Conversable Economist

The Greek scientist and philosopher Archimedes is reputed to have said: \”Give me a lever and a place to stand, and I will move the world.\” Well, the World Bank doesn\’t have a long enough lever.

Total outstanding World Bank loans are $152 billion. If one could magically take that $152 billion and distribute it outright to the 2.1 billion people who  live on less than $3.10 per day, it works out to about $72 per person–which is a month or so of consumption even for these very poor people. If it is only the returns from World Bank loans are spread among those 2.1 billion people (an assumption that can be questioned), the gains to consumption would last longer, but also be much smaller.

There\’s not much prospect that World Bank funding will increase dramatically. So how can the bank choose a place to stand, so that it can leverage its resources as much as possible? Two papers in the Winter 2016 issue of the Journal of Economic Perspectives tackle this problem: Michael A. Clemens and Michael Kremer discuss \”The New Role for the World Bank\”, while Martin Ravallion explores \”The World Bank: Why It Is Still Needed and Why It Still Disappoints.\”  Both papers, in related but different ways, argue that the World Bank needs to shift its focus.

The original idea of the World Bank at its founding back in 1944, in the aftermath of World War II, was that global capital markets were not very functional, and that in particular low-income countries were unlikely to have the access they needed to investment capital from  high-income countries. Whatever the merits of that argument in the 1940s and 1950s, it doesn\’t hold up so well in the 21st century. The last quarter century has seen dramatic rises in international foreign direct investment, foreign portfolio investment, and in remittances that emigrants send back to their home countries.
Ravallion in his paper estimates that \”World Bank lending in 2012 represented only about
5 percent of the aggregate private capital flows to developing countries. Also, it\’s become fairly common to hear that certain low-income countries have borrowed so much that repayment of that debt has become a burden hindering their growth–which is pretty much the opposite of not being able to borrow.

In short, the idea of the World Bank as a major player in global capital markets for low-income countries no longer fits with the facts of the world economy. Indeed, Clemens and Kremer detail an estimate that if you just look at how of a subsidy the World Bank is offering low-income countries (which includes loans made at below-market interest rates and outright grants), \”total subsidy provided by the World Bank Group’s shareholders to clients overall is in the range of $11.0–14.2 billion per year.\” The alternative vision offered by the JEP articles is that the World Bank should use its loan and grant programs so that it has legitimacy as a player in development issues, but that the real leverage of the World Bank isn\’t from the money. Instead, the Bank\’s leverage, its place to stand, is to focus on policies for reducing extreme poverty (rather than being broadly concerned with helping economic growth) and with being a knowledgeable and trusted  source of advice on what works.

But here the problem arises in thinking about the World Bank as a trusted source of knowledge about how to help the poor. In the post-modern 21st century, we are often quick view ideas like \”knowledge\” and \”trust\” as little more than pretty language designed to fool the suckers for long enough to impose a desired politics and ideology. It can be hard to work one\’s way around to a mature view that even though knowledge is almost never truly dispassionate or disconnected from politics, knowledge is still real and possible. Or to recognize that while  trust should only be given conditionally and incrementally in matters of public policy and finance, trust can lead to working relationships that are far more smooth and productive than the perpetual embrace of mistrust.

The JEP authors are quick to note that the World Bank hasn\’t always acted in a way that would lead to it being regarded as a trusted source of knowledge. On the basic question of how the World Bank evaluates its own loans, Ravallion points out:

The first question we would surely ask of a knowledge bank is whether it establishes a sound prior case for its own interventions and systematically assesses whether that case turned out to be valid. The World Bank has not, however, lived up to this ideal. Evaluation is generally weak and unbalanced, both before and after implementation. This reflects a lack of focus on the welfare outcomes of projects and policies. Instead of studying the effect on its stated goal of poverty reduction, the focus tends to be on monitoring inputs—for example, schools built rather than education attainments … The Bank was once a leader in cost–benefit analysis, but this is no longer true. While the Bank’s operational directives call for cost–benefit analysis, it is not implemented for most Bank projects (World Bank 2010). The proportion of projects quoting an expected rate of return has fallen over time. Cost–benefit analysis has clearly fallen out of favor among World Bank staff and managers.

From the beginning, the World Bank had a political mission—to use aid to keep countries in the Western political orbit and to compete with the USSR for economic and political influence in third world countries—as well as a narrower economic mission. The political nature of the institution has continued. In particular, the United States has effective veto power over major Bank decisions, and Bank lending tends to follow the commercial and financial interests of the United States. Indeed, US officials explicitly demanded such behavior in recently declassified documents from long ago. Also, countries temporarily on the UN Security Council receive more Bank loans, and Bank projects may be used to reward countries for General Assembly votes that support priorities of the United States and other high-income countries. The United States has successfully intervened to limit Bank lending to some countries, including Iran.

Ravallion\’s paper offers a detailed blueprint for how the World Bank might reshape itself from being a \”lending bank\” to a \”knowledge bank,\” including its practices concerning loan-granting and evaluation, gathering and dissemination of  data and research findings, policy advice, and other dimensions. Perhaps counterintuitively, if the US thinks that it\’s useful to have a World Bank that is viewed as knowledgeable and trusted, and not (primarily) politically motivated, then the US needs to loosen its grip on the bank. For example, the tradition that only a US citizen can be president of the Bank needs to end, and voting power in the governance of the Bank needs to be adjusted over time so that the emerging economies of the world have have bigger say.

This kind of advice seems eminently sensible, if perhaps harder than it sounds. But perhaps the hardest point is that if the World Bank is to use knowledge as its leverage to fight poverty, then it will need to actually take a stand on what works and what doesn\’t. And when the Bank takes a stand, it will inevitably inject itself into policy disputes where its motives will be called into question. Clemens and Kremer offer a list of examples of knowledge-based poverty-fighting policies that have been pushed by the World Bank in recent years. Here\’s the Clemons-Kremer list (citations omitted):

Agriculture. In the past, many African farmers could only sell to agricultural marketing boards that operated state-run processing facilities and paid a fraction of the world price for export crops. For example, Ghanaian cocoa farmers shortly after independence were only receiving 55 percent of what the board received for selling their produce; Kenyan cotton farmers in the mid-1970s were getting only 48 percent. This practice was common … The state also ran markets for inputs, such as fertilizer, often delivering them not at all, or only to politically connected farmers, or too late for planting. The Bank promoted liberalization of agriculture and these monopsonistic agricultural marketing boards are now mostly gone. …

Health. The World Bank promoted a shift in budgets away from tertiary-care hospitals in capital cities towards community health centers and rural clinics providing basic primary care—for example through Ethiopia’s Health Extension Program and Brazil’s Family Health Extension Program. Health budgets are now substantially more oriented toward primary care. At one point the Bank pushed for charging fees to at least certain categories of patients, although it has now backed away from this. It now frequently promotes the adoption of pay-for-performance programs within government health services. 

