CROSS SECTIONAL AND

PANELS IIIPaul Gompers

EMCF 2009

March 5, 2009

TODAY Examine two papers that use interesting

cross sectional variation to identify their tests.

Find a discontinuity in the data. In how much you have to fund your pension

plan. In how easy

INVESTMENT AND FINANCING

CONSTRAINTS: EVIDENCE FROM THE

FUNDING OF CORPORATE PENSION

PLANSJosh Rauh

JF 2006

SETTING Sponsors of defined benefit – DB –

pension plans need to make contributions to their pension pools that are legally specified, i.e., formulaic.DB plans pledge future payments to

workersBased on retirement cohorts, promised

benefits, assumed returns, and existing assets, pension plans are either under-, fully-, or overfunded.

Funding status has non-linear affects on amount of corporate contributions to the pension fund.

This funding amount varies from year to year for a variety of reasons.

SETTING Funding requirement can vary because:

Fund returnsChanges in the discount rate applied to

future benefits.Voluntary funding decisions.Changes in the future benefit structures.

IDEA Does the required level of contributions to

the pension fund (Mandatory contributions – MC) affect the capital expenditure and R&D of public companies?

Changes in the contribution may be correlated with firm prospects, its cash flow, and its investment opportunities.

But… There are non-linearities in the contribution

requirements for certain levels of underfunded status.

Also for overfunded pensions, lose tax benefits of contributions.

IDEA Estimate the effect using these non-

linearities. Get identification of whether external

finance is costly. Instrument for internal cash.

FUNDING REQUIREMENTS Figure 1 – Funding status of all public

traded firms on Compustat.Big cross sectional and time series

variation.Data from SEC filings.

Unfortunately, SEC filings don’t give enough information to run test.

Get plan information from IRS form 5500 filings.Hence, sample runs from 1990 to 1998.

FUNDING REQUIREMENTS Underfunded plans must contribute amount

equal to new benefits accrued plus a fraciton of the funding shortfall. Contribute the larger of:

Minimum funding contribution (MFC) Deficit reduction contribution (DRC) Prior to 1994 = min{0.30, [0.30-0.25*(funding status-

0.35)] After1994 = min{0.30, [0.30-0.40*(funding status-

0.60)] Figure 2.

Overfunded plans are not required to contribute. Can make voluntary contributions, but big

contributions lose tax advantage.

ANALYSIS Typical regression of investment on Q

and cash flow.

Include Zit, mandatory contribution (MC)

Include firm and year FE

INSIGHT Still endogeneity concerns, but will

utilize the kink in MC to identify exogenous variation in internal cash.

DATA Unbalanced panel using the IRS 5500 filings

for all firms that report DB pension assets. Typically older manufacturing firms. 8,030 firm-year observations for 1,522 firms. Table I – Summary stats. Compare actual to required contributions.

Figure 3. Use a kernel estimation of the density function. Definition of The Epanechnikov Kernel: The

Epanechnikov kernel is this function: (3/4)(1-u2) for -1<u<1 and zero for u outside that range. Here u=(x-xi)/h, where h is the window width and xi are the values of the independent variable in the data, and x is the value of the scalar independent variable for which one seeks an estimate.

DATA Graph mandatory contributions/book

assetsFigure 4

RESULTS Look at relationship between funding

status and capital expenditure and funding status and pension contributions, both scaled by assets.Do non-parametric univariate analysis using

the Epanechnikov kernel.Figure 5.

Looks as if there are similar inverted patterns in capex and pension contributions.

RESULTS Regression with a variety of specifications.

Baseline without MC. Breakout contributions into mandatory and total

contributions. Look at funding status of pension fund. Cluster standard errors by firm to correct for

within firm serial correlation of error terms. Alternatively, cluster by year (ala Fama-McBeth) or

use an arbitrary AR(1) correction in the panel. Table II.

Only mandatory contributions matter. Include funding status as an independent variable. Hence, it is the shape of the contribution curve that

identifies the effect, not funding status.

IV APPROACH Can instrument for pension

contributions by using MC as an instrument for either total pension contributions or total firm cash flow. Identifying that component of pension

contribution or cash flow that is correlated with MC.

Include funding status as an independent variable.

Hence, it is the shape of the contribution curve that identifies the effect, not funding status.

Table III.

DOES MC AFFECT OTHER USES OF CASH? Examine a variety of other uses of

corporate cash. R&D, Acquisitions, dividends, repurchases, and

changes in debt. Table IV.

No affect on R&D. Perhaps R&D has big startup and stopping costs.

Reduction in acquisitions. In Tobit regressions, reduction in dividends and

repurchases. Increase in debt. Reduction in trade credit. Increase in working capital.

WHICH COMPONENT OF MC? Divide MC into the unexpected and

expected components.Need to calculate the expected pension

assets and liabilities.

Utilize information on pension liabilities, expected return (based upon share of stock and bonds in portfolio).

Table V. Both expected and unexpected MCs matter.

OBSERVABLE MEASURES OF FINANCING CONSTRAINTS Prior literature has utilized a variety of measures

of funding constraint proxies. These have been shown to be related to cash flow-

investment sensitivities in a variety of papers. Utilize:

Age Credit rating Dividend ratio Cash balances Those firms with Capex > cash flow.

Table VI. In general, variables that are proxies for financing

constraints are associated with greater sensitivity of investment to cash flow.

GENERAL EQUILIBRIUM CONSIDERATIONS

Is there a cost to the firm of MC? Level of investment around large MCs (>0.1% of

book value of assets). Figure 6.

Do other firms take up the slack? Estimate total industry pension requirements.

