Growth strategies of companies in emerging capital markets

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ФЕДЕРАЛЬНОЕ ГОСУДАРСТВЕННОЕ АВТОНОМНОЕ ОБРАЗОВАТЕЛЬНОЕ УЧРЕЖДЕНИЕ ВЫСШЕГО ОБРАЗОВАНИЯ «НАЦИОНАЛЬНЫЙ ИССЛЕДОВАТЕЛЬСКИЙ УНИВЕРСИТЕТ

«ВЫСШАЯ ШКОЛА ЭКОНОМИКИ»

Международный институт экономики и финансов

Выпускная квалификационная работа

Growth strategies of companies in emerging capital markets

Стратегии роста компаний на развивающихся рынках капитала

Шабалин Даниил Александрович

Москва 2020



Abstract

This work analyzes the question of choosing either internal or external growth strategies, for a company wishing to grow in Emerging capital markets. The result of the analysis of 114 companies for the period from 2009 till 2018 from countries included in MSCI Emerging Markets Index, data for which was obtained using Bloomberg and World Bank stated that a company searching for growth in developing countries should spend more of a free cash flow for capital expenditures rather than for acquisition of businesses. This result was also compared with the one from developed markets in which the best decision was the opposite.

В данной работе исследуется вопрос выбора стратегии внутреннего или внешнего роста для развития компании на развивающихся рынках капитала. Результат исследования, основанного на выборке из 114 компаний в период с 2009 по 2018 год из стран, включённых в MSCI Emerging Markets Index, показал, что наиболее эффективный рост может быть достигнут в том случае, когда компания растет изнутри, используя собственные ресурсы. Также было произведено сравнение с компаниями, представляющими развитые рынки, в ходе которого было выявлено, что на развитых рынках лучше использовать сделки слияния и поглощения.



Table of contents

Introduction

1. Literature Overview

1.1 Growth

1.2 Choice of the strategy

1.3 Mergers and acquisitions

1.4 Organic growth

2. Empirical Set-up

2.1 Data Sample

2.2 Model

2.3 Explanatory variables

2.4 Control variables

2.5 Method

3. Results

3.1 Emerging markets

3.2 Developed vs Emerging markets

3.3 Robustness check

Conclusion

References

Appendix



Introduction

The topic that is going to be discussed in this work can be referred to a much wider theme of corporate strategies which is very important and will always be up to date due to the fact that any company, that is operating in a market and wants to achieve success, has to grow and in order to do it in the most efficient way it should be done by choosing the right strategy. Many researchers have already touched the theme of corporate strategies, their classification, application, comparison and effects, for example, one of the first and very recognizable works in this field “Corporate Strategy” written by I. Ansoff (1965) shows how and why strategies should be implemented, the author also brought the concept of synergy which is often used now, considering M&A deals. Such a long history of works on the theme of corporate strategies and the fact that there are still many aspects that can be discussed and developed once again highlights the importance of the theme.

In particular, my article of “Growth strategies of companies on emerging capital markets” will serve to any of the companies that are willing to expand and acquire any growth in emerging markets, and this are not only the companies operating in emerging markets now, but also can be very large and well-known corporations from developed markets that are looking for more growth and thus may find it attractive to start their operation in the emerging markets.

The relevance of the theme cannot be argued as long as companies always have to develop and grow due to the fact that there is no other way to survive in such a competitive market that can be seen today, and specially in emerging capital markets, one of the main characteristics of which is high potential for growth (Amadeo 2020). For example, this year due to COVID-19 companies lost on average about 30% over just March. Consequently, in order not to go bankrupt companies had to adapt as fast as possible, trying to maintain their growth. What is more, in addition we could see the influence of Russia and OPEC failure to agree on oil production cuts, which was immediately followed by the 25% fall in oil price. This decline had a huge impact on companies in oil industry, which were faced with the question of choosing a strategy.

Although the emerging markets are not as large as the developed and they are more volatile, their importance also cannot be missed. This was well analyzed from the view of corporate finance in the article of Jack D. Glen and Brian Pinto (1994). Authors highlight the role of emerging equity markets in funding of firms and compare the growth of emerging and developed markets in 90s, which, as expected, differed significantly. Talking about the countries which can be referred to as the ones having emerging capital markets we can turn to The Morgan Stanley Capital International Emerging Market Index, in which 26 developing countries are presented, the list of them can be found in Appendix 1. The Index is used in order to show the overall performance of emerging countries and is a float-adjusted market capitalization index, representing 13% of global market capitalization. The list of developed markets according to MSCI Developed Markets Indexes can also be found in the Appendix 1.

Also, for the better understanding, growth strategies themselves should be explained. In general, they can be divided simply in two parts, internal and external. Firstly, the internal or organic growth is the type of growth when a company is developing by using its internal resources, they work on the development of existing products, finding new ones, or just optimizing their product line, making the production more efficient. So, the internal growth may be further divided into expansion, diversification and modernization. Secondly, the external growth strategies, these are commonly considered as mergers and acquisitions. In these strategies, a company is involved in the acquisition of another one, which may be a competitor, a company that is operating in a different country or even a different type of business. The motivation behind any of them is quite similar, generally focused on the diversification, expansion or modernization.

