Tag Archives: GDP

Local land-use restrictions harm everyone

In a recent NBER working paper, authors Enrico Moretti and Chang-Tai Hsieh analyze how the growth of cities determines the growth of nations. They use data on 220 MSAs from 1964 – 2009 to estimate the contribution of each city to US national GDP growth. They compare what they call the accounting estimate to the model-driven estimate. The accounting estimate is the simple way of attributing city nominal GDP growth to national GDP growth in that it doesn’t account for whether the increase in city GDP is due to higher nominal wages or increased output caused by an increase in local employment. The model-driven estimate that they compare it to distinguishes between these two factors.

Before I go any further it is important to explain the theory behind the author’s empirical findings. Suppose there is a productivity shock to City A such that workers in City A are more productive than they were previously. This productivity shock could be the result of a new method of production or a newly invented piece of equipment (capital) that helps workers make more stuff with a given amount of labor. This productivity shock will increase the local demand for labor which will increase the wage.

Now one of two things can happen and the diagram below depicts the two scenarios. The supply and demand lines are those for workers, with the wage on the Y-axis and the amount of workers on the X-axis. Since more workers lead to more output I also labeled labor as L = αY, where α is some fraction less than 1 to signify that each additional unit of labor doesn’t lead to a one unit increase in output, but rather some fraction of 1 unit (capital is needed too).

moretti, land use pic

City A can have a highly elastic supply of housing, meaning that it is easy to expand the number of housing units in that city and thus it is relatively easy for people to move there. This would mean that the supply of labor is like S-elastic in the diagram. Thus the number of workers that are able to migrate to City A after labor demand increases (D1 to D2) is large, local employment increases (Le > L*), and total output (GDP) increases. Wages only increase a little bit (We > W*). In this situation the productivity shock would have a relatively large effect on national GDP since it resulted in a large increase in local output as workers moved from relatively low-productivity cities to the relatively high-productivity City A.

Alternatively, the supply of housing in City A could be very inelastic; this would be like S-inelastic. If that is the case, then the productivity shock would still increase the wage in City A (Wi > W*), but it will be more difficult for new workers to move in since new housing cannot be built to shelter them. In this case wages increase but since total local employment stays fairly constant due to the restriction on available housing the increase in output is not as large (Li > L* but < Le). If City A output stays relatively constant and instead the productivity shock is expressed in higher nominal wages, then the resulting growth in City A nominal GDP will not have as large of an effect on national output growth.

As an example, Moretti and Hsieh calculate that the growth of New York City’s GDP was 12% of national GDP growth from 1964-2009. But when accounting for the change in wages, New York’s contribution to national output growth was only 5%: Most of New York’s GDP growth was manifested in higher nominal wages. This is not surprising as it is well known that New York has strict housing regulations that make it difficult to build new housing units (the recent extension of NYC rent-control laws won’t help). This makes it difficult for people to relocate from relatively low-productivity places to a high-productivity New York.

In three of the most intensely land-regulated cities: New York, San Francisco, and San Jose, the accounting contribution to national GDP growth was 19.3%. But these cities actual contribution to national output as estimated by the authors was only 6.1%. Contrast that with the Rust Belt cities (e.g. Detroit, Pittsburgh, Cleveland, etc.) which contributed -28.5% according to the accounting method but +6.1% according to the author’s model.

The authors conclude that less onerous land-use restrictions in high-productivity cities New York, Washington D.C., Boston, San Francisco, San Jose, and the rest of Silicon Valley could increase the nation’s output growth rate by making it easier for workers to migrate from low to high-productivity areas. In an extreme migration scenario where 52% of American workers in 2009 lived in a different city than they actually did, the author’s calculate that GDP per worker would have been $8,775 higher in 2009, or $6,345 per person. In a more realistic scenario (only 20% of workers lived in a different city) it would have been $3,055 more per person: That is a substantial increase.

