Conversable Economist

Watching the travails of the euro area in the last few years, it seems as if the negative consequences of high government debt are likely manifest themselves with a bang: that is, a scenario in which investors fear that the debt will not be repaid, and thus begin demanding much higher interest rates for being willing to hold the debt, which then makes it impossible for the government to repay. Rounds of financial panic alternating with recrimination follow, while the economy of the country flounders. In a roundabout way, this scenario is oddly comforting for Americans, because there is no sign in the financial markets (and remember, financial markets look toward future interest rates, not just current rates ) that U.S. Treasury debt is anywhere near to experiencing a surge in its perceived riskiness.

But in the Summer 2012 issue of my own Journal of Economic Perspectives, Carmen M. Reinhart, Vincent R. Reinhart and Kenneth S. Rogoff offer a different scenario in \”Public Debt Overhangs: Advanced-Economy Episodes since 1800.\” They argue that very high levels of government debt can also lead to a debt-without-drama situation in which interest rates rise little or not at all, and no deep financial crisis occurs–but the economy nonetheless suffers a prolonged slowdown in its long-term growth rate. 

They begin by collecting the available data on advanced economies from 1800 to 2011, and found 26 situations in which the ratio of gross government debt/GDP in a certain country exceeded 90% for at least five years. U.S. government debt passed the gross debt/GDP ratio in 2010, but because it has not remained in that zone for five years, the current U.S. debt experience is not included in their group of 26 examples. They point out many patterns in this data, but here, I would emphasize three:

• When the government debt/GDP ratio climbs above 90%, it tends to remain there for awhile. They find only a few examples where the 90% ratio was reached that lasted less than five years–mainly cases of wartime debts that declined quickly after the war. As they note: \”the 26 episodes of public debt overhang in our sample had an average duration of 23 years.\” Some countries had multiple lengthy episodes of high government debt. \”For example, since 1848 (when the public debt data is available), Greece leads the way with 56 percent of the debt/GDP ratio observations above 90 percent.\”
• \”However, we find that countries with a public debt overhang by no means always experience either a sharp rise in real interest rates or difficulties in gaining access to capital markets. Indeed, in 11 of the 26 cases where public debt was above the 90 percent debt/GDP threshold, real interest rates were either lower, or about the same, as during the lower debt/GDP years.\”
• \”Consistent with a small but growing body of research, we find that the vast majority of high debt episodes—23 of the 26— coincide with substantially slower growth. On average across individual countries, debt/GDP levels above 90 percent are associated with an average annual growth rate 1.2 percent lower than in periods with debt below 90 percent debt; the average annual levels are 2.3 percent during the periods of exceptionally high debt versus 3.5 percent otherwise.\” The cases of high debt/GDP ratios and fast growth are typically cases of a bounceback from postwar rebuilding.

In discussing how government debt might lead to slower growth, there is a challenging problem of determining cause and effect. It is possible that high government debt leads to reduced growth, perhaps by leading to lower levels of domestic investment as government borrowing soaks up the available financial capital. (The authors do not have long-term data on investment levels to test this hypothesis.) But it is also possible that a country with slow economic growth might find it easier to build up excessive government debt and harder to muster the economic resources or political decision-making to reduce that debt. In all of these scenarios, high government debt and slow growth accompany each other–but which is the cause and which is the effect?

Reinhart, Reinhart, and Rogoff cite a number of studies using different groups of countries over different time frames, along with statistical approaches that seek to clarify the question of cause and effect (for example, instrumental variables, generalized method of moments estimation, measuring growth with five-year averages that are determined by other variables and thus not subject to feedback effects, fitting data to an endogenous growth model, and the like). They find:

