The U.S. corporate sector has high profits. Interest rates are near historical lows. Those factors would seem to encourage investment, expansion, and hiring. But here we are in 2012, with the official end of the Great Recession nearly three years in the rear-view mirror, and many firms are still holding back. Scott R. Baker, Nick Bloom, and Steven J. Davis have written \”Is Policy Uncertainty Delaying the Recovery?\” as a policy brief for the Stanford Institute for Economic Policy Research. The underlying research paper and data are available here
There are lots or theoretical reasons why a high level of uncertainty might cause managers to be hesitant about starting new projects, investing, or hiring workers. But how does one collect data on the level of uncertainty–and in particular, on the level of uncertainty related to economic policy? Baker, Bloom and Davis mix together three sources of data into a single index: \”We construct our index of policy uncertainty by combining three types of information: the frequency of newspaper articles that reference economic uncertainty and the role of policy; the number of federal tax code provisions that are set to expire in coming years; and the extent of disagreement among economic forecasters about future inflation and future government spending on goods and services.\” Here is their index, where a level of 100 is set arbitrarily to be equal to the average of the index for the 25 years from 1985 up to 2010.
Constructing any index like this involves some more-or-less arbitrary choices, so there will always be room for dispute. In addition, while the authors offer some arguments that this index is emphasizing \”policy\” uncertainty, I suspect that it\’s picking up other kinds of swings in economic confidence as well.
But for what it\’s worth, it does seem that the index is spiking at times one might expect: 9/11, the \”Black Monday\” stock market meltdown in 1987, wars, presidential elections, and the like. In addition, policy uncertainty by this measure has been especially high since 2008, although in early 2012 the measure has fallen back to 2009 levels. When the authors look more closely at the newspaper articles underlying their index, they find that the greatest sources of uncertainty are those related to monetary issues, which includes many steps taken by the Federal Reserve, and tax issues, like whether various tax provisions will be extended or ended.
How much does policy stability matter? As the authors ask (references to figures omitted):
\”How much near-term improvement could we expect from a stable, certainty-enhancing policy regime? We use techniques developed by Christopher Sims, one of the two 2011 Nobel laureates in economics, to estimate the effects of economic policy uncertainty. The results for the United States suggest that restoring 2006 (pre-crisis) levels of policy uncertainty could increase industrial production by 4% and employment by 2.3 million jobs over about 18 months. That would not be enough to create a booming economy, but it would be a big step in the right direction.\”
By the time one takes into account the problems of creating an index to measure policy uncertainty and the problems of blending policy uncertainty into a macroeconomic model, I wouldn\’t place much confidence in these exact numbers. But at a broader level, the calculations make a strong argument that the effects of policy uncertainty on output and employment have probably been a substantial contributor to the sluggishness of the U.S. economic recovery.
Movies are usually shown to critics before being released to the general public. But about one-tenth of movies are not released. What do you as a movie-goers infer when a movie isn\’t released for review? But then, what is an appropriate strategy for movie studios in sending movies out for review, if they recognize what movie-goers like you are likely to infer? And what is the appropriate strategy for movie-goers, if they recognize what the movie studios are likely to infer about what they are likely to infer? You have just crossed the border into the land of strategic game theory. In the most recent issue of the American Economic Journal: Microeconomics, Alexander L. Brown, Colin F. Camerer, and Dan Lovallo sort through the implications and inferences in \”To Review or Not to Review? Limited Strategic Thinking at the Movie Box Office\” (vol. 4, number 2, pp. 1-26). The journal is not freely available on-line, although many academics will have access to it through a library subscription or their personal membership in the American Economic Association.
The usual starting point for analyzing these kinds of strategic interactions is to consider what would happen if all parties were completely rational. It might seem intuitively obvious that movie studios will send out their better-quality movies to be reviewed, but not send out their lower-quality movies. However, it turns out that if all parties are fully rational, movie studios would release every movie for review. The authors explain the underlying mathematical game theory by offering an illustration along these lines.
Say that the quality of a movie can be measured on a scale between 0 and 100. Now say that studios decide that they will only release their better movies for review: for example, the studios might decide not to release for review any movie with quality below 50. In this situation, when moviegoers see that a movie has not been released for review, they will infer that it has a quality ranging from 0 to 50–on average, a value of 25.
But if consumers are going to assume that all unreviewed movies have a quality value of 25, then it makes sense for the movie studios to release for review all movies with qualities higher than 25, because they suffer diminished profits if a movie has quality of, say, 40, but moviegoers are assuming it\’s only a 25.
Now movie studios are releasing for review all movies with a quality score over 25, and movie goers will assume that the remaining movies are between 0 and 25, or an average of 12.5. Given these expectations of moviegoers, it will pay for the studios to release for review all movies with a quality score above 12.5, so that they don\’t face a situation where their movie with a true quality score of 20, when movie-goers are expecting only a 12.5.
