Board of Directors’ Cultural Proximity and Investment Efficiency of Multinational
Corporations

ABSTRACT: This paper investigates how cultural proximity, defined as a shared cultural
background between the board of directors and the regions in which the firm’s foreignsubsidiaries operate, affects the investment efficiency of multinational corporations (MNCs). We
argue that ethnicity can be viewed as a proxy for board members’ cultural backgrounds and that
cultural proximity creates an alignment between the board’s comparative advantage and the
MNC’s great need for mitigating cross-border information frictions and monitoring foreignsubsidiaries. Consistent with our prediction, we find that U.S. MNCs with cultural proximity
make more efficient investment decisions at both the firm- and the subsidiary-level. This study
suggests that cultural proximity, instead of the board diversity per se, is an effective internal
control mechanism.

Keywords: Culture, Information asymmetry, Investment efficiency, Multinational corporations

I. INTRODUCTION
This paper investigates how cultural proximity affects the investment efficiency ofmultinational corporations (MNCs) at both the firm- and the subsidiary-level. Cultural proximity
is defined as a shared cultural background between the board of directors and the regions inwhich the firm’s foreign subsidiaries operate. MNCs play a leading role in today’s global
economy (e.g., UNCTAD 2012). They are inherently complex due to their extreme diversity invarious dimensions, such as geographic distances, differences in culture, language, andoperational styles between parents and subsidiaries. These cross-border factors lead to more
severe information frictions within MNCs than in domestic firms. Cross-border frictions, in turn,increase information asymmetry between parents and subsidiaries and thus increase the cost ofmonitoring (e.g., Roth and O'Donnell 1996; Bushman, Chen, Engel, and Smith 2004).
The board of directors serves as a key internal control mechanism that firms use to
mitigate information asymmetry and reduce the cost of monitoring. With a dual role ofmonitoring and advising the firm’s management, the corporate board has long been a subject of
research in a variety of disciplines (e.g., Mace 1971; Johnson, Schnatterly, and Hill 2012). In thepast two decades, one important trend in the U.S. corporate boardroom is that director profiles
have shifted toward the inclusion of minorities, often identified by gender, race and/or ethnicity,from the traditional white male directors (Directorship’s Annual Survey 1999). Although suchdiversity in the boardroom is still at a relatively low level, researchers and regulators have shown
great interest in understanding its impact on firm outcomes (Walt and Ingley 2003; Malberti andSironi 2007). However, extant studies exclusively treat board members’ ethnicity as anobservable demographic characteristic and hypothesize that ethnicity directly and explicitly
affects firm performance, but they find little consensus as to whether such diversity has any

1impact on outcomes (e.g., Oxelheim and Randoy 2003; Carter, D’Souza, Simkins, and Simpson
2010).
In contrast to prior studies, we argue that ethnicity can be viewed as a proxy for board
members’ cultural backgrounds and that the control mechanism being used to mitigate
information asymmetry is not board diversity per se, but the alignment between what the board
can provide and what the company needs. The intuition is that if an individual director from acertain ethnic group is more familiar with the dominant culture of that ethnicity, he or she may
also have better knowledge about operational regions sharing the same culture, including ashared language or dialect, familiarity with foreign institutional characteristics and political orsocial norms, and awareness of opportunities and risks in the local business environment (Ryan
2010). In addition, sharing the same cultural may make it easier for the director to establish
social connections with local personnel. When an MNC has subsidiaries operating in areas towhich one or more board members have a cultural tie, this cultural proximity aligns thecomparative advantage of those board members with the company’s need for better monitoring
those subsidiaries. Such an alignment can help decrease information asymmetry within MNCs
and enhance the board’s monitoring ability, improving the firms’ decision-making efficiency.
In this study, we focus on MNCs’ investment efficiency because a firm’s investment
decision is one of the most important managerial decisions and one of the most fundamental
drivers of firm value (Hubbard 1998). Studying firms’ investment decisions also provides us aunique setting to investigate whether cultural proximity would adversely affect the board’s
monitoring and advising ability. If a board member sharing a cultural tie with the firm’s
subsidiaries is also socially connected with local personnel, the social ties may foster nepotism,
moral hazard, and even collusion problems. A board member with cultural proximity may also2

be subject to overconfidence and the familiarity bias, defined as people’s tendency to develop apreference for certain things merely because they are familiar with them (e.g., Zajonc 1968;
Zajonc 1980). He or she may intentionally or unintentionally allow non-optimal investment
decisions and therefore decrease the firm’s investment efficiency.
We hypothesize that the board of directors’ cultural proximity is positively associatedwith MNCs’ investment efficiency. If board diversity per se leads to higher efficiency, twoboards with directors from the same cultural background, ceteris paribus, should benefit the
MNCs similarly, regardless of whether a cultural tie with subsidiaries exists. However, wepredict that the board of directors’ cultural proximity, not diversity, results in higher investment
efficiency at the firm-level. To further ensure that the improved efficiency comes fromsubsidiaries sharing cultural ties with one or more board members, we also predict that the boardof directors’ cultural proximity is associated with higher investment efficiency at the subsidiarylevel.
Two key constructs in this study are cultural proximity and investment efficiency. We use
directors’ ethnicity, identified based on their surnames from the BoardEx database, as the proxy
for their cultural backgrounds. Only four cultural groups: Chinese, Indian, Japanese, and Korean,
are considered because they allow us to identify the ethnic origin of an individual director bysurname with significantly less ambiguity relative to other western cultural groups (Du, Yu, andYu 2013). Information on subsidiaries comes from ORBIS, which contains detailed informationon the ownership structure of public and private companies from over 220 countries and 1,000
registrants. Taking advantage of the long cultural distance between western and eastern cultures,
we focus on only U.S. MNCs and their subsidiaries in seven Asian countries and regions:
Mainland China, Hong Kong, India, Japan, Korea, Malaysia and Singapore, which are classified

3into the four cultural groups. Cultural proximity exists if one or more board members fall into
one cultural group to which one or more subsidiaries also belong.
At the firm-level, we follow Biddle, Hilary, and Verdi (2009) and Chen, Hope, Li, andWang (2011) and define investment efficiency as the deviation from the expected level ofinvestment directly modeled based on a firm’s investment opportunities. Using a large sample of
11,205 firm-year observations with 1,684 unique U.S. MNCs among which 132 are with cultural
proximity from 2005 to 2012, we find that cultural proximity is associated with higher
investment efficiency. To preclude the possibility that a board member with a certain cultural
background is more likely to influence the firm’s decision of locating its subsidiaries in regions
sharing the same culture, we retain a subsample of 1,868 observations that only consists of firmsthat have subsidiaries operating in the seven areas of interest. These firms either create cultural
proximity by bringing one or more directors with a tie onto the board or continue operating
without such cultural proximity after the establishment of these subsidiaries. The result again
supports the prediction that cultural proximity increases MNCs’ firm-level investment efficiency.
At the subsidiary-level, because the data on capital expenditures and/or acquisitions are
not available for our sample of largely private subsidiaries, we follow Shroff, Verdi, and Yu(2014) and use the sensitivity of a subsidiary’s investment to its growth opportunities as its
investment efficiency indication. Using a sample of 6,205 parent-subsidiary-year observations,
we find a positive association between board of directors’ cultural proximity and the U.S. MNCs’
subsidiary-level investment efficiency, suggesting that the higher investment efficiency at thefirm-level is a result of culture proximity rather than ethnic diversity of the board per se.
This paper makes several contributions. First, MNCs have been underexplored by priorresearch due to data limitations. This study contributes to the literature on corporate governance4