 \”We would not claim that the cause-and-effect problems involved in determining how public debt overhang affects economic growth have been definitively addressed. But the balance of the existing evidence certainly suggests that public debt above a certain threshold leads to a rate of economic growth that is perhaps 1 percentage point slower per year. In addition, the 26 episodes of public debt overhang in our sample had an average duration of 23 years, so the cumulative effect of annual growth being 1 percentage point slower would be a GDP that is roughly one-fourth lower at the end of the period. This debt-without-drama scenario is reminiscent for us of T.S. Eliot’s (1925) lines in “The Hollow Men”: “ This is the way the world ends/Not with a bang but a whimper.” Last but not least, those who are inclined to the belief that slow growth is more likely to be causing high debt, rather than vice versa, need to better reconcile their beliefs with the apparent nonlinearity of the relationship, in which correlation is relatively low at low levels of debt but rises markedly when debt/GDP ratios exceed the 90 percent threshold. Overall, the general thrust of the evidence is that the cumulative economic losses from a sustained public debt overhang can be extremely large compared with the level of output that would otherwise have occurred, even when these economic losses do not manifest themselves as a financial crisis or a recession. …\”

\”This paper should not be interpreted as a manifesto for rapid public debt deleveraging exclusively via fiscal austerity in an environment of high unemployment. Our review of historical experience also highlights that, apart from outcomes of full or selective default on public debt, there are other strategies to address public debt overhang including debt restructuring and a plethora of debt conversions (voluntary and otherwise). The pathway to containing and reducing public debt will require a change that is sustained over the middle and the long term. However, the evidence, as we read it, casts doubt on the view that soaring government debt does not matter when markets (and official players, notably central banks) seem willing to absorb it at low interest rates—as is the case for now.\”

One striking feature of the Great Recession is that the unemployment rate for men spiked higher than that for women–but now has recovered to roughly the same rate. Here\’s an illustrative figure, courtesy of a chartbook published by the Stanford Institute for Economic Policy Research.

How unexpected is this pattern of \”mancession\” and \”he-covery\”? Although I wasn\’t expecting it, perhaps I should have been. Looking on the graph at the aftermath of the \”jobless recoveries\” that followed the 2001 and the 1990-91 recessions, in both cases the jobless rate spiked higher for men than for women. In the Summer 2012 issue of my own Journal of Economic Perspectives, Hilary Hoynes, Douglas L. Miller and Jessamyn Schaller investigate the question: \”Who Suffers During Recessions?\”(All articles in the JEP, from the current issue back to 1994, are freely available on-line courtesy of the American Economic Association. Starting this year, entire issues can also be downloaded in PDF, Kindle, or ePub formats.)

Here is the conclusion from Hoynes, Miller, and Schaller:

\”The labor market effects of the Great Recession have not been not uniform across demographic groups. Men, blacks, Hispanics, youth, and those with lower education levels experience more employment declines and unemployment increases compared to women, whites, prime-aged workers, and those with high education levels. However, these dramatic differences in the cyclicality across demographic groups have been remarkably stable since at least the late 1970s and across recessionary periods versus expansionary periods. These gradients persist despite the dramatic changes in the labor market over the past 30 years, including the increase in labor force attachment for women, Hispanic immigration, the decline of manufacturing, and so on.\”

 \”The general tone of these findings might be surprising given much emphasis in the press on the “man-cession”—that is, the greater effect that the Great Recession has had on men … Our analysis shows that men, across recessions and recoveries, experience more cyclical labor market outcomes. This is largely the result of the higher propensity of men to be employed in highly cyclical industries such as construction and manufacturing, while women are more likely to be employed in less-cyclical industries such as services and public administration. More generally, much of the difference in the cyclical effect across groups during the 2007 recession is explained by differing exposure to fluctuations due to the industries and occupations in which the groups are employed.

\”Although overall the 2007–2009 recession appears similar to the 1980s recession, responsiveness by women’s employment and by that of the youngest and oldest workers was somewhat greater in the more recent recession. Further, we do find evidence of a “he-covery;” and the extent to which the current recovery is being experienced more by men than women (compared to the 1980s recovery) is largely due to a drop in women’s cyclicality during the current recovery.\”

\”Despite these various distinctions, the overarching picture is one of stability in the demographic patterns of response to the business cycle over time. Who loses in the Great Recession? The same groups who lost in the recessions of the 1980s and who experience weaker labor market outcomes even in the good times. Viewed through the lens of these demographic patterns across labor markets, the Great Recession is different from business cycles over the three decades earlier in size and
length, but not in type.\”

What is the value of a human life? For U.S. regulatory purposes, the Environmental Protection Agency has a FAQ page up on the subject. Here\’s the bottom line:

