Determinants of corporate profitability: an empirical study of Indian drugs and pharmaceutical industry.
This paper provides an empirical evidence about the determinants of profitability of selected companies in drugs and pharmaceutical industry in India. It is based on a sample of fifty firms in drugs and pharmaceutical industry drawn from PROWESS database developed by CMIE. It covers a period of ten years from 1995-6 to 2004-5. The profitability of firms has been measured in terms of average return on capital employed. In order to study the determinants of profitability, ten explanatory variables, i.e., size (total assets), past profitability (OPR, NPR), age, advertising intensity, retention ratio, liquidity, efficiency ratios (inventory turnover ratio, debtor turnover ratio, asset turnover ratio), long term finance, market share, and research and development intensity were chosen for empirical investigation. Multiple regression analysis was used to develop a model to identify the determinants of profitability of firms in this industry. The results revealed that age, efficiency ratio, past profitability, and research and development intensity are statistically significant in determining the profitability of firms in drugs and pharmaceutical industry.

Key words : Performance, Profitability, Efficiency, Turnover, Variables.

Article Type:
Pharmaceutical industry
Liquidity (Finance)
Beef cattle
Chander, Subhash
Aggarwal, Priyanka
Pub Date:
Name: Paradigm Publisher: Institute of Management Technology Audience: Academic Format: Magazine/Journal Subject: Business, general Copyright: COPYRIGHT 2008 Institute of Management Technology ISSN: 0971-8907
Date: July, 2008 Source Volume: 12 Source Issue: 2
Event Code: 242 Advertising Advertising Code: 52 Advertising Activity Computer Subject: Return on investment
Product Code: E324000 Corporate Profits; 2830000 Drugs & Pharmaceuticals; 2834000 Pharmaceutical Preparations; 2834190 Metabolic Agents; 2834199 Metabolic Agents NEC; 0212000 Beef Cattle & Calves NAICS Code: 3254 Pharmaceutical and Medicine Manufacturing; 325412 Pharmaceutical Preparation Manufacturing; 1121 Cattle Ranching and Farming SIC Code: 2830 Drugs; 2834 Pharmaceutical preparations; 5122 Drugs, proprietaries, and sundries; 0210 Livestock, Except Dairy and Poultry
Geographic Scope: India Geographic Code: 9INDI India
Accession Number:
Full Text:

In today's business world of cut throat competition, performance is an ambiguous phenomenon and it can be measured and interpreted in a variety of different ways (Bains 1951; Mehta 1955; Kakani et al. 2001; Jones et al. 2006). Performance simply reflects the degree of success achieved in terms of stated objectives and as the objectives differ widely so does the concept of performance (Pandey 2006). That is why it cannot be put into the tight framework of a definition.

Performance is viewed differently from different perspectives. For an economic planner it is efficient utilization of resources (Miles and Snow 1978), while a welfare economist views it as equitable distribution of gains apart from the efficient utilization of resources. From the national viewpoint, performance indicators would consist of overall socio-economic development, generation of employment, and health facilities, etc. (Zahra 1993, Jennings 2000).

Steer and Cable (1979) observed that there is a separation between ownership and management in large companies and in such companies, different interested parties view the company's performance from different standpoints. Penrose (1959) has rightly remarked that shareholders, i.e., owners are directly interested in the relationship between profits (after fixed interest payments) and the nominal capital issued, while managers are in the 'effective' utilization of capital, i.e., relationship between profits (before interest payments) and the total real capital employed. Therefore, a company's performance can be measured incorporating both these dimensions, i.e., growth and profitability. Thus, the present study is an attempt to analyse a company's performance on profitability dimensions.

As per US Department of Commerce, profitability provides a summary measure of corporate success or failure and thus serves as an essential indicator of economic performance. Profits are a source of retained earnings (Kaur 1997), providing much of the funding in plant and equipment that raises productive capacity (Kakani et al. 2001). Profits are also frequently used in measuring the rate of return on investment and relationship between earnings and equity valuation (Fenny and Roger 1999). Profits are also used to evaluate the effect of changes in policy or in economic conditions on corporations (Mehta 1955; Esposito and Esposito 1975). Finally, corporate profit is also an important component of the nation's overall income and plays a role in measuring the total income resulting from production and distribution of income across the sectors (UN Committee Report, 1963). Thus, profits affect corporate performance significantly.

There are a number of factors that affect a firm's profitability position: an industry in which a company operates (Bains 1951; Jones et al. 1973; Ito and Fuka 2006), size (Mehta 1955; Camanor and Wilson 1969), and leverage (Bothwell et al. 1984; Kakani et al. 2001). Besides, the lowest cost of debt magnifies shareholders' earnings subject to the condition that the cost of debt is less than the returns earned by a company. Capacity utilization also leads to better asset utilization resulting in the firm's good performance (Kumar 1982; Kaur 1997). Firms with higher market share are also able to take advantages of product differentiation and have higher profits due to lower break-even points (Shepherd 1972a, 1972b; Bothwell et al. 1984; Nagarajan and Barthwal 1990). Similarly, advertisement and marketing expenditure helps in increasing profitability by helping a firm to build and cash its intangible assets at its brand name (Comanor and Wilson 1969; Esposito and Esposito 1971; Shepherd 1972a, 1972b; Kaur 1997; Kakani et al. 2001). But such an expenditure must also lead to economies of scale by spreading them to multiple product lines and take their full advantage. The most important variable affecting the profitability of firms in this ever-changing business environment is that of R&D that helps firms to achieve benefits of innovation and uniqueness (Nagarajan and Barthwal 1990; Fenny and Rogers 1999). Thus, all these variables and many more are assumed to affect profitability significantly.

