1

Is International Diversification of Emerging Market’s

Firms Beneficial? Evidence from BRIC Companies

Irina Ivashkovskaya, Pavel Yakovenko

Abstract

In recent years corporate international diversification has become a widely used growth

strategy for companies from both developed and emerging markets. Nevertheless, academic

papers provide controversial results of whether the influence of international diversification

on firm performance is positive or negative. This chapter presents the results of an empirical

analysis of corporate international diversification efficiency on a sample of companies from

BRIC countries that expanded geographically during 2005–2015. We contribute to the

existing literature by applying a new methodology of identifying the efficiency of corporate

international diversification based on the economic profit measure. The results indicate that

there is a non-linear form of relationship between degree of international diversification and

performance of BRIC companies. Additionally we found that the predictive power of entropy

measure of international diversification is similar to combination of foreign-sales-to-total-

sales measure and HHI measure. Moreover, international diversification produces long-term

positive performance effects (measured by Tobin’s Q) even when in the short-term

performance effects may be negative (measured by economic profit spread).

1 Introduction

During the last decades, international diversification became one of the main firms’ growth

strategies both on developed and emerging markets.

One of the most popular directions of research about corporate international diversification

(CID) is looking for the pattern of relationship between degree of internationalization (DOI)

and firms’ performance. But still in the contemporary economic and financial literature there

is no common opinion on how internationalization affects firms’ performance. It happens due

to the trade-off between the costs and benefits of international diversification. On the one

hand, companies benefit from competitive advantages that are not accessible in the home

market. On the other hand, CID brings various risks, transactional costs and agency problems.

So, one group of researches demonstrates increase of firms’ performance involved in process

of internationalization (Ramaswamy et al., (1996), Beamish et al., (1999), Cardinal (2011),

Hennart (2011), etc.). Other results stand for negative impact of CID on corporate

performance (Zaheer, Mosakowski (1997), Singla, George (2013) etc.). Most of recent studies

illustraate more complicated non-linear pattern of DOI-performance relationship (Elango and

Prakash Sethi, (2007), Xiao et al, (2013), Hitt et al., (1997), Lu, Beamish, (2004) etc.)

Current chapter is devoted to the topic of relationship between degree of internationalization

and firms’ performance on BRIC markets. We investigate the most prevailing in recent

literature research problems, related to this field, including: form of DOI-performance

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relationship on corporate performance; choice of relevant measures and methodology; impact

of product diversification on effectiveness of internationalization process.

The research objective of this chapter is: to determine the form of relationship between the

degree of internationalization and corporate performance for companies from BRIC countries

in long run and short run. These results can be used in prediction of internationalization

performance.

2 Theoretical Background and Hypotheses

2.1 Research Approach

The internationalization-performance relationship is typically studied in two paradigms1:

event studies and accounting studies. While the first is based on the analysis of corporate

performance change within a time window around a cross-border M&A deal, the second

approach is based on identification of relationship between corporate performance (typically

accounting-based measures) and a degree of internationalization of business (DOI). One may

find a thorough review of research literature of both event-based and accounting-based

internationalization studies in the papers of Bruener R. (2004) or Hitt et al. (2006).

The current research is based on the approach of regression analysis of influence of degree of

internationalization on corporate performance measures. The existing researches differ a lot

by the use of different performance indicators and measures of degree of internationalization.

2.1.Choice of DOI Measures

In existing research literature there is no unified approach to choice of quantitative measures

of DOI and performance measures.

Depending on the choice of measure of DOI it is possible to control different

internationalization patterns. Usually international diversification is classified into two classes

– diversification of assets and diversification of markets. The most commonly used measures

of these types are foreign-assets-to-total-assets (FATA) and foreign-sales-to-total-sales

(FSTS) ratios correspondently. In opposite to event-studies approach the use of FATA and

FSTS measure allows to analyze not only non-organic foreign growth (cross-border M&As)

but also foreign greenfield investments.

One more frequently-used measure is Herfindahl-Hirshman Index (HHI), calculated as:

𝐻𝐻𝐼 = 1 − ∑ 𝑝𝑖2

𝑛

𝑖=1

(1)

where 𝑝𝑖 - share of sales of country i (or share of assets, if measure is asset-based) in overall

sales volume (overall assets value) of the company. HHI incorporates not only foreign share

of sales/assets, but also the distribution of these measures among countries. One may find the

example of HHI usage in research by Elif (2015).

1 There exists the third paradigm of case studies analysis, but it remains a rather niche study-field.

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The mentioned variables are well studied and frequently used, however, they have significant

weakness: they do not account for the number of regions or countries in which the firm

operates. Other things equal, from our model we will expect the same performance from firms

with equal FSTS or FATA even if they operate in different number of countries. But since

economic conditions in different countries are different, performance of these firms is likely

to be different. We can just add the number of countries of operations to our model as a

control variable, but it is likely to be correlated with FSTS and FATA (the more the number

of countries in which the firm operates, the more would be FSTS and FATA). One of the

possible solutions for this problem is to use entropy index as a proxy for DOI. (Hitt et al.

1997) Entropy index can be calculated as follows:

𝐸𝑛𝑡𝑟𝑜𝑝𝑦 = ∑(𝑃𝑖 ∗ ln (1

𝑃𝑖

))

𝑁

𝑖=1

(2)

where 𝑃𝑖 is a revenue from country i if the firm operates is N countries. This index considers

both diversity (in how many countries does the firm operate) and intensity (what is the weight

of revenue from a single country in overall revenue) of firm’s revenue.

Following Grigorieva (2007) the Entropy index illustrates following aspects of

internationalization:

• Number of countries/regions, where the company operates;

• Distribution of sales/assets among geographic segments;

• Degree of relatedness between different regions, where the company operates.

Moreover, Hitt et al. (1997) argued that entropy is the most efficient index for international

diversification. The implication of this measure can be also found in research by Bany-Arifin

(2016).

2.1.2.Choice of Performance Indicators

A usage of various corporate performance indicators also allows to study different types of

effects of internationalization in different time horizons. A classification of typically used

performance measures is described in Table 1.

Table 1. Accounting studies by the types of corporate performance measures

Type of

measure

Type of corporate

performance

Examples of

measures

Papers

Current

performance

measure

(expected

performance

change is not

considered)

Operational

efficiency

Revenue, operating

cash flow, EBIT-

based measures

(EBIT margin, ROS,

ROE, ROA, etc.),

others

Qian and Li (2002), Guler et

al. (2003), Moeller and

Schlingemann (2004), Lu

and Beamish (2004),

Contractor et al. (2007),

Bobillo et al. (2010),

Rugman and Chang (2010)

Tian (2017), Wu (2012)

Financial

efficiency

WACC and other

cost-of-capital

related measures

Singh and Nejadmalayeri

(2004), Joliet and Hubner

(2006)

Measures

incorporating

Operational and

financial efficiency

Tobin’s Q, PE,

market-to-book

Bodnar et al. (2003), Chang

and Wang (2007), Rugman

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expectations ratio, others and Chang (2010); Bany

Ariffin (2016), Elif (2015)

As for performance measurement issue, it is shown in Table 10.1, that accounting studies

typically use the following two types of corporate performance measures:

1. The first group of measures represents the current corporate performance during a

particular period of time (usually 1 year) but does not incorporate expectations of

potential efficiency changes in the future (usually benefits from internationalization are

fully realized in the period of several years). The group of these measures consists of

operational and financial performance measures which are studied separately.

2. The second group of measures incorporates expectations of the future corporate

performance by combining accounting measures with market-based metrics represented

by different multiples.

The weakness of the first group of measures is that they do not simultaneously count for

operational and financial efficiency effects of internationalization. In fact, the change in

operational efficiency measures should be compared to the change in opportunity costs

measured by the change in the cost of capital. Therefore, we follow the approach of a

simultaneous analysis of operational and financial efficiency changes related to corporate

international diversification. The research model is based on the economic profit concept.

Since economic profit comprises the cost of capital, which represents the risks associated with

a firm and its internationalization decisions, it is an appropriate measure of strategic

performance of a firm (Sherbakov, 2013, Ivashkovskaya, 2008). The economic profit or

residual income is measured as follows:

(3)

where RI is the measure of economic profit of company i in period t, ROCE – return on

capital employed, WACC - weighted average cost of capital, CE – capital employed.

