Views expressed do not necessarily reflect official positions of the Federal Reserve System. On the first Friday of each month, the Bureau of Labor Statistics (BLS) releases its closely scrutinized monthly employment report. Data in this report are derived from two surveys: the Census Bureau’s Current Population Survey, also known as the household survey; and the Current Employ ment Statistics survey (CES), which is a survey of nonagricultural business establishments (including government offices).1 One of the limitations of the CES is that data for average weekly hours and average hourly earnings are reported for only a subset of workers: currently, production, construction, and non-supervisory workers. The production classification is used in the goods-producing sector, while the non-supervisory classification is used in the service-producing industries. Workers in these two categories account for about 80 percent of private nonagricultural employment. According to the BLS, this classification system has become increasingly archaic. Many employers do not classify workers by these two categories, which has led to relatively high non response rates.2 Looking to the future, the BLS began publishing an experimental series in April 2006 that measures average hourly earnings and average weekly hours of all nonfarm privatesector employees. The BLS also began publishing an experimental gross monthly earnings series that includes both wages and salaries and benefits such as bonuses, stock options, and employer contributions to 401(k) plans. The existing BLS average hourly earnings series excludes these kinds of benefits. These experimental data are relatively new and so are not seasonally adjusted, as the existing data are. Moreover, these data are published with a two-month lag. (For example, if the official data are available for January 2008, the experimental series are available only through November 2007.) The experimental all-employee series for hours and earnings will become the official data in February 2010, with the release of the January 2010 data. At that time the BLS believes that it will have had enough time to reliably estimate monthly seasonal factors. These experimental hours and earnings series have potentially significant implications for measures of nonfarm business productivity and personal income. For example, the BLS uses the CES-based series of hours paid of production and nonsupervisory workers as the key input into its measure of hours worked (the denominator in output per hour).3 If the existing CES series is not reporting a complete picture of hours worked, then measures of productivity will be affected. The charts show the percentage difference between the existing and experimental series for average hourly earnings and average weekly hours (not seasonally adjusted): The experimental measure of hours is on average between 1 and 3 percent higher than the existing measure. The experimental measure of average hourly earnings is
本文所表达的观点不一定反映联邦储备系统的官方立场。每个月的第一个星期五，美国劳工统计局(BLS)都会发布经过严格审查的月度就业报告。本报告中的数据来自两项调查:人口普查局的当前人口调查，也被称为住户调查;当前就业统计调查(CES)，这是对非农业商业机构(包括政府办公室)的调查CES的局限性之一是，平均每周工作时间和平均每小时收入的数据只报告了一小部分工人:目前，生产、建筑和非监督工人。生产分类用于商品生产部门，而非监管分类用于服务生产行业。这两类工人约占私营非农业就业人数的80%。根据劳工统计局的说法，这种分类系统已经变得越来越过时了。许多雇主没有将员工分为这两类，这导致了相对较高的不回复率展望未来，劳工统计局于2006年4月开始发布一系列实验数据，衡量所有非农业私营部门雇员的平均时薪和平均周薪。劳工统计局还开始发布一个试验性的月度总收入系列，其中包括工资、薪金和福利，如奖金、股票期权和雇主对401(k)计划的缴款。现有的劳工统计局平均时薪数据不包括这些福利。这些实验数据相对较新，因此不像现有数据那样经过季节性调整。此外，这些数据的发布有两个月的滞后。(例如，如果官方数据是2008年1月的，那么实验系列数据只能到2007年11月。)2010年2月，随着2010年1月数据的发布，所有员工工作时间和收入的实验系列将成为官方数据。到那时，劳工统计局相信它将有足够的时间来可靠地估计月度季节性因素。这些实验时间和收入序列对非农业企业生产率和个人收入的衡量具有潜在的重要意义。例如，劳工统计局将生产和非监督工人的工资时间序列作为衡量工作时间(每小时产出的分母)的关键输入如果现有的CES系列没有报告工作时间的完整情况，那么生产力度量将受到影响。图表显示了现有和实验系列的平均小时收入和平均每周工作时间(不经季节性调整)之间的百分比差异:实验测量的工作时间平均比现有测量高1%到3%。平均时薪的实验指标也一直高于现有的指标:截至2007年11月，这一差异约为19.5%(分别为21.12美元和17.63美元)。这些图表似乎暗示，目前未被归类为生产或非管理员工的工人往往工作时间更长，每小时收入更高。-Kevin L. Kliesen 1欲了解更多细节，请参阅BLS的《方法手册》;www.bls.gov opub /轨/ home.htm。
{"title":"An expanded look at employment","authors":"Kevin L. Kliesen","doi":"10.20955/es.2008.7","DOIUrl":"https://doi.org/10.20955/es.2008.7","url":null,"abstract":"Views expressed do not necessarily reflect official positions of the Federal Reserve System. On the first Friday of each month, the Bureau of Labor Statistics (BLS) releases its closely scrutinized monthly employment report. Data in this report are derived from two surveys: the Census Bureau’s Current Population Survey, also known as the household survey; and the Current Employ ment Statistics survey (CES), which is a survey of nonagricultural business establishments (including government offices).1 One of the limitations of the CES is that data for average weekly hours and average hourly earnings are reported for only a subset of workers: currently, production, construction, and non-supervisory workers. The production classification is used in the goods-producing sector, while the non-supervisory classification is used in the service-producing industries. Workers in these two categories account for about 80 percent of private nonagricultural employment. According to the BLS, this classification system has become increasingly archaic. Many employers do not classify workers by these two categories, which has led to relatively high non response