by focusing on indirect cultural control systems within MNCs and suggesting a new mechanism
at the parent-level, cultural proximity. Management research categorizes internal control
mechanisms into two main systems: bureaucratic control and cultural control (Child 1972; Child
1973; Edstrom and Galbraith 1977). Cultural control systems can be classified as the direct“personal” type of control and the indirect “control by socialization” (Balgia and Jaeger 1984).
Prior studies on MNCs mainly focus on direct cultural control mechanisms at the subsidiarylevel, such as sharing ownership with local partners and using parent country expatriates insubsidiaries (e.g. Desai, Foley, and Hines Jr. 2004; Colakoglu and Caligiuri 2008). Because thesemechanisms all require a deep involvement in subsidiaries’ operating activities, they are
effective but also costly. Indirect cultural control mechanisms can be of greater value to MNCs
because these mechanisms reduce the cost of monitoring by having impact on a larger number ofoperating units. Second, this paper is related to a growing body of literature on board
composition, specifically board diversity. The results provide a potential explanation for theinconclusive research investigating the influence of board members’ demographic characteristics
on firm outcomes (e.g., Oxelheim and Randoy 2003; Staples 2008; Carter et al. 2010). My study
suggests that diversity per se does not enhance a firm’s performance. The purpose of composing
a diverse board is to increase the likelihood of benefiting from the alignment between what thefirms’ need and what the board can provide. Third, this paper is one of the few studies that
exploit a novel dataset of the ownership structure of MNCs (Shroff et al. 2014). This study
provides preliminary evidence on the role of cultural proximity in facilitating MNCs’ investment
decisions not only at the firm-level but also at the subsidiary-level.
The rest of the paper is organized as follows: Section 2 presents the background anddevelops our hypotheses. Section 3 describes the sample and discusses the empirical5

methodology. In Section 4, we present the empirical findings. Section 5 includes the discussionof limitations, suggestions for future research, and concluding remarks.

II. PRIOR LITERATURE AND HYPOTHESIS DEVELOPMENT
2.1. Information asymmetry within MNCs
Various cross-border factors, such as geographic distances, differences in culture,
language, and operational styles between parents and subsidiaries, create severe information
frictions within MNCs. These information frictions increase information asymmetry between
parents and subsidiaries (e.g., Bushman et al. 2004). As a result, it is more difficult for MNCs toefficiently allocate resource, incentivize subsidiary managers, and monitor the dispersed
activities (e.g., Hope and Thomas 2008; Dellestrand and Kappen 2012).
Management research suggests that internal control mechanisms can be used to mitigate
information frictions and reduce the cost of monitoring. Child (1972) and Child (1973) claim that,when choosing internal control mechanisms to monitor output or behavior, organizations can use
either personal control systems or bureaucratic control systems. In the MNC context,
bureaucratic control utilizes an extensive set of rules, regulations, and procedures to constrain
subsidiary management’s role and authority. Personal control, on the other hand, involves
placing a number of trustworthy personnel from headquarters in key positions in the subsidiary
to directly supervise subsidiary operations. However, Edstrom and Galbraith (1977) assert that athird type exists: indirect control by socialization, which includes frequent information exchange
between headquarters and subsidiaries, a de-emphasis of formalization, and an improvement in
information technology systems within MNCs (Bloom, Sadun, and Reenen 2012). Balgia andJaeger (1984) categorize both the direct “personal” type of control and the indirect “control by6socialization” as “cultural control”. The majority of management studies focus on direct culturalcontrol mechanisms at the subsidiary-level, such as sharing ownership with local partners andusing parent country expatriates in subsidiaries (e.g. Desai et al. 2004; Colakoglu and Caligiuri2008). Due to their deep involvement in subsidiaries’ operating activities, these mechanisms are
effective but also costly. Bloom et al. (2012) suggest that indirect cultural control mechanisms
are of great value to MNCs because they affect a large number of operating units at reduced cost.
In this study, we investigate an indirect cultural control mechanism at the parent-level,
specifically the board of directors’ cultural proximity.

2.2. Board of directors and cultural proximity
The board of directors serves as an important internal governance mechanism in a firm’s
decision- making process. It performs the dual role of monitoring and advising the firms’
management (e.g., Mace 1971; Jensen 1993). The monitoring role involves overseeing
management with a goal of minimizing potential agency problems, while the advising role
involves assisting management in strategy formulation and execution, as well as providing
counsel in other areas of top-level decision making. In MNCs, the board faces greater challenge
in performing these two functions, and board composition is a critical element in its ability toaffect firms’ decision-making process and operational outcomes (Zald 1969).
My study is closely related to the growing literature on board diversity (e.g., Carpenter,
Geletkanycz, and Sanders 2004; Carter et al. 2010). The traditional argument for selecting adiverse board is based on the research dependence theory, which suggests that individual board
members bring resource to the organization as a result of their backgrounds (Zald 1969; Pfeffer
and Salancik 1978). Understanding differences in backgrounds helps individuals learn new7

perspectives, so a diverse board will more effectively draw upon talent, intellectual capital, andmotivate more employees (Worthy and Neuschel 1984).
Erhardt et al. (2003) classify board diversity into two categories: the observable(demographic) and the non-observable (cognitive). Examples of observable diversity are age,gender, educational level, race, and ethnicity (e.g., Milliken and Martins 1996; Timmerman 2000;
Carpenter et al. 2004). Non-observable diversity can be categorized into two groups: humancapital and social capital (Johnson et al. 2012). Individual directors’ human capitalcharacteristics are experiences and knowledge, such as experience as a CEO, financial expertise,and familiarity with a specific event or industry (e.g., Wang and Dewhirst 1992; Chhaochharia
and Grinstein 2007; Kroll, Walters, and Wright 2008). Social capital characteristics are social
relationships, such as ties to other firms and personal relationships with firm managers (e.g.,
Ruigrok, Peck, and Keller 2006; Rhee and Lee 2008).
Not all board member characteristics can, or should be, easily classified into only one
category. Extant studies exclusively consider board members’ ethnicity as an observable
demographic characteristic that directly and explicitly affects firm-level outcomes. However,they find no consistent relationship between ethnic diversity and firm performance. For example,
Oxelheim and Randoy (2003) find a positive association between foreign-born directors andhigher firm value, using a sample of Scandinavian firms. Staples (2008) suggests that having a
multinational and ethnically diverse board increases the chance of cross-national acquisitions. On
the other hand, Carter et al. (2010) find no evidence that ethnic diversity affects U.S. firms’
financial performance.
In contrast to prior studies, we argue that the classification of board members' ethnicity is
ambiguous because it can also be viewed as a proxy for their cultural backgrounds (e.g., Fisman,8