2.1.3.Financial Efficiency Impact

In context of internationalization, scholars identify three factors of financial efficiency:

change in capital structure, change in cost of equity and change in cost of debt. Singh and

Nejadmalayeri (2004) have identified an increase of financial leverage related to corporate

internationalization. This fact is motivated by a corresponding increase of debt supply on

capital market, which is driven by diminishing bankruptcy risks of internationalizing firms

due to overall risk diversification. But conversely there exist other studies that state for a

downturn in debt supply related to corporate internationalization due to the following factors

(see e.g. Doukas and Pantzalis, (2003)):

a) typically internationalization is associated with higher growth rates and a growing

complexity of organizational design of a business both of which increase agency costs

of debt holders;

b) amount of intangible assets are likely to increase with international diversification of

business which implied additional risks to debt holders as these assets cannot be

monetized in case of bankruptcy.

Corporate international diversification influences cost of equity through the following three

factors:

a) change in level of risks: there may exist a non-linear relationship between DOI and

level of risks to shareholders due to an addition of new internationalization-specific

risks on initial stage of international diversification, meanwhile on a latter stages of

itititit CEWACCROCERI )(

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corporate international diversification one could expect a decrease of shareholders’

risks due to their diversification;

b) rise of shareholders’ agency costs: it is supposed that as far as DOI grows the costs of

monitoring and controlling company’s management also increase;

c) change in capital structure: different levers are described in above in paragraph.

Singh and Nejadmalayeri (2004) state for a higher risk price for shareholder determined by

beta coefficient for MNCs.

As for cost of debt, the most significant debt-specific factors are as follows:

a) change in debt maturity: as it was identified by Singh and Nejadmalayeri (2004) that

MNCs typically raise a longer-term debt than domestic firms do. It is thus resulted in

higher cost of debt;

b) change in efficient tax rate driven by a move of company’s profit center to countries

with different corporate taxation: this factor directly influences the after-tax cost of

debt.

As an economic profit measure for estimation of internationalization-performance relationship

the ratio of residual income to capital employed may be used. Thus, both ROCE and WACC

as functions of the degree of internationalization (DOI) and other control variables should be

estimated.

2.1.4.Prior Results

Using the measures of internationalization and efficiency listed above, the researchers

obtained different and often contradictory results. Some of them, which were derived by

analyzing companies from developing markets, are presented in Table 2. The analysis of

Indian companies by using both operational and financial measures of efficiency, has shown

no relationship between internationalization and performance. For Chinese and Mexican

companies, we observe non-linear curve shape when using operational performance measures.

Among the most interesting conclusions, we should mention the paper of Chen and Tan

(2012) who derived different internationalization-performance relationships for each of the

three DOI measures.

Table 2. The results of the developing countries analysis

Paper

Sample

Performance

variable DOI variable Relationship

Thomas

(2005) 500 Mexican firms ROS FSTS U-shaped curve

Chen, Tan

(2012) 887 Chinese firms Tobin’s Q

FSTS Linear negative

RSTS (regional

sales to total sales) U-shaped curve

RSTS (Intragreater

China) S-shaped curve

Singla,

George

(2013)

237 Indian firms ROA,

Tobin's Q

FSTS No relationship

Composite index

(FSTS, FATA, Negative linear

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OSTS, scope)

Xiao et al.

(2013)

114398 Chinese

firms ROA FSTS S-shaped curve

Chen et al

(2014) 685 Chinese firms ROA FSTS

Inverse U-

shaped curve

Borda

(2016)

103 Latin firms

(Brazil, Chili,

Mexico)

ROA FSTS Inverse U-

shaped curve

Wu (2012) 318 China firms ROA Entropy index S-shaped curve

Since the results of previous papers are very different, a number of researchers done a meta-

analysis of empirical data from different studies in order to test whether the hypothesis that

diversification influences the corporate performance holds for the sample of overall data. The

results of some meta-analytical papers are presented in Table 3.

All authors used number of performance variables, both historic and future performance. The

main difference is explanatory variable, is it usually FSTS with some additions (in Kirca et al.

these are firm-specific assets, while in Carney et al. specific variable is an amount of export in

firm overall sales).

Table 3. Meta-analytical researches on diversification performance

Paper Sample Explanatory

variable Relationship

Carney ae al. (2011) 141 studies FSTS, product

diversification

Moderating effect

(affected by other

factors)

Kirca et al. (2011) 111 studies FSTS Positive

Bausch, Pills (2007) 104 studies FSTS, number of

foreign countries Positive

2.1.5 Side Factors

Scholars suggest that there is also a wide array of side effects, also called moderators, which

affect the DOI-performance relationship. Basing on existing studies these factors include

firm-, industry- and country-specific factors. First, firm-specific ones are marketing and

technological resources (Chen et al. 2014), R&D level, (Kotabe et al., 2002), absorptive

capacity (Wang et al., 2012a), financial capabilities and managers’ competencies (Zeng et al.,

2009). Second, industry-specific factors are degree of competition, industry policies and the

technology levels within the industry etc. (Wang et al. 2012). And finally, both home and host

country-specific factors can affect the effects of internationalization on performance. (Wan

and Hoskisson, 2003).

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Firms balance their growth between two directions: regional and product diversification.

Thus, one more factor, which has an impact on internationalization performance is the level of

product diversification. (Bausch and Pills, 2009, Oh et al., 2015, Ref, 2015, Hashai and

Delios, 2012, Chen et al., 2014) Scholars divide the directions of product diversification into

related diversification (expanding to industries, which are similar with firm’s core recourses)

and unrelated diversification (expanding to industries dissimilar to the firm’s core recourses).

Expanding into related foreign markets, firms transfer home business capabilities and

intellectual capital in combination with local technologies and resources, increasing their

competitive advantage in the local markets (Weston, 1970). On the other hand, firms

following unrelated diversification strategy in foreign markets are unable to effectively apply

the advantages, crated on the home market. Thus, these firms will incur double costs related

both with internationalization and developing new products, which in result can exceed the

mentioned benefits of diversification. (Chen et al. 2014) All growth strategies increase the

complexity of business design, increasing the transaction costs (Sherbakov, 2013).

Pro

duct d

ivers

ificatio

n

Unrelated

PD

Related

PD

Initial

Initial Local ID Global ID

Geographic diversification

Lower transaction costs Higher transaction costs

Figure 1. Diversification matrix (Sherbakov, 2013)

Basing on existing research results the most frequently mentioned side factors and their

moderating effect on DOI-performance relationship are illuatrated in Table 4.

Table 4. Frequently studied side-effects (moderators) on DOI-performance relationship

Factor Moderating effect Research examples

Degree of product

diversification

+ Riahi-Belkaoui (1996), Hitt

et al. (1997)

- Vermeulen & Barkema

(2002), Chen et al. (2014)

Share of intangible assets + Lu, Beamish (2004)

R&D intensity +

Zahra, Ireland, & Hitt (2000),

Kotabe, Srinivasan, &

Aulakh (2002)

Company size + Dragun (2002)

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Overall risk level + Hejazi & Santor (2010)

2.2 Hypotheses

Based on existing studies as well as our analysis of internationalization processes in BRIC

countries we have formulated several research hypotheses for a sample of Chinese, Indian,

Brazilian and Russian companies.

2.2.1 Hypotheses for Economic Profit Spread-DOI Relationship

As it was stated before, RI, except amount of capital employed, depends on two elements:

ROCE, standing for operational efficiency, and WACC, illustrating the impact of change in

cost of capital, representing the risks associated with internationalization. Both components

affect the overall internationalization performance, but prior results show, that operational

effectiveness has stronger impact in total effect of internationalization performance

(Sherbakov, 2013), thus we assume the relation between RI and DOI has the same pattern as

ROCE and DOI.

