Tag Archives: growth

What is the greatest threat to freedom and prosperity?

FLORENCE— Bernardo Caprotti was a 45-year-old entrepreneur when he agreed to buy a suburban plot of land for a new supermarket.

Building permits recently came through. He’s now 88.

So begins an enlightening story in today’s Wall Street Journal on Italy’s sclerotic economy. The story continues:

Italy has emerged as a Technicolor example of the [EU’s] problems. Its growth has been stuttering for 20 years. Since 2008, its economy has shrunk by 9%, and this year it is struggling to expand by even 1%.

It is tempting to think that a simple solution is new leadership, that Italy just needs to elect more market-oriented politicians to sweep away the layers of red-tape and barriers to entrepreneurship that have ensnared the country’s entrepreneurs.

But the problem is much more intractable because established businesses benefit from the status quo:

The roots of the problem, say many Italians, lie in how vested interests in the private and public sectors gum up the economy, preventing change that replaces old practices with new, more efficient ones, and repeatedly frustrating political attempts to shake up the country.

It adds up to “deep-seated cultural obstacles to growth,” says Tito Boeri, a professor at Milan’s Bocconi University who is one of Italy’s top economists.

Years ago, Milton Friedman put his finger on the problem:

A few months ago, I attended a conference on the intersection between politics and capitalism (what we’ve called government-granted privilege). The eminent economic historian Robert Higgs was there and he said something that has stuck with me (I’m paraphrasing, but he just approved the quote):

I believe crony capitalism—the alliance between business and government—is the biggest problem of our age. And the reason is that it is robust. As alternatives to free-market capitalism, communism and old-fashioned fascism are thankfully dead. And genuine socialism has no real constituency in America. But crony capitalism, unfortunately, has a very active, organized, well-funded, and vocal constituency. It is the greatest threat to our prosperity and our freedom.

 

9 Farm Bill Figures

In my last post, I made the case that the farm bill (which has now emerged from conference committee and just passed the House) makes an excellent teaching tool.

Many students, of course, are visual learners. So I thought I might suggest a few farm bill figures.

Let’s begin with farm subsidy outlays. These are the most conspicuous privileges afforded farmers. As Veronique de Rugy’s figure below shows, these were around $13 billion per year in the late ‘90s, then surged up to $28 billion in 2000, then settled into annual levels that were about twice their pre-surge levels after that (readers of Robert Higgs will recognize this as a “ratchet” pattern in government growth).

farm-subsidies-chart-original (Click on any image in this post to enlarge it)

The last bar in Vero’s chart shows projected subsidies of about $29 billion in 2014. Another of Vero’s charts, however, suggests that this figure may be optimistic. The chart below shows projected and actual farm bill spending for the last couple farm bills (note: these figures include the entire bill not just subsidies, which is why the numbers are so much larger than those in the previous chart). 

farm-bills-cbo-score-chart-1000

Though subsidies are the most conspicuous privilege afforded to farmers they are by no means the only or even the most important. In addition to cash outlays, farmers also benefit from an assortment of trade barriers (some of which have gotten us into trouble with the WTO), various marketing programs, and artificial price supports. My chart from last week shows how active farm assistance programs have grown over the years (along with farmer incomes):

The chart below by Vero shows how one of these price support programs drives up the price of sugar:

sugar-subsidies-original

As I write in my Mercatus on Policy piece: 

This might seem trivial, but sugar isn’t the only item that is more expensive because of agricultural price supports. The House version of the farm bill imposes artificial price floors on wheat, corn, grain sorghum, barley, oats, long and medium grain rice, soybeans, oilseeds, peanuts, dry peas, lentils, chickpeas, sugar, and dairy products.

Farm privileges are often justified on the common assumption that farming is unprofitable. But this isn’t so. The figure below, taken from Vincent Smith’s paper on the 2013 bill, shows that both median and mean farm household income has exceeded median and mean US household income for more than a decade. Today, the average farm household makes 53 percent more than the average US household:

Farm income

Farm supports are also often justified on the basis that farming is uniquely risky. As Smith explains, though, the business failure rate of the typical American business is 14 times greater than the failure rate of the typical farm. Moreover, as the figure below from his report demonstrates, the agriculture sector’s debt-to-asset ratio is lower than that of other sectors and has been falling for two decades:

Though the average farm has done quite well, it is not the average farm that receives privileges. As demonstrated by Smith’s chart below, since 1995, 83 percent of subsidy payments have flowed to the largest 15 percent of farms:

As I put it in my piece, “Given that these subsidies and price supports distort free market signals and transfer wealth from the relatively poor to the relatively wealthy, one would think they would face bipartisan opposition.” Why, then, do they persist?

