M ost financial economists would agree that expected stock returns vary somewhat across time. Of course, this variation in expected returns explains only a small fraction of the variation in actual returns. Consequently, attempting to time the market remains a risky endeavor and many risk-averse investors favor buy-and-hold investing. Busi ness investment projects, on the other hand, are unavoidably “lumpy” by nature and firms have strong incentives to wait for the most profitable periods to invest in irreversible, largescale projects. Do we see any evidence that business investment keys off expected stock market returns, and, if so, what is the current outlook for business investment? Recently, Lettau and Ludvigson (2001) showed that the deviation of consumption from aggregate wealth, which they label as the consumption-wealth ratio, is a useful indicator of expected stock returns, especially long-lasting shifts in expected returns.1 Their measure of aggregate wealth includes both financial assets and the present value of labor income. The predictive ability of the consumption-wealth ratio is consistent with the economic theory that views consumption as a forwardlooking variable. If investors foresee higher stock returns in the future, they will boost their consumption now to smooth consumption. Therefore, a high (low) level of the consumptionwealth ratio indicates high (low) expected stock returns in the future. Like consumption decisions, business investment is also forward-looking with respect to expected returns to capital. If expected returns shift upward, new capital put in place now is expected to garner those high returns. It follows that movements in the consumption-wealth ratio should presage movements in business investment because the consumption-wealth ratio contains information about expected stock returns. Lettau and Ludvigson (2002) show that this relationship is present in the post-World War II data.2 The accompanying chart demonstrates their main results. The thin dashed line is the consumption-wealth ratio and the thick solid line is the average growth rate of fixed, private nonresidential investment in the three subsequent years. In general, a high level of the consumption-wealth ratio is associated with high rates of future investment and the coefficient of correlation between these two variables is about 0.40. The business cycle, highlighted with recession bars in the chart, appears to be a significant source of variation in expected stock market returns. The correlation between the consumption-wealth ratio and business investment appears particularly strong in the last decade. Relatively high levels of the consumption-wealth ratio in the early 1990s preceded dramatic increases in both stock market prices and business invest ment in the subsequent years. Later in the 1990s, however, consumption did not rise at the same pace that financial wealth increased and the consumptionwealth ratio fell to an unusually low level. With
{"title":"Expected stock market returns and business investment","authors":"Hui Guo","doi":"10.20955/ES.2002.17","DOIUrl":"https://doi.org/10.20955/ES.2002.17","url":null,"abstract":"M ost financial economists would agree that expected stock returns vary somewhat across time. Of course, this variation in expected returns explains only a small fraction of the variation in actual returns. Consequently, attempting to time the market remains a risky endeavor and many risk-averse investors favor buy-and-hold investing. Busi ness investment projects, on the other hand, are unavoidably “lumpy” by nature and firms have strong incentives to wait for the most profitable periods to invest in irreversible, largescale projects. Do we see any evidence that business investment keys off expected stock market returns, and, if so, what is the current outlook for business investment? Recently, Lettau and Ludvigson (2001) showed that the deviation of consumption from aggregate wealth, which they label as the consumption-wealth ratio, is a useful indicator of expected stock returns, especially long-lasting shifts in expected returns.1 Their measure of aggregate wealth includes both financial assets and the present value of labor income. The predictive ability of the consumption-wealth ratio is consistent with the economic theory that views consumption as a forwardlooking variable. If investors foresee higher stock returns in the future, they will boost their consumption now to smooth consumption. Therefore, a high (low) level of the consumptionwealth ratio indicates high (low) expected stock returns in the future. Like consumption decisions, business investment is also forward-looking with respect to expected returns to capital. If expected returns shift upward, new capital put in place now is expected to garner those high returns. It follows that movements in the consumption-wealth ratio should presage movements in business investment because the consumption-wealth ratio contains information about expected stock returns. Lettau and Ludvigson (2002) show that this relationship is present in the post-World War II data.2 The accompanying chart demonstrates their main results. The thin dashed line is the consumption-wealth ratio and the thick solid line is the average growth rate of fixed, private nonresidential investment in the three subsequent years. In general, a high level of the consumption-wealth ratio is associated with high rates of future investment and the coefficient of correlation between these two variables is about 0.40. The business cycle, highlighted with recession bars in the chart, appears to be a significant source of variation in expected stock market returns. The correlation between the consumption-wealth ratio and business investment appears particularly strong in the last decade. Relatively high levels of the consumption-wealth ratio in the early 1990s preceded dramatic increases in both stock market prices and business invest ment in the subsequent years. Later in the 1990s, however, consumption did not rise at the same pace that financial wealth increased and the consumptionwealth ratio fell to an unusually low level. With ","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"105 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":"115226488","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. As energy costs have risen, so have fears that these