Financial Constraints, Corporate Characteristics and Investment:

Financial Constraints, Corporate Characteristics and Investment:
Evidence from China Stock Market (1995---2006)
CAO Yanming
School of Management, China University of Mining & Technology, P.R.China, 221116
Abstract: In this paper, we introduce the standard regression model and use Erickson and Whited’s
(2000) Generalized Method of Moments (GMM) estimation model to examine the possible influence of
financial constraints and corporate characteristics on corporate investment based on china listed
company sample from 1995 to 2006. Our empirical findings indicate that cash flow remains important
for investment. We find that less-constrained firms exhibit larger cash flow sensitivities than
more-constrained firms, which do not support the hypothesis for a positive relationship between the
sensitivities and the degree of financial constraints. We also find evidence that young firms adjust
investment by observing cash flow realizations. The corporate other characteristics, depreciation rate
and volatility have no significant influence on the cash flow sensitivities of investment.
Key words: GMM, financial constraints, corporate characteristic, investment
1 Introduction
As is known, financial frictions and other firm characteristics have important effects on corporate
investment. Because of information asymmetry, the external financing cost is higher than that of internal
funds in the capital market, which makes firms investment confront with financing constraints and more
sensitive to its internal cash flow. There is much investigation on how financial constraints affect the
corporate investment decisions. But the issue has not got a unified result up to now. The recent
theoretical literature suggests that firm characteristics other than financial constraints may explain the
observed cash flow sensitivities. In this paper, we examine how financial constraints and other firm
characteristics affect investment using a large balanced sample of china firms over the period 1995 to
2006.
The rest of the paper is organized as follows: Section 2 presents literature review in this field.
Section 3 describes our research design. In this section, we describe the sample and data. presents the
model and the method of parameter test. Section4 is the empirical results and its analysis. Section 5
concludes this paper.
2 literature review
There are many literatures concerning the influence of financial constraints and other firm
characteristics on investment since Fazzari, Hubbard, and Petersen’s (1998) hypothesis that more
constrained firms should rely heavily on internal cash flows to finance investment. Because financial
constraints are not directly observable from an empirical perspective, using different proxies obtains
different results. Fazzari, Hubbard, and Petersen (1998) used dividend payouts rate as financing
constraints screening criterion. Their studies indicated that firms maintaining high dividend payouts
encountered lower financial constraints in comparison with those maintaining low dividend payouts.
Because this screening criterion is very simple and easily understood, it is accepted by most of the
literature. Kaplan and Zingales(1997 and 2000) conclude that a monotonic relation between the degree
of external market constraints and cash-flow sensitivity does not exist. To avoid the limitation of single
variable, Cleary (1999) use a discriminate function to estimate future dividend changes. Cleary classify
the samples using five key company financial variables, four of which are the total debt ratio, the return
on equity, the times interest earned and the current ratio. According to Cleary’s method, the samples are
divided into three groups: those with an increase rate of dividend, those with a decrease rate of dividend
and those with a constant one. Moreover, Erickson and Whited (2000) document that errors in
measuring Tobin’s marginal Q, not financial constraints, explain the observed relationship between
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firms’ investments and their cash flows.
The recent theoretical literature suggests that firm characteristics other than financial constraints
may explain the observed cash flow sensitivities. Anti (2003) predicts that the learning process within a
firm and predicts that the investments of young firms should be more sensitive to cash flow than those
of mature firms. Abel and Eberly (2003) model a neoclassical environment without financial constraints
and predict that the investments of high-growth, high-depreciation, or high-volatility firms should be
more sensitive to cash flow than those of low-growth, low-depreciation, or low-volatility firms. Small,
young, fast-growing, or volatile firms are widely perceived as more likely to be financially constrained.
The models of Alti (2003) and Abel and Eberly (2003) provide mechanisms other than through financial
constraints for small, young, fast-growing, or volatile firms to exhibit different investment-cash flow
sensitivities. The effect of depreciation on investment-cash flow sensitivities has been examined using
the proxy of firm size, where higher depreciation firms are smaller. The effects of firm age, growth rates,
and volatilities have not yet been examined empirically. Contrary to common belief, Erickson and
Whited (2000) find that it is the larger firms, not the smaller firms, which exhibit larger investment-cash
flow sensitivities.