The aim of the paper is to analyze the question of choosing a growth strategy for a company that will be better to apply in emerging capital markets. The result will state whether a company should use internal or external growth. In order to investigate the question, I am going to use the following steps:

Analyze previous papers and researches on similar themes;

Perform an empirical analysis of the question, using econometric models applied to the panel data of 114 companies from Emerging capital markets;

Compare the results of the models with those for 97 companies, representing Developed markets.

The work is going to be structured in the following way: I will start with the discussion of the existing articles and researches which have already covered the similar questions in the Literature Overview, then passing to the main part of this thesis, which will be the empirical analysis, describing the data, estimating models measuring growth and the effect from application of two types of growth strategies, and then I will come to the Results and Conclusion of the research.



1. Literature Overview

The theme I am discussing in my work is quite popular among different researchers due to its relevance and importance when making a choice of a strategy for the development of any company, which will finally result in growth. In this part, I am going to describe some of the researches already made, their empirical tests and contribution to the theme.

1.1 Growth

Considering my work, the thesis of Frederic Delmar “Measuring growth: methodological considerations and empirical results” is of great use. From his analysis of 55 studies the result showed that researchers used the following growth indicators: sales, employment, market share, asset and multiple indicators. However, the author argues some of the indicators, saying, for example, that the changes in assets are only appropriate for the manufacturing sector, while changes in sales will not be a great indicator when there is high inflation. Summing this, the researcher said that the use of multiple indicators is a good way to measure the growth. From his research, it can also be seen that the usage of relative growth is much more popular than absolute and makes more than half of the total sample of researches on growth. The preference of relative growth to absolute is simple, this is due to the fact that some companies may have the same absolute growth, however their relative will differ a lot. The author also says that the measure was often logarithmized in order to adjust for skewness. One of the examples of usage of sales and earnings growth can be the work of L. E. Palich; L. B. Cardinal; C. C. Miller (2000).

1.2 Choice of the strategy

In terms of choosing the strategy there are many works on discussing the first step of a company, or its entrance to a market. The theme was covered by Gwendolyn K. Lee and Marvin B. Lieberman (2010) in their work “Acquisition vs internal development as modes of market entry”. The researchers have analyzed the question of whether the model of organic or not organic growth will be used when a new product is less related to the firm's existing products. Building the logistic regression which tells whether the company would enter the market with the help of M&A deal or using its own capital helped the researchers in answering the main question of their work. The relatedness was measured using the pairwise similarity index and there were also included financial measures and such market controls as its newness and density. The result they got stated that the use of acquisition increases with the degree of firm-market relatedness. However, this holds only if the entry is inside the primary business domain. The researchers made their analysis on the data of US telecommunications companies, resulting in 1,719 observations. The similar result was also found by Karim and Mitchell (2000), who said that the choice of external growth strategy would more likely be made by a firm if its production is very similar to those of the target, their study was based on the sample of companies from the medical sector, consequently, they got the result for the inside primary business domain mergers.

Following this we can also come up with the work of George S. Yip “Diversification entry: Internal Development Versus Acquisition”, which can be compared to the just discussed one. The researcher also wanted to examine the effect of relatedness to the market and barriers to entry in choosing the way of entry. By barriers author means things that give disadvantages to companies wishing to enter the market relative to those already in, such as economies of scale, product differentiation, absolute costs and the capital requirement. His work is based on the binary regression model telling whether the company would enter the market via acquisition or internal development. As the independent variables, the researchers chose variables that will give information on barriers and other parameters which can influence the choice of the entering strategy, such as Entrant Parent Size (as revenue), Investment Intensity (in net plant, equipment and working capital) and Market Growth Rate (change in market sales). Yip's paper provided the opposite result, compared to the work of Lee and Lieberman, he stated that costs of the direct market entry decrease with higher relatedness, despite high barriers, consequently, the higher is the relatedness the better it is to choose internal growth compared to acquisition.

Also, there are many works that want to estimate the efficiency and performance of different growth strategies. Firstly, discussing this theme, I cannot not mention the book of Ivashkovskaya “Strategic financial decisions of companies on emerging capital markets”. Authors have provided a good analysis of realization of diversification methods via both acquisitions and internal growth in order to answer the question of which type of strategy if applied will be more effective in the emerging markets. In the research in order to measure the strategic efficiency as the dependent variable was chosen economic profit, however, due to the absence of the needed data it was changed for the Equity Residual Income, which is almost the same but for the debt inclusion. As independent variables author chose diversification, measured by generalized entropy index and Herfindahl-Hirschman index, its square and three groups of control variables, those characterizing the activity of the company, its industry and time. The result that they got stated that for developing markets the dependence between the degree of diversification and the strategic efficiency is U-shaped using external growth, while choosing the organic growth strategy results in a decrease of the strategic efficiency. The researchers also stated that the results are different for developing and developed markets when talking about internal diversification method, as I have already mentioned above, the result of the developing market is that increase in the number of different products in a company results in lower value, while on the developed markets it is U-shaped, like for the companies choosing acquisitions instead. There are also some opposite results may be found that would say that the dependence between the degree of diversification and firm's performance has the concave form (L. E. Palich; L. B. Cardinal; C. C. Miller 2000).



