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Industrial and Labor Relations Review, Vol. 58, No. 2 (January 2005). © by Cornell University.0019-7939/00/5802 $01.00

H

THE INFLUENCE OF WAGES AND

INDUSTRIAL RELATIONS ENVIRONMENTS ON

THE PRODUCTION LOCATION DECISIONS

OF U.S. MULTINATIONAL CORPORATIONS

MARIO F. BOGNANNO, MICHAEL P. KEANE, and DONGHOON YANG*

Using the Benchmark and Annual Surveys of U.S. Direct Investment Abroadcollected by the Bureau of Economic Analysis, the authors examine the opera-tions of U.S. multinational corporations (MNCs) in seven manufacturing indus-tries and twenty-two countries over the years 1982–91. They analyze how tariffs,wages, and industrial relations environments influenced U.S. MNCs’ decisionsabout where to locate assets and employment. The results imply that wages andthe industrial relations environment were statistically significant determinantsof the extent to which MNCs located operations in a particular host country.However, while these factors were important, their impact was much smallerthan that of host country market size, which was by far the main determinant ofMNC location decisions. Furthermore, the authors find no evidence that tariffreductions increased the share of U.S. MNC activities located abroad. Thus,concerns that tariff reductions may lead to loss of “American jobs” appear to beexaggerated.

*Mario Bognanno is Professor in the IndustrialRelations Center, University of Minnesota; MichaelKeane is Professor of Economics, Yale University; andDonghoon Yang is an Associate Professor in the Schoolof Business at Sogang University in Seoul, SouthKorea. For helpful comments, the authors thankseminar and conference participants at the Depart-ment of Economics, University of Le Havre, France;the Industrial Relations Research Association; theKorea Development Institute, Seoul; and the Indus-trial Relations Center (IRC), University of Minne-sota.

The data and computer programs used in thisstudy are available on request from Donghoon Yang([email protected]).

1For instance, a spokesman for the AFL-CIO stated,“A new trade agreement with Mexico . . . will onlyencourage greater capital outflows from the UnitedStates, bring about an increase in imports fromMexico, and reduce domestic employment” (seeAnderson 1993:55). See also Betcherman (1993) orRobinson (1994).

ow multinational corporations(MNCs) decide where to locate pro-

duction has been a subject of considerableinterest in recent years. For example, inthe debate over the North American Free

Trade Agreement (NAFTA), U.S. laborunions and many politicians, as well as someacademics, expressed concern that tradeliberalization would cause U.S. MNCs tomove production facilities from the UnitedStates to Mexico, so as to take advantage oflower Mexican wages.1 The basic logic is

172 INDUSTRIAL AND LABOR RELATIONS REVIEW

that in the absence of U.S. tariffs, it wouldin many instances be optimal for U.S. firmsto produce in Mexico, where labor costs arelower, and then to ship finished productsback to the United States for sale. Accord-ing to this view, U.S. tariff walls provide animportant incentive for corporations toproduce within the United States thosegoods intended for final sale in the UnitedStates.

This raises the empirical question of justhow tariffs and cross-country wage differ-entials affect the production location deci-sions of MNCs. Production location deci-sions include how to allocate capital andemployment across countries, how muchoutput to produce in each country, and thevolumes of goods to ship between coun-tries. There now exists a large literature onthe factors that influence MNCs’ foreigndirect investment (FDI), employment, andproduction in various countries. Interest-ingly, this work has typically found thathost country wages either are not a statisti-cally significant determinant of MNCs’ pro-duction location decisions or are a statisti-cally significant determinant but with the“wrong” sign. That is, conditional on othercontrol variables, the correlation betweenthe amount of capital, employment, or pro-duction MNCs locate in a country, and theaverage wage rate in the country, is usuallyfound to be essentially zero or even positive(see, for example, Swedenborg 1979; Dun-ning 1980; Cooke 1997; Cooke and Noble1998).2

Findings of zero or positive correlationsbetween MNC investments and wage ratesacross countries have been viewed as anoma-lous given the widespread expectation that,ceteris paribus, returns on investment shouldbe greater in countries with lower laborcosts. There are at least four potentialexplanations for this anomalous finding.First, if MNC organization is primarily “hori-zontal” rather than “vertical,” meaning that

most FDI is driven by market access consid-erations (rather than the desire to exploitfactor price differentials), we would expectto see most FDI in large markets (that is,industrialized high-wage countries).

A second possibility is that, if labor qual-ity is much lower in low-wage countries,then wages per efficiency unit of labor maybe relatively high in such countries. Third,unobserved (or unmeasured) characteris-tics of countries that make them attractiveplaces in which to invest (such as favorablefactor endowments, low corruption, oropenness to FDI) may be positively corre-lated with wage rates. In the second andthird scenarios, the effect of the wage perefficiency unit of labor on the attractive-ness of a country as a production location isin fact negative, but omitted variables maylead to bias in the regression estimates ofthe true effect.

A fourth possibility is that average wagesin foreign countries are not relevant forMNC production location decisions. AnMNC that uses a particular technology in itsproduction process may require workerswith particular skills. In that case, even ifthe average wage rate in a country is low,the wage rate among the relevant set ofworkers with the necessary skills to imple-ment the production process may be high.

Under this fourth scenario, it may makesense to view U.S. MNCs not as wage takersin competitive labor markets in each hostcountry, but rather as bargaining over wageswith the set of workers possessing the nec-essary skills to operate their productionprocesses. In that case, the attractivenessof a host country may be influenced byfeatures of its industrial relations (IR) envi-ronment that affect bargaining outcomes,such as union power and collective bar-gaining structure. Indeed, Ulman (1975)and Carlton (1979) argued that firms avoidinvestment in locations where unions arepowerful, independent of the wage rate. Akey objective of this paper is to evaluatewhether features of the IR environment arein fact important determinants of MNCproduction location decisions.

Our paper contributes to the literatureby using industry-level panel data to exam-

2Of course, conditional on an MNC having chosento operate in a country, a positive wage effect oncapital can be easily rationalized, provided the substi-tution effect dominates the scale effect.

LOCATION DECISIONS OF U.S. MULTINATIONALS 173

ine U.S. MNCs’ production location deci-sions. Specifically, we use data on the op-erations of U.S. MNCs in seven manufac-turing industries and twenty-two foreigncountries over the years 1982–91. The dataare from the Benchmark and Annual Sur-veys of U.S. Direct Investment Abroadcollected by the Bureau of EconomicAnalysis (BEA), plus a number of addi-tional data sources that we have mergedwith the BEA data. In contrast, mostprevious work in this area has used cross-sectional data on MNC activities in asingle year, or used data aggregated tothe national level.

For example, a paper to which ours isclosely related is that by Grubert and Mutti(1991a), who found, using data from theBEA’s 1982 Benchmark survey aggregatedto the national level, that both corporatetax rates and tariffs are important deter-minants of U.S. MNCs’ production loca-tion decisions (they did not, however,examine the influence of wages or IRfactors). Another related paper that usedcountry-level panel data, Kleiner andHam (2003), reported evidence that more“progressive” IR systems do reduce FDIflows. As we discuss below, however, thereare important reasons to examine FDIlevels as well as flows. The papers byCooke (1997), Cooke and Noble (1998),and Cooke (2001) used cross-sectionaldata at the country/industry level, andfound evidence that IR systems affect theallocation of MNC assets across coun-tries. To our knowledge, our paper is thefirst to use industry-level panel data toaddress these issues.

The use of industry-level panel data tostudy FDI is desirable for two reasons. First,panel data enable one to exploit the tem-poral co-variation (within industries) be-tween FDI and the covariates of interest inorder to better identify the effects of thosecovariates (that is, these data provide infor-mation beyond that in the cross-sectionalco-variation).

Second, there are likely to be unobserved(or unmeasured) characteristics of particu-lar countries that make them attractive aslocations for particular industries. Ex-

amples are countries’ factor endowments,3

as well as differences in worker quality thatare not captured by observed characteris-tics of the labor force. But we are inter-ested in how certain “generic country fac-tors,” such as wages and features of the IRenvironment, affect MNC production loca-tion decisions. If such generic factors arecorrelated with unmeasured country char-acteristics, then failure to control for coun-try/industry effects will lead to inconsis-tent estimates of wage and IR effects. Oneneeds industry-level panel data to imple-ment such controls.

As will become apparent when we presentour results, we find that controlling forindustry- and country-specific unobserv-ables using panel data techniques does in-deed lead to more reasonable estimates ofwage and other effects on MNC productionlocation decisions than have been obtainedin prior work. This highlights the impor-tance of using industry-level panel data tostudy these questions.

Theoretical and EmpiricalLiteratures on MNC Behavior

To guide our empirical specifications,we turn to the theoretical literature onMNC behavior. Markusen and Maskus(2001, 2002) provide an excellent overviewof the current state of the literature. Theydivide theories of the MNC into those thatgenerate “vertical” versus “horizontal”MNCs. Vertical MNCs fragment the pro-duction process across countries to takeadvantage of factor price differentials, forexample, by locating unskilled labor-inten-sive parts of the process in low-wage coun-tries. Horizontal MNCs basically replicatethe entire production process in multiplecountries, thus avoiding tariff and trans-port costs.4

3For example, MNCs in the pulp, paper, and boardmills industry would presumably invest heavily inCanada even if its business climate were not particu-larly attractive, simply because of its substantial tim-ber endowment.

4Theories of the MNC originated in the interna-tional business literature, with the so-called “Owner-


These theoretical models generate clearpredictions for the set of variables we ex-pect to have an important impact on FDI.The vertical models obviously imply thatfactor price differentials (wage differen-tials, for example) between potential hostcountries and the United States should beimportant. Vertical models also imply thattariffs (as well as geographic distance andtransport costs) discourage FDI by prevent-ing MNCs from exploiting factor price dif-ferentials. This predicted mechanism isconsistent with the notion that trade liber-alization could cost U.S. jobs.

In contrast, horizontal models imply thatmarket size (that is, host country gross do-mestic product) is the main factor drivingFDI, since market access is MNCs’ mainmotivation. The horizontal models alsoimply that higher tariffs (and distance)should encourage FDI because they increasethe advantage to locating production in thehost country. Both vertical and horizontalmodels imply that variables affecting thecost of foreign operations (such as Englishlanguage, cultural openness, local corrup-tion, corporate taxes, and the IR environ-ment) should matter.

As Markusen and Maskus point out, ifU.S. MNCs direct most of their FDI toward

large countries with factor prices similar tothose in the United States, the horizontalmodel will be the better predictor of FDIpatterns. But if most FDI is directed towardcountries with very different factor prices(for example, lower wages), the verticalmodel is the better predictor. Since ourgoal is to focus on whether wages and IRfactors have an important impact on FDI,without taking an a priori stand on whetherthe vertical or horizontal model is mostappropriate, we simply include a rathercomplete set of control variables that mightbe relevant under one or the other model.

