razing arizona: anti-immigrant sentiment and foreign direct … · 2014-07-29 · law enforcement...

Razing Arizona: Anti-Immigrant Sentiment and Foreign Direct Investment into US States!

Sarah Andrews PhD Candidate

[email protected]

Sonal S. Pandya Associate Professor

[email protected]

David Leblang Professor

[email protected]

Department of Politics University of Virginia

June 2014

Abstract

Although economic calculations are central in immigration policy debates, the economic costs of the debates themselves have been overlooked. We analyze patterns of foreign direct investment (FDI) flows into US states and show that states with stronger anti-immigrant sentiment receive less FDI. Using a new dataset of dyadic FDI flows from 125 countries into the 50 US states during 2002-2011 and original measures of anti-immigrant sentiment, we isolate the role of anti-immigrant sentiment by comparing two distinct types of FDI: greenfield FDI, the establishment of new foreign-owned firms and foreign mergers and acquisitions (M&As) of existing firms. Both types of investors are sensitive to states’ business climate but M&A investors are less flexible in location decisions, constrained by the location of acquisition targets. Modeling both types of FDI via seemingly unrelated regressions controls for unobservable state characteristics that correlate with both anti-immigrant sentiment and business climate. Our findings contribute to migration research and demonstrate FDI investors’ sensitivity to sub-national politics unrelated to expropriation risk.

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In recent years US state legislatures have introduced hundreds of bills and resolutions

pertaining to undocumented immigration. Figure 1 illustrates that these laws are wide ranging—

impacting an individual’s eligibility for drivers’ license eligibility, employment, in-state tuition

for public higher education, and basic social programs. One of the most prominent examples is

Arizona’s 2010 Support Our Law Enforcement and Safe Neighborhoods Act. That act requires

all non-citizens over the age of 14 to carry proof of legal residency with them at all times.

Despite challenges to the law’s constitutionality and its potential to encourage racial profiling,

six other state legislatures proposed similar legislation.1

While most of this legislation targets undocumented immigrants, it can fuel a hostile

environment for all migrants, including those who contribute greatly to the local economy.

Alabama’s 2011 immigration law, among the country’s toughest, encouraged undocumented

migrants to “self-deport”, stripped access to all public benefits including schools, and mandated

law enforcement to verify immigration status in all traffic stops, detention, and arrests. That

same year Alabama police stopped a German national driving a rental car and arrested him

because he could not produce his passport. The driver was a Mercedes-Benz executive visiting

his company’s sport utility vehicle plant just outside of Tuscaloosa. Opened in 1996, the plant

has created over 41,000 jobs in the state.2 Amid widespread media coverage, the St. Louis Post-

Dispatch invited Mercedes-Benz to relocate: “You should move your SUV plant to Missouri

where we are the Show Me State, not the ‘Show me your papers State.’” Just a month later a

visiting Japanese executive of Honda, also present in Alabama, met with the same fate.

1 2010: Pennsylvania, Rhode Island, Michigan, Minnesota and South Carolina; 2011: Alabama (www.ncls.org/immigration). 2 http://www.reuters.com/article/2011/11/22/us-immigration-alabama-mercedes-idUSTRE7AL0DT20111122

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In this article we analyze how anti-immigrant sentiment—defined as the introduction of

anti-immigrant bills and the public’s attitude towards immigration in general—influences the

amount of foreign direct investment (FDI) that states receive. The Alabama example highlights

how anti-immigrant sentiment can deter foreign investors, even when the status of legal

immigrants is not under debate and state policymakers have strong incentives to attract and retain

foreign investors. We argue that, all else equal, foreign firms are less likely to invest in US states

with greater anti-immigrant sentiment. Foreign firms must relocate home country personnel for

extended periods and frequently send additional personnel for shorter stays in order to manage

the subsidiary and maintain its integration with the larger multinational firm. Anti-immigrant

sentiment undermines foreign investors’ management of their US subsidiaries.

We develop novel empirical tests that isolate the consequences of anti-immigrant

sentiment for FDI. An obvious threat to inference is that anti-immigrant sentiment correlates

with unobserved characteristics of a state’s business climate (labor market factors, location, etc.)

that actually drive foreign firms’ investment decisions. We address this concern by exploiting the

presence of two distinct types of FDI. Greenfield FDI is FDI via the establishment of a new firm,

whereas foreign mergers and acquisitions (M&A) transfer ownership of existing firms to a

foreign investor. Greenfield investments to designed to introduce new production technologies

into the host country whereas foreign M&As are typically to acquire location-specific assets such

as distribution networks (Bruner 2004, Nocke and Yeaple 2007, 2008).

Both types of FDI investors consider the same basic features of a state’s business climate

including wages, infrastructure, and regulation. Foreign M&A investors, however, are less

flexible in their location decisions because they are constrained by the ex ante location of

acquisition targets. We use seemingly unrelated regressions to directly compare models

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greenfield and M&A flows into the same state. This approach holds constant any cross-state

variation in the business climate that correlates with anti-immigrant sentiment because both types

of FDI are sensitive to states’ broader business climate. Thus, we expect that anti-immigrant

sentiment discourages greenfield FDI more than FDI via mergers and acquisition (M&A) of

existing firms. Our empirical analysis draws on an original dataset of dyadic greenfield and

M&A investments that spans 124 source countries and the 50 US states. We also rely on original

measures of anti-immigrant sentiment based on state legislation and public opinion.

