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The author is grateful for comments on earlier drafts of this paper from Piman Limpaphayom, Roy Kouwenberg, and Steen Thomsen. All remaining errors are my own.
Investment Policy at Family Firms: Evidence from Thailand J. Thomas Connelly Faculty of Commerce and Accountancy, Chulalongkorn University
[This version: 15 Jan 2014]
Abstract Using a unique, multi-year sample of publicly traded non-financial firms in Thailand, this study finds that firms’ ownership characteristics influence the level of investment. The results are from an emerging market, which features concentrated, family-dominated corporate ownership structures, including ownership pyramids. Firms with high family ownership have higher investment ratios and exhibit greater sensitivity to financial slack than low family ownership firms. However, with the use of a lower threshold to designate family ownership, the investment ratios and sensitivity to financial slack are not statistically different between firms with high or low family ownership. Both high and low family ownership firms exhibit higher investment ratios when facing growth opportunities. The investment ratios for both types of firms are more sensitive to growth opportunities than cash flow, in contrast with earlier findings for developed markets. Keywords: Family ownership; Investment-cash flow sensitivity; Capital investment; Financing constraints; Pyramids; Thailand JEL Classification: G31, G32 Corresponding Author: J. T. Connelly, Department of Banking and Finance, Faculty of Commerce and Accountancy, Chulalongkorn University, Phayathai Road, Bangkok 10330, Thailand. Telephone: +66-2-218-5674. Facsimile: +66-2-218-5676. E-mail: [email protected].
Investment Policy at Family Firms: Evidence from Thailand
Abstract Using a unique, multi-year sample of publicly traded non-financial firms in Thailand, this study finds that firms’ ownership characteristics influence the level of investment. The results are from an emerging market, which features concentrated, family-dominated corporate ownership structures, including ownership pyramids. Firms with high family ownership have higher investment ratios and exhibit greater sensitivity to financial slack than low family ownership firms. However, with the use of a lower threshold to designate family ownership, the investment ratios and sensitivity to financial slack are not statistically different between firms with high or low family ownership. Both high and low family ownership firms exhibit higher investment ratios when facing growth opportunities. The investment ratios for both types of firms are more sensitive to growth opportunities than cash flow, in contrast with earlier findings for developed markets. Keywords: Family ownership; Investment-cash flow sensitivity; Capital investment; Financing constraints; Pyramids; Thailand JEL Classification: G31, G32
1
Investment Policy at Family Firms: Evidence from Thailand 1. Introduction
A firm’s ownership characteristics can have a significant effect on the company’s
investment policy. Myers and Majulf (1984) articulate the ways that information
asymmetries can affect investment policy. For example, their model shows instances when
managers may make sub-optimal decisions and underinvest, forgoing value-creating projects.
The model also explains why financial slack may be valuable because financial slack will
give managers the flexibility to undertake investments without issuing new shares to secure
funds.
The effect of a firm’s ownership characteristics on its investment policy may be
accentuated for firms in an emerging market such as Thailand. Thai firms, like companies in
most emerging markets, have concentrated ownership structures that are quite often
dominated by families. At high levels of family ownership, the degree of information
asymmetry between the large family shareholders and the smaller outside shareholders can be
quite substantial. However, the high level of family ownership may also temper the effects of
the information disparity. Certainly the family owners –who are often the managers as well –
are in a position to know more about the investment opportunities facing the firm. Yet the
family owners would have an incentive to pursue an optimal investment policy. Should the
managers choose to over- or underinvest, their wealth as shareholders would be directly
affected. The effect on wealth could be large, as their ownership stake is large. The net
effect would be a reduction in the consequences of information symmetries, leading to pursuit
of an optimal investment policy.
2
At firms with lower levels of family ownership, information asymmetries between the
family owner-managers and minority shareholders could still be significant. Though their
ownership stake is smaller, the managers at these firms still control the investment decision.
At lower levels of family ownership, the owner-managers may be more prone to deviate from
an optimal investment policy in order to enjoy other benefits of control. The family
shareholders may be more likely to use their position to expropriate the minority
shareholders. This risk of expropriation would be most acute at firms which employ control-
enhancing structures like pyramidal ownership. In these instances, the net effect would be an
increase in the consequences of information asymmetries, leading to overinvestment.
This paper makes several contributions to the literature. Few studies have examined
the extent to which ownership characteristics affect firms’ investment policies, especially in
emerging markets. Next, I use a unique time series dataset of firm ownership patterns after
the 1997 Asian financial crisis. I also examine the effects that growth opportunities and
financial constraints have on the investment decision for firms with high levels and with low
levels of family ownership.
I find firms with high family ownership show higher levels of investment than low
family ownership firms. However, this distinction depends on the level of family ownership
and the absence of a control-enhancing (pyramidal) ownership structure. These results imply
family firms follow an investment policy more close to optimal when their ownership stake is
high. Firms with growth opportunities have higher investment ratios, no matter if the level of
family ownership is high or low. I find a weak positive association between financial slack
and the investment ratio. However, this association does not hold for lower levels of family
ownership or firms with a control-enhancing pyramidal ownership structure.
3
2. Motivation and Theory
Myers and Majulf (1984) create a model connecting the investment decision and the
financing decision, specifically when a firm is contemplating issuing new equity to fund a
new investment opportunity. The model makes two important assumptions. First, managers
have information that other investors do not have. A second vital assumption is that
managers act in the interest of the old stockholders, the existing owners of the firm, who own
shares before a new project is undertaken and before the firm finances the new project with
additional equity. Based on these two assumptions, Myers and Majulf (1984) show instances
where managers may make a sub-optimal decision and decide not to invest in a value-
creating project. The authors make an example of an investment in a positive NPV project.
The project lowers the value of the shares held by the old shareholders, since the old
shareholders do not capture the increase in value created by the new, positive NPV project.
The gains go to the new shareholders. Thus, the old shareholders would not want the firm to
undertake the investment if the investment requires the issuance of new shares to new
stockholders. The model also explains why firms prefer to have financial slack (cash,
marketable securities, or unused borrowing capacity). Slack is valuable, as it permits firms to
take investment opportunities without issuing shares.
Myers and Majulf (1984) note two instances where slack would not be valuable. The
first instance occurs if a firm were able to communicate privately and preferentially to the old
shareholders rather than the new shareholders.1 Another instance would be if the old
shareholders would agree to purchase, and then hold, the newly issued shares, eliminating the
conflict between old and new shareholders.
1 Myers and Majulf (1984, p. 195) note that this action “…would be difficult and also illegal”.
4
The two instances where financial slack would not be valuable are precisely the
conditions that apply at family owned and managed firms. At a family firm, the managers are
insiders, they are shareholders, and they have information that outside shareholders do not
have. Also, the owner-managers may be extremely reluctant to sell shares to outsiders and
thus compromise their control. Potential purchasers of the new shares, knowing that the
insiders would only be selling if the shares are overvalued, would demand a discount. The
discount would further discouraging insiders from selling new shares. For these reasons,
financial slack would be more valuable at firms where family ownership is high, but less
valuable at firms where family ownership is lower.
Since Thai firms are quite often family owned and managed, the owner-managers
would indeed know more about the firm and its investment opportunities – the first
assumption made by Myers and Majulf (1984). In addition, the owner-managers would act in
the interest of themselves, the existing owners of the firm, according to the second
assumption. For example, secondary equity offerings are rare for Thai public companies.
Firms more commonly undertake a rights offering to the existing shareholders as a means to
secure new additional equity capital.
At a family owned and managed firm, the family owners often have much of their
wealth tied up in the firm, and the wealth may not be especially liquid. Their own money is
at risk. Thus, they would not make decisions that would impair their wealth. For example, if
the family owner-managers overinvest by accepting value-destroying, negative NPV projects,
they reduce their own wealth because they have reduced the value of the company.
Similarly, if the family owner-managers underinvest, they have refused to fund positive NPV
projects that would increase the value of their firm, missing opportunities to increase their
wealth. At higher levels of family ownership, family members would have a greater
5
incentive to pursue an optimal investment policy, since their investment decisions have a
larger direct effect on their wealth. Conversely, as family ownership declines,
overinvestment would become more of a problem than underinvestment because the family
typically does not own all of a firm’s shares. Minority shareholders may have sizeable stakes
in aggregate. Since the family owner-managers do control the investment decision, their
investment decisions may bring other private benefits of control (increasing the firm size, for
example, or consumption of perks, or the opportunity to divert assets) that they alone can
enjoy. For this reason, a less-than-optimal investment policy would be the more likely
outcome at firms with lower levels of family ownership.
