Continuing crises in U.S. housing and financial markets have increased concerns about an impending recession, which typically involves two quarters of negative GDP growth. All recessions since the early 1950s, except the one in 2001, included a contraction in personal consumption expenditures (PCE). Because roughly 70 percent of the country’s GDP can be attributed to PCE on goods and services, any contraction in consumption expenditures weighs heavily on economic growth. The Bureau of Economic Analysis (BEA) measures GDP and, two months after the end of each quarter, releases its GDP estimates for that quarter.1 In addition to releasing data on total output, the BEA releases data on each component of GDP: PCE, investment, government expenditures, and net exports. Unlike the other components, PCE estimates are also released monthly as part of the BEA’s “Personal Income and Outlays” report. Quarterly PCE is the average of monthly PCE over the three months in the quarter, and both monthly and quarterly data are subsequently revised multiple times. These monthly PCE releases have been spotlighted recently: As it turns out, the PCE measure for the second month of a given quarter is close to the measure for the entire quarter. For example, the PCE for February 2008 (reported March 28) was $8,365.5 billion; the advance release of 2008:Q1 PCE (reported May 1) was $8,369.1 billion. The rationale for using the second month as a proxy for a quarter’s average is simple: It is available sooner— one month before the advance release of the quarterly figure. It is also quite reliable: On average, the economy tends to grow; if the growth rate of consumption from month to month within a quarter is constant, the PCE for the second month is close to that quarter’s average. The chart plots the approximated (second-month) and actual growth rates of PCE since 1991 using real-time data: That is, the growth rates at each point on the chart are computed using only the data that would have been available to a researcher at the time of the estimate. The approximated measure for 2008:Q3 is –2.3 percent, suggesting the first decline in PCE since the fourth quarter of 1991. On average, the second-month PCE is close to the reported quarterly data; however, it under predicts the advance release by an average of 0.15 percentage points over the sample. The correlation between the two series in the chart is 0.90. Most of the difference between the two series disappears when data are revised. In fact, the actual quarterly PCE growth rate and the approximation using second-month data is almost perfect, differing by only 0.03 percentage points when both growth rates are computed using current data. In summary, this simple calculation can provide a quite accurate picture of the quarterly consumption growth rate a month ahead of the BEA’s first official release. In addition, the use of real-time (or unrevised) data is essential for evaluating the performance of this or any calculation.
{"title":"Predicting consumption: a lesson in real-time data","authors":"Riccardo DiCecio, Charles S. Gascon","doi":"10.20955/ES.2008.28","DOIUrl":"https://doi.org/10.20955/ES.2008.28","url":null,"abstract":"Continuing crises in U.S. housing and financial markets have increased concerns about an impending recession, which typically involves two quarters of negative GDP growth. All recessions since the early 1950s, except the one in 2001, included a contraction in personal consumption expenditures (PCE). Because roughly 70 percent of the country’s GDP can be attributed to PCE on goods and services, any contraction in consumption expenditures weighs heavily on economic growth. The Bureau of Economic Analysis (BEA) measures GDP and, two months after the end of each quarter, releases its GDP estimates for that quarter.1 In addition to releasing data on total output, the BEA releases data on each component of GDP: PCE, investment, government expenditures, and net exports. Unlike the other components, PCE estimates are also released monthly as part of the BEA’s “Personal Income and Outlays” report. Quarterly PCE is the average of monthly PCE over the three months in the quarter, and both monthly and quarterly data are subsequently revised multiple times. These monthly PCE releases have been spotlighted recently: As it turns out, the PCE measure for the second month of a given quarter is close to the measure for the entire quarter. For example, the PCE for February 2008 (reported March 28) was $8,365.5 billion; the advance release of 2008:Q1 PCE (reported May 1) was $8,369.1 billion. The rationale for using the second month as a proxy for a quarter’s average is simple: It is available sooner— one month before the advance release of the quarterly figure. It is also quite reliable: On average, the economy tends to grow; if the growth rate of consumption from month to month within a quarter is constant, the PCE for the second month is close to that quarter’s average. The chart plots the approximated (second-month) and actual growth rates of PCE since 1991 using real-time data: That is, the growth rates at each point on the chart are computed using only the data that would have been available to a researcher at the time of the estimate. The approximated measure for 2008:Q3 is –2.3 percent, suggesting the first decline in PCE since the fourth quarter of 1991. On average, the second-month PCE is close to the reported quarterly data; however, it under predicts the advance release by an average of 0.15 percentage points over the sample. The correlation between the two series in the chart is 0.90. Most of the difference between the two series disappears when data are revised. In fact, the actual quarterly PCE growth rate and the approximation using second-month data is almost perfect, differing by only 0.03 percentage points when both growth rates are computed using current data. In summary, this simple calculation can provide a quite accurate picture of the quarterly consumption growth rate a month ahead of the BEA’s first official release. In addition, the use of real-time (or unrevised) data is essential for evaluating the performance of this or any calculation.","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"219 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131849053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
