Year: 2016

James Guszcza has a lively interview with Richard Thaler in the Deloitte Review (Issue 18, published January 26, 2016). Last week I offered a link to video and slides from Thaler\’s Presidential Address to the American Economic Association in early January on the subject of \”Behavioral Economics: Past, Present and Future.\” The interview covers the same broad subject, but in a more free-flowing way. Here are a few of Thaler\’s comments.

There\’s an argument a few blocks down from where I work about whether to install parking meters in a small-but-busy shopping district, and judging from some of the rhetoric, we should give strong consideration to amending the Declaration of Independence so that we can be endowed by our Creator with certain unalienable rights, Life, Liberty, Free Parking, and Other Less Important Aspects of the Pursuit of Happiness. Eren Inci asks \”Who Pays for Free Parking?\” in the Milken Institute Review (First Quarter 2016, pp. 66-74). He starts with the obvious point that a parking space can be a a substantial resource.

\”For starters, most economic transactions you make depend in part on the ability of someone (you, a long-haul trucker, the UPS delivery person, an ambulance driver, and so on) to park. And stationary vehicles occupy vast amounts of land everywhere in the world. Indeed, a simple back-of-the-envelope calculation suggests that, in the United States, parking takes up more space than the whole state of Massachusetts. In Europe, where cars are smaller and fewer in number, parking still takes up an area half the size of Belgium.\”

The discussion in this article focuses on free parking in two particular contexts: Inci suggests that there is a plausible if unexpected economic justification for free parking at shopping malls, but not in residential urban areas. Here\’s his explanation on free parking at shopping malls.

 One can think of shopping as participation in a lottery of sorts in which shoppers either win (find what they’re looking for) or lose (don’t find it). Shoppers who make the purchases leave the mall satisfied. But, of course, not all mall trips have happy endings: sometimes shoppers leave emptyhanded. Since they “pay” for parking only by making purchases at prices that include an implicit charge for parking, those who buy nothing don’t pay for parking. Thus, in a very real sense, bundling the cost of parking with the price of goods is a form of insurance for shoppers: if they don’t buy, they pay nothing; if they do buy, they effectively pay for the parking of unsuccessful shoppers as well as their own. …  Shopping-mall parking may appear to be free, but in fact you pay for it every time you buy something at the mall. Happily, though, what looks like distorted pricing serves the broader interests of society as well as those of the mall owners.

Here\’s the argument about free parking in urban areas:

Although there has been a trend away from free to paid curbside parking almost everywhere as municipalities attempt to generate revenue and ration scarce space, there is still an enormous amount of free curbside parking available.But if you live in a place with free parking, don’t be so sure you’re the lucky one. Our research suggests that at least some of the value of that free parking is reflected in your rent or in the market value of your home. So, the issue is not whether you pay, but how you pay – directly or bundled in the cost of housing. This will all be clearer if we first look at onsite parking that is sold as a bundle with housing. Most cities impose minimum parking requirements that determine how many parking spaces each new land use must include. In most American cities, developers must provide at least one and, in many cases, two spaces for each housing unit. This is no small deal: after taking into account the space allocated for ramps and maneuvering, the area occupied by two parking spaces is usually larger than a two-bedroom apartment. … In fact, Michael Manville of Cornell University found that in San Francisco bundled parking increases the average asking price for an apartment by $22 per square foot. … Curbside parking in front of your house may also appear to be free, but in fact its costs are already capitalized in housing prices and rents. Although I can’t claim the last word on the subject, our estimate hints that free curbside parking produces negative welfare consequences.

There\’s also an interesting discussion of what happened in Istanbul, when the city decided to move away from an \”informal\” system of paying for parking, in which \”self-appointed parking attendants stood by the road and demanded money to “protect” parked cars,\” to a formal anc city-run system of paying for parking.

For a couple of earlier posts on the economics of parking, see:

• \”Donald Shoup and the Economics of Parking\” (January 5, 2012)
• \”Free Parking: A Gift to Whom?\” (June 19, 2014)

There\’s a considerable argument over federal borrowing. Were the deficits during the Great Recession too small to help boost the recovery, or too large? Were the deficits reduced too quickly, or not quickly enough? Are the ongoing deficits too large, or too small? That gentle \”whoosh\” you hear in the distance is me sidestepping those questions in this post. (For the record, I\’ve taken the unpopular position that the path of federal borrowing was approximately correct during the Great Recession and its aftermath, thus managing to prove yet again the old adage that if you try to stand in the middle of the road, you\’ll get hit by traffic from both directions.)

