Tag Archives: Department of Transportation

The Economics of Regulation Part 1: A New Study Shows That Regulatory Accumulation Hurts the Economy

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.

Delaying the Rearview Camera Rule is Good for the Poor

A few weeks ago, the Department of Transportation (DOT) announced it would delay implementation of a regulation requiring that rearview cameras be installed in new automobiles. The rule was designed to prevent backover accidents by increasing drivers’ fields of vision to include the area behind and underneath vehicles. The DOT said more research was needed before finalizing the regulation, but there is another, perhaps more important reason for delaying the rule. The costs of this rule, and many others like it, weigh most heavily on those with low incomes, while the benefits cater to the preferences of those who are better-off financially.

The rearview camera regulation was expected to increase the cost of an automobile by approximately $200. This may not seem like much money, but it means a person buying a new car will have less money on hand to spend on other items that improve quality of life. These items might include things like healthcare or healthier food. Those who already have access to quality healthcare services, or who shop regularly at high end supermarkets like Whole Foods, may prefer to have the risk of a backup accident reduced over the additional $200 spent on a new car. Alternatively, those who don’t have easy access to healthcare or healthy food, may well prefer the $200.

A lot of regulation is really about reducing risks. Some risks pose large dangers, like the risk of radiation exposure (or death) if you are within range of a nuclear blast. Some risks pose small dangers, like a mosquito bite. Some risks are very likely, like the risk of stubbing your toe at some point in your lifetime, while other risks are very remote, like the chance that the Earth will be hit by a gigantic asteroid next week.

Risks are everywhere and can never be eliminated entirely from life. If we tried to eliminate every risk we face, we’d all live like John Travolta in the movie The Boy in the Plastic Bubble (and of course, he could also be hit by an asteroid!). The question we need to ask ourselves is: how do we manage risks in a way that makes the most sense given limited resources in society? In addition to this important question, we may also want to ask ourselves to what degree distributional effects are important as we consider which risks to mitigate?

There are two main ways that society can manage risks. First, we can manage risks we face privately, say by choosing to eat vegetables often or to go to the gym. In this way, a person can reduce the risk of cardiovascular disease, a leading cause of death in the United States, as well as other health problems. We can also choose to manage risks publicly, say through regulation or other government action. For example, the government passes laws requiring everyone to get vaccinated against certain illnesses, and this reduces the risk of getting sick from those around us.

Not surprisingly, low income families spend less on private risk mitigation than high income families do. Similarly, those who live in lower income areas tend to face higher mortality risks from a whole host of factors (e.g. accidents, homicide, cancer), when compared to those who live in wealthier neighborhoods. People with higher incomes tend to demand more risk reduction, just as they demand more of other goods or services. Therefore, spending money to reduce very low probability risks, like the risk of being backed over by a car in reverse, is more in line with preferences of the wealthy, since the wealthy will demand more risk reduction of this sort than the poor will.

Such a rule may also result in unintended consequences.  Just as using seat belts has been shown to lead to people driving faster, relying on a rearview camera when driving in reverse may lead to people being less careful about backing up.  For example, someone could be running outside of the camera’s view, and only come into view just as he or she is hit by the car.  Relying on cameras entirely may increase the risk of some people getting hit.

When the government intervenes and reduces risks for us, it is making a choice for us about which risks are most important, and forcing everyone in society to pay to address these risks. But not all risks are the same. In the case of the rearview camera rule, everyone must pay the extra money for the new device in the car (unless they forgo buying a new car which also carries risks), yet the risk of accident in a backup crash is small relative to other risks. Simply moving out of a low income neighborhood can reduce a whole host of risks that low income families face. By forcing the poor to pay to reduce the likelihood of tiny probability events, DOT is essentially saying poor people shouldn’t have the option of reducing larger risks they face. Instead, the poor should share the burden of reducing risks that are more in line with the preferences of the wealthy, who have likely already paid to reduce the types of risks that low income families still face.

Politicians and regulators like to claim that they are saving lives with regulation and just leave it at that. But the reality is often much more complicated with unintended consequences and regressive effects. Regulations have costs and those costs often fall disproportionately on those with the least ability to pay. Regulations also involve tradeoffs that leave some groups better off, while making other groups worse off. When one of the groups made worse off is the poor, we should think very carefully before proceeding with a policy, no matter how well intentioned policymakers may be.

The DOT is delaying the rearview camera rule so it can conduct more research on the issue. This is a sensible decision. Everyone wants to reduce the prevalence of backover accidents, but we should be looking for ways to achieve this goal that don’t disadvantage the least well off in society.