imperfect information and agency cost models: further empirical tests

IMPERFECT INFORMATION

AND AGENCY COST MODELS:

FURTHER EMPIRICAL TESTS

DAVID BERNSTEIN

ABSTRACT

The theory of agency costs and imperfect information suggests that some types of firms may prefer to finance investment with internal funds. Fazzari, Hubbard, and Petersen (1988) and other researchers reported differences across groups of firms in the sensitivity of investment to cash flow that appear to be caused by market imperfections. Previous researchers used a fixed-effects regression model that implicitly assumed investment behavior was the same for all firms with common financial characteristics. This paper provides evidence of substantial intragroup heterogeneity in fixed-effects estimates of investment behavior and a Random Coefficients Regression model is shown to display less intragroup variability. A format statistical test rejects the hypothesis that firms with similar characteristics have fixed and identical regression coefficients. Some of the evidence supports the existence of intergroup differences in cash-flow coefficients; however, the large intragroup variability of the fixed-effects cash-flow coefficients may be. evidence of an incorrectly specified model.

I. INTRODUCTION

There are two different schools of thought regarding the desirability of corporations

possessing financial slack. Jensen (1986) suggests that most managers of major corporations

Direct all correspondence to: David Bernstein, U.S. Department of Treasury, 1500 Pennsylvania Ave., Washington, DC 20220.

International Review of Economics and Finance 3(2) 183-193 Copyright Q 1994 by JAI Press, Inc.

ISSN: 10.59-0560 All rights of reproduction in any fom reserved.

183

DAVID BERNSTEIN

do not act to maximize firm value. Instead, managers with a preference for controlling larger

firms may have a tendency to overinvest and accept negative net present value projects when

cash is available. The imperfect capital market models of Stiglitz and Weiss (1981), Myers

and Majluf (1984), and Bernanke and Gertler (1989) suggest that because of asymmetric

information, investment expenditures may be determined by cash flow (or financial slack)

even for firms with managers that act in the interests of their shareholders. In contrast to the

Jensen theory, the relation between cash flows and investment in these models arises because

of a tendency to forego positive net present value projects that cannot be financed with

externally generated funds, rather than a tendency to accept negative net present value

projects.

Although the asymmetric information models and the free cash flow theory generate many

similar empirical predictions (e.g., they both suggest that stock prices should fall when firms

issue new equity’), their policy implications can be very different. The free-cash-flow theory

suggests that firms have a tendency to overinvest while the information-based models

indicate that firms underinvest. Depending on the relative importance of the two effects,

investment should be either taxed or subsidized.

These contrasting implications of agency cost and imperfect capital market theories

motivated recent research on investment by Fazzari, Hubbard, and Petersen (PHP) (1988),

Gertler and Hubbard (1988), Cantor (1989), Hoshi, Kashyap, and Scharfstein (1991),

Calomiris and Hubbard (1991, mimeo), and Oliner and Rudebusch (1992). The test of

potential credit rationing and/or agency costs, developed by FHP (1988), involves a model

in which investment is explained by cash flow and q. These researchers grouped firms on

the basis of common characteristics that, according to financial theories, were likely to affect

investment behavior. Observed differences in the coefficients of cash flow and q were

interpreted as evidence in support of certain theories.

The previous researchers have assumed that each separate group of firms have identical

investment models. The method used to partition firms into groups is highly subjective and

may be incorrect. In addition, previous researchers have relied upon a fixed-effects estima-

tion method. If intragroup differences in investment behavior exist, a Random Coefficients

Regression (RCR) model may be a more appropriate estimation method. The RCR model

has been criticized by Mundlak (1978) for potentially being biased. However, the potentially

biased RCR estimator could possess a smaller variance than the unbiased fixed-effects

estimator and both the potential bias and variability of these estimators should be considered.

The empirical results presented in this paper indicate that the RCR estimator may be

superior to the fixed-effects estimator. A formal statistical test rejects the hypothesis that

firms with similar financial characteristics have fixed and identical regression coefficients.

The RCR estimator exhibits less intragroup variability and appears more robust than the

fixed-effects estimator. Some evidence supports the existence of systematic differences

across groups of firms in the sensitivity of investment to cash flow; however, the high

intragroup variability of cash-flow coefficients exhibited by the fixed-effects model may

indicate an incorrect model specification.