Here, I want to offer the mild suggestion that debates about what budget deficits should be, or should have been, might be usefully informed by the path they have actually taken, and where they are headed. Here\’s the quick summary from the \”Budget and Economic Outlook: 2016-2026,\” just released by the Congressional Budget Office.  Lots more detail on spending and taxes is available in the report.

Here\’s the path of federal budget deficits over time, and the \”baseline\” projections for the next 10 years, which assume that existing laws and rules relating to the budget will remain in place. The deficits are expressed as a share of the GDP–that is, relative to the size of the overall economy.

And here\’s the path of the debt/GDP ratio over time. The deficit is the amount borrowed by the federal government in a given year. The debt is the accumulation of borrowing over time. Again, it is expressed relative to GDP.

When mulling over appropriate size of deficits in the next few years, here are a couple of points to keep in mind.

1) As noted earlier, the CBO estimates are \”baseline\” forecasts, which means that if Congress has passed a law saying that spending will be reduced or certain taxes will be raised three or five or ten years into the future, the CBO assumes that these changes will actually happen. However, the CBO recognizes that Congress has a propensity to play games by passing budgets that project smaller deficits in the future through tax and spending policies that don\’t actually arrive. Thus, CBO also offers some \”alternative\” forecasts if some of these changes that are part of current law don\’t actually happen.

For example, the CBO forecast for 2020 is for a deficit of $810 billion. But this assumes that certain automatic future spending reductions in the 2011 Budget Control Act are actually enforced. If these cuts are not enforced, the deficit would be $97 billion higher in 2020. As another example, there is a rule in the tax code allowing for \”expensing\” of certain business investments, which means that a firm can count the entire cost of an investment in the year the spending happens, rather than depreciating the investment over time. If that expensing provision in tax law is extended into the future at a 50% rate, the projected deficit for 2020 would be an additional $52 billion larger. Bottom line: the \”baseline\” deficit estimate may be an underestimate, because it is based on a willingness of Congress to make some tough decisions in the future.

2) Keeping the debt/GDP ratio roughly constant or even reducing it gradually over time doesn\’t require anything close to a balanced budget. For example, the debt/GDP ratio is falling through the 1960s and 1970s, although the US government ran annual deficits almost every year. The fact that the debt/GDP ratio has been relatively flat during the last year or two means that annual deficits have been of a size that total debt is growing at about the same speed as GDP. The rise in debt/GDP ratio projected by the CBO a few years out means that using the baseline calculation of existing law, annual deficits are projected to be large enough that debt (the numerator of the calculation) will be growing at a faster rate than GDP (the denominator of the calculation).

The Wall Street Reform and Consumer Protection Act of 2010, commonly known as the Dodd-Frank Act, set up an Office of Financial Research. Its website describes its purpose this way: \”The Office of Financial Research (OFR) helps to promote financial stability by looking across the financial system to measure and analyze risks, perform essential research, and collect and standardize financial data.\” In December 2015, the OFR published its Financial Stability Report for 2015.

The report emphasizes three main risks facing the US economy: 1) credit risks for US nonfinancial businesses and emerging markets; 2) the behaviors encouraged by the ongoing environment of low interest rates; and 3) situations in which financial markets are not resilient, as manifested in shortages of liquidity, run and fire-sale risks, and other areas. Here are some thoughts from the report.

\”First and most important, credit risks are elevated and rising for U.S. nonfinancial businesses and many emerging markets. … In 2015, U.S. nonfinancial business debt continued to grow rapidly, fueled by highly accommodative credit and underwriting standards. The ratio of that debt to gross domestic product has moved above pre-crisis highs, and corporate leverage continues to rise. So far, distress in U.S. credit markets has been largely limited to the lowest-rated debt issuers and the energy and commodity industries. However, that distress may spread, because investors now appear to be reassessing the credit and liquidity risks in these markets. … The combination of higher corporate leverage, slower global growth and inflation, a stronger dollar, and the plunge in commodity prices is pressuring corporate earnings and weakening the debt-service capacity of many U.S. and emerging market borrowers. A shock that significantly further impairs U.S. corporate or emerging market credit quality could potentially threaten U.S. financial stability.\”

Here\’s a figure showing growth in outstanding US nonfinancial corporate bonds, which as a share of GDP have risen back close to where they were just before the 2008 financial crisis. The report also notes: \”“Covenant-lite” loans, which contain less legal protections for creditors, accounted for approximately two-thirds of institutional leveraged loan volumes this year, and the share of low-rated debt with weak covenants has continued to increase …\”