Feinberg and Keane (2003) presentedan estimable structural model of MNC be-havior that guides the reduced form em-pirical work here (as well as that in Feinberg,Keane, and Bognanno 1998 and Feinbergand Keane 2001). Their model considersan MNC with an affiliate in a single foreigncountry. The MNC chooses eight inputs,{Kj, Lj, Mj , Nj }j=d,f, where K, L, M, N denotecapital, labor, raw materials, and interme-diate inputs, respectively, and the j = d, fsubscript denotes whether the input is usedin the parent (domestic) or affiliate (for-eign) production process. The MNC alsochooses a level of exports (E) from theparent to the host country, and of imports(I) from the affiliate to the domestic mar-ket. The intermediate input Nd denotes apart of the affiliate’s output that is shippedback to the parent for further processing,and conversely for Nf. This model nestsboth the vertical and horizontal models ofMNC organization. A purely horizontalMNC, for example, would have Nd = Nf = E =I = 0, so the affiliate production processreplicates the parent’s, all parent output issold in the United States, and all affiliateoutput is sold in the host country.

The ten MNC choice variables, as well asany functions of those variables (like out-put or sales), are endogenous in the model.The MNC chooses these ten variables tomaximize profits, given a set of constraintsdetermined by the parent and affiliate pro-duction functions, the wage rate, prices ofcapital and materials, tariffs and transportcosts, and four demand functions for thegoods produced by the parent and affiliate

ship-Location-Internalization (OLI) paradigm”—seeHymer (1976) and Dunning (1975). In the OLItheory, in order to rationalize FDI, an MNC must havean “ownership advantage,” such as a superior produc-tion process or a well-known brand name, whichenables it to compete with foreign firms in their ownmarkets (despite language barriers, communicationand transport costs, and so on). The MNC must alsohave an “internalization advantage”—see Rugman(1985)—which makes FDI preferable to licensing.For example, a licensee might steal production pro-cess knowledge or dilute a brand name by skimpingon quality. Finally, the MNC must possess a “locationadvantage” that makes FDI preferable to serving for-eign markets via exports. In “horizontal” MNC mod-els the location advantage arises from the desire tojump the tariff wall or save on transport costs, orbecause proximity to final customers is important(for example, to provide service). In contrast, in“vertical” MNC models, the main purpose of FDI is tofragment the production process across countries toexploit factor price differentials, so tariffs discourageFDI.


(the domestic and foreign demands for thegood produced by the parent, and the do-mestic and foreign demands for the goodproduced by the affiliate). The demandfunctions are shifted by domestic and for-eign gross domestic product (GDP). Ex-change rates enter the model implicitly byaltering the relative factor prices and out-put prices in the two countries. Any vari-ables that affect MNC behavior through theconstraints are exogenous in the model.

Brainard (1995) also provided a usefuldiscussion of which variables are viewed asexogenous in the dominant theoreticalmodels of MNCs. These include transpor-tation costs, tariffs, corporate tax rates, andmeasures of income and market size for thedomestic and foreign markets. In theFeinberg and Keane (2003) structuralmodel these variables are also exogenous,as are wages, the price of capital,5 and otherfactor input prices. Here we augment thislist of assumed exogenous variables to in-clude national-level IR environment vari-ables, which we argue may affect the firm’slabor costs. Additional factors enteringthrough costs of doing business in a hostcountry include English language profi-ciency, cultural openness, and corruptionmeasures.

In this theoretical framework, the con-struction of a valid reduced form equationrequires that one of the endogenous vari-ables (or some function thereof) be re-gressed on the complete set of exogenousvariables. In contrast, in the FDI literature,when authors have estimated equations inwhich the dependent variables are such

quantities as MNCs’ assets, sales, employ-ment, or output in various host countries,they have commonly used as “explanatory”variables such quantities as capital/laborratios, exports to the host country, R&Dspending, advertising expenditure, and firmsize. But such “explanatory” variables arejointly determined along with the MNCs’decisions about production location and,therefore, they are potentially endogenous.

The common practice of including mea-sures of exports to a host country (E in ournotation) as a predictor of the level of MNCassets located in that county (Kf in ournotation) has been rationalized by the ar-gument that exports are an alternative toFDI, and they therefore substitute for FDI.However, by analogy, to include exports onthe right-hand side of a “reduced form”equation to predict FDI (that is, Kf) is nomore appropriate than to include laborinput on the right-hand side of an equationto predict demand for capital. Rather, avalid reduced form for FDI should includeon the right the complete set of exogenousvariables that may affect the cost of servingthe foreign market via either FDI or ex-ports. Examples of the former are wagesand the corporate tax rate in the foreigncountry, and an example of the latter is theforeign tariff rate.

With a few notable exceptions (see, forexample, Grubert and Mutti 1991a;Cummins and Hubbard 1995; Brainard1995; Cooke and Noble 1998), it is difficultto find examples in the literature on MNCproduction location decisions and FDI inwhich authors have estimated valid reducedforms in the sense that the explanatoryvariables include only quantities that areassumed exogenous in standard theoreti-cal models of MNC behavior.

We turn next to a discussion of the de-pendent variables of interest. The mostobvious measure of FDI in a particular hostcountry is the capital of U.S. MNCs’ affili-ates in that country (Kf in our notation). Ofcourse, there are well-known problems withmeasuring capital stocks. We proxy foraffiliates’ capital stock using the assets ofmajority-owned foreign affiliates (MOFAs)in a particular country, as reported by the

5We realize that in a richer model of capital invest-ment decisions the price of capital is also endog-enous, depending, for instance, on firm-specific capi-tal adjustment costs, risk/return factors, and finan-cial structure and how this interacts with taxes. Fur-thermore, in equilibrium the price of capital will becorrelated with aggregate productivity shocks thatare common to all firms. We abstract from thisproblem and simply control for aggregate-level deter-minants of the cost of capital, like interest rates andcorporate tax rates, which are plausibly exogenousfrom the perspective of individual firms (that is, ifproductivity shocks are largely idiosyncratic).


Bureau of Economic Analysis (BEA).6 Wethen model the share of U.S. MNCs’ world-wide capital stock (within each manufac-turing industry) that is allocated to each of22 potential host countries.

Working with shares has several virtues.First, it enables us to avoid modeling thedeterminants of the overall level of theMNC capital stock. This would obviouslyrequire a dynamic model of investmentbehavior, raising a host of serious compli-cations that are tangential to our mainfocus. Instead, we focus on how the relativeattractiveness of potential host countries(as determined by the set of assumed exog-enous variables described earlier) affectstheir share of MNCs’ worldwide capitalstock.7 Second, a related point is that work-ing with shares alleviates some of the well-known problems associated with finding anappropriate price index to measure growthof the real capital stock over time. Third,working with shares helps to alleviateheteroskedasticity problems that typicallyarise in a panel data context when onepools data from industries or countries (orboth) that differ in size. We discuss thisfurther below, under “Econometric Issues.”

Of course, our focus is not just on U.S.MNCs’ allocation of capital to various po-tential host countries, but also on MOFA

employment (Lf in our notation). Also,following earlier literature, we examineanother measure of the size of MNCs’ for-eign operations: the sales of U.S. MOFAs ineach host country, net of their importsfrom the U.S. parent. Again, we look atspecifications in which the dependent vari-ables are specified as the shares of U.S.MNCs’ worldwide employment or net saleslocated in each host country.

We hasten to point out a key problemwith the net sales measure: since salesequals price times quantity, one cannotinterpret the coefficients on covariates in anet sales regression as necessarily captur-ing effects on scale of MNC operations.Rather, the coefficients will capture theeffects of covariates on both the volume ofproduction and the price level. To theextent that the MNC has market power inthe host country (and theories of the MNCimply that it should), a drop in localproduction may lead to a higher price.Still, since it has been common in FDIliterature to use net sales as a measure ofthe size of MNCs’ local operations ineach host country, we look at this depen-dent variable for the sake of comparabil-ity with earlier studies.

We conclude this section with a specificdiscussion of some earlier studies that fo-cused on the role of wages and IR factors inMNC production location decisions. Karier(1995) estimated equations for value added,sales, and employment of foreign affiliatesof U.S. MNCs in 32 industries and 10 geo-graphic regions, using data from the BEA’s1982 benchmark survey. He found thatunionization rates in host countries werestatistically insignificant in the value addedand sales equations, and actually were posi-tive and statistically significant in the em-ployment equation. However, he includedcapital-labor ratios, R&D, and advertisingexpenditures as explanatory variables, allof which we would argue are endogenous,and this clouds the interpretation of theresults. He did not control for wage rates orunobserved country/industry effects (sincehe used data for only one year).

Cooke (1997) examined data from the1989 BEA survey. He looked at assets of

6In contrast, many existing studies look at balanceof payments measures of FDI. As Stevens (1972) andGrubert and Mutti (1991b) pointed out, this is inap-propriate for studying real investment decisions—which is what we are interested in. The problem isthat FDI financed by local borrowing in the hostcountry does not affect the balance of payments. Thispoint is not just academic. Grubert and Mutti foundthat the correlation over time between the U.S. bal-ance of payments measure of FDI in Canada and thefixed investment expenditures of U.S. MNC affiliatesin Canada is essentially zero. And Culem (1988)found that FDI flows are driven by nominal interestrate differentials.

7By analogy, in the consumption literature, it iscommon to model shares of household spending allo-cated to each category of goods, as a function ofrelative prices of those goods, in a static framework.This avoids the complex task of modeling the overalllevel of consumption, which would require dynamicmodeling of saving behavior.


U.S. MNC foreign affiliates in nine indus-tries and nineteen industrialized host coun-tries, and considered four IR factors: theunionization rate, decentralized bargain-ing, layoff restrictions, and whether workscouncils were required. His results weremixed: centralized bargaining, hiring/fir-ing restrictions, and higher unionizationrates were all associated with lower assets in1989 (as expected), while work council re-quirements were associated with higherassets (which is unexpected). When Cookeand Noble (1998) extended this analysis toa sample that included developing coun-tries and also looked at employment, theyobtained similar results. However, sincethese papers used only one year of data,they could not control for country/indus-try effects. Cooke found that wage rateswere statistically insignificant, even thoughhe used average education levels of coun-tries to control for worker quality. AndCooke and Noble found the typical positivecoefficient on wages in both asset and em-ployment equations (provided wages wereabove a certain threshold level).8

As indicated in the introduction, oneplausible reason for obtaining the “wrong”sign on wages in MOFA employment equa-tions is failure to control adequately forintrinsic differences in worker productivityacross countries. Some authors try to ad-dress this problem by using unit labor costsrather than wage rates as measures of laborcosts (see, for example, Devereux andGriffith 1996, or Erickson and Kuruvilla1994). But this is problematic, becauseunit labor costs are themselves endogenous.They depend in general on the productiontechnology of firms and their factor input

decisions.9 We argue that a better way todeal with the problem is to use panel datatechniques to control for country-specificunobservables that drive productivity dif-ferences.

Data

Our data on assets, employment, and netsales of U.S.-based MNCs and their major-ity-owned foreign affiliates (MOFAs) comefrom the Annual and Benchmark Surveysof U.S. Direct Investment Abroad, adminis-tered by the Bureau of Economic Analysis(BEA). These are the most comprehensiveavailable data on the activities of U.S.-basedMNCs and their foreign affiliates.

We use the BEA data on 7 manufacturingindustries and 22 countries for the years1982–91. The industries are food and kin-dred products, chemicals and allied prod-ucts, primary and fabricated metals, ma-chinery and nonelectronic equipment, elec-tronic equipment, transportation, and“other.” The 22 countries include 15 Euro-pean countries, Japan, Australia, NewZealand, Singapore, Korea, Mexico, andCanada. We chose these countries becausewe were able to obtain complete data forthem on the IR and other control variablesof interest. The BEA reports data on assetsand sales in U.S. dollars, based on averagespot exchange rates for the fiscal year.

