Financial Decisions by Business Groups in India: Is it “Fair and Square”?
Debarati Basu and Kaustav Sen
Indian Institute of Management Calcutta
Diamond Harbour Road, Joka, Kolkata 700104, INDIA
August, 2012
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Financial Decisions by Business Groups in India: Is it “Fair and Square”?
Abstract
Using a large sample of business group affiliated firms in India, we investigate if insiders expropriate
outside shareholders. Our study revisits three important business decisions: capital investments, dividend
policies and corporate performance reporting. We do not assume business groups are organized as
pyramids and use the one share-one vote rule for our analyses. To examine if expropriation exists, we
focus on how the deviation of a firm’s insider ownership and other attributes from that of the group affect
these three decisions. First, we find that firms with high insider ownership make lower group investments
and pay lower dividends. Second, we find that high insider ownership firms receive capital even when
they have low growth opportunities or are suffering losses. There is only one exception to this rule: large
unprofitable firms get funding even when insider ownership is low. Third, we find that insiders behave
opportunistically in managing firm performance and transfer volatility from firms where their ownership
is high to other group firms. Taken together, our findings indicate that business group insiders expropriate
other shareholders, even without assuming that business groups are organized as pyramids.
JEL: G15, G34
Keywords: Business Groups, Dividends, Expropriation
2
1. Introduction
There is a large literature that has examined whether control rights in excess of cash flow rights, often
referred to as wedge, explain the behavior of insider owners within business groups. The underlying
assumption in this literature is that business groups are organized as pyramids, allowing insiders to have
more control over the assets of a firm than what they really own. Khanna and Yafeh (2007) question this
assumption and suggest that not all business groups around the world are pyramids. If that is true, then the
wedge measure may not be the appropriate metric to use. Among the nine East-Asian countries examined,
Classens et al. (2000) find that the median value of the cash flow to control rights ratio equals one in
majority of the countries. Masulis et al. (2011) finds that of the 194 family group firms identified by them
in India, only 6.34 percent are part of a pyramid 1. In this paper, we do not assume a pyramidal structure
and instead assume insiders focus on the deviation of a particular firm’s attributes from that of the group
as a whole in taking their decisions. Using this alternative lens, we examine if insiders expropriate outside
shareholders.
There is a unique benefit to our approach of using firm attributes relative to the group as a whole. It starts
from the premise that business group insiders take the view that they hold a portfolio of businesses. Their
decision to vertically integrate which gives rise to cross holdings and pyramids, or their decision to
diversify into unrelated industries similar to a conglomerate, can be driven by different considerations.
However, given either organizational form, viewing each firm relative to its group should play an
important role in various corporate decisions that insiders can influence. Our approach allows us to study
the effect of variation of cash flow rights of insiders across firms on variation of corporate decisions
within each business group.
There are many ways in which business group insiders can expropriate the wealth of other shareholders.
They may deviate from an appropriate dividend policy befitting the fundamental characteristics of the
firm. Paying lower dividends and retaining more cash in the firm gives the insiders control over economic
resources in excess of the norm (see La Porta et al., 1999). They may also transfer capital from one firm
to another even though it may not be in the best interests of the other stakeholders, commonly known as
tunneling (e.g. Johnson et al., 2000). These can be accomplished using varied transactions, such as loans
or inter-corporate investments, asset sales or purchases or other related party transactions. A slightly
different behavior that eventually helps expropriate is to manage earnings and transfer performance across
firms with different cash flow rights (see Bertrand et al, 2002).
1
Table 2 in Masulis et al. (2011) indicates that India lies almost in the bottom quartile of the forty-five countries
studied in terms of the number of listed firms where ownership and control diverge.
3
It is also possible that a different type of incentive, not indicating expropriation, exists in certain settings.
This may be driven by reputation or internal capital market considerations. Perhaps, higher dividends are
paid out when insider ownership is high (see La Porta et al., 1999). Faccio et al. (2001) find this to be true
for business groups in Western Europe and attribute it to better shareholder protection rights. Business
groups may also perform the function of internal capital markets (Gopalan et al., 2011). Thus intercorporate capital transfers are made not due to the vested interests of the insider but to maintain the
group’s reputation. Our approach to examining firm level decisions as deviations from the group level
makes it easier to resolve this alternative explanation for any observed results as well.
In line with the insider behavior described above, we look for evidence of expropriation by examining
dividend and inter-company financing and investing activities of firms. India has witnessed instances of
expropriation, as defined above. In the late 1990s, Kalyani Steel was investigated by the Serious Fraud
Investigation Office (SFIO)2 for having more than two-thirds of its net worth invested in group companies
and subsidiaries, yielding a return less than 1 percent. In 2004, SFIO started an investigation of Daewoo
India following charges of accounts manipulation and records falsification to enable siphoning of funds to
group companies through illegal inter-corporate deposits. Similarly, in 2005, Mardia Chemicals, the
flagship company of Mardia Group was found falsifying accounts and violating sections 77 and 297 3 of
the Companies Act, 1956; it owed over INR 1450 crores to twenty-two lenders, including banks and
insurance companies.4
We restrict our analysis to business group firms since we want to understand the intra-group decisions
taken by such insiders. Additionally, we focus on a sample of Indian firms only, primarily due to two
reasons. First, business groups have been around in India for a long time and still continue to play an
important role in the country’s economy. This together with the fact that India is a large emerging
economy, that by some estimates will eventually play a dominant role in the global economy, makes it an
important setting to examine. Second, India has the largest number of business group affiliated firms for a
single country (1821 during 1990-97, Table 1, Khanna and Yafeh, 2007). So the possibility of variation in
the organizational form of a business group in India is higher. Using our approach for evaluating business
group decisions without assuming a pyramidal structure seems more appropriate in such a setting.
2
The SFIO is a multi-disciplinary organization that detects and prosecutes or recommends for prosecution whitecollar crimes and frauds. It was created under the Ministry of Corporate Affairs, Government of India in 2003, to
stop rising white-collar crimes including stock market scams, vanishing companies and plantation companies.
3
Section 77 of the Companies Act, 1956 puts a restriction on purchase by a company, or loans by a company for
purchase, of its own or its holding company's shares. Section 297 of the Companies Act, 1956 requires the
company's board of directors' consent for certain contracts in which particular directors of the company are
interested.
4
These examples of fraud have been retrieved from articles published in various editions of the financial daily, The
Hindu Business Line.
4
When firms within the same business group engage in inter-corporate financing and investing activities,
the capital stays within the group and gets reallocated. Firms can make direct inter-corporate investments
(e.g. Gopalan et al., 2007) or pay it out as dividends that get reinvested in other group companies
(Gopalan et al., 2011). In our analysis, we separately examine the movement of capital raised from
outside the group, and that raised from group firms. We also examine whether excess ownership in a
particular firm relative to the group plays a role in influencing these decisions. If more capital is provided
to or retained in firms where insiders have higher ownership, it suggests that they want to retain control
over the capital, confirming expropriation.
Focusing first on firms that raise capital from external sources, we examine what happens to the capital
subsequently. If insiders think for the group as a whole instead of just focusing on their own cash flows,
we expect them to raise capital and then channel it to other group firms, regardless of their ownership
levels. However, if they have vested interests and are not really the guardians of the group, they may be
reluctant to channel it to group companies where their ownership is lower. In other words, they want to
keep it in a firm where they have more ownership. Here, we focus on the direct inter-corporate investment
in other group companies only. In a similar vein, we examine the behavior of firms that receive capital
from other group companies net of any investments made into other group companies. In particular, we
analyze the dividend policies of such firms subsequent to receiving intra-group capital. If they pay out
less than normal dividends when insiders have high ownership, it is again due to their penchant for
exercising control. Because their stakes in these firms are high, insiders may be taking suboptimal
decisions and investing in negative net present value projects. Given the evidence on expropriation (e.g.
La Porta et al., 1999; Faccoio, 2001), we hypothesize that after raising new capital, group affiliated firms
with high insider stakes prefer to retain it in the firm, regardless of its source.