\”Another key source of risk is the eventual normalization of Federal Reserve monetary policy. Past U.S. monetary tightening cycles have been associated with increased volatility in corporate bond markets and, in some cases, increased spreads … The next cycle could be more destabilizing for corporate debt markets, given that it will unwind an extraordinary period of low interest rates and associated yield-seeking investor behavior. U.S. corporate bond markets may face an exodus of these investors as yields on safer instruments increase. … The fact that U.S. nonfinancial business debt has expanded so rapidly since the financial crisis suggests that even a modest default rate could lead to larger absolute losses than in previous default cycles.\”

These issues also spill over into emerging market debt. Here\’s a figure showing the level of corporate debt (not including government or household debt) in some emerging markets. Speaking of this debt in emerging markets, the report notes:

\”A significant deterioration in the liquidity of large nonfinancial corporates could create financial difficulties for domestic banks and governments. In some emerging market countries, more than half of banks’ outstanding loans are to corporate borrowers, leaving many banking sectors vulnerable to losses in the event of broad-based corporate distress. In addition, a large share of emerging market corporate external bonds is issued by quasi-governmental borrowers, meaning that corporate distress could also activate contingent or legal liabilities for some governments, with associated effects on government debt markets. This is a particular problem for countries where the central government has less fiscal space, such as Brazil. Increased dependence on foreign exchange-denominated debt is also a concern, given that depreciating currencies increase the difficulty of servicing the debt.\”

\”Second, the low interest rate environment may persist for some time, with associated excesses that could pose financial stability risks. … The persistence of low rates contributes to excesses that could pose financial stability risks, including investor reach-for-yield behavior, tight risk premiums in U.S. bond markets, and, as noted, the high level and rapid growth of U.S. nonfinancial business debt. … The duration of investors’ U.S. bond portfolios remains at historic highs …, increasing their exposure to interest rate risk. …\”

\”Third, although the resilience of the financial system has improved significantly in the past five years, it is uneven. Since the financial crisis, regulatory reforms and changes in risk management practices have strengthened key institutions and markets critical to financial stability. Yet, existing vulnerabilities persist and some new ones have emerged. Financial activity and risks continue to migrate, challenging existing regulations and reporting requirements. Market liquidity appears to be episodically fragile in major U.S. financial markets, diminishing sharply under stress. Run and fire-sale risks persist in securities financing markets. Interconnections among financial firms are evolving in ways not fully understood, for example, in the growing use of central clearing. … The financial system is highly complex, dynamic, and interrelated, making it exceedingly challenging to monitor developments in every corner of the system and adequately assess the probability and magnitude of all important risks. …\”

\”Funding conditions remain broadly stable, though market liquidity episodically appears to be fragile — an amplifier of financial stress. This fragility was evident in the 2010 U.S. equity flash crash, the 2013 U.S. Treasury market sell-offs, the October 2014 Treasury “flash rally,” and other episodes … Runs in short-term wholesale funding markets were a key source of systemic stress during the financial crisis. Although progress has been made to address this vulnerability, run risk persists in these markets years later. … Banks’ reliance on repo financing has diminished and concentration risks have eased as these institutions have diversified their funding sources and reduced their client-financing operations. Broker-dealers have also reduced their reliance on repo financing, particularly in net terms, but overall these firms remain highly dependent on such financing and at risk of runs in a stress scenario …\”

Of course, saying something is a \”risk\” doesn\’t mean that disaster is imminent, but rather that attention on the topic should be elevated. I\’d expect that some of these topics–not sure which ones!–will be the stuff of headlines in the next year or so.

It\’s not actually true that all econometrics students are required to get a tattoo reading \”Correlation is not causation\” on an easily visible part of their body. But I suspect that late at night, when grading midterm exams, some professors have considered the merits of imposing such a requirement. Richard Nisbett is a prominent professor of psychology at the University of Michigan. He offers a 36-minute lecture titled \”The Crusade Against Multiple Regression Analysis\” which was posted on The Edge website on January 21, 2016. For those would rather read than watch, a transcript is also posted.

Although the talk is aimed more at psychologists than economists, it\’s a nice accessible overview to these subjects, which apply across the social sciences–and toward the end, Nisbet has some comments that should be thought-provoking for economists about the potential strength of social influence. But at the start, Nisbet says:

\”A huge range of science projects are done with multiple regression analysis. The results are often somewhere between meaningless and quite damaging. I find that my fellow social psychologists, the very smartest ones, will do these silly multiple regression studies, showing, for example, that the more basketball team members touch each other the better the record of wins.

