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CHAPTER 6

DATA ANALYSIS: CORPORATE DIVIDEND POLICY

DETERMINANTS

The body of literature dealing with dividend determinants can be grouped into two

distinct categories: (1) those based on the implicit assumption of symmetric

information and (2) those based on the explicit assumption of asymmetric

information.

In the symmetric information models, the seminal work is that of Lintner (1956).

According to Lintner’s model, current dividends are predicated upon past dividends

and current profits. Lintner’s “two-variable” model is supported by the empirical

evidence of Fama and Babiak (1968)35.

Theories based on the supposition of asymmetric information include agency, pecking

order, and most profoundly, dividend-signaling theories. Agency theory explanations

of dividend build on the works of Easterbrook (1984) and Jensen (1986). The pecking

order theory (the complete opposite of the agency theory explanation) is espoused by

Myers (1984) and Myers and Majluf (1984).[19],[20]

Signaling theory, first proposed by Bhattacharya (1979, 1980), asserts that “good”

firms are able to signal their expected fortunes via the disbursement of dividends. The

tax costs of which are fully recovered by ensuing stock price increases.[[33],[34]

According to Miller and Modigliani, investment decisions are independent of

financing decisions such as new debt or dividends because, in perfect capital markets,

the value of a firm is affected only by its investment decision. And both the financing

decision and dividend policy are irrelevant in the process of determining the firm’s

value.[9]

35 A somewhat different work is that of Marsh and Merton (1987), who developed a dynamic aggregate dividend behavior model based on both accounting data and stock market data. In contrast to Marsh and Merton, the present study treats the dividend policy problem endogenous to the firm. In addition, there are marked differences between the two studies, both in method of analysis and in data used. Nevertheless, the conclusions of this study and the Marsh and Merton study concerning the validity of signaling models are surprisingly similar.

194

Dhrymes and Kurz (1967) and McCabe (1979) found that the firm’s investment

decision is linked to its financing decision. Higgins (1972), Fama (1974), and

Smirlock and Marshall (1983) documented no interdependence between investments

and dividends. It is argued here that these tests seem to be misspecified or

inappropriate because they have omitted important explanatory variables and/or

lagged variables. [92]

The agency theory explanation of dividend payment is that the firm should pay

dividends to reduce agency costs associated with excess cash flows. Easterbrook

(1984) further argued that paying dividends can reduce agency costs because it forces

managers to return to the market for cash, thus keeping them under constant

monitoring. [19]

In contrast, the pecking order hypothesis offers and Majluf (1984) states that dividend

payments are negatively influenced by free cash flows and Majluf (1984) claim that

“firms can build up financial slack by restricting dividends when investment

requirements are modest”.

Thus, Dividend decision in the corporate sector is governed by a large number of

determinants. The foregoing discussions on the review of empirical studies disclose

that profit, cash flow, lagged dividend, capital expenditure, retained earnings, flow of

external fund, cost of debt, changes in sales, share price behaviour etc., are expected

to have a direct bearing on the dividend policy decision of the firms. In the following

segment these variables and their relationship with dividend payout has been

discussed at length.

6.1 LEADING DETERMINANTS OF DIVIDEND POLICY

Dividend decisions in the corporate sector are governed by large number of

determinants. The various research studies done abroad and in Indian context have

been detailed in the literature review chapter. Enumerated below are the key variables

identified as per available literature along with the relationship with dividend payout

ratio of the firm.

195

• PROFITABILITY

The key determinant of dividend payments is the current earnings which represents

the capacity of a firm to pay dividends. Profitability has a positive relationship with

dividends. Research studies have used PAT, ROE and RONW as proxies for

profitability of the company.

• CASH FLOW

Brittain (1966) suggested that cash flow (net current profit after tax + depreciation) is

a more appropriate measure of the company’s capacity to pay dividend. Further the

regulations and accounting practices regarding depreciation allowance keep on

changing and as such cash flow may be a better indicator of true earnings than net

profit.

• LAGGED DIVIDEND

The specification of dividend equation by Lintner (1956) suggested that lagged

dividend is the only other explanatory variable of dividend policy (first being net

profit). The rationale of lagged dividend as a determinant of dividend policy is

provided by the speed of adjustment mechanism, which states that companies try to

achieve a certain desired payout ratio in the long run. In order to follow a stable

dividend policy management has to allow the past dividend trend to influence the

current dividend payments.

• DEBT EQUITY RATIO

The demand for external finance usually arises in a company on account of

constraints imposed by its internal resources. Higher the internal flows, given the

investment requirements, lesser will be the demand for borrowings and vice-versa.

That is, higher the dividend, larger the demand for borrowings and higher is the debt

equity ratio. Firms with high debt ratios ought to pay lower dividends as they have

already precommitted their cash flows to make debt payments. Through lower

dividend payout firm may avoid borrowing more capital.

196

• SALES GROWTH

An increase in sales generates increased working capital requirement, which in turn

may adversely affect dividend payments. Some research studies have also taken sales

growth as proxy for growth and investment opportunities available to firm. This

implies a negative relationship between dividend and sales growth.

• LIQUIDITY

A firm may have adequate earnings to declare dividends, but it may not have

sufficient cash to pay the same. The liquidity position of a company is expected to be

positively related to dividend payment. Current ratio and quick ratio has been used as

proxy to measure liquidity position of the company by various researchers.

• SHARE PRICE BEHAVIOUR

Researchers have proposed negative relationship of lagged share prices with current

year dividends and positive relationship of current year share prices with dividend

distributed during current year. This relationship suggests dividend policy decisions

have an impact on shareholders wealth which is mirrored by share prices of a

company.

• CAPITAL EXPENDITURE

The extent to which the company decides to finance capital expenditure from internal

resources, both dividend and capital expenditure decisions would compete with each

other, therefore, capital expenditure is negatively related to its dividend payments.

• RETAINED EARNINGS

A firm that plans to finance future investment opportunities from retained earnings

would distribute lesser profits as dividends. Thus, retained earnings of the current year

are negatively associated with dividend paid.

• BETA

It measures the systematic risk (systemic risk) of the company. Higher the market risk

lower will be the dividend payments.

197

• PE RATIO

There is a debate in corporate finance literature that out of PE ratio and Dividend

Payout ratio which is the cause and which is the effect. However in the present study

a positive relationship between PE ratio and dividend payout has been assumed.

• PB or MTBV RATIO:

Theoretically higher the growth opportunities available to a firm lower will be the

dividend payout. A common proxy used for investment opportunities is MTBV and

expected to be negatively correlated with dividend payment.

