Conversable Economist

On June 24, 2009, President Obama signed into law the \”cash for clunkers\” act. The idea was that if people traded in an older and less fuel-efficient car, they could receive a voucher for $3,500 or $4,500 to be put toward the purchase of a new and more fuel efficient car. The program had two goals in mind: 1) stimulating the economy by encouraging people to buy new cars; and 2) reducing auto emissions as people used newer cars that had better pollution-control equipment and were  fuel-efficient cars. The program handed out $2.85 billion in vouchers in July and August 2009. How did it work? Ted Gayer and Emily Parker tackle that question in \”Cash for Clunkers: An Evaluation of the Car Allowance Rebate System.\” The more readable \”Policy Brief\” version of their analysis is here; the more detailed background paper is here. Short take: Having the government hand out vouchers for buying new cars was not a cost-effective program.

CARS (that is, Car Allowance Rebate System) did encourage people to buy cars during the two months it was in operation. Here\’s a figure showing monthly car sales. The gray band is the recession. The red band is the actual cash-for-clunkers program. Clearly, there\’s a boost when the program was in operation, but just as clearly, there\’s no particular deviation from the long-run trend.

Indeed, it looks as if the main effect of the program was that some of the people who were thinking about buying a car in the next few months anyway accelerated their purchase, so the sustained effect was minimal. Gayer and Parker write that the \”CARS program led to approximately 380,000 additional vehicle sales during the time of the program. This number represents the number of vehicles sales that would not have occurred during this time period without the CARS program. The existing evidence also suggests that these sales were pulled forward from sales that would have occurred otherwise in the future. Ten months after the end of the program, the cumulative purchases from July 2009 to June 2010 were nearly the same, showing little lasting effect.\”

What does this mean in terms of jobs? The additional spending can be linked to 2,000 to 3,000 extra jobs during and right after the bill. Gayer and Parker put this in context using estimates from the Congressional Budget Office about cost-per-job-saved of various policy steps taken during the recession. As many readers know, the details of these job-saved estimates can be controversial. But the bottom line is that if you are using federal spending to save jobs, cash-for-clunkers is a highly ineffective way of doing so.

What about on the environmental side? About 700,000 cars were purchased with the cash-for-clunkers vouchers, which is less than 1% of the total cars in the United States. Moreover, most of these cars would have been purchased in the next few months, even without the program. So the potential environmental gains are a modest speed-up in the purchase of cars with better fuel efficiency and pollution-control equipment. Gayer and Parker write: \”Overall, the average fuel economy of the vehicles traded in under the CARS program was 15.7 miles per gallon and that of new vehicles purchased under the program was 24.9 miles per gallon.\”

The social cost of carbon emissions is again a controversial topic, but the current US government estimate is a cost of $38 per ton; that is, a method of reducing carbon emissions that costs less than $38/ton is a good deal for society, but a method of reducing carbon emissions that costs more than $38/ton is a poor deal. Even if one has qualms about the specific number, even the most ardent environmentalists should prefer methods of reducing carbon emissions at the lowest cost–because more carbon will be reduced in that way. By this standard, cash-for-clunkers wasn\’t as bad as some alternatives, but neither was it cost-effective. Here\’s some context on cost-per-ton of reduced carbon emissions from Gayer and Parker:

Gayer and Parker also write: \”Note that these estimates of the reduction in gasoline consumption and emissions do not account for the energy consumed by prematurely disposing of used vehicles and the manufacturing of additional vehicles due to the CARS program, which would offset some of the program’s environmental benefits.\”

So to sum up: The taxpayer-funded cash-for-clunkers program was not a cost-effective way of job creation or helping the environment. Indeed, the main benefits probably went to those who were already thinking about a new car and were in a financial position to proceed immediately with buying one. Not surprisingly, those who used the taxpayer-funded cash-for-clunkers vouchers were usually people with above average-incomes.

I should say up front that I know little about financial investment strategies; indeed, all of my retirement assets are in various Vanguard no-load passively managed index funds. But after watching the financial sector jolt the U.S. economy both in recession that followed the dot-com boom of the 1990s and in the more recent Great Recession, I do spend a little more time looking at broad financial indexes than I used to. Here\’s what\’s called the Shiller P/E ratio. A P/E ratio looks at the total price of stocks compared with the earnings of companies, and thus gives a sense of how the stock market is being valued.  If you calculate a price/earnings ratio using annual data, then in a dismal economic year like 2008 when profits are very low, the P/E ratio will spike dramatically. To avoid these somewhat meaningless short-term spikes, the Shiller P/E ratio looks the current price of stocks divided by the average profit levels over the previous 10 years, so that it is less influenced by economic conditions this year.

The Shiller P/E is now 24.8. As the figure shows, it is higher than at any time except the peak of the dot-com boom and its aftermath, and Black Tuesday back in 1929 at the front edge of the Great Depression. In other words, when the P/E ratio has reached this level in the past, sometimes it has gone still higher (as in the dot-com boom), but over the last 130 years it has then always fallen back.  

I\’m not the right person to ask about what this means from an investment point of view: as I said, I\’m a boring index-fund investor. (John Hussman, who knows quite a bit about investments, discusses this pattern over at his website. Full disclosure: Many years back at Stanford, John was a teaching assistant for me and had an office down the hall. We are friends who have fallen out of touch.) My broader concern for some time has been that the extremely low interest rate policies that have been pursued by the Federal Reserve and central banks around the world run a risk of pumping up asset bubbles in other markets, which may pop painfully later on. The IMF has expressed similar concerns, along with Raghuram Rahan (a University of Chicago economist now heading India\’s central bank), economists at the Bank of International Settlements, and others. 

There are two main responses to concerns about the U.S. manufacturing sector: either the changes in manufacturing are mainly part of a long-run structural shift that goes back decades in the U.S. economy and is happening in high-income economies across the world, or there is something new and worrisome happening in the last decade or so.  Robert Z. Lawrence and Lawrence Edwards discuss \”US Employment Deindustrialization: Insights from History and the International Experience,\” written as Policy Brief #PB13-27 for the Peterson Institute for International Economics.  They come down more heavily on the \”long-run structural shift\” side of the argument.

