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Regional Science and Urban Economics 29 (1999) 197–218

Attracting foreign manufacturing: Investment promotion and agglomeration C. Keith Head a , John C. Ries a , Deborah L. Swenson b ,c , * b

a University of British Columbia, Vancouver, Canada Department of Economics, University of California, Davis, CA 95616, USA c National Bureau of Economic Research, Cambridge, MA, USA

Received 29 November 1996; received in revised form 20 May 1998; accepted 17 June 1998

Abstract We study Japanese investments between 1980 and 1992 to assess the effectiveness of US state promotion efforts in light of strong agglomeration effects in Japanese investment. The provision of foreign trade zones, lower taxes, and job-creation subsidies have statistically significant effects on the location of investment. Simulations indicate that unilateral withdrawal of promotion would have caused individual states to lose substantial amounts of Japanese investment. However, because state promotional policies tended to offset each other, their impact on the geographic distribution of Japanese investment appears small.  1999 Elsevier Science B.V. All rights reserved. Keywords: Foreign direct investment; Agglomeration; Incentives JEL classification: H32; H2; F2; R38

1. Introduction State governments in the United States compete aggressively for new manufacturing plants by offering a variety of incentives. During the 1980s state governments made a number of policy changes in an effort to attract foreign investment. *Corresponding author. Corresponding address: Department of Economics, University of California, Davis, CA 95616, USA. Tel.: (1-916) 752-1569; fax: (1-916) 752-9382; e-mail: [email protected] 0166-0462 / 99 / $ – see front matter  1999 Elsevier Science B.V. All rights reserved. PII: S0166-0462( 98 )00029-5


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For example 19 US states opened foreign trade zones (FTZs), ten established investment promotion offices in Japan, and ten eliminated the unpopular policy of taxing investors on a unitary basis. Investors are not unaware of these efforts, and press accounts suggest that investment incentives have come to be taken for granted.1 This paper assesses the influence of state policies on the location decisions of 760 new Japanese-owned manufacturing plants that began operations in the US between 1980 and 1992. The principal contribution lies in the investigation of interstate competition for investment in the context of nationalitybased agglomeration. Agglomeration effects operate when the presence of similar firms raises the probability that subsequent investors will choose that location. If a study successfully controls for other factors that influence site selection, the estimated agglomeration effects represent the positive externalities conferred by proximate location choice. Typically, similarity is defined in terms of industry. For instance, Carlton (1983) finds that new firms appear to be attracted to states with high employment in their 4-digit industry. Head et al. (1995) observe that, in addition to the positive effect of same-industry establishments, Japanese investors also appear to be attracted to US states with other Japanese plants in the same industry or keiretsu. Agglomeration effects between Japanese firms may arise from a number of sources. Japan-based firms may have systematically different factor intensities than American firms. For instance, land scarcity in Japan may have induced the development of land-saving techniques, such as just-in-time (JIT) inventory systems, which are transplanted to the new US locations. Implementation of JIT might generate agglomeration effects since the firms supplying parts may need to make substantial investments in upgrading production processes to facilitate JIT delivery. Their willingness to make such investments would presumably be an increasing function of the number of downstream Japanese producers. Another source of Japan-level agglomeration would be a preference for higher skilled workers because of a stronger desire for quality control or greater use of complex machinery. This mechanism might also be self-reinforcing if newcomers can hire away employees trained in Japanese methods by earlier arrivals. Finally, the greater the total number of Japanese manufacturers in an area, the more likely that Japanese employees will be accommodated in preferences for particular types of restaurants, schooling, and other amenities. Self-reinforcing agglomeration effects have important policy implications. A state that moves first in offering a subsidy or removing a tax will attract investment. The resulting increase in the stock of firms in the state will make it more attractive than it was before, even if other states subsequently match the 1

The Financial Times (October 1991) reported that expected subsidies to new investors almost doubled in the decade due to the intensification of competition among states.

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policy change. Thus, a quantification of the full impact of investment promotion policies should address the potential for dynamic effects of policy through the mechanism of increased agglomeration. Incorporating nationality-specific agglomeration into a location choice model is valuable for two primary reasons. First, it may improve the estimated coefficients of policy variables. Japanese agglomeration and policies to attract investment are likely to be correlated and the exclusion of the former may introduce omitted variable bias in the estimation. Second, nationality-specific agglomeration implies that less industrialized states may be able to compete effectively for foreign investment. Kentucky, Tennessee and Georgia are states that have relatively small shares of manufacturing but were able to attract large amounts of Japanese investment. Today, by virtue of a large base of Japanese manufacturing activity, these states may be more attractive to new Japanese investment than more industrialized states such as Massachusetts. Thus, policies that succeed in establishing a national agglomeration may offset disadvantages associated with a lack of overall manufacturing activity. The paper is organized as follows. In Section 2 we develop a discrete choice model that can be estimated using conditional logit. The data used to implement the model are described in Section 3. While Head et al. (1995) do not consider state attributes other than agglomeration and a state fixed effect, the current paper examines the efficacy of six state policies. Estimation results are analyzed and compared to the previous literature in Section 4. The statistical results indicate that the provision of lower corporate taxes, employment subsidies, and foreign trade zones attracted Japanese investment. We turn from statistical significance to investigate the economic significance of promotional policies using simulations in Section 5. The simulations reveal that policies designed to attract investment were often thwarted by emulation; however, we show that individual states would have lost significant investment had they not offered incentives when other states were offering them. We offer some tentative policy implications in the Conclusion (Section 6. The data sources used are included in Appendix A.

