example, Harris & Todaro,1970).

Kerr (2007a) incorporates into the basic model an agri-cultural sector in the follower. In this extension, technologytransfer from the leader to the follower induces sectorreallocation, with labor shifting from agriculture to themanufacturing and research sectors. Thus, output growthoccurs through both labor productivity gains, as in thesteady-state scenario, and through employment growthalong the transition path. After a sufficient number offrontier innovations are imitated, the follower’s economytransitions to full employment in the manufacturing andresearch sectors. Thus, the steady state of the expandedeconomy is the same as the basic framework described insection II.20

To test these transition path predictions for developingeconomies, table 2 lists the 1980 share of national employ-ment in agriculture for each economy. The three smallestagricultural sectors are found in Hong Kong (1%), Singa-pore (2%), and Belgium (3%), while the three largestsectors are India (70%), Vietnam (73%), and MainlandChina (74%). A modified form of equation (11) interacts theethnic scientific community regressor with this preperiodagricultural share,

� ln�Ycit� � � � � ln�PATeitUS�

� �� ln�PATeitUS� � AGR%c,1980 � �it � εcit,

where the main effect for the agricultural share is absorbedinto the first-differencing. The main effects are demeanedprior to the interaction to restore the coefficient to close toits base level. A positive � coefficient indicates outputgrowth due to scientific integration is stronger in countrieswith larger agricultural workforces in 1980.

Table 6 reports the results from these interacted regres-sions. Foreign country output growth due to stronger U.S.ethnic research integration is higher in economies with largeagricultural shares in 1980. Panels B and C again disaggre-gate the output regression into labor productivity and em-ployment shifts, respectively. Labor productivity gains areweaker in the less developed economies, though the differ-ence is usually not statistically significant, while substantial

sector reallocation through employment growth is clearlyevident in panel C. The interacted regressions thus supportthe model’s predictions regarding the stage of developmentbeing important for how technology transfer gains arerealized. Economies with large agricultural sectors facilitateemployment reallocation across sectors that aid manufac-turing output expansion.21

V. Exogenous Changes from U.S. Immigration Reforms

While OLS regressions establish partial correlationspresent in the data, they frequently fail to identify causalrelationships because of the endogenous relationships be-tween outcomes or because of omitted variable biases.Domestic human capital developments in Chinese econo-mies, for example, could lead to both higher productivityand output growth at home and the export of scientists to theUnited States. Alternatively, R&D in Japan might be re-sponsible for the growth of its Asian neighbors and feed intohigher U.S. research output. Despite the strong fixed-effectspecifications employed, further exercises can aid in theinterpretation of the positive outcomes evident in patent-based regressions.

The earlier model helps us to understand and addressthese concerns. Consider the initial transition from theequilibrium described in section II following an industrial-ization push in the follower. The follower’s government

20 The output gains through labor productivity and employment growthare of similar magnitude in the numerical simulations Kerr (2007a)models. In alternative models, output growth would come only throughlabor reallocation (such as, fixed physical capital stocks and constantoutside wages).

21 These sector reallocation findings are robust to the earlier sampledecompositions. Notably, the interactions are more robust than the maineffects to dropping high-tech industries and the Chinese ethnicity (Kerr,2007a).

TABLE 6.—UNIDO SECTOR REALLOCATION SPECIFICATIONS

NoWeights

PatentWeights

OutputWeights

(1) (2) (3)


� log U.S. ethnic researchcommunity

0.043(0.062)

0.315(0.153)

0.252(0.086)

� log US ethnic comm.� 1980 agriculture share

0.765(0.185)

0.442(0.353)

0.647(0.242)




0.105(0.047)

0.225(0.106)

0.228(0.068)

� log U.S. ethnic comm.� 1980 agriculture share

�0.284(0.097)

�0.191(0.162)

�0.216(0.120)




�0.062(0.037)

0.091(0.084)

0.024(0.047)