Given our 10-year sample period, andthe fact that we use 7 industries and 22countries, there are 1,540 total potentialobservations on each dependent variable.But the BEA suppresses data in cases wherethe information might reveal the activitiesof particular firms (in particular, if onlyone or two U.S. MNCs have an affiliate in aparticular industry located in a particularcountry). Thus, the data actually contain1,202 total observations on assets, 1,273 on

8More recently, Cooke (2001) examined the allo-cation of FDI by MNCs of each of 16 OECD countriesamong the other 15 OECD countries, using OECDdata from 1994. He found that centralized bargain-ing and layoff restrictions discourage FDI. He at-tempted to control for worker quality differencesusing the average education level in each country.But he still obtained an unexpected positive coeffi-cient on the wage per year of education.

9For example, given a Cobb-Douglas technology y= K α(βL)1-α, where β is efficiency units per unit oflabor, the firm sets wL/y = (1–α). Thus, the unit laborcost is (1–α), regardless of the wage per efficiencyunit (w/β).


employment, and 941 on net sales. We alsodrop cells with zero values, leaving 1,150,1,218, and 881 observations, respectively.

For manufacturing industries, the 22countries in our sample account for over80% of all U.S. MOFA sales and 70–76% ofall U.S. MOFA employment for this period.Furthermore, the sample countriesclosely parallel the time-series pattern ofworldwide sales and employment of allU.S. MOFAs in manufacturing indus-tries.10 Thus, we doubt that includingother (smaller) countries as potentialtargets of FDI would have much effect onour results.

We model the production location deci-sions of U.S. MNCs as depending on a set ofindependent variables that capture the at-tractiveness of both the United States andthe set of foreign host countries as produc-tion locations. These independent vari-ables include U.S. and host country aver-age manufacturing wage rates, corporatetax rates, and tariff rates. Proxies for U.S.and host country market size and relativedemand, as well as transportation costs, arealso included, along with miscellaneousmacroeconomic variables meant to capturetime effects on costs of capital or demand.Finally, we include a set of variables thatcapture features of the IR environment inthe United States and each host country.We next describe each of these variables.

Data on foreign and U.S. hourly manu-facturing wages are obtained from the U.S.Department of Labor (1993), and theyare converted into U.S. dollar values us-ing nominal exchange rates. They arethen put on a 1985 base using the U.S.GDP deflator (the same is true of allnominal variables described below). Toconserve on parameters, we enter thewage rates in ratio form, as the ratio ofthe foreign wage to the U.S. wage.

Average corporate tax rates for the 22

foreign countries are constructed from theBEA data, by calculating the percentage oftotal affiliate income paid in taxes by coun-try and by year. We obtained U.S. corpo-rate income tax rates from Cummins, Har-ris, and Hassett (1995). Differences be-tween foreign tax rates and the U.S. tax rateare then included in the model.

U.S. and foreign tariff rates are takenfrom the International Data Base compiledby the World Trade Organization (WTO).The ad valorem nominal tariff rates com-piled by the WTO are industry averagerates, unweighted by imports. Since theBEA uses a system of ISI industry codesbased on 2- to 3-digit U.S. SIC codes, weconverted the 3-digit SITC codes used inthe WTO tariff data into 3-digit SIC codes,and subsequently into the BEA’s ISI codes.11

Tariff rates are Most Favored Nationduties because all 22 of the sample coun-tries are members of GATT (the GeneralAgreement on Trade and Tariffs). Partici-pants in the Tokyo round of the GATTagreed to implement an average one-thirdtariff reduction in eight equal annual in-crements beginning January 1, 1980 (seeKowalczyk and David 1998). Thus, tariffrates set by the Tokyo Round apply to thefirst six years of our data set, 1982–87.There were no further major multilateralnegotiations until the Uruguay round,which specified additional tariff reductionsbeginning in 1995. Thus, we use the initialtariff levels from the Uruguay round tomeasure tariffs in the years 1988–91. Anexception is made in the case of Canada,since the U.S.-Canada Free Trade Agree-ment (FTA) went into effect in 1989. Thus,rates specified by the FTA were used after1989. We include both current and five-year leads of the U.S. and host countrytariff rates in our models, to account for the

10It is worth noting that MOFA total assets (inmanufacturing industries) grew rapidly from $213billion in 1982 to $369 billion in 1991. But totalemployment showed no trend (that is, it fluctuated inthe 3.0–3.4 million range).

11To do this we use a concordance table publishedby the U.S. Department of Commerce (1995) in U.S.Exports and Imports of Merchandise (CD-ROM). Tariffrates for Mexico were not classified by U.N. SITCcodes but rather by Harmonized System codes, so theconcordance was made between Harmonized Systemand BEA industry codes.


possibility that current MNC decisions de-pend on both current and anticipated fu-ture tariffs.

Foreign and U.S. real GDP are used tomeasure market size, which the “horizon-tal” model of MNCs suggests should be akey determinant of FDI. To capture rela-tive market size, we enter these variables inratio form (host country GDP divided byU.S. GDP). We also include the per capitaincome ratio (host country PCI divided byU.S. PCI). This is meant to capture thecomposition of demand (that is, the rise inthe demand for manufactured goods as percapita income rises), but it should also helpcapture cross-country differences in workerproductivity.

The earnings-to-price (EP) ratio for eachnational stock market is obtained from datasets compiled by Morgan Stanley CapitalInternational and International FinanceCorporation. We include these EP ratios as(arguably exogenous) determinants of thecost of raising equity capital in each coun-try, and enter the foreign-U.S. differencein the model.12

The GDP, PCI, and EP ratio variables canall be viewed more generally as a set ofcontrols for time-varying macroeconomicfactors that influence MNC location deci-sions. We include an overall time trend aswell, to capture omitted macroeconomicfactors that may be altering the share ofMNC assets, employment, and net salesthat occur abroad.

The geographic distance between theUnited States and a host country is in-cluded to capture transportation costs. Adummy variable indicating whether a coun-

try is a member of the European Commu-nity (EC) is also included, in recognition ofthe fact that locating a production facilityinside Europe enables a U.S. MNC to jumpthe EC tariff wall, and also that location ofproduction in Europe may be relativelyeasy due to cultural similarities.

We also include a “cultural openness”variable, which measures the extent to whicha country is culturally varied and open toforeign influence, and a “corruption” vari-able that measures the extent to whichimproper practices such as bribery prevailin the public sphere. These are both takenfrom the World Competitiveness Report(1992, 1990).

We characterize the IR environment ofeach host country by five variables: uniondensity, strike intensity, collective bargain-ing structure, works councils, and layoffrestrictions. Union density is meant tocapture the likelihood that an affiliate lo-cated in a particular host country will beunionized. To the extent that U.S. MNCswould prefer to operate non-union, theywill tend to locate production in countrieswith relatively low union densities. Uniondensities for the OECD countries are takenfrom Visser (1991). For Singapore, Korea,and Mexico, we refer to government re-ports and Foreign Labor Trends (U.S. Depart-ment of Labor). Since data on union den-sities for all sample countries are only avail-able for 1982, 1985, and 1988, the uniondensities in those years are carried forwardfor 1983–84, 1986–87, and 1989–91, re-spectively.

The strike intensity variable is meant tocapture the potential cost of unionizationin terms of lost production. It is measuredby the number of working days lost to strikesper employee. For OECD countries, we useU.K. Department of Labor statistics on strikeintensity reported in Employment Gazette.For non-OECD countries, the ILO’s Year-book of Labor Statistics or government publi-cations of the particular countries are used.

The collective bargaining variable mea-sures the extent to which bargaining iscentralized (that is, largely conducted atthe national, regional, industrial, or occu-pational level) versus decentralized (largely

12We do not attempt to include firm- (or industry-)specific measures of the cost of capital because thesemay be endogenous. We assume that firm-specificactivities do not affect the return on equity at thenational level. We abstract from the problem that theoverall level of FDI directed toward a country mightaffect the price of capital in that country (or factorprices more generally). To deal with this sort ofendogeneity problem would require a worldwide gen-eral equilibrium framework, taking us well beyondthe scope of our analysis.


conducted at the firm or plant level). Weexpect U.S. MNCs to prefer one-on-onenegotiations with individual unions, ratherthan a multi-employer framework. Wemeasure centralization using an adapta-tion of the Calmfors and Driffill (1988)index, which is available for 16 of our 22countries. The index is on a scale of 0 to 6,with 0 indicating the maximum degree ofcentralization. Calmfors and Driffill didnot classify Korea, Mexico, Greece, Portu-gal, Singapore, and Spain, so we appliedthe same criteria to classify them, usingdescriptions given in Rothman, Briscoe,

and Nacamulli (1993), Commission of theEuropean Communities (1994), andTorriente (1997). Our classifications arereported in Appendix Table A1.

Finally, the Works Council dummy vari-able is set equal to one if works councils arelegally recognized or prevail in the coun-try, as reported in Kleiner and Ay (1996),and the Layoff Restriction dummy equalsone if the country requires employers tonotify the government and consult withunions or works councils about layoffs, asreported by Cooke and Noble (1998). Wehave rated countries that were not included

Table 1. Variable Definitions and Sources.

Variable Definition and Source

MOFA total asset ratio U.S. majority-owned foreign affiliates’ total assets in industry i,country j, year t, divided by U.S. MNCs’ worldwide total assets inindustry i, year t. Source: U.S. Department of Commerce (1985–94).

MOFA total employment ratio U.S. majority-owned foreign affiliates’ total employment in industry i,country j, year t, divided by U.S. MNCs’ worldwide total employmentin industry i, year t. Total employment is the sum of full-time andpart-time workers. Source: U.S. Department of Commerce (1985–94).

MOFA net sale ratio U.S. majority-owned foreign affiliates’ net sales in industry i, countryj, year t, divided by U.S. MNCs’ worldwide net sales in industry i, yeart. Net sales is total sales minus MOFA imports from the United Statesand other countries. Source: U.S. Department of Commerce (1985–94).

Wage ratio Ratio of the hourly compensation rate of manufacturing productionworkers in host country j at time t to that in the United States at timet. Compensation is the sum of base pay and fringe benefits per hour.Source: U.S. Department of Labor (1993).

Union density Proportion of wage and salary workers in country j in year t who areemployed union members. Source: Visser (1991) and variousgovernment reports of Asian countries.

Strike intensity Days lost per worker due to work stoppages in country j in year t.Sources: U.K. Department of Labor (1992) and ILO (1995).

Collective bargaining structure Degree of decentralization in bargaining structure. Sources: Calmforsand Driffill (1988) and the authors’ own calculations.

Works councils Equals 1 if works councils are legally recognized or prevail in the hostcountry; equals 0 otherwise. Source: Kleiner and Ay (1996).

Layoff restrictions Equals 1 if country requires employers to notify government laboroffice and consult with unions or works councils about layoffs; equals0 otherwise. Sources: Cooke and Noble (1998) and authors’ owncalculations.