However, now consumers will assume that all unreviewed movies have a quality value below 12.5. And as this cycle of inference and counterinference continues, eventually the movie studios will release all movies (except perhaps the single worst movie, which consumers will then know is the single worst movie) for review.
After identifying the purely rational outcome, the next step in this kind of analysis is to look at the underlying assumptions, and to think about which assumptions are mostly likely to be violated in this setting. The authors emphasize two such assumptions: 1) Consumers are always aware when movies haven\’t been released for review; and 2) Consumers draw fully rational conclusions when a movie isn\’t released for review. Of course, in the real world neither of these assumptions holds true. The authors find that of the 1,414 that had wide release in the U.S. market from 2000 through 2009, about 11% were not released for review. In addition, that number has been higher in recent years.
Movie-goers who often don\’t notice that a movie hasn\’t been released for review, or who don\’t draw the rational inference when that happens, are likely to end up going to low-quality movies they would not otherwise have attended. As a result, they are more likely to be disappointed in their movie experience when going to an unreviewed movie than to a movie that was released for review. The authors set out to test whether this implication holds true.
To measure what critics think of the quality of a movie movie, they use data from Metacritic.com, a website that pulls together and averages ratings of more than 30 movie critics from newspapers, magazines, and websites. To measure what audiences think of a movie, they look at user reviews of movies at the of Internet Movie Database (IMDB). They plot a graph with the movie critic ratings on the horizontal axis and the movie-watcher ratings on the vertical axis. Movies that were released for review are solid dots; movies that had a \”cold open\” without a review from the critics before they were released (although they were reviewed later) are hollow dots. Here is the graph:
What patterns emerge here?
1) Notice that the dots form a generally upward-sloping pattern, which tells you that when the critics tend to rate a movie more highly (on the horizontal axis), moviegoers also tend to rate the movie more highly (on the vertical axis).
2) Cold-opened movies, the hollow dots, tend to have lower quality. \”No cold-opened movie has a metacritic rating higher than 67. The average rating for those movies is 30, 17 points below the sample average of 47.\”
3) The darker straight line is the best-fit line looking only at movies that were screened in advance. The lighter straight line is the best-fit line looking at movies that were not screened in advance. The lighter line is below the darker line. Think about a movie of a certain quality level as defined by the critics: if that movie is reviewed, people are more likely to enjoy that movie than if the movie was not released for early review. This pattern suggests that the reviews are helping people to sort out which movies they would prefer seeing, and that without reviews, people are more likely to end up disappointed.
After doing statistical calculations to adjust for factors like whether the movie features well-known stars, the size of the production budget, the rating of the movie, the genre of the movie, and other factors, they find: \”[C]old opening is correlated with a 10 –30 percent increase in domestic box-office revenue, and a pattern of fan disappointment, consistent with the hypothesis that some moviegoers do not infer low quality from cold opening.\”
So here\’s some advice you can use: If you\’re not sure whether a movie has been released for review by critics before it was distributed, find out. If it hasn\’t been releasedm think twice about whether you really want to see it. Maybe you do! Or maybe you are not paying enough attention to the signal the movie studio is sending by choosing a cold opening. Here is the authors\’ explanation, based in part on interviews with studio executives (footnotes omitted):
\”[P]roduction budgets and personnel are decided early in the process. The number of theaters which agree to show the film is contracted far in advance of any cold-opening decision. Cold-opening decisions are made after distribution contracts have been signed and according to a major distributor and studio executives “are not a part of the contract.” There are no contracted decision rights about whether to cold open or not. The cold-opening decision is almost always made late in the process. After the film is completed, there is often audience surveying and test screenings. As one senior marketing and public relations (PR) veteran put it, “If a movie is not shown to critics, a decision has been made that the film will not be well received by them … After the PR executives have seen the film, if they believe the film will be poorly reviewed, they will have a heart to heart with the marketing execs and filmmakers about the pros and cons of screening for critics. …
\”A key ingredient in this story is that executives must think some moviegoers are strategically naïve, in the sense that those moviegoers … will not deduce from the lack of reviews that quality is lower than they think. (Otherwise, the decision to cold open would be tantamount to allowing critics to say the quality is low).\”
The percentage of the U.S. adult population that is either working or unemployed and looking for a job is called the labor force participation rate. From the early 1960s to the late 1990s, this percentage rose more-or-less steadily, from 59% to 67%. But since then, starting well before the Great Recession, the labor force participation rate has been falling. In my own Journal of Economic Perspectives, Chinhui Juhn and Simon Potter wrote about \”Changes in Labor Force Participation inthe United States\” back in the Summer 2006 issue. Willem Van Zandweghe has a more recent take, with updated evidence, in \”Interpreting the Recent Decline in Labor Force Participation\” in the Economic Review of the Federal Reserve Bank of Kansas City.