Education. The number of out-of-school children and adolescents worldwide fell from 196 million to 124 million between 2000 and 2013  despite population growth over the period. The Bank has been an important part of the movement for universal primary education, and now that the vast majority of primary-school-age children in the developing world are enrolled in school, the Bank is shifting its focus to improving learning. 

Social protection. The Bank has played an important role in the spread of “conditional cash transfer programs”—in which cash transfers to low-income households are linked to children attending school or seeing health care providers. After promising results from Mexico’s PROGRESA in the 1990s (now referred to as Oportunidades) and Brazil’s Bolsa Alimentação program, the Bank now supports conditional cash transfer programs in 26 countries. The Bank both financially supported national programs and vigorously promoted conditional cash transfer programs, including at international conferences convened for that purpose in Mexico in 2002, Brazil in 2004, and Turkey in 2006. The Bank’s researchers also played an important role in rigorously evaluating the impact of these programs, a factor in their rapid diffusion. Such programs have been found to reduce poverty and improve child health and education. 

Regulatory policy. The World Bank’s Doing Business reports, which provide objective and internationally comparable measures of how different countries regulate the private sector, have been very influential in motivating countries to reduce regulatory barriers to establishing new firms. 

Tax policy. While the International Monetary Fund has played a bigger role, the World Bank has supported the dramatic worldwide shift to value-added taxes, which have replaced other taxes widely considered less efficient. Since 1960, a VAT has been adopted as the main consumption tax in over 140 countries. 

Trade policy. The World Bank, along with the IMF, has supported shifts from rigid import quotas to more flexible tariffs, along with reductions in tariffs and movements toward “unified” exchange rates in which the same exchange rates are applied to all types of trade. From the 1980s to 1990s, most World Bank adjustment operations were made conditional on trade liberalization. Tariffs and statutory barriers to business creation have declined dramatically. In India, for example, the weighted tariff rate has fallen from 54 to 7 percent between 1990 and 2009. 

Conflict recovery. In post-conflict situations, the Bank has supported community-driven development programs and procedures for demobilizing and providing transitional support to ex-combatants, for example, in Bosnia, Cambodia, El Salvador, Lebanon, and Uganda. 

Property rights. Since the 1960s, the Bank has supported land demarcation and titling programs in Armenia, Bolivia, Guatemala, Indonesia, Malawi, and elsewhere across the developing world. Thailand used World Bank support to partition and distribute land to rural residents. Whereas governments of developing countries once regularly appropriated private assets, they are now more likely to privatize state assets. 

Few of us will be in total agreement with any list like this one. My point is that one\’s feeling about the World Bank as a knowledge bank and a poverty-fighting institution will depend in substantial part on whether you think this kind of list is mostly right or mostly wrong. Clemens and Kremer sum up their own views this way: \”Indeed, we do not agree with all of these policies or believe they were all well implemented, but we do agree with the general thrust of most of them and believe that they reflect mainstream views within the economics profession.\”

The popular saying that \”demography is destiny\” is of course not literally true. Economic factors like demography set the stage, but actions still affect outcomes. As Stephen Hawking wrote in his 1993 book: \”I have noticed that even people who claim everything is predetermined and that we can do nothing to change it, look before they cross the road.\”

That said, demographic patterns can greatly affect the economy. The Council of Economic Advisers has published the 2016 Economic Report of the President, which I commend to readers who would like a nice overview of the state of the US and world economy along many dimension. In the next week or so, I\’ll put up some posts on a few aspects of the report that jumped out at me. One such discussion was about the global workforce and patterns of global aging. The report shows the number of people in the working-age population in different regions of the world, both going back to 1950 and then projecting forward to 2070.

What patterns jump out? The working-age population in Europe didn\’t expand much since 1950, and has already peaked. In North America and Latin America, there\’s been some relatively modest growth in the working age population since 1950, but that will continue to be modest or flatten out in the next half-century. Asia has seen a skyrocketing workforce since 1950. However, the size of that workforce has already topped out and started declining in east Asia (think China and Japan, and surrounding countries), while it will continue to expand dramatically for a few more decades in south Asia (think India and surrounding countries). And in Africa, working-age population has already surpassed Europe and and in the next few decades is projected to first overhaul eastern Asia and then pass southern Asia. The report notes:

\”Over the next 30 years, half of the world’s population will live in Africa and Southern Asia; global population growth will be driven by their high fertility and relatively young populations. As a result, the bulk of new workers in the global economy will be added in economies that have lower levels of education, technology, and capital, implying those workers will not be as productive, if current circumstances continue. By 2035, the number of people joining the working-age population from Sub-Saharan Africa and Southern Asia will exceed that from the rest of the world combined. This means both South Asia and Africa will be increasingly important to global growth.\”

These shifts in the workforce will be accompanied by shifts in the \”dependency ratio,\” which is calculated by adding the the number of people 14 and under to the number who are 65 and older, and then dividing that total by the size of the working age population.  Most areas of the world have seen a fall in the dependency ratio from, say about 1960 or 1970 up to the present. But in most areas of the world–Africa with its rapid workforce growth excepted–are now headed for a sharp rise in the dependency ratio in the dccades ahead.

The report notes (citations omitted):

\”Global demographic trends are at a turning point. Population growth is slowing and, after increasing for the previous five decades, the proportion of the population that is working-age peaked at 66 percent in 2012. This proportion is projected to decline steadily for the next century. This slower growth in the working-age population—or outright contraction—will continue to be a drain on global growth for the foreseeable future. …  Demographic changes also indirectly affect the amount of resources per capita through changes in household savings behavior across their life cycles. Lower dependency ratios (the ratio of people younger than 15 or older than 64 to the working-age population) can raise savings, which helps finance more investments and increases output. Finally, demographics indirectly affect productivity growth through changes in the quality of human capital formation and innovation. Nevertheless, the reverse is also true. Demographic changes can act as a drag on economic growth.

In thinking about the relationship between population, working-age population, dependency ratios, and economic growth, it\’s going to become more important over time to divide economic statistics by various relevant population  measures. Doing this can sometimes lead to unexpected conclusions.

For example, the report offers a US-Japan comparison. We all know that Japan\’s real GDP growth in the last 25 years has been disappointingly sluggish, right? The first two bars show that real annual GDP growth in the US has been roughly double that of Japan. But if you look at growth in real GDP per capita, you are then taking into account that Japan\’s population growth has been very slow–indeed, Japan\’s total population peaked back around 2008 and has been declining since. Thus when you look at growth of per capita GDP, the US lead over Japan is greatly reduced, as shown by the second set of bars. Now consider the working-age population in Japan, which peaked back around 1995 at about 87 million, and now has declined to about 77 million. If you look at annual growth of real GDP growth per working age population, Japan\’s proverbially sluggish economy has actually exceeded the US rate.

