panels and cross-sections 1

Panels and Cross-sections 1Paul A. Gompers

Empirical Topics in Corporate Finance

February 19, 2009

Panel and Cross Sectional Data

• Today look at panel and cross sectional data.

• Covers lots of interesting papers and data sets.

• Methodological issues arise in the cross section and we will deal with those in a variety of settings.

Agenda

• Look at methodological papers as well as applications of cross sectional and panel data.

• Hopefully this examination will provide insights into how to approach many of the most interesting problems in CF.

Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches

Mitch Petersen

RFS 2009

Papers Contribution

• Examines a variety of approaches to estimating standard errors and statistical significance in panel data sets

• Interesting look at a variety of papers published from 2001-2004.– Only 42% of papers adjusted standard errors for

possible dependence in residuals. Many different approaches. Which are correct under what circumstances.

Overview

• OLS standard errors are unbiased when residuals are independent and identically distributed.

• Residuals in panel data may be correlated by firm-specific effects that are correlated across time.– Firm effect.

• Residuals of a given year may be correlated across different firms (cross sectional dependence)– Time effect.

Paper’s Approach

• Simulate data that has either firm effect or a time effect.

• Test various estimation techniques.• See how they deal with the simulated data.• Then takes regression approaches to actual

data and compares them.

Firm Fixed Effects

• Assumption of OLS is that cross product matrix has only non-zero numbers on the diagonal.

• Figure 1 – Example of a firm effect.– Cluster standard errors by firm.

OLS vs. Clustering by Time vs. FM with Firm Effect

• Simulate 5000 samples with 5000 observations.– 500 firms and ten years of observations.

• Let the residual and independent variable variance due to the firm effect vary between 0 and 75%.

• 500 clusters by firm.

OLS vs. Clustering on Firm vs. FM with Firm Effect

• Table 1– Compare average coefficients, st. dev. of coefficient

estimates, % significant, average SE clustered and % significant with clustered SE.

– Vary how much of the independent variable variation is due to firm effect and how much of the residual variation is due to firm effect.

• Figure 2 – Compare OLS, Clustered by firm, and Fama-McBeth.

• Table 2- Fama-McBeth

OLS vs. Clustering by Time vs. FM with Time Effect

• Simulate 5000 samples with 5000 observations.

• Let the residual and independent variable variance due to the time effect vary between 0 and 75%.

• Not this is the situation that FM developed FM for.

• Clustering will be by the 10 years.

OLS vs. Clustering by Time vs. FM with Time Effect

• Table 3 – Compare OLS and Clustering by time.

• OLS does pretty poor job.• Table 4 – Using FM to estimate.

OLS vs. Clustering by Time vs. FM with Firm and Time Effect

• In many typical examples, could have both a firm and time effect.

• Figure 6, typical structure with both.• Can cluster by firm and time together.

– See Samuel Thompson’s 2006 working paper for math.

OLS vs. Clustering by Time vs. FM with Firm and Time Effect

• Simulate 5000 samples with 5000 observations.

• Let the residual and independent variable variance due to firm and time effect vary

• Table 5 – Compare OLS, with and without firm dummies, Clustered by firm and time, GLS, and FM.

Real Data

• Table 6 – Look at asset pricing application.– Equity returns on asset tangibility.– Different methods matter.– OLS and firm clusters do poorly.– Time and firm clustering and FM work well.– Seems to say that for returns may be more

affected by a time effect.

Recommendations

• Think about the structure of the panel data structure.

• What is the likely source of dependence.• Comparing different methods may provide

additional information about the research question.

Real Data

• Table 7 – Capital structure regressions.– OLS, clustering by time, and FM do poorly.– Clustering by firm and clustering by firm and

time do well.– Says that within corporate finance a lot of the

effects seem to have firm level persistence.

Market timing and capital structure

Malcolm Baker

Harvard Business School

Jeffrey Wurgler

NYU

Research question

• Why do similar firms have different capital structures?

• Temporary fluctuations in market value have a lasting impact on capital structure outcomes

Overview• A new fact: Temporary fluctuations in market value

have a lasting impact on capital structure outcomes• Possible explanations:

– (1) Trade-off theories Taxes, costs of financial distress, and agency lead to an optimal

leverage ratio

– (2) Pecking order Adverse selection dominates other considerations, leading to a

pecking order

• Neither appears to explain the new fact– Consistent with the idea that managers are motivated by

market-timing

Some motivation• Prevailing market values are the most important empirical

determinant of financing decisions…– Equity issues

IPOs: Loughran, Ritter, Rydqvist (1994),Pagano, Panetta, Zingales (1998) et al.SEOs: Asquith and Mullins (1986), Marsh (1982) et al.

– Debt issues and repurchases Debt: Marsh (1982) et al.