Here\’s the pattern of the labor force participation rate. The first figure shows the overall number. The second figure shows the breakdown by gender: that is, the declining labor force participation rate for men, and the women\’s labor force participation rate that was rising until about 2000, but then flattened out and has now declined.
How much of the recent sharp decline in the labor force participation rate is the Great Recession, and how much is other factors? \”At the turn of the 21st century, labor force participation in the United States reversed its decades-long increase and started trending lower. A more startling development has been the recent sharp decline in the labor force participation rate—from 66.0 percent in 2007 to 64.1 percent in 2011—a far bigger drop than in any previous four-year period. … This article presents a variety of evidence—including data on demographic shifts, labor market flows, gender differences, and the effects of long-term unemployment—to disentangle the roles of the business cycle and trend factors in the recent drop in participation. Taken together, the evidence indicates that long-term trend factors account for about half of the decline in labor force participation from 2007 to 2011, with cyclical factors accounting for the other half.\”
What are these long-term trend factors?
1) The baby boom generation (roughly those born starting in 1946 and up until about 1960) pushed up the labor force participation rate while they were moving through their prime earning years, and now are starting to pull down the labor force participation rate as they head into retirement.
2) Women entered the (paid) labor force in large numbers starting after World War II, which helped drive the overall labor force participation rate higher for decades. But the labor force participation rate for women seemed to top out at around 60%, and has flattened out since then.
3) Young adults in the 16-24 age group have become less likely to work. This group had a labor force participation rate of nearly 70% back in the 1970s and 1980s, but it has now fallen to about 55%. Part of the decline is that more young people are attending at least some college. Another part of the decline is that for many of the relatively low-skilled in this age group, the low wages they would earn don\’t seem worth taking a job.
4) The long-term trend of declining male labor force participation rates continues. What are these men doing when they leave the labor force? One doorway out of the labor force for many of them takes the form of applying for disability, which has nearly tripled in the last 10 years from 1 million to 3 million applications per year. For a discussion of \”Disability Insurance: One More Trust Fund Going Broke,\” see this post from August 11, 2011.
A long-term decline in the labor force participation rate isn\’t good news for long-term economic growth, nor for the long-term solvency of Social Security and Medicare. There are two particular areas worth focus.
2) The labor force participation of the elderly has been rising since the mid-1990s, albeit slowly. For example, the labor force participation rate of the 55-64 age group rose from 59.3% in 2000 to 64.9% in 2010; for the over 65 group, the increase was from 12.9% in 2000 to 17.4% in 2010; and for the over-75 group, the rise was 5.3% in 2000 to 7.4% in 2010. As the population ages, we need to think about design of retirement programs and labor force institutions in a way that certainly doesn\’t penalize–and perhaps can even reward–the decision to work a few more years.
The Federal Reserve has set up \”swap lines\” with other central banks around the world. What are these? Galina Alexeenko, Sandra Kollen, and Charles Davidson offer a nice overview in \”Swap Lines Underscore the Dollar\’s Global Role,\” in EconSouth from the Atlanta Fed.
The economic issue here is the central role of the U.S. dollar in global economic transactions. As they write, \”[O]ne of the major business lines of European banks is providing financing in dollars on a global scale—for trade, purchasing dollar-denominated assets, or syndicating loans to corporations. Banks the world over, in fact, have a great need for dollars because much of the world’s trade, investment, and lending is conducted in U.S. currency.\” But during an international financial crisis, as various financial markets freeze up, it may be very expensive or even impossible at certain time for banks around the world to get the U.S. dollars they need to carry out transactions. The Federal Reserve\’s swap lines are a temporary measure to make U.S. dollars available at such time around the world, so that financial instability is less likely to persist and grow.
How does a swap line work? Alexeenko, Kollen, and Davidson explain:
\”The swaps involve two steps. The first is literally a swap—U.S. dollars for foreign currency—between the Federal Reserve and a foreign central bank. The exchange is based on the market exchange rate at the time of the transaction. The Fed holds the foreign currency in an account at the foreign central bank, while the other central bank deposits the dollars the Fed provides in an account at the Federal Reserve Bank of New York. The two central banks agree to swap back the money at the same exchange rate, thus creating no exchange rate risk for the Federal Reserve. The currencies can be swapped back as early as the next day or as far ahead as three months.
The second step involves the foreign central bank lending dollars to commercial banks in its jurisdiction. The foreign central bank determines which institutions can borrow dollars and whether to accept their collateral. The foreign central bank assumes the credit risk of lending to the commercial banks, and the foreign central bank remains obligated to return the dollars to the Fed. At the conclusion of the swap, the foreign central bank pays the Fed an amount of interest on the dollars borrowed that is equal to the amount the central bank earned on its dollar loans to the commercial banks. The interest rate on the swap lines is determined by the agreement between the Fed and foreign central banks.\”
The description helps to clarify why such swap lines are not a \”bailout\” or any such prejudicial term. The exchange rate for the swap is locked in, and any U.S. dollar loans that are made will pay interest to the Fed. Because the U.S. dollar plays such a central role in global transactions, the Fed is just making sure that a temporary shortfall of dollars in a foreign financial system doesn\’t make a financial crisis worse.