• PROMOTER HOLDING

It has been recently observed that promoters of the company play a dominant role in

dividend decisions of the company. In India promoter companies have earned sizeable

income from dividends since dividends are tax free in hands of recipients i.e.

promoters of companies in India and dividend forms substantial part of their earnings.

• SIZE OF THE FIRM

Studies have used natural log of total assets and market capitalisation as a surrogate

for size of the firm. In particular, larger firms have easier access to external capital

markets and can borrow on better terms. Moreover, larger firms tend to be more

diversified and their cash flows are more regular and less volatile. Thus, larger firms

should be more willing to pay higher dividends. A positive relationship is expected

between Dividend payout ratio and firm size as larger firms face lower issuing cost.

• VOLATILITY IN EARNINGS

Volatility in earnings can be measured by taking standard deviations in the earnings

per share. An inverse relationship is expected between volatility of earnings and

dividend payout ratio.

• INTEREST COVERAGE RATIO

It measures the debt servicing capacity of the firm. A positive relationship is expected

between ICR and dividend payout ratio.

198

Undeniably all the variables discussed above are important determinants of dividend

policy as is evident from the review of the past empirical studies. As such, they need

proper consideration by the management while formulating a dividend policy, which

will be appropriate both from the point of view of shareholders and that of the

company as a whole. This chapter aims at identifying key determinants of dividend

policy in three sectors chosen for study.

6.2 DATA AND VARIABLE CONSTRUCTION

6.2.1 KEY VARIABLES THAT AFFECT THE DIVIDEND PAYOUT RATIO

OF A FIRM

On the basis of literature review, the following key variables have been identified that

influence the dividend payout ratio of the firm.

Y= Equity dividend (in crores), X1=PAT (in Rs crore), X2=Lagged dividend (Rs.

crore), X3=Current ratio of firm ‘i’ during period’t’, X4=Debt equity ratio of firm ‘i’

during period’t’, X5= Quick ratio of firm ‘i’ during period’t’, X6= Annual sales

growth of firm ‘i’ during period’t’, X7= Natural log National Stock Exchange adjusted

average closing stock prices of the firm ’i’ during period ‘t’, X8= Cashflows of firm

‘i’ during period ‘t’, X9= Retained profits of the firm ‘i’ during period‘t’, X10=

Capital expenditure or Gross fixed assets (t-(t-1)),X11= Nifty beta of firm ‘i’ during

period‘t’,X12=Market capitalisation of firm ‘i’ during period ‘t’,X13=Price earning

ratio of firm ‘i’ during period ‘t’,X14=Price to book value ratio of firm ‘i’ during

period’t’, X15= Promoter holding of firm ‘i’ during period’t’, X16= Natural Log of

Total assets of firm ‘i’ during period’t’, X17= Interest coverage ratio of firm ‘i’ during

period’t’, X18= RONW of the firm ‘i’ during period ‘t’, X19= ROE of firm ‘i’ during

period ‘t’, X20=Lagged PAT (in Rs crore), X21= Standard deviation of earnings per

share.

Therefore, final analysis was carried by reckoning the following standardised key

variables.

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• Y= dividend payout ratio

• X1=PAT to assets ratio36

• X2=Lagged dividend ratio

• X3=Current ratio of firm ‘i’ during period’t’

• X4=Debt equity ratio of firm ‘i’ during period’t’

• X5= Quick ratio of firm ‘i’ during period’t’

• X6= Annual sales growth of firm ‘i’ during period’t’37

• X7= Natural log National Stock Exchange adjusted average closing stock prices of the firm ’i’ during period ‘t’

• X8= Cashflows ratio of firm ‘i’ during period ‘t’ 38

• X9= Retained ratio of the firm ‘i’ during period‘t’

• X10= Capital expenditure or Gross fixed assets (t-(t-1)) to fixed asset ratio)

• X11= Nifty beta of firm ‘i’ during period‘t’

• X12=Natural log of Market capitalisation of firm ‘i’ during period ‘t’

• X13=Price earning ratio of firm ‘i’ during period ‘t’

• X14=Price to book value ratio of firm ‘i’ during period ‘t’

• X15= Promoter holding of firm ‘i’ during period ‘t’

• X16= Log of Total assets of firm ‘i’ during period ‘t’

• X17= Interest coverage ratio of firm ‘i’ during period ‘t’

• X18= RONW of the firm ‘i’ during period ‘t’

• X19= ROE of firm ‘i’ during period ‘t’

• X20=Lagged PAT to lagged assets ratio (in Rs crore) 39

• X21= Standard deviation of earning per share

36 In FMCG and Service sector respectively PAT has been expressed as % of total assets. At the same time to obtain better results total assets was substituted by gross fixed assets in IT sector 37 In case of constituent companies of CNX Service sector the annual sales growth was replaced with growth in revenue as majority of the constituents of this Index are banks where sales growth figure is not available 38 In case IT and FMCG sector cashflows have been expressed as a percentage of Netsales. However, in case of Service sector cashflows ratio has been computed by expressing cashflow as a% of PBIT 39 The results in IT sector are reported by expressing lagged PAT as a% of gross fixed assets.

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6.3 IT SECTOR

6.3.1. Kaiser-Meyer-Olkin Measure of Sampling Adequacy Table 6.1. depicts

KMO values. It is a measure that judges the sampling adequacy. The value obtained is

0.688 which ensures the sample size is adequate to apply Factor Analysis. The value

of KMO is quite encouraging to apply factor analysis.

Table 6.1: KMO Test Results: IT Sector

Kaiser-Meyer-Olkin Measure of

Sampling Adequacy

0.688

Bartlett's Test of Sphericity Approx. Chi-Square 1374.160

df 210

Sig. .000

6.3.2 EXTRACTION METHOD:

Principal Component Analysis method was used extraction of factors. The Table 6.2

shows the factor pattern matrix, which highlights variance exhibited by extracted

factors.

6.3.3. SCREE PLOT

Component Number212019181716151413121110987654321

Eige

nval

ue

6

5

4

3

2

1

0

Scree Plot

Figure6.1: Scree plot :IT sector

201

Figure 6.1 shows the scree plot, which is used to determine the number of factors to

retain. An elbow in the scree plot indicates the point at which the inclusion of

additional factors does not contribute significantly in explaining the variance in data

set. Factors above the elbow of the plot are retained. The Scree plot shown above has

an elbow at Factor 8.Therefore a set of 8 Factors were chosen which accounts for

about 75% of the variations in the data.