Serious discussion of the U.S. manufacturing sector is based on three claims:

1) If you measure the output of the U.S. manufacturing sector in terms of the value-added in that sector, there is no trend up or down in recent decades. As Lawrence and Edwards write: \”Measured in 2005 dollars, manufacturing share of gross US output was 17.5 percent in 1947 and 17.3 percent in 2005. Between 1947 and 2005 the share averaged 17.3 percent and was essentially trendless …\” Value-added is calculated by taking the total revenues of a firm and then subtracting the value of purchased inputs of goods and services: thus, it includes not only profit but costs of labor and depreciation incurred by the firm itself in the course of production.

2) The number of jobs in manufacturing has fallen over time; in particular, the total number of U.S. manufacturing jobs didn\’t change much in the 1990s, but the U.S. manufacturing sector lost 5.8 million jobs from 2000-2010. However, Lawrence and Edwards point out if you look at the proportion of total U.S. jobs that are in manufacturing, it looks like a straight-line drop over time.

3) The manufacturing sector has a particular importance in any advanced economy. As Lawrence and Edwards write: \”[M]anufacturing activity is strongly associated with economic growth, because manufacturing serves as the fulcrum of supply chains that combine and process raw materials and services to produce goods.1 In addition, the sector is among the most dynamic—accounting for about 70 percent
of US spending on business research and development—and it regularly outstrips the rest of the economy in productivity growth.\”

Taking these factors together, a pattern arises that is familiar across high-income economies. For the U.S. economy, the price of manufactured goods relative to services has been dropping about 2% per year since 1960, driven by the relatively faster gains in productivity in the manufacturing sector. Consumers have increased their purchases of goods relative to service by about 0.5% per year since 1960. Putting these together, the amount spent on goods relative to services by U.S. consumers has been dropping about 1.5% per year since 1960. Thus, it takes fewer workers in U.S. manufacturing to produce the goods that American consumers want to buy, and this would hold true even if there was no international trade.

Lawrence and Edwards readily admit that trade makes a difference to the U.S. manufacturing sector, too. If the U.S. economy didn\’t run large trade deficits, they estimate that U.S. manufacturing jobs would be at a higher level–but such jobs would still be steadily declining over time at about the same rate.

These issues are common across high-income countries. Here\’s a table showing the decline in U.S. manufacturing jobs compared with some other high income countries, and the U.S. experience is about average. Notice that countries like Japan and Germany, which have often had substantial trade surpluses in recent decades, have still experienced a decline in manufacturing jobs.

The drop in the share of consumption represented by goods is also common across these countries.

There are some potential bright spots for U.S. manufacturing. Cheaper energy in the U.S. economy would give the energy-intensive manufacturing sector a boost. A reduced trade deficit and stronger recovery from the Great Recession would help manufacturing jobs. Advances in automation could improve the competitiveness of the U.S. manufacturing industry, although it would also contribute to the long-run decline in jobs. Similarly, wrapping the U.S. manufacturing sector into global supply chains can be a way to preserve U.S. manufacturing expertise–and in the 21st century, being apart from global supply chains in some sectors be an economic death sentence. New high-value, high-technology products may be manufactured in the U.S. economy. But while these steps can help keep manufacturing a lively and important part of the U.S. economy, the long-term decline in manufacturing jobs seems likely to continue.

Finally, here\’s a post from about a year ago on a global manufacturing report from the McKinsey Institute that includes useful figures showing patterns of manufacturing output and jobs over time in different countries.

President George W. Bush signed the Troubled Asset Relief Program into law on October 3, 2008. What has happened with it five years later? For me, the main difficulty in thinking about TARP has been keeping track of all the things it ended up doing. The U.S. Treasury has a useful website that runs through the details of TARP. As one might expect, it\’s slanted toward the position that TARP was a good idea. But that bias doesn\’t affect the actual numbers of what the government spent and the extent to which it has been paid back.

TARP was authorized to spend $700 billion. What did it actually do? The money went five places: 1) $68 billion to the insurance company AIG; 2) $80 billion to the auto companies; 3) $245 billion to bank investment programs; 4) $27 billion to credit market programs; and 5) $46 billion to housing programs. The other $235 billion in spending authorization was cancelled. Let\’s unpack these categories and see what happened.

AIG

The government justification for investing in AIG looks like this: \”At the height of the financial crisis in September 2008, American International Group (AIG) was on the brink of failure. At the time, AIG was the largest provider of conventional insurance in the world. Millions depended on it for their life savings and it had a huge presence in many critical financial markets, including municipal bonds. AIG’s failure would have been devastating to global financial markets and the stability of the broader economy. Therefore, the Federal Reserve and Treasury acted to prevent AIG’s disorderly failure.\” The Treasury used its $67 billion to buy AIG stock and it has sold off that stock since then, finishing in December 2012. The Treasury ended up making a gain of $5 billion. The Federal Reserve Bank of New York also loaned AIG $112 billion, and has ended up making a gain of $7 billion as that loan has been repaid. These gains end up in the U.S. Treasury.

The auto industry

Treasury reports: \”The Automotive Industry Financing Program (AIFP) was created to prevent the collapse of the U.S. auto industry, which would have posed a significant risk to financial market stability,threatened the overall economy, and resulted in the loss of one million U.S. jobs. Treasury invested approximately $80 billion in the auto industry through its Automotive Industry Financing Program. As of September 30, it has recovered $53.3 billion or more than 66% of the funds disbursed through the AIFP program.\” The Treasury exited its investment in Chrysler in 2011, and the government now owns only about 7% of GM stock, down from 60% at the peak. The website reports that \”the auto industry rescue may end up as a net cost to the government.\” It\’s useful to remember that the TARP money wasn\’t all that the government did. As I discuss here, the government also stage-managed an accelerated bankruptcy process that reorganized the ownership of Chrysler, in a way that did much less for the bondholders than a standard bankruptcy process, and more  for the the employees. The firms were also handed created tax breaks not usually available to bankrupt firms worth tens of billions of dollars.

