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Introduction

• Firm size has been a major area of investigation in corporate finance.

• Coase [1] is credited for seminal work in this area, questioning what determines firm boundaries and how they affect resource allocation.

• Researchers have examined the impact of firm size on various outcomes, including:

  • Executive compensation [4–6]

  • Innovation [7–11]

  • Organizational change [12]

  • Functional complexity [13]

  • Hiring practices and job search behavior [14]

  • Unemployment [15]

  • Managerial succession [16]

  • Buying influences [17]

  • Job shift patterns [18]

  • Individual’s ethical predispositions [19]

  • Corporate social responsibility [20, 21]

• In finance, firm size has been studied in relation to:

  • Capital structure [22, 23]

  • Financial policy [24]

  • Dividend policy [24–27]

  • Leverage [28]

  • Merger and acquisition [29]

• Dang et al. [30] investigated the impact of firm size on eight practices of empirical corporate finance using data from Latin America.

• Hashmi et al. [31] conducted research on the same issue using data from Sharı ‘ah compliant firms.

• All measures of firm size are theoretically different and capture different aspects of size.

• Ebel Ezeoha [24] argued that mixed results of past researchers on relationship between size and leverage is due to the difference in definitions of firm size employed by all the papers.

• This study examines the impact of different measures of firm size, namely total assets, total sales, market capitalization and total number of employees on seven important areas/practices of corporate finance including financial policy, investment policy, dividend policy, diversification, managerial compensation and incentives, firm performance and corporate governance.

• This study checks for R2 sensitivity, beta coefficient sensitivity and significance level sensitivity of all four different measures of firm size with these seven areas.

• This study uses data from five emerging economies, i.e. Brazil, Russia, India, China and South Africa (BRICS).

• The study replicates and extends Dang et al. [30] study, includes number of employees as another measure of firm size, and uses data from BRICS economies.

• Overall, results supported the formulated hypotheses.

• Different proxies of firm size have been found to differently relate to all areas/practices of corporate finance based on beta coefficient value, R2 and sign of coefficient.

Theory and Hypotheses

Firm Size and Financial Policy

• Financial policy includes decisions about debt–equity mix, maturity structure, cash holdings, and financing and hedging decisions.

• Large firms are generally more levered than small firms because they have more investment opportunities and are more creditworthy.

• Ebel Ezeohai [24] argued that large firms would be able to get more financing because of its growth.

• Banks are more willing to give debt to institutions with more creditworthiness and large firms because of their reputation have higher creditworthiness.

• Mixed results in previous research regarding the relationship between firm size and leverage are attributed to differences in the definitions of firm size used [24].

• H1: Firm size has a significant impact on financial policy.

Firm Size and Dividend Payout Policy

• Dividend policy explains whether a firm pays out dividend to investors or retains its earnings for future investments.

• Small firms focus more on growth, causing them to pay fewer dividends.

• Large firms are more stable and issue dividends to gain investor trust, signaling financial stability [25–27, 42–44].

• Agency theory suggests dividends reduce cash available to managers, aligning with shareholder interests [40].

• H2: Firm size has a significant impact on dividend policy

Firm Size and Investment Policy

• Investment policy refers to the capital expenditure a firm is willing to make.

• Large companies can handle larger projects and afford sophisticated investment appraisal techniques.

• Large firms have more access to financing and internal resources, enabling more capital expenditure [31].

• H3: Firm size has a significant impact on investment policy

Firm Size and Diversification

• Diversification is studied in managerial research including strategic management, industrial organization and financial management.

• Business diversification requires financing, which is more accessible to large firms through banks and stock markets [49].

• Large firms can hire experts for diversified operations due to higher managerial compensations and incentives.

• Small firms tend to grow by using concentration strategy.

• Large firms tend to diversify their product and business portfolio.

• H4: Firm size has a significant impact on diversification

Firm Size and Firm Performance

• Firm performance refers to financial performance, i.e. return on investments made by the firm and its shareholders in firm itself.