higher costs will derail economic activity. Professor James Hamilton of the University of California at San Diego has noted that sharp increases in the price of oil have preceded each post-World War II recession in the United States. Yet, some analysts suggest that energy prices today put less pressure on the economy than they did in the past— because less energy is used to produce each unit of GDP; said another way, the economy’s “energy efficiency” has increased. But such a conclusion must be drawn with care. The chart displays annual U.S. energy use relative to use in 1970. The top line shows aggregate energy use, which in 2007 was 50 percent more than in 1970. The bottom line shows energy use per unit of real GDP, which in 2007 was 50 percent less than in 1970. Correctly assessing these trends requires adding one more variable: labor productivity (that is, increases in GDP per hour of work). The chart’s center line adjusts roughly for productivity gains by displaying the quantity of energy consumed per capita. Since 1970 energy use per capita has risen and fallen with energy prices and the business cycle, with notable decreases during 1975, 1979-82, 1990-91, and 2001. Yet, the quantity of energy consumed per capita in 2007 was approximately unchanged from that in 1970. Energy use per capita is only a rough measure of the economy’s energy dependence because it does not separate the economy’s varied uses of energy. It does, however, emphasize an important underlying theme of America’s energy use: While energy efficiency has improved in almost every aspect of business and life at home, higher living standards have fully consumed that gain—overall energy use per person has changed little during the past four decades. Examples abound. In 1970, the average passenger automobile was driven 10,000 miles annually and consumed 737 gallons of fuel; in 2005, annual mileage was 12,400 using 554 gallons. In 1970, light trucks (then used almost exclusively by business) averaged 8,700 miles annually, consuming 866 gallons of fuel; in 2005, near-ubiquitous trucks and SUVs averaged 11,000 miles annually, consuming 612 gallons of fuel. For the typical household, heating and cooling comprises half of its housing-related energy usage. In 1970, the average new American single-family house was approximately 1,500 square feet; by 2005, the average home was 2,350 square feet. Appliances are more energy efficient, but there are more of them. Survey data for 1980 and 2001 show increases in the share of households with microwave ovens from 14 percent to 86 percent, dishwashers from 37 percent to 53 percent, personal computers from zero to 56 percent, and central air conditioning from 27 percent to 55 percent (the share of households with no air conditioning dropped from 42 percent in 1980 to 23 percent in 2001). The constancy o
{"title":"Energy and the economy","authors":"George Mobus","doi":"10.20955/ES.2008.9","DOIUrl":"https://doi.org/10.20955/ES.2008.9","url":null,"abstract":"Views expressed do not necessarily reflect official positions of the Federal Reserve System. As energy costs have risen, so have fears that these higher costs will derail economic activity. Professor James Hamilton of the University of California at San Diego has noted that sharp increases in the price of oil have preceded each post-World War II recession in the United States. Yet, some analysts suggest that energy prices today put less pressure on the economy than they did in the past— because less energy is used to produce each unit of GDP; said another way, the economy’s “energy efficiency” has increased. But such a conclusion must be drawn with care. The chart displays annual U.S. energy use relative to use in 1970. The top line shows aggregate energy use, which in 2007 was 50 percent more than in 1970. The bottom line shows energy use per unit of real GDP, which in 2007 was 50 percent less than in 1970. Correctly assessing these trends requires adding one more variable: labor productivity (that is, increases in GDP per hour of work). The chart’s center line adjusts roughly for productivity gains by displaying the quantity of energy consumed per capita. Since 1970 energy use per capita has risen and fallen with energy prices and the business cycle, with notable decreases during 1975, 1979-82, 1990-91, and 2001. Yet, the quantity of energy consumed per capita in 2007 was approximately unchanged from that in 1970. Energy use per capita is only a rough measure of the economy’s energy dependence because it does not separate the economy’s varied uses of energy. It does, however, emphasize an important underlying theme of America’s energy use: While energy efficiency has improved in almost every aspect of business and life at home, higher living standards have fully consumed that gain—overall energy use per person has changed little during the past four decades. Examples abound. In 1970, the average passenger automobile was driven 10,000 miles annually and consumed 737 gallons of fuel; in 2005, annual mileage was 12,400 using 554 gallons. In 1970, light trucks (then used almost exclusively by business) averaged 8,700 miles annually, consuming 866 gallons of fuel; in 2005, near-ubiquitous trucks and SUVs averaged 11,000 miles annually, consuming 612 gallons of fuel. For the typical household, heating and cooling comprises half of its housing-related energy usage. In 1970, the average new American single-family house was approximately 1,500 square feet; by 2005, the average home was 2,350 square feet. Appliances are more energy efficient, but there are more of them. Survey data for 1980 and 2001 show increases in the share of households with microwave ovens from 14 percent to 86 percent, dishwashers from 37 percent to 53 percent, personal computers from zero to 56 percent, and central air conditioning from 27 percent to 55 percent (the share of households with no air conditioning dropped from 42 percent in 1980 to 23 percent in 2001). The constancy o","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"118 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":"117322581","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}