Bank investment programs

This was actually five separate sub-programs; for example, it includes the \”stress tests\” under which bank regulators re-examined the balance sheets of many banks under a variety of different scenarios, and pushed some of them to raise more outside private capital as a result. But in terms of spending, the biggest element was the Capital Purchase Program that provided investments and loans to about 700 banks. \”Treasury has recovered almost $225 billion from CPP through repayments, dividends, interest, and other income – compared to the $204.9 billion initially invested. Treasury has recovered more than 100 percent of that amount through repayments, dividends, interest, and other income. Treasury continues to recover additional funds.\”

Credit Market Programs

\”Three programs were launched: the Public-Private Investment Program (PPIP), the SBA 7(a) Securities Purchase Program, and the Term Asset‐Backed Securities Loan Facility (TALF). Although the specific goals and implementation methods of each program differed, the overall goal of these three programs was the same—to restart the flow of credit to meet the critical needs of small businesses and consumers.​\” These three programs are no longer making loans and are in the process of being wound up. They have either repaid the government money invested, or are on their way to doing so.

Housing Programs

There are two main programs here. The Making Home Affordable Program is aimed at assisting households that are faced with foreclosure on their home mortgage. The website reports that 1.2 million households have negotiated lower mortgage payments (usually with some write-down of the principal) and another 200,000 have arranged to sell their homes for less than the mortgage. The Hardest Hit Fund aimed at mortgage borrowers in 18 states where the fall in housing prices was especially severe and/or the unemployment rate was exceptionally high. It allocated funds to state-based programs. As of second quarter 2013, the summary report is that it has spent $1.6 billion to assist about 126,000 households nationally. Here in late 2013, these programs are still taking applicants, which seems misguided to me. They were badly needed during the past few years, when they did relatively little.

Overall, the Treasury reports that TARP is likely to end up costing the federal government about $40 billion. As this review should help to clarify, most of that is for the auto company bailout, and the rest is the housing and credit market programs. The bank investment programs and the AIG investment have ended up making money for the government. This outcome wasn\’t unexpected. The economic theory behind a lender-of-last-resort program is well-established. In the middle of a financial panic, as in fall 2008, financial markets can lock up. In that setting, having a deep-pockets government agency like the Treasury or the Federal Reserve provide capital can restart the financial markets. Even better, when the government provides financial liquidity during a crisis, it can then often make money when it cashes out its financial stake after the crisis has passed, when the economy has improved.

Of course, the fact that most of the TARP spending has ended up being repaid doesn\’t settle the issue of whether it was a good idea. Here are some outstanding issues:

1) The bailouts of 2008 raise an issue of whether systemically important financial institutions can reasonably expect future bailouts if they get in trouble. If so, they may engage in overly risky behavior in the future. The government can make many promises that it won\’t bail out large institutions in the future, but if push comes to shove, will those promises be kept?

2) We don\’t get to replay history to find out how alternative policies would have worked. What if AIG, the car companies, or some of the banks had been required to reorganize through a normal bankruptcy process?

3) Many firms went broke in the Great Recession. As a matter of fairness, what makes the firms that TARP helped so special?

4) TARP was intertwined with separate but related actions by the Federal Reserve, by bank regulators, with bankruptcy processes for the car companies, various tax law changes, and so on. As a result, it\’s hard or impossible to evaluate the effect of TARP in a vacuum.

5) When you step in during a crisis, there is some luck involved if your intervention works out well.

Here\’s the pattern of American daily calorie consumption since the early 20th century. I suspect that some of the the ups and downs from 1900 up to about 1980 are due to issues with data and measurement, as well as social trends that came and went. But there\’s no mistaking the rise in the last few decades.

From an international perspective, Americans consume a large amount of sugar (whether from cane or beets) and related high-calorie sweeteners like high-fructose corn syrup.

The Credit Suisse Research Institute ponders the economic background and health consequences of these patterns in \”Sugar Consumption at a Crossroads,\” a report released in September 2013. A sizeable share of the report looks at the evidence linking sugar consumption in various forms to obesity, the health consequences of obesity, and possible policy options like a tax on sugary beverages.  Here, I\’ll stick to looking at patterns of consumption and production.

The report emphasizes that the growth in U.S. consumptin of calories can be linked to sweetened beverages: \”Sweetened  beverages are now delivering  an increasingly greater percentage of the sugars that are ingested in an average diet. Between 1955  and 2000,  the consumption  of  soft drinks in the USA increased from about ten gallons/person to  54 gallons/person and then declined by around 20% until 2012, but with an equivalent increase in the consumption  of fruit juices and bottled water. According to  the USDA, the beverage industry now accounts for 31% of total sweetener  deliveries  and we estimate  that 43% of added sugars in a normal US diet come from sweetened  beverages. A similar stabilizing trend can be seen in most other developed markets, while consumption is still on the rise in emerging markets.:

and is unsurprisingly dominated by sugar. Each of the major groups (high-intensity/artificial sweeteners,
sugar, and high-fructose corn syrup) has been growing at a similar rate of circa 2% per annum, though the most recent numbers have natural high intensity\\sweeteners growing rather faster.\”

\”Many countries have regimes that protect the local production through various mechanisms including support prices, import restrictions, production quota, etc. Examples include the US Farm Act, the European Union Sugar Regime, or the Chinese government’s controls on imports. Put simply, the complexity of the infrastructure surrounding sugar is significant. Thus, the traded market (or the “world market”) is only 55–60 million tons, and is sometimes referred to as the residual market (where the sugar that is not a part of the special agreements is bought and sold). The largest producer of sugar by some distance is Brazil (22% of world production), followed by India (15%), China (8%) and Thailand (6%). However, India and China consume all they produce, so if we look at the supply to the “world market” instead, this is dominated by Brazil (supplies typically half the “world market”) and Thailand (10%–15%). 