\”EPA recommends that the central estimate of $7.4 million ($2006), updated to the year of the analysis, be used in all benefits analyses that seek to quantify mortality risk reduction benefits regardless of the age, income, or other population characteristics of the affected population until revised guidance becomes available …\”

On what sort of numbers is that estimate based? EPA offers this illustrative calculation:

\”In the scientific literature, these estimates of willingness to pay for small reductions in mortality risks are often referred to as the \”value of a statistical life.” This is because these values are typically reported in units that match the aggregate dollar amount that a large group of people would be willing to pay for a reduction in their individual risks of dying in a year, such that we would expect one fewer death among the group during that year on average. This is best explained by way of an example. Suppose each person in a sample of 100,000 people were asked how much he or she would be willing to pay for a reduction in their individual risk of dying of 1 in 100,000, or 0.001%, over the next year. Since this reduction in risk would mean that we would expect one fewer death among the sample of 100,000 people over the next year on average, this is sometimes described as \”one statistical life saved.” Now suppose that the average response to this hypothetical question was $100. Then the total dollar amount that the group would be willing to pay to save one statistical life in a year would be $100 per person × 100,000 people, or $10 million. This is what is meant by the \”value of a statistical life.” Importantly, this is not an estimate of how much money any single individual or group would be willing to pay to prevent the certain death of any particular person.\”

Other studies look a jobs that pose different mortality risks, and seek to estimate how much additional pay is required for people to take such jobs. Again, the willingness to take a certain amount of money in exchange for a change in the risk of dying can be translated into an estimated \”value of a statistical life.\” 

The estimate raises obvious questions, but hard experience has taught me that the obvious questions for me are not always the obvious questions for others! For many people, the obvious question is whether it isn\’t just morally wrong to put any value on life. To me, that question missed the point. Every time we set a rule or regulation at one level, and not another level, we are implicitly making a decision about the value of a human life. Think the speed limit should be slower, or faster, or the same? No matter your choice, you are implicitly setting a value on human life vs. other tradeoffs of time and money.

For me, one interesting question lies in the EPA assumption that every life has the same value, regardless of age or health characteristics. Thus, society should be willing to spend the same amount to save the live of an 80 year-old, a 40 year-old and a 10 year-old. EPA has some difficult history here. Back in 2003 it proposed a cost-benefit analysis of an air pollution regulation in which the statistical value of a life saved was lower for those over 70 than for those under 70. After a public outcry, this distinction was eliminated. Studies (like this one) show only weak support for the idea that those who are actually old or sick would place a lower value on their own live: indeed, some of those who are extremely ill tend to place a higher value on their lives in survey data. But when government is drawing up rules and regulations, it may choose other priorities.

Another interesting distinction about what value to place on life saved in the present vs. lives saved in the future–perhaps even several decades in the future. Ben Trachtenberg outlines some of these issues in a recent note for the UCLA Law Review. In the past, standard practice at the EPA and the U.S. Department of Transportation was that the value of lives in the future, like all costs and benefits arising in the future, was adjusted downward by a \”discount rate.\”\” He writes: \”Because, however, lives saved in the future were given the same nominal value as lives saved in the present, the real value of future lives was substantially eroded by discounting to present value, generally at annual rates of 3 and 7 percent. In other words, if a life saved today is worth $8 million, a life saved in ten or twenty years would be worth far less. A discount rate of 7 percent erodes half the value of a life expected to be saved in 2022 and three-quarters of one expected to be saved in 2032.\”

However, the rules have now changed. \”Before subjecting lifesaving benefits to the same discounting applied to other costs and benefits, the agencies adjust the values upward to reflect the expected higher income (and associated willingness to pay to avoid risks of harm) enjoyed by future persons. This seemingly minor procedural change can radically alter the expected benefits of major regulations …\” I suspect that this adjustment will prove quite controversial: after all, it suggest that future lives of those yet unborn have a higher value, before applying a discount rate, than present lives.

Yet another set of intriguing questions have to do with the fact that not all regulatory agencies use the same value for a statistical life: the EPA, the U.S. Department of Transportation, and the FDA use values that can be millions of dollars apart.