Control-enhancing structures like pyramids make it much easier for controlling
shareholders to take advantage of the minority owners through expropriation actions like
transfers of assets or funds through related-party transactions. However, these control-
enhancing structures are somewhat rare for Thai firms. Thus, the family owners ideally
would follow an optimal investment strategy, suppressing the inclination to overinvest and
destroy firm value, and suppressing the temptation to underinvest, for the same reasons. The
costs of pursuing a less-than-optimal investment policy, and the resultant wealth destruction
or foregone increases in value, would fall disproportionately on the dominant (controlling)
owners.
In contrast, managers at firms with low family ownership may have little or no
ownership of the company they manage. Managers at these firms may be risk-averse and
prefer to underinvest, or not to invest at all, to reduce the variability of profits and to increase
job security. The managers would bear little or none of the consequences their decision to
forego investment opportunities. Firms with low family ownership may also be subject to
greater monitoring by the shareholders or creditors. Greater monitoring, for example, may
6
take the form of creditors place restrictions on the firm, such as loan covenants or limits on
capital expenditures. Given the absence of an active market for corporate control in the Thai
stock market, the managers could underinvest with relative impunity.
Based on the discussion above, I can make clear theoretical predictions about the
relation between firm type and the investment ratio, growth opportunities, and the effect of
financing constraints. I expect firms with high family ownership to follow an optimal
investment policy. Firms with low family ownership, on the other hand, may exhibit signs of
underinvestment. Thus, I predict low family ownership firms will have investment ratios less
than the ratios at high family ownership family firms. I will interpret lower investment ratios
at low family ownership firms as a sign of underinvestment.
Next, I expect that the relation between the investment ratio and growth opportunities
is positive among high family ownership firms. This implies that high family ownership
firms invest to capture the full benefits of the growth opportunities they possess. I do not
expect a significant relation between the investment ratio and growth opportunities among
low family ownership firms, based on the expectation that managers at low family ownership
firms will underinvest. Lastly, I predict no relation between the investment ratio and
financing constraints at high family ownership firms. Financial slack, in the form of cash or
unused borrowing capacity, can be thought of as the opposite of a financing constraint. At
high family ownership firms, slack is not valuable because the owners are the managers and
possess knowledge of the value of investment opportunities. The value of a potential
investment does not have to be communicated to outside equity investors, thus preserving
control and confidentiality. As noted earlier, financial slack may be more valuable to low
family ownership firms.2 For low family ownership firms, I expect a negative relation
2 The information asymmetry argument (Myers and Majluf, 1984) expresses a relation between investment policy and financing constraints. At low family ownership firms, managers have information that other
7
between the investment ratio and financing constraints; that is, the relation between the
investment ratio and financial slack is positive.
Theoretical Prediction for Variables of Interest Concerning:
Type of firm
Degree of Information Asymmetry
Investment Ratio Growth Opportunities
Financial Slack
High Family Ownership
Lower Investment policy is optimal
Positive relation with investment
ratio
No relation with
investment ratio
Low Family Ownership
Higher Investment policy is not optimal;
underinvestment
No relation with investment ratio
Positive relation with investment
ratio
3. Prior Empirical Research
There are just a few studies examining the connection between a dominant owner or
family ownership and the investment policies of firms.
Goergen and Renneboog (2001) use UK firms to evaluate whether the availability of
internal funds influences the investment spending of firms. The authors document no
positive relation between internally generated funds and investment for the random sample of
UK firms covering a six-year period. However, the authors note interesting differences in
their subsamples. For example, financially constrained firms underinvest, judging from the
strong positive relation between investment spending and the amount of internally generated
funds at these companies. Ownership moderates this relation. The authors also show firms
with higher levels of managerial ownership have less underinvestment.
investors do not have, and managers may act in the interest of the existing owners of the firm. The authors suggest this could lead to underinvestment.
8
Andres (2008) uses a sample of German public company covering 1997 to 2004 to
determine the influence of founding family ownership on investment policy. Family firms
show investment patterns that are less sensitive to the availability of cash and more sensitive
to investment opportunities compared with other types of firms.
These two studies for developed markets arrive at consistent conclusions that
ownership characteristics influence investment expenditures. This conclusion is supported by
Wei and Zhang (2008), who examine the sensitivity of investment cash flows for eight East
Asian nations in 1993-1996, the four years immediately preceding the Asian Financial Crisis.
They find a positive relation between capital expenditures (investment) and cash flow, and a
positive relation between capital expenditures and growth opportunities. They also examine
the way in which the control rights of the largest shareholder (owning 50 percent of the
outstanding shares) affect the relation between investment and cash flow. The authors find
that increases in the cash flow rights of the largest shareholder reduce the sensitivity of
capital expenditures to cash flow. Wei and Zhang (2008) also find as managers become more
entrenched (measured by a greater deviation between the cash flow rights and voting rights),
the sensitivity of capital expenditures to cash flow increases.
However, two studies for other emerging markets arrive at different conclusions.
Prasetyantoko (2007) observes no difference in the sensitivity to financial constraints
between firms in the tradable versus non-tradable sectors of the Indonesian stock market.
The author also notes no difference in firm investment and liquidity (measured by cash flow)
between these two types of firms. Pallathitta, Kabir and Qian (2011) separate a sample of
Indian businesses into group-affiliated and non-affiliated firms. The authors make the
assumption that group-affiliated firms should have easier access to funds and thus investment
should be less sensitive to cash flow. They find a strong positive relation between investment
9
and cash flow sensitivity, but the relation is indistinguishable between group-affiliated and
non-affiliated firms.
Fazzari, Hubbard, and Petersen (1988) find that financing factors affect firms’
investment decisions. They use investment cash flow sensitivity as a measure of financing
constraint. Kaplan and Zingales (1997) return to this issue, examining the relation between
investment-cash flow sensitivities and financing constraints for firms previously studied
Fazzari, Hubbard, and Petersen. Kaplan and Zingales (1997) find that firms appearing less
financially constrained have higher investment-cash flow sensitivities, and conclude that
sensitivities cannot be used to show financial constraints. The debate continued in a series of
papers (see Fazzari, Hubbard, and Petersen (2000) and Kaplan and Zingales (2000) for
dissections of each other’s work). Cleary (1999) strives to resolve the debate by using
financial statement information to classify firms based on the level of financing constraint.
Cleary (1999) finds that investment decisions are related to financial factors, supporting the
findings of Kaplan and Zingales (1997).
3. Data and Methodology
3.1 Identification of High Family Ownership versus Low Family Ownership Firms
Family ownership is common among Thai public companies, even though the total
amount of family ownership may be significantly below 25 percent of the shares, the legally-
designated threshold for control. For example, a family could own 12 percent of the shares,
yet retain a significant amount of influence over the firm’s policies, by virtue of occupying
key management positions, or holding seats on the board of directors. There are arguably
very few true widely-held firms in Thailand, where widely-held means small, atomistic
10
shareholdings, as described by Berle and Means (1932). Appendix 1 gives a more detailed
description of the extent of family ownership among publicly-traded non-financial firms in
Thailand.
I divide my sample into high family ownership and low family ownership firms, using
an ownership classification scheme similar to the method described by La Porta, Lopez-de-
Silanes, and Shleifer (1999) and other researchers, such as Claessens, Djankov, and Lang,
(2000) and Claessens, Djankov, Fan, and Lang, (2002). The designation of high family
ownership is set at 25 percent of the outstanding shares, since this is the threshold for control
according to Thai law.3
For each sample firm in each sample year, the ownership of the voting rights4
determines whether a firm has high or low family ownership. I examine the list of the top ten
shareholders, plus annual reports and other outside references, to determine if a firm has an
ultimate controller. An ultimate controller is a shareholder owning 25 percent or more of the
equity.5 I trace ownership of the shares upwards through networks of companies, both private
and public.6 If family members own 25 percent of the shares, either directly or indirectly, I
classify the company as a ‘high family ownership’ firm. If a firm does not have a family
owning 25 percent of the shares, I classify the firm as a ‘low family ownership’ firm.
