A fundamental tenet of monetary policymaking is that a surprise increase in the short-term interest rate will lower price inflation from what it otherwise would have been. Thus, it has been disconcerting to macroeconomists that many empirical estimates of the relationship between the federal funds rate and inflation have suggested that a surprise interest rate hike is followed immediately by a sustained increase in the inflation rate. This result has become known as the “price puzzle,” starting with Eichenbaum (1992). Hanson (2004) showed that it is not easy to explain away the price puzzle, especially in the pre-1980 period. The attached chart highlights circumstances in which the price puzzle flourished; specifically, it shows the tendency of the federal funds rate to precede change in inflation in the same direction during the 1970s. Interpretations of the price puzzle can differ in an important aspect: A conventional view is that nobody should believe that surprise interest rate hikes are ever inflationary in reality. Accord ing to this view, any empirical finding of the price puzzle is necessarily a false reading and a sign of a problem with the empirical model that generated such a result. A relatively new explanation for the price puzzle admits the possibility that surprise interest rate hikes really could be inflationary in some circumstances. The view that the price puzzle is a genuine phenomenon—especially in the pre-1980 period—can be based on indeterminacy. Loosely speaking, an economy’s characteristics correspond to indeterminacy when there is no way to identify the exact sources of forecast errors (for inflation and GDP, for example) in terms of clearly identifiable sources of shocks (such as surprise changes in the federal funds rate and productivity surprises). In general, it is possible to show in a macroeconomic model that some combinations of characteristics (such as how risk-averse people are, how sticky prices are updated, and how monetary policy is set) pertain to “determinacy” and others pertain to “indeterminacy.” Lubik and Schorfheide (2003) provided the necessary tools to allow for empirical estimates of an economy under indeterminacy. Hence, only recently have macroeconomists been able to explore how closely the data from a given time period conform to determinacy or indeterminacy. Belaygorod and Dueker (2006) estimate a model of the U.S. economy that also attempts to discern the precise period when indeterminacy was relevant. Their estimates suggest that the indeterminacy period was roughly from 1972 through 1981. Importantly for the price puzzle, the model estimates imply that in this period of indeterminacy, inflation would rise immediately and in a sustained fashion in response to an interest rate hike. Reassuringly for monetary policymakers, the model estimates for both determinacy periods— before 1972 and after 1981—suggest that increases in the federal funds rate unambiguously help rein in inflation. In the type
{"title":"The price puzzle: an update and a lesson","authors":"Michael J. Dueker","doi":"10.20955/ES.2006.24","DOIUrl":"https://doi.org/10.20955/ES.2006.24","url":null,"abstract":"A fundamental tenet of monetary policymaking is that a surprise increase in the short-term interest rate will lower price inflation from what it otherwise would have been. Thus, it has been disconcerting to macroeconomists that many empirical estimates of the relationship between the federal funds rate and inflation have suggested that a surprise interest rate hike is followed immediately by a sustained increase in the inflation rate. This result has become known as the “price puzzle,” starting with Eichenbaum (1992). Hanson (2004) showed that it is not easy to explain away the price puzzle, especially in the pre-1980 period. The attached chart highlights circumstances in which the price puzzle flourished; specifically, it shows the tendency of the federal funds rate to precede change in inflation in the same direction during the 1970s. Interpretations of the price puzzle can differ in an important aspect: A conventional view is that nobody should believe that surprise interest rate hikes are ever inflationary in reality. Accord ing to this view, any empirical finding of the price puzzle is necessarily a false reading and a sign of a problem with the empirical model that generated such a result. A relatively new explanation for the price puzzle admits the possibility that surprise interest rate hikes really could be inflationary in some circumstances. The view that the price puzzle is a genuine phenomenon—especially in the pre-1980 period—can be based on indeterminacy. Loosely speaking, an economy’s characteristics correspond to indeterminacy when there is no way to identify the exact sources of forecast errors (for inflation and GDP, for example) in terms of clearly identifiable sources of shocks (such as surprise changes in the federal funds rate and productivity surprises). In general, it is possible to show in a macroeconomic model that some combinations of characteristics (such as how risk-averse people are, how sticky prices are updated, and how monetary policy is set) pertain to “determinacy” and others pertain to “indeterminacy.” Lubik and Schorfheide (2003) provided the necessary tools to allow for empirical estimates of an economy under indeterminacy. Hence, only recently have