Table 6.2: Factor Pattern Matrix: IT Sector

The table provides the factor loadings from the Principal Component Analysis (PCA). The factor

loadings may be viewed as ordinary correlation between a variable and the factor. Underlying loadings

in excess of 0.3 are significant

FACTOR LABEL Firm size and

Dividend stability

Liquidity ratios

Profitability and Pecking

order

Longterm solvency

Leverage and

retained earnings

Shareholders wealth and

earnings variability

Valuation ratios

VARIABLES FACTOR 1 FACTOR 2 FACTOR 3 FACTOR 4 FACTOR 5 FACTOR 6 FACTOR 7

Pat to gross asset ratio .436 .054 .535 .350 -.367 .283 -.154 Lagged dividend ratio -.329 .386 -.066 .657 -.037 .045 .332

Current Ratio -.106 .961 -.007 .055 -.022 -.017 -.122 Debt Equity ratio -.205 -.164 -.038 -.186 .529 .338 -.127

Quick Ratio -.059 .963 .008 .034 -.055 .013 -.080 Sales Growth -.037 -.035 .879 -.099 .057 -.185 -.010 Ln shareprices .364 -.029 .254 .032 -.248 .640 -.153

CFO to sales ratio .447 .150 -.029 -.213 -.314 -.074 .099 Retention ratio .214 .019 -.012 .019 .787 -.058 .025

Capex to gross fixed asset ratio

-.248 .124 .643 .218 .129 .137 .236

Beta .904 -.031 -.011 -.082 -.162 .145 .008 Mcap -.320 .115 .286 .674 .166 -.171 .128

PE ratio .012 -.199 .017 .047 -.045 -.025 .895 PB ratio .519 -.202 .103 -.333 -.292 .270 .173

Promoter holding .895 -.080 -.070 -.110 .224 .033 -.013 Ln of total assets .738 -.107 .137 .143 .053 -.147 -.224

Interest coverage ratio .031 -.084 .040 .788 -.133 -.101 -.094 RONW .244 -.120 .740 .132 -.315 .286 -.166

ROE .903 -.019 -.008 -.062 -.145 .086 .020 Lagged profit to lagged

fixed assets ratio .807 -.127 -.040 -.144 .251 -.112 .010

Standard deviation of EPS

-.134 .043 -.046 -.188 .186 .786 .081

Cumulative Variance Explained

22.216 33.803 44.339 53.872 61.724 69.083 74.806

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6.3.4 FACTOR PATTERN MATRIX:

The Variables Cashflow from operations to sales ratio, beta, LnMcap, PB ratio,

Promoter holding , lagged profit to lagged fixed asset ratio , ROE are heavily loaded

on Factor 1. Factor 1 is labelled as Factor of dividend stability and firm size.

Higher and more stable the cash flows, higher would be the capacity of the company

to pay dividend to the shareholders’. In particular, larger firms should have easier

access to external capital markets and can borrow on better terms. Even the conflicts

between creditors and shareholders are more severe for smaller firms than larger

firms. Moreover, larger firms tend to be more diversified and their cashflows are more

regular and less volatile. Thus, larger firms are in a better position to pay higher

dividends. If the dividend payment of the current year is governed by dividend

payment of the previous year, it implies firm follows a policy of paying consistent and

stable dividends. Promoters also being one of the major ownership groups have an

influence on dividend payout ratio of a firm. It is also commonly observed that firms

with larger operations are able to pay consistent and regular dividends. Therefore, a

positive relationship is expected between this factor and dividend payout ratio.

Variables like Current ratio and Quick ratio are heavily loaded on Factor 2. This

Factor has been labeled as Factor of liquidity ratios. Thus, Factor 2 is projected to

have positive relationship with equity dividend.

PAT to asset ratio, RONW, Capex to asset ratio and sales growth are the two

variables that have higher loadings on Factor 3. This factor has been named as Factor

of Profitability and Pecking order hypothesis. Dividend and investment decisions

of the firm are closely interlinked and cannot be taken in isolation. Higher the growth

opportunity available to the firm lesser is the dividend payout of the firm.

Lagged dividend ratio and interest coverage ratio (ICR) have significant positive

loadings on factor 4. Therefore Factor 4 can be said to represent Factor of long term

solvency. Larger the ICR higher is the capacity of the firm to pay stable and

consistent dividends.

Debt Equity ratio and retention ratio are heavily loaded on factor 5. Therefore; this

factor has been termed as Factor of leverage and retained earnings. Higher the risk,

more volatile are the earnings and lower is the dividend payout ratios of a company.

203

Higher debt equity ratio increases the fixed financial burden of the company in form

of interest payments. This in turn puts pressure on firms’ capacity to pay steady and

regular dividends to shareholders. Two arguments support this relationship. First, in

an agency theory framework, debt can play a disciplinary role: by increasing the debt

level, the free cashflow will decrease (Grossman and Hart 1982; Jensen 1986; Stulz

1990). Indeed shareholders may expropriate wealth from bondholders who try to

tackle this problem through indenture restrictions (Jensen and Meckling 1976).

Besides, firms with high debt ratios ought to pay lower dividends as they have already

precommited their cash flows to make debt payments. Lower dividends also reduce

needs to borrow more capital. Thus Dividend payout (DP) ratio of all equity firms is

significantly higher than DP ratio of levered firm. Dividend decision of the firm is

also closely interlinked with how much profit it expected to retain (Agrawal &Jayram

1994). Hence the Factor 5 is anticipated to be negatively related to dividend payout

ratio.

Standard deviations in EPS and Ln shareprices have significant positive loadings on

Factor 6. Therefore this factor has been labeled as Factor of Shareholders’ wealth

and earnings variability. A firm whose earnings are stable has a better capacity to

pay regular and consistent dividend and this leads to creation of shareholders’ wealth.

PE ratio has significant positive loadings on Factor 7. Therefore, this factor can be

represented as Factor of Valuation and Capital Market ratios. These ratios reflect

the investor’s perception of a company. Equity shareholders use these valuation ratios

to make investment decisions. It enables them to take hold or exit decisions. Firms

with higher PE or PB ratio usually pay generous dividends. Consequently

shareholders place higher valuation on share prices of such firms. This factor is

expected to be positively associated with Dividend payout ratio.

6.3.5. RESULTS OF REGRESSION ON EXTRACTED FACTORS

The regression coefficients of Factors 1 and 6 have expected signs. Only two factors

i.e. Factor 2 & 5 have regression coefficients, which are statistically significant at 5%

level of significance. Both factor 2 and 5 have exactly opposite signs of regression

coefficients compared to what was expected based on previous research studies. The

value of Adjusted R2 is 0.375. The value of tolerance is close to 1 which shows that

204

there is no problem of multicollinearity in the data. However, multicollinearity is not

present in the factor analysis model as indicated by condition index, which is below

the critical value 20, and strong stability of coefficients. The Anova Table 6.4 depicts

F values, which are significant at 5% level of significance. (For regression results

refer to Table 6.3, 6.4 and 6.5). The time dummy variable is also significant at 5%

level. The time dummy variable has been introduced to test the structural break in the

data set which was expected to prevail as the IT sector was at the bottom of charts in

terms of dividend payments till 2004 and in 2005 scenario changed as there was

sudden spurt in the dividend payments by the companies in IT sector.

