Podcast
Questions and Answers
According to the provided text, what is the relationship between market size and entry for generic drugs?
According to the provided text, what is the relationship between market size and entry for generic drugs?
- Generic drug entry is equally responsive to changes in market size as non-generic drug entry.
- Generic drug entry is less responsive to changes in market size than non-generic drug entry.
- Generic drug entry is not responsive to changes in market size.
- Generic drug entry is more responsive to changes in market size than non-generic drug entry. (correct)
In the context of the model, what is used as a proxy for market size, mj(t)?
In the context of the model, what is used as a proxy for market size, mj(t)?
- The number of clinical trials for drug category c at time t.
- The number of patents for new drugs in category c.
- Actual market size data for drug line j.
- Potential market size driven by demographic changes, Mct. (correct)
What does the provided equation log nj(t) = constant + log δj + log mj(t) represent?
What does the provided equation log nj(t) = constant + log δj + log mj(t) represent?
- The relationship between the number of clinical trials and drug approvals.
- The relationship between generic and non-generic drug market sizes.
- The relationship between new drug entry and market size when r approaches zero. (correct)
- The relationship between patents and drug innovation rates.
Which of the following is NOT a direct proxy for innovation rates?
Which of the following is NOT a direct proxy for innovation rates?
What is a key distinction the study intends to investigate regarding the relationship between market size and drug entry?
What is a key distinction the study intends to investigate regarding the relationship between market size and drug entry?
Approximately how many non-generic drugs were approved compared to generic drugs in the dataset from 1970-2000?
Approximately how many non-generic drugs were approved compared to generic drugs in the dataset from 1970-2000?
How were the 34 drug approval categories grouped for analysis?
How were the 34 drug approval categories grouped for analysis?
What trend was observed when comparing the income share and drug approvals for the 0-20 age group?
What trend was observed when comparing the income share and drug approvals for the 0-20 age group?
What is the stated reason for fluctuations in the total number of drug approvals?
What is the stated reason for fluctuations in the total number of drug approvals?
What happened to the drug approval process for generics in the early 1990s?
What happened to the drug approval process for generics in the early 1990s?
What was the implication of finding positive association between contemporaneous changes in population share and changes in drug approvals for corresponding age groups?
What was the implication of finding positive association between contemporaneous changes in population share and changes in drug approvals for corresponding age groups?
What statistical methods were used for analysis, according to the passage?
What statistical methods were used for analysis, according to the passage?
What does the text suggest about the drug entry and income shares for the 30-50 age group?
What does the text suggest about the drug entry and income shares for the 30-50 age group?
According to the model, what condition must be met for steady-state equilibrium regarding R&D level?
According to the model, what condition must be met for steady-state equilibrium regarding R&D level?
In the steady state, which of the following factors would lead to an increase in R&D for a given drug line?
In the steady state, which of the following factors would lead to an increase in R&D for a given drug line?
In this extended model, how does the equilibrium behavior of R&D for a specific drug line relate to other drug lines?
In this extended model, how does the equilibrium behavior of R&D for a specific drug line relate to other drug lines?
What conclusion can be drawn about the dynamics of $z_j(t)$ based on the provided analysis?
What conclusion can be drawn about the dynamics of $z_j(t)$ based on the provided analysis?
Consider a scenario where a future increase in market size $Y_j$ is announced at time $t_0$ for a future date $t̂ > t_0$. According to the model, what happens to $z_j(t)$ at $t_0$?
Consider a scenario where a future increase in market size $Y_j$ is announced at time $t_0$ for a future date $t̂ > t_0$. According to the model, what happens to $z_j(t)$ at $t_0$?
If a future market size is expected to increase, what occurs with the value $V_j(t|q_j)$ before the change in market size is realized?
If a future market size is expected to increase, what occurs with the value $V_j(t|q_j)$ before the change in market size is realized?
What is the implication of the fact that the right hand side of equation (15) is strictly increasing in $z_j(t)$ at $z_j(t)=z^S_j$?
What is the implication of the fact that the right hand side of equation (15) is strictly increasing in $z_j(t)$ at $z_j(t)=z^S_j$?
In the provided model, what does the term $M_{ct}$ represent?
In the provided model, what does the term $M_{ct}$ represent?
In the given model when it is said that there are no transitional dynamics, what does this imply regarding R&D?
In the given model when it is said that there are no transitional dynamics, what does this imply regarding R&D?
What is the primary reason for using the logarithm of $N_{ct}$ as the dependent variable?
What is the primary reason for using the logarithm of $N_{ct}$ as the dependent variable?
Why is the original model (equation 23) problematic when $N_{ct}$ equals 0?