These issues matter because for most individuals and countries, the value of your life is the single largest asset you have. If my life is worth the EPA-approved $7.4 million, that is substantially higher than the value of any assets I am likely to accumulate in my life. A couple of weeks ago I posted about an effort by a group at the United Nations called the International Human Dimensions Program to measure the extent to which economic growth was sustainable by estimating the value of human capital, produced capital, and natural capital across countries in the Inclusive Wealth Report 2012.

That report included one of those thoughts that seemed obviously true, once someone had pointed it out. They excluded \”health capital\” from the calculations, because including it would have swamped everything else. \” \”Health capital of a nation’s population reflects the expected discounted value of years of life remaining. This is, understandably, a large number; indeed, we find that health capital makes up more than 90% of the capital base for all countries in the study. In the nations under study, the amount of health capital that each person owns outweighs all other forms of capital combined. Given a population, slight changes in mortality rates result in more or less health capital each
year.\”

Gains to health and life expectancy are extraordinarily important, but in a world of inevitable costs and tradeoffs, values and limits must still be set.

Ramanan Laxminarayan discusses \”A Matter of Life and Death: The Economics of Antibiotic Resistance,\” in the Third Quarter 2012 issue of the always-interesting Milken Institute Review. (The magazine is freely available, with registration, on-line.) Not only is the topic of considerable importance, but for teachers of introductory economics, it offers a nontraditional example of a shared natural resource–and the risk of a tragedy of the commons.

The problem arises, paradoxically, because antibiotics are such a miraculous medical invention that they are heavily and broadly prescribed, even for relatively minor conditions like bronchitis or ear infections, and even for virus-caused conditions like flu where antibiotics don\’t even work. When antibiotics are so widely used, bacteria mutate in response and build up resistance.

\”In the United States, for example, resistance to the bacterium methicillin-resistant Staphylococcus aureus (MRSA), has reached 60 percent. This means six out of 10 patients with this virulent staph infection can no longer be treated with oxacillin, a relatively low cost drug. But what still amounts to a cost problem in rich countries is becoming a serious threat to public health in the developing world: lower-income countries face a growing toll of death and morbidity from curable infections because the generally available antibiotics no longer work.\”

The problems only start with infections that are resistant. Without antibiotics, almost every form of surgery lead to additional and potentially severe infections.

One obvious answer is to invent new antibiotics, but it has gotten more difficult and costly to do so. \”[O]minously, the pace of development is slowing: 14 of the 16 classes of antibiotics in use
were introduced before 1970. Accordingly, options for treating patients who do not respond to older, less effective antibiotics are shrinking. … Fortunately, there seems to be a glimmer at the end of this tunnel. Two new antibiotic drug classes have been introduced during the past decade, ending a 40-year drought. Moreover, the pharmaceutical industry seems to be returning to antibiotic development, especially for soft-tissue skin infections …\”

Another issue is that the U.S. health care financing system reimburses the costs of antibiotics. However, it does not reimburse–at least not in a direct way–for alternative ways of reducing the spread of infection. One study found that Medicare alone spent $20 billion on addressing costs of hospital-caused infections in 2004. But except for some pilot programs, the health care system is readier to pay for costs of fighting infection than for costs of preventing its spread in the first place. \”The market for antibiotics may fail to produce economically efficient outcomes for other
reasons – notably underinvestment in other means of infection control like vaccinations
and good hospital management practices.\”

Laxminarayan does a nice job of talking through the possible solutions: encouraging development of additional antibiotics, encouraging a range of antibiotics, discouraging overuse of antibiotics when not especially necessary, and encouraging alternative ways of fighting infection. Here, I\’d just emphasize the author\’s point that for teachers of economics, antibiotics resistance offers an example of a shared natural resource. 

Many natural resources–like fisheries or forests or clean air–share the trait that if they are used in moderation, they have an ability to renew themselves and to continue. However, if they are overused, the resource can be depleted in a way that it has great difficulty in recovering. Moreover, as the \”tragedy of the commons\” scenario points out, every individual has an incentive to overuse a common resource, because the gains of using that resource all flow to the individual, while the social costs of overusing the resource are shared across society. This is the economic basis for arguing that natural resources need to be managed in some way: perhaps through private property rights like ownership or marketable quotas, or perhaps through more direct regulation of use, to prevent their overuse and depletion.