T he application of computing technologies by U.S. firms has exploded in recent years. Between 1990 and 2000, the average annual growth rate of real investment in computer capital was about 33 percent. Ever-declining prices of computer equipment, in quality-adjusted terms, allow firms to pursue more powerful and sophisticated applications of computers and improve the efficiency of basic business functions, such as supply-chain management. Although the rapid growth of computer investment contributes directly to business investment spending, economists have for a long time wondered how computer investment affects productivity. The impact could be substantial if the use of computers facilitates a broad collection of complementary innovations within firms. Until recently, however, the connection between computer use and business productivity growth has been unclear.1 Reexamining standard growth-accounting techniques, similar to those used in this publication, economists have identified in recent studies that computers do have potentially large effects on productivity growth—particularly in the long run, when computing technologies interact fully with a firm’s business practices. Because the benefits of computers are largely oriented toward intangible aspects of business activity, the impact of computer use may not be reflected in aggregate statistics. Indeed, investment in these technologies may have little direct impact on a firm’s productivity; only when these technologies are combined with organizational aspects over time do the contributions of computer investment become apparent. One example is Wal-Mart’s proprietary software that processes a large array of computer-collected data to determine specific goods to stock at specific stores at specific times of year, as well as their prices. Brynjolfsson and Hitt (2003) estimate the impact of computers on productivity by using firm-level data to reduce measurement problems of outputs and inputs that exist in industry-level data.2 They find that computer use accounts for a substantial share of total factor productivity (TFP) and output growth.3 They also find that computer investment has its maximal impact on productivity after about seven years. They estimate that, between 1987 and 1994, about 0.25 to 0.50 percentage points of TFP growth at the firm level was generated by the use of computer capital, which grew by about 25 percent per year. Although many studies focus on TFP growth, computers also contribute to labor productivity growth by increasing the stock of capital per worker. As seen in the chart, computer investment accelerated early in the 1990s, long before the “new economy” productivity acceleration took place. If the firm-level results are translated to the overall economy, the gains in TFP growth from the 1995-99 flurry of computer investment growth (which exceeded 40 percent per year) should peak around 2006. Hence, there is cause for optimism regarding productivity over the next few ye
{"title":"Computer use and productivity growth","authors":"Rubén Hernández-Murillo","doi":"10.20955/ES.2003.29","DOIUrl":"https://doi.org/10.20955/ES.2003.29","url":null,"abstract":"T he application of computing technologies by U.S. firms has exploded in recent years. Between 1990 and 2000, the average annual growth rate of real investment in computer capital was about 33 percent. Ever-declining prices of computer equipment, in quality-adjusted terms, allow firms to pursue more powerful and sophisticated applications of computers and improve the efficiency of basic business functions, such as supply-chain management. Although the rapid growth of computer investment contributes directly to business investment spending, economists have for a long time wondered how computer investment affects productivity. The impact could be substantial if the use of computers facilitates a broad collection of complementary innovations within firms. Until recently, however, the connection between computer use and business productivity growth has been unclear.1 Reexamining standard growth-accounting techniques, similar to those used in this publication, economists have identified in recent studies that computers do have potentially large effects on productivity growth—particularly in the long run, when computing technologies interact fully with a firm’s business practices. Because the benefits of computers are largely oriented toward intangible aspects of business activity, the impact of computer use may not be reflected in aggregate statistics. Indeed, investment in these technologies may have little direct impact on a firm’s productivity; only when these technologies are combined with organizational aspects over time do the contributions of computer investment become apparent. One example is Wal-Mart’s proprietary software that processes a large array of computer-collected data to determine specific goods to stock at specific stores at specific times of year, as well as their prices. Brynjolfsson and Hitt (2003) estimate the impact of computers on productivity by using firm-level data to reduce measurement problems of outputs and inputs that exist in industry-level data.2 They find that computer use accounts for a substantial share of total factor productivity (TFP) and output growth.3 They also find that computer investment has its maximal impact on productivity after about seven years. They estimate that, between 1987 and 1994, about 0.25 to 0.50 percentage points of TFP growth at the firm level was generated by the use of computer capital, which grew by about 25 percent per year. Although many studies focus on TFP growth, computers also contribute to labor productivity growth by increasing the stock of capital per worker. As seen in the chart, computer investment accelerated early in the 1990s, long before the “new economy” productivity acceleration took place. If the firm-level results are translated to the overall economy, the gains in TFP growth from the 1995-99 flurry of computer investment growth (which exceeded 40 percent per year) should peak around 2006. Hence, there is cause for optimism regarding productivity over the next few ye","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134294013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Views expressed do not necessarily reflect official positions of the Federal Reserve System. There has been considerable discussion of the possibility that ongoing troubles in the housing market could push the economy into recession.1 But it is very unlikely that the decline in housing alone will cause a recession. Any recessionary effect on the economy caused by housing will be a consequence of its effect on consumption. Real GDP is a measure of the economy’s current production. Sales of existing houses have no impact on current production because these