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Cross Sectional and Panel Data II. Paul Gompers Harvard Business School February 26, 2009. Today. Look at a variety of papers that examine panel data. Start with methodological papers.

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Cross sectional and panel data ii

Cross Sectional and Panel Data II

Paul Gompers

Harvard Business School

February 26, 2009


Today

Today

  • Look at a variety of papers that examine panel data.

  • Start with methodological papers.

    • Differences in differences is common approach to estimated an effect in panel data when there is an “exogenous” intervention and a contemporaneous non-intervention group.

    • Look at some of the statistical issues that may plague these tests.


Today1

Today

  • A variety of natural experiments that can be viewed as “exogenous” events.

    • Election cycles

    • Lawsuits

    • Oil price shocks

    • State law passage

  • Key point from these papers.

    • All of them examine a particular issue in a panel data set.

    • Find an intervention that allows the researcher to look at pre and post behavior of the firms to answer an economic question.


How much should we trust differences in differences estimates

How much should we trust differences in differences estimates?

Bertrand, Dufo, and Mullainathan

QJE 2004


Motivation

Motivation

  • Lots of papers try to control for events by comparing changes in one group that has been affected by an event to a contemporaneous group has not been affected.

  • Essentially, the non-affected group becomes the control for the affected group.

  • Look at differences in the differences between these groups.

  • Most papers focus solely on whether the intervention is endogenous

  • Standard errors may be inconsistent.


Problems

Problems

  • DD estimates are usually OLS run in repeated cross sections.

  • Potential for significant serial correlation in residuals of OLS regression.

    • DD estimates usually use long time series.

    • Dependent variables in DD are typically highly positively serially correlated.

    • Treatment variables typically changes very little within a state over time.


Paper s approach

Paper’s Approach

  • Create a series of placebo laws and look at employment data on female wages.

    • Laws are not real. They randomly assign years and states for a test.

    • Would only expect significant relationship between intervention (i.e., the placebo law) and female real wages 5% of the time at typical significance levels.

  • Also create Monte Carlo simulation.

  • Compare a variety of fixes to deal with the serial correlation in residuals.


Survey of the literature

Survey of the literature

  • Looked at 92 papers published in six journals from 1990 to 2000 that utilized differences in differences estimation.

  • Table I.

    • Review of papers that use DD.


Placebo tests

Placebo Tests

  • Utilize CPS and a sample of women’s wages

    • Table II

    • Look at 1979 to 1999

    • 540,000 reports and (50*21-1050) state year observations

    • Do 200 independent observations of placebo laws.

    • Also, because there are only 50 actual states in the real sample, perform a Monte Carlo simulation where the “states” are generated from the state-level empirical distribution from the CPS with replacement.

    • Look at Type I and Type II error (inducing a true 2% effect)


Placebo tests1

Placebo Tests

  • Look at effect of sample size and time series length of panel.

    • Small T reduces problem of the Type I error.

    • Table III


Corrections

Corrections

  • Look at a variety of parametric corrections

    • To adjust for serial correlation, typical to set an AR(1) structure to residuals.

    • Table IV.

      • Typical corrections don’t do very well.


Block bootstrapping

Block Bootstrapping

  • Correction for dealing with autocorrelation structure.

  • Keep all observations from the same state together.

  • Construct a bootstrap sample by drawing with replacement 50 matrices using the entire time series of observations for each state (Ys,Vs)

    • Run the regression of Y on V.

    • Draw large number of bootstrap samples (200).

  • Table V.

    • Works well when N is large.


Simple method

Simple Method

  • Aggregate time series into two periods.

    • Works reasonable well.

    • If all laws passed same time, can do simple aggregation into average before and after intervention.

    • If interventions are at different times:

      • Run regressions without intervention variables.

      • Take residuals for intervention states before and after intervention then average them before and after.

    • Table VI.

      • With small N, works okay.

      • As N increases, works better.

      • Downside is the power of Type II tests decreases.


Empirical variance covariance matrix

Empirical Variance-Covariance Matrix

  • Can estimate the Var-covar matrix by assuming that it is block diagonal with each state having the same autocorrelation process.

  • Use the states to estimate the Var-covar matrix.

  • Table VII.

    • Works well with large N.

    • Does poorly with small.


Conclusion

Conclusion

  • Need to worry about serial correlation within the DD sample.

  • A number of techniques to deal with problem.