This chart by Vero, showing annual lobbying expenditures by the sugar industry is one explanation:

Finally, this chart from my piece shows that political donations tend to be concentrated on those who actually write the bill:

The minimum wage and slower job growth

In my December post on Willie Lyons I linked to a new article on the minimum wage by Jonathan Meer and Jeremy West, both of Texas A&M.

The paper merits a closer look as it points to a more nuanced minimum wage / unemployment relationship than you typically see in public policy debates. Namely, the authors find that a minimum wage reduces employment growth rather than employment levels.

This insight is inspired by the work of Nobel laureate Peter Diamond who analyzed employment as a searching and matching-type game between employers and employees (Diamond argued that unemployment insurance can make workers more selective in the jobs that they take, improving the “match” between employer and employee). As a number of subsequent authors have emphasized, this means that the transition to a new employment equilibrium can take time. “In this case,” Meer and West write, “the effects of the policy [minimum wage] may be more evident in net job creation.” (7)

The theoretical prediction, however, is ambiguous. On the one hand, a minimum wage reduces employer demand for labor by raising the cost of employment. On the other, it induces greater search effort among potential employees.

To resolve this theoretical ambiguity the authors consult the data. They study the problem by looking at three different datasets that encompass all fifty states, plus DC and cover the years 1975-2012. What did they find? First:

Our findings are consistent across all three data sets, indicating that job growth declines significantly in response to increases in the minimum wage. (2)

To be precise, they find:

a ten percent increase in the minimum wage results in a reduction of approximately one-quarter of the net job growth rate. (19)

To gain a better understanding of whether this happens because a minimum wage retards job creation or because it accelerates job destruction, the authors then look at each of these factors separately and conclude that the minimum wage mostly seems to retard creation of new jobs in expanding firms.

Lastly, they find that “the effect on job growth is concentrated in lower-wage industries and among younger workers.”

Not so incidentally, my colleague Keith Hall (a former Bureau of Labor Statistics commissioner) calculates that the latest seasonally-adjusted unemployment rate among the 18-24 age group is 12.4 percent. This is nearly twice the national average.

Credit Warnings, Debt Financing and Dipping into Cash Reserves

As 2013 comes to an end recent news brings attention to the structural budgetary problems and worsening fiscal picture facing several governments: New Jersey, New York City, Puerto Rico and Maryland.

First there was a warning from Moody’s for the Garden State. On Monday New Jersey’s credit outlook was changed to negative. The ratings agency cited rising public employee benefit costs and insufficient revenues. New Jersey is alongside Illinois for the state with the shortest time horizon until the system is Pay-As-You-Go. On a risk-free basis the gap between pension assets and liabilities is roughly $171 billion according to State Budget Solutions, leaving the system only 33 percent funded. This year the New Jersey contributed $1.7 billion to the system. But previous analysis suggests New Jersey will need to pay out $10 billion annually in a few years representing one-third of the current budget.

New Jersey isn’t alone. The biggest structural threat to government budgets is the unrecognized risk in employee pension plans and the purely unfunded status of health care benefits. Mayor Michael Bloomberg, in his final speech as New York City’s Mayor, pointed to the “labor-electoral complex” which prevents employee benefit reform as the single greatest threat to the city’s financial health. In 12 years the cost of employee benefits has increased 500 percent from $1.5 billion to $8.2 billion. Those costs are certain to grow presenting the next generation with a massive debt that will siphon money away from city services.

Public employee pensions and debt are also crippling Puerto Rico which has dipped into cash reserves to repay a $400 million short-term loan. The Wall Street Journal reports that the government planned to sell bonds, but retreated since the island’s bond values have, “plunged in value,” due to investor fears over economic malaise and the territory’s existing large debt load which stands at $87 billion, or $23,000 per resident.

This should serve as a warning to other states that continue to finance budget growth with debt while understating employee benefit costs. Maryland’s Spending Affordability Committee is recommending a 4 percent budget increase and a hike in the state’s debt limit from $75 million to 1.16 billion in 2014. Early estimates by the legislative fiscal office anticipate structural deficits of $300 million over the next two years – a situation that has plagued Maryland for well over a decade. The fiscal office has advised against increased debt, noting that over the last five years, GO bonds have been, “used as a source of replacement funding for transfers of cash” from dedicated funds projects such as the Chesapeake Bay Restoration Fund.

 

Does the minimum wage increase unemployment? Ask Willie Lyons.

President Obama recently claimed:

[T]here’s no solid evidence that a higher minimum wage costs jobs, and research shows it raises incomes for low-wage workers and boosts short-term economic growth.