W ith the tax filing season in full swing, these summary figures may provide some perspective on the issue of who is paying federal individual income taxes and, more importantly, the relative shares paid by different groups of taxpayers. (Note that, because I do not include the impact of other taxes, such as payroll taxes, my discussion does not address the overall tax system.) Federal tax receipts for 2003 totaled $1.7 trillion, with the largest share (43 percent) coming from individual income taxes (IIT). As shown in the table, receipts from IIT totaled $747.9 billion for 2003, the result of 128.6 million filed returns that reported total adjusted gross income (AGI) of $6.3 trillion. Thus, the ratio of taxes to income, known as the total average tax rate, was 11.9 percent. Under taking the same calculation for earlier years, one finds that the average tax rate for 2003 is the lowest rate for 1985-2003. The table also provides data on the income tax burdens of specific groups. By ordering the returns based on AGI from highest to lowest, one can calculate useful income and tax information. Let’s start with the top 50 percent and the bottom 50 percent: The former group accounted for 86 percent of AGI and paid more than 96.5 percent of IIT, while the latter group accounted for 14 percent of AGI and 3.5 percent of IIT. The average tax rate for the top 50 percent was 13.4 percent, while the rate for the bottom 50 percent was 3 percent. Both of these rates for 2003 are at their lowest levels for 19852003. Next, let’s look at groups in the top 50 percent. The top 25 percent accounted for 64.9 percent of AGI and paid 83.9 percent of IIT. Their tax rate was 15.4 percent. Meanwhile, the top 10 percent accounted for 42.4 percent of AGI and paid 65.8 percent of IIT. Their tax rate was 18.5 percent. Comparing the top 25 percent with the top 10 percent, one sees that those with higher incomes pay higher average tax rates. This fact continues to hold as we examine those with even higher income, which is a characteristic of a progressive income tax system (i.e., the income tax rate increases as income increases). The top 5 percent accounted for 31.2 percent of AGI and paid 54.4 percent of IIT. Their tax rate was 20.7 percent. Finally, the top 1 percent accounted for 16.8 percent of AGI and paid 34.3 percent of IIT. Their tax rate was 24.3 percent, roughly eight times the average rate of the bottom 50 percent. The rate of 24.3 percent is approximately twice the average rate for all taxpayers, which is roughly the midpoint of the range of 1.8 to 2.3 based on annual calculations for 1985-2003. Reaching political consensus on a tax system that simultaneously (i) provides desirable incentives to work, save, and invest; (ii) is viewed as fair; (iii) is easy to understand; and (iv) generates sufficient revenues to fund spending decisions has proven to be a major challenge in the United States.1 One place to start the discussion is with some basic facts about the exist
{"title":"Income taxes: who pays and how much?","authors":"Cletus C. Coughlin","doi":"10.20955/es.2006.7","DOIUrl":"https://doi.org/10.20955/es.2006.7","url":null,"abstract":"W ith the tax filing season in full swing, these summary figures may provide some perspective on the issue of who is paying federal individual income taxes and, more importantly, the relative shares paid by different groups of taxpayers. (Note that, because I do not include the impact of other taxes, such as payroll taxes, my discussion does not address the overall tax system.) Federal tax receipts for 2003 totaled $1.7 trillion, with the largest share (43 percent) coming from individual income taxes (IIT). As shown in the table, receipts from IIT totaled $747.9 billion for 2003, the result of 128.6 million filed returns that reported total adjusted gross income (AGI) of $6.3 trillion. Thus, the ratio of taxes to income, known as the total average tax rate, was 11.9 percent. Under taking the same calculation for earlier years, one finds that the average tax rate for 2003 is the lowest rate for 1985-2003. The table also provides data on the income tax burdens of specific groups. By ordering the returns based on AGI from highest to lowest, one can calculate useful income and tax information. Let’s start with the top 50 percent and the bottom 50 percent: The former group accounted for 86 percent of AGI and paid more than 96.5 percent of IIT, while the latter group accounted for 14 percent of AGI and 3.5 percent of IIT. The average tax rate for the top 50 percent was 13.4 percent, while the rate for the bottom 50 percent was 3 percent. Both of these rates for 2003 are at their lowest levels for 19852003. Next, let’s look at groups in the top 50 percent. The top 25 percent accounted for 64.9 percent of AGI and paid 83.9 percent of IIT. Their tax rate was 15.4 percent. Meanwhile, the top 10 percent accounted for 42.4 percent of AGI and paid 65.8 percent of IIT. Their tax rate was 18.5 percent. Comparing the top 25 percent with the top 10 percent, one sees that those with higher incomes pay higher average tax rates. This fact continues to hold as we examine those with even higher income, which is a characteristic of a progressive income tax system (i.e., the income tax rate increases as income increases). The top 5 percent accounted for 31.2 percent of AGI and paid 54.4 percent of IIT. Their tax rate was 20.7 percent. Finally, the top 1 percent accounted for 16.8 percent of AGI and paid 34.3 percent of IIT. Their tax rate was 24.3 percent, roughly eight times the average rate of the bottom 50 percent. The rate of 24.3 percent is approximately twice the average rate for all taxpayers, which is roughly the midpoint of the range of 1.8 to 2.3 based on annual calculations for 1985-2003. Reaching political consensus on a tax system that simultaneously (i) provides desirable incentives to work, save, and invest; (ii) is viewed as fair; (iii) is easy to understand; and (iv) generates sufficient revenues to fund spending decisions has proven to be a major challenge in the United States.1 One place to start the discussion is with some basic facts about the exist","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":"123358364","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}