2. The location choice model In this section we develop a model in which heterogeneous investors choose locations from among the 50 states. Our model specifies the probability that a state yields the highest profits for a particular investor. We choose functional forms to obtain a final specification that is linear in the parameters. We first consider the decision of a representative investor. This firm manufactures a unique product with log demand curve ln D( p) 5 h ln y 2 h p ln p6e d , where y is the income of consumers of the good, p is the price, and e d is a demand shock. We assume an elastic demand curve, i.e. h p . 1. Profit maximizing firms select a price that is


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determined by a markup over the marginal cost, c: p 5 (h p /(h p 2 1))c. Log profits are given by ln p 5 ln (1 2 tp ) 1 ln ( p 2 c) 1 ln D( p). Substituting the log demand and log price functions into log profits provides ln p 5 ln(1 2 tp ) 1 h y ln y 2 (h p 2 1) ln c 1 (h p 2 1) ln(h p 2 1) 2 h p ln h p 1 e d .


The log cost function is assumed to be ln c 5 uw ln(w(1 2 s w )) 1 uk ln(k (1 2 s k )) 1 uv ln v 2 a ln(1 1 N) 1 e c .


In this expression w, k and v represent the wage, the cost of capital and the price of imported inputs and uw , uk , and uv are cost-share parameters. Wage and capital subsidy rates are denoted s w and s k . Unmeasured interstate variation in costs is captured with e c . N is the count of related investors that chose the state previously. As we detail later, relatedness is determined by industry, national origin and keiretsu affiliation. We add one because the investor recognizes that it would constitute an increment of one to the number of plants already in the state. The parameter a is the agglomeration elasticity. It can be positive or negative depending on whether investors are attracted or deterred by the existence of prior investment in a location. A positive agglomeration coefficient could arise from a number of sources. First, there may be direct positive spillovers between proximate firms. One reason would be the sharing of information on how to operate efficiently in a new economic environment. Second, large values of N could induce diverse and inexpensive supplies of specialized inputs.2 Third, if investors observe e c but the econometrician does not, then N could be high for certain industries that have common unmeasured advantages. Thus, N might serve as a proxy for endowments of factors or infrastructure used intensively by an industry. Fourth, if investors only receive signals about e c but cannot directly observe it, they might mimic each others’ location decisions.3 We now substitute Eq. (2) into Eq. (1). Since investors compare the profitability 2 Heisley and Strange (1990) provide a labor matching model in which the expected match quality is increasing in the number of firms. Krugman (1991) provides a simple model in which workers with industry-specific skills will accept a lower expected wage in order to work in an area with a large number of firms because of the riskpooling benefit. Rivera-Batiz (1988) uses a monopolistic competition model to show that an increase in the size of the downstream sector stimulates entry by specialized providers of producer services. Venables (1996) examines location decisions of vertically related industries and argues that for small enough transport costs, activity in the upstream and downstream industries will agglomerate. 3 Banerjee (1992) provides a model of herding based on this idea that actions can convey information about the private information of prior investors. DeCoster and Strange (1993) argue that clustering could occur because of an agency problem. The managers making location decisions might elect to follow prior investors because they are afraid of the reputational consequences of an ‘eccentric’ decision that fails.

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of each of the states, any terms that are common to all states drop out of the estimating equation. The term (h p 21) ln (h p 21)2h p ln h p can be removed since the elasticity of demand is assumed to be a preference parameter that does not vary across states. The term ln k also falls out because we assume capital market integration equalizes the price of capital across states. Since we use a number of variables to capture labor cost conditions, it is useful to separate ln w from ln (12s w ). Finally, we assume the cost of imported inputs varies across regions due to transportation costs and across states depending on the availability of a tariff-lowering foreign trade zone (FTZ). This introduces a set of indicator variables for regions, denoted Ireg and states with FTZs, denoted Iftz . Defining x;[ln (12tp ) ln y ln w ln(12s w ) ln (12s k ) Iftz Ireg ln (11N)] and u5 e d 1(h p 21)e c we obtain ln P 5xb1u, where b is the vector of parameters to be estimated. Maximization of profits with respect to location implies choosing the state with the highest ln p.4 Define Pk as the probability that state k is chosen by the representative investor. For some state k, that implies Pk equals the joint probability that (x k 2x, )b.u, 2u k for all k± ,. For most distributions of u, the calculation of Pk would require a numerical integration of the same order as the number of choices. McFadden (1974) demonstrates that the assumption of independent errors with cumulative distribution function exp[2exp(2u k )] will generate a probability function that does not require any numerical integration: exp(x k b) Pk 5 ]]]]] . 50 , 51 exp(x, b)



The parameter vector, b, is estimated by maximizing the likelihood of the choices of the investors. The particular form assumed for the CDF of u is not as restrictive as it appears. Differences in profitability for two observably equal states depend on differences in u which, under the error term assumption made above, will be distributed according to the logistic function, a reasonably close approximation of the normal distribution (and hence many other distributions). Hausman and Wise (1978) find 4

We use a static model of the investment decision. It is appropriate if firms discount future profits heavily or if they base expectations of future values of x for each state on current values. The agglomeration effects present in our model create the possibility of multiple equilibria. In our setup, the equilibrium realized is determined by the historical path of investments. However, Matsuyama (1991) shows that it is possible to realize an equilibrium that is contrary to that predicted by history if, for example, a policy successfully changes investor expectations. Here investors might choose a state that has enacted a tax cut, not because they expect to directly benefit from the cut, but because they expect that the cut will attract other investors to the state. If this shift in expectations has a similar influence on other investors, a shift in agglomeration may occur, validating their expectations. In principal, very patient investors might disregard the current agglomeration pattern if they believe future investors will all select a new location. We leave to future research the difficult task of incorporating expectations into location decisions.