2.2.1.1 ROCE-DOI Relationship

The majority of internationalization-performance researches state for a non-linear pattern of

relationship between DOI and operational efficiency measures for the firms from developed

economies. Lu and Beamish (2004) identified the most general pattern of this relationship

demonstrated by horizontal S-shape curve which was also supported by Bobillo et al. (2010),

Rugman and Chang (2010), Oh et al (2015). The S-shape curve consists of 3 sequential

intervals:

1) at a low level of international diversification the operating performance is decreasing with

an increase in DOI since internationalization-related costs (learning costs, cost of

coordination and control of foreign divisions, other transaction costs) are too high in

comparison with a low marginal increase in efficiency and growth of foreign sales;

2) at a medium level of internationalization the performance the firm is capable to gain

significant benefits derived from economy of scale and scope, diversification of country

risks, access to foreign knowledge and cheaper resources, increase of market power, etc.

which are higher than transaction costs. Therefore, we can observe an increase in

performance;

3) at a high level of DOI the performance may start declining again due to the unmanageable

international complexity of organizations (over-internationalization stage) and high

transaction costs based on the complexity.

For the emerging markets (India) a U-shaped relationship has been identified by Contractor et

al. (2007) and for Chinese companies be Chen and Tan (2012). It is presumed that the

companies from the emerging markets typically do not reach such degree of complexity

related to an over-internationalization stage when further internationalization becomes value

destroying So we expect U-shaped pattern of relationship between ROCE and DOI for

companies from BRIC countries.

Hypothesis 1: The relationship between Degree of international diversification and firm

performance has a U-shaped curve form for BRIC countries.

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2.2.2 Choice of Relevant DOI Measure

As it was mentioned before, there are studies, which emphasize the advantages of using

entropy index as DOI measure. (Hitt et al. 1997) It incorporates not only intensity of

internationalization (share of all foreign sales in total sales), measured with FSTS, but also a

diversity factor (number of countries and sales distribution among them), which is commonly

measured with HHI, and degree of relatedness between the regions. That fact positively

distinguishes the Entropy index from other ones in terms of economic sense. That is why we

expect, that it is more relevant and precise in measuring and forecasting the relationship

between DOI and firms’ performance, in comparison to FSTS and HHI measures.

Hypothesis 2: Entropy index has a higher forecasting power of firm performance comparing

to the combination of FSTS and HHI in researches for BRIC countries.

2.2.3 Impact of Product Diversification

A group of researches demonstrated, that the performance of internationalization is also

affected by level and form of product diversification of the company. (Chang, Wang, (2007),

Hitt et al.,(1997), Chen et al., (2014) etc.)

Hitt et al., (1997) have shown that the internationalization-performance relationship

significantly depends on the product diversification of a company. Typically, the

internationalization effect is more positive when the company is characterized by a higher

level of related product diversification (Chang, Wang, 2007). It is described by the

organizational design of product-diversified companies which is usually better adapted to

international diversification. Chen et al. (2014), who conducted their research on Chinese

manufacturing firms, found that related product diversification enhances the performance

effects, while unrelated product diversification does the contrary. Following the

abovementioned ideas, the hypothesis is:

Hypothesis 3: Related product diversification has positive effect on the relationship between

internationalization and performance.

Hypothesis 4: Unrelated product diversification has negative effect on the relationship

between internationalization and performance.

2.2.4 Impact of Internationalization in long run and short run

Some researches state, that both the benefits and costs of multinationality can have different

impacts in the short versus long term (Thomas, 2004). For example, investments in R&D have

negative impact in the short run, as the costs are incurred in favor of future benefits. The

benefits from investments in the intangible assets are also reflected in the long run

performance. What is more, going internationally, enterprises should adopt new mechanisms

and consequently they increase the complexity of business design, what raises their overall

costs over time (Hitt et al. 1997). Otherwise, firms also learn to manage the new processes,

and adopt the changes (Barkema & Vermeulen, 1998). Because the benefits are more likely to

be longer term in nature, relative to the costs (Thomas, 2004), we hypothesize that:

Hypothesis 5: Impact of international diversification on long run performance is stronger on

long run performance.

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3 Methods

3.1 The Sample

The proposed research framework is applied on a sample of 109 companies from BRIC

countries. Overall, there are 40 Russian, 29 Chinese, 25 Brazilian and 15 Indian companies in

the sample.

All chosen companies satisfy the following criteria:

1. Company is public and discloses all the key information.

2. Company closed at least one acquisition of a foreign company worth more than $10

mln.

3. Company discloses distribution of its foreign sales.

While the first criterion is rather natural and controls for the availability of data, the second

one ensures that companies in the sample have foreign businesses that are large enough to be

disclosed in the financial statement. However, it does not necessarily mean that all companies

in the sample have a subsidiary in other countries since we do not specify the share of the

company bought in the deal, so both strategic and financial deals may be included in the

sample. The third criterion is required to calculate entropy index and HHI index based on the

foreign and domestic revenue. If company discloses only export sales there is not enough

information to analyze the sources of foreign revenue.

The data set is derived from Bloomberg database. The data has been collected for a time span

from year 2005 to year 2015. All figures are given in millions of US dollars. Overall, we have

an unbalanced panel of 440 observations for Russian, 330 for Chinese, 187 for Indian and 308

for Brazilian companies. Descriptive statistics of all variables by country after the exclusion

of outliers are depicted in Table 10.5. The sample includes companies from four industries

following the NAICS standard.

For the companies included in the sample Indian firms have the highest average value of both

performance variables. Russian and Chinese companies have almost the same mean value of

economic profit spread but Tobin’s Q is significantly higher in case of China (1.92 versus

1.46 for Russia). On the other hand, Chinese companies are less internationally diversified

than Russian (measured by all DOI variables used in this research) while Indian companies

have the highest degree of international diversification.

The majority of firms in the sample are manufacturing companies but this share differs across

countries: while in Brazil 75% of the sample are manufacturing firms in China their share is

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Table 5. Variable’s description and statistics for the sample

Russia China India Brazil

Variable Description Obs Mean S. D. Obs Mean S. D. Obs Mean S. D. Obs Mean S. D.

Dependent variables

RI_CE Economic profit spread (%) 286 9.47 13.49 233 9.46 11.49 113 16.26 13.02 147 2.00 11.21

Q Tobin’s Q 299 1.46 1.05 265 1.92 1.72 151 2.21 1.44 262 1.33 0.60

Internationalization measures (DOI)

Entropy Entropy index 422 0.54 0.64 302 0.33 0.49 169 0.86 0.55 280 0.57 0.58

Intensity FSTS 422 0.29 0.35 302 0.23 0.35 169 0.59 0.38 280 0.35 0.37

Diversity HHI 422 0.27 0.30 302 0.18 0.26 169 0.45 0.27 280 0.30 0.29

Control variables

ln_sales Company size (log of sales) 383 8.00 1.84 285 7.33 285 151 7.20 1.98 273 7.70 1.71

asset_turnover Asset turnover ratio 372 0.83 0.55 281 0.59 281 141 0.91 0.36 269 0.80 0.69

int_assets Intangibles to total assets, % 422 0.07 0.12 297 0.03 297 169 0.13 0.13 280 0.12 0.15

3roe 3-year average return on equity, % 314 14.55 18.52 261 15.39 261 129 17.90 17.54 240 11.09 17.22

3sales 3-year average revenue growth, % 331 22.81 29.67 262 29.52 262 129 27.07 28.84 252 19.45 24.73

ebit_sales EBIT/sales, % 383 14.80 31.52 285 15.45 285 150 11.80 10.15 273 9.24 22.48

Related Related product diversification

measure 422 0.72 0.43 302 0.67 0.46 169 0.82 0.37 280 0.68 0.45

Unrelated Unrelated product diversification

measure 422 0.71 0.43 302 0.67 0.46 169 0.82 0.37 280 0.69 0.46

Country-specific variables

Log_GDP Natural logarithm of county’s GDP 422 28.69 0.24 302 30.12 0.34 169 29.29 0.28 280 28.64 0.15

Curr % change of national currency

exchange rate 422 0.08 0.19 302 -0.03 0.03 169 0.03 0.07 280 0.03 0.16

DB World Bank’s Doing Business

“distance to frontier” rating 422 71.13 1.98 302 62.06 2.55 169 39.83 3.98 280 44.69 2.73

Industry dummies (NAICS)

NAICS1 Mining industry dummy 422 0.21 0.41 302 0.07 0.25 169 0.00 0.00 280 0.04 0.19

NAICS2 Manufacturing industry dummy 422 0.45 0.50 302 0.32 0.47 169 0.62 0.49 280 0.75 0.43

NAICS3 Transportation and public utilities

dummy 422 0.13 0.33 302 0.21 0.41 169 0.13 0.34 280 0.09 0.29

NAICS4 Services sector dummy 422 0.11 0.31 302 0.10 0.30 169 0.20 0.40 280 0.00 0.00

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almost one-third (32%). Some industries are not presented in several countries; there is no

services companies for Brazilian part of the sample and also there are no Indian mining

companies.