Here\’s a timeline for these swap lines. (I found it interesting that these authors differentiate between the \”Global Financial Crisis (2007-2008)\” and the \”European Financial Crisis (2009-current).\” I\’ve been trying to sort out in my own mind, without yet reaching firm conclusions, about how to think of these episodes as connected in some ways and separate in others.)
How large have these swap lines been? This graph shows the sharp rise in assets held by the Federal Reserve starting in mid-2008. A fairly substantial portion of these assets (say, $500 billion or so) were held in the form of swap lines at the height of the global financial crisis in late 2008 and early 2009, but these swap lines were ended by February 2010. More recently, you can see on the graph the much smaller swap lines–in the range of $100 billion–established to address the European financial crisis.
One of the most sizzling of all hot-button issues over the last 40 years has been the sharp rise in immigration from Mexico, much of it illegal. Thus, it\’s intriguing to read the report by Jeffrey Passel, D’Vera Cohn, Ana Gonzalez-Barrera called \”Net Migration from Mexico Falls to Zero—and Perhaps Less,\” from the Pew Research Center.
They write (footnotes and citations omitted): \”The largest wave of immigration in history from a single country to the United States has come to a standstill…. The U.S. today has more immigrants from Mexico alone—12.0 million—than any other country in the world has from all countries of the world. Some 30% of all current U.S. immigrants were born in Mexico. The next largest sending country—China (including Hong Kong and Taiwan)—accounts for just 5% of the nation’s current stock of about 40 million immigrants…. Beyond its size, the most distinctive feature of the modern Mexican wave has been the unprecedented share of immigrants who have come to the U.S. illegally. Just over half (51%) of all current Mexican immigrants are unauthorized, and some 58% of the estimated 11.2 million unauthorized immigrants in the U.S. are Mexican.\”
Here\’s are two illustrative figures. The first shows the total Mexican-born population in the United States, showing how the total just takes off from about 1970 up through the middle of this decade. The second figure breaks the total down into legal and \”unauthorized,\” and shows that the decline in the unauthorized total actually started back about 2007.
Will immigration from Mexico surge again in the next few years, if and when U.S. employment gradually recovers? I suspect that such immigration may rise again, but much more mildly than in the past. There are a number of reasons, going back several years, why net immigration from Mexico has leveled out or perhaps even turned slightly negative. Start by looking at a graph of annual immigration (that is, not the total Mexican-born population, but the annual flow). It actually peaked back in the late 1990s, and there has been an especially sharp decline going back to about 2004.
What are the causes of this decline? In no particular order, here are some of the longer-term reasons dating back to before the Great Recession hit full force:
1) Border enforcement is way up. \”Appropriations for the U.S. Border Patrol within the Department of Homeland Security (DHS)—only a subset of all enforcement spending, but one especially relevant to Mexican immigrants—more than tripled from 2000 to 2011, and more than doubled from 2005 to 2011. The federal government doubled staffing along the southwest border from 2002 to 2011, expanded its use of surveillance technology such as ground sensors and unmanned flying vehicles, and built hundreds of miles of border fencing. .. In spite of (and perhaps because of) increases in the number of U.S. Border Patrol agents, apprehensions of Mexicans trying to cross the border illegally have plummeted in recent years—from more than 1 million in 2005 to 286,000 in 2011—a likely indication that fewer unauthorized migrants are trying to cross. Border Patrol apprehensions of all unauthorized immigrants are now at their lowest level since 1971.\”
2) Deportations are way up. \”As apprehensions at the border have declined, deportations of unauthorized Mexican immigrants–some of them picked up at work sites or after being arrested for other criminal violations–have risen to record levels. In 2010, 282,000 unauthorized Mexican immigrants were repatriated by U.S. authorities, via deportation or the expedited removal process.\”
3) Mexico\’s demography is changing, with fewer children per woman and an older population, so the pressures on young men to leave and look for work in the U.S. are much reduced. \”In Mexico, among the wide array of trends with potential impact on the decision to emigrate, the most significant demographic change is falling fertility: As of 2009, a typical Mexican woman was projected to have an average 2.4 children in her lifetime, compared with 7.3 for her 1960 counterpart.\”
4) Mexico\’s economy was a train wreck for substantial periods of the 1970s and 1980s, and the U.S. economy was an incredible jobs locomotive in the second half of the 1990s in particular. But Mexico\’s economy is maturing, and the gap between economic opportunities in Mexico and those in the U.S. seems less gaping.