On October 4, 2006, President Bush signed the Department of Homeland Security (DHS) Appro pri ations Act for fiscal year 2007. The law provides $525 million for state homeland security grants and, as in previous years, allocates the funds according to a formula written into law by the Patriot Act. This formula guarantees each state 0.75 percent of the total funds appropriated in a fiscal year for state and local terrorism preparedness grants. In the initial years of the program, the allocation of remaining funds was left to the discretion of the DHS, which distributed the remaining funds according to each state’s share of the national population. In June 2004, the 9/11 Commission report recommended against the population-based distribution of these grants. The Commission favored instead an allocation based on risk and vulnerability. Critics of the state grant formula pointed to Wyoming’s total grant receipts per capita, which were the largest among all states because Wyoming has the smallest share of national population. In contrast, states such as California and New York, with a presumably greater terrorist threat, received much less per capita funding. The U.S. House and Senate responded to the 9/11 Commission by seeking to base state grant levels more on risk assessments and less on population, while still guaranteeing a minimum share to each state. The distinct House and Senate proposals serve as a case study in political economy, in the way political bodies seek to allocate economic resources. The House bill proposed a state minimum allocation of 0.25 percent; the Senate bill proposed 0.55 percent. These numbers are interesting: In the House, each state has a minimum voting share of 1/435 or 0.23 percent of the representatives. In the Senate, the allocation of two senators for each state, regardless of population, increases the relative representation for small states in the full U.S. Congress to a minimum of 3/535 or 0.56 percent. It is remarkable how close these two percentages are to the minimum allocations that the House and Senate proposed. Political economy considerations would suggest that the median voter on this issue in the Senate would be from a state with below-average population—hence, the relatively generous 0.55 percent minimum share. In conference committee, however, the House and Senate did not agree on whether or how to amend the Patriot Act, so each state’s 0.75 percent minimum share has remained intact. Instead, the House and Senate decided to cut the size of the state grant program, both in its share of DHS spending and in absolute terms. In addition, the state grant funds not committed by the minimum guaranteed levels are to be distributed according to risk and not simply population. Accordingly, the attached chart shows how per capita grants to the states shifted between 2005 and 2006. The distribution of per capita grants across states became much more concentrated in the range of $1 to $3 per capita in 2006 and grant
{"title":"Political economy of state homeland security grants","authors":"Michael J. Dueker, Christopher J. Martinek","doi":"10.20955/es.2006.29","DOIUrl":"https://doi.org/10.20955/es.2006.29","url":null,"abstract":"On October 4, 2006, President Bush signed the Department of Homeland Security (DHS) Appro pri ations Act for fiscal year 2007. The law provides $525 million for state homeland security grants and, as in previous years, allocates the funds according to a formula written into law by the Patriot Act. This formula guarantees each state 0.75 percent of the total funds appropriated in a fiscal year for state and local terrorism preparedness grants. In the initial years of the program, the allocation of remaining funds was left to the discretion of the DHS, which distributed the remaining funds according to each state’s share of the national population. In June 2004, the 9/11 Commission report recommended against the population-based distribution of these grants. The Commission favored instead an allocation based on risk and vulnerability. Critics of the state grant formula pointed to Wyoming’s total grant receipts per capita, which were the largest among all states because Wyoming has the smallest share of national population. In contrast, states such as California and New York, with a presumably greater terrorist threat, received much less per capita funding. The U.S. House and Senate responded to the 9/11 Commission by seeking to base state grant levels more on risk assessments and less on population, while still guaranteeing a minimum share to each state. The distinct House and Senate proposals serve as a case study in political economy, in the way political bodies seek to allocate economic resources. The House bill proposed a state minimum allocation of 0.25 percent; the Senate bill proposed 0.55 percent. These numbers are interesting: In the House, each state has a minimum voting share of 1/435 or 0.23 percent of the representatives. In the Senate, the allocation of two senators for each state, regardless of population, increases the relative representation for small states in the full U.S. Congress to a minimum of 3/535 or 0.56 percent. It is remarkable how close these two percentages are to the minimum allocations that the House and Senate proposed. Political economy considerations would suggest that the median voter on this issue in the Senate would be from a state with below-average population—hence, the relatively generous 0.55 percent minimum share. In conference committee, however, the House and Senate did not agree on whether or how to amend the Patriot Act, so each state’s 0.75 percent minimum share has remained intact. Instead, the House and Senate decided to cut the size of the state grant program, both in its share of DHS spending and in absolute terms. In addition, the state grant funds not committed by the minimum guaranteed levels are to be distributed according to risk and not simply population. Accordingly, the attached chart shows how per capita grants to the states shifted between 2005 and 2006. The distribution of per capita grants across states became much more concentrated in the range of $1 to $3 per capita in 2006 and grant","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"95 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":"125283121","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}