We then examine the characteristics of firms that receive funding from the group to determine if
expropriation occurs, commonly known as tunneling. Around the world, evidence of tunneling has been
documented by Johnson et al. (2000) and by Classens et al. (2000) in East-Asian countries. Kali and
Sarkar (2010) find Indian business groups tunnel resources from high wedge firms to low wedge firms in
unrelated industries, concluding that diversification is driven by the desire to expropriate. An alternative
explanation has been offered for such inter-corporate transfers. Distressed firms facing bankruptcy can
cause loss of reputation for the group and are therefore ‘propped’ (see Goplan et al. 2007). We
hypothesize that more capital is tunneled to the firms with higher insider ownership, regardless of whether
it is to avoid bankruptcy or fund firms with low growth opportunities. However, the reputation costs of a
large firm bankruptcy may be deep, so funds may be used to support large distressed firms even with low
insider ownership.
5
Finally, the internal markets created by business groups do not only relate to reallocation of capital.
Performance can be moved from one firm to another in order to reduce the impact of external economic
shocks. Bertand et al. (2002) find that Indian business groups engage in moving profits from low cash
flow to high cash flow firms. Khanna and Yafeh (2007) question their findings, asking why negative
shocks from high wedge firms should also trickle to those in low wedge firms. We try to resolve that
conundrum: performance smoothing does occur, but only among firms where insider ownership is high,
at the expense of the other group firms. We hypothesize that firms with high insider ownership engage in
performance smoothing by transferring volatility in their performance to other group firms.
The rest of the paper is organized as follows. Section 2 reviews the literature and develops the
hypotheses. Section 3 discusses the variables used and outlines the research methodology. Section 4
describes the sample. Section 5 analyses the results. Section 6 presents results of the sensitivity analyses
conducted. Section 7 presents a comparison with alternative measures of our primary variable. Section 8
concludes the paper.
2. Literature Review and Hypotheses Development
Business groups are a significant economic occurrence, particularly in emerging economies. In India,
about 35 percent of the BSE A and B group companies (2697 companies as on 1st September, 2011) are
business group affiliated, and account for almost sixty percent of total assets. Typically, firms belonging
to such groups have characteristics very different from standalone firms, like more concentrated
ownership, more diversified businesses, better performance and larger size (see George and Kabir, 2012;
Carney, 2005; Khanna and Palepu, 2000a; Guillén, 2000; La Porta et al., 1999). While Claessens et al.
(2000a) characterizes business groups as ‘heroes or villains’, Khanna and Yafeh (2007) calls them
‘paragons or parasites’, drawing attention to both advantages and disadvantages of such affiliation.
Recent literature like Kali and Sarkar (2011), Gopalan et al. (2007), Lu and Yao (2006), Morck et al.
(2004), Bertrand et al. (2002) and Scharfstein and Stein (2000), focus on the misallocation of resources
and costs associated with business group affiliation, ranging from rent extraction to political influence.
A number of papers have focused on value-destroying business group activities related to intra-group
capital reallocation through internal capital markets. Claessens et al. (2006) find business groups enhance
agency problems through capital reallocation, especially in emerging markets with weak institutions. In a
study across forty-five countries, Masulis et al. (2011) conclude that internal capital market
considerations drive the creation of business group like structures. Gopalan et al. (2011) examine the
group financing function of business groups to explain why group affiliated firms pay higher dividends.
6
La Porta et al. (1999) is one of the first papers that discuss dividend decisions taken by business groups. It
proposes two alternate models to explain dividend decisions in this context. The outcome model views
that insiders desire more control and thus may pay out lower dividends. The substitute model argues that
higher dividends can act as a substitute for good corporate governance, so the relation between insider
ownership and dividends may be positive. Faccio et al. (2001) find that group affiliated firms with high
insider control pay higher dividends in Europe than in Asia and conclude it is most likely because of
better investor protection rights. Chen et al (2009) find that despite weak legal and institutional pressure,
firms in China with high insider ownership levels pay higher dividends. They conclude that these findings
indicate evidence for tunneling and not any alternative explanation.
We explore the existence of internal capital markets and its association with high levels of insider
ownership in two steps. First, we trace the movement of capital raised by a group affiliate from outside
the group and test if insider ownership plays a role in reallocation decisions. Next, we track the movement
of capital within the group and examine whether insider ownership level impacts dividend decisions. We
expect high insider ownership firms retain more capital, confirming the outcome model discussed above.
Hypothesis 1: After raising new capital, firms with high insider ownership retain more capital. In
particular, these firms invest lower amounts into other group firms or distribute lower dividends.
In addition to expropriation of minority investors through cash flow retention, insiders can also
expropriate through intra-group financing activities. We first explore whether business group affiliates
tunnel funds to enhance control. In the literature that assumes business groups are organized as pyramids,
tunneling has been defined as the flow of funds from a firm where insiders have low control i.e. nearer to
the base to a firm where they have high control, nearer to the peak of the pyramid (Riyanto and Toolsema,
2008; Johnson et al., 2000). Bertrand et al. (2002) and Bae et al. (2002) examine the extent of tunneling
among group firms in India and Korea respectively, and find that considerable diversion of funds takes
place across group-affiliated firms.
Riyanto and Toolsema (2008) question why rational outside investors are willing to invest in a group firm
in spite of evidence of resources being tunneled and conclude that implicit group insurance for affiliates
in distress, is an important consideration for outside investors. This type of behavior is called propping in
the literature and has been defined as negative tunneling by Friedman et al. (2003). Claessens et al. (2003)
examining 644 financially distressed firms in five East Asian countries during the crisis, discover that the
probability of filing for bankruptcy is lower for group affiliated firms. Bae et al. (2008) use sensitivity of
returns to earnings announcements of group firms to investigate whether Korean chaebols engage in
propping; they find non-announcing stocks’ returns are sensitive to the announcing firms, more so when
7
the latter are cash rich, larger, better performers or have higher debt guarantee ratios. Gopalan et al.
(2007) study bankruptcy effects on group firms in India and find group support is provided to financially
weaker firms. A study using related party transactions by Cheung et al. (2009) in China find that although
both forms of transfers exist, tunneling is more significant.
For the purpose of our study, we define tunneling as movement of capital to low growth firms, which
suffer low market valuations and are unable to raise capital on their own. We define propping as
providing support to loss making or distressed firms, suffering due to temporary adverse shocks. It is
expected that insiders act in their own self-interests when it comes to engaging in tunneling and propping.
In particular, the firms receiving capital through tunneling and propping are expected to have high insider
ownership. However, there is one exception: if the insiders do not provide support to large loss making
firms within their group, then the group’s reputation will suffer. Large firms receive this special
consideration as the costs of such distress are high for the group as a whole. 5 We expect that large firms
incurring financial losses will receive support regardless of the level of insider ownership.
Hypothesis 2: Firms that exhibit low growth receive capital from the group only if they have high insider
ownership. However, the group’s decision to provide capital to firms suffering losses is asymmetric: all
such large firms, but only small firms with higher insider ownership receive support.
The final form of expropriation that we examine is of performance smoothing. Khanna and Yafeh (2007)
discuss how group affiliation enables risk sharing through profit smoothing and reallocation of profits
across affiliates. Bertrand et al. (2002) use the sensitivity of firm performance to performance benchmarks
and group performance and find that tunneling occurs through non-operating components of profits.
Nakatani (1984) finds that the variance of operating profitability and growth rates is lower for group
affiliated companies in Japan. George and Kabir (2008) in a study on business groups and profit
redistribution in India, examines a similar phenomenon and relates redistribution with the
underperformance of group-affiliated firms relative to unaffiliated firms.
Motivated by the sensitivity approach used by Bertrand et al. (2002), we examine whether firms with high
insider ownership levels indulge in performance smoothing, to build or save reputation. We confirm the
Bertrand et al. (2002) results that firms with higher promoter ownership are more sensitive to industry
benchmarks. They conclude that within a business group, profits move to firms where the insiders benefit
the most at the cost of all other group firms. However, an unresolved issue in their findings is why
negative economic shocks should also move in this way (see Khanna and Yafeh, 2007).