Our empirical findings demonstrate the economic costs of anti-immigrant sentiment in

terms of forgone FDI. For each additional anti-immigrant legislation introduced in the state

legislature, the state loses on average nearly one million dollars in greenfield investment the

following year. Similarly, a one-percent decline in state public opinion towards immigrants

corresponds to a quarter of million dollars less of greenfield investment in the subsequent year.

We, however, find no correlation between states’ anti-immigrant public sentiment and the

amount of foreign M&A investment received. This finding supports our claim that, all else equal,

M&A investors have less flexibility in their location decisions. We verify the robustness of our

findings by controlling for the size of the sending country’s migrant population residing in the

state, the volume of passenger air traffic within the investor country-US state dyad, and

agglomeration patterns that influence investors’ location decisions.

This research contributes to scholarship on the political economy of migration in a few

ways. While there is extensive research on the sources of immigration sentiment (see Hopkins

and Hainmueller 2014 for a review), existing scholarship does not address the economic

repercussions of those sentiments. The existence of these costs can be used to tease out the

relative importance of economic and non-economic factors in preference formation. These costs

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are material consequences of immigrant sentiment distinct from labor market competition and

fiscal burdens, standard considerations in current research. Further, Jensen et al (forthcoming)

suggests that voters reward politicians who successfully attract FDI to their localities. Survey

experiments can include forgone FDI as a treatment to assess whether it moderates anti-

immigrant sentiment. Additionally, we uncover a new mechanism that contributes to migration-

FDI linkages (Leblang 2010; Rauch and Trindade 2002).

This study also expands political economy theories of FDI determinants.3 Extant political

economy theories of FDI emphasize how host country politics influence where foreign firms

invest (Li 2006, Blanton and Blanton 2007, Jensen 2008). These theories however provide little

guidance in explaining variation in FDI location within large countries like the US despite

relatively uniform political systems, legal protections, and macroeconomic policies.4 Our

analysis of FDI patterns within the US highlights that even in the most advanced industrialized

countries, host country politics can still deter foreign investors. Additionally, our theoretical

emphasis on different modes of FDI entry and corresponding novel measures of FDI address

well-known inaccuracies of standard FDI data (Beugelsdijk et al. 2010).

In addition to our innovations to political economy theories of FDI flows, our findings

have clear implications for how subnational policymakers can attract greater foreign investment.

As states continue to actively debate immigration policy, they should be aware of the fact that

policies that create a more hostile environment for migrants also have the effect of deterring

3 See Jensen et al 20132012. 4 Though see Malesky 2008 on the influence of FDI on subnational policymaking in Vietnam and Jensen et al forthcoming on the subnational electoral consequences of FDI.

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some types of foreign investment. This finding should be factored into the highly politicized

debate surrounding immigration in the United States.

In the next section we develop the theoretical proposition that FDI investors are sensitive

to local anti-immigrant sentiment and elaborate on the distinctions between greenfield and M&A

investment that form the basis of our research design. The following section describes our

empirical tests and includes measures of key concepts, empirical tests, findings, and robustness

tests. We conclude with a discussion of possible extensions of this research.

Local Politics and Subnational FDI Location Decisions

Just as firms consider economic conditions when selecting markets for investment we

argue that they also evaluate the political environment. While these calculations are similar to

the considerations regarding political risk when investing abroad we are not arguing that state

governments pose an expropriation risk similar to that of foreign governments. Rather, we argue

that a state’s immigration environment —regardless of who is targeted—sends a signal about

how hospitable the community is to foreign investment and the company’s employees that may

be part of investment activity. In response to his legislature’s adoption of its immigration law,

David Bronner, chief of the Retirement Systems of Alabama, stated "If the Hispanic feels he's

not welcome, the Chinese will think the same thing, then the Japanese and the mathematician

from India especially when you can go 50 or 100 miles and not have to put up with it."5

A state’s immigration environment may influence the willingness of a company’s

personnel to relocate to a particular destination. Scholars have long recognized the importance

of a community’s characteristics on local migration decisions (Agnew 1987). Migrants are often

5 http://blog.al.com/jkennedy/2011/11/joey_kennedy_alabama_immigrati.html

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drawn to destinations where there are a large number of co-ethnics, where jobs are available, and

where there are economic, social and political opportunities for their family members (Clark,

Deurloo and Dieleman 1984; Huff 1986). A hospitable environment for investment can help a

foreign firm lower the transactions costs associated with relocating management to a new

location and can also assist in the retention of key personnel. This, we argue, should matter more

for greenfield investment as these investors will be building a management team from scratch, a

team that will likely consist of a large number of individuals from the investing country.