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that the assumption of independent normally distributed errors produces similar econometric results to the assumption of independent Type-I Extreme Value errors. The assumption of independent errors is more important and can, if violated, lead to biased coefficient estimates. Three features of our specification will tend to reduce bias in estimates of policy effects caused by correlated errors. First, it seems likely that investors view neighboring states as closer substitutes than more distant states. This could be for geographic reasons or because of similar factor endowments. We use region dummies for the nine Divisions designated by the US Census Bureau to address this issue.5 Second, our agglomeration counts, N, may capture unobserved attributes of states that are valued more highly by investors in the same industry. They may also capture unobserved cross-industry variation in investor preferences for measured state attributes.6 Third, we also include neighbor-state agglomeration counts. They are included because there is little reason to expect that the geographic scope of agglomeration mechanisms would be limited by state borders. Neighbor-state counts also provide a convenient way to capture unobserved attributes common to neighboring states.

3. Empirical implementation of the model We study the 760 Japanese manufacturing establishments that began (or were scheduled to begin) operations between 1980 and 1992, as recorded by the Japan Economic Institute (1990). For each investment we have information on the year of completion, the identity of the parent firm, the industry in which the investment occurred, and the number of employees. Our sample comprises investments in 225 different 4-digit industries. Almost a third of the investors manufacture autorelated products, although they span over 40 4-digit SICs. The location choices constitute our dependent variable. The independent variables are the characteristics of the 50 states as viewed by the investors. We now discuss the data used to implement the model.7

5 We do not estimate state-level fixed effects since it would be impossible to estimate state effects for the eight states that received no investment at all and the seven that received only one. In addition, variables that exhibit limited time-series variation may have important effects on location decisions which can be inferred from intraregional variation. 6 Schmenner et al. (1987) handle heterogeneity in investor preferences by conducting a survey of their sample of investors. They interact survey information on plant characteristics and investor goals with state attributes. This approach requires the econometrician to impose a great deal of structure by deciding which state attributes to interact with which investor characteristics. Schmenner et al. use one to four interactions per state attribute, thus substantially increasing the number of parameters to be estimated. 7 Further information on data sources is contained in Appendix A.

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3.1. Income and labor costs For investors manufacturing easily transportable goods, income in a particular state will have little influence on location decisions. Such investors may desire a US production location to avoid trade barriers. Regarding the entire country as their market, they would choose location based on cost considerations. On the other hand, when transport costs are important, populous high-income states could have substantial drawing power. We measure the market potential of a state with two variables: ln y s , the log of personal income in that state, and ln y s a , the log of personal income in adjacent states. For a given state s9, we calculate y s 9 a , as o s ±s 9 Ds y s where Ds equals 1 if s9 and s are contiguous and zero otherwise. In measuring observed factor prices, we focus on wages because of data availability and public policy interest. Past location choice studies summarized in Friedman et al. (1992) have characterized labor market conditions with a measure of a state’s average manufacturing wage, its unionization rate, and its unemployment rate. While industry-level wages would be preferable to the manufacturing average, comprehensive and reliable data are not available.8 We measure unionization as the percentage of manufacturing workers who are members of unions. After controlling for the wage, we might obtain a negative effect if unions insist on restrictive work rules that lower labor productivity. To provide further information on the threat of unionization, we also include a dummy variable for states that have laws prohibiting union membership as a condition for continued employment. Such laws are referred to as ‘right-to-work’ statutes. Finally, we include a state’s unemployment rate. A high unemployment rate could increase the attractiveness of a state by increasing the size of the job applicant pool. In some efficiency wage models, the high unemployment raises effort because it increases the cost of being fired for shirking. States with high unemployment rates may also have a tendency to engage in unmeasured efforts to attract investment.

3.2. Agglomeration effects We use different measures of agglomeration to capture three different types of similarity: industry, nationality and group affiliation. Let Ns denote the number of manufacturing establishments in a state prior to the decision of a particular investor. Nis provides the same information at the level of a 4-digit industry. We do not use Ns in our reported regressions because ln Ns and ln y s have a 0.9 correlation, making it infeasible to distinguish the two effects statistically. We also

8 The data source with the best coverage (400 000 establishments accounting for 40% of the total non-farm workforce), the Current Employment Statistics Survey Bureau of Labor Statistics, 1993, does not have data for all states at even the 2-digit level of disaggregation.


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consider agglomeration that operates with respect to investors based in the same nation. Let Ns J and Nis J measure the number of Japanese-owned greenfield (acquired firms were not included) investments in a state for all manufacturing and by 4-digit industry. We also use information on established keiretsu affiliations between vertically related firms such as the Toyota and Matsushita groups. We denote this variable as Nks .9 For each within-state agglomeration count, we also calculate an adjacent-state count using the method described above for calculating adjacent-state income. Taken together, then, there are eight agglomeration variables: ln (11Ns J ), ln (11 Nis ), ln (11Nis J ), ln (11Nks ) and their four adjacent state counterparts. While the US counts are measured at census years only, the Japanese variables are updated annually to include the previous year’s new investment.