3.2 The Model

We use two different performance variables to test the efficiency of international diversification.

The short run performance is represented by economic profit spread, which is calculated as

follows:

𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝑝𝑟𝑜𝑓𝑖𝑡 𝑠𝑝𝑟𝑒𝑎𝑑 = 𝑅𝑂𝐶𝐸 − 𝑊𝐴𝐶𝐶 (4)

Economic profit spread, as well as economic profit itself captures both operational and financial

consequences of international diversification on the company’s performance, but unlike

economic profit, it does not depend on the company’s size (both in terms of revenue profit

earned by the company and the value of assets owned by the company).

Long run performance is measured by Tobin’s Q, one of the most common metrics measuring

firm’s long term growth and investors’ expectations about it. It is calculated as a ratio of market

value of the firm to the book value of its assets. We choose this variable among different market

multiples due to the following reasons:

• Denominator of Tobin’s Q (book value of assets) is far less volatile than other operating

variables (like EBITDA, revenue, etc.) and thus it is less exposed to the short-term

industry and macroeconomic fluctuations.

• It reflects the expectations of investors that are focused on the stable growth of the

company.

Based on the hypothesis proposed in the Section 10.2 the following regression equations will be

estimated:

𝑅𝐼𝑖𝑡𝑐 − 𝐶𝐸𝑖𝑡𝑐 = 𝛽0 + 𝛽1 ∗ 𝑋 + 𝛽2 ∗ 𝑒𝑛𝑡𝑟𝑜𝑝𝑦𝑖𝑡𝑐 + 𝛽3 ∗ 𝑟𝑒𝑙𝑎𝑡𝑒𝑑𝑖𝑡𝑐 ∗ 𝑒𝑛𝑡𝑟𝑜𝑝𝑦𝑖𝑡𝑐

+ 𝛽4 ∗ 𝑢𝑛𝑟𝑒𝑙𝑎𝑡𝑒𝑑𝑖𝑡𝑐 ∗ 𝑒𝑛𝑡𝑟𝑜𝑝𝑦𝑖𝑡𝑐 + 𝛽5 ∗ 𝐺𝐷𝑃𝑡𝑐 ∗ 𝑒𝑛𝑡𝑟𝑜𝑝𝑦𝑖𝑡𝑐 + 𝛽6

∗ 𝐶𝑢𝑟𝑟𝑡𝑐 ∗ 𝑒𝑛𝑡𝑟𝑜𝑝𝑦𝑖𝑡𝑐 + 𝛽7 ∗ 𝐷𝐵𝑡𝑐 ∗ 𝑒𝑛𝑡𝑟𝑜𝑝𝑦𝑖𝑡𝑐+𝛽8 ∗ 𝑒𝑛𝑡𝑟𝑜𝑝𝑦𝑡𝑐2

+ 𝜖𝑖𝑡𝑐

RI-

Entropy

model

𝑄𝑖𝑡𝑐 = 𝛽0 + 𝛽1 ∗ 𝑋 + 𝛽2 ∗ 𝑒𝑛𝑡𝑟𝑜𝑝𝑦𝑖𝑡𝑐 + 𝛽3 ∗ 𝑟𝑒𝑙𝑎𝑡𝑒𝑑𝑖𝑡𝑐 ∗ 𝑒𝑛𝑡𝑟𝑜𝑝𝑦𝑖𝑡𝑐 + 𝛽4

∗ 𝑢𝑛𝑟𝑒𝑙𝑎𝑡𝑒𝑑𝑖𝑡𝑐 ∗ 𝑒𝑛𝑡𝑟𝑜𝑝𝑦𝑖𝑡𝑐 + 𝛽5 ∗ 𝐺𝐷𝑃𝑡𝑐 ∗ 𝑒𝑛𝑡𝑟𝑜𝑝𝑦𝑖𝑡𝑐 + 𝛽6

∗ 𝐶𝑢𝑟𝑟𝑡𝑐 ∗ 𝑒𝑛𝑡𝑟𝑜𝑝𝑦𝑖𝑡𝑐 + 𝛽7 ∗ 𝐷𝐵𝑡𝑐 ∗ 𝑒𝑛𝑡𝑟𝑜𝑝𝑦𝑖𝑡𝑐+𝛽8 ∗ 𝑒𝑛𝑡𝑟𝑜𝑝𝑦𝑡𝑐2

+ 𝜖𝑖𝑡𝑐

Q-

Entropy

model

13

𝑅𝐼𝑖𝑡𝑐 − 𝐶𝐸𝑖𝑡𝑐 = 𝛽0 + 𝛽1 ∗ 𝑋 + 𝛽2 ∗ 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦𝑖𝑡𝑐 + 𝛽3 ∗ 𝑟𝑒𝑙𝑎𝑡𝑒𝑑𝑖𝑡𝑐 ∗ 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦𝑖𝑡𝑐

+ 𝛽4 ∗ 𝑢𝑛𝑟𝑒𝑙𝑎𝑡𝑒𝑑𝑖𝑡𝑐 ∗ 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦𝑖𝑡𝑐 + 𝛽5 ∗ 𝐺𝐷𝑃𝑡𝑐 ∗ 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦𝑖𝑡𝑐

+ 𝛽6 ∗ 𝐶𝑢𝑟𝑟𝑡𝑐 ∗ 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦𝑖𝑡𝑐 + 𝛽7 ∗ 𝐷𝐵𝑡𝑐 ∗ 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦𝑖𝑡𝑐 + 𝛽8

∗ 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦𝑖𝑡𝑐2 + 𝛽9 ∗ 𝑑𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦𝑖𝑡𝑐 + 𝛽10 ∗ 𝑟𝑒𝑙𝑎𝑡𝑒𝑑𝑖𝑡𝑐 ∗ 𝑑𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦𝑖𝑡𝑐

+ 𝛽11 ∗ 𝑢𝑛𝑟𝑒𝑙𝑎𝑡𝑒𝑑𝑖𝑡𝑐 ∗ 𝑑𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦𝑖𝑡𝑐 + 𝛽12 ∗ 𝐺𝐷𝑃𝑡𝑐 ∗ 𝑑𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦𝑖𝑡𝑐

+ 𝛽13 ∗ 𝐶𝑢𝑟𝑟𝑡𝑐 ∗ 𝑑𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦𝑖𝑡𝑐 + 𝛽14 ∗ 𝐷𝐵𝑡𝑐 ∗ 𝑑𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦𝑖𝑡𝑐 + 𝛽15

∗ 𝑑𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦𝑖𝑡𝑐2 + 𝜖𝑖𝑡𝑐

RI-

Int+Div

model

𝑄𝑖𝑡𝑐 = 𝛽0 + 𝛽1 ∗ 𝑋 + 𝛽2 ∗ 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦𝑖𝑡𝑐 + 𝛽3 ∗ 𝑟𝑒𝑙𝑎𝑡𝑒𝑑𝑖𝑡𝑐 ∗ 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦𝑖𝑡𝑐 + 𝛽4

∗ 𝑢𝑛𝑟𝑒𝑙𝑎𝑡𝑒𝑑𝑖𝑡𝑐 ∗ 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦𝑖𝑡𝑐 + 𝛽5 ∗ 𝐺𝐷𝑃𝑡𝑐 ∗ 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦𝑖𝑡𝑐 + 𝛽6

∗ 𝐶𝑢𝑟𝑟𝑡𝑐 ∗ 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦𝑖𝑡𝑐 + 𝛽7 ∗ 𝐷𝐵𝑡𝑐 ∗ 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦𝑖𝑡𝑐 + 𝛽8

∗ 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦𝑖𝑡𝑐2 + 𝛽9 ∗ 𝑑𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦𝑖𝑡𝑐 + 𝛽10 ∗ 𝑟𝑒𝑙𝑎𝑡𝑒𝑑𝑖𝑡𝑐 ∗ 𝑑𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦𝑖𝑡𝑐

+ 𝛽11 ∗ 𝑢𝑛𝑟𝑒𝑙𝑎𝑡𝑒𝑑𝑖𝑡𝑐 ∗ 𝑑𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦𝑖𝑡𝑐 + 𝛽12 ∗ 𝐺𝐷𝑃𝑡𝑐 ∗ 𝑑𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦𝑖𝑡𝑐

+ 𝛽13 ∗ 𝐶𝑢𝑟𝑟𝑡𝑐 ∗ 𝑑𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦𝑖𝑡𝑐 + 𝛽14 ∗ 𝐷𝐵𝑡𝑐 ∗ 𝑑𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦𝑖𝑡𝑐 + 𝛽15

∗ 𝑑𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦𝑖𝑡𝑐2 + 𝜖𝑖𝑡𝑐

Q-

Int+Div

model

where i, t, c stand for company, year and country, respectively.