macroeconomists been able to explore how closely the data from a given time period conform to determinacy or indeterminacy. Belaygorod and Dueker (2006) estimate a model of the U.S. economy that also attempts to discern the precise period when indeterminacy was relevant. Their estimates suggest that the indeterminacy period was roughly from 1972 through 1981. Importantly for the price puzzle, the model estimates imply that in this period of indeterminacy, inflation would rise immediately and in a sustained fashion in response to an interest rate hike. Reassuringly for monetary policymakers, the model estimates for both determinacy periods— before 1972 and after 1981—suggest that increases in the federal funds rate unambiguously help rein in inflation. In the type ","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":"126897881","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":"Look who's still working now","authors":"Kristie M. Engemann, Michael T. Owyang","doi":"10.20955/ES.2007.14","DOIUrl":"https://doi.org/10.20955/ES.2007.14","url":null,"abstract":"","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"84 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":"123709756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
NationalEconomicTrends Views expressed do not necessarily reflect official positions of the Federal Reserve System. P revious generations of workers held lifelong jobs. Today's workers no longer expect to work for the same employer for their entire career. Even in the past 20 years, expected job tenure has declined in the United States, with a few differences between the trends for men and women. Some notable trends in job tenure, and their potential causes, may provide some insight into the employment landscape of the future. Friedberg and Owyang (2002) use data from the Survey of Consumer Finances (SCF) to find job tenure trends. 1 According to the SCF, from 1983 to 1998, average expected remaining tenure—how long a worker expects to continue working for his current employer—for full-time male employees declined from 18.6 to 14.7 years. The trend for female workers is similar. Their expected remaining tenure starts at 15.9 in 1983 and eventually falls to 12.8 years in 1998. 2 The accompanying graph shows these numbers broken down by years of experience at workers' current jobs. Note that for nearly every subsample, expected remaining tenure has decreased. An interesting trend arises with the percentage of employees aged 25 and older who have been with their current employer for at least ten years. The total percentage of men in this group decreased by nearly 5 points from 1983 to 2002, whereas the opposite holds for women— their percentage increased by almost 4 points. All age groups for men saw declines, but those affected most negatively were men aged 40-44, 45-49, and, especially, 60-64. In contrast, the percentage of women aged 35-54 with ten-year tenure or longer increased, but the remaining age groups suffered the same fate as the men's age groups. 3 What could have brought about these trends? Changing labor market characteristics in the past couple of decades have had pronounced effects on tenure. For instance, countering the general decreasing tenure trend, women's rising labor force participation beginning in the 1980s undoubtedly contributed to the proportion of 35-to 54-year-olds with ten years of tenure. In the graph, these same women demonstrate their growing attachment to the workforce through their increased expected remaining tenure. Numerous factors have contributed to the decline in job tenure. The composition of payroll employment has shifted to more heavily favor service jobs at manufactur-ing's expense. Because the median tenure for a worker in manufacturing exceeds that …
{"title":"Your current job probably won't be your last","authors":"Kristie M. Engemann, Michael T. Owyang","doi":"10.20955/ES.2004.5","DOIUrl":"https://doi.org/10.20955/ES.2004.5","url":null,"abstract":"NationalEconomicTrends Views expressed do not necessarily reflect official positions of the Federal Reserve System. P revious generations of workers held lifelong jobs. Today's workers no longer expect to work for the same employer for their entire career. Even in the past 20 years, expected job tenure has declined in the United States, with a few differences between the trends for men and women. Some notable trends in job tenure, and their potential causes, may provide some insight into the employment landscape of the future. Friedberg and Owyang (2002) use data from the Survey of Consumer Finances (SCF) to find job tenure trends. 1 According to the SCF, from 1983 to 1998, average expected remaining tenure—how long a worker expects to continue working for his current employer—for full-time male employees declined from 18.6 to 14.7 years. The trend for female workers is similar. Their expected remaining tenure starts at 15.9 in 1983 and eventually falls to 12.8 years in 1998. 2 The accompanying graph shows these numbers broken down by years of experience at workers' current jobs. Note that for nearly every subsample, expected remaining tenure has decreased. An interesting trend arises with the percentage of employees aged 25 and older who have been with their current employer for at least ten years. The total percentage of men in this group decreased by nearly 5 points from 1983 to 2002, whereas the opposite holds for women— their percentage increased by almost 4 points. All age groups for men saw declines, but those affected most negatively were men aged 40-44, 45-49, and, especially, 60-64. In contrast, the percentage of women aged 35-54 with ten-year tenure or longer increased, but the remaining age groups suffered the same fate as the men's age groups. 