houses were produced sometime in the past. The only direct effect housing has on current economic growth comes through the “residential investment” component of GDP, which includes current construction and improvements of singleand multi-family housing. Residential investment accounts for only about 5 percent of GDP; consequently, the effect of residential investment on economic growth is relatively modest. The chart shows this effect by plotting quarterly GDP growth with and without residential investment. (It also shows the quarterly growth rate of residential investment.) Excluding residential investment has only a small effect even during the 1970s and early 1980s, when the growth of residential investment was considerably more volatile than during the past two decades. Since residential investment peaked in the fourth quarter of 2005, its decline has reduced growth of real GDP by an average of about 0.85 percentage points. This decline has largely been offset by nonresidential investment, which has continued to grow at a brisk pace. Thus, as noted above, if the troubles in the housing industry were to cause a recession, it would have to be because of their effect on consumer spending. Consumers base their spending decisions not only on their current income, but also on their wealth. Other things the same, an increase in wealth should induce consumers to spend more of their current income. Hence, a decline in wealth could generate a decline in consumer spending. For many people the net worth of their home is the single most important source of wealth. Consequently, a decline in home prices can make people less wealthy, causing them to consume less. Because consumption accounts for about 70 percent of GDP, even relatively small changes in consumer spending can have a relatively large effect on output growth. Estimates suggest that the wealth effect associated with changes in equity values is weak or nonexistent. The wealth effect associated with housing wealth is stronger.2 Wealth effects are very difficult to identify and measure, however. Consequently, it is difficult to precisely determine the effect of the recent decline in home prices on consumption and, hence, output growth. It is interesting to note that growth of real consumption expenditures since the second quarter of 2006 has remained strong—in the range of 3 percent— despite the downturn in the S&P/Case-Shiller home price index sin
{"title":"Housing and the \"R\" word","authors":"D. Thornton","doi":"10.20955/ES.2008.5","DOIUrl":"https://doi.org/10.20955/ES.2008.5","url":null,"abstract":"Views expressed do not necessarily reflect official positions of the Federal Reserve System. There has been considerable discussion of the possibility that ongoing troubles in the housing market could push the economy into recession.1 But it is very unlikely that the decline in housing alone will cause a recession. Any recessionary effect on the economy caused by housing will be a consequence of its effect on consumption. Real GDP is a measure of the economy’s current production. Sales of existing houses have no impact on current production because these houses were produced sometime in the past. The only direct effect housing has on current economic growth comes through the “residential investment” component of GDP, which includes current construction and improvements of singleand multi-family housing. Residential investment accounts for only about 5 percent of GDP; consequently, the effect of residential investment on economic growth is relatively modest. The chart shows this effect by plotting quarterly GDP growth with and without residential investment. (It also shows the quarterly growth rate of residential investment.) Excluding residential investment has only a small effect even during the 1970s and early 1980s, when the growth of residential investment was considerably more volatile than during the past two decades. Since residential investment peaked in the fourth quarter of 2005, its decline has reduced growth of real GDP by an average of about 0.85 percentage points. This decline has largely been offset by nonresidential investment, which has continued to grow at a brisk pace. Thus, as noted above, if the troubles in the housing industry were to cause a recession, it would have to be because of their effect on consumer spending. Consumers base their spending decisions not only on their current income, but also on their wealth. Other things the same, an increase in wealth should induce consumers to spend more of their current income. Hence, a decline in wealth could generate a decline in consumer spending. For many people the net worth of their home is the single most important source of wealth. Consequently, a decline in home prices can make people less wealthy, causing them to consume less. Because consumption accounts for about 70 percent of GDP, even relatively small changes in consumer spending can have a relatively large effect on output growth. Estimates suggest that the wealth effect associated with changes in equity values is weak or nonexistent. The wealth effect associated with housing wealth is stronger.2 Wealth effects are very difficult to identify and measure, however. Consequently, it is difficult to precisely determine the effect of the recent decline in home prices on consumption and, hence, output growth. It is interesting to note that growth of real consumption expenditures since the second quarter of 2006 has remained strong—in the range of 3 percent— despite the downturn in the S&P/Case-Shiller home price index sin","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132535428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