\”The social indicators presented in this chapter illustrate in broad terms how the Nation is faring in selected areas in which the Federal Government has significant responsibilities. Indicators are drawn from six selected domains: economic, demographic and civic, socioeconomic, health, security and safety, and environment and energy. …   In choosing indicators for these tables, priority was given to measures that are broadly relevant to Americans and consistently available over an extended period. Such indicators provide a current snapshot while also making it easier to draw comparisons and establish trends.\”

Kahn notes that there is some stirring of enthusiasm about prizes as a tool for innovation. She writes (footnotes omitted):

By way of contrast, both academics and American policy makers today are increasingly enthusiastic about prizes. The White House urges that “history should be our guide” and “the Federal Government should… use high-risk, high-reward policy tools such as prizes and challenges to solve tough problems.” The federal government has begun to finance prizes as a means of generating new ideas and products, claiming that prizes “have a good track record of spurring innovation.” Numerous businesses have also offered large privately-funded prizes for objectives that range from specific targets to solutions for more general problems.

But it\’s unwise to taunt economic historians like Khan with that about how \”history should be our guide.\” A substantial part of the article is devoted to debunking popular stories about the efficacy of prizes as a spur for past innovation. One famous example is the prize for discovering how to calculate longitude while at sea. Khan writes:

The British Parliament passed a bill in July 1714 “for providing a public reward for such person or persons as shall discover the longitude at sea.” The bill offered “10,000 pounds if the method were accurate to within 1 degree, or 60 nautical miles; 15,000 pounds if the method were accurate to within 2/3 degree, or 40 nautical miles; 20,000 pounds if the method were accurate to within ½ degree, or 30 nautical miles.” The panel of judges comprised 22 commissioners, including the astronomer royal, the Speaker of the House of Commons, and the lords of Admiralty.

A poor clockmaker named John Harrison eventually came up with a solution, but the Longitude Board was too busy fighting among its members over the prize money. Indeed, it never actually awarded the prize, although Harrison got a partial reward 47 years later from another source. Perhaps even more to the point, there had been prizes offered for a solution to the longitude problem for centuries by Spain, Venice, Holland, and others–and all those prizes had failed.

Other famous historical examples of prizes turn out to be unsatisfactory in various ways, as well.

\”[T]he French Academy of Sciences in 1775 offered a cash prize for the discovery of a process to create sodium carbonate from the cheaper sodium chloride. Nicolas Leblanc succeeded in finding a viable manufacturing solution, but he never received the prize and his factory was expropriated by the revolutionary government.\”

In other cases, a prize was offered and awarded, but the inventor made vastly more money out of patenting and selling the invention over time. Thus, it\’s not at all clear that the very small additional incentive provided by the prize was important. This theme applies, for example, to when Hippolyte Mège won a prize from Napoleon III  for making margarine production commercially viable, and when John Wesley Hyatt won a prize offered by the billiard table producers Phelan and Collender in 1863 for a method of making billiard balls out of something other than ivory.

A policy related to prizes is to buy out the inventor\’s patent. A classic historical example here is when the French government in 1839 bought the rights from Louis Daguerre for his daguerrotype photographic process. Inconveniently, Khan points out that Daguerre never had a patent. Instead, he used powerful patrons to lobby with the French government for a payment, on the grounds that getting a patent was too expensive and difficult, and if the French didn\’t pay him for the invention, some other country would. As soon as the French paid him, he then applied for a patent in England, and tried to sell the rights to the British government–who refused to bite.

Khan also considers at some length the series of 18th- and 19th-century institutions in various European countries that offered prizes for inventions, including France\’s Société d\’Encouragement pour l\’Industrie Nationale (Society to Encourage National Industry or SEIN) founded in 1801, England\’s Royal Society for the Encouragement of Arts, Manufactures and Commerce (commonly known as the Royal Society of Arts or the RSA) founded in 1754.

But the problems of prizes were so widely recognized that these institutions largely shut down their prize operations, and instead started lobbying for a mixture of patents, research grants, and spreading technical information. Khan explains:

It is therefore not surprising that, in both England and France, the systematic institution of “inducement prizes” that had prevailed in the eighteenth and early nineteenth centuries failed to survive except for sporadic instances. In England, by the 1820s the Royal Society realized the inefficiencies associated with prizes, and instead switched to lobbying in favour of patents. … The system of inducement prizes in France and England was typically replaced by research grants to underwrite the costs of R&D inputs into the technology production process. Both institutions also switched their mandate towards the provision of information and technical education. The RSA even refused to accept further funding from benefactors who wished to designate prizes, because such endowments hampered their desire to reform their policies away from such targeted awards and towards more productive endeavours for “the advancement of Natural Knowledge.”

During the run-up to the US Constitution, there was an actual choice made between encouraging innovation through prizes or through patents. Khan says:

The use of prizes and bounties was common in the colonial period, and the Continental Congress in 1783 “recommended to the Legislatures of the several states to … encourage the establishment of useful manufactures either by premiums or by such other means as they may find most effectual.” …  The framers of U.S. policies were aware of the options that had prevailed in the colonial period and in Europe, but rejected the use of “premiums” in favour of property rights in patents. …

Whereas, the majority of organizations that had specialized in granting prizes for industrial innovations ultimately became disillusioned with this policy, and the practice of bestowing technology awards declined among both private and public institutions. As observers noted in the nineteenth century, industrial prizes faltered in part because of their lack of market-orientation, and even the democratic nature of economic institutions in the United States could not overcome such drawbacks in administered prize systems.Judges had to combine technical and industry-specific knowledge with impartiality, but even the most competent personnel could not ensure consistency; decision-making among panels was complicated by differences in standards, interpretation, capture, and risk-aversion. Such difficulties tended to lead to haphazard decisions, or were often overcome by simply making the award to the person or the firm with the most established reputation. Juries were not immune to the effects of outright bias, capture, cognitive dissonance, lobbying, and “marketing.” Prizes tended to offer private benefits to both the proposer and the winner, largely because they served as valuable advertisements, with few geographical spillovers. Winners of such awards were generally unrepresentative of the most significant innovations, in part because the market value of useful inventions would typically be far greater than any prize that could be offered by private or state initiative.  … 

In any event, history indicates that the evolution of the institution of innovation prizes over the past three centuries serves as a cautionary tale rather than as a success story. 

Khan also quotes a passage from Adam Smith on merits of patents over prizes, on the grounds that patents are more likely to reflect actual social benefits. It\’s from his Lectures on Jurisprudence  apparently delivered in 1763:

Thus the inventor of a new machine or | any other invention has the exclusive priviledge of making and vending that invention for the space of 14 years by the law of this country, as a reward for his ingenuity, and it is probable that this is as equall an one as could be fallen upon. For if the legislature should appoint pecuniary rewards for the inventors of new machines, etc., they would hardly ever be so precisely proportiond to the merit of the invention as this is. For here, if the invention be good and such as is profitable to mankind, he will probably make a fortune by it; but if it be of no value he also will reap no benefit.

Of course, none of this means that certain kinds of carefully targeted prizes are a bad idea. But it does suggest some reasons that it comes to tools for encouraging innovation, prizes should remain a sideshow rather than taking center stage.