The “residual” nature of the world market has made the “world price” very volatile and sensitive to movements in global supply versus demand. … Brazil’s cost of production is generally thought to be USD 18 cents/lb. and, in the long term, this should be the floor of the market. As we mentioned earlier, most of the markets are protected/controlled, which means the local price bears little significance to the world price – and trades at a significant premium (see Figure 20). These regimes have been in place for many years and are designed to protect the local farmers from the vagaries of the world price and guarantee them an economic return. …
Finally, the market for HFCS [high fructose corn syrup] is similar in size to that of HIS, but is concentrated in three major markets: USA, China and Japan. The principal requirement for HFCS to flourish is government support. HFCS can only truly become established where it is allowed and where there is enough supply of starch. …

Here\’s a figure showing the retail price of sugar. It can\’t be compared directly to Brazil\’s cost of production of 18 cents per pound, but the comparison is nonetheless interesting.

If one is concerned about the effects of high levels of sugar consumption on public health, what might be done? One option is to have a tax on foods that are high in sugar, which presumably would need to include not just sugared soft drinks, but also many kinds of drinks flavored with sugar or fruit juice, and other high-sugar foods. The Credit Suisse report leans in favor of such taxes; my own somewhat more pessimistic review of the workability and desirability of such taxes is here.

Another option is technological: that is, find a way to sweeten foods that doesn\’t have (many) calories. The difficult here is that while most societies have no regulation that limit loading up food and drink with sugar, they often have strict regulations about using alternative sweeteners. The Credit Suisse report writes: \”The market for high-intensity sweeteners, both natural and artificial, is completely open, but the products are the most heavily regulated among sweeteners. These regulations vary from country to country. A high-intensity sweetener cleared in one country may be banned in another. The artificial sweetener industry’s profile on health is somewhat colored and many still see some of these products in a bad light. This is not the case for natural HIS, the largest portion of which is made of polyols (sugar alcohols).\” At least so far, many people have more fear of how artificial sweeteners may affect their health than about how high sugar intake might affect their health. Also, many of the artificial sweeteners don\’t really taste like sugar.

A final option is some sort of social attitude adjustment. Just as shifts in social attitudes brought us the explosion in soft drink consumption, and more recently the explosion in bottled water consumption, a future change could mean lower consumption of added sugars. The Credit Suisse report has some suggestive if not dispositive evidence here. If one looks across regions of the United States, those regions with a higher average level of education and higher average levels of income are more likely to consume diet soda. Of course, this kind of correlation doesn\’t prove a cause and effect. But it does suggest that there are different cultural norms even within the United States about drinking sugared soda, and thus some hope for a healthier set of sugar-consumption norms to emerge.

Douglas Clement has a lively and incisive interview with Richard Thaler in the September 2013 issue of the The Region magazine, which is published by the Federal Reserve Bank of Minneapolis. Thaler is well-known as one of the leading figures in \”behavioral\” economics, which involves thinking about how common psychological factors may cause people to act in ways that differ from what is predicted in a basic economic model of people purposefully pursuing their own self-interest. Here are a few of Thaler\’s comments that jumped out at me.

Getting started in thinking about behavioral economics

[L]ater I would call them anomalies, but for a while I just called them “the list.” And I started writing a list of funny behaviors on my blackboard, such as paying attention to sunk costs. I mean, at first they were just stories. Like, a buddy of mine and I were given tickets to a basketball game. Then there’s a blizzard and we don’t go. But he says, “If we had paid for the tickets, we would have gone.” Another thing on the list was a story about having a group of fellow grad students over for dinner and putting out a large bowl of cashew nuts. We started devouring them. After a while, I hid the bowl in the kitchen and everyone thanked me.But as econ grad students, of course, we immediately started asking why we were happy about having a choice removed. For years, some of my friends referred to my new research interests as “cashew theory.”

Behavioral economics and finance

The biggest surprise about behavioral economics, I think, looking back on it all, is that the subfield where behavioral has had the biggest impact is finance.If you had asked me in 1980 to say which field do you think you have your best shot at affecting, finance would have been the least likely, essentially because of the arguments that [Gary] Becker’s making: The stakes are really high, and you don’t survive very long if you’re a trader who loses money.But for me, of course, that was exactly the attraction of studying finance …

The random walk and the rationality assumption

Bob Shiller has this great line in one of his early papers to the effect that if you see a random walk, concluding from that that prices are rational is the greatest error in the history of economic thought. Why? Because it could be a drunken walk. A drunken man will have a random walk and it’s not rational.

No free lunch and price is right?

I separate these two aspects of the efficient markets argument: Whether you can get rich (the “no-free lunch” part) and whether the “price is right.” It’s hard to get rich because even though I thought Scottsdale real estate was overpriced, there was no way to short it. Even if there were a way—[Robert] Shiller tried to create markets in that, so that you could have shorted it—you might have gone broke before you were right. …  I think it’s hard to beat the market. Nobody thinks it’s easy, and so that part of the hypothesis is truer, but if we look at what happened to Nasdaq in 2000, and then the recent crash, well, of course, we’ve never gotten back to 5,000. So it’s very hard to accept that markets always get prices right. … If anything, the Internet has wildly exceeded our expectations, but the Nasdaq has still not gotten close to where it was in 2000. So I think it’s pretty obvious that market was overheated, just like the Las Vegas and Phoenix real estate markets were, but you couldn’t say necessarily when it was going to end.

A story of a non-price behavioral economics intervention in the United Kingdom

Let me tell you another story about the U.K. We had a meeting with the minister in charge of a program to encourage people to insulate their attics, which they call “lofts”—I had to learn that. Now, any rational economic agent will have already insulated their attic because the payback is about one year. It’s a no-brainer. But a third of the attics there are uninsulated. The government had a program to subsidize insulation and the takeup was only 1 percent.

