Political Winds, Financing Constraints and Pharmaceutical Innovation

Joshua Linn (UIC)

and

Robert Kaestner (UIC and NBER)

November 9, 2007

Presentation for the DIME Workshop on Finance, Innovation and Inequality

Public Policy and Pharmaceutical Prices

• Recent policy debates in the US– Price controls, Canadian imports– Medicare Part D– Should the government negotiate prices?– Common theme: prices are too high

• What are the welfare effects?– Static effect on consumer surplus (holding fixed available drugs)– Dynamic effect (innovation)– Tradeoff between consumer surplus today and tomorrow

Profits, Policy and Pharmaceutical Innovation

• Recent empirical evidence that innovation responds to profit opportunities– Acemoglu and Linn (2004), Finkelstein (2004) and Qian (2007)– Relative profit opportunities (potential market size, regulation or

patent protection) drive innovation

• Prices should create similar incentives– The evidence suggests a large effect, but it is difficult to

calculate directly– Magnitude is important for policy questions

Empirical Evidence on Prices and Innovation

• Separately estimate different stages from prices to innovation– Prices → R&D → new drugs → consumer surplus– Golec and Vernon (2005), Lichtenberg (2002)– Alternative: structural model of supply and demand

• Conclusion– Hughes, Moore and Snyder (2002) and Santerre and Vernon

(2006)– Dynamic effect is much larger

• Difficulties– Estimates combined from many studies– Identification– Interpretation

Existing Empirical Evidence

• Identification usually relies on time series or cross-country evidence– Correlation between prices and R&D– Variables move slowly over time – Other differences between countries?

• What causes the relationship between prices and R&D?– Expected profitability or financing constraints? (Scherer, 2001)– Are current prices correlated with future prices?– Does an increase in prices allow firms to invest more in

developing new drugs?

• Different policy implications for raising drug prices in countries with less developed financial markets

Open Questions

• How do pharmaceutical prices affect innovation?– Would price regulation affect the types of new drugs?– How does the dynamic effect compare with static effect?– Expected prices vs. financing constraints

• Our approach– Focus on firms’ asset values– A firm’s assets include marketed and pre-market drugs– How does a price decrease affect the values of both types of

assets?– How strong is the effect of prices on profit incentives?– Separate financing constraints from other effects

Policy Context

• Medicare Prescription Drug, Improvement, and Modernization Act (MMA) passes in 2003– Subsidizes prescription drug insurance for the elderly– Passes narrowly (in House) after many years of debate, and

along party lines

• Should the government negotiate prices?– Rangel-Dingell Amendment would have required government to

negotiate prices– Amendment would have resulted in lower prescription drug

prices, but fails

• Midterm election (2006)– Before election, Democratic position was well known– Democratic victory was unexpected (Iowa Electronic Market)– Large change in expected drug prices

A Closer Look at Financing Constraints

• Evidence from the pharmaceutical industry– External cost of capital is greater than the internal cost – Lerner, Shane and Tsai (2003) and Metrick and Nicholson (2006)

• Value of a project depends on expected price and available cash– Asymmetric information or agency problems create wedge– Invest available cash and external funds– The firm is constrained, so an increase in cash raises investment

• Two effects of a price decrease on the value of a pre-market drug– Cost of marketing the drug is higher if less cash is available– Potential revenue from the drug also decreases if expected output

price is lower

Empirical Strategy

• Election causes a discrete change in asset values– Age profile varies widely across therapeutic categories

(antibiotics vs. cardiovascular)– Price negotiation (mainly) affects drugs used by elderly– Value of marketed drugs declines because expected revenue

falls– Value of pre-market drugs (pipeline) also falls, because of

revenue and financing effect

• Effect of election on asset values varies across firms– Change in value of marketed drugs proportional to sales of

drugs to elderly– Average revenue effect for pre-market drugs proportional to

number of drugs in elderly therapeutic classes (e.g., cardiovascular)

– Firms with large decrease in cash flow should experience large decline in average value of pre-market drugs

Empirical Strategy (cont.)

• Link abnormal returns to firm assets– Estimate abnormal returns over seven days following election– Construct measures of elderly drug sales and R&D capital stock– Relate abnormal returns to asset measures and interaction

(financing effect)– Assume changes in asset values uncorrelated with unobserved

variables

• Interpretation of results– Discrete change in expected prices– Distinguish financing effect from price effect– How valuable is the average pre-market drug?

Post-Election Stock Returns

• Compute abnormal returns for two months surrounding election– Construct value-weighted index of two samples of firms– Firms with prescription drug sales to the elderly (2004 MEPS)– Large pharmaceutical and biotech firms with zero elderly drug

sales

• Decrease in stock prices for pharmaceutical firms with elderly drug sales (Figure 1)– Large, statistically significant decrease for first sample– No change for firms without elderly drug sales

• Change in stock price negatively correlated with elderly drugs sales (Figure 2)

Figure 1: Cumulative Abnormal Returns 10/1/06 - 11/30/06

-0.05

-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

0.04

10/2/2006 10/12/2006 10/22/2006 11/1/2006 11/11/2006 11/21/2006

Date

CA

R (

pe

rce

nt)

Firms with Elderly Sales Other Firms

Figure 2: Cumulative Abnormal Returns vs Elderly Sales

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Ratio of Elderly Sales to Market Cap

CA

R (

pe

rce

nt)

Estimation Results

• Estimating equation:

• Relate cumulative abnormal returns (CAR) to firm assets:– Dependent variable is post-election CAR– is firm’s sales to elderly (65+) (from MEPS)– RDi is firm’s capitalized R&D (from Compustat)– Final variable is the interaction of elderly drug sales and R&D

• Interpretation:– β1 is percent change in elderly drug sales– β2 is percent change in value of capitalized R&D– β3 is the financing effect: do firms with a large decline in elderly

drug sales also experience a large decline in the value of their R&D stock?

eiM

ieiii

eii MRDRDMCAR 3210

(1) (2) (3)

-0.027 -0.024 -0.037(0.012) (0.013) (0.015)

-0.017 -0.002(0.013) (0.022)

-0.231(0.186)

Number of Observations

20 20 20

R2 0.09 0.12 0.19

Table 1

Effect of the 2006 Election on Asset Values

Dependent Variable: Cumulative Abnormal Return

Elderly Prescription Drug Sales

Capitalized R&D

Elderly x R&D

Conclusions

• How does prescription drug price regulation affect innovation?– Expect innovation to follow profit opportunities– Regulation affects revenue from a potential innovation and cash

available for investment

• Focus on stock prices– 2006 election provides a discrete change in expected profitability– Stock prices immediately incorporate change in profitability– Disentangle two effects on return to innovation

• Evidence that regulation affects revenue of current and future drugs, and that financing effect is important