Firms engaging in FDI, either greenfield or M&A, often have employees, or at least

upper level management, from the country of origin working in the new foreign affiliate, as well

as temporary trips to the host country or state by home country nationals for visits or trainings.

Therefore, FDI should inherently bring with it a concern for immigration procedures and the

immigration experience. Living in a place hostile to or suspicious of foreigners could be

generally unpleasant for those working in foreign affiliates. Also, immigration policy is

officially the purview of the federal government in the United States, but especially since 9/11,

state and local governments have played an increasing role in immigration policy enforcement

and in making laws related to immigration enforcement and there is variation among states in

approaches taken to immigration enforcement (Rodriguez 2008). A number such laws are

targeted at undocumented immigrants, but immigrant sentiment on the part of voters may have

either real or perceived practical consequences for those connected with FDI inflows. This

suggests that anti-immigrant legislation anti-immigrant opinion could deter FDI.

Varying Sensitivities of Greenfield vs. M&A Investment

The location decisions of greenfield investors are more sensitive to immigration

considerations because these investors have greater flexibility in their location choices than do

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M&A investors. Greenfield investors introduce their own productive assets and choose where to

locate them geographically. The entry of foreign automakers into new and different US states is

the classic illustration of this flexibility of greenfield location decisions. Consistent with this

flexibility, it is not surprising that these foreign automaker investments have garnered some of

the most visible investment promotion efforts that US states have undertaken. By contrast, M&A

investment opportunities are ex ante geographically determined by the location of acquisition

targets.

Our analysis relies on the determinants of greenfield and M&A as being essentially

similar except for their relative tolerance to a hostile local political climate. At the level of

aggregation at which we are modeling investments (e.g. not disaggregating by industry) the same

set of factors should influence where both types of investors locate. One exception is the

importance of investment incentives in influencing greenfield location decisions. Precisely

because greenfield investors have more flexibility in location decisions, state and local

governments vigorously compete for greenfield investments through the provision of tax and

other incentives (Jensen et al forthcoming). M&As typically do not receive such incentives

because they do not create additional jobs. Indeed M&As can often result in job loss in the early

and medium run while foreign acquirers exploit higher economies of scale (Arnold and Javorik

2009).

It could be that we are picking up cross-state variation in the preference for FDI rather

than for immigration. This would be true if state opinion about immigration correlated with

public opinion about FDI. We lack systematic state public opinion data about FDI with which to

directly test this but the difference in distributive effects suggests that M&A should produce

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more opposition than greenfield investments and, at the margins, M&As should exhibit greater

sensitivity to immigrant sentiment than greenfield. This is the opposite of our expected finding.

We note that due to a lack of data we cannot control for the presence of investment

incentives in our model of greenfield FDI. States and localities sometimes compete vigorously

for greenfield investments by offering tax incentives, creating free trade zones, and other forms

of subsidies (Adams et al 2014). They favor greenfield investments to M&A because of the

greater job creation potential. The implications of this omission depend on if and how anti-

immigrant sentiment correlates with the propensity of state and local governments to offer FDI

incentives. If incentives are more generous in states with an anti-immigrant climate then any

negative effect of climate on investment understates the negative correlation.

If states with less hostility towards migrants are also more likely to furnish investment

incentives, we cannot readily interpret model estimates for immigrant climate variables because

we cannot distinguish between incentives and sentiment. This latter scenario is highly

improbable because the states that must use incentives to attract investment tend to be states with

more negative sentiment towards immigrants. While it is possible that we are picking up the

effects of investment incentives, available evidence indicates that incentives are rarely the

deciding factor in investment decisions (Morrisett and Pirna 1999). Head et al (1999) show that

incentives have had a marginal effect on the location of Japanese affiliates in the US because

competition among states washed out incentives’ effects on location decisions.

Empirical Analysis

We test our hypotheses using annual dyadic data on two forms of FDI, greenfield and

M&A investment, from up to 125 sending countries i into the 50 US states j in year t. The unit of

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analysis in this study is the dyad-year, where a dyad consists of a sending country and a recipient

state. By using state level data we hold constant all national factors such as political risk and

regime type that are the subject of existing studies of FDI location decisions, and thus gain some

purchase on the effects of subnational factors. Greenfield is the dollar value of all greenfield

investments in each dyad-year. Greenfield data are from the fdiIntelligence database of the

Financial Times and cover the years 2004-2011. M&A is the number of foreign merger and

acquisition investment “deals,” in each dyad-year. These data are from the SDC Platinum

database and span the period 2002-2011.6 By construction, our sample includes all possible

dyad-years given the sending countries in our data. Thus we have dyads within which there was

zero investment for one of the FDI types. This strategy assumes that there is the potential for

investment deals between the partners in each dyad of our samples in each year.7 To minimize

distortion from outlier values we estimate the natural log of both FDI measures.8

By distinguishing between FDI through different modes of entry we capture and parse

conceptually distinct aspects of FDI that standard balance of payments FDI measures cannot.