3 What could have brought about these trends? Changing labor market characteristics in the past couple of decades have had pronounced effects on tenure. For instance, countering the general decreasing tenure trend, women's rising labor force participation beginning in the 1980s undoubtedly contributed to the proportion of 35-to 54-year-olds with ten years of tenure. In the graph, these same women demonstrate their growing attachment to the workforce through their increased expected remaining tenure. Numerous factors have contributed to the decline in job tenure. The composition of payroll employment has shifted to more heavily favor service jobs at manufactur-ing's expense. Because the median tenure for a worker in manufacturing exceeds that …","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"15 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":"115520466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
T he Baltic Dry Index (BDI) is an index published daily by the Baltic Exchange in London, the leading global marketplace for brokering shipping contracts. Exchange members include companies from the international bulk-shipping industry, shipbrokers, freight derivative brokers, trading houses, shipowners, and other cargo interests.1 Every day, the Baltic Exchange asks brokers around the world the cost of booking cargo of various sizes to move raw materials across various ocean routes. The exchange uses this information to compute the BDI—an indicator of maritime transportation costs for major raw materials. The BDI has generated interest as a leading indicator of economic activity because manufacturers increase their demand for raw materials to meet expected increases in the demand for finished products.2 Likewise, a slowdown in economic activity reduces the demand for raw materials when manufacturers detect increases in unsold inventory. The BDI can be viewed as the equilibrium price of shipping raw materials, determined by the supply of cargo ships and the demand for transporting raw materials by ship. First, the supply of cargo ships is inelastic relative to demand because cargo ships are costly and time-consuming to build. In contrast, companies can simply stop booking cargo ships when demand drops. Second, the index is sensitive to changes in the price of crude oil. Fuel and port-related charges are significantly large components of the total cost of maritime shipping.3 Although port-related expenses such as dockage charges are relatively stable, oil prices are notoriously volatile. As a result, the BDI is sensitive to changes in the demand for raw materials and oil price changes. Movements in the BDI can be traced to changes in global demand for manufactured goods. In addition, this demand affects the price of crude oil, which in turn affects the cost of maritime shipping. Between January 2006 and October 2007, the BDI increased more than 400 percent, from 2,081 to 10,656 points (see chart). This steady rise was largely due to the significant growth in the global economy for manufactured products. The growth rate of total industrial production (excluding construction) for member countries of the Organisation for Economic Co-operation and Development (OECD) and six major non-members (Brazil, China, India, Indonesia, Russia, and South Africa) was more than 6 percent per annum from March 2006 to October 2007. Movements in this data series closely mirror the (lower) growth rate of industrial production of manufactured goods in the United States. Such high growth rates of industrial production around the world are likely to have also contributed to a surge in crude oil prices, which increased from $67 per barrel to $95 per barrel during the same period, thereby exacerbating the rise in the BDI. Recent Movements in the Baltic Dry Index
{"title":"Recent movements in the Baltic Dry Index","authors":"Rajdeep Sengupta, Y. Tam","doi":"10.20955/ES.2009.12","DOIUrl":"https://doi.org/10.20955/ES.2009.12","url":null,"abstract":"T he Baltic Dry Index (BDI) is an index published daily by the Baltic Exchange in London, the leading global marketplace for brokering shipping contracts. Exchange members include companies from the international bulk-shipping industry, shipbrokers, freight derivative brokers, trading houses, shipowners, and other cargo interests.1 Every day, the Baltic Exchange asks brokers around the world the cost of booking cargo of various sizes to move raw materials across various ocean routes. The exchange uses this information to compute the BDI—an indicator of maritime transportation costs for major raw materials. The BDI has generated interest as a leading indicator of economic activity because manufacturers increase their demand for raw materials to meet expected increases in the demand for finished products.2 Likewise, a slowdown in economic activity reduces the demand for raw materials when manufacturers detect increases in unsold inventory. The BDI can be viewed as the equilibrium price of shipping raw materials, determined