Did someone say "research" and "patents" in the same sentence? :-)
I was going to rifle through my vast collection of references to studies about patents, but I just came across a pretty good review of the research of economics of patents:
"Recent Research on the Economics of Patents" - Bronwyn H. Hall, Dietmar Harhoff (Google for PDF)
I'm still going through it, but it will give you an idea of what the current research looks like.
Here's the deal: Whether patents promote innovation, and whether their benefits outweigh the harms, are extremely difficult questions to answer. I mean, how do you even measure "innovation"? You could say by "counting patents", but that's simply a circular argument! Not to mention the complaints that many patents are low quality, or that companies like Twitter are regularly called "innovative".
So in absence of any direct indicators, what the vast majority of studies do is roughly this:
1. Pick a few metrics that act as proxies for whatever they measure (innovation, patent quality, economic benefit, economic harm, productivity, R&D efforts, etc.);
2. Gather data from which these metrics can be gleaned, typically constrained along many dimensions such as time, industry sectors, sources, etc. to make gathering it feasible. (Sometimes this step is actually optional and author outright just run simulations on what they think are "reasonable" approximations of data. Sometimes they don't even do that. In the paper above, they're referred to as "Theoretical Evidence");
3. Present a hypothesis;
4. Construct a model;
5. And test the model to see if their hypotheses stand up to the data or not.
And at each step they provide varying degrees of explanations of their methodologies, assumptions, controls, potential confounding factors, flaws in their data, and so on.
As you can imagine, it is rather difficult to make solid, generalizable conclusions. For one, the metrics may be pretty poor approximations. Or the model may be poor. Or the hypothesis may be flawed - an unfortunate problem with many studies is that they do not take into account changes in the legal environment (such as the changes wrought by the AIA and decisions like Medimmune v. Genentech) so their very premises are flawed.
Fortunately, many studies are all about finding flaws in other previous studies, so there's some semblance of balance.
Really, go through the paper above and the papers it cites, and you'll see why it's downright impossible to make broad assertions like "patents promote innovation" or "patents harm innovation".
I was going to rifle through my vast collection of references to studies about patents, but I just came across a pretty good review of the research of economics of patents:
"Recent Research on the Economics of Patents" - Bronwyn H. Hall, Dietmar Harhoff (Google for PDF)
I'm still going through it, but it will give you an idea of what the current research looks like.
Here's the deal: Whether patents promote innovation, and whether their benefits outweigh the harms, are extremely difficult questions to answer. I mean, how do you even measure "innovation"? You could say by "counting patents", but that's simply a circular argument! Not to mention the complaints that many patents are low quality, or that companies like Twitter are regularly called "innovative".
So in absence of any direct indicators, what the vast majority of studies do is roughly this:
1. Pick a few metrics that act as proxies for whatever they measure (innovation, patent quality, economic benefit, economic harm, productivity, R&D efforts, etc.);
2. Gather data from which these metrics can be gleaned, typically constrained along many dimensions such as time, industry sectors, sources, etc. to make gathering it feasible. (Sometimes this step is actually optional and author outright just run simulations on what they think are "reasonable" approximations of data. Sometimes they don't even do that. In the paper above, they're referred to as "Theoretical Evidence");
3. Present a hypothesis;
4. Construct a model;
5. And test the model to see if their hypotheses stand up to the data or not.
And at each step they provide varying degrees of explanations of their methodologies, assumptions, controls, potential confounding factors, flaws in their data, and so on.
As you can imagine, it is rather difficult to make solid, generalizable conclusions. For one, the metrics may be pretty poor approximations. Or the model may be poor. Or the hypothesis may be flawed - an unfortunate problem with many studies is that they do not take into account changes in the legal environment (such as the changes wrought by the AIA and decisions like Medimmune v. Genentech) so their very premises are flawed.
Fortunately, many studies are all about finding flaws in other previous studies, so there's some semblance of balance.
Really, go through the paper above and the papers it cites, and you'll see why it's downright impossible to make broad assertions like "patents promote innovation" or "patents harm innovation".