3 The threshold used to designate family ownership varies in the literature. In early studies, the ownership threshold or cutoff value may be set by the researchers or set by law. For example, other researchers have used 20 percent ownership of the voting rights to determine the extent of family control, or even lower levels. See for example La Porta, Lopez-de-Silanes, and Shleifer (1999), Claessens, Djankov, and Lang (2000), Claessens, Djankov, Fan, and Lang (2002), Vilalonga and Amit (2006), and Wei and Zhang (2008). Wiwattanakantang (2001) notes that 25 percent ownership designates control of a Thai firm, consistent with Thai law. In more recent studies, researchers consider a family firm to be on where a founder or family shareholder manages the company or sits on the board. Researchers also use lower cutoff values to establish family ownership. For example, Anderson, Duru, and Reeb (2012) set a minimum family ownership threshold of 5 percent. 4 Thai law requires one share, one vote. 5 Individual family members and family-controlled affiliated firms are grouped as “family”. Shareholders with the same last name are counted as family, as are shareholders with known familial relationships (spouses, children, and other relatives), even if the last names are different. Corporations, either public or private, that are part of a family-controlled network are included in the family ownership tally. 6 I eliminate from the sample firms that have the state, a foreign corporation, a widely-held domestic financial institution, or another type of institution (such as a charitable foundation) as an ultimate controller.
11
I repeat the classification methodology using a lower cutoff value to determine the
high family ownership and low family ownership firms. I use the lower cutoff value (family
ownership of 10 percent of the shares) as a robustness check.
3.2 Identification of pyramidal ownership structures
The “wedge” is measured as the ratio of the cash flow (or ownership) rights divided
by the voting (or control) rights. This variable is frequently used by researchers as a proxy
for the extent of agency problems in firms. I employ the wedge as a proxy for the degree of
information asymmetry at family firms. An ownership “wedge” arises through the use of a
pyramidal ownership structure.7 I hypothesize the wedge will have a negative relation with
investment. A smaller wedge value means a wider deviation between the cash flow rights
and voting rights. The wider deviation implies the information asymmetries grow more
severe as the wedge value declines. The owner-managers of a family firm would be less
likely to overinvest and destroy the wealth they have built up in their firms; their personal
fortunes would suffer. However, control-enhancing structures, like pyramidal shareholdings,
permit the controlling shareholders to take advantage of smaller shareholders more easily.
Combining these two points, the differences in information will be higher when the owners
employ control-enhancing structures, as shown by smaller values for the wedge. As the
values of the wedge gets smaller, the degree of information asymmetry rises. Firms with
pyramidal ownership structures are more likely to overinvest, shown by higher investment
ratios. Thus, I predict a negative sign for the wedge. In contrast, firms with wedge values
7 The “wedge” is defined as the ratio of the cash flow rights divided by the voting rights. The “wedge” is determined by first finding the chain of control for a firm. If a firm does not have a chain of control, that is, the shareholders own their shares directly rather than through a network of affiliated public or private companies, the cash flow rights are equal to the voting rights. For firms with a chain of control, the cash flow rights are measured as the product of the family’s ownership percentages at each level in the chain of control. The voting or control rights for a firm are the smallest ownership percentage along the chain of control. This is the method used by La Porta et al. (1999).
12
equal to one will have less information asymmetries, despite differences in the level of family
ownership. These firms will not overinvest and thus exhibit lower investment ratios.
Due to data availability, I use only a single year (2005) of values for the wedge
variable. I extend this single year to cover all ten years in my sample (2001 – 2010).
Anecdotal evidence and a visual inspection of the data show ownership characteristics, such
as the use of pyramidal shareholdings, are static for most firms.
Early research, such as a study by Claessens et al. (2000), find that Thai firms rarely
employ ownership pyramids before the 1997 Asian financial crisis. However, a more recent
paper by Connelly et al. (2012) documents that by 2005, well after the Asian financial crisis
of 1997, the incidence of pyramidal ownership structures by Thai firms had increased. By
2005, more Thai firms used ownership pyramids to enhance their owners’ control rights.
3.3 Empirical methods
Equation 1 is an augmented version of the equation tested in Cleary (1999) and Cleary
(2005).
Capex Ratioi,t = β1 + β2 High Family Ownership Dummyi,t + β3 Growth Opportunitiesi,t
+ β4 High Family Ownershipi,t * Growth Opportunities i,t + β5 Financing
Constraintsi,t + β6 High Family Ownership Dummyi,t * Financing
Constraintsi,t + β7 Cash Flowi,t + β8 Wedgei,t + β9 Profitabilityi,t
+ β10 Sizei,t + β11 Leveragei,t + Yeari,t + Industryi,t + εi,t Eqn. (1)
13
From the theoretical background, the expected signs are:
Type of firm
Invest-ment Ratio
Growth Oppor-tunities
Financing Constraints
Cash Flow
Profit-ability
Size Lever-age
Wedge
High Family Owner
ship
( + ) ( + ) ( - ) ( + ) ( + ) ( + ) ( - ) ( - )
Low Family Owner
ship
Base category No relation No relation ( + ) ( + ) ( + ) ( - ) ( - )
The dependent variable, the Capex Ratio, is the firm’s capital expenditures in year t
divided by the book value of net plant, property, and equipment in year t. A dummy variable
for family ownership reveals the relation between the investment ratio and family firms. The
regressions include interaction terms to show the differences between the investment ratio for
high family control firms and low family control firms, with respect to growth opportunities,
and with respect to financing constraints. 8 The regressions also include control variables.
For high family control firms, the proxy for growth opportunities is expected to have
a positive relation with the investment ratio (Fazzari, Hubbard, and Petersen, 1988 and 2000;
Kaplan and Zingales, 1997 and 2000; Cleary, 1999 and 2005). I expect to see no relation
between growth opportunities and investment ratio for low family ownership firms. With
respect to the financing constraint variable, previous research by Kaplan and Zingales (1997,
2000) and Cleary (1999, 2005) show that the investment sensitivities are highest for firms
that are unconstrained. Thus, the expected relation between financing constraints and the
investment ratio is negative for high family ownership firms but I expect no relation for low
family ownership firms. Cash flow is expected to have a positive relation to investment
8 Cleary (1999) and (2005) estimates a regression equation similar to Equation (1) but without the interaction terms. He estimates regressions for three subsamples: financially constrained firms (FC); partially financially constrained (PFC); and not financially constrained (NFC).
14
policy, since the availability of cash will increase capital investment. The wedge is expected
to have a negative relation the investment ratio, showing that as the wedge value falls
(indicating rising information asymmetries), the investment ratio rises.
The model I estimate includes control variables for profitability, size, and leverage,
plus dummy variables for industry and year. Profitability is expected to have a positive
relation to investment policy, as more profitable firms have more cash available to invest.
Firm size is expected to be positively related to investment policy, since larger firms would
be more likely to have higher investment needs, if only to maintain their existing bases of
assets. Lastly, leverage is expected to have a negative relation with the investment ratio.
Firms with higher amounts of leverage have less financial slack, and less access to additional
debt capital. These firms may also have bank loans or bond issues that carry restrictive
covenants and limit capital expenditures.
3.4 Description of Variables
Table 1 shows the variables used in the investment policy analyses. The dependent
variable is the Capex Ratio, defined as the capital expenditures in year t, divided by the book
value of net plant, property, and equipment, measured at the beginning of year t.
The market to book ratio (MKT_TO_BOOK) is the proxy for growth opportunities.
This variable is defined as the ratio of the market value of equity divided by the book value of
common equity. Both the market value of equity and the book value of equity are measured
at the end of the previous year (the beginning of year t). The beginning of the period values
reflect the growth opportunities present at the beginning of the year, which drive a firm’s
investment spending during the year. D_FAMILY_25 and D_FAMILY_10 are dummy
variables that equals one if the firm is a family firm and zero otherwise. KZ_SCORE is a
measure of the lack of financing constraint, or a measure of financial slack. The construction
15
of the KZ_SCORE is discussed in detail in Appendix 2. CF2_NET_PPE is the cash flow in
year t, divided by book value of net plant, property, and equipment at the beginning of year t.
Cash flow is defined as net income before extraordinary items (or earnings before interest but
after tax) plus depreciation and amortization.
The control variable for profitability is NOI_TA, measured as earnings before interest
and taxes in year t divided by total assets at the end of year t. SIZE_TA is the variable that
captures firm size, calculated as the natural logarithm of total assets at the end of fiscal year t.