Paravisini, and Vig 2012; Du et al. 2013). Sapienza, Zingales, and Guiso (2006) define culture as“those customary beliefs and values that ethnic, religious, and social groups transmit fairly
unchanged from generation to generation.” One of the most common mechanisms of thetransmission of cultural traits is socialization within families as well as ethnic or religious groups(e.g., Hayes and Pittelkow 1993; Bisin and Verdier 2000). If an individual director from a certain
ethnic group is more familiar with the dominant culture of that ethnicity, he or she may also havebetter knowledge about operational regions sharing the same culture, which can, but does nothave to, include a shared language or dialect, familiarity with foreign institutional characteristics
and political or social norms, and awareness of opportunities and risks in the local businessenvironment (Ryan 2010). The board can use such external information available in subsidiaries’
operating environments to better monitor and evaluate the subsidiaries’ managerial decisions(Shroff et al. 2014). In addition, sharing the same cultural background may make it easier for theboard member to establish connections with local personnel. These knowledge and social ties are
human and social capital attributes of directors’ ethnicity.
Merely composing an ethnically diverse board is not sufficient to alleviate information
asymmetry. When an MNC has subsidiaries operating in areas to which one or more board
members have a cultural tie, the knowledge and connections in these areas become crucial. Sucha tie aligns the comparative advantage of those board members with the MNC’s need for better
monitoring and evaluating those subsidiaries. Thus, a necessary condition for a diverse board tobe effective in performing its monitoring and advising role is to create the alignment between
what the board of directors can provide and what the company needs. In other words, the indirect
cultural control mechanism being used to mitigate information asymmetry within MNCs is not

9board diversity per se, but the board of directors’ cultural proximity. Thus, we assert that theenhanced ability of the board leads to an increase in the firm’s investment efficiency.
We focus on MNCs’ investment efficiency because a firm’s investment decision is one of
the most important managerial decisions and one of the most fundamental drivers of firm value(Hubbard 1998). Studying firms’ investment decisions also provides us a unique setting toinvestigate whether cultural proximity would negatively affect the board’s monitoring andadvising ability. First, because a board member with cultural proximity can easily makeconnections with subsidiaries’ local personnel, such social ties may cause nepotism, moral
hazard, and even collusion problems. Extant studies document that social ties between executives
or CEO/CFO and the board of directors have a negative impact on firms’ financial reporting
quality and mergers and acquisitions. (Krishnan, Raman, Yang, and Yu 2011; Fracassi and Tate2012). Research in auditing also finds that social ties between auditors and client executives oraudit committee members impair audit quality (e.g., Guan, Su, Wu, and Yang 2014; He, Pittman,
Rui, and Wu 2014). Second, prior research suggests that insiders or experts are subject to a more
severe overconfidence bias (Kahneman and Lovallo 1993). For example, Tetlock (2005)
investigates individuals’ predictions of political and economic trends and shows that experts tend
to be overconfident with their predictions than non-experts. A board member with cultural
proximity may perceive himself or herself as an insider or expert. He or she is then more likely
to overestimate the returns to the potential investment projects or misperceive negative NPV
projects as value creating. Third, board members with the same cultural background can havedifferent degrees of understanding and familiarity with the culture and the regions sharing thesame culture. Even not being an insider or expert, a board member with a cultural tie may still be
subject to the familiarity bias. The familiarity bias, also called “the mere-exposure effect” from10

social psychology, describes people’s tendency to develop a preference for certain things merely
because they are familiar with them (e.g., Zajonc 1968; Zajonc 1980). Thus, a board with
cultural proximity may have impaired monitoring and advising ability, which adversely affects
the firm’s investment efficiency.
If board diversity per se leads to higher efficiency, two boards with directors from thesame cultural background, ceteris paribus, should benefit the MNCs similarly, regardless ofwhether a cultural tie with subsidiaries exists. However, if it is cultural proximity that makes adifference, we make the following firm-level hypothesis:
Hypothesis 1: The board of directors’ cultural proximity is positively associated with MNCs’
investment efficiency at the firm-level.

To further ensure that the improved efficiency comes from subsidiaries sharing cultural
ties with one or more board members, we also make the subsidiary-level prediction:
Hypothesis 2: The board of directors’ cultural proximity is positively associated MNCs’
investment efficiency at the subsidiary-level.

III. SAMPLE SELECTION AND RESEARCH DESIGN
3.1 Data
Cultural proximity
There are a number of challenges in empirically identifying the cultural proximity within
MNCs. Following Du et al. (2013) We use individual directors’ ethnicity as the proxy for culture
and identify individual directors’ ethnic origins based on their surnames. We consider only four
cultural groups: Chinese, Indian, Japanese, and Korean, because the unique nature of these11cultures allows us to identify the ethnic origin of an individual director by surname withsignificantly less ambiguity relative to other western cultural groups. One limitation of thisapproach is that surnames may be changed upon marriage, but given the low level ofrepresentation of women on boards, this issue is less of a concern.
We focus on only U.S. MNCs and their subsidiaries in seven Asian countries and regions:
Mainland China, Hong Kong, India, Japan, Korea, Malaysia, and Singapore, which are classified
into the four cultural groups. Extant research in cultural sociology, history, and linguistics
provide support for categorizing Mainland China, Hong Kong, Malaysia, and Singapore into one
cultural group. In Hong Kong, 93.6% of the residents are reported as ethnically Chinese as of2011 (Population Census 2011). Chinese and English are both official languages in Hong Kong.
Since the 1997 handover, there is an increasing impact of Mainland China on Hong Kong, both
economically and politically (Cullinane, Wang, and Cullinane 2007). In Malaysia as of 2010,seven million Malaysian self-identify as “Chinese”, about one-third of the population(Department of Statistics Malaysia 2010). Malaysian Chinese are dominant in both the businessand commerce sectors, controlling about 70% of the country’s market capitalization (Lee andTham 2007). In Singapore, over 77% of Singaporeans are of Chinese descent or consider
themselves as ethnic Chinese (Lee 2002). They are the largest ethnic group in Singapore and are
well represented in all levels of Singaporean society. Mandarin is one of the four official
languages recognized by the Singapore Government. Thus, although with limitations, it isreasonable to classify these three countries and regions all into the Chinese cultural group.
Basing our study on these area and their cultures has merit for several reasons. First,
based on the Hofsted Cultural Distance Model, archetypical eastern cultures in Asian countries
are of a long distance from western culture (e.g., Singh, Zhao, and Hu 2005; Shi and Wang 2011).