5
In a slightly different context but with the same idea in mind, the United States Treasury provided $ 700 billion of
support to the big banks during the financial crisis of 2008, arguing that these were “too big to fail”.
8
Performance management can be of two types: one is to smooth performance to reduce the cost of capital
and the other is to inflate performance. Inflating performance when a company is actually generating
profits may be motivated by trying to meet benchmarks, or prior to raising new equity. Inflating losses for
unprofitable firms, commonly known as big-bath, is motivated with future considerations in mind; when
the firm eventually turns around, it will be able to show higher profits if it has taken a big-bath in profits
earlier. Firms within a business group can be motivated to either inflate or smooth performance. If
detected, inflating performance carries higher reputation costs as compared to performance smoothing.
Using a sample of 500 Indian firms that are not necessarily group affiliated, Sarkar et al., 2008 find that
insider ownership increases smoothing of performance, whereas good quality boards keeps it in check.
We believe groups engage in both types of activities but insiders act opportunistically. They smooth
performance in firms where they have high ownership and inflate performance in the other firms, in effect
transferring performance volatility from the first group to the second.
Hypothesis 3: Insiders transfer performance volatility from firms where they have high ownership to
other firms in the group.
3. Research Design
3.1. Variables of Interest
In this section, we describe the variables used in our study, segregated into our primary variable of
interest, the dependent and key variables of our equations and the control variables. As discussed in the
first section, we assume insiders focus on the deviation of a particular firm’s attributes from that of the
group as a whole.
All our variables except some dummy and industry adjusted variables are group adjusted. The value of a
particular variable z for firm k, denoted as d_zk, is measured as the difference of z from the weighted
average value of z for k’s group, using total assets as weights i.e. d_zk = zk – zG, where zG = ∑k=1
to n
(total_assetk * zk) / ∑k=1 to n total_assetk; where group G has n number of firms.
3.1.1.
Primary variable
Our primary variable of interest is the level of insider ownership. The wedge measure i.e. divergence
between cash flow and voting rights was first used in the US by Berle and Means (1932) and has been the
primary variable of interest in extant literature. While control enhancing mechanisms like pyramids
provide disproportionate influence, the power to control can also be captured with a large blockholding in
a one share-one vote structure. In a review of the empirical literature on the wedge, Adams and Ferreira
9
(2008) find that evidence is inconclusive and the role of ownership disproportionality on firm value often
disagree. Eklund and Poulsen (2010) in a recent empirical study find that existence of large blockholders
in a one share-one vote structure impacts firm value more negatively than the divergence of cash flow
from control rights. The same voting right can thus provide differential power and influence. While
theory suggests both rights are important, the incentive to expropriate varies more with cash flow rights
(see Jensen and Meckling, 1976).
Moreover, Khanna and Palepu (2000b) find no support for pyramids in Chile and Khanna and Yafeh
(2007) highlight the lack of clarity in understanding what percentage of all groups are organized as
pyramids. Panel C of Table 4 in Claessens et al. (2000) reveals a median value of one or very close to one
for six of the nine East Asian countries studied and an overall median of one as well. The evidence thus
suggests that examining business group behavior using the wedge measure may be inappropriate in the
Asian context. We examine the corporate financing behavior of a sample of Indian firms that are affiliated
to business groups using an alternative variable that measures ownership levels i.e. cash flow rights. Our
focus is not on the divergence between control rights and cash flow rights, but rather on the deviation of
cash flow rights across different companies within a business group. Our primary measure of insider
ownership is d_own, which is the group adjusted value of own, defined as the percentage of outstanding
shares held by non-institutional promoters (Pt).
We use two other measures of insider ownership. To allow for non-linearity of the relationship (see
Anderson and Reeb, 2003), we use d_own_sq, which is the group adjusted value of the square of own.
The final measure of insider ownership is d_exp, which is the group-adjusted value of exp. The measure
exp = (Pt - It - Mt) is defined as the percentage of outstanding shares held by non-institutional promoters
(Pt) less the percentage of outstanding shares held by non-promoter institutions (It) and non-institutional
non-promoter investors i.e. the minority shareholders (Mt). This measure offsets promoter control against
the better governance and monitoring provided by non-promoter institutions (e.g. Sarkar and Sarkar,
2000) and minority investors.
3.1.2.
Dependent and other key variables
The key variables we use are as described below. In the descriptions below, we use the terms group
adjusted and relative to the group interchangeably. They both represent the measure outlined in section
3.1.

Dividend: d_div is the group adjusted value of div, defined as (dividend / total assets at t-1).
10

Financial loss: loss is a dummy variable which takes the value one if current year return on assets
(roa) is negative, zero otherwise.

Low Growth: f_lgrowth is a dummy variable that equals one for firms with low growth opportunities
relative to other firms in its group. We first calculate (book value of debt + book value of equity) /
(book value of debt + market value of equity) for a firm at t-1. If this ratio for a firm is less than the
weighted average (by total assets) value of this ratio for its group, then the dummy variable equals
one, zero otherwise.

Unpredicted performance: unpred_perf is defined as actual performance minus predicted
performance. A firm misses its benchmark when the unpredicted performance is negative. We group
all positive values of unpred_perf into quartiles; if it is in the first quartile, then the firm just meets
the benchmark. For all higher quartiles, the firm beats the benchmark. A firm’s performance is
defined as earnings before interest, depreciation and amortization and its predicted performance is
calculated by multiplying a firm’s total assets by its industry’s weighted average (by total assets)
return on assets (see Bertrand et al., 2002) i.e. pred_perfk,t = roaIk,t x total_assetk,t.

Group predicted performance: for a particular firm k, opred_pref is the total predicted performance of
the rest of the group i.e. excluding the predicted performance of k. It is the total predicted
performance of all group affiliates minus the predicted performance of firm k. If there are n firms in
the group, then opred_perfk,t =∑j=1 to n,j ≠k pred_perfj,t.

Net investment from the group: d_ninvf1 is the group adjusted value of net investment from the group
at t-1, ninvf1. This is defined as total investment from all other group firms into firm k minus the total
investment made by firm k into all other group firms, divided by firm k’s total assets from a year
earlier. If we denote investment from firm j to firm k (both in group G) at time t as Ij,k,t, then the total
investment by G into k is IG,k,t =∑j=1 to n, j≠k Ij,k,t and similarly by k into the group is Ik,G,t =∑j=1 to n, j≠k Ik,j,t.
Then net investment by the group to k at t is NIG,k,t = IG,k,t - Ik,G,t. To compute our measure we scale net
investment from last year, ninvf1 = NIG,k,t-1 / total_assett-2.

Increase in internal investing into the group: d_dninvt is the group adjusted value of net investment
by firm k into the group, defined as (NIk,G,t - NIk,G,t-1) / total_assett-1.

Increase in internal financing from the group: d_dninvf is the group adjusted value of financing
received by firm k from the group, defined as (NIG,k,t - NIG,k,t-1) / total_assett-1.

External financing: f_extfinex1 is a dummy variable that equals one for firms that have raised more
external finance relative to other firms in its group. We first calculate (capital raised from outside the
group / lagged total assets) for a firm at t-1. If this ratio for a firm is more than the weighted average
(by total assets) value of this ratio for its group, then the dummy variable equals one, zero otherwise.
11
3.1.3.
Control variables
Besides the variables stated above, our tests control for other firm characteristics identified in the
literature as possible determinants of the dependent variables. For purposes of our analyses, all the
variables below are adjusted by their corresponding weighted average values for the group.

ROA: Return on Assets i.e. roat = net_incomet / total_assett

Age: number of years since incorporation

Size: log of total assets (size); an alternative measure is log of net sales (log_sales)

Lev: leverage i.e. levt = total_debtt / total_assetst..
--- Insert Table 1 ---
We also incorporate industry fixed effects in all our models. Table 1 displays the definitions of all the
variables considered in our study.