by the supply of cargo ships and the demand for transporting raw materials by ship. First, the supply of cargo ships is inelastic relative to demand because cargo ships are costly and time-consuming to build. In contrast, companies can simply stop booking cargo ships when demand drops. Second, the index is sensitive to changes in the price of crude oil. Fuel and port-related charges are significantly large components of the total cost of maritime shipping.3 Although port-related expenses such as dockage charges are relatively stable, oil prices are notoriously volatile. As a result, the BDI is sensitive to changes in the demand for raw materials and oil price changes. Movements in the BDI can be traced to changes in global demand for manufactured goods. In addition, this demand affects the price of crude oil, which in turn affects the cost of maritime shipping. Between January 2006 and October 2007, the BDI increased more than 400 percent, from 2,081 to 10,656 points (see chart). This steady rise was largely due to the significant growth in the global economy for manufactured products. The growth rate of total industrial production (excluding construction) for member countries of the Organisation for Economic Co-operation and Development (OECD) and six major non-members (Brazil, China, India, Indonesia, Russia, and South Africa) was more than 6 percent per annum from March 2006 to October 2007. Movements in this data series closely mirror the (lower) growth rate of industrial production of manufactured goods in the United States. Such high growth rates of industrial production around the world are likely to have also contributed to a surge in crude oil prices, which increased from $67 per barrel to $95 per barrel during the same period, thereby exacerbating the rise in the BDI. Recent Movements in the Baltic Dry Index","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"4 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":"123335255","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. A Case for Oil? As economic growth slowed during the first half of 2003, many analysts again focused on increases in the price of oil. In the United States, increases in the price of oil generally have preceded business-cycle downturns since World War II.1 In late February, oil prices were close to $40 as oil supplies were throttled in Venezuela and Nigeria and the world anticipated war in the Middle East. Some analysts feared prices could reach $50 if the war in Iraq bogged down into an urban guerilla conflict or spread to other nearby oil-producing nations. At the same time, nuclear power-supply troubles in Japan and unusually cold weather in the United States boosted demand. Fears of further sharp oil price increases seemed well-founded. An extensive economics literature has explored the various mechanisms whereby higher oil prices affect economic activity.2 One of the more plausible mechanisms operates by means of the postponement effect. In this scenario, increases in the current price of oil increase uncertainty about future oil prices which, in turn, causes households and businesses to postpone purchases of durable goods and equipment. Unraveling the economy’s recent performance depends, at least in part, on understanding the extent to which businesses and consumers believed that this year’s oil price increases would be reversed in the near future. If this belief was widely held, then oil prices might have affected business and consumer spending very little— and the economy’s slow growth might have been signaling broader underlying weakness. It seems plausible that many firms and households judged world events during 2002-03 by comparing them to those that surrounded the first Gulf War. Looking back, oil price movements during 2002-03 in fact were quite similar to those during the 1990-91 Iraqi invasion of Kuwait and subsequent Gulf War, albeit with somewhat different timing. The figure shows the spot price for benchmark Texas-type light, sweet crude oil before and after the peak price observed during each period. (We aligned the prices based on the peak price because of the differing timing of events.) In 1990, prices peaked after the Kuwait invasion but well before the beginning of the American liberation of Kuwait. In 2003, similarly, prices rose sharply during the military buildup, when uncertainty regarding war was high, and then decreased after the degree of uncertainty was reduced by the American entry into Iraq. During both episodes, oil futures prices (not shown) moved in similar patterns; indeed, even during 2003, futures prices generally remained below the spot price, suggesting that the price run-up would be short-lived. The similarity of oil price movements during 1990-91 and 2002-03 suggests that the slow pace of economic activity during this year’s first-half should not be attributed to higher oil prices. It also suggests that the re