TD_TA is a measure of leverage, measured as the book value of total interest-bearing debt
(including short-term financing) at the end of year t divided by total assets at the end of year
t. Dummy variables for each of the nine years in the sample (2002 – 2010) are included in
the regression analyses. I also include 21 industry dummy variables.9
3.5 Description of Sample and Data
The sample is drawn from non-financial public companies in Thailand, from 2001 –
2010. Firms must have a complete set of financial and ownership data available for each
fiscal year, 2001 through 2010.
The dataset contains 2,091 firm-year observations across the ten-year sample period.
Some observations are winsorized to eliminate extreme values. The values of the Capex
Ratio and market to book (MARKET_TO_BOOK) are winsorized at the 99th percentile. The
values of the cash flow measure, CF2_NET_PPE, are winsorized at the 99th and 1st
percentiles, as is the profitability measure, NOI_TA.
Table 2 contains the descriptive statistics for the variables used. The Capex Ratio
shows a significant amount of variation, even after winsorizing. Figure 1 is a histogram of
9 I use 2001 as the reference year for the year dummy variables, while “Other” is the reference industry.
16
the Capex Ratio values, illustrating the fact that the Capex Ratio is bounded at zero.10 Table 3
contains the Pearson correlation coefficients for the variables used in the regression analyses.
The low values for the correlation coefficients imply there are no issues with
multicollinearity.
4. Results
In Table 4, the dependent variable is the Capex Ratio. Model 1 regresses the high
family ownership dummy, the market-to-book ratio, and an interaction term on Capex Ratio,
while Model 2 regresses the high family ownership dummy, the financing constraint measure
KZ_SCORE, and an interaction term on Capex Ratio. Model 3 is a full model as shown in
Equation 2. Model 4 is a Tobit model.
The family dummy variable, D_FAMILY_25, is not significant in Models 1 and 2. In
Model 1, the coefficient for MKT_TO_BOOK is positive and significant. This result is
logical and as expected, indicating higher investment at firms with growth opportunities. The
coefficient for the interaction term is not significant. This implies no significant difference in
the sensitivity to growth opportunities at high family ownership firms versus low family
ownership firms. In Model 2, the coefficient for the financing constraint measure is positive
and significant. Firms with higher KZ_SCORE values (meaning a lower level of financing
constraint or more financial slack) have higher levels of investment. This result is also
expected, because firms with less financing constraints (meaning a higher KZ_SCORE value)
have more financial slack, and be able to invest more. The insignificant coefficient for the
10 According to the Worldscope data item used to construct the Capex Ratio, capital expenditures is defined as additions to fixed assets. Any divestitures are accounted for as part of another datatype. See the definitions of the Capex Ratio in Table 1.
17
interaction term indicates no difference in the sensitivity of the Capex Ratio to financing
constraints, whether a firm is a high family ownership or a low family ownership firm.
Model 3 is the full ordinary least squares (OLS) model, including control variables,
plus industry and year dummy variables. The coefficient of the dummy variable for high
family ownership is positive and significant in this model. This result shows high family
ownership firms have higher investment ratios than low family ownership firms. The
coefficient for growth opportunities (MKT_TO_BOOK) is positive and significant, and so is
the coefficient for KZ_SCORE. These two results are the same as in Models 1 and 2.
Neither of the coefficients for the two interaction terms is significant. The coefficient for
cash flow (CF2_NET_PPE) is positive and significant, indicating firms with higher cash flow
have higher Capex Ratio values.
Of the control variables, only the measure of size, SIZE_TA, is positive and
significant, indicating larger firms have a higher Capex Ratio. This result is as expected, as
larger firms would need to invest more, at the very least to maintain their larger asset bases.
The coefficients for profitability (NOI_TA) and leverage (TD_TA) are not significant in any
model. These results are different than the hypothesized relations. Firms with higher
profitability were expected to have more cash available for investment and thus higher Capex
Ratio values. Firms with higher leverage were expected to have lower Capex Ratio values
because these firms may face difficulties securing additional capital to fund investments. For
example, firms with greater leverage may carry restrictive loan covenants which limit capital
expenditures. The effects of theses the profitability and leverage variables may have been
subsumed by other variables in the multiple regression model.
Model 4 uses a different econometric specification: a Tobit model. I include this
model because the distribution of the Capex Ratio is bounded at zero (see Figure 1). The
18
results from Model 4 confirm the results of Model 3, the full model. However, one
interaction term, high family ownership interacted with the growth opportunities proxy
variable, is negative and significant. When the coefficient of the interaction term is combined
with the coefficient for the high family ownership dummy, the net effect is that high family
ownership firms have higher Capex Ratio values than low family firms, but the difference is
smaller among firms with high growth opportunities.
The conclusions from Table 4 are as follows:
• High family ownership firms have on average a greater Capex Ratio than low
family ownership firms.
• Firms with growth opportunities have on average a greater Capex Ratio than
firms without. As growth opportunities become higher, the gap in the Capex
Ratio between high and low ownership firms narrows.
• Firms with lower levels of financing constraints have greater Capex Ratio
values. There is no difference in the relation whether a firm is a low or high
family ownership firm.
• Higher investment ratios are observed at larger firms, and at firms with higher
cash flow. There is no difference in the relation whether a firm is a low or
high family ownership firm.
• There is no relation between profitability or leverage and the Capex Ratio.
Table 5 repeats the regression analyses in Table 4, but I use a lower ownership cutoff
value to classify firms as high or low family ownership. The results in Table 5 reflect the use
of a 10 percent cutoff value, rather than the 25 percent cutoff value used in Table 4.
19
Using the lower (10 percent) cutoff value, there are far fewer firms with low family
ownership. At the 25 percent cutoff, about 29 percent (605 out of 2,091 firm-year
observations) of the sample was low family ownership firms. At the 10 percent cutoff, only
6.4 percent (117 out of 1,935 firm-year observations) are considered low family ownership
firms.11
The regression results in Table 5 are notably different from the results shown in Table
4. The family dummy variable, D_FAMILY_10, is not significant in any regression. The
proxy for growth opportunities, MKT_TO_BOOK, is significant in two OLS models (Models
1 and 3) as well as the Tobit model (Model 4). The coefficient of the interaction term is not
significant in Model 1 or Model 3, but it is significant at the 10 percent level in Model 4. The
coefficient of cash flow (CF2_NET_PPE) is positive and significant in Models 3 and 4. The
coefficients for KZ_SCORE are not significant in any model and the coefficients of the
interaction terms are not significant either. Of the control variables, only the coefficient for
size is positive and significant (Models 3 and 4).
A comparison of the results in Tables 4 and 5 leads to the conclusion that the results
are influenced by firms where family ownership is between 10 percent and 25 percent. This
group of firms is included in the low family ownership group in Table 4, but in the high
family ownership group in Table 5. Firms with higher levels of family ownership, and firms
with lower levels of financing constraints (higher KZ_SCORE values), have higher Capex
Ratio values. These results are driven by firms with higher (25 percent or above) family
ownership. The positive relations between family ownership and investment, and between
11 The lower cutoff value causes the sample to become slightly smaller, as I eliminate 156 firm-year observations (7.5 percent of the original 2,091 firm-year observations) from the sample. At the higher 25 percent cutoff, 156 firm-year observations are classified as low family ownership firms, since a family or other organization or institution does not own more than 25 percent of the outstanding shares. However, at the lower 10 percent cutoff value, these firms now have the state, a foreign corporation, a widely-held domestic financial institution, or another type of institution (such as a charitable foundation) as the ultimate controller.
20
investment and lower levels of financing constraint, do not hold at firms where family
ownership is between 10 percent and 25 percent. However, the positive relation between
growth opportunities and Capex Ratio, and the positive relation between cash flow and Capex
Ratio, are both robust to the choice of the cutoff value delineating high versus low family
ownership.
One possible explanation behind the inconsistent relation between family ownership
and investment rests on the classification of the firms themselves. At the higher threshold
designating family ownership, family firms have higher investment ratios (Table 5). The
information asymmetries may seem to be higher at firms with higher family ownership. This
is true when considering the differences in information between large (family) shareholders
and the minority shareholders. However, effect of the asymmetries are counterbalanced by
the desire of the family members to pursue a rational investment policy in order to protect
their wealth. As family members have a great deal of their wealth tied up in their firm, they
would be more likely to pursue a rational investment policy. They would be less inclined to
overinvest or underinvest.
