Discussion of "Developing Novel Drugs"(Krieger, Li, and Papanikolaou)

Borja Larrain

FinanceUC December 2017

• Very nice paper: great data and identification

• Main results:

— Novel drugs (↓ similarity) are high-risk-high-return invest-ments.

— ↓ Financial constraints ⇒ ↑ Risk-taking

— Exogenous shock to financial constraints:

Expansion Medicare × Remaining Exclusivity

6/20/2015 A Windfall From Shifts to Medicare - New York Times

http://www.nytimes.com/2006/07/18/business/18place.html?_r=0&pagewanted=print 1/4

July  18,  2006

MARKET  PLACE

A  Windfall  From  Shifts  to  Medicare

By  MILT  FREUDENHEIM

The pharmaceutical industry is beginning to reap a windfall from a surprisingly lucrative niche market:

drugs for poor people.

And analysts expect the benefits to show up in many of the quarterly financial results that drug makers will

begin posting this week.

The windfall, which by some estimates could be $2 billion or more this year, is a result of the transfer of

millions of low-income people into the new Medicare Part D drug program that went into effect in January.

Under that program, as it turns out, the prices paid by insurers, and eventually the taxpayer, for the

medications given to those transferred are likely to be higher than what was paid under the federal-state

Medicaid programs for the poor.

About 6.5 million low-income elderly people or younger disabled poor people were automatically

transferred into the Part D program for drug coverage. Because their other health needs are still covered by

Medicaid, they are called dual eligibles.

The advent of Part D has not affected the drug coverage for the 45 million other low-income people whose

drugs are still paid for under state Medicaid programs. Those programs closely monitor drug prices, and

drug makers often typically end up paying rebates to the states.

It is too early to calculate the full effect of the shift of the former Medicaid patients now covered by Part D.

But analysts expect it to generate hundreds of millions of additional dollars this year for the drug

companies, which have long chafed under the pricing restraints of the state programs.

Drugs tend to be cheaper under the Medicaid programs because the states are the buyers and by law they

receive the lowest available prices for drugs.

But in creating the federal Part D program, Congress — in what critics saw as a sop to the drug industry —

barred the government from having a negotiating role. Instead, prices are worked out between drug makers

and the dozens of large and small Part D drug plans run by commercial insurers.

Since Part D went into effect, the pharmaceutical industry has raised the wholesale prices of its brand-

name drugs an average of 3.6 percent. Although the actual amount spent depends on what each insurer

negotiates, in many cases the drugs for those 6.5 million people who used to receive their medicines

through Medicaid will cost more now.

http://topics.nytimes.com/top/reference/timestopics/people/f/milt_freudenheim/index.html?inline=nyt-perManuel Hermosilla

Manuel Hermosilla

http://www.nytimes.com/http://www.nytimes.com/adx/bin/adx_click.html?type=goto&opzn&page=www.nytimes.com/printer-friendly&pos=Position1&sn2=336c557e/4f3dd5d2&sn1=a622d194/e708ef44&camp=FoxSearchlight_AT2015-1977459-June-A&ad=MistressAmerica_120x60-DATE&goto=http%3A%2F%2Fwww%2Emistressamericathemovie%2Ecom%2F

Plan:

1. Theory

2. Identification

3. Kitchen-sink comments

1 Theory

• This paper: ↓ Financial constraints ⇒ ↑ Risk-taking

• In the spirit of:

— Acemoglu and Zilibotti (JPE 1997)

— Acharya and Subramanian (RFS 2009)

— Schroth and Szalay (RoF 2010)

• In theory, the relationship can be reversed.

• Risk-shifting (Jensen and Meckling, 1976):

— Constrained firms (closer to bankruptcy) choose more risky projects.

• Tirole (2006, in particular exercise 3.15):

— Risky projects are less likely to suffer credit rationing.

2 Identification

• I’m "paid" to be the devil’s advocate.

• 2 alternative hypotheses + 1 proposal.