1.3 Mergers and acquisitions

Mergers are very popular in corporate finance and the theme is quite deep, so the book I would like to start with is “Mergers & Acquisitions from A to Z” written by Andrew J. Sherman and Milledge A. Hart, the book helped me in understanding mergers and acquisitions much better. This work also showed the importance of M&As for the economy, providing the fact that in 2004 these deals formed 92% of liquidity events from venture capital funded firms, 8% left were achieving liquidity through an IPO. What is more, it also gives some information on alternative external growth strategies, such as joint ventures, franchising, licensing, strategic alliances and distributorships, however authors mention that choosing these strategies involves a company in sharing its control.

Another work that is vital to mention is “The performance of mergers and acquisitions in emerging capital markets: new evidence” made by Svetlana Grigorieva and Tatiana Petrunina. The sample they used consists of 80 m&a deals that took place during the period from 2002 to 2009 in emerging markets. The hypothesizes authors set addressed the question of mergers' performance in different ways, beginning with the overall M&A performance in emerging capital markets and going further, comparing local and cross-border deals, those paid for by stock and by cash and so on. In order to measure the long-run impact of a deal the researchers have also been using the concept of economic profit. As a result, they found that M&A in emerging markets provide a negative effect on the performance of a merged company, median industry-adjusted economic profit declines by $4.0 million in the post- acquisition period.

The conclusion of the previously mentioned researchers can be supported by the work of Ghosh A. “Does operating performance really improve following corporate acquisitions?”. In this work Aloke Ghosh compares pre- and post-acquisition operating performance of 315 companies. The method is based on the comparison of the growth of a merging company to its matched firm, the match is basing on the performance and size of companies before the deal. The result of the study states that there is no increase in the operational performance of merging companies. However, the researcher goes deeper in the analysis and compares the results of two subsamples which differ in the payment method used for a deal. The author says that, unlike stock acquisition, deals paid by cash are followed by a significant increase in the operational performance of a merging company, resulting from the higher sales growth.

However, researchers have not yet come to the same conclusion on the question of the efficiency of mergers. The opposite result is provided by Erik Devos, Palani-Rajan Kadapakkam and Srinivasan Krishnamurthy (2009) in their work “How Do Mergers Create Value? A Comparison of Taxes, Market Power, and Efficiency Improvements as Explanations for Synergies”. Basing on the analysis of 264 large mergers, the researchers have found the evidence that the overall performance of a merged firm increases by 10.03% and the main source of this improvement comes from the operational synergy and not from the tax shields benefits. The underlying method based on the comparison of the present values of incremental cash flows of the merged firm relative to the sum of those before the merger.

To continue, I would like to take attention to the article of Ronan G. Powell and Andrew W. Stark “Does operating performance increase post-takeover for UK takeovers? A comparison of performance measures and benchmarks”. In order to answer their question the data on 191 takeovers in the United Kingdom was obtained. Researchers used operating cash flows to measure the pre- and post-merger performance. Pre-merger operating cash flows are constructed by the sum of the flows of the acquirer and the target prior the takeover. Measures were then transferred to relative using four different denominators. Powell and Stark used both the regression based analysis and the change model (comparison with benchmarks) in order to obtain the results. The conclusion made by the authors was that there are improvements in the post-takeover operating performance of companies. This result supports the work of Paul M. Healey, Krishna G. Palepu and Richard S. Ruback (1992), who were basing their research on 50 largest U.S. mergers during the period of 1972-1984.

1.4 Organic growth

Going further with the literature I should pass to the analysis of some works made on the theme of internal growth. And to begin with I would like to look at the work of John McMahan and Tom Hester “Is organic change a better option than acquisitions as a growth strategy?”. The great part of the article is devoted to the types of different organic growth strategies. Furthermore, the author provides a few examples from the real estate market and gives some ways of how an organic growth strategy can be applied to an existing business. Thus, the work is very helpful in better understanding of the ways how a company may develop internally.

Looking more closely at organic growth I would refer to the article of Bowman, Singh, Useem and Bhadury “When does restructuring improve economic performance?”. By restructuring authors see changes in assets, lines of business, capital structure and organizational structure, specifically employment. The total effect of restructuring is said to be captured by the changes in the operating profit of a company. Looking at the effect on both the accounting performance, including such measures as Return On Equity and Return On Investment and market performance, abnormal movements in stock price, researchers provided both case studies and research studies on all three types of restructurings: portfolio, financial and organizational. The conclusion made by the researchers is the following: the improvement of indicators from the financial restructuring is the largest, followed then by portfolio, organizational restructuring, however, showed the mean decrease in the performance. However, the constructed sample included many leveraged and management buyouts in the part of financial restructuring, thus there is some effect that can be prescribed to the external growth strategies. The controversial result is also presented by James A. Brickley and D. Van Drunen (1989), they stated that on average internal changes in structure result in value increase, however, those companies that were using restructuring in order to cut costs or increase their efficiency, on average experience a decrease in their stock price and revenue.

As we can see there are many works that cover the theme of internal and external growth, those that compare the choice among different strategies and those analyzing them separately. However, there are still many uncovered parts in this large part of corporate finance, and what is more, there will be even more with the future development of new markets, industries and new types of companies.