Table VII: Look at firms based upon cash and capex to CF. Unconstrained firms seem to increase investment.

CONCLUSION Kink in MC based on funding status is a

nice natural variation that allows for examination of Cash flow-investment puzzle.

Financing constraints do seem to matter for these types of firms.

Remember, these are DB companies which may have very different types of investment behavior.

DID SECURITIZATION LEAD TO LAX SCREENING?

EVIDENCE FROM SUBPRIME LOANS

Benjamin Keys, Tanmoy Mukherjee, Amit Seru, and Vikrant Vig

Working paper 2008

IDEA Incentive problems potentially exist

when banks/lenders originate loans and then sell them in a securitized pool.Perhaps less incentive to fully gather

information if you are selling the loan off.Papers have looked at this issue, but

endogeneity issues plague most of them. Exploit an industry “rule of thumb” to

identify any effects on monitoring/information gathering.

RULE OF THUMB Prominent rule of thumb is to not lend to

people with FICO scores less than 620.FICO scores

FICO is the acronym for Fair Isaac Corporation, a publicly-traded corporation (under the symbol "FIC") that created the best-known and most widely used credit score model in the United States.

Calculated statistically, with information from a consumer's credit files.

Primarily used in credit decisions made by banks and other providers of secured and unsecured credit.

Intended to show the likelihood that a borrower will default on a loan

Range is 400 to 900.

RULE OF THUMB (2) This rule of thumb makes securitizing

loans of borrowers with FICO scores less than 620 more difficult, i.e., they are less liquid.Guidelines by Freddie Mac – Cautious

Review Category Research design:

Look at borrowers just above and just below the 620 break.

Should have similar default probabilities.Look at quality of the loans, terms, etc.

620+

620-

ASSUMPTIONS Borrowers on either side of 620 should

look similar.Only small differences in characteristics.

Screening is costly for lenders.

TERMS Look at loans where collection of soft

information may be important.Low documentation loans.

More likely that soft information would be important to estimate default probabilities.

Full documentation loans.

BACKGROUND 60% of loans trade as mortgage-backed

securities. Most are agency-pass through pools.

Freddie Mac (Federal Home Loan Mortgage Corporation), Fannie Mae (Federal National Mortgage Association), Ginnie Mae (Government National Mortgage Association).

Agency eligibility is based upon loan size, credit score, and underwriting standards.

Implicit government guarantee. Non-agency loans – “subprime”

More expensive. Price of loan depends upon credit score, debt to

income, and documentation level. No guarantee of loan.

DATA LoanPerformance.

Detailed data on non-agency securities markets.

8,000 home equity and nonprime loan pools.

16.5 millionloans.$1.6 trillion outstanding.90% of all securitized subprime loans.

METHODOLOGY Hard information:

FICO ScoreLoan terms (LTV, interest rate)

Soft information:Measure of future income stability of

borrower, years of information provided by borrower, joint income status).

When securitized, only hard information provided.

METHODOLOGY

Y is number of loans of score I T = indicator if FICO>620 and 0 if

FICO<620. T*f(FICO) is a flexible seventh-order

polynomial.Fit smooth curve.Data recentered so that FICO = 620 is 0.At cutoff, polynomial is evaluated at 0.b is measure of discontinuity for FICO>620.

SUMMARY STATS Table I Focus only on low documentation loans.

Those where soft information will be important.

Figure 2 – Increase in number of loans above 620. Yearly.

Table 2 – b coefficient by year. Do permutation test.

Look for discontinuity at other places.Allow the 0 to be at different FICO scores.Do not find any other discontinuities.

BORROWER AND LOAN CHARACTERISTICS Look at LTV and interest rates.

Figures 3 and 4.

LOAN PERFORMANCE Look at whether or not performance of

loan differs around 620. Look at default rates.

Dollar weighted.Default within 10-15 months of origination.Collapse data into 1 point FICO bins.Figures 6A-F.Table III.Figure 7 – Delinquencies by age of loan.

ALTERNATIVE TEST Look at loans on either side of 620.

Group for loans 615 to 619 and 620-624.

T=1 if FICO is between 620 and 624 and 0 for FICO between 615 and 619.

Control for type of loan – Adjustable or Fixed rate.

Age.Logit is Panel C of Table III.

ADDITIONAL TEST OF CAUSATION Look at Georgia and New Jersey.

Both passed Fair Lending Laws.Strong restrictions on predatory lending.Made securitizing loans very difficult when

law was in effect.Both laws later repealed.Run same regression and include

interaction for when Fair Lending law was in effect and not.

Table IV. Big increase in loans above 620 when law not in

effect in those states. No effect when Fair Lending law in place.

ENDOGENEITY OF FICO SCORE Do borrowers manipulate their FICO

score to be just above 620 Fair Isaac says that it takes time and is

hard to do. Look at six months immediately after

repeal of Fair Lending laws.Table IV Panel B.Change in delinquencies happens

immediately after law for loans with FICO > 620.

HARD INFORMATION Does the effect exist in full

documentation loans.Greater information about borrower’s ability

to repay.Fair Isaac advises lenders that below FICO

of 600, very troubled borrower.Figure 11 – Substantial increase in full

documentation loans with FICO>600.Look at default rates above and below FICO

600 for full documentation loans. Figures 12 and 13. Table VI.

INTERPRETATION Seems as if loan default differential in

low documentation loans is due to “soft information”

For full documentation loans, i.e., with more hard information, no difference at loan inflection point.

OVERALL Interesting use of kink in the behavior of

lenders. Find that this leads to an effect on loan

repayment. Nice set of robustness tests.