Capital Structure and Industry
Equilibrium Models
Review by
Gordon Phillips
University of Maryland and NBER
(Also covers MacKay and Phillips, RFS (2005))
Presentation Outline
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Capital Structure Research
Industry-Equilibrium Models
MacKay Phillips
Results Preview
Data & Detailed Results
Conclusions
TRADE-OFF THEORY of Capital Structure
• Value of firm (V)
Maximum
firm value
Present value of tax
shield on debt
VL = VU +TC B = Value of firm under
MM with corporate
taxes and debt
Present value of financial distress costs
V= Actual value of firm
VU= Value of firm with no debt
Debt (B)
B*
Optimal amount of debt
The tax shield increases the value of the levered firm. Financial distress
costs lower the value of the levered firm. The two offsetting factors produce
an optimal amount of debt.
In Search of Optimal Capital Structure
Trade-off Theory had not fared well. Simple
pecking order theory has fared much better.
Harris and Raviv ’91 state that the consensus of
existing literature is:
“leverage increases with fixed assets, nondebt tax shields,
growth opportunities and firm size and decreases with
volatility, advertising expenditure, bankruptcy probability,
profitability and uniqueness of the product.”
Other Puzzles:
Risk and Capital Structure?
Linear Empirical Relations
Leverage
Kim & Sorensen (1986)
Titman & Wessels (1988)
Bradley et al. (1984)
Risk (Capital/Labor, s)
Empirical Testing Strategies:
Partial-Equilibrium Models :
Exploit intra-industry variation (exogenous) to fit
representative-firm regression models.
Tests generally based on cross-sectional data (Titman &
Wessels (1988), Rajan and Zingales(1995).
Question: What is exogeneous/endogeneous?
Importance of Industries:
Dummy variables: Bradley, Jarrell, and Kim (1984) find:
Beginning with NOL, Advertising/R&D explain 23.6% of
cross-sectional variation, industry dummies add an
incremental 10.1%. Industry dummies alone: 25.6%
Industry-Equilibrium Models (untested)
Key is intra-industry variation (endogenous) in both risk
and capital structure.
Trade-off/Pecking order Theories
Firm
Endogenous
Exogenous
Finance
Characteristics
Static trade-off theories (agency & information problems, etc.)
Jensen and Meckling (1976), Myers and Majluf (1984)
Titman and Wessels (1988), Rajan and Zingales (1995)
Agency Distortions
Firm
Finance
Characteristics
Industry
Conduct
Structure
Financial distress & real decisions distortions
Shleifer and Vishny (1992), Sharpe (1994)
Opler and Titman (1994), Lang, Ofek, and Stulz (1996)
Strategic Interaction
Firm
Finance
Characteristics
Industry
Conduct
Structure
Strategic debt interaction under imperfect competition
Brander and Lewis (1986), Maksimovic (1988)
Chevalier (1995), Phillips (1995), Kovenock & Phillips (1997)
Industry Equilibrium
Firm
Finance
Characteristics
Industry
Conduct
Structure
Real and financial interactions under perfect competition
Maksimovic & Zechner (1991), Williams (1995), Fries et al. (1997)
… MacKay and Phillips
Capital Structure is a WIP...
Empirical literature has stalled:
Not because the issue is closed,
But because the approach is partial equilibrium.
What’s endogenous and exogenous?
Firm-level:
real and financial decisions
Industry-level: conduct and structure
Aggregation issues
Firm-level:
linear empirical relations
Industry-level: nonlinear industry patterns
Industry Equilibrium Models
Maksimovic & Zechner (1991): Set Up
Debt, Agency Costs, and Industry Equilibrium
Perfect competition, set number of firms
Time 0: Firms choose debt
Time 1: Firms choose investment project,
Time 2 production: Max pq – c(q,P)
Inverse Demand Function: p=a-bQ
(Q is industry quan.)
Maksimovic & Zechner (1991): Set Up
Two projects (technologies):
S: Safe: certain marginal cost & efficient (IS < IR)
R: Risky: uncertain mc & inefficient (IR > IS)
Safe: MC = k + gq
Risky: MC= k-h + gq in state L
= k+h + gq in state H
Maksimovic & Zechner (1991): Set Up
Analysis:
First, production, project selection, lastly t0
capital structure.
Maksimovic & Zechner (1991): Set Up
Industry equilibrium: number of firms
that choose each project adjust until
expected profits from each are equal.
Solution
• Define I* = Is – Ins
• Remainder of paper assume I* > 0,
stochastic technology less efficient.
Single-firm Equilibrium: E[PS] > E[PR]
Debt destroys firm value if high enough to
cause shareholders to pick R (intractable) –
Risk shifting problem of debt.