I hope that in the future, if I’m successful in communicating with people about this, there’ll be a kind of upfront warning in New York Times articles: These data are based on multiple regression analysis. This would be a sign that you probably shouldn’t read the article because you’re quite likely to get non-information or misinformation. …

What I most want to do is blow the whistle on this and stop scientists from doing this kind of thing. … I want to do an article that will describe, similar to the way I have done now, what the problem is. I’m going to work with a statistician who can do all the formal stuff, and hopefully we’ll be published in some outlet that will reach scientists in all fields and also act as a kind of \”buyer beware\” for the general reader, so they understand when a technique is deeply flawed and can be alert to the possibility that the study they\’re reading has the self-selection or confounded-variable problems that are characteristic of multiple regression.\”

The basic issue arises here when a study looks at two variables that are correlated with each other. However, the study doesn\’t take into account all the relevant factors which may be causing the correlation, and thus it misinterprets the correlation. Here\’s a quick-and-dirty example from Nisbet:  

\”A while back, I read a government report in The New York Times on the safety of automobiles. The measure that they used was the deaths per million drivers of each of these autos. It turns out that, for example, there are enormously more deaths per million drivers who drive Ford F150 pickups than for people who drive Volvo station wagons. Most people’s reaction, and certainly my initial reaction to it was, \”Well, it sort of figures—everybody knows that Volvos are safe.\”

Let’s describe two people and you tell me who you think is more likely to be driving the Volvo and who is more likely to be driving the pickup: a suburban matron in the New York area and a twenty-five-year-old cowboy in Oklahoma. It’s obvious that people are not assigned their cars. We don’t say, \”Billy, you’ll be driving a powder blue Volvo station wagon.\” Because of this self-selection problem, you simply can’t interpret data like that. You know virtually nothing about the relative safety of cars based on that study.\”

Again, Nisbet\’s point is that just because pickup trucks are correlated with more accidents or deaths does not in any way prove that pickup trucks are less safe. Maybe they are. Or maybe the reason behind the correlation is that pickup trucks are more likely to attract certain kinds of drivers, or to be driven in certain ways, and thus have 

Consider how you would answer this question from the Gallup poll organization: \”With which one of these statements about the environment and the economy do you most agree–protection of the environment should be given priority, even at the risk of curbing economic growth (or) economic growth should be given a priority, even if the environment suffers to some extent?\” James W. Boyd and Carolyn Kousky show the results when Gallup surveyed Americans on this question in \”Are We Becoming Greener? Trends in Environmental Desire,\” which appears in the Winter 2016 issue of Resources, published by Resources for the Future.

As the figure shows, when this question was first being asked in the mid-1980s and through the 1990, a typical answer was that two-thirds or more of American gave priority to the environment. But there is a substantial shift in the answers from about 2000 up through 2004. The proportions giving each priority shift to roughly half and  half; indeed, during the Great Recession and its aftermath, people put a higher priority on the economy than on the environment.

Of course, interpreting poll results is a tricky business, and Boyd and Kousky offer a useful discussion of all sorts of interpretations one might place on these results. Perhaps those taking the poll are interpreting not whether they value environmental protection steps taken in the past, but whether they think additional steps should be a high priority moving forward. Perhaps the term \”environmental protection\” has over time become equated in some people\’s minds with \”big government,\” and so the results are showing a rise in negative feelings about government. Perhaps in the aftermath of the dot-com recession of 2001, Americans started to feel more concerned about future economic prospects.

The question of what people \”prefer\” is a delicate one for economists, who try to make a logical and analytical separation between what people\’s preferences and what they end up choosing. For example, imagine that people have a certain preference for coffee, which means that given a certain  price for quantity, they will demand a specific quantity of coffee. But now imagine that poor weather damages the coffee crop, the price of coffee rises, and people choose to purchase a lower quantity of coffee as a result. An economist would argue that even though people are buying a reduced quantity coffee, their \”preference\” for coffee didn\’t change. Instead, the change in what was bought and sold in the market was the result of a change in growing conditions for coffee. For an economist, a change in preference would involve buying more coffee at the same market price (and the same income level, and holding other factors constant, too). In the present context, people may perceive that the cost of environmental protection has been rising over time, but given a specific environmental protection policy with a high ratio of benefits/costs, they might favor it.

I\’m sure lots of other interpretations are possible, but I wanted to mention one more point. Opinion surveys sometimes seem to suggest that problems can be solved right now, but both the environmental protection and economic growth have a long-run aspect. Many of the costs of environmental protection are felt in the shorter-term, while benefits (say, in human health or environment protected) are longer-term. Similarly, economic growth often is built on investments in the short run–human capital, physical capital, technology–which can seem pricey in the present, but pay off over longer periods of time. Choosing either environmental protection or economic growth, properly understood, is about less consumption in the present and planning for the future.