Table 6.3: Regression Results on Extracted Factors: IT Sector

R R Square Adjusted R Square

Std. Error of the

Estimate Durbin-Watson

.640 .409 .375 27.53667 1.514

Table 6.4 : ANOVA: IT Sector

Model Sum of Squares Df Mean Square F Sig.

Regression 73024.616 8 9128.077 12.038 .000

Residual 105399.241 139 758.268

Total 178423.856 147

Table 6.5: Regression Coefficients of Extracted Factors: IT Sector

FACTOR LABEL Regression

coefficients

Prob.value Condition

Index

Tolerance

(collinearity

diagnosis)

Firm size and stability Factor 1 .001 .702 1.734 .856

Liquidity ratios Factor 2 -1.229 .034* 1.821 .903

Profitability and Pecking order Factor 3 -1.22E-006 .988 1.855 .987

Long term solvency Factor 4 -.007 .508 1.893 .947

Leverage and Retained earnings Factor 5 3.466 .000* 2.030 .972

Shareholders wealth and earnings

variability

Factor 6 -.199 .295 2.712 .945

Valuation ratios Factor 7 -.007 .782 3.063 .974

D1 (Dummy variable for structural break) 13.450 .009* 5.705 .808

Note: * indicates values significant at 5% level of significance

205

6.3.6 CONCLUSION

This study examines the determinants of dividend payout ratios of companies listed

on CNX IT Index in India. The period undertaken for study i.e. 2000-2008 covers

both recessionary and booming phase of Indian information technology sector. Till

2003, there was recession and from 2003, onwards IT sector witnessed exponential

growth. After 2006, linear growth has persisted in IT sector. This sector is now,

steadily approaching towards maturity. The Return on equity of this sector is very

high compared to other sectors of Indian economy. Before 2003 the profitability of

the IT firms was scanty and consequently this sector was at the bottom of the list in

terms of dividend payments. The average payout of the IT sector during this period

was 21.53%. This can be attributed two factors. Firstly, the industry presented

immense growth opportunities for the companies hence the managers were of opinion

that they can provide the investors better returns if they plough back the earnings into

business. Secondly, most firms in Industry were facing volatile earnings stream,

which deterred them from paying more dividends. After 2003, there was a substantial

spurt in dividend payout ratios of the IT companies. Infosys Technologies, Wipro

Technologies, HCL Technologies were among the highest dividend paying

companies. Infosys Technologies paid dividend as high as 2590% in the year 2004.

The surprising results are that factor 2 and 5 have exactly opposite signs in contrast to

what was expected based on literature survey. This implies that firms in IT sector do

not use dividends as a medium to signal their prosperity to the shareholders. It also

reflects that there is lesser information asymmetry in this sector. The information is

becoming more and more symmetrical due to better Corporate Governance practices

adopted by IT companies.

IT sector is a human intensive sector and do not require huge capital asset base like

manufacturing companies for their operations. The major asset of this sector is

manpower. The funds required for recruitment and retention of manpower is

comparatively less than funds required for purchasing capital assets. So these firms

can easily release funds for payment of dividends. Also a negative relationship

between liquidity can be attributed to the fact that agency problems are not very

relevant so that monitoring mechanism i.e. dividend payout may be less needed. A

206

negative regression coefficient of Factor 2 and DP ratio can be attributed to the fact

that in IT sector capital gains are preferred to cash dividends.

Retained earnings are a vital variable governing the Dividend Payout ratio of IT firms.

The results show that Factor of retained earnings and Leverage is positively related to

DP ratio. Generally, higher debt equity ratio may negatively influence the dividend

payout of company. But in case of IT firms the proportion of debt in the total capital

structure of the company is relatively low as they are very low debt or zero debt

companies eg. Infosys ( a zero debt company). Therefore, bondholders do not

consider dividend payment a way to expropriate their value. This relationship also

highlights lesser conflicts between two groups of stakeholders i.e. shareholders and

bondholders in the Indian IT sector. Stockholders may expropriate wealth from

bondholders by paying themselves dividends. But the results show that Bondholders

may also try to contain this problem through restrictions on dividend payments in

bond indenture. Thus, the positive relation depicts that debt holders do not reduce the

cash available for the dividend by imposing debt covenants and related restrictions.

This positive relationship between Dividend payout ratio and Debt equity ratio is in

alignment with the findings of Easterbrook (1984). According to him firms with high

leverage are those whose value shifting is potentially costly. Such firms are expected

to pay large dividends.

The Factor of profitability and pecking order, long-term solvency, Shareholders’

wealth and earnings variability, have not emerged as an imperative factors

affecting the dividend payout ratios of IT firms

The existing variables explain just 37% of Indian Information Technology

dividend behavior; future research can be focused on discovering variables that can

offer better explanation of dividend payments.

207

6.4 FMCG SECTOR

6.4.1 Kaiser-Meyer-Olkin Measure of Sampling Adequacy

KMO was calculated as a first step. It is a measure that judges the sampling

adequacy. The value obtained is .691 which ensures the sample size is sufficient

to apply Factor Analysis. Table 6.6 below shows the test values.

Table 6.6: KMO test values: FMCG SECTOR

Kaiser-Meyer-Olkin Measure of

Sampling Adequacy.

0.691

Bartlett's Test of Sphericity Approx. Chi-Square 2042.453

Df 210

Sig. .000

6.4.2 EXTRACTION METHOD: Principal Component Analysis method was used

for extraction of factors. The Table 6.7 shows the factor pattern matrix, which

highlights variance exhibited by extracted factors.

6.4.3. SCREE PLOT

Component Number212019181716151413121110987654321

Eig

enva

lue

6

5

4

3

2

1

0

Scree Plot

Figure 6.2: The Scree plot: FMCG sector

The Scree plot shows the factor eigen values in descending order .The eigen values

of a factor represents the variance explained by each factor. An elbow in the Scree

plot occurs at Factor 6, which indicates the point at which the inclusion of additional

208

factors does not contribute significantly in explaining the variance of the data set.

The results of the analysis are presented in the form of factor pattern matrix.

Factors above the elbow of the plot are retained. A set of 6 Factors that were chosen

accounts for about 76% of the variations in the data.