• Larger firms typically have larger operations, higher sales, and greater revenue, leading to higher profits and returns on investments/assets and return on equity.

• Large firms are able to generate investor’s trust which translates to high investments.

• H5: Firm size has a significant impact on firm performance

Firm Size and Managerial Compensation and Incentives

• Managerial compensation and incentives refer to the salary and other benefits which top executive of any company receive.

• Agency theory suggests managerial compensation minimizes conflicts between shareholders and managers [40, 54].

• RBV assumes top management as a unique pool of resources that are valuable, rare, imperfectly imitable and non-substitutable (VRIN), and thus, this top executive human resource is one which is the source of competitive advantage of firm [56].

• Large firms enhance compensation to retain top management and mitigate agency costs [55, 57].

• H6: Firm size has a significant impact on compensation and incentives

Firm Size and Corporate Governance Mechanism (Board Structure)

• Corporate governance involves rules and structures for wielding power and includes consideration of use and abuse of power.

• Three theories base the governance mechanisms: agency theory of Jensen and Meckling [40], resource dependence theory and stewardship theory.

• Effective board structures include outside independent directors [58, 59].

• Larger firms tend to have larger boards and more independent directors to comply with regulations and represent shareholders [30].

• H7: Firm size has a significant impact on board structure.

Sensitivity of Firm Size Measures

• Different measures of firm size yield different results, with mixed outcomes, which is attributed to the difference in definitions of firm size [24, 30].

• H8: Different measures of firm size would have different sensitivities regarding different practices of corporate finance.

Methods

Data

• The study used data from BRICS countries (Brazil, Russia, India, China, and South Africa), treating them as one block.

• It included data from the non-financial sector, excluding financial firms.

• Data of 25 companies from each country over a period of 10 years (2006–2015) were collected.

• Selection of companies was based on capitalization.

• Data were collected from company’s filings to SEC of that respective country, website of that firm, annual reports or independent websites.

• Pay level information was determined based on whether companies disclosed compensation details beyond the mandatory requirements.

Measures

• Firm size

  • Total assets: $Ln (total assets)$

  • Total sales: $Ln (total sales)$

  • Market value of equity: $Ln (market cap)$

  • Number of employees: $Ln (number of employees)$

• Financial policy

  • Financial leverage: Book value of debt/equity

  • Financial leverage: Total asset/total equity

• Payout policy

  • Dividend payout: Dividend payment (dummy)

• Investment policy

  • CAPEX: Net CAPEX/total asset

• Diversification

  • Business segments: $Ln (no. of business segments)$

• Firm performance

  • ROA: Profit after taxes/total asset

  • ROE: PAT/total equity

• Managerial compensation and incentives

  • Pay level: Pay level and disclosers (dummy)

• Corporate governance (board structure)

  • Board independence: $Ln (no. of independent directors on board)$

  • $Ln (no. of non-executive directors on board)$

Control Variables

• Control variables were identified based upon the benchmark papers of that area and those as used by Dang et al. [30].

• The benchmark paper for firm performance was of Mehran [63]; for board structure, it was of Linck et al. [64]; Frank and Goyal [65] for leverage; and Coles et al. [66] for investment policy and diversification.

Results

Correlation Analysis

• The total number of observations of the study was 1250.

• Firm size measured by different proxies are differently related to corporate choices.

  • Total assets is significantly correlated with financial leverage (assets/equity) (r=0.11, p<0.05), financial leverage (debt/equity) (r=0.17, p<0.05), business segments (r=0.20, p<0.05), dividend policy (r=0.18, p<0.05), CAPEX (r=0.16, p<0.05), independent directors (r=0.20, p<0.05), non-executive directors (r=0.23, p<0.05), pay level (r=0.18, p<0.05), ROA (r=0.19, p<0.05), and ROE (r=0.06, p<0.10).

