# Foreign-origin managers and FDI location choice

Massimiliano Coda Zabetta, Ernest Miguelez, Francesco Lissoni, Sarah Hegarty

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Massimiliano Coda Zabetta, Ernest Miguelez, Francesco Lissoni, Sarah Hegarty. Foreign-origin managers and FDI location choice. Journal of International Business Studies, 2025, 56 (2), pp.194-202. 10.1057/s41267-024-00726-2 . hal-04783226

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# Foreign-origin managers and FDI location choice

Massimiliano Coda Zabetta<fnref n="1" />,<fnref n="3" /> · Ernest Miguelez<fnref n="2" />,<fnref n="3" />,<fnref n="4" />,<fnref n="6" /> · Francesco Lissoni<fnref n="3" />,<fnref n="5" />,<fnref n="6" /> · Sarah Hegarty<fnref n="7" />

Received: 4 February 2022 / Revised: 27 June 2024 / Accepted: 14 July 2024
© The Author(s) 2024

## Abstract

Building upon the upper echelons theory of organizations as applied to internationalization strategies, we investigate the impact of foreign-origin top management team (TMT) members on their companies’ location choice. We propose that a *manager-from-target* effect may exist, by which a manager’s knowledge of their country of origin increases the likelihood of choosing that country as a target. We expect it to be stronger for acquisitions than for greenfield investments, the inputs of foreign-origin managers being more relevant in the first case. Based on a large sample of foreign direct investment from 2013 to 2019, and using name analysis to identify TMT members’ origins, we find that the effect is present in both establishment modes but larger and more robust for acquisitions. It is also larger in companies with less diverse TMTs, for which targeting one manager’s country of origin instead of others’ may be less likely to cause conflict. Future research should explore whether investments in locations suggested by managers from the target country outperform others. If this is the case, it implies that recruiting foreign-origin top managers can improve a company’s internationalization strategy via better location choices.

**Keywords** Foreign direct investment · Location choice · International migration

## Introduction

International migration has grown incessantly over the last
half century, from 2.3% of the world population in 1970
to 3.6% in 2019 (IOM, 2020). Such growth has gone hand
in hand with that of international capital flows, at least up

until the Great Recession of 2008. Several country-level
economic studies have detected a causal link between the
two phenomena and proposed that international diasporas,
including second-generation migrants and their descend-
ant, lower search and transaction costs, thus facilitating
the entry of foreign investors into the migrants’ country
of origin (Kugler & Rapoport, 2007; Kugler et al., 2018;
Fang & Wells, 2023). A number of international business
studies have complemented this proposal with direct evi-
dence on diasporas’ role in attracting investments from
migrants’ country of origin to their host country (Hernan-
dez, 2014; Li et al., 2019) or vice versa (Gillespie et al.,
1999). None, however, has examined the role played by the
most important decision-makers, namely the increasing
number of foreign-origin managers in apical positions in
multinational enterprises (MNEs).

We propose to fill this gap by investigating whether
and under what circumstances such managers contribute to
direct their companies’ foreign direct investments (FDIs)
towards their countries of origin. This research question is
motivated by both theoretical and empirical reasons, which
we derive from the upper echelons theory of organizations,
as applied to internationalization strategies (Popli et al.,
2022; Cuypers et al., 2022).

Accepted by Anu Phene, Guest Editor, 14 July 2024. This article has
been with the authors for five revisions.

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Massimiliano Coda Zabetta
massimiliano.codazabetta@unito.it

<footnote n="1">¹ Department of Economics and Statistics Cognetti de Martiis, University of Turin, Turin, Italy</footnote>

<footnote n="2">² Instituto de Políticas y Bienes Públicos, Consejo Superior de Investigaciones Científicas, Madrid, Spain</footnote>

<footnote n="3">³ BSE UMR CNRS 6060, University of Bordeaux, Pessac, France</footnote>

<footnote n="4">⁴ Department of Econometrics, Statistics and Applied Economics, University of Barcelona, Barcelona, Spain</footnote>

<footnote n="5">⁵ Department of Economic Policy, Università Cattolica del Sacro Cuore, Milan, Italy</footnote>

<footnote n="6">⁶ French Collaborative Institute on Migration, Paris, France</footnote>

<footnote n="7">⁷ Centre for Transformative Innovation, Swinburne University of Technology, Melbourne, Australia</footnote>
We address our question in the following way. First, we review the evidence on how international diasporas affect FDI and discuss how a focus on top management teams (TMTs) can complement it. We argue that foreign-origin managers may be in a position to provide their companies with idiosyncratic inputs, such as personal knowledge of their countries of origin and a non-stereotyped view of their business potential. We then propose that a *manager-from-target* effect may exist, by which such managers help steer their companies toward investments in their country of origin, the strength of this effect varying with the relevance of their inputs.

