Wednesday, February 13, 2013

Quickly thrown together thesis for the Gender Macro paper proposal

Not the best writing, but you get the idea of what I'm going after. I'll be using the NSF's SESTAT data:

The science and engineering labor markets have been persistently dominated by men, and although women are making inroads into the life sciences the underutilization of female scientific talent is substantial. New growth theory’s emphasis on endogenously determined research and development as the principle source of economic growth implies that gender disparities in the science and engineering labor market may negatively impact economic growth. This paper will review long-run trends in female higher education across the OECD. It will then estimate R&D production functions for men and women, which will be used to discuss the growth costs of gender disparities. Three sorts of R&D production functions will be considered to triangulate the impact of different policy choices. First, a production function will estimate the expected output of additional female scientists and engineers, with no adjustments in the resources available to those women (i.e., no changes to the provisioning of the average female scientist), and no adjustments in the culture of science (i.e., no adjustment for the assumption that male and female R&D production functions are equal in the absence of gender norms). The second production function will explore the expected output of additional female scientists and engineers conditional on the availability of resources available to the typical male (i.e. – provisioning women with the educational backgrounds, grant resources, etc. that would make them comparable to male scientists). The third approach will estimate expected output of additional female scientists if their R&D production function is identical to that of men. Tying these gendered R&D production functions to economic growth is difficult and will only be discussed generally. For example, it is not clear how much an additional patent or scientific article will boost growth. But this analysis will provide the basis for a discussion of the importance of gender disparities in a new growth theory model.


  1. Uh.... intriguing stuff, I'm sure, But what is it about? Splitting up 2-3 of the entire economy between handfuls of well educated men and women. We could use laws and customs and armed troops to remove all women from the R&D business -- or all men, or all people above or below the age of 30 -- and the overall impact on the economy would be minimal.

    You know what strikes me as the best way to increase "fairness" in the science and engineering fields? A relatively small boost in federal spending that would increase R&D to say 3.5 % of GNP. This would probably have very large -- positive -- effects on the growth of the economy while bettering the lives of both men and women who wished to enter technical careers. Is there some reason why virtually no modern economist can consider the possibility?

  2. I don't mean to be rude or to go off-topic, but...

    Have you ever considered using the techniques of non-parametric statistics in your research, Daniel Kuehn? Non-parametric statistics would go a long way in resolving problems with econometric studies...or at least that's what I've been told.

    1. I used some non-parametric methods for a paper last semester.

      Exactly what do you have in mind here?

    2. What class was that paper for? Are you going to have it published in a journal?

      And what I have in mind is what one typically means by the term non-parametric statistics...

    3. Econometrics - yes I'm going to try to submit it.

      I know what non-parametric statistics are. What were you thinking of in light of this paper? "Non-parametric statistics" is a pretty broad term.

    4. With regard to your gender economics dissertation chapter?

      I simply meant techniques that take into account discontinuity and short-run or long-run dependence/persistence. Nothing more or less.

      Will you use non-parametric approaches in the quantitative aspect of this dissertation chapter?

    5. I'm not doing a dissertation chapter on gender, I'm guessing.

      I'm still not sure what you you think is worth doing here. I think of non-parametric statistics as work that doesn't impose a specific functional form on the model. I'm not quite sure what discontinuity or dependence/persistence have to do with it.

      In my chapter on the job creation tax credit that I'm anticipating, I'm sure I'll use non-parametric methods in that sense. I actually think they're weaker than parametric methods (in the context of the RDD model I'm doing - certainly not elsewhere), but I'm sort of obligated to do it because everybody uses non-parametric RDD methods.

      If you have something specific in mind here I'm all ears, but I am not really following what you are thinking of.

    6. *face-palm*

      So you're not doing a dissertation chapter on gender economics. Never mind.

      Let me try to be clearer.

      Discontinuity and dependence/persistence are properties that can be found in economic data. Non-parametric statistics are also used because they have greater "robustness" than parametric methods. If discontinuity and dependence/persistence are found in the data, then you have to use something to accomodate those properties.

      You can't just assume a log-normal distribution *without* a goodness-of-fit test, then just run a regression. That's what a lot of econometricians have done.

      What I meant by "non-parametric statistics" is the first definition found in the Wikipedia article. Not the second with regard to structures. I was suggesting that you use non-parametric statistics in the context of this study with regard to gender economics because I think it might be more useful and insightful to use non-parametric approaches.


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