  • Total sales is significantly correlated with financial leverage (assets/ equity) (r=0.19, p<0.05), financial leverage (debt/equity) (r=0.16, p<0.05), business segments (r=0.19, p<0.05), dividend policy (r=0.25, p<0.05), CAPEX (r=0.34, p<0.05), independent directors (r=0.23, p<0.05), non-executive directors (r=0.18, p<0.05), pay level (r=0.20, p<0.05), ROA (r=0.13, p<0.05), and ROE (r=0.17, p<0.05).

  • Market value of equity is significantly correlated with financial leverage (assets/equity) (r=−0.08, p<0.05), financial leverage (debt/equity) (r=−0.06, p<0.10), business segments (r=0.21, p<0.05), dividend policy (r=0.23, p<0.05), CAPEX (r=0.05, p<0.05), independent directors (r=0.46, p<0.05), non-executive directors (r=0.14, p<0.05), pay level (r=0.27, p<0.05), ROA (r=0.33, p<0.05), and ROE (r=0.16, p<0.10).

  • Total number of employees is significantly correlated with financial leverage (assets/equity) (r=−0.05, p<0.10), business segments (r=0.24, p<0.05), dividend policy (r=0.28, p<0.05), CAPEX (r=0.14, p<0.05), independent directors (r=0.13, p<0.05), non-executive directors (r=0.19, p<0.05), pay level (r=0.24, p<0.05), and ROE (r=0.13, p<0.10).

Regression Analysis

Firm Size and Financial Policy

• All proxies of firm size are significantly related to financial leverage of firm:

  • total assets–financial leverage (β=−0.03, p<0.01)

  • total sales–financial leverage (β=−0.21, p<0.01)

  • MVE– financial leverage (β=−0.37, p<0.01)

  • number of employees and financial leverage (β=−0.003, p<0.01).

• Value of R2 for all these four models was 0.03, 0.17, 0.05 and 0.02, respectively.

• Hausman test was done to see whether fixed effect model is appropriate or random is appropriate.

• All proxies of firm size are significantly yet differently related to financial leverage:

  • total assets–financial leverage (β=0.05, p<0.1)

  • total sales– financial leverage (β=−0.07, p<0.01)

  • MVE–financial leverage (β=−0.19, p<0.05)

  • number of employees and financial leverage (β=−0.01, p<0.01).

• The value of R2 for these relationships was 0.07, 0.07, 0.07 and 0.20, respectively.

• Firm size as measured by total assets is significantly related to debt–equity (β=0.07, p<0.01), total sales are also significantly related to debt–equity (β=0.08, p<0.01), MVE is significantly related to debt–equity (β=0.03, p<0.01) and number of employees and debt–equity relationship is also significant (β=0.03, p<0.01).

• The value of R2 for these relationships was 0.04, 0.04, 0.03 and 0.09, respectively.

• The table shows that there exists insignificant relationship between firm size as measured by number of employees and debt–equity $(β=0.02, ns)$. However, relationship between firm size as measured by total assets and debt–equity is significant (β=0.21, p < 0.01), firm size as measured by MVE and debt–equity is significant (β=0.06, p < 0.01) and firm size as measured by total sales and debt–equity is significant (β=0.11, p < 0.01).

• These results thus support hypothesis H1.

Firm Size and Investment Policy

• All proxies of firm size are significantly negatively related to CAPEX, i.e. total assets (β=−0.08, p < 0.01), sales (β=−0.09, p < 0.01), MVE (β=−0.08, p < 0.01) and number of employees (β=−0.09, p < 0.01).

• Value of R2 for all these four models was 0.18, 0.19, 0.17 and 0.14, respectively.

• Results of fixed effect regression show that relationship of number of employee–CAPEX is insignificant $(β=0.002, ns)$, total assets–CAPEX relationship is significant (β=−0.04, p < 0.05), total sales–CAPEX is insignificant $(β=−0.01, ns)$ and MVE–CAPEX relationship is also insignificant $(β=−0.0001, ns)$.

