Robust economic modeling offers guidance for policymakers

For two decades voluntary pollution abatement programs have shifted in and out of popularity with national — popular in the George W. Bush administration, not so much under President Obama — state and even international policymakers.

The programs are supposed to coax rather than compel businesses, industries and in some cases individuals to reduce, for example, airborne emissions, agricultural runoff or the use of toxic chemicals with incentives ranging from expert advice and public recognition to market advantages and easing of regulations.

Despite considerable experience with them, however, questions remain about how effective the voluntary programs are, and under what circumstances. Michael Delgado, a Purdue assistant professor of agricultural economics, is using statistical models and DiaGrid to generate answers that could help guide policymakers in the future.

"Recently, environmental issues are at the forefront of policy at the state level, the national level, the global level," Delgado says. "I think it's a great environment to apply econometric models to get a really rich set of results."

Delgado also uses modeling to look at questions involving economic development, for instance the effects of education on growth rates in developing countries, and his research has involved issues such as consumer demand for hybrid cars as well.

His models tend to be nonparametric and semiparametric. That means they make fewer assumptions than parametric models, in which assumptions are used to simplify problems, and demand more computational muscle for timely solutions. The payoff is more robust results. He also employs computationally demanding nonlinear optimization problems to set parameter values for his models.

"The standard approach with a lot of simplifying assumptions can be run on any laptop or any desktop," Delgado says. "What I do can take a standard computer weeks. Even if you want to do it that way, maybe you can run one model. But to get a robust set of results we want to run a model multiple times, or we want to change some parameter values and run it a little bit differently and we want to do that multiple times."

DiaGrid can make thousands of processors available, and SubmitR, a Web-based interface for the R statistical computing environment available on the DiaGrid hub, makes it easier to get jobs running.

"SubmitR is particularly suited for running large simulations that require thousands of independent computations, at least for my purposes," Delgado says.

Dr. Michael Delgado
Assistant Professor of Agricultural Economics
Purdue University