The Organization for Economic Cooperation and Development recently forecasted that real U.S. GDP (output) in 2004 will average 0.3 percent less than potential output. In the third quarter of 2003, though, real GDP grew at a surprising 8.2 percent annual rate, the economy’s fastest rate of growth in nearly 20 years. If real GDP has increased by 4 percent (annual rate) in the fourth quarter of 2003, which many economists expect, then the economy will have grown at a 6.1 percent annual rate over the second half of 2003. Although few economists expect this growth rate to persist into 2004, it seems apparent that recent economic growth has been boosted by expansive monetary and fiscal policies. Hence, an important question for policymakers is when will the percentage difference between the economy’s hypothesized level of potential output and actual output—termed the output gap— be closed? A highly expansionary monetary policy entails little risk of an acceleration of inflation when there is considerable resource slack. But as the gap closes and the economy increases its use of resources, continuing such a policy carries significant risk of a rapid acceleration of inflation. Key to this framework, though, is a correct measurement of the gap. Thus, perhaps a more pertinent question is how accurate are measures of the output gap? The chart plots three different measures of the output gap using three different vintages of data. The first measure is derived from the Con gres sional Budget Office’s (CBO) measure of potential real GDP, which is estimated from an econometric model. This gap is measured in 1996 dollars, which are the estimates prior to the Dec. 10, 2003, 12th comprehensive revision of the national income and product accounts (NIPA). The remaining two measures are derived from two different statistical filtering (detrending) techniques that extract the long-run component of real GDP, which approximates potential output. The first, using the band-pass (BP) technique, measures the gap in “real time.”1 For example, the output gap for the first quarter of 2001 is calculated from data available to policymakers as of May 2001 (initial estimate of first-quarter real GDP). The third measure uses the HodrickPrescott (HP) technique, which measures the gap with the current vintage of NIPA data (in 2000 dollars). From the chart, it is apparent that estimates of the output gap can differ significantly across both estimation techniques and data vintages. For example, in the first quarter of 2000, the difference between the CBO estimate and the real-time BP estimate was 2 percent of potential GDP. There are two key reasons why estimates of the gap should be viewed cautiously. First, the output gap depends on a value that can be measured with reasonable accuracy (real GDP) and a value that cannot (potential output); moreover, there is no agreed upon method for calculating potential output. Second, actual GDP is continually revised to incorporate improved data
{"title":"Mind the gap: measuring actual vs. potential output","authors":"Kevin L. Kliesen","doi":"10.20955/ES.2004.2","DOIUrl":"https://doi.org/10.20955/ES.2004.2","url":null,"abstract":"The Organization for Economic Cooperation and Development recently forecasted that real U.S. GDP (output) in 2004 will average 0.3 percent less than potential output. In the third quarter of 2003, though, real GDP grew at a surprising 8.2 percent annual rate, the economy’s fastest rate of growth in nearly 20 years. If real GDP has increased by 4 percent (annual rate) in the fourth quarter of 2003, which many economists expect, then the economy will have grown at a 6.1 percent annual rate over the second half of 2003. Although few economists expect this growth rate to persist into 2004, it seems apparent that recent economic growth has been boosted by expansive monetary and fiscal policies. Hence, an important question for policymakers is when will the percentage difference between the economy’s hypothesized level of potential output and actual output—termed the output gap— be closed? A highly expansionary monetary policy entails little risk of an acceleration of inflation when there is considerable resource slack. But as the gap closes and the economy increases its use of resources, continuing such a policy carries significant risk of a rapid acceleration of inflation. Key to this framework, though, is a correct measurement of the gap. Thus, perhaps a more pertinent question is how accurate are measures of the output gap? The chart plots three different measures of the output gap using three different vintages of data. The first measure is derived from the Con gres sional Budget Office’s (CBO) measure of potential real GDP, which is estimated from an econometric model. This gap is measured in 1996 dollars, which are the estimates prior to the Dec. 10, 2003, 12th comprehensive revision of the national income and product accounts (NIPA). The remaining two measures are derived from two different statistical filtering (detrending) techniques that extract the long-run component of real GDP, which approximates potential output. The first, using the band-pass (BP) technique, measures the gap in “real time.”1 For example, the output gap for the first quarter of 2001 is calculated from data available to policymakers as of May 2001 (initial estimate of first-quarter real GDP). The third measure uses the HodrickPrescott (HP) technique, which measures the gap with the current vintage of NIPA data (in 2000 dollars). From the chart, it is apparent that estimates of the output gap can differ significantly across both estimation techniques and data vintages. For example, in the first quarter of 2000, the difference between the CBO estimate and the real-time BP estimate was 2 percent of potential GDP. There are two key reasons why estimates of the gap should be viewed cautiously. First, the output gap depends on a value that can be measured with reasonable accuracy (real GDP) and a value that cannot (potential output); moreover, there is no agreed upon method for calculating potential output. Second, actual GDP is continually revised to incorporate improved data ","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"59 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":"126143348","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. As measured by the Bureau of Economic Analysis (BEA), the U.S. personal saving rate (PSR) has trended down over the past two decades, bottoming out in negative territory within the past year. The PSR is defined as disposable income minus consumption expenditures, all divided by disposable income. Simply put, a negative PSR means that U.S. households are consuming more than their current after-tax income. Consequently, many