NationalEconomicTrends Views expressed do not necessarily reflect official positions of the Federal Reserve System. S ome economists believe that the 2001 recession ended in the fourth quarter of 2001 (see the inside cover of this publication). Since then, real business fixed investment (BFI)—expenditures on structures and equipment and software—has declined at a 2.2 percent annual rate. By contrast, in the first four quarters of the typical recovery, real BFI increases a little more than 8 percent. One reason why growth of real BFI has remained weak is that real GDP growth during the recovery has been weaker than normal, which is probably related to the mildness of the 2001 recession. Geopolitical uncertainties arising from the conflict with Iraq and tensions with North Korea may be another reason why business investment spending has been unusually weak. According to this argument , firms have been postponing plans for new capital projects until the risks become clearer. Indeed, in the policy statement issued after the March 18 Federal Open Market Committee meeting, Fed policymakers said that, until these uncertainties abate, they will not be able to " usefully characterize " the risks to the outlook. Another explanation is that a recovery in business investment is being hampered by a capital " overhang. " According to this view, the economy's actual capital stock currently exceeds its desired capital stock because of the investment boom of the late 1990s, which was perhaps exacerbated by the euphoria in the stock market. Some data support this argument: During the record-long 1991-2001 expansion, real BFI increased 113 percent and real GDP increased by about 39 percent. In contrast, during the 1961-69 expansion (the second-longest) real BFI increased 95 percent and real GDP increased by about 51 percent; and in the 1982-90 expansion (the third-longest), real BFI increased 42 percent and real GDP increased by about 37 percent. The strongest rates of business capital spending during the 1991-2001 expansion occurred toward its end. The accompanying chart shows this by plotting the Federal Reserve's measure of manufacturing capacity and real GDP since 1955. Economic theory says that growth of output (real GDP) will be commensurate with the growth of capital inputs and, by extension, capacity. From the first quarter of 1955 to the first quarter of 1994, growth of manufacturing capacity (3.4 percent per year) was nearly identical to the growth of real GDP (3.3 percent per year). Since 1994, …
{"title":"Waiting for the investment boom? It might be a while","authors":"Kevin L. Kliesen","doi":"10.20955/es.2003.12","DOIUrl":"https://doi.org/10.20955/es.2003.12","url":null,"abstract":"NationalEconomicTrends Views expressed do not necessarily reflect official positions of the Federal Reserve System. S ome economists believe that the 2001 recession ended in the fourth quarter of 2001 (see the inside cover of this publication). Since then, real business fixed investment (BFI)—expenditures on structures and equipment and software—has declined at a 2.2 percent annual rate. By contrast, in the first four quarters of the typical recovery, real BFI increases a little more than 8 percent. One reason why growth of real BFI has remained weak is that real GDP growth during the recovery has been weaker than normal, which is probably related to the mildness of the 2001 recession. Geopolitical uncertainties arising from the conflict with Iraq and tensions with North Korea may be another reason why business investment spending has been unusually weak. According to this argument , firms have been postponing plans for new capital projects until the risks become clearer. Indeed, in the policy statement issued after the March 18 Federal Open Market Committee meeting, Fed policymakers said that, until these uncertainties abate, they will not be able to \" usefully characterize \" the risks to the outlook. Another explanation is that a recovery in business investment is being hampered by a capital \" overhang. \" According to this view, the economy's actual capital stock currently exceeds its desired capital stock because of the investment boom of the late 1990s, which was perhaps exacerbated by the euphoria in the stock market. Some data support this argument: During the record-long 1991-2001 expansion, real BFI increased 113 percent and real GDP increased by about 39 percent. In contrast, during the 1961-69 expansion (the second-longest) real BFI increased 95 percent and real GDP increased by about 51 percent; and in the 1982-90 expansion (the third-longest), real BFI increased 42 percent and real GDP increased by about 37 percent. The strongest rates of business capital spending during the 1991-2001 expansion occurred toward its end. The accompanying chart shows this by plotting the Federal Reserve's measure of manufacturing capacity and real GDP since 1955. Economic theory says that growth of output (real GDP) will be commensurate with the growth of capital inputs and, by extension, capacity. From the first quarter of 1955 to the first quarter of 1994, growth of manufacturing capacity (3.4 percent per year) was nearly identical to the growth of real GDP (3.3 percent per year). Since 1994, …","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"2003 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131218352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Views expressed do not necessarily reflect official positions of the Federal Reserve System. The effect that housing has on the economy has received increased attention in recent years—first for the recordhigh boom in house prices and homeownership and then for the decline in house prices and the subprime market meltdown. Part of the boom was fostered by important developments in housing finance such as the introduction of new mortgage products, a reduction in the cost of providing mortgage services, and the expansion of subprime lending and private securitization of mortgages. For example, instruments such as “piggyback” loans and option adjustable-rate mortgages accounted for 12.5 percent of the originations in 2004 and 32.1 percent in 2006. Mortgage market innovations and their importance in increasing house prices and homeownership have a historical precedent: After the collapse of mortgage markets during the Great Depression, policymakers’ goal was to increase owneroccupied housing. In the late 1930s, the creation of the Federal Housing Administration (FHA) led to changes in the terms of existing mortgage contracts. Prior to the Great Depression, the typical mortgage contract had a maturity of less than ten years, a 50 percent downpayment, no amortization, and a balloon payment at expiration. The FHA sponsored the use of a new mortgage with a longer duration, lower downpayment requirement, and self-amortizing structure. Government intervention provided uniform lending throughout the country that resulted in low and stable mortgage rates. Between 1942 and 1947, house prices and homeownership attained historical heights with an annual inflation-adjusted appreciation of 6.5 percent and a total increase in owner-occupied housing of 20 percent. Shortages of building materials and delays in construction of housing for lowincome families fueled the house price boom, followed by a bust once the supply of new construction caught up. The similarities between these time periods become clear by plotting inflation-adjusted U.S. house prices (see the chart), which suggests that periods following innovations in housing finance also include high appreciation. The magnitudes of the house price increases from both periods are qualitatively the same when the OFHEO index is used, and the most recent period is slightly higher (10 percent) according to the Case-Shiller index. However, the homeownership rate differs substantially across periods. Innovations in housing finance after the Great Depression reduced the barriers to homeownership for many middle-income households. These individuals had at least 20 percent of equity in the house to buffer a large decline in the price. That, combined with some legal restrictions to “walk away” from the house, kept the foreclosure rates at historically low levels. By contrast, the innovations in the past decade allowed firsttime (young and low-income households) and repeat buyers to purchase or refinance a house with a very