While I agree with the author’s conclusion that less land-use restrictions would result in a more productive allocation of labor and thus more stuff for all of us, the author’s policy prescriptions at the end of the paper leave much to be desired.  They propose that the federal government constrain the ability of municipalities to set land-use restrictions since these restrictions impose negative externalities on the rest of the country if the form of lowering national output growth. They also support the use of government funded high-speed rail to link  low-productivity labor markets to high-productivity labor markets e.g. the current high-speed rail construction project taking place in California could help workers get form low productivity areas like Stockton, Fresno, and Modesto, to high productivity areas in Silicon Valley.

Land-use restrictions are a problem in many areas, but not a problem that warrants arbitrary federal involvement. If federal involvement simply meant the Supreme Court ruling that land-use regulations (or at least most of them) are unconstitutional then I think that would be beneficial; a broad removal of land-use restrictions would go a long way towards reinstituting the institution of private property. Unfortunately, I don’t think that is what Moretti and Hsieh had in mind.

Arbitrary federal involvement in striking down local land-use regulations would further infringe on federalism and create opportunities for political cronyism. Whatever federal bureaucracy was put in charge of monitoring land-use restrictions would have little local knowledge of the situation. The Environmental Protection Agency (EPA) already monitors some local land use and faulty information along with an expensive appeals process creates problems for residents simply trying to use their own property. Creating a whole federal bureaucracy tasked with picking and choosing which land-use restrictions are acceptable and which aren’t would no doubt lead to more of these types of situations as well as increase the opportunities for regulatory activism. Also, federal land-use regulators may target certain areas that have governors or mayors who don’t agree with them on other issues.

As for more public transportation spending, I think the record speaks for itself – see here, here, and here.

State and local spending growth vs. GDP growth.

A few years ago, I produced a figure which showed inflation-adjusted state and local expenditures alongside inflation-adjusted private GDP.

It’s been some time since I made that chart and so I thought I might revisit the question. This time around, I compared state and local expenditures with overall GDP, not just private GDP.

The results are below (click to enlarge).

State and Local expenditures vs. GDPAfter adjusting for inflation, the economy is about 5.79 times its 1950 size. This is a good thing. It means more is being produced and more is available for consumption. And since the population has only doubled over this period, it means that per capita production is way up.

Over the same time period, however, state and local government expenditures have not just gone up 5 or 6 or even 8 times. Instead, after adjusting for inflation, state and local governments are spending about 12.79 times as much as they spent in 1950.

State and local governments, of course, depend entirely on the economy for their resources. As I put it when I produced the original chart, this is like a household whose income has grown about 6-fold but whose spending habits have grown nearly 13-fold.

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.

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.

Does economic freedom matter among wealthy countries?

In response to my last post, alert Neighborhood Effects reader Shane Phillips writes:

Are there charts like these that just compare the nations in the top quintile? It’s good to know that economic freedom leads to these positive outcomes, but knowing the difference between the Central African Republic, for example, and the US doesn’t really tell me as much as the US vs other modern, developed countries would.

This is a great question and the answer is yes, there has been some attempt to examine these relationships in a sub-set of wealthier countries. One area in which this has been done is in the literature looking at the effect of government size on economic growth. Government size, remember, is just one aspect of economic freedom (in the EFW, the other components are “legal system and property rights,” “sound money,” “freedom to trade internationally,” and “regulation”).

Speaking broadly, most economists who have looked at this, tend to approach the question with the following theoretical relationship in mind:

government size and growth in theory

In other words, at low levels of government spending, additional spending may be able to increase growth by financing things like property protection and public goods. But at higher levels of government spending, marginal increases in government size detract from growth as taxes become more distortionary and as government becomes less effective.

Andreas Bergh and Magnus Henrekson have a very nice survey of this literature. The whole study is worth a read, but one of the more important findings is that while the relationship is fairly ambiguous when all countries are included, it is less-so when you look at the sub-sample of wealthy countries:

The literature on the relationship between the size of government and economic growth is full of seemingly contradictory findings. This conflict is largely explained by variations in definitions and the countries studied. An alternative approach—of limiting the focus to studies of the relationship in rich countries, measuring government size as total taxes or total expenditure relative to GDP and relying on panel data estimations with variation over time—reveals a more consistent picture. The most recent studies find a significant negative correlation: An increase in government size by 10 percentage points is associated with a 0.5 to 1 percent lower annual growth rate.