Students of economics may find this a curious claim. Many of them will have been assigned Steven Landsburg’s Price Theory and Applications where, on page 380, they will have read:

Overwhelming empirical evidence has convinced most economists that the minimum wage is a significant cause of unemployment, particularly among the unskilled.

Or perhaps they will have been assigned Hirschleifer, Glazer, and Hirschleifer’s widely-read text. In this case, they will have seen on page 21 that 78.9 percent of surveyed economists either “agree generally” or “agree with provisions” with the statement that “A minimum wage increases unemployment among young and unskilled workers.”

More advanced students may have encountered this January 2013 paper by David Neumark, J.M. Ian Salas, and William Wascher which assesses the latest research and concludes:

[T]he evidence still shows that minimum wages pose a tradeoff of higher wages for some against job losses for others, and that policymakers need to bear this tradeoff in mind when making decisions about increasing the minimum wage.

Some students may have even studied Jonathan Meer and Jeremy West’s hot-off-the-presses study which focuses on the effect of a minimum wage on job growth. They conclude:

[T]he minimum wage reduces net job growth, primarily through its effect on job creation by expanding establishments. These effects are most pronounced for younger workers and in industries with a higher proportion of low-wage workers.

Students of history, however, will be aware of another testimonial. It comes not from an economist but from an elevator operator. Her name was Willie Lyons and in 1918, at the age of 21, she had a job working for the Congress Hall Hotel in Washington, D.C. She made $35 per month, plus two meals a day. According to the court, she reported that “the work was light and healthful, the hours short, with surroundings clean and moral, and that she was anxious to continue it for the compensation she was receiving.”

Then, on September 19, 1918, Congress passed a law establishing a District of Columbia Minimum Wage Board and setting a minimum wage for any woman or child working in the District. Though it would have been happy to retain Ms. Lyons at her agreed-upon wage, the Hotel decided that her services were not worth the higher wage and let her go.

Ms. Lyons sued the Board, claiming that the minimum wage violated her “liberty of contract” under the Due Process clauses of the 5th and 14th Amendments.* As the Supreme Court would describe it:

The wages received by this appellee were the best she was able to obtain for any work she was capable of performing, and the enforcement of the order, she alleges, deprived her of such employment and wages. She further averred that she could not secure any other position at which she could make a living, with as good physical and moral surroundings, and earn as good wages, and that she was desirous of continuing and would continue the employment, but for the order of the board.

For a time, the Supreme Court agreed with Ms. Lyons, finding that the minimum wage did, indeed, violate her right to contract.

The minimum wage was eliminated and she got her job back.

——————-

*Legal theorists might well claim that the Immunities and/or Privileges clauses of these amendments would have been more reasonable grounds, but those had long been gutted by the Supreme Court.

Maryland’s “severe financial management issues”

Budgetary balance continues to evade Maryland. In FY 2015 the state anticipates a deficit of $400 million. A fact that is being blaming on entitlements, mandated spending, and fiscal mismanagement in the Developmental Disabilities Administration. The agency has been cited by the HHS Inspector General as over billing the Federal government by $20.6 billion for Medicaid expenses.

For over a decade the state has struggled with structural deficits, or,  spending exceeding revenues. The state’s method of controlling spending – the Spending Affordability Commission – has overseen 30 years of spending increases, and its Debt Affordability Commission has compounded the problem by increasing the state’s debt limits in order to expand spending.

For the details, visit my blog post for the Maryland Public Policy Institute. Of related interest is the Tax Foundation’s recent ranking of government spending the states. Maryland ranks 19, and has increased spending by 30.5% since 2011  2001.

It’s Time to Change the Incentives of Regulators

One of the primary reasons that regulation slows down economic growth is that regulation inhibits innovation.  Another example of that is playing out in real-time.  Julian Hattem at The Hill recently blogged about online educators trying to stop the US Department of Education from preventing the expansion of educational opportunities with regulations.  From Hattem’s post:

Funders and educators trying to spur innovations in online education are complaining that federal regulators are making their jobs more difficult.

John Ebersole, president of the online Excelsior College, said on Monday that Congress and President Obama both were making a point of exploring how the Internet can expand educational opportunities, but that regulators at the Department of Education were making it harder.

“I’m afraid that those folks over at the Departnent of Education see their role as being that of police officers,” he said. “They’re all about creating more and more regulations. No matter how few institutions are involved in particular inappropriate behavior, and there have been some, the solution is to impose regulations on everybody.”