{"title":"Boom & gloom in housing markets: the sequel","authors":"Carlos Garriga","doi":"10.20955/es.2008.12","DOIUrl":"https://doi.org/10.20955/es.2008.12","url":null,"abstract":"Views expressed do not necessarily reflect official positions of the Federal Reserve System. The effect that housing has on the economy has received increased attention in recent years—first for the recordhigh boom in house prices and homeownership and then for the decline in house prices and the subprime market meltdown. Part of the boom was fostered by important developments in housing finance such as the introduction of new mortgage products, a reduction in the cost of providing mortgage services, and the expansion of subprime lending and private securitization of mortgages. For example, instruments such as “piggyback” loans and option adjustable-rate mortgages accounted for 12.5 percent of the originations in 2004 and 32.1 percent in 2006. Mortgage market innovations and their importance in increasing house prices and homeownership have a historical precedent: After the collapse of mortgage markets during the Great Depression, policymakers’ goal was to increase owneroccupied housing. In the late 1930s, the creation of the Federal Housing Administration (FHA) led to changes in the terms of existing mortgage contracts. Prior to the Great Depression, the typical mortgage contract had a maturity of less than ten years, a 50 percent downpayment, no amortization, and a balloon payment at expiration. The FHA sponsored the use of a new mortgage with a longer duration, lower downpayment requirement, and self-amortizing structure. Government intervention provided uniform lending throughout the country that resulted in low and stable mortgage rates. Between 1942 and 1947, house prices and homeownership attained historical heights with an annual inflation-adjusted appreciation of 6.5 percent and a total increase in owner-occupied housing of 20 percent. Shortages of building materials and delays in construction of housing for lowincome families fueled the house price boom, followed by a bust once the supply of new construction caught up. The similarities between these time periods become clear by plotting inflation-adjusted U.S. house prices (see the chart), which suggests that periods following innovations in housing finance also include high appreciation. The magnitudes of the house price increases from both periods are qualitatively the same when the OFHEO index is used, and the most recent period is slightly higher (10 percent) according to the Case-Shiller index. However, the homeownership rate differs substantially across periods. Innovations in housing finance after the Great Depression reduced the barriers to homeownership for many middle-income households. These individuals had at least 20 percent of equity in the house to buffer a large decline in the price. That, combined with some legal restrictions to “walk away” from the house, kept the foreclosure rates at historically low levels. By contrast, the innovations in the past decade allowed firsttime (young and low-income households) and repeat buyers to purchase or refinance a house with a very","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"239 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116232507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Views expressed do not necessarily reflect official positions of the Federal Reserve System. The largest retirement plan in the United States is the Old Age, Survivors, and Disability Insurance (OASDI) program of the Social Security Administration. During 2004, 48 million persons received $493 billion in benefits, and 157 million persons with earnings subject to the program’s payroll tax furnished the bulk of the program’s $658 billion income. The $165 billion in excess revenue was added to the program’s trust fund, which at the end of 2004 totaled $1.7 trillion. During the next 25 years, it is projected that the OASDI program’s ratio of beneficiaries to taxpayers will decrease from its current 3.3 to 1 to about 2.2 to 1. As a result, it is projected that outlays will persistently exceed revenues after 2018 or so. Under current tax and benefit rules, and by drawing down the trust fund, it is projected that all scheduled benefit payments can be made for at least the next 25 years, at which time the trust fund will be exhausted. Thereafter, if no changes are made to benefits or taxes, it is projected that incoming tax revenue will be sufficient to fund about 70 percent of scheduled benefit payments through 2080. Many analysts (and politicians) have argued that this shortfall is unacceptable and that the OASDI program must change. Proposals include increasing the payroll tax, reducing benefit levels, increasing earnings on the trust fund’s investments, and delaying the age at which new retirees are eligible for full benefits. Economic analysis suggests it is important to analyze these proposals carefully because each is likely to have different effects on various groups in the economy. Consider, for example, the Social Security trust fund. Prior to 1983, the OASDI program operated largely as a pay-as-yougo system. Established by Congress in 1940, the program’s trust fund grew little prior to 1983 as a result of Congressional deferrals of proposed tax increases.1 In 11 of the years between 1940 and 1983, the level of the fund decreased as benefit outlays exceeded revenues. In 1983, the Greenspan commission on OASDI recommended that the program be changed from one in which benefits were paid solely from current revenues to a partially funded retirement plan. Payroll tax rates were increased, and the trust fund began to grow. Recently, some analysts have proposed that the trust fund ought to seek to earn a higher rate of return so as to mitigate the future OASDI shortfall. This proposal is problematic because the fund consists solely of Treasury securities. Not until benefit payments begin to exceed payroll tax revenues, say in 2018, will the Treasury be required to redeem these securities and transfer funds to OASDI. How the Treasury chooses to raise those funds will have tax-incidence implications. Increases in income taxes, to fill the Treasury’s general fund for payment to OASDI, will fall more heavily on upperincome households; decreases in OASDI