GDP ratio The ratio of host country GDP to U.S. GDP. Sources: OECD, NationalAccount—Main Aggregates (various years) and IMF, InternationalFinancial Statistics Yearbook (various years).

Geographic distance Direct land and sea distance between capitals of the U.S. and foreigncountries. Source: Fitzpatrick and Modlin (1986).

Continued


in Cooke and Noble’s study (see AppendixTable A1).

Table 1 contains a complete list of vari-able definitions, and descriptive statisticsare given in Table 2. Appendix Table A1gives the IR environment variables for eachcountry in our sample.

Table 3 presents the fraction of MOFAassets and employment in each of the 22sample countries in 1982 versus 1991. Thestriking impression conveyed by this tableis that the relative size of U.S. MNCs’ opera-tions in the various countries is very stableover the sample period. For instance, forthe share of MNC employment located ineach host country, the top 8 ranked coun-tries are identical in 1982 and 1991, and the

top 3 are all industrialized countries (theUnited Kingdom, Canada, and Germany).Thus, there is no overall pattern of move-ment of MNC activities toward low-wagecountries during this period. The one ob-vious exception is Mexico: the share of U.S.MNCs’ worldwide employment located inMexico increased from 1.67% in 1982 to2.86% in 1991, a substantial increase. Nev-ertheless, Mexico’s employment rank staysfixed at 4.

Note, however, that the failure to seeMNC activities moving into low-wage coun-tries over time has no bearing on the extentto which MNCs prefer to locate in low-wagecountries. As Grubert and Mutti (1991a)pointed out in a different context, results

Table 1. Continued.

Variable Definition and Source

English dummy Equals 1 if the country uses English as a native language; equals 0otherwise. Australia, Canada, Ireland, New Zealand, and the UnitedKingdom are coded as 1.

PCI ratio Ratio of host country per capita income to U.S. per capita income.Source: United Nations Conference on Trade and Development(1982–93).

EC membership Equals 1 if the country is a member of the European Community;equals 0 otherwise. The European countries that were EC membersare Belgium, Denmark, France, Germany, Greece, Ireland, Italy, theNetherlands, Portugal, Spain, and the United Kingdom. TheEuropean countries that were not EC members (during our sampleperiod) are Austria, Norway, Sweden, and Switzerland.

Corporate income tax rate The host country corporate income tax rate in year t minus the U.S.difference corporate income tax rate in year t. Sources: The percentage of total

affiliate net income paid as income taxes in country j in year t wascalculated from BEA annual and benchmark surveys. For the U.S. taxrates, see Cummins, Harris, and Hassett (1995).

Earnings-to-price ratio difference Difference between foreign and U.S. earnings-to-price ratio, for stockmarket in host country j in year t. Monthly ratios were averaged toyearly ratios. Source: unpublished data from Morgan Stanley CapitalInternational and International Finance Corporation.

Cultural openness The extent to which the culture of a host country is open to foreigninfluence. Source: IMD (1992).

Corruption The extent to which improper practices such as bribing prevail in thepublic sphere. Source: IMD (1990).

Foreign tariff rate Ad valorem tariff rates in industry i, country j, and year t.Source: WTO (1995).

U.S. tariff rate Ad valorem tariff rates in industry i and year t. Between U.S. andCanada, FTA tariff rates were used after the year 1989. Source: WTO(1995).

Time trend Ranges from 1 to 10 for the period 1982–91.


for changes in assets and employment aredifficult to interpret because, in equilib-rium, one would not see any associationbetween levels of exogenous variables andchanges in endogenous variables. For ex-ample, if relative wages across countrieswere fairly stable during the sample period,then lack of movement is consistent with ascenario in which relative wages are a veryimportant determinant of MNC locationdecisions, provided that the equilibriumallocation of MNC activities across coun-tries in 1981 already reflected this factor.This highlights why a multivariate analysisthat exploits both cross-sectional and tem-poral co-variation in FDI and its determi-nants is essential in order to shed furtherlight on the determinants of MNC produc-tion location decisions.

Econometric Issues

An important problem in panel data stud-ies in which data for different countries,industries, or both are combined is the

potential for heteroskedasticity. This prob-lem often arises because of intrinsic differ-ences in size between the different coun-tries or industries. The variance of theerror terms (over time) tends to be muchgreater for the larger industries or coun-tries. We found that heteroskedasticity wasindeed a severe problem in our data.

Typical methods for dealing withheteroskedasticity often involve transfor-mations that “rein in” large values of thedependent variable (thus reducing skew-ness). Common examples are normalizingby some measure of size, or taking logs. Inour analysis, we normalize the dependentvariables by converting them to shares. Forinstance, for employment, the total MOFAemployment of each industry in each hostcountry is divided by the worldwide em-ployment of U.S. MNCs in that industry.Similar normalizations are done for MOFAassets and net sales.

Conversion to shares substantially re-duced the heteroskedasticity in our data,but fell far short of eliminating it. That is,

Table 2. Descriptive Statistics.

Variable Mean Standard Deviation

Dependent Variables:

MOFA total asset ratio 0.94 1.20MOFA total employment ratio 1.21 1.55MOFA net sales ratio 1.43 1.68

Independent Variables:

Wage ratio (foreign/U.S.) 0.72 0.35Foreign union density (% union member) 39.91 18.44Foreign strike intensity (days per worker) 0.25 0.74Collective bargaining structure 2.16 0.96Works council 0.55 0.50Layoff restriction 0.32 0.46GDP ratio (foreign/U.S.) 0.07 0.08Geographic distance (in 1000s of km) 6.57 3.26English language dummy 0.26 0.44Per capita income (foreign/U.S.) 0.67 0.30European Community membership 0.52 0.50Corporate tax rate difference –0.05 0.17Earnings-to-price ratio difference –0.008 0.03Cultural openness (1992) 60.49 10.95Corruption (1990) 61.93 9.07Foreign nominal tariff rate 10.82 15.42U.S. nominal tariff rate 4.19 1.18Time trend 5.64 2.87


industry/country cells with relatively largeshares tend to have greater variability intheir shares over time. A log transforma-tion resulted in an over-correction: indus-try/country cells with smaller shares tendto have greater variability in their log sharesover time. Hence, we used a Box-Cox trans-formation (intermediate between the logand linear cases) to effect the degree ofskewness correction needed to eliminateheteroskedasticity.

Thus, our estimating equations are ofthe form

(1) (Y λict – 1)/λ = Xict β + φic + εict,

where Yict is the dependent variable (shareof assets, employment, or net sales) forindustry i allocated to host country c inyear t, λ is the Box-Cox parameter, Xict is avector of (assumed) exogenous influences

on MNC behavior, φic is an industry/coun-try-specific effect, and εict is an idiosyncraticerror component. As λ → 0, we approach alog transformation.

In the OLS specification, the φic are ig-nored. The random effects (RE) modelassumes that the φic are present but thatE(φic | Xic1 , …, XicT) = 0 (that is, there is meanindependence). The fixed effects (FE)model does not restrict the distribution ofthe φic in this way. If the RE assumptionholds, the RE effects estimator is consistentand efficient.13 The OLS estimator is also

Table 3. Shares of U.S. MNC Assets and Employment Located in Each Host Country.

MOFAs’ Share of Worldwide Assets MOFAs’ Share of Worldwide Employment

Host Country 1982 Rank 1991 Rank 1982 Rank 1991 Rank

Canada 2.76 1 2.79 2 3.34 2 3.14 2United Kingdom 2.58 2 2.97 1 3.79 1 3.48 1Germany 1.92 3 2.77 3 2.97 3 3.08 3France 0.96 4 1.49 4 1.53 5 1.57 5Italy 0.64 5 1.03 5 0.96 6 0.90 6

Netherlands 0.60 6 0.96 6 0.51 10 0.62 10Mexico 0.52 7 0.42 11 1.67 4 2.86 4Australia 0.45 8 0.57 8 0.67 8 0.73 8Japan 0.39 9 0.39 12 0.35 11 0.64 9Belgium 0.36 10 0.47 9 0.64 9 0.34 12

Spain 0.25 11 0.70 7 0.68 7 0.74 7Ireland 0.20 12 0.42 10 0.23 13 0.30 13Singapore 0.17 13 0.27 13 0.23 12 0.52 11Sweden 0.08 14 0.06 15 0.09 15 0.13 15Switzerland 0.06 15 0.05 17 0.11 14 0.04 18

Denmark 0.06 16 0.03 19 0.06 19 0.05 17Austria 0.04 17 0.03 18 0.08 17 0.04 19Greece 0.03 18 0.02 20 0.03 21 0.04 20Portugal 0.02 19 0.05 16 0.08 18 0.07 16New Zealand 0.01 20 0.02 21 0.05 20 0.04 21

Korea 0.01 21 0.08 14 0.09 16 0.15 14Norway 0.004 22 0.003 22 0.01 22 0.02 22

Note: The MOFA share of worldwide assets is the ratio of MOFA total assets in a host country to U.S. MNCs’worldwide total assets (that is, parent plus MOFA assets). Employment share is defined similarly. The Bureauof Economic Analysis denominates MOFAs’ assets and U.S. worldwide assets in U.S. dollars using annual averageexchange rates.

13Consistency of the RE estimator actually requiresthe additional assumption of strict exogeneity,E(εictXics) = 0 for all t,s. The same is true of the FEestimator. Consistency of pooled OLS does not re-quire this assumption.


consistent in this case, but it is inefficientand produces inconsistent standard errors.However, one can construct robust stan-dard errors (which we do). On the otherhand, if the φic are correlated with the Xict,

only the fixed effects estimator is consis-tent, and both OLS and RE are inconsis-tent.

In our case, we would like the RE as-sumption to hold, because if we must rely

Table 4. Majority-Owned Foreign Affiliates’ Total Asset Ratio.

Random Fixed Random FixedVariable OLS Effect Effect Variable OLS Effect Effect

Wage Ratio 1.974** 0.232 0.164 (Foreign/U.S.) (0.243)a (0.120) (0.126)

Industrial Relations Variables:

Foreign Union –0.0029 –0.0025 –0.0021 Density (0.0025) (0.0024) (0.0027)

Foreign Strike –0.085** 0.006 0.006 Intensity (0.031) (0.018) (0.019)

Collective Bargaining 0.117** 0.155 — Structure (0.042) (0.086)

Works Council –0.569** –0.042 — Dummy (0.146) (0.234)

Layoff Restriction –0.073 –0.256 — Dummy (0.120) (0.234)

F-Test for IR Variables:

F-Statistics (p-value)b 13.16** 7.69** 0.33(0.000) (0.000) (0.718)

Control Variables:

GDP Ratio 38.214** 28.711** –5.193 (Foreign/U.S.) (2.382) (4.151) (10.307)

Geographic Distance 0.111** 0.053 —(0.014) (0.029)

GDP Ratio × Distance –3.685** –2.414** 1.715(0.279) (0.513) (1.250)

English Dummy –0.504 0.461 —(0.279) (0.256)

GDP Ratio × English 5.590** 6.354 2.898(1.322) (3.286) (16.168)

PCI Ratio –2.148** –0.093 0.050 (Foreign/U.S.) (0.258) (0.139) (0.152)

EC Dummy 0.345* 0.341 —(0.161) (0.183)

PCI Ratio × EC 0.255 0.524** 0.626**(0.203) (0.121) (0.129)

PCI Ratio × English 0.830* 0.165 0.258(0.390) (0.180) (0.200)

Corporate Income Tax Rate Difference –0.060 –0.093 –0.163 (Foreign – U.S.) (0.186) (0.081) (0.086)

Earnings-to-Price Ratio Difference 4.041** 0.619 0.193 (Foreign – U.S.) (0.782) (0.339) (0.348)

aFigures in parentheses are standard errors.bCritical values of the F-test are 2.21 for OLS and random effect models and 3.00 for the fixed effect model.cOmitted industry is “other.”*Statistically significant at the .05 level; **at the .01 level.