• The value of R2 for these models was 0.09. These results thus support hypothesis H3.

Firm Size and Diversification

• Firm size as measured by total assets is significantly related to business segments of firm (β=0.0731, p<0.05) and total sales–business segments relationship is also significant (β=0.058, p<0.05).

• There exists insignificant relationship between MVE and business segments $(β=0.032, ns)$ and number of employees and number of business segments $(β=0.0057, ns)$.

• Value of R2 for all these four models was 0.06, 0.06, 0.05 and 0.05, respectively.

• Results of random effect show that there exists significant relationship between firm size as measured by number of employees and number of business segments (β=−0.123, p<0.01).
  Relationship between total sales and number of business segments is also significant (β=0.055, p<0.1), relationship of MVE and number of business segments is significant (β=0.0309, p<0.1) and total assets and number of business segments is insignificant $(β=0.0232, ns)$.

• These results support hypothesis H4.

Firm Size and Firm Performance

• All proxies are significantly related to ROA: total assets and ROA (β=−1.18, p<0.01), MVE–ROA relationship (β=−1.24, p<0.01), total sales and ROA (β=−1.23, p<0.01) and number of employees and ROA (β=0.95, p<0.01).

• Value of R2 for all these four models was 0.08, 0.08, 0.07 and 0.03, respectively, for total asset–ROA, total sales–ROA, MVE–ROA and number of employees–ROA.

• The table shows that there exists significant relationship between firm size as measured by total assets and ROA (β=0.20, p<0.01). The relationship between firm size as measured by total sales and ROA is insignificant (β=−0.84, p<0.01). The relationship of firm size as measured by MVE and ROA is significant (β=0.39, p<0.01). The relationship between firm size as measured by number of employees and ROA is also significant (β=0.56, p<0.01).

• In case of ROE as DV, pooled OLS regression as shown in Table 9 shows that all proxies are significantly related to ROE: total asset (β=−1.05, p<0.01), total sales (β=−1.10, p<0.01), MVE (β=−1.12, p<0.01) and number of employees (β=0.84, p<0.01).

• The table shows that total asset–ROE relationship is significant (β=0.11, p<0.01), total sales–ROE relationship is insignificant $(β=−0.03, ns)$, MVE–ROE relationship is significant (β=0.11, p<0.01) and number of employee– ROE relationship is significant (β=0.17, p<0.01).

• Support for hypothesis H5 is thus found from this result.

Firm Size and Board Structure

• Firm size as measured by total assets is significantly related to number of independent directors (β=0.03, p < 0.05). It can also be seen that number of employees– number of independent directors relationship (β=0.09, p < 0.01) and MVE and number of independent directors relationship (β=0.05, p < 0.01)are significant.

• There exists an insignificant relationship between total sales and number of independent directors $(β=0.02, ns)$.

• The table shows that there exists insignificant relationship between firm size as measured by number of employees and number of independent directors $(β=−0.01, ns)$. All other models are insignificant: total assets and number of independent directors (β=0.04, p<0.01), sales and number of independent directors (β=0.03, p<0.05) and MVE and number of independent directors (β=0.09, p<0.01).

• All proxies are significantly related to number of non-executive directors, i.e. total assets (β=0.18, p<0.01), total sales (β=0.20, p<0.01), MVE (β=0.16, p<0.01) and number of employees (β=0.03, p<0.01).

• The table shows that all proxies are significantly related to number of non-executive directors, i.e. total assets (β=0.16, p<0.01), total sales (β=0.11, p<0.01), MVE (β=0.15, p<0.01) and number of employees (β=0.04, p<0.01).

• Hypothesis H7 is thus supported.

Firm Size and Dividend Policy

• Firm size as measured by total assets is insignificantly related to dividend policy $(β=0.013, ns)$. For total sales as measure of firm size, results are also same, i.e. insignificant $(β=0.045, ns)$. Relationship between MVE and dividend policy is significant (β=0.136, p < 0.01). The last proxy of firm size taken by this study, i.e. number of employees, is significantly related to dividend policy (β=0.185, p < 0.01) and positive.