{"title":"TIPS for social security","authors":"R. Anderson","doi":"10.20955/ES.2005.9","DOIUrl":"https://doi.org/10.20955/ES.2005.9","url":null,"abstract":"Views expressed do not necessarily reflect official positions of the Federal Reserve System. The largest retirement plan in the United States is the Old Age, Survivors, and Disability Insurance (OASDI) program of the Social Security Administration. During 2004, 48 million persons received $493 billion in benefits, and 157 million persons with earnings subject to the program’s payroll tax furnished the bulk of the program’s $658 billion income. The $165 billion in excess revenue was added to the program’s trust fund, which at the end of 2004 totaled $1.7 trillion. During the next 25 years, it is projected that the OASDI program’s ratio of beneficiaries to taxpayers will decrease from its current 3.3 to 1 to about 2.2 to 1. As a result, it is projected that outlays will persistently exceed revenues after 2018 or so. Under current tax and benefit rules, and by drawing down the trust fund, it is projected that all scheduled benefit payments can be made for at least the next 25 years, at which time the trust fund will be exhausted. Thereafter, if no changes are made to benefits or taxes, it is projected that incoming tax revenue will be sufficient to fund about 70 percent of scheduled benefit payments through 2080. Many analysts (and politicians) have argued that this shortfall is unacceptable and that the OASDI program must change. Proposals include increasing the payroll tax, reducing benefit levels, increasing earnings on the trust fund’s investments, and delaying the age at which new retirees are eligible for full benefits. Economic analysis suggests it is important to analyze these proposals carefully because each is likely to have different effects on various groups in the economy. Consider, for example, the Social Security trust fund. Prior to 1983, the OASDI program operated largely as a pay-as-yougo system. Established by Congress in 1940, the program’s trust fund grew little prior to 1983 as a result of Congressional deferrals of proposed tax increases.1 In 11 of the years between 1940 and 1983, the level of the fund decreased as benefit outlays exceeded revenues. In 1983, the Greenspan commission on OASDI recommended that the program be changed from one in which benefits were paid solely from current revenues to a partially funded retirement plan. Payroll tax rates were increased, and the trust fund began to grow. Recently, some analysts have proposed that the trust fund ought to seek to earn a higher rate of return so as to mitigate the future OASDI shortfall. This proposal is problematic because the fund consists solely of Treasury securities. Not until benefit payments begin to exceed payroll tax revenues, say in 2018, will the Treasury be required to redeem these securities and transfer funds to OASDI. How the Treasury chooses to raise those funds will have tax-incidence implications. Increases in income taxes, to fill the Treasury’s general fund for payment to OASDI, will fall more heavily on upperincome households; decreases in OASDI ","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124457868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S ome analysts have suggested that there is a statistically reliable relationship between the severity of a recession and the strength of the subsequent recovery. Specifically, the suggestion is that severe recessions are followed by robust recoveries and that mild recessions are followed by relatively weak recoveries. Because the 2001 recession appears to have been the mildest during the postWWII period, can we expect a below-average recovery? One frequently cited example that appears to support this proposition is the 1990-91 experience. That recession was very mild, and it was followed by a relatively weak and protracted recovery. The unemployment rate, for instance, peaked more than a year after the official end of the recession (March 1991). Similarly, the rather severe 1981-82 recession was followed by a robust recovery. While interesting, these examples do not constitute a significant regularity that tends to hold for all recessions and recoveries. To investigate this proposition, we analyzed data on post-WWII recessions and recoveries. According to the National Bureau of Economic Research (NBER), there have been ten postwar recessions, including the 2001 recession. One of these, the 1980 recession, was immediately followed by another, the so-called 1981-82 “double dip” recession. Because the recovery period following the 1980 recession was relatively short, we eliminated it from our analysis. We measured the severity of each recession by the decline in output, measured both by real GDP and industrial production (IP), from the NBER date of the business cycle peak to the date of the trough. Likewise, the strength of the recovery is measured by the growth in output, using the same two measures, during the year following the NBER-dated business cycle trough. A scatter plot of these data for the eight postwar recessions prior to 2001 is presented in the accompanying figure. The “lines of best fit” for both output measures indicate that there is a positive relationship between the severity of the recession (horizontal axis) and the strength of the recovery (vertical axis) as hypothesized. Using either measure, the correlation between severity of recession and strength of recovery is not statistically significant, although the relationship is somewhat stronger using IP.1 Hence, while there is a positive correlation between the severity of the recession and the strength of the recovery, this relationship alone is not strong enough that knowledge of the depth of the recession is useful for predicting the strength of the subsequent recovery. Conse quently, the mildness of this recession would appear to provide little if any guidance about the strength of the recovery.
