Monday, April 11, 2022

Methods in Open Policy Analysis: An Application to California's Building Energy Codes

 I have just posted a new working paper, "Methods in Open Policy Analysis: An Application to California's Building Energy Codes."

Abstract: Have building energy codes succeeded in lowering energy consumption, and have their benefits outweighed their costs? Using survey data from the 2000 US Census, I estimate household electricity and natural gas expenditures by decade of home construction, controlling for household and home characteristics, to study the impact of the first decade of California's energy codes. I then use the estimates in a social cost-benefit analysis (CBA) for a representative household. I find homes built in the 1980s used \$35 less in electricity and \$46 less in natural gas, per year, compared to homes built in the 1970s. For the Sacramento region, the energy codes pass a cost-benefit test (the present value of social benefits exceed compliance costs) when the best-case (low-end) policy cost estimates are used, but fail the test with base-case (mid-point) policy costs. This study paves the way for future analysis by clarifying how a CBA for a representative household fits into a comprehensive CBA.

This paper directly builds on my 2021 book Data and the American Dream. In that book, I include end of chapter questions that contain ideas for original research. The empirical model in my new working paper carries out the analysis I suggest in Part d of Question 3 from Chapter 1 (below I paste an image of that question.) Thus the paper is an example of the type of analysis I suggest to readers of the book.

In the paper I also carry out a Cost-Benefit Analysis (CBA) that uses the empirical results. If I would have been able to write this paper sooner, I would have included it in the Conclusion to Data and the American Dream. The title of the conclusion is, "What do we know and What should we do?" In it, I introduce the reader to CBA and argue causal inference teaches us facts about the world, but causal inference is not a decision making science, so to decide what to do we need CBA. Of course, accurate facts are a key input to CBA.

This new working paper is an example of using careful empirical estimates to help decision makers, and the methods it uses are designed to be general so that they could be applied to different states or regions.

Here's Question 3 from Chapter 1 of Data and the American Dream:

No comments:

Post a Comment