At a lower threshold for family ownership, some firms previously designated as low
family ownership are now shifted into the high ownership group. Family members at these
firms hold a medium-sized stake in their firm. They own in total between 10 percent and 25
percent of the shares. Once these firms are added to the group of high family ownership
firms, the effect of family ownership on investment policy vanishes (Table 6, Panel B). The
effect of financing constraints also vanishes. Clearly these firms exert an influence on the
results. The information asymmetries at firms in the 10 percent to 25 percent ownership
group would also be quite high. Family members in this type of firm may have a reduced
21
incentive to pursue a rational investment policy. They may be inclined to underinvest and
enjoy private benefits of control, given their influential ownership stake.
Managers at low family ownership firms may have very little of their wealth tied up
in the firm. These managers may be tempted to ensure that they keep their jobs, with the
consequences being underinvestment, perhaps due to monitoring by a wider base of
shareholders or even creditors. For family firms with pyramidal ownership structures, the
results are different. The results show evidence of overinvestment, which could be evidence
of expropriation of minority shareholders.
The final set of results, shown in Table 6, include an additional explanatory variable.
I add the “wedge”, a measure of the deviation between cash flow rights and control or voting
rights, as an additional explanatory variable. My sample size shrinks slightly due to data
availability for the wedge. I have a total of 1,837 firm-year observations in Table 6. The
largest portion of the sample (1,495 firm-year observations or about 81 percent of the sample)
shows no evidence of pyramidal shareholding and the wedge value is one. The balance of the
sample (342 observations or about 19 percent of the sample; all are family firms) shows
evidence of a control-enhancing pyramidal shareholding structure.
Table 6 contains regression results using two cutoff values for high family ownership,
and two different econometric specifications. Models 1 and 2, an OLS model and a Tobit
model respectively, use 25 percent as the cutoff value for high family ownership. Models 3
and 4, an OLS model and a Tobit model, repeat the analyses but use 10 percent as the cutoff
value.
The results show that high family ownership firms have higher values of the Capex
Ratio, on average. The coefficients of the high family ownership dummy variable are
significant in Models 1 and 2, but not significant in Model 3, and significant at the 10 percent
22
level in Model 4. The coefficients for the wedge are negative and significant in Models 1, 3
and 4, but at the 10 percent level in Models 1 and 4. These results indicate that family firms
with pyramidal ownership have higher values of the Capex Ratio. The greater the deviation
between cash flow rights and voting rights, shown by lower values for the wedge, the higher
the Capex Ratio. These results are consistent with overinvestment, as expected.
The coefficients of the growth proxy (MKT_TO_BOOK) are positive and significant
across all four models. This result is robust to the use of different cutoff values to determine
high family ownership. The coefficients of the interaction terms are negative and significant
in the two Tobit models (Model 2 and Model 4), and negative and significant at the 10
percent level in Model 1. These results indicate family firms have lower Capex Ratio values,
irrespective of the cutoff value. The coefficients for the cash flow measure are positive and
significant in every model. The coefficient of the KZ_SCORE is significant at the 10 percent
level only in Model 3, while none of the coefficients for the interaction terms is significant.
The coefficients for profitability are significant only in the two Tobit models, Models 2 and
4, with Model 2 showing significance at the ten percent level. The coefficients for size are
positive and significant in all models.
As mentioned earlier, the coefficient for cash flow (CF2_NET_PPE) is positive and
significant in nearly every model in Tables, 4, 5, and 6. These results show firms with higher
cash flow have higher Capex Ratio values. My finding for the cash flow term is similar to
earlier work by Cleary (1999, 2005). In Cleary’s two papers (1999 and 2005), he shows the
coefficient of the cash flow ratio (the sensitivity of investment to changes in cash flow) is
positive, that the investment ratio has a positive relation to growth opportunities. My results
support these previous findings. Cleary (2005) finds firms with financial slack (that is, the
lack of financing constraint) have higher investment ratios. However, I find mixed evidence
23
in support of this result. My findings show a positive relation between financial slack and the
investment ratio, but only when using the 25 percent share ownership cutoff to designate with
high family ownership.
My results differ in a second aspect. Cleary (2005, p. 149) notes “…firm investment
decisions are sensitive to investment opportunities as proxied by market-to-book, but are
even more sensitive to cash flow.” In contrast, my results show that investment ratios are
more sensitive to growth opportunities than cash flow. My finding show the growth
opportunities themselves induce the firms to act and invest, irrespective of the level of family
ownership. One reason for the difference in sensitivities could be due to the difference in the
level of market development. Cleary’s 2005 study used developed nations, while my study is
only for firms in one developing market. One explanation for my results is that managers at
public companies, in a developing market, are more attuned to grabbing available growth
opportunities. The availability of the cash needed for investment seems to be a secondary
concern. This could imply that firms in a developing market can more easily find the capital
needed for their investment opportunities.
5. Conclusion
The results discussed in the preceding section show that firms with high family
ownership have investment ratios which are higher, on average, than the investing ratios of
low family ownership firms. I see these results for firms where a family owns 25 percent or
more of the outstanding shares. However, the results depend on the threshold used to
determine high family ownership. At a lower family ownership threshold of 10 percent share
ownership, high family and low family ownership firms have investment policies that are not
significantly different.
24
The same pattern holds when I include the wedge, a measure of the likelihood of
expropriation, as an additional explanatory variable. I find a negative relation between the
investment ratio and the wedge. The wedge, defined as the deviation of the cash flow rights
and ownership rights, measures an increased potential for expropriation of minority
shareholders. I find evidence of overinvestment at firms with pyramidal ownership
structures. With the wedge included in the regression analyses, I again find high family
ownership firms (using the 25 percent cutoff) have higher investment ratios. But when the
cutoff value is lowered to 10 percent, the investment ratios of high family and low family
ownership are not different.
The results show growth opportunities are associated with higher investment ratios.
This result is robust, irrespective of the ownership cutoff level used, and no matter whether a
firm has high or low family ownership. Lastly, I find mixed evidence that the lack of
financing constraints is associated with higher values of the Capex Ratio. The positive
association is not robust to the use of lower threshold for family ownership, and the effect
vanishes when the wedge is included as an additional explanatory variable.
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Figure 1: Histogram of Capex Ratio
0.0%5.0%
10.0%15.0%20.0%25.0%30.0%35.0%40.0%45.0%50.0%
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8
Percent of SampleCapex Ratio
Table 1: Variables Used in the Analyses
Variable of Interest
Variable Name Definition
Measures of Investment Investment in Fixed Assets
Capex Ratio Capital expenditures in year t, divided by the book value of net plant, property, and equipment, measured at the beginning of year t. Capital expenditures is defined as Worldscope code WC04601 from the cash flow statement: Capital Expenditures (Additions to Fixed Assets)
Proxy for Growth Opportunities Market to book ratio
MKT_TO_BOOK Ratio of market value (common shares outstanding times price per share divided by the book value of common equity). The measurement is made using the market equity and book equity values from the end of the previous year (the beginning of year t).
Proxy for Amount of Funds Available to Invest Cash flow CF2_NET_PPE Cash flow in year t, divided by book value of net plant,
property, and equipment at the beginning of year t. Cash flow is defined as net income before extraordinary items (or earnings before interest after tax) plus depreciation and amortization.
Control variables
High family ownership firm dummy variable
D_FAMILY_25
Equals one if the firm is a high family ownership firm (25 percent or greater family ownership of the outstanding shares, owned directly or through a chain of control), and zero otherwise.