\”Mexico today is the world’s 11th-largest country by population with 115 million people and the world’s 11th-largest economy as measured by gross domestic product (World Bank, 2011). The World Bank characterizes Mexico as an “upper-middle income economy,” placing it in the same category as Brazil, Turkey, Russia, South Africa and China. Mexico is also the most populous Spanish-speaking country in the world. … In the three decades from 1980 to 2010, Mexico’s per capita GDP rose by 22%—from $10,238 in 1980 to about $12,400 in 2010.17 This increase is somewhat less than the average for all Latin American/Caribbean countries during the same period (33%) and significantly less than the increase in per capita GDP in the United States during this period (66%). Meantime, during this same period, the per capita GDP in China shot up thirteenfold—from $524 in 1980 to $6,816 in 2010. In more recent years, Mexico’s economy, like that of the United States and other countries, fell into a deep recession in 2007-2009. But since 2010 it has experienced a stronger recovery than has its neighbor to the north …\”
5) Prospects for education and health care in Mexico have improved, as well. \”For example, 92.4% of all Mexicans ages 15 and older were literate in 2010, up from 83% in 1980.19 In 2010, the average number of years of education of Mexicans ages 15 and older was 8.6, compared with 7.3 years in 2000. In terms of health care, almost three-in-five (59%) Mexicans in 2000 lacked health care coverage. In 2003, the Mexican federal government created a health care program, Seguro Popular, that provides basic coverage to the uninsured and is free for those living under the poverty line. The share of the Mexican population with access to health care had increased from less than half (41%) in 2000 to slightly more than two-thirds (67%) in 2010, an increase of 26 percentage points.\”
In short, Mexico in the 1970s and 1980s was demographically top-heavy with teenagers and young adults from large families living in a country with a weak economy and limited prospects for education and health care, right next to a much richer country with a weakly enforced border. A flood of immigration followed. Now, Mexico is on average older, with smaller families, and the prospects for education, health, and finding economic opportunity in Mexico are notably better. Enforcement at the border and within the U.S. economy have ramped up considerably. In that situation, a large resurgence of immigration from Mexico seems unlikely.
The global financial crises was preceded by a huge run-up in household debt, which in a number of countries helped to fuel a rise in housing prices. When housing prices then deflated, households were left with oversized debt burdens that they couldn\’t meet. One of the reasons behind the sluggish \”recovery\” since the Great Recession is that so many households have been struggling to pay down or renegotiate their debts. How to reduce these housing-related debt burdens? Some aggressive policy steps to reduce housing debt are recommended by (to me, at least) an unlikely source: the International Monetary Fund in the April 2012 World Economic Outlook.
Chapter 3 of the report, \”Dealing with Household Debt,\” first rehearses facts about about this cycle of rising debt, housing price bubbles, and then after the bubble pops, a sluggish recovery. This story is fairly conventional; for example, I posted on \”Leverage and the Business Cycle\” about a month ago on March 23. What surprised me about the IMF report was the policy recommendations: \”[B]old household debt restructuring programs such as those implemented in the United States in the 1930s … can significantly reduce debt repayment burdens and the number of household defaults and foreclosures. Such policies can therefore help avert self-reinforcing cycles of household defaults, further house price declines, and additional contractions in output.\”
The IMF uses the U.S. Home Owners\’ Loan Corporation, established in 1933, as it main example of how best to address housing debt–and contrasts it unfavorably with the policy steps the U.S. has taken since 2009. Here\’s the IMF\’s description of how the U.S. Home Owners\’ Loan Corporation worked (footnotes, citations, and references to tables and boxes omitted):
\”To prevent mortgage foreclosures, HOLC bought distressed mortgages from banks in exchange for bonds with federal guarantees on interest and principal. It then restructured these mortgages to make them more affordable to borrowers and developed methods of working with borrowers who became delinquent or unemployed, including job searches. HOLC bought about 1 million distressed mortgages that were at risk of foreclosure, or about one in five of all mortgages. Of these million mortgages, about 200,000 ended up foreclosing when the borrowers defaulted on their renegotiated mortgages. The HOLC program helped protect the remaining 800,000 mortgages from foreclosure, corresponding to 16 percent of all mortgages. HOLC mortgage purchases amounted to $4.75 billion (8.4 percent of 1933 GDP), and the mortgages were sold over time, yielding a nominal profit by the time of the HOLC program’s liquidation in 1951. The HOLC program’s success in preventing foreclosures at a limited fiscal cost may explain why academics and public figures called for a HOLC-style approach during the recent recession.
A key feature of HOLC was the effective transfer of funds to credit-constrained households with distressed balance sheets and a high marginal propensity to consume, which mitigated the negative effects on aggregate demand discussed above…. Accordingly, HOLC extended mortgage terms from a typical length of 5 to 10 years, often at variable rates, to fixed-rate 15-year terms, which were sometimes extended to 20 years. … In a number of cases, HOLC also wrote off part of the principal to ensure that no loans exceeded 80 percent of the appraised value of the house, thus mitigating the negative effects of debt overhang discussed above.