Imperfect Information and Agency Cost Models 185

II. THE PREVIOUS WORK

The empirical tests used in this paper are a modification of a methodology introduced by

FHP. FHP postulate that firm investment expenditures are determined by their contempora-

neous cash flows and by Tobin’s 4. The model, estimated with pooled cross-section and

time-series data, is of the form;

ZZf = Yt + Fj + clqjl+ CzCFAjt + ej,

where, Zjt is the ratio of investment to the replacement value of the capital stock;

Y, is the shift in the intercept for year Yt;

Fj is the shift in the intercept for firmj;

Qjt = (Vjt + Bjt - Njt)/Kjt; V’t is the market value of equity;

Bjt is the book value of debt;

Njt is the value of inventories;

Kjt is the replacement value of the capital stock; and

CFAjt is the ratio of cash flow to the replacement value of the capital stock.

The replacement value of the capital stock and the value of inventories were calculated by

an iterative procedure similar to the method developed by Salinger and Summers (1983).

This procedure uses aggregate price indices to convert book value accounting measures to

market value.

The major innovation introduced by FHP and adopted by subsequent researchers was to

divide firms according to their prior beliefs about which types of firms are likely to be subject

to the information problems suggested by theory, and to see if the coefficients on cash flow

for these firms differ from those that a priori are expected not to be subject to these

information problems. For instance, FHP argue that low-dividend firms are credit con-

strained and, on the basis of imperfect capital market models, the sensitivity of investment

to cash flow will be larger for low-dividend firms. In fact, FHP find that the relation between

cash flows and investment, after controlling for changes in Tobin’s q, is stronger for firms

with low dividend yields.

Hoshi, Kashyap, and Scharfstein (HKS) (1991) use the FHP methodology to analyze two

samples of Japanese firms. The first sample consists of firms with strong ties to a major bank

and the second sample consists of independent firms. Firms with close ties to banks are

thought to be more closely monitored and less subject to credit rationing than independent

firms. HKS find that the investment behavior of the first group is better explained by Tobin’s

q than by its cash flows and that the reverse is true for the second sample. The HKS results

suggest that firms with close ties to banks more easily can raise external funds than firms

which do not have these ties. In addition, HKS argue that the prohibition of close ties between

banks and firms in the United States may have increased the cost of capital for U.S. firms.

Oliner and Rudebusch (O&R) (1992) study how a variety of factors, including age, stock

exchange listing, ownership structure, and the pattern of insider trading, affect the investment

DAVID BERNSTEIN

cash flow relationship. The O&R study differs from many of the other papers by using

interaction variables instead of classifying firms into groups. This method allows for the

cash-flow coefficient of firms to change over time. However, if too many interaction

variables are used there will be a high degree of collinearity and estimated coefficients may

be imprecise.

Gertler and Hubbard (1988) show that low-retention firms invest less during recessions.

This finding supports work conducted by Stiglitz and Weiss (1981) and Bernanke and Gertler

(1989) which emphasizes that market imperfections can be especially severe during eco-

nomic downturns. The study by Cantor (1989) showed that the sensitivity of investment to

cash flow was larger for leveraged firms.

Calomiris and Hubbard (C&H) (1991) study the impact of the cost of funds on the

sensitivity of firm investment to cash flow during the 1930s. Due to a surtax on undistributed

profits (SUP), C&H were able to identify firms with a high cost of capital. Survey evidence

confirmed that external funds were very costly for some firms in this period and some firms

would retain earnings and voluntarily pay a surtax. These high-SUP firms also tended to be

small and pay low dividends. In all of the regressions presented by C&H, only the high-SUP

firms exhibited a significant relationship between investment and cash flow.

This approach to the study of credit rationing relies upon a model that uses 4 and cash

flow to explain investment. However, the measured average q will diverge from marginal q

and the cash flow variable may measure an accelerator effect or investment opportunities in

addition to the liquidity effect. Whited (1992) Gilchrist (1990, mimeo), Hubbard and

Kashyap (1990), and Himmelberg (1990, mimeo) use an Euler equation investment model

instead of the q and cash flow models. Investment in the Euler equation is determined from

the firm’s maximization problem which can be modified to include borrowing constraints.