3.2. Empirical models
3.2.1.
Hypothesis 1
We use two tests to examine the first hypothesis. The first one verifies the effect of insider ownership
level on a firm’s decision to invest in other group firms. It uses the following regression, while controlling
for age, size, leverage and growth. The second explanatory variable is the interaction of insider ownership
with the external financing.
d_dninvt= α + β1* f_extfinex1 + β2* down_fextfin1 + controls + fixed effects
--- (1)
We expect a positive β1, in line with the existence of internal capital markets since certain group firms
raise capital from external sources and reallocate it to other group firms. However, we expect β2 to be
negative since we hypothesize that firms which have high insider ownership levels retain more capital
rather than share it with its group.
The second test examines the effect of insider ownership levels on dividend distribution decisions. We
use the following model and control for performance, age, size and growth. The second explanatory
variable is the interaction of insider ownership with investments from the group.
d_div= α + β1* d_ninvf1 + β2* down_dninvf1 + controls + fixed effects
--- (2)
Here also we expect β2 to be negative, as we expect high insider ownership levels enhances the desire to
expropriate and retain funds rather than distribute it.
12
3.2.2.
Hypothesis 2
For the second hypothesis, we test whether tunneling exists using the following model, after controlling
for age and size. The second explanatory variable is the interaction of insider ownership with low growth.
d_dninvf= α + β1* f_lgrowth + β2* down_flgrowth + controls + fixed effects
--- (3)
We expect a negative β1 since we expect low growth firms to receive lower financing. However, we
expect β2 to be positive since groups engage in tunneling i.e. transfer funds to firms with high insider
ownership in spite of low growth opportunities.
Next, we test for propping, where funds are transferred to firms incurring financial losses using the
following regression, after controlling for age and size. The second explanatory variable is the interaction
of insider ownership with the loss indicator.
d_dninvf= α + β1* loss + β2* down_loss + controls + fixed effects
--- (4)
Equations 3 and 4 are each tested separately on two subsamples, one for small firms and the other for
large firms. This helps explore whether tunneling and propping decisions are affected by a firm’s
visibility considerations. For this purpose, we divide the sample into three groups based on size and then
estimate the slopes separately for the group of small firms and the group of large firms. We expect a
positive β1, since group firms provide an implicit insurance to firms facing an adverse shock through
propping. We also expect a positive β2, since we expect more propping for firms with high insider
ownership levels. We expect β1 to be more significant for large firms since reputation will matter more
for these firms. Similarly, we expect β2 to be more significant for small firms since the incentive to
protect group reputation will be weaker, while the need to protect their own stakes will still exist.
3.2.3.
Hypothesis 3
To test whether group firms with high insider ownership smooth earnings, we first estimate abnormal
performance as the unpredicted portion of performance, unpred_perf. We then split the sample into three
parts: (a) firms that miss the benchmark, i.e. unpred_perf < 0, (b) firms that have met or just beaten the
benchmark (0 <= unpred_perf <= first quartile of positive values) and (c) firms that have convincingly
beaten the benchmark i.e. unpred_perf > first quartile of positive values. We then test the sensitivity of
firm performance to group performance by running the following regression for each of the three
subsamples, controlling for age and size. The second explanatory variable is the interaction of insider
ownership with group performance.
13
unpred_perf= α + β1* opred_perf + β2* down_opred + controls + fixed effects
--- (5)
For firms that have missed the benchmark, we expect a positive β2 since high insider ownership level
firms performing poorly will borrow profits from the group. Similarly, for firms that have convincingly
beaten the benchmark, we expect a negative β2 as they can afford to lend profits to lower performing
group firms.
We estimate all the above regressions using the Fama-MacBeth approach. The slopes are estimated for
each cross-section, which in our case is for every year. Then the mean values of the estimated slopes
across the ten years and the corresponding statistical significance is reported. Petersen (2009) suggests
that for empirical analyses of issues related to corporate finance, where levels variables are mainly
examined (e.g. leverage), cross sectional models are the appropriate specification. He concludes that the
Fama-Macbeth method of averaging the estimates of the cross-sectional regressions is the proper way to
allow for year fixed effects.
4. Data
Our sample comprises all business group affiliated firms (as defined by Prowess) listed on the Bombay
Stock Exchange (BSE) A and B groups as on 1st September, 2011. We study a 10-year period, from the
financial year 2001-02 to the most recent year with available data i.e. financial year 2010-11. Our sample
starts from the first year when corporate governance data became available; the K. M. Birla committee
recommended that all listed firms must file a Corporate Governance Report from 2001-02. All required
data are annual and have been extracted from the CMIE database Prowess.
---Insert Table 2 --Our final sample comprises 5637 firm-years across 10 years, 215 groups, 51 industries and 650 firms. The
test based on dividends uses a smaller sample of 3474 firm-years across 10 years, 198 groups, 50
industries and 486 firms. The steps taken to arrive at this sample have been described in Table 2.
---Insert Table 3a --Panel A of Table 3 presents the descriptive statistics of the variables used in the study. We find that our
insider ownership proxies are slightly skewed to the right, with median values of zero and mean values
marginally positive. The positive mean for loss implies there are more unprofitable than profitable firms
in our sample, while a negative median for unpred_perf implies more than half the firms do not meet
benchmarks.
14
---Insert Table 3b --Panel B of Table 3 displays Pearson’s correlation coefficients. When all variables are group adjusted, we
find a significant negative correlation between dividend (d_div) and insider ownership (d_own, d_own_sq
and d_exp). Firms with higher insider ownership also have lower leverage. We find a positive correlation
between insider ownership and a proxy for low growth (f_lgrowth); surprisingly, we also find insider
ownership to be positively correlated with growth (d_lgrowth), which should not be the case. The
Spearman correlations (unreported) confirm that insider ownership is negatively correlated with
d_lgrowth.
---Insert Table 3c --To develop better intuition, we also perform univariate tests on our key variables, for our entire sample
and separately for a few groups. We compare the mean values of these variables for firms in the top 67th
percentile with those in the bottom 33rd percentile, ranked on the basis of d_own within each group. Panel
C of Table 3 presents these results. The first column displays the differences for the entire sample; the
second column is for a group that was investigated for fraud by the SFIO (Kalyani Group) and the
remaining six columns for a sample of representative groups based on number of firms within a group.
We partition our sample comprising of 215 business groups into three smaller samples based on the
number of firms within each group. Groups with seven or more firms was in one sub-sample, groups with
five or six firms in the second and groups with less than five firms in the third sub-sample. Two groups
from each of these three sub-samples are presented in Table 3c. We find that firms with high d_own are
smaller in size, raise more external finance, receive more group funding, perform better and beat
benchmarks more often. Overall the results provide support for our hypotheses, discussed earlier in
section 3.2.
5. Results
After raising capital, typically a firm invests it to generate returns for its investors. For firms within a
business group, the choice of investment is not just limited to that firm but can also include other
affiliated firms. It is also possible that firms pay out dividends a year after raising capital because of
realized or anticipated profits. Our first hypothesis tests whether such behavior is influenced by the level
of insider ownership in the firm. If business group insiders want to expropriate from other shareholders,
we expect that the amount of investment into other group firms or the dividends paid will be lower in
firms where insider ownership is high.
---Insert Table 4a --15
The results in table 4a examine the behavior of firms after they have raised capital from sources outside
the group. The regression model is as specified in equation (1). The dependent variable is the total amount
of investment from this firm to all other firms in the same business group. Since it is the total amount
invested, at the baseline we expect such a decision to be influenced by some attributes of this firm relative
to other firms in the group. In particular, we expect firm age, size, leverage and the firm value to
influence the intra-group investing decision of this firm. We expect leverage to have a negative effect
since lenders would act as monitors; the other three to have a positive effect on the amount invested into
the group, since typically these firms are expected to have capital slack.