� log U.S. ethnic comm.� 1980 agriculture share

1.049(0.146)

0.633(0.266)

0.863(0.198)


Notes: Row titles document the dependent variable studied; column titles document the weightingscheme employed. Panel estimations consider country-industry-year observations taken from the 1985–1997 UNIDO manufacturing database. 1980 agriculture shares for foreign countries are listed in table 2.Log U.S. ethnic research communities are estimated at the ethnicity-industry-year level from the U.S.ethnic patenting data set. Main effects are demeaned prior to interactions. Regressions include industry-year fixed effects. Standard errors are conservatively clustered at the ethnicity-industry level.


temporarily subsidizes invention until condition (7) nolonger holds. As I � I�[M/I](HF), it is more profitable forresearchers in the follower to invent rather than imitate; thefollower’s output growth and sector reallocation are nowdriven solely by domestic innovations. In the leader, re-searchers of the follower’s ethnicity switch from inventingto imitating, as the latter is initially very easy (that is, �[0]is high). If international property rights are weak, so thatethnic researchers in the leader can register their imitationswith the leader’s patent office, a positive coefficient willbe found in the core estimating equations even though thefollower’s manufacturing gains no longer depend on itsfrontier research community. In fact, data trends will showcontemporaneous accelerations in the growth of foreignoutput and the leader’s ethnic patenting.22

The leader’s population of the follower’s ethnic research-ers is a foothold for establishing greater confidence in thedirection of technology flows as the expatriates only influ-ence the follower’s development through their transmissionof knowledge regarding frontier innovations. If the size ofthis research population is exogenously determined by im-migration restrictions, a reduced-form strategy for the sizeof the ethnic research community can be developed withinthe quotas system. In this paper’s context, U.S. immigrationlaw does not control the population size of foreigners in theUnited States, but it does control the inflow of new immi-grants. Define the quota on the follower’s inflows of re-searchers to the United States to be QUOTARF,t. Assumingthat only the previous three years of immigration matter fora research stock,23 a reduced-form immigration estimatorfor ethnic scientific integration to the United States ismodeled as

ln�IMMRF,tRF � � ln��

s�1

5

�QUOTARF,t�s

� QUOTARF,t�s�1 � QUOTARF,t�s�2��.

(12)

The summation over the previous five years maintains thehuman capital stock modeling technique employed with theethnic patenting data set. This section designs and imple-ments an empirical version of equation (12) using exoge-nous changes in U.S. immigration quotas.

A. The Immigration Act of 1990

The disproportionate influence of immigrant scientistsand engineers (ISEs) in the United States is staggering:while immigrants account for 10% of the U.S. working

population, they represent 25% of the U.S. science andengineering workforce and 50% of those with doctorates.Even looking within the PhD level, immigrant researchershave made an exceptional contribution to science as mea-sured by Nobel Prizes, election to the National Academy ofSciences, patent citation counts, and so on.24 Yet, the U.S.immigration system significantly restricted the inflow ofISEs from certain nations prior to its reform with theImmigration Act of 1990 (1990 Act).

Immigrants can obtain permanent residency in the UnitedStates through numerically unrestricted categories (such as,immediate family members) or numerically restricted cate-gories (such as, extended family members, employment-based applications). The immigration exercises focus on thenumerically restricted categories that admit 75% of ISEs,versus 43% of all immigrants. U.S. immigration law appliestwo distinct quotas within these restricted categories. Bothof these quotas were increased by the 1990 Act, and theircombined change dramatically released pent-up immigra-tion demand from researchers in constrained countries.