12

A long cultural distance between the parent and its subsidiaries increases information asymmetry
within MNCs, and may lead to a stronger demand for cultural proximity. Second, the selected
countries and regions are all located close to each other but geographically far away from the
U.S. This setting can help disentangle cultural proximity from geographic proximity.
Sample
The two main databases used in this study are ORBIS published by Bureau van Dijk
(BvD) and BoardEx. ORBIS covers 130 million public and private companies from over 220
countries and 1,000 registries. We obtain ownership and financial information for U.S. MNCs
and their subsidiaries from 2004 to 2013. Data on independent directors of U.S. MNCs iscollected from BoardEx, which covers biographical information on board members andexecutives associated with over 800,000 organizations around the world. Information on
individual directors’ surnames is available from 1999 to 2012.
We first follow Shroff et al. (2014) and construct the business group of U.S. MNCs bylinking subsidiaries to parents using the ownership database in ORBIS. The ultimate owners aredefined as firms in which no single corporate shareholder owns more than 25% of the firms’shares. We select non-financial holding subsidiaries located in Mainland China, Hong Kong,
India, Japan, Korea, or Singapore and restrict their ultimate owners to be in the U.S. only. We
exclude subsidiaries indirectly owned via ownership of other subsidiaries, so subsidiaries in our
sample are all directly owned by their parent companies in the U.S. These restrictions return asample of 11,380 parent-subsidiary-year observations from ORBIS.
The Committee details file in BoardEx contains information on individual directors’ fullnames, board roles, and board positions by company and year. We include only U.S. firms withmore than three board members. We then restrict directors to be independent outside directors

13that stay on the board of a given company for at least two years (Matos, Ferreira, Matos, andMergulhao 2008). Using Perl, we compare each director’s surname with four master lists
containing common Chinese, Indian, Japanese, and Korean surnames, respectively. If one ormore directors’ surnames on the board match with one of the lists in a given year, we identify“board culture(s)” as having cultural diversity with the matched ethnicity. It is possible that one
board is matched to more than one ethnicity. Through this process, we construct the BoardEx
sample to be at the firm-year instead of the firm-year-director level, with 54,872 observations,
The ORBIS sample and BoardEx sample do not share a common identifier, so We matchcompanies ISINs from BoardEx with Tickers from ORBIS by using the CRSP/COMPUSTAT
Merged database. Each firm(parent)-year observation from the BoardEx sample can be matched
to multiple parent-subsidiary-year observations from the ORBIS sample, so We obtain a sample
of 52,149 parent-subsidiary-year observations after the BoardEx/ORBIS merge (Table 1 Panel
A). We identify cultural proximity by comparing each subsidiary’s country of location with the“board culture(s)” constructed in the BoardEx sample. For example, if a subsidiary locates in
Mainland China, Hong Kong, Malaysia, or Singapore, and the board has a member with aChinese surname, then the cultural proximity indicator for Chinese culture is set to 1. The
cultural proximity indicators for Indian, Japanese, and Korean cultures are created in the samemanner. Because one single board can have multiple “board cultures”, it is also possible to havecultural proximities for more than one culture.
To test Hypothesis 1 at the firm-level, we aggregate the four cultural proximity indicators
at the subsidiary-level to one firm-level indicator. If one or more subsidiary-level indicators
equal to 1, the firm-level cultural proximity indicator is set to be 1. After this aggregation process,we retain 43,601 firm(parent)-year observations. In the unmatched control group (cultural

14

proximity indicator = 0), there are both U.S. MNCs that do not have cultural proximity andpurely domestic U.S. firms that do not have operations overseas. To compare U.S. MNCs with
cultural proximity versus those with no cultural proximity, we merge the sample withCOMPUSTAT based on GVKEYs and exclude observations that have no foreign pretax income,resulting in a significant decrease in the sample size. After also excluding parents that are
financial holding companies and observations missing data necessary for computing theaccounting and financial market variables, My final sample to test Hypothesis 1 consists of
11,205 firm-year observations. There are 1,684 unique U.S. MNCs in the sample, among which
132 have cultural proximity with their subsidiaries. There is a potential endogeneity problem thata board member with a certain cultural background is more likely to influence the firm’s decisionof locating its subsidiaries in regions sharing the same culture. To mitigate this concern, we
retain a subsample of 1,868 observations that only consists of firms that have subsidiaries
operating in the seven areas of interest. These firms either create cultural proximity by bringing
one or more directors with a tie onto the board or continue operating without such cultural
proximity after the establishment of these subsidiaries. Details related to the impact of each of thesample inclusion criteria on the final determination of both the full sample and the subsample aresummarized in Table 1 (Panel B).

To test Hypothesis 2 at the subsidiary-level, we identify parent firms for subsidiaries in
ORBIS based on Tickers and retain 9,542 parent-subsidiary-year observations. For observations
that have at least one subsidiary-level cultural proximity indicator equaling 1, we identify which
subsidiary or subsidiaries are the beneficiaries of the indentified cultural proximity. For example,
if a board is identified as having a Chinese “board culture,” subsidiaries located in Mainland
China, Hong Kong, Malaysia, or Singapore would benefit. We require each subsidiary to have atleast three successive years of total assets and acquire several accounting and financial market
15measures at the firm-level or at the country-industry-year level from COMPUSTAT, I/B/E/S,
Datastream and/or Worldbank. My final sample to test Hypothesis 2 consists of 6,205 parentsubsidiary-year observations. Table 1 (Panel C) presents the detailed sample selection procedure.

3.2 Research design
To test Hypothesis 1, we investigate how board proximity in the current year affects next
year’s investment efficiency at the firm-level. Following Biddle et al. (2009) and Chen et al.
(2011), we define investment efficiency as the deviation from the expected level of investment
directly modeled based on a firm’s investment opportunities. We proceed by first estimating afirm-specific model of investment as a function of growth opportunities (as measured by revenue
growth). Because the relation between investment and revenue growth could differ between
revenue decreases and revenue increases (McNichols and Stubben 2008), we allow fordifferential predictability for revenue increases and revenue decreases by employing a piecewise
liner regression model, as described below:

Investmenti,t+1=β0+β1NEGi,t+β2RevGrowthi,t+β3NEG*RevGrowthi,t+εi,t+1(1)

Investmenti,t+1 is the total investment, defined as the sum of research and development
expenditure, capital expenditure, and acquisition expenditure less cash receipts from sale ofproperty, plant, and equipment, multiplied by 100 and scaled by lagged total assets for firm i inyear t+1. RevGrowthi,t is the annual percentage change in revenue firm i from year t-1 to t. The
indicator variable NEGi,t takes the value of 1 for negative revenue growth, and 0 otherwise. Eq.
(1) is estimated for each industry-year based on the Fama and French 48-industry classification.