Ebersole has it right – the incentive for people at the Department of Education, and at regulatory agencies in general, is to create more regulations.  Economists sometimes model the government as if it were a machine that benevolently chooses to intervene in markets only when it makes sense. But those models ignore that there are real people inside the machine of government, and people respond to incentives.  Regulations are the product that regulatory agencies create, and employees of those agencies are rewarded with things like plaques (I’ve got three sitting on a shelf in my office, from my days as a regulatory economist at the Department of Transportation), bonuses, and promotions for being on teams that successfully create more regulations.  This is unfortunate, because it inevitably creates pressure to regulate regardless of consequences on things like innovation and economic growth.

A system that rewards people for producing large quantities of some product, regardless of that product’s real value or potential long-term consequences, is a recipe for disaster.  In fact, it sounds reminiscent of the situation of home loan originators in the years leading up to the financial crisis of 2008.  Mortgage origination is the act of making a loan to someone for the purposes of buying a home.  Fannie Mae and Freddie Mac, as well as large commercial and investment banks, would buy mortgages (and the interest that they promised) from home loan originators, the most notorious of which was probably Countrywide Financial (now part of Bank of America).  The originators knew they had a ready buyer for mortgages, including subprime mortgages – that is, mortgages that were relatively riskier and potentially worthless if interest rates rose.  The knowledge that they could quickly turn a profit by originating more loans and selling them to Fannie, Freddie, and some Wall Street firms led many mortgage originators to turn a blind eye to the possibility that many of the loans they made would not be paid back.  That is, the incentives of individuals working in mortgage origination companies led them to produce large quantities of their product, regardless of the product’s real value or potential long-term consequences.  Sound familiar?

Does Anyone Know the Net Benefits of Regulation?

In early August, I was invited to testify before the Senate Judiciary subcommittee on Oversight, Federal Rights and Agency Action, which is chaired by Sen. Richard Blumenthal (D-Conn.).  The topic of the panel was the amount of time it takes to finalize a regulation.  Specifically, some were concerned that new regulations were being deliberately or needlessly held up in the regulatory process, and as a result, the realization of the benefits of those regulations was delayed (hence the dramatic title of the panel: “Justice Delayed: The Human Cost of Regulatory Paralysis.”)

In my testimony, I took the position that economic and scientific analysis of regulations is important.  Careful consideration of regulatory options can help minimize the costs and unintended consequences that regulations necessarily incur. If additional time can improve regulations—meaning both improving individual regulations’ quality and having the optimal quantity—then additional time should be taken.  My logic behind taking this position was buttressed by three main points:

  1. The accumulation of regulations stifles innovation and entrepreneurship and reduces efficiency. This slows economic growth, and over time, the decreased economic growth attributable to regulatory accumulation has significantly reduced real household income.
  2. The unintended consequences of regulations are particularly detrimental to low-income households— resulting in costs to precisely the same group that has the fewest resources to deal with them.
  3. The quality of regulations matters. The incentive structure of regulatory agencies, coupled with occasional pressure from external forces such as Congress, can cause regulations to favor particular stakeholder groups or to create regulations for which the costs exceed the benefits. In some cases, because of statutory deadlines and other pressures, agencies may rush regulations through the crafting process. That can lead to poor execution: rushed regulations are, on average, more poorly considered, which can lead to greater costs and unintended consequences. Even worse, the regulation’s intended benefits may not be achieved despite incurring very real human costs.

At the same time, I told the members of the subcommittee that if “political shenanigans” are the reason some rules take a long time to finalize, then they should use their bully pulpits to draw attention to such actions.  The influence of politics on regulation and the rulemaking process is an unfortunate reality, but not one that should be accepted.

I actually left that panel with some small amount of hope that, going forward, there might be room for an honest discussion about regulatory reform.  It seemed to me that no one in the room was happy with the current regulatory process – a good starting point if you want real change.  Chairman Blumenthal seemed to feel the same way, stating in his closing remarks that he saw plenty of common ground.  I sent a follow-up letter to Chairman Blumenthal stating as much. I wrote to the Chairman in August:

I share your guarded optimism that there may exist substantial agreement that the regulatory process needs to be improved. My research indicates that any changes to regulatory process should include provisions for improved analysis because better analysis can lead to better outcomes. Similarly, poor analysis can lead to rules that cost more human lives than they needed to in order to accomplish their goals.

A recent op-ed penned by Sen. Blumenthal in The Hill shows me that at least one person is still thinking about the topic of that hearing.  The final sentence of his op-ed said that “we should work together to make rule-making better, more responsive and even more effective at protecting Americans.” I agree. But I disagree with the idea that we know that, as the Senator wrote, “by any metric, these rules are worth [their cost].”  The op-ed goes on to say:

The latest report from the Office of Information and Regulatory Affairs shows federal regulations promulgated between 2002 and 2012 produced up to $800 billion in benefits, with just $84 billion in costs.