analysts have expressed concern that households are saving too little to support the levels of investment required to sustain economic growth, without excessive dependence on foreign sources of capital. During 1970-93 the monthly U.S. PSR averaged 8.9 percent, but it subsequently fell to an average 2.8 percent in the period 1994 to February 2006. In the past 12 months, the rate dropped to –0.6 percent, leading analysts to wonder whether the United States has become a spendthrift nation. The downward trend of the U.S. PSR is not simply the product of accounting and measurement practices that may distort the BEA’s calculations.1 For example, even after adjusting the BEA’s treatment of capital gains, pension benefits and contributions, and durable goods purchases to better reflect actual disposable income and consumption, the resulting PSR figures remain at or below levels reported by the BEA. One way to gain further insight into the declining U.S. PSR is by comparing the recent dynamics of PSRs across a few other developed countries. The chart plots the PSRs for five such countries during 1970-2004. Two blocks of countries emerge: the Anglo-Saxon “group” (United States, United Kingdom, Australia), in which the PSR steadily declines after the late 1980s; and continental Europe (Germany) and Japan, where the PSR oscillates, but no clear trend emerges. The table highlights three interesting associations that emerge from our cross-country data: (i) The PSR usually declined more in countries where access to domestic credit grew faster. This trend may have resulted if households achieved easier access to credit to finance consumption in excess of income. Alternatively, higher consumption may have led to lower savings and thus higher demand for credit. (ii) In countries where public (government) debt was higher, households tended to save more. This makes sense since households, in the face of higher government deficits, may increase savings in anticipation of higher future taxes (economists call this “Ricardian equivalence”). (iii) The PSR also declined in countries with higher income inequality (Gini index), a higher percentage of labor force employed in the service sector, and less health infrastructure. Untangling the causes of different trends of the PSRs across countries will be the subject of future study. The data nonetheless reveal interesting associations that may inform our current understanding of the negative U.S. PSR puzzle.
{"title":"Cross-country personal saving rates","authors":"Massimo Guidolin, Elizabeth A. La Jeunesse","doi":"10.20955/ES.2006.12","DOIUrl":"https://doi.org/10.20955/ES.2006.12","url":null,"abstract":"Views expressed do not necessarily reflect official positions of the Federal Reserve System. As measured by the Bureau of Economic Analysis (BEA), the U.S. personal saving rate (PSR) has trended down over the past two decades, bottoming out in negative territory within the past year. The PSR is defined as disposable income minus consumption expenditures, all divided by disposable income. Simply put, a negative PSR means that U.S. households are consuming more than their current after-tax income. Consequently, many analysts have expressed concern that households are saving too little to support the levels of investment required to sustain economic growth, without excessive dependence on foreign sources of capital. During 1970-93 the monthly U.S. PSR averaged 8.9 percent, but it subsequently fell to an average 2.8 percent in the period 1994 to February 2006. In the past 12 months, the rate dropped to –0.6 percent, leading analysts to wonder whether the United States has become a spendthrift nation. The downward trend of the U.S. PSR is not simply the product of accounting and measurement practices that may distort the BEA’s calculations.1 For example, even after adjusting the BEA’s treatment of capital gains, pension benefits and contributions, and durable goods purchases to better reflect actual disposable income and consumption, the resulting PSR figures remain at or below levels reported by the BEA. One way to gain further insight into the declining U.S. PSR is by comparing the recent dynamics of PSRs across a few other developed countries. The chart plots the PSRs for five such countries during 1970-2004. Two blocks of countries emerge: the Anglo-Saxon “group” (United States, United Kingdom, Australia), in which the PSR steadily declines after the late 1980s; and continental Europe (Germany) and Japan, where the PSR oscillates, but no clear trend emerges. The table highlights three interesting associations that emerge from our cross-country data: (i) The PSR usually declined more in countries where access to domestic credit grew faster. This trend may have resulted if households achieved easier access to credit to finance consumption in excess of income. Alternatively, higher consumption may have led to lower savings and thus higher demand for credit. (ii) In countries where public (government) debt was higher, households tended to save more. This makes sense since households, in the face of higher government deficits, may increase savings in anticipation of higher future taxes (economists call this “Ricardian equivalence”). (iii) The PSR also declined in countries with higher income inequality (Gini index), a higher percentage of labor force employed in the service sector, and less health infrastructure. Untangling the causes of different trends of the PSRs across countries will be the subject of future study. The data nonetheless reveal interesting associations that may inform our current understanding of the negative U.S. PSR puzzle.","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"38 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":"129967157","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}
{"title":"School and work","authors":"Joseph A. Ritter","doi":"10.4324/9780203125281-8","DOIUrl":"https://doi.org/10.4324/9780203125281-8","url":null,"abstract":"","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"23 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":"130070433","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}