Control Variables (continued):

Cultural Openness 0.028** 0.047** —(0.004) (0.007)

Corruption –0.007 –0.023* —(0.005) (0.011)

Foreign Nominal 0.006 0.032** 0.029** Tariff (0.017) (0.007) (0.008)

Foreign Nominal –0.010 –0.032** –0.059** Tariff (+5) (0.018) (0.008) (0.012)

U.S. Nominal Tariff –0.030 0.077* 0.101**(0.079) (0.037) (0.038)

U.S. Nominal 0.372** 0.048 0.017 Tariff (+5) (0.073) (0.032) (0.034)

Time Trend 0.046** 0.048** 0.037**(0.012) (0.008) (0.009)

Industry Dummy 1 0.749** 0.382 — (Food)c (0.123) (0.211)

Industry Dummy 2 1.215** 0.730** — (Chemical) (0.163) (0.217)

Industry Dummy 3 0.724** 0.098 — (Metals) (0.188) (0.227)

Industry Dummy 4 1.229** 0.625** — (Equip.) (0.147) (0.215)

Industry Dummy 5 0.207* –0.016 — (Elect.) (0.095) (0.203)

Industry Dummy 6 0.162 –0.239 — (Transport) (0.165) (0.226)

Constant –5.978** –5.421** —(0.629) (0.883)

No. of Observations 1,150 1,150 1,150Box-Cox Lambda 0.2 0.2 0.2

LM-Statistics (p-value) 0.67 0.09 0.14(0.412) (0.763) (0.708)

Hausman Test (critical value= 7.96 ) — — 74.67**

F Test for Significance — — 62.37** of Fixed Effects (p-value) (0.000)

R-Square 0.78 0.75 0.98


on the FE estimator, then the coefficienton any country characteristic that is con-stant over time is not identified—and thisapplies to three of our five IR environmentvariables. We can test whether the REassumption holds using a Durbin-Wu-Hausman (DWH) test (see Hausman 1978).While this test is often treated as definitive, itis important to recognize that it is actuallyonly valid under a strong set of auxiliaryassumptions that are tangential to the REversus FE distinction. Thus, we will advocatea more pragmatic approach to choosing be-tween the RE and FE specifications that alsoconsiders the a priori reasonableness of theestimates produced by each model. As apractical matter, in order to minimize theimportance of the unobserved φic , we shouldcontrol for as many observed (exogenous)country characteristics as possible.

The Box-Cox parameter λ was chosen inan effort to minimize heteroskedasticity inthe error terms εict. We test for heteroskedas-ticity using the Breusch-Pagan LM test. It isuseful to find a common value of λ thateliminates heteroskedasticity in the OLS,RE, and FE models, because only then arethe magnitudes of the coefficient estimatescomparable across models.14 We were infact able to do this for assets, employment,and net sales (see Appendix Table A2).

Empirical Results

Our main estimation results, which useeither the share of MOFA assets or employ-ment (by industry) located in each hostcountry as the dependent variable, are re-ported in Tables 4 and 5, respectively.Appendix Table A2 describes the grid searchwe used to settle on a Box-Cox parameter

for each dependent variable. As the resultsin Appendix Table A2 show, homoskedas-ticity was overwhelmingly rejected (by theBreusch-Pagan LM test) for both linear andlog specifications for all dependent vari-ables. We settled on a Box-Cox parameterof 0.20 for MOFA assets and 0.45 for MOFAemployment. These values eliminate anystatistically significant heteroskedasticity.

In Tables 4 and 5 we first report OLSresults, in which the data for all countries,industries, and time periods are simplypooled. The fit of the OLS regressions isquite good. The R-squared values are .78 inthe asset share equation and .80 in theemployment share equation. These R-squared values are unusually high for cross-country/cross-industry regressions of thisgeneral type. Thus, the covariates that wehave included in our models appear to doa good job of explaining the allocation ofU.S. MNC assets and employment acrosscountries.

The OLS coefficient estimates are, forthe most part, not surprising. Notably, theyindicate that market size is a very importantdeterminant of the share of MNC opera-tions located in each host country (for ex-ample, the coefficient on the ratio of hostcountry to U.S. GDP is 38.2 in the assetshare equation, with a standard error ofonly 2.4). Table 6 presents a list of effectsizes implied by each of the estimates. Ef-fect sizes measure the change (in standarddeviations) of the dependent variable asso-ciated with a one standard deviation changein an independent variable. Hence, theyprovide measures of the influence of eachindependent variable that are adjusted bothfor the units in which the variable is mea-sured and for the variability of that variablein the sample.15 The figures in Table 6

14Also, the usual method for implementing theDWH test relies on the assumption that, under thenull hypothesis that the RE assumption is valid, theRE estimator is efficient. This means we should choseλ to eliminate heteroskedasticity under the RE speci-fication in order to implement the test. The FEmodel must be estimated using exactly the same λ,since the choice of λ affects the scale of the coeffi-cients and the DWH test is based on a comparison ofRE- and FE-estimated coefficients.

15In contrast, elasticities adjust for units but notfor variability. Effect sizes, unlike elasticities, can becompared across independent variables to get a feelfor the relative importance of each variable in gener-ating the in-sample variation of the dependent vari-able. We have adjusted the effect size estimates sothey measure effects on the dependent variable itselfat the mean of the data, as opposed to the Box-Coxtransform of the dependent variable.


indicate that the GDP ratio and the GDPratio interacted with geographic distancehave substantially larger effect sizes thanany of the other covariates. The findingthat host country market size is the main

determinant of FDI is quite standard, and,as noted by Markusen and Maskus (2001,2002), it is consistent with “horizontal” MNCmodels.

Given that our main focus is on the wage

Table 5. Majority-Owned Foreign Affiliates’ Total Employment Ratio.


Wage Ratio 1.469** –0.363** –0.424** (Foreign/U.S.) (0.192)a (0.108) (0.113)




Collective Bargaining 0.070 0.140* — Structure (0.036) (0.070)

Works Council –0.348** 0.185 — Dummy (0.126) (0.202)

Layoff Restriction –0.465** –0.603** — Dummy (0.104) (0.201)


F-Statistics (p-value)b 17.44** 10.54** 0.18(0.000) (0.000) (0.833)

Control Variables:


Geographic Distance 0.113** 0.063* —(0.012) (0.026)

GDP Ratio × Distance –4.433** –3.425** 1.211(0.236) (0.450) (1.112)

English Dummy –0.764** 0.295 —(0.239) (0.223)

GDP Ratio × English 8.680** 8.401** 0.983(1.164) (2.861) (14.664)

PCI Ratio –2.118** 0.133 0.308* (Foreign/U.S.) (0.212) (0.124) (0.135)

EC Dummy 0.406** 0.548** —(0.142) (0.160)

PCI Ratio × EC 0.008 0.062 0.175(0.168) (0.107) (0.114)


Corporate Income Tax Rate Difference 0.387* 0.010 –0.101 (Foreign – U.S.) (0.160) (0.071) (0.076)

Earnings-to-Price Ratio Difference 2.991** –0.688* –1.163** (Foreign – U.S.) (0.657) (0.291) (0.299)


Control Variables (continued):


Corruption –0.010* –0.023* —(0.004) (0.010)

Foreign Nominal –0.014 –0.0003 –0.005 Tariff (0.015) (0.007) (0.007)

Foreign Nominal 0.018 0.010 –0.026* Tariff (+5) (0.016) (0.008) (0.011)

U.S. Nominal Tariff –0.087 0.066* 0.100**(0.068) (0.034) (0.034)

U.S. Nominal 0.324** –0.023 –0.057 Tariff (+5) (0.063) (0.029) (0.030)

Time Trend 0.028** 0.044** 0.030**(0.011) (0.007) (0.008)

Industry Dummy 1 0.347** –0.044 — (Food)c (0.103) (0.182)

Industry Dummy 2 0.885** 0.393* — (Chemical) (0.138) (0.187)


Industry Dummy 4 1.040** 0.424* — (Equip.) (0.124) (0.185)

Industry Dummy 5 0.513** 0.209 — (Elect.) (0.078) (0.172)

Industry Dummy 6 0.534** 0.034 — (Transport) (0.138) (0.193)

Constant –3.171** –3.117** —(0.538) (0.752)

No. of Observations 1,218 1,218 1,218Box-Cox Lambda 0.45 0.45 0.45


Hausman Test (critical value = 7.96) — — 113.48**

F Test for Significance of — — 60.04**Fixed Effects (p-value) (0.000)

R-Square 0.80 0.75 0.98


and IR environment variables, we will com-ment only briefly on the other control vari-ables. As expected, cultural openness has apositive effect, and corruption a negativeeffect, on the share of assets or employ-ment located in a host country (althoughcorruption is not statistically significant inthe OLS asset equation). The finding thatthe host country corporate tax rate is posi-tively related to the share of MNC employ-ment located in a host country is surpris-ing, especially given the results of Grubertand Mutti (1991a). As we will see below,however, this result vanishes when we go toa random or fixed effects model. We in-cluded four interaction terms in the modelthat considerably improved fit.16 Theseimply, for instance, that host country GDPand English language are complements interms of attracting MNC assets. The owncoefficient on geographic distance is posi-tive, but the negative interaction with GDPimplies that, at the mean of GDP, the effectof distance is negative (that is, according tothe OLS estimates, the effect of distance is0.111–3.685([GDP ratio], and the meanGDP ratio is 0.07). Regarding tariffs, notethat there is no theoretical prediction, be-cause horizontal MNC models imply thattariffs encourage FDI, while vertical mod-els imply that they discourage it.

Not surprisingly, given past research, theOLS results imply a positive association be-tween the host country average wage inmanufacturing and the size of U.S. MNCoperations in the country (measured byeither assets or employment). The wagecoefficient in the employment equation is1.469 with a standard error of .192. Accord-ing to the effect size estimates in Table 6,this implies that a one standard deviationincrease in the host country wage wouldincrease the share of MNC worldwide em-ployment allocated to a country by .377standard deviations. At the mean of the

data, this implies an increase from 1.21% to1.79%—a 48% increase. Viewed anotherway, this corresponds to a wage elasticity ofdemand for labor of positive 0.97 at themean of the data.17 This is clearly an anoma-lous result.

We turn now to the other key variables ofinterest in this study, namely, the IR envi-ronment measures. The OLS estimatesimply that U.S. MNCs prefer to locate pro-duction in countries with low strike inten-sity, decentralized bargaining, and no workscouncils or layoff restrictions. Host coun-try union density is not statistically signifi-cant in either the asset or employmentequation. To give an idea of the magni-tudes implied by the estimates, in the assetequation, the point estimate for decentral-ized bargaining (0.117) implies that a onestandard deviation increase in the measureof decentralization (0.96 points) would in-crease the share of MNC assets in a particu-lar host country by 0.089 standard devia-tions (see Table 6). This is a 0.11 percent-age point increase, which corresponds to a12% increase at the mean of the data (re-call from Table 1 that the mean percentageof assets in each host country is 0.94%).