Review of Literature

A number of studies have been undertaken on the issue of determinants of corporate profitability in different countries. A synoptic view of these studies has been presented in Table 1.

The review of empirical studies show the relationship between profitability and its determinants, which have been carried out worldwide. The literature broadly provides us with the variables that determine a company's profitability. All these variables are equally popular among researchers. These are: firm's size, risk, leverage, industry type, age, capital intensity, skill, concentration ratio, capacity utilization, market share, advertising intensity, R&D intensity, liquidity, retention ratio, long-term finance, turnover ratios, ownership characteristics, exports, working assets, indebtedness level, growth in revenue, etc. These studies differ from each other because of periods taken ranging from one year (as seen in Jones et al. 1973; Barthwal 1984) to 20 years (Kaur 1997). The studies also vary from country-specific and -firm specific (Kaur 1997; Glancey 1998; Ito and Fukao 2006) to inter-industry-specific (like Mehta 1955; Radice 1971; Jones et al. 1973; Barthwal 1984; Nagarajan and Barthwal 1990; Grinyer and Mc. Kiernan 1991). The review also shows that most of the research work relating to inter-industry profitability determinants had been undertaken in industrially advanced countries, particularly in the USA. Largely, the studies have used multiple regression analysis to find out the significant determinants of profitability of firms.

Need and Objective of the Study

The review of literature reveals that profitability is one of the important areas of research. However, the studies done in India are very few and relate to a comparatively older period, i.e., Mehta 1955; Barthwal 1977; Nagarajan and Barthwal 1990; Kaur 1997; and Kakani et al. 2001. If they relate to a recent period, the trend and impact has been studied for a very short period. One recent study (Kakani et al. 2001) no doubt serves as a major contribution to the work done in India, but in this study only limited variables have been considered and it covers the period up to 2000 only.

The policy of liberalization, privatization, and globalization initiated in 1991 in India has provided enormous opportunities to the companies to grow and enhance their profitability, but there is dearth of empirical research to study the determinants of corporate profitability in the post-liberalization period. Hence, the need to carry out this study was felt. Thus, it may be seen from the above that it becomes necessary that the determinants of profitability be studied in detail in the post-liberalization scenario.

The major objective of this study is to identify the factors which determine the profitability of firms in drugs and pharmaceutical industry in India in the post-liberalization period.

This industry has been chosen because it is one of the significant industry in India. It has shown a tremendous growth over a period. About 20 per cent of global generic drugs in volume are manufactured in India (The Hindu, 12 November, 2006).

According to a Federation of Indian Chamber of Commerce and Industry (FICCI) study, between 2000 and 2006, sixty-two companies in the health care and pharmaceutical sector abroad were acquired by Indian companies and Ranbaxy, Dr Reddy's Laboratory, Nicolas Piramal, Sun Pharma, and Glenmark Pharma account for thirty acquisitions. According to ASSOCHAM, domestic pharma industry grew at a rate of 9.5 per cent between 2000 and 2005 and touched a market size of 5.13 billion dollars by March 2005. The chamber forecasts a growth rate of 13.6 per cent for the industry in the next four years to achieve a turnover of 9.48 billion dollars by 2010 (The Financial Express, 11 November, 2006).

Data Base and Methodology

The study covers a period of ten years from 1995-6 to 2004-5. To avoid factors such as temporal stability and business cycle fluctuations, we used a longer period of ten years. The significance of this period for the Indian firms needs hardly to be emphasized, as Indian economy passed through a phase of increasing competition, deregulation, and restructuring during this period. The ten year period of study shows full impact of liberalization. This was the period when many policy changes occurred and the regulators such as, Securities & Exchange Board of India (SEBI), Bombay Stock Exchange (BSE), National Stock Exchange (NSE), and Reserve Bank of India (RBI) streamlined themselves. A longer time span of ten years would generally make the performance more rigorous to take the impact of business cycles on the firms chosen for the study.

Data Collection

The universe of the study constitutes the companies representing drugs and pharmaceutical industry given in PROWESS database of Centre for Monitoring Indian Economy (CMIE) as on 15 July, 2006. There were 341 companies representing drugs and pharmaceutical industry. Out of these, top 100 companies ranked on the basis of Return on Capital Employed (ROCE) were selected to which following filters were applied:

* The companies whose financial information about return on capital employed as on 31 March, 2005 was not available were eliminated.

* Moreover, those companies for which the data regarding all explanatory variables for a period of ten years (i.e. from 1995-6 to 2004-5) was not available were also eliminated.

Thus, because of these filters, out of 100 companies, a resultant sample size of fifty companies was selected and studied.

The data relating to all the dependent and independent variables were taken from 'PROWESS'. Annual reports of the leading drugs and pharmaceutical firms have also been considered. For analysis of data, regression analysis was applied using SPSS version 10.05.