rates.2 Looking to the future, the BLS began publishing an experimental series in April 2006 that measures average hourly earnings and average weekly hours of all nonfarm privatesector employees. The BLS also began publishing an experimental gross monthly earnings series that includes both wages and salaries and benefits such as bonuses, stock options, and employer contributions to 401(k) plans. The existing BLS average hourly earnings series excludes these kinds of benefits. These experimental data are relatively new and so are not seasonally adjusted, as the existing data are. Moreover, these data are published with a two-month lag. (For example, if the official data are available for January 2008, the experimental series are available only through November 2007.) The experimental all-employee series for hours and earnings will become the official data in February 2010, with the release of the January 2010 data. At that time the BLS believes that it will have had enough time to reliably estimate monthly seasonal factors. These experimental hours and earnings series have potentially significant implications for measures of nonfarm business productivity and personal income. For example, the BLS uses the CES-based series of hours paid of production and nonsupervisory workers as the key input into its measure of hours worked (the denominator in output per hour).3 If the existing CES series is not reporting a complete picture of hours worked, then measures of productivity will be affected. The charts show the percentage difference between the existing and experimental series for average hourly earnings and average weekly hours (not seasonally adjusted): The experimental measure of hours is on average between 1 and 3 percent higher than the existing measure. The experimental measure of average hourly earnings is","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"41 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":"131885407","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}
I n November and December of 2002, the producer price index (PPI) declined unexpectedly, contributing to concerns about deflation. In November, the PPI for finished goods fell 0.4 percent. In December, although analysts expected a 0.3 percent increase, it was unchanged. In each of these months the core PPI (that is, the PPI excluding food and energy) surprised analysts by falling 0.3 percent. Should we be concerned about declines in the PPI as a possible harbinger of economy-wide deflation? The chart, which presents a long-run perspective on the consumer price index (CPI) and the PPI, includes four price measures that are indexed to one in January 1967: (i) the PPI, (ii) the all-item CPI, and (iii) the services and (iv) goods components of the CPI. Since at least 1980, services prices have tended to rise relative to goods prices. In just the past five years, the services component of the CPI has increased at an average rate of 3.2 percent per year, while the goods component has increased at only a 1.1 percent rate. Why have goods prices risen so much more slowly than services prices? The answer, as shown by the data, is tremendous advances in technology and productivity in goods production. For example, the PPI component for electronic com puters and computer equipment has fallen an average of 12.3 percent per year for the past five years. Com puter prices are expected to decline far into the future, and no one thinks that this is a sign of deflation. Rather, it is a sign of technological advances and cost saving in computer manufacturing. The average price changes for the eight major components of the CPI have been widely dispersed in the past five years. The food component of the CPI increased at an average rate of 2.2 percent— which is close to the all-item CPI inflation rate (2.3 percent). Four components had lower average price increases than the all-item CPI: apparel (–1.6 percent), transportation (1.5 percent), recreation (1.3 percent), and education and communications (1.8 percent). The fall in electronic equipment prices has helped moderate the relative rise in communications prices. Three components had higher average price increases than the all-item CPI: housing (2.8 percent), medical care (4.1 percent), and the “other” category (5.0 percent). The “other” category includes tobacco products and personal care services. We should not be too concerned about price declines in the PPI (or any subset of the CPI basket) as long as the broad price indexes such as the CPI are stable. If CPI inflation remains at about 2 percent per year, then recent trends suggest that the PPI will increase at a rate of about 1 percent. Similarly, CPI inflation would have to be below 1 percent before we would expect to see ongoing price declines in the goods sector. PPI versus CPI Inflation