3.3. Investment-promotion policies Our regressions consider the influence of profit taxes as well as factor subsidies on location decisions. We also include three other state policies that might influence prospective Japanese investors: the presence of an investment promotion office in Japan, the existence of a foreign trade zone in the state, and the use of unitary taxation by the state. During the 1980s, many states opened investment offices in Japan as a means of disseminating information to potential investors and encouraging them to choose that state when they located in the United States. Only eleven states had such offices in 1982. In the next eight years, ten more states opened investment promotion offices in Japan. We employ a time-varying dummy variable indicating states with these offices.10 Over the same time interval eighteen states that did not have foreign trade zones added them. By 1990 all but three states (Idaho, South Dakota, and West Virginia) had FTZs. The presence of a foreign trade zone lowers the tariff costs of imported intermediate goods via three mechanisms: payment delay, re-export, and reclassification. Payment delay is a benefit open to all users of foreign trade zones. Users of foreign trade zones do not have to pay any tariff duties until goods are shipped from the foreign trade zones to final market destinations in the United States. In addition, operation in a foreign trade zone enables the firm to avoid all tariffs on imported intermediate goods that are re-exported in final products. Finally, reclassification reduces costs when goods assembled within the zones are subject to a lower tariff than are the component parts imported into the zone. Although the federal government ultimately approves each zone, all applications are made with 9 We consider only manufacturer-centered (vertically linked) keiretsu and exclude bank-centered (horizontal) keiretsu affiliations. 10 Only one state, Illinois, established more than one office in Japan.

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the support of state and local governments. Hence, we consider the provision of FTZs as part of state promotional efforts. We employ an indicator variable to measure the influence of the existence of at least one FTZ in a state. This variable takes a value of 1 for states with ‘general-purpose’ zones as long as the investor manufactures a product that qualifies for automatic tariff treatment. General-purpose FTZs are open to use by multiple businesses in contrast to ‘subzones’ that encompass single-user facilities. Products such as automobiles, auto parts, and televisions are considered ‘sensitive’ industries that must apply for subzone states in order to obtain tariff reductions. Since firms in sensitive industries must apply individually to gain subzone status, we assume that they will not be attracted by the presence of a general-purpose zone. We test the hypothesis that higher taxes deter investment by gathering data on the corporate tax rate and unitary taxation. The latter is a method of taxing firms based on a proportion of their US or even worldwide profits rather than the accounting profits of the affiliate attributable to the state of operation. Foreign firms actively opposed the tax because it exposes them to the possibility of positive tax payments in states even if they earned no direct profits in that state of operation. As an attempt to attract more investment, many states rescinded or modified their unitary taxes during the 1980s. Fifteen states utilized employment subsidies and sixteen used capital subsidies to attract investment (eight used both at the same time). Eligibility for subsidies was often contingent on minimum employment or investment levels. This causes the subsidies to vary across both firms and states. In the model we express the subsidies in rate form. For a few states this choice requires that we convert subsidies specified in dollars per job to rate form by dividing by the state’s wage.11 This rate form is useful because it allows us to estimate distinct effects of wages and subsidies; instead of ln (w(12s w )), we introduce ln w and ln (12s w ) separately. There are three rationales for this less restrictive specification. First, we want to test for the significance of subsidy programs in their own right. A related issue is that some studies find positive wage effects which could be attributed to unmeasured worker quality variation. Finally, the subsidies were often administered as tax credits for the first year of operation. Thus, their impact would be different from wage savings that last for the lifetime of the investment.

4. Estimation results The primary goal of our estimation is to assess the statistical significance of states’ investment promotion efforts. We then use simulations to explore the 11

Further details on the construction of the subsidy variables are available from the corresponding author.


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economic significance of policy. To obtain consistent estimates of the effects of promotion, it is necessary to control for agglomeration as well as regional fixed effects. Nevertheless, we begin without these controls and estimate a baseline model that resembles the specifications used by Friedman et al. (1992); Woodward (1992). The advantage of beginning with a specification that is similar to others employed in the literature is that we can first check that there are no peculiarities in our sample of Japanese data that result in different estimated effects for regressors that have been found previously. Moreover, having begun from a common framework, we can determine the contribution that more precise agglomeration and policy measures bring to the estimates. The first column of Table 1 contains the baseline estimates of the effects of income, labor market characteristics and policy variables on Japanese investment. The following four columns list the coefficients when regional dummies and agglomeration variables are added sequentially as follows: Column (2) adds regional dummies, Column (3) includes the total count of Japanese investments, Column (4) incorporates US and Japanese industry counts, and Column (5) adds counts of the agglomeration variables in adjacent states. The table is split onto two pages with the regional dummies and the agglomeration estimates appearing on the second page. First note that the inclusion of controls for unobservables improves the fit of the model substantially. The log likelihood starts at 22570 in the baseline case and improves in each successive column, ultimately reaching 22213. As a measure of the goodness of fit, we provide the correlation between the predicted investment share for each state and the actual share. In the full specification it equals 0.99, while it is only 0.77 in Column (1). Turning to the coefficient estimates, in the baseline model both in-state and adjacent state income are positive and significant. Recall that the count of all manufacturing establishments was omitted due to its 0.9 correlation with state income. Thus, the final demand variables may be reflecting agglomeration at the manufacturing level. Neither in-state nor adjacent state income, however, is significant once we add the industry-level agglomeration variables. We find that the effects of labor costs are sensitive to the addition of the agglomeration variables. In the final specification, two out of the four labor cost measures are significant at the 5% level. The unionization rate is negative and the wage is positive. One explanation for the latter result may be that interstate variation in average wages mainly reflects variation in the skill composition of the work force. High skill intensity of Japanese manufacturing plants would result in the apparent preference for high-wage states.12 A comparison of the coefficients on 12 Smith and Florida (1994) also find a positive and significant wage effect using county-level data on Japanese auto parts investment. We have attempted to test the skill hypothesis directly, using the log of the average grade attained by workers in a state as proxy for skills. The estimated coefficient had the ‘wrong’ sign (negative) with a T-statistic of 20.36. This suggests the need for improved skills measures or, perhaps, an alternative interpretation of the positive wage coefficient.