Balance of payments data measure FDI as foreign equity ownership in excess of ten percent

equity ownership but cannot distinguish between mode of entry and the number of distinct

investors entering the market. Both of our measures are dyadic which is in and of itself an

advance over current measures. Additionally our measure of M&A, by capturing the number of

deals, distinct investments, focuses attention on the analytically salient part of investment –

foreign ownership – rather than the financial value of external financing of FDI that can be

6 See appendix for a list of sending countries for both samples. 7 Inclusion of the zero dyads in our analysis does not alter the results of our hypothesis tests but it does, of course, influence parameter estimates. 8 We add one to all measures in order to retain the zero dyads.

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distorted by investors raising financing locally and through reinvested earnings. The distinction

between greenfield and M&A is particularly useful for our analytical purposes; different modes

of entry should have varying levels of sensitivity to domestic level factors, allowing us to better

parse the effects of anti-immigration legislation and sentiment.

Our primary explanatory variables capture immigrant sentiment within a state. We

conceive of immigrant sentiment both as it is codified in legislation and in terms of public

opinion within a state. First, we measure the number of anti-immigrant bills passed by each in

state legislature in a given year. We construct this variable using data from the National Council

of State Legislatures’ (NCLS) database which tracks all bills introduced and enacted on a variety

of key immigration issues.9 The NCLS database allows us to code whether legislation was

passed in four primary areas, each with the potential to impact immigration levels: immigrant

access to state welfare benefits, immigrant eligibility for in-state tuition, the implementation of

workplace enforcement mechanisms (e.g., E-VERIFY), and any requirement specifying the need

to present a a birth certificate or greencard in order to obtain a driver’s license. It is possible, in

fact likely, that a piece of legislation passed in one year will continue to have an effect in future

years. To account for this potential stickiness of legislation, the state immigration legislation

index is calculated using a formula whereby for any given year, the number of squared anti-

immigrant pieces of legislation is subtracted from the number of pro-immigrant pieces of

legislation and then the average for the past three years is taken. Anti-immigration measures are

squared to account for the fact that pro-immigration legislation likely has less of effect on

attracting immigrants than does anti-immigration legislation on deterring immigrants.

9 www.ncsl.org

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As observed attempts at legislation may also reflect a variety of political characteristics

unrelated to immigration, we also measure state public opinion towards immigrants. This is

measured as the attitude towards migrants or US immigration policy as expressed in public

opinion polls. Rather than take a sample of polls, we collected all Gallup polls conducted

between 1980 and 2012 which asked at least one question related to immigration.10 For the most

part Gallup consistently asked either “do you favor increasing the [country’s] level of

immigration” or “do you favor liberalizing [US] immigration policy” at least four times a year;

in election years these questions were asked more often. Our measure of attitudes towards

migrants is the average negative response to either of these questions for each state-year. As

argued above, we expect that greater anti-immigrant sentiment will deter FDI.

A key control variable measures the size of the college-educated migrant community

from country i residing in state j in year t. We calculate Bilateral Migrant Stock w/ College from

the US Census Bureau’s American Community Survey (ACS).11 Prior literature has found that

educated migrants drive cross-national investment and we anticipate a similar result using state-

level data.12 Using ACS data we also calculate Share of State Pop that is Foreign Born, the

proportion of the state population that is foreign born across all countries. This control variable

allows us to distinguish the role of skilled immigrants from that of immigrants more generally.

Figures 2 and 3 plot, for M&A and greenfield investment respectively, the volume of

investment and the number of college-educated migrants over the sample period for each US

state. Both figures provide preliminary confirmation of a positive correlation between the size of

10 Specifically we use the universe of polls in the Roper Center’s iPoll database. 12 See Kugler and Rapoport 2007, Leblang 2010, and Pandya and Leblang 2013 for discussions of the relevant literature.

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the skilled migrant population in a state and the volume of FDI that it receives. Not surprisingly

the largest volumes of skilled migrants and FDI are in particularly large markets like California,

New York, and Texas.

We include a battery of control variables that have been found to influence investment

into US states to minimize spurious correlation between migrant networks and FDI. The first set

of variables draws on canonical gravity-type models of factor flows, which hold that the volume

of economic activity between a sending and receiving unit is driven by the size of those units and

the distance between them. We include the recipient state’s gross state product and its total

population13 and the source country’s gross domestic product and total population.14 We

measure the distance between the source country and receiving state as the great circle distance

based on the central longitude and latitude of each unit.

As gravity models of trade, FDI and migration also include other indicators of

transactions costs; we also include a dummy variable indicating whether the source country has

English as an official language—shared language should decrease the costs of writing contracts

and enhance the appeal of assets that are language-specific such as marketing campaigns.

Investing firms also consider cultural characteristics distance to be a determinant of foreign

direct investment. Investment deals involve substantial interaction between actors in both the

home and host country including government officials, managers, and even employees; such

interactions occur with greater ease among actors from similar cultures (Loree and Guisinger

1995, Grosse and Trevino 1996). Therefore, we include controls for cultural distance between

the home country and US, based on the World Values Survey, and for common dominant

13 These variables are collected from the US Census Bureau: http://www.census.gov/popest/ 14 These variables are from the World Bank’s World Development Indicators.

