Conversable Economist

In this figure, the horizontal axis shows \”Birth income quintile,\” that is, if you divide the income distribution into fifths, or \”quintiles,\” where were children born. \”For each birth quintile, five bars describe the distribution of income rank as adults. For example, for all those born into the bottom quintile, 44% are still in that quintile as adults. About half as many, 22%, rise to the second quintile by adulthood. The percentages go down from there. … Similarly, those born into the top income quintile are relatively likely to remain in the top. Among children born into the top quintile, 47% are still there as adults. Only 7% fall to the bottom quintile. The experiences of those born into the middle three quintiles are quite different. The distribution among income quintiles as adults is much more even for those born in these three middle groups, suggesting significant mobility for these individuals. … This pattern has led researchers to conclude that the U.S. income distribution has a fairly mobile middle, but considerable “stickiness at the ends” …\”

What about if we bring education into the picture? Again, start with birth income quintile, but now just look the bottom quintile–no college or college–and the top quintile–no college or college. \”For example, only 5% of children born into the bottom quintile who don’t graduate from college end up in the top quintile. By contrast, 30% of bottom-quintile children who graduate rise to the top quintile. The pattern is different for children born into the top quintile. Most stay in or near the top quintile regardless of whether they graduated from college. Still, the tendency to stay at the top is much more pronounced for those with a college degree. The distributions among income quintiles are similar for children born to parents in the bottom quintile who complete college and for children born into the top quintile who do not get a degree. This suggests that a child born to a bottom quintile family who graduates from college has similar mobility to a child born to a top quintile family who does not finish college. … However, it’s important to note that access to college is not equal across income distribution. Over half of children born into the top quintile graduate from college. By contrast, only 7% of those born to parents in the bottom quintile get a college degree. … This indicates that birth circumstances contribute to the stickiness at the top and bottom of income distribution, either directly or through differential access to education.\”

Here are a couple of additional comments on economic mobility:

First, the appropriate level of mobility across the income distribution is a difficult question upon which to be honest. Most parents I know are more in favor of economic mobility in the abstract than in the particular case of their own children. In the abstract, sure, it\’s easy to argue that every child should have an equal opportunity for their efforts and abilities to take them to the top of the income distribution. But it\’s a hard mathematical fact that not everyone will end up at the top; indeed, half of all students will inevitably fall below the median. It\’s a little less comfortable to argue that every child should have an equal opportunity for their efforts and talents to land them in the middle of the income distribution; and it\’s downright uncomfortable to argue that every child should have an equal opportunity for their talents and efforts to land them at the bottom of the income distribution. As parents, my wife and I make a considerable effort to assure that the opportunities for the efforts and talents of our own children will be above-average.

Second, for the record, the original idea of the \”American dream\” as discussed by the Pulitzer prize-winning historian James Truslow Adams in his 1931 book The Epic of America wasn\’t just about economic mobility, but was also about social equality and the greater freedom that Americans had to to choose their own personal path than did European societies that were more bound by class and social expectations. For my post from back in July 2011 on this subject, see here.

The total value of outstanding student loans has nearly tripled in the last eight years–and 17% that total value is owed by people over the age of 50. These and more disturbing facts are apparent from a presentation by Donghoon Lee of the New York Federal Reserve on \”Household Debt and Credit: Student Debt,\” given last week as part of the quarterly release of data on overall household debt and credit trends.

Start with the big picture. Total student debt outstanding has risen from about $350 billion in 2004 to $950 billion by fourth quarter 2012. One-third of that debt is owed by people over the age of 40, and shockingly, at least to me, 5% is owed by people over the age of 60.

Other kinds of debt like credit card loans, auto loans, and home equity loans are down from the peaks they hit just before the recession, while student loans are way up. The increase is built on more students taking out loans each year, and the average balance per borrower is rising.

Perhaps not surprisingly, given these borrowing trends combined with poor job prospects and continued high unemployment, the rate of delinquencies on loans is up. This is measured in two ways. Some borrowers are not yet \”in repayment,\” because they are able to defer their loan for some reason–like they have continued on to another degree. The figures on the right don\’t count the loan as delinquent if you aren\’t yet \”in repayment.\” But for those in repayment, on the right, about one-third of all borrowers are more than 90 days delinquent on their payments, compared with one-fifth back in 2004.

To me, the difficult issue with student loans is that, on average, they are still a good deal, in the sense that on average the income gains from a college education make it possible to repay the average loan and still to come out way ahead. But of course, not everyone is borrowing the average amount. One-eighth or so of borrowers have more than $50,000 in outstanding loans, and 3.7% have more than $100,000 in outstanding loans. Here\’s a distribution of how much student debt people have incurred.

Not everyone will earn the average income of a college graduate, either. Those who borrow to fund a year or two of higher education but then don\’t complete a degree, for example, are less likely reach that average. Those who attend certain schools with poor job placement records, or who major in areas that typically have limited job prospects or low average pay, are going to have a tougher time.

As a father of teenagers, I\’m acutely aware of the fail-safe parenting rule: \”Just don\’t let them screw up their lives before the age of 20.\” Very large numbers of young people–if not still teenagers, still in their early or mid-20s–are in grave danger of screwing up their financial lives even before they are launched in the adult world of work. Borrowing for higher education is on average a good deal, but there\’s often a lot of cheerleading around the student loan process, too. If students are going to be on the hook for these loans, they need to be made aware of how their choices about how much to borrow, where to attend, and what to study affect the risk of ending up delinquent on the loan.