To me this suggests that the theoretical prediction may not be far from the mark. It’s interesting to note, by the way, that government size is generally negatively correlated with other aspects of economic freedom. So the freer, more-developed countries are often the ones with the largest public sectors. This helps explain why the relationship isn’t consistent across a larger sample: some of the countries with the smallest size governments are also those with the most regulation, the most barriers to trade, etc.

What about economic freedom more broadly defined? Has this been studied among the subset of relatively wealthy and relatively economically-free countries? I’m unaware of any formal studies, but as it turns out I’ve done some simple correlations myself. In the chart below, I graph economic freedom along with per capita GDP in OECD countries. The relationship is positive and statistically significant, though I’d caution that it is a small sample size and I have no control variables.

economic freedom and per capita GDP in OECDOne nice thing about focusing on the subset of OECD countries is that doing so allows me to examine the relationship between economic freedom and median income (which isn’t readily available for non-OECD countries). Per capita measures are problematic because they are sensitive to outliers. A handful of super-wealthy people in the U.S. or Luxembourg may give the false impression that everyone is wealthy. The median, however, doesn’t have this problem because it is unaffected by the levels at the extremes of the sample. Here the relationship is in terms of median income:

economic freedom and median income in oecdAs before, the same caveat applies: This is statistically significant; but it is a small sample and I have no control variables.

Does stimulus displace private economic activity?

According to Keynesian economic theory, many recessions have little or nothing to do with underlying (structural) economic problems. Instead, recessions are the result of a crisis in confidence. People are simply freaked out and therefore not spending. And when they are not spending, others are not earning income and so the economy suffers.

Keynesians argue that the government can cure this crisis in confidence by borrowing (deficit spending) to fund an increase in government purchases. If people are too freaked out to spend, the logic goes, the government can spend for them. And this spending has a multiplier effect, rippling throughout the economy.

You might be wondering how the government is able to get something for nothing. Government has to borrow the resources from the private economy, doesn’t that mean that the government is competing with private borrowers who have their own plans to invest in the economy? Doesn’t that mean that government investment displaces or crowds-out private investment? The Keynesians, being clever economists, have an answer for this. Their answer is that during a recession there are “idle resources.” That is, individuals and businesses are too freaked out to undertake any major investments and so there is money just lying around. The government can borrow it without displacing any private activity.

Most Keynesians (and by this I mean the economists, not most politicians and pundits who subscribe to Keynesian theory) recognize that this is only a short term phenomenon. Obviously, there comes a time when government borrowing will, indeed, displace private economic activity. That’s why Keynesians believe that the multiplier is larger during a recession and its why they counsel that stimulus should be “timely, targeted, and temporary,” as Lawrence Summers famously put it in December 2007.

Leaving aside the question of whether government can effectively spend the money, is it true that the government purchases multiplier is larger during recessions? A new paper by Michael Owyang (St. Louis Fed), Sarah Zubairy (Bank of Canada) and Valerie Ramey (UCSD) examines this question:

A key question that has arisen during recent debates is whether government spending multipliers are larger during times when resources are idle. This paper seeks to shed light on this question by analyzing new quarterly historical data covering multiple large wars and depressions in the U.S. and Canada. Using an extension of Ramey’s (2011) military news series and Jordà’s (2005) method for estimating impulse responses, we find no evidence that multipliers are greater during periods of high unemployment in the U.S. In every case, the estimated multipliers are below unity. We do find some evidence of higher multipliers during periods of slack in Canada, with some multipliers above unity.

Remember, the way the government calculates GDP, $1.00 in government purchases, automatically increases measured GDP. So a multiplier “below unity” (<1) implies that government purchases displace private economic activity, that stimulus shrinks the private economy.