Second, we produce an original data set by linking many worldwide FDI operations between 2013 and 2019 to the name, surname, role, and activity years of TMT members within each investor company. Based on ethnolinguistic analysis of such managers’ names and surnames, we both identify those whose country of origin is likely to coincide with the target country (*managers from target*) and produce a measure of TMT diversity.

Last, we use these data to undertake a fixed-effects, matched-sample regression. We find statistically significant and quantitatively relevant evidence of a manager-from-target effect for mergers and acquisitions (M&As) but not or not as much for greenfield investments (GIs) (depending on the matching strategy). We also find an inverse relationship between the strength of the manager-from-target effect and TMTs’ diversity.

The paper proceeds as follows. First, we revisit the literature and formulate our hypotheses ("Background literature and hypotheses" section). Then, we describe our data ("Data" section) and empirical strategy ("Empirical strategy" section). We present and discuss our results in "Results" section. "Discussion and conclusions" section concludes.

## Background literature and hypotheses

Among the numerous studies exploring the connections between international diasporas and FDI, only a few focus on how the former affect location choices. In the context of the United States, Hernandez (2014) suggests that migrants residing in specific states help companies from their countries of origin to better understand local business conditions, thereby increasing their chances of successful investment, while Shukla and Cantwell (2018) provide details on the role of migrants’ clubs and associations. Li et al. (2019) focus on ethnic communities originating from pre-World War II Korean migration in China provinces and find that they function as informal institutions, facilitating transactions between present-day Korean investors and local customers or suppliers.

As for outward investments, Gillespie et al. (1999) – based on survey data – measure the intensity of the homeland orientation of four diaspora communities in the United States as expressed through the frequency of their visits, their language proficiency, and the intensity of their contact with relatives, family friends, or local institutions. They find such orientation to be positively associated with an interest in homeland investment, based on both altruistic considerations and a superior knowledge of local business conditions. Burchardi et al. (2019) find that the regional distribution of immigrants in the United States at the beginning of the 20th century predicts the geographical distribution of present-day outward FDI.

None of these studies, however, investigate the specific role of top managers, meaning those sitting on the board of directors or closely advising it (see Table 1 in Carpenter et al. (2004); see also Hambrick (2016)). Rather, they mostly refer to agents acting outside the investing companies, such as customers, business partners, or prospective employers.

Some indirect evidence suggests that filling this research gap is both feasible and valuable. For example, Zaheer et al. (2009) investigate the location choices of US companies that offshore their administrative and technical work to India, and they explain the choices with the association between the ethnic background of the companies’ executives and that of the majority of the population in the chosen cities. Two other studies focus on inventors in R&D-intensive MNEs, some of whom have managerial roles, especially in technology-related operations. First, Foley and Kerr (2013) examine the case of US-resident “ethnic inventors” working for US MNEs, where *ethnicity* is broadly defined via name analysis. Besides finding a correlation between the ethnic share of a company’s patents and the shares of assets held by the company in countries associated with that ethnicity, the authors prove that the effect is stronger for MNEs with no previous experience in the country of investment. In a similar vein, Useche et al. (2020) study the cross-border acquisitions undertaken by R&D-active firms and find that those employing foreign inventors in their home operations have a higher probability of picking target firms in such inventors’ countries of origin.

Our intent is to generalize this evidence and directly investigate the managers most involved in strategic decision-making. To do so, we revisit the upper echelons theory of organizations and its use of the concept of managers’ nationality in relation to internationalization processes.

Based on the behavioral theory of the firm, the upper echelons research program assumes that strategic decision-making is conditioned by their top managers’ field of vision, selective perception and interpretation of information (Hambrick & Mason, 1984). These, in turn, are associated

<figure><img src="image_3.png" /></figure>
with some observational equivalents such as age, education, and other indicators of socioeconomic background, from which the managers' different cognition processes can be inferred (Carpenter et al., 2004).

One such indicator is nationality. This is meant to capture not so much a manager's legal status as their cultural traits (Tung & Verbeke, 2010), and it is "open to various definitions," including "the identities of one's parents" or their "personal sense of identity," whichever is more relevant for the decision-making process under study (Hambrick et al. (1998), p. 183). In the context of our study, we use the broader term *foreign origin*, which is more clearly distinct from migrant-versus-native legal status, spans multiple generations, and relates more clearly to an individual's ties with their country of origin.