Summing up the analysis of the previous researches and the results of their tests I have come up with the following hypothesis, which I would like to test empirically:

H1: Choosing the external growth strategies will be better for a company willing to grow in emerging capital markets.

Also, I wanted to compare the results of the model when applied to a different sample of companies, consequently I set another hypothesis which I would like to look at: capital strategy diversification resource

H2: The results of the same models will be different for emerging and developed markets.

I decided to include this hypothesis basing on the results of previous studies that were saying that the choice of a strategy can be different in different markets, but they previously have analyzed not exactly the same question. For example, the work of Ivashkovskaya (2019) compared performance of diversification, while I am going to assess the impact of different strategies on growth.



2. Empirical Set-up

2.1 Data Sample

The sample obtained for the study was created manually, using Bloomberg data base, it includes the largest companies from countries, which are included in the MSCI Emerging Market Index. The main idea of this Morgan Stanley Index is in representing the performance of companies from emerging markets, it covers around 99% of the free float of emerging markets. The sample does not include the financial and banking industry due to the fact that they can account for a large part of their business performed off their balance sheet. The period taken for the analysis accounts for 10 years, from 2009 till 2018. Consequently, the sample resulted in 114 companies observed over a 10-year period. The companies were selected on the basis of their inclusion in the list of Forbes Global 2000, which represents 2000 largest listed companies in the world.

For another part of the work I have also collected data from the Bloomberg data base but for the developed markets. The countries included in the sample are US, UK, Germany, France and Italy. The period of observations is the same, from 2009 till 2018, and the sample consists of 97 companies. This sample is going to be used in order to compare the obtained results of emerging markets with those from the same models but applied to the new sample, representing developed markets. Companies represented in this sample are also those from the list of Forbes Global 2000.

In order to obtain data for the control variables and account for the effect of each country I will use the data taken from the World Bank. The time period is the same and is used for both samples of developed and emerging markets. The data is provided for each country included in the sample.



2.2 Model

In order to provide the analysis, I have chosen to regress three models that differ only in the dependent variable as long as the choice of exactly one variable that will show that a company grows is not obvious. Consequently, with the help of the works of L. E. Palich, L. B. Cardinal and C. C. Miller (2000), F. Delmar (2006) and M. S. Kumar (1985) the choice resulted in looking at Sales, Employment and Assets as size measures and measuring total growth is done, again as in the work of M. S. Kumar, as size at period t as a proportion of size in period t-1. Expecting that the result of the growth can be seen in an increase in Sales or Assets is quite predictable, but it cannot be said so about the employment. However, it can be explained, growth of a company can be described as a higher demand for its products and in order to be able to cope with this new demand the company will have to hire new employees. In some way, the usage of Employment can be even better then Sales or Assets as long as these measures may be subject to inflation, when being compared in their absolute values. Here are the models which will be used in order to test the set hypothesizes:

Log(Salesi,t/Salesi,t-1) = a0 + a1CoRi,t + a2MAoRi,t+ a3Leverage ratioi,t + a4EonGDPi,t + ctTime dummyt + biIndustry dummyi +

Log(Employmenti,t/Employmenti,t-1) = a0 + a1CoRi,t + a2MAoRi,t+ a3Leverage ratioi,t + a4EonGDPi,t + ctTime dummyt + biIndustry dummyi +

Log(Assetsi,t/Assetsi,t-1) = a0 + a1CoRi,t + a2MAoRi,t+ a3Leverage ratioi,t + a4EonGDPi,t + ctTime dummyt + biIndustry dummyi +

The description of the variables can be found in the (Appendix 1). The growth variables are logarithmized in order to adjust for the skewness of the sample.



2.3 Explanatory variables

Talking about the independent variables, the CoR variable is going to be used as a reflection of an internal growth strategy, growth by using its internal resources, the variable is constructed as capital expenditures of a company relative to the revenue for the previous year.

The MAoR represents the usage of a nonorganic growth. As M&A the Acquisition of affiliates/business/etc from the part of investing activities of Cash Flow statement will be used. The variable will also be taken relative to the revenue of the previous year.

Taking the revenue of the previous year is done due to the fact that the current revenue depends on the amount of money invested in the period.

2.4 Control variables

The use of control variables cannot be argued, they are very important in the analysis of the data, due to the fact that there can be some effects present which will differ, for example, from one country to another. Control variables used in this research can be described by their division into 3 parts, those controlling for effects of different countries, industry controls and the control of the capital structure of a firm.

In order to control for the effects that may arise due to the fact that the sample consists of large number of different countries I would use Government Expenditures. EonGDP, stands for the expenditures as a percentage of the GDP of a country. It is quite logical to use this indicator because the revenue of a company is related to this. Government spends money in different purposes the main ones in which we are interested are investments and also the case when government expenditures are used as an expansionary fiscal policy, providing both consumers and businesses with more money, which will boost the economy, motivating companies to grow.