These investment models also have been separately estimated for firms that are or are not

likely to be subject to credit rationing problems.

III. DATA AND METHODOLOGY

This investigation into the potential impacts of credit rationing and agency costs on

investment relies upon 586 manufacturing firms obtained from the Compustat tape. All firms

have standard industrial classification of two or three and all firms existed for the entire 1970

to 1987 time period.

Following FHP and subsequent researchers, the sample is partitioned into groups on the

basis of prior beliefs about which types of firms are likely to experience credit rationing or

managerial-stockholder incentive problems. In particular, the sample is partitioned on the

basis of dividend policy and size. The dividend policy partition is based on whether the sum

of dividends paid by a firm divided by the sum of its earnings between 1970 and 1987 was

above or below the median of this ratio in our sample. The partition on size is based on

whether a firm is above or below the median of the mean level of assets. All partitions are

formed so that a firm is placed in a single group for the entire time period.


The method used to classify firms is a crucial methodological issue in this type of study.

Factors other than dividend policy and size may affect the relationship between investment

and cash flow and the assumption that all firms in the same group have the same regression

coefficient may be incorrect. The Myers-and-Majluf model describes how a firm with many

investment opportunities might want to pay zero dividends. However, since there exists

pressure on management to maintain a previously established dividend level, credit rationing

might also occur when firms are forced to cut dividends. Information on the decision to pay

zero dividends and the decision to reduce dividends below a previously established level for

the four classifications of firms is displayed in Table 1. The data reveals that high-dividend

small firms forego dividends with close to the same frequency as low-dividend big firms

and that high-dividend firms may be more likely to cut dividends than low-dividend firms.

This data demonstrates the potential for misclassification under a rule that assigns a firm to

a particular group for an entire time period.

The previous studies have concentrated upon the process of discovering which economic

variables affect the sensitivity of investment to cash flow. Unlike previous researchers, the

assumption that firms inside a particular partition have fixed and identical coefficients is not

taken for granted. This hypothesis is formally tested and measures of intragroup cash-flow

coefficient variability are obtained. A Random Coefficient Regression (RCR) model,

developed by Swamy (1970), is proposed as an alternative to the fixed-effects method used

in previous studies. The RCR model is represented below in matrix form;

Ijt = X,Bi + Uj

In the above equation, Xjf is a three column matrix composed of a unit vector, qjt, and

CFAjt and Zjt is investment. Each coefficient is random with;

Swamy (1970) derives an estimator of Z? which is a weighted average of the ordinary least

squares estimates of Bj from each firm separately. Swamy’s estimator can be written as

Table I. Dividend Policies of Firms in the Four Groups

Skipped Dividends Low Dividend

SITU11 Low Dividend

Big High Dividend

Sl?Ulll High Dividend

Big

0

1-5

35

Dividend Cuts

0

l-5

>5

23.6% 67.6% 62.9% 88.3%

63.9 10.7 22.0 10.6

9.5 21.7 15.1 1.1

26.6% 21.9% 12.4% 20.2%

63.9 73.4 63.8 67.6

9.5 4.7 23.8 12.2

Note: Frequency of zero dividends and dividend cuts for four dividend/size groups of firms.

188 DAVID BERNSTEIN

Swamy’s weights are of the form

1 /[s2 + OF/(x Xi)] wj= n

Cl/[6 + C#/(XjXj)]

1

Maddala (1977) points out that Swamy’s estimates of the weights could result in a negative

variance. Maddala’s estimates, which were used in this study, are based on;

1 1

In the above, Vi is a vector of ordinary least squares residuals.

In order to motivate the use of the RCR model, a statistical test of whether the regression

coefficients are fixed and equal is conducted for each group of firms. The following

hypothesis is tested for each of the four dividend/size partitions.

This hypothesis is tested with the homogeneity statistic also developed by Swamy.

f&=x (bi - bP)'X{Xi(bi - bp)

Sii i=l

where

N X!X. N XiXi bP=IC *!I-’ C ybi

1 1

Swamy’s test statistic, &/K(N- l), has an F distribution with K*(N - l), fl(T- ZC)

degrees of freedom where K is number of parameters in regression model, N is number of

firms and T is number of years.