The effectiveness of antibiotics fits this scenario. Each doctor and patient has an individual incentive to use a wide spectrum of antibiotics to treat any given condition. The benefits to the patient are immediate, while the potential costs of creating greater resistance to antibiotics are shared across society. The effectiveness of antibiotics is an extraordinarily important social resource, but it is being eroded by overuse. Exactly how to prevent overuse of this resource is debatable, but the need to take steps to do so is clear.

The nature of what the federal government does is shifting over time, away from providing goods and services to the public and toward becoming a conduit for payments to households. Jessica Perez, Gabe Horwitz, and David Kendall summarize some trends in a report for a Democrat-leaning think tank called Third Way. They write: \”Entitlements are squeezing out public investments. In 1962, spending on investments was two and a half times that of entitlements. But today, as a result of this Great Inversion, entitlement spending is three times that of investments. And this trend will only accelerate in time as the Baby Boomers retire and their benefits grow faster than inflation and wages.\” 

What exactly is included in these categories? It takes a quick skip and a hop through federal budget documents to find out.

In the proposed Budget of the United States for FY2013 from the Office of Management and Budget, Table 8.5 in the Historical Tables summarizes \”mandatory spending.\” This category totaled about $2 trillion in 2011. Of that total, about three-quarters was payments from Social Security ($725 billion), Medicare ($480 billion) and Medicaid ($275 billion). Other big categories include payments to federal retirees ($124 billion), unemployment insurance ($117 billion) and food and nutrition assistance ($96 billion).  

For an overview of \”Federal Investment,\” the useful starting point is Chapter 21 with that title in the
Analytical Perspectives volume of the federal budget. It defines federal investment broadly:

\”Federal investment is the portion of Federal spending intended to yield long-term benefits for the economy and the country. It promotes improved efficiency within Federal agencies, as well as growth in the national economy by increasing the overall stock of capital. Investment spending can take the form of direct Federal spending or of grants to State and local governments. It can be designated for physical capital, which creates a tangible asset that yields a stream of services over a period of years. It also can be for research and development, education, or training, all of which are intangible but still increase income in the future or provide other long-term benefits.\”

Along with annual spending numbers, the chapter makes an interesting attempt to estimate the total value of the accumulated stock of federal investment in several areas. For example, it estimates the total stock of federally financed physical capital in 2011 was worth $3,054 billion, of which a bit under  one-third ($925 billion) is national defense, almost exactly one-third is transportation $1,017 billion), and the rest is for water and power, natural resources, community and regional resources, and \”other.\”

The chapter estimates the value of the total stock of federally financed research and development at $1.5 trillion in 2011. This total can be divided more-or-less 50:50 into the value of the stock of basic and applied research, or it can be divided about 60:40 into the value of the stock of non-defense and defense-related research and development.

Finally, the stock of federally financed education capital is given as a shade over $2 trillion in 2011, of which three-quarters is the total value of federally-financed elementary and secondary education, and the rest is fedearally-financed higher education.

I\’m sure the exact assumptions behind these numbers are debateable, but the broad theme seems well-established. Back in the 1960s, about one-third of all federal spending was devoted to building up these various types of capital. Now only half that share of federal spending goes to these purposes, and the Third Way report projects that public investment spending may be headed for just 5% of all federal spending in a few decades.  Conversely, entitlement spending was only about 15% of all federal spending back in the 1960s. Now it\’s more than half of all federal spending, and the share is rising. The main functions of what the U.S. government actually does are shifting before our eyes.

A half-century ago, white men dominated the high-skilled occupations in the U.S. economy, while women and minority groups were often barely seen. Unless one holds the antediluvian belief that, say, 95% of all the people who are well-suited to become doctors or lawyers are white men, this situation was an obvious misallocation of social talents. Thus, one might predict that as other groups had more equal opportunities to participate, it would provide a boost to economic growth. Pete Klenow reports the results of some calculations about these connections in \”The Allocation of Talent and U.S. Economic Growth,\” a Policy Brief for the Stanford Institute for Economic Policy Research.