To explain the conflicting empirical evidence on the effect of financial constraints, Moyen (2004)
develop constrained and unconstrained firm value-maximizing models. Firms simulated from the
constrained model exhibit smaller cash flow sensitivity than unconstrained firms. Unconstrained firms
finance their investments with cash flows and external funds. When cash flow increases, firms raise
external funds to finance more investment, magnifying the cash flow sensitivity. In addition, the models
show that Cleary’s index is a good empirical proxy for the degree of financial constraint. Alti (2003)
considers firm age as a proxy for learning about a firm’s quality. Young firms, uncertain about their
quality, update their beliefs by observing cash flow realizations. The investment decisions of young
firms are therefore more sensitive to cash flow realizations.
Recently, the study on the influence of financial constraints and other corporate characters on
investment in Chinese market make a great progress. Feng Wei (1999) suggested that the internal cash
flow plays an important role in firm’s investment decision which tends to be significant when facing
financial constraints. He Jin-gang (2001) initiated the financing factor into investment model to exploit
the decision-making motivation behind controlling shareholders. Moreover, the other scholars( see, for
example, Zheng Jiang-hun2001;Jinang Xiu-zhen, 2003; Wei Feng, 2004; Lu Zhengfei,2005; Tong Pan
2006)also made investigations on the issue from various angles.
（
3 Research design
3.1 Samples and data
The sample consists of the firms existing between 1995 through 2006 that satisfy the requirements
below. Erickson and Whited’s minimus distance estimator combining all cross-sectional GMM estimates
requires a balanced panel, we construct three balanced panels of four years each: 1995-1998, 1999-2002,
and 2003-2006. Firms in the sample are required to have positive values for Total Assets, Net Property,
Fixed Asset, and Net Sales. We also exclude financial institutions and utilities from the sample.
In order to test the effect of firm age on investment, we divide the sample into two subset groups:
young and mature group. Furthermore, we consider different definitions of the young firms: at most 5
years, at most 6 years and at most 7 years. This allows us to explore different criteria to classify firms
into young and mature subsets, and to observe changes in the sensitivity with the age of the firms.
We use Cleary’s (1999) Z index to identify firms’ degree of financial constraints. The index takes
into account many variables characterizing a firm’s financial status and is based on a discriminate
analysis procedure classifying firms into a group likely to increase dividends, and a group likely to
decrease them. Cleary (1999) assumes that firms increase dividends only when they are in good
financial standing and expect to remain so. Therefore, firms with higher Z index values are identified as
less constrained, while firms with lower index values are identified as more constrained. Firms in
different group with the lower half of that year’s Z index distribution are labeled more-constrained,
while firms with the higher half are labeled less-constrained. Firms are allowed to move between more
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constrained and less-constrained groups every year as their financial status changes.
Furthermore, in order to test for differential effect of firm characteristics on the sensitivity of
investment to cash flow, we also divide firms into different groups according to their sales growth rates
over the last year, their depreciation rates, and the standard deviation of their sales (scaled by the capital
stock) over the last ten years. Firms with values in the lower half of the distribution are considered
low-growth, low-depreciation, or low-volatility, while firms with values in the higher half are
considered high-growth, high-depreciation, or high-volatility. Firm-year observations with negative
operating income before depreciation are considered distressed.
3.2 Models selection
In order to study the effect of the corporate characteristics of firm age, growth rates, and volatilities
on investment, we introduce the standard regression specification augmented to capture the possible
effects of firm characteristics other than financial constraints. A linear relationship between the
investment and Tobin’s marginal q arises as a first-order condition from the firm’s investment problem
with capital adjustment costs. It suggests that marginal q is a sufficient statistic for the investment.
Marginal q is not equal to average q in discrete time, even when one assumes linear homogeneity of
the production and adjustment costs functions, as in Hayashi’s(1982) continuous time framework.
Therefore, measurement error in marginal Tobin’s q is likely to arise.
The typical and standard model in the investment-cash flow sensitivities in the literature is:
I it
= Z it α + β 1Qit + errorit
K it
i = 1,..., N , t = 1,..., T
Where subscript i denotes the i th firm, t denotes the t th period, N is the number of firms and T is
I
the total number of periods. The dependent variable it is typical of investment-to-capital ratio. Q is
K it
the proxy of Tobin’s Q. Allowing for our market conditions; Tobin’s Q is measured as the ratio of the
sum of equity market value and liabilities book value to assets book value. Row vector Zit typically
contains firm and year fixed effects, as well as cash flow terms associated with different groups of firms.