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Table 1 Conditional logit results for location choice model Dep. Var.: Prob (investor j chooses state s)

Market size Log of state income Log of adjacent state income

Labor costs Log of manufacturing wage Unionization rate Right-to-work state Unemployment rate

Policy instruments Log of (12Corporation tax rate) Log (12Labor subsidy) Log (12Capital subsidy) Unitary tax dummy FTZ dummy Japan office dummy Pseudo R 2 Cor (P, Pˆ ) Log-likelihood Region effects Mid-Atlantic East North Central West North Central South Atlantic East South Central






0.966 a (0.058) 0.331 a (0.067)

0.942 a (0.082) 0.509 a (0.091)

0.268 a (0.094) 0.321 a (0.074)

20.232 b (0.106) 0.337 a (0.080)

20.009 (0.111) 0.091 (0.107)

20.564 (0.556) 1.168 (1.231) 0.027 (0.149) 0.0980 (0.026)

1.585 c (0.812) 23.793 (2.399) 0.234 (0.171) 20.026 (0.036)

2.200 a (0.850) 24.456 c (2.316) 20.032 (0.174) 0.021 (0.038)

2.118 b (0.866) 23.676 (2.314) 20.056 (0.177) 0.035 (0.039)

1.941 b (0.853) 24.768 b (2.293) 20.009 (0.178) 0.032 (0.039)

8.084 a (1.445) 27.370 a (1.266) 21.362 a (4.094) 0.161 (0.123) 1.29 a (0.332) 0.022 (0.091) 0.135 0.77 22570.5

3.027 (1.865) 25.640 a (1.348) 0.469 (1.467) 20.397 b (0.165) 1.201 a (0.338) 20.168 (0.106) 0.175 0.91 22451.5

2.921 (1.857) 24.695 a (1.403) 20.233 (1.134) 20.497 a (0.176) 1.141 a (0.340) 0.03 (0.111) 0.202 0.99 22371.2

5.659 a (1.921) 24.254 a (1.446) 20.517 (1.068) -0.498 a (0.181) 1.3 a (0.343) 0.104 (0.112) 0.247 0.99 22237.3

6.134 a (1.899) 23.448 b (1.466) 20.524 (1.072) 20.454 b (0.181) 1.188 a (0.343) 0.070 (0.113) 0.256 0.99 22213.4

0.087 (0.371) 1.502 a (0.327) 0.008 (0.365) 1.141 a (0.340) 1.764 a (0.337)

20.342 (0.368) 0.399 (0.356) 20.349 (0.368) 0.456 (0.357) 0.703 b (0.355)

20.159 (0.371) 0.19 (0.364) 20.14 (0.368) 0.798 b (0.359) 0.805 b (0.361)

20.206 (0.382) 0.122 (0.391) 20.199 (0.378) 0.638 c (0.377) 0.698 c (0.380)


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Table 1 Conditional logit results for location choice model Dep. Var.: Prob (investor j chooses state s) (1) West South Central

(2) 0.023 (0.424) 0.033 (0.410) 2.004 a (0.312)

Mountain Pacific

Within-state agglomeration Japan manufacturing



20.508 (0.441) 20.229 (0.407) 0.438 (0.355)

20.211 (0.441) 0.048 (0.412) 0.542 (0.360)

20.334 (0.444) 20.035 (0.424) 0.608 (0.393)

0.743 a (0.062)

0.532 a (0.065) 0.547 a (0.054) 0.670 a (0.091) 0.709 a (0.125)

0.510 a (0.064) 0.4440 (0.057) 0.590 a (0.094) 0.736 a (0.130)

US Industry Japan Industry Keiretsu

Adjacent-state agglomeration Japan Manufacturing US Industry Japan Industry Keiretsu Log-likelihood






20.087 (0.099) 0.303 a (0.069) 0.3580 (0.094) 0.300 b (0.140) 22213.436

Standard errors in parentheses. a, b, c indicate significance in a two-tail test at the 1, 5 and 10% levels. Pseudo R 2 is the percentage improvement in the likelihood function relative to the model with all coefficients set equal to zero. Cor (P, Pˆ ) is the correlation between predicted and actual shares of investment for each state. New England’s region effect is normalized to be zero.

the unemployment and unionization rates in the baseline and final specification indicates the importance of precise controls for agglomeration effects. In the former, both are positive with unemployment being significant.13 Arguably, the unemployment result may indicate that slack labor markets appeal to investors or, alternatively, reflect unobserved efforts by high unemployment states to solicit investment. However, the fact that the unemployment becomes insignificant and unionization negative and significant once agglomeration measures are included suggest the initial positive result is a consequence of omitted variable bias: US 13

Friedman et al. (1992); Coughlin et al. (1991) find positive and significant effects of both variables in specifications similar to our Column (1).

