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Capital Structure and Industry Equilibrium Models
Review by
Gordon Phillips
University of Maryland and NBER
(Also covers MacKay and Phillips, RFS (2005))
Presentation Outline
• Capital Structure Research
• Industry-Equilibrium Models
• MacKay Phillips
• Results Preview
• Data & Detailed Results
• Conclusions
TRADE-OFF THEORY of Capital Structure
Present value of financial distress costs
• Value of firm (V) VL = VU +TC B = Value of firm under
MM with corporateMaximum taxes and debtfirm value
V= Actual value of firm
VU= Value of firm with no debt
Debt (B) B *
Optimal amount of debtThe tax shield increases the value of the levered firm. Financial distresscosts lower the value of the levered firm. The two offsetting factors producean optimal amount of debt.
Present value of tax
shield on 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
Risk (Capital/Labor, )
Kim & Sorensen (1986)
Bradley et al. (1984)
Titman & Wessels (1988)
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
Endogenous Exogenous
Firm 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
Finance Characteristics
Financial distress & real decisions distortionsShleifer and Vishny (1992), Sharpe (1994)Opler and Titman (1994), Lang, Ofek, and Stulz (1996)
Firm
Conduct StructureIndustry
Strategic Interaction
Finance Characteristics
Conduct Structure
Strategic debt interaction under imperfect competitionBrander and Lewis (1986), Maksimovic (1988)Chevalier (1995), Phillips (1995), Kovenock & Phillips (1997)
Firm
Industry
Industry Equilibrium
Characteristics
Conduct
Real and financial interactions under perfect competitionMaksimovic & Zechner (1991), Williams (1995), Fries et al. (1997)
Firm
Industry
… MacKay and Phillips
Finance
Structure
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 issuesFirm-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 + q
Risky: MC= k-h + q in state L
= k+h + q 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[S] > E[R]
Debt destroys firm value if high enough to cause shareholders to pick R (intractable) – Risk shifting problem of debt.
Safety in numbers: price = marginal costAll 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
ProjectValue E[R] E[S]
Interior Industry Equilibrium:NS and NR adjust until E[S] = E[R]
Low (high)-debt firms choose S (R)
n NR0% 100%
Core FringeFringe
Industry Equilibrium Models Maksimovic & Zechner (1991): Predict
Nonlinear Industry Patterns
LeverageRisk ()E[profit]
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 debtFringe L: small, risky, unprofitable, no debt
Industry Equilibrium Models Williams (1995): Predicts
Linear Industry Patterns
Leverage1/E[profit]Size
Capital/Labor
CoreFringe
MacKay and Phillips (RFS, 2005)
Examine intra-industry variation (ANOVA)
Examine intra-industry patterns & relationsSum Stats: entering, exiting, & incumbent firms Evolution: transition frequencies across quintiles
Estimate simultaneous debt, K/L, risk modelsFirm-level: own decisions & characteristics Industry: own technology versus industry mean
actions of intra/extra-quintile firms
What We FindEvidence 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
Figure 1a. Dispersion in Fin. Lev for Competitive Industries
Low
Medium
High
20th40th
60th80th
0
0.1
0.2
0.3
0.4
0.5
0.6
Debt/Asset Ratio
Debt/Asset Percentiles
Intra-Industry Debt/Asset Dispersion
Some Evidence - 2
Figure 1a. Dispersion in Fin. Lev for Competitive Industries
Low
Medium
High
20th40th
60th80th
0
0.1
0.2
0.3
0.4
0.5
0.6
Debt/Asset Ratio
Debt/Asset Percentiles
Intra-Industry Debt/Asset Dispersion
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/L1: 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 R
2
Adjusted R2
with Firm Fixed Effects
Firm-years
A: Importance of Industry Financial Structure
Lagged Industry Median Debt/Assets
Firm Debt/Assets 0.149 0.032 0.118 9% 66% 19,374 (4.66)
a (0.78) (2.88)
a
Lagged Industry Mean Debt/Assets
Firm Debt/Assets 0.105 0.076 0.115 5% 66% 19,374 (3.75)
a (2.62)
a (3.38)
a
B: Importance of Common Industry Shocks
Change in Industry Median Debt/Assets
Change in Firm Debt/Assets 0.182 0.059 0.117 2% 19,374 (7.28)
a (1.84)
c (3.25)
a
Change in Industry Mean Debt/Assets
Change in Firm Debt/Assets 0.150 0.016 0.064 1% 19,374 (8.82)
a (0.94) (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 -0.052 -0.070 8% 19,374 (-3.33)
a (-3.06)
a (-5.00)
a
Lagged Decile Rank Difference between Firm and Industry Mean Debt/Assets
Change in Firm Debt/Assets -0.004 -0.001 -0.004 6% 19,374 (-4.00)
a (-1.00) (-4.00)
a
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 25th
Percentile 50th
Percentile 75th
Percentile
Dependent variables: Debt K/L Risk Debt K/L Risk Debt K/L Risk Leverage n/a -2.81 -5.21 n/a 0.94 1.68 n/a 4.01 7.32 Capital / Labor -3.22 n/a 2.91 0.06 n/a -0.32 2.74 n/a -2.97 Risk -2.08 1.06 n/a 0.27 -0.32 n/a 2.50 -1.61 n/a
Industry Variables Natural hedge 6.38 -3.75 -6.81 1.40 -0.91 -1.73 -6.22 3.40 6.05 Intra-quantile change -1.60 -0.64 0.11 Extra-quantile change -0.53 -0.83 -1.16
Control Variables Profitability 2.94 -1.47 -2.94 -1.30 0.55 0.99 -4.78 2.21 4.21 Size (log of assets) -5.99 3.42 5.94 -1.11 0.49 0.85 4.22 -2.71 -4.72 Tobin’s q -4.51 2.43 4.31 2.15 -1.38 -2.48 7.09 -4.20 -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.
ConclusionsIndustries 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 industry-equilibrium model predictions.