{"title":"A case for oil","authors":"R. Anderson, Michelle T. Meisch","doi":"10.20955/es.2003.14","DOIUrl":"https://doi.org/10.20955/es.2003.14","url":null,"abstract":"Views expressed do not necessarily reflect official positions of the Federal Reserve System. A Case for Oil? As economic growth slowed during the first half of 2003, many analysts again focused on increases in the price of oil. In the United States, increases in the price of oil generally have preceded business-cycle downturns since World War II.1 In late February, oil prices were close to $40 as oil supplies were throttled in Venezuela and Nigeria and the world anticipated war in the Middle East. Some analysts feared prices could reach $50 if the war in Iraq bogged down into an urban guerilla conflict or spread to other nearby oil-producing nations. At the same time, nuclear power-supply troubles in Japan and unusually cold weather in the United States boosted demand. Fears of further sharp oil price increases seemed well-founded. An extensive economics literature has explored the various mechanisms whereby higher oil prices affect economic activity.2 One of the more plausible mechanisms operates by means of the postponement effect. In this scenario, increases in the current price of oil increase uncertainty about future oil prices which, in turn, causes households and businesses to postpone purchases of durable goods and equipment. Unraveling the economy’s recent performance depends, at least in part, on understanding the extent to which businesses and consumers believed that this year’s oil price increases would be reversed in the near future. If this belief was widely held, then oil prices might have affected business and consumer spending very little— and the economy’s slow growth might have been signaling broader underlying weakness. It seems plausible that many firms and households judged world events during 2002-03 by comparing them to those that surrounded the first Gulf War. Looking back, oil price movements during 2002-03 in fact were quite similar to those during the 1990-91 Iraqi invasion of Kuwait and subsequent Gulf War, albeit with somewhat different timing. The figure shows the spot price for benchmark Texas-type light, sweet crude oil before and after the peak price observed during each period. (We aligned the prices based on the peak price because of the differing timing of events.) In 1990, prices peaked after the Kuwait invasion but well before the beginning of the American liberation of Kuwait. In 2003, similarly, prices rose sharply during the military buildup, when uncertainty regarding war was high, and then decreased after the degree of uncertainty was reduced by the American entry into Iraq. During both episodes, oil futures prices (not shown) moved in similar patterns; indeed, even during 2003, futures prices generally remained below the spot price, suggesting that the price run-up would be short-lived. The similarity of oil price movements during 1990-91 and 2002-03 suggests that the slow pace of economic activity during this year’s first-half should not be attributed to higher oil prices. It also suggests that the re","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"103 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":"115379170","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}
L ate last year, the National Bureau of Economic Research (NBER) announced that the U.S. economy entered a recession in March 2001. Some have questioned whether the recent downturn really qualifies as a recession, as by some measures it has been quite mild. For example, the broadest measure of the economy’s output, real gross domestic prod uct (GDP), experienced only a modest decline in the current recession. However, given that more timely measures of economic activity are available, the NBER gives relatively little weight to GDP in determining recession dates. Instead, the NBER defines a recession as a “significant decline in activity spread across the economy, lasting more than a few months, visible in industrial production, employ ment, real income, and wholesale-retail sales.”1 To investigate whether the recent economic downturn meets this definition, I use past recessions as a yardstick. The table details the significance and length of decline in the four variables included in the NBER recession definition for each recession over the past 40 years. Signi fi cance is measured by the percentage decline in each variable from its individual peak to its individual trough. This is computed by finding the lowest point each variable reached during or a year following the recession (called the trough) and subtracting this value from the highest point the variable reached in the past (called the peak). Beside this statistic, in parentheses, is the time in months between individual peak and trough for each series, a mea sure of the length of the decline. The statistics for the current recession assume the troughs have been reached for all of these variables. The table shows that both employment and industrial production have experienced significant and lengthy declines in the current recession; the magnitudes of the declines are similar to those observed in several past recessions and lasted for 13 and 18 months, respectively, longer than the average of the previous recessions. The employment decline is of particular interest, as the NBER gives extra emphasis to employment movements in determining recession dates. The decline in sales during the current recession has been notably mild relative to previous recessions, dropping less than half as much as in the 1990-91 recession. Nevertheless, such a protracted sales decline has never occurred within any expansion. In the sample period considered here, there have been only three months not related to an NBER recession in which sales had declined 2.4 percent below their prior peak, the level reached in the current recession. In all three cases these episodes lasted just one month, with sales bouncing back above their peak in the following month. By contrast, sales in the current recession remained below their peak for 10 consecutive months before the trough was reached, suggesting the recent sales data have more in common with past recessions than past expansions. Finally, as in the 1960-61 and 1969-70 re