When applied to the study of internationalization strategies, the upper echelons theory places a special emphasis on TMTs' diversity. On the positive side, TMT members from different countries of origin are expected to have different views of the costs and benefits of entering new geographic areas, thus easing information processing, reducing uncertainty, and helping to overcome domestic myopia (Barkema & Shvyrkov, 2007). More diverse TMTs may then decide to target countries that less diverse ones consider too risky or do not consider at all. On the negative side, diversity may induce teams to fragment into like-minded groups, whose communication problems or conflicts may retard or undermine decision-making (Harrison & Klein, 2007). The positive effects seem to generally prevail. For example, Greve et al. (2009) find a positive association between the country-of-origin diversity of a company's TMT and that of its operations, which Pisani et al. (2018) suggest comes from a causal link running from the former to the latter.

These findings, however, do not specify which countries the TMTs decide to target. Determining this requires shifting the emphasis from diversity per se to the specific knowledge that diverse managers may have of different countries. For example, Xie and Wang (2022) find that returnee managers in China increase their new employers' probability of completing cross-border acquisitions in their former host countries. In the same vein, Belderbos et al. (2022) find that the performance of foreign R&D operations of MNEs benefits from the work experience gained by former expats in the operations' host countries. We extend this line of argument by suggesting that foreign-origin managers may play a similar role with respect to their country of origin. Based on this, we postulate the existence of a manager-from-target effect and put forward the following hypothesis:

**Hypothesis 1** Conditional on investing abroad, companies with foreign-origin managers on their TMT are more likely

to target the managers' country of origin, other things being equal.

We expect the manager-from-target effect to be more relevant when the individual inputs of foreign-origin managers are more valuable, assuming all other factors remain constant. This is likely influenced by the nature of the investment, including the establishment mode. In particular, we expect the effect to be stronger for acquisitions than for greenfield investments (GIs). Acquisitions offer quicker access to local resources and assets but involve higher costs, including upfront payments and integration expenses due to the need to merge with existing management and corporate cultures (Dikova & Brouthers, 2016). When undertaking acquisitions, which involve less gradual implementation compared to GIs, firms face higher risks. These risks are particularly pronounced if the investments are directed towards culturally or institutionally diverse environments or if the managers lack international experience (Kogut & Singh, 1988; Slangen & Hennart, 2008; Dikova et al., 2010). Additionally, acquisitions are more prone to adverse selection and moral hazard issues because of information asymmetries between foreign investors and local targets. These issues can be mitigated by having a reliable information source within the target country (Dikova & Van Witteloostuijn, 2007). This may either exclude the country from consideration or lead the managers to prefer other, better-known ones. But managers from the target country might have personal connections and a deeper understanding of local practices, reducing perceived risks and offering more comprehensive information, thus favoring the country in the selection process. This leads us to the following hypothesis:

**Hypothesis 2** The manager-from-target effect will be stronger for foreign acquisitions than for greenfield investments.

We also expect the strength of the manager-from-target effect to depend on the consideration the TMT gives to the personal inputs provided by individual managers. This can be influenced by the TMT's overall diversity. A diverse TMT, particularly one with multiple foreign-origin members, is likely more open to such inputs, fostering a multicultural environment. However, this diversity may also lead to conflicts if multiple foreign-origin managers advocate investing in their respective countries, potentially leading to a decision to avoid investing in any of them. These considerations suggest two alternative hypotheses:
**Hypothesis 3a** The manager-from-target effect will be stronger for companies with a TMT with more, and more diverse, foreign-origin managers.

**Hypothesis 3b** The manager-from-target effect will be weaker for companies with a TMT with more, and more diverse foreign-origin managers.

## Data

We source our investment data from Bureau van Dijk's (BvD) Orbis Cross-border Investment (OCI) database, which, for the period 2013–2019 and most countries worldwide, provides data on two types of FDIs: GIs and M&As. For each operation, OCI reports the completion year plus the country and sector of both the investor company and the investment (subsidiary's activity). It also provides information on the size of the investment (in million dollars), albeit with a high number of missing values (especially for M&As). Finally, and only for GIs, OCI reports a brief description of the investment motives.<fnref n="1" />

In the case of GIs, we take into account only the operations consisting in, according to OCI's glossary, "new ...manufacturing plant[s], regional headquarters, sales office[s,] etc." Hence, we exclude expansions, co-locations, and relocations. Nor do we consider GIs in business, retail, travel, and wholesale services, because their size and strategic importance is often negligible (they mostly consist in the opening of new retail points and are concentrated in the bottom quartile of the investment-size distribution). As for M&As, we consider both acquisitions (defined as deals "in which the acquirer ends up with a stake of 50% or more in the target's equity") and mergers (deals "in which a one-for-one share swap takes place"). We do not consider stake increases in companies the investor already participates in. For both GIs and M&As we retain only the operations whose size is not missing from the data set. In total, we consider 10,010 GIs, 9180 M&As, 172 target countries, and 9869 investor companies.