Specification of the Model

The review of empirical literature (Esposito and Esposito 1971; Barthwal 1977; Kaur 1997; Feeny and Rogers 1999; Kakani et al. 2001) shows that most of the studies have used net profit margin (PAT/SALES) and return on capital employed (ratio of earnings before interest but after tax by net worth plus total debt--ROCE), as these are theoretically justifiable measures to evaluate the efficiency of a company in terms of profits. We employed both, and found that ROCE provides better statistical results in terms of R2, F values, and t values of the independent variables and Durbin-Watson. Therefore, average return on capital employed has been used to represent the corporate profitability. A brief snap shot of the above multivariate analysis has been presented in Table 2.

Model 1 in Table 2 shows the results of multiple regression relationship between dependent variable (i.e., average net profit margin) and ten independent variables. In model 2, in the same table average return on capital employed has been used as a dependent variable instead of average net profit margin. Model 2 is giving better results because the percentage variation by the independent variables is quiet high, i.e., 71 per cent, in comparison to 27 per cent in model 1. Moreover, F value is significant at 0.00 level and the Durbin-Watson (DW) is 2.076, which is very near to the rule of thumb of two. The more the value of DW is closer to 2.0 the better are the results (Gujrati 2006 : 467). Therefore, average return on capital employed has been used to represent corporate profitability. Further, it is important to mention here that two proxies for size, i.e., total assets and net sales have been taken respectively in model 2 and 3, but after experimentation, it has been found that total assets provide better explanation to the behaviour of a company's profitability than net sales. This has been evidenced by the results of models 2 and 3.

In order to study the impact of various determinants on corporate profitability, regression analysis has been done. The following model has been developed:

Y = [[beta].sub.0] + [[beta].sub.1][X.sub.1] + [[beta].sub.2][X.sub.2] + [[beta].sub.3][X.sub.3] + [[beta].sub.4][X.sub.4] + [[beta].sub.5][X.sub.5] + [[beta].sub.6][X.sub.6] + [[beta].sub.7][X.sub.7] + [[beta].sub.8][X.sub.8] + [[beta].sub.9][X.sub.9] + [[beta].sub.10][X.sub.10] + [epsilon]


Y = Profitability in average return on capital employed by a company;

[X.sub.1] = Average size of the company by total assets;

[X.sub.2] = Age of the company (from year of incorporation till March 2005);

[X.sub.3] = Average advertising intensity of the company;

[X.sub.4] = Average retention ratio of the company;

[X.sub.5] = Average liquidity of the company expressed in quick ratio;

[X.sub.6] = Average efficiency ratio of the company measured by inventory, debtors, and assets turnover ratio;

[X.sub.7] = Average profitability ratio in the respective previous years as measured by net profit ratio and operating profit ratio of the company;

[X.sub.8] = Average long term finance of the company;

[X.sub.9] = Average market share of the company;

[X.sub.10] = Average research and development intensity of the company;

[beta] = Slope of the independent variables while b0 is a constant or the value of Y when all values of X are zero;

[epsilon] = The error term, normally distributed about a mean of 0.

Hypotheses Development

Based on the theoretical framework and the review of literature, the following hypotheses were developed:

H-1: The size of a company as measured by total assets has a positive impact on its profitability.

H-2: The age of a company positively influences its profitability.

H-3: Advertising intensity of a company measured as the ratio of the sum of advertising and marketing expenditure to net sales is positively related to its profitability.

H-4: Retention ratio measured as the ratio of retained profits to net profits positively affects the profitability of a company.

H-5: Liquidity position of a company measured as ratio of net liquid assets (cash and bank balances, marketable securities, and advance payments) to current liabilities positively affects the profitability of a company.

H-6: The efficiency of a company as measured by inventory, debtors, and asset turnover ratios positively influences its profitability.

H-7: Short-run profits in the respective previous years as measured by the net profit ratio and operating profit ratio has a positive impact on its profitability.

H-8: Long-term finance of a company as measured by the ratio of long-term finance to total net assets positively influences its profitability.

H-9: Market share of a company as measured by the proportion of the firm's sales to the total sales of the industry positively influences its profitability.

H-10: R&D intensity of a company as measured by the ratio of R&D expenditure to sales has a positive impact on its profitability.

Analysis and Discussions

The model described above has been estimated for all the fifty companies in drugs and pharmaceutical industry for the whole period of ten years. The analysis is based on simple linear model, wherein the profitability of a company is determined by some explanatory variables. These have been chosen both for their importance in the context of this study and ease of their measurement. We have taken profitability in average return on capital employed as dependent variable. In this part, ten years' averages have been taken for all the dependent and independent variables in order to remove the minor fluctuations in the data.

Correlation Analysis

Before proceeding to the results of regression analysis, it is an implied condition to check the existence of multicollinearity or collinearity, the situation where two or more of the independent variables are highly correlated. It can have damaging effect on the results of multiple regressions. The correlation matrix is a powerful tool for developing a degree of relationship between predictors. The suggested rule of thumb is that, if the pair-wise or zero-order correlation coefficient between two regressors is high, say in excess of 0.8, multi-collinearity is a serious problem (Gujrati 2006: 345, 359). The solution is to drop that variable and then run regression analysis with the rest. To examine the correlation between different variables, Pearson product moment correlation (r) was computed. A correlation matrix of all the values of r for the explanatory variables along with dependent variables was constructed and has been shown in Table 3.

Table 3 reveals that some of the highest correlations or multi-collinearity exists between those variables, where there is an almost tautological relationship, i.e., between various measures of efficiency (inventory, debtors, and asset turnover ratios); size (total assets and net sales), and net profit and operating profit ratio. We got rid of this problem by omitting a highly collinear variable. Thus, the given set of independent variables is transformed into a new set of predictors that are mutually independent by using only one of the variables in a highly correlated set of efficiency and past profitability variables.