The OLS results also imply that workscouncils have very important negative ef-fects. The point estimate of –.569 in theasset equation implies that a shift to havingworks councils would reduce the share ofMNC assets located in a country from 1.28%to 0.72% at the mean of the data. Thisestimate is so large that it may strain cred-ibility. According to the effect size esti-mates in Table 6, works councils explainthe most variation in assets among the IRvariables, and the second most variation inemployment (being slightly less importantthan layoff restrictions).

Next, we report estimates of a fixed ef-fects model in which each of the industry/

16If the four interaction terms are dropped, the R-squared in the asset equation drops from .78 to .71,while that in the employment equation drops from.80 to .68.

17Given our Box-Cox specification, the elasticity ofY with respect to X is given by β × X × Y λ. Using themean wage ratio (0.72), the mean employment share(1.21), and λ = 0.45, this gives the elasticity of 0.97 atthe mean of the data.

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LA

TIO

NS R

EV

IEW

Table 6. Effect Size.a

MOFA Total Asset Ratio MOFA Total Employment Ratio MOFA Net Sales Ratio

Random Fixed Random Fixed Random FixedVariable OLS Effect Effect OLS Effect Effect OLS Effect Effect

Wage Ratio (Foreign/U.S.) 0.551 0.065 0.046 0.377 –0.093 –0.109 0.128 –0.016 –0.030Foreign Union Density –0.043 –0.036 –0.031 –0.053 –0.022 –0.017 –0.014 –0.015 –0.016Foreign Strike Intensity –0.050 0.004 0.003 –0.058 0.003 0.003 –0.017 0.001 0.002Collective Bargaining Structure 0.089 0.118 — 0.048 0.098 — 0.016 0.021 —Works Council Dummy –0.225 –0.016 — –0.124 0.066 — –0.027 0.046 —Layoff Restriction Dummy –0.027 –0.095 — –0.154 –0.200 — –0.017 –0.047 —GDP Ratio (Foreign/U.S.) 2.463 1.851 –0.335 2.504 2.063 –0.191 0.711 0.480 –0.191Geographic Distance 0.288 0.136 — 0.268 0.150 — 0.102 0.039 —GDP Ratio × Distance –2.008 –1.315 0.934 –2.234 –1.726 0.610 –0.585 –0.339 0.295English Dummy –0.176 0.161 — –0.237 0.091 — –0.036 0.073 —GDP Ratio × English 0.149 0.170 0.077 0.205 0.198 0.023 0.029 0.051 0.090PCI Ratio (Foreign/U.S.) –0.514 –0.022 0.012 –0.466 0.029 0.068 –0.119 0.020 0.037EC Dummy 0.137 0.135 — 0.146 0.197 — 0.069 0.035 —PCI Ratio × EC 0.073 0.150 0.179 0.002 0.016 0.046 –0.012 0.045 0.056PCI Ratio × English 0.202 0.040 0.063 0.177 0.0004 0.024 0.069 0.011 0.013Corporate Income Tax Difference –0.008 –0.013 –0.022 0.047 0.001 –0.012 0.005 –0.001 –0.003Earnings-to-Price Ratio Difference 0.111 0.017 0.005 0.074 –0.017 –0.029 0.024 –0.003 –0.008Cultural Openness 0.245 0.412 — 0.107 0.236 — 0.067 0.113 —Corruption –0.051 –0.168 — –0.066 –0.151 — –0.013 –0.057 —Foreign Nominal Tariff 0.070 0.390 0.354 –0.140 –0.003 –0.051 –0.091 0.117 0.118Foreign Nominal Tariff (+5) –0.117 –0.370 –0.685 0.177 0.099 –0.256 0.087 –0.110 –0.190U.S. Nominal Tariff –0.028 0.072 0.095 –0.074 0.057 0.085 0.023 0.018 0.023U.S. Nominal Tariff (+5) 0.340 0.044 0.016 0.271 –0.019 –0.047 0.089 0.022 0.014Time Trend 0.105 0.110 0.085 0.058 0.091 0.062 0.057 0.049 0.043Industry Dummy 1 (Food) 0.223 0.114 — 0.091 –0.012 — 0.059 0.025 —Industry Dummy 2 (Chemical) 0.365 0.219 — 0.239 0.106 — 0.133 0.061 —Industry Dummy 3 (Metals) 0.203 0.028 — 0.181 0.018 — 0.092 0.017 —Industry Dummy 4 (Equip.) 0.350 0.178 — 0.269 0.110 — 0.124 0.056 —Industry Dummy 5 (Elect.) 0.060 –0.004 — 0.132 0.054 — 0.039 0.006 —Industry Dummy 6 (Transport) 0.038 –0.057 — 0.117 0.007 — 0.042 –0.011 —

aThe effect size is calculated using the formula βY–1–λ(σx/σy), where λ is the Box-Cox parameter and Y– is the mean of the dependent variable.


country pairs is treated as a cross-sectionalunit with its own intercept. By includingindustry/country-specific intercept termsin the model, the fixed effects specificationignores the cross-sectional covariance ofthe dependent and independent variables,and identifies the model coefficients purelyfrom the over-time covariance. As a conse-quence, independent variables that areconstant over time (within an industry/country pair) drop out. This is problem-atic, because three of the IR variables ofinterest (decentralized bargaining, workscouncils, and layoff restrictions) drop out.

The fixed effects estimates are radicallydifferent from the OLS estimates. For ex-ample, the fixed effects estimates indicatethat there is no statistically significant asso-ciation between the size of U.S. MNCs’operations in a host country, as measuredby assets or employment, and host countryGDP. This means that as the ratio of hostcountry to U.S. GDP changes over time, theshare of U.S. MNCs’ assets or employmentallocated to that country does not vary sig-nificantly. It would, of course, be highlyimplausible to argue that host country sizeis not a key determinant of FDI locationdecisions in the long run. This is an ex-ample in which neither OLS nor fixed ef-fects are giving us the “right” answer, butwhere, instead, each is answering a differ-ent question.

The anomalous wage effects noted ear-lier are no longer present in the fixed ef-fects specification. In the employment equa-tion, the wage coefficient is –.424 with astandard error of .113. This implies that aone standard deviation increase in the hostcountry to U.S. wage ratio would lower theshare of MNC employment located in thatcountry by .109 standard deviations (seeTable 6). At the mean of the data thisimplies that a 49% wage increase woulddecrease the host country employmentshare from 1.21% to 1.04%—a 14% de-crease. The implied wage elasticity of labordemand is –0.28. The fact that the wagecoefficient switches to the “right” sign whenwe move from OLS to fixed effects suggeststhat the potential bias in the wage coeffi-cient resulting from the failure to control

for industry/country effects, which we dis-cussed in the introduction, is in fact impor-tant.

In the fixed effects asset equation, thewage coefficient is still positive, but muchsmaller in magnitude and no longer statis-tically significant. This can be rationalizedif substitution and scale effects on MOFAs’demand for capital roughly cancel, so thatwhen host country wages rise MOFAs’ capi-tal stocks remain roughly constant. In-deed, Cummins and Hassett (1996) foundevidence of strong substitutability betweenMOFA capital and employment. Thus, oncewe control for unobserved industry/coun-try effects, the wage coefficient takes ontheoretically plausible values in both theasset and employment equations. Hence, itseems likely that unobserved country ef-fects that make high-wage countries desir-able places to locate assets drive the largepositive effect of wages on employmentimplied by OLS.

Only two IR environment variables—union density and strike intensity—are iden-tified in the fixed effects specification, be-cause these are the only two that vary overtime. Neither variable is statistically signifi-cant in either the asset or employmentequation. One concern is potential col-linearity between these two variables, suchthat their separate statistical insignificanceconceals joint statistical significance. How-ever, in Table 7 we report correlations ofseveral key variables, and these show thatunion density and strike intensity are onlyslightly negatively correlated (–0.069).Moreover, the joint F-tests reported inTables 4 and 5 show that these IR variablesare not jointly statistically significant ineither the asset or employment equations.These results imply that there is no statisti-cally significant association between changesin the IR environment in a host country (interms of union density or strike activity)and changes in the fraction of worldwideassets or employment that U.S. MNCs lo-cate in that country.

The fixed effects results leave no doubtthat there are important unmeasured in-dustry/country effects in these data, mean-ing unmeasured country- and industry-spe-


cific idiosyncratic factors that make par-ticular countries attractive locations forparticular industries. The inclusion of in-dustry/country-specific intercepts causesthe R2 for the asset model to increase from.78 to .98, and that for employment toincrease from .80 to .98. Thus, the unmea-sured industry/country effects, which pre-sumably include such things as factor en-dowments, account for at least 20% of thevariance of U.S. MNCs’ decisions abouthow to allocate assets and employmentacross various host countries.18 The factorswe have measured and included in themodel, such as GDP, geographic distance,wages, the IR environment, cultural open-ness, and so on, can account for at most80% of the variance. This is actually quitea large proportion of variance to be ex-plained by observables in a cross-country/cross-industry regression.

The fact that adding industry/country-specific intercepts substantially improvesmodel fit does not necessarily mean thatfixed effects should be the preferred speci-fication. A key issue is whether the unmea-sured industry/country effects are corre-lated with the determinants of MNC pro-duction location that we have measuredand included in the model. If not, then thefixed effects estimator is inefficient (be-cause it does not exploit the lack of corre-lation between the unmeasured effects andthe regressors), and it needlessly causesloss of identification of the time-invariantregressors. In this case, it may be prefer-able to use a random effects model.

In the random effects model each indus-try/country pair is again treated as havingits own intercept, in order to capture indus-try/country-specific unobservables. In the

random effects specification, however, theindustry/country effects are assumed to bemean independent of the observedcovariates. In that case, the unobservablescreate serial correlation of the residualswithin each industry/country pair. Thus, itis appropriate to use a GLS procedure thataccounts for the serial correlation in theerrors. The random effects estimator, whichimplements this GLS procedure, is effi-cient because it optimally weights the infor-mation in the cross-sectional and over-timevariation in the data. Unlike fixed effects,the random effects estimator does not throwaway all the information contained in thecross-sectional variation in the data. How-ever, it in effect down-weights that informa-tion relative to OLS, because it recognizesthat, due to serial correlation, we do notreally have T independent cross-sectionalobservations on each industry/country pair.

As we see in Tables 4 and 5, the fixed andrandom effects estimates are dramaticallydifferent. Most obviously, the random ef-fects estimates imply that GDP, and its in-teractions with geographic distance andEnglish language, are very important de-terminants of the share of MNC assets andemployment located in a country. At themean of the data, a 1% increase in the GDPratio is predicted to raise the share of MNCassets allocated to a country by 1.36% if it isEnglish-speaking, and 0.91% if not.19 Theseappear to be very plausible estimates.