Here\’s a table that illustrates some of the movement to greater equality of opportunity in the U.S. economy. White men are no longer 85% and more of the managers, doctors, and lawyers, as they were back in 1960. High skill occupation is defined in the table as \”lawyers, doctors, engineers, scientists, architects, mathematicians and executives/managers.\” The share of white men working in these fields is up by about one-fourth. But the share of white women working in these occupations has more than tripled; of black men, more than quadrupled; of black women, more than octupled.

Moreover, wage gaps for those working in the same occupations have diminished as well. \”Over the same time frame, wage gaps within occupations narrowed. Whereas working white women earned 58% less on average than white men in the same occupations in 1960, by 2008 they earned 26% less. Black men earned 38% less than white men in the typical occupation in 1960, but had closed the gap to 15% by 2008. For black women the gap fell from 88% in 1960 to 31% in 2008.\”

Much can be said about the causes behind these changes, but here, I want to focus on the effect on economic growth. For the purposes of developing a back-of-the-envelope estimate, Klenow builds up a model with some of these assumptions: \”Each person possesses general ability (common to
all occupations) and ability specific to each occupation (and independent across occupations). All groups (men, women, blacks, whites) have the same distribution of abilities. Each young person knows how much discrimination they would face in any occupation, and the resulting wage they would get in each occupation. When young, people choose an occupation and decide how
much to augment their natural ability by investing in human capital specific to their chosen
occupation.\”

With this framework, Klenow can then estimate how much of U.S. growth over the last 50 years or so can be traced to greater equality of opportunity, which encouraged many in women and minority groups who had the underlying ability to view it as worthwhile to make a greater investment in human capital.

\”How much of overall growth in income per worker between 1960 and 2008 in the U.S. can be explained by women and African Americans investing more in human capital and working more in high-skill occupations? Our answer is 15% to 20% … White men arguably lost around 5% of their earnings, as a result, because they moved into lower skilled occupations than they otherwise would have. But their losses were swamped by the income gains reaped by women and blacks.\”

At least to me, it is remarkable to consider that 1/6 or 1/5 of total U.S. growth in income per worker may be due to greater economic opportunity. In short, reducing discriminatory barriers isn\’t just about justice and fairness to individuals; it\’s also about a stronger U.S. economy that makes better use of the underlying talents of all its members.

Like a lot of teachers of economics, I have a riff about the deregulation of various industries in the late 1970s and early 1980s. For example, the Motor Carrier Act of 1935 allowed the Interstate Commerce Commission to set prices and limit entry for the trucking industry. However, in the 1970s Presidents Ford and Carter started appointing commissioners to the ICC who favored more deregulation, and the Motor Carrier Act of 1980 gave them the power to decontrol trucking.

Deregulation of industries at around this time (not just trucking, but also airlines, rail, banking, natural gas, and others) turned out to be a boon to consumers and innovation. For those who want the overall story, Clifford Winston had a nice overview of \”U.S. Industry Adjustment to Economics Deregulation\” in the Summer 1998 issue of my own Journal of Economic Perspectives. The Concise
Encyclopedia of Economics has a short article by Thomas Gale Moore on the history and benefits of \”Trucking Deregulation\” here.

The short story is that after decades of regulation that had set prices and blocked entry and exit, trucking was ready to evolve. Winston notes that the trucking industry traditionally has two parts:
\”‘less-than-truckload\’ (LTL), which uses a network of terminals to consolidate shipments of more than one shipper’s goods on a truck, and `truckload\’ trucking, which provides point-to-point
service for one shipper’s goods that fill an entire truck.\” In addition, some firms provide their own trucking services, while other firms contract for that service. Winston cites evidence that after deregulation, trucks carried fuller loads and service times improved. Costs and prices in the LTL segment dropped 35%; costs and prices in the truckload segment dropped 75%.

However, my riff on the effects of deregulation s largely draws on evidence from the 1980s and 1990s. I have no clear idea what\’s up with the trucking industry these days. As a starting point to getting me back up to speed, I was delighted to see the article by Nancy Condon called \”Truckonomics: An Industry on the Move\” that appeared in the Second Quarter 2012 issue of Econsouth, published by the Federal Reserve Bank of Atlanta. It has lots of intriguing tidbits:

The tonnage of goods carried by trucks is sometimes used as an indicator for the overall health of the economy. Thus, it\’s interesting that as of early 2011, truck tonnage had regained its pre-recession level.

