Technological Regimes and Demand Structure in the Evolution of the Pharmaceutical Industry

Abstract

This paper examines how the nature of the technological regime governing innovative activities and the structure of demand interact in determining market structure, with specific reference to the pharmaceutical industry. The key question concerns the observation that—despite high degrees of R&D and marketing-intensity—concentration has been consistently low during the whole evolution of the industry. Standard explanations of this phenomenon refer to the random nature of the innovative process, the patterns of imitation, and the fragmented nature of the market into multiple, independent submarkets. We delve deeper into this issue by using an improved version of our previous “history-friendly” model of the evolution of pharmaceuticals. Thus, we explore the way in which changes in the technological regime and/or in the structure of demand may generate or not substantially higher degrees of concentration. The main results are that, while technological regimes remain fundamental determinants of the patterns of innovation, the demand structure plays a crucial role in preventing the emergence of concentration through a partially endogenous process of discovery of new submarkets. However, it is not simply market fragmentation as such that produces this result, but rather the entity of the “prize” that innovators can gain relative to the overall size of the market. Further, the model shows that emerging industry leaders are innovative early entrants in large submarkets.

Reprinted from Journal of Evolutionary Economics 22(4), 677-709, Springer (2012)

Appendices

Appendix 1: Parameters and variables reported in the text

f :

index for firms

t :

index for time

TC :

index for therapeutic categories

General model parameters

${\it nF = 50}$ :

Initial number of possible entrants (firms)

n = 200:

Number of TCs

time = 100:

Periods of simulation

Exogenous industry characteristics

a = U(0.5,0.6):

Exponent of product quality (PQ)

b = U(0.15,0.20):

Exponent of inverse of price 1/Price_j,t

c = U(0.35,0.4):

Exponent of launch marketing expenditures M

${\it eA} = 0.01$ :

Erosion coefficient of launch marketing expenditure

${\it Mol}_{TC} = 400$ :

Number of molecules per TC

${\it PD} = 20$ :

Patent duration

${\it PW}= 5$ :

Patent width

ϕ = 0.97:

Probability of drawing a zero-quality molecule

${\it Pat}_{TC}\sim N(\mu _{p},\sigma_{p})$ :

Number of patients per TC

μ _p  = 600:

Mean of normal distribution of number of patients per TC

σ _p  = 200:

Standard deviation of normal distribution of the number of patients per TC

Q~N(μ _Q ,σ _Q ):

Quality of the molecule

μ _Q :

Mean of normal distribution of positive quality molecules

σ _Q :

Standard deviation of normal distribution of positive quality molecules

ν _Q  = 30:

Minimum quality of the product to be sold on the market

ε = 1.5:

Price sensitivity of demand

Endogenous industry characteristic

H _TC :

Average Herfindahl index in submarkets (TCs)

H :

Herfindahl index in the overall market

Exogenous firm characteristics

B _start  = 4500:

Starting budget given to each entrant

h = U[0.25, 0.75]:

Firm’s strategy

ω = U(0.05, 0.15):

Firm’s share of budget dedicated to search

C _s  = 20:

Firm’s cost of draw new molecules

x = 7:

blank periods of search that leads to exit the market

χ = 0.4 %:

lower bound to exit the market

Endogenous firm characteristics

B _D,t :

Budget dedicated to development of products at time t

B _M,t :

Budget dedicated to marketing of products at time t

B _S,t :

Budget dedicated to search of molecules at time t

X _t :

Number of draws of a firm f at time t

${\it Pr}_{t}$ :

Number of products belonging to firm f at time t

M _t :

marketing expenditure at time t

${\it Price}_{j,t}$ :

Price of drug j at time t

Appendix 2: Robustness of results

We check the robustness of our results with a Monte Carlo exercise for different degrees of fragmentation of the market: TC = 1, 10 and 200. For each of these three cases, we draw 100 different parameterizations of the model from a uniform multinomial distribution. Each marginal distribution of the multinomial is the value of the parameter i for the parameterization n, where i is between 1 and 8, and n between 1 and 100. Table 2 reports the parameters of the robustness check. We exclude the parameters that are the center of our analysis in order to isolate the effects of the i.

Table 2 Parameters’ values of the robustness check

Robustness check is successful (Figs. 15 and 16). In the three baseline cases (TC = 1, 10 and 200), the effect of market fragmentation on H _TC and H is confirmed, according to the analyses in the text, even applying the random parameterization of the model.

Fig. 15

Robustness check: average H _TC index

Fig. 16

Robustness check: overall H