My long-ago memory is that basic classroom experiments in physics and chemistry would produce a graph of data that was pretty close to a straight line, or a smooth curve. In social science, it\’s more common to see a rougher pattern, with the points scattered around. As a result, the social science research on \”power laws\” is full of graphs that raise my eyebrows, because there are lots of graphs where the points fall very close to a straight line. The fit looks too good! Xavier Gabaix provides a readable overview in \”Power Laws in Economics: An Introduction,\” appearing in the Winter 2016 issue of the Journal of Economic Perspectives, which pushed my  (Full disclosure: I\’ve worked as Managing Editor of JEP for 30 years now. All JEP article back to the first issue are freely available on-line compliments of the publisher, the American Economic Association.)

Here are some of Gabaix\’s examples from the paper, but he also refers to power law results in a wide array of other papers. (For some readers, it may be useful to add a few words on what a \”power law\” is. On a typical linear graph, each equal distance on the graph represents a change of the same absolute amount–say, 1, 2, 3, 4, …–although the units may be expressed in millions or years or percentage points or dollars or whatever is useful. In a \”power law,\” each equal distance on the graph represents a rise in an exponential power–say, 10¹ , 10², 10³, 10⁴… As a result, what appears to be an equal visual distance on the graph now represents not an absolute change, but a proportional change: for example, each equal visual distance in the powers-of-10 example represents a 10-fold increase.)

 Consider a graph based on the population of US cities, which in this data is all cities with population above more 250,000. The horizontal axis is population, expressed as powers-of-10. The vertical axis is the rank of the city size–that is cities are ranked by population from #1 New York to #2 Los Angeles and so on. Again, the vertical axis is expressed as powers-of-10. The result is very close to a straight line with a slope of -1.

As Gabaix writes: \”A slope of approximately 1 has been found repeatedly using data spanning many cities and countries (at least after the Middle Ages, when progress in agriculture and transport could make large densities viable, see Dittmar 2011). There is no obvious reason to expect a power law relationship here, and even less for the slope to be 1.\”

Now here\’s an example looking at the distribution of the size of US firms, measured by the number of employees on the horizontal axis, and the number of firms of this size, measured on the vertical axis. Again, both axes are measured in powers-of-10. Again, the slope is very close to -1. But why should the ranks of cities as measured by population look similar to the frequency of firms as measured by number of employees? (As I said, these are the sorts of graphs that make your eyebrows go up,)

Or here\’s an example about the distribution of daily stock market market returns. You can read the details of the calculations in the Gabaix article, but again, the axes are expressed as powers-of-10, and a linear relationship seems to emerge.

Or here\’s an example of a power law in the relationship between the pay of CEOs and the size of firms. Here, the labels on the graph are expressed as logarithms. When the size of the firm rises, the so does CEO pay–but in an exponential power-law kind of way. Gabaix writes: \”In a given year, the compensation of a CEO is proportional to the size of the firm to the power of 1/3, S(n)^1/3, an empirical relationship sometimes called Roberts’ (1956) law.\” He argues that this pattern of larger firms paying more to their CEOs can explain much the rise in CEO pay over time, as well as cross-differences in what CEOs are paid.

Why do these kinds of power law relationships show up so often? Say you start of with a random distribution of something, and the different points in your data all tend to experience proportional growth (positive or negative). However, this particular data (like city size) can\’t turn negative. In addition, the total size of the system can\’t grow in an unbounded way. Gabaix explains how with a few additional assumptions this process will result in the kind of straight-line power laws shown here. Of course, this kind of explanation then needs to be adapted and applied to each specific situation.

Want a power law outside of economics? Here\’s a graph where the typical mass of an animal is shown on the horizontal axis with a power-of-10 scale, and the metabolic rate, or energy requirement of that animal each day, is shown on the vertical axis. I\’m sure that clever biologists can give reasons for why this should be so. But given what certainly seem to be substantial differences in animal behavior, it\’s not obvious to me that, before the data was available, they would have expected straight-line power-law to emerge here, either.

Twice each year, the Federal Reserve is required to submit to Congress a report about \”the conduct of monetary policy and economic developments and prospects for the future.\” The most recent Monetary Policy Report went to Congress on February 10, 2016. A number of the themes will be familiar to regular readers of this blog. For example, there\’s a discussion of how US unemployment rates have more-or-less bottomed out, and whether there are some signs of a pickup in wage growth (see my post on \”Unemployment is Bottoming Out, So What\’s Next?\” January 25, 2016). There\’s a discussion of how the inflation rate is affected by fluctuations in energy prices, food prices (see \”Breaking Down US Inflation Rates by Category,\” February 9, 2016). There\’s a discussion of how Federal Reserve policy of raising interest rates may not have a huge effect on emerging markets, because any negative effects they experience from capital outflows will be largely offset by how a lower exchange rate spurs their exports (\”Bernanke on the Fed, the US Dollar, and the Global Economy,\” January 8, 2016).

Here, I\’ll just note a couple of other figures that caught my eye.

A divergence has emerged in the interest rates of advanced market economies, between the US and UK on one hand and the euro-zone and Japan on the other. Here are a couple of examples.
The first shows nominal yields on 10-year government debt.

The other is what\’s called the \”overnight index swap rate.\” In general, an interest rate \”swaps\” contract occurs when one party that\’s getting a variable interest rates over time swaps with another party that is getting a fixed interest rate over time. Obviously, the fixed interest rate in the swap will reveal what the average of the variable rate is expected to be. The figure shows the two-year overnight index swap rate for several economies. For the US, this is the fixed interest rate which shows what the average value of the US federal fund interest rate (the interest rate targeted by the Federal Reserve) is expected to be in the next two years. The other lines show what average interest rate is expected for the target interest rate of other central banks–and notice that it is turning negative for the Bank of Japan and the European Central Bank.

Both sets of interest rates show that the rates for the US and the UK are substantially above those for Japan and the euro-zone. The reasons for such a divergence are a jumble of expectations about growth rates, inflation rates, financial stability, and central bank policies. But whatever the reasons, this difference helps to explain why demand for US dollar assets is up and the US dollar exchange rate has been rising.

The big shift in the Federal Reserve balance sheet has leveled off. Just about anyone who is teaching a serious course about Federal Reserve Policy in recent years uses some version of this figure. It shows the assets and liabilities of the Federal Reserve system. For example, two of the main assets of the Fed are the US Treasury securities that is owns, along with the mortgage-backed securities and housing-related debt that it owns. The \”other assets\” are mainly certain premiums or discounts on these two other categories that for one accounting reason or another aren\’t built into the usual price of the asset. The two main liabilities of the Fed are currency–that is, Federal Reserve notes in circulation–along with the deposits it holds from member banks. The remaining \”Capital and other liabilities\” includes, for example, the US Treasury General Account, which is more-or-less the checking account through which the federal government pays its bills, and also includes some financial deals like reverse repurchase agreements.

The figure shows how the Fed balance sheet has evolves over time. Back in mid-2007, Fed assets were basically all Treasury securities, and its liabilities were basically all currency. But when the financial crisis hit in late 2007, several shifts happened.