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religion between the home country and US in order to account for the fact that more investment

might come from countries that are more culturally similar.

Firms consider a number of state-level economic characteristics that influence production

costs and local market opportunities. 15 Firms that rely on access to international markets, say

for production inputs, are more likely to select states where the costs of conducting international

trade are low. We account for this consideration with Number of Free Trade Zones, the number

of foreign trade zones per state-year.16 Free trade zones exempt foreign firms engaged in certain

trade-related production from a variety of taxes and regulations.

Features of a state’s labor market such labor supply and wages are crucial to firms’

production goals. The state’s unemployment rate comes from the Bureau of Labor Statistics, as

does each state’s average hourly wage. Human capital, understood as the level of education or

skill-level of the population, is relevant to investors as higher human capital is associated with

greater productivity and innovation. As a measure of human capital, we use the share of a state’s

total population that has a college degree, calculated using ACS data.

An alternative determinant of increased bilateral FDI flows is agglomeration, or the

geographic clustering of FDI. Firms may prefer to cluster their activity in order to facilitate

knowledge spillovers or to create a larger market for specialized skills, as well as to facilitate

informal knowledge sharing such as an understanding of the culture and business environment

(Bobonis and Shatz 2007). We account for agglomeration effects by taking the sum of FDI

(either greenfield or M&A) from all countries other than country i into state j at time t-1.

15 For example, see Coughlin et al. 1991, Friedman et al. 1992, Bobonis and Shatz 2007, and Kandogan 2012. 16 These data are from the U.S. Foreign-Trade Zones Board, an arm of the US International Trade Administration.

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We control for unobserved time-varying factors in investment flows by including year fixed

effects.17

To estimate our statistical models we utilize a seemingly unrelated tobit model.18 The

tobit model is appropriate when the dependent variable is truncated or censored below a certain

level; a situation that certainly pertains to our use of investment data and our coding thereof. The

tobit model helps capture two facets of our data: first, that we have a large number of zeros and,

second, that the non-zero observations are normally distributed. This second condition applies as

we use the log (plus one) of the size of greenfield investments and the number of M&A deals

across dyads. Using the seemingly unrelated regression framework—which allows the error

terms across models to be correlates—enables us to test hypotheses about the differential effect

of key variables across models of greenfield and M&A deals.

Table 1 contains our baseline estimates for seemingly unrelated tobit models of greenfield

investment and M&A activity. All right hand side variables are lagged by one year to decrease

the chance of simultaneity or endogeneity. Column 1 of Table 1 contains the results for

greenfield investment while the dependent variable in column 2 is for M&A deals.

The measure of anti-immigrant legislation is negative and statistically significant indicating

that firms interested in greenfield investment shy away from those states that are—as evidenced

by the enactment of anti-immigration legislation—hostile those immigrants. More hostile state

public opinion toward immigration correlates with lower average greenfield investment into that

17 We opt not to include fixed effects for states or dyads because of the large number of time invariant and slow-moving covariates in our models. 18 We also estimate instrumental variables (IV) tobit models, using the stock of migrants from all countries except country i into state j as the instrument. We do not report the results here because the IV models fail to reject the null hypothesis of exogeneity with regard to the migrant stock variable. The IV estimates do not differ meaningfully from the reported estimates.

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state. Table 1 also includes a measure of the size of the migrant network between country i and

state j; this variable is statistically significant and positive in the greenfield model as reported in

Column 1. The estimated parameter indicates that a one percent increase in the bilateral co-

ethnic population is associated with a two-percent increase in the dollar value of greenfield

investment. We should note that this is a fairly “clean” estimate of the effect of high-skilled

migrant networks as we also include the share the state that are foreign born along with the share

of the state’s population with a college degree.

The determinants of greenfield investment on the origin/source country side are statistically

significant and reasonably signed. We find that richer source countries supply more greenfield

FDI. The coefficient on the size of the source country’s population is negative and statistically

significant which runs contrary to gravity models of international investment but is compatible

with models of FDI which are driven by a demand to pay lower wages as countries with larger

populations plausibly have lower wage rates. Greenfield investment, in the specification in

Table 1, is influenced by transactions costs—both bilateral distance and common official

language are statistically significant—which is consistent with more traditional cross-country

gravity models of FDI.19

Our measure of agglomeration –the total sum of greenfield or M&A investment from

countries other than country i into state j at time t-1 - is statistically significant and positive

confirming earlier research which finds positive spillovers or externalities associated with

agglomeration. Interestingly state economic and demographic characteristics—state per capita

19 In other specifications we also controlled for the number of bilateral air passengers between state i and the origin of the greenfield or M&A investment. Inclusion of this variable did not alter the substantive results reported here; we do not include this variable in our reported specifications because it results in the loss of over ten percent of our observations.