The first quota governs the annual number of immigrantsadmitted per country. This quota is uniform across nations,and the 1990 Act increased the limit from 20,000 toapproximately 25,620. Larger nations are more con-strained by country quotas than smaller nations andbenefited most from these higher admission rates. Sec-ond, separately applied quotas govern the relative admis-sions of family-based versus employment-based immi-grants. Prior to the 1990 Act, the quotas substantiallyfavored family-reunification applications (216,000) toemployment applications (54,000). The 1990 Act shiftedthis priority structure by raising employment-based im-migration to 120,120 (20% to 36% of the total) andreducing family-based admissions to 196,000. Moreover,the relative admissions of high-skilled professionals tolow-skilled workers significantly increased within theemployment-based admissions.25

The uniform country quotas and weak employment pref-erences constrained high-skilled immigration from largenations, and long waiting lists for Chinese, Indian, andFilipino applicants formed in the 1980s. When the 1990 Actsimultaneously raised both of these quotas, the number ofISEs entering the United States dramatically increased.Figure 3 uses records from the INS to detail the response.This graph plots the number of ISEs granted permanent

22 The follower’s economy still depends on previously imitated products,as well as new inventions. Kerr (2007a) further discusses the transitionsfollowing this disturbance.

23 The reform below produced a very sharp immigration surge thatmakes this assumption more reasonable.

24 For example, Stephan and Levin (2001), Burton and Wang (1999),Johnson (1998, 2001), and Streeter (1997).

25 Kerr (2007a) describes the 1990 Act in greater detail and discussesISE immigration through temporary visas and numerically unrestrictedcategories. This supplement further catalogues the construction of the INSdata employed in this section. The worldwide ceiling for numericallyrestricted immigration now fluctuates slightly year-to-year based on pastlevels; maximum immigration from a single country is limited to 7% ofthe worldwide ceiling. The employment limit increased to 140,000, but120,120 corresponds to the previously restricted categories. Jasso, Rosen-zweig, and Smith (2000) also discuss behavioral responses to the 1990Act.


residency in the United States from 1983 to 1997 forselected ethnicities (summed over countries within eachethnicity). Prior to the 1990 Act, no trends are evident inISE immigration. The 1990 Act took effect in October 1991,and a small increase occurred in the final three months of1991 for Chinese and Indian ISEs. Immigration furthersurged in 1992–1995 as the pent-up demand was released.Low-skilled immigration did not respond to the 1990 Act.

The extremely large Chinese response and sharp declineis partly due to a second law that slightly modified thetiming of the 1990 Act’s reforms. Following the TiananmenSquare crisis in June 1989, Chinese students present in theUnited States from the time of the crisis until May 1990were permitted to remain in the United States until at least1994 if they so desired. The Chinese Student Protection Act(CSPA), signed in 1992, further granted this cohort theoption to change from temporary to permanent status duringa one-year period lasting from July 1993 to July 1994. TheCSPA stipulated, however, that excess immigration from theCSPA over Mainland China’s numerical limit be deductedfrom later admissions. The timing of the CSPA partlyexplains the 1993 spike, and the ability of graduatingChinese science and engineering students to remain in theUnited States in 1990 should factor into the timing of thereduced-form estimator.

Finally, NSF surveys of graduating science and engineer-ing doctoral students—the group most important for devel-oping human capital with respect to U.S. innovations—confirm the strong responses evident in the INS data. Thequestionnaires ask foreign-born PhD students in their finalyear of U.S. study about their plans after graduation. Figure4 exhibits the percentage intending to remain in the UnitedStates after graduation for available countries. The 60% to

90% jump for Mainland China from 1990 to 1992 isstriking. Substantial increases are also apparent for Indiaand Western Europe.