{"title":"Was the recent economic downturn a recession","authors":"Jeremy Piger","doi":"10.20955/es.2002.20","DOIUrl":"https://doi.org/10.20955/es.2002.20","url":null,"abstract":"L ate last year, the National Bureau of Economic Research (NBER) announced that the U.S. economy entered a recession in March 2001. Some have questioned whether the recent downturn really qualifies as a recession, as by some measures it has been quite mild. For example, the broadest measure of the economy’s output, real gross domestic prod uct (GDP), experienced only a modest decline in the current recession. However, given that more timely measures of economic activity are available, the NBER gives relatively little weight to GDP in determining recession dates. Instead, the NBER defines a recession as a “significant decline in activity spread across the economy, lasting more than a few months, visible in industrial production, employ ment, real income, and wholesale-retail sales.”1 To investigate whether the recent economic downturn meets this definition, I use past recessions as a yardstick. The table details the significance and length of decline in the four variables included in the NBER recession definition for each recession over the past 40 years. Signi fi cance is measured by the percentage decline in each variable from its individual peak to its individual trough. This is computed by finding the lowest point each variable reached during or a year following the recession (called the trough) and subtracting this value from the highest point the variable reached in the past (called the peak). Beside this statistic, in parentheses, is the time in months between individual peak and trough for each series, a mea sure of the length of the decline. The statistics for the current recession assume the troughs have been reached for all of these variables. The table shows that both employment and industrial production have experienced significant and lengthy declines in the current recession; the magnitudes of the declines are similar to those observed in several past recessions and lasted for 13 and 18 months, respectively, longer than the average of the previous recessions. The employment decline is of particular interest, as the NBER gives extra emphasis to employment movements in determining recession dates. The decline in sales during the current recession has been notably mild relative to previous recessions, dropping less than half as much as in the 1990-91 recession. Nevertheless, such a protracted sales decline has never occurred within any expansion. In the sample period considered here, there have been only three months not related to an NBER recession in which sales had declined 2.4 percent below their prior peak, the level reached in the current recession. In all three cases these episodes lasted just one month, with sales bouncing back above their peak in the following month. By contrast, sales in the current recession remained below their peak for 10 consecutive months before the trough was reached, suggesting the recent sales data have more in common with past recessions than past expansions. Finally, as in the 1960-61 and 1969-70 re","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"68 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":"128933126","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":"Look who's working now","authors":"Kristie M. Engemann, Michael T. Owyang","doi":"10.20955/ES.2006.9","DOIUrl":"https://doi.org/10.20955/ES.2006.9","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":"125319697","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":"Japanese deflation loses something in the translation","authors":"James Bullard, John Seiffertt","doi":"10.20955/es.2003.22","DOIUrl":"https://doi.org/10.20955/es.2003.22","url":null,"abstract":"","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"6 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":"122750281","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}
Economic analysts and policymakers pay a great deal of attention to employment data. The Bureau of Labor Statistics’ monthly employment report is often taken as a key early indicator of aggregate economic activity. Moreover, the National Bureau of Economic Research’s business cycle dating committee uses employment to help determine business cycle peaks and troughs. Thus, changes in employment can reflect the economy’s evolving strength or weakness over the near-term. (Over longer periods, the change in employment depends more on trend growth of labor productivity and labor force participation rates.) When using employment to predict near-term economic growth, analysts must choose which employment survey to use. The Bureau of Labor Statistics presents two measures of employment: one from the Current Population Survey (CPS), with about 60,000 households; the other from the Current Employment Statistics (CES), with about 