Browse by Tags

With the Federal Reserve turning 100, San Francisco Fed economists Early Elias and Òscar Jordà take a moment to look at the impact of their parent institution on crisis mitigation. They point to fewer crises over the last 100 years than in the previous century, and the less severe results of the Great Recession compared to the Panic of 1907 as evidence of the Fed's relative success. Here is an excerpt from their Economic Letter: Recessions originating from a financial event were common in the late 19th and early 20th centuries. Many stemmed from banking panics. Figure 1 provides a global historical perspective. We calculate by decade the number of countries that experienced financial crises among a sample of 17 industrialized economies representing more than half of global GDP during the past 140 years (for details, see Jordà, Schularick, and Taylor 2012). Figure 1 shows a notable downward global trend in the incidence of these highly disruptive events, with the conspicuous exceptions of the Great Depression and the Great Recession of 2007–09. In the United States, the rate of banking crises declined markedly after the 1913 creation of the Federal Reserve System. Other than the Great Depression and Great Recession, the only significant banking crisis of the past century was the savings and loan crisis. By contrast, ten significant banking crises occurred in the 19th century. The panic of 1907 and the resulting recession are generally credited with providing the catalyst for the creation of the Federal Reserve System. When the Federal Reserve was chartered, the United States had been without a central bank for about 70 years. Congress chartered The First Bank of the United States in 1791 during the Washington presidency, under the guiding hand of Secretary of Treasury Alexander Hamilton. However, its 20-year charter was allowed to expire in 1811. Then, under President Madison, the Second Bank of the United States was created in 1817 for another 20-year period. Once again, the charter was allowed to expire amid President Jackson’s strong opposition to the central bank. Read the full letter here .

San Francisco Fed President John C. Williams visited the Forecasters Club in New York last Thursday and gave his assessment of the economy. He named four key factors behind the slow, or "tepid" recovery: 1) the effects of the housing bubble and crash; 2) austerity measures reducing aggregate demand; 3) eroding demand for exports with a weakened global economy; and 4) unusually high levels of uncertainty. He then addressed the question of whether these factors affect supply and demand: So, is the problem today inadequate supply, or demand, or both? A useful way to think about this question is to compare the unemployment rate with the natural rate of unemployment. By the natural rate, I mean the unemployment rate that minimizes labor market imbalances and pressures—either upward or downward—on inflation. The unemployment gap—the difference between the unemployment rate and its natural rate—measures the degree to which labor demand is unequal to supply. Movements in the natural rate itself reflect changes in supply. Of course, we can’t directly measure the natural rate of unemployment. Rather, we must estimate it. This topic has appropriately garnered a great deal of attention among economists at the Federal Reserve and elsewhere in recent years. Extensive analysis of the labor market comes to a clear conclusion: Supply-side considerations explain only some of the rise in unemployment. Most of that rise is explained by a lack of labor demand. Let’s look at this more closely. Prior to the recession, a typical estimate of the natural rate of unemployment was between 4¾ and 5% (see Williams 2013). The empirical evidence suggests that the recession and policy responses to it, such as extended unemployment insurance benefits, contributed to dislocations in the labor market. These have pushed the natural rate above its pre-recession level by about 1 percentage point (see Congressional Budget Office 2013 and Daly et al. 2012b). Consistent with these findings, my estimate of the current natural rate of unemployment is about 6%, roughly 2 percentage points below the current unemployment rate. This 6% figure is consistent with many other estimates, including the most recent median estimate of the Survey of Professional Forecasters (Federal Reserve Bank of Philadelphia 2012). Fortunately, many of the influences that have elevated the natural rate of unemployment since the crisis and recession should fade over time. In fact, this process is already under way. The extended unemployment insurance programs have been scaled back and are affecting fewer and fewer people. Eventually these programs will be phased out. In addition, measures of mismatch between workers and available jobs are receding (Lazear and Spletzer 2012 and Șahin et al. 2012). And, at least so far, we are not seeing permanent scarring effects of long-term unemployment (Valletta 2013). I expect that, in coming years, the natural rate will return to a more historically typical level of about 5½%. I should note that the fact that economists are busily studying, debating, and revising their assessments of the supply side of the economy is encouraging. It makes a repetition of the mistakes of the late 1960s and 1970s much less likely. In our research, Orphanides and I found that, if economists and policymakers had similarly reevaluated their views back in the 1960s and 1970s, the stagflation of that period could have been avoided (Orphanides and Williams 2013). The conclusion that the economy is suffering primarily from weak demand rather than a shortage of supply receives additional support when the factors weighing on recovery are analyzed. The finding of the research that I mentioned on the economic effects of uncertainty—that heightened uncertainty raises unemployment and depresses inflation—is evidence that uncertainty primarily acts as a barrier to demand, not supply. Other research supports that view. In recent work published by the San Francisco Fed, Mian and Sufi (2013) compare state-level employment performance during the recession and recovery with state-level survey data from the National Federation of Independent Business. The NFIB survey asks small business owners to identify the single most important problem they face. Answers include taxes, poor sales, labor costs, government regulation, insurance costs, et cetera. Mian and Sufi find that declines in state employment were highly correlated with the percentage of respondents in each state citing lack of demand as their most important business problem. Read the full speech here .