KZ Score KZ_SCORE Calculated by discriminant analysis Net operating income ratio NOI_TA Earnings before interest and taxes in year t divided by total
assets at the end of year t Total assets SIZE_TA Natural logarithm of total assets at the end of the fiscal year
Total debt / total assets TD_TA
Book value of total interest-bearing debt (including short-term financing) at the end of year t divided by total assets at the end of year t
Table 2: Descriptive Statistics Table 2 presents summary statistics of variables used in the study. The sample consists of non-financial firms listed on the Stock Exchange of Thailand from 2001 – 2010. Financial services companies (banks, finance and securities companies, and insurance firms) are not included in the sample. Panel A shows the full sample, while Panel B divides the sample depending on whether the firm has a high level or low level of family ownership. High family ownership means 25 percent or greater family ownership of the outstanding shares, owned directly or through a chain of control), while low family ownership means family holdings are less than 25 percent. Capex Ratio is capital expenditures in year t, divided by the book value of net plant, property, and equipment, measured at the beginning of year t. MKT_TO_BOOK is the ratio of market value of equity (common shares outstanding times price per share) divided by the book value of common equity, measured at the end of the previous year (the beginning of year t). CF2_NET_PPE is the cash flow in year t, divided by book value of net plant, property, and equipment at the beginning of year t, where cash flow is defined as net income before extraordinary items (or earnings before interest after tax) plus depreciation and amortization. KZ_SCORE is a measure of financing constraint. NOI_TA is earnings before interest and taxes divided by total assets. SIZE_TA is the natural logarithm of total assets at the end of the fiscal year. TD_TA is the book value of total interest-bearing debt (including short-term financing) divided by total assets. Some variables in the sample have been winsorized to eliminate extreme values: 99th percentile for Capex Ratio and MKT_TO_BOOK; 99th and 1st percentiles for CF2_NET_PPE and NOI_TA. There are a total of 2,091 firm-year observations. Panel A: Full Sample
Variable Mean Median Std Dev Maximum Minimum Skewness Kurtosis
Capex Ratio MKT_TO_BOOK CF2_NET_PPE KZ_SCORE NOI_TA SIZE_TA TD_TA
0.189 1.416 0.587 2.126 0.052
14.767 0.254
0.1150.9490.2611.7590.051
14.5890.230
0.2421.4121.6342.3170.0971.3040.213
1.600 9.300
11.900 13.000 0.330
19.081 0.857
0.000 0.094
-1.900 -8.053 -0.300 11.220 0.000
3.196 2.947 5.297 0.809
-0.356 0.438 0.427
13.029 11.236 31.350 3.569 2.179
-0.074 -0.865
Panel B: Divided by Level of Family Ownership
N Variable Mean Median Std Dev Max Min Skewness Kurtosis
Low Family
Ownership 605
Capex Ratio MKT_TO_BOOK CF2_NET_PPE KZ_SCORE NOI_TA SIZE_TA TD_TA
0.1851.3540.5471.6130.029
14.8030.277
0.0920.8570.2121.6060.040
14.5850.273
0.2761.4991.8582.2530.1001.2960.206
1.6009.300
11.90011.9620.330
19.0810.857
0.0000.151
-1.900-8.053-0.30011.2260.000
3.354 3.238 4.825 0.260
-0.822 0.442 0.328
12.974 12.519 26.032 4.070 1.819
-0.158 -0.842
High Family
Ownership 1,486
Capex Ratio MKT_TO_BOOK CF2_NET_PPE KZ_SCORE NOI_TA SIZE_TA TD_TA
0.1911.4420.6032.3350.061
14.7530.245
0.1230.9880.2811.8230.054
14.5910.216
0.2271.3751.5342.3110.0941.3070.215
1.6009.300
11.90013.0000.330
18.6520.844
0.0000.094
-1.900-7.403-0.30011.2200.000
3.025 2.802 5.550 1.040
-0.116 0.437 0.477
12.199 10.542 34.175 3.311 2.165
-0.037 -0.852
Table 3: Pearson Correlation Coefficients Table 3 presents the Pearson correlation coefficients for the variables used in the study. The sample consists of non-financial firms listed on the Stock Exchange of Thailand from 2001 – 2010. Financial services companies (banks, finance and securities companies, and insurance firms) are not included in the sample. Capex Ratio is defined as capital expenditures in year t, divided by the book value of net plant, property, and equipment, measured at the beginning of year t. MKT_TO_BOOK is the ratio of market value of equity (common shares outstanding times price per share) divided by the book value of common equity, measured at the end of the previous year (the beginning of year t). CF2_NET_PPE is the cash flow in year t, divided by book value of net plant, property, and equipment at the beginning of year t, where cash flow is defined as net income before extraordinary items (or earnings before interest after tax) plus depreciation and amortization. KZ_SCORE is a measure of financing constraint. NOI_TA is earnings before interest and taxes divided by total assets. SIZE_TA is the natural logarithm of total assets at the end of the fiscal year. TD_TA is the book value of total interest-bearing debt (including short-term financing) divided by total assets. Some variables in the sample have been winsorized to eliminate extreme values: 99th percentile for Capex Ratio and MARKET_TO_BOOK; 99th and 1st percentiles for CF2_NET_PPE and NOI_TA. Correlations that are statistically significant at the 10 percent level or better are shown in bold.
Variable ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) ( 6 ) ( 7 ) ( 1 ) Capex Ratio 1.00 ( 2 ) MKT_TO_BOOK 0.23 1.00 ( 3 ) CF2_NET_PPE 0.26 0.13 1.00 ( 4 ) KZ_SCORE 0.11 0.07 0.13 1.00 ( 5 ) NOI_TA 0.10 0.18 0.31 0.40 1.00 ( 6) SIZE_TA 0.08 0.11 0.14 -0.09 0.15 1.00 ( 7 ) TD_TA -0.03 0.05 -0.02 -0.56 -0.22 0.35 1.00
Table 4: Regression Results Table 4 presents regression results with the Capex Ratio as the dependent variable, using 25 percent ownership as the cutoff value to determine whether or not a firm is a high family ownership firm. A total of four regression models are shown: three ordinary least squares (OLS) models and a Tobit model. The sample consists of non-financial firms listed on the Stock Exchange of Thailand from 2001 – 2010. Financial services companies (banks, finance and securities companies, and insurance firms) are not included in the sample. The Capex Ratio is defined as capital expenditures in year t, divided by the book value of net plant, property, and equipment, measured at the beginning of year t. D_FAMILY_25 is a dummy variable that equals one if the firm is a firm with high family ownership (25 percent or greater family ownership of the outstanding shares, owned directly or through a chain of control), and zero otherwise. MKT_TO_BOOK, a proxy for growth opportunities, is the ratio of market value of equity (common shares outstanding times price per share) divided by the book value of common equity, measured at the end of the previous year (the beginning of year t). CF2_NET_PPE is the cash flow in year t, divided by book value of net plant, property, and equipment at the beginning of year t, where cash flow is defined as net income before extraordinary items (or earnings before interest after tax) plus depreciation and amortization. KZ_SCORE is a measure of financing constraint. NOI_TA is earnings before interest and taxes divided by total assets. SIZE_TA is the natural logarithm of total assets at the end of the fiscal year. TD_TA is the book value of total interest-bearing debt (including short-term financing) divided by total assets. MKT_TO_BOOK * Family and KZ_SCORE * Family are interaction terms, multiplying MKT_TO_BOOK and KZ_SCORE, respectively, with the D_FAMILY_25 dummy variable. Some variables in the sample have been winsorized to eliminate extreme values: 99th percentile for Capex Ratio and MARKET_TO_BOOK; 99th and 1st percentiles for CF2_NET_PPE and NOI_TA. The standard errors of the coefficients have been adjusted for heteroskedasticity. t-statistics are shown in parentheses. *, **, and *** denote statistically significant differences at the 10, 5, and 1 percent level (two-tailed) respectively.