In thinking about main concerns for the trucking industry looking forward, fuel prices do not seem a major concern, because truckers can typically pass along fuel costs to consumers. However, short-term price spikes can be a concern: \”There is typically a 45- to 60-day lag between when the carrier makes a shipment and when the shipper pays the carrier for that shipment. If the shipper is paying a fuel surcharge based on the cost of fuel two months ago, and the prices are experiencing a double-digit rise, the carrier can be seriously harmed.\” (Of course, the economist in me notes that it is quite possible for even small companies to hedge against short-term price spikes, if they are really concerned about this risk.)

The two issues of greater concern are constraints on the capacity of the industry and a potential shortage of drivers in the future.

The issue of capacity for the industry grows out of decisions made back in 2006, when the industry was expecting \”mandated decrease by the U.S. Environmental Protection Agency (EPA) in diesel
emissions for heavy-duty trucks, to take effect in 2007. So in 2006, many trucking companies conducted what is called a “pre-buy.” To avoid having to buy the upgraded, costlier trucks, they purchased inventory ahead of their normal schedule. So when the recession began in late 2007 and demand plummeted, these companies were left holding all their new equipment … With so much excess capacity, truck values also plummeted and the companies ended up upside down (that
is, they had negative equity) on their equipment. When times are good, most companies run their trucks through the warranty period—about 48 months—and then buy new ones to replace the old. More recently, many companies have held onto their trucks longer than normal for several
reasons: they lost the equity in their trucks, did not have enough cash reserves, or could not get credit even if they did have equity. All these factors combined to increase the age of the fleet in
the postrecession industry. … In 2011, the trucking industry was operating with 12 percent
fewer trucks than at the height of their business in 2006 …\” For now, capacity constraints are allowing trucking companies to raise their rates. But the demand for new trucks started rising again in 2011.

During the recession, and with fewer trucks on the road than several years ago, both the number of drivers and the number of recruiters has diminished. \”What’s more, many of the largest truckload carriers in the industry—including Swift, headquartered in Phoenix, Arizona, and Schneider National Inc. in Green Bay, Wisconsin—had eliminated their driver training programs altogether.\” Employment in trucking hasn\’t yet recovered to its pre-recession levels, and the industry is apparently thinking about ways to find new drivers: looking to demographic groups not well-represented among truckers in the past like women, redesigning routes so that long-haul truckers don\’t have to be away from home so long, and building heavy-duty trucks with automatic transmissions, which are physically easier to operate than the old 13-speed transmissions. It looks likely that the trucking industry will be looking to hire those who have the necessary licenses in the next few years.

Condon\’s six-page article whetted my appetite for more on the trucking industry. If anyone out there knows of a nice recent overview paper or report about structure, costs, industry practices, employment and wages in the trucking industry over the last decade or two, I\’d love to see it.

When we refer to an \”economy,\” we are often talking about a national economy, or in some cases the global economy. But of course, local economies operate as well. In fact, $13.1 trillion of the total U.S GDP of $14.5 trillion in 2010–roughly 90%– happened in urban economies.

In the table below, the first column lists the 20 largest U.S. metropolitan areas (which are broader than official city boundaries) ranked by size of the metro-area economy, while the second column shows the gross metropolitan product of those cities in 2010, using data from the U.S. Bureau of Economic Analysis. Just because comparisons like this always scramble my brain a bit, the last column shows some cities and countries of comparable economic size around the world. The city data is for 2008 data from a PriceWaterhouseCooper report. The estimates of country GDP are for 2010 from the World Bank.

At least for me, these kinds of intuition don\’t always fit my pre-existing intuitions. Now and again, it\’s useful to align one\’s thought with the data! For example:

• The New York City metro area has an economy more than twice the size of Chicago. The Los Angeles metro area has an economy about twice the size of Dallas or Philadelphia.
•  Shanghai, which is the largest city in China by the size of its metro-area economy, is roughly equal to the city of Seattle (or the country of Portugal). 
• My own metro area of Minneapolis is roughly equal in economic size to the country of Ireland or the city of Mumbai. 
• Saudi Arabia has an economy the size of the Dallas metro area. Nigeria has an economy about the size of Phoenix. Pakistan has an economy about the size of San Diego. Kuwait has an economy about the size of Baltimore.