6.4.4. FACTOR PATTERN MATRIX:

Table 6.7: Factor Pattern Matrix: FMCG Sector

The table provides the factor loadings from the Principal Component Analysis (PCA). The factor loadings may be

viewed as ordinary correlation between a variable and the factor. Underlying loadings in excess of 0.3 are

significant

FACTOR LABEL Dividend signaling and

smoothing

Cash flow quality and

firm size

Systematic and financial

risk

Pecking Order

hypothesis

Liquidity ratios and ownership dispersion

Longterm solvency andshareholders

wealth

VARIABLES FACTOR 1 FACTOR 2 FACTOR 3 FACTOR 4 FACTOR 5 FACTOR 6

Pat to total asset ratio .909 .084 -.216 -.127 -.116 .100 Lagged dividend ratio .370 -.086 -.006 -.646 -.192 .177

Current Ratio -.298 .015 -.073 .221 .848 -.073 Debt Equity ratio -.191 -.186 .473 .325 .029 -.392

Quick Ratio -.212 -.057 -.076 .052 .914 .055 Sales Growth -.075 -.063 .721 .468 -.049 -.067 Ln shareprices -.065 .325 .338 -.145 .129 .652

CFO to sales ratio -.061 -.780 -.051 -.063 .023 .129 Retention ratio -.331 .067 .026 .671 .225 -.181

Capex to gross fixed asset ratio

.074 .012 .378 .724 -.084 .069

Beta -.344 .158 .373 -.156 -.133 -.532 LnMcap .299 .858 .149 -.147 -.071 .165 PE ratio .008 .089 .894 .035 -.055 .113 PB ratio .853 .013 .323 -.188 -.081 -.053

Promoter holding .240 -.536 .108 -.117 .546 .068 Ln of total assets -.129 .930 .040 -.056 -.030 -.076

Interest coverage ratio -.049 -.180 -.022 -.105 -.087 .666 RONW .951 -.065 .015 -.104 -.109 -.073

ROE .942 -.045 -.011 -.155 -.111 -.047 Lagged profit to lagged

total assets ratio .852 .092 -.240 .005 -.098 .179

Standard deviation of EPS -.072 .704 -.332 .361 .027 .053 Cumulative Variance

Explained

22.851

38.279

49.271

59.197

68.988

76.171

209

An analysis of the factor pattern matrix potrays that the variables like PAT to asset

ratio, lagged DP ratio, lagged PAT to asset ratio, ROE and RONW have significant

positive loadings on Factor 1. Factor 1 is labelled as Factor of dividend signaling

and smoothing. Dividend carries a signal of firm’s prosperity. Shareholders tend to

applaud dividend increases and frown dividend cuts. Several research studies have

convincingly proven that dividend has information content. Higher the profitability,

higher is the dividend payout of the firm. A firm strives to maintain stability and

consistency in dividend payout if Dividend Payout ratio of the current year is

governed dividend paid during previous and profit of the current year Therefore, a

positive relationship is expected between this factor and dividend payout ratio.

Variables like Cashflow from operations to sales ratio, LnMcap and Lntotal assets are

heavily loaded on Factor 2. This Factor has been labeled as Factor of cash flow

quality and firm size .A negative loading of cashflow to sales ratio on this factor

depicts that firms with larger size suffer from liquidity problems and shortage of cash.

Higher and more stable the cash flows, higher would be the capacity of the company

to pay dividend to the shareholders. Generally larger the firm size greater is the

capacity of the firm to pay dividend .Thus, Factor 2 is projected to have positive

relationship with equity dividend.

Variables like Debt equity ratio, Beta and Standard deviation in earnings per share are

heavily loaded on factor 3. This factor has been named as Factor of financial risk

and systematic risk. Higher the risk, more volatile are the earnings and lower is the

dividend payout of a company. Higher the risk, more volatile are the earnings and

lower is the dividend payout of a company. Higher debt equity ratio increases the

fixed financial burden of the company in form of interest payment. This in turn puts

pressure on firms’ capacity to pay steady and regular dividends to shareholders. Thus

factor 3 is projected to show a negative relationship with DP ratio.

Variables like sales growth, Capex to asset ratio, retention ratio and lagged dividend

payout ratio have significant positive loadings on factor 4. Therefore this Factor 4 can

be said to represent Factor of Pecking order Hypothesis. Higher the growth

opportunities available to a firm lower will be dividend payout and lesser would be

the firms capacity to pay consistent dividends. Therefore, the lagged DP ratio has a

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negative loading on factor 4. Thus, Factor 4 is expected to have negative relationship

with Dividend Payout ratio.

Current ratio, Quick ratio display significant positive loadings on Factor 5, indicating

that liquidity and promoter holding positively impact dividend payout. It can also be

interpreted in the sense that firms with high promoter holding are better managed and

enjoy higher liquidity. This factor is labeled as Factor of Liquidity ratios and

Ownership dispersion.

Lnshareprices, interest coverage ratio have higher loadings on Factor 6. Therefore this

factor can be termed as Factor of longterm solvency and shareholders’ wealth.

Higher is the ICR higher is the firm’s capacity to pay dividends. At the same time

higher debt equity ratio induces firm to pay regular dividends to the shareholders to

mitigate agency conflicts. Also shareholders’ wealth as proxied by Lnshareprices are

positively loaded on this factor depicting that a firm, which is highly solvent in long

term, enjoys higher share prices. Hence the Factor 6 is anticipated to be positively

related to dividend payout ratio.

6.4.5. RESULTS OF REGRESSION ON EXTRACTED FACTORS

The regression results have been reported in Table 6.8, 6.9 and 6.10 respectively. The

regression results indicate that Factors 1,2,4 and 6 have expected signs of regression

coefficients. Out of these factors only one factor i.e. Factor 6 has regression coefficient,

which is statistically insignificant at 5% level of significance. Factor 5 has exactly

opposite signs of regression coefficient compared to what could be expected based on

previous research studies. The value of Adjusted R2 is 32.8% The value of tolerance is

close to 1 which shows that there is no problem of multicollinearity in the data. This is

also confirmed by the fact that the value of condition Index is below the critical value of

20. The DW statistics is 2.127 The Anova table 6.9 depicts F values which are

significant at 5% level of significance. The dummy variable has also been introduced to

take care of structural break that is anticipated from 2000-2004 and 2005-2008. The

FMCG sector revived from prolonged slump in the beginning of 2005 and a change in

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the taxation structure with the introduction of VAT had several implications for the

sector40.