Our baseline regression (model 1) shows none of these have a significant effect. However, with the
exception of age, all the others have the expected sign. And among all of these, firm value as proxied by
Tobin’s Q appears to have the biggest influence. The influence of insider ownership is captured in Model
2. The additional term is an interaction of insider ownership with capital raised from external sources in
the prior period. If our first hypothesis is true, then we expect it to have a negative sign. We find this term
to have a slope of -2.16 with a t value of 4.52, significant at the 1% level. We find the results to be robust
to alternative measures of insider ownership, square of insider ownership percentage and expropriation
percentage, as seen from Models 3 and 4. From these results, it is clear that having raised capital from
outside, firms with high insider ownership do not help in facilitating internal capital markets.
A natural question that arises but is not directly related to our hypothesis is to understand the
characteristics of firms that do raise external capital. In unreported univariate tests, we find that these
firms enjoy very large stock return momentum over the last year, have high abnormal accruals, are
younger and have higher block holdings as compared to the group average. However, the level of insider
ownership is not significantly higher than in the rest of the group.
---Insert Table 4b --We next examine the situation where a group firm receives capital from other group firms. The regression
model for this setting is specified in equation (2). The results are presented in Table 4b. Unlike the earlier
specification, the dependent variable is dividend distributed. However, in spirit it measures any capital
surplus a firm may have. Three of the control variables in this regression are as earlier viz. size, age and
Tobin’s Q; however, we include profitability instead of leverage. We expect that profitable, old, large
firms enjoying higher valuations will pay out more dividends. The baseline regression, Model 1, indicates
that indeed dividends are paid out by more profitable and valuable firms, but they are younger and smaller
when all variables are measured relative to the group. The interaction term in Model 2 indicates a
negative slope of -0.002, marginally significant at the 10% level. These results hold for alternative
16
specifications of insider ownership, as seen from Models 3 and 4. Taken together, these results suggest
that propensity to retain capital by insiders is still present even when capital is received from other group
firms, but is considerably weaker. It should be noted that explanatory power of both specifications
changes a little if insider ownership is used as an explanatory variable; for equation 1, it increases by
about 2% from 16% to 18% and for equation 2, it increases marginally by 0.6% from 56.2% to 56.8%.
Our second hypothesis suggests that if insiders transfer funds from one group to another for their vested
interests and not because the firm’s fundamentals justify it, then the level of insider ownership will
influence which firm receives capital. We distinguish between two types of firms that receive capital: (i)
those with low valuations and so are not the best candidates for raising capital on their own from the
external markets and (ii) those that are suffering losses and run the risk of becoming financially
distressed. We refer to these activities as tunneling and propping respectively.
---Insert Table 5a --The regression model for testing if insiders expropriate through tunneling is specified in equation (3). The
results are presented in Table 5a. The variable to measure low valuation is book to market ratio for the
firm’s assets; it is operationalized as an inverse measure so that the slope is expected to be positive. This
is done to ease interpretation of the interaction term in model 2. The control variables are size and age.
We expect younger and smaller firms to receive funding from the group, especially if they are suffering
low valuations. We present all our results separately for the largest and smallest groups by size. This is to
see if the behavior is any different between the two groups.
The baseline regression, models 1 and 5, indicates that a group firm with low valuation (F_lgrowth),
receives no group capital. So a simple internal capital markets story, without any vested interest by the
insiders does not appear to be true. On introducing the interaction term in models 2 and 6, the picture
becomes clearer. It is only those firms that have higher insider ownership that receive group capital. The
slope coefficient for the smallest group by size is 3.79, with a t value of 2.93 significant at the 5% level
and for the largest group by size is 0.766, with a t value of 2.72 significant at the 5% level as well.
Tunneling from visible firms carries higher reputation costs for insiders. Thus the lower slopes for larger
firms are expected. The results are robust to alternate specifications of insider ownership.
---Insert Table 5b --When we examine the set of firms that receive group capital in spite of incurring financial losses (loss) as
specified in equation (4), we find a somewhat different picture, compared to the results above. The results
are asymmetric with regard to the size groupings; in particular, comparing the two baseline models, 1 and
17
5, we find that large firms get group funding whereas small firms do not. The slope of loss has a value of
+0.201, with a t value of 2.1, significant at the 10% level. The results do not hold in model 1. Next, when
we introduce the interaction term, we find another set of interesting results. The slope for this term has a
value of 6.84 with a t value of 2.4, significant at the 5% level for the small firms, but insignificant for the
large firms, as seen from models 2 and 6, respectively. The results are robust to alternate insider
ownership specifications. Taken together, what this suggests is that group capital is provided to large
firms irrespective of insider ownership levels, whereas it is provided to small firms only if insiders have
large stakes in the loss making firm.
Our final hypothesis tests if firms within a business group engage in smoothing profits, perhaps through
inter-corporate transactions. Bertrand et al (2002) find that Indian business group affiliated firms with
high insider ownership display a higher correlation with the industry performance. They conclude that
within a business group, profits move to firms where the insiders benefit the most at the cost of all other
group firms. However, an unresolved issue in their findings is why negative economic shocks should also
move in this way. Business groups are interested in smoothing profits across all firms so as to reduce the
cost of capital. We hypothesize that insiders’ act opportunistically, and transfer volatility in performance
from firms where their ownership is high to other firms in the group.
---Insert table 6 --We test our hypothesis using equation (5), where we evaluate the determinants of the unexpected
performance of a firm. In particular, we are interested in examining whether performance of the group
influences the unexpected performance of a firm, and if there is a moderating effect of insider ownership.
We include age and size as control variables. The results are as presented in Table 6. We split the sample
into three different groups, i.e. firms that have fallen short of the benchmark (negative unexpected
performance), firms that have just met the benchmark (positive unexpected performance in the first
quartile) and firms that have comfortably beaten the benchmark (positive unexpected performance in the
second or higher quartiles).
The baseline results (model 1) indicate that the unexpected performance of firms that meet the benchmark
is positively correlated with the group performance. However, the results are in the opposite direction for
firms with negative unexpected performance. Certainly this indicates that some firms take a big-bath.
Collectively, model 1 results do not confirm any income smoothing within a group, but provide evidence
of performance inflation. However, the regressions that include an interaction term (model 2) do indicate
income smoothing among firms with high insider ownership. Better performing firms with unexpected
18
performance in the second quartile and above forego some of their profits to help out poorer performing
firms with negative unexpected performance, only when insider ownership in these firms is high.
6. Sensitivity Analysis
In addition to the percentage of insider ownership, we use two additional measures to examine the
robustness of our results. These are d_own_sq and d_exp and have been described in section 3.1.1. These
sensitivity results have been included in the tables discussed earlier along with the results based on insider
ownership percentage, d_own. As can be seen in models 3 and 4 in tables 4a, 4b and 6 and in models 3, 4,
7 and 8 in tables 5a and 5b, the results discussed in the section above continue to hold. So we conclude
that our results are robust to alternate specifications of our primary variable, with similar levels of
statistical significance.
7. Alternative Measures
It is important to assess if alternative measures used to study expropriation yield similar results to what
we observe using d_own. Specifically, we examine if the level of ownership (not the deviation from group
average) as well as proxies for wedge provide similar insights. We run our tests using the level measure
of own and two proxies for wedge, pac and domc. The percentage of outstanding shares held by persons
acting in concert (pac) has been used by Kali and Sarkar (2011), but is available only till 2005-06 in
Prowess. The percentage of outstanding shares held by non-promoter domestic corporations (domc) has
been used by George and Kabir (2008) and is available for all ten years in our sample. These are the only
two wedge proxies that have been used in literature studying business groups in India.
---Insert Table 7a,b and c --From Table 7a, we observe that own shows very little significance in testing our hypotheses. Moreover,
unlike our conclusions, higher value of own leads to higher dividend payouts, which appears to confirm
the substitute model. But this result may also be due to tunneling (which essentially agrees with the
rationale of the outcome model), similar to Chen et al. (2009). The results using pac (Table 7b) as a
proxy for wedge yield no insightful results. The coefficients using domc (Table 7c) indicate some
differences from our results; in particular, capital is withdrawn from large unprofitable firms with high
insider ownership. Of all the three measures, domc confirms our results to a large extent.