400,000 establishments, which cover about a third of all nonfarm payroll workers. Although the household and establishment measures of employment differ considerably, they tend to show similar growth trends over longer periods of time.1 The two series had been moving in two distinct patterns: From January 1994 to March 2001, the establishment survey averaged about 233,000 additional jobs per month, while the household survey averaged only about 184,000 per month. But, since the recession trough in November 2001, the opposite has occurred—household employment has increased more, by an average of about 148,000 per month, while payroll employment has increased by only 82,000 per month. So, should analysts continue to rely more on the payroll survey or put more weight on the household survey? The table shows simple correlations between the growth of two measures of economic activity—industrial production and real GDP—and three measures of labor input: the CES, the CPS, and the average of the two surveys over three separate periods. The 1994 breakpoint is chosen because the CPS was changed in several important ways; the 2001 break point was chosen because it is the peak of the 1991-2001 expansion. The table shows that the correlation between employment growth and industrial production is generally stronger than between employment and real GDP. Second, from 1950 to 1993, the correlation between the growth of payroll employment and real output was larger than the correlation between the growth of household employment and output; this is consistent with the conventional wisdom noted earlier. Third, the correlation between output growth and either measure of employment growth was much weaker during the 1990s, possibly due to the increase in the trend growth of labor productivity and the sharp rise in stock prices. For the most recent period, the CPS is more highly correlated with industrial production growth (0.77) than is the CES (0.67). An interesting finding is that, since 2001, the correlation between the growth of the combined CES and C
经济分析师和政策制定者非常关注就业数据。美国劳工统计局(Bureau of Labor Statistics)的月度就业报告通常被视为总体经济活动的关键早期指标。此外，美国国家经济研究局(National Bureau of Economic Research)的商业周期确定委员会利用就业情况来帮助确定商业周期的高峰和低谷。因此，就业的变化可以反映经济在短期内的强弱变化。(在较长时期内，就业的变化更多地取决于劳动生产率和劳动力参与率的趋势增长。)当使用就业预测近期经济增长时，分析师必须选择使用哪种就业调查。美国劳工统计局(Bureau of Labor Statistics)提供了两种就业衡量标准:一种来自当前人口调查(CPS)，涉及约6万户家庭;另一个来自当前就业统计(CES)，大约有40万家企业，覆盖了大约三分之一的非农业就业人员。虽然家庭和企业的就业指标差别很大，但它们往往在较长时期内显示出类似的增长趋势从1994年1月到2001年3月，机构调查平均每月增加约23.3万个工作岗位，而住户调查平均每月仅增加约18.4万个工作岗位。但是，自2001年11月经济衰退达到低谷以来，情况正好相反——家庭就业人数增加得更多，平均每月增加约14.8万人，而工资单就业人数每月只增加8.2万人。那么，分析师应该继续更多地依赖于就业调查，还是更看重家庭调查?该表显示了两种衡量经济活动的指标——工业生产和实际gdp——和三种衡量劳动力投入的指标——消费消费指数(CES)、CPS，以及三个独立时期两次调查的平均值之间的简单相关性。之所以选择1994年的断点，是因为CPS在几个重要方面发生了变化;之所以选择2001年这个断点，是因为它是1991-2001年经济扩张的峰值。该表显示，就业增长与工业生产之间的相关性通常强于就业与实际GDP之间的相关性。第二，从1950年到1993年，工资单就业增长与实际产出之间的相关性大于家庭就业增长与产出之间的相关性;这与前面提到的传统智慧是一致的。第三，在20世纪90年代，产出增长与就业增长之间的相关性要弱得多，这可能是由于劳动生产率趋势增长的增加和股票价格的急剧上涨。在最近的一段时间里，CPS与工业生产增长的相关性(0.77)比CES(0.67)更高。一个有趣的发现是，自2001年以来，综合CES和CPS措施的增长与实际GDP之间的相关性大于单独的机构或家庭措施之间的相关性，这与最近的结果一致因此，经济分析师可能希望使用工资和家庭调查的平均值来衡量潜在的就业趋势，从而衡量近期实际GDP增长的趋势。这些相关性的变化可能是由个体经营的相对增长引起的，这在CPS中被计算，而在CES中没有被计算。从1994年1月到2001年11月，个体经营下降了1.6%。然而，自2001年11月以来，个体经营者增加了9.3%，达到约950万人;与此同时，家庭总就业人数只增加了6.3%。如果这种趋势继续下去，经济分析人士可能需要更密切地关注住户调查。
{"title":"Are two employment surveys better than one","authors":"Kevin L. Kliesen","doi":"10.20955/ES.2006.27","DOIUrl":"https://doi.org/10.20955/ES.2006.27","url":null,"abstract":"Economic analysts and policymakers pay a great deal of attention to employment data. The Bureau of Labor Statistics’ monthly employment report is often taken as a key early indicator of aggregate economic activity. Moreover, the National Bureau of Economic Research’s business cycle dating committee uses employment to help determine business cycle peaks and troughs. Thus, changes in employment can reflect the economy’s evolving strength or weakness over the near-term. (Over longer periods, the change in employment depends more on trend growth of labor productivity and labor force participation rates.) When using employment to predict near-term economic growth, analysts must choose which employment survey to use. The Bureau of Labor Statistics presents two measures of employment: one from the Current Population Survey (CPS), with about 60,000 households; the other from the Current Employment Statistics (CES), with about 400,000 establishments, which cover about a third of all nonfarm payroll workers. Although the household and establishment measures of employment differ considerably, they tend to show similar growth trends over longer periods of time.1 The two series had been moving in two distinct patterns: From January 1994 to March 2001, the establishment survey averaged about 233,000 additional jobs per month, while the household survey averaged only about 184,000 per month. But, since the recession trough in November 2001, the opposite has occurred—household employment has increased more, by an average of about 148,000 per month, while payroll employment has increased by only 82,000 per month. So, should analysts continue to rely more on the payroll survey or put more weight on the household survey? The table shows simple correlations between the growth of two measures of economic activity—industrial production and real GDP—and three measures of labor input: the CES, the CPS, and the