Table 6.8: Regression Results on Extracted Factors: FMCG Sector

R R Square

Adjusted R

Square

Std. Error of the

Estimate Durbin-Watson

.604 .365 .328 27.6591839 2.127

Table 6.9: ANOVA: FMCG Sector

Model Sum of

Squares

Df Mean Square F Sig.

Regression 53172.145 7 7596.021 9.929 .000

Residual 92568.685 121 765.030

Total 145740.830 128

Table 6.10: Regression Coefficients of Extracted Factors: FMCG Sector

FACTOR LABEL Regression

coefficients

Prob.value Condition

Index

Tolerance

(collinearity

diagnosis)

Dividend signaling and

smoothing

Factor 1 .198 .000* 2.243 .881

Cash flow quality and firm size Factor 2 3.433 .003* 2.649 .585

Systematic risk and financial risk

Factor 3 5.876 .000* 3.154 .636

Pecking order hypothesis

Factor 4 -.727 .002* 3.591 .851

Liquidity ratios and ownership

dispersion

Factor 5 -.158 .440 4.682 .876

Factor of long-term solvency and

shareholders wealth

Factor 6 .005 .064** 8.709 .955

D1 ((Time dummy for structural

break)

-7.169 .201 17.515 .769

Note: * and ** indicates values significant at 5% and 10% level of significance respectively.

40 For details refer to the Industry overview chapter

212

6.4.6. CONCLUSION

The period undertaken for study (2000-2008) covers a complete business cycle of

Indian FMCG sector. This sector recovered from its prolonged slump in 2005 FMCG

companies have been known to be generous dividend distributors to its shareholders.

These stocks were known as ‘dividend yield’ stocks till 2004.The companies

maintained consistent dividend payouts to some extent even when the profits were not

on surge. After 2004, FMCG stocks were purely viewed as ‘dividend growth stocks’

since the companies deployed their resources for sustaining larger product baskets. It

exposed the companies to greater competition. The FMCG companies adopted a

CAPEX mode and started ploughing profits for future expansion plans. But the high

profitability enabled the firms to continue pay encouraging dividends even after

retaining a part of the profits. HLL, Godrej, ITC are among the top dividend payers.

FMCG companies’ business is easy to understand, as it is relatively simple and stable

in character. A complex business causes difficulty in predicting future cash flows. The

products have a quick turnover, and relatively low input cost. The consumers think

less while purchasing FMCG products as they are meant for daily requirements.

FMCG companies typically require very little incremental Capex. They do not have to

invest huge new capital in assets to grow earnings. That is what makes these

companies so discreet in terms of capital efficiency. Further, they also have strong

cash flows and a low debt to equity ratio. Most of the FMCG and food companies are

debt free, thus are not affected by interest rate cycles. Companies with stable, simple

and have sustainable competitive advantages over peers are likely to generate

materially higher cash flows with the passage of time. The FMCG companies usually

fulfill these criterions. Their strong brands and multiple product innovations help

them sustain their revenue stream over long periods. Also, the consumers buy the

same product several times in a year. FMCG companies are known to be generous

payers of dividend due to their strong cash flow and minimal Capex requirement.

Indian FMCG companies like their global peers have developed some strong brands,

sustained stable growth, high dividend payout and high return on net worth (RONW).

It can be stated from above analysis that Profitability is a primary determinant factor

for dividend distribution. FMCG companies score high on dividend stability and

consistency, as Lagged dividend and PAT are important factors governing dividend

213

distribution. These findings are in alignment with the findings of Aharony and Wary

(1980), Asquith and Mullins(1983), Petit(1972), John and Williams(1980),

Bhattacharya(1979), Miller and Rock(1985).

The quality of cash flows, which is a measure of liquidity of the firm and firm size are

found be to be a note worthy determinant of the dividend payout. According to

previous research studies larger firms face lower issuing cost and external debt

financing making it easier to raise funds. Thus, they can go for generous dividend

payouts. These findings are consistent with the findings of Smith and Watts

(1992)[118].

The opportunities for future growth and expansion are found to be negatively related

to dividend payout ratio. Larger is the growth and investment opportunities available

to the firm, lesser is the incentive to pay dividends as the firms prefer to retain larger

proportion of profits. According to Pecking order hypothesis, firms should prefer to

finance investment by retentions rather than debt. The regression results also disclose

negative and significant relationship with Retained earnings and Capital Expenditure

during the current year. This result is in alignment with the existing literature, which

suggests that results are logically, and theoretically correct. A company, which prefers

retention of profits for financing the capital expenditure from internal resources,

distributes fewer dividends compared to a firm, which finances the investment

expenditure from external sources. Thus, the extent to which the company decides to

finance CAPEX from retained earnings; both dividend and CAPEX in a company

would be negatively related to dividend payments. In other words, dividend decisions

are not independent of uses of corporate funds and changes in fixed assets i.e. capital

expenditure is an important determinant of dividend payments in FMCG sector.

The results depict a negative relationship between liquidity and Dividend Payout ratio

and promoter holding. This relationship cannot be validated as regression coefficient

of Factor Liquidity ratios and ownership dispersion is found to be insignificant at 5%

level.

The systematic risk, earnings variability and financial risk obstruct the stable dividend

payout but the results report that in case of FMCG sector in India the Dividend Payout

ratio is increasing even if the firm faces higher risk.

214

Dividend Payout ratio is found to be positively related to long term solvency of the

firm. But this relationship is significant at 10% level. The firms in FMCG sector

operate with very low level of debt. At the same time these firms are highly liquid

firms, therefore increase in debt proportion in capital structure do not put pressure on

firms capacity to pay dividend and consequently a positive relation can exist between

Debt Equity ratio and Dividend Payout ratio through the results. Dividend are less

important to investors in high growth firms who seek out these firms in the

expectation of receiving little dividend income as these firms need outside financing

regularly and therefore are subject to the discipline of frequent capital market

scrutiny. This also indicates lesser degree of conflicts between bondholders and

shareholders. The bondholders do not reduce the cash available for dividend

distribution by imposing indenture restrictions.

6.5. SERVICE SECTOR

6.5.1 Kaiser-Meyer-Olkin Measure of Sampling Adequacy : The first step was to

calculate KMO. It is measure that judges the sampling adequacy the value obtained is

0.506 which ensures the sample size is ample to apply Factor Analysis.

Table 6.11: KMO Test Values: Service Sector

Kaiser-Meyer-Olkin Measure of

Sampling Adequacy.

0.506

Bartlett's Test of Sphericity Approx. Chi-Square 2643.368

df 210

Sig. .000

Bartlett test of spherecity is the statistical test for overall significance of all

correlations with in a correlation matrix. It judges the appropriateness of factor

analysis.