The ministry comes to us and says, “We have this program, but no one’s using it.” They came to us because they had first gone to the PM or whomever and said, “We need to increase the subsidy.” You know, economists have one tool, a hammer, and so they hammer. You want to get people to do something? Change the price. Based on theory, that’s the only advice economists can give. …

So we sent some team members to talk to homeowners with uninsulated attics. “How come you don’t have insulation in your attic?” They answered, “You know how much stuff we have up there!?” So, we got one of the retailers, their equivalent of Home Depot, that are actually doing the [insulation] work, to offer a program at cost. They charge people, say, $300; they send two people who bring all the stuff out of the attic. They help the homeowners sort it into three piles: throw away, give to charity, put back in the attic. And while they’re doing this, the other guys are putting in the insulation. You know what happened? Up to a 500 percent increase. So, that’s my other mantra. If you want to get somebody to do something, make it easy. 

On August 29, 2005, Hurricane Katrina slammed into the Gulf Coast area, causing $125 billion in property damage and more than 1,800 deaths. According to U.S. Census Bureau estimates, in the July 2005 the population of the New Orleans-Metairie-Kenner metropolitan area was area at a shade over 1.3 million, essentially unchanged since 2000. By the July 2006 count, dropped to 978,000. The population has rebuilt slowly since then, up to nearly 1.2 million by July 2009, but remains below the pre-storm level. What about the economy of New Orleans? As I\’ll try to explain, it\’s a story with twists and turns, but perhaps without any clear policy implication.

Charles Davidson has written \”\”The Big Busy: A radical reset after the Katrina catastrophe is transforming the economy of New Orleans,\” in the Third Quarter 2013 issue of Econ South, published by the Federal Reserve Bank of Atlanta. He draws to some extent on data from the Greater New Orleans Community Data Center and its report \”The New Orleans Index at Eight.\” For some of the good news, here\’s a figure showing job growth in various metro areas since 2008. New Orleans lost fewer jobs than the rest of the country. The group of \”aspirational metros\” is seven Southern metro areas with populations of more than 1 million that have seen at least 10% job growth since 2000: Orlando, Nashville, Raleigh, Charlotte, Austin, Houston, and San Antonio. Just the fact that job growth in those cities since 2008 are closely comparable to those in New Orleans since 2008 is a big change.

Here\’s a figure showing wage levels in New Orleans compared to the U.S. level. Notice that a substantial gap opened up between wages in the New Orleans metro in the 1980s and 1990s, but after 2005 that gap closed somewhat. Of course, there\’s a difficult question here. A substantial share of those who left New Orleans and didn\’t return after Katrina were those with lower incomes, whose presence had been holding down average wages. Right after the storm, the area received considerable funds from insurance companies and government programs for clean-up and construction. Thus, are these higher wages an artifact of altered population composition and demographics, along with a short-term gusher of rebuilding money? Or a phenomenon that might have longer-term sustainability?

The argument that the gains in wages might be sustainable is based in part on the fact that New Orleans has experienced a wave of entrepreneurial behavior. This figure shows the number of individuals per 100,000 starting up businesses. New Orleans has risen from well below the U.S. average to well above it.

Much of Davidson\’s article is focused on how the storm transformed attitudes and governance in New Orleans. He quotes Michael Hecht, president of the largest economic development agency in the region, in this way: “New Orleans was like a morbidly obese person who finally had a heart attack that was strong enough to scare them, but not strong enough to kill them … Katrina laid bare that this was a city and a
region that had been in slow, decadent decline, probably since  the ’60s …”

The economic analysis of the city of New Orleans looks back  before Hurricane Katrina, and sees a city in long-term decline. Jacob Vigdor laid out the issues in \”The Economic Aftermath of Hurricane Katrina,\”
which appeared in the Fall 2008 issue of the Journal of Economic Perspectives. (Full disclosure: I\’ve been the Managing Editor of JEP since the first issue in 1987.) Vidgor points out that many cities which have experienced disasters revert over time to their previous pattern of population: for example, this graphs shows population levels before and after for some cities that experienced heavy bombing during World War II, along with San Francisco and the 1906 earthquake, and Chicago and its 1871 fire.

So what was the long-term trend for New Orleans before Hurricane Katrina? As Hecht says, it was \”slow, decadent decline.\” Here\’s a figure from Vigdor showing the population of New Orleans as a share of U.S. population. It\’s no surprise that the New Orleans share of the U.S. population falls during the 19th century, at a time when the U.S. was adding states. What is perhaps more interesting is that the New Orleans share of the U.S. population was largely unchanged from about 1880 to 1950, when it began to decline. Vigdor argues that U.S. cities since 1950 have experienced two transforming changes: the rise of suburbs and a reliance on \”knowledge-based\” industries as the basis for growth–and that New Orleans essentially  missed both of these transformations. The population of the city of New Orleans itself had fallen from more than 600,000 in 1960 to less than 400,000 in 2005, before Katrina hit.

Vigdor also emphasizes a recent lesson in the economic thinking about cities in economic decline: When a city is declining, low-quality housing can become quite inexpensive. The result is that those with low incomes find it hard to leave the city, because although their prospects for earning income aren\’t good, their cost of housing is low, and moving to some other area with a higher cost of housing seems like a high-risk choice. But Hurricane Katrina blasted the New Orleans housing stock. Vigdor wrote: \”The 2000 Census counted just over 215,000 housing units in the city of New Orleans. By 2006, the estimated number of units had declined to 106,000, of which more than 32,000 were vacant. Although these vacant units appeared intact from the exterior, most of them undoubtedly required significant interior rehabilitation prior to occupation. Hurricane Katrina thus rendered two-thirds of the city’s housing stock uninhabitable, at least in the short term.\” To be sure, a substantial amount of this housing stock was eventually refurbished. But some of the cycle of low-income people living in low-cost housing was diminished, partly because a number of those low-income people ended up relocated in other cities, and partly because much of the refurbished housing was no longer as inexpensive as it had previously been.