8. Conclusion
In this paper we examine the inter-corporate behavior of firms belonging to a business group for a sample
of 5637 Indian firms-years, over ten years and across 215 business groups. Our paper investigates
19
whether insiders expropriate other shareholders when taking important corporate finance decisions. In
particular, we study decisions related to raising new capital, distributing dividends, investing in other
group firms and reporting firm performance. While the evidence from the existing literature does confirm
a difference in the behavior of firms affiliated to business groups with respect to these decisions, the
motivation behind such behavior remains elusive, especially in the Indian context.
Our first hypothesis examines whether high insider ownership has an impact on a firm’s investing and
dividend distribution decisions. We find that firms with higher insider ownership provide lower group
financing and pay lower dividends in order to control more resources. Next, we examine the financing
decisions of business group affiliates to investigate whether tunneling or propping occurs. We find that
business groups tunnel resources to low growth firms with high insider ownership, thus expropriating the
minority shareholders of the firm being tunneled, especially the smaller firms. We also find evidence of
groups propping firms that are incurring financial losses. The groups provide capital to all unprofitable
large firms, regardless of insider ownership whereas unprofitable small firms get support only if they have
high insider ownership. We then examine whether firms in a group firms share performance and engage
in performance smoothing. Our results reveal that only firms with high insider ownership levels engage in
such smoothing at the cost of other group firms, essentially transferring performance volatility from high
insider ownership firms to the other firms.
Our contribution to the business group literature lies in providing robust and lucid empirical results that
not everything is “fair and square” when it comes to financial decisions taken by business groups in India.
We find evidence that insiders look out for their own interests rather than of all investors in the group.
The use of direct ownership instead of the wedge and the group adjustment to every variable helps us
understand the issues better. It also helps us question the evidence that the extant literature provides on
intra-group activities and their business decisions. For example, while a group adjusted test of dividends
and insider ownership exposes a negative association as outlined in the outcome model theorized by La
Porta et al. (1999), in unreported analysis of actual dividend values on insider ownership we find a
positive association, in line with the conclusions of many papers in the emerging markets setting (see
Gopalan et al., 2011; Ramli, 2010; Chen et al., 2009). This new approach could thus, enable future
research to develop more robust and appropriate tools for within group analysis.
20
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23
Table 1: Definition of Variables
Variable
d_own
d_own_sq
d_exp
d_div
d_ninvf1
d_dninvt
Description
percentage of shares held by non-institutional promoters, minus the weighted average value
of this ownership percentage at the group level
own_sq is the square of percentage of shares held by non-institutional promoters, minus
the weighted average value of this measure at the group level
percentage of shares held by (non-institutional promoters - non-promoter institutions - nonpromoter non-institutional shareholders), minus the weighted average value of this net
measure at the group level. This is a measure of expropriation, using excess ownership by
promoters after adjusting for ownership by institutions and minority shareholders.
(dividend paid / lagged total assets), minus the weighted average of this ratio at the group
level
(investment received from group companies - investment made into group companies)/
lagged total assets, minus the weighted average of this ratio at the group level
annual increase in net investment made by a firm into the group, minus the weighted
average of the annual increase at the group level
d_dninvf
annual increase in net financing received by a firm from the group, minus the weighted
average of the annual increase at the group level
d_extfinex1
(capital raised from outside the group / lagged total assets) at t-1, minus the weighted
average value of this ratio at the group level
f_extfinex1
a dummy variable that takes the value 1 if d_extfinex1 is greater than the group average,
else 0
loss
a dummy variable that takes the value 1 if net profit is negative, else 0
pred_perf
industry roa x total_assets, where industry roa is the weighted industry average value of
(net profit / average total assets) for all firms in the industry
unpred_perf earnings before interest, depreciation and amortization – predicted performance (see
pred_perf above)
opred_perf total predicted performance (see pred_perf above) of all other firms in the group
d_roa
(net profit / average total assets), minus weighted average value of the ratio at the group
level
d_lgrowth
(book value of debt + book value of equity) / (book value of debt + market value of equity)
at t-1, minus the weighted average value of this ratio for the group
f_lgrowth
a dummy variable that takes the value 1 if d_lgrowth is negative, else 0
d_age
age calculated as financial year less year of incorporation, minus the weighted average age
of all group firms
log of total assets, minus the weighted average size of all group firms
log of net sales, minus the weighted average log of net sales for all group firms
total debt divided by total assets, minus the weighted average of the ratio for all group
firms
d_size
d_log_sales
d_lev
24
Table 2: Sample Selection Steps
Steps
1 BSE A & B listed firms as on September 1st, 2011, for years 2001-2010
2 less: observations with missing age
3 less: observations for firms not belonging to a business group
4 less: observations with less than two firms in the same business group
5 less: observations with net sales <0, total assets <0 and |ROA| > 50%
Final Sample
6 less: Dividend > 0 (only for dividend based tests)
Final Dividend Sample
25
Firms Firm-years
2697
26970
(3156)
23814
(15098)
8716
(2333)
6383
(746)
650
5637
(2163)
486
3474
Table 3a: Sample Descriptive Statistics
Lower
Quartile
Mean
Median
Upper
Quartile
Maximum
Std Dev
5242 -0.709
-0.044
0.014
0.000
0.067
0.611
0.129
d_own_sq
5242 -0.704
-0.045
0.014
0.000
0.061
0.652
0.130
d_exp
5637 -1.417
-0.092
0.023
0.000
0.131
1.210
0.254
d_div
3451 -0.271
-0.004
-0.001
0.000
0.003
0.316
0.023
d_ninvf1
4666 -51.923
-0.713
2.000
0.000
1.761
211.353
9.592
d_dninvf
4955 -50.580
-0.056
0.072
0.000
0.031
128.173
3.008
d_dninvt
5363 -211.353
-1.812
-2.102
0.000
0.683
51.923
9.717
unpred_perf
5637 -35914.99
-174.53
0.00
-17.60
76.39
39357.36
2352.35
f_extfinex1
4835 0.000
0.000
0.502
1.000
1.000
1.000
0.500
d_extfinex1
4835 -73.884
-0.207
0.183
-0.002
0.108
255.895
6.422
loss
5637 0.000
0.000
0.131
0.000
0.000
1.000
0.338
d_lgrowth
5168 -10.118
-0.202
0.015
0.000
0.093
44.800
1.598
f_lgrowth
5637 0.000
0.000
0.612
1.000
1.000
1.000
0.487
d_roa
5637 -0.642
-0.043
-0.011
0.000
0.022
0.548
0.086
d_age
5637 -109.957
-11.925
-3.828
-0.363
3.602
96.571
18.936
d_size
5637 -8.363
-1.637
-0.943
-0.320
0.171
1.058
1.504
d_log_sales
5634 -11.018
-1.750
-0.956
-0.202
0.195
4.618
1.957
d_lev
4757 -0.660
-0.072
-0.013
0.000
0.037
0.820
0.141
Variable
N
d_own
Minimum
26
Table 3b: Pearson’s Correlation Coefficients
d_own
d_own_sq
d_exp
d_div
d_dninvf
unpred_perf
d_own
1.000
0.970
0.983
-0.054***
0.073
0.016
d_own_sq
0.970
1.000
0.956
-0.055***
0.067
0.021
d_exp
0.983
0.956
1.000
-0.036**
0.071
0.006
d_ninvf1
0.123
0.115
0.126
0.115
-0.047***
0.000
f_extfinex1
0.002
-0.004
0.001
-0.002
-0.023
-0.012
d_extfinex1
0.052***
0.047***
0.049***
-0.016
-0.050***
-0.016
loss
-0.047***
-0.033**
-0.048***
-0.046***
0.002
-0.118
d_lgrowth
0.034**
0.046***
0.037***
0.468
0.009
0.027*
f_lgrowth
0.052***
0.044***
0.054
-0.174
-0.021
-0.025*
d_roa
0.010
0.012
0.017
0.474
0.020
0.149
d_age
-0.136
-0.122
-0.133
-0.042**
-0.015
-0.005
d_size
-0.158
-0.146
-0.126
0.023
-0.027*
0.004
d_log_sales
-0.089
-0.086
-0.070
0.081
-0.022
0.014
d_lev
-0.058***
-0.047***
-0.051***
-0.153
0.016
-0.039***
Note: ***, **, * indicate that the coefficient is significant at the 1 percent, 5 percent, and 10 percent level,
respectively.