Repurchases: Ikenberry, Lakonishok, Vermaelen (1995) et al.

– … and capital structure is the sum of past financing decisions (accounting identity)

– So, capital structure may depend on the historical path of market valuations

Sample• Trace the evolution of capital structure as firms mature

– Require a known IPO date– Natural starting point for the historical path of market

valuations– And, can trace the determinants of capital structure as firms

mature

• Capital structure Et / At

– Compustat coverage from 1969 to 1998• Empirical approaches

– IPO time– Calendar time

Capital structure changes

• Changes in the equity ratio come in two forms

(1) New issues and repurchases– Active change in capital structure (Table 3A)

• (2) Retained earnings– Passive change in capital structure (Table 3B)

• Examine the determinants of each– Market-to-book, asset tangibility, profitability, and size– Table 3

ttt A

eE

A

e

A

E

Measures of M/B

• Summarize the historical path of valuations with a single statistic:

(1) Maximum market-to-book ratio– The highest year-end value from the IPO through t-1

(2) Weighted average market-to-book ratio– The weights are the amount of external finance (debt

plus equity) raised in each year from the IPO through t-1

– Financing events represent a practical opportunity to change capital structure

Temporary fluctuations in M/B and capital structure

• Cross-section regressions in Table 7

(1) Include controls x– Fixed assets intensity– Profitability– Firm size

(2) b2 captures the impact of temporary fluctuations in market value

– Control for endpoints at IPO and t-1

ttefwatt

uB

Mb

B

Mb

B

Mba

A

E

kx 10

321

1

ttefwatt

uB

Mb

B

Mba

A

E

kx 121

1

Is M/B a measure of mispricing?• M/B predicts stock returns ...

– Basu (1977, 1983)Fama and French (1992)LSV (1994)

… partly because of errors in expectations– La Porta (1996)

LLSV (1997)• But M/B captures both mispricing and legitimate growth

prospects ...– Growth could be correlated with agency, asymmetric

information, financial distress costs

… so control for the level of M/B– Starting point (IPO), ending point (t-1), both

Some robustness checks

(1) Market value capital structure(2) Industry effects

IPO year effects(3) Fama and French (2000)

Five profitability lags(4) Outliers included

Mature firms included(5) TobitTable 8.

Economic significance

• Figures 1• Large relative to the other determinants

– Asset tangibility and size die– Profitability emerges

Possible explanations(1) Trade-off theories

– Taxes, costs of financial distress, and agency lead to an optimal leverage ratioAncillary prediction: Temporary fluctuations in market-to-book (or anything else) should have a temporary impact

(2) Pecking order– Adverse selection dominates other considerations, leading to a

pecking orderAncillary prediction: Temporary increases in market-to-book should lead to lower cash balances or higher future investment

(3) Market timing– Managers believe they can time the market

Trade-off theories• Taxes, costs of financial distress, and agency lead to an optimal capital

structure• Market-to-book could be connected to one or more inputs to the trade-

off, and… – Costly financial distress– Debt overhang– Agency– Perhaps tax benefits

… may have some persistence, but… – Adjustment costs

… temporary fluctuations in market-to-book (or anything else) should have a temporary impact

• Table 9.

Long-term impact

• Temporary fluctuations in market-to-book have a lasting impact on capital structure– The half-life of the initial effect is at least ten

years (Table 9)– Hatched bars are the five percent lower bound

Pecking order

• Adverse selection dominates other considerations, leading to a pecking order

• Market-to-book is related to investment opportunities, but…– High market-to-book means investment opportunities

exceed internally generated funds and debt capacity

… extra equity raised should be spent or at least be earmarked for future investment

• So, temporary increases in market-to-book should lead to lower cash balances or higher future investment

Cash balances

• Temporary fluctuations in market-to-book also have a lasting impact on cash– Increases in market-to-book have a permanent

impact on cash balances (Table 10)– No lasting impact on investment (Table 11)

Market timing• A variant of Myers and Majluf (1984)

Like Myers-Majluf:– Managers have the incentive to try to time the market because

they care more about existing shareholders– Investors react to financing decisions, and this adverse selection

dominates other considerations, so… … there is no optimal capital structure

Unlike Myers-Majluf:– Managers think that they can successfully time the market,

believing(1) Shares are occasionally under or overvalued(2) Investors underreact to new issues

Other evidence of timing• Managers admit to timing the market

– Graham and Harvey (2000)• It looks like they’re trying

– Marsh (1982)Pagano, Panetta, and Zingales (1998)

• Although investors recognize it ...– Asquith and Mullins (1986)

… they underreact– Ritter (1991), Loughran and Ritter (1995)– Baker and Wurgler (2000)

• Is it actually successful?– A separate debate – Market-timing attempts affect capital structure

Conclusions• Managers try to time their equity issues and this

influences capital structure outcomes:(1)Low leverage firms raised external finance

when valuations were comparatively highHigh leverage firms raised external financewhen valuations were comparatively low

(2)Temporary fluctuations in market-to-booklead to lasting changes in capitalstructure and cash balances

(3) Trade-off theories and pecking order do notappear to explain the results

(4) Market timing fits the new fact and old facts

Testing Trade-Off and Pecking Order Predictions about Dividends and Debt

Fama and French

RFS 2002

Agenda

• Look at two competing theories of capital structure and dividends.– Pecking order– Trade-off theory

• Utilize Fama-McBeth techniques.– Very important technique to understand.