  • Based on the structure of your panel, a different approach may be appropriate.


Fixing market failures or fixing elections agricultural credit in india

Fixing Market Failures Or Fixing Elections? Agricultural Credit in India

Shawn Cole


Motivation1

Motivation

  • India nationalized banks

    • “Reduce poverty, encourage growth”

      • Private banks controlled by industrial houses

      • Farmers victim to rural money-lender

      • Industrial credit requires co-ordination

    • At a potential cost?

      • Inefficiency (lazy bureaucrats)

      • Capture of banks by politicians


Cross sectional and panel data ii

...don't worry, I spoke to banks for loans, told agriculturalists for seeds, discussed with firms for fertilisers, ordered weather dept to ensure a good monsoon!


This paper

This Paper

  • Provide micro foundations for observed cross-country relationships between bank ownership and performance

  • Test theories of capture

    • Credit follows election year cycles

      • Agricultural credit is up to 20% higher in election years than non-election years

      • Rule out other causes for cycles

    • Captured credit is used strategically targeted

      • Cycles are twice as large in “swing districts” than in non-swing districts

      • No evidence politicians reward their supporters

  • Measure costs of distortions:

    • Default rates are higher in election years

    • The marginal political loan has no measurable effect on agricultural output

    • Even the average loan does not affect agricultural investment


Setting

Setting

  • Banking in India

    • In 1969, and 1980, government nationalized largest banks

    • Branch expansion law vastly increases scale and scope of banking

      • Currently more than 60,000 bank branches

      • Most banks government-owned (ca. 90% assets)

    • Government intervention increases importance of agricultural lending

      • Nationalized banks lend more to agriculture

      • All banks required to lend minimum percentage to agriculture

  • Agricultural credit is important

    • Government banks lend substantial amounts to agriculture

      • 17% of value of portfolio

      • 40% of loans, or over 20m loans

    • Private banks lend (less) to agriculture

  • Agriculture is important

    • 60% of labor force works in agriculture

    • 24% of GDP


Politicization of lending

Politicization of Lending

  • Politicians promise agricultural credit prior to elections

  • Informal interaction between bankers and politicians

  • “State Level Bankers Committees”

    • Comprised of political appointees and bankers

    • Quarterly meetings to monitor lending

    • Staff turns over when government changes


Cross sectional and panel data ii

Data

  • Panel of 412 districts in 19 states, annual data 1992-1999

  • Credit Data

    • “Basic Statistical Returns,” Reserve Bank of India

      • Census of loans

      • Identifies bank, loan amount, location, industry, interest rate, repayment status

  • Elections Data

    • Election Commission of India

      • Constituency-level results, aggregated to district-level

      • 32 elections in 19 states

  • Output Data

    • Planning Commission of India


Political cycles empirical strategy

Political Cycles: Empirical Strategy

  • “Naïve” OLS: Is credit higher in election years?

  • But, elections may be called early by optimizing politicians. Use constitutional schedule to create an instrument for election year

  • Stronger test - impose structure of entire election cycle


Political cycles

Political Cycles

  • OLS:

  • District fixed effect: ad

  • Region-year fixed effect: grt

  • Dummy for election year: Est:

  • (Rain in district as additional control): Raindt

  • Comparing districts in Gujarat, when Gujarat is having an election, to districts in Maharashtra, when Maharashtra is not having an election

Ydt = ad + grt + d Raindt + bEst + edt


Ols effect of elections on total credit

OLS: Effect of Elections on Total Credit

  • Table 2

    • Examine credit by type of bank.

    • Use a variety of estimation techniques.

Sumstat


Political cycles1

Political Cycles

  • Election cycles in a state are required every five years

  • Elections may be called early (10 of 32 in sample are called early)

  • Instrument: a dummy for whether it is a scheduled election (Khemani, 2004)

    • Does not take credit for elections called during “booms”


Political cycles lessons

Political Cycles: Lessons

  • In election years, level of agricultural credit from public sector banks is 6% higher than in non-election years.

  • No differences for non-agricultural credit (precise)

  • No differences for private banks (imprecise)

  • Table 3 – Examines by year and loan type and bank type.