The paper can be found here.

To Raise Taxes or to Close Loopholes?

Imagine for a moment that you are interested in lowering your nation’s debt-to-GDP ratio. Let’s assume you are determined to ignore the experience of other nations and you are dead-set on lowering your debt-to-GDP ratio by raising revenue rather than by cutting spending.

This leaves you with two choices:

Choice A: increase tax rates.

Choice B: leave rates where they are but close loopholes.

President Obama’s erstwhile deficit commission, Simpson-Bowles, favored Choice B. And I think it is fair to say that most economists do as well. Why? Put simply, a rate increase has deleterious demand and supply-side effects, whereas a loophole closing only has deleterious demand-side effects. If you raise rates, people are incentivized to spend less and work less (or hide more of their income from the IRS). But if you close loopholes, people are incentivized to spend less while their incentive to work is unchanged.  What’s more, when you close loopholes, you tend to remove other distortions in the economy (think: mortgage interest deduction) and you diminish the incidence of government-favoritism.

There are at least three strikes against the fiscal cliff deal struck this week:

  1. It ignored the evidence that tax increases are more economically harmful than spending cuts.
  2. It opted to raise revenue through rate increases rather than loophole closings.
  3. It actually expanded corporate tax loopholes!

On the last point, don’t miss Vero’s pieces here and here, Tim Carney’s pieces here and here, Matt Stoller’s piece here, and Brad Plumer’s piece here.

Don’t like the fiscal cliff? You’ll hate the fiscal future.

Absent an eleventh-hour deal—which may yet be possible—the Federal government will cut spending and raise taxes in the New Year. In a town that famously can’t agree on anything, nearly everyone seems terrified by the prospect of going over this fiscal cliff.

Yet for all the gloom and dread, the fiscal cliff embodies a teachable moment. At the risk of mixing metaphors, we should think of the fiscal cliff as the Ghost of the Fiscal Future. It is a bleak lesson in what awaits us if we don’t get serious about changing course.

First, some background. Over the last four decades, Federal Government spending as a share of GDP has remained relatively constant at about 21 percent. This spending was financed with taxes that consumed about 18 percent of GDP and the government borrowed to make up the difference.

After a decade of government spending increases and anemic economic growth, federal spending is now about 24 percent of GDP (a post WWII high, exceeded only by last year’s number) and revenues are about 15 percent of GDP (the revenue decline can be attributed to both the Bush tax cuts and to the recession).

But the really telling numbers are yet to come.

The non-partisan Congressional Budget Office now projects that, absent policy change, when my two-year-old daughter reaches my age (32), revenue will be just a bit above its historical average at 19 percent of GDP while spending will be nearly twice its historical average at 39 percent of GDP. This is what economists mean when they say we have a spending problem and not a revenue problem: spending increases, not revenue decreases, account for the entirety of the projected growth in deficits and debt over the coming years.

Why is this so frightful? The Ghost of the Fiscal Future gives us 3 reasons:

1) As spending outstrips revenue, each year the government will have to borrow more and more to pay its bills. We have to pay interest on what we borrow and these interest payments, in turn, add to future government spending. So before my daughter hits college, the federal government will be spending more on interest payments than on Social Security.

2) When the government borrows to finance its spending, it will be competing with my daughter when she borrows to finance her first home or to start her own business. This means that she and other private borrowers will face higher interest rates, crowding-out private sector investment and depressing economic growth. This could affect my daughter’s wages, her consumption, and her standard of living. In a vicious cycle, it could also depress government revenue and place greater demands on the government safety net, exacerbating the underlying debt problem.

This is not just theory. Economists Carmen Reinhart and Kenneth Rogoff have examined 200-years’ worth of data from over 40 countries. They found that those nations with gross debt in excess of 90 percent of GDP tend to grow about 1 percentage point slower than otherwise (the U.S. gross debt-to-GDP ratio has been in excess of 90 percent since 2010)

If, starting in 1975, the U.S. had grown 1 percentage point slower than it actually did, the nation’s economy would be about 30 percent smaller than it actually is today. By comparison, the Federal Reserve estimates that the Great Recession has only shrunk the economy by about 6 percent relative to its potential size.