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states with high unemployment and unionization rates also tend to have high agglomeration. Table 1 also reveals that high corporate taxes and the presence of unitary taxation deterred Japanese investment.14 Corporate taxes are the one fiscal element common to most studies of investment location. Our finding is consistent with work by Bartik (1985); Coughlin et al. (1991) who find that high taxes deter investment and contrasts with the early Carlton (1983) study of domestic investment as well as with the studies of foreign investment conducted by Wheeler and Mody (1992); Friedman et al. (1992); Woodward (1992) who fail to find clear evidence that high taxes reduce investment.15 However, the absence of significant tax effects in many studies of foreign investment, may instead reflect inadequate controls for the benefits provided by tax revenues. This is important, since higher taxes will not necessarily deter investment if those taxes finance services that prospective investors value.16 Labor and capital subsidies, as well as support for gaining federal approval of general-purpose foreign trade zones, are benefits provided to investors out of tax revenues. Very few papers have included subsidies in their policy analysis, and our paper is unique in its use of subsidy measures that are matched to the investment characteristics. Moreover, we are not aware of any previous studies that include the provision of FTZs. Previous work by Luger and Shetty (1985); Woodward (1992) implicitly includes the effects of labor and capital subsidies through their use of an ‘effort index’ that provides a count of programs offered by each state to lure investors. The major drawback to the effort index is that it simply counts the number of activities without measuring the intensity of the efforts. We find that the presence of FTZs and labor and capital subsidies are positively correlated with Japanese investment, although the capital subsidy is not statistically significant.17 We do not find a significant effect for investment promotion offices in Japan. This latter finding contrasts with the result of Woodward (1992) that state investment promotion offices opened in Japan are positively correlated with investment.18 Promotion offices, like other forms of advertising, would be more likely to work when investors have little information about the choices they face. The low efficacy of this policy suggests that Japanese investors may already be well-informed about state characteristics and therefore unswayed by the in14

A positive coefficient implies that higher taxes deter investment, as the tax variable comes from the profit function and is measured as ln (12tp ). 15 Hines (1996) explains why some foreign investors, such as those from Japan, may be less sensitive to interstate tax variation. 16 Work by Papke (1987), (1991) provides insight into these issues. Papke includes measures of state expenditures on fire and police protection to measure some of the benefits provided by tax revenues. 17 A negative coefficient implies that subsidies increase investment as our specification measures the subsidies as ln (12s w ). 18 Woodward’s results are based on cross-sectional variation alone, as his Japan office measure is based on a single year.


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formation the offices provide. A second factor that may dilute the measured impact of Japan offices is that reverse causality may drive state adoption of these offices. States that anticipated faring poorly in the competition for new investment may have been the ones to implement proactive policies. The problem of reverse causality is mitigated somewhat by the structure of our analysis. If ‘unattractive’ states have a higher propensity to take remedial action this proclivity should be captured in part by the agglomeration counts since these variables indicate which states have been less successful in previous years. Our results suggest that significant policy effects emerge when the specification contains detailed state policies linked to the after-tax profits of investors. Moreover, the magnitudes of the policy effects are sensitive to the inclusion of the agglomeration variables. The estimated coefficients on the labor subsidy and the corporate tax fall when we add agglomeration variables. The coefficients on the capital subsidy and the unitary tax have the ‘wrong’ sign in the baseline specification but enter with the expected sign in the final specification, with the latter estimate being significant. Now consider estimates of the coefficients for the region and agglomeration effects that appear in the second page of Table 1. Column (2) reveals that Japanese investors preferred the Pacific region, the East North central region, and the South Atlantic region over the omitted region, New England. However, the region effects are much less important when the count of Japanese manufacturing investment is added in Column (3). The highly significant estimated coefficient indicates a tendency of Japanese manufacturers to cluster regardless of industry. This might arise because Japanese manufacturers seek workers with specific attributes that are not captured in our labor variables. In addition, states with a concentration of Japanese manufacturers may endogenously develop schools for Japanese children and other culture-based amenities. Column (4) adds the within-state counts of same-industry and same-group establishments. The results indicate that the numbers of both US and Japanese firms that operate in the same 4-digit industry as the investor exert a strong positive influence. The keiretsu counts also enter positively and significantly. It is informative that Japanese and US industry counts are each statistically significant in the same regression. Suppose that plants in the same industry have the same factor intensities regardless of the nationality of the parent firm and that there are no sunk costs or externalities that prevent the US pattern from reflecting current relative input price conditions. Then we would expect the Japanese investment to mirror the US pattern and the Japan-industry counts would not be statistically significant. The results suggest that one or both of the above suppositions is false. Alternatively, they may indicate the existence of nationality-specific endogenous agglomeration effects. The last column incorporates the adjacent-state agglomeration measures. With the exception of the count of Japanese manufacturers, these variables enter positively and significantly. As expected, the adjacent-state effects are sys-

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tematically smaller than the in-state effects.19 One interpretation is that firms care about counts in adjacent states because they indicate nearby sources of endogenously supplied specialized inputs. Another possibility is that both the in-state and adjacent-state counts serve as imperfect proxies for unobserved features of states that are spatially correlated. In summary, we find that state policies that raise the after-tax profits of investors provide a positive boost to investment. The inclusion of detailed agglomeration measures appears important for obtaining accurate estimates of policy variables and for ensuring a good fit of the model. The provision of FTZs and labor subsidies raises investment significantly while corporate taxes reduce investment.