It can also be observed from Table 3 that the average return on capital employed by a company is significantly and positively associated with its age, advertising intensity, efficiency ratio, past year's profitability, market share at 1 per cent and 5 per cent level of significance whereas it is negatively associated with long-term finance at 5 per cent level. Thus, this relationship is evidencing that older drugs and pharmaceutical firms, holding large market shares, with strong profitable background, and which are also utilizing their assets effectively, are showing a positive growth in profits and capital employed. Hence, the correlation results show the predicted direction as evidenced by empirical research.

Regression Analysis

In order to identify the determinants of profitability of firms in drugs and pharmaceuticals industry, multiple regression analysis was done. It was applied to find the strength of the relationship between independent and dependent variables. The results of analysis has been presented in the Table 4. In all the seven formulated models in Table 4, age, liquidity, efficiency either measured as inventory, debtors or assets turnover ratios, past profitability of a company either measured as operating profit ratio or net profit ratio, market share, and R&D intensity are positively associated with profitability as measured by average return on capital employed by the pharma companies. However, out of these variables the relationships of age, efficiency ratio, past profitability, and R&D intensity are statistically significant. In addition, coefficients of long-term finance and size as measured by total assets were negatively associated with profitability, though this relationship was statistically non-significant. Also, in these formed models, the R2, F values, and t values of the independent variables suggest that these are providing identical statistical inference and interpretation of the coefficients.

Thus, all the seven models in the multivariate analysis were found to be valid. However, based on Adjusted R2, which is highlighting that the various independent variables are justifying their impact on ROCE and the value of Durbin-Watson, which is suggesting about the problem of auto-correlation, model 7 is the most preferred model.

Model 7

Y = [[beta].sub.0] + [[beta].sub.2][X.sub.2] + [[beta].sub.5][X.sub.5] + [[beta].sub.6][X.sub.6] + [[beta].sub.7][X.sub.7] + [[beta].sub.8][X.sub.8] + [[beta].sub.9][X.sub.9] + [[beta].sub.10][X.sub.10] + [epsilon]

This model explains 71.4 per cent of variation in the profitability of firms under study. Adjusted R2 is highest in this model, i.e., 0.659 which suggests that approximately 66 per cent impact on ROCE is explained by these independent variables while the remaining 34 per cent is because of some other variables that need to be explored. The value of F-test shows that the value of Adjusted R2 is significant at 1 per cent level of significance. The value of Durbin-Watson is 1.911, which suggests that there is no serious problem of auto-correlation. The coefficients offer strong support to our hypotheses that age, efficiency as measured by assets turnover ratio, operating profit ratio as a measure of past years profitability, and R&D intensity are positively associated with profitability as measured by average return on capital employed by the pharma companies. These coefficients are significant at 1 per cent and 10 per cent levels. Liquidity and market share are also positively associated with profitability whereas, against our hypothesis, long-term finance is negatively associated, though, these relationships are statistically non-significant.

Testing the Hypotheses

Age, as an explanatory variable, has a highly significant positive relation with profitability as measured by average return on capital employed at 5 per cent level. Highly significant positive association of age with profitability is because of older firm's experience and efficiency in the production process, which decreases the cost of production (Glancey 1998; Kakani et al. 2001; Harabi 2003). For example Cipla, Dr Reddy's Laboratory, Glenmark, etc., are the leaders in Indian drugs and pharma industry with more than fifty years of their operations. Therefore, the findings of this study are in tune with that of previous studies that a firm's age positively affects its profitability. Thus H-2 has been accepted.

Efficiency of a firm, measured in terms of assets turnover ratio, has a significant positive relation with return on capital employed at 10 per cent level. Since assets turnover ratio measures the efficiency with which assets are utilized to boost sales, positive association of assets turnover ratio with the profitability shows that pharma firms are very efficient in the effective utilization of their assets (Kumar 1982; Kaur 1997; Mak and Kusnadi 2001). Thus, this study shows that firms that have high assets turnover ratio tend to record a high profitability. Therefore, hypothesis H-6 has been accepted.

Past year's performance of a company either measured by operating profit ratio or by net profit ratio has a significant positive association with average return on capital employed at 1 per cent level. Past performance acts as a guide to the future and measures the overall efficiency and profitability of an enterprise. Thus, the positive association signifies a profitable and economical use of the resources by the pharma firms. The pharma companies in their financial statements have supported this fact. For example, Cadila has shown a growth of 25.49 per cent in their profitability. In addition, Hikal Ltd has increased its profitability approximately thrice in four years, i.e., Rs199 crore in 2002 to Rs414 crore in 2006. Thus, this study supports the findings of Shepherd (1972a, 1972b), Barthwal (1977), Bothwell et al. (1984), Kaur (1997), and Fenny and Rogers (1999). So, hypothesis H-7 has been accepted that companies with good past records have a high profitability ratio.

R&D intensity has a positive association with ROCE at 10 per cent level. Positive association with profitability shows that drugs and pharma firms are emerging as a leading research base and globally integrated firms. That is why the bulk of investment continues to be in generic formulation development and API (Active Pharmaceutical Ingredients) process research. This means that firms with higher R&D expenditure are upgrading their technology, enhancing their capability in product innovation, which adds to their profitability by successfully adopting changes and contributing to reduction of production cost (Fenny and Rogers 1999). Thus, hypothesis H-10 has been accepted which shows that pharma firms emphasizing more on R&D show higher profitability.