Political cycles conclusion

Political Cycles-Conclusion

  • Public sector banks increase agricultural credit by approximately 5-8 percentage points during elections

  • Private banks too small to “undo” cycle

  • Amount dwarfs legal campaign spending limits

    • Average constituency has roughly ~$1.4m in agricultural credit from public sector banks

    • 5 percent of $1.4m is $70,000

      • Implied Subsidy:

        • Lower interest rate: 3%=$2,100

        • Outside option (moneylender) or nonpayment: 51-100%, $70,000

    • Total spending on state elections by candidate is limited to $1,000-$14,000


Targeted allocation

Targeted Allocation

  • Competing theories of targeted allocation of resources:

    • Politically close areas, to win elections

    • Areas which support majority party (patronage)

  • Need measure of the local strength of state governing party (SGP) in previous election

    • Define SGP as party with more than 50% of seats, or member of ruling coalition

    • Define Margin of Victory in a constituency (Mcdst) as

      • Share of votes of SGP minus share of votes of next strongest challenger

      • 100% if SGP candidate runs unopposed

      • –(Share of Winner) if SGP does not field a candidate


Measure of political support

Measure of Political Support

  • Aggregation

    • Test for Patronage

      • Credit targeted to areas in which party enjoys more support

      • Mdst= District average of Mcdst

    • Test for Swing Voter

      • Credit targeted to areas in which previous election was close


Test for constant swing targeting

Test for Constant Swing Targeting

  • Table 7

    • Look at amount of credit controlling for years to election and how close the election was.


Interpreting coefficients

Interpreting Coefficients

  • Standard deviation of absolute margin of victory is .11

    • Moving from a district with a margin of victory of 0 to a margin of victory of .11 reduces the size of the cycle by approximately 5 percentage points


Targeted allocation concluded

Targeted Allocation (Concluded)

  • Evidence consistent with “swing voter” models; patronage and programmatic redistribution can be ruled out

  • Government ownership of banks introduces distortions in credit (but cannot yet make welfare statements)


Do elections affect loan repayment

Do Elections Affect Loan Repayment?

  • Challenges

    • Do not observe panel of loans, only district aggregate

  • Evidence of cycles in repayment suggest costliness

  • Use “more than 6 months” late as indicator for default

    • Many agricultural loans are for harvest/season


Cross sectional and panel data ii

Lending Cycles and Non-Performing Loans

  • Table 8

    • Examine being late on loans.

Table 7


Elections affect loan repayment

Elections Affect Loan Repayment

  • There are cycle in loan repayment rates

  • Suggests electoral cycle is costly

    • Enforcement is more lax in election years

    • The marginal electoral loan may be more likely to default

    • Non-performing loans are written off following an election


Do political loans affect output

Do Political Loans Affect Output

  • Data - Agricultural Output at the state level, 1992-1999

    • Log real value of agricultural output

  • Election Schedule serves as instruments for agricultural credit


Do political loans affect output first stage reduced form iv

Do Political Loans Affect Output?First Stage & Reduced Form/ IV

  • Table 9

    • Look at output and credit.

    • Panel A is reduced form and Panel B is IV.


Output conclusion

Output Conclusion

  • No measurable effect on output

  • TWO

  • FOUR


Conclusion1

Conclusion

  • Combining theories of political cycles and tactical transfers helps identify manipulation of public resources

    • Agricultural lending exhibits substantial lending cycles

    • Lending is targeted in election years, not in non-election years

    • Results unlikely to be caused by omitted factors

    • Private banks do not exhibit these distortions


Conclusion2

Conclusion

  • Combining theories of political cycles and tactical transfers helps identify manipulation of public resources

    • Agricultural lending exhibits substantial lending cycles

    • Lending is targeted in election years, not in non-election years

    • Results unlikely to be caused by omitted factors

    • Private banks do not exhibit these distortions


Lessons

Lessons

  • Strong evidence for “Political” view of government involvement in bank credit

  • Evidence against “Development” view

  • Explains why politicians favor public banks, and agricultural credit in particular

  • Politicians may care more about re-election than delivering patronage to core supporters


What do firms do with cash windfalls

What do firms do with cash windfalls?

Blanchard, Lopez-de-Silanes, and Shleifer

JFE 1994


Motivation2

Motivation

  • How does one provide evidence of investment policy, internal opportunities, and cash flow sensitivity?

  • What do firms do with cash windfalls when investment opportunities are unchanged?

    • In perfect capital markets, would give money back to shareholders if holding cash inside firm is tax disadvantaged.