It\’s common to describe the U.S. as having employer-based health insurance, and while that statement is broadly true, it doesn\’t capture the limitations of this approach. Hubert Janicki of the U.S. Census Bureau lays out some of the basic trends and patterns in \”Employer-Based Health Insurance: 2010,\” a February 2013 report. Here are a few facts:

The share of the U.S. population over the age of 15 covered by employment-based health insurance (either by their own employer or as a dependent) has been falling, dropping from 64.4% in 1997 to 56.5% in 2010.

Of the employed, 70.2% have employment based health insurance in 2010, down from 76.2% back in 2002. Of the employed, 18% have no health insurance in 2010, compared with 14.5% of the employed back  in 2002.

In other words, employer-provided health insurance has long fallen short of universal coverage, and it\’s been getting skimpier over the last decade or so. For example, those with lower incomes and those working for smaller firms are less likely to have employer-based health insurance.

\”By family income, the likelihood of working for an employer that offers any health insurance benefits increased with family income. Individuals with family incomes less than 138 percent of the federal poverty level were the least likely to work for an employer that offered health insurance benefits.Among these workers, 43.3 percent were employed in firms that offered health insurance benefits. In comparison, 63.9 percent of individuals with family incomes between 139 percent and 250 percent of the federal poverty level worked for such an employer. Among workers with family incomes 251 percent to 400 percent of the federal poverty level, 74.8 percent were employed in firms that offered health insurance benefits. Workers with family incomes 401 percent and above of the federal poverty level were themost likely to work for an employer that offered health benefits (80.9 percent). …

\”Less than one half (45.3 percent) of people working in firms with fewer than 25 employees received health insurance benefits compared with 88.8 percent for people who worked for firms employing 1,000 or more employees …\”

Of course, the limitations of employer-provided health insurance are not sufficient to prove that the Affordable Care Act passed into law in 2009 is a useful solution. Indeed, the looming presence of that act soon to take effect, together with the economic wreckage of the Great Recession, may help to explain the drop-off in employer-provided health insurance in the last few years. But whatever the limitations of that legislation, the shortcomings of employer-provided health insurance are very real.

It\’s always worth remembering (and I have noted before on this blog) that the predominance of employer-provided health insurance in the U.S. economy is an historical accident. Melissa Thomasson offers a nice overview in \”From Sickness to Health: The Twentieth-Century Development of U.S. Health Insurance,\” in the July 2002 issue of Explorations in Economic History, but that\’s not freely available on-line. However, Thomasson offers a brief overview at the Economic History Association website here. Thomasson points out that the number of Americans with health insurance went from 15 million in 1940 to 130 million in 1960. Blue Cross/Blue Shield plans began to be established in the 1930s. Then in World War II, the fateful decision was made to encourage employers to provide health insurance, and not to tax individuals on the value of that health insurance they received. Here\’s Thomasson:
 

\”During World War II, wage and price controls prevented employers from using wages to compete for scarce labor. Under the 1942 Stabilization Act, Congress limited the wage increases that could be offered by firms, but permitted the adoption of employee insurance plans. In this way, health benefit packages offered one means of securing workers. … [I]n 1949, the National Labor Relations Board ruled in a dispute between the Inland Steel Co. and the United Steelworkers Union that the term \”wages\” included pension and insurance benefits. Therefore, when negotiating for wages, the union was allowed to negotiate benefit packages on behalf of workers as well. This ruling, affirmed later by the U.S. Supreme Court, further reinforced the employment-based system.

\”Perhaps the most influential aspect of government intervention that shaped the employer-based system of health insurance was the tax treatment of employer-provided contributions to employee health insurance plans. First, employers did not have to pay payroll tax on their contributions to employee health plans. Further, under certain circumstances, employees did not have to pay income tax on their employer\’s contributions to their health insurance plans. The first such exclusion occurred under an administrative ruling handed down in 1943 which stated that payments made by the employer directly to commercial insurance companies for group medical and hospitalization premiums of employees were not taxable as employee income. While this particular ruling was highly restrictive and limited in its applicability, it was codified and extended in 1954. Under the 1954 Internal Revenue Code (IRC), employer contributions to employee health plans were exempt from employee taxable income. As a result of this tax-advantaged form of compensation, the demand for health insurance further increased throughout the 1950s …\”

If you feed any industry with enormous tax breaks, especially especially an insurance industry that separates both providers and ultimate consumers from facing costs directly, you are likely to get high levels of spending that, on the margin, bring only very slight benefits.

At least as measured by emissions of carbon dioxide, \”Africa is the green continent,\” as Paul Collier and Anthony Venables note in the most recent issue of the World Economic Review. Of course, the reason is that the standard of living across Africa is so low that not much energy is being consumed. As the economies of Africa develop, can its energy demand be green?

The common relationship between economic growth and environmental pollution is sometimes called the \”environmental Kuznets curve.\” It\’s an inverted-U; that is, economic development first brings a rise in pollution, but then later leads to a reduction in pollution. Much of the underlying reason involves political tradeoffs: the very poor are more willing to sacrifice environmental protection for gains in consumption, while those who are better off become less willing to do so. For a review of these arguments in my own Journal of Economic Perspectives from back in 2002, see \”Confronting the Environmental Kuznets Curve\” by Susmita Dasgupta, Benoit Laplante, Hua Wang and David Wheeler.( Like all articles in JEP back to the first issue in 1987, it is freely available on-line courtesy of the American Economic Association.) Here\’s a figure from Collier and Venables, showing production of carbon dioxide relative to economic output as measured by GDP.