27
Table 3c: Univariate Tests
Firms in a group
Variable
t_asset
extfinex
dgfinf
gfinf
div1
roa
tobin1
unpred_perf
>= 7
>= 5 and <= 6
<= 4
SAMPLE
KALYANI
BIRLA
OPJINDAL
HINDUJA
HERO
JP
ESSAR
-4734.6*
-11544.2***
-66488.5**
-23058
-25029.6***
-22523.6***
-60490.4
-50628.5*
(1.79)
(3.11)
(2.47)
(1.03)
(3.57)
(3.55)
(1.14)
(1.81)
0.3627*
0.58*
0.8965
1.002
0.6287
-0.3891
25.0867
0.5116
(1.85)
(1.9)
(1.57)
(1.35)
(0.8)
(0.34)
(0.98)
(0.42)
0.2566**
0.307*
0.4419
0.538
0.3801
-0.2688
14.9495
0.4193
(2.5)
(1.86)
(1.45)
(1.32)
(0.94)
(0.38)
(0.99)
(0.76)
2.65***
1.771***
5.48***
3.480**
3.035***
5.286***
28.646*
6.351***
(8.32)
(5.03)
(4.23)
(2.54)
(5.1)
(5.65)
(2.33)
(7.25)
-0.0004
0.001
-0.0053**
-0.0081
0.0142
-0.0407*
0.019**
-0.008
(0.42)
(0.15)
(2.35)
(0.83)
(1.68)
(2)
(3.79)
(1.11)
0.0063**
0.011
0.0362*
-0.0225
0.0024
-0.0647
0.0181
0.0422**
(2.11)
(0.56)
(1.88)
(1.17)
(0.14)
(1.58)
(0.89)
(2.11)
0.2149***
-0.667
0.1142
0.3383**
0.0328
-0.8803**
-0.1075
-0.412**
(3.48)
(0.45)
(0.53)
(2.13)
(0.18)
(2.17)
(0.43)
(2.56)
62.4
561.3**
1309.6
2161.4**
345.7
-3290.4***
92.6957
8935.2***
(0.9)
(2.61)
(0.77)
(2.12)
(1.32)
(3.48)
(0.08)
(3.45)
Note: t-values presented below the coefficients; ***, **, * indicate that the coefficient is significant at the 1 percent, 5 percent, and 10 percent
level, respectively. The first column uses all observations in our sample; the second column reports the values for Kalyani Group, which was
investigated for fraud by the SFIO; the six other columns present representative groups from three subsamples created on the basis of the number
of firms in a group.
28
Table 4a: Results for Equation (1) relating to Hypothesis 1
Model: d_dninvt= α + β1* f_extfinex1 + β2* down_fextfin1 + controls + industry fixed effects
Variable
Intercept
F_extfinex1
Model 1
0.193
Model 2
0.228
Model 3
0.2286
Model 4
0.233
1.36
1.63
1.64
1.81
0.044
0.059
0.058
0.065
0.90
1.25
1.22
1.35
down_fextfin1
-2.160***
-4.52
down_sq_fextfin1
-2.227***
-4.51
dexp_fextfin1
-1.131***
-4.94
d_age
d_size
d_lev
d_lgrowth
RSq (%)
Obs
-0.001
-0.001
-0.001
-0.001
-0.37
-0.37
-0.39
-0.35
0.054
0.037
0.0376
0.042
1.28
0.73
0.76
0.95
-0.428
-0.454
-0.446
-0.404
-0.90
-0.98
-0.96
-0.87
0.205
0.215
0.214
0.197
1.51
1.43
1.42
1.45
16.07
4162
18.07
4162
18.22
4162
17.36
4162
Note: The results in this table examine whether capital raised from sources outside of the group influence
investments into other group firms in the following year; the value of β2 indicates the role of insider
ownership in modifying this relationship. t-values presented below the coefficients; ***, **, * indicate
that the coefficient is significant at the 1 percent, 5 percent, and 10 percent level, respectively. The slopes
and r-squares reported above are the average of the annual cross-sectional regressions (Fama-Macbeth);
the total observations across all cross-sections are also reported.
29
Table 4b: Results for Equation (2) relating to Hypothesis 1
Model: d_div= α + β1* d_ninvf1 + β2* down_dninvf1 + controls + industry fixed effects
Variable
Model 1
Model 2
Model 3
Model 4
Intercept
0.002
0.002
0.002
0.002
0.78
0.74
0.75
0.79
0.000
0.000
0.000
0.000
-0.14
1.25
1.31
0.84
d_ninvf1
down_dninvf1
-0.002*
-1.93
down_sq_dninvf1
-0.002*
-2.11
dexp_dninvf1
-0.001*
-2.08
d_roa
0.094***
0.094***
0.095***
0.095***
8.13
8.02
8.04
8.23
-0.000**
-0.000**
-0.000**
-0.000**
-2.49
-2.77
-2.82
-2.72
-0.001**
-0.001**
-0.001**
-0.001**
-2.65
-2.8
-2.81
-2.74
0.017***
0.017***
0.017***
0.017***
5.17
5.58
5.58
5.2
RSq (%)
56.19
56.83
56.90
56.84
Obs
3302
3302
3302
3302
d_age
d_size
d_tobin
Note: The results in this table examine whether capital raised from other firms within the group influence
dividends paid out in the following year; the value of β2 indicates the role of insider ownership in
modifying this relationship. t-values presented below the coefficients; ***, **, * indicate that the
coefficient is significant at the 1 percent, 5 percent, and 10 percent level, respectively. The slopes and rsquares reported above are the average of the annual cross-sectional regressions (Fama-Macbeth); the
total observations across all cross-sections are also reported.
30
Table 5a: Results for Equation (3) relating to Hypothesis 2
Model: d_dninvf= α + β1* f_lgrowth + β2* down_flgrowth+ controls + industry fixed effects
Small firms
Large firms
Variable
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Intercept
-0.066
-0.359
-0.342
-0.380
-0.070
-0.063
-0.073
-0.079*
-0.14
-0.78
-0.74
-0.85
-1.53
-1.61
-1.75
-2.02
-0.049
-0.052
-0.070
-0.039
-0.146
-0.142
-0.140
-0.150*
-0.19
-0.2
-0.27
-0.15
-1.81
-1.8
-1.76
-1.86
F_lgrowth
down_flgrowth
3.796**
0.766**
2.93
2.72
down_sq_flgrowth
3.611**
0.849**
3.07
2.81
dexp_flgrowth
d_age
1.822**
0.234
2.71
1.52
-0.002
-0.002
-0.002
-0.002
-0.002
-0.002
-0.002
-0.002
-0.59
-0.42
-0.47
-0.61
-0.91
-0.83
-0.83
-0.84
-0.009
0.041
0.040
0.001
-0.098
-0.085
-0.087
-0.097
-0.12
0.58
0.57
0.02
-1.71
-1.61
-1.66
-1.68
RSq (%)
25.71
27.85
27.84
26.79
23.78
25.59
25.77
24.79
Obs
1491
1423
1423
1491
1796
1758
1758
1796
d_size
Note: The results in this table examine whether slow growing firms receive investments from the group;
the value of β2 indicates the role of insider ownership in modifying this relationship. t-values presented
below the coefficients; ***, **, * indicate that the coefficient is significant at the 1 percent, 5 percent, and
10 percent level, respectively. The slopes and r-squares reported above are the average of the annual
cross-sectional regressions (Fama-Macbeth); the total observations across all cross-sections are also
reported.