Motivation

• Most previous studies are pure cross section or small panels.

• Results can be wildly overstated.– Cross correlation can reduce standard errors.

Correlation of the residuals across firms are ignored.

– Auto correlation can reduce standard errors. Panels can have residuals correlated across years.

• Fama-McBeth gives robust standard errors in these types of situations.– Particularly when there are multiple observations on the

same firm and you have unbalanced panels potentially.

Methodology

• Run a series of cross sectional regressions.– Can be run annually, monthly, daily, etc.

Only depends upon number of unique observations you have.

• Report the average coefficient and test significance by using the standard deviation of the time series of coefficients.– Also can report number of positive and negative coefficients.

• Recognize that there may be firm persistence.– Arbitrarily argue that there is a need to increase t-statistics

critical value by 2.5x, i.e., 5.00 to get significance.

Pecking Order

• Dividends– Less attractive for less profitable firms, large current

and expected investments, high leverage.

• Leverage– Depends if one period or care about future financing.

Lower leverage for firms with large future investments.

• Volatility.– Low dividends and low leverage.

Trade-off Model

• Bankruptcy costs• Taxes• Agency costs• Adjustment costs.

Independent Variables

• ET/A – Pretax earnings to assets• V/A = Market value to book value (future

investments)• RD/A – R&D to assets• Dp/A – Depreciation over assets• Ln(A) – log assets as proxy for volatility.

Dependent Variables

• Dividends– D/A – Dividends over assets

• Leverage– Market leverage – L/V– Book leverage – L/A

Regressions

• Data from 1965-1999.• Table 1 – Dividend payout ratio

– Use target leverage from leverage regression in Table 4.

• Table 3 – Level of leverage.– Strong evidence of pecking order.

More profits yields less debt.

Sorting the sorts

• Table 5– Sort firms based on dividend paying or not and

leverage.– Find that low leverage nonpaying firms have

better investment opportunities and more equity issuances.

At odds with pecking order.

Conclusion

• Neither theory wins out.• Perhaps a third theory is at work.• Perhaps both have merits.• Interesting techniques.

Conclusion

• Interesting paper.• Highly technical with attention to detail.• Firms don’t take full advantage of potential

tax benefit savings from debt.

Understanding the Determinants of Managerial Ownership and the Link

Between Ownership and Performance

Himmelberg, Hubbard, and Palia

JFE 1999

Motivation

• Lots of studies show non-linear relationship between firm value and inside ownership.– Mork, Shleifer, and Vishny (1988).– McConnell and Servaes (1990).– Hermalin and Weisbach (1991).

• Problem is that ownership may be endogenous.– All these studies are large cross-sections.

Game Plan

• Examine the determinants of equity holdings.– In particular, does the availability of information to

monitor and track company affect inside ownership?

– Create panel data set of inside ownership to control for unobserved firm factors that affect ownership.

Ideally would use IV estimation, but no good instruments.

Data

• Find firms on Compustat that have complete data on sales, book value, and stock prices from 1982-1984.

• Randomly select 600 firms.• Get ownership data from proxies.• Unbalanced panel.

– Table 1.

Summary Statistics

• Collect data on:– Number of top managers (from proxies).– Ownership of top managers (from proxies).

• Table 2 and Figure 1.

Determinants of Ownership• Stock price volatility.

– Managerial risk aversion.– Demsetz and Lehn (1985).

Interpretation?• Ease of monitoring.

– Size - ln(sales)– Capital intensity - K/S.

Measures scope for discretionary spending.– R&D intensity - R&D/K– Advertising intensity - Ad/K

Determinants of Ownership

• Ease of monitoring. – Cash flow.– Gross investment rate.

Results

• Table 4 (A) - Examines total inside ownership.– Fixed effects matter.

– Size has differential impact in different specifications.

– Increasing capital intensity, less inside ownership.

– Increase volatility, less ownership.

– Increase R&D, increased ownership with fixed effects.

Results (2)

• Table 4 (B) - Examines average inside ownership per top manager.– Size increasing in pooled regressions. No effect

with FE.– Increasing capital intensity, less inside

ownership per manager.– Increase volatility, less ownership per manager.– Increase R&D, increased ownership with fixed

effects.