• These results thus support hypothesis H2.

Firm Size and Pay Level

• All proxies are significantly related to pay level, i.e. total assets (β=0.71, p<0.01), total sales (β=0.53 p<0.01), MVE (β=0.51, p<0.01) and number of employees (β=0.21, p<0.01).

• These results are thus supporting hypothesis H6.

Sensitivity of Firm Size Measures

• Different measures of firm size are differently related to different practices.

• Firm size does change its sign from proxy to another even for a same area.

• These results are thus supporting hypothesis H8 and have serious implications.

Robustness Test

• For robustness issue, we used firm fixed effect regression as suggested by Dang et al. [30]. Although none of the papers in the field have applied GMM to look for dynamic modelling, we applied GMM to test for dynamic modelling. Our results of GMM however forced us to stick with original OLS estimates.

Discussions

• The study was done with the purpose of determining impact of different measures of firm size on seven important areas of corporate finance (financial policy, dividend policy, investment policy, diversification, firm performance, compensation and incentives and board structure (corporate governance).

• Overall, the results indicate that firm size is significantly related to these areas of corporate finance. More importantly, results show that different measures of firm size are differently related to these areas.

• This difference can be seen in the beta coefficient value. It can also be seen that in some cases, for one practice different measures of firm size have different signs of coefficient meaning that they are differently related to that practice, i.e. one might be positive, and the other proxy is negatively related to that practice.

• Similarly, each proxy has different explanatory powers towards each practice of corporate finance (in most cases). This means that different proxies have different relationship towards different areas of corporate finance.

• The use of proxy of firm size in that context is not well justified.

• The first hypothesis of the study was that firm size has a significant impact on financial policy. This hypothesis has been supported by the results.

• The second hypothesis of the study was that firm size has a significant impact on dividend policy. This hypothesis has also been supported.

• The third hypothesis of the study was that firm size has a significant impact on investment policy. Results support this hypothesis too.

• The fourth hypothesis of the study was that firm size has a significant impact on diversification. This hypothesis has also been supported by the results of the study.

• The fifth hypothesis of the study was that firm size has a significant impact on firm performance. Using ROA and ROE as measures of performance study found support for the relationship between size and performance.

• The sixth hypothesis of the study was that firm size has a significant impact on compensation and incentives. Using pay level as proxy of compensation, study has found some support towards hypothesis.

• The seventh hypothesis of the study was that firm size has a significant impact on board structure. Results of the study support this hypothesis.

• Last hypothesis of the study was that different measures of firm size have different sensitivities regarding different practices of corporate finance. The hypothesis has been fully supported.

Conclusion

• The aim of the study was to examine impact of different measures of firm size on seven important areas of corporate finance which are financial policy, dividend policy, investment policy, diversification, firm performance, compensation and incentives and board structure (corporate governance).

• Data of five countries, i.e. Brazil, Russia, India, China and South Africa, were analysed. Overall results supported the hypotheses. Study concludes that different proxies of firm size are differently related to practices of corporate finance based on sign, significance and R2.

• All proxies capture different aspects of firm size and have different implications for corporate finance. Thus, this study confirms “measurement effect” in “size effect”. Unfortunately, this means that many of past studies are not robust and are biased. Researchers thus need to be careful when selecting any proxy of firm size for their research keeping in mind the scope and context of their work. Choosing a proxy thus is a theoretical and empirical question.

• Our study provides important guidelines for researchers, managers and investors at large.

• Every study has its own merits and demerits. Similarly, this study also has some limitations

  • Data availability was the major limitation of the study

  • The study used data only for 10 years

  • The study used data only from five countries and from only 25 companies of each country

  • The study used only four proxies of firm size which were felt most important

  • Study used only seven major practices of corporate finance.

  • Study used sign, significance and R2 sensitivity.

  • Study used dummy variable for dividend policy.