In an Economic Letter for the San Francisco Fed , Sylvain Leduc and Daniel Wilson conclude that building highways has a significant positive effect on both short and medium-term economic activity. The real "bang for the buck" of federal highway grants to states, comes in the relatively large fiscal multipliers over the medium term. Leduc and Wilson estimate that "each dollar of federal highway grants received by a state raises that state’s annual economic output by at least two dollars." In our analysis of how changes in forecasts of highway grants to the states affect state GSP, we control for lags in state GSP, lags in receipt of highway grants, average state GSP levels, and national movements of gross domestic product (GDP) over the sample period from 1990 to 2010. In Figure 1, the solid line shows the average percentage change in a state’s GSP following a 1% increase in forecasted future highway grants to the states. The shaded area around the line represents a 90% probability range. The horizontal axis indicates the number of years after the unanticipated change in forecasted highway grants to the states. The figure shows that changes in the forecasts have a significant short-term effect on state output in the first one to two years. This effect fades, but then increases sharply six to eight years after the forecast revisions, before declining again. This pattern holds up well with alternative estimation techniques, the inclusion of different control variables, and with different data samples. This pattern is consistent with New Keynesian theoretical models in which public infrastructure, such as roads, are used by the private sector in the production of goods and services and take time to be built (see Leduc and Wilson, forthcoming). In this framework, the initial impact is due to a traditional Keynesian effect of an increase in aggregate demand. The medium-term effect on output arises once the public infrastructure is built, thus increasing the economy’s productive capacity. Read Highway Grants: Roads to Prosperity? here .

We are expecting that growth in China will have to slow down as the median income level in the country rises. But that analysis may be the result of neglecting what is happening across China. In a new Economic Letter for the San Francisco Fed , Israel Malkin and Mark Spiegel point out that there is a lot of room for income growth in emerging cities throughout China. Recent evidence suggests that the poorer Chinese provinces are catching up with their richer counterparts, at least on average. This phenomenon, called convergence, has been observed widely in other countries, including the United States, where poorer states have tended to grow income faster than richer states (see Barro and Sala-i-Martin 1992). Figure 1 displays average growth rates for Chinese provinces since 2000. There is a negative relationship between a province’s income in 2000 and its subsequent growth. China’s two wealthiest provinces, Beijing and Shanghai, have had the lowest per capita growth since 2000. We divide China’s provinces into higher and lower-income groups, examining their prospects for continued growth based on current income levels. Provinces with per capita income below $10,000 are identified in blue as emerging. Provinces above that level are identified in red as developed. We then perform a statistical exercise, using data from a group of other Asian economies that have had rapid growth experiences to predict expansion rates for these two groups of Chinese provinces. Our results indicate that growth of the wealthier portion of China is likely to slow, but substantial room remains for continued growth in China’s interior. For example, among the advanced Chinese provinces, average growth is predicted to slow to 7% in the five years beginning in 2016. However, growth among China’s emerging provinces is not expected to fall to that rate until sometime during the five years beginning in 2024. Thus, the emerging Chinese provinces are predicted to enjoy more than an additional decade of high growth before succumbing to the middle-income trap. Read the full Economic Letter here .

To the extent that faster recovery depends on consumer spending, it is important to watch trends in household debt. In a new Economic Letter , John Krainer , senior economist at the San Francisco Fed , unpacks some data on debt and the behavior of borrowers. And he ties household spending today to tighter lending for the people who were more likely to build up debt during the housing boom: In the fourth quarter of 2011, of the nearly $12 trillion in total consumer debt, about 70% consisted of mortgages, according to the data sample. Home equity lines and home equity installment loans accounted for another 10%. In the nonmortgage category, auto loans represented 7% and bank credit card balances 6% of total consumer debt. Analysis of the data uncovers a number of interesting patterns in the evolution of household debt. For example, as Figure 2 shows, borrowers who defaulted at some point in the sample period increased their nonmortgage debt loads during the housing boom at a much faster pace than did nondefaulting borrowers. To a large extent, this ramping up of nonmortgage debt levels occurred among younger borrowers and those with lower credit scores, that is, subprime borrowers. The reverse occurred once the housing boom ended. Mortgage borrowers who defaulted reduced their nonmortgage debt levels at a much faster pace than nondefaulters. This decline in debt most likely occurred because defaulters lost access to credit. Read Consumer Debt and the Economic Recovery here .