3.3 Estimating process
We test the predictions about these firm characteristics and financial constraints, by using Erickson
and Whited’s (2000) Generalized Method of Moments (GMM) estimation model. To facilitate the
accuracy of the Critical Minimum Distance estimator by providing larger number of cross-sections, we
combine the three consecutive five-year panels into one fifteen-year panel. This is especially important
for the GMM estimator, the use of which provides us with robustness on the expense of efficiency.
In order to test for differential effect of firm characteristics on the sensitivity of investment to cash
flow, we split the sample firm-year observations in mutually exclusive subsets according to one or more
characteristics, and estimate sensitivities specific to each group, in a panel data setting.
We also consider function, in which dummy variables control for the level of financial constraints,
age, firm growth, depreciation rate and volatility, in order to take into account any direct (not through
cash flow) effect of these characteristics on investment. First, we test the theoretical predictions on
financial constraints, firm age, growth rates, depreciation rates, and volatilities separately, to determine
which hypotheses receive support. After that, the hypotheses that receive initial support in the data are
tested jointly.
In our tests we control also for the economic status of the firms, since as demonstrated by Allayannis
and Mozumdar (2004) and by Cleary, Povel and Raith (2004), firms in distress exhibit investment that is
negatively related to cash flow. Failing to take into account economic distress labeled as negative
Operating Income before Depreciation can confound the tests.
After estimating the sensitivities for each group, we test for the difference between them using
Cleary’s(1999) bootstrap procedure and report the p value.
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4 Empirical results
In this model, Investment I/K is regressed on Tobin’s average Q, cash flow CF/K terms accounting
for financial constraints, firm age, and distress, as well as dummy variables controlling for the level of
firm characteristics. Table 1 summarizes our results on the firm characteristics affecting investment.
We provide additional robustness checks on our results. We report estimates coefficient and τ 2 , a similar
measure for the equation specifying the measurement error in Tobin’s Q. standard errors are in
parenthesis. (See table 2: Difference of CF/K sensitivities: P-value)
Most firms have investment that is positively related to cash flow, while distressed firms have
investment that is negatively related to cash flow, which does not support the Tobin’s theory. In contrast
to Erickson and Whited (2000), we obtain significant cash flow coefficients, even when accounting for
possible errors in measuring marginal q. Our different result arises because we control for fixed firm
effects and use a larger sample of firms.
Financial constraints identified by Cleary’s (1999) index do affect investment-cash flow sensitivities.
Less-constrained firms exhibit larger sensitivities than more-constrained firms, regardless of other firm
characteristics, in all estimation periods. Once again, controlling for fixed firm effects is important.
When we omit doing so, the result is opposite: more- constrained firms exhibit larger sensitivities.
Firm age also affects investment-cash flow sensitivities. For the subset of less-constrained firms
where learning from cash flow realizations is allowed to be reflected in the investment decisions, young
firms exhibit larger sensitivities than mature firms, consistent with the prediction of Alti(2003).
Table1: regression results with financial constraints and firm age across different age definitions
when young is at most when young is at six when young is at seven
five years
years
years
0.147***
0.146***
0.154***
Tobin’s Q
(0.011)
(0.010)
(0.011)
-0.014
-0.016*
-0.016**
More-constrained
(0.008)
(0.009)
(0.009)
0.012
0.018
-0.003
Young
(0.022)
(0.024)
(0.024)
0.019***
0.020***
0.019***
High growth
(0.004)
(0.004)
(0.004)
0.093***
0.095***
0.097***
High depreciation
(0.007)
(0.007)
(0.007)
0.009
0.008
0.008
High volatility
(0.009)
(0.009)
(0.009)
-0.004
0.005
0.005
distressed
(0.010)
(0.010)
(0.008)
CF/K:
0.130***
0.189***
0.278***
Young and less-constraints
(0.010)
(0.066)
(0.092)
-0.006
-0.045
0.079
Young and more-constrained
(0.076)
(0.101)
(0.199)
0.066***
0.060***
0.060***
Mature and less-constrained
(0.011)
(0.011)
(0.010)
0.047***
0.045***
0.047***
Mature and more-constrained
(0.010)
(0.011)
(0.010)
-0.031***
-0.032***
-0.032***
distressed
(0.009)
(0.009)
(0.009)
∗∗∗ ∗∗ ∗
,
, , indicate significance at the one, five, and ten percent level
Growth rates, depreciation rates, and volatilities do not affect investment-cash flow sensitivities in
the way predicted by Abel and Eberly (2003). Without controlling for the direct effect of firm
characteristics on investment levels, high-growth and high-depreciation firms have larger sensitivities.