If you want to get up to speed on what behavioral economics is all about, two prominent speeches recently given at the annual meetings of the American Economic Association, held earlier this month in San Francisco, offer a useful starting point. The AEA Presidential Address was given by Richard Thaler on the subject of \”Behavioral Economics: Past, Present, and Future.\” The Richard T. Ely lecture was given by John Y. Campbell on the subject of \”Restoring Rational Choice: The Challenge of Consumer Financial Regulation.\” Campbell\’s lecture can be thought of as an application of behavioral economics to a specific policy area. The lectures will be published in print in the next few months. However, video of the roughly hour-long lectures accompanied by the slides, is available at the AEA website.

Thaler\’s lecture is, as the title suggests, is a broad overview of behavioral economics, and it should be quite accessible to a broad listenership. His basic argument is to point out simple economic models assume that economic agents (people and firms) seek to act in an optimal way. I would add that just to be clear, this approach to economic modelling is not the same as as arguing that people always have full and complete information, or that people have perfect abilities to perceive and calculate in all situations. It\’s not all that hard to do standard economic modelling with agents who lack full information or who can\’t do high-powered calculations very well, and as a result they will sometimes make mistakes. One way of restating the basic assumption of standard economics is that people will try to optimize, in the sense that at least they won\’t make the same mistakes over and over again.

Behavioral economics suggests that people often do make certain mistakes over and over again. People are often overconfident, and believe that they are well-above average in many categories. People have self-control problems when it comes to saving, exercising, diet, working hard. They are more averse to losses than they are enthusiastic about gains. They often stick with default choices, rather than considering alternatives. People often care about fairness, especially in social settings. They may be motivated in some settings, but not others. The idea  of not making the same mistake over and over again probably works well when it comes to certain market with frequent and low-stakes interactions: int he market for restaurant meals, for example, if you hate the meal, don\’t go back. But lots of life decisions get made only once or a limited number of times, like choice of college major, career, who you marry, buying a house, or how much you save for retirement. Some of these choices are easier to alter than others! No one gets to rewind life, over and over again, so that you can learn from your mistakes and avoid them the second or third or 15th time around.

Thaler gives lots of example of how the kinds of people with these very real psychological biases will act in ways that, from the viewpoint of a standard economic model, will act \”irrationally.\” As a result, they will be situations where people make choices that they might later regret, ranging from small choices like paying for an extended warranty that never gets used up to large choice like those leading to bubbles and crashes in housing or stock markets. One of the most prominent examples in this literature is that if an employer puts workers into a retirement saving plan by default, they tend to stay in the plan, and if an employer doesn\’t put workers into a retirement saving plan by default, they tend to stay out of the plan. Thus, whether a given person ends up with retirement savings is often not a matter of personal choice, but a matter of what default was chosen by their employer. There are a very wide array of applications of these choices in many context: tax evasion, health insurance, Food Stamps, and many others. Thaler\’s lecture is a very nice broad overview.

John Campbell offers an application of these ideas in the area of household finance. His lecture is quite accessible, if not quite as accessible as Thaler\’s (that is, Campbell has a very small number of equations and reports a small number of statistical results). Campbell points out that people seem to systematically make what surely look like mistakes in their household decision-making. For example, some employers offer an \”employer match\” in retirement accounts–that is, if you put some amount like 5% of your income in a retirement account, the employer will also put in 5%. It\’s free money! But lots people don\’t take it. As another example, many people don\’t refinance their home mortgage when interest rates fall. Or many people put their retirement savings in a low-return financial instrument (like bonds) instead of a higher-return instrument (like stocks). Or many people take on lots of short-term debt at high interest rates, including with credit card debt, overdrawing their bank account and paying fees, or through payday loans. People are often financially illiterate, in the sense that they don\’t know the difference between a nominal and a real rate of interest, or how interest compounds over time, and similar problems.

Thus, Campbell argues that there is a case for financial regulation in certain settings that goes beyond the standard rules requiring disclosure of information, and moves toward setting rules. The tradeoff here is that setting specific rules can protect people from their own behavioral biases and ignorance. On the other side, rules may impose costs on those who are not so susceptible to such biases. As one example, consider the market for credit cards. The credit card companies offer what sounds like a very attractive deal: cash-back refunds, frequent flyer miles, and the like. Indeed, if you follow the rules and make all your payments in full and on time, and take advantage of the benefits, a credit card more than pays for itself. But if you fall behind in your payments, and start paying interest and fees, credit card borrowing can be very costly. If a new rule places sharp limits on credit card interest rates and fees, it will in some ways benefit those who could have become trapped in credit card debt (although they will also find it harder to get a credit card). But it also seems likely that if the credit card companies make less money from late fees and interest, they will also charge higher annual fees and offer less attractive options like cash-back and airline flights.