16

Both underinvestment (negative deviations from expected investment) and overinvestment(positive deviations from expected investment) are considered inefficient investment. We
multiple the absolute values of residuals from Eq. (1), the deviations from the predictedinvestment levels, by -1 to create the investment efficiency measure, InvEffi,t+1. A higher value of
InvEff i,t+1 suggests a more efficient investment.
We then test whether board proximity is positively associated with investment efficiency
by estimating the following model:

InvEffi,t+1=β0+β1SubIni,t+β2BodIni,t+β3CulProxi,t+βnControlsi,t+∑SubCountries+∑Industries+∑Years+εi,t+1(2)where:SubIn = an indicator variable that takes the value of one if the firm has at least one
subsidiary in the areas of interest, and zero otherwise;
BodIn = an indicator variable that takes the value of one if the firm has at least one board
member with the cultural background of interest, and zero otherwise;
CulProx = an indicator variable that takes the value of one if the board of directors hascultural proximity as described above, and zero otherwise.
Motivated by prior research, we include a set of control variables, including firm size
(LogAsset), market-to-book ratio (MB), bankruptcy cost (Tangibility), analysts following(Analysts), institutional ownership (Institutions), financial reporting quality (AQ), market
leverage (K-structure), average industry leverage (Ind. K-struc.), financial slack (Slack and
CFOsale), dividend payout ratio (Dividend), the length of the operating cycle (OperCycle), past
performance (Loss), the cash ratio (Cash), firm (Age), and investment, cash flow and sales

17

volatility. The Appendix details the definitions of all variables. We control for industry based on
Fama-French 48 industry classification and year fixed effects. To address correlated omitted
variable problems at the subsidiary country-level subsidiary, we also include subsidiaries’
country-specific effects.
We test hypothesis 1 by examining if the coefficient on CulProx is greater than zero (i.e.,
H1: β3>0). When the subsample is used to test Hypothesis 1, SubIn all equal to 1, and BodIn and
CulProx take the same value. The prediction stays the same.
To test Hypothesis 2, we investigate how board proximity in the current year affects next
year’s investment efficiency at the subsidiary-level. Ideally, we would proxy for investmentusing capital expenditures and use the same model as the firm-level analysis. However, thesedata are not available for our sample of largely private subsidiaries. We follow Shroff et al.
(2014) and use the sensitivity of a subsidiary’s investment to its growth opportunities as its
investment efficiency indication. The intuition is that investment is more responsive toinvestment opportunities when the adjustment costs are low (Hubbard 1998). We examine
whether the sensitivity of a subsidiary’s investment to its growth opportunities is positively
affected by the board’s cultural proximity by estimating the following model:

Invi,t+1=βPEs,j,t*CulProxs,j,t+∑βsPEs,j,t*Countrys+∑βkMechanismsk+∑βkPEs,j,t*Mechanismsk+βnControlsi,t+∑Industriesj+∑Years+εi,t+1
where:
i = subsidiaries;s = subsidiary countries;
j = subsidiary industries;

18

(3)

Inv = the subsidiary investment, defined as the percentage change in total assets in a year;
PE = growth opportunities, defined as the price-to-earnings (PE) ratio of the countryindustry-year in which the subsidiary operates obtained from Datastream.
Following prior literature, we include a set of internal and external mechanisms
associated with investment. Internal mechanisms that could affect investment and/or be used tomonitor a subsidiary’s decisions include parent firm’s cash flow (Parent_CFO), because Shinand Stulz (1998) find that parent cash flows affect subsidiary investment through internal capital
markets; and the availability of local bank financing, proxied by the total banking credit
extended in the subsidiary’s country (Credits), to control for additional bank monitoring.
External mechanism is the quality of the information environment in which a subsidiary operates,
measured by the number of analysts following (Sub_Ext). We allow the coefficient for PEs,j,t tovary by subsidiary country to control for the effect of subsidiary country-level institutional
factors, such as financial development and capital market integration, on investment efficiency(Wurgler 2000; Bekaert et al. 2007). Because data limitations preclude us from directly
controlling for all possible mechanisms, controlling for this interaction between PE andsubsidiary country indicators allows us to indirectly control for them as long as the mechanisms
are largely driven by country-level factors. The subsidiary firm size (Sub_lnAsset) andperformance (Sub_ROA) are included in our model to control for subsidiary scale andprofitability. We also control for subsidiary ICB industry, country, and year fixed effects.
The coefficient of interest is β, which captures the incremental sensitivity of investment
to growth opportunities (Inv-PE) when cultural proximity exists. We test hypothesis 2 byexamining whether subsidiaries with cultural proximity exhibit greater Inv-PE sensitivity (i.e.,H2: β>0).

19

IV. Empirical Results
4.1 Descriptive statistics
Panel A of Table 2 presents descriptive statistics for the firm-level samples used to test
Hypothesis 1. Columns 2 to 5 contain information for the full sample of 11,205 observations andColumns 6 to 8 are for the subsample of 1,868 observations. The average investment level acrossall firms deviates from the expected level by USD 10.54 (7.36) million for the full sample(subsample). Only 4% of the firm-year observations in the full sample are identified to havecultural proximity. Due to the sample selection, 13% of the observations in the subsample arewith cultural proximity. Panel B of Table 2 presents the correlations for the dependent,
explanatory and control variables. Both the full sample (lower diagonal) and the subsample(upper diagonal) correlations show that as expected, CulProx is positively correlated with InvEff.
Panel A of Table 5 presents descriptive statistics for the subsidiary-level sample of 6,205
observations used in the Hypothesis 2 testing. The average investment rate among subsidiaries is
4.08% of assets and the average PE ratio is 2.48. 19% of the parent-subsidiary-year observations
are with cultural proximity. The distribution of them is presented in Table 4 which shows thatsubsidiaries located in Mainland China, Korea, and Malaysia make up a large proportion of thesample. Thus, Chinese culture is over-represented in our subsidiary-level sample. Panel B of
Table 5 presents the correlations among our main variables for Hypothesis 2. CulProx is
positively correlated with InvEff, PE, Sub_Ext, Parent_CFO, and Credits.

20

4.2 Main results
Table 3 reports the results for our analyses of Hypothesis 1. We find evidence that
cultural proximity is positively associated with investment efficiency at the firm level. That is,
the estimated coefficient on CulProx, 1.628 (1.107), is positive and statistically significant using
the full sample (subsample), with a t-statistic equal to 2.080 (1.670). In terms of the economics
significance, cultural proximity makes firms’ investment 15.4% (15.0%) more efficient in thefall sample (subsample). These findings provide consistent support for Hypothesis 1. BodIn is
negatively associated with the firm-level investment efficiency (β=-0.753， t-statistic=-2.080),
supporting our assertion that board diversity per se does not enhance firm outcomes.
The estimation results for the control variables are as follows. Using the full sample,
consistent with prior literature, we find that LogAsset (β=0.789, t-statistic=6.810), Analysts(β=0.214, t-statistic=8.250), AQ (β=8.245, t-statistic=2.610), K-structure (β=2.031, tstatistic=2.250), CFOsale (β=0.003, t-statistic=3.270), Dividend (β=1.294, t-statistic=4.290), Ind.
K-struc. (β=3.040, t-statistic=2.030) and Age (β=0.019, t-statistic=2.250) are positively
correlated with InvEff. These results suggest that firms that are larger and older, have more
analysts following, better financial reporting quality, higher leverage, more financial slack,higher average industry leverage, and pay dividend, have higher firm-level investment efficiency.
Tangibility (β=-5.082, t-statistic=-6.090), Loss (β=1.359, t-statistic=-4.600), Cash (β=-13.731, tstatistic=-20.030), and std_Inv (β=-0.026, t-statistic=-5.940) are negatively associated with
InvEff. These results confirm the findings in prior literature and indicate that firms with higher
bankruptcy cost, past losses, more cash, and more volatility in investment have lower investment
efficiency at the firm-level. It is puzzling to find that MB (β=-0.013, t-statistic=-3.010) is
negatively and significantly associated with InvEff, suggesting that higher investment21