    • In imperfect capital markets:

      • If firm was capital constrained, should increase investment in projects it couldn’t do.

      • If managers pursue their own objectives, then could get very perverse behavior.


Cross sectional and panel data ii

Data

  • Process of identifying cash windfall firms is quite selective:

    • Looked in WSJ index for mentions of “Antitrust”, “Patents”, and “Suits” from 1980 to 1986.

    • Find 110 firms that won awards

      • (Easier today with the web and ability to electronically search news.)

    • Use four criteria to create sample:

      • Should not affect firm’s marginal Q

      • Award should be significant

      • Require 10K and proxies.

      • Focus only on award winners.

    • Final sample is 11 firms.

    • Table 1.


Significance of award

Significance of Award

  • Table 2

    • Look at award as fraction of sales and assets.

  • Table 3

    • Look at main line of business

    • Look at firm Qs, sales, investment, and debt.

      • Subtract size of award from Q.

        • These are pretty bad firms with poor investment opportunities.


Market s reaction

Market’s Reaction

  • Do event study.

  • Don’t know the exact amount of leakage/anticipation of award.

  • Look at CARs centered on award date for firms:

    • 100, 10, and 3 days.

  • Deflate market value change imputed by the CAR by the size of the award.

    • In general, increase in market cap is small fractio nof award.

  • Interpretation.

    • Award is wasted.

    • Award is anticipated.


Investment

Investment

  • Look at change in investments pre and post award.

  • Deflate change in investment rate by assets or net award.

  • Table 6.

    • Small increases in investment on average.

    • Jamesbury is interesting example.

      • Starts constructing new plant.


Asset sales and acquisitions

Asset sales and acquisitions

  • Table 7.

    • Not much asset sales.

  • Table 8.

    • More troubled firms seem to buy assets.

    • DASA (formerly a telecomm equipment company) buys oil wells and credit collection business.

    • UNC quires a communications carrier and starts air services like pilot training.


Change in financial policies

Change in Financial policies

  • Look at change in debt pre and post-award.

  • Firms seem to add debt after award.

    • Table 9.

    • Consistent with either commitment to pay out cash or increase in debt capacity.

  • Look at payout policy.

    • No increase in dividends.

    • Increase in repurchases – Table 10.

    • Repurchases tend to be targeted at large investors.

      • Reduction in oversight.


Executive compensation

Executive Compensation

  • Look at changes in executive compensation.

    • Cash and stock compensation.

    • Table 11 – Looks like increases in cash primarily.


Conclusions

Conclusions

  • Examination looks like cash windfalls are typically used to entrench management.

  • Increase in pet projects, acquisition.

  • Increase in exec comp.

  • Targeted repurchases.

  • Methodology is interesting in approach of trying to cleanly identify sample.

    • One more table than data points.


Cash flow and investment evidence from internal capital markets

Cash flow and investment: Evidence from Internal capital markets

Owen Lamont

JF 1997


Motivation3

Motivation

  • Similar to Blanchard, et al., trying to identify group of firms that have change in cash flow and see how it affects investment.

  • Want a shock that is unanticipated (exogenous) and its affect on multi-divisional firms that have businesses whose prospects are uncorrelated with the other division’s prospects.

  • Natural experiment:

    • Oil shock of 1986.

      • Severe and rapid real price decline in oil prices.

      • Look at diversified oil companies who had other businesses unrelated to oil.


Internal capital markets

Internal capital markets

  • If external finance is costly, then internal capital markets can allocate money across divisions to overcome some of the financing wedges that exist between internal and external cash.

  • If capital markets were perfect, would expect no change in investment with a decline in the unrelated segment cash flow.


Oil shock of 1985

Oil shock of 1985

  • Increase production by Saudi Arabia

    • Figure 1 – Real crude prices.

    • Large change in production and investment plans for real crude companies.

    • Table I.

      • Real declines in profitability.


Sample

Sample

  • Look at multi-segment reporting firms.

    • Issue with multi-segment firms.

      • Firms report segments that are more than 10% of firm revenues.

      • Reporting is endogenous.

      • Can change from year to year.

      • Does not necessarily represent how firm is organized.

    • Extract all firms from Compustat in 1985 that had either their primary or secondary SIC code in oil and gas extraction (SIC code 13).

      • Firms classified as oil-dependent if at least 25% of their cash flow came from oil extraction.