{"title":"Does a mild recession imply a weak recovery","authors":"","doi":"10.20955/ES.2002.9","DOIUrl":"https://doi.org/10.20955/ES.2002.9","url":null,"abstract":"S ome analysts have suggested that there is a statistically reliable relationship between the severity of a recession and the strength of the subsequent recovery. Specifically, the suggestion is that severe recessions are followed by robust recoveries and that mild recessions are followed by relatively weak recoveries. Because the 2001 recession appears to have been the mildest during the postWWII period, can we expect a below-average recovery? One frequently cited example that appears to support this proposition is the 1990-91 experience. That recession was very mild, and it was followed by a relatively weak and protracted recovery. The unemployment rate, for instance, peaked more than a year after the official end of the recession (March 1991). Similarly, the rather severe 1981-82 recession was followed by a robust recovery. While interesting, these examples do not constitute a significant regularity that tends to hold for all recessions and recoveries. To investigate this proposition, we analyzed data on post-WWII recessions and recoveries. According to the National Bureau of Economic Research (NBER), there have been ten postwar recessions, including the 2001 recession. One of these, the 1980 recession, was immediately followed by another, the so-called 1981-82 “double dip” recession. Because the recovery period following the 1980 recession was relatively short, we eliminated it from our analysis. We measured the severity of each recession by the decline in output, measured both by real GDP and industrial production (IP), from the NBER date of the business cycle peak to the date of the trough. Likewise, the strength of the recovery is measured by the growth in output, using the same two measures, during the year following the NBER-dated business cycle trough. A scatter plot of these data for the eight postwar recessions prior to 2001 is presented in the accompanying figure. The “lines of best fit” for both output measures indicate that there is a positive relationship between the severity of the recession (horizontal axis) and the strength of the recovery (vertical axis) as hypothesized. Using either measure, the correlation between severity of recession and strength of recovery is not statistically significant, although the relationship is somewhat stronger using IP.1 Hence, while there is a positive correlation between the severity of the recession and the strength of the recovery, this relationship alone is not strong enough that knowledge of the depth of the recession is useful for predicting the strength of the subsequent recovery. Conse quently, the mildness of this recession would appear to provide little if any guidance about the strength of the recovery.","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"125 2-3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124474159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A lthough national income inequality is a subject of great concern in the popular press and in political rhetoric, income inequality at the local level is more evident because inequality tends to be much higher in densely populated areas; consequently, some economists focus their attention there. The chart shows a correlation of 45 percent between county-level income inequality and population density for 2007. Differ ences in local inequality reflect, for the most part, three important factors: differences in the distribution of job skills, differences in the returns that individuals gain from their skills, and differences in government policies. Economists Glaeser, Resseger, and Tobio find that almost one half of the variation in income inequality across metropolitan areas can be explained by differences in the distribution of skills.1 They identify two reasons for this: First, particularly at the medium and high skill levels, the differences in skills observed today can be explained largely by the historical patterns from several decades ago; in other words, historical patterns of skill level are very persistent. Second, where people choose to live, especially those with lower skill levels, affects the distribution of skills: For example, recent Hispanic immigrants have disproportionately lower skill levels than immigrants from other ethnic backgrounds and lower skill levels than immigrants from previous decades. Historical patterns are also important in this case because many Hispanic immigrants often locate in the same areas of the country—those geographically close to Latin America, such as California, Texas, and Florida. Inequality across cities also reflects differences in the returns to skill, although it is not clear why skill is rewarded more in some places than others. One potential explanation is that densely populated areas generate human capital spillovers (i.e., the sharing or passing on of skills) because of the concentration of skilled individuals working together, which causes the returns to rise. Economists also recognize that highly skilled individuals are sensitive to differences in these returns and can easily migrate to those places where their skills are valued more. Differences in government policies also explain some of the variation in income inequality. However, the high mobility of more skilled, and consequently richer, individuals severely limits the ability of local governments to reduce inequality by redistributing wealth: As noted, the richer and highly skilled will move if returns to skill decline. Communities with more generous redistributive policies, in the form of welfare programs, also affect the location patterns of less skilled immigrants and will tend to attract more poor individuals. Glaeser, Resseger, and Tobio suggest that education policies to improve the skill levels of individuals at the bottom of the distribution might be more effective at reducing inequality than redistributive policies. They warn
{"title":"Local income inequality","authors":"Rubén Hernández-Murillo","doi":"10.20955/es.2008.31","DOIUrl":"https://doi.org/10.20955/es.2008.31","url":null,"abstract":"A lthough national income inequality is a subject of great concern in the popular press and in political rhetoric, income inequality at the local level is more evident because inequality tends to be much higher in densely populated areas; consequently, some economists focus their attention there. The chart shows a correlation of 45 percent between county-level income inequality and population density for 2007. Differ ences in local inequality reflect, for the most part, three important factors: differences in the distribution of job skills, differences in the returns that individuals gain from their skills, and differences in government policies. Economists Glaeser, Resseger, and Tobio find that almost one half of the variation in income inequality across metropolitan areas can be explained by differences in the distribution of skills.1 They identify two reasons for this: First, particularly at the medium and high skill levels, the differences in skills observed today can be explained largely by the historical patterns from several decades ago; in other words, historical patterns of skill level are very persistent. Second, where people choose to live, especially those with lower skill levels, affects the distribution of skills: For example, recent Hispanic immigrants have disproportionately lower skill levels than immigrants from other ethnic backgrounds and lower skill levels than immigrants from previous decades. Historical patterns are also important in this case because many Hispanic immigrants often locate in the same areas of the country—those geographically close to Latin America, such