The hope that Africa might be able to minimize the rise or even sidestep the rise in pollution that often comes with technological development is rooted in several underlying facts. Africa has strong natural potential for use of some renewable energy resources, like solar power. In addition, Africa has what economists have long referred to as \”the advantages of backwardness\” (the phrase comes from the writings of Alexander Gerschenkron back in 1962, available at various places on the web like here ). The notion is that countries which start out behind may be able to catch up rapidly because they can draw on technologies already developed elsewhere. In some cases, they may even be able to leapfrog certain stages of techology; for example, many areas of Africa may move directly to mobile phones rather than land lines for all and, for example, to retail banking based on these phones, rather than following the historical path of phones and banking from high-income countries.

Could Africa also use modern technologies for energy conservation and alternative sources of energy to sidestep the environmental Kuznets curve? Collier and Venables pose this question in \”How Rapidly Should Africa Go Green? The Tension Between Natural Abundance and Economic Scarcity.\” The essay is a nice readable version of a more technical research paper that they published last year in Energy Economics–\”Greening Africa? Technologies, Endowments and the latecomer effect\”–which is available as a working paper here.  Their conclusion is not optimistic: 

\”Superficially, Africa appears well-suited for green energy. Sunshine, water, land, forests, and being a latecomer all confer significant advantages. However, energy generation, energy saving, and carbon capture are intensive in capital, governance capacity and skills. Unfortunately, all of these factors are scarce in Africa. These factor scarcities offset the advantages conferred by natural endowments and are often decisive. Similarly, the historic advantage of being a latecomer to the installation of generating capacity is offset by the historic disadvantage of the acute energy scarcity inherited from past under-investment: Africa cannot afford to wait for further developments in green technologies. Nevertheless, there is scope for Africa’s natural advantages for green energy to be harnessed to a global advantage. But to do so will require international action that brings global factor endowments to bear on Africa’s natural opportunities.\”

What sort of international action would be especially useful? They emphasize three possibilities: 1)
\”It is cheaper for the international community to pay for the installation of green technology in Africa’s new plants than to retrofit it in existing Northern plants;\” 2) \” A second Africa-specific opportunity in generation is for international public finance, perhaps through guarantees, to subsidize the cost of switching from gas flaring to either LNG or gas-fired electricity generation;\” 3) \”A third would be to provide international public subsidies or guarantees for hydropower mega-projects.\”

For an overview of the scale of this issue, a useful starting point is a 2011 World Bank report by

Last November, I pointed out that President Obama had campaigned in 2008 on a pledge to raise the minimum wage, but that this proposal had vanished during the rest of his first term. Now, after the election, Obama somewhat unexpectedly resurrected the proposal in his State of the Union address. For a review of the controversy over the economics of the minimum wage, a useful starting point is
\”Why Does the Minimum Wage Have No Discernible Effect on Employment?\” written by John Schmitt for the Center for Economic and Policy Research.

While Schmitt\’s title suggests, albeit in the form of a question, that it is an agreed-upon truth that the minimum wage has \”no discernible effect on employment,\” I would say that his own review of the evidence suggests that there is still a genuine controversy between those who see the employment effects of the minimum wage as nil and those who see it as small. As Schmitt writes in the conclusion: \”[W]hat is striking about the preceding review of possible channels of adjustment – including employment – is how often the weight of the empirical evidence is either inconclusive (statistically insignificant or positive in some cases and negative in others) or suggestive of only small economic effects.\”

There is a difficult problem of inferring causality here. Compared to the overall costs of firms, or even compared to the costs of low-wage labor, the effects of a slightly higher minimum wage are going to be hard to distinguish from everything else that\’s happening in the economy. The employment prospects for low-skilled workers have been falling for decades, and it would clearly be incorrect to blame that on the minimum wage. Rises in the minimum wage are more likely to occur when the economy is doing well and adding jobs, but it would clearly be incorrect to infer from this correlation that a higher minimum wage causes an increase in jobs. In addition, there are difficult questions of what is sometimes called \”publication bias\” in the minimum wage literature, in which researchers of different political bents may–surprise, surprise–tend to publish the results that confirm their pre-existing beliefs.

Rather than try to unpick this empirical puzzle here–for those who are interested, Schmitt provides a nice overview of the key paper and their methods–I\’d like to focus on a separate issue, which I call the Law of Many Margins. The \”law\” simply points out that when a rule is imposed, like a minimum wage, there are almost always a wide variety of possible reactions to that law. Schmitt provides a list of 11 possible reactions (!) to a higher minimum wage. They are:

1. Reduction in hours worked (because firms faced with a higher minimum wage trim back on the hours they want)
2. Reduction in non-wage benefits (to offset the higher costs of the minimum wage)
3. Reduction in money spent on training (again, to offset the higher costs of the minimum wage)
4. Change in composition of the workforce (that is, hiring additional workers with middle or higher skill levels, and fewer of those minimum wage workers with lower skill levels)
5. Higher prices (passing the cost of the higher minimum wage on to consumers)
6. Improvements in efficient use of labor (in a model where employers are not always at the peak level of efficiency, a higher cost of labor might give them a push to be more efficient)
7. \”Efficiency wage\” responses from workers (when workers are paid more, they have a greater incentive to keep their jobs, and thus may work harder and shirk less)
8. Wage compression (minimum wage workers get more, but those above them on the wage scale may not get as much as they otherwise would)
9. Reduction in profits (higher costs of minimum wage workers reduces profits)
10. Increase in demand (a higher minimum wage boosts buying power in overall economy)
11. Reduced turnover (a higher minimum wage makes a stronger bond between employer and workers, and gives employers more reason to train and hold on to workers)

The evidence on many of these  points is ambiguous at best, and indeed may vary across industries or geographic areas or employers. But it\’s worth noting that which of these effects arise, and with what magnitude, can only be settled by empirical evidence, not theoretical assertions.

I confess that I find it hard to get too excited about modest increases in the federal minimum wage every few years, which has been happening for decades. As Schmitt points out, the evidence is that this pattern of minimum wage increases has had at most a small effect on employment and other outcomes. But the minimum wage was $5.15/hour in 2007, when President Bush signed legislation to raise it to $7.25/hour by 2009. Given an unemployment rate that has been stuck near or above 8% for four solid years now, my preference would be to de-emphasize rises in the minimum wage for awhile longer–and instead focus on other methods to help the working poor.

 The Clean Water Act of 1972 regarded water pollution a something that came out of a pipe–typically from either an industrial facility or a sewage treatment plant–and passed into streams, rivers, lakes, or ocean. Thus, the legislation was based on a process of issuing permits for what could come out of these pipes, and on phasing back those pollutants. But the success of the Clean Water Act in reducing these \”point-source\” discharges means that the primary source of U.S. water pollution is \”nonpoint\” pollution–that is, runoff from agricultural and urban areas.

Karen Fisher-Vanden and Sheila Olmstead set the stage for one way in which environmental regulators are trying to tackle the issue of nonpoint source pollution in their article, \”Moving Pollution Trading from Air to Water: Potential, Problems, and Prognosis,\” which appears in the most recent (Winter 2013) issue of my own Journal of Economic Perspectives. Like all articles in JEP back to the first issue in 1987, it is freely available on-line courtesy of the American Economic Association. Fisher-Vanden and Olmstead write (citations and footnotes omitted):

 \”The stated goals of the Clean Water Act were: 1) the attainment of fishable and swimmable waters by July 1, 1983; and 2) the elimination of all discharges of pollutants into navigable waters by 1985. Obviously, those deadlines have been postponed through amendments, and distinctions have since been made between different types of pollutants. …  The Clean Water Act’s main tool is a set of effluent standards, implemented through point-source permitting. The National Pollutant Discharge Elimination System (NPDES) specifies quantitative effluent limits by pollutant, for each point source, based on available control technologies. For the most part, industrial point source compliance with these permits has been high. Municipal sewage treatment has also expanded dramatically, resulting in impressive improvements in urban water quality—for examples, see Boston Harbor and the Hudson River near New York City.

\”But the gains from point source controls are reaching their limits. Even if all point sources were to achieve zero discharge, only 10 percent of US river and stream miles would rise one step or more on EPA’s water quality ladder. Nonpoint source pollution such as agricultural and urban runoff, atmospheric deposition, and runoff from forests and mines has become the major concern of water pollution abatement efforts. In fact, nonpoint source pollution from agricultural activities is now the primary source of impairment in US rivers and streams. Nonpoint source pollution involving nutrients like nitrogen and phosphorus causes excessive aquatic vegetation and algae growth and eventual decomposition, which deprives deeper waters of oxygen, creating hypoxic or “dead” zones, fish kills, and other damages. This problem is geographically widespread; seasonal dead zones in US coastal waters affect Puget Sound, the Gulf of Mexico, the Chesapeake Bay, and Long Island Sound. However, agricultural nonpoint source pollution is essentially unregulated by the Clean Water Act …\”

Before discussing what efforts are being made to address nonpoint source pollution, it\’s worth nothing that there are legitimate questions about whether the costs of the Clean Water Act have exceeded the benefits.  For example, in the Winter 2002 issue of my own Journal of Economic Perspectives, A. Myrick Freeman III reviewed studies bearing on \”Environmental Policy Since Earth Day I:What Have We Gained?\” Even if one goes beyond just looking at immediate economic gains and takes into account survey evidence on people\’s willingness to pay for knowing that water is cleaner (so-called \”contingent valuation\” evidence), the overall costs seem to far outstrip the benefits.

The intuition behind this result is that there were some prominent bodies of water that were highly contaminated, and that have improved substantially since the passage of the law. But the law was not just applied to a few high-profile cases of water pollution: it imposed costs everywhere. Moreover, as noted above, the law called for (eventually) the total elimination of all discharges, and even a passing acquaintance with the law of diminishing returns suggests that reducing pollution by one-third or one-half or more might be done at fairly low cost, but when it comes to figuring out how to reduce that last bit of pollution, the marginal costs may climb very high. .