B. Immigration Responses

The reduced-form strategy exploits differences in theextent to which countries were affected by the 1990 reform.It is inappropriate, however, to use the outcomes exhibitedin figures 3 and 4 to determine treatment and control groups.A proper designation of the affected countries requires amore formal analysis of researcher immigration responsesto the legislation change. Kerr (2007a) undertakes such ananalysis and further characterizes immigration waiting listsaround the time of the reform. From this analysis, thetreated groups are determined to be India, Mainland China,the Philippines, and Taiwan. The reduced-form immigrationestimator (12) then takes the form

ln�IMMcitRF� � ln��

s�1

5

�QUOTAc,t�sEff � QUOTAc,t�s�1

Eff

� QUOTAc,t�s�2Eff ��. (13)

where QUOTActEff is the effective quota for country c in year

t. Raising the numerical ceilings did not change the effectivequotas for nations unconstrained by the former immigrationregime, and their effective quotas are held constant at theprereform theoretical limit. For constrained countries, theeffective quota increases to reflect both the higher country

FIGURE 3.—SCIENCE AND ENGINEERING IMMIGRATION

Science and engineering immigration trends are calculated from INS surveys of permanent residency admissions at the immigrant level. The solid line marks the passage of the Immigration Act of 1990 that tookeffect in 1991. Science and engineering categories are defined as engineers, natural scientists, and mathematical and computer scientists. Ethnic trends sum over the countries within each ethnicity. Kerr (2007a)discusses different admission categories and low-skilled immigration during this period.


limit of 25,600 and the larger employment preference allo-cation of 36% (that is, 120,120/336,000). This quota in-crease occurs in 1991 and is moved forward to 1990 forMainland China to account for the CSPA.

This simple reduced-form approach abstracts from sev-eral issues: return migration (such as, Taiwanese scientistsin the mid-1990s), occupational or industry changes byISEs, second-generation immigrant demographics, shifts inresearcher productivity, and others. If these types of con-cerns are overwhelming, panel regressions of U.S. ethnicpatenting on the reduced-form estimator will yield weakcoefficients. Unreported regressions find this relationship isquite strong, however, despite the design’s simplicity. How-ever, two more serious reservations regarding the estimatorshould be addressed before viewing the results.

First, the quota change affected all skilled workers seek-ing admission into the United States, not just researchers,and the impact of other occupations should be considered.The reduced-form estimator should only influence foreignmanufacturing output and productivity through the devel-opment of human capital with respect to U.S. technologies.Most skilled occupations can be dismissed immediately, yetimmigration of business executives and lawyers also in-creased after the 1990 Act. It is possible this business groupmight influence foreign output growth through better salescontacts or higher foreign investment independent of tech-nology transfer. The relative volumes argue against thisconcern, as the size of the influx relative to the existing basefor advanced-degree researchers dwarfs other occupations.The planned inflow of Chinese science and engineeringPhDs for 1991–1995, as measured by the NSF surveys,would have doubled the existing Chinese-born PhD stock inthe 1990 Census. The business inflow over this period isonly about 20% of the 1990 stock.

A second liability is that the reduced-form estimator maybe correlated with other factors. Here, the simplicity of itsdesign is a concern. While determined by the data, thequotas technique only distinguishes between the treatmentgroup (that is, India, Mainland China, the Philippines, andTaiwan) and the remainder of the sample. Other changesoccurring around 1991 that affect the output growth of thetreatment group differentially from the control group couldconfound the analysis. As with any country-level change,possible confounding factors can be hypothesized for eachtreatment member. While the results are robust to excludingany one country from the treatment group, it is of course notpossible to drop them all.

These concerns are why the U.S. quotas are employed fora reduced-form estimator rather than in an instrumentalvariables specification. Immigration quotas directly influ-ence the size of ethnic research communities in the UnitedStates, and thus the unobserved human capital stocks. Assuch, these quotas offer a nice complement to and check onthe earlier metrics derived from ethnic patents; moreoverthe quotas-based metric is more robust to reverse-causalitycriticisms. The estimator does not resolve omitted variableconcerns, however, and lacks industry-level variation thatcan be exploited. As the exclusion restriction for two-stageleast squares would not hold, this study concentrates on thereduced-form outcomes.