Another piece of evidence is from another report by Vicki Mack and Elaine Ortiz of the Greater New Orleans Community Data Center, \”Who lives in New Orleans and the metro area now? Based on 2012 U.S. Census Bureau data.\” Here is some of their data on education. The first bar, the parish of Orleans, is the same as the city of New Orleans. The next two bars show a couple of nearby parishes,  then the metro area around New Orleans. Notice that for the city of New Orleans in particular, the share of those with only a high school diploma fell sharply and the share of those with at least a bachelor\’s degree rose sharply. The main industry for New Orleans is tourism. But for a city hoping to build a stronger future in knowledge-based service industries, this rise in educational attainment is encouraging.

New Orleans as a city and a metropolitan area has a chance to move into the future with a different kind of momentum: that is, as a city in which tourism and dilapidation plays a relatively less important role, and entrepreneurs with higher education levels living in a refurbished housing stock play a greater role. But of course, these changes have arrived only after the gut-wrenching destruction and deaths from Hurricane Katrina, and the forces it created for resettlement and rebuilding.  There\’s no question that the economy, schools and governance of New Orleans circa mid-2005 were stagnating in a way that wasn\’t providing opportunity for many of its residents–but what a dreadful and disruptive way for some welcome changes to arrive.

Americans on average are healthier and living longer. U.S. jobs on average are moving away from hard physical labor and toward service jobs and brainwork. And yet, the percentage of Americans who are officially too disabled to work has been rising for a quarter-century. Tad DeHaven lays out some of the trends in \”The Rising Cost of Disability Insurance,\” written as Cato Institute Policy Analysis #733 (August 6, 2013).

Here\’s a figure showing the share of those receiving federal disability payments per 1,000 U.S. workers. Back in the mid-1980s, there were about 30 recipients of disability for every 1,000 workers; now, it\’s up to 75 recipients of disability for every 1,000 workers.  .

As you might guess, this sharp rise in disability has less to do with a sharp drop in levels of physical health, and more to do with a sharp rise in people who are disabled with \”nonexertional conditions,\” like someone who has a high level of depression or anxiety, or who experiences pain, often back pain, from a \”musculoskeletal condition.\” These are real conditions, and they are conditions where is can be hard to verify their severity. DeHaven points out that the National Academy of Medicine estimates that 116 million Americans suffer from some form of chronic pain, and the National Institute of Mental Health estimates that 61 million Americans suffer from some mental disease. Of course, most people with these conditions manage to continue their day-to-day functioning, including holding a job.

The Social Security Disability Insurance program is funded by a payroll tax of 1.8 percent of income up to a certain level, which is $113,700 this year. With the rising number of recipients, it\’s no wonder that the SSDI trust fund is even now dropping below the minimum level for financial solvency and will probably be empty in a few years, according to the annual report of the system\’s actuaries.  (The top line shows the path of the Social Security trust fund, with three scenarios for the future; the bottom line shows the path of the disability insurance trust fund, again with three scenarios.)

There are essentially two broad approaches for fixing disability insurance, and it would behoove us to choose both of them. The first approach is to encourage people to treat disability as a short term event, and to allow disability to be partial, and thus allowing for some work. This might be accomplished through some combination of program restructuring and financial carrots. For example, one approach along these lines would have employers purchase private-sector disability insurance that could last up to a couple of years. During this time, the private-sector insurance company would have an incentive to find ways to help the person get back to work, at least part-time. If such efforts didn\’t succeed after a couple of years, only then would the person migrate over to the federal disability insurance program. Here\’s a discussion of such a proposal from David Autor and Mark Duggan. Another approach would tinker with letting those who are receiving disability benefits continue to receive some of those benefits even if they find work. One never wants to set up a situation in which earning $1 means losing $1 of government benefits, because the incentives are much the same as a 100% marginal tax rate. Instead, disability benefits could be phased down so that for every $1 earned, the person might lose only 25 cents in disability benefits. Here\’s an example of such a proposal from Jagadeesh Gokhale.

The other approach is to get tougher about demanding that those who receive disability insurance payments are really and truly disabled. DeHaven points to a number of troubling anecdotes that suggest the possible scale of the problem. For example, anyone denied a disability claim can appeal the decision at five levels, represented by lawyers working for contingency fees. Fees paid to lawyers as a part of disability appeals tripled from $425 million in 2001 to $1.4 billion in 2011. Some judges find that almost 100% of claims of back pain should receive disability status, while others find that fewer than 20% of back pain claims deserve disability status. During a recent four-year period a single judge in Pennsylvania overruled the Social Security Administration on 2,285 cases, and made these 2,285 people eligible for disability insurance payments. The decisions of that single judge have led to $2 billion in disability insurance payments. There are private-sector consulting companies who are hired by states and paid several thousand dollars for every person who they manage to shift from a cash welfare program, which is partly funded by the state, over to disability insurance, which is funded by the federal government. One news story (for National Public Radio, no less) referred to all this as the \”Disability-Industrial Complex.\”

For some people, disability insurance has become a way of exiting the labor force. It\’s hard for me to get into high dudgeon over these people, because I suspect that many of them have at least mild disabilities and also lousy job prospects, especially the last few years. But the hard fact is that the disability insurance program has limited funds, and is headed for bankruptcy. If it pays those funds to a substantial number people who are only marginally disabled,  and could be working, it cannot pay higher benefits to the more severely disabled.

The costs of regulations can be measured by the money that must be spent for compliance. But many of the benefits of regulation are measured by lives saved or injuries avoided. Thus, comparing costs and benefits requires putting some kind of a monetary value on the reduction of risks to life and limb. For example, the US Department of Transportation estimates the \”value of a statistical life\” at $9.1 million in 2012. In a memo called \”Guidance on Treatment of the Economic Value of a Statistical Life in the U.S. Department of Transportation Analyses,\” it explains how this number was reached. I\’ll run through the DoT estimates, and then raise some of the broader issues as discussed in a recent paper by Cass Sunstein called \”The value of a statistical life: some clarifications and puzzles,\” which appeared in a recent issue of the Journal of Benefit Cost Analysis (4:2, pp. 237-261).