But the question of past costs and benefits of the Clean Water Act is, well, water under the bridge. At present, the situation is that the law has been so effective at reducing point-source emissions that the main source of water pollution is nonpoint sources, and especially runoff from agriculture. There are a variety of voluntary programs to encourage reducing nonpoint source pollution. But such programs generally lack teeth. Thus, environmental regulators have been trying in some areas to tackle the problem through a back door–by creating a structure for tradeable emissions permits. As Fisher-Vanden and Olmstead describe it, the current clean water law

\”…  requires states to establish a Total Maximum Daily Load (TMDL)—basically a “pollution budget”—for each water body that does not meet ambient water quality standards for its designated use, despite point source controls. Designated uses include recreational use, public water supply, and industrial water supply, and each designated use has an applicable water quality standard. State courts began ordering the developmentof TMDLs in the 1980s and 1990s in response to lawsuits by environmental groups.Since 1996, the states in cooperation with the Environmental Protection Agency have completed thousands of TMDLs. Establishing a TMDL is a “holistic accounting exercise” in which all permitted sources and land uses within a watershed drainage area, including agriculture and urban runoff, are inventoried and allocated responsibility for portions of the pollution budget. While regulators cannot implement enforceable caps on agricultural pollution through this process, they have recognized the importance of incorporating agricultural abatement into clean-up processes, and water quality trading is one tool they have employed for this purpose.\”

They discuss a number of examples. In one fairly straightforward program here in Minnesota, the \”Southern Minnesota Beet Sugar Cooperative, a beet processor, pays its 256 grower-members to invest in phosphorus-reducing land management changes so that the processor can meet its permit requirements for expanded production. In this case, the beet growers and the processing facility are treated under the processor’s permit as a single source to meet an overarching phosphorus effluent cap.\” A more complicate case involves the Chesapeake Bay, which receives discharges from six states and the District of Columbia, and in which three of the states are allowing for trading of water quality permits. Fisher-Vanden and Olmstead discuss several dozen of these programs around the country.

The practical and political advantage of using marketable permits are well-known among economists, and are a staple of most intro econ classes: specifically, those who need to reduce emissions can think about whether to do it themselves, or whether to pay some other economic actor–like a farm–for reducing emissions. With this choice, emissions get reduced, which after all is the goal, at lowest possible cost. But the practical problems of implementing such a scheme over a large area like the Chesapeake Bay, making sure that reductions in nonpoint source pollution really happen, and in a way that doesn\’t clean up one area of the Bay at the expense of another area, can be quite complex. My own sense is that the Total Maximum Daily Load concept is a very useful one for thinking about the causes of water pollution, but it\’s time to stop putting all the requirements on the point-source emitters of water pollution. For bodies of water that are not meeting ambient quality standards, there should be requirements for both point and nonpoint emitters to reduce their water pollution–with trading of emissions permits allowed between them.

When talking about ways of curbing health care spending, someone always brings up the costs of acute care at the very end of life. Could we save significant money by not spending so much on people who are the verge of dying? To what extent are we already changing the patterns of end-of-life care?

We spend about 25-30% of Medicare spending on patients who are in their last year of life, according to Gerald F. Riley and James D. Lubitz in their 2010 study, \”Long-term trends in Medicare payments in the last year of life\” (Health Services Research, April 2010, 45(2):565-76). They also find that this number hasn\’t changed much over the last 30 years–that is, health care spending during the last year of life is rising at about the same pace as other Medicare spending– and that the percentage isn\’t much affected by adjusting for changes in age or gender of the elderly.  On their estimates, Medicare spending on those who die in a given year is much higher than on those who survive the year: in 2006, Medicare spending in 2006 on those who died in that year was $38,975, while Medicare spending in 2006 on those who survived the year was $5,993.

Total Medicare spending in 2012 was about $560 billion. Thus, 25% of that amount would be $140 billion spent during the last year of life. It\’s often unclear at the time whether someone is actually in their last year of life, but say for the sake of argument that such cases could be identified, and spending in this area could be reduced by half. If attainable, cuts of this size would be $70 billion in annual savings, which is certainly a substantial sum. But to keep it in perspective, total U.S. health care spending is in the neighborhood of $2.6 trillion. Thus, the potential gains from even fairly aggressive limits on end-of-life health care spending through Medicare is a little under 3% of total U.S. healthcare spending.

To what extent is the U.S. health care system changing its practices in end-of-life care? In the February 6, 2013 issue of JAMA, a team of writers led by Joan Temo address this question in an article called: \”Change in End-of-Life Care for Medicare Beneficiaries\” (vol. 309, #5, pp. 470-477). They find an intriguingly mixed set of patterns.

One common measure of end-of-life care is to see what share of patients died in a hospice or at home, compared to dying in the acute-care ward of a a hospital. Their results show that from 2000 to 2009, the share of patients who died in the acute care section of a hospital declined from 32.6% to 24.6%; the share of patients who died at home rose from  30-7% to 33.5%; and the share of patients who died in a hospice rose dramatically from 21.6% to 42.2%. On the surface, these kinds of numbers certainly suggest a pattern of less aggressive end-of-life care.

But when Temo et al. dug just a bit deeper, they found that many of the hospice stays were extremely short–just a few days. Looking at the use of intensive care units in the last month of life, they found that it has risen from 24.3% in 2000 to 29.2% in 2009. In addition, the number of health care \”transitions\” from one care setting to another has risen both in the last 90 days of life and the last three days of life. For example, 10.3% of patients had a \”transition\” in the last three days of life in 2000, while 14.2% of patients had a transition in the last three days of life in 2009.