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GDP and state population—are not statistically significant at conventional levels. The state

unemployment rate is significant in the expected direction, but none of the other state level

variables are statistically significant in explaining greenfield investment: variables for state

income tax, average wage and college educated population are all statistically insignificant.

While this is contrary to prior scholarship it is sensible as the agglomeration variable tends to

capture state level factors that have already attracted FDI from countries other than country i.

Column 2 of Table 1 reports the results of our analysis for M&A activity. Note that the

dependent variable here is the number of bilateral deals between the source country and recipient

state—not the dollar amount as for greenfield investment. The measure of anti-immigrant

legislation is negative and but not statistically significant for M&A activity, supporting our

proposition that while investors evaluate not only the informational environment but the political

environment when choosing across potential locations for investment, this matters more for

greenfield investment than for M&A activity. The same is true of anti-immigrant public opinion,

which is not a significant predictor of M&A deals but was a predictor of greenfield investment.

What is additionally interesting about the M&A results is that the coefficient on the foreign born

population is negative and statistically significant. This result suggests that bilateral M&A deals

are driven not by the opportunity to exploit a potentially low-wage labor force but rather by the

knowledge supplied by co-ethnic networks.

The effect of college-educated migrant networks is, as for greenfield investment, positive and

statistically significant. A one-percent increase in bilateral college-educated migrants increases

the number of bilateral M&A deals by a little over one percent. While seemingly small, this is

equivalent to an increase from the (non-zero) average of two deals to almost three and one-half

deals. As expected we find that the effect of bilateral college-educated migrants is larger for

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greenfield investment than for M&A activity as indicated by the 95% confidence intervals

surrounding these parameter estimates.

Similar to greenfield investment we find a positive and statistically significant effect of

agglomeration for M&A deals in column 2. In contrast with the greenfield results, we also find

that a state’s economic size—measured by gross state product—in addition to the size of its

population positively influence bilateral M&A investment. Interestingly we again find that the

size of the sending country’s population exerts a negative and statistically significant effect on

M&A investment, perhaps adding a layer of support to the idea that country population is a

proxy for its potential labor force and/or wage rate. As with greenfield investment bilateral

distance has a negative effect and common official language a positive effect on M&A. We also

find that state economic conditions have no statistically significant effect on M&A activity with

the exception of the average hourly wage rate which, as expected, has a negative effect on M&A

deals.

In Tables 2 and 3 we push these results by controlling for other factors that may be correlated

with both foreign direct investment and the propensity of migrants from various countries to

move to locate in the United States. These measures—of common culture and common

dominant religion—are unfortunately not available at the state level but there is considerable

variance across sources of investment. The results in Table 2 include a measure of culture

distance—how different the political culture is in country i relative from that of the United

States—while the results in Table 3 include a dummy variable coded “1” if country i and the

United States share a common religion. In both cases this additional measure is statistically

significant and in the expected direction for both greenfield and M&A investment. Greater

cultural distance decreases foreign direct investment while sharing a common dominant religion

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increases FDI. In both cases we find that the effect of cultural distance or religious similarity

matters more for greenfield investment than for M&A activity consistent with our hypothesis

that greenfield investors have more flexibility in their location choices.

In Table 4 we test the hypothesis that migrants exert an information effect that is separate

from the agglomeration effect, which could contribute to the observed correlation between

migrants and investment. Correlation between the two could reflect the role of agglomeration

externalities in MNCs’ local investment decisions. When several firms are in close geographic

proximity there can be positive externalities to co-location including access to specialized labor

pools, higher provisions of specialized public goods, and proximity to suppliers. Agglomeration

can refer to groupings of firms regardless of country of origin (Nachum 2000). Firms of like

nationality may also choose to cluster to facilitate informal knowledge sharing, such as an

understanding of the culture and business environment relative to their shared home country

context (Bobonis and Shatz 2007, Head et al 1999).

We test for this possibility by interacting our measure of bilateral migrants with the measure

of agglomeration. In column 1 of Table 4 we find a statistically significant and negative

correlation of the interaction on greenfield investment. We interpret this finding to mean that

agglomeration of greenfield investments and college educated migrants are substitutes to some

extent. However, the effect of this interaction is small but positive and statistically significant

for M&A, suggesting that migrants help provide additional information about acquisition

opportunities; however, this effect is sensitive to different specifications. These different

findings by mode of entry are consistent with our expectation that greenfield investors are

attracted to locations where they can draw on home-country social capital to tap as a source of

positive externalities. M&A investments, by contrast, flow to where acquisition opportunities are

19

available. Overall, the results support our conclusion that greenfield investments are more

sensitive host political conditions than M&A investments.

Conclusion

This paper identifies political economy determinants of MNC investment decisions that go

beyond standard national-level considerations regarding contractual risks. By analyzing sub-

national patterns of foreign investment we can build richer political economy theories of FDI

flows that move beyond contractual risks to incorporate other mechanisms through which host

country politics and policies influence MNCs’ location decisions. Specifically, we find that a

state’s sentiment towards immigration, whether measured as the passage of anti-immigration

legislation or public opinion towards immigration, is an important determinate of the location

decision of foreign firms. However, the effect of the immigration environment varies across

modes of investment. Our use of new FDI data allows us to distinguish between different modes

of entry for MNCs – greenfield investment and mergers and acquisitions. Doing so provides

evidence that it is both theoretically and empirically useful to separate different types of FDI as

both political and economic determinants impact different modes of entry differently.