The wage coefficients also appear plau-sible in both the asset and employmentequations. The coefficient on wages isstatistically insignificant in the asset equa-tion. In the employment equation the pointestimate is –.363 with a standard error of.108. This implies an elasticity of labordemand at the mean of the data of –0.24.Thus, the random effects estimate of the

18Note that the increase in the R-squared when weinclude the fixed effects only gives a lower bound onthe fraction of variance explained by unobservedtime-invariant characteristics of countries. It is ofcourse possible that some portion of the variance“explained” by variables we have measured and in-cluded in our models is spurious, meaning that it onlyarises because these variables are correlated with theexcluded unobserved country characteristics.

19According to the point estimates in Table 4, theelasticity of assets with respect to GDP is given by[28.71 – 2.41×D+6.35×E]×G×A–0.2, where D, E, G, andA denote distance, English, GDP ratio, and assetshare, respectively. Plugging in means from all vari-ables (see Table 2), we obtain an elasticity of 0.91 if E= 0 and 1.36 if E = 1.


wage coefficient is similar to the fixed ef-fects estimate, and both are far from theanomalous OLS estimate.

The IR environment variables are notindividually statistically significant in theasset equation. However, the joint F-testfor these variables is 7.69, which is highlystatistically significant. As we see in Table7, some moderately high correlations arepresent among the IR variables. Layoffrestrictions and works councils are posi-tively correlated (.58), layoff restrictionsand union density are negatively correlated(–.47), and decentralized bargaining isnegatively correlated with both union den-sity (–.44) and works councils (–.35). Thiscollinearity does not seem severe, but itcomplicates sorting out the relative influ-ence of each variable.

Nevertheless, all the point estimates forthe IR variables look reasonable in the assetequation. The decentralized bargainingvariable comes closest to statistical signifi-cance at the 5% level. Its point estimate is.155 with a standard error of .086. Thepoint estimate implies that at the mean ofthe data, a one standard deviation increasein the measure of decentralization (0.96points) would increase the share of MNCassets in a particular host country by 0.118

standard deviations (see Table 6). This is a0.14 percentage point increase, which cor-responds to a 15% increase at the mean ofthe data (recall that the mean percentageof assets in each host country is 0.94%).The point estimates also imply that layoffrestrictions are quite important. Their in-troduction is predicted to reduce the shareof MNC assets allocated to a host countryfrom 1.08% to 0.84% at the mean of thedata—a 23% reduction.

In the employment equation, the bar-gaining decentralization and layoff restric-tion coefficients are individually statisti-cally significant. The point estimate im-plies that, at the mean of the data, a onestandard deviation increase in the measureof decentralization (0.96 points) would in-crease the share of MNC employment allo-cated to a particular host country by 0.098standard deviations (see Table 6). This is a0.15 percentage point increase, which cor-responds to a 13% increase at the mean ofthe data (recall that the mean percentageof employment in each host country is1.21%). The estimated effect of layoff re-strictions is larger. At the mean of the data,the point estimates imply that introductionof layoff restrictions would reduce the shareof MNC employment allocated to a host

Table 7. Correlation Matrix for Selected Variables.

Collective CulturalWage Union Strike Bargaining Works Layoff Open-Ratio Density Intensity Structure Council Restrictions ness

Union Density 0.3185(0.0000)a

Strike Intensity –0.1479 –0.0688(0.0000) (0.0131)

Collective Bargaining –0.1163 –0.4422 –0.1634 Structure (0.0000) (0.0000) (0.0000)

Works Councils 0.2304 0.0859 0.0908 –0.3495(0.0000) (0.0019) (0.0011) (0.0000)

Layoff Restrictions –0.1969 –0.4705 0.2037 0.1601 0.5805(0.0000) (0.0000) (0.0000) (0.0000) (0.0000)

Cultural Openness 0.0309 –0.0414 –0.0071 –0.1758 –0.0643 0.0462(0.2656) (0.1360) (0.7981) (0.0000) (0.0205) (0.0959)

Corruption 0.1189 0.1467 –0.1733 –0.1588 –0.4743 –0.6263 0.2887(0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000)

aFigures in parentheses are p-values.


country from 1.44% to 0.80%—a 44% re-duction.

The works council variable, which wasthe most important IR factor according tothe OLS estimates, appears to be much lessimportant according to the random effectsestimates. In our view, the OLS estimate ofthe effect of works councils seemed implau-sibly large, so the random effects estimatesseem more credible.

The estimated coefficients of the controlvariables in the random effects specifica-tion also look reasonable. For instance, theestimates imply that cultural openness en-courages MNCs to allocate assets and em-ployment to a host country, while corrup-tion discourages FDI. According to theeffect size estimates in Table 6, these areamong the more important factors explain-ing the variation in asset and employmentshares across countries.

The disparity between the fixed and ran-dom effects estimates leaves us in a quan-dary as to which specification to prefer. Acommon way to choose between them is theDurbin-Wu-Hausman (DWH) test. Thelogic of this test is that, under the nullhypothesis that the unit-specific effects aremean independent of the covariates, boththe fixed and random effects estimators areconsistent (although fixed effects is ineffi-cient because it ignores the cross-sectionalcovariance in the data). However, underthe alternative that correlation is present,the fixed effects estimator remains consis-tent, while the random effects estimatorbecomes inconsistent. Thus, the degree ofsimilarity or dissimilarity between the ran-dom and fixed effects estimates can beconstrued as evidence of the extent to whichthe unmeasured effects are, respectively,uncorrelated with the regressors or corre-lated with them.

The DWH test statistic values are 74.67for the asset models and 113.48 for theemployment models. Under the null, thesestatistics are distributed as χ2 random vari-ables with 16 degrees of freedom (sincethere are 16 common parameters betweenthe fixed and random effects specifica-tions), so the 5% critical value is 7.96. Thus,the DWH overwhelmingly rejects the ran-

dom effects specification. This outcome isnot surprising, since we have already notedthe striking differences between the fixedand random effects estimates. It is impor-tant to note, however, that this result is notconclusive. The DWH test relies criticallyon the assumption that any differences be-tween the random effects and fixed effectsestimates are due only to correlation be-tween the unit-specific effects and thecovariates. In the present context, how-ever, it seems likely that other factors areimportant.

As we noted earlier, the most strikingdifference between the random and fixedeffects results is that the fixed effects esti-mates imply that host country GDP is not astatistically significant determinant of U.S.MNC assets and employment allocated tothat country. This strikes us as completelyimplausible. What might account for thisresult? As long as there are costs to adjust-ing the level of assets or employment thatan MNC maintains in a particular host coun-try, short-run changes in GDP that MNCsperceive as transitory should have little af-fect on MNC decisions (see Das, Roberts,and Tybout 2001). Thus, it is not at allsurprising that the fixed effects estimate ofthe effect of GDP, which relies exclusivelyon short-run covariation, would be close tozero. On the other hand, the cross-sec-tional variation in GDP across host coun-tries is dominated by permanent or long-lived differences. If MNCs are responsiveto these, as seems likely, then the randomeffects estimator, which does incorporatethe cross-sectional variation in the data,will imply that GDP is an important deter-minant of MNC decisions.

More generally, the supposition that anydifference between the random and fixedeffects estimates stems solely from correla-tion between the unit-specific effects andthe covariates relies on the implicit assump-tion that the dependent variable respondsin the same way to permanent and transi-tory changes in covariates.20 This seems

20Keane and Wolpin (2002) made this point in adifferent but perfectly analogous context. They con-


highly implausible in the present context.In particular, the same argument we madefor why this is not true for GDP applies foraspects of the IR environment as well. Forexample, suppose strike intensity exhibitssubstantial permanent differences acrosscountries (very high in Greece and low inNorway, say), and also exhibits transitoryvariation over time within countries. Givenadjustment costs, it is unlikely that a transi-tory increase in strike activity would lead anMNC to reduce the assets it allocates to aparticular host country, even if the MNC’slong-run decisions are quite sensitive to theaverage level of strike activity in a country.21

In light of these considerations, we pre-fer the random effects specification overthe fixed effects specification, preciselybecause we are more interested in the ef-fects of long-lived or permanent differencesin wages and the IR environment on MNClocation decisions than on the effects ofmore transitory developments. The ran-dom effects specification does have theproblem that, if the unit-specific effects arecorrelated with the covariates, the estimatesmay be inconsistent. However, to counterthis concern, we appeal to the fact that wehave included quite a rich set of control

variables in our models. Indeed, the frac-tion of variance explained by our measuredcovariates (78–80%) is very high relative tolevels achieved in most panel data studies.Thus, unobserved unit-specific effects maybe a less important source of bias here thanthey often are in panel data work. We alsoappeal to the fact that our random effectsestimates have considerable face validity.This is the only specification in which allthe statistically significant estimated coeffi-cients have quite reasonable signs and mag-nitudes.

In summary, our preferred random ef-fects estimates support a conclusion thatboth wages and the IR environment areimportant determinants of U.S. MNCs’ pro-duction location decisions. While somemild collinearity problems make it difficultto sort out the relative importance of all theIR factors we have examined, the clearestevidence seems to be that U.S. MNCs preferto locate assets and employment in hostcountries with decentralized bargainingstructures and with no important restric-tions on layoffs.

We have reserved until now any detaileddiscussion of the tariff coefficients. In therandom effects specification, the currentU.S. and host country tariff coefficients arestatistically significant and positive in theasset equation, and the current U.S. tariffcoefficient is statistically significant andpositive in the employment equation. Thefixed effects results are similar. Thus, thereis no support for the notion that tariffreductions increase FDI. The failure tofind statistically significant positive effectsof trade liberalization on FDI is consistentwith the finding that market access (“jump-ing tariff walls”) is the main factor drivingFDI (the horizontal model).

Theory suggests that the vertical modelshould better explain FDI in relatively low-wage countries like Mexico, while the hori-zontal model should better explain FDI inhigh-wage countries (which account formost U.S. FDI). Thus, interactions be-tween tariffs and wages should be impor-tant. That is, higher tariffs should encour-age FDI in high-wage countries while dis-couraging FDI in low-wage countries, lead-

sidered a model of welfare program participation inwhich single mothers are eligible for welfare benefitsuntil their child reaches age 18. In the model, women’sdecisions about whether to become single mothersdepend on the average level of welfare benefits theyexpect to prevail during the period until a childreaches 18. Hence, their decisions are quite insensi-tive to transitory fluctuations in benefits. Using simu-lated data from this model, Keane and Wolpin showedthat a fixed effects model falsely implies that women’sdecisions about whether to become single mothersare insensitive to welfare benefits. As Keane andRunkle (1992) discussed, another reason for differ-ences between the random and fixed effects estima-tors is failure of the strict exogeneity assumption thatunderlies both estimators. If that assumption fails,then both are inconsistent, and the DWH test isuninformative.

21It is interesting that the random and fixed effectsestimates of the wage coefficient are quite close. Thiscould be rationalized if the labor input is indeedquite variable in the short run (in contrast to capital).In that case, the responses of employment to perma-nent and transitory wage changes may not differgreatly.


ing to a negative linear coefficient on tariffsand a positive coefficient on the interac-tion with the host country wage rate. Whenwe included such interactions, they werecompletely statistically insignificant in theasset equation. But the expected sign pat-tern did emerge in the employment equa-tion (although magnitudes of the negativelinear terms were small). This result isconsistent with the notion that trade liber-alization with a single low-wage country likeMexico, where FDI is more likely to bemotivated by factor price differentials,would lead to an increased share of U.S.MNCs’ employment being located in thatcountry. However, if trade liberalization isa worldwide trend, we would expect, givenour results, that it would reduce the overallshare of U.S. MNCs’ employment locatedabroad.