As Temo et al. put it: \”Although a hospice stay of 1 day may be viewed as beneficial by a dying patient and family, an important yet unanswered research question is whether this pattern of care is consistent with patient preferences and improved quality of life. … Our findings of an increase in the number of short hospice stays following a hospitalization, often involving an ICU stay, suggest that increasing hospice use may not lead to a reduction in resource utilization. Short hospice lengths of stay raise concerns that hospice is an \”add-on\” to a growing pattern of more utilization of intensive care services at the end of life.\”

Few questions in health care policy are harder than what should be spent on end-of-life care. It\’s fairly common for the elderly, when healthy, to say that they don\’t want extreme end-of-life measures. But when those same people become very ill, both they and their families often start thinking that extreme care makes a lot of sense. In addition, while perhaps the diagnostic and statistical techniques for figuring out a few months or a year in advance who is likely to die will improve over over time, right now they are not very accurate. Thus, the common sense policies in this area tend to revolve around earlier counseling for the elderly, so that patients (and their families) can have a more clear sense of what they want in terms of end of life care, and improving hospice and end-of-life home care–after all, basic palliative services like intravenous fluids and antibiotics don\’t need to happen in a hospital setting.

In the aftermath of the Great Recession, mainstream macroeconomists have been seeking in various ways to bring the financial sector into their models. As that activity implies, the financial sector had not previously been playing much of a role mainstream models. Claudio Borio lays out a perspective on treating cycles in the financial sector as having a life of their own in \”The financial cycle andmacroeconomics: What have we learnt?\”, published in December 2012  as working paper #395 for the Bank of International Settlements. I should note that Borio\’s view of how the financial sector interrelates with the real economy is not conventional macroeconomic wisdom, but I should also note that conventional macroeconomics hasn\’t exactly covered itself with glory in the last few years.

Borio begins by point out that conventional macroeconomics was paying little attention to the financial sector in the years before the Great Recession, and argues that the strategies for trying to add a  financial sector to existing models doesn\’t go nearly far enough. (Citations and footnote are omitted from quotations throughout.)  Here\’s Borio: \”The financial crisis that engulfed mature economies in the late 2000s has prompted much soul searching. Economists are now trying hard to
incorporate financial factors into standard macroeconomic models. However, the prevailing,
in fact almost exclusive, strategy is a conservative one. It is to graft additional so-called
financial “frictions” on otherwise fully well behaved equilibrium macroeconomic models … The main thesis is that macroeconomics without the financial cycle is like Hamlet without the Prince. In the environment that has prevailed for at least three decades now, just as in the one that prevailed in the pre-WW2 years, it is simply not possible to understand business fluctuations and their policy challenges without understanding the financial cycle.\”

Borio argues that there is a \”financial cycle\” with its own dynamics. Here\’s a figure with U.S. data showing the regular business cycle, measured by variations in GDP, compared with the \”financial cycle,\” based on estimates of credit, the credit/GDP ratio, and property prices. He argues that while business cycles are usually in the range of 1-8 years, \”the average length of the financial cycle in a sample of seven industrialised countries since the 1960s has been around 16 years.\”

Borio argues that the peaks of the financial cycle are associated with financial crises. When a business cycle recession happens at the same time as the contraction part of a financial cycle, the recession is about 50% deeper.

This perspective on the financial cycle also offers some policy advice. Central banks and financial regulators should pay attention to credit/GDP ratios and to property prices. Borio writes: \”The idea is to build up buffers in good times, as financial vulnerabilities grow, so as to be able to draw them down in bad times, as financial stress materialises. There are many ways of doing so, through the appropriate design of tools such as capital and liquidity standards, provisioning, collateral and margining practices, and so on. … In the case of monetary policy, it is necessary to adopt strategies that allow central banks to tighten so as to lean against the build-up of financial imbalances even if near-term inflation remains subdued – what might be called the “lean option”. Operationally, this calls for extending policy horizons beyond the roughly 2-year ones typical of inflation targeting regimes and for giving greater prominence to the balance of risks in the outlook, fully taking into account the slow build-up of vulnerabilities associated with the financial cycle. … In the case of fiscal policy, there is a need for extra prudence during economic expansions associated with financial booms. …  Financial booms are especially generous for the public coffers, because of the structure of revenues. And the sovereign inadvertently accumulates contingent liabilities, which crystallise as the
boom turns to bust and balance sheet problems emerge, especially in the financial sector.\”

However, once the double-whammy of a financial crisis and a business cycle recession has hit simultaneously, Borio also argues that conventional policy responses may not work well. In a \”balance sheet recession,\” fiscal and monetary policy may not be very capable of stimulating demand, and instead may encourage financial firms and businesses to put off the necessary hard steps they need to take, leaving the economy too dependent on government stimulation rather than on the private sector moving forward. As he writes:  \”On reflection, the basic reason for the limitations of monetary policy in a financial bust is not hard to find. Monetary policy typically operates by encouraging borrowing, boosting asset prices and risk-taking. But initial conditions already include too much debt, too-high asset prices (property) and too much risk-taking. There is an inevitable tension between how policy works and the direction the economy needs to take.\”

The concept of a \”financial cycle\” has a plausible back-story. When times are good, borrowers and investors of all kinds tend to let down their guard, worry less about risks, and gradually become overextended–which can then brings on a counterreaction, or even in some cases a financial crisis. It\’s easy to point to financial crises, but it\’s harder to show convincingly that an earlier financial boom is the cause of the crisis. The nice smooth curve of financial cycles above is created by using statistical tools (\”filtering\”) to blend together tBorio is up front about this difficulty and others, and has some suggestions for how the appropriate modeling might proceed. But even if one doesn\’t buy into the notion of a self-perpetuating financial cycle, standing apart from the regular business cycle, one lesson that everyone seems to have learned from the Great Recession is that rapid expansions of credit and rapid rises in property values have real macroeconomic risks–and thus are an appropriate target for policy.