31
Table 5b: Results for Equation (4) relating to Hypothesis 2
Model: d_dninvf= α + β1* loss + β2* down_loss + controls + industry fixed effects
Small firms
Large firms
Variable
Model 1
Model 2
Model 3
Model 4
Model 5 Model 6 Model 7 Model 8
Intercept
-0.139
0.053
0.053
-0.089
-0.203*
-0.026
-0.046
-0.056
-0.46
0.18
0.18
-0.27
-1.99
-0.30
-0.59
-0.77
-0.084
0.109
0.097
0.114
0.2012*
0.216**
0.225**
0.132*
-0.24
0.35
0.31
0.39
2.10
2.55
2.43
1.84
loss
down_loss
6.841**
3.818
2.40
1.25
down_sq_loss
7.541**
3.723
2.29
1.24
dexp_loss
d_age
3.285**
1.937
2.50
1.22
-0.002
0.000
0.000
-0.001
-0.002
-0.002
-0.002
-0.002
-0.51
-0.04
-0.04
-0.27
-0.90
-0.92
-0.90
-0.92
-0.003
0.056
0.057
0.012
-0.082
-0.050
-0.056
-0.062
-0.04
0.72
0.72
0.13
-1.63
-1.09
-1.21
-1.19
RSq (%)
25.59
28.79
28.70
27.30
23.50
30.88
30.81
29.19
Obs
1491
1423
1423
1491
1796
1758
1758
1796
d_size
Note: The results in this table examine whether firms incurring losses receive investments from the group;
the value of β2 indicates the role of insider ownership in modifying this relationship. t-values presented
below the coefficients; ***, **, * indicate that the coefficient is significant at the 1 percent, 5 percent, and
10 percent level, respectively. The slopes and r-squares reported above are the average of the annual
cross-sectional regressions (Fama-Macbeth); the total observations across all cross-sections are also
reported.
32
Table 6: Results for Equation (5) relating to Hypothesis 3
Model: unpred_perf= α + β1* opred_perf + β2* down_opred + controls + industry fixed effects
Misses benchmark
Meets benchmark
Beats benchmark
Variable
Model 1
Model 2
Model 1
Model 2
Model 1
Model 2
Intercept
-483.562***
-501.847***
10.063*
8.772
1127.962***
1010.436***
-4.42
-4.56
2.07
1.79
3.93
3.28
-0.0075***
-0.0089***
0.0004*
0.0004
0.0231***
0.0262***
-7.05
-6.56
2.17
1.2
4.59
4.57
opred_perf
down_opred
d_age
0.025**
0.003
-0.067*
2.69
1.09
-2.1
-1.659**
-0.982
0.047
0.056
14.315***
14.879***
-2.41
-1.22
0.97
1.29
4.61
4.93
-164.786***
-165.246***
0.625
0.622
441.952***
431.933***
-4.86
-5.28
1.26
1.18
6.5
5.72
RSq (%)
37.97
39.12
60.07
62.97
53.96
56.08
Obs
3316
3071
5290
4840
1759
1656
d_log_sales
Note: The results in this table examine whether volatility in firm performance is transferred among group
affiliated firms, in order to meet performance benchmarks; the value of β2 indicates the role of insider
ownership in modifying this relationship. t-values presented below the coefficients; ***, **, * indicate
that the coefficient is significant at the 1 percent, 5 percent, and 10 percent level, respectively. The slopes
and r-squares reported above are the average of the annual cross-sectional regressions (Fama-Macbeth);
the total observations across all cross-sections are also reported.
33
Table 7a: Results for All Equations using own
Hypothesis 1
Variable
Intercept
F_extfinex1
own_fextfin1
ninvf1
own_ninvf1
F_tobin1
own_ftobin1
loss
own_loss
opred_perf
own_opred
control variables
RSq
Obs
Hypothesis 2
Equation 1
Equation 2
Equation 3
Small
Large
firms
firms
-0.155
0.554
-0.790*
-0.005
1.675
0.501
-1.078
1.314
-0.054
-0.313
Equation 4
Small
Large
firms
firms
0.503
0.071
-1.657
3.163
-0.311
1.415
Misses
benchmark
Hypothesis 3
Equation 5
Meets
benchmark
Beats
benchmark
1468.435*** -1.631
-5032.969***
-0.005
0.005
Yes
0.41561
3071
-0.001
0.002
Yes
0.04**
-0.077*
Yes
0.6098
484
0.63428
1656
-0.005
0.001**
Yes
0.2047
2181
Yes
0.5595
3280
Yes
0.2765
1404
Yes
0.2943
1717
Yes
0.2699
1423
Yes
0.3056
1758
Note: The results in this table are for all five equations. t-values presented below the coefficients; ***, **, * indicate that the coefficient is
significant at the 1 percent, 5 percent, and 10 percent level, respectively. The slopes and r-squares reported above are the average of the annual
cross-sectional regressions (Fama-Macbeth); the total observations across all cross-sections are also reported. The control variables and t-statistics
have been omitted to save space.
34
Table 7b: Results for All Equations using pac
Hypothesis 1
Variable
Intercept
F_extfinex1
pac_fextfin1
ninvf1
pac_ninvf1
F_tobin1
pac_ftobin1
loss
pac_ loss
opred_perf
pac_opred
control variables
RSq
Obs
Hypothesis 2
Small firms
Large firms
Small firms
Large firms
Misses
benchmark
Hypothesis 3
Equation 5
Meets
Beats
benchmark benchmark
-0.144
0.725
0.079
0.402
670.94***
-1.856
-3254.018**
-0.242
-0.12975
-0.16
-1.25
-0.925
1.96634
0.548*
-3.551
-0.002**
-0.541***
Yes
0.40225
2788
0
0.001
Yes
0.54053
570
0.014*
0.024
Yes
0.61981
1524
Equation 3
Equation 1
Equation 2
-1.003
0.057
0.191
-0.011
Equation 4
0.0004
0.000
Yes
0.1982
1968
Yes
0.6084
2910
Yes
0.2585
1478
Yes
0.3477
1136
Yes
0.2516
1502
Yes
0.3031
1154
Note: The results in this table are for all five equations. t-values presented below the coefficients; ***, **, * indicate that the coefficient is
significant at the 1 percent, 5 percent, and 10 percent level, respectively. The slopes and r-squares reported above are the average of the annual
cross-sectional regressions (Fama-Macbeth); the total observations across all cross-sections are also reported. The control variables and t-statistics
have been omitted to save space.
35
Table 7c: Results for All Equations using domc
Hypothesis 1
Variable
Intercept
F_extfinex1
domc_fextfin1
ninvf1
domc_ninvf1
F_tobin1
domc_ftobin1
Loss
domc_ loss
opred_perf
domc_opred
control variables
RSq
Obs
Equation 1
Equation 2
-0.373
0.278
-1.964*
-0.005
Hypothesis 2
Equation 3
Small
Large
firms
firms
-0.224
0.587
Equation 4
Small
Large
firms
firms
0.111
0.186
Hypothesis 3
Equation 5
Misses
Meets
Beats
benchmark benchmark
benchmark
1477.445*** -2.454
-5076.002***
0.001**
-0.001
-0.386
0.746
-0.275**
0.547
-0.620
0.08722
Yes
0.2157
2181
Yes
0.5549
3280
Yes
0.2602
1404
Yes
0.2836
1717
Yes
0.2447
1425
0.637**
-3.804*
Yes
0.2830
1758
-0.009***
0.079***
Yes
0.41672
3076
-0.001**
0.008**
Yes
0.61328
485
0.009***
-0.019
Yes
0.61716
1657
Note: The results in this table are for all five equations. t-values presented below the coefficients; ***, **, * indicate that the coefficient is
significant at the 1 percent, 5 percent, and 10 percent level, respectively. The slopes and r-squares reported above are the average of the annual
cross-sectional regressions (Fama-Macbeth); the total observations across all cross-sections are also reported. The control variables and t-statistics
have been omitted to save space.
36