Finally, in Appendix Table A3 we reportresults for net sales for the sake of compa-rability with several earlier studies that ex-amined this variable. The results are broadlysimilar to our results for assets and employ-ment. OLS estimates imply that the size ofMNC operations in a host country as mea-sured by net sales is strongly positively re-lated to the host country wage rate. But thisanomalous finding is reversed when wecontrol for unobserved country/industryeffects using either a random or fixed ef-fects specification. OLS and random ef-fects estimates imply that host country GDPis the major factor driving MNC locationdecisions, while the fixed effects estimatesproduce the implausible result that hostcountry market size is unimportant. Theset of IR environment variables is not jointlystatistically significant in the net sales equa-tion, but as we noted in the introduction,we view the net sales results as relativelyuninformative, because sales equal pricetimes quantity. Thus, how the covariatesaffect sales does not tell us directly howthose covariates affect the scale of MNCoperations.

Conclusion

We have examined the production loca-tion decisions of U.S. MNCs, using BEA

industry level data on U.S. MNC operationsin 22 countries over 10 years (1982–91).Four main conclusions emerge.

First, we find evidence that the IR envi-ronment does have statistically significanteffects on U.S. MNCs’ production locationdecisions. U.S. MNCs prefer to locate pro-duction in foreign host countries with adecentralized bargaining structure andwithout appreciable restrictions on layoffs.Our preferred random effects model esti-mates imply that if a host country is onestandard deviation above average in termsof our measure of decentralization, theshare of U.S. MNC assets allocated to thatcountry will be, ceteris paribus, 15% higher.The corresponding figure for employmentis also 15%. Our estimates imply that theintroduction of important layoff restric-tions would, ceteris paribus, reduce the sharesof assets and employment allocated to aparticular host country by 23% and 44%,respectively. Other features of the IR envi-ronment, like union density, strike inten-sity, and works councils, do not appear tobe as important.

Second, we conclude that the priorliterature’s dominant and anomalous find-ing of a positive or zero association be-tween a host country’s wage rate and MNCs’allocation of employment to the country isdue largely to the tendency to identify thewage coefficient purely from cross-sectionalvariation, and the failure to control forindustry/country-specific unobservables.Indeed, we find that wage coefficients inmodels for MNCs’ allocation of employ-ment to a country switch sign from positiveto negative when we control for industry/country effects using either a random ef-fects or fixed effects specification. Thus,contrary to most prior studies, we find thatU.S. MNCs do prefer to locate employmentin low-wage countries, ceteris paribus. Ourpreferred random effects estimates implythat the elasticity of employment with re-spect to host country wages is –0.24.

Third, while we find that both wages andthe IR environment are important, it isworth emphasizing that by far the mostimportant determinant of MNC produc-tion location decisions appears to be sim-


ply host country GDP (that is, market size).This finding is consistent with horizontalmodels of MNCs. Other variables withstatistically significant influence werecultural openness, corruption, geo-graphic distance, corporate tax rates, andtariffs. Our complete set of covariatesexplains a very large share of the varia-tion in U.S. MNCs’ production alloca-tion across countries (with the R-squaredvalues in our asset and employment equa-tions being 78–80%).

Fourth, we find no evidence that tariffreductions would lead to significant in-creases in the share of U.S. MNC assets andemployment located abroad. In fact, ourestimates imply the reverse. This is againconsistent with the horizontal MNC model,in which market access is the main factordriving FDI, so that tariff reductions actu-ally discourage FDI. The concern—fre-quently expressed in the debate overNAFTA—that trade liberalization wouldlead to an increased share of MNC employ-ment being located abroad may be exag-

gerated, since the vertical model, in whichthe exploitation of factor price differen-tials drives FDI, explains a relatively smallpart of FDI.

The key importance of stable factors likerelative market size implies that trade liber-alization is unlikely to lead to any suddenand substantial shifts of U.S. MNC opera-tions toward countries with lower wages ormore favorable IR environments, despiteour findings that these factors are impor-tant. As further evidence for this conclu-sion, we also found that the allocation ofU.S. MNC operations across host countrieschanged little between 1982 and 1991, de-spite the tariff reductions in the Tokyoround of the GATT that took effect from1982 though 1987. Thus, it appears thatthe allocation of U.S. MNC operations (thatis, capital and employment) across foreigncountries and the U.S. has been in some-thing close to an equilibrium position formany years, and that the factors we con-sider are only leading to small and gradualshifts in that equilibrium.


Appendix Table A1

Industrial Relations Environment Indicators

Union Strike DegreeDensity Intensity of

(Averaged (Averaged Bargaining Works LayoffCountry over 1982–91) over 1982–91) Decentralization Councils Restriction

Australia 55.43 252 2 0 0Austria 61.43 7 0 1 0Belgium 54.60 45 2 1 0Canada 35.20 428 4 0 0Denmark 76.00 143 1.33 1 0

France 15.77 64 1 1 1Germany 36.07 31 1.67 1 0Greece 32.40 3,630 0 1 1Ireland 61.67 318 1.67 0 0Italy 43.63 492 2.67 1 1

Japan 28.93 8 3 0 0Korea 14.97 306 4 1 1Mexico 29.00 218 3 0 0Netherlands 29.67 20 1.67 1 1New Zealand 48.13 386 2 0 0

Norway 56.57 100 1 1 0Portugal 46.80 106 2 1 1Singapore 22.43 0 1 0 0Spain 18.00 582 2 1 1Sweden 83.10 83 1 0 0

Switzerland 28.50 5 3 0 0United Kingdom 45.90 272 2.67 0 0

Note: For strike intensity we report days lost per thousand workers, but days lost per worker is included in theregressions.


Appendix Table A2

LM Heteroskedasticity Test Statistics under Alternative Box-Cox Transformations

DependentVariable Asset Share Employment Share Net Sales Share

Box-Cox Fixed Random Fixed Random Fixed RandomTransform Effects Effects Effects Effects Effects EffectsParameter OLS Model Model OLS Model Model OLS Model Model

1.0 209.86 318.69 216.12 68.55 209.63 64.90 143.57 301.13 134.17(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

0.9 102.87 253.28 102.34 39.05 181.33 35.03 135.48 294.01 115.71(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

0.8 54.45 190.92 56.82 28.35 152.85 26.07 104.85 239.47 87.21(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

0.7 13.66 114.72 16.38 15.16 113.34 15.49 65.59 168.13 51.97(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

0.6 0.11 39.21 0.01 3.32 63.37 5.54 26.05 88.75 18.22(0.739) (0.000) (0.908) (0.068) (0.000) (0.019) (0.000) (0.000) (0.000)

0.5 11.06 1.02 8.68 0.53 16.55 0.24 2.87 24.41 0.80(0.001) (0.314) (0.003) (0.469) (0.000) (0.622) (0.090) (0.000) (0.372)

0.45 15.81 1.69 13.51 3.73 3.44 0.15 0.05 6.71 0.38(0.000) (0.193) (0.000) (0.053) (0.063) (0.698) (0.817) (0.010) (0.536)

0.4 15.68 8.84 14.12 8.21 0.02 1.20 0.59 0.23 3.07(0.000) (0.003) (0.000) (0.004) (0.887) (0.273) (0.441) (0.628) (0.079)

0.3 4.90 12.88 5.27 10.72 2.61 2.56 0.63 2.00 5.23(0.027) (0.000) (0.022) (0.001) (0.106) (0.110) (0.429) (0.157) (0.022)

0.2 0.67 0.14 0.09 2.71 2.87 0.49 2.64 3.33 0.22(0.412) (0.708) (0.763) (0.100) (0.09) (0.482) (0.104) (0.068) (0.635)

0.1 16.03 19.42 9.48 0.98 51.94 1.18 23.64 68.32 6.53(0.000) (0.000) (0.002) (0.321) (0.000) (0.278) (0.000) (0.000) (0.011)

Log(y) 44.79 61.60 33.19 12.04 139.48 9.09 51.59 143.12 30.06(0.000) (0.000) (0.000) (0.001) (0.000) (0.003) (0.000) (0.000) (0.000)

Note: p-values are in parentheses.


Appendix Table A3

Majority-Owned Foreign Affiliates’ Net Sales Ratio


Wage Ratio 1.864** –0.239 –0.430** (Foreign/U.S.) (0.298)a (0.153) (0.163)




Collective Bargaining 0.087 0.112 — Structure (0.054) (0.093)

Works Council –0.277 0.463 — Dummy (0.181) (0.266)

Layoff Restriction –0.184 –0.515 — Dummy (0.144) (0.265)


F-Statistics (p-value)b 5.79** 0.46 0.69(0.000) (0.809) (0.499)

Control Variables:


Geographic 0.149** 0.056 — Distance (0.017) (0.032)

GDP Ratio × –4.394** –2.547** 2.217 Distance (0.340) (0.571) (1.526)

English Dummy –0.398 0.804** —(0.333) (0.286)

GDP Ratio × English 4.009* 7.008* 12.254(1.554) (3.448) (17.752)

PCI Ratio –2.091** 0.351* 0.647** (Foreign/ U.S.) (0.330) (0.173) (0.194)

EC Dummy 0.705** 0.360 —(0.217) (0.206)

PCI Ratio × EC –0.160 0.624** 0.765**(0.261) (0.153) (0.169)


Corporate Income Tax Rate

Difference 0.154 –0.039 –0.107 (Foreign – U.S.) (0.226) (0.100) (0.107)

Earnings-to-Price Ratio

Difference 3.621** –0.455 –1.148** (Foreign – U.S.) (0.980) (0.424) (0.440)


Control Variables (cont’d):


Corruption –0.007 –0.030* —(0.006) (0.012)

Foreign Nominal –0.039 0.050** 0.050* Tariff (0.027) (0.016) (0.020)

Foreign Nominal 0.038 –0.049** –0.084** Tariff (+5) (0.027) (0.017) (0.019)

U.S. Nominal Tariff 0.097 0.077 0.100*(0.087) (0.041) (0.042)

U.S. Nominal 0.385** 0.096** 0.063 Tariff (+5) (0.080) (0.037) (0.039)

Time Trend 0.101** 0.087** 0.075**(0.016) (0.010) (0.011)

Industry Dummy 1 0.895** 0.375 — (Food)c (0.152) (0.222)

Industry Dummy 2 1.762** 0.802** — (Chemical) (0.197) (0.230)


Industry Dummy 4 1.725** 0.780** — (Equip.) (0.181) (0.227)

Industry Dummy 5 0.515** 0.080 — (Elect.) (0.116) (0.218)

Industry Dummy 6 0.686** –0.175 — (Transport) (0.200) (0.244)

Constant –7.662** –5.833** —(0.842) (0.979)

No. of Observations 881 881 881Box-Cox Lambda 0.2 0.2 0.2


Hausman Test(critical value = 7.96 ) — — 98.18**

F-Test for Significance of 50.01**Fixed Effects (p-value) — -— (0.000)

R-Square 0.79 0.73 0.98


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