Electronic copy available at: http://ssrn.com/abstract=1129265
Forthcoming: Review of Financial Studies
New Evidence on Measuring Financial Constraints: Moving Beyond the KZ Index*
Charles J. Hadlock
Michigan State University
Joshua R. Pierce
University of South Carolina
First Draft: December 3, 2007
This Draft: September 14, 2009
*Prior versions of this paper circulated under different titles. We thank Julian Atanassov,
Sreedhar Bharath, Murillo Campello, Jonathan Carmel, Jonathan Cohn, Ted Fee, Jun-Koo Kang,
Michael Mazzeo, Uday Rajan, David Scharfstein, Michael Weisbach, two anonymous referees,
and seminar participants at George Mason, Michigan, North Carolina, Oregon, Pittsburgh, South
Carolina, Texas, Texas Tech, and Wayne State for helpful comments. Tehseen Baweja and
Randall Yu provided superb research assistance. All errors remain our own.
Electronic copy available at: http://ssrn.com/abstract=1129265
New Evidence on Measuring Financial Constraints: Moving Beyond the KZ Index
We collect detailed qualitative information from financial filings to categorize financial
constraints for a random sample of firms from 1995 to 2004. Using this categorization, we
estimate ordered logit models predicting constraints as a function of different quantitative
factors. Our findings cast serious doubt on the validity of the KZ index as a measure of financial
constraints, while offering mixed evidence on the validity of other common measures of
constraints. We find that firm size and age are particularly useful predictors of financial
constraint levels, and we propose a measure of financial constraints that is based solely on these
JEL Classification: G31; G32; D92
Key words: financial constraints; KZ index; qualitative information; investment
A large literature in corporate finance examines how various frictions in the process of
raising external capital can generate financial constraints for firms. Researchers have
hypothesized that these constraints may have a substantial effect on a variety of decisions
including a firm’s major investment and capital structure choices (e.g., Hennessy and Whited
(2007)). Additional research suggests that financial constraints may be related to a firm’s
subsequent stock returns (e.g., Lamont, Polk, and Saa-Requejo (2001)).
To study the role of financial constraints in firm behavior, researchers are often in need of
a measure of the severity of these constraints. The literature has suggested many possibilities,
including investment-cash flow sensitivities (Fazzari, Hubbard, and Petersen (1988)), the KZ
index of constraints (Lamont, Polk, and Saa-Requejo (2001)), the WW index of constraints
(Whited and Wu (2006)), and a variety of different sorting criteria based on firm characteristics.
We describe these approaches in more detail below.
While there are many possible methods for measuring financial constraints, considerable
debate exists with respect to the relative merits of each approach. This is not surprising, since
each method relies on certain empirical and/or theoretical assumptions that may or may not be
valid. In addition, many of these methods rely on endogenous financial choices that may not
have a straightforward relation to constraints. For example, while an exogenous increase in cash
on hand may help alleviate the constraints that a given firm faces, the fact that a firm chooses to
hold a high level of cash may be an indication that the firm is constrained and is holding cash for
In this paper we study financial constraints by exploiting an approach first advocated by
Kaplan and Zingales (1997). In particular, we use qualitative information to categorize a firm’s
financial constraint status by carefully reading statements made by managers in SEC filings for a
sample of randomly selected firms from 1995 to 2004.1 This direct approach to categorizing
financial constraints is not practical for large samples, since it requires extensive hand data
collection. However, by studying the relation between constraint categories and various firm
characteristics, we can make inferences that are useful for thinking about how to measure
financial constraints in larger samples.
We exploit our qualitative data on financial constraints for two purposes. First, we
critically evaluate methods commonly used in the literature to measure financial constraints. We
pay particular attention to the KZ index, given its relative prominence in the literature and the
fact that our data are particularly useful for evaluating this measure. Second, after examining
past approaches, we propose a simple new approach for measuring constraints that has
substantial support in the data and considerable intuitive appeal. We then subject this new
measure to a variety of robustness checks.
To evaluate the KZ index, we estimate ordered logit models in which a firm’s categorized
level of constraints is modeled as a function of five Compustat-based variables. This modeling
approach parallels the analysis of Lamont, Polk, and Saa-Requejo (2001), who create the original
KZ index by estimating similar models using the original Kaplan and Zingales (1997) sample.
The KZ index, which is based on the estimated coefficients from one of the Lamont, Polk, and
Saa-Requejo (2001) models, loads positively on leverage and Q, and negatively on cash flow,
cash levels, and dividends.
In the ordered logit models we estimate, only two of the five components of the KZ
index, cash flow and leverage, are consistently significant with a sign that agrees with the KZ
index. For two of the other five components, Q and dividends, the coefficients flip signs across
estimated models and in many cases are insignificant, particularly for the dividend variable.
1 The information we use includes statements regarding the strength of a firm’s liquidity position and the firm’s
ability to raise any needed external funds. Additional details are provided below.
Finally, in contrast to its negative loading in the KZ index, we find that cash holdings generally
display a positive and significant coefficient in models predicting constraints. This positive
relation is consistent with constrained firms holding cash for precautionary reasons.
Our estimates differ substantially from the KZ index coefficients even though we use a
parallel modeling approach. Upon further investigation, we find that the differences most likely
arise from the fact that the dependent variable in the original modeling underlying the KZ index
includes quantitative information in addition to qualitative information. This treatment adds a
hard-wired element to the estimates underlying the KZ index, since the same information is
mechanically built into both the dependent and the independent variables. In our treatment, we
are careful to avoid this problem. Once this problem is addressed, our findings indicate that
many of the estimated coefficients change substantially.
Clearly our evidence raises serious questions about the use of the KZ index. To explore
this issue further, we calculate the KZ index for the entire Compustat universe and compare this
to an index constructed using the coefficient estimates from one of our models. We find that the
correlation between the traditional index and our alternative version of this index is
approximately 0. This provides compelling evidence that the KZ index is unlikely to be a useful
measure of financial constraints. Thus, it would appear that researchers should apply extreme
caution when using the traditional KZ index or interpreting results based on index sorts.
An alternative index of financial constraints has been proposed by Whited and Wu
(2006), who exploit an Euler equation approach from a structural model of investment to create
the WW index. This index loads on six different factors created from Compustat data. When we
use these six factors as explanatory variables in ordered logit models predicting constraints, only
three of the six variables have significant coefficients that agree in sign with the WW index.
Two of these variables, cash flow and leverage, are essentially the same variables that figure