In a new Economic Letter for the San Francisco Fed , Brian Lucking and Dan Wilson take a look ahead at the potential impact of current fiscal policy on future growth. Their projections are based on aggregate local, state, and federal policies, which they say have historically been countercyclical. But that seems to be shifting: To investigate the extent to which recent fiscal policy has been countercyclical and expansionary, we created a statistical model by estimating how federal, state, and local budgets have moved through past business cycles. Our analysis uses quarterly data since 1949 from the National Income and Product Accounts of the U.S. Commerce Department Bureau of Economic Analysis. We use historical fiscal patterns to predict what budgetary policy would have been in recent years based solely on business cycle factors, and then compare that prediction to actual policy. We measure the business cycle based on the difference between actual GDP and the CBO’s estimate of potential GDP, which is known as the output gap. This allows us to determine how expansionary current policy has been compared with fiscal policy’s typical countercyclical response, given the severity of the recent downturn. One pattern independent of the business cycle is worth noting. State, local, and federal spending and tax revenue have both trended up as a share of GDP since 1949. Spending has grown faster than revenue, so the aggregate government deficit has also trended up. Our statistical model incorporates these long-run trends. Figure 1 summarizes the results of our analysis based on four measures of fiscal policy, calculated as a percentage of GDP. Figure 1, panel A plots federal spending against the level predicted based on historical patterns. It turns out that federal spending was highly stimulatory during the recession, rising well above the level predicted by normal cyclical factors. Our statistical model predicted a sharp rise in spending, based on the historical relationship between spending and the business cycle, and the unusually large output gap that developed during the Great Recession. But the actual rise in federal spending was even greater than our model predicted. By comparison, federal spending also rose during the severe recession of 1981–82 (not shown), but less than our statistical model would predict. Read U.S. Fiscal Policy: Headwind or Tailwind? here .

Philip Strahan --professor of finance at Boston College's Carroll School of Management , and a visiting scholar at the Federal Reserve Bank of San Francisco --has an Economic Letter on liquidity risks at banks during and following the 2007-2008 financial crisis. Strahan highlights the importance of traditional deposits to mitigating risk: Banks finance their balance sheets with more than just deposits and equity capital. Other liabilities include uninsured wholesale deposits, repurchase agreements, and other short-term unsecured debt instruments. These sources became scarce during the crisis. For example, repurchase agreements, known as repos, were often used to finance risky assets such as private-label mortgage-backed securities. Gorton and Metrick (2011) show that, in the middle of 2007, mortgage-backed securities could be almost completely financed with short-term borrowed funds in the repo market. However, by the fourth quarter of 2008, only about 55% of each dollar invested in such securities could be financed this way. Banks that used repos to finance purchases of mortgage-backed securities faced an unpleasant choice. They could sell their securities holdings into a falling market and take a big loss. Or they could find new, and presumably expensive, sources of credit. In the case of nonbank brokerage firms, the collapse of the repo market was a calamity. However, it was less of a disaster for commercial banks because they could use increases in deposits to bridge the financing gap. Figure 1 shows how these sources of liquidity risk affected overall bank credit during the crisis. Off-balance-sheet loan commitments rose steadily from 1990 to 2007. Overall bank credit production, including both on- and off-balance-sheet credit commitments, started to fall in the middle of 2007. The decline accelerated sharply in the last quarter of 2008. By contrast, loans held on bank balance sheets continued to rise until the end of 2008. That rise in on-balance-sheet loans during the crisis was due to borrowers drawing down preexisting credit lines. Banks began cutting back new lending in the middle of 2007. This illustrates how bank obligations to existing borrowers crowded out new borrowers. Read Liquidity Risk and Credit in the Financial Crisis here .

In a new Economic Letter , San Francisco Fed economists Mary Daly , Bart Hobjin , and Brian Lucking take a look at wage growth. Wage growth has been strong compared to the overall rate of recovery and growth of GDP in the US. So what is to explain this growth? Daly, Hobjin, and Lucking provide one quick answer: One reason real wage growth has been so solid is that inflation has been low, with the personal consumption expenditures price index increasing at an average annual rate of 1.8% since the start of 2008. Low inflation means that employers cannot reduce real wages simply by letting inflation erode the value of worker pay. Instead, if they want to reduce real labor costs, they must cut the actual dollar value of wages. Employers generally avoid doing so because cuts to nominal wages can reduce morale and prompt resistance even in difficult economic times (Kahneman, Knetsch, and Thaler 1986). The inability or unwillingness of employers to reduce nominal pay is known as downward wage rigidity. When economic conditions are poor, this rigidity can disrupt normal labor market functioning, especially in a low-inflation environment. If wages are downwardly rigid, workers may receive false signals about the value of remaining in a particular occupation or industry. For example, consider construction workers who are less productive now than they were five years ago because of the bursting of the housing bubble. If their wages fell, they might seek jobs in other industries. Because of downward wage rigidity, they may stay in construction instead. On the labor demand side, employers that can’t cut wages may delay expanding payrolls as conditions improve. Either way, downward nominal wage rigidities can result in misallocation of resources in the economy. Read Why Has Wage Growth Stayed Strong? here .