Liquidity ratio, commonly known as quick or liquid assets ratio, has given a more stringent test of short-term solvency of drugs and pharma firms. It has shown a positive association with ROCE because the coefficient is at the border line of significance. Pharma firms like Ranbaxy, Cadila, Aarti Drugs Ltd, Alembic, and many more prefer to maintain high liquidity for meeting their working capital requirements, particularly, in this frequently changing regime of credit policies and credit controls. Further, funds have to be kept in liquid form by these firms for financing rapidly changing technological development in R&D and awaiting for long for government approvals for their expansion plans (Annual Reports, 2005-6) of the above listed Companies. This is how liquidity is exerting a positive influence on profitability through growth. Thus, this study corroborates the results of past research (Barthwal 1977; Bothwell et al. 1984; Kaur 1997; Kakani et al. 2001). So, hypothesis H-5 has been accepted.

ROCE is also positively influenced by market share, though the coefficients are statistically non-significant. These results indicate that greater the market share acquired by these firms greater is their profitability. A recent McKinsey Report on the Indian pharmaceutical industry says: 'It is poised to grow to a staggering $25 billion by 2010. It ranks very high in the third world, in terms of technology, quality, and range of medicines manufactured. From simple headache pills to sophisticated antibiotics and complex cardiac compounds, almost every type of medicine is now made indigenously' (Annual Report, Pfizer India, 2005, p.11). Thus, as akin to our findings H-9 is in support of our hypothesis that profitability is positively influenced by market share.


The primary objective of this study was to look into the nexus between Indian drugs and pharmaceuticals firms' characteristics and their profitability as measured by ROCE. We performed an analysis of fifty Indian firms over a time span of ten years, 1995-6 to 2004-5. We used a firm's size, age, advertising intensity, retention ratio, past year's profitability, R&D intensity, long-term finance, liquidity, and market share as independent variables. Table 6 provides a snap shot of significant results of this study with the direction (positive or negative relation) of the particular independent variables.

This means that older drugs and pharma firms which are efficiently utilizing their assets, also possessing an impressive past performance record and having high R&D intensity, are showing a positive growth in profitability as measured by average return on capital employed.


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Subhash Chander, Department of Commerce and Business Management, Guru Nanak Dev University, Amritsar, India. Email:

Priyanka Aggarwal, Department of Commerce and Business Management, Guru Nanak Dev University, Amritsar, India. Email:
Table 1 : Empirical Studies on Determinants of Corporate Profitability

Authors        Objective                Time period/      Dependent
                                        Sample size/      variable

Bains          The firms operating      1936-40           Average
(1951)         in oligopoly market      42 four digit     after tax
               structure would          US manu-          return on
               tend to earn high        facturing         equity
               profit rates than all    industries

Mehta          Studying the             1938-47           Rate of
(1955)         relationship             Jute, sugar,      return on
               between size and         paper, coal,      shareholders'
               profitability of         cement, iron      equity
               Indian industries.       and steel

Comanor        Examining the            1954-57           Average
& Wilson       relevance of             35 US             after tax
(1967)         advertising              consumer          return on
               intensity and            goods             equity
               market structure         industries
               on US industrial

Radice         Effect of control        1957-67           Average
(1971)         system on the            89 firms          rate of
               growth and               from three        return
               profitability of large   industries

Esposito &     Examining the            1963-65           Net profit
Esposito       influence of foreign     78 industries,    rate
(1971)         competition              i.e., 44
               on industrial            consumer
               profitability.           goods and
                                        34 producer

Shepherd       Analyzing the            1960-69           After tax
(1972)         behavior of market       231 large US      return on
               structure on firm's      corporations      equity

Jones et al.   Relationship             1965              Ratio of
(1973)         between market           30 Canadian       profits to
               structure and            consumer          equity and
               profitability            goods             Ratio of
               in Canadian              industries        profits plus
               manufacturing                              interest to
               industry                                   total assets

Barthwal       Determinants of          1972              Current
(1984)         profitability in         Cotton textile    profitability
               Indian cotton textile    industry of       of the
               industry                 Maharashtra,      industry
                                        Tamil Nadu,
                                        Gujarat, and
                                        rest of India

Steer and      Examining the            1967-71           Profitability
Cable          role of internal         82 large UK       as average
(1979)         organization on the      companies         rate of
               profitability of the                       return and
               companies                                  growth as
                                                          total assets

Bothwell et    Identifying the          1960-67           Net profit
al. (1984)     determinants of          156 large US      rate
               profitability            manufacturing

Nagarajan      Examining the            1970-82           Ratio of net
& Barthwal     relationship             38 Pharma         profits to
(1990)         between                  firm's of India   total sales
               profitability and                          revenue and
               market structure of                        ratio of net
               Indian pharmacy                            profits to
               firms                                      total assets

Grinyer &      Identifying the          1973-77           Operating
McKiernan      determinants             45 companies      profit ratio
(1991)         of corporate             in UK
               profitability            electrical

Kaur           Identifying              1971-90,          Profitability
(1997)         the various              235 Indian        ratio as
               determinants of          firms             operating
               profitability                              net
                                                          profits as
                                                          of net sales