For all such companies, OCI provides unique identifiers that are consistent across all BvD products, including Orbis Historical Data (OHD). This provides yearly information on – among other things – the names, surnames, roles, and employment years of the companies' managers. For each operation in year *t* in the OCI database, we then extract from OHD the first names and surnames of all managers

in apical positions in the investor company at time *t* − 1 (as well as *t* − 2 and *t* − 3, for robustness checks). Based on BvD's classification of managers' roles, apical positions include members of the board of directors, the corporate governance committee, the executive board (or committee), and the supervisory board, plus the rather generic category of senior management.<fnref n="2" />

OHD does not report any information related to the managers' country of origin, such as their or their parents' legal status or country of birth. Other BvD products do so, but only for recent years and for a very few companies. We fill this information gap by means of name analysis, which is quite common in migration and innovation studies focusing on the international mobility of inventors (Kerr, 2008; Foley & Kerr, 2013; Breschi et al., 2017; Marino et al., 2020) and adaptable to our purpose.

To this end, we use the IBM-GNR data library, which links both first and last names to vectors of countries where their usage is documented, along with their frequency across and within these countries. While this information does not allow us to definitively determine the country of origin for each manager in our database, it enables us to estimate the likelihood that a manager's country of origin matches that of their company's FDI target.

More precisely, we ask the following question: given the investment *i* at time *t* undertaken by company *j* from country *w* and with target country *z*, does *j*'s TMT at *t* − 1 include at least one member *m* with foreign origins in country *z*? We answer by elaborating the information from IBM-GNR to produce, for each company-investment pair (*j*,*i*), the variable *Managers from target*, which takes value 1 when the answer is positive and 0 otherwise. Online appendix B.1 contains all details on our algorithm. Here it suffices to say that we suppose a manager *m* working for company *j* in country *w* to come from or have origins in *z* if they bear a first name or surname very common in country *z* but not in *w*; and we calibrate our algorithm with the aim of minimizing false positives (mistaking *z* as manager *i*'s country of origin, when it is not).<fnref n="3" />

A major limitation of our algorithm is that it cannot be applied to investments between countries whose populations belong to the same linguistic group. In fact, names common to both countries would produce too many false negatives. Therefore, we exclude investments between such country

<figure><img src="image_4.png" /></figure>

<footnote n="1">¹ For the full list of motives and their frequency see Table A.1 in the online appendix A. The Orbis sectoral classification system is based on the four-digit level codes from the European Union's National Classification of Economic Activities. It assigns both the investing firms and their investments to their respective principal sector of activity.</footnote>

<footnote n="2">² For a complete list of roles, refer to Table A.2 in online appendix A. These roles are identified based on publicly available company information and press releases. Because of variation in vocabulary and level of detail, the average number of managers per company and the roles provided by BvD differ across countries (see Table A.3).</footnote>

<footnote n="3">³ Notice that our method allows for the possibility that managers with mixed heritage may be identified as coming from different target countries in various investments, though this is uncommon in practice.</footnote>
pairs from our regression sample. This reduces the sample to 1190 GIs and 359 M&As, involving 911 investor companies and 111 target countries. Table B.4 in online appendix B.1 reports descriptive statistics for the top countries and corridors we retain for our analysis. In online appendix B.3, we also examine the accuracy of our name analysis in identifying first-generation migrants and its potential impact on our results.

While our main application of name analysis consists in identifying managers from target countries, we also use it for measuring diversity within the TMTs. To do so, we associate each TMT member's combination of first name and surname with one or more possible countries of origin and measure the country-of-origin diversity within the TMT with a fractionalization index (more details in the next section and online appendix B.2).

## Empirical strategy

Our empirical strategy relies on building a case-control matched sample of FDIs targeting a set of countries Z ($z = 1...Z$), and on using it to test whether companies investing in country z are more likely to have a least one TMT member from the same country, relative to similar companies that have undertaken similar investments at the same time but in different target countries (for similar sampling schemes, see Hall (1988); Hussinger (2010); Useche et al. (2020)).