One further difference between the fixed-effects and the RCR Model involves the

estimation of the constant term. The fixed-effect constant term differs for each firm and year

since firm and year dummies were included in the model. The existence of firm dummies

complicates interpretation of tests of the shift of the investment function. By contrast, the

constant term obtained from the RCR model is, like the other coefficients, simply a weighted

average of the random constant terms.


IV. EMPIRICAL RESULTS

Table 2 contains coefficient estimates for the four dividend/size categories with the fixed-

effects regression model. The larger cash-flow coefftcients, holding constant size, for

low-dividend firms is compatible with the credit rationing hypothesis. The larger cash-flow

coefficients, holding constant dividend policy, for large firms supports the agency cost

hypothesis. However, if small firms are more likely to be credit rationed, this result conflicts

with the imperfect capital market theory.

The fixed-effects model relies upon the assumption that regression coefficients for firms

inside a group are fixed and identical. Swamy’s test of this hypothesis was conducted and

the results are presented in Table 3. The null hypothesis that regression coefficients are fixed

and identical was rejected for each of the four dividend/size partitions. This statistical test

provides justification for the use of the RCR model. The RCR results for the four divi-

dend/size classifications are presented in Table4. As was the case for the fixed-effects model,

low-dividend/big firms had the largest cash-flow coefficient and high-dividend/small firms

had the lowest cash-flow coefficient. However, the intergroup variability of cash-flow

coefficients is substantially smaller for the RCR model than the fixed-effects model.

The focus of the work by FHP and the subsequent researchers was on the intergroup

dispersion in cash-flow coefficients. The existence of intragroup cash-flow coefficient

dispersion makes it more difficult to make a strong case for either the agency cost or

imperfect capital market model. Direct measures of intragroup cash-flow coefficient disper-

sion were obtained by dividing each of the four groups into five random subgroups. The

intragroup cash-flow coefficients are presented in Table 5 for the fixed-effects model and

Table 6 for the RCR model. In each of the four groups, the intragroup range of the cash flow

sensitivity for the fixed-effects model is substantially larger than for the RCR model.

Interestingly, for the fixed-effects model the median cash-flow coefficient is lowest for

low-dividend/small firms and the median cash-flow coefficients for large firms holding

constant dividend policy, exceeds the median cash-flow coefficient for small firms. These

Group

Table 2. Fixed Effects Regression Results

Q CFA R2 MSE Number of Finns

Low Dividend

S?TUlll

Low Dividend

Big

High Dividend

S?TUlll

High Dividend

Big

0.013

(8.7)

0.003

(1.3)

0.002

(1.3)

0.001

(1.5)

0.476 35.8 0.032 188

(29.7)

0.746 44.4 0.013 10.5

(24.9)

0.334 33.4 0.013 10.5

(17.8)

0.425 35.6 0.008 188

(22.4)

Note: Fixed-effects regression results for four dividend/size groups.

190 DAVID BERNSTEIN

Table 3. Hypothesis Test Results

Group F-Statistic

Low Dividend

Small

Low Dividend

Big

High Dividend

.%&I

High Dividend

Big

6.9

7.0

3.7

4.7

Note: All tests statistics are significant at the 0.01

level.

results conflict with the credit rationing hypothesis. By contrast, the RCR results in Table 6

confirm that, holding constant size, the median RCR cash-flow coefficient of low-dividend

firms exceeds the median cash-flow coefficient of high-dividend firms. Also, holding

constant dividend policy, the median RCR cash-flow coefficient for large firms exceeds the

median cash-flow coefficient for small firms. In many respects, the RCR results in Table 6

present a stronger case for the existence of intergroup differences in cash-flow coefficients

than do the fixed-effects results in Table V.

It is the view of this author that the RCR model is superior to the fixed-effects model. The

similarity in the ordering of the cash-flow coefficients suggests that the potential bias of the

RCR method, discussed by Mundlak, may not be too important for this data set. The null

hypothesis that coefficients inside homogenous groups are fixed and equal is rejected by a

formal statistical test. The high intragroup dispersion of fixed-effects cash-flow coefficients

suggests that this estimator is not robust. Mosteller and Tukey argue that it is extremely

important for estimators to be robust or not drastically change with small changes in the data.