as California, Texas, and Florida. Inequality across cities also reflects differences in the returns to skill, although it is not clear why skill is rewarded more in some places than others. One potential explanation is that densely populated areas generate human capital spillovers (i.e., the sharing or passing on of skills) because of the concentration of skilled individuals working together, which causes the returns to rise. Economists also recognize that highly skilled individuals are sensitive to differences in these returns and can easily migrate to those places where their skills are valued more. Differences in government policies also explain some of the variation in income inequality. However, the high mobility of more skilled, and consequently richer, individuals severely limits the ability of local governments to reduce inequality by redistributing wealth: As noted, the richer and highly skilled will move if returns to skill decline. Communities with more generous redistributive policies, in the form of welfare programs, also affect the location patterns of less skilled immigrants and will tend to attract more poor individuals. Glaeser, Resseger, and Tobio suggest that education policies to improve the skill levels of individuals at the bottom of the distribution might be more effective at reducing inequality than redistributive policies. They warn","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126782626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Views expressed do not necessarily reflect official positions of the Federal Reserve System. The Federal Open Market Committee (FOMC) has increased the target federal funds rate at each meeting since June 2004, to 2.5 percent following the February 2005 meeting. A puzzling aspect of recent financial market developments has been that, despite the rise of 150 basis points in the Fed’s target rate, longer-term rates have remained roughly constant. In recent testimony, Fed Chairman Alan Greenspan commented extensively on this issue, eventually concluding that the market behavior “remains a conundrum.”1 There was a time when this same behavior would not have been considered so unusual. It occurred in the early 1960s, a cherished era among monetary economists and policymakers. That era sported relatively rapid growth in both real output and productivity, low inflation rates, and low rates of interest, not unlike the present day. Although many years have passed since that time, the intervening years have been associated with higher inflation—at times substantially higher—and have been dominated by Federal Reserve efforts to move inflation lower and build credibility for continued low inflation. The low level of inflation and high level of Fed credibility characteristic of the early 1960s returned in the early 2000s. Thus, the early 1960s may give a better indication of the nature of today’s financial markets than most of the intervening years. The chart shows the evolution of shortand longerterm interest rates during the 38 months following the last month of the recession relevant to each era. The most recent recession ended in November 2001, while for the earlier era it ended in February 1961. The chart shows the effective federal funds rate along with the longer-term 10-year Treasury note yield. The most striking characteristic is that in both eras, once the federal funds rate began rising following the recession, the longer-term bond yield remained anchored near 4 percent. One explanation is that, in both eras, the private sector viewed movements in short-term interest rates as exactly those necessary to keep inflation low and steady, so that longer-term inflation expectations and hence longer-term bond yields could remain anchored. Similarities in the interest rate environment may also be attributable in part to similarities in economic performance. The average annualized quarterly growth rates of key variables were a lot alike during 2002:Q1 to 2004:Q4 as compared with the corresponding 1961:Q2 to 1964:Q4 period. Average nonfarm business sector productivity growth, for instance, was almost identical during the two periods, 3.90 percent today versus 3.80 percent in the early 1960s. Inflation rates were similar as well, with the core consumer price index increasing, on average, 1.80 percent during the current period versus 1.40 percent during the earlier era. Real output growth was robust in both periods as well, 3.50 percent in the early 2000s versu
{"title":"Twist and shout, or back to the sixties","authors":"James Bullard","doi":"10.20955/es.2005.7","DOIUrl":"https://doi.org/10.20955/es.2005.7","url":null,"abstract":"Views expressed do not necessarily reflect official positions of the Federal Reserve System. The Federal Open Market Committee (FOMC) has increased the target federal funds rate at each meeting since June 2004, to 2.5 percent following the February 2005 meeting. A puzzling aspect of recent financial market developments has been that, despite the rise of 150 basis points in the Fed’s target rate, longer-term rates have remained roughly constant. In recent testimony, Fed Chairman Alan Greenspan commented extensively on this issue, eventually concluding that the market behavior “remains a conundrum.”1 There was a time when this same behavior would not have been considered so unusual. It occurred in the early 1960s, a cherished era among monetary economists and policymakers. That era sported relatively rapid growth in both real output and productivity, low inflation rates, and low rates of interest, not unlike the present day. Although many years have passed since that time, the intervening years have been associated with higher inflation—at times substantially higher—and have been dominated by Federal Reserve efforts to move inflation lower and build credibility for continued low inflation. The low level of inflation and high level of Fed credibility characteristic of the early 1960s returned in the early 2000s. Thus, the early 1960s may give a better indication of the nature of today’s financial markets than most of the intervening years. The chart shows the evolution of shortand longerterm interest rates during the 38 months following the last month of the recession relevant to each era. The most recent recession ended in November 2001, while for the earlier era it ended in February 1961. The chart shows the effective federal funds rate along with the longer-term 10-year Treasury note yield. The most striking characteristic is that in both eras, once the federal funds rate began rising following the recession, the longer-term bond yield remained anchored near 4 percent. One explanation is that, in both eras, the private sector viewed movements in short-term interest rates as exactly those necessary to keep inflation low and steady, so that longer-term inflation expectations and hence longer-term bond yields could remain anchored. Similarities in the interest rate environment may also be attributable in part to similarities in economic performance. The average annualized quarterly growth rates of key variables were a lot alike during 2002:Q1 to 2004:Q4 as compared with the corresponding 1961:Q2 to 1964:Q4 period. Average nonfarm business sector productivity growth, for instance, was almost identical during the two periods, 3.90 percent today versus 3.80 percent in the early 1960s. Inflation rates were similar as well, with the core consumer price index increasing, on average, 1.80 percent during the current period versus 1.40 percent during the earlier era. Real output growth was robust in both periods as well, 3.50 percent in the early 2000s versu","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133411084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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