opportunities are correlated with lower investment efficiency. Biddle and Hilary (2006) also finda negative association which requires further investigation. Biddle et al. (2009) find mixed
results for std_CFO in some of their analyses. We find a negative and statistically significant
coefficient on this variable, -24.618 (t-statistic=-13.500). It suggests that when cash flows fromoperations are difficult to predict, a firm’s investment inefficiency decreases. Some control
variables lose their significance in the subsample test, but the signs of estimated coefficients are
generally consistent with those in the full sample.
Table 6 presents the results for our analysis of Hypothesis 2. The coefficient for theinteraction between PE and CulProx, 0.011, is positive and statistically significant with a tstatistic equal to 2.360. In economic terms, a one standard deviation increase in growth
opportunities translate to approximately 0.4% increase in investment for firms with cultural
proximity. Given that the average investment in our sample equals 4.08%, this 0.4% represents a
relative increase of approximately 9.8% in the subsidiary-level investment efficiency. As
expected, the estimated coefficient for PE is positive and statistically significant (β=0.940, tstatistic=2.730). It suggests that firms’ investment decisions are associated with industry-level
PE ratios. However, one major concern of this analysis is that the adjusted R-squared is only
4.00%, which is significantly lower than that in Shroff et al. (2014). Untabulated results indicatethat the variation in most of the control variables within each country is not large, possibly due tothe uneven distribution of subsidiaries shown in Table 4. The low within-country standard
deviation in these variables may cause the low R-squared and insignificant coefficients on
interaction terms in our analysis, because our research design exploits only within-country
variation in CulProx and a series of mechanisms to explain Inv-PE sensitivities.

22

V. Conclusion
This study examines whether the board of directors’ cultural proximity is positively
associated with the investment efficiency of MNCs. Prior research considers board members’
ethnicity as an observable demographic characteristic and hypothesizes that ethnic diversity
directly and explicitly affects firm performance, but they find mixed results. In this study, we
assert that board members’ ethnicity can be a proxy for their cultural backgrounds. A sharedcultural background between one or more directors and the regions in which the firm’s foreignsubsidiaries operate aligns the board’s comparative advantage with MNC’s need for mitigating
severe cross-border information frictions. Such cultural proximity, as an indirect control
mechanism, can help the board better monitor and advise foreign subsidiaries, leading to an
improvement in the decision-making efficiency.
Consistent with our prediction, we find evidence that the board’s cultural proximity has
an positively impact on U.S. MNCs’ investment efficiency at both the firm- and subsidiary-level,
after controlling for the potential endogeneity concern that the existence of board members withcertain cultural backgrounds results in the establishment of subsidiaries in areas sharing acultural tie.
This paper contributes to the literature on corporate governance by focusing on theindirect cultural control system within MNCs and suggesting a new mechanism at the parentlevel, cultural proximity. Because MNCs face more severe cross-border frictions than domestic
firms, indirect cultural control mechanisms play an important role in the entire internal control
system of MNCs. The results of our study also provide a potential explanation for theinconclusive research investigating the influence of board members’ demographic characteristics
on firm outcomes. We document that diversity per se does not enhance a firm’s performance.

23

The purpose of composing a diverse board is to increase the likelihood of benefiting from thealignment between what the firms’ need and what the board can provide. This paper is one of the
few studies that exploit a novel dataset of the ownership structure of MNCs. It provides
preliminary evidence on the role of cultural proximity in facilitating MNCs’ investment
decisions not only at the firm-level but also at the subsidiary-level.
This study is subject to a number of limitations. First, our use of board members’
surnames to identify their ethnicity may create noise in identifying cultural proximity. Althoughethnicity is a reasonable proxy for culture, other omitted variables, such as directors’ educational
backgrounds and work experience, may also affect a board member’s understanding of a certain
culture. Second, the ORBIS dataset exhibits significant cross-country variation in subsidiary
coverage, primarily due to cross-country differences in the reporting requirements for private
firms. This caveat may affect the generalizability of our findings. Third, our subsidiary-level
analysis is preliminary. The control variables in the model need further refinement. Forth, weonly focus on the investment decision of MNCs. Other managerial behaviors, such as financing
decisions and incentive system designs, are also worth exploring.

24

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27

Appendix
Variable definitions
Dependent variables
Investment (Eq. 1) = the sum of research and development expenditure, capital expenditure,
and acquisition expenditure less cash receipts from sale of property, plant,
and equipment, multiplied by 100 and scaled by lagged total assets
InvEff (Eq. 2)= the absolute value of residual from Eq. (1) multiplied by -1
Inv (Eq. 3)= the percentage change in total assets of a subsidiary in a year
Independent variables
Eq. 1
RevGrowth= the annual percentage change in revenue
NEG= an indicator variable that takes the value of 1 for negative revenue
growth, and 0 otherwise
Eq. 2
SubIn
= an indicator variable that takes the value of one if the firm has at least
one subsidiary in the areas of interest, and zero otherwise
BodIn
a= n indicator variable that takes the value of one if the firm has at least
one board member with the cultural background of interest, and zerootherwise
CulProx= an indicator variable that takes the value of one if the board of directors
has cultural proximity as described above, and zero otherwise
LogAsset= the log of total assets
MB= the ratio of the market value of total assets to book value of total assets
Tangibility= the ratio of PPE to total assets
Slack= the ratio of cash to PPE
Institutions= the percentage of firm shares held by institutional investors
Analysts= the number of analysts following the firm as provided by I/B/E/S
AQ= the standard deviation of the firm-level residuals from the Dechow andDichev model from years t-3 to t-1 and multiplied by negative one. The
model is a regression of working capital accruals on lagged, current, andfuture cash flows plus the change in revenue and PPE.
K-structure= the ratio of long-term debt to the sum of long-term debt to the market
value of equity
CFOsale= the ratio of CFO to sales
Dividend= an indicator variable that takes the value of one if the firm paid a
dividend, and zero otherwise
OperCycle= the log of receivables to sales plus inventory to COGS multiplied by 360
Loss= an indicator variable that takes the value of one if net income before
extraordinary items is negative, and zero otherwise.
Cash= the ratio of cash to total assets
Ind. K-struc.
= the mean K-structure for firms in the same SIC3-digit industry
Age= the difference between the first year when the firm appears in CRSP and
the current year
std_Inv= The standard deviation of investment from years t-3 to t-1
28std_CFO
std_Sales
Eq. 3
PE
Sub_Ext
Sub_roa
Sub_lnAsset
Parent_CFO
Credits= the standard deviation of the cash flow from operations deflated byaverage total assets from years t-3 to t-1= the standard deviation of the sales deflated by average total assets fromyears t-3 to t-1= the price-to-earnings (PE) ratio of the country-industry-year in which thesubsidiary operates as provided by Datastream= the median number of analysts following firms in the country-industryyear= the return on assets, that is, net income by total assets, at the subsidiarylevel= the natural log of assets at the subsidiary level= the cash flows from operations scaled by total assets for each parent= Domestic Banking Credit including all credit provided domestically by
the banking sector as a percentage of the Gross Domestic Product (GDP) as
provided by Worldbank

