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.