Several findings emerge from our analysis. First, we find evidence that state-level anti-

immigrant legislation deters greenfield investment into the state, but this negative effect is not

significant for M&As; we attribute this finding to the fact that greenfield investors select where

to locate productive assets geographically whereas M&A investments are geographically

constrained by the pre-existing locations of acquisition targets. Similarly, more hostile public

opinion towards immigration deters greenfield investment but does not have a significant effect

on M&A deals. We also find that high skilled immigrants increase FDI into US states, an effect

20

that holds for both greenfield investment and M&A; a finding consistent with a larger set of

results in the international economics and IPE literatures. We attribute this impact of skilled

migrants on FDI to the role that migrants play in providing private information to investors in

their home countries through informal networks or in more formal capacities as MNC managers,

entrepreneurs, or market intermediaries. Our empirical strategy allows us to see that migrants

have an effect that goes beyond the information that firms can receive from other foreign firms in

the host state. While college educated migrants have a significant positive effect on both

greenfield investment and M&As, this effect is stronger for greenfield investment.

Future research on immigration and FDI linkages can build on our findings. Using micro-

level data about skilled immigrants, including immigrants to the US on skilled worker (H1-B)

visas and biographical information about immigrant entrepreneurs and managers of foreign-

based MNCs, may allow us to more precisely measure the strength of informational links among

immigrants and foreign firms. Additionally, we should continue to explore other, and perhaps

underappreciated, barriers that states face in attracting FDI. In particular, with the collection of

data on state-level FDI incentives it is possible to estimate the financial cost of anti-immigrant

sentiment by identifying how much states have to “compensate” foreign firms to overcome this

barrier. Studies like these contribute more nuanced political economy models in the study of both

FDI and immigration.

Finally we note that our findings have substantively significant policy implications. We still

do not know if policies that restrict the rights of undocumented workers or that mandate

workplace enforcement (e.g., E-VERIFY) deter illegal migrants but the evidence provided above

demonstrates that these policies cost states millions of dollars in lost investment a year. It may

also be the case that anti-immigrant sentiment leads to a reduction in foreign (and perhaps

21

domestic) tourism and negatively influences the attractiveness of a state to foreign students. The

extent of these costs is likely far from trivial.

22

Table 1: Seemingly Unrelated Tobit Models of FDI Greenfield M&A State Anti-Immigration Legislation Index -0.0664**

[-0.123,-0.00966] 0.00154

[-0.0194,0.0225] Anti-Immigrant Public Opinion -1.413**

[-2.283,-0.542] 0.0966

[-0.197,0.390] Agglomeration 1.272**

[1.232,1.312] 0.460**

[0.397,0.523] Log(Gross State Product Per Capita) 0.997

[-1.101,3.095] 1.456**

[0.512,2.401] Log(State Population) 0.0821

[-0.252,0.416] 0.258**

[0.102,0.414] Log(Real GDP Per Capita) 2.741**

[1.801,3.680] 1.604**

[1.310,1.899] Log(Population) -2.054**

[-2.975,-1.134] -1.143**

[-1.437,-0.849] Bilateral Distance -1.584**

[-1.867,-1.300] -1.132**

[-1.251,-1.012] Common Official Language 1.967**

[1.634,2.300] 1.720**

[1.558,1.883] Number of Free Trade Zones -0.0144

[-0.0616,0.0329] 0.00447

[-0.0168,0.0257] State Unemployment Rate 0.142**

[0.00587,0.278] 0.0414

[-0.0213,0.104] State has Income Tax -0.0552

[-0.349,0.238] 0.0474

[-0.0945,0.189] Average Hourly Wage -0.0610

[-0.249,0.127] -0.116**

[-0.201,-0.0313] Share of State Pop w/College Degree 52.71

[-118.4,223.9] 41.96

[-37.82,121.7] Share of State Pop that is Foreign Born -55.41**

[-89.85,-20.96] -27.84**

[-43.40,-12.28] Bilateral Migrant Stock w/College Education 0.203**

[0.0971,0.308] 0.103**

[0.0571,0.150] Right to Work State 0.0717

[-0.400,0.544] -0.152

[-0.369,0.0646] Constant -27.41**

[-48.37,-6.453] -24.58**

[-34.17,-15.00] Observations 33253 Models are seemingly unrelated tobit models. 95% confidence intervals based on robust standard errors in brackets; independent variables lagged by one year. * p < 0.10, ** p < 0.05

23

Table 2: Determinants of FDI: Controlling for Cultural Distance Greenfield M&A State Anti-Immigration Legislation Index -0.0524*