General methodology of estimation for all models is Hausman-Taylor method. This method

controls for the possible endogeneity of data. We assume endogeneity since not only

international diversification can lead to the increase in firm performance, but also more

profitable firms have higher resources to participate in international diversification. To check the

presence of endogeneity we run Hausman test for each specification.

In all equations X stands for matrix of control variables described in the Table 10.5. Control

variables are basic performance measures associated with corporate performance and

international diversification in the existing academic literature (see Scherbakov (2013). X matrix

also include country-specific variables, namely, natural logarithm of GDP as a proxy of

economic activity in a specific country and percentage year-to-year change of national currency

exchange rate since all figures are in USD. Industry dummies based on the NAICS codes are also

included in the X matrix.

Our hypotheses are tested based on the results of the estimation of the four models stated above

in the following way:

Hypothesis 1 is tested by the significance of of coefficients for squared variables in each model

(𝛽8 in Entropy models and 𝛽8 and 𝛽15 in Int+Div models).

To test the Hypothesis 2 we compare the forecasting power of models that have the same

dependent variable (economic profit spread or Tobin’s Q) but different DOI variables (entropy or

intensity+diversity). To measure the forecasting power we employ several statistics that

compares the efficiency of forecasts of two competing models. The description of these statistics

are presented in Table 10.6.

14

Table 6. Forecast efficiency measures

Forecast efficiency measure Formula

Mean error

𝑀𝐸𝑡 =∑ 𝐸𝑡

𝑁𝑡=1

𝑁=

1

𝑁∑ (𝐹𝑡 − 𝐴𝑡)𝑁

𝑡=1 , where 𝐹𝑡 –

forecast at moment t, 𝐴𝑡 –actual value at

moment t

Mean average percentage error (MAPE) 𝑀𝐴𝑃𝐸 =100

𝑁∑ |

𝐴𝑡 − 𝐹𝑡

𝐴𝑡|

𝑁

𝑖=1

Forecast bias 𝐵𝑖𝑎𝑠 = ∑ 𝐸𝑡

𝑁

𝑡=1

Mean absolute deviation 𝑀𝐴𝐷 =∑ |𝐸𝑡|𝑁

𝑡=1

𝑁

Tracking signal 𝑇𝑆 =𝐵𝑖𝑎𝑠

𝑀𝐴𝐷

RMSE (root mean standard error) 𝑅𝑀𝑆𝐸 = √∑ 𝐸𝑡

2𝑁𝑡=1

𝑁

The closer the value of each statistic to zero, the more efficient is the forecast.

Additionally, to test the Hypothesis 2 we run Diebold-Mariano test (DM) that statistically

compares the forecasting power of two models with the same dependent variable. Under null

hypothesis that two models have the same forecasting power the distribution of differences of

forecast errors of two models is standard normal distribution. Test statistics for DM test is

calculated as follows.

Let 𝑒𝑖𝑝(𝑠) be a forecast error of model s for company i at moment p. Then 𝑑𝑖𝑝 = 𝑒𝑖𝑝(1) −

𝑒𝑖𝑝(2) is a difference in errors of two competing models. If two models gave the same

forecasting power then 𝐸(𝑑𝑖𝑝) = 0 and test statistics has a standard normal distribution. The test

statistics is:

𝐷𝑀 = 𝜃√𝑁/𝑉��,

where

15

𝜃 =1

𝑁𝑃∑ ∑ 𝑑𝑖𝑝 =

1

𝑁∑

1

𝑃∑ 𝑑𝑖𝑝 =

1

𝑁∑ 𝑑𝑖.

= 𝑑..

𝑁

𝑖=1

𝑃

𝑝=1

𝑁

𝑖=1

𝑃

𝑝=1

𝑁

𝑖=1

and

𝑉�� =1

𝑁−1∑ (𝑑𝑖.

− 𝑑.. )

2𝑁𝑖=1 .

If DM is less than a critical value at 5% level of significance we conclude that entropy and

combination of FSTS and HHI gave the same forecasting power. If DM is greater than critical

value, we reject the null hypothesis and choose DOI measure with the highest forecasting power

based on the measures of the forecast efficiency stated above.

Hypothesis 3 and 4 are tested by significance and signs of the coefficients of joint products of

product diversification measures (related and unrelated) and DOI variables (𝛽3 and 𝛽4 in Entropy

models and 𝛽3, 𝛽4, 𝛽10 and 𝛽11 in Int+Div models).

Hypothesis 5 is tested by the comparison of change in average level of performance measures

that is attributed to the average level of international diversification. For each model we calculate

the average change in performance by taking mean values of DOI and moderating variables

(related and unrelated product diversification, GDP, change in currency exchange rate and Doing

Business rating) and subtracting them to the corresponding model. Then we compare the change

of different performance variables (economic profit spread and Tobin’s Q) over the mean value

forecasted by the same DOI measure.

4 Findings

4.1 Multi-Country Models

The results of multi-country models are presented in Table 10.7. All regressions are significant at

5% level.

Table 7. Results of multi-country models

Model RI-Entropy Q-Entropy RI-Int+Div Q-Int+Div

LN_SALES -1.3118603*** -.35463501*** -1.3468968*** -.35957743***

ASSET_TURNOVERR 8.2183756*** .56856847*** 8.4015155*** .56696641***

INT_ASSETS 8.0037612** -1.6528482*** 7.8311898** -1.6720421***

3ROE .26357287*** .00725766*** .26165339*** .00734781***

EBIT_SALES .37904087*** .00259209* .37939337*** .00251754*

UNRELATED -0.36592944 0.09679964 -0.73652029 0.10726376

LOG_GDP -1.256501 0.03315159 -1.4326276 0.05863074

CURR -11.299529*** 0.04657593 -9.9698468*** 0.11182575

ENTROPY*RELATED -7.8065243 -1.0292807

ENTROPY*UNRELATED 5.519736 1.1401531

ENTROPY*GDP 2.8748069** -0.08465714

ENTROPY*CURR -1.8243261 -0.26187283

ENTROPY*DB -0.00109728 0.00566657

16

Model RI-Entropy Q-Entropy RI-Int+Div Q-Int+Div

ENTROPY -83.811643** 2.3484344

ENTROPY2 1.4497347 -0.12048634

NAICS1 3.755496 .95558441* 3.6871181 .97759602*

NAICS2 6.1242661*** 0.37790471 6.1229779*** 0.38656235

NAICS3 5.661941** 1.0241025** 5.5095279* 1.045437**

NAICS4 8.7405656*** 1.3530938*** 8.3104627*** 1.3592677***

RUSSIA -4.3011876 -.77242979* -3.1615257 -.78301675*

CHINA -1.9294141 -0.32428127 -0.7832231 -0.35297325

BRAZIL -10.235828*** -0.49698518 -9.5219995*** -0.48082498

3SALES

0.00118061

0.00110891

INTENSITY*RELATED

28.300755 -1.5348323

INTENSITY*UNRELATED

-20.861606 1.1739707

DIVERSITY*RELATED

-44.55424 -0.55903318

DIVERSITY*UNRELATED

32.396277 1.1978447

INTENSITY*GDP

0.31156541 -0.08165718

INTENSITY*CURR

-13.996871 0.27368413

DIVERSITY*GDP

5.9076522* -0.12003036

DIVERSITY*CURR

8.5165787 -1.1378425

INTENSITY*DB

-0.17594724 -0.00259707

DIVERSITY*DB

0.14639113 0.01865713

INTENSITY

7.4920653 1.4280591

INTENSITY2

-8.9970214 1.2039976*

DIVERSITY

-184.9786** 3.6126583

DIVERSITY2

13.203481 -1.4398333*

INTERCEPT 37.442138 2.7187841 41.959545 2.0101392

Number of observations 711 885 711 885

Wald chi-squared 542.31 198.19 588.70 204.26

p-value 0.00 0.00 0.00 0.00

Note: * p<.1; **p<.05; *** p<.01

All models are estimated with Hausman-Taylor method. For each model a Hausman test is run in