The industries should also be controlled (Saptarshi Purkayastha, Tatiana S. Manolova and Linda F. Edelman 2012). In my research, as another control group, I am using industry dummies. The performance of companies differs not only from country to country but also from one industry to another. It was described in the work of just mentioned authors, they provided an analysis of different empirical researches, proving that the inclusion of industry controls is significant in an analysis. For example, they stated that: «Bettis and Hall (1982) found that the differences between the profitability of Rumelt's categories disappeared after correcting for the industry bias in the sample» Saptarshi Purkayastha, Tatiana S. Manolova and Linda F. Edelman (2012) Diversification and Performance in Developed and Emerging Market Contexts: A Review of the Literature* International Journal of Management Reviews .

Another control is used for the leverage, the capital structure is very important when talking about the performance and growth of a company. The reason for that is that managers may use debt in order to finance different projects and also just increase the value of a company by exploiting the benefit of a tax shield. This control was used in the research of Ivashkovskaya (2019), what is more this type of variable was used in two different parts of the book. In one chapter it was used in the format of Debt to Assets, while in another as Debt to Equity. In this work, the control for the capital structure of a firm was chosen to be Total Debti,t/Total Equityi,t.

Also, year dummies are of great use, economy and market are things that are changing, specially in the emerging markets. These changes occur very fast and sometimes are not really predictive, so in order to account for the fixed-time effects we can use year dummies (Bae, Kwon, Lee 2011)

2.5 Method

In order to estimate the three models proposed for this work the Fixed Effect method of estimation is going to be used. The motivation behind the usage of the FE models lies in the fact that it allows for the existence of some unobserved variables that are time-invariant. (Cameron & Trivedi 2005) The fact that the data is collected from different industries and countries may result in the omitted variable bias. If in case of the existence of this problem and if the model is estimated using Random Effects estimator, then the estimates will be inconsistent.

I ran the regressions using Fixed Effects and faced the fact that the (2) model is insignificant basing on the p-value for the whole model and, consequently, should not be used any further. (Appendix 2)

So, the further analysis is performed for models (1) and (3), which have log of changes in sales and assets as the dependent variables respectively. Then, the following tests were applied in order to check that the models are fine:

The Breusch-Pagan test was applied and the results stated that for both models (1) and (3) there is no heteroscedasticity and there is no need in usage of robust standard errors.(Appendix 3)

Another test used in the analysis is the Hausman LM test. The results were different for two models:

For the model (1) it turned out that Fixed Effects are present and, consequently, we should go on with the analysis of FE model;

For the model (3) the test says that the RE estimation is preferred, however this result does not make the FE estimator insufficient. And considering the data it will still be preferred to others.(Appendix 4)

I also looked at the variance inflation factors and correlation matrix in order to check for multicollinearity. The results showed that VIFs of all three models were not worrisome, consequently, we might not be afraid of the presence of the multicollinearity in the models.(Appendix 5, 6)

Summing everything up, in the analysis of the question set by the theme of the article the formed models were estimated using the Fixed Effects estimators. Which require weaker assumptions than those of RE or pooled models and are more stable. Stability of the model is one of the main things that are important on practice, it is though more important than the efficiency of the model, which can motivate the choice of either OLS method of estimation or Random Effects. However, fixing the effect of each company results in the creation of too many dummy variables, consequently, in order not to deal with problems that may occur from this, I decided to use Fixed Effects based on industries in which companies operate. The method results in the same estimates as would have LSDV estimation, in which dummies are created.

3. Results

The main aim of the work was to identify which types of strategies are more efficient in their use for companies developing their businesses in the emerging markets. Also, it is interesting to see the difference in choice between emerging and developed markets. Estimating models provided the following results, which will be discussed in this section of the work.

3.1 Emerging markets

Firstly, I would like to start with the discussion of the model (1). In order to provide the answer to the first hypothesis we have to focus on the coefficients of two factors, CoR and MAoR. As we can see, both of the coefficients turned out to be significant and have a positive effect on growth of Sales. However, the difference between them is significant, as we can see a 1 dollar increase in Capital Expenditures per each dollar of Revenue for the previous year of a company operating in an emerging market on average will result in an approximately 6.8% growth in sales growth rate. The increase coming from Mergers and Acquisitions is much higher, from the estimated model we see that the same amount of money but spent not on CAPEX will result in an approximately 26.3% increase. So, the results of the model stated that, considering the increase in sales as a measure of growth, the usage of Mergers and Acquisitions should be preferred relative to the internal growth strategies. (Appendix 2)

Continuing the analysis, I would like to pass to the second model, model (3). Focusing on the two main variables again we can see that the answer will be the same, in favor of the acquiring external growth. The difference in coefficients, when using assets as growth measure is even larger. This model says that a 1 dollar increase in capital expenditures for each dollar of revenue for the previous year will increase the growth rate of sales by approximately 4.3%, while the same increase on M&A deals will have an effect of 78.7% increase. (Appendix 2)

In order to sum this part up, I have to conclude that in emerging capital markets using external growth strategies is much more efficient. Consequently, the Hypothesis 1 is not rejected, supporting the results of Ivashkovskaya (2019), which said that the relationship between diversification and strategic efficiency in developing markets, using external growth has U-shaped dependence, while using internal development results in a decrease of efficiency in a company.