Here\’s a figure showing the U.S. housing market in recent years. As the IMF reports: \”There were about 2.4 million properties in foreclosure in the United States at the end of 2011, a nearly fivefold increase over the precrisis level, and the “shadow inventory” of distressed mortgages suggests that this number could rise further.\” The area shade in blue shows the number of properties in foreclosure. The area shaded in yellow is an estimate of \”shadow inventory\”–that is, additional properties likely to go into foreclosure.\”Shadow inventory indicates properties likely to go into foreclosure based on a number of assumptions. It includes a portion of all loans delinquent 90 days or more (based on observed performance of such loans); a share of modifications in place (based on redefault performance of modified mortgages); and a portion of negative equity mortgages (based on observed default rates).\”
Notice that the spike in foreclosures starts at about the same time as housing prices top out, in late 2006, and peaks around early 2009. These numbers don\’t include the larger number of people– about 11 million, which is one in every four mortgages in the country–who have \”underwater\” mortgages where the value of the mortgage exceeds the value of the property.
What policies has the U.S. followed to deal with this foreclosure problem? The IMF reports that the main policy is \”the Home Affordable Modification Program (HAMP), the flagship mortgage debt restructuring initiative targeted at households in default or at risk of default.\” It was adopted in February 2009, and has been revised a number of times since. But so far, the policy hasn\’t accomplished much. Here\’s the IMF:
\”However, households already in default are excluded from HARP, and the impact on preventing foreclosures is likely to be more limited. HAMP had significant ambitions but has thus far achieved far fewer modifications than envisaged. … Meanwhile, the number of permanently modified mortgages amounts to 951,000, or 1.9 percent of all mortgages. By contrast, some 20 percent of mortgages were modified by the Depression-era HOLC program, and HAMP’s targeted reach was 3 to 4 million homeowners. By the same token, the amount disbursed … as of December 2011 was only $2.3 billion, well below the allocation of $30 billion (0.2 percent of GDP).\”
Of course, there is a list of reasons for this minimal effect. It requires the cooperation of creditors and loan officers, which is voluntary. When mortgages have been bundled together and sold as securities, it\’s not clear how the renegotiation should work. Tight eligibility rules mean that the unemployed and those who have suffered big drops in income often aren\’t eligible. The policy typically relied on lower interest rates and longer mortgage terms, but only rarely could reduce the outstanding principal on a house that had lost value. The reductions in mortgage payments often wasn\’t large, so roughly a third of those who made it through the program ended up defaulting again–which of course reduces anyone\’s incentive to participate in the first place. Fannie Mae and Freddie Mac, which hold about 60% of outstanding U.S. mortgages, don\’t participate.
So here we are, six years after the wave of foreclosures started and three years after it peaked, still arguing about whether something substantial ought to be done–and if so, what. Without drilling down into details of the alternative proposals, it seems to me that a modest share of the trillions in federal borrowing in the last few years, along with the trillions of assets that the Federal Reserve has accumulated through its \”quantitative easing\” policy, might have been better applied to assisting the millions of American households who took out a mortgage and bought a house–implicitly relying on the ability of supposedly better-informed lenders to tell them what they could afford–and then were blindsided by the national downturn in housing market prices.
What jobs do those in the top 1% of the income distribution hold? How have those jobs shifted in recent decades? Jon Bakija, Adam Cole, and Bradley T. Heim have evidence on this question in \”Jobs and Income Growth of Top Earners and the Causes of Changing Income Inequality: Evidence from U.S. Tax Return Data.\” An April 2012 version of their working paper is here; a very similar March 2012 version is here. They have a bunch of interesting tables and analysis: here, I\’ll give a sampling with two columns from two of their tables about the occupations of the top 1%, and some evidence on how what\’s driving the top 1% is really the top tenth of 1%.
Here\’s are the occupations of who was in the top 1% of the income distribution, in the tax return data, in 1979 and in 2005. What occupations have a smaller share of the top 1? The share of the top 1% who get their income as \”Executives, managers and supervisors (non-finance)\” has dropped 5.3 percentage points. The breakdown at the bottom of the table suggests that much of this fall is due to the subcategory of \”Executive, non-finance, salaried.\” The share of the top 1% in the medical profession falls by 1.7 percentage points. The share of the top 1% who are \”Farmers & ranchers\” falls by 1.5 percentage points.
What occupations comprise a larger share of the top 1%? The share of the top 1% whose occupation is classified as \”Financial professions, including management\” rises by 5.5 percentage points. The share in \”Real Estate\” rises by 1.8 percentage points–although surely this gain would be smaller if calculated on data after the drop in housing prices. The share who are lawyers rises by 1 percentage point.