Based on our data sample, we consider I focal investments ("cases"; $i = 1...I$) taking place in any year $t$ from 2013 to 2019. Each case involves an investor firm $j$ from country $w$ and a target country $z$. From the same sample, and for each case $i$, we select one or more control investments $C_i$ (with $C_i \ge 1$ and $c_i=1...C_i$), such that each $c_i$

When it comes to GIs, we use both the matching scheme just described and an alternative one, based on the investment motive as an additional criterion. To do so, we first aggregate the original motives reported by OCI in five classes, four of which are based on Dunning (1994), namely resource-, market-, efficiency-, and asset-seeking investments.<fnref n="7" /> We then drop all the observations for which the investment motives are not reported or fall in the last category, and we match on one of the four classes above. This comes at a considerable cost in terms of sample size, which is why we first introduce it as an alternative matching scheme.

We combine the case and control investments in our regression sample, which consists of 4348 observations, of which 887 are M&As (359 cases and 528 controls) and 3454 are GIs (1190 cases and 2264 controls; these figures fall to 308 and 411, respectively, for a total of 719 when matching also on investment motives). Each observation in the regression sample consists of an investment $n$ ($n = 1...N$), where $n$ may be either a case (it comes from the set of focal investments $I$) or a control (it comes from the control set $C$, with $C = C_1 \cup C_2 \cup ... \cup C_I$). We test Hypothesis 1 by means of the following baseline equation:

$$FDI_{n(j,w,z)} = \alpha \text{Managers from target}_{(j,z,t-1)} + \beta \text{Subsidiary in target}_{(j,z,t-1)} + \mu_j + \nu_s + \gamma_t + \phi_{(w-z)} + \epsilon_n \quad (1)$$

where $FDI_{n(j,w,z)}$ takes value 1 for cases and 0 for controls; $\mu_j$, $\nu_s$, $\gamma_t$, and $\phi_{(w-z)}$ are fixed effects for, respectively, investors, target sectors, investment years, and country pairs (to which we will also refer as "corridors"); and $\epsilon_n$ is a random error term.<fnref n="8" />

<figure><img src="image_5.png" /></figure>

<footnote n="4">⁴ We define the size classes of the investments by considering their entire value range in OCI and splitting it into quintiles. Each quintile corresponds to a class.</footnote>

<footnote n="5">⁵ To define the size classes of the investors, we rely on Orbis classification. This splits companies into four size classes, ranging from very large to small. The classes are defined on the basis of four size measures (not all of which are available for each firm), namely operating revenue (OPRE), total assets (TOAS), number of employees (EMPL), and whether they are publicly listed. Very large companies are those that match at least one of the following conditions: (i) $OPRE \ge 100M$ EUR; (ii) $TOAS \ge 200M$ EUR; (iii) $EMPL \ge 1000$; or (iv) they are listed. The remaining companies are classified as: large if they match at least one of the following conditions: i) $OPRE \ge 10M$ EUR; ii) $TOAS \ge 20M$ EUR; iii) $EMPL \ge 150$. Those that are neither very large nor large are classified as medium sized if one of following holds: (i) $OPRE \ge 1M$ EUR; (ii) $TOAS \ge 2M$ EUR; (iii) $EMPL \ge 15$. They are classified as small otherwise.</footnote>

<footnote n="6">⁶ Notice that $C_i$ may include more than one control investment by the same company, as long as the investments satisfy all conditions and have different target countries. When two or more potential control investments by the same company target the same country, we retain only one, through random extraction.</footnote>

<footnote n="7">⁷ The fifth category consists of "other" motives, with less than 8% of records.</footnote>

<footnote n="8">⁸ Notice that, for simplicity, we use $j$ to refer indifferently to the investor company both for cases and controls. As for $w$, this is – by construction – the same for each case and its controls. Notice also that $z$ always refers to the target country of cases, that of controls being different by construction. See Tables A.4, A.5, and A.6 in online appendix A for summary statistics.</footnote>

<footnote n="6">⁶ Notice that $C_i$ may include more than one control investment by the same company, as long as the investments satisfy all conditions and have different target countries. When two or more potential control investments by the same company target the same country, we retain only one, through random extraction.</footnote>

<footnote n="7">⁷ The fifth category consists of "other" motives, with less than 8% of records.</footnote>

<footnote n="8">⁸ Notice that, for simplicity, we use $j$ to refer indifferently to the investor company both for cases and controls. As for $w$, this is – by construction – the same for each case and its controls. Notice also that $z$ always refers to the target country of cases, that of controls being different by construction. See Tables A.4, A.5, and A.6 in online appendix A for summary statistics.</footnote>
Managers from target<sub>(j,z,t-1)</sub> is our variable of interest and takes value 1 if the investor *j* employs, 1 year before the investment, at least one manager from the case investment-target country *z*, and 0 otherwise. Its value depends on the linguistic analysis of the managers' first names and surnames we describe in "Data" section and in online appendix B.1. As for *Subsidiary in target*<sub>(j,z,t-1)</sub>, it takes value 1 if investor *j* had already invested in country *z* at any time before *t*, and 0 otherwise. *Subsidiary in target* controls for the fact that companies already active in country *z* may be more likely than others both to reinvest there and to hire locally.<fnref n="9" />