Table 4. Random Coefficient Regression Results

Group Constant Q CFA

Low Dividend 0.036 0.022 0.454

Small (4.2) (4.7) (8.4)

Low Dividend 0.032 0.007 0.628

Big (2.2) (0.9) (6.6)

High Dividend 0.046 0.018 0.313

SW11 (4.2) (2.1) (5.7)

High Dividend 0.038 0.002 0.526

Big (4.8) (0.5) (9.1)

Note: RCR regression results for four dividend/size classifications.


Table 5. Intragroup Cash Flow Sensitivities

(Fixed-E@& Regression Results)

Low Dividend

Small

Lav Dividend High Dividend High Dividend

Big SnUlll Big

1 0.277 0.989 0.333 0.489

(9.4) (18.4) (10.9) (12.8)

2 0.249 0.499 0.322 0.349

(9.3) (6.6) (6.5) (7.5)

3 0.675 0.668 0.458 0.239

(19.5) (6.4) (9.9) (7.3)

4 0.374 0.353 0.238 0.612

(8.3) (5.9) (5.9) (11.9)

5 0.264 0.575 0.199 0.581

(6.2) (10.5) (4.8) (12.8)

Median 0.277 0.575 0.322 0.489

Minimum 0.249 0.353 0.199 0.239

Maximum 0.675 0.989 0.458 0.612

Range 0.426 0.636 0.259 0.373

Note: Fixed-effects cash-flow coefficients for five random groups in each of four dividend/size

firm types.

Table 6. Intragroup Cash Flow Sensitivities (RCR Method)

Law Dividend Low Dividend High Dividend High Dividend

Big SmLlll Big

1 0.443 0.724 0.312 0.568

(4.0) (3.6) (2.5) (4.4)

2 0.424 0.436 0.307 0.425

(4.4) (2.9) (3.2) (3.9)

3 0.458 0.744 0.367 0.392

(4.2) (2.6) (3.0) (3.8)

4 0.559 0.538 0.299 0.560

(3.4) (3.5) (2.4) (3.8)

5 0.368 0.644 0.245 0.641

(3.5) (3.3) (2.6) (4.7)

Median 0.443 0.644 0.307 0.560

Minimum 0.368 0.436 0.245 0.392

Maximum 0.559 0.744 0.367 0.641

Range 0.191 0.308 0.122 0.249

Note: RCR cash-flow coefficients for five random groups in each of four dividend/size

classifications.

192 DAVID BERNSTEIN

Also, high intragroup variability in cash-flow coefficients makes it extremely difficult to

reach conclusions on intergroup differences that are allegedly a consequence of different

firms’ financial characteristics.

V. CONCLUSIONS

Previous researchers have partitioned data sets and estimated separate investment models

over different categories of firms. These researchers did not consider whether firms, which

placed in separate categories, had identical investment models. The results in this paper

suggests that firms with similar dividend policies and of similar size may exhibit very

different investment behavior. Differences in investment behavior among firms in the same

group makes it difficult to reach conclusions regarding differences in investment behavior

for different types of firms.

The intragroup dispersion in cash-flow coefficients is more pronounced for the fixed-

effects than the RCR estimator and the RCR model appears to be more robust. The use

of the RCR model is also supported by a formal hypothesis test. It is possible that this study

and previous work have not properly identified all of the financial factors that affect the

sensitivity of investment to cash flow. If this is the case, then the model on how credit

rationing and agency costs affect investment policy is incorrectly specified and a significant

amount of work remains. However, in the absence of complete information, it appears the

RCR method can add insight into investment behavior.

ACKNOWLEDGMENTS

I would like to thank Sheridan Titman, Jonathan Jones, Carl R. Chen and an anonymous referee. Any

problems that remain are solely the responsibility of the author.

NOTE

1. See Smith (1986) for a review of the empirical literature on the announcement effect of new

equity issues.

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imperfect information and agency cost models: further empirical tests

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