29

TABLE 1
Sample Selection
Panel A: ORBIS and BoardEx merge
ORBIS

BoardEx

Merging BoardEx with the
CRSP/COMPUSTAT Merged
database (identifier: ISIN)
Merging ORBIS and BoardEx(identifier: Ticker)# of parent-subsidiary-year observations(Subsidiaries: Mainland China, Hong Kong, India,
Japan, Korea, or Singapore; Parents: U.S.; Year:
2004-2013)# of firm(parent)-year observations(Companies: MNCs and domestic firms with board
members’ ethnicity identifications; Year: 1999-2012)# of firm(parent)-year observations

11,380# of parent-subsidiary-year observations

52,14954,872

43,698Sample B: Sample selection for Hypothesis 1

Aggregating the four cultural proximity indicators at the subsidiary-level to one firmlevel indicator
Merging ORBIS/BoardEx with COMPUSTAT(identifier: GVKEY)
Excluding parents that are financial holding companies or firms with no foreign pre-tax
incomeExcluding observations missing data items from COMPUSTAT, CRSP and/or I/B/E/S
necessary for computing the accounting and financial market variables
30# of firm (parent)-year observations(Year: 2005-2012)
43,601
31,481
15,352
11,205

H1 full sample
Excluding firms that have no subsidiaries in areas of interest
Excluding firms that have cultural proximity by establishing subsidiaries after having
board members with the cultural background of interest
H1 subsample

11,205
4,365
1,868
1,868

Panel C: Sample selection for Hypothesis 2

Matching the 11,380 ORBIS subsidiary-level observations with H1 full sample (before
excluding observations missing parent-level accounting and financial market measures)(identifier: Ticker)
Excluding subsidiaries missing at least three successive years of data on total assets
Excluding subsidiaries located in country-industry-years with no PEs
Excluding subsidiaries and parents missing firm-specific accounting and financial
market measure
H2 sample# of parent-subsidiary-year observations(Year: 2005-2012)
9,542

7,180
6,738
6,205
6, 205This table describes the details related to the impact of each of the sample inclusion criteria on the final determination of both the Hypothesis 1 and Hypothesis 2
samples. Panel A presents the ORBIS/BoardEx merge procedure. Panel B presents the Hypothesis 1 sample selection procedure. Panel C presents the Hypothesis
2 sample selection procedure.

31

TABLE 2
Descriptive statistics and correlations at the firm-level
Panel A: Descriptive statistics

Variable

Mean

Full Sample
Std Dev

InvEff
CulProx
LogAsset
MB
Tangibility
SlackInstitutions
Analysts
AQ
K-structure
CFOsaleDividend
OperCycle
Loss
Cash
Ind. K-struc.
Age
std_Inv
std_CFO
std_Sales-10.54
0.04
5.93
3.09
0.19
8.10
-0.58
-6.34
-0.06
0.12
-5.04
0.33
180.76
0.32
0.25
0.13
20.61
8.97
0.06
0.16

13.97
0.19
2.04
28.12
0.17
67.24
0.33
7.03
0.04
0.17
146.310.47
1,567.06
0.47
0.23
0.10
16.54
27.78
0.08
0.16

N

Mean

Subsample
Std Dev

11,205
11,205
11,205
11,205
11,205
11,205
11,205
11,205
11,205
11,205
11,205
11,205
11,205
11,205
11,205
11,205
11,205
11,205
11,205
11,205

-7.36
0.13
7.71
3.13
0.20
1.90
-0.75
-10.66
-0.04
0.15
0.11
0.55
145.09
0.18
0.18
0.14
30.24
6.79
0.04
0.13

9.75
0.33
1.86
13.69
0.13
3.59
0.22
7.88
0.03
0.16
0.12
0.50
71.63
0.38
0.16
0.10
22.49
12.67
0.03
0.11

32

N
1,868
1,868
1,868
1,868
1,868
1,868
1,868
1,868
1,868
1,868
1,868
1,868
1,868
1,868
1,868
1,868
1,868
1,868
1,868
1,868

Panel B: Correlations
1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

0.04

0.08

-0.03

0.07

-0.11

0.02

0.07

0.10

0.20

-0.08

0.15

-0.03

0.03

-0.18

0.21

0.12

-0.03

-0.11

-0.04

0.09

-0.01

-0.02

-0.01

-0.11

-0.03

0.03

0.08

0.00

-0.01

-0.05

0.00

0.05

0.05

0.02

-0.03

-0.01

-0.02

0.01

0.15

-0.24

-0.30

-0.69

0.32

0.22

0.25

0.42

-0.13

-0.20

-0.33

0.21

0.51

0.01

-0.33

-0.16

-0.03

0.01

-0.01

-0.04

0.00

0.00

0.05

0.02

0.02

-0.04

0.02

-0.06

0.02

0.00

0.02

0.00

-0.42

0.04

0.03

0.14

0.27

-0.01

0.14

-0.21

0.03

-0.33

0.36

0.16

-0.16

-0.04

-0.04

-0.01

-0.04

-0.16

-0.27

0.08

-0.26

-0.01

0.04

0.70

-0.25

-0.21

-0.02

0.17

0.00

0.20

-0.11

-0.15

-0.12

0.00

0.04

0.01

0.05

-0.08

0.06

-0.07

0.15

0.05

-0.17

0.08

-0.36

-0.18

0.08

0.21

-0.04

0.07

-0.22

-0.01

0.12

0.11

0.04

0.14

0.26

-0.11

-0.13

-0.19

0.13

0.20

-0.12

-0.28

-0.22

-0.15

0.03

-0.16

0.24

-0.38

0.47

0.08

0.12

-0.17

-0.04

0.11

-0.03

-0.37

0.10

-0.14

0.05

0.05

-0.15

-0.19

-0.09

-0.27

-0.32

0.23

0.45

-0.08

-0.26

-0.14

0.05

0.08

-0.19

-0.07

0.13

0.01

-0.08

0.09

0.02

-0.15

0.12

0.13

0.07

-0.36

-0.26

-0.09

0.29

0.03

0.21

-0.08

-0.11

-0.02

-0.12

-0.16

-0.10

0.00

0.09

1

InvEff

2

CulProx

0.06

3

LogAsset

0.22

0.19

4

MB

-0.04

0.00

0.00

5

Tangibility

0.12

0.01

0.22

-0.01

6

Slack

-0.09

-0.02

-0.09

0.01

-0.11

7

Institutions

-0.10

-0.12

-0.59

-0.01

-0.06

0.06

8

Analysts

-0.04

-0.12

-0.70

-0.03

-0.08

0.04

0.49

9

AQ

0.19

0.09

0.39

-0.02

0.26

-0.09

-0.29

-0.22

10

K-structure

0.16

0.08

0.31

-0.02

0.31

-0.05

-0.13

-0.04

0.13

11

CFOsale

0.08

0.01

0.05

0.01

0.02

-0.07

-0.03

-0.01

0.04

0.02

12

Dividend

0.19

0.08

0.38

-0.01

0.21

-0.06

-0.11

-0.17

0.25

0.10

0.02

13

OperCycle

-0.05

-0.01

-0.04

-0.01

-0.02

0.00

0.02

0.01

-0.04

-0.02

-0.49

-0.02

14

Loss

-0.18

-0.05

-0.36

0.01

-0.11

0.07

0.28

0.25

-0.25

0.05

-0.05

-0.28

0.04

15

Cash

-0.35

-0.05

-0.33

0.04

-0.41

0.22

0.13

0.06

-0.25

-0.36

-0.08

-0.29

0.03

0.28

16

Ind. K-struc.