Geroski et     Relationship             1976-82,          Return on
al. (1997)     between corporate        271 large         sales
               growth and               quoted UK
               profitability            firms

Glancey        To investigate           1988-90,          Return on
(1998)         the determinants         38 Tayside        total assets
               of growth and            region firms      as average
               profitability in small                     operating
               entrepreneurial                            profits
               firms                                      divided by
                                                          total assets

Fenny and      Measuring                1993-96,          Return on
Rogers         performance              653               assets and
(1999)         of large private         Australian        return on
               Australian               enterprises       equity

Kakani et      Measuring the            1992-96, &        Profitability
al. (2001)     post-liberalization      1996-2000,        as sales
               financial                566 large         turnover,
               performance.             Indian firms      profit
                                                          margin, and

Kaen and       Measuring the            1990-2001         EBITDA
Baumann        profitability of US      64 industries     margin and
(2003)         companies                of US             EBIT margin

Claver et      Determining the          2001-3            Average
al. (2006)     growing profitable       444 Spanish       annual
               factors of hotel         firms             return on
               firms                                      assets and
                                                          return on

Ito and        Analysing the            1989-2002         Return on
Fukao          performance              2000+             sales as
(2006)         of Japanese              parent            ratio of
               multinationals           Japanese          ordinary
                                        firms             profits
                                        and their         before tax
                                        approximately     to total
                                        14000             sales

Authors        Independent variable

Bains          Industry concentration (as
(1951)         the proportion of combined
               production volume of such
               a group of close substitute
               output supplied by one,
               four, eight, or twenty firms).

Mehta          Size, demand, prices,
(1955)         degree of internal and
               external competition,
               measures of state
               protection, technical and
               mechanical efficiency

Comanor        Advertising intensity, plant
& Wilson       size, and concentration ratio

Radice         Control type, and opening
(1971)         size of net assets

Esposito &     Product differentiation,
Esposito       i.e., advertising sales
(1971)         ratio, imports, seller
               concentration, technical
               barriers to entry, capital
               requirement, economies
               of scale, and the rate of
               growth of demand

Shepherd       Market share, four firm
(1972)         concentration ratio,
               absolute size of the firm,
               advertising to sales ratio,
               estimated barriers to entry
               and revenue growth.

Jones et al.   Product differentiation,
(1973)         i.e., ratio of advertising to
               sales, economies of scale,
               absolute capital req. of
               plant, growth of demand
               variable, concentration
               variable, regional
               concentration, herfindahl
               index, high import

Barthwal       Past profitability, cost
(1984)         leverage, age, capital
               intensity, liquidity, size

Steer and      Ownership characteristics,
Cable          i.e., whether a firm is
(1979)         owner-controlled or

Bothwell et    Seller concentration,
al. (1984)     business risk, advertising
               intensity, economies of
               scale, absolute capital
               requirement, leverage, profit
               variability, firm's growth,
               size, and market share.

Nagarajan      Vertical integration, size,
& Barthwal     advertising intensity, growth
(1990)         rate of sales, R&D intensity,
               market share, and p/v ratio.

Grinyer &      Measures of centralization,
McKiernan      average OP margin,
(1991)         average RONC or net
               worth, total assets to sales
               ratio, real export sales to
               average annual export ratio,
               real sales to average annual
               sales ratio.

Kaur           Size as measured by total
(1997)         assets, fixed assets, and net
               assets, age, growth rate
               of sales, past profitability,
               advertising intensity,
               diversification, retention
               ratio, liquidity ratio, turnover
               ratio, valuation ratio, long
               term finance, market share,
               and capacity utilization.

Geroski et     Firm size as log of sales,
al. (1997)     growth in average annual

Glancey        Growth as average annual
(1998)         growth rate of total assets,
               size, age, location, diversity,
               and inter-industry difference

Fenny and      Growth in revenue, EBDIT
Rogers         Margin, debt to equity,
(1999)         export intensity, R & D
               intensity, Tobin's q

Kakani et      Size, age, leverage, public
al. (2001)     holding, net exports,
               working capital ratio,
               industry effects, business
               group affiliation, internal
               diversification, marketing

Kaen and       Sales, total assets, and
Baumann        number of employees.

Claver et      Size, indebtedness level,
al. (2006)     age, working assets, and
               initial risk level as standard
               deviation of return on

Ito and        Log of total sales, age, local
Fukao          procurement ratio, local
(2006)         sales ratio, equity ownership
               ratio, DV (1) if the host
               country is a WTO member
               or (0) otherwise, DV (1) if
               the affiliate was established
               by a joint venture or (0)
               otherwise, DV (1) if the
               affiliate was established by
               a wholly owned affiliate
               otherwise (0).

Authors        Statistical              Significant variables

Bains          Simple                   Regression line is
(1951)         regression               showing a downward
               and                      slope for profit rates as
               correlation              concentration decreases.

Mehta          Correlation              High degree of positive
(1955)         and regression           correlation between size
                                        and rate of return, i.e.,
                                        rate of return increases
                                        with size after controlling
                                        other variables.

Comanor        Linear                   Advertising intensity
& Wilson       regression               turned out to be a major
(1967)         analysis                 determinant of industrial

Radice         Simple                   Management control
(1971)         regression               firms are better in growth
               and correlation          and profitability.