order to control for possible endogeneity of panel data estimation with random effect and

presence of endogeneity. In each model, we assume that both linear and quadratic parts of DOI

variable (entropy and entropy2 in RI-Entropy and Q-Entropy models and intensity, intensity2,

diversity and diversity2 in RI-Int+Div and Q-Int+Div, respectively) are endogenous. The logic

behind this assumption is that not only international diversification affects firm performance but

also performance has an effect on DOI as firms that are more profitable have more resources to

participate in international diversification. The results of Hausman test are presented in Table 8.

17

Table 8. Results of Hausman test for endogeneity

Model RI-Entropy Q-Entropy RI-Int+Div Q-Int+Div

Р-value 0,0018 0,0000 0,0000 0,000

Conclusion Null hypothesis

is rejected

Null hypothesis

is rejected

Null hypothesis

is rejected

Null hypothesis

is rejected

This test compares the estimates of two models, Hausman-Taylor model and the one with

random effects. Under the null hypothesis, there is no significant difference in estimates of these

two models and thus we choose simple random effect model. But if we reject the null hypothesis

of Hausman test, we should choose Hausman-Taylor model which in means that there is an

endogeneity in the data. According to the results, null hypothesis (absence of endogeneity) is

rejected in all models. There are several possible reasons for endogeneity in the data: omission

of some significant variables, measurement error or simultaneity (situation when dependent and

some independent variables are codetermined, with each affecting the other). In our case the

most possible reason of endogeneity is simultaneity which means that not only international

diversification affects firm performance but also performance has an effect on DOI as firms that

are more profitable have higher resources to participate in international diversification.

The findings indicate that unrelated product diversification measure and GDP dynamics have no

significant influence on firm performance, but other control variables are significant. Currency

exchange rate affects performance only in case of economic profit spread since all variables are

presented in US dollars, but not in case of Tobin’s Q. Firm size has a negative effect on

performance in each specification which indicates that there is no economy on scale for BRIC

countries firms and large firms tends to be less effective in terms of operating and financial

performance as they are more complex and thus harder to manage. Another interesting result is

that ratio of intangible assets to total assets has a significant positive effect on economic profit

spread but significant negative on Tobin’s Q.

Our conclusions on the hypotheses are the following.

Hypothesis 1 is rejected in all multi-country models except for Q-Int+Div model. DOI from a

statistical point of view have a linear effect on performance measured by both economic profit

spread and Tobin’s Q. In the Q-Int+Div model quadric parts of both DOI variables (FSTS and

HHI) are statistically significant and positive while linear parts are statistically insignificant. In

RI-Int+Div, only diversity variables are significant which means that for economic profit spread

dynamics distribution of sales across countries has a significantly higher impact on spread than

the ratio of foreign sales in total sales. We can conclude that there are no phases of international

diversification for BRIC companies and the impact on performance is monotonous.

Additionally, the results of RI-Int+Div and Q-Int+Div models show that different DOI measures

(FSTS and HHI) have different signs of coefficients and thus affect firm’s performance

differently. Since these DOI variables are included in the regressions not only as single linear

terms but also as joint products with five moderators (related and unrelated product

diversification, GDP, national currency exchange and Doing Business rating), value of

moderators will affect the form of linear dependence of DOI and firm performance. For instance,

in Q-Int+Div model:

18

𝑅𝐼𝑖𝑡𝑐 − 𝐶𝐸𝑖𝑡𝑐 = 𝛽0 + 𝛽1 ∗ 𝑋 + 𝛽2 ∗ 𝑒𝑛𝑡𝑟𝑜𝑝𝑦𝑖𝑡𝑐 + 𝛽3 ∗ 𝑟𝑒𝑙𝑎𝑡𝑒𝑑𝑖𝑡𝑐 ∗ 𝑒𝑛𝑡𝑟𝑜𝑝𝑦𝑖𝑡𝑐 + 𝛽4

∗ 𝑢𝑛𝑟𝑒𝑙𝑎𝑡𝑒𝑑𝑖𝑡𝑐 ∗ 𝑒𝑛𝑡𝑟𝑜𝑝𝑦𝑖𝑡𝑐 + 𝛽5 ∗ 𝐺𝐷𝑃𝑡𝑐 ∗ 𝑒𝑛𝑡𝑟𝑜𝑝𝑦𝑖𝑡𝑐 + 𝛽6 ∗ 𝐶𝑢𝑟𝑟𝑡𝑐

∗ 𝑒𝑛𝑡𝑟𝑜𝑝𝑦𝑖𝑡𝑐 + 𝛽7 ∗ 𝐷𝐵𝑡𝑐 ∗ 𝑒𝑛𝑡𝑟𝑜𝑝𝑦𝑖𝑡𝑐+𝛽8 ∗ 𝑒𝑛𝑡𝑟𝑜𝑝𝑦𝑡𝑐2 + 𝜖𝑖𝑡𝑐

the linear coefficient for intensity will be:

𝛽2 + 𝛽3 ∗ 𝑟𝑒𝑙𝑎𝑡𝑒𝑑𝑖𝑡𝑐 + 𝛽4 ∗ 𝑢𝑛𝑟𝑒𝑙𝑎𝑡𝑒𝑑𝑖𝑡𝑐 + 𝛽5 ∗ 𝐺𝐷𝑃𝑡𝑐 + 𝛽6 ∗ 𝐶𝑢𝑟𝑟𝑡𝑐 + 𝛽7 ∗ 𝐷𝐵𝑡𝑐

In order to determine the average impact of international diversification on performance we can

apply moderators’ means. The total coefficients for linear and quadratic parts of FSTS and HHI

in RI-Int+Div and Q-Int+Div models are presented in Table 10.9.

Table 9. Linear and quadratic coefficients for models with two DOI variables

Performance variable Economic profit spread Tobin’s Q

Linear coefficient for FSTS 11.96 -1.38

Quadric coefficient for FSTS -8.99 1.20

Linear coefficient for HHI -13.37 1.63

Quadric coefficient for HHI 13.20 -1.44

As can be seen from the table, FSTS and HHI have different signs in different models but also

they change sighs with different performance measures. Since these variables captures different

aspects of international diversification (intensity and diversity), this result is quite natural. It also

states that form of relationship and influence of international diversification highly depends on

the choice of DOI measure. This conclusion corresponds with the meta-analytical papers on this

topic (see Kirca et al. (2011) or Yang, Driffield (2012).

The results of the Hypothesis 1 can be also presented in form of graphs.

On the Figure 2, we present the outcome pattern of DOI–performance relationship for all

countries and outcomes from general model for countries with the highest number of

observations in the sample – Russia and China. General model predicts that for Russian

companies international diversification is value destroying and leads to the decline in economic

profit spread up to the 2% while for Chinese companies international diversification is highly

profitable and results in more than 5% increase in economic profit

19

Figure 2. The pattern of entropy – change in economic profit spread relationship

Note: these grapths are plotted for mean value of all variables except for DOI measure. Equasion is: 𝑦 = 𝛼 ∗ 𝑥 +𝛽 ∗ 𝑥2, where у and х – performance variables and DOI measure, correspondingly

spread. On average, international diversification has almost no impact on the company

performance in case of all BRIC countries.