0.16

0.05

0.25

-0.02

0.38

-0.06

-0.09

-0.02

0.17

0.46

0.02

0.21

-0.02

-0.08

-0.33

17

Age

0.17

0.12

0.39

-0.01

0.14

-0.06

-0.11

-0.16

0.20

0.13

0.02

0.42

-0.01

-0.21

-0.26

0.18

18

std_Inv

-0.10

-0.02

-0.04

0.00

-0.06

0.05

0.04

0.02

-0.15

0.05

-0.10

-0.07

0.11

0.08

0.06

-0.03

-0.07

19

std_CFO

-0.29

-0.06

-0.38

0.02

-0.18

0.17

0.27

0.20

-0.38

-0.16

-0.09

-0.21

0.08

0.27

0.37

-0.12

-0.16

0.13

20

std_Sales

-0.01

-0.04

-0.14

0.01

-0.04

-0.01

0.10

0.10

-0.18

-0.04

0.02

-0.06

-0.02

-0.02

-0.08

0.02

-0.07

0.05

0.40
0.21

This table presents descriptive statistics and correlations at the firm-level. All variables are defined in the Appendix. Panel A provides descriptive statistics for the dependent,
explanatory and control variables. Panel B presents the correlation matrix for the dependent, explanatory and control variables. The full sample (subsample) correlations are provided
in the lower (upper) diagonal of the panel.

33

TABLE 3
Association between cultural proximity and investment efficiency at the firm-level

VariableIntercept
SubIn
BodIn
CulProx
LogAsset
MB
Tangibility
SlackInstitutions
Analysts
AQ
K-structure
CFOsaleDividend
OperCycle
Loss
Cash
Ind. K-struc.
Age
std_Inv
std_CFO
std_Sales
Year
Industry
SubCountryN
Adj R-Sq

Full Sample
Coefficient-10.790***-0.054-0.753**
1.628**
0.789***-0.013***-5.082***
0.000
0.688
0.214***
8.245***
2.031**
0.003***
1.294***
0.000-1.359***-13.731***3.040**
0.019**-0.026***-24.618***
0.959
YES
YES
YES
11,2050.185

Subsample
Coefficient-6.320***t-stat-12.880-0.140-2.0802.080
6.810-3.010-6.090-0.250
1.460
8.250
2.610
2.250
3.270
4.290
-0.150-4.600-20.030
2.030
2.250-5.940-13.500
1.190

1.107*-0.016-0.013-2.421
0.093
2.289**
0.072
17.072*
7.263***-2.543
1.792***
0.002
0.419
-4.138*9.183***
0.016-0.022-12.913*
0.342
YES
YES
YES
1,868
0.084

t-stat-3.1401.670-0.070-0.790-1.200
1.040
2.110
1.600
1.910
4.120
-1.230
3.300
0.670
0.620
-1.9203.180
1.290-1.210-1.6900.150

This table reports the regression estimation of the association between cultural proximity and investment efficiency
at the firm-level. All variables are defined in the Appendix. *, **, and *** denote statistical significance at the 10
percent, 5 percent, and 1 percent levels, respectively, based on a two-tailed t-test.

34

TABLE 4
Distribution of geographic locations at the subsidiary-level

Subsidiary location
Mainland China
Hong Kong
India
Japan
Korea
Malaysia
Singapore
Total

Total
# of obs
2,475
19
660
299
1,170
1,045
537
6,205

% of obs
39.89%
0.31%
10.64%
4.82%
18.86%
16.84%
8.65%
100%

With cultural proximity# of obs% of obs
398
33.14%
10
0.83%
107
8.91%
63
5.25%
240
19.98%
201
16.74%
182
15.15%
1,201
100%

This table presents the distribution of parent-subsidiary-year observations by country and region.

35

TABLE 5
Descriptive statistics and correlations at the subsidiary-level
Panel A: Descriptive statistics
VariableMean

Std Dev

P25

P50

P75

N

Inv

4.08

88.96

-0.03

0.10

0.28

6,205

CulProx

0.19

0.28

0

0

0

6,205

PE

37.30

107.15

13.00

18.00

28.00

6,205

Sub_lnAsset

10.02

2.17

9.12

10.20

11.10

6,205

Sub_roa

8.87

14.26

1.50

6.70

15.00

6,205

Parent_CFO

0.08

0.08

0.03

0.07

0.13

6,205

Credits

131.7550.80

115.00128.00145.006,205

Sub_Ext

2.50

0.62

2.00

2.00

3.00

6,205

Panel B: Correlations

1
2
3
4
5
6
7
8

Inv
CulProxPE
Sub_roa
Sub_lnAsset
Sub_Ext
Parent_CFO
Credits

1
1.00
-0.01
-0.01
0.00
0.03
-0.03
-0.01
-0.01

2

3

4

5

6

7

8

1.00
-0.03
0.01
0.02
0.02
-0.02
-0.05

1.00
-0.04
0.07
-0.14
0.00
0.00

1.00
-0.07
-0.11
0.06
-0.11

1.00
0.08
0.05
0.02

1.00
-0.05
0.24

1.00
-0.04

1.00

This table presents descriptive statistics and correlations at the subsidiary-level. All variables are defined in the
Appendix. Panel A provides descriptive statistics for the dependent, explanatory and control variables. Panel B
presents the correlation matrix for the dependent, explanatory and control variables.

36

TABLE 6
Association between cultural proximity and investment efficiency at the subsidiary-level
Variable

Coefficientt-stat

PE_CulProx
PE
CulProxSub_lnAsset
Sub_roa
Parent_CFO
Credits
Sub_Ext
PE*Parent_CFO
PE*Credits
PE*Sub_Ext
Year
SubIndustry
SubCountryN
Adj R-Sq

0.011**
0.940**-5.407*1.450-0.035-17.650
0.033-6.997-0.039
0.000
0.000
YES
YES
YES
6,205
0.040

2.360
2.730
-1.9101.500-0.510-1.6900.310-1.260-1.360-0.740-1.080This table reports the regression estimation of the association between cultural proximity and investment efficiency
at the subsidiary-level. All variables are defined in the Appendix. *, **, and *** denote statistical significance at the
10 percent, 5 percent, and 1 percent levels, respectively, based on a two-tailed t-test.

37