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However, when controlling for the direct effect of firm characteristics, this result no longer holds.
In sum, our results show that investments increase with Tobin’s in accord with most investment
models. Cash flow continues to have significant explanatory power for investment after controlling for
measurement error in Tobin’s Q. Cash flow can be important for investment if it proxies well for
short-term profitability. It is also clear that the investments of less-constrained firms are more sensitive
to cash flow fluctuations than those of more-constrained firms, in accord with the structural estimation
of Hennessy and Whited, the model prediction of Moyen, and a number of empirical papers that do not
account for errors in measuring Tobin’s Q, including Cleary’(1999),cleary, povel, and Raith(2004),
Kadapakkam, Kumar, and Riddick(1998),and Kaplan and Zingales(1997, 2000). Therefore, the rejection
of the monotonic hypotheses can not be attributed to the presence of measurement error in Tobin’sQ, or
the lack of control for economic status. The investment of financially less-constrained firms responds
stronger to a unit change in cash flow than that of more-constrained firms, because less-constrained
firms have access to less expensive external funds. When a positive cash flow shock arrives,
less-constrained firms can fully exploit its information content by investing an optimal amount. On the
other hand, external funds are either increasingly expensive or even rationed for more-constrained firms.
Thus, when a positive cash flow shock arrives such firms increase investment, but only to the extent to
which the cost of an additional unit of external financing equals the marginal benefit from investment, or
up to the available limit, if funds are rationed. Following the same cash flow shock more-constrained
firms underinvested, which leads to a lower sensitivity. The evidence on firm age is supported for the
less-constrained subset of firms. When the presence of financial constraints is less likely, the cash flow
realizations provide useful information about the quality of young firms, and thus affect investment. The
evidence on growth rates, depreciation rates and volatilities is not supported in the data. The investments
of high-growth and high-depreciation firms become less sensitive to cash flow after we control for the
direct effect of growth rate and depreciation rate on investment. The evidence on volatility rates is
strong but opposite to the theoretical prediction, after we take into account firm-specific effects.
We provide additional robustness checks on our results in table 2. To observe the stability of the
estimates through time we test the hypotheses on financial constraints and firm age in each individual
five panel. Finally, we re-estimate the critical minimum distance coefficients excluding and
cross-sections in which the identification hypothesis was not rejected at the 5% level to alleviate any
concerns that our estimates are contaminated by the influence of poorly identified cross-sections. Our
results remain qualitatively unchanged after performing the robustness checks
Table 2: Difference of CF/K sensitivities: P-value
when young is at
when young is
most five years
at six years
young: less constrained vs more constrained
0.000***
0.009***
**
mature: less constrained vs more constrained
0.040**
0.050
less constrained: young vs mature
0.065*
0.012**
**
more constrained:young vs mature
0.996*
0.729
R2
τ2
0.317
(0.019)
0.269
(0.021)
0.334
(0.020)
0.266
(0.021)
when young is at
seven years
0.001***
0.016**
0.309
0.995
0.322
(0.019)
0.282
(0.022)
∗∗∗ ∗∗ ∗
,
, , indicate significance at the one, five, and ten percent level.
5 Conclusions
Our findings confirm the explanatory power of cash flow in investment-Tobin’s Q regressions even
after controlling for measurement error in the latter. We find that cash flow is positively and
significantly related to investment and less-constrained firms exhibit larger cash flow sensitivities than
more-constrained firms, which consistent with the structural estimation of Hennessy and Whited with
the theoretical model predictions of Moyen, and with the studies of Kaplan and Zingales and Cleary.
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The above results do not support the hypothesis for a positive relationship between the sensitivities and
the degree of financial constraints. Our analysis confirms also the importance of taking into account the
economic status of a company when investigating the sensitivity of its investment to internal funds. Our
tests confirm also the effect of learning as hypothesized by Alti when firms are likely to be less
financially constrained. Specifically, young firms, uncertain about the quality of the projects they engage
in, rely on cash flow realizations for feedback and subsequently adjust investment. We can not find
evidence for the role of firm growth rate, depreciation rate of volatility for the investment-cash flow
sensitivity. The study of the simultaneous effects of financial constraints and learning on the sensitivity
of firm’s investment to its cash flow seems a promising venue for future research.
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The author can be contacted from e-mail : [email protected]
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