The steady rise in oil prices since 2002 has evoked concern about possible negative effects on the economy.1 Could current energy prices push the United States into recession? Many economists have studied this question—in particular, how oil shocks affect both economic growth and inflation. When consumer demand shifts away from energy-intensive goods as a result of rising oil prices, output declines in the near term as industries’ capital and labor adjust. Timothy Bresnahan and Valerie Ramey studied the effect of the oil shocks of the 1970s and early 1980s on the automobile industry. Specifically, they examined how changing demand from standard-sized vehicles to smaller, more fuel-efficient cars affected the economy. They found that capacity utilization—the ratio of total cars produced to potential cars produced without using overtime—fell from above 100 percent in 1973 to around 50 percent in 1975. Similarly, capacity utilization fell from 100 percent to around 40 percent in 1982.2 Both instances followed sustained high levels of gasoline prices, which caused such a shift in consumer choices. Today’s high gas prices may do the same. The National Automobile Dealers Association reported that year-to-date SUV sales were down 19 percent in July compared with the same time last year. Steven Davis and John Haltiwanger examined the effect of oil price shocks on manufacturing employment between 1972 and 1988. They found that the sharp rise in oil prices in 1973-74 led to an 8 percent decline in employment after two years and total job reallocation (job destruction plus job creation) of 11 percent after four years. In the short term, job reallocation was higher in industries that may require more time to adjust labor and capital (e.g., apparel, rubber and plastics, furniture, primary metals, and transportation equipment).3 Steep increases in oil prices might also translate into inflation of prices for other goods by increasing the cost of production. Mark Hooker examined the effects that oil has on personal consumption expen diture inflation, excluding food and energy (core PCE inflation). He found that, before 1981, a doubling of the price of oil would lead to a 3-percentage-point increase in core PCE inflation.4 The stance of monetary policy, however, ultimately dictates long-run inflation. Since 1981, the Fed has adopted a more anti-inflationary stance of monetary policy, potentially lessening economic disruption from oil price changes.5 Indeed, Hooker found that, post-1981, oil price increases have had statistically no effect on core PCE inflation. Finally, can we expect a recession to occur as a result of current oil prices? James Hamilton believes that oil shocks affect economic growth only when, as a result of the higher prices, consumers’ spending behavior changes.6 As the accompanying chart shows, PCE growth has remained positive since the real price of oil began to rise. The negative PCE growth that accompanied the previous oil shocks has not y
{"title":"Barreling down the road to recession","authors":"Kristie M. Engemann, Michael T. Owyang","doi":"10.20955/ES.2006.22","DOIUrl":"https://doi.org/10.20955/ES.2006.22","url":null,"abstract":"The steady rise in oil prices since 2002 has evoked concern about possible negative effects on the economy.1 Could current energy prices push the United States into recession? Many economists have studied this question—in particular, how oil shocks affect both economic growth and inflation. When consumer demand shifts away from energy-intensive goods as a result of rising oil prices, output declines in the near term as industries’ capital and labor adjust. Timothy Bresnahan and Valerie Ramey studied the effect of the oil shocks of the 1970s and early 1980s on the automobile industry. Specifically, they examined how changing demand from standard-sized vehicles to smaller, more fuel-efficient cars affected the economy. They found that capacity utilization—the ratio of total cars produced to potential cars produced without using overtime—fell from above 100 percent in 1973 to around 50 percent in 1975. Similarly, capacity utilization fell from 100 percent to around 40 percent in 1982.2 Both instances followed sustained high levels of gasoline prices, which caused such a shift in consumer choices. Today’s high gas prices may do the same. The National Automobile Dealers Association reported that year-to-date SUV sales were down 19 percent in July compared with the same time last year. Steven Davis and John Haltiwanger examined the effect of oil price shocks on manufacturing employment between 1972 and 1988. They found that the sharp rise in oil prices in 1973-74 led to an 8 percent decline in employment after two years and total job reallocation (job destruction plus job creation) of 11 percent after four years. In the short term, job reallocation was higher in industries that may require more time to adjust labor and capital (e.g., apparel, rubber and plastics, furniture, primary metals, and transportation equipment).3 Steep increases in oil prices might also translate into inflation of prices for other goods by increasing the cost of production. Mark Hooker examined the effects that oil has on personal consumption expen diture inflation, excluding food and energy (core PCE inflation). He found that, before 1981, a doubling of the price of oil would lead to a 3-percentage-point increase in core PCE inflation.4 The stance of monetary policy, however, ultimately dictates long-run inflation. Since 1981, the Fed has adopted a more anti-inflationary stance of monetary policy, potentially lessening economic disruption from oil price changes.5 Indeed, Hooker found that, post-1981, oil price increases have had statistically no effect on core PCE inflation. Finally, can we expect a recession to occur as a result of current oil prices? James Hamilton believes that oil shocks affect economic growth only when, as a result of the higher prices, consumers’ spending behavior changes.6 As the accompanying chart shows, PCE growth has remained positive since the real price of oil began to rise. The negative PCE growth that accompanied the previous oil shocks has not y","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"2006 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":"129089873","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}