3.2 Developed vs Emerging markets

Considering developed markets, the same models were estimated using Fixed Effects. The results were exactly opposite. The model (1), with sales as an indicator of growth, showed that a company, choosing to grow using its own resources, will on average outperform those companies that acquire external growth. The coefficient of Capital Expenditures over the Revenue for the previous year was estimated to be 0.576, which states that if the whole revenue for the previous year will be invested in Capital Expenditures that will increase the growth rate of sales by 57.6%. And if the revenue is used for M&As then the increase will be even less than 1%. (Appendix 7)

The model (3) supports the results of the first model that internal growth strategies should be preferred. It even says that mergers result in a decrease in the growth of a company, the coefficient of MAoR is (-0.00316). (Appendix 7)

Due to the fact that the data on employees from this sample is better than those provided by companies from emerging markets, model (2) became also significant and the results of its estimation using FE also supports those of two previous models. It can be seen that the coefficients of CoR and MAoR are 0.497 and 0.00795 respectively. (Appendix 7)

Summing up the results of the regressions we see that Hypothesis 2 is not rejected as well. We see that the results are different for the emerging and developed markets. This may be due to the fact that for the companies in developed markets it is more cost-efficient to choose the way to grow using the resources of a company rather than buying companies.

3.3 Robustness check

The robustness check is performed in order to prove that the models are good and stable. The exact thing that I would like to look at is, according to Leamer, whether the sign and significance of the main coefficients of interest will not change.

The first way I am going to test this is by addition of more control variables. The effect of presence of different countries was fixed, by creating dummies for each of them, in addition to industries. The results of the check stated that the models are good as long as the signs of coefficients of CoR and MAoR did not change, as well as the level of their significance. The conclusion on the Robustness was the same for both models (1) and (3). (Appendix 8)

The second way is dropping some observations from the existing sample. I dropped the observations of the year 2018. However, the results did not change, the signs of the variables stayed the same and kept their significance unchanged, consequently, the model proved to be significant. (Appendix 9)

The results can also be supported by the fact that the models turned out to be significant when regressed on the sample of data of companies from developed markets. Nevertheless, the results stated different effects, but they were significant, adding more confidence in the fact that the tested models are good. (Appendix 7)

In case if the coefficients of the variables turned out to be insignificant, then I would have to conclude that the growth of a company is not related to either the cash spend on acquisition of fixed or intangible assets or on acquisition of subsidiaries/businesses/affiliates and etc.



Conclusion

I would like to divide the conclusion into two parts, the first one will be focused on the analysis of the main question of the choice between two ways to grow in emerging markets, while in the second part I will look at the comparison between the emerging and developed markets.

This work was done in order to analyze and answer such an important question of the choice of a strategy for the development and growth of a company in emerging markets. Considering the previous researches, the conclusion is not very clear, some of the works say that, for example, M&A deals are not good for a company (Grigorieva, S., & Petrunina, T. 2013; Ghosh A. 2001), while some say that they increase the performance of a company (Powell, R., & Stark, A. 2005). Comparing strategies in terms of obtaining diversification in the work of Ivashkovskaya (2019) stated that external growth may be good from some levels, while organic only decreases the value of a company.

The analysis in this article was performed on the panel data that consists of 114 companies from developing countries, which are included in the MSCI Emerging Markets Index (Appendix 1). The results of this empirical study stated that for a company willing to grow in an emerging capital market the external growth strategies should be preferred to the internal ones. This result was supported by both models that account for different growth measures.

The second part of the analysis is concentrated on the comparison between two different types of market. The sample, representing developed markets, consists of 97 companies from the USA, the UK, Germany and Italy. The result is different, as it was expected, due to different growth opportunities, difference in stability of the market types and so on. Estimating the same models provided the result that it is better to develop from the inside of a company, using its own resources. The result that the strategies chosen should differ from one market to another can also be seen in the work of Ivashkovskaya (2019).

Models constructed proved that they are applicable and can be further used in order to investigate the question of choosing a strategy. They have been applied to two samples of different companies, and worked well in both.



References

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12. Grigorieva, S., & Petrunina, T. (2013). The Performance of Mergers and Acquisitions in Emerging Capital Markets: New Evidence. SSRN Electronic Journal.

13. Healy, P., Palepu, K., & Ruback, R. (1992). Does corporate performance improve after mergers?. Journal Of Financial Economics, 31(2), 135-175.

14. Hussey, D. (1999). Igor Ansoff's continuing contribution to strategic management. Strategic Change, 8(7), 375-392.

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20. McMahan, J., & Hester, T. (2000). Is organic change a better option than acquisitions as a growth strategy?. Real Estate Issues. Retrieved 10 June 2020, from.

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27. Ивашковская, И. (2019). Стратегические финансовые решения компаний на развивающихся рынках капитала.