Why is the original model (equation 23) problematic when $N_{ct}$ equals 0?
In equation (24), how is $Ñ_{ct}$ transformed for cases where $N_{ct}$ = 0?
In equation (24), how is $Ñ_{ct}$ transformed for cases where $N_{ct}$ = 0?
What is the potential drawback of introducing the dummy variable $d_{ct}$ in equation (24)?
What is the potential drawback of introducing the dummy variable $d_{ct}$ in equation (24)?
According to the provided equations, what is the impact on $z_j(t)$ if $\delta_j$ increases, assuming that Assumption (19) holds?
According to the provided equations, what is the impact on $z_j(t)$ if $\delta_j$ increases, assuming that Assumption (19) holds?
Based on the model, what happens to R&D, represented by $z_j(t)$, if Assumption (19) is not valid?
Based on the model, what happens to R&D, represented by $z_j(t)$, if Assumption (19) is not valid?
According to the model, the entry rate of non-generics, $n_j(t)$, is influenced by which of the following factors?
According to the model, the entry rate of non-generics, $n_j(t)$, is influenced by which of the following factors?
What does the model predict about the relationship between market size and the entry rates of both non-generics, $n_j(t)$, and generics, $g_j(t)$?
What does the model predict about the relationship between market size and the entry rates of both non-generics, $n_j(t)$, and generics, $g_j(t)$?
In the context of the model, if $\mu = 0$, what is the state of the equilibrium?
In the context of the model, if $\mu = 0$, what is the state of the equilibrium?
According to the model, what factor determines whether the entry of generics or non-generics will respond more to changes in market size, $Y_j(t)$?
According to the model, what factor determines whether the entry of generics or non-generics will respond more to changes in market size, $Y_j(t)$?
What happens to the limiting value of $h_j(t)$, which represents a measure of product quality, as $t$ approaches infinity, given that there is no R&D?
What happens to the limiting value of $h_j(t)$, which represents a measure of product quality, as $t$ approaches infinity, given that there is no R&D?
In the model, what is the relationship between the entry rate of non-generics, $n_j(t)$, and the parameter $\delta_j$?
In the model, what is the relationship between the entry rate of non-generics, $n_j(t)$, and the parameter $\delta_j$?
What does the use of maximum likelihood standard error compared to the robust standard error indicate in the context of the given study?
What does the use of maximum likelihood standard error compared to the robust standard error indicate in the context of the given study?
How does using income and expenditure to compute market size affect estimates, compared to estimates based on a different methodology?
How does using income and expenditure to compute market size affect estimates, compared to estimates based on a different methodology?
What is the primary distinction between panels A and B in Table 3B, regarding estimation procedures?
What is the primary distinction between panels A and B in Table 3B, regarding estimation procedures?
What does the negative binomial model account for that the standard Poisson model does not?
What does the negative binomial model account for that the standard Poisson model does not?
What effect does using demographic information from multiple regions have on the study's market size measure?
What effect does using demographic information from multiple regions have on the study's market size measure?
If a potential market size measure based on the NAMCS data produces similar weighted and unweighted results, what does this suggest?
If a potential market size measure based on the NAMCS data produces similar weighted and unweighted results, what does this suggest?
In the context provided, what does the term 'overdispersion' refer to?
In the context provided, what does the term 'overdispersion' refer to?
What is the main advantage of using a robust standard error over maximum likelihood standard error?
What is the main advantage of using a robust standard error over maximum likelihood standard error?
Flashcards
Market Size Responsiveness
Market Size Responsiveness
In economic models, the responsiveness of a variable (like new drug entry) to changes in market size. It measures how much the variable changes for every unit change in market size.
Potential Market Size
Potential Market Size
A measure of the size of a potential market for drugs, considering factors like population growth and aging, rather than actual sales data.
Logarithmic Model of Drug Entry
Logarithmic Model of Drug Entry
Using the logarithm of drug entry (new drug approvals) to estimate the relationship between market size and drug entry.