The case for rules that block certain fees or charges shouldn\’t be made casually, and the reason for the existence of economists in the universe is to warn about tradeoffs. But Campbell makes a strong case that in several contexts–getting people to invest long-term retirement accounts in stock, limiting the fees and charges from short-term borrowing through credit cards and payday loans, and reverse mortgages for the elderly–such rules are worth serious consideration.

The unemployment rate, now hovering around 5%, has dropped a lot more quickly during the last three years than mainstream forecasters predicted. For example, back in February 2013, the Congressional Budget Office was forecasting that the unemployment rate would be 7.1% in 2015 and 6.3% in 2016. As another example, all participants in the meetings of the Federal Reserve Open Market Committee present their forecast of economic conditions looking ahead. In the \”Summary of Economic Projections\” prepared for its December 2012 meeting, the predicted range for the unemployment rate in 2015 was 5.7% to 6.8%. To put it another way, the unemployment rate in the last three years has fallen by more than even the most optimistic member of the Federal Reserve Open Market Committee believed was likely.

But now, the unemployment rate is close to bottoming out. The Congressional Budget Office forecast for January 2016 is that the unemployment rate will fall to 4.5% in 2016 and 2017, but will then rise back to 5.0% in the long run. In the Federal Reserve\’s \”Summary of Economic Projections\” for its meeting of December 16, 2015,  the median prediction is that unemployment will fall to 4.7% for the next three years from 2016 to 2018 but in the long run will rise back to 4.9%.

Other measures of the labor market also suggest that it is very close to returning to pre-recession levels. For example, here\’s a figure from the latest Job Openings and Labor Turnover Survey that came out in mid-January, which is a survey with a rotating sample of about 16,000 firms run by the US Bureau of Labor Statistics. The figure shows that the pace of hiring has returned almost to pre-recession levels, and that job openings have surged faster. The number of workers who quit typically drops during a recession, because it\’s harder to find an alternative job, so the rising number of \”quits\” is a signal of a healthier labor market.

In short, while the unemployment rate may dip a bit lower or bounce a bit higher, the long fall in the unemployment rate from its peak of 10% in October 2009 is mostly finished.  So what\’s the next step in looking for whether the labor market continues to improve? There are two broad metrics that come to mind. Are wages starting to rise more quickly, as one would expect in a tighter labor market? and what\’s the share of Americans who have jobs?

There are some early and very preliminary signs that wages might finally be starting to rise. For example, here\’s a figure from the \”US Economy in a Snapshot\” published earlier in January 2016 by the Federal Reserve Bank of New York.

Of course, what actually matters is not just wages, but whether wages are keeping ahead of inflation. Here\’s a figure in which the blue line shows the Employment Cost Index, a broad measure of how much costs of compensation (including benefits) are rising. The red line shows the rate of inflation based on the Personal Consumption Expenditures index. Notice that back around 2003-2004, compensation increases were running above inflation. The rate of inflation leaps up and down, in part driven by short-term shifts in prices of oil and food, but you can see that compensation wasn\’t running much ahead of inflation–if at all–from when the recession got underway up through about 2012. However, just in the last year or so, compensation has again been ahead of inflation

Another labor market measure that deserves a close look moving ahead is the relationship between the unemployment rate and some broader measures of labor market involvement. In this figure, again taken from the \”US Economy in a Snapshot,\” the blue line shows the unemployment rate rising during the recession and falling again. The red line shows the labor force participation rate, which I\’ve written on this blog a number of times.  The beige line shows the employment/population ratio

The downward trend in the labor force participation rate started well before the recession, and as I\’ve discussed on this website before (for example, here and here), the long-term trend reflects factors like young adults being more likely to attend school, low-wage workers having fewer job opportunities, and the baby boom generation reaching retirement age.  This long-term fall in the labor force participation rate accelerates during the Great Recession, but more recently, it the rate of decline has slowed again.

A pattern I haven\’t written about much on this blog is the employment-to-population ratio, which has declined over time  for many of the same reasons as the labor force participation rate. The economic concept of the \”labor force\” includes both the employed and the unemployed–that is, everyone who has a job or is actively looking for one. However, the employment/population ratio includes only the employed,which is why it drops more sharply during recessions. It\’s interesting that the employment/population ratio actually stabilized back around 2010 and has even increased a little since then.

Maybe it goes without saying, but just because unemployment is bottoming out doesn\’t mean that everything is hunky-dory for everyone in the labor market. But the labor market problems of having the unemployment rate at 5% is at least a different and less terrible than the problems of having an unemployment rate at 10%.