Appendix

MSCI Emerging Markets:

Argentina, Brazil, Chile, China, Colombia, Czech Republic, Egypt, Greece, Hungary, India, Indonesia, Korea, Malaysia, Mexico, Pakistan, Peru, Philippines, Poland, Qatar, Russia, Saudi Arabia, South Africa, Taiwan, Thailand, Turkey, and United Arab Emirates

MSCI Developed countries:

Canada, USA, Austria, Belgium, Denmark, Finland, France, Germany, Ireland, Israel, Italy, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, Australia, Hong Kong, Japan, New Zealand and Singapore

Descriptive Statistics

Variable	Obs	Mean	Std.Dev.	Min	Max
ldRev	1024	.08	.268	-2.016	3.696
ldEMPL	716	.051	.357	-2.812	5.17
ldTA	1023	.073	.251	-.785	2.434
CoR	939	.153	.403	0	11.58
MAoR	948	.018	.076	-.197	1.037
td_te	1137	3636.038	44285.11	0	566000
EonGDP	964	26.382	9.327	3.895	59.901

	(1)	(2)	(3)
VARIABLES	ldRev	ldEMPL	ldTA
CoR	0.0680***	0.0154	0.0426**
	(0.0150)	(0.0252)	(0.0171)
MAoR	0.263***	0.00687	0.787***
	(0.0899)	(0.216)	(0.102)
td_te	1.84e-07	1.35e-07	4.33e-08
	(1.90e-07)	(3.17e-07)	(2.17e-07)
EonGDP	-0.00224**	-0.00106	-0.00328***
	(0.000983)	(0.00180)	(0.00112)
2011.year	-0.0110	-0.0130	-0.0602*
	(0.0274)	(0.0558)	(0.0312)
2012.year	-0.165***	-0.0726	-0.00481
	(0.0272)	(0.0550)	(0.0310)
2013.year	-0.177***	-0.0320	-0.0878***
	(0.0272)	(0.0544)	(0.0310)
2014.year	-0.218***	-0.0785	-0.212***
	(0.0270)	(0.0542)	(0.0308)
2015.year	-0.360***	-0.00394	-0.193***
	(0.0271)	(0.0542)	(0.0308)
2016.year	-0.216***	-0.101*	-4.02e-05
	(0.0269)	(0.0536)	(0.0306)
2017.year	-0.0871***	-0.0724	0.00755
	(0.0268)	(0.0534)	(0.0305)
2018.year	-0.151***	-0.0730	-0.127***
	(0.0272)	(0.0541)	(0.0310)
Constant	0.266***	0.124**	0.202***
	(0.0316)	(0.0625)	(0.0360)
Observations	802	567	802
R-squared	0.304	0.017	0.214
Number of IND	25	21	25

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

For model (1):

Breusch and Pagan Lagrangian multiplier test for random effects

ldRev[IND,t] = Xb + u[IND] + e[IND,t]

Estimated results:

| Var sd = sqrt(Var)

---------+-----------------------------

ldRev | .0453021 .2128429

e | .0313853 .177159

u | 0 0

Test: Var(u) = 0

chibar2(01) = 0.00

Prob > chibar2 = 1.0000

For model (3):

Breusch and Pagan Lagrangian multiplier test for random effects

ldTA[IND,t] = Xb + u[IND] + e[IND,t]

Estimated results:

| Var sd = sqrt(Var)

---------+-----------------------------

ldTA | .0553109 .2351826

e | .0407738 .2019253

u | 0 0

Test: Var(u) = 0

chibar2(01) = 0.00

Prob > chibar2 = 1.0000

Hausman (1978) specification test for model (1)

	Coef.
Chi-square test value	19.997
P-value	.045

Hausman (1978) specification test for model (3)

	Coef.
Chi-square test value	4.283
P-value	.961



	VIF	1/VIF
IIND 25	3.725	.268
IIND 20	3.681	.272
IIND 18	3.302	.303
IIND 4	2.797	.358
IIND 24	2.768	.361
IIND 11	2.737	.365
IIND 23	2.678	.373
IIND 21	2.378	.42
IIND 27	2.299	.435
IIND 2	2.229	.449
Iyear 2017	1.912	.523
Iyear 2016	1.908	.524
IIND 3	1.902	.526
IIND 5	1.894	.528
Iyear 2018	1.881	.532
Iyear 2015	1.864	.536
Iyear 2014	1.855	.539
Iyear 2011	1.832	.546
Iyear 2013	1.829	.547
Iyear 2012	1.827	.547
IIND 22	1.783	.561
IIND 26	1.688	.592
IIND 13	1.658	.603
IIND 16	1.623	.616
IIND 9	1.615	.619
EonGDP	1.615	.619
IIND 12	1.324	.755
IIND 10	1.311	.763
IIND 6	1.309	.764
IIND 19	1.305	.766
IIND 17	1.179	.848
IIND 14	1.144	.874
IIND 28	1.143	.875
td te	1.104	.906
CoR	1.085	.922
MAoR	1.071	.934
Mean VIF	1.924	.