-3

-2

-1

0

1

2

3

4

5

6

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 1,1 1,2 1,3 1,4 1,5

% c

han

ge in

Eco

no

mic

pro

fit

spre

ad

Entropy

M1B China Russia

20

Figure 3. The pattern of entropy – change in Tobin’s Q relationship

Note: these grapths are plotted for mean value of all variables except for DOI measure. Equasion is: 𝑦 = 𝛼 ∗ 𝑥 +𝛽 ∗ 𝑥2, where у and х – performance variables and DOI measure, correspondingly

In the case of long-run performance measured by Tobin’s Q, international diversification is value

creating both in case of all BRIC companies and single country results (for Russia and China).

Long run performance of Russian companies increases greater than for all countries and China.

For Russian companies maximum increase in Tobin’s Q equals to roughly 0.28 while for BRIC

and Chinese companies maximum increase equals to 0.16 and 0.12, respectively. In addition, we

can see that for Russian companies Tobin’s Q increases with the growth of entropy while for

BRIC and China there is a DOI value when long-run performance starts to decline.

In case of models RI-Int+Div and Q-Int+Div models we are unable to plot these graphs because

FSTS and HHI are correlated. In these models, we use two measures of DOI that are

interdependent and change their values jointly, and both have an impact on performance.

Therefore, it is also impossible to plot separate graphs since in this case we ignore their joint

influence on firm performance. Thus, for RI-Int+Div and Q-Int+Div models we present the

results for Hypothesis 1 only in table with models estimation outcomes.

Hypothesis 2 is rejected. We calculate several measures of efficiency of forecasts. The results

are presented in Table 10.

Table 10. Forecast efficiency measures

Measure RI-Entropy RI-Int+Div Q-Entropy Q-Int+Div

ME -0,05927 -0,06871 0,02358 0,025778

Bias -42,1399 -48,8559 20,86843 22,81335

MAD 6,407336 6,310541 0,76109 0,770194

TS -6,57682 -7,74196 27,41914 29,62027

0

0,05

0,1

0,15

0,2

0,25

0,3

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 1,1 1,2 1,3 1,4 1,5

Chan

ge

in T

ob

in's

Q

Entropy

M2B China Russia

21

RMSE 8,714936 8,644594 1,143217 1,149525

We can see that in general models with entropy (1 and 2 column) are more efficient than models

with FSTS and HHI (3 and 4 column), but this difference is small and can be insignificant. To

test this difference and provide another way to test Hypothesis 2, we performed DM test based

on the results of the forecast we obtained after the estimation of each model. This test compares

the efficiency of forecasts of competing models (Model 1 versus Model 3 and Model 2 versus

Model 4). The results of the test are presented in Table 11.

Table 11. Results of DM test for forecast efficiency

Performance

variable Test statistics value Critical value Conclusion

Economic profit

spread 1,076 1,96

Null hypothesis is not

rejected

Tobin’s Q 0,012 1,96 Null hypothesis is not

rejected

In both cases we do not reject null hypothesis about equal forecasting power of competing

models. Thus, we conclude that entropy and combination of FSTS and HHI have equal

forecasting power of firm performance and reject Hypothesis 2. According to the DM test the

difference in forecast efficiency measures is statistically insignificant. However, entropy index

can be more useful in deferent ways since usage of one DOI measure instead of two allows us to

present the results graphically. In addition, entropy index lowers the level of multicollinearity

that is common for HHI.

Hypothesis 3 is rejected in all models. Both related and unrelated product diversification have

no significant impact on DOI–performance relationship. Moreover, the results are unstable: for

example, in Model 3 related product diversification has a positive impact on intensity–economic

profit spread relationship while in Model 4 this coefficient is negative. In most cases, related

product diversification has a negative sign and unrelated product diversification has a positive

sign.

The sign of related product diversification coefficient can be explained that simultaneous

diversification to the new markets from both geographical and product points of view allows the

firm to low its exposure to macroeconomic and industry trends since company’s revenue

distributes across higher number of firms. These gains can overweight the increased costs from

growth in complexity of the business.

Hypothesis 4 is not rejected. The results of testing of Hypothesis 5 are presented in Table 12.

Table 12. Average change in performance variables attributed to the average level of DOI.

DOI measure Entropy index FSTS+HHI

Average change in

economic profit spread -0.52 0.40

Average change in

Tobin’s Q 0.12 0.58

When we use entropy index as a DOI measure economic profit spread declines on average while

Tobin’s Q increases, thus, we conclude that international diversification can be value destroying

22

in short term period. This result can also be viewed on the Figure 1 and Figure 2: while

economic profit spread declines with the growth of entropy, Tobin’s Q increases. In case of

FSTS and HHI economic profit spread increases but less than growth in Tobin’s Q in absolute

terms. If we compare the ratios of average changes in performance variables to its mean values

(which is 9.04% for economic profit spread and 1.67 for Tobin’s Q) we see that increase in long

run performance measure to its mean is much higher than in for short run performance. Thus, we

can conclude that these DOI measures do not state that diversification is value destroying in the

short term but they also support the fact that it has more influence on the long term performance,

since in short period of time company generally can’t integrate new assets to its business

structure, but investors already include the effects of international diversification to the

company’s fair market price.

4.2. Results for single country models

The results for single country models are presented in the Table 13. We run the regressions only

for Russian and Chinese companies as these countries have the highest number of observations.

For Indian and Brazilian companies there is no enough observation to have a high power of

statistical tests.

For each country we run the same four regressions as we did for all BRIC companies. The results

indicate a sufficient difference in variables that have a significant influence on firm performance

for single country and multi-country models. For example, asset turnover ratio is significant and

has a positive impact on both performance variables in BRIC countries models, it is significant

only in 5 out of 8 single country models and in two of them it has a negative sign. Variable

3ROE, which was also significant in all multi-country models, has a significant influence only on

residual income in case of Russian and Chinese companies and on Tobin’s Q of Russian firms

(but the level of significance is only 10% while in multi-country models 3ROE is significant at

1% level). There are opposite examples when some variable was insignificant in multi-country

models but significant in single country: for instance, natural logarithm of GDP is significant

positively and significant negatively as a control variable for Tobin’s Q of Chinese and Russian

firms, correspondingly, while it has no significant influence on Tobin’s Q for companies from all

BRIC markets. These results support our initial statement that country specific factors have a

great impact on efficiency of international diversification, but they also affect the choice of

control variables for short term and long term performance variables.

However, result that is much more important is connected with the effects of DOI variables on

firms performance. In 5 of 8 models that we estimated DOI variables and joint products of DOI

with moderators have no significant influence on firm performance. Still, in three models we got

a significant result for DOI variables that we can compare with predictions of multi-country

models for separate countries. This can be a part of robustness check for multi-country models

since the comparison of the results of single and multi-country models allows to conclude

whether the multi-country model produce the same pattern of DOI-performance relationship as

single country model and, thus, does it captures the country specific factors that affect this

pattern. This comparison also answers the question whether the country specific variables that

we included in the model reflects the influence of these country specific factors on international

diversification of firms. We take Q-Entropy model for Chinese companies and Q-Int+Div model

for Russian companies and plot the change of Tobin’s Q against the level of DOI variables

predicted by single and multi-country models. The results are presented below.