6.5.2 Extraction Method: Extraction method used is Principal Component Analysis.

The Table 6.9 highlights the variance exhibited by extracted factors. It shows that the

first factor accounts for highest amount of variance, the second factor accounts for

second highest and so on. The principal components analysis using ‘varimax rotation

method’ of correlation matrix of the 22 variables have led to the extraction of seven

215

broad components of dividend policy of the corporate India which are explained in

the forthcoming paragraphs. These factors accounted for 20%, 12%, 10%, 10%, 7%,

7% and 5% of the total variance explained respectively.

6.5.3. SCREE PLOT

The scree plot is used to determine the number of factors to retain. Factors above the

elbow of the plot are retained. The procedure involves certain amount of subjectivity

if no clear elbow appears in the curve. The Scree plot shown below shows a clear

elbow at Factor 7. These seven factors cumulatively accounts for about 72% of the

variations in the data. Consequently, these seven Factors are considered for the

analysis.

Component Number212019181716151413121110987654321

Eig

en

valu

e

5

4

3

2

1

0

Scree Plot

Figure6.3: The Scree plot: Service sector

216

Table 6.12: Factor Pattern Matrix: Service Sector

The table provides the factor loadings from the Principal Component Analysis (PCA). The factor loadings may be

viewed as ordinary correlation between a variable and the factor. Underlying loadings in excess of 0.3 are

significant

FACTOR LABEL Dividend signaling and profitability

Liquidity ratios and systematic

risk.

Agency conflicts

and ownership dispersion

Firm size and

dividend stability

Cash flow quality and

valuation ratios

Pecking Order

hypothesis

Long-term solvency

and earnings

variability

VARIABLES FACTOR 1 FACTOR 2 FACTOR 3 FACTOR 4

FACTOR 5 FACTOR 6 FACTOR 7

Pat to total assets ratio .842 -.139 .295 -.118 -.115 .062 .174 Lagged profit to lagged

total assets ratio .713 -.227 .369 -.126 -.132 -.195 .122

Lagged dividend ratio .204 -.089 -.463 -.477 -.361 -.199 -.052 Current Ratio -.018 .913 .105 -.058 -.061 -.085 .004

Debt Equity ratio -.151 -.041 -.795 .040 -.111 .045 -.054 Quick Ratio -.004 .940 .044 .055 -.089 -.017 .007

Growth in total Inome .015 -.120 .039 .012 -.067 .838 -.035 Ln shareprices .413 -.191 -.331 .635 -.153 -.042 .210

CFO to EBIT ratio -.041 -.104 .013 -.178 .770 -.047 -.005 Retention ratio -.012 .208 .137 .560 .402 .240 -.214

Capex to gross fixed asset ratio .348 .012 -.013 .073 -.027 .743 .078

Beta -.548 .504 .168 -.014 -.175 -.052 .052 LnMcap .455 -.384 .012 .675 -.138 -.043 .026 PE ratio -.011 -.081 -.009 .032 .667 -.066 .032 PB ratio .557 .070 -.064 .068 .237 .020 -.129

Promoter holding .206 .180 .766 -.088 -.132 .066 -.082 Ln of total assets -.367 .138 -.188 .717 -.268 .006 -.028

Interest coverage ratio -.043 -.062 .183 -.042 -.029 .032 .844 RONW .808 .091 -.052 .087 -.136 .293 .087

ROE .859 .016 .231 .038 -.099 .232 .076 Standard deviation of

EPS .267 .148 -.339 .075 .080 -.002 .694

Cumulative Variance Explained 19.991

32.069

41.762 51.239 59.045

66.592

72.002

6.5.4. FACTOR PATTERN MATRIX:

The results of the analysis are presented in the form of factor pattern matrix.

The Variables PAT to asset ratio, PB ratio, Beta, ROE and RONW Lagged PAT to

asset ratio have significant positive loadings on Factor 1. Factor 1 is labelled as

Factor of dividend signaling and profitability. As it is known that dividend carries

217

a signal of firms’ prosperity. Shareholders tend to welcome dividend increases and

scowl dividend cuts. Thus, dividend has information content. Higher the profitability,

higher is the dividend payout of the firm. Dividend is a medium to signal profitability

of the company to the sharholders. Dividend stability exists when dividend paid

during current year is governed by previous year profits and dividend paid during the

year ‘t-1’(Lintner 1956). Therefore, a positive relationship is expected between this

factor and dividend payout ratio.

Variables like Current ratio and Quick ratio are heavily loaded on Factor 2. These

ratios are proxies for firms liquidity. Also beta, a proxy for systematic risk and

standard deviation in earnings have significant positive loadings on the same factor

indicating that firms with higher systematic risk tend to maintain higher liquidity .The

variability in earnings leads to fluctuations in the dividend paid. Firms having higher

systematic risk are expected to show greater variability in the earnings. If the issuing

costs are significant, then firms are likely to finance investments through retention of

earnings rather than from external sources. To the extent that dividends compete with

investments for internally generated funds, such costs are likely to affect dividend

policy. Hence under the residual theory of dividends a negative relationship is

expected between dividend payout and external financing costs. Rozeff (1982) has

used equity beta to proxy for the cost of external financing. Therefore a negative

relationship is expected between beta and Dividend Payout ratio. This Factor has been

labeled as Factor of liquidity ratios and systematic risk. Previous research studies

have shown both positive and negative signs of the variables loaded on this factor

with dividend payout ratio.

Debt Equity ratio, promoters holding are heavily loaded on factor 3. Debt Equity ratio

is positively loaded while the promoter holding is negatively loaded. This implies

firms where promoter holding is high tend to have lower debt equity ratio. Therefore,

this factor has been termed as Factor of agency conflict and ownership dispersion.

Higher the risk, more volatile is the earnings and lower is the dividend payout of a

company. Highly Levered firms can write strict dividend policy constraints. Heavy

debt service obligations limit these firms ability to over invest and frequent

refinancing provides capital market discipline. The same time higher the money raised

through debt lead to conflicts between bondholders and equity shareholders.

218

Therefore, the firm is required to pay higher dividends to shareholders to mitigate the

agency conflicts. The financial risk proposition as proxied by Debt Equity ratio might

suggest that firms with greater financial risk ought to pay higher dividends to

investors in order to compensate for risk. Therefore this factor is estimated to have

positive relationship with dividend payout ratio.