5. Simulation results We turn to simulations to quantify the changes in the geographic distribution of the Japanese manufacturing investments that would have occurred if particular state incentive programs had been removed. The distribution of investment is first derived by calculating the probabilities that each investment will locate in each state under different realizations of the policy variables using Eq. (3). For each simulation we estimate a static and dynamic version. The static version corresponds to the case where agglomeration effects reflect unobserved characteristics of states. In these simulations, counterfactual policy experiments do not alter the agglomeration counts which are exogenously determined by the actual historic pattern of investment. In contrast, the dynamic simulations allow policy changes to affect agglomeration counts in successive years. Instead of historical counts, these simulations apportion Japanese investment according to the predicted probabilities that arise under the new set of government policies. Thus, policies that raise Japanese investment in one year will raise the probability of investment in subsequent years through increased Japanese agglomeration.20 The simulations employ the coefficients from Column (5) of Table 1. The provision of foreign trade zones and labor subsidies are estimated to have a significant and robust statistical influence on the location choice of Japanese investors. We simulate how each of these policies affected the distribution of investment. We then ask what the geographic pattern of Japanese investment would have been in the absence of all policy differences. Table 2 groups states into three categories based on the year in which each state established its first general-purpose FTZ and describes each group’s share of 19 The likelihood ratio test statistic for the hypothesis that all adjacent coefficients are zero is 48, where 13 is sufficient to reject at the 1% level. 20 The USA industry counts are always treated as exogenous since updating the 230 000 USA establishments for our 760 Japanese establishments would have no measurable impact in the simulation.


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Table 2 Effects of eliminating or freezing the FTZ program FTZ timing Actual share (%) Pre-1980 76.8 1981–1984 23 Post-1988 0.1

Baseline share (%)

Elim. (shr D)

Freeze (shr D)







76 23.5 0.4

77.3 22.3 0.4

22.2 1.5 0.8

25.5 4.7 0.8

12.3 212.3 0.1

14.8 214.9 0.1

Explanation: Investment shares generated by simulation using coefficients from Column (5) in Table 1.

investment under different policy scenarios. The first group comprises the 28 states that had FTZs in 1980, the second includes the eighteen states that adopted them from 1981–1984, while the four states in the third group introduced FTZs after 1988 or not at all. The first three columns compare the actual share of investments for each group against the shares predicted in the baseline static and dynamic simulations where all policy variables are given their historic values. Both simulations fit the actual shares well.21 To compute changes caused by policy, however, the baseline simulation results are used as the benchmark rather than the actual number of investments. This prevents reported policy changes from reflecting deviations attributable to the lack of exact fit of the model to the data. The fourth and fifth columns show the changes in group shares in the dynamic and static simulations under the scenario that all FTZs were eliminated in 1980. The 28 states that had FTZs at the beginning of the sample period are slightly worse off in terms of investment while the later adoption states gain. The dynamic simulation generates larger changes than the static case. However, the overall distribution remains largely unaltered since 46 states offered FTZs by 1984, removing them as a reason to prefer one state over another. The last two columns measure the effect of a policy ‘freeze’, where FTZs exist only for the 28 states that had them by 1980. The columns reveal that these states’ share of total investment increases by 12.3 and 14.8 percentage points in the static and dynamic simulations, respectively. This gain comes at the expense of the other states which do not introduce FTZs in this policy experiment. Their share of investment declines by more than one-half. This experiment reveals that while FTZs had little effect on the total distribution, if states who did not offer FTZs in 1980 had not subsequently adopted them, they would have received much less Japanese investment in the following decade. Unilateral FTZ removal would have led to large reductions in the number of Japanese investments a state received. Fig. 1 shows the ratios of the number of 21

The good fit of the static simulation is not surprising, given the result reported in the regression table that there is 0.99 correlation between the predicted and actual state shares. However, the dynamic simulation may not track the data as well since an overprediction early in the sample will cause overprediction throughout.

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Fig. 1. Consequences of the removal of foreign trade zones.

investments under multilateral and unilateral FTZ elimination to the number of investments in the baseline simulation. The six states chosen are the three largest investment recipients in each of the first two groupings reported in Table 2 with the actual number of investments they received shown in parentheses. The figure indicates that while a state’s investments do not change much under multilateral elimination, each would have lost between 50% and 75% of its investment if it had not kept pace with competing states by providing its own foreign trade zone. These results point to the potential for a prisoners’ dilemma in FTZ creation: multilateral elimination might have increased welfare but each state had the incentive to offer FTZs in order to attract investment at the expense of other states.22 Our estimation and simulations consider only location choices of investors, conditional on choosing the United States. If the US FTZ program lured large numbers of investors from other countries, it may have been the case that states were collectively better off with the FTZs, and the prisoners’ dilemma analogy would not hold. The next set of simulations considers labor subsidies. Unlike FTZs which 46 states had adopted by 1984, only fifteen states used labor subsidies during the 1980s. Table 3 displays the effects of unilateral removal of subsidy programs by 22

Reich (1991) argues that the coordination provided by the European Commission may provide a superior outcome to the unconstrained bid competition that takes place among states. As long as the investor has decided to invest within a particular region (the USA, Europe), he argues that bid competition only reduces the benefits reaped by the ultimate hosts.