| ldRev CoR MAoR td_te EonGDP

-------------+---------------------------------------------

ldRev | 1.0000

CoR | 0.1639 1.0000

MAoR | 0.1406 0.0021 1.0000

td_te | -0.0476 -0.0218 -0.0136 1.0000

EonGDP | -0.0943 -0.0417 -0.0792 0.2261 1.0000

| ldTA CoR MAoR td_te EonGDP

-------------+---------------------------------------------

ldTA | 1.0000

CoR | 0.0813 1.0000

MAoR | 0.2791 0.0021 1.0000

td_te | -0.0498 -0.0218 -0.0136 1.0000

EonGDP | -0.1477 -0.0417 -0.0792 0.2261 1.0000

	(1)	(2)	(3)
VARIABLES	ldRev	ldEMPL	ldTA
CoR	0.576***	0.497***	0.526***
	(0.0582)	(0.0633)	(0.0734)
MAoR	0.00796***	0.00795***	-0.00316***
	(0.000617)	(0.000667)	(0.000777)
td_te	9.18e-07	3.10e-06	-7.90e-06
	(6.38e-06)	(6.88e-06)	(8.04e-06)
EonGDP	-0.00241***	-0.00144**	-0.00253***
	(0.000657)	(0.000709)	(0.000829)
2011.year	0.00265	0.0147	-0.0152
	(0.0206)	(0.0225)	(0.0259)
2012.year	-0.0941***	0.00984	-0.0297
	(0.0205)	(0.0223)	(0.0259)
2013.year	-0.0755***	-0.00722	-0.0372
	(0.0205)	(0.0223)	(0.0259)
2014.year	-0.0926***	0.00436	-0.0760***
	(0.0205)	(0.0223)	(0.0258)
2015.year	-0.171***	0.00178	-0.0675***
	(0.0205)	(0.0223)	(0.0258)
2016.year	-0.0961***	-0.0218	-0.0359
	(0.0204)	(0.0223)	(0.0258)
2017.year	-0.0455**	-0.0129	0.0229
	(0.0205)	(0.0223)	(0.0258)
2018.year	-0.0304	0.0151	-0.0366
	(0.0205)	(0.0223)	(0.0258)
Constant	0.148***	0.0357	0.134***
	(0.0263)	(0.0286)	(0.0332)
Observations	840	835	839
R-squared	0.516	0.406	0.101
Number of IND	38	38	38

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

	(1)	(2)	(3)
VARIABLES	ldRev	ldEMPL	ldTA
CoR	0.0605***	0.0124	0.0347**
	(0.0154)	(0.0264)	(0.0173)
MAoR	0.265***	0.0684	0.806***
	(0.0902)	(0.223)	(0.102)
td_te	1.85e-07	1.28e-07	2.07e-07
	(2.18e-07)	(3.84e-07)	(2.45e-07)
EonGDP	-0.00236	0.00169	0.00442
	(0.00342)	(0.00695)	(0.00385)
2011.year	-0.00973	-0.00909	-0.0507*
	(0.0273)	(0.0564)	(0.0307)
2012.year	-0.165***	-0.0716	-0.00335
	(0.0269)	(0.0554)	(0.0303)
2013.year	-0.177***	-0.0339	-0.0896***
	(0.0270)	(0.0548)	(0.0304)
2014.year	-0.223***	-0.0812	-0.224***
	(0.0269)	(0.0547)	(0.0303)
2015.year	-0.365***	-0.0111	-0.217***
	(0.0280)	(0.0563)	(0.0315)
2016.year	-0.221***	-0.106*	-0.0224
	(0.0277)	(0.0554)	(0.0311)
2017.year	-0.0921***	-0.0758	-0.0127
	(0.0273)	(0.0548)	(0.0308)
2018.year	-0.157***	-0.0768	-0.146***
	(0.0276)	(0.0554)	(0.0311)
2.CNT	-0.308***	-0.0758	-0.459***
	(0.0725)	(0.138)	(0.0816)
3.CNT	-0.361***	-0.0599	-0.463***
	(0.0764)	(0.155)	(0.0860)
5.CNT	-0.300***	-0.0585	-0.405***
	(0.0749)	(0.137)	(0.0843)
6.CNT	-0.291***	-0.0867	-0.411***
	(0.0998)	(0.188)	(0.112)
7.CNT	-0.292**	-0.133	-0.629***
	(0.123)	(0.239)	(0.139)
8.CNT	-0.288**	-0.212	-0.547***
	(0.112)	(0.212)	(0.126)
9.CNT	-0.303***	-0.0895	-0.300***
	(0.0830)	(0.164)	(0.0934)
10.CNT	-0.303***	-0.0851	-0.302***
	(0.0838)	(0.156)	(0.0944)
11.CNT	-0.281***		-0.335***
	(0.0741)		(0.0834)
12.CNT	-0.336***	-0.103	-0.389***
	(0.0753)	(0.155)	(0.0848)
13.CNT	-0.293***	-0.0245	-0.371***
	(0.0674)	(0.126)	(0.0759)
14.CNT	-0.264***	0.0789	-0.151*
	(0.0762)	(0.142)	(0.0858)
15.CNT	-0.267***	-0.146	-0.440***
	(0.0831)	(0.158)	(0.0935)
17.CNT	-0.313***	-0.0175	-0.430***
	(0.0650)	(0.127)	(0.0732)
18.CNT	-0.268***	-0.0358	-0.386***
	(0.0687)	(0.131)	(0.0773)
19.CNT	-0.383***	-0.158	-0.496***
	(0.0831)	(0.157)	(0.0935)
21.CNT	-0.290***	-0.146	-0.329***
	(0.0705)	(0.161)	(0.0794)
22.CNT	-0.306***	-0.0741	-0.480***
	(0.0844)	(0.160)	(0.0951)
23.CNT	-0.326***	-0.120	-0.289**
	(0.106)	(0.214)

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