The Fed set up an alphabet soup of agencies to make temporary loans to various financial institutions during the recession: for example, the Primary Dealer Credit Facility, the Asset-Backed Commercial Paper Money Market Mutual Fund Liquidity Facility, the Commercial Paper Funding Facility, and the Term Asset-Backed Securities Loan Facility. On the asset side, you can see how these loans blossomed for a time, then were repaid after the crisis, and these temporary agencies have now been shut down. Almost no one talks about these temporary agencies any more, but they were a rapid and innovative success in helping to prevent financial contagion from spreading.

As the temporary lending facilities phased out, you can see the quantitative easing policies come into play. The Fed started investing directly in mortgage-backed securities. It greatly increased its investments in US Treasury debt. On the liability side, banks began to hold much larger reserves at the Fed than the bare legal minimum they used to hold. The banks now receive interest on these reserve, too. These changes leveled out in the later part of 2014. But clearly, the Fed as a financial institution has been fundamentally changed from those not-so-long-ago days when it was pretty much all about US Treasury securities and currency.

The \”gig economy\” essentially refers to workers who are available when someone wants to hire them, but who don\’t have any long-term guarantee of how many hours they will work or in some cases even how much they will earn. Those who work for temp agencies are part of the gig economy, and so are those who drive for a company like Uber. But one problem overhangs all discussions of the gig economy. The discussions often end up being about anecdotal cases, either of those who find the gig economy to be a useful and preferred arrangement, or others who feel pressured into the gig economy because they couldn\’t find a steadier ongoing job.  There\’s not broad agreement on how to define the gig economy, and partly as a result, there also isn\’t good systematic evidence on how many workers are in the gig economy or how those workers perceive their jobs.

The Secretary of the US Department of Labor, Thomas Perez, announced a couple of weeks ago that the DoL would be working with the US Census Bureau to add as set of survey questions about \”contingent workers\” to the May 2017 Current Population Survey. But while we\’re waiting for that survey to happen, and the results to be tabulated and released, what do we know now?

The US Government Accountability Office (GAO) published a report in April 2015 called \”Contingent Workforce: Size, Characteristics, Earnings, and Benefits.\”  The GAO writes:

\”The size of the contingent workforce can range from less than 5 percent to more than a third of the total employed labor force, depending on the definition of contingent work and the data source.  … However, no clear consensus exists among labor experts as to whether contingent workers should include independent contractors, self-employed workers, and standard part-time workers, since many of these workers may have long-term employment stability. There is more agreement that workers who lack job security and those with work schedules that are variable, unpredictable, or both—such as agency temps, direct-hire temps, on-call workers, and day laborers—should be included. We refer to this group as the “core contingent” workforce. We estimate that this core contingent workforce comprised about 7.9 percent of employed workers in the 2010 GSS [General Social Survey] and also made up similar proportions of employed respondents in the roughly comparable 2005 CWS [Contingent Worker Survey] and 2006 GSS—5.6 percent and 7.1 percent, respectively.\”

The data surveyed by the GAO suggests that the contingent workers tend to be younger and less educated, with about a 15% chance of leaving the labor force or being unemployed one month later. Even after adjusting for other observable factors that affect wages (like experience and education), contingent workers earn about 10% less per hour. They are less likely to have benefits, and less likely to be satisfied with their jobs overall.

But notice that the definition of a \”contingent worker\” in this GAO study is not especially new. Job categories like \”agency temps, direct-hire temps, on-call workers, and day laborers\” have been around for awhile. The evidence they cite is only updated through 2010. Somehow, these categories and teh timing of the data don\’t quite seem to cover what it means to work for a company like Uber.

A more recent piece of evidence comes from \”Changing Patterns in Informal Work Participation
in the United States 2013–2015,\” by Anat Bracha, Mary A. Burke, and Arman Khachiyan, which the the Federal Reserve Bank of Boston published as a Current Policy Perspectives in October 2015. These authors designed the Survey of Informal Work Participation, which was then included as part of the Federal Reserve Bank of New York’s Survey of Consumer Expectations in December 2013 and January 2015. They define \”informal work\” like this:

By informal work we refer to any income-generating activity that does not involve a contract between an employer and an employee (except possibly for contracts involving a single task). This definition includes activities that monetize possessions (such as selling used goods or renting out one’s property) as well as activities that monetize free time and skills (such as babysitting). Typical features of informal work are the following: (1) it involves a greater degree of scheduling freedom than a formal job would, (2) the worker is paid on a per-service or per-good basis, and (3) the work does not provide benefits such as health insurance or pension contributions. … The number and types of paid informal work opportunities have expanded in recent years, in no small part due to the appearance of new technologies facilitating the so-called peer-to-peer economy. Well-known peer-to-peer businesses include Uber, a taxicab-like business that connects drivers with riders via mobile phones; Airbnb, which enables individuals to rent out their home for brief stays; Amazon Mechanical Turk, which offers the opportunity to do basic computing work from home on a fee-for-service basis; and Taskrabbit, which facilitates spot contracting for personal services.\”

Bracha, Burke, and Khachiyan are quick to point out that the US economy was in a different place int the two survey dates: for example, the unemployment rate was 6.7% at the time of their Survey 1 in December 2013 but had fallen to 5.7% by the time of their Survey 2 January 2015. Thus, drawing comparisons between the two surveys needs to be done cautiously. Also, although this survey is designed to be nationally representative, it is carried out online and pays the respondents $15, which could introduce some biases in terms of who is likely to answer. That said, here are some findings:

\”In Survey 2 [the January 2015 survey], the share of survey-takers who reported participating in informal paid work increased significantly—from 40 percent to 52 percent among men and from 40 percent to 60 percent among women. Among both women and men, participation rates became more equal across education classes in Survey 2. Among women, this equalization reflected in part a large increase in participation among those with high school or less, while among men, the equalization embedded a large increase in participation among those with a graduate degree.
Among men, participation rates became more equal across groups classified based on
employment status. As of Survey 2, men from across the formal income distribution are roughly equally likely to participate in informal work, while among women, the negative association between formal income and informal participation remains in force. … 

At the same time, however, informal participation increased between the surveys among highly educated and highly paid men, an outcome that likely reflects the fact that recent technological innovations have expanded the set of informal work opportunities and made it easier to engage in such work. Indeed, among both men and women and in both surveys, more than half of those who report engaging in informal work are performing internet-based tasks. In addition, one of the categories with the highest increase in participation between surveys was “online tasks,” which refers to activities such as rating pictures or copy-editing online. Female informal work participants in Survey 2 were more likely than those in Survey 1 to report both that informal earnings were their main source of income and that informal work helped at least somewhat to offset recent negative employment shocks. Taken together, our results suggest that some individuals continue to seek out informal work in order to offset negative economic shocks, while others engage in informal work—despite already being fairly well off—because it offers an easy way to earn extra cash.

They slice up the data in a number of ways, based on whether people have other full-time jobs, other part-time jobs, or no other jobs, as well as by education level. But as an overall summary, it\’s fair to say that men in the informal economy in this survey were working 10-15 hours per week and earning about $240 per month. Women in the informal economy in this survey were working in the range of 8-18 hours per month at these jobs, and earning in the range of $135-$185 per month. The most common activity for both men and women was \”selling online.\” These results are quite heterogenous: those working in the informal sector include high-educated people who have full-time jobs,  low-educated people without a job, and everyone in between.

