In June, John Dawson and John Seater, economists at Appalachian State University and North Carolina State University, respectively, published a potentially important study (ungated version here) in the Journal of Economic Growth that shows the effects of regulatory accumulation on the US economy. Several others have already summarized the study’s results (two examples here and here) with respect to how the accumulation of federal regulation caused substantial reductions in the growth rate of GDP. So, while the results are important, I won’t dwell on them here. The short summary is this: using a new measure of federal regulation in an endogenous growth model, Dawson and Seater find that, on average, federal regulation reduced economic growth in the US by about 2% annually in the period from 1949 to 2005. Considering that economic growth is an exponential process, an average reduction of 2% over 57 years makes a big difference. A relevant excerpt tells just how big of a difference:

We can convert the reduction in output caused by regulation to more tangible terms by computing the dollar value of the loss involved. […] In 2011, nominal GDP was $15.1 trillion. Had regulation remained at its 1949 level, current GDP would have been about $53.9 trillion, an increase of $38.8 trillion. With about 140 million households and 300 million people, an annual loss of $38.8 trillion converts to about $277,100 per household and $129,300 per person.

These are large numbers, but in fact they aren’t much different from what a bevy of previous studies have found about the effects of regulation. The key differences between this study and most previous studies are the method of measuring regulation and the model used to estimate regulation’s effect on economic growth and total factor productivity.

In a multi-part series, I will focus on the tools that allowed Dawson and Seater to produce this study: 1. A new time series measure of total federal regulation, and 2. Models of endogenous growth. My next post will go into detail on Dawson and Seater’s new time series measure of regulation, and compares it to other metrics that have been used. Then I’ll follow up with a post discussing endogenous growth models, which consider that policy decisions can affect the accumulation of knowledge and the rates of innovation and entrepreneurship in an economy, and through these mechanisms affect economic growth.

Why should you care about something as obscure as a “time series measure of regulation” and “endogenous growth theory?” Regulations—a form of law that lawyers call administrative law—create a hidden tax. When the Department of Transportation creates new regulations that mandate that cars must become more fuel efficient, all cars become more expensive, in the same way that a tax on cars would make them more expensive. Even worse, the accumulation of regulations over time stifle innovation, hinder entrepreneurship, and create unintended consequences by altering the prices of everyday purchases and activities. For an example of hindering entrepreneurship, occupational licensing requirements in 17 states make it illegal for someone to braid hair for a living without first being licensed, a process which, in Pennsylvania at least, requires 300 hours of training, at least a 10th grade education, and passing a practical and a theory exam. Oh, and after you’ve paid for all that training, you still have to pay for a license.

And for an example of unintended consequences: Transportation Security Administration procedures in airports obviously slow down travel. So now you have to leave work or home 30 minutes or even an hour earlier than you would have otherwise, and you lose the chance to spend another hour with your family or finishing some important project. Furthermore, because of increased travel times when flying, some people choose to drive instead of fly. Because driving involves a higher risk of accident and death than does flying, this shift, caused by regulation, of travelers from plane to car actually causes people to die (statistically speaking), as this paper showed.

Economists have realized the accumulation of regulation must be causing serious problems in the economy. As a result, they have been trying to measure regulation in different ways, in order to include regulation in their models and better study its impact. One famous measure of regulation, which I’ll discuss in more detail in my next post, is the OECD’s index of Product Market Regulation. That rather sanitized term, “product market regulation,” actually consists of several components that are directly relevant to a would-be entrepreneur (such as the opacity of a country’s licenses and permits system and administrative burdens for sole proprietorships) and to a consumer (such as price controls, which can lead to shortages like we often see after hurricanes where anti-price gouging laws exist, and barriers to foreign direct investment, which could prevent multinational firms like Toyota from building a new facility and creating new jobs in a country). But as you’ll see in the next post, that OECD measure (and many other measures) of regulation miss a lot of regulations that also directly affect every individual and business. In any science, correct measurement is a necessary first step to empirical hypothesis testing.

Dawson and Seater have contributed a new measure of regulation that improves upon previously existing ones in many ways, although it also has its drawbacks. And because their new measure of regulation offers many more years of observations than most other measures, it can be used in an endogenous growth model to estimate how regulation has affected the growth of the US economy. Again, in endogenous growth models, policy decisions (such as how much regulation to create) affect economic growth if they affect the rates of accumulation of knowledge, innovation, and entrepreneurship. It’s by using their measure in an endogenous growth model that Dawson and Seater were able to estimate that individuals in the US would have been $129,300 richer if regulations had stayed at their 1949 level. I’ll explain a bit more about endogenous growth theory in a second follow-up post. But first things first—my next post will go into detail on measures of regulation and Dawson and Seater’s innovation.