There are essentially two ways to estimate what value people place on a reduction in risk. Revealed preference studies look at how people react to different combinations of risk and price. For example, one can look at what workers are paid in jobs that involve a greater risk of death or injury, or at what people are willing to pay for safety equipment that reduces risks.  As DoT explains: \”Most regulatory actions involve the reduction of risks of low probability (as in, for example, a one-in-10,000 annual chance of dying in an automobile crash).  For these low-probability risks, we shall assume that the willingness to pay to avoid the risk of a fatal injury increases proportionately with growing risk.  That is, when an individual is willing to pay $1,000 to reduce the annual risk of death by one in 10,000, she is said to have a VSL of $10 million.  The assumption of a linear relationship between risk and willingness to pay therefore implies that she would be willing to pay $2,000 to reduce risk by two in 10,000 or $5,000 to reduce risk by five in 10,000.   The assumption of a linear relationship between risk and willingness to pay (WTP) breaks down when the annual WTP becomes a substantial portion of annual income, so the assumption of a constant VSL is not appropriate for substantially larger risks.\”

As the report also points out, while the result of this calculation is called the \”value of a statistical life,\” it\’s not actually putting value on a life, but on a reduction in risk. \”What is involved is not the valuation of life as such, but the valuation of reductions in risks.\”

The alternative method is called a \”stated preference\” approach, in which people work their way through a sophisticated survey tool that informs them about various combinations of risks and costs, and seeks to elicit their preferences. This method is sometimes called \”contingent valuation,\” and it\’s a controversial subject as to whether the values that are inferred from surveys can capture \”real\” preferences. (For a three-paper symposium on the use of contingent valuation techniques in estimating environmental damages, see the Fall 2012 issue of the Journal of Economic Perspectives.) When it comes to estimating value of reductions in risk, the DoT dismisses this method, on these grounds: \”Despite procedural safeguards, however, SP [stated preference] studies have not proven consistently successful in estimating measures of WTP [willingness-to-pay] that increase proportionally with greater risks.\”

The DoT gets its value of $9.1 million with a literature review: specifically, it looks at nine recent studies that consider risk and pay in various occupations and that seem methodologically sound, and takes the average value from those studies. DoT also looks at costs of health or injury, as measured by research on what are called \”quality-adjusted life years,\” which sets up criteria for categorizing the severity of an injury. They set up a scale with six levels of severity of injury: minor, which is worth .003 of the value of a statistical life, moderate, .047 of a VSL; serious, .105; severe, .266; critical, .593, and unsurvivable, 1.0. 

The DoT memo lays out where its numbers come from, but quite appropriately, it doesn\’t venture into a broader discussion of using the value of a statistical life in the first place. Cass Sunstein was for several years Administrator of the Office of Information and Regulatory Affairs in the Obama White House, so his views are of more than ordinary interest. While he supports using the value of a statistical life, he is also clear and thoughtful about a number of the tricky issues involved. Here are some of the tough questions raised by his article.

1) If the benefits of a regulation outweigh the costs, why is the regulation even necessary? Presumably, the answer is that there is some reason that buyers and sellers in the market cannot coordinate on an appropriate safety outcome. Potential reasons might include that people lack information or a range of choice between safety and price options.

2) Should the value of a statistical life be different across people? For example, perhaps reducing the risks faced by a child who lacks capacity to weigh and measure risk should be weight more heavily than risks faced by an adult. Or perhaps reducing the risks for a young adult, with a long life expectancy, should carry a higher value than reducing the risks faced by an elderly person. This point seems logically sound, but administratively and politically difficult.

3) If the reduction in risk is based on willingness to pay, then don\’t those with low incomes end up with less protection than those with higher incomes? Sunstein faces up to this point and accepts it. As he writes: \”The reason is not that poor people are less valuable than rich people. It is that no one, rich or poor, should be forced to pay more than she is willing to pay for the reduction of risks.\” Guaranteeing low-income people a level of safety where the costs are higher than what they wish to pay ultimately doesn\’t make sense. \”Government does not require people to buy Volvos, even if Volvos would reduce statistical risks. If government required everyone to buy Volvos, it would not be producing desirable redistribution. A uniform VSL has some of the same characteristics as a policy that requires people to buy Volvos. In principle, the government should force exchanges only on terms that people find acceptable, at least if it is genuinelyconcerned with their welfare.\”

4) What if the costs of risk reduction are carried by one group, but the benefits are received by another? Sunstein points out that in some cases, like regulation of drinking water, much of the cost of safer water is passed along in the form of higher water prices, and thus paid by everyone. Similarly, the cost of the worers\’ compensation program basically means that the benefits received by (nonunionized) workers are essentially offset by lower take-home pay. However, in regulation of air pollution, it\’s quite possible that the costs are spread across companies that pollute and their shareholders, while the benefits are realized by people regardless of income. Here, Sunstein points to the classic and controversial argument that if overall benefits for society exceed overall costs to society, even if there are some individual winners and losers, the policy can be justified. But he argues that redistribution is not the right goal for regulatory policy: \”It is important to see that the best response to unjustified inequality is a redistributive income tax, not regulation – which is a crude and potentially counterproductive redistributive tool …\”

5) Maybe people aren\’t knowledgeable or rational their thinking about costs and benefits of risk reduction? Maybe they place a high value on avoiding some risks, but not on avoiding others, even though the objective level of risk seems much the same? Sunstein takes the technocratic view here: \”Regulators should use preferences that are informed and rational, and that extend over people’s life-histories.\”

6) Instead of thinking about willingness to pay to reduce risk, the problem instead can be formulated as one of rights: that is, people have a right not to have certain risks imposed on them. Sunstein argues that this idea of rights applies in situations where the risk is extremely high, but doesn\’t apply well to issues of changes in statistically small risks. He further argues that if the issue is one of rights, then the cost-benefit calculation no longer applies (as implied in the DoT quotation above). Sunstein notes that in issues involving, say race and gender discrimination or sexual harassment, we quite rightly don\’t apply a cost-benefit calculation. But a regulatory issue like what kinds of bumpers should be put on cars to reduce risks during a crash is not a \”right\” in this sense, and so a cost-benefit calculation becomes appropriate.