[-0.110,0.00486] 0.00177


[-2.363,-0.639] 0.0847

[-0.180,0.350] Agglomeration Greenfield 1.152**

[1.108,1.196] 0.445**


[-1.039,3.185] 1.398**


[-0.106,0.574] 0.315**


[2.958,5.123] 1.764**

[1.398,2.130] Log(Population) -3.445**

[-4.495,-2.395] -1.451**


[-1.547,-0.899] -0.703**


[0.762,1.478] 1.021**


[-0.0598,0.0358] 0.00591


[0.0353,0.311] 0.0349


[-0.333,0.254] 0.0525


[-0.246,0.131] -0.0979**

[-0.174,-0.0219] Share of State Pop w/College Degree 195.3**

[11.65,378.9] 144.8**

[55.55,234.0] Share of State Pop that is Foreign Born -82.74**

[-119.8,-45.73] -46.19**


[0.104,0.326] 0.0803**


[-0.336,0.617] -0.0916

[-0.284,0.101] Cultural Distance -0.984**

[-1.207,-0.761] -0.778**

[-0.884,-0.673] Constant -32.72**

[-54.04,-11.41] -24.28**


24

Table 3: Determinants of FDI: Controlling for Common Religion Greenfield M&A State Anti-Immigration Legislation Index -0.0641**

[-0.120,-0.00868] 0.00165


[-2.306,-0.632] 0.0406

[-0.241,0.322] Agglomeration 1.122**

[1.077,1.166] 0.453**


[-0.693,3.135] 1.517**


[-0.0636,0.547] 0.279**


[2.460,4.318] 1.977**

[1.682,2.273] Log(Population) -2.667**

[-3.573,-1.762] -1.556**


[-2.178,-1.664] -1.249**


[0.449,1.038] 1.032**


[-0.0518,0.0348] 0.00414


[0.0399,0.291] 0.0473


[-0.290,0.271] 0.0527


[-0.241,0.0910] -0.123**

[-0.196,-0.0491] Share of State Pop w/College Degree 175.9**

[27.31,324.6] 122.7**

[46.08,199.3] Share of State Pop that is Foreign Born -71.14**

[-103.1,-39.18] -38.26**


[0.149,0.344] 0.108**


[-0.219,0.624] -0.130

[-0.318,0.0590] Shared Dominant Religion w/US 3.278**

[2.989,3.566] 1.773**

[1.646,1.901] Constant -31.55**

[-50.84,-12.26] -24.65**

[-33.30,-16.01] Observations 33253 95% confidence intervals based on robust standard errors in brackets; independent variables lagged by one year. * p < 0.10, ** p < 0.05

25

Table 4: Determinants of FDI: Agglomeration as Complement or Substitute? Greenfield M&A State Anti-Immigration Legislation Index -0.0649**

[-0.122,-0.00798] 0.000758


[-2.395,-0.594] 0.101

[-0.190,0.392] Agglomeration 1.742**

[1.609,1.876] 0.355**

[0.227,0.484] Bilateral Migrant Stock w/College Education 0.491**

[0.347,0.635] 0.0665**

[0.0123,0.121] Agglomeration*Educated Migrants -0.0644**

[-0.0816,-0.0472] 0.0133*

[-0.00120,0.0277] Log(Gross State Product Per Capita) 0.999

[-1.143,3.142] 1.510**

[0.565,2.455] Log(State Population) -0.00845

[-0.355,0.338] 0.269**


[1.822,3.745] 1.602**

[1.308,1.897] Log(Population) -2.067**

[-3.010,-1.125] -1.144**


[-1.889,-1.320] -1.130**


[1.609,2.279] 1.721**

[1.559,1.883] Number of Free Trade Zones 0.00418

[-0.0439,0.0522] 0.00256

[-0.0189,0.0240] State Unemployment Rate 0.134*

[-0.00412,0.272] 0.0427


[-0.315,0.288] 0.0388


[-0.234,0.148] -0.118**

[-0.202,-0.0330] Share of State Pop w/College Degree 75.67

[-81.52,232.9] 37.68

[-44.02,119.4] Share of State Pop that is Foreign Born -53.56**

[-88.95,-18.17] -27.94**

[-43.39,-12.50] Right to Work State 0.0607

[-0.420,0.541] -0.149

[-0.365,0.0675] Constant -28.90**

[-50.34,-7.454] -24.95**


26

Figure 1: Distribution of Proposed Anti-Immigrant Legislation in US States, 2006-2011

Source: The National Council of State Legislatures (NCSL). NCSL collects information on all bills introduced in state legislatures during each session. We coded all immigration-related bills; these fell into four categories: access to public benefits, document requirements (i.e., birth certificates or passports) to obtain a driver's license, workplace enforcement (i.e., E-VERIFY), and in-state tuition access for the children of undocumented migrants. Vertical bars represent the number of bills in each of these areas that were introduced into state legislatures during the respective session.

010

2030

2006 2007 2008 2009 2010 2011

Num

ber o

f ant

i-im

mig

rant

bills

intro

duce

d ac

ross

US

stat

es.

Benefit Restrictions Education RestrictionsWorkplace Enforcement Driver's License Requirements

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