Sample1

Sample

  • Look at non-oil segments.

    • Exclude firms with financial services operations or by-products of oil and gas.

    • First pass is to use judgment.

    • Second test, look at time series of correlation of profits and investments from Annual Survey of Manufacturing for non-oil segment and real oil prices.

    • Table II- Sample of firms.

    • Table III – Non-oil segment at firms.

      • Several firms have multiple non-oil segments in data sample.


Tests

Tests

  • Examine investment pre- and post-oil shock for non-oil segments.

    • Table V – Raw and industry adjusted investment for non-oil segments.


Cash flow or collateral

Cash flow or collateral

  • Change in investment could be due to higher cost of external finance.

  • Look at firms that have a bond downgrade.

  • Table VII – all done at the industry level for non-oil segments.

    • Change in investment not driven by bond rating or shock variable or cash flow.


Over or underinvestment

Over or underinvestment

  • Testing between capital market imperfection (underinvesment after shock) vs. agency costs (overinvestment prior to shock).

    • Look at industry adjusted investment rates.

    • Table VIII

      • Go from roughly average/median to below industry average/medians.

    • Table IX

      • Look at profitability.

      • Non-oil segments were less profitable than industry averages prior to shock and then increase to average after shock.


Subsidies to non oil divisions

Subsidies to non-oil divisions

  • Table X.

    • Cut deepest for divisions in which CF<I prior to shock.

  • Also, look at segment investment as a function of oil cash flows to firm sales.

    • Table XI


Observations

Observations

  • Appears that diversified oil companies subsidize non-oil divisions with free cash flow generated by the oil profits.

  • This subsidization appears to be the results of agency costs.

  • More generally, nice natural experiment to provide evidence of investment behavior within firms.


Enjoying the quiet life corporate governance and managerial preferences

Enjoying the quiet life? Corporate governance and managerial preferences

Bertrand and Mullainathan

JPE 2003


Motivation4

Motivation

  • Managers typically own very little of their company stock.

    • 90% own less than 5% (Ofek and Yermack (2000))

    • Are managers who are entrenched empire building (Williamson (1964) vs. quiet life consumers (Hicks (1935))?

  • Lots of studies look at the relationship between measures of corporate governance and firm performance/activities.

    • But internal governance is endogenous.

      • Firms choose what level of shareholder protection, etc. they have.


Dealing with endogeneity

Dealing with Endogeneity

  • Look at enactment of state antitakeover laws.

    • Typically viewed as outside the control of any one firm.

    • Can look at effect on companies incorporated in states that pass these lasws.

  • Research design.

    • Examine both firm level and plant level data.

    • Plant level data is key.

      • Firms have plants in many states.

      • Anti-takeover rules apply to where firm is incorporated.


State anti takeover laws

State anti-takeover laws

  • Good history of first and second generation anti-takeover laws.

  • Table 1.

    • List of states and years that the laws passed.

      • Focus on Business Combination Laws

        • Moratorium (3 – 5 years) on specified transaction between target and a raider holding a certain % of the stock unless board votes

  • Literature on impact of laws.

    • Stock price reaction – small negative.

      • Hard to pin down date.

    • Evidence on actual takeover incidence is mixed.


Paper s strategy

Paper’s Strategy

  • Use difference in differences comparisons between states that adopt BC laws and those that don’t.

  • Have data at the firm level and the plant level.

    • Can control for local economic conditions.


Cross sectional and panel data ii

Data

  • LRD – Longitudinal Research Datafile

    • Census – large probability sample of US manufacturing firms

    • Have data on individual plants.

    • Matched to Compustat

      • Issue with Compustat – has only most recent state of incorporation, i.e., it is not historical.


Variables

Variables

  • Average hourly production worker wage

  • Capital stock

  • Return on capital

  • Plant births and deaths

  • Table 2 – Summary stats


Regressions

Regressions.

Firm Level

yjklt = at + aj + g Xjklt + d BCkt + bEst + ejklt

Plant Level

yijklt = at + ai + ak + g Xijklt + rylt(-i) + d BCkt + bEst + ejklt


Wages

Wages.

  • Do managers increase wages after BC law?

    • Why would they increase wages above profit maximizing levels?

    • Reduce turnover.

    • Reduce complaints.

    • Allow for clustering at the state of location level to adjust for serial correlation.

    • Table 3

      • Column 5 – look at reverse causality.