{"title":"PPI versus CPI inflation","authors":"W. Gavin","doi":"10.20955/ES.2003.5","DOIUrl":"https://doi.org/10.20955/ES.2003.5","url":null,"abstract":"I n November and December of 2002, the producer price index (PPI) declined unexpectedly, contributing to concerns about deflation. In November, the PPI for finished goods fell 0.4 percent. In December, although analysts expected a 0.3 percent increase, it was unchanged. In each of these months the core PPI (that is, the PPI excluding food and energy) surprised analysts by falling 0.3 percent. Should we be concerned about declines in the PPI as a possible harbinger of economy-wide deflation? The chart, which presents a long-run perspective on the consumer price index (CPI) and the PPI, includes four price measures that are indexed to one in January 1967: (i) the PPI, (ii) the all-item CPI, and (iii) the services and (iv) goods components of the CPI. Since at least 1980, services prices have tended to rise relative to goods prices. In just the past five years, the services component of the CPI has increased at an average rate of 3.2 percent per year, while the goods component has increased at only a 1.1 percent rate. Why have goods prices risen so much more slowly than services prices? The answer, as shown by the data, is tremendous advances in technology and productivity in goods production. For example, the PPI component for electronic com puters and computer equipment has fallen an average of 12.3 percent per year for the past five years. Com puter prices are expected to decline far into the future, and no one thinks that this is a sign of deflation. Rather, it is a sign of technological advances and cost saving in computer manufacturing. The average price changes for the eight major components of the CPI have been widely dispersed in the past five years. The food component of the CPI increased at an average rate of 2.2 percent— which is close to the all-item CPI inflation rate (2.3 percent). Four components had lower average price increases than the all-item CPI: apparel (–1.6 percent), transportation (1.5 percent), recreation (1.3 percent), and education and communications (1.8 percent). The fall in electronic equipment prices has helped moderate the relative rise in communications prices. Three components had higher average price increases than the all-item CPI: housing (2.8 percent), medical care (4.1 percent), and the “other” category (5.0 percent). The “other” category includes tobacco products and personal care services. We should not be too concerned about price declines in the PPI (or any subset of the CPI basket) as long as the broad price indexes such as the CPI are stable. If CPI inflation remains at about 2 percent per year, then recent trends suggest that the PPI will increase at a rate of about 1 percent. Similarly, CPI inflation would have to be below 1 percent before we would expect to see ongoing price declines in the goods sector. PPI versus CPI Inflation","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"41 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":"133109570","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 U.S. economy has experienced sustained trend growth of GDP and a decline in the volatility of macroeconomic variables since the mid-1980s, but has individual real income shared these same trends? Thanks to the increasing availability of individual and household-level data, economists have studied income dispersion and volatility in greater detail. Indeed, many recent studies focus not only on average income, but also on differences within and among groups of individuals (e.g., baby boomers or the top 5 vs. bottom 95 percent of the income distribution). A common finding is that both dispersion between individuals at any point in time and volatility of labor income over time are significantly different across various groups. This twofold finding prompts two questions. The first is whether income has become more or less dispersed over the years. U.S. data show that the median real wage for all workers grew by 1.4 percent per year between 1995 and 2003, when productivity growth was almost 3 percent per year and the labor share of national income remained flat. Although mean real income has kept pace with productivity growth, various analyses have found a sizable difference between mean and median income. This wedge can be explained by the disproportionate increase in real income in the top 10 percent of the income distribution; as a group, the top earners drive the mean but barely affect the median. Who are these individuals? They are mostly the very best (and rare) “superstars” with sizable wage premia in various occupational niches, particularly in the top 1 percent of the income distribution.1 The second question is whether income volatility has changed over time. Based on the Panel Study on Income Dynamics (a nationally representative panel of U.S. households), average income volatility has increased significantly since the 1970s. This finding contrasts with other studies based on administrative records on pretax earnings that show small changes.2 These two apparently contradictory results can be reconciled by considering that, again, “averages” do not tell the whole story because they neglect heterogeneity among individuals. A look at different income groups reveals that about 95 percent of the U.S. population experienced minimal or no change in income volatility (see chart). Rather, the rise in average volatility appears to be entirely explained by increased income volatility of individuals who have had the largest income changes in the past— those at the top end (right tail) of the distribution.3 In other words, individuals who experienced a disproportionate past increase in their income continued to experience a disproportionate increase in its volatility. Although we don’t know for sure, some of these “volatile” earners could be the aforementioned superstars. Jensen and Shore (2008) observe that increased volatility is more likely among individuals who describe themselves as risk-tolerant and the self-employed, whose income swings are al