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Table 3 The impact of unilateral removal of labor subsidies on state employment State

Baseline employment

Employment losses

Impact a

California Colorado Iowa Illinois Indiana Kansas Louisiana Missouri Mississippi New York Oklahoma

20731 385 555 6510 6955 297 335 1734 1121 1765 835

1617 4 121 58 1752 4 8 26 55 126 6

2 4 3 3 3 3 3 3 3 3 3


Employment attributable to subsidies per job subsidized, see explanation in text.

each of the eleven states that attracted investment.23 The first column lists the employment by Japanese plants in the baseline static simulation where all states offered their historic set of subsidies. The second column indicates the employment that would have been lost had each state unilaterally removed its subsidy. The table reveals that these programs had strong effects on investment in certain states. Over 20% of the employment received by Indiana and Iowa, who both implemented job-creation credits that paid up to 10% of the first year wage bill in new plants, is attributable to the programs. For other states the results are less dramatic because they offered small subsidies, often just a nominal inducement for each job created. The third column shows the additional employment attributable to the labor subsidies per job paid for by the state, which we call the ‘impact’. The numerator of this statistic is Column (2), the employment increase caused by the subsidy. The denominator is the baseline simulation’s calculation of the total number of employees in Japanese plants that were paid for through the subsidy program. The impact statistic ranges from two to four. Thus, on average, for every one employee paid for entirely by a state, employment in Japanese plants increased by two to four workers. Finally, we consider the overall impact of promotional policies of states. While FTZ programs largely offset each other in the simulations, states simultaneously pursued a number of other incentive programs. Did state efforts offset each other through their combined labor and capital subsidies, foreign trade zones and taxes? To answer this question, we compare the number of Japanese plants predicted in baseline static simulation to those predicted by a static simulation of harmonized policy. Since the simulation experiment equalizes policies, only economic 23

The four states whose labor subsidies did not yield investment (Delaware, Idaho, Montana and North Dakota) had subsidy rates that averaged about 3%.

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Fig. 2. The effects of a policy harmonization.

fundamentals—income, labor markets, agglomeration and regional effects—distinguish states. Fig. 2 shows the change in the number of plants from the baseline case to the harmonization case for the twelve states who realized changes exceeding two investments. California is the big winner in the simulation as it receives 52 additional investments on top of the 100 it receives in the baseline. California’s gains come largely at the expense of Indiana, Michigan, Georgia and Washington. California gains because it has strong fundamentals and its high corporate taxes (on average 9.5% compared to the overall average of 6.6%) and unitary taxation do not deter investment in the harmonized policy simulation. The states that lose investment had low taxes (Indiana, Michigan and Washington had average rates below 3%) and Indiana offered relatively large labor subsidies as well. The policies, however, did not appear to significantly alter the rank ordering of the number of investments each state received: the correlation between the estimated investment in the baseline and harmonization simulations is 0.95.24 Thus, while the figure indicates that particular states would have substantially different levels of investment if states had harmonized fiscal policies starting in 1980, economic fundamentals were the most important determinant of the location of Japanese investment. 24

The dynamic simulation generates somewhat greater policy-induced deviations; the correlation falls to 0.88.


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6. Conclusions Japanese manufacturing investment in the United States exhibits pronounced agglomeration effects. Other things being equal, Japanese investors appear to prefer states chosen by preceding investors, particularly those in the same 4-digit industry or keiretsu. We find that states which offered foreign trade zones, job-creation subsidies, and low taxes received significant increases in investment. However, individual state policies were often matched by other states. For example, by the middle of the 1980’s 46 states had at least one foreign trade zone. With endogenous agglomeration effects, a state that adopts pro-investment policies first can retain an advantage even after the policy change is emulated by rivals. The simulations suggest that dynamic effects were not strong enough to produce major alterations in the geographic pattern of Japanese investment. Nevertheless, we find that unilateral elimination of FTZs or labor subsidy programs would have led to substantial decreases in investment.

Acknowledgements We thank two anonymous referees, Jim Hines, Jim Poterba, participants at the NBER summer institute, AEA, and CEA meetings for helpful suggestions. Meng Zhang provided valuable research assistance.

Appendix A

Data sources The Japan Economic Institute (1990) provides the operation date and SIC of each Japanese manufacturing plant. We computed keiretsu counts based on affiliations found in Keizai (1993), a publication of the Toyo Keizai company. Manufacturing establishment counts in the United States were obtained from the USA Bureau of the Census (1982, 1987). Unemployment rates and manufacturing wages were obtained from the Bureau of Labor Statistics (1993). We used the USA Bureau of the Census (1993) to calculate wages for Indiana and unionization rates for each state. Mills (1989) provided the list of states with Right-to-Work laws. State income and the corporate tax rate were collected from the USA Department of Commerce (1982, 1986, 1991). Data on the unitary tax were collected from Tannenwald (1984) and from the Wall Street Journal and New York Times. Data on foreign trade zones were collected from the Foreign Trade Zones Board at the US Department of Commerce. The Japan investment promotion office data and labor and capital subsidy program information were gathered from the

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National Association of State Development Agencies (NASDA) publication: Directory of Incentives for Business Investment and Development in the United States: A State-by-State Guide (National Association of State Development Agencies, 1983, 1986, 1991). In each case where the data were not available annually, investors were matched to data close to the year the plant began operations. More detail on the construction of these variables, particularly the factor subsidies, is supplied in a longer version of this appendix that is available from the corresponding author.

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