OLS OLS OLS Tobit
Expected
Sign ( 1 ) ( 2 ) ( 3 ) ( 4 )
D_FAMILY_25 ( + ) 0.006 0.011 0.046*** 0.045*** (0.33) (0.70) (2.62) (2.63)
MKT_TO_BOOK ( + ) 0.040*** 0.038*** 0.038*** (3.59) (3.64) (6.22)
Mkt to Book * Family Dummy ( + ) -0.002 -0.018 -0.017** (-0.18) (-1.41) (-2.33)
CF2_NET_PPE ( + ) 0.030*** 0.030*** (4.78) (9.15)
KZ_SCORE ( + ) 0.016*** 0.015*** 0.015*** (3.50) (3.19) (3.44)
KZ_SCORE * Family Dummy ( + ) -0.007 -0.007 -0.007 (-1.30) (-1.40) (-1.47)
NOI_TA ( + ) -0.089 -0.091 (-1.12) (-1.46)
SIZE_TA ( + ) 0.016*** 0.016*** (3.23) (3.29)
TD_TA ( - ) -0.023 -0.022 (-0.70) (-0.70)
Intercept 0.130*** 0.158*** -0.118* -0.118* (8.49) 12.64 (-1.75) (-1.64)
Time (Year) Dummies No No Yes Yes Industry Dummies No No Yes Yes
Adj. R-squared 0.050 0.011 0.172 F-Statistic 37.74*** 8.994*** 12.11*** Log Likelihood 206.47 No. of Observations 2,091 2,091 2,091 2,091
Table 5: Regression Results Using a Lower Cutoff Value to Determine High Family Ownership Firms Table 5 presents regression results with the Capex Ratio as the dependent variable, using a lower cutoff value (10 percent ownership) to determine whether or not a firm is a high family ownership firm. A total of four regression models are shown: three ordinary least squares (OLS) models and a Tobit model. The sample consists of non-financial firms listed on the Stock Exchange of Thailand from 2001 – 2010. Financial services companies (banks, finance and securities companies, and insurance firms) are not included in the sample. The Capex Ratio is defined as capital expenditures in year t, divided by the book value of net plant, property, and equipment, measured at the beginning of year t. D_FAMILY_10 is a dummy variable that equals one if the firm is a firm with high family ownership (10 percent or greater family ownership of the outstanding shares, owned directly or through a chain of control), and zero otherwise. MKT_TO_BOOK, a proxy for growth opportunities, is the ratio of market value of equity (common shares outstanding times price per share) divided by the book value of common equity, measured at the end of the previous year (the beginning of year t). CF2_NET_PPE is the cash flow in year t, divided by book value of net plant, property, and equipment at the beginning of year t, where cash flow is defined as net income before extraordinary items (or earnings before interest after tax) plus depreciation and amortization. KZ_SCORE is a measure of financing constraint. NOI_TA is earnings before interest and taxes divided by total assets. SIZE_TA is the natural logarithm of total assets at the end of the fiscal year. TD_TA is the book value of total interest-bearing debt (including short-term financing) divided by total assets. MKT_TO_BOOK * Family and KZ_SCORE * Family are interaction terms, multiplying MKT_TO_BOOK and KZ_SCORE, respectively, with the D_FAMILY_10 dummy variable. Some variables in the sample have been winsorized to eliminate extreme values: 99th percentile for Capex Ratio and MARKET_TO_BOOK; 99th and 1st percentiles for CF2_NET_PPE and NOI_TA. The standard errors of the regression coefficients have been adjusted for heteroskedasticity. t-statistics are shown in parentheses. *, **, and *** denote statistically significant differences at the 10, 5, and 1 percent level (two-tailed) respectively.
OLS OLS OLS Tobit
Expected
Sign ( 1 ) ( 2 ) ( 3 ) ( 4 )
D_FAMILY_10 ( + ) 0.041 -0.010 0.038 0.036 (1.22) (-0.34) (1.10) (1.20)
MKT_TO_BOOK ( + ) 0.045** 0.045** 0.045*** (2.01) (2.15) (4.69)
Mkt to Book * Family Dummy ( + ) -0.004 -0.020 -0.019* (-0.19) (-0.88) (-1.84)
CF2_NET_PPE ( + ) 0.026*** 0.025*** (3.99) (7.67)
KZ_SCORE ( + ) 0.005 0.012 0.012 (0.59) (1.55) (1.42)
KZ_SCORE * Family Dummy ( + ) 0.007 -0.001 -0.001 (0.81) (-0.17) (-0.14)
NOI_TA ( + ) -0.053 -0.056 (-0.67) (-0.89)
SIZE_TA ( + ) 0.016*** 0.016*** (3.35) (3.30)
TD_TA ( - ) -0.006 -0.004 (-0.17) (-0.14)
Intercept 0.089*** 0.172*** -0.122 -0.121 (2.72) (6.36) (-1.59) (-1.57)
Time (Year) Dummies No No Yes Yes Industry Dummies No No Yes Yes
Adj. R-squared 0.062 0.010 0.169 F-Statistic 43.23*** 7.56*** 11.09*** Log Likelihood 248.18 No. of Observations 1,935 1,935 1,935 1,935
Table 6: Analyses Including Ownership Wedge as an Additional Explanatory Variable Table 6 presents regression results with the Capex Ratio as the dependent variable, and incorporating the ownership wedge as an additional explanatory variable. The sample consists of non-financial firms listed on the Stock Exchange of Thailand from 2001 – 2010. Financial services companies (banks, finance and securities companies, and insurance firms) are not included in the sample. The Capex Ratio is defined as capital expenditures in year t, divided by the book value of net plant, property, and equipment, measured at the beginning of year t. WEDGE is the ratio of the cash flow (ownership) rights and the voting (control) rights. A WEDGE value of one indicates that a firm shows no evidence of pyramidal ownership, while a value less than one means pyramidal shareholding is present because the cash flow rights are less than the control rights. The values of WEDGE are for a single year (2005) but are assumed to be constant across the whole ten-year sample period. D_FAMILY_25 and D_FAMILY_10 are dummy variables that equals one if the firm is a high family ownership firm and zero otherwise. D_FAMILY_25 uses a cutoff value equal to 25 percent family ownership (25 percent or greater family ownership of the outstanding shares, owned directly or through a chain of control) to designate high family ownership firm, while D_FAMILY_10 uses 10 percent as the cutoff value. MKT_TO_BOOK, a proxy for growth opportunities, is the ratio of market value of equity (common shares outstanding times price per share) divided by the book value of common equity, measured at the end of the previous year (the beginning of year t). CF2_NET_PPE is the cash flow in year t, divided by book value of net plant, property, and equipment at the beginning of year t, where cash flow is defined as net income before extraordinary items (or earnings before interest after tax) plus depreciation and amortization. KZ_SCORE is a measure of financing constraint. NOI_TA is earnings before interest and taxes divided by total assets. SIZE_TA is the natural logarithm of total assets at the end of the fiscal year. TD_TA is the book value of total interest-bearing debt (including short-term financing) divided by total assets. MKT_TO_BOOK * Family and KZ_SCORE * Family are interaction terms, multiplying MKT_TO_BOOK and KZ_SCORE, respectively, with the family ownership dummy variable (D_FAMILY_25 or D_FAMILY_10). Some variables in the sample have been winsorized to eliminate extreme values: 99th percentile for Capex Ratio and MARKET_TO_BOOK; 99th and 1st percentiles for CF2_NET_PPE and NOI_TA. The standard errors of the regression coefficients have been adjusted for heteroskedasticity. t-statistics are shown in parentheses. *, **, and *** denote statistically significant differences at the 10, 5, and 1 percent level (two-tailed) respectively.
Regression Results
OLS Tobit OLS Tobit
Expected
Sign ( 1 ) ( 2 ) ( 3 ) ( 4 )
WEDGE ( - ) -0.031* -0.032 -0.036** -0.037* (-1.70) (-1.55) (-2.01) (-1.83)
D_FAMILY_25 (Family dummy) ( + ) 0.053*** 0.054*** (2.80) (2.75)
D_FAMILY_10 (Family dummy) 0.060 0.059* (1.41) (1.77)
MKT_TO_BOOK ( + ) 0.047*** 0.047*** 0.059** 0.059*** (3.10) (6.43) (2.11) (5.25)
Mkt to Book * Family Dummy ( + ) -0.027* -0.027*** -0.036 -0.036*** (-1.64) (-3.19) (-1.26) (-3.01)
CF2_NET_PPE ( + ) 0.032*** 0.032*** 0.032*** 0.032*** (4.28) (8.56) (4.26) (8.59)
KZ_SCORE ( + ) 0.010 0.010* 0.013 0.012 (1.60) (1.75) (1.12) (1.24)
KZ_SCORE * Family Dummy ( + ) -0.001 -0.001 -0.003 -0.003 (-0.18) (-0.22) (-0.28) (-0.28)
NOI_TA ( + ) -0.122 -0.124* -0.124 -0.125** (-1.51) (-1.95) (-1.54) (-1.98)
SIZE_TA ( + ) 0.024*** 0.024*** 0.025*** 0.025*** (4.95) (4.79) (5.04) (4.93)
TD_TA ( - ) -0.002 -0.001 -0.004 -0.003 (-0.06) (-0.01) (-0.11) (-0.08)
Intercept -0.210*** -0.209*** -0.229*** -0.228*** (-3.14) (-2.76) (-2.77) (-2.81)
Time (Year) Dummies Yes Yes Yes Yes Industry Dummies Yes Yes Yes Yes
Adj. R-squared 0.185 0.184 F-Statistic 11.44*** 11.36*** Log Likelihood 265.95 264.56 No. of Observations 1,837 1,837 1,837 1,837
Appendix 1: Ownership Characteristics of Non-Financial Public Companies in Thailand, 2001 – 2010
To keep the ownership statistics as comparable as possible with previous studies of
ownership, the classification categories I use match the classification scheme described by La
Porta, Lopez-de-Silanes, and Shleifer (1999). These authors identify six types of ultimate
controllers: widely held (the firm has no ultimate controller); family (members of the same
family with the same last name); state (government ownership); widely held financial
institutions (financial institutions that do not have a single controlling large shareholder);
widely held corporations (corporations that do not have a single controlling large shareholder;
and widely held groups (other widely held entities not fitting into the above categories;
examples would be a voting trust or a cooperative). 12
The thresholds for determining control (ownership) may be set by the researchers or
by law. For example, 50 percent ownership is the cutoff for absolute control; other
researchers have used 20 percent or even as low as 10 percent ownership of the voting rights
to determine the extent of the control that a firm’s owners have over the company. The lower
level(s) are also important because prior research has shown that it is possible to control a
firm by owning a significantly lower portion of the shares.13
Wiwattanakantang (2001) notes that 25 percent can be used to give practical control
of Thai firms. Thai law states that rather than having an absolute majority of shares (greater
than 50 percent), the ownership threshold for effective control is 25 percent. I set the
designation of control at 25 percent of the outstanding shares, since this is the threshold for
control by Thai law. I use 25 percent ownership to determine high versus low family
12 Essentially the same classification scheme is used by others in several subsequent studies. See for example Claessens, Djankov, and Lang, (2000) and Claessens, Djankov, Fan, and Lang, (2002). 13 See La Porta, Lopez-de-Silanes, and Shleifer (1999) who find that 80% of firms can be controlled by stockholders owning less than 20% of the shares.