{"title":"What are the chances","authors":"Kristie M. Engemann, Michael T. Owyang","doi":"10.20955/es.2007.27","DOIUrl":"https://doi.org/10.20955/es.2007.27","url":null,"abstract":"","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"11 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":"128450607","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. Corporate social responsibility (CSR) is a concept that promotes expanded social stewardship by businesses and organizations and has gained popularity in recent years. CSR suggests that corporations embrace responsibilities toward a broader group of stakeholders (customers, employees, and the community at large) in addition to their customary financial obligations to shareholders. Social activists often pressure corporations to engage in CSR by integrating some ethical feature in their product or undertaking some type of social investment. Firms are also ranked in terms of their CSR. Although some economists are concerned about the viability of CSR in a competitive environment, recent studies suggest that engaging in CSR can be consistent with profit-maximization behavior. These studies argue that CSR can be considered a form of strategic investment for building and maintaining the firm’s reputation. Other benefits derived from CSR may include the ability to charge a premium for products or the ability to recruit and retain certain types of workers. In fact, consumers’ shopping patterns suggest that some socially concerned individuals are willing to pay a price premium for goods that incorporate a social or ethical component (e.g., hybrid automobiles or beauty products not tested on animals) because they value these characteristics. Economists Donald Siegel and Donald Vitaliano analyzed the role of CSR as a signaling device to convey information about the firm’s product quality.1 Their study of a large sample of publicly traded firms classified the firms using North American Industry Classification System codes into the following five categories: search goods (those whose quality can be readily evaluated before purchase, e.g., clothing, footwear, furniture); nondurable experience goods (those whose quality is experienced over multiple uses and frequent purchases, e.g., food, health and beauty products); durable experience goods (those that must be consumed before their true value can be determined, permit less learning from repeated purchases, and require a longer period for the product’s characteristics to be fully known, e.g., automobiles, appliances); and finally, experience services and credence services (those that often involve strong information asymmetries between sellers and buyers, who may find it difficult to assess the service’s value even over a long period, e.g., banking, financial counseling, auto repairs, weight loss programs). Siegel and Vitaliano found that firms selling experience and credence goods and services are more likely to engage in CSR than those selling search goods. The difference is explained by consumers’ perception of a firm’s involvement in CSR (even when its product does not directly include an ethical component) as a valuable signal, particularly when associated with upscale goods and services for which prices do not co
{"title":"Strategic social responsibility","authors":"Rubén Hernández-Murillo","doi":"10.20955/ES.2008.22","DOIUrl":"https://doi.org/10.20955/ES.2008.22","url":null,"abstract":"Views expressed do not necessarily reflect official positions of the Federal Reserve System. Corporate social responsibility (CSR) is a concept that promotes expanded social stewardship by businesses and organizations and has gained popularity in recent years. CSR suggests that corporations embrace responsibilities toward a broader group of stakeholders (customers, employees, and the community at large) in addition to their customary financial obligations to shareholders. Social activists often pressure corporations to engage in CSR by integrating some ethical feature in their product or undertaking some type of social investment. Firms are also ranked in terms of their CSR. Although some economists are concerned about the viability of CSR in a competitive environment, recent studies suggest that engaging in CSR can be consistent with profit-maximization behavior. These studies argue that CSR can be considered a form of strategic investment for building and maintaining the firm’s reputation. Other benefits derived from CSR may include the ability to charge a premium for products or the ability to recruit and retain certain types of workers. In fact, consumers’ shopping patterns suggest that some socially concerned individuals are willing to pay a price premium for goods that incorporate a social or ethical component (e.g., hybrid automobiles or beauty products not tested on animals) because they value these characteristics. Economists Donald Siegel and Donald Vitaliano analyzed the role of CSR as a signaling device to convey information about the firm’s product quality.1 Their study of a large sample of publicly traded firms classified the firms using North American Industry Classification System codes into the following five categories: search goods (those whose quality can be readily evaluated before purchase, e.g., clothing, footwear, furniture); nondurable experience goods (those whose quality is experienced over multiple uses and frequent purchases, e.g., food, health and beauty products); durable experience goods (those that must be consumed before their true value can be determined, permit less learning from repeated purchases, and require a longer period for the product’s characteristics to be fully known, e.g., automobiles, appliances); and finally, experience services and credence services (those that often involve strong information asymmetries between sellers and buyers, who may find it difficult to assess the service’s value even over a long period, e.g., banking, financial counseling, auto repairs, weight loss programs). Siegel and Vitaliano found that firms selling experience and credence goods and services are more likely to engage in CSR than those selling search goods. The difference is explained by consumers’ perception of a firm’s involvement in CSR (even when its product does not directly include an ethical component) as a valuable signal, particularly when associated with upscale goods and services for which prices do not co","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"118 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":"134582657","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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