average of the two surveys over three separate periods. The 1994 breakpoint is chosen because the CPS was changed in several important ways; the 2001 break point was chosen because it is the peak of the 1991-2001 expansion. The table shows that the correlation between employment growth and industrial production is generally stronger than between employment and real GDP. Second, from 1950 to 1993, the correlation between the growth of payroll employment and real output was larger than the correlation between the growth of household employment and output; this is consistent with the conventional wisdom noted earlier. Third, the correlation between output growth and either measure of employment growth was much weaker during the 1990s, possibly due to the increase in the trend growth of labor productivity and the sharp rise in stock prices. For the most recent period, the CPS is more highly correlated with industrial production growth (0.77) than is the CES (0.67). An interesting finding is that, since 2001, the correlation between the growth of the combined CES and C","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"209 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":"126321791","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. House prices in the United States were 14.1 percent lower in the first quarter of 2008 than they were a year earlier, according to a widely cited measure of U.S. house prices, the SP it also showed a more rapid decline in house prices during 2007-08. The OFHEO index tracks the sales prices of houses financed with conforming, conventional mortgages purchased by Fannie Mae and Freddie Mac. In 2007, conforming loans were limited to a maximum of $417,000. Home purchases involving larger, “jumbo” mortgages, or unconventional mortgages— including many subprime mortgages—do not influence the OFHEO index. By contrast, the SP it includes data on houses financed by jumbo mortgages, subprime mortgages, and home purchases that do not involve a mortgage. The S&P/CS index is also value weighted: More expensive homes have relatively greater influence on the index, whereas the OFHEO index is unit weighted. Further more, the S&P/CS index assigns greater weight to census regions with greater total residential real estate value. The OFHEO index, by contrast, weights regions based on the number of residential units. Hence, regions with relatively higher average home prices have more influence on the S&P/CS index than on the OFHEO index.1 The S&P/CS index exhibited faster growth in house prices before 2006 because house prices rose more rapidly in the regions with more influence on that index. These regions also tended to have a higher percentage of transactions involving mortgages ineligible for purchase by Fannie Mae and Freddie Mac. The relatively rapid decline in house prices since 2006 in the West and East Coast regions, and the relatively greater weight given these regions in the S&P/CS index help explain why the S&P/CS index shows a more rapid decline in house prices for the United States as a whole.
{"title":"How much have U.S. house prices fallen","authors":"C. Aubuchon, David C. Wheelock","doi":"10.20955/ES.2008.20","DOIUrl":"https://doi.org/10.20955/ES.2008.20","url":null,"abstract":"Views expressed do not necessarily reflect official positions of the Federal Reserve System. House prices in the United States were 14.1 percent lower in the first quarter of 2008 than they were a year earlier, according to a widely cited measure of U.S. house prices, the SP it also showed a more rapid decline in house prices during 2007-08. The OFHEO index tracks the sales prices of houses financed with conforming, conventional mortgages purchased by Fannie Mae and Freddie Mac. In 2007, conforming loans were limited to a maximum of $417,000. Home purchases involving larger, “jumbo” mortgages, or unconventional mortgages— including many subprime mortgages—do not influence the OFHEO index. By contrast, the SP it includes data on houses financed by jumbo mortgages, subprime mortgages, and home purchases that do not involve a mortgage. The S&P/CS index is also value weighted: More expensive homes have relatively greater influence on the index, whereas the OFHEO index is unit weighted. Further more, the S&P/CS index assigns greater weight to census regions with greater total residential real estate value. The OFHEO index, by contrast, weights regions based on the number of residential units. Hence, regions with relatively higher average home prices have more influence on the S&P/CS index than on the OFHEO index.1 The S&P/CS index exhibited faster growth in house prices before 2006 because house prices rose more rapidly in the regions with more influence on that index. These regions also tended to have a higher percentage of transactions involving mortgages ineligible for purchase by Fannie Mae and Freddie Mac. The relatively rapid decline in house prices since 2006 in the West and East Coast regions, and the relatively greater weight given these regions in the S&P/CS index help explain why the S&P/CS index shows a more rapid decline in house prices for the United States as a whole.","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"97 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":"127386425","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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