Ultimately, Sunstein is a supporter of using the value of a statistical life in setting regulatory policy. As he notes, there are easier and harder cases for applying this principle. What he doesn\’t emphasize in this article is that if we can figure out which regulations have greater benefits for their cost, and which regulations have lower benefits for their cost, we should then be able to tighten up the very cost-effective regulations and loosen up the cost-ineffective regulations, and end up helping more people at the same or even lower cost.

Earlier this week, this Conversable Economist blog reached 1,000,000 pageviews. Of course, for big-time blogs this would be small-time news. But for this one-person blog, which offers links and discussion of economic analysis 4-5 times per week, it seems like a landmark worth noting. Of course, being me, I can \’t commemorate a landmark without worrying about it. Is focusing on 1,000,000 pageviews just another example of round-number bias? Are pageviews a classic example of looking at what is easily measureable, when what matters is not as easily measurable?

Round number bias is the human tendency to pay special attention to numbers that are \”round\” in some way. For example, in the June 2013 issue of the Journal of Economic Psychology (vol. 36, pp. 96-102) ,Michael Lynn, Sean Masaki Flynn, and Chelsea Helion ask \”Do consumers prefer round prices? Evidence from pay-what-you-want decisions and self-pumped gasoline purchases.\” They find, for example, that at a gas station where you pump your own, 56% of f sales ended in .00, and an additional 7% ended in .01–which probably means that the person tried to stop at .00 and missed. They also find evidence of round-number bias in patterns of restaurant tipping and other contexts.

Another set of examples of round number bias come from Devin Pope and Uri Simonsohn in a 2011 paper that appeared in Psychological Science (22: 1, pp. 71-79): \”Round Numbers as Goals: Evidence from Baseball, SAT Takers, and the Lab.\” They find, for example, that if you look at the batting averages of baseball players five days before the end of the season, you will see that the distribution over .298, .299, .300, and .301 is essentially even–as one would expect it to be by chance. However, at the end of the season, the share of players who hit .300 or .301 was more than double the proportion who hit .299 or .298. What happens in those last five days? They argue that batters already hitting .300 or .301 are more likely to get a day off, or to be pinch-hit for, rather than risk dropping below the round number. Conversely, those just below .300 may get some extra at-bats, or be matched against a pitcher where they are more likely to have success. Pope and Simonsohn also find that those who take the SAT test and end up with a score just below a round number–like 990 or 1090 on what used to be a 1600-point scale–are much more likely to retake the test than those who score a round number or just above. They find no evidence that this behavior makes any difference at all in actual college admissions.

Round number bias rears its head in finance, too. In a working paper called \”Round Numbers and Security Returns,\” Edward Johnson, Nicole Bastian Johnson, and Devin Shanthikumar desribe their results this way: \”We find, for one-digit, two-digit and three-digit levels, that returns following closing prices just above a round number benchmark are significantly higher than returns following prices just below. For example, returns following “9-ending” prices, which are just below round numbers, such as $25.49, are significantly lower than returns following “1-ending” prices, such as $25.51, which are just above. Our results  hold when controlling for bid/ask bounce, and are robust for a wide collection of subsamples based on year, firm size, trading volume, exchange and institutional ownership. While the magnitude of return difference varies depending on the type of round number or the subsample, the magnitude generally amounts to between 5 and 20 basis points per day (roughly 15% to 75% annualized).\”

In \”Rounding of Analyst Forecasts,\” in the July 2005 issue of Accounting Review (80: 3, pp. 805-823), Don Herrmann and Wayne B. Thomas write: \”We find that analyst forecasts of earnings per share occur in nickel intervals at a much greater frequency than do actual earnings per share. Analysts who round their earnings per share forecasts to nickel intervals exhibit characteristics of analysts that are less informed, exert less effort, and have fewer resources. Rounded forecasts are less accurate and the negative relation between rounding and forecast accuracy increases as the rounding interval goes from nickel to dime, quarter, half-dollar, and dollar intervals.\”

In short, the research on round-number bias strongly suggests not getting too excited about 1,000,000 in particular. There is very little reason to write this blog post now, as opposed to several months in the past or in the future. However, I hereby acknowledge my own personal round number bias and succumb to it.

The other reason not to get too excited about this particular round number is that pageviews themselves are only one measure of the benefits of a blog. For example, page views don\’t count those who receive the blog by RSS feed, or those who read part of a post as it is reprinted on another site, or those who are emailed a post by a friend. Pageviews also don\’t measure the intentionality and seriousness of readers, and of course everyone who reads this blog is considerably above average in every way.

In addition, I often tell myself that the compelling reason to carry on with this blog is for my own personal needs. The blog has become a sort of extended memory for me, where I can easily track down that article I dimly remember reading a few months back. It\’s a filing system, where I can store figures and quotations in a searchable form. My efforts at explaining something here at the blog can serve as a dry run for later explanations that will be smoother and better-developed.

Of course, I could achieve these kinds of self-focused and mildly anti-social goals with a private blog, closed off to the world. The fact that I publish the blog is an admission that I like having readers. If you have become a regular reader in the last couple of years–whether or not you are counted in the pageviews–thanks for spending some time with me. If you are an irregular reader, check in more often. This blog is a specialized flavor, reflecting the randomness of my own reading and interests. But if you feel like recommending the blog to anyone–well, my round-number bias tells me that I would be irrationally delighted to have some help in reaching 2,000,000 page views.