The Philanthropy Roundtable has published its most recent Almanac of American Philanthropy. The report offers lots of possibilities for browsing.  For example, there\’s a lengthy list of of \”Great Philanthropy Quotations,\”  \”Timeline of American Philanthrophy: 1936-2015,\”  a \”Philanthropy Hall of Fame,\” and more. Alex Reid, a tax attorney who was a former counsel to the Joint Committee on Taxation of the U.S. Congress, offers a rock-ribbed historical and philosophical (if not especially economic) defense of why charitable contributions should be tax-deductible. But as is my wont, I\’m inevitably drawn to the graphs and figures.

For example, here\’s a graph of giving over time, with the top line (measured on the left axis) showing total real charitable giving in the US, and the bottom line (measured on the right axis) showing per capita giving. As the report points out, another way of describing these figures is that Americans give about 2% of GDP: \”But it’s interesting that even as we have become a much wealthier people in the post-WWII era, the fraction we give away hasn’t risen. There seems to be something stubborn about that 2 percent rate.\”

Where do the charitable donations go? As the report notes, donations to \”religion\” end up being spent in a variety of areas: \”Much religious charity, however, ultimately goes into sub-causes like relief for the poor, medical care, education, or aid sent to low-income countries or victims of disaster.\”

And for all the talk of charitable giving by big foundations, the vast majority of charitable donations are by individuals. About two-thirds of Americans donate to charity, including over 90% of those with annual incomes above $125,000.

Here\’s a graph showing \”Output of the Nonprofit Sector\” as a share of GDP. A few points about this calculation are worth remembering: 1) With some industries, like steel mills or haircuts, it\’s relatively easy to measure output. With the nonprofit sector, what you\’re really measuring is money spent, rather than outcomes provided; 2) Volunteer time of over 8.1 billion hours per year isn\’t counted in the \”output\” statistics of nonprofits, for example, because although it would have a market value well above $100 billion (depending on how you value people\’s time), it\’s not paid for; 3) Also, it turns out that some charities registered with the IRS are counted as \”businesses,\” rather than as nonprofits. That said, it\’s still interesting that, as the report notes: \”For perspective, consider that annual U.S. defense spending totals 4.5 percent of GDP. The nonprofit sector surpassed the vaunted “military-industrial complex” in economic scope way back in 1993.\”

If one looks at private philanthropy as a share of GDP, Americans lead the high-income countries of the world, with Canada in second place.

Finally, there\’s a potentially interesting discussion topic for students–and for us all, really–on the subject of \”Good Charity, Bad Charity?\” The Almanac takes the following stance:

Some activists today are eager to define what is good or bad, acceptable or unacceptable, in other people’s giving. Princeton professor Peter Singer has lately made it almost a career to pronounce that only certain kinds of philanthropic contributions ought to be considered truly in the public interest. Only money given directly to “the poor” should be counted as charitable, he and some others argue.

Former NPR executive Ken Stern constructed a recent book on this same idea that charity must be “dedicated to serving the poor and needy.” Noting that many philanthropists go far beyond that limited population, he complains that it is “astonishingly easy to start a charity; the IRS approves over 99.5 percent of all charitable applications.” He disapprovingly lists nonprofits that have “little connection to common notions of doing good: the Sugar Bowl, the U.S. Golf Association, the Renegade Roller Derby team in Bend, Oregon, and the All Colorado Beer Festival, just to name a few.”

Is that a humane argument? Without question, the philanthropy for the downtrodden launched by people like Stephen Girard, Nicholas Longworth, Jean Louis, the Tappans, Milton Hershey, Albert Lexie, and Father Damien is deeply impressive. But the idea that only generosity aimed directly at the poor (or those who agitate in their name) should count as philanthropic is astoundingly narrow and shortsighted. Meddling premised on this view would horribly constrict the natural outpouring of human creativity.

Who is to say that Ned ­McIlhenny’s leaps to preserve the Negro spiritual, or rescue the snowy egret, were less worthy than income-boosting? Was the check that catalyzed Harper Lee’s classic novel bad philanthropy? Were there better uses for Alfred Loomis’s funds and volunteer management genius than beating the Nazis and Imperial Japanese military?

Even if you insist on the crude utilitarian view that only direct aid to the poor should count as charity, the reality is that many of the most important interventions that reduce poverty over time have nothing to do with alms. By building up MIT, George Eastman struck a mighty blow to increase prosperity and improve the health and safety of everyday life—benefiting individuals at all points on the economic spectrum. Givers who establish good charter schools today are doing more to break cycles of human failure than any welfare transfer has ever achieved. Donors who fund science, abstract knowledge, and new learning pour the deep concrete footings of economic success that have made us history’s most ­aberrant nation—where the poor improve their lot as much as other citizens, and often far more.