C. Reduced-Form Results

The reduced-form regressions for 1985-1997 mirror thepatent-based approach,

� ln�Ycit� � � � � ln�IMMctRF� � �it � εcit, (14)

FIGURE 4.—U.S. SE PHD GRADUATES STAYING

PERCENTAGE OF GRADUATES FROM COUNTRY EXPECTING TO STAY IN THE UNITED STATES

Trend lines show percentages of graduating foreign doctoral students from U.S. science and engineering PhD programs who intend to remain in the United States after graduation. Shares are calculated from NSFsurveys for available countries. The solid line marks the passage of the Immigration Act of 1990 that took effect in 1991.


with ln(I M MctRF) defined by equation (13). Table 7 exhibits

the main results in a format similar to that of table 3. Thereduced-form estimator suggests foreign output increaseswith an elasticity of about 0.3 to higher ethnic research inthe United States. While the coefficients should not bedirectly compared to the patent-based approach, the inter-pretation that greater scientific integration with the UnitedStates boosts foreign manufacturing development is sup-ported. The lower variance in table 7’s estimates acrossweights reflects the country-level design of the immigrationestimator.

In contrast to the patent-based results, panels B and C findoutput growth comes mainly through higher employmentlevels rather than labor productivity gains. This difference iseasily explained with the sector reallocation model. Three ofthe four treated economies had large agricultural sectors in1980 that supported significant expansions in employment;Taiwan is the one exception at 8%. The immigration esti-mator contrasts the outcomes in these economies with thecontrol sample and thus emphasizes the sector reallocationprocess. The patent-based regressions, on the other hand,paid greater attention to the outcomes of Hong Kong,Macao, and Singapore through the application of the U.S.Chinese ethnic patenting series to all economies within theChinese ethnicity. Without an agricultural sector from whichto draw labor, these economies experienced sharper laborproductivity gains.

Table 8 next turns to robustness checks on the outputgrowth finding. As a test of the foreign human capitaldevelopment story, panel B again incorporates the log trendsin foreign graduates from U.S. science and engineering PhDprograms. The technology transfer coefficients hold up wellin the augmented specification. Panel C finds that MainlandChina can again be excluded from the sample with onlyminor shifts in the outcomes. The results are also robust todropping any other country, the computer and drug industries, the full Chinese ethnicity, and the other sample de-

compositions studied above.26 Panel D incorporates a linearethnic time trend that removes the trend growth in both theforeign country output and the U.S. immigration estimator.By doing so, the framework emphasizes the discontinuity ofthe 1990 Act for identification of the parameter. Thecoefficients remain economically and statistically signifi-cant in this stringent specification, providing confidenceagainst the estimator reflecting a spurious correlation.

The last two rows incorporate into equation (14) twocounterfactual estimators that move the 1991 effective dateof the immigration reform earlier to 1987 or later to 1995.The results with the 1987 counterfactual are mixed. Encour-agingly, the coefficients on the true estimator retain most oftheir value and are still statistically different from 0. More-over, the standard errors for the placebo estimators are400% larger than those of the true estimator, and the placeboestimators are not statistically significant. The coefficient

26 These decompositions are more stable than those with the patent-based metric due to the country-level design of the estimator.

TABLE 7.—IMMIGRATION QUOTAS SPECIFICATIONS

NoWeights

PatentWeights

OutputWeights

(1) (2) (3)


� log U.S. immigrationquotas estimator

0.294(0.044)

0.370(0.086)

0.320(0.057)




0.054(0.069)

0.135(0.080)

0.086(0.073)




0.240(0.045)

0.236(0.052)

0.234(0.037)


Notes: Row titles document the dependent variable studied; column titles document the weightingscheme employed. Panel estimations consider country-industry-year observations taken from the 1985–1997 UNIDO manufacturing database. Log U.S. immigration quotas estimators are developed fromquotas changes due to the 1990 Act. Regressions include industry-year fixed effects. Standard errors areconservatively clustered at the ethnicity level.