Here\’s the share of GDP received as income by those in each occupation in the top 1%, again comparing 1979 and 2005. The top line shows that the share of income going to the to 1% roughly doubled over this time. Thus, the interesting question here is whether in some occupations the rise in income was substantially more or less than a doubling. For example, the share of income going to the top 1% in the \”Financial Professions, including management\” more than tripled, as did the share of income going to those \”Real Estate.\” The share of income going to \”Business operations (nonfinance)\” and to \”Professors and scientists\” almost tripled. On the other side, the share of income in the top 1% going to \”Medical\” rose by \”only\” about 50%, and the share of income in the top 1% going to \”Farmers & ranchers\” declined.
Finally, here\’s a striking figure that compares income growth from 1979 to 2005 for the top 0.1% to income growth for those in the bottom half of the top 1%: that is, those from the 99th percentile to those in the 99.5th percentile. The bottom line shows that on average, the growth rate of real income (in this case, excluding capital gains) was 2.4 times as fast for the top 0.1% as it was for those in the 99-99.5 percentiles. For certain occupations like \”Executive, non-finance\” and \”Supervisor, non-finance,\” the multiple is much higher. The overall pattern here is that while slogans often refer to the top 1%, for most occupations, referring to the top 0.1% might be a more accurate description of where the largest income gains have been occurring.
Every intro econ class points out that the total aggregate demand economy is the sum of consumption plus investment plus government plus exports minus imports. It also points out that the \”government\” category for \”government demand\” in this equation isn\’t the total government budget, but rather government spending on purchasing goods and services and paying employees. Parts of the government budget that involve a transfer of funds to consumers are not treated as part of demand by government , but instead are treated as demand by consumers. Daniel Carroll gives the facts behind this distinction in \”The Shrinking Government Sector,\” published in the April 2012 issue of Economic Trendsfrom the Cleveland Fed.
First, here\’s a figure showing government demand or the \”government sector\” as a share of GDP. Total budgets for federal, state, and local government have been over one-third of GDP. But total government demand for goods and services has actually been falling. Here\’s Carroll\’s exposition:
\”While it is true that the ratio of government expenditures—including federal, state, and local government—to GDP increased precipitously during the crisis (reaching 21.1 percent in 2009), it has been trending down sharply since. At 19.7 percent as of the fourth quarter of 2011, it has given back 70 percent of its post-crisis increase.
This downward trend is the result of decreasing shares at all levels of government; however, the most significant factor has been cuts at the state and local level. Unlike the federal government share, which currently sits at 15.7 percent, state and local government spending is now nearly 3 percent below its first-quarter 2007 level. Because state and local government accounts for about 60 percent of total government spending, the trend in this component has more weight than the federal component on the overall government share.\”
Carroll also provides a graphs of income transfers by government: again, in the breakdown of GDP into components of aggregate demand, these are allocated to the \”consumption\”category. Note that this graph isn\’t directly comparable to the one above, because it starts in 1997 rather than in 1970.
Several intriguing patterns emerge from these graphs:
1) I hadn\’t known that government spending on goods and services was actually higher in the much of the 1970s than it is today, nor that government demand for goods and services had such a big decline in the 1990s. For those who have a vision of government doing things like building roads, providing education and national defense, enforcing laws, and paying for research and development, government is doing less of those things as a share of the economy now than it was a few decades ago.
2) The recent rise in government transfer payments is extraordinarily large 4%: nearly 4% of GDP during the recent recession, or more than 5% of GDP if one compares from the peak of the business cycle in 2000 to the trough in 2009 and 2010. For comparison, total defense spending in 2011 was 4.7% of GDP. Thus, just rise in government transfer payments has been roughly comparable to total defense spending.