There\’s reason to believe that these kinds of informal jobs are going to increase in number. The Boston Fed researchers also point to some estimates by PricewaterhouseCoopers, who back in 2014 made predictions about sales growth in five \”sharing economy\” sectors, \”peer-to-peer finance, online staffing, peer-to-peer accommodation, car sharing and music and video streaming. PwC estimates that these five sectors had $15 billion in sales in 2013, but are headed for $335 billion in sales by 2025.

One more source that offers some discussion of these issues is Sarah A. Donovan, David H. Bradley, and Jon O. Shimabukuro who wrote a report \”What Does the Gig Economy Mean forWorkers?\” published by the Congressional Research Service on February 5, 2016. They offer a nice specific definition of the gig economy:

The gig economy is the collection of markets that match providers to consumers on a gig (or job) basis in support of on-demand commerce. In the basic model, gig workers enter into formal agreements with on-demand companies to provide services to the company’s clients. Prospective clients request services through an Internet-based technological platform or smartphone application that allows them to search for providers or to specify jobs. Providers (i.e., gig workers) engaged by the on-demand company provide the requested services and are compensated for the jobs. Business models vary across companies that control tech-platforms and their associated brands. Some companies allow providers to set prices or select the jobs that they take on (or both), whereas others maintain control over price-setting and assignment decisions. Some operate in local markets (e.g., select cities) while others serve a global client base. Although driver services (e.g., Lyft, Uber) and personal and household services (e.g., TaskRabbit, Handy) are perhaps best known, the gig economy operates in many sectors, including business services (e.g., Freelancer, Upwork), delivery services (e.g., Instacart, Postmates), and medical care (e.g., Heal, Pager).

The authors also emphasize various ways that gig workers are different from freelance workers. On-demand firms that contract with gig workers control the brand name, place various requirements on how the job is done, and take a percentage of what is earned. As they write:

However, gig jobs may differ from traditional freelance jobs in a few ways. The established store-front and brand built by the tech-platform company reduces entry costs for providers and may bring in groups of workers with different demographic, skill, and career characteristics. Because gig workers do not need to invest in establishing a company and marketing to a consumer base, operating costs may be lower and allow workers’ participation to be more transitory in the gig market (i.e., they have greater flexibility around the number of hours worked and scheduling).

But while this definition of the gig economy is nice and specific, the report then runs into the problem that there is no systematic survey evidence on the \”gig economy\” defined in this way. As one example, the number of what the Census Bureau calls \”Nonemployer Establishments,\” which are firms that don\’t employ anyone but earn at least $1,000 in a year and pay income tax, seems to be on the rise. But it is not at all clear what share of this increase is the \”gig economy\” as narrowly defined, or those in the \”informal economy\” or in the category of \”contingent workers.\”

There\’s considerable discussion over whether the rise of the gig economy, or the informal economy more generally, represents a broad shift in the conditions of the labor force that should push us to broader labor market reforms about issues involving minimum wages, overtime pay, unemployment insurance, or other benefits for these kinds of workers. I discussed one such proposal in \”New Rules for the Gig Economy?\” (December 9, 2015). The problem at this stage is that it isn\’t yet clear, even roughly, how many workers are facing what kinds of problems. Full-time workers picking up some extra income in the gig economy are one thing. Part-time workers or those without other jobs who are earning most of their income in the gig economy pose other issues. With a broad array of new labor force relationships, it\’s  very hard to sort out the costs and benefits of different set of rules–especially given the ability of on-demand firms and workers to alter their labor force relationships in response to any new rules that are enacted.

Whenever the US economy looks shaky, one of the most common questions I hear is whether this recovery has \”run its course\” or \”gotten old.\” The downturn of the US economy during the Great Recession ended back in June 2009, so it\’s now been about 80 months of an economy on an (often frustratingly slow) upswing. There\’s a basic statistical answer to this question, but there\’s also a broader issue that tackles of how to think about a \”business cycle.\”

When looking at the path of economies over time, you see recessions and recoveries. But there also a well-known is a common cognitive pattern of \”paraedolia,\” which refers to looking at randomness and perceiving patterns that aren\’t really there.  Even using the conventional term \”business cycle\” for patterns of recession and recovery hints at a belief that the economy is be based on underlying patterns and dynamics that will cause it to rotate in a preordained way from recovery to recession and back again. When people ask whether the recovery is \”getting old\” or has has gone on \”long enough,\” they are presuming this kind of \”cycle.\”

The statistical answer to whether economic upswings die of old age can be answered statistically, and Glenn D. Rudebusch summarizes the conventional wisdom very nicely in \”Will the Economic Recovery Die of Old Age?\”  written as the Federal Reserve Bank of San Francisco \”Economic Letter\” for February 8, 2016. Rudebucsh uses a kind of graph called \”survival analysis,\” which can be applied to people\’s chance of dying, to part of a machine wearing out or breaking, to whether economies fall into recession, and many other applications.

As an example, here\’s a survival curve for the probability of an American male dying in the next year, The graph shows that the chance of dying in the next year doesn\’t rise very much at all for men up to the age of about 50 or 60, but then it starts to rise steadily with age.

A survival curve for the economy asks a question like: \”What\’s the chance of an economic recovery ending in the next month?\” Based on data for US business cycles going back to 1858, the patterns look quite different for before and after World War II. Here\’s Rudebusch\’s figure. Before World War II, there was a substantial rise in the chance of recession as an expansion aged: that is, after about four years, the chance of a recession int he next month has reached 20% and climbing. But since World War II, the chance of a recession rises by comparatively little as a recession ages: it\’s maybe  2% chance of recession in the next month after four years, but still only a 4% chance of recession in the next months after 10 years.

In short, US business cycles in the last 70 years or so don\’t seem to have a natural lifespan.

However, the notion of predictable cycles was once very hot stuff in the economics profession. One classic exposition is the great economist Joseph Schumpeter\’s 1939 book on Business Cycles (an abbreviated version is available on-line here). Schumpeter suggested that the rise and fall of the economy could be understood through a mixture of three different kinds of cycles: short-run, medium-run, and long-run. The short-term 3-5 year cycles were called Kitchin cycles, and Joseph Kitchin argued in 1923 that they were based on variations in of psychological factors and crop yields. (If you want more, see Joseph Kitchen, \”Cycles and Trends in Economic Factors,\” Review of Economics and Statistics, January 1923, 5:1, pp. 10-16.) The medium-run Juglar cycles were based on fluctuations in levels of fixed investment often stemming from waves of innovation, as first argued in an 1869 book by by Clément Juglar (a condensed English translation is available here). The long-run Kondratieff cycles happened every 50 years or so, give or take a decade or two, and were based on major technological shifts (an English translation of Nikolai Kondratieff\’s 1922 article is available here).  For example, Schumpeter suggests in his 1939 book that a Kondratieff cycle had run from the start of the Industrial Revolution in the 1780s up through 1842, when it was followed by what he called \”the age of steam and steel\” from 1842 and 1897, and then age of \”electricity, chemistry, and motors\” after about 1898.