2.1 Certification

• The assumption in the paper is that the remaining exclusivity period isuncorrelated with investment opportunities or firm quality.

• Similar to Almeida et al. (CFR 2012) who use remaining debt duration.

• What if better drugs/firms receive longer exclusivity periods? Is the exclu-sivity period a certification of quality?

• Even if the Medicare expansion is a surprise to everyone, good drugs/firmswill have longer remaining exclusivity periods on average.

2.2 Real Options

• What if the expansion of Medicare implies a reduction of uncertainty, inparticular for firms with longer exclusivity periods?

• ↓ Uncertainty ⇒ ↑ Investment

• ↓ Uncertainty ⇒ ↑ Risk-taking (?)

2.3 Proposal

• Use only 2-segment firms, where one segment is covered by Medicare andthe other is not (e.g., pediatric drugs).

• Focus on the effect on the other segment (hinted by Table 5)

• Similar strategy as:

— Lamont (JF 1997): non-oil segments in oil-shocked conglomerates

— Peek and Rosengren (AER 2000): U.S. lending of Japanese-related banks

— Froot and O’Connell (1997): earthquake insurance after hurricanes

— Larrain, Sertsios, Urzua (2017): 2-firm business groups

Figure A.8: Distribution of Medicare Drug Life in 2003

010

2030

40P

erce

nt

0 .2 .4 .6 .8 1Drug Life, MMS

All Firms

05

1015

20P

erce

nt

0 .2 .4 .6 .8 1Restricted Drug Life, MMS

Firms with Medicare Drug Life in (0,1)

Notes: Figure A.8 plots the distribution of Medicare Drug Life in 2003. Each observation is a firm in our

main analysis sample.

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3 Kitchen-sink comments

3.1 Innovation process

• Non-monotonicity of the effect (Figure 5): Some innovation, but notcomplete disruption. Why?

— Is similarity non-linear? Our DNA is 96% the same as the monkey, but90% the same as the cat.

— Limited ability to try new combinations. Weitzman (QJE 98)

Figure 5: Impact of Additional Resources on Novelty of Drug Investments

(a) Coefficients

0.0

5.1

.15

.2.2

5Im

pact

of M

edic

are

Dru

g Li

fe o

n Lo

g(1

+ #

Can

dida

tes)

1 2 3 4 5 6 7 8 9 10Similarity to Prior Candidates, Binned

(b) Elasticities

-2-1

01

23

Ela

stic

ity o

f # C

andi

date

s w

.r.t.

Med

icar

e D

rug

Life

1 2 3 4 5 6 7 8 9 10Similarity to Prior Candidates, Binned

Notes: Figure 5 plots the estimated coefficients on Post×Medicare Drug Lifef,2003 from our main regressionspecification defined by (6). Each point represents a different outcome variable: the number of new drug

candidates in a given bin of similarity. Bins are specified by absolute similarity scores: Bin 1, for example,

counts the impact of our treatment on the number of drugs with similarity score between 0 and 0.1, while

Bin 10 is the impact on drugs with similarity between 0.9 and 1.0. The bottom figure reports the estimated

elasticities for drugs in each novelty bin. We note that for a regression of the form log(1 + y) = bx+ e, the

elasticity is given by b× 1+yxy . We evaluate these elasticities at the corresponding means of x and y.

41

• Endogenous differentiation (Hoberg and Phillips, JPE 2016):

— Differentiation is a long process and not only due to cash-flow shocks.

— R&D and marketing before differentiation.

— Correlation with exclusivity periods?

3.2 Capital Structure Implications

• Pharmaceutical firms use very little debt (Table A.2): Leverage 10%.

• What does the Medicare shock imply for k-structure? Take onmore debt? Pecking order vs. trade-off theory.

• Related: Sertsios and Phillips (RFS 2016)

4 Conclusions

• Very nice paper. I learned a lot!

• Small editorial comments:

— Too much important stuff in the appendix.

— I couldn’t understand why the # obs is the same in Tables 4, 5, and A.14.