O bservers of the economy have clearly documented that U.S. aggregate output has become much less volatile since the early 1980s. The accompanying chart plots the annualized standard deviation of quarterly growth of real gross domestic product (GDP) using a 60quarter rolling window. The value corresponding to 1962:Q2 is the standard deviation of GDP growth between 1947:Q3 and 1962:Q2, for example. The downward movement in output volatility is particularly pronounced after 1984: The standard deviation of economic growth declined sharply from over 4 percent to about 2 percent in recent years. Economists have put forth three explanations why output growth may have become more stable in the past 20 years. One focuses on the conduct of monetary policy and the accompanying decline in inflation. Prior to the early 1980s, the Federal Reserve relied at times on recessions to rein in inflation. Since then, the Federal Reserve has been proactive in keeping inflation contained. Another explanation is that the U.S. economy simply has enjoyed good fortune in that there have been, for example, fewer tumultuous oil price shocks, which can cause volatility in economic activity. The third explanation suggests that improvements in inventory management are important for understanding the reduction in volatility. That is, while the durable goods sector has experienced a dramatic decline in output volatility in the past two decades, final sales of durable goods have seen only a moderate decline in volatility. Therefore, durable goods inventories—the difference between production and final sales—account for a substantial reduction in output variability in the durable goods sector and in the aggregate economy. Stock and Watson (2002) conduct a comprehensive analysis on this issue and provide some insights on the relative importance of the three hypotheses in explaining the decline in output volatility.1 Their results indicate that improved monetary policy could account for 20 to 30 percent of the volatility reduction and that smaller shocks probably account for most of the rest. However, they acknowledge that their conclusions are tentative and are open to further investigation. The fact that U.S. output growth is more stable now than it was two decades ago has important implications in interpreting economic data. For example, in the 1970s, changes in annualized GDP growth that seem large by today’s standards were, back then, within one standard deviation of the mean and thus policymakers could consider them noise. In contrast, a shock to output growth of a similar magnitude today would be cause for believing that the economy might be near a business cycle turning point and would be more likely to elicit a prompt response from monetary policymakers. Perhaps for this reason, Federal Reserve policymakers began cutting the federal funds rate aggressively in January 2001, based on a slowing economy that would not actually enter a recession until March 2001.
{"title":"The less volatile U.S. economy","authors":"Hui Guo","doi":"10.20955/ES.2003.24","DOIUrl":"https://doi.org/10.20955/ES.2003.24","url":null,"abstract":"O bservers of the economy have clearly documented that U.S. aggregate output has become much less volatile since the early 1980s. The accompanying chart plots the annualized standard deviation of quarterly growth of real gross domestic product (GDP) using a 60quarter rolling window. The value corresponding to 1962:Q2 is the standard deviation of GDP growth between 1947:Q3 and 1962:Q2, for example. The downward movement in output volatility is particularly pronounced after 1984: The standard deviation of economic growth declined sharply from over 4 percent to about 2 percent in recent years. Economists have put forth three explanations why output growth may have become more stable in the past 20 years. One focuses on the conduct of monetary policy and the accompanying decline in inflation. Prior to the early 1980s, the Federal Reserve relied at times on recessions to rein in inflation. Since then, the Federal Reserve has been proactive in keeping inflation contained. Another explanation is that the U.S. economy simply has enjoyed good fortune in that there have been, for example, fewer tumultuous oil price shocks, which can cause volatility in economic activity. The third explanation suggests that improvements in inventory management are important for understanding the reduction in volatility. That is, while the durable goods sector has experienced a dramatic decline in output volatility in the past two decades, final sales of durable goods have seen only a moderate decline in volatility. Therefore, durable goods inventories—the difference between production and final sales—account for a substantial reduction in output variability in the durable goods sector and in the aggregate economy. Stock and Watson (2002) conduct a comprehensive analysis on this issue and provide some insights on the relative importance of the three hypotheses in explaining the decline in output volatility.1 Their results indicate that improved monetary policy could account for 20 to 30 percent of the volatility reduction and that smaller shocks probably account for most of the rest. However, they acknowledge that their conclusions are tentative and are open to further investigation. The fact that U.S. output growth is more stable now than it was two decades ago has important implications in interpreting economic data. For example, in the 1970s, changes in annualized GDP growth that seem large by today’s standards were, back then, within one standard deviation of the mean and thus policymakers could consider them noise. In contrast, a shock to output growth of a similar magnitude today would be cause for believing that the economy might be near a business cycle turning point and would be more likely to elicit a prompt response from monetary policymakers. Perhaps for this reason, Federal Reserve policymakers began cutting the federal funds rate aggressively in January 2001, based on a slowing economy that would not actually enter a recession until March 2001.","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"310 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":"133746742","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. Modern economic theory of foreign exchange rates stipulates that the Deutsche mark/U.S. dollar rate, for example, is equal to discounted future fundamentals—e.g., aggregate income, interest rates, and monetary aggregates—in both the United States and Germany. A substantial portion of the variation in these fundamental macroeconomic variables is predictable across time; therefore, fundamentals should provide important information about future movements in exchange rates. In an influential paper, Meese and Rogoff (1983), however, find that a simple random walk model, in which the forecasted value is the most recent realization, outperforms various forecasting models, including those using economic fundamentals as predictors.1 Meese and Rogoff’s result has inspired numerous empirical investigations of exchange rate predictability, and their conclusion has proven to be strikingly robust after 20 years of fresh data and intensive academic research. In light of seemingly compelling evidence, some recent authors argue that exchange rates are indeed unpredictable—possibly because some shocks have a permanent effect on economic fundamentals. In particular, if people discount the future very little relative to the present, then exchange rates could follow a process close to a random walk. Other economists, however, argue that exchange rates are predictable and that existing empirical studies suffer from various misspecifications. For example, some crucial fundamental determinants