But as Schumpeter was quick to note, the idea of three overlapping cycles wasn\’t meant to be definitive. He wrote: \”There are no particular virtues in the choice made of just three classes of cycles. Five would perhaps be better, although, after some experimenting, the writer came to the conclusion that the improvement in the picture would not warrant the increase in cumbersomeness.\”

For the modern economist, this notion of maybe three or maybe five overlapping cycles, happening over maybe 3-5 or 7-11 or 40-60 years, sounds a lot like an attempt to impose an overall template pattern that isn\’t really there on an essentially random set of events. Sure, one can look back after the fact and analyze the proximate causes of recessions, like the Federal Reserve raising interest rates to fight inflation in the early 1980s, or the aftermath of the dot-com investment boom in the later 1990s, or the housing price bubble leading up to the Great Recession. But those proximate causes were not an inevitable cycle; instead, they were the result of other economic events and policy choices.

So the good news is that the US economy doesn\’t seem to be doomed by any mechanical law of aging recoveries to enter a recession soon. After all, there was a period between recessions in the 1960s that lasted 106 months and the another period between recessions from the 1990s into the early 2000s that lasted 120 months. But on the other side, the US economic recovery is far from bulletproof, and remains vulnerable to twists of policy and fate.

Hopeful onlookers sometimes point to two possible escape hatches from the problems of burning fossil fuels. One escape hatch is \”peak oil\”–that is, the argument that production of fossil fuel resources is near or its peak. In this view, the impending fall in fossil fuel production might well bring higher prices and other economic hardship, but at least emissions from burning fossil fuels would drop. The other escape hatch is a large rise in cost-competitive non-carbon sources of energy, like solar and wind, but also nuclear and hydroelectric power. If these sources of energy undercut fossil fuels on price, then the economy could make a transition away from fossil fuels to an economy that used on cheaper and abundant energy from these other sources.

But there\’s yet another possibility, and it\’s the one laid out by Thomas Covert, Michael Greenstone, and Christopher R. Knittel in their article, \”Will We Ever Stop Using Fossil Fuels?\” appearing in the Winter 2016 issue of the Journal of Economic Perspectives.  In this outcome, supply of fossil fuels isn\’t going to run out in the next few decades, and alternative non-carbon energy sources aren\’t going to become cost-effective for enough uses in that timeframe to substantially reduce consumption of fossil fuels, either. One might wish it was otherwise. As the authors write: \”After all, who wouldn’t prefer to consume energy on our current path and gradually switch to cleaner technologies as they become less expensive than fossil fuels? But the desirability of this outcome doesn’t assure that it will actually occur—or even that it will be possible.\” Because they believe that neither of the two escape hatches from the problems of burning fossil fuels are likely to be available, they argue that addressing issues like climate change and conventional air pollutants will require a strong policy intervention to reduce the use of fossil fuels.

When it comes to the supply of fossil fuels, an important lesson to remember that technological progress happens in many areas. It happens in solar and wind power, but it also happens in finding, developing, and extracting fossil fuels. examples include the discovery of  how to drill in ever-deeper water, as well as the more recent developments in getting oil and gas from tar sands and from hydraulic fracturing, As the authors write: \”It is an empirical regularity that, for both oil and
natural gas at any point in the last 30 years, the world has 50 years of reserves in the
ground. The corollary, obviously, is that we discover new reserves, each year, roughly
equal to that year’s consumption.\” Here\’s a figure showing the growth proven reserves of oil and gas reserves over time.

\”Proven reserves\” is a specific term referring to reserves that are available at (more-or-less) current prices, and given current levels of technology.  Geologists also estimate fossil fuel \”resources,\” which are the quantities of fossil fuels known to exist, but not economically viable–yet. The known resources are maybe 3-4 times the size of the \”proven reserves. And then there are enormous other fossil fuel resources, like oil shale and methane hydrates, which are not currently counted as either reserves or resources, but technological  developments over time could bring them into the market as well.  As Covert, Greenstone, and Knittel write: \”If the past 35 years is any guide, not only should we not expect to run out of fossil fuels any time soon, we should not expect to have less fossil fuels in the future than we do now. In short, the world is likely to be awash in fossil fuels for decades
and perhaps even centuries to come.\”

When thinking about non-carbon technologies, it would take a book-length manuscript to go through all the possible developments. The authors thus focus on a few key points. Global demand for energy seems certain to rise dramatically in the decades ahead with overall economic development in today\’s low-income and emerging economies. The question about non-carbon energy sources is not whether they will expand (spoiler alert: they will expand), but whether they will expand so quickly and dramatically that they undercut fossil fuels in a wide array of uses. This outcome may be desirable, but that doesn\’t make it likely or even possible. As the authors write:

[T]he International Energy Administration Agency (2015) projects that fossil fuels will account for 79 percent of total energy supply in 2040 under the current, business-as-usual policies, which already takes into account some rise in these alternative noncarbon energy production technologies. In the medium-run of the next few decades, none of these alternatives seem to have the potential based on their production costs (that is, without government policies to raise the costs of carbon emissions)
to reduce the use of fossil fuels dramatically below these projections.

The paper offers a few comments in passing about carbon capture technology, nuclear power, and hydro power, but the main focus is on solar and wind technologies as alternative methods of generating electricity, and on whether developments in battery technology will make fully electric cars viable. Overall, Covert, Greenstone, and Knittel write:

Our conclusion is that in the absence of substantial greenhouse gas policies, the US and the global economy are unlikely to stop relying on fossil fuels as the primary source of energy. The physical supply of fossil fuels is highly unlikely to run out, especially if future technological change makes major new sources like oil shale and methane hydrates commercially viable. Alternative sources of clean energy like solar and wind power, which can be used both to generate electricity and to fuel electric vehicles, have seen substantial progress in reducing costs, but at least in the short- and middle-term, they are unlikely to play a major role in base-load electrical capacity or in replacing petroleum-fueled internal combustion engines. Thus, the current, business-as-usual combination of markets and policies doesn’t seem likely to diminish greenhouse gases on their own.

Twenty years ago in 1996, President Bill Clinton signed into law the Personal Responsibility and
Work Opportunity Reconciliation Act, more commonly known as \”welfare reform.\” The welfare reform of 1996 sought to \”end welfare as we know it,\” as President Clinton had often stated. The Winter 2016 issue of the Journal of Policy Analysis and Management has a \”Point/Counterpoint\” exchange on the effects, which at least for now is freely available on-line, although many readers will also have access through library subscriptions.  The intellectual combat here isn\’t in the binary, black vs. white. fire vs. ice, war-of-the-worlds style. Instead, Ron Haskins takes the position the glass-half-full position in \”TANF At Age 20: Work Still Works\” (pp. 224-231), and then the team of  Sandra K. Danziger, Sheldon Danziger, Kristin S. Seefeldt, and H. Luke Shaefer takes the glass-half-empty position in \”From Welfare to a Work-Based Safety Net: An Incomplete Transition\” (pp. 231-238). The authors then offer a response-and-rejoinder to each other, as well.