Safety in numbers: price = marginal cost
All firms alike  each is naturally hedged as
industry cost shocks are reflected in price
Gains to defection: convex payoff
 output if state is bad relative to industry
 output if state is good relative to industry
Role of debt: induce risk-taking & choice of R
Industry Equilibrium Models
Maksimovic & Zechner (1991): Outcome
Interior Industry Equilibrium:
NS and NR adjust until E[PS] = E[PR]
Low (high)-debt firms choose S (R)
Project
Value
E[PR]
0%
E[PS]
n
100% NR
Industry Equilibrium Models
Maksimovic & Zechner (1991): Predict
Nonlinear Industry Patterns
Leverage
Risk (s)
E[profit]
Fringe
Core
Fringe
Capital/Labor
Issues & Extensions
• Fixed number of firms, no entry.
• Competitive industries.
• Timing of moves: debt, project, production
– Could be simultaneous.
• Other problems: Agency?
Industry Equilibrium Models
Williams (1995): Set Up
Homogeneous good
Endogenous entry and exit
Excess perks consumption (intractable)
Two projects (technologies):
L: High-variable cost, labor-intensive (IL = 0)
K: Low-variable cost, capital-intensive (IK > 0)
Industry Equilibrium Models
Williams (1995): Outcome
Perks: underinvestment at industry-level
An equilibrium # of firms obtain capital:
Consume perks, invest, produce, NPV > 0
Remaining firms obtain no capital:
Use labor to produce, NPV  0
Equilibrium Industry Structure:
Core K: large, stable, profitable, with debt
Fringe L: small, risky, unprofitable, no debt
Industry Equilibrium Models
Williams (1995): Predicts
Linear Industry Patterns
Leverage
1/s
E[profit]
Size
Fringe
Core
Capital/Labor
MacKay and Phillips (RFS, 2005)
Examine intra-industry variation (ANOVA)
Examine intra-industry patterns & relations
Sum Stats: entering, exiting, & incumbent firms
Evolution: transition frequencies across quintiles
Estimate simultaneous debt, K/L, risk models
Firm-level: own decisions & characteristics
Industry: own technology versus industry mean
actions of intra/extra-quintile firms
What We Find
Evidence supports some (but not all)
industry-equilibrium model predictions.
Industry structure:
Linear & nonlinear patterns & relations
Firm-level debt, K/L, risk determinants:
Own & rivals decisions & characteristics
Simultaneity/endogeneity are real issues:
Key discrepancies between OLS & 3SLS
Some Evidence
0.6
0.5
0.4
Debt/Asset
0.3
Ratio
0.2
0.1
0
80th
High
60th
Debt/Asset
Percentiles
40th
Medium
20th Low
Intra-Industry
Debt/Asset Dispersion
Figure 1a. Dispersion in Fin. Lev for Competitive Industries
Some Evidence - 2
0.6
0.5
0.4
Debt/Asset
0.3
Ratio
0.2
0.1
0
80th
High
60th
Debt/Asset
Percentiles
40th
Medium
20th Low
Intra-Industry
Debt/Asset Dispersion
Figure 1a. Dispersion in Fin. Lev for Competitive Industries
Data & Sample Selection
• Compustat –Crsp Merged database.
• Years 1981- 2000, unbalanced panel.
• Include Firm, time and industry effects.
• Explicit measure of how firms deviate on real-side
dimensions as well as industry financial structure.
Key Variable: Natural Hedge
Definition: similarity of firm’s technology (and
cost structure) to the industry norm.
Deviation: D = abs[K/L - Median(K/L)]
Normalize: NH = [Range(K/L] – Median (K/L)]
Range:
NH  [0, 1]
0: Furthest from median industry K/L
1: Nearest to median industry K/L
Estimate Simultaneous Equations
•
Leverage
= f(Capital/Labor, Risk; industry position,
controls, fixed effects) + error
• Capital/Labor = g(Leverage, Risk; industry position,
controls, fixed effects) + error
• Risk = h(Leverage, Capital/Labor; industry position,
controls, fixed effects) + error
Results: Summary Stats Table 1
• Mean [median] financial leverage is about 17% [21%]
higher in concentrated industries (0.274 [0.250]) than in
competitive industries (0.235 [0.207]).
This is consistent with evidence by Spence (1985) and
predictions by Brander and Lewis (1986, 1988) and
Maksimovic (1988).
• Competitive and concentrated industries differ
significantly along financial & real-side variables.
Competitive industries exhibit greater risk levels and
dispersion in financial structure & risk. Profitability and
asset size are both substantially higher for concentrated
industries,
Summary Statistics: Entry & Exit
Table 2
• First, entrants start off with high financial leverage ratios
compared to incumbents, suggesting a greater reliance on
debt at inception.
• Second, entrants begin with lower capital-labor ratios than
incumbents but trend toward incumbent levels.
• Third, exiters leave their industries much more leveraged,
risky, and unprofitable than incumbents,
 consistent with ideas of asymmetric information & distress
on exit.
Analysis of Variance
Table 3
• Competitive industries: firm fixed effects account for sixty
percent of the variation in financial leverage. Industry
fixed effects combined account for only twelve percent of
the variation .