Sen. Blumenthal’s op-ed would make sense if his facts were correct.  However, the report to Congress from OIRA that his op-ed referred to actually estimates the costs and benefits of only a handful of regulations.  It’s simple enough to open that report and quote the very first bullet point in the executive summary, which reads:

The estimated annual benefits of major Federal regulations reviewed by OMB from October 1, 2002, to September 30, 2012, for which agencies estimated and monetized both benefits and costs, are in the aggregate between $193 billion and $800 billion, while the estimated annual costs are in the aggregate between $57 billion and $84 billion. These ranges are reported in 2001 dollars and reflect uncertainty in the benefits and costs of each rule at the time that it was evaluated.

But you have to actually dig a little farther into the report to realize that this characterization of the costs and benefits of regulations represents only the view of agency economists (think about their incentive for a moment – they work for the regulatory agencies) and for only 115 regulations out of 37,786 created from October 1, 2002, to September 30, 2012.  As the report that Sen. Blumenthal refers to actually says:

The estimates are therefore not a complete accounting of all the benefits and costs of all regulations issued by the Federal Government during this period.

Furthermore, as an economist who used to work in a regulatory agency and produce these economic analyses of regulations, I find it heartening that the OMB report emphasizes that the estimates it relies on to produce the report are “neither precise nor complete.”  Here’s another point of emphasis from the OMB report:

Individual regulatory impact analyses vary in rigor and may rely on different assumptions, including baseline scenarios, methods, and data. To take just one example, all agencies draw on the existing economic literature for valuation of reductions in mortality and morbidity, but the technical literature has not converged on uniform figures, and consistent with the lack of uniformity in that literature, such valuations vary somewhat (though not dramatically) across agencies. Summing across estimates involves the aggregation of analytical results that are not strictly comparable.

I don’t doubt Sen. Blumenthal’s sincerity in believing that the net benefits of regulation are reflected in the first bullet point of the OMB Report to Congress.  But this shows one of the problems facing regulatory reform today: People on both sides of the debate continue to believe that they know the facts, but in reality we know a lot less about the net effects of regulation than we often pretend to know.  Only recently have economists even begun to understand the drag that regulatory accumulation has on economic growth, and that says nothing about what benefits regulation create in exchange.

All members of Congress need to understand the limitations of our knowledge of the total effects of regulation.  We tend to rely on prospective analyses – analyses that state the costs and benefits of a regulation before they come to fruition.  What we need are more retrospective analyses, with which we can learn what has really worked and what hasn’t, and more comparative studies – studies that have control and experiment groups and see if regulations affect those groups differently.  In the meantime, the best we can do is try to ensure that the people engaged in creating new regulations follow a path of basic problem-solving: First, identify whether there is a problem that actually needs to be solved.  Second, examine several alternative ways of addressing that problem.  Then consider what the costs and benefits of the various alternatives are before choosing one. 

The Myth of Deregulation and the Financial Crisis

In an opinion piece on American Banker, Rep. Jeb Hensarling wrote that:

The great tragedy of the financial crisis, however, was not that Washington regulations failed to prevent it, but instead that Washington regulations helped lead us into it.

Even putting aside the issue of causality, my colleague Robert Greene and I recently examined the data on regulatory growth as we sought to answer the question, “Did Deregulation Cause the Financial Crisis?” Our conclusion was that there was no measurable, net deregulation leading up to the financial crisis.

The data on regulatory growth came from RegData, which uses text analysis to measure the quantity of restrictions published in regulatory text each year.  The graph below shows the number of regulatory restrictions published each year in Title 12 of the Code of Federal Regulations, which covers the subject area of banks and banking, and Title 17, which covers commodity futures and securities trading.  Deregulation would show a general downward trend.  Instead, we see that both titles grew over that time period. The only downward ticks we see occurred because of some consolidation of duplicative regulations from 1997 to 1999 (see our article for more details on that).

As we wrote at the time:

[W]e find that between 1997 and 2008 the number of financial regulatory restrictions in the Code of Federal Regulations (CFR) rose from approximately 40,286 restrictions to 47,494—an increase of 17.9 percent. Regulatory restrictions in Title 12 of the CFR—which regulates banking—increased 18.2 percent while the number of restrictions in Title 17—which regulates commodity futures and securities markets—increased 17.4 percent.

The Economics of Regulation Part 3: How to Estimate the Effect of Regulatory Accumulation on the Economy? Exploring Endogenous Growth and Other Models

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.