Esposito &     Multivariate             The effect of foreign
Esposito       regression               competition on
(1971)                                  profitability was
                                        significant and negative
                                        where as it was positive in
                                        case of advertising sales
                                        ratio, seller concentration
                                        and market growth of

Shepherd       Correlation              Rate of return was found
(1972)         and regression           to be positively and
                                        significantly related to
                                        market, advertising to
                                        sales ratio, and revenue
                                        growth. However, size
                                        showed a negative
                                        coefficient throughout.

Jones et al.   Multiple                 Product differentiation,
(1973)         regression               growth in demand,
               analysis                 and concentration
                                        are positively and
                                        significantly associated
                                        whereas import variable
                                        is showing a significant
                                        negative relationship with
                                        profit ratios.

Barthwal       Regression               Past profitability and
(1984)         and                      cost leverage emerged
               correlation              as the most significant
                                        determinants, explaining
                                        90% of the variation. Size
                                        variable was found to be

Steer and      Univariate               Owner-controlled firms
Cable          regression               outperformed manager-
(1979)         analysis                 controlled both in
                                        terms of growth and

Bothwell et    Correlation              Positive correlation
al. (1984)     and                      between seller
               regression               concentration, market
                                        share, growth of demand,
                                        business risk, advertising
                                        expenditure, and profit
                                        rate was found. Profit
                                        rates were negatively
                                        related with the extent of
                                        economies of scale and
                                        capital requirement.

Nagarajan      Multivariate             The most significant
& Barthwal     regression               determinants in
(1990)         analysis                 determining the
                                        profitability of Indian
                                        pharmacy firms are their
                                        size, growth rate of sales,
                                        and R&D.

Grinyer &      Multiple                 Market share, capital
McKiernan      regression               intensity, growth of sales,
(1991)         analysis                 tightness of control
                                        of working capital,
                                        and decentralization
                                        contribute significantly
                                        in determining the
                                        corporate profitability.

Kaur           Correlation              Past profitability, market
(1997)         regression               share of the firm,
               and chi-                 advertising intensity,
               square.                  rate of diversification,
                                        valuation ratio, and
                                        growth were significantly
                                        affecting profitability.

Geroski et     Simple                   Variations in profit
al. (1997)     correlation              rate are significantly
               and                      associated with both
               regression               size as well as growth in

Glancey        Simple                   Larger of smaller firms
(1998)         regression               grew faster with more
               and                      profit. Older firms
               correlation              grow less rapidly. Weak
                                        evidence is found that
                                        profitable firms are
                                        located in urban area.

Fenny and      Multiple                 Profitability is positively
Rogers         regression               and significantly
(1999)         and                      associated with growth of
               correlation              revenue, export intensity,
                                        and innovation.

Kakani et      Correlation              Sales turnover is
al. (2001)     and                      significantly and positively
               multiple                 associated with marketing
               regression               expenditure, age;
                                        profit margin with net
                                        exports, size and working
                                        capital management;
                                        and shareholder value
                                        maximization with size,
                                        marketing expenditure
                                        and with internal

Kaen and       Regression               In about half the
Baumann        and                      industries firm profitability
(2003)         correlation              increases at decreasing
                                        rate and eventually
                                        declines as the firms
                                        become larger. For
                                        remaining half no
                                        relationship between
                                        profitability and size was

Claver et      Binomial                 Diversification either
al. (2006)     logistic                 related or unrelated, age
               regression               and size are positively
               model and                and significantly related
               Correlation              to profit ratios.

Ito and        Regression               Log of sales, age, and
Fukao          analysis                 local procurement
(2006)                                  ratio have positive and
                                        significant coefficients
                                        whereas local sales ratio,
                                        and WTO affiliates have
                                        negative and significant
                                        coefficients with return on

Table 2 : Summary Results of Multivariate Analysis

Independent Size                   Size        Size     Age       Adv
Variables [right arrow] (TA)       (TA)        (Net               Int
Dependent                                      Sales)
Variable [down arrow]

AvgNPRatio(1)                      1.96 **     X        .370      1.24
Avg ROCE (2)                       1.46        X        2.78 *    .372
Avg ROCE (3)                       X           .606     1.653 *   .549

Independent Size                   Ret.        Leq.     Eff.    NP
Variables [right arrow] (TA)       Ratio       Ratio    Ratio   Ratio
Variable [down arrow]

AvgNPRatio(1)                      -1.89 **    -1.378   .094    X
Avg ROCE (2)                       .304        1.263    .877    6.979 *
Avg ROCE (3)                       .328        1.015    .622    6.206 *

Independent Size                   LTF         Mkt      R&D
Variables [right arrow] (TA)                   Share    Int.
Variable [down arrow]

AvgNPRatio(1)                      .792        -.035    -2.05 **
Avg ROCE (2)                       .971        1.079    2.39 **
Avg ROCE (3)                       .694        .943     1.097

Independent Size                   [R.sup.2]   Adj
Variables [right arrow] (TA)                   [R.sup.2]
Variable [down arrow]

AvgNPRatio(1)                      .278        .116
Avg ROCE (2)                       .703        .640
Avg ROCE (3)                       .502        .497

Independent Size                   F Ch        DW
Variables [right arrow] (TA)
Variable [down arrow]

AvgNPRatio(1)                      .117        1.59
Avg ROCE (2)                       0.00        2.076
Avg ROCE (3)                       0.00        1.795

Table 3 : Correlation Matrix

Independent        Total       Net         ROCE       Age       Adv.
Variables          Asset       Sales                            Int.