• Concentrated industries: Iindustry explains a far greater
percentage of variation in financial leverage (34% versus
12%), consistent with the lower levels of intra-industry
dispersion in leverage we noted in discussing Table 2 .
• Industry fixed effects are substantially more important for
entrants and exiters than they are for incumbents
Industry Mean Reversion
Table 4
• Statistical significance but little economic significance:
Firms maintain their industry positions.
• We find annual industry-mean reversion rates of 5.0% for
two-digit, 5.2% for three-digit, and 7.0% for four-digit
industries
Table 4
Industry Reversion in Financial Structure for Competitive Industries
Industry Financial Structure
2-SIC
3-SIC
4-SIC
Adjusted R2
Adjusted R2
with Firm
FirmFixed Effects years
A: Importance of Industry Financial Structure
Lagged Industry Median Debt/Assets
Firm Debt/Assets
0.149
(4.66) a
0.032
(0.78)
0.118
(2.88) a
9%
66%
19,374
5%
66%
19,374
Lagged Industry Mean Debt/Assets
Firm Debt/Assets
0.105
(3.75) a
0.076
(2.62) a
0.115
(3.38) a
B: Importance of Common Industry Shocks
Change in Industry Median Debt/Assets
Change in Firm Debt/Assets
0.182
(7.28) a
0.059
(1.84) c
0.117
(3.25) a
2%
19,374
1%
19,374
Change in Industry Mean Debt/Assets
Change in Firm Debt/Assets
0.150
(8.82) a
0.016
(0.94)
0.064
(2.78) a
C: Reversion to Industry Mean Financial Structure
Lagged Difference between Firm and Industry Mean Debt/Assets
Change in Firm Debt/Assets
-0.050
(-3.33) a
-0.052
(-3.06) a
-0.070
(-5.00) a
8%
19,374
Lagged Decile Rank Difference between Firm and Industry Mean Debt/Assets
Change in Firm Debt/Assets
-0.004
(-4.00) a
-0.001
(-1.00)
-0.004
(-4.00) a
6%
19,374
Dynamic Patterns of Reversion
Table 5: Transition Frequencies
• Substantial Persistence in industry position.
• For all variables, we find persistence rates that
significantly diverge from 20%, the rate expected if
incumbents were uniformly randomly redistributed across
quintiles between 1981-1990 and the 1990-2000 time
period.
 Consistent with large, capital-intensive, profitable, stable
incumbent firms tend to maintain their dominant industry
position over time, and represent a Williams-style industry
core
Multivariate Evidence – Tables 6-8
 financial leverage is positively related to capital-labor
ratios, cash-flow volatility, asset size, and Tobin’s q.
• Inverse relation between natural hedge and debt –
consistent with MZ ’91.
• Significant differences between OLS & GMM
 Supports many of MZ ’91 predictions.
Significant non-monotonicities, outside of MZ.
• Multivariate evidence that entrants start out with less
leverage – consistent with Williams ’95.
Table 8
Economic Significance of the Determinants
of Financial Leverage, Capital Intensity, and Risk
Competitive Industries
Dependent variables:
Leverage
Capital / Labor
Risk
Industry Variables
Natural hedge
Intra-quantile change
Extra-quantile change
Control Variables
Profitability
Size (log of assets)
Tobin’s q
25th Percentile
Debt K/L Risk
50th Percentile
Debt K/L Risk
75th Percentile
Debt K/L Risk
n/a
-3.22
-2.08
-2.81
n/a
1.06
-5.21
2.91
n/a
n/a
0.06
0.27
0.94
n/a
-0.32
1.68
-0.32
n/a
n/a
2.74
2.50
4.01
n/a
-1.61
7.32
-2.97
n/a
6.38
-1.60
-0.53
-3.75
-6.81
1.40
-0.64
-0.83
-0.91
-1.73
-6.22
0.11
-1.16
3.40
6.05
2.94
-5.99
-4.51
-1.47
3.42
2.43
-2.94
5.94
4.31
-1.30
-1.11
2.15
0.55
0.49
-1.38
0.99
0.85
-2.48
-4.78
4.22
7.09
2.21
-2.71
-4.20
4.21
-4.72
-7.52
Multivariate Evidence 2
Concentrated Industries
Financial structure is affected by the competitive
environment.
Leverage does not depend on capital-intensity or risk in
these industries.
 financial leverage is positively related to profitability –
consistent with trade-off theories.
Conclusions
Industries are important: Cohorts within industries
exhibit similar patterns.
Dispersion on real-side variables associated with
financial side dispersion. Deviate on one
dimension, likely to deviate on other.
Substantial persistence within industries.
Capital structure positively related to risk and Tobin’s
q within industries
Conclusions - 2
Natural Hedge and firm’s position within
industries are important.
Firm-level debt, K/L, risk determinants:
Own & rivals decisions & characteristics
Simultaneity/endogeneity are real issues
Key discrepancies between OLS & 3SLS
 Evidence supports many industryequilibrium model predictions.