{"title":"Some incomes are less average than others","authors":"Silvio Contessi, Ariel Weinberger","doi":"10.20955/ES.2008.24","DOIUrl":"https://doi.org/10.20955/ES.2008.24","url":null,"abstract":"The U.S. economy has experienced sustained trend growth of GDP and a decline in the volatility of macroeconomic variables since the mid-1980s, but has individual real income shared these same trends? Thanks to the increasing availability of individual and household-level data, economists have studied income dispersion and volatility in greater detail. Indeed, many recent studies focus not only on average income, but also on differences within and among groups of individuals (e.g., baby boomers or the top 5 vs. bottom 95 percent of the income distribution). A common finding is that both dispersion between individuals at any point in time and volatility of labor income over time are significantly different across various groups. This twofold finding prompts two questions. The first is whether income has become more or less dispersed over the years. U.S. data show that the median real wage for all workers grew by 1.4 percent per year between 1995 and 2003, when productivity growth was almost 3 percent per year and the labor share of national income remained flat. Although mean real income has kept pace with productivity growth, various analyses have found a sizable difference between mean and median income. This wedge can be explained by the disproportionate increase in real income in the top 10 percent of the income distribution; as a group, the top earners drive the mean but barely affect the median. Who are these individuals? They are mostly the very best (and rare) “superstars” with sizable wage premia in various occupational niches, particularly in the top 1 percent of the income distribution.1 The second question is whether income volatility has changed over time. Based on the Panel Study on Income Dynamics (a nationally representative panel of U.S. households), average income volatility has increased significantly since the 1970s. This finding contrasts with other studies based on administrative records on pretax earnings that show small changes.2 These two apparently contradictory results can be reconciled by considering that, again, “averages” do not tell the whole story because they neglect heterogeneity among individuals. A look at different income groups reveals that about 95 percent of the U.S. population experienced minimal or no change in income volatility (see chart). Rather, the rise in average volatility appears to be entirely explained by increased income volatility of individuals who have had the largest income changes in the past— those at the top end (right tail) of the distribution.3 In other words, individuals who experienced a disproportionate past increase in their income continued to experience a disproportionate increase in its volatility. Although we don’t know for sure, some of these “volatile” earners could be the aforementioned superstars. Jensen and Shore (2008) observe that increased volatility is more likely among individuals who describe themselves as risk-tolerant and the self-employed, whose income swings are al","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"28 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":"115461180","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}
I n late 2000 and early 2001, the U.S. economy closed a chapter of very strong economic growth and entered its tenth recession since the end of the Second World War. Since this recession began, the economy has experienced significant overall declines in production and employment. For example, payroll employment fell by 1.4 percent, totaling 1.8 million jobs, from its peak in March 2001 to its low point in December 2002.1 How ever, such national statistics need not accurately represent the economy’s strength in any particular geographic region. An interesting question to ask is then whether the recent recession was a truly “national” event, or whether it instead was localized in just a few geographic areas. To investigate this question, I compute the percentage decline in payroll employment during the recent national recession for each state and the District of Columbia. I also compute employment losses for each of the eight geographic regions of the United States, as defined by the Bureau of Economic Analysis. The percentage employment declines are measured by first recording the highest level that state and regional employment reached in the six months before and after the peak in the national employment data, which occurred in March 2001. This peak is then compared with the lowest level state and regional employment have reached since March 2001. For some states, employment had not yet begun to recover by the end of the sample period, which is January 2003. Thus, in these states the employment