In his overview called \”Welfare Reform: A 20-Year Retrospective,\” Richard V. Burkhauser offers some reminders of the intensity of the rhetoric back in 1996 when the welfare reform bill was on the verge of being signed into law. On one side, here\’s Democratic New York Senator Daniel Patrick Moynihan:

“The welfare bill terminates the basic federal commitment to support dependent children. It endangers children with absolutely no evidence that this radical idea has even the slightest chance of success . . . The current batch in the White House have only the flimsiest grasp of social reality, thinking anything doable and equally undoable. As, for example, the horror of this legislation …” 

And here\’s an opposing view, from Republican Florida Congressman Clay Shaw:

“When the Senate passed, with a good bipartisan vote, with half the Democrats joining the Republicans, I began to think the President would sign this bill . . . July 31st has got to go down as Independence Day for those who have been trapped in a system that has been left dormant and left to allow people to actually decay on the layers of inter-generational welfare which has corrupted their souls and stolen their future . . . It will work …\” 

In terms of nomenclature, the previous welfare program called Aid to Families with Dependent Children (AFDC) now became Temporary Assistance for Needy Families program (TANF). The change in name was mean to reflect a change in emphasis. AFDC had been an \”entitlement\” program, meaning that if you qualified for the program as a low-income family with children, you were entitled to the payment. The central change in TANF was that welfare became a work-based program. Recipients now had to either work or be preparing for work through education or job training to be eligible for welfare. In addition, time limits were placed on the length of time welfare could be received during a lifetime.

The adoption of TANF was not the disaster that some had predicted. Welfare enrollments did drop dramatically and work levels rose, as Haskins describes:

The welfare caseload, which had increased almost every year since the beginning of the War on Poverty in the mid-1960s, fell every year after 1994 before increasing slightly during the Great Recession of 2007. Over the six years between 1994 and 2000, the caseload fell by about 60 percent to a level roughly equal to the 1971 level. The decline in the rolls over this period was accompanied by a 16 percent increase in work by all single mothers and a 35 percent increase in work by never-married mothers, the subgroup of single mothers who were most likely to go on welfare. Meanwhile, the poverty rate among single mothers and their children fell from 44 percent to 33 percent, a decline of 25 percent to its lowest level on record.

Moreover, poverty rates among the key group of households headed by single mothers. Here\’s a figure from Hansen\’s paper. The top line shows the poverty rate in this group if you look at earned income only. The next line down shows the poverty rate if you include cash benefits including TANF, but also unemployment insurance, general assistance, Supplemental Security Income, and others). The next line down adds in the value of food stamps. The line under that adds the value of the Earned Income Tax Credit. The bottom line also adds income from other household members and government stimulus/recovery payments. Right after 1996 welfare reform, the poverty rate for this group declines, and at least according to the bottom line in the figure hasn\’t changed much since then.

But of course, nothing is truly simple in social science. As all the authors in the symposium point out, the welfare reform law of 1996 was operating in a broader economic and policy context. The economic context was the dot-com boom of the late 1990s. When President Clinton signed the welfare reform law in August 1996, the unemployment rate was 5.1%, and it would fall all the way to 3.9% by late in 2000. In other words, it was a good economic time to impose work requirements.

The policy context was that the shift in welfare from AFDC to TANF was accompanied at about the same time by a substantial shift in other programs to provide work support. As the Danziger et al. team of authors point out, \”For example, the Earned Income Tax Credit (EITC), which provides tax credits to families with children and low earnings, was increased significantly in the early 1990s, the minimum wage was increased in 1997, and access to medical care was expanded by the State Child Health Insurance Program of 1997.\” For an earlier post about how a Congressional Budget Office report on how government work-support programs largely offset the falls in welfare payments, see \”Where Has Welfare Reform Taken Us?\” (January 23, 2015).

The broad shift to work requirements as part of welfare remains popular, and on its own specific terms, successful. Both sets of authors in this symposium support the shift, which represents a real change from how welfare was regarded before 1996. As Haskins writes: \”Low-income working families with children receive more help from government than ever before—and there is bipartisan agreement that this is good policy.\” But the shift raises some obvious questions. What about assistance to those adults who are disconnected from work and don\’t have children, or to parents who are disconnected from work and do have children?  And given that nearly half of all households headed by single mothers do not earn enough to be above the poverty line based on their own income, as shown in the figure above, what can be done to raise the payoff for low-skilled and low-wage work?

The Danziger, Danziger, Seefeldt, and Shaefer group writes: \”We want to make the work-based
safety net more effective without returning to AFDC.\” Their opening essay lists four proposals along these lines, and the follow-up essay lists four more. I\’m not endorsing everything on their list, but here are the eight items, in two groups of four:

1. Adoption of a public responsibility to provide work opportunities to those for whom employer demand is limited, especially when unemployment is high. This includes transitional jobs or public subsidies to private-sector firms, nonprofit agencies or government agencies.

2. Expanded child care subsidies.

3. Reducing barriers to TANF entry by requiring states to spend a larger fraction of block grant funds on cash assistance and raising the TANF block grant to reflect economic and demographic changes. 

4. Modifying the SSI [Supplemental Security Income] program by adding part-time or temporary disability benefits. …

First, because the federal minimum wage has not increased since 2009, an increase would reduce earnings poverty for single mothers, who are disproportionately represented among minimum wage workers. The Congressional Budget Office (2014) estimated that a $10.10 per hour wage would raise earnings for 16.5 million workers and reduce poverty by about 900,000, while reducing employment by about 0.3 percent (about 500,000 jobs).

Second, Hoynes (2014) proposes to raise the EITC for families with one child so that it is equivalent to that of two-child families, adjusted for family size. This would increase the maximum EITC for one-child families by about 40 percent for those in the bottom two quintiles. …

Third, Ziliak (2014) proposes to convert the Child and Dependent Care Credit from a nonrefundable to a refundable credit. For example, for children under the age of five whose families have less than $25,000 in adjusted gross income, the refundable credit would be $4,000 for the first child in a licensed facility and half that for a child in an unlicensed facility. Low-income families do not benefit much from the current credit because they have little taxable income.

Fourth, families with incomes below $3,000 do not benefit at all from the $1,000 per child tax credit and other low-income families do not receive the full credit because their income tax liability is lower than $1,000 per child. The Center for American Progress (2015) has proposed making the credit fully refundable. This would provide needed cash income for the disconnected and additional income for TANF recipients, particularly if the credit could be delivered on a monthly basis.

These changes are essentially incremental. For example, proposals like a substantial increase in the Earned Income Tax Credit for all recipients are not includes. Moreover, I don\’t think of all of these changes as being work-related: for example, making the Child and Dependent Care Credit refundable doesn\’t encourage work in any direct way. Nonetheless, I\’m broadly sympathetic toward proposals that support the households of low-income workers, and especially those with children.