In theory,  the federal government sets minimum guidelines for each state\’s unemployment insurance system, and then each state sets its own rules for what is paid in and and what benefits are offered. Each state has its own unemployment trust fund. The idea is that the the trust fund will build up in good economic times, and then be drawn down in recessions. But it hasn\’t actually worked that way for a long time, and the problem is getting worse.  Christopher J. O’Leary lays out the issue and possible solutions in \”A Changing Federal-State Balance in Unemployment Insurance?\” written for the January 2013 Employment Research Newsletter published by the Upjohn Institute.

When a recession hits, the federal government has developed a habit of stepping in with extra unemployment insurance funds. For example, the feds stepped in with additional funding for extending unemployment benefits in 1958, 1961, 1971, 1974, 1982, 1991 and 2002–as well as during the most recent recession. With the feds stepping up, it has been easier and easier for the states to keep their unemployment taxes as low as possible. For example, average unemployment insurance taxes (adjusted for inflation) were $274/employee in 2008, lower than the $350/employee in 1994 and the $515/employee in 1984, according to Ronald Wilus of the U.S. Department of Labor.

As a result, over time the feds are paying for a larger share of unemployment insurance during recessions. Here\’s an illustrative figure from O\’Leary.

For some perspective on the revenues coming into the unemployment trust funds from the regular unemployment tax, as opposed to how much money is going out, here\’s a table from a Congressional Research Service report on \”Unemployment Insurance: Programs and Benefits,\” by Julie M. Whittaker and Katelin P. Isaacs, dated December 31, 2012. Notice that when unemployment rates were fairly low from 2005-2007, revenue exceeded outlays by about $10 billion per year. Then from 2009, 2010, and 2010, outlays exceeded revenue by something like $100 billion per year. The difference was made up by general taxpayer spending.

The intergovernmental incentives in the unemployment insurance system are clearly messed up. States have an incentive to keep unemployment insurance premiums fairly low, promise significant benefits, and then let the federal government pick up the tab when a recession occurs. What would be needed to get back to a system where states save up funds for unemployment insurance money in trust funds–even if some federal help might occasionally be needed?

One step suggested by O\’Leary is to raise the \”tax base.\” At present, the minimum federal standard requires that states collect unemployment insurance taxes on the first $7000 of taxable wages–a level that was established back in 1983. Just adjusting that $7,000 base for inflation would mean increasing it to about $16,000. O\’Leary notes that 35 states currently have a taxable wage base at or below $15,000.

A second step would be to have a rule that unemployment insurance benefits would not kick in until after a waiting period. O\’Leary writes: \”A much neglected potential reform on the benefit side would be to institute waiting periods of 2–4 weeks, with the duration of the wait depending inversely on the aggregate level of unemployment. … A somewhat longer waiting period will reduce program entry by those with ready reemployment options, and help to preserve the income security strength of the system for those who are involuntarily jobless for 4, 5, or 6 months.\”

Yet another step would be to use federal rules to discourage states from lowballing the funding of their unemployment insurance and relying on an influx of federal funding. Here\’s O\’Leary: \”[T]he federal partner should institute minimum standards on weekly benefit levels and durations, and also tie potential durations of any future federal emergency benefits to the existing state maximum durations. For example, a state providing up to 26 weeks would get 13 weeks of federal temporary benefits, but if the state maximum were 20 weeks the federal supplement would be 10 weeks.\”

It\’s worth pointing out that unemployment insurance has a number of problems other than whether it is pre-funded. You need to meet certain qualification tests for unemployment insurance, typically based on earnings in the previous year or so, and as a result, many of the unemployed do not receive unemployment insurance. In January 2013, about 3.5 million people were receiving unemployment insurance benefits, but about 12.3 million people were unemployed.

There are also a number of proposals that seek to adjust the incentives so that unemployment insurance can better co-exist with incentives to find a new job. Some proposals are that unemployment benefits should be larger, so as to soften the economic blow of unemployment, but for a shorter time, to hasten the incentive to find a new job. Some proposals would require or allow people to set up individual unemployment accounts, which they could keep at retirement, so that people would tap their own money before turning to the government fund. One proposal would offer a bonus to those receiving unemployment insurance if they found a job quickly, because it could be less costly for the unemployment insurance trust fund if they find a job faster rather than linger on receiving benefits.

The Great Recession and its aftermath have wrecked the premises of the existing unemployment insurance system. It\’s time to rebuild.