TABLE 8.—IMMIGRATION QUOTAS COUNTRY CONTROLS SPECIFICATIONS

NoWeights

PatentWeights

OutputWeights

(1) (2) (3)



0.294(0.044)

0.370(0.086)

0.320(0.057)


B. Including Foreign PhDs in the United States


0.280(0.057)

0.350(0.110)

0.297(0.078)

� log foreign PhDstudents in theUnited States

0.033(0.068)

0.054(0.088)

0.054(0.076)


C. Excluding Mainland China


0.210(0.094)

0.306(0.152)

0.252(0.111)


D. Including Ethnic Time Trend


0.317(0.077)

0.385(0.135)

0.327(0.101)


E. Including 1987 Counterfactual


0.262(0.047)

0.335(0.061)

0.287(0.050)

� 1987 counterfactualquotas estimator

0.296(0.199)

0.326(0.262)

0.311(0.232)


F. Including 1995 Counterfactual


0.266(0.081)

0.439(0.096)

0.345(0.067)

� 1995 counterfactualquotas estimator

0.069(0.152)

�0.170(0.099)

�0.063(0.110)


Notes: See table 7. Panel A replicates the foreign country-industry output regressions from table 7.Panels B through F incorporate the country controls indicated by the row titles. All regressions maintainindustry-year fixed effects and the clustering of standard errors.


estimates on the 1987 estimator, however, are of similarmagnitude to the true reform, and it cannot be rejected thatthe coefficients are the same. Panel F, on the other hand,shows better performance with the 1995 counterfactual.These results support the conclusion of stronger scientificintegration leading to foreign output growth, but also high-light that the estimated elasticity with the immigrationestimator may be partly capturing an earlier differentialchange for the treatment group.

Establishing the causal direction of international technol-ogy flows is a very daunting task. The reduced-form quotasestimator offers more confidence than the patent-basedapproach that coefficient estimates are not determined byreverse causality (especially foreign human capital devel-opments). The price for this exogenous determinant, how-ever, is the loss of industry variation that can be exploited.This reduced variation may leave the quotas estimatorexposed to omitted variable biases contemporaneous to orslightly preceding the reform, although the multiple robust-ness checks suggest spurious correlations are not solelyresponsible for the outcomes measured. Overall, the reduced-form regressions support the conclusion in section IV thatforeign manufacturing output increases with stronger ethnicscientific integration to the U.S. frontier.

VI. Conclusions

The international diffusion of new innovations from fron-tier countries is necessary for broad economic growth.Successful transfer, however, is complicated by the difficultdissemination of the codified and tacit knowledge necessaryfor adoption. This project considers the role and importanceof knowledge networks for exchanging this informationthrough the observable channel of ethnicity, examining theties between U.S. ethnic research and entrepreneurial com-munities and their home countries. The findings suggest thatthese frontier expatriates do play an important role intechnology transfer, and more generally that inadequateaccess to the codified and tacit knowledge complementingnew frontier innovations can slow development in followingregions.

This study concentrates on estimating the general elas-ticities for technology transfer across multiple ethnicitiesand manufacturing industries. The platform, however, iden-tifies particular strength for high-tech industries and Chi-nese communities. While still measurable, the responses areweaker elsewhere. Future work should investigate whetherthese patterns hold in other samples too. Chinese economiesexperienced exceptional manufacturing development duringthe 1985–1997 period. The extent to which these resultsextend to nonmanufacturing sectors will shed light onwhether the strong Chinese outcomes are due to the manu-facturing focus, as all data sets have above-average out-comes, or due to unique qualities of this ethnicity’s network(for example, size and network effects). Likewise, charac-terizing portions of nonmanufacturing sectors like financial

and business services that are conducive to technologytransfer will refine our understanding of the traits of indus-tries (such as, vertical integration, product cycles) whereU.S. ethnic scientists and entrepreneurs can most aid theirhome countries.

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Documents

William R. Kerr* Files...While informative, citation patterns do not quantify the extent to which following countries realize economic ben-eﬁts from better access to U.S. innovations