This post is the third part in a three part series spurred by a recent study by economists John Dawson and John Seater that estimates that the accumulation of federal regulation has slowed economic growth in the US by about 2% annually. The first part discussed generally how Dawson and Seater’s study and other investigations into the consequences of regulation are important because they highlight the cumulative drag of our regulatory system. The second part went into detail on some of the ways that economists measure regulation, highlighting the strengths and weaknesses of each. This post – the final one in the series – looks at how those measures of regulation are used to estimate the consequences of regulatory policy. As always, economists do it with models. In the case of Dawson and Seater, they appeal to a well-established family of endogenous growth models built upon the foundational principle of creative destruction, in the tradition of Joseph Schumpeter.
So, what is an endogenous growth model?
First, a brief discussion of models: In a social or hard science, the ideal model is one that is useful (applicable to the real world using observable inputs to predict outcomes of interest), testable (predictions can be tested with observed outcomes), flexible (able to adapt to a wide variety of input data), and tractable (not too cumbersome to work with). Suppose a map predicts that following a certain route will lead to a certain location. When you follow that route in the real world, if you do not actually end up at the predicted location, you will probably stop using that map. Same thing with models: if a model does a good job at predicting real world outcomes, then it sticks around until someone invents one that does an even better job. If it doesn’t predict things well, then it usually gets abandoned quickly.
Economists have been obsessed with modeling the growth of national economies at least since Nobel prize winner Simon Kuznets began exploring how to measure GDP in the 1930s. Growth models generally refer to models that try to represent how the scale of an economy, using metrics such as GDP, grows over time. For a long time, economists relied on neoclassical growth models, which primarily use capital accumulation, population growth, technology, and productivity as the main explanatory factors in predicting the economic growth of a country. One of the first and most famous of such economic growth models is the Solow model, which has a one-to-one (simple) mapping from increasing levels of the accumulated stock of capital to increasing levels of GDP. In the Solow model, GDP does not increase at the same rate as capital accumulation due to the diminishing marginal returns to capital. Even though the Solow model was a breakthrough in describing the growth of GDP from capital stock accumulation, most factors in this growth process (and, generally speaking, in the growth processes of other models in the neoclassical family of growth models) are generated by economic decisions that are outside of the model. As a result, these factors are dubbed exogenous, as opposed to endogenous factors which are generated inside of the model as a result of the economic decisions made by the actors being modeled.
Much of the research into growth modeling over the subsequent decades following Solow’s breakthrough has been dedicated to trying to “endogenize” those exogenous forces (i.e. move them inside the model). For instance, a major accomplishment was endogenizing the savings rate – how much of household income was saved and invested in expanding firms’ capital stocks. Even with this endogenous savings rate, as well as exogenous growth in the population providing labor for production, the accumulating capital stocks in these neoclassical growth models could not explain all of the growth in GDP. The difference, called the Solow Residual, was interpreted as the growth in productivity due to technological development and was like manna from heaven for the actors in the economy – exogenously growing over time regardless of the decisions made by the actors in the model.
But it should be fairly obvious that decisions we make today can affect our future productivity through technological development, and not just through the accumulation of capital stocks or population growth. Technological development is not free. It is the result of someone’s decision to invest in developing technologies. Because technological development is the endogenous result of an economic decision, it can be affected by any factors that distort the incentives involved in such investment decisions (e.g., taxes and regulations).
This is the primary improvement of endogenous growth theory over neoclassical growth models. Endogenous growth models take into account the idea that innovative firms invest in both capital and technology, which has the aggregate effect of moving out the entire production possibilities curve. Further, policies such as increasing regulatory restrictions or changing tax rates will affect the incentives and abilities of people in the economy to innovate and produce. The Dawson and Seater study relies on a model originally developed by Pietro Peretto to examine the effects of taxes on economic growth. Dawson and Seater adapt the model to include regulation as another endogenous variable, although they do not formally model the exact mechanism by which regulation affects investment choices in the same way as taxes. Nonetheless, it’s perfectly feasible that regulation does affect investment, and, to a degree, it is simply an empirical question of how much.
So, now that you at least know that Dawson and Seater selected an accepted and feasible model—a model that, like a good map, makes reliable predictions about real world outcomes—you’re surely asking how that model provided empirical evidence of regulation’s effect on economic growth. The answer depends on what empirical means. Consider a much better established model: gravity. A simple model of gravity states that an object in a vacuum near the Earth’s surface will accelerate towards the Earth at 9.81 meters per second squared. On other planets, that number may be higher or lower, depending on the planet’s massiveness and the object’s distance from the center of the planet. In this analogy, consider taxes the equivalent of mass – we know from previous endogenous growth models that taxes have a fairly known effect on the economy, just like we know that mass has a known effect on the rate of acceleration from gravitational forces. Dawson and Seater have effectively said that regulations must have a similar effect on the economy as taxes. Maybe the coefficient isn’t 9.81, but the generalized model will allow them to estimate what that coefficient is – so long as they can measure the “mass” equivalent of regulation and control for “distance.” They had to rely on the model, in fact, to produce the counterfactual, or to use a term from science experiments, a control group. If you know that mass affects acceleration at some given constant, then you can figure out what acceleration is for a different level of mass without actually observing it. Similarly, if you know that regulations affect economic growth in some established pattern, then you can deduce what economic growth would be without regulations. Dawson and Seater appealed to an endogenous growth model (courtesy of Perreto) to simulate a counterfactual economy that maintained regulation levels seen in the year 1949. By the year 2005, that counterfactual economy had become considerably larger than the actual economy – the one in which we’ve seen regulation increase to include over 1,000,000 restrictions.