losses of the recent recession could end up being more severe than reported here. The table shows these declines for the eight states with the largest percentage job losses, the eight states with the smallest percentage job losses, and each of the eight geographic regions. The table suggests that the recent recession was a national event, with wide geographic dispersion: All eight of the geographic regions experienced a decline in jobs. This geographic dispersion is seen at the state level as well. Of the eight states with the largest percentage job declines, there is at least one state from seven of the eight geographic regions represented (the Southwest is the exception). Like wise, of the eight states with the smallest percentage job declines, five of the geographic regions are represented. Finally, no state escaped a decline in employment during the recession. The economic pain of the recession was truly felt nationally. The Federal Reserve assesses regional economic conditions as an input into its monetary policy decisions. Eight times per year, each of its 12 regional banks performs a survey of local business conditions, summaries of which are published in the Beige Book.2 However, while regional conditions are of interest to monetary policymakers, it is unlikely that the direction of monetary policy would be determined by the fortunes of any one region, as it is generally thought that monetary policy is too blunt a tool to fine-tune the economic pe
2000年末和2001年初，美国经济结束了强劲增长的一段时期，进入了二战结束以来的第十次衰退。自这次衰退开始以来，经济在生产和就业方面经历了显著的总体下降。例如，从2001年3月的高峰到2002年12月的最低点，就业人数下降了1.4%，共计180万人。然而，这样的国家统计数据并不需要准确地代表任何特定地理区域的经济实力。一个有趣的问题是，最近的经济衰退是一个真正的“全国性”事件，还是仅仅局限于几个地理区域。为了研究这个问题，我计算了在最近的全国经济衰退期间，每个州和哥伦比亚特区的就业人数下降的百分比。我还计算了美国经济分析局(Bureau of Economic Analysis)定义的八个地理区域的就业损失。就业下降的百分比是通过首先记录州和地区就业在2001年3月全国就业数据达到峰值前后六个月内达到的最高水平来衡量的。然后将这一峰值与自2001年3月以来各州和地区就业率的最低水平进行比较。对一些州来说，到样本期结束时，也就是2003年1月，就业还没有开始复苏。因此，在这些州，最近的经济衰退造成的就业损失最终可能比这里报道的更为严重。该表显示了8个就业岗位流失比例最高的州、8个就业岗位流失比例最低的州以及8个地理区域的降幅。该表表明，最近的经济衰退是一个全国性事件，具有广泛的地理分布:所有八个地理区域都经历了就业下降。这种地理上的分散在州一级也可以看到。在就业下降比例最大的8个州中，至少有一个州来自8个地理区域中的7个(西南地区除外)。同样，在就业下降百分比最小的8个州中，有5个地理区域是有代表性的。最后，在经济衰退期间，没有一个州能幸免于就业率的下降。全国都切实感受到经济衰退带来的痛苦。美联储将评估地区经济状况作为其货币政策决策的参考因素。每年8次，12家地区银行都会对当地商业状况进行调查，其摘要发表在褐皮书中。2然而，尽管货币政策制定者对地区状况感兴趣，但货币政策的方向不太可能由任何一个地区的命运决定，因为人们普遍认为货币政策是一种过于迟钝的工具，无法对特定地理区域的经济表现进行微调。
{"title":"A national recession","authors":"Jeremy Piger","doi":"10.20955/ES.2003.9","DOIUrl":"https://doi.org/10.20955/ES.2003.9","url":null,"abstract":"I n late 2000 and early 2001, the U.S. economy closed a chapter of very strong economic growth and entered its tenth recession since the end of the Second World War. Since this recession began, the economy has experienced significant overall declines in production and employment. For example, payroll employment fell by 1.4 percent, totaling 1.8 million jobs, from its peak in March 2001 to its low point in December 2002.1 How ever, such national statistics need not accurately represent the economy’s strength in any particular geographic region. An interesting question to ask is then whether the recent recession was a truly “national” event, or whether it instead was localized in just a few geographic areas. To investigate this question, I compute the percentage decline in payroll employment during the recent national recession for each state and the District of Columbia. I also compute employment losses for each of the eight geographic regions of the United States, as defined by the Bureau of Economic Analysis. The percentage employment declines are measured by first recording the highest level that state and regional employment reached in the six months before and after the peak in the national employment data, which occurred in March 2001. This peak is then compared with the lowest level state and regional employment have reached since March 2001. For some states, employment had not yet begun to recover by the end of the sample period, which is January 2003. Thus, in these states the employment losses of the recent recession could end up being more severe than reported here. The table shows these declines for the eight states with the largest percentage job losses, the eight states with the smallest percentage job losses, and each of the eight geographic regions. The table suggests that the recent recession was a national event, with wide geographic dispersion: All eight of the geographic regions experienced a decline in jobs. This geographic dispersion is seen at the state level as well. Of the eight states with the largest percentage job declines, there is at least one state from seven of the eight geographic regions represented (the Southwest is the exception). Like wise, of the eight states with the smallest percentage job declines, five of the geographic regions are represented. Finally, no state escaped a decline in employment during the recession. The economic pain of the recession was truly felt nationally. The Federal Reserve assesses regional economic conditions as an input into its monetary policy decisions. Eight times per year, each of its 12 regional banks performs a survey of local business conditions, summaries of which are published in the Beige Book.2 However, while regional conditions are of interest to monetary policymakers, it is