Empirical Legal Studies

Med Mal Insurance Premia & Economic Theory

The medical malpractice insurance market is inherently complex and made even more so by the growing number of state efforts to regulate it. Complexities aside, however, standard economic theory should, in the main, explain, to some degree, med mal premia trends. Findings from a recent paper, How Do Insurers Price Medical Malpractice Insurance?, by Bernie Black et al. (Northwestern), however, "are not easily explained by standard economic theory."

Prof. Black et al.'s paper analyzes almost three decades worth of med mal premia data (gathered by the Medical Liability Monitor). Core findings include:

• Med mal premia are only loosely connected to insurer costs, even over long periods;
• Premia are much higher in states with damage caps, even after controlling for costs; and
• The Premium/Cost Ratio varies widely both across states at a given time and within states across time.

Taken together, the paper's findings imply that med mal insurance companies do not fully adjust premia to changes in direct costs even over long time periods as standard economic theory might predict.

Poisson Tutorial Videos

The good folks at Stata recently posted a series of video tutorials focusing on how to fit various Poisson regression models which assist when modeling count data. The various videos take up a Poisson model with: a) continuous predictors (here); b) categorical predictors (here); and c) Poisson models that include both continuous and categorical variables (here).

4th annual Association for Interdisciplinary Metaresearch and Open Science Conference (AIMOS2022) -- Registration

At the request of an ELS colleague, I want to help circulate the following registration information for the 4th annual Association for Interdisciplinary Metaresearch and Open Science Conference (AIMOS2022). Please note that the conference is scheduled for November 28-30, 2022, at the University of Melbourne. AIMOS2022 will be an in-person conference, with support to attend virtually.

As an association, AIMOS aims to make the research process more trustworthy and efficient and to promote the study of how research is done and how it can be improved.

The conference program will also explore other areas of metaresearch including a host of mini-note sessions on:

• Big Team Science
• Credible Science for Decision Making
• Correcting the Scientific Record
• History of Metascience 
• Unpacking Trust in Science
• The Many-Analysts EcoEvo Project
• The Systematizing Confidence in Open Research and Evidence (SCORE) Program.

In addition to invited speaker sessions, submissions are now open for: discussions about norms, practices, and cultures with science and scholarship more broadly; learning and development through practical skills workshops in open science (e.g., R, lab notebooks, pre-registration); and getting things done in hackathons.

Finally, a small number of AU$1,000 travel grants are also available to support in-person attendance.

A Small--But Important--Logistical Matter Incident to Dataset Merging

Those who have merged (or appended or crossed) datasets may have inadvertently bumped into a not uncommon problem--where the two separate datasets share a variable with the identical variable label name. For relatively smaller datasets, and where the researcher is aware of this hurdle ex ante, one can always, by hand, rename variables prior to combining the datasets. For larger datasets with many more variables, however, while this may not be practical it nevertheless remains critical.

A recent discussion of how to manage this nettlesome problem is found here. The comments' leading suggestion references Daniel Klein's -elabel- package, available from SSC (as it involves user-written code). Put briefly, the elable command allows a user to add a suffix (e.g., "_1") to every variable name in "dataset1." An analogous manipulation to datast2 can add another unique suffix (e.g., "_2") to every variable name in dataset2. And so forth. The end result includes datasets with distinct variable label names that can then be combined.

Helpful Statistics Resources

On the better late than never front, I just stumbled across a wonderfully rich "one-stop" resource for statistics novices. Germán Rodríguez (Princeton) hosts a site (here) that includes tutorials for both Stata and R. As to the tutorials' scope, the Stata tutorial, e.g., ranges from a "gentle introduction" to coding examples for customizable tables. Many--especially students--may find this resource helpful.