Total Assets       1.00
Net Sales          .971 *      1.00
ROCE               .257        .257        1.00
Age                .322 **     .443 *      .526 *     1.00
Advertising Int.   .637 *      .103        .285 *     .386 *    1.00
Retention Ratio    -.032       -.028       -.183      -.044     -.029
Liquidity Ratio    -.096       -.100       -.038      -.031     -.072
Inv TR             -.075       .076        .171       .238      -.042
Dr TR              -.026       .142        .326 **    .436 *    .022
Asset TR           -.098       .068        .291 **    .335 **   -.037
N P Ratio          .244        .249        .685 *     .212      .200
0 P Ratio          .211        .188        .681 *     .192      .176
LTF ratio          -.213       -.262       -.286 **   -.364 *   -.281 **
Mkt Share          .337 **     .407 *      .324 **    .418 *    .394 *
R&D Int.           .637 *      .689 *      .138       .146      .697 *

Independent        Retention   Liquidity   Inv.       Dr. TR    Asset
Variables          Ratio       Ratio       TR                   TR

Total Assets
Net Sales
Advertising Int.
Retention Ratio    1.00
Liquidity Ratio    -.084       1.00
Inv TR             -.048       .009        1.00
Dr TR              .002        -.071       .908 *     1.00
Asset TR           -.094       -.061       .940 *     .928 *    1.00
N P Ratio          -.190       -.267       .034       .070      .134
0 P Ratio          -.122       -.279       -.030      -.004     .056
LTF ratio          .361 **     -.395 *     -.006      -.068     -.065
Mkt Share          .136        -.094       .132       .287 **   .181
R&D Int.           -.105       -.066       -.048      -.050     -.083

Independent        NP          OP          LTF        Mkt       R&D
Variables          Ratio       Ratio       Ratio      Share     Int.

Total Assets
Net Sales
Advertising Int.
Retention Ratio
Liquidity Ratio
Inv TR
Asset TR
N P Ratio          1.00
0 P Ratio          .955 *      1.00
LTF ratio          .031        .065        1.00
Mkt Share          .140        .117        -.310 **   1.00
R&D Int.           .184        .137        -.132      .153      1.00

*, ** and ***: Significant at 1 %, 5% and 10% level respectively.

Table 4 : Multivariate Regression Results

Dependent variable [right arrow]       Average     Average    Average
Independent variables [down arrow]      ROCE        ROCE       ROCE

Size (Total Assets)                   -1.46       -1.189      -1.333
Age                                    2.758 *     2.138 **    2.520 *
Advertising Int.                        .372        .583        .413
Retention Ratio                         .304        .292        .392
Liquidity Ratio                        1.263       1.356       1.375
Inv. TR                                0.877          X           X
Dr. TR                                    X        1.519          X
Asset TR                                  X           X        1.350
NP Ratio                               6.979*      7.066 *     6.983 *
OP Ratio                                  X           X           X
LTF Ratio                             -0.971      -1.032      -0.968
Mkt Share                              1.079       0.825       1.035
R&D Int.                               2.390 **    2.244 **    2.530 *
R                                       .845        .851        .849
[R.sup.2]                               .713        .724        .721
Adj. [R.sup.2]                          .640        .653        .649
F Ch                                   0.00        0.00        0.00
DW                                     2.076       2.046       2.049

Dependent variable [right arrow]        Average      Average
Independent variables [down arrow]       ROCE         ROCE

Size (Total Assets)                     -.876        -.550
Age                                     2.056 *      2.007 **
Advertising Int.                        -.278        -.032
Retention Ratio                         -.206        -.213
Liquidity Ratio                         1.092        1.237
Inv. TR                                 1.140           X
Dr. TR                                     X         2.029 **
Asset TR                                   X            X
NP Ratio                                   X            X
OP Ratio                                6.773 *      7.042 *
LTF Ratio                              -1.181       -1.293
Mkt Share                               1.063        0.739
R&D Int.                                1.532        1.361
R                                       .839          .850
[R.sup.2]                               .704          .723
Adj. [R.sup.2]                          .628          .652
F Ch                                    0.00         0.00
DW                                      1.938        1.908

Dependent variable [right arrow]        Average      Average
Independent variables [down arrow]       ROCE         ROCE

Size (Total Assets)                    -.738          X
Age                                    2.488 **      2.6 *
Advertising Int.                       -.259          X
Retention Ratio                        -.089          X
Liquidity Ratio                        1.242         1.266
Inv. TR                                   X           X
Dr. TR                                    X           X
Asset TR                               1.703 **      1.851 ***
NP Ratio                                  X           X
OP Ratio                               6.858 *       7.432*
LTF Ratio                             -1.183          X
Mkt Share                              1.016         1.025
R&D Int.                               1.726 ***     1.781 ***
R                                       .846          .845
[R.sup.2]                               .715          .714
Adj. [R.sup.2]                          .642          .659
F Ch                                   0.00          0.00
DW                                     1.908         1.911

*, ** and ***: Significant at 1 %, 5% and 10% level respectively.

X : Variables were excluded from the regression model.

Table 6 : A Snap Shot of Regression Models

Dependent Variable [right arrow]      Profitability in Average
[down arrow] Independent Variable     Return on Capital Employed

Age                                   Positive
Liquidity Ratio                       Positive
R & D Intensity                       Positive
Efficiency Ratio                      Positive
Past Year's Profitability             Positive
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