unlikely that the direction of monetary policy would be determined by the fortunes of any one region, as it is generally thought that monetary policy is too blunt a tool to fine-tune the economic pe","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"86 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":"126327587","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. In the 1980s, the United States and most other developed countries adopted monetary policies based on the goal of first achieving, and then maintaining, price stability. Price stability can be defined as an economic environment in which people can make plans and contracts without worrying about inflation. Interest rates are a good indicator of expectations about future inflation because most long-term shifts in the level of interest rates are due to changes in the market’s expectations about future inflation. During the long period of achieving price stability in the United States—from about 1983 until the mid-1990s— interest rates declined. In 1984, the yields on the 3-month Treasury bill and the 10-year bond peaked at 10.90 percent and 13.56 percent, respectively. By 1993, the yield on the 3-month bill had fallen to 3 percent and the yield on the 10-year bond dipped to 5.33 percent. Since the mid1990s, inflation and interest rates have been relatively stable, reflecting the relative success of monetary policy in maintaining price stability. The table shows statistics for shortand long-term interest rates for two periods. The first period, from January 1983 until December 1996, is one of declining inflation and inflation expectations. The average 3-month interest rate over this period was 6.36 percent and the average 10-year rate was 8.35 percent. The second period, from January 1997 to the present, is one of relative price stability. A comparison of the two periods clearly shows the advantage of price stability: Interest rates shown in the bottom panel are, on average, 2 to 3 percentage points lower across all maturities, with the largest declines in the longest maturities. At the short end, monthly average rates have varied from a low of 0.90 percent to a high of 6.36 percent, which was the average in the earlier period. At the long end, the yield on 10-year bonds has averaged 5.02 percent and, on a monthly average basis, has never risen as high as 7 percent. Another benefit of price stability is that it stabilizes people’s expectations about inflation. Hence, indications of strong economic growth are less likely to foment expectations of a long-lasting shift in the inflation rate. Also, under price stability, monetary policymakers are less compelled to quell inflation fears during periods of fast economic growth by raising short-term interest rates. The table illustrates this benefit by showing the average monthly standard deviations of the respective interest rates, calculated from daily data. This measure shows that expectations in the current era of price stability have been well anchored—that is, intra-month developments, such as data releases, have less effect on interest rates.
{"title":"Stable interest rates follow stable prices","authors":"W. Gavin","doi":"10.20955/ES.2007.17","DOIUrl":"https://doi.org/10.20955/ES.2007.17","url":null,"abstract":"Views expressed do not necessarily reflect official positions of the Federal Reserve System. In the 1980s, the United States and most other developed countries adopted monetary policies based on the goal of first achieving, and then maintaining, price stability. Price stability can be defined as an economic environment in which people can make plans and contracts without worrying about inflation. Interest rates are a good indicator of expectations about future inflation because most long-term shifts in the level of interest rates are due to changes in the market’s expectations about future inflation. During the long period of achieving price stability in the United States—from about 1983 until the mid-1990s— interest rates declined. In 1984, the yields on the 3-month Treasury bill and the 10-year bond peaked at 10.90 percent and 13.56 percent, respectively. By 1993, the yield on the 3-month bill had fallen to 3 percent and the yield on the 10-year bond dipped to 5.33 percent. Since the mid1990s, inflation and interest rates have been relatively stable, reflecting the relative success of monetary policy in maintaining price stability. The table shows statistics for shortand long-term interest rates for two periods. The first period, from January 1983 until December 1996, is one of declining inflation and inflation expectations. The average 3-month interest rate over this period was 6.36 percent and the average 10-year rate was 8.35 percent. The second period, from January 1997 to the present, is one of relative price stability. A comparison of the two periods clearly shows the advantage of price stability: Interest rates shown in the bottom panel are, on average, 2 to 3 percentage points lower across all maturities, with the largest declines in the longest maturities. At the short end, monthly average rates have varied from a low of 0.90 percent to a high of 6.36 percent, which was the average in the earlier period. At the long end, the yield on 10-year bonds has averaged 5.02 