\”Big data\” has become a buzzword. It conveys the notion that our interconnected world is generating a vast array of data–and asks how that data can be used for analysis, social problem-solving, and private profit. However, I had not known that the United Nations has an organization called Global Pulse, which focuses on issues of Big Data from a development perspective. The Global Observatory, a publication of the International Peace Institute, had an interview last November with Robert Kirkpatrick, Director of UN Global Pulse.  Here, I\’ll quote from the interview with Kirkpatrick, and will also refer to a May 2012 white paper from Global Pulse called \”Big Data for Development: Challenges and Opportunities.\” 

As  a starting point, here\’s Kirkpatrick defining Big Data: \”[Bbig data is a term that has come into vogue only in the last couple of years, and it refers to the tremendous explosion in volume and velocity and variety of digital data that is being produced around the world. The statistics are somewhat astonishing: there was more data produced in 2011 alone than in all of the rest of human history combined back to the invention of the alphabet.\”

The May 2012 report offers this comment (footnotes and references to figures omitted: \”The world is experiencing a data revolution, or “data deluge”. Whereas in previous generations, a relatively small volume of analog data was produced and made available through a limited number of channels, today a massive amount of data is regularly being generated and flowing from various sources, through different channels, every minute in today’s Digital Age. It is the speed and frequency with which data is emitted and transmitted on the one hand, and the rise in the number and variety of sources from which it emanates on the other hand, that jointly constitute the data deluge. The amount of available digital data at the global level grew from 150 exabytes in 2005 to 1200 exabytes in 2010. It is projected to increase by 40% annually in the next few years .. This rate of growth means that the stock of digital data is expected to increase 44 times between 2007 and 2020, doubling every 20 months.\”

 The flood of data relevant for development issues includes four categories, according to Global Pulse: 1) \”Data exhaust\” created by people\’s transactions with digital services, including web searches, purchases, and mobile phone use; 2) \”Online information\” available in news media and social media, as well as job postings and e-commerce sites; 3) Physical sensors that look at landscapes, traffic patterns, weather, earthquakes, light emissions, and much else; 4) Citizen reporting, when information is submitted by citizens through surveys, hotlines, updating of maps,and the like.

Of course, there are enormous challenges in dealing with Big Data, including privacy concerns, the sheer size of the datasets, how quickly they are expanding, and how to digest and interpret it. But the potential for understanding what is happening much more quickly is becoming apparent. As Kirkpatrick says: \”[W]e now live in this hyper-connected world where information moves at the speed of light, and a crisis can be all around the world very, very quickly, but we’re still using two- to three-year-old statistics to make most policy decisions. The irony is, we’re swimming in this ocean of digital data, which is being produced for free all around us.\”

Private sector firms like Google are already using Big Data. Some of the public sector and research studies include:

• A country\’s GDP can be estimated based on light emissions at night, as perceived by satellites. 
• Outbreaks of flu or cholera or dengue fever can be identified much more quickly by looking at web searches. Another study used Twitter mentions of earthquakes as a way to get a faster response to quakes.
• One study was able to predict where people were at any time with greater than 90% accuracy based on cell-phone records showing past movements. Another study in developing countries could predict income with 90% accuracy based on how often you top off the air time on your mobile phone. Kirkpatrick says: \”Even if you are looking at purely anonymized data on the use of mobile phones, carriers could predict your age to within in some cases plus or minus one year with over 70 percent accuracy. They can predict your gender with between 70 and 80 percent accuracy.\”
• A study in Indonesia was able to approximate a consumer price index for basic foods by looking at comments on social media. (Apparently, Jakarta produces more tweets than any other city in the world.) Other studies have sought evidence on food shortages or food price volatility by looking at social media.

I confess that the social scientist within me finds the research possibilities here to be fascinating. Kirkpatrick says:\” Now think about this, this is astonishing: the ability to see in real time where beneficiaries are can allow us to understand exactly where the population is that we need to reach, and if you combine that with information on the size of air-time purchases, you can tell how much money these people have. You start to be able to extract basic demographic information, population movement, and behavior data from this information while fully protecting privacy in the process.

What we’re focused on now is working with mobile carriers around the world, including in Indonesia, to get access to archives of anonymized call records and purchase records, because what we do is essentially correlate that data with official statistics. You look at the movement patterns, the mobile service consumption patterns, the social-network patterns that you can derive from how people interact and compare that to food prices, fuel prices, unemployment rates, disease outbreaks, earthquakes, and look at how a population was affected. Or, you compare it to when a program was initiated in the field or when a policy initiative got off the ground: did it actually work? The potential for monitoring and evaluation here as well is quite remarkable.\”

Moreover, Kirkpatrick describes the effort by Global Pulse to find a middle ground in concerns about privacy and access to Bid Data: \”Right now, the conversation around big data is very polarized. You might call it \”Germany vs. Mark Zuckerberg.\” You have the very conservative prohibition against reuse without explicit permission that has become pervasive in the European Union; it’s a very guarded approach. At the opposite end of the spectrum, you have companies that live on big data, which are saying privacy is dead, profit is king. We’re trying to insert a third pole into this debate, which is to say, big data is a raw public good. But to do that we have to create a kind of R & D sandbox where we can experiment with it and learn how to use it safely.\”

At least to me, many of the existing efforts to use Big Data seem to me interesting–but relatively small potatoes. As the existing data increases 40-fold in the next few years, along with techniques and capabilities to digest and analyze that data, challenges and possibilities will probably emerge that I can\’t even imagine now.  The May 2012 report quotes the comment from social technology guru Andreas Weigend, who said: \”[D]ata is the new oil; like oil, it must be refined before it can be used.\”