Lnshareprices, Lagged dividend payout ratio retention ratio, LnMCap and Lntotal

assets have significant positive loadings on factor 4. It has been seen larger the firm

size higher is the dividend payout of a company. It may be expected that smaller firms

grow faster through retention and so there would be a negative relationship between

retention ratio and firm size, and hence a positive relationship between the dividend

payout ratio and firm size. Larger firms are in a better position to maintain stable

dividends and create value for shareholders as reflected by the shareprices of a

company.Therefore, Factor 4 can be said to represent Factor of firm size and

stability. Factor 3 is projected to have positive relationship with equity dividend. It

can also have negative sign with the Dividend Payout ratio as it is observed larger

firms face larger cost of external financing thus prefer to retain funds and pay lower

dividends.

Variables like, cash flow to EBIT ratio and PE ratio have higher loadings on Factor

5.Therfore this factor can be termed as Factor of cash flow quality and valuation

ratios. If the dividend payout of the current year is governed by dividend payout of

previous year, it implies firm follows a policy of paying consistent and stable

dividend. Dividend decision of the firm is also closely interlinked with how much

cash flows are generated from operating activities. Hence the Factor 5 is anticipated to

be positively related to dividend payout ratio.

Capex to asset ratio and growth in total income are the two variables that have higher

loadings on Factor 6. This factor has been named as Factor of Pecking Order

Hypothesis. Dividend and investment decisions of the firm are closely interlinked and

cannot be taken in isolation. Higher the growth opportunity available to the firm lesser

is the dividend payout of the firm. According to pecking order hypothesis, firms

should prefer to finance investment by retentions rather than debt. A higher retention

ratio implies a lower dividend payout ratio, so lower payout ratio should be associated

219

with lower gearing rather than higher gearing (financial risk). Conversely, a higher

payout ratio should be associated with higher gearing.

Interest coverage ratio and Standard deviation in Earnings per share have significant

positive loadings on Factor 7. Therefore, this factor has been phrased as Factor of

long term solvency and earnings variability.

6.5.5 REGRESSION ANALYSIS

The regression results are highlighted in the Tables 6.13, 6.14, 6.15. Out of 7 factors

only 2 factors have statistically significant regression coefficients. Factors 1 and 2

have exactly opposite signs as established by previous research studies but their

regression coefficients are not significant at 5% level. The value of Adjusted R2 is

0.113, which is not very encouraging. This indicates that significant factors combined

together explain only 11% of the dividend payout pattern of Indian Service sector.

The value of tolerance is close to 1, which shows that there is no problem of

Multicollinearity in the data. The condition index values are below the critical value

of 20. The Anova Table 6.14 depicts F values are significant at 5% level of

significance. The DW statistics is 1.897.

Table 6.13: Regression Results On Extracted Factors: Service Sector

R R Square

Adjusted R

Square

Std. Error of the

Estimate Durbin-Watson

.387 .150 .113 21.5495787 1.897

Table 6.14: ANOVA: Service Sector

Model Sum of Squares df Mean Square F Sig.

Regression 15027.247 8 1878.406 4.045 .000

Residual 85446.719 184 464.384

Total 100473.966 192

220

Table 6.15: Regression Coefficients of Extracted Factors: Service Sector

FACTOR LABEL Regression

coefficients

Prob. value Condition

Index

Tolerance

(collinearity

diagnosis)

Dividend signaling and

profitability

Factor 1

-.078 .183 2.111 .838

Liquidity ratios and systematic risk. Factor 2 -.559 .335 2.156 .861

Agency conflict and ownership Factor 3 -.029 .721 2.164 .851

Firm size and dividend stability Factor 4 -2.196 .003* 2.815 .884

Cash flow quality and Valuation ratios Factor 5 .000 .051 3.800 .981

Pecking order hypothesis Factor 6 .000 .347 4.616 .954

Factor of long term solvency and

earnings variability

Factor 7 .002 .041* 5.635 .984

D1 (Regulated Vs. Unregulated) -7.963 .021* 24.531 .837

Note: * indicates values significant at 5% level of significance

6.5.6 CONCLUSION

Indian Service sector comprises of trade hotels, transport, communication, IT and

software, banking and insurance etc. Till 2002 service sector was ignored in India and

the main emphasis was on manufacturing and agricultural sector. It was only after

2002 that service sector started growing at a healthy rate of 8-10%. Today it is the

highest contributor to the GDP of our economy.

IT sector recovered from recession after 2004. Consequently there was a sudden spurt

in the dividend payout of IT firms. Companies like Wipro, Satyam Computers,

Infosys Technologies and HCL technologies have topped the list in terms of dividend

payout. Infosys Technologies paid dividend of 900% in 2005-06. This hefty doll out

of dividends can be attributed mainly to hiked profitability of these companies. IT

sector has seen significant fluctuations in its payout ratio over last years probably

indicating that the sector is still evolving. The payout ratio for the sector has moved

up and down ranging between 45% and 23%, over the last five years.

NTPC Ltd., Reliance industries, TCS declared more than Rs1000 crores dividends in

FY07. State Bank of India and VSNL Ltd. have been generous dividend payers.

NTPC was among the top 10 dividend paying companies in the FY07. In private

221

sector, Reliance Industries have been the largest dividend payer. The dividend payout

was Rs1,440.44 crore in 2007, Rs.1394 crores in 2006 against Rs.1045.13crores in

2005.

These figures indicate that dividend payout of service sector in India has increased

leaps and bounds in last few years. These hefty dividend payments can be attributed

to surging profitability of firms in this sector.

The results portray that higher the earnings variability higher will be dividend paid by

the companies in Service sector. A positive relationship has also been reported

between Dividend payout ratio and long term solvency. Higher ICR indicates that the

firm is financially sound to meet its precommited cash outlays in form of interest.

A finding of the study that refutes the existing literature is that through the analysis a

negative relationship has been found between firm’s size and the dividend payout

ratio.

This finding is not consistent with Pecking order hypothesis and stands in sharp

contrast with results in Smith and Watts (1992)[118]. Larger companies despite

having the opportunity to tap easily the financial markets by issuing stocks or bonds

prefer to retain dividends so as to avoid the costly external financing. Moreover, small

firms, which are more risky, need to have a high payout ratio, in order to attract

investors to buy their stocks.

The extracted factors are found to offer low explanatory power of dividend

determinants of Indian service sector. This can be attribute to the fact the banks

constitute major chunk of the sample chosen for study. Out of 29 companies included

in the CNX Service Index, 9 are banks. Payment of dividends by banks is covered

under the Banking Regulation Act and may not have anything to do with the variables

identified in the study. Banks have to fulfill the norms of the RBI before distributing

any dividends. To tackle this issue dummy variable has been used.

Similarly in this index, 6 are Government companies. Again their dividend payment

would depend upon the policy decisions by the Government of India and may have

little to do with the variables identified in the study. Therefore the results should be

interpreted keeping in view these two limitations.