percent and, on a monthly average basis, has never risen as high as 7 percent. Another benefit of price stability is that it stabilizes people’s expectations about inflation. Hence, indications of strong economic growth are less likely to foment expectations of a long-lasting shift in the inflation rate. Also, under price stability, monetary policymakers are less compelled to quell inflation fears during periods of fast economic growth by raising short-term interest rates. The table illustrates this benefit by showing the average monthly standard deviations of the respective interest rates, calculated from daily data. This measure shows that expectations in the current era of price stability have been well anchored—that is, intra-month developments, such as data releases, have less effect on interest rates.","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"2007 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":"128721261","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}
Real U.S. house prices, on average, have appreciated by 6 percent annually since 2000, a historically high rate when compared with the 2.7 percent annual rate between 1975 and 1999. Certain states have had especially high average annual rates since 2000: 12 percent in California, 11 percent in Rhode Island, and 10 percent in Nevada, Hawaii, and Florida. With such high rates of house price growth, many experts in the press and academic circles have debated whether we are currently facing a house price bubble. House price bubbles are characterized by homebuyer expectations of unusually rapid price appreciation. Thus, many buy a home they consider expensive, in relation to current rental prices, under the expectation of continued price increases. When buyers perceive that prices have stopped increasing, however, expectations normalize and demand falls. House prices fall as the bubble “bursts.” Economic consequences of a potential housing burst are especially worrisome. Lower home values reduce homeowners’ wealth, causing significant declines in consumer demand and thus in GDP. In fact, Case, Quigley, and Shiller (2001) point out that the elasticity of consumption to housing wealth appears much higher than the elasticity to stock market equity. History further indicates that banks’ balance sheets—when proper risk management strategies are not in place—can be heavily exposed to price bursts in real estate. For these reasons, policymakers are keen on trying to identify the presence of a bubble. Although criteria for detecting a bubble are debatable, we present a standard indicator of house affordability— prices divided by per capita income (P/I)—for all states plus the District of Columbia. (See map.) P/I provides a better measure than house price appreciation because it accounts for the evolution of income, a key factor in housing demand. California, New York, and Massachusetts, for instance, have high P/I ratios. According to our calculations of P/I growth rates, if bubble conditions do exist, they appear only on the two coasts and in Michigan. Since 2000, for example, the average P/I ratios for California, Massachusetts, Oregon, Rhode Island, and New York have been at least 13 percent above their respective averages for the 1975-99 period. On the other hand, the same 2000-05 P/I measure for Texas, Oklahoma, Mississippi, and North Dakota has declined by 24 percent below its average for the 1975-99 period. Yet, do the P/I ratios observed on the two coasts constitute a bubble? Note that when real estate is evaluated as a potential investment, housing prices should be determined by discounting the expected flow of income (rents) and other services using an appropriate risk-adjusted capitalization rate. Considering the difference between capitalization rates implied by house price indices and long-term government bond yields, we find indications against the presence of a bubble. House price data imply that the spread between capitalization rates and long
自2000年以来，美国实际房价平均每年上涨6%，创历史新高，而1975年至1999年间的年涨幅为2.7%。自2000年以来，某些州的平均年增长率特别高:加利福尼亚州为12%，罗德岛州为11%，内华达州、夏威夷和佛罗里达州为10%。在如此高的房价增长率下，媒体和学术界的许多专家都在争论我们目前是否面临房价泡沫。房价泡沫的特征是购房者对房价异常快速升值的预期。因此，许多人在预期房价会继续上涨的情况下，购买了他们认为相对于当前租金价格昂贵的房子。然而，当买家认为价格已经停止上涨时，预期就会恢复正常，需求就会下降。随着泡沫“破裂”，房价下跌。潜在的房地产泡沫破裂的经济后果尤其令人担忧。较低的房屋价值减少了房主的财富，导致消费需求大幅下降，从而导致GDP大幅下降。事实上，Case、Quigley和Shiller(2001)指出，消费对住房财富的弹性似乎远高于对股票市场权益的弹性。历史进一步表明，当适当的风险管理策略不到位时，银行的资产负债表可能会严重暴露在房地产价格飙升的风险之下。由于这些原因，政策制定者热衷于试图确定泡沫的存在。尽管检测泡沫的标准存在争议，但我们提出了一个衡量住房负担能力的标准指标——价格除以人均收入(P/I)——适用于所有州和哥伦比亚特区。(见地图)。P/I比房价升值提供了一个更好的衡量标准，因为它反映了收入的演变，而收入是住房需求的一个关键因素。例如，加州、纽约和马萨诸塞州的市盈率就很高。根据我们对市盈率增长率的计算，如果泡沫条件确实存在，它们只出现在两个海岸和密歇根州。例如，自2000年以来，加州、马萨诸塞州、俄勒冈州、罗德岛州和纽约州的平均市盈率至少比1975年至1999年期间各自的平均水平高出13%。另一方面，同样的2000- 2005年，德克萨斯州、俄克拉荷马州、密西西比州和北达科他州的市盈率比1975- 1999年的平均水平下降了24%。然而，在两岸观察到的市盈率是否构成泡沫?请注意，当房地产作为一项潜在投资进行评估时，房价应通过使用适当的风险调整资本化率贴现预期收入流(租金)和其他服务来确定。考虑到房价指数和长期政府债券收益率隐含的资本化率之间的差异，我们发现了反对泡沫存在的迹象。房价数据表明，资本化率和长期债券收益率之间的差距已经从1975- 1999年的平均水平0.7%上升到2000年以来的平均水平2.3%。这些正价差意味着，房价实际上与未来租金的风险调整折现保持一致。事实上，在1975- 1999年和2000年以来的最近时期，在所有50个州(和哥伦比亚特区)的传播都大幅增加。即使在息差相对较低的州(如加利福尼亚和马萨诸塞州)，这一数字仍为正值，在这两个时期之间翻了一倍多。总之，支持近期房地产泡沫的证据充其量是有争议的。正在进行的研究正在努力将基本面因素所证明的实际房价上涨与非理性的、可能有害的过度上涨区分开来。Karl E. Case, John M. Quigley, Robert J. Shiller， <财富效应的比较:股票市场与房地产市场>工作文件第8606号，国家经济研究局，2001。房价泡沫(还是只是泡沫)?