3) Things get worse. The CBO no longer projects out beyond 2042, the year my daughter turns 32. In other words, the CBO recognizes that the whole economic system becomes increasingly unsustainable beyond that point and that it is ludicrous to think that it can go on.

What’s more, if Congress waits until then to make the necessary changes, it will have to enact tax increases or spending cuts larger than anything we have ever undertaken in our nation’s history. As Vero explains:

By refusing to reform Social Security, lawmakers are guaranteeing automatic benefit cuts of about 20-something percent for everyone on the program in 2035 (the Social Security trust fund will be exhausted in 2035, the combined retirement and disability trust funds will run dry in 2033, and both will continue to deteriorate).

In other words, if we fail to reform, the fiscal future will make January’s fiscal cliff look like a fiscal step. I’ve never understood why some people think they are doing future retirees a favor in pretending that entitlements do not need significant reform.

You might think that we could tax our way out of this mess. But taxes, like debt, are also bad for economic growth.

But it is not too late. Like Scrooge, we can take ownership of the time before us. We can make big adjustments now so that we don’t have to make bigger adjustments in a few years. There is still time to adopt meaningful entitlement reform, to tell people my age to adjust our expectations and to plan on working a little longer, to incorporate market incentives into our health care system so that Medicare and Medicaid don’t swallow up more and more of the budget.

Some characterize these moves as stingy. In reality, these types of reforms would actually make our commitments more sustainable. And the longer we wait to make these inevitable changes, the more dramatic and painful they will have to be.

For all the gloom and dread, the Ghost of Christmas Yet to Come was Scrooge’s savior. In revealing the consequences of his actions—and, importantly, his inactions—the Ghost inspired the old man to take ownership of the “Time before him” and to change his ways.

Let us hope that Congress is so enlightened by the Ghost of the Fiscal Cliff.

Eileen Norcross on News Channel 8 Capital Insider discussing Virginia and the fiscal cliff

Last week I appeared on NewsChannel 8’s Capital Insider to discuss how the fiscal cliff affects Virginia. There are several potential effects depending on what the final package looks like. Let’s assume the deductions for the Child Care Tax Credit, EITC, and capital depreciation go away. This means, according to The Pew Center, where the state’s tax code is linked to the federal (like Virginia) tax revenues will increase. That’s because removing income tax deductions increases Adjusted Gross Income (AGI) on the individual’s income tax filing (or on the corporation’s filing) thus the income on which the government may levy tax increases. According to fellow Mercatus scholar, Jason Fichtner, that could amount to millions of dollars for a state.

On the federal budget side of the equation,the $109 billion in potential reductions is now equally shared between defense and non-defense spending. Of concern is the extent to which the region’s economy is dependent on this for employment. Nearly 20 percent of the region’s economy is linked to federal spending. Two points: The cuts are reductions in the rate of growth in spending. For defense spending, they are relatively small cuts representing a return to 2007 spending levels as Veronique points out. So, these reductions not likely to bring about the major shakeup in the regional economy that some fear. Secondly, the fact that these cuts are causing worry is well-taken. It highlights the importance of diversification in an economy.

Where revenues, or GDP, or employment in a region is too closely tied to one industry, a very large and sudden change in that industry can spell trouble. An analogy: New Jersey’s and New York’s dependence on financial industry revenues via their income tax structure led to a revenue shock when the market crashed in 2008, as the New York Fed notes.

On transportation spending there are some good proposals on the table in the legislature and the executive. Some involve raising the gas tax (which hasn’t been increased since 1986), and others involve tolls. The best way to raise transportation revenues is via taxes or fees that are linked to those using the roads. Now is no time to start punching more holes in the tax code to give breaks to favored industries (even if they are making Academy-award quality films) or to encourage particular activities.