We test Hypothesis 2 by running our regressions for both our entire sample and separately for M&As and GIs. As for Hypothesis 3a and 3b, we add to Eq. (1) an additional variable on TMTs' diversity (*TMT diversity*<sub>(j,t-1)</sub>) and its interaction term with *Managers from target*<sub>(j,z,t-1)</sub>. In particular, we measure diversity with a simple fractionalization index as follows:

$$ \left[ 1 - \sum_{c=1}^{C} S_c^2 \right]_{(j,t-1)} $$

Here, $S_c^2$ is the share of investor *j*'s TMT members from country of origin *c* out of the *C* countries of origin represented in the TMT (including *j*'s; see online appendix B.2 for details).

We run all our regressions based on linear probability models with high-dimensional fixed effects (Correia et al., 2020).

## Results

Table 1 presents our baseline estimates of Eq. (1). In column 1, we report the results for the full sample of FDI, while columns 2 and 3 refer to split regressions for, respectively, M&As and GIs.

The estimated coefficient for *Managers from target* is positive and statistically significant for the whole FDI sample (column 1), and it is positive, large, and significant for

<table><caption>Table 1 Baseline results</caption><thead><tr><th></th><th>(1)<br>All</th><th>(2)<br>M&As</th><th>(3)<br>GI<sup>a</sup></th><th>(4)<br>GI<sup>b</sup></th></tr></thead><tbody><tr><td>Managers from target</td><td>0.091<br>(0.039)<br>[0.021]</td><td>0.429<br>(0.127)<br>[0.001]</td><td>0.039<br>(0.041)<br>[0.349]</td><td>0.223<br>(0.116)<br>[0.055]</td></tr><tr><td>Subsidiary in target</td><td>0.145<br>(0.039)<br>[0.000]</td><td>0.315<br>(0.130)<br>[0.016]</td><td>0.123<br>(0.042)<br>[0.003]</td><td>0.241<br>(0.132)<br>[0.070]</td></tr><tr><td>Investor FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Year FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Target's sector FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Corridor FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Observations</td><td>4341</td><td>887</td><td>3454</td><td>719</td></tr><tr><td>R<sup>2</sup></td><td>0.110</td><td>0.140</td><td>0.111</td><td>0.097</td></tr></tbody></table>

the M&A sample (column 2). For the GI sample (column 3), conversely, it is not significant. As for *Subsidiary in target*, it is always positive and significant, as expected.

Taken together, these results suggest that foreign-origin managers exert a considerable influence on the companies' investment-location choices, especially for M&A operations. On average, a company with a manager who originates from country *z* is 43% more likely to choose an acquisition target in country *z* compared to a company with no foreign-origin managers or with managers from other countries.

Concerning GIs, we first investigate whether the null result we get in column 3 is because the GI sample is much larger than the M&A one but also much more heterogeneous with respect to the strategic objectives pursued by the investors. To attenuate this problem, we resample our data by including investment motives among the matching criteria, which, as explained in "Empirical strategy" section, reduces the number of observations considerably. Our regression results, however, change in the expected direction. Comparing columns 3 and 4, we see that the coefficient of *Managers from target* both increases and becomes significant. All these results are in line with Hypotheses 1 and 2.

Table 2 reports our results for Hypotheses 3a and 3b, which we obtain by adding to the baseline model the variable *TMT diversity* and its interaction term with *Managers from target* (MFT). The interaction between TMT diversity and *Managers from target* is significant only in the GI sample (column 4), and it has a negative sign. This speaks in favor of Hypothesis 3b and against Hypothesis

<figure><img src="image_6.png" /></figure>

<footnote><sup>a</sup>Sample obtained using the basic matching scheme (see "Empirical strategy" section).</footnote>

<footnote><sup>b</sup>Sample obtained using the alternative matching scheme (see "Empirical strategy" section).</footnote>

<footnote>Deal clustered SE in parentheses; *p* value in square brackets</footnote>