And what of the private donors who stoke the fires of imagination, moral understanding, personal character, and inspiration? Is artistic and religious philanthropy just the dabbling of bored and vain wealthholders? Aren’t people of all income levels lifted up when the human spirit is cultivated and celebrated in a wondrous story, or haunting piece of music, or awe-engendering cathedral?

When a donation is offered to unlock some secret of science, or feed an inspiring art, or attack some cruel disease, one can never count on any precise result. But it’s clear that any definition which denies humanitarian value to such giving, because it doesn’t go directly to income support, is crabbed and foolish. Much of the power and beauty of American philanthropy derives from its vast range, and the riot of causes we underwrite in our millions of donations.

In practice, my own answer to all those rhetorical questions is that my personal tax-deductible giving goes to a range of cause that include the arts, education, and conservation, as well as to activities supporting the poor. But one can make a case that it\’s too easy to use tax deductible contributions, and that the rules should be tightened.

For me, figures based on data about the global economy are like reliable sources of indirect light in a room where I\’m working. Even when they are out of view, or when I\’m not thinking about them explicitly, they shed some light on whatever the topic of my work is that day. For example, I recently ran across this breakdown of world GDP by country on the cost information website \”How Much\” in the short article, \”One Diagram That Will Change the Way You Look At the US Economy\” (July 21, 2015).

The country share of GDP (using market exchange rates to convert national currency values) is shown by the dark like. The lighter subdivisions within each country show the share of the economy that is services (darker area), manufacturing (medium area), and agriculture (lighter area). The color scheme differentiates continents, as the key to the figure shows.

Here\’s a quick collection of some other global economy figures that have appeared on this blog during the last year or two. If you want more detailed commentary about the figures and their sources, you can check at the original blog post.

Here\’s a different representation of world GDP, this one from the World Bank\’s International Comparison project. The horizontal axis shows population for various countries. The vertical axis shows per capita GDP. Thus, the area for each country (per capita GDP multiplied by population) gives the size of the country\’s economy.  The original post was \”GDP Snapshots from the International Comparison Project\” (May 9, 2014),

This is a similar figure with population and per capita GDP from a McKinsey Global Institute report at \”Global Economic Growth: All Productivity, All the Time\”  (January 20, 2015). However, this figure shows the distribution for 1964 and also the distribution 50 years later in 2014, thus illustrating the rise in population, standard of living, and the size of the world economy over those 50 years.

Some areas of the world obviously have more economic activity than others. What\’s the geographic center of the world economy? It\’s a little tricky to carry out this calculation on the sphere of the world, but here\’s the result of one such calculation in \”The Shifting Geographic Center of the World Economy\” (June 3, 2015). The center of the world economy was over near China 2000 years ago. During the industrial revolution period of the 19th century and on into the 20th century, the geographic center moves to Europe and then over toward the United States. But now the geographic center is shifting back toward China.

One way of illustrating income inequality is with a graphs that has the range of incomes on the horizontal axis, and the share of people receiving any given level of income on the vertical axis. This kind of graph shows the most common level of income and the distribution of income. In this figure on \”Global Income Inequality in Decline\” (May 5, 2015), the global distribution of income for 2003 (brown line) is compared with the distribution in 2013 (blue line) and a projected distribution for 2035 (red line). You can see the rise in mean and median incomes over time. Moreover, economic growth around the globe, but especially in previously low-income places like China and India, means that not as much of the world population is bunched down at the bottom of the income distribution, and for that reason the world distribution of income is becoming more equal.

Over the last couple of centuries, a common pattern has been that the high-income economies were also the faster-growing and largest economies. But over the last few decades, that pattern has been changing. The consulting firm PricewaterhouseCoopers estimates that by 2050, the six largest economies in the world will be, in order, China, India, United States, Indonesia, Brazil, Mexico. Thus, we are entering a world \”When High GDP No Longer Means High Per Capita GDP\” (October 20, 2015). Here\’s a figure showing GDP in per capita terms for some large high-income countries the also a group of lower-income emerging market countries. By 2050, economic growth will make many of the countries on the right-hand side of this figure among the largest in the world. But even after several more decades of faster-than-global-average growth, their per capita GDP will not have yet caught up to the levels n the US and other high-income countries.

Finally, here\’s a depiction of the distribution of global wealth from Credit Suisse in \”Snapshots of Global Wealth\” (October 15, 2014). If you have more than $100,000 in wealth (and yes, your housing equity and your retirement account are included here), then you are sitting above the 90th percentile of the world wealth distribution. If you have more than $1,000,000 in wealth (or if you plan to end up at that level of wealth by the time you reach retirement age), you are in the 99th percentile of world wealth.