3) One way to look at the government budgets is that tax and other revenues pay for transfers, and borrowing pays for all government demand for goods and services. Carroll writes: \”\”[G]overnment as a component of GDP does not include transfers; however, transfers greatly exceed tax revenue and nearly exhaust total revenues. This leaves little funding to pay for government consumption and investment, and so the difference must be borrowed.\”
What is the \”Short Africa\”? \”\’Africa\’ in this talk is \’the short Africa\’: excluding N Africa, Madagascar, Mauritius and South Africa. All these are sharply distinct from the rest of Africa environmentally, agriculturally and economically, and generally well ahead in mean income; poverty reduction; growth; farming (irrigation, fertilizer, seeds); and demographic transition. The short Africa is itself highly diverse, but no more so than is India or China.\”
What\’s the demographic and economic challenge for this region? \”Between 1950 and 2012, population in the \’short Africa\’ rose fivefold. It will more than double again in 2012-50 to 11.3 times its 1950 level. Workforces – people aged 15-65 – are rising faster still, thanks to better child survival and some fall in fertility. In 1985 sub-Saharan Africa had 106 people of prime working age for every 100 dependents. By 2012 there were 120; in 2050 there will be 196. That\’s a 63% rise in workers-per-dependent from now to 2050 – and a 3.5% rise each year in the number of people aged 15-64. In South and East Asia, a similar rise in workers-per-dependent proved a demographic window of opportunity, contributing about a third of the \’miracle\’ of growth and poverty reduction – because those extra workers found productive employment: first, in smallholdings, gaining from a green revolution and usually land redistribution; later, in industry and services, as farm transformation released workers. In \’the short Africa\’, will the swelling ranks of young workers produce Asian miracles – or worsening poverty, unemployment and violent unrest?\”
Why smallholder farmers are of central importance. \”Farming will decide in Africa, as it did in Asia. Farms remain the most important income and work source for over 2/3 of the short Africa\’s economically active – more among the young and the poor. This will change, but not fast.\”
More land under cultivation isn\’t the answer. \”Farmers\’ strategy of feeding themselves by land expansion – forced on them by insufficient public atten-tion to irrigation, fertilizer access and seed improvement – not only failed to maintain living standards: it has run out of steam and is, or is fast becoming, unsustainable in most of Africa. That is, farmland ex-pansion is inducing, or soon will induce, soil depletion that means net farmland loss.\”
Improvements in irrigation, fertilizer and seeds are a possible answer. In \’the short Africa\’, below 1% of cropland is irrigated (20-25% in S/E/SE Asia in 1965; 35-40% now). Below 2 kg/ha of main plant nutrients – nitrogen, phosphorus, potash – are applied (>150kg/ha in S/E/SE Asia). … [F]ast yield growth without fertilizers and water-control is bricks without straw.\”
Summing up. \”\’Scientific smallholder intensification\’ in Africa is no easy path to development. From global evidence, we know it\’s possible. Is it necessary? Initially, yes. Farm development is only the start of modernization away from agriculture; I\’m no agricultural or smallholder fundamentalist. But I\’m an income-from-work fundamentalist. \’The short Africa\’ by 2050 will have 2.3 times today\’s population – but 3.7 times today\’s 15-64-year-olds. They need an affordable initial path to workplaces giving income and respect. Other-wise, potential demographic dividend will become demographic disaster. But, with half the people still in severe poverty and States cash-strapped too, what initial path is \’affordable\’? One, trodden elsewhere, is scientific intensification of smallholder farms. If there\’s an alternative, what is it?\”
The U.S. Treasury has published \”The Financial Crisis Response In Charts.\” The labels on the charts largely tell the following four-part story: 1) There was a deep financial crisis; 2) The government did things; 3) The crisis did not continue; 4) Therefore, what the government did was beneficial and useful and responsible for the recovery. Even those of us who are generally supportive of many of the steps taken during the worst of the financial crisis late in 2008 and early 2009 can spot some logical flaws in that syllogism.
But two of the charts in particular, about the U.S. banking system, caught my eye. The first one is titled: \”The financial industry is less vulnerable to shocks than before the crisis. The panels show two lessons. \”Banks have added nearly $400 billion in fresh capital as a cushion against unexpected losses and financial shocks. Banks are also less reliant on short-term funding, which can disappear in a crisis and leave them more vulnerable to panics.\”
The second panel of interest shows that, relative to the U.S. economy, \”The U.S. banking system is proportionally smaller than that of other advanced economies.\” The horizontal axis shows total assets of banks as a share of the economy of their home country. The four largest U.S. banks by assets are JPMorgan Chase, Bank of America, Citigroup, and Wells Fargo. The vertical axis shows total assets of all commercial banks as a share of GDP. By either measure, U.S. banks are relatively small in international terms.
Of course, this comparison is somewhat misleading. U.S. banks are being compared to the huge U.S. economy, while banks in Belgium and Sweden and Switzerland are being compared to their much smaller national economies, not to overall economy of the European Union. However, the figure still makes a useful point that while the biggest U.S. banks are enormous, by some standards they aren\’t so large.
Along similar lines, I blogged on December 7, 2011, about \”The Rise of Global Banks in Emerging Markets,\” where I quoted Neeltje van Horen: \”In fact, the world’s biggest bank in market value is China’s ICBC. The global top 25 includes eight emerging-market banks. Among these, three other Chinese banks (China Construction Bank, Agricultural Bank of China, and Bank of China), three Brazilian banks (Itaú Unibanco, Banco do Brasil, and Banco Bradesco) and one Russian bank (Sberbank). While excess optimism might have inflated these market values, these banks are large with respect to other measures as well. In terms of assets all these banks are in the top 75 worldwide, with all four Chinese banks in the top 20.\”
My broader point is that in thinking about the financial system, it\’s important not to overemphasize just the large U.S. banks. The U.S. financial system is much larger than the banking system, and includes all the ways of borrowing funds like asset-banked securities, commercial paper, and bonds. In addition, there are enormous banks in other countries as well. In addition, U.S. banks have largely returned to health in terms of holding more assets and being less reliant on short-term financing. Looking ahead, the big issues about stability of the financial system go well beyond the big U.S. banks–although they pose too-big-to-fail issues of their own–and require looking more broadly at international banks and global finance.