{"title":"Bubbling (or just frothy) house prices","authors":"Massimo Guidolin, Elizabeth A. La Jeunesse","doi":"10.20955/ES.2005.27","DOIUrl":"https://doi.org/10.20955/ES.2005.27","url":null,"abstract":"Real U.S. house prices, on average, have appreciated by 6 percent annually since 2000, a historically high rate when compared with the 2.7 percent annual rate between 1975 and 1999. Certain states have had especially high average annual rates since 2000: 12 percent in California, 11 percent in Rhode Island, and 10 percent in Nevada, Hawaii, and Florida. With such high rates of house price growth, many experts in the press and academic circles have debated whether we are currently facing a house price bubble. House price bubbles are characterized by homebuyer expectations of unusually rapid price appreciation. Thus, many buy a home they consider expensive, in relation to current rental prices, under the expectation of continued price increases. When buyers perceive that prices have stopped increasing, however, expectations normalize and demand falls. House prices fall as the bubble “bursts.” Economic consequences of a potential housing burst are especially worrisome. Lower home values reduce homeowners’ wealth, causing significant declines in consumer demand and thus in GDP. In fact, Case, Quigley, and Shiller (2001) point out that the elasticity of consumption to housing wealth appears much higher than the elasticity to stock market equity. History further indicates that banks’ balance sheets—when proper risk management strategies are not in place—can be heavily exposed to price bursts in real estate. For these reasons, policymakers are keen on trying to identify the presence of a bubble. Although criteria for detecting a bubble are debatable, we present a standard indicator of house affordability— prices divided by per capita income (P/I)—for all states plus the District of Columbia. (See map.) P/I provides a better measure than house price appreciation because it accounts for the evolution of income, a key factor in housing demand. California, New York, and Massachusetts, for instance, have high P/I ratios. According to our calculations of P/I growth rates, if bubble conditions do exist, they appear only on the two coasts and in Michigan. Since 2000, for example, the average P/I ratios for California, Massachusetts, Oregon, Rhode Island, and New York have been at least 13 percent above their respective averages for the 1975-99 period. On the other hand, the same 2000-05 P/I measure for Texas, Oklahoma, Mississippi, and North Dakota has declined by 24 percent below its average for the 1975-99 period. Yet, do the P/I ratios observed on the two coasts constitute a bubble? Note that when real estate is evaluated as a potential investment, housing prices should be determined by discounting the expected flow of income (rents) and other services using an appropriate risk-adjusted capitalization rate. Considering the difference between capitalization rates implied by house price indices and long-term government bond yields, we find indications against the presence of a bubble. House price data imply that the spread between capitalization rates and long","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"19 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":"127608916","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":"China's strategic petroleum reserve: a drop in the bucket","authors":"Christopher J. Neely","doi":"10.20955/es.2007.2","DOIUrl":"https://doi.org/10.20955/es.2007.2","url":null,"abstract":"","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":"117102458","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}
E conomists and analysts are devoting much attention to the commercial banking sector, whose health is fundamental to directing household savings to firms, institutions, and other consumers in need of loans. During the financial crisis of 2007-2008, aggregate data on bank lending show little deviation from trend until mid-October 2008. How ever, aggregate data may hide much of the microeconomic diversity that characterizes the U.S. banking system.
{"title":"Gross credit flows of U.S. commercial banks until 2008:Q3","authors":"Silvio Contessi, Johanna L. Francis","doi":"10.20955/es.2009.3","DOIUrl":"https://doi.org/10.20955/es.2009.3","url":null,"abstract":"E conomists and analysts are devoting much attention to the commercial banking sector, whose health is fundamental to directing household savings to firms, institutions, and other consumers in need of loans. During the financial crisis of 2007-2008, aggregate data on bank lending show little deviation from trend until mid-October 2008. How ever, aggregate data may hide much of the microeconomic diversity that characterizes the U.S. banking system.","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"2005 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":"125809417","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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