<footnote n="9">⁹ Endogeneity issues in our research design are common to all upper echelons studies (Hambrick, 2007). First, managers may wish to join the companies whose strategic orientation better suits their characteristics. Second, the association between those managers' characteristics and their decisions may depend on the expectation that such an association exists on the part of those who hired the manager, combined with a mandate to operate accordingly. When applied to the manager-from-target effect, these two mechanisms imply that either foreign-origin managers join the company they expect will invest in their country of origin or they are admitted to the upper echelons in view of investing there. While our controls, including the fixed effects, and the time lag between the dependent variable and variable of interest go a long way in reducing bias, some caution must still be exercised before interpreting our results causally.</footnote>
3a. Notice that in all columns, the new coefficients for *Managers from target* increase considerably and that those for the interaction terms tend to be very close to them in absolute value. Summing the former and the latter in each column produces near-zero effects in all columns except that for M&As (column 2), which implies that the manager-from-target effect approaches zero when the TMT diversity indicator reaches its theoretical maximum value of 1. But this is never the case in the sample, the maximum value of the indicator being 0.94, the average around 0.84, and the median around 0.88 (see online appendix B.2 for discussion). We conclude that even in presence of high TMT diversity, the manager-from-target effect may still be relevant.<fnref n="10" />

Our results withstand a large number of robustness checks. In particular, we test whether our results are affected by the noise and potential bias due to having based the identification of foreign managers on name analysis or by other, non-name-related aspects of our operationalization of the *Managers from target* variable. All results are reported, respectively, in online appendices B.3 and C.

Discussion and conclusions

In this paper, we investigated whether foreign-origin managers in apical positions help attract FDI to their country of origin, in what we call a manager-from-target effect. To do so, we revisited the upper echelons theory and its definition of nationality and related nationality to companies' location choices. From that we derived our main theoretical argument, namely that foreign-origin managers may provide their companies with some surrogate experience of the cost and opportunities of doing business in their country of origin and that the value of such experience varies by establishment mode and with the extent of country-of-origin diversity in TMTs.

To test our hypotheses, we assembled and exploited a new and original database on a large number of FDI operations. Our regression results point to the existence of a significant manager-from-target effect that, in accordance with our hypotheses, is stronger and more significant (i) for acquisitions than for greenfield investments and (ii) when TMTs' diversity is low. These findings generalize those obtained by Useche et al. (2020) for foreign-origin inventors in innovation-oriented FDIs by R&D-active companies; and those by Foley and Kerr (2013) on the correlation between the weight

<table><caption>Table 2 Testing for moderating effects: TMT diversity</caption><thead><tr><th></th><th>(1)<br>All</th><th>(2)<br>M&As</th><th>(3)<br>GI<sup>a</sup></th><th>(4)<br>GI<sup>b</sup></th></tr></thead><tbody><tr><td>MFT</td><td>0.350<br>(0.494)<br>[0.478]</td><td>0.737<br>(2.139)<br>[0.731]</td><td>0.329<br>(0.507)<br>[0.517]</td><td>1.733<br>(0.210)<br>[0.000]</td></tr><tr><td>TMT diversity</td><td>-0.190<br>(0.705)<br>[0.787]</td><td>-2.826<br>(6.796)<br>[0.678]</td><td>-0.167<br>(0.718)<br>[0.817]</td><td>0.614<br>(2.738)<br>[0.823]</td></tr><tr><td>MFT × TMT diversity</td><td>-0.296<br>(0.560)<br>[0.596]</td><td>-0.343<br>(2.420)<br>[0.887]</td><td>-0.331<br>(0.575)<br>[0.564]</td><td>-1.730<br>(0.260)<br>[0.000]</td></tr><tr><td>Observations</td><td>4341</td><td>887</td><td>3454</td><td>719</td></tr><tr><td>R<sup>2</sup></td><td>0.110</td><td>0.140</td><td>0.111</td><td>0.108</td></tr></tbody></table>

a Sample obtained using the basic matching scheme (see “Empirical strategy” section).

b Sample obtained using the alternative matching scheme (see “Empirical strategy” section).

Subsidiary in target and fixed effects for investor, year, target sector, and investment corridor included in all models. Deal clustered SE in parentheses; *p* value in square brackets

of such inventors on a company's patent portfolio and the share of assets held in their country of origin.