Ownership and Firm Value

• Create three categories of inside ownership based on Morck, et al.– m1 = ownership if ownership<5% =.05

if ownership >5%.

– m2 = 0 if ownership<5% =ownership level minus 5% if ownership >5% and <20%. =.20 if ownership > 25%.

– m3 = 0 if ownership<25% =ownership level minus 25% if ownership >25%.

Results

• Table 5B.– Without fixed effects, get non-linear

relationship between ownership and Q.– With FE, becomes insignificant.– Even SIC dummies and other controls don’t

help.

Instrumental Variables Approach

• Perhaps we can solve the problem with IV estimation.– Use size and size squared as well as volatility

as instruments for ownership.

• Results in Table 6.– Once again, without FE get inverse relationship

between ownership and Q.– Goes away with FE.

Conclusions

• Inside ownership appears to be related to monitoring/agency environment.

• The relationship between ownership and Q appears to be driven by unobservable firm factors.– Argue that each firm is choosing ownership

optimally to maximize Q.

Concerns

• Equity ownership perhaps not best measure of incentives.– Need to consider other elements of

compensation and relationship to executives’ wealth.

• Why look at ownership of all top executives?– Is looking at CEO better or worse?

Concerns

• Why not control for other governance provisions?– Structure of board of directors.– Anti-takeover provisions.– Institutional ownership.

Extensions and Questions

• CEO ownership and compensation over time.– CEOs in our sample own substantially more

than seen in typical, large public firms.– When do the CEO begin to sell equity in large

quantities?– Is there a performance difference? Do

ownership and incentives even matter?

Extensions and Questions (2)

• Relationship to other corporate governance mechanisms.– Boards of directors.

Insider vs. outsider.– Detailed description of types of outsiders.

Role of block shareholders.

– Changes over time. Who gets added and why?

– Real effects of directors?

Managing with Style: The Effect of Managers on Firm Policies

Bertrand and Schoar

QJE 2003

Motivation

• Most theories have treated managers as being undifferentiated.

• Real differences in managers may have impact on firm policies.– Investment and financial.– Look at:

Financial leverage, investment-CF sensitivity, organizational strategy (R&D, advertising, diversification, and cost-cutting), and performance.

Key Issue

• How do you separate out manager effects from firm effects?

• Strategy:– Collect long panel of top execs at US firms.– Look at those who change firms and estimate

the “Manager Fixed Effects”

Sample

• Forbes 800 data from 1969 through 1999 and Execucomp from 1992 through 1999.– Need managers to be in a firm for at least three

years.– Find 600 firms with over 500 managers that

spend at lease three years at two different firms.– Table I – Firm sample characteristics.– Table II – data on managers and transitions.

Methodology

• Look at dependent variable as a function of firm and manager characteristics –– Utilize manager fixed effects.– Extract out these fixed effects.– Table III – Look at investment and financial policies on

CEO characteristics. CEOs matter. CFOs matter for financial policy.

– Table IV – Look at organizational strategies. Once again, CEOs matter.

Individual Characteristics

• Look at two important characteristics.– MBA.– Birth year.

Did you grow up in Depression.

• Table IX.– Find that Depression babies are more

conservative and MBAs are more aggressive.

Conclusion

• Manager characteristics matter.– Where you were before.– MBA and birth year.

• Nice paper.– Clever idea and clever test.

US cross-listings and the private benefits of control: evidence

from dual-class firms

Craig Doidge

JFE 2003

Motivation

• Can you use the decision of dual-class foreign firms to list on the US on NYSE or Nasdaq to get estimates of value of private benefits?

Background

• Coffee (1999) and Stulz (1999) show that firms issuing ADRs on US causes stock price increases.– Commitment to better governance.

Data

• Compile panel sample of 745 non-US firms that have dual class shares.

• 137 cross list on US– 75 on NYSE or Nasdaq– Table 1 - Summary

Voting Premium Analysis

• Look at voting premium of superior voting class shares.

• Table 2• Table 3

– Look at voting premium based upon type of ADR and the level of protection in the domestic market.

– Utilize OLS and random effects model.

Announcement Period Returns• Figure 1

– Look at voting premium and cumulative return difference between low and high voting shares.

• Table 6 – CAR analysis.– Estimate market model from day -244 to day -6.– Also, just subtract return on the high voting shares

minus low voting shares.

• Table 7 – Regression of LMH on protection of shareholders variables.

Conclusion

• Bonding hypothesis seems to have support.– Better protection leads to lower voting

premium.– Higher level of disclosure of ADRs leads to

lower premium.– Better protection of shareholders in domestic

markets leads to lower premium.