One of the things that frustrates me to no end about accusations of "scientism" and "physics envy" is that they are always hopelessly vague and wishy washy. What precisely do you have concerns about? It's rarely made explicit because in my opinion the critique is rarely carefully considered by those making it. Constrained optimization is occassionally cited specifically for censure. It's a convenient case because Samuelson explicitly mentioned thermodynamics in his early work on constrained optimization, which painted a bulls-eye on him for people who were more interested in painting him as a physics-envier than they were in good economic science.
Outside of that, few specific critiques of why a technique is appropriate or not are offered.
I thought of that this morning when I was looking at this new NBER working paper titled "Quantile Regression with Censoring and Endogeneity". This is precisely the sort of thing a lot of the "scientism/physics envy" people would talk about: fancy math for fancy math's sake according to them. That's really unfortunate. Here's a summary of what this paper does:
"In this paper, we develop a new censored quantile instrumental variable (CQIV) estimator and describe its properties and computation. The CQIV estimator combines Powell (1986) censored quantile regression (CQR) to deal semiparametrically with censoring, with a control variable approach to incorporate endogenous regressors. The CQIV estimator is obtained in two stages that are nonadditive in the unobservables. The first stage estimates a nonadditive model with infinite dimensional parameters for the control variable, such as a quantile or distribution regression model. The second stage estimates a nonadditive censored quantile regression model for the response variable of interest, including the estimated control variable to deal with endogeneity. For computation, we extend the algorithm for CQR developed by Chernozhukov and Hong (2002) to incorporate the estimation of the control variable. We give generic regularity conditions for asymptotic normality of the CQIV estimator and for the validity of resampling methods to approximate its asymptotic distribution. We verify these conditions for quantile and distribution regression estimation of the control variable. We illustrate the computation and applicability of the CQIV estimator with numerical examples and an empirical application on estimation of Engel curves for alcohol."
I wish they came out with this five years ago, because this actually sounds like a very useful technique. We could have made good use of this in an evaluation of child welfare reform that I helped perform for the Department of Health and Human Services a couple years back (the evaluation itself is still working its way through the DHHS bureaucracy, but background is available online and we've presented preliminary results). We used quantile regressions for the analysis, but one thing we didn't have was an IV version of a quantile regression. This would have helped tremendously because we were investigating a series of highly endogenous child welfare outcomes - but we did have a quasi-random treatment which would have worked with the CQIV presented here. Some of our variables were also censored because of institutional time constraints within the child welfare system. A quantile regression approach that deals with both censoring and endogeneity in such a clean way is great stuff.
The trouble is, if you open up that working paper you see a lot of math. I would have had to spend a fair amount of time sitting down and reading it very closely to understand the gist of it. Then I'd probably have to take the time of walking down the hall to Doug Wissoker's office - a senior econometrician here, whose door I knock on often - and humbly profess my remaining ignorance and ask him to help me. And then he'd have to sit and look at for a while to figure out what they were getting that. And then we'd also share what we eventually did with it in meticulous detail to our technical working group and then they'd make sure we were doing something reasonable. It would have been quite a process - but it would have considerably improved our understanding of how abused and neglected children fare in the child welfare system in this country. And that is a very good thing.
These are the sorts of papers, though, that often just get dismissed as "scientism" with no details on exactly what is wrong with investing the time to learn and use it. That bothers me a great deal.