ownership. I also use a lower cutoff value (10 percent) and re-determine the high versus low
family ownership classifications.
The main source for the ownership data is the SETSMART data service, published by
the Stock Exchange of Thailand. In addition to company shareholding records and annual
reports, it is often necessary to consult outside sources to trace the ownership chains.
Examples of these outside sources would be company filings at the Ministry of Commerce,
an online database of company records provided by Business Online Co., Ltd., and numerous
business directories (for example, Brooker Group, 2003).
For each sample firm, the owner(s) of the voting rights determines the ultimate
controller (ownership) classification. Though it is possible to have differences in voting
rights and cash flow rights, Thai law requires one share, one vote.
I classify each firm into one ownership category based on the shareholder record
available that is closest to the end of the fiscal year. I examine the list of the top ten
shareholders to see if any individual, family, or organization owns 25 percent or more of the
outstanding shares. If no ultimate controller is present, the firm is classified as ‘low family
ownership’. As needed, ownership of the shares is traced upwards through the network of
companies, both private and public. Individual family members and family-controlled firms
are all grouped together as “family”. Shareholders with the same last name are counted as
family, as are known familial relationships (relatives, spouses, children, and other relatives)
even if the last names are different. Corporations that are part of a family-controlled network
are classified as ‘family’. Firms classified as ‘corporate controlled’ are companies that have
another non-family company as the ultimate controller, whether public or private, domestic or
foreign. If an ultimate owner of the shares can be determined, the company is classified into
one of the five remaining categories depending on the type of ultimate controller.
Table A1 shows the number (Panel A) and percentages (Panel B) of non-financial
publicly-traded Thai firms based on the type of ultimate controller. Six ultimate controller
classifications are used: low family ownership (no ultimate controller at the given ownership
cutoff level), State (government), Family, a widely held financial institution, another widely
held corporation, and some other type of controller (Other). I exclude firms with missing or
incomplete data, or if the firm is undergoing financial rehabilitation. New listings and
delisted firms are not included because the ownership information for these firms was not
complete for a full year.
Table A1 shows that for the vast majority of Thai public non-financial companies, a
family is the ultimate controller. From 2001 through 2010, covering over 3,100 firm-year
observations, on average more than one-half (53.2 percent) of Thai public non-financial
companies have a family as the controlling shareholder. The next largest group is firms that
can be considered low family ownership, that is, no ultimate controller owns more than 25
percent of the voting rights. Firms controlled by other widely held corporations, such as
foreign or domestic subsidiaries, comprise the third largest group at about 16 percent of
firms. Table A1 shows the other types of organizations or institutions (the state, widely held
financial institutions, and other types of organizations) are not commonly ultimate
controllers. The combined percentage of these three categories is about 9 percent of firms.
These percentages do not vary much from year to year across the 10 year period.
In this paper, only two groups are of interest: the low family ownership group and the
high family ownership group. The other ultimate controller categories (the state, widely held
financial institutions, and other types of organizations) are not included in the analyses.
Table A1: Ownership Characteristics of Non-financial Thai Public Companies, 2001 – 2010 Panels A and B show the number and percentages of firms for which the ultimate controller can be determined, covering 2001 - 2010. All financial services companies (banks, finance and securities companies, and insurance firms) have been deleted from the sample. The classification of ultimate controller is based on ownership of 25 percent or more of the voting rights. Six ultimate controller classifications are used: Low family ownership (no ultimate controller), State (government), High family ownership, Widely held financial institution, Widely held corporation, and some other type of controller (Other). Panel A
YEAR
Low Family
Ownership State
High Family
Ownership
Widely held Financial
Institution Widely held Corporation Other
Grand Total
2001 63 13 126 6 48 5 261 2002 56 13 134 5 45 5 258 2003 59 13 145 4 44 9 274 2004 65 16 171 3 48 9 312 2005 71 17 205 1 48 10 352 2006 76 17 188 1 55 7 344 2007 82 14 181 1 54 8 340 2008 80 13 176 1 54 8 332 2009 79 14 171 56 8 328 2010 74 13 164 53 9 313 Total 705 143 1,661 22 505 78 3,114
Panel B
YEAR Low
Family Ownership
State High
Family Ownership
Widely held Financial Institution
Widely held Corporation Other Grand
Total
2001 24.1% 5.0% 48.3% 2.3% 18.4% 1.9% 100.0% 2002 21.7% 5.0% 51.9% 1.9% 17.4% 1.9% 100.0% 2003 21.5% 4.7% 52.9% 1.5% 16.1% 3.3% 100.0% 2004 20.8% 5.1% 54.8% 1.0% 15.4% 2.9% 100.0% 2005 20.2% 4.8% 58.2% 0.3% 13.6% 2.8% 100.0% 2006 22.1% 4.9% 54.7% 0.3% 16.0% 2.0% 100.0% 2007 24.1% 4.1% 53.2% 0.3% 15.9% 2.4% 100.0% 2008 24.1% 3.9% 53.0% 0.3% 16.3% 2.4% 100.0% 2009 24.1% 4.3% 52.1% 0.0% 17.1% 2.4% 100.0% 2010 23.6% 4.2% 52.4% 0.0% 16.9% 2.9% 100.0%
Average 22.6% 4.6% 53.2% 0.8% 16.3% 2.5% 100.0%
Appendix 2: Calculation of the Financial Constraint Measure (KZ Score)
The development of the financial constraint measure has its origins in the study of
firms’ investment decisions. 14 I use multiple discriminant analysis, following the
methodology employed by Cleary (1999). Discriminant analysis produces a function which
distinguishes between two groups: firms that are financially constrained and firms that are
not. The function, which uses common financial indicators, takes the form:
KZ Score = β1 CURRENT + β2 TIE + β3 ROE + β4 NI_PCT + β5 SLS_GROWTH
+ β6 DEBT ( A1 )
where CURRENT is the current ratio, TIE is times interest earned, ROE is return on equity,
NI_PCT is the net income margin, SLS_GROWTH is the percentage growth in sales
compared with the previous year, and DEBT is the debt ratio.
The six variables, and the corresponding Worldscope data codes, are:
CURRENT = Current ratio, defined as: (WC03101) sLiabilitieCurrent
(WC02201) AssetsCurrent
TIE = Times interest earned, defined as: (WC01251)debt on expenseInterest
(WC18191) taxesandinterest before Earnings
ROE = Return on equity, defined as: (WC03501)equity Common
Return
NI_PCT = Net income margin, defined as: (WC01001) SalesNet
Return
For the ROE and NI_PCT measures, Return is defined as: Net income before
extraordinary (XO) items minus or plus any extraordinary charges: WC01551 –
WC01254 + WC01253
14 See for example Fazzari, Hubbard, and Petersen (1988, 2000), Kaplan and Zingales (1997, 2000), and Cleary (1999, 2005).
SLS_GROWTH = Percentage growth in sales compared with the previous year using Net
Sales (WC01001), 1-year tin Sales
1-year tin Sales -year t in Sales
DEBT = Debt ratio, defined as: (WC02999) assets Total
(WC03255) sobligation bearing-interest all debt; Total