Plant births and deaths

Plant births and deaths

  • Look at death of plants

    • Table 5 – lower rate of plant closings.

  • Plant births.

    • Table 6 – BC law decreases plant birth by 2%.

      • Large relative to unconditional plant birth probability of 7%.

  • No effect on capex or firm size – Tables 7 and 8.


Productivity

Productivity

  • Seems as if managers are paying higher wages and being less aggressive.

  • Does this affect plant level performance.

  • Look at TFP

  • TFP is the residual from this regression.

  • Second measure of productivity is the return on capital.

log(output)= atlog(wage billi) + b log(capitali ) + g log(materiali ) + ei


Regressions1

Regressions

  • Table 9

    • Results appear to show lower productivity when TFP percentiles are measuresd.

    • Marginal effect on return on capital.


Observations1

Observations

  • Results consistent with managers that are insulated from takeover “enjoying the quiet life”

  • Good natural experiment.

  • Lots of interesting variation and results.

  • Exploits location/incorporation differences.

  • Nice data set on plant level variables.


Does corporate governance matter in competitive industries

Does corporate governance matter in competitive industries

Groud and Mueller

Working paper 2007


Cross sectional and panel data ii

Idea

  • Notion that market competition weeds out firm inefficiencies.

  • Would expect that corporate governance is less important when firms face highly competitive markets.

  • Do the Bertrand Mullainathan analysis, but add in the level of competition in the industry.

    • Expect that in highly concentrated industries, the Bertrand/Mullainathan result would be particularly strong.

    • Not so in highly fragmented/competitive industries.


Cross sectional and panel data ii

Data

  • Main data is Compustat:

    • Exclude utilities – regulated.

    • Use state of location and state of incorporation.

      • Not necessarily the same thing.

    • Table I – Summary

    • Compute 3-digit Herfindahl index.

      • Test robustness at 2 and 4 digit.

      • Industry definition is particularly tough.

        • SIC codes not very good for high technology companies.

        • Mapping to actual competitors may be problematic.


Main approach

Main Approach

  • Use differences in differences

  • Look at operating performance about how it depends upon concentration

yijklt =at+ai+g Xijklt+b1BCkt+b2Herfindahljt+b3(BCkt x Herfindahljt)+ejklt


Caveat

Caveat

  • Not looking at LRD data

  • Looking at Compustat

  • Table III – Look at ROA/assets

    • BC alone associated with lower ROA

    • Once interaction term put in, all the effect of BC is due to concentrated industries

    • Standard errors are clustered at the state level to deal with serial correlation of residuals at the state level.


Measurement error

Measurement Error

  • Perhaps Herfindahl is measured with error.

    • Imprecise industries

    • Wrong measure of competitiveness.

  • Use dummy variable for Herfindahl above and below the median or with three Herfindahl dummies.

    • No dependent upon functional form of the Herfindahl.

    • Table IV.


Robustness checks

Robustness Checks

  • Use past Herf to control for reverse causality – Table VII.

  • Look at alternative performance measures – Table VIII.

  • Look only at manufacturing – Table IX.

  • Alternative correction from Bertrand, Duflo, and Mallainathan (2004) – Table X.


Event study

Event Study

  • Try to replicate Karpoff and Malatesta (1989)

  • Difficulty

    • When is first announcement?

    • What date do you use?

    • What is the market expectation through time?

  • Look for first announcement of the law.

    • Exhaustive search where possible.

    • Only find news stories about 19 of the 30 laws.

    • But this covers the big states.


Event study issues

Event Study Issues

  • Typical event study methodology assumes that all events are independent.

  • Solutions:

    • Typically, just form portfolios of affected and non-affected firms.

  • Here, authors form equally weighted portfolios of firms that are incorporated in the state and run market model on equally weighted CRSP market portfolio.

  • Estimate model for days -241 to -41.

  • Get factor loadings.

  • Create predicted returns and subtract from actual return.


Event study1

Event Study

  • Look at CARs for days -40 to -, -3 to -2, -1 to 0, 1 to 2 and 1 to 10.

  • Separate out firms based upon Herfindahl in the previous groups.

  • Table XII.

    • Biggest price drop is in the concentrated industries.

    • BC laws affect performance of the concentrated firms that face less competition.


Contribution

Contribution

  • Interesting extension of prior work.

  • Thoughtful methodology.


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