Virginia’s in a good starting position to handle what may be in store for the US over the coming years. Virginia has a relatively flat tax structure with low rates. It has a good regulatory environment. This is one reason why people and businesses have located here.

Keep the tax and regulatory rules fair and non-discriminatory and let the entrepreneurs discover the opportunities. Don’t develop an appetite for debt financing. A tax system  is meant to collect revenues and not engineer individual or corporate behavior. Today, Virginia beats all of its neighbors in terms of economic freedom by a long shot. The goal for Virginia policymakers: keep it this way.

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States Look to Rainy Day Funds to Avoid Future Crises

For the past nine quarters, state revenue collections have been increasing and are now approaching 2008 levels after adjusting for inflation. Many state policymakers are no longer facing the near-ubiquitous budget gaps of fiscal year 2012, but at the moment those memories seem to remain fresh in their minds.

Many states are looking to rainy day funds as a tool to avoid the revenue shortfalls they have experienced since the recession. In Wisconsin, for example, Governor Walker recently made headlines by building up the states’ fund to $125.4 million. In Texas, the state’s significant Rainy Day Fund has reached over $8 billion, behind only Alaska’s fund that holds over $18 billion.

A June report from the Tax Foundation shows Texas and Alaska are the only states with funds that are significant enough to protect states from budget stress in future business cycle downturns. As the Tax Foundation analysis explains, state rainy day funds can be a useful to smooth spending over the business cycle. Research that Matt Mitchell and Nick Tuszynski cite demonstrates that rainy day funds governed by strict rules about when they may be tapped do achieve modest success in smoothing revenue volatility. Because most states have balanced budget requirements, when tax revenues fall during business cycle downturns, states must respond by raising taxes or cutting spending, both pro-cyclical options. If states are required to contribute to rainy day funds when they have revenue surpluses and then are able to draw on these savings during downturns in order to avoid tax increases or spending cuts, this pro-cyclical trend can be avoided.

The Texas Public Policy Foundation points out some of the benefits of large rainy day funds:

Maintaining large “rainy day” funds  benefits Texas and Alaska in three ways:

1) These states do not rely  on large pots of one-time funding to pay for ongoing expenses, but rather balance their books by bringing spending in line with revenues;

2) These states  have reserves on hand to deal with emergencies; and

3) Having a large “rainy day” fund improves the states’ bond rating which means lower interest rates for borrowing.

However, even as more states begin making significant contributions to their rainy day funds, they have not fulfilled their pension obligations. According to states’ own estimates of their pension liabilities, states’ unfunded pension liabilities total about $1 billion. However using private sector accounting methods, states are actually on the hook for over $3 trillion in unfunded pension liabilities. Because states do not use the risk-free discount rate to value these liabilities, the surpluses they think they have to contribute to rainy day funds are illusions.

Even if states were already contributing appropriately to their pension funds and systematically contributed to rainy day funds during revenue upswings, it’s not clear that rainy day funds are a path toward fiscal discipline.  Because of the perpetual tendency for government to grow, it’s unlikely that state policymakers will take any steps to reduce the growth of government during times of economic growth. If states successfully save tax revenues in rainy day funds to avoid having to make spending cuts during recessions, states will not have to decrease spending at any point during the business cycle. States’ balanced budget requirements can provide a mechanism that helps states cut spending in some areas when revenues drop off, but rainy day funds obviate this requirement. Successful use of rainy day funds could contribute to the trend of states’ spending growing fast than GDP.

Supporters of substantial rainy day funds should acknowledge that these cushions — which on the one hand may provide significant benefits to taxpayers — come at the expense of cyclical opportunities to cut the size of state governments to bring them in line with tax revenues. Without the necessity of cutting spending at some point, state budgets might grow more rapidly that they already are, hindering economic growth in the long run. Whether or not rainy day funds increase the growth rate is an empirical question that advocates should research before recommending this strategy, and this possible drawback should be weighed against their potential to reduce revenue volatility.