New Drug Approval Process
New Drug Approval Process
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Drug Entry Rate
Drug Entry Rate
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Steady-State R&D Level (zjS)
Steady-State R&D Level (zjS)
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Factors affecting Steady-State R&D Level
Factors affecting Steady-State R&D Level
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Independence of Drug Line R&D
Independence of Drug Line R&D
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Instability of Equilibrium and Instantaneous Adjustment
Instability of Equilibrium and Instantaneous Adjustment
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Anticipated Future Changes and R&D
Anticipated Future Changes and R&D
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Gradual Increase in R&D with Anticipated Changes
Gradual Increase in R&D with Anticipated Changes
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Equilibrium in the pharmaceutical market model
Equilibrium in the pharmaceutical market model
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Entry rate of non-generics (nj(t))
Entry rate of non-generics (nj(t))
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Entry rate of generics (gj(t))
Entry rate of generics (gj(t))
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Positive relationship between entry rates and market size
Positive relationship between entry rates and market size
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Non-generic entry rate's independence from generic entry cost (δj)
Non-generic entry rate's independence from generic entry cost (δj)
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Zero profit condition for generics
Zero profit condition for generics
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Asymmetric response of non-generics and generics to market size changes
Asymmetric response of non-generics and generics to market size changes
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Importance of positive innovation cost (µ) for equilibrium existence
Importance of positive innovation cost (µ) for equilibrium existence
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New Drug Entry (Nct)
New Drug Entry (Nct)
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Potential Market Size (Mct)
Potential Market Size (Mct)
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Control Variables (Xct0)
Control Variables (Xct0)
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No Approval Dummy (dct)
No Approval Dummy (dct)
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Relationship between market size and drug entry
Relationship between market size and drug entry
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Steady-State R&D Level
Steady-State R&D Level
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Poisson Model
Poisson Model
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Negative Binomial Model
Negative Binomial Model
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Non-Linear Least-Squares (NLLS)
Non-Linear Least-Squares (NLLS)
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Study Notes
Market Size in Innovation: Theory and Evidence from the Pharmaceutical Industry
- This paper examines the effect of market size on new drug entry and pharmaceutical innovation.
- The study uses U.S. demographic trends to analyze exogenous market size changes.
- A 1% increase in potential market size for a drug category correlated with a 4-6% increase in new drugs within that category.
- The increase stems from both generic drug entry and new, non-generic drug entry.
- The findings remain robust after controlling for various non-profit factors, pre-existing trends, and health care coverage changes.
Theory
- A basic model links innovation rates to current and future market size.
- Profit incentives and market size drive innovation, especially in the pharmaceutical industry.
- Profit driven models and induced/directed technical change models consider profit incentives, market size, and specific innovations.
- The study uses demographic trends as exogenous market size variations to overcome endogeneity issues inherent in other studies.
- The analysis considers potential delays in drug development and approval processes.
- This results suggest delays have no substantial effect but do affect calculations of the timing of R&D and patenting.
Empirical Strategy
- The paper measures entry and innovation using FDA drug approvals.
- A 1% increase in potential market size is correlated with an increase in new drug entry by 4-6%.
- The robustness of the findings is reinforced by checking against lagged drug approvals, trends in biotechnology, insurance coverage, and economic incentives.
- The study investigates whether current, past, or future market sizes have more impact on drug entry rates; current and short-term future market sizes yielded the strongest results.
- The paper explores the relationship between market size and drug patents.
- The analysis incorporates alternative measures of market size, including those based on demographic changes and health insurance coverage.
Generics and Non-Generics
- The analysis extends to distinguish between generic and non-generic drug entry.
- Introducing a new generic drug correlates with cost savings and improved availability, impacting market share and revenues.
- Generic drug entry into an existing market is easier than introducing a fundamentally new drug, with lower required resources.
- The study investigates whether different responses to market size changes exist between different types of drugs, and concludes that generics have a stronger response than non-generics.
Delays in Development
- Delays in drug development and approval process are briefly addressed to mitigate potential biases.
- The model accounts for delays using differential equations and shows that the response to market size is not significantly affected by delays.
- Anticipation effects on drug entry are explored in a more general setting.
Supply-Side Determinants
- The robustness of the findings is examined by controlling for non-profit factors, scientific incentives, and government funding (e.g., CRISP data).
- No significant effects from any of the tested factors are observed on market size impacts.
Patents
- The paper also examines the effect of market size on patents.
- The analysis shows that the relationship between patents and market size is weaker and less statistically significant due to potential noise from various factors.
- Evidence suggests that drug approvals are affected by past and anticipated market size, yet the results are less significant than other considerations.
Conclusion
- The study finds a substantial and robust positive correlation between market size and new drug approval rates, both for generics and non-generics.
- The response to market size changes is largely independent of factors like pre-existing trends in R&D, scientific funding sources, and health-related factors.
- The results have implications for endogenous growth, directed innovations, and health related profit-based innovation incentives.
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Description
This study explores the relationship between market size and pharmaceutical innovation, particularly focusing on new drug entries. It reveals that larger potential markets significantly boost the number of new drugs, emphasizing the role of profit incentives. The analysis includes demographic trends to understand variations in market size affecting innovation rates.