Our research could be expanded in several directions. In particular, our findings on the moderating variables affecting the size and significance of the manager-from-target effect suggest the value in investigating other conditions under which the managers' country-of-origin orientation is more valuable and hence decisive. These may have to do with the lack of alternative knowledge resources, the characteristics of the target countries' business environment, or those of the investment. Researchers may want to explore the possibility that foreign-origin managers may have a biased view of their target locations' potential or strong personal interests in them. Drawing a parallel with the field of behavioral finance, and in particular with Schijven and Hitt's (2012) discussion of why investors delegate to managers decisions on acquisitions, TMT members may assume that their colleagues from a given country of origin know the country better than they do and therefore trust their judgment, even given the risk of cognitive bias due to hubris or self-interest. To establish whether this is the case, it would be desirable to investigate whether FDI, and in particular cross-border M&As, ultimately performs better if undertaken under the influence of foreign-origin managers from the target country. This would allow researchers to draw some managerial implications from our work concerning whether it would be advisable to solicit or rely on such managers' advice about investing in their country of origin. If so, it would provide one further reason, besides those explored already in the upper echelons literature, to promote diversity in TMTs.

<figure><img src="image_7.png" /></figure>

<footnote n="10">¹⁰ As for the coefficient of *TMT diversity*, this is never significant; but nothing in the literature nor in our discussion suggests it should be otherwise.</footnote>
Whatever research avenue one undertakes, there are wide margins to improve upon our empirical exercise, in at least two directions. First, our name-based methodology for identifying managers from target countries could be usefully supplemented with additional information on the strength of the managers’ ties with their country-of-origin. Knowing the managers’ country of birth or legal status would help, but it would not be enough. The key information is the time spent in the country of origin, whether for education or work, and any form of business activity or civic engagement that may reveal their orientation toward it. Social media may be useful sources, as they provide information on the geographical distribution of contacts or message contents from personal profiles. One major difficulty in proceeding in this way, however, is the legal limitations concerning access to sensitive data.

Second, concerning the econometric strategy, our matched-sample regression is a second-best approach, as it relies on the assumption that locations in control investments are equivalent to those in case ones. A more straightforward, but also much more data-intensive, strategy would consist in collecting information on rumors of FDI under consideration in one or another country and explaining whether the investment actually takes place when any TMT member comes from the target country.

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1057/s41267-024-00726-2.

**Acknowledgements** We thank Vittoria Scalera for her encouragement and in-depth discussion of the relevant literature. We benefited from comments by participants in GEOINNO2020 (Stavanger Univ.), DRUID21 (Copenhagen Business School), the 2021 Migration & Organizations Conference (Wharton School), the 2021 and 2022 Workshops on Migration, Globalization and the Knowledge Economy (Bordeaux Univ. and Gran Sasso Science Inst.), GCEC2022 (Trinity and University Colleges Dublin), the Workshop on New Data and Methods for Migration Studies 2022 (Paris School of Economics and French Collaborative Institute on Migration), and seminars at CRENoS (Cagliari Univ.) and JSEC (Jena Univ.). Maxime Pelissier Hermitte and Martin Jean-Jullien contributed high-quality research assistantship. We acknowledge funding from the French National Research Agency (TKC project – ref.: ANR-17-CE26-0016) and the Spanish Ministerio de Ciencia e Innovación (PID2020-119096RB-I00/MCIN/AEI; Ernest Miguelez). All errors are ours.

**Funding** Open access funding provided by Università degli Studi di Torino within the CRUI-CARE Agreement.

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**Massimiliano Coda Zabetta** (PhD, University of Turin and Collegio Carlo Alberto) is Assistant Professor at the University of Turin (Italy). His research focuses on the economics of science and innovation, particularly the effects of international mobility and migration on innovation and scientific outcomes. Recently, he has been investigating the scientific basis of technologies that facilitate the transition to a circular economy paradigm.

Ernest Miguelez (PhD, University of Barcelona) is a research scientist at the IPP-CSIC (Spain). He previously worked as a tenured scientist at the CNRS (France), attached to the Bordeaux School of Economics, where he is still affiliated. He is a research affiliate at the AQR-University of Barcelona (Spain), external Research Fellow at CReAM-UCL (UK), and Fellow of the Institute Convergence Migrations (France). His research interests cover economic geography, innovation economics, migration, and intellectual property, and more recently, the diffusion/adoption of green technologies and products.

Francesco Lissoni (PhD, University of Manchester) is Full Professor of Economics at the University of Bordeaux (France), Fellow of the Department of Economic Policy of the Università Cattolica del Sacro Cuore (Milan, Italy), and Fellow of the Institute Convergence Migrations (France). He got his PhD with a thesis on innovation adoption. His more recent and current research activities cover the economics of science, intellectual property, university–industry technology transfer, the geography of knowledge diffusion, and highly skilled migration.

Sarah Hegarty is a data scientist at the Centre for Transformative Inno-
vation of the Swinburne University of Technology (Australia). Her doc-
toral studies were in the field of data-intensive astrophysics, examining
best practices in extracting insight from very large, complex datasets.
She specializes in the analysis and linking of large economic databases.