23

Table 13. Results of single country models

Model RI-Entropy_C RI-Entropy_R Q-Entropy_C Q-Entropy_R RI-Int+Div_C RI-Int+Div_R Q-Int+Div_C Q-Int+Div_R

LN_SALES -1.0954056 -1.5393816** -.84636208*** -0.15523479 -1.0726039 -1.5702811** -.8803996*** -0.1289759

ASSET_TURN~R 4.9770888* 8.8105493*** .98151922*** -0.02806658 3.905557 8.473013*** 1.0510124*** -0.01627327

INT_ASSETS 24.939803*** 6.4671897 -0.00301258 -3.8658349*** 24.89023** 5.3280702 -0.14613954 -3.4499393***

3ROE .21832684*** .30365801*** 0.00291398 0.00768065* .21621819*** .30843713*** 0.00262185 .00737867*

EBIT_SALES .25663984*** .39218513*** .01171962** 0.00067522 .25238639*** .39872755*** .01371149*** 0.00056

UNRELATED 0.31634564 -3.8091726 -0.29829084 -0.3781952 -0.27289706 -3.8884028 -0.30545471 -0.36534101

LOG_GDP -2.2237047 4.4224779 1.3145185*** -1.5015722*** -1.8487042 3.6347168 1.2962445*** -1.4502131***

CURR -18.008887 -11.10652** 1.5704963 -0.15534795 -17.502433 -9.5437037* 2.1630437 -0.06187468

ENT*REL 6.5816776 3.2009338 0.63145592 -0.38041867

ENT*UNREL -9.3139624 1.1228118 0.3313229 0.50366928

ENT*GDP -12.164726 0.5463949 -2.1972702* -0.07728014

ENT*CURR 47.288427 6.9192347 2.1333349 -0.26495197

ENT*DB 0.47276287 -0.90845274 0.15892419 0.07216182

ENTROPY 351.9103 39.722076 56.446179** -2.6943517

ENTROPY2 -5.3785449 2.7193878 -0.39842861 -0.17096623

NAICS1 1.8209074 3.2799218 0.26712381 -0.03575789 2.0906132 2.9538305 0.19443663 -0.06825665

NAICS2 3.4074892 6.2082702 0.5797709 -0.13803639 3.5918353 6.245769 0.56229744 -0.19635031

NAICS3 5.9631028 3.7270341 2.1834956*** -0.5423091 6.238436 3.3977235 2.2009973** -0.57561472

NAICS4 5.8414294 3.8596876 -0.0570167 1.0157994 6.0365644 4.0131866 -0.11733411 0.88638446

3SALES

-0.00015443 0.00016301

-0.00004127 0.00013816

INT*REL

0.89818446 16.888652 0.2645255 -5.0540959

INT*UNREL

4.1578911 -9.7618214 -1.3756343 5.7800134

DIV*REL

25.313593 -10.516434 0.26111542 3.7854132

DIV*UNREL

-31.798502 11.627565 2.7602814 -4.2024374

INT*GDP

11.119245 -5.6674754 2.9335359 -3.8843849**

INT*CURR

-25.588657 -25.316609 6.740167 -2.1506319

24

Model RI-Entropy_C RI-Entropy_R Q-Entropy_C Q-Entropy_R RI-Int+Div_C RI-Int+Div_R Q-Int+Div_C Q-Int+Div_R

DIV*GDP

-37.677672 11.402153 -7.0454183 3.5159131

DIV*CURR

80.419169 37.795797 -2.2325472 1.4929664

INT*DB

-1.5248456 2.4175895 -0.17232963 0.3902923

DIV*DB

2.8679393 -4.5124641 0.43036939 -0.27449731

INTENSITY

-262.53132 1.0170121 -76.063427 81.983143*

INTENSITY2

16.674985 -14.410297 -0.84593545 1.6174259

DIVERSITY

996.86602 -30.009898 185.71115* -78.92283

DIVERSITY2

-27.950997 18.99835 -2.9892823 -2.8121848*

Intercept 67.284559 -125.76709 -32.824168*** 46.37736*** 56.799261 -102.41464 -32.087571*** 44.657038***

Number of

observations 217 247 256 266 217 247 256 266

Wald chi-squared 149.07 165.71 82.03 101.98 151.11 165.67 92.06 111.59

p-value 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Note: * p<.1; **p<.05; *** p<.01. C and R stand for models run for Chinese and Russian companies, correspondingly

25

Figure 4. The pattern of entropy – change in Tobin’s Q relationship predicted by single and multi-country models

for Chinese companies.

Note: these grapths are plotted for mean value of all variables except for DOI measure. Equasion is: 𝑦 = 𝛼 ∗ 𝑥 +

𝛽 ∗ 𝑥2, where у and х – performance variables and DOI measure, correspondingly

Figure 5. The pattern of FSTS and HHI – change in Tobin’s Q relationship predicted by single and multi-country

models for Russian companies.

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

0,4

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 1,1 1,2 1,3 1,4 1,5

Chan

ge

in T

obin

's Q

Entropy

Single country model Multi country model

-0,6

-0,4

-0,2

0

0,2

0,4

0,6

0,8

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1

chan

ge

in T

obin

's Q

DOI

Multi country model HHI Multi country model FSTS

Single country model HHI Single country model FSTS

26

Note: these grapths are plotted for mean value of all variables except for DOI measure. Equasion is: 𝑦 = 𝛼 ∗ 𝑥 +

𝛽 ∗ 𝑥2, where у and х – performance variables and DOI measure, correspondingly

We can see that for Russian companies multi-country models estimated for all BRIC companies

produces almost the same pattern of change in performance–DOI relationship. For small values

of DOI, the predicted change in Tobin’s Q is the same for different models, but starting from

about 0.4 the lines move in slightly different directions. The forecasts of the effect of HHI on

Tobin’s Q for high values of HHI differ greater than for FSTS impact on Tobin’s Q (maximum

difference between forecasts for HHI is 0.79 against 0.23 for FSTS). But overall, the form of the

pattern for both FSTS and HHI are the same.

In case of predictions for Chinese companies the forms of relationships between entropy and

change in long term performance is also the same but single country model forecasts higher

changes in Tobin’s Q for all levels of entropy. Maximum values of change in Tobin’s Q

attributed to the change in international diversification variables is 0.33 for single and 0.11 for

multi-country models. But again we can conclude that multi-country model for BRIC companies

produces the same result as single country one with a high level of accuracy.

5 Conclusion

This paper contributes to the existing literature on the performance – international diversification

relationship by shedding a new light on the mechanism of this relationship for companies from

BRIC markets. We used economic profit concept which allows us to take into account two types

of effects of international diversification (namely, effect on financial and operating metrics of the

firm) and applied this methodology to a sample of 109 companies from 4 BRIC countries. We

also used Tobin’s Q as a proxy of firms’ long run performance. Degree of international

diversification was measured by three types of variables – entropy index, intensity index as

measured by FSTS ratio and diversity index as measured by Herfindahl-Hirshman index – and

the later two were used simultaneously. Our analysis shows the following results.

After running several panel data random effect models estimated with Hausman-Taylor method

we conclude that the pattern of relationship between performance and DOI measure differs

across models. In case of entropy index we found linear form of relationship while for diversity

and intensity this pattern tends to be nonlinear. This result demonstrates that choice of measure

of degree of international diversification plays an important role in studies of performance –

international diversification relationship and corresponds to the results of the previous

researchers (Kirca et al. (2011) or Yang, Driffield (2012)).

Additionally, we studied the difference in effect of international diversification on short run and

long run performance measures of the firms. We found that in short run international

diversification has a smaller effect on performance measured by economic profit spread and in

some cases can be even value destroying while in long run performance measured by Tobin’s Q

increases significantly higher.

Another important result of this research is a comparison of forecasting power of different

measures of international diversification. Our findings demonstrate that entropy index and the

combination of FSTS and Herfindahl-Hirshman index have the same predictive power in

forecasting both short run and long run performance measured. This result shows that despite the

fact that these measures take into account different aspects of international diversification and

thus produces different patterns of performance – international diversification relationship they

predict the change in performance measure with the same quality.

27

Perhaps the most interesting result of our research is the predictive power of general model for

all BRIC countries compared to single-country models. We used three country-level variables to

take into account differences in economic conditions of BRIC countries (natural logarithm of

county’s GDP, percentage change in national currency exchange rate and World Bank’s Doing

Business rating as a measure of institutional development). The results show that general model

estimated for companies from 4 countries produces the same pattern of performance –

international diversification relationship as a model estimated for companies from only one

country (on the example of Russian and Chinese companies). This fact demonstrates that the

mechanism of influence of international diversification on firm performance is the same for

different BRIC countries and the differences can be explained by country level variables such as

general economic trend and level of institutional development.

As an implication of the present research for corporate decision makers it can be used for solving

a vast number of practical problems, such as determination of the most appropriate degree of

international diversification for a particular company or prediction of the effect of international

diversification on both short run and long run firm performance. However, the results of this

research should be treated with cautious as it has some limitations. First of all, the statistical

insignificance of some DOI measures may be caused by a low number of companies in the

sample as the present research studied only one way of diversification, mergers and acquisitions.

Additionally, the level of institutional development is measured only by Doing Business rating;

inclusion of additional variables such as cultural, political and economic distance between

countries can improve the results of both multi-country and single-country models. Future

research in this area can focus on these limitations.

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