First there was gross domestic product, and it was useful, but limited. After all, it measured only economic output, and left out considerations like the health or education of the population, or the quality of the environment, or whether a country was investing enough to sustain continued growth in the future. In the Spring  2008 issue of my own Journal of Economic Perspectives, J. Steven Landefeld, Eugene P. Seskin, and Barbara M. Fraumeni offer a tour through the history and the estimation of this statistic in \”Taking the Pulse of the Economy: Measuring GDP.\” The U.S. Bureau of Economic Analysis page for GDP is here.

In response to these shortcomings, the United Nations Human Development Program created the Human Development Index. Instead of ranking countries by GDP alone, it added life expectancy as a measure of health and years of schooling as a measure of education.  The Human Development Reports going back to 1990 are available here. This was a useful step, but there was controversy over how to combine and to weight these different characteristics. Moreover, while the Human Development Index offered a snapshot of comparisons across a single year, it didn\’t address in an direct way whether the country was on a sustainable trajectory. It also had nothing to say about the environment.

Now the United Nations has taken another stab at developing a broader measure of economic performance, this one focused on sustainability. It appears as the Inclusive Wealth Report 2012, and is apparently intended to be an ongoing report in the future. The effort was led by Partha Dasgupta, who long ago in graduate school taught me pretty much everything I know about the details of welfare economics. The report seeks to construct an inclusive measure of total wealth. For this first report, the emphasis is on three categories: the total value of human capital, produced capital, and natural capital. (There are also supplementary calculations to expand these measures to consider technological productivity, carbon damages, and capital gains on oil reserves.) The notion is that a sustainable economy will be increasing its total wealth over time–which allows for the possibility that gains in human capital or produced capital would offset declines in natural capital.

For detail on how these numbers are constructed, you can look at the report and the supporting working papers. Here, I\’ll just say that human capital wealth is based on a mixture of statistics on mortality, employment, education, pay, divided by age and gender. Produced capital is based on levels of investment, depreciation, the lifetime of assets, output growth, and productivity. Natural capital looks at statistics and prices related to on fossil fuels, minerals, forests, agricultural land, and fisheries. These measures of wealth are compiled for 20 countries from 1990-2008.

Here\’s a figure showing the calculations for the United States. The rising top line shows the growth in physical capital wealth over time. The bottom declining line shows the drop in natural capital wealth. The blue-gray line in the middle shows the rise in human capital over time. The green line combines these into an overall Inclusive Wealth Index. The rising level of the inclusive wealth index implies that U.S. economic growth is sustainable, in the sense that the country\’s stock of wealth is rising.

Compared to other advanced economies, the U.S. economy has a relatively high dependence on natural wealth and on human capital. Here\’s a graph for 2009. Compared to Norway, with its oil reserves, the U.S. economy relies less on natural capital. But relies more on natural capital than Japan, France, or the United Kingdom. The U.S. relies less on human capital than does the UK, but more than the other comparison countries here, which tend to rely more on physical capital.

Here is a figure showing the comparison for 1980-2009 for each of the 20 countries. For each country, the first bar combines annual growth rates for natural capital, human capital, and produced capital, as shown in the bar chart. The second bar for each country then shows the Inclusive Wealth Index. Among the top five countries on inclusive wealth, the growth of China, India, and Chile were  primarily driven by a rise in produced capital, while the growth of Germany and France were primarily driven by a arise in human capital.  The countries at the other end are largely resource exporters, and the decline in the value of their natural capital over time is not being offset by rises in human or produced capital.

The authors of this first Inclusive Wealth Index would be the first to say that the measures will need to develop and evolve over time. Rather than pick nits with the index, I\’ll just say that when it comes to economic statistics, I\’m a believer that \”more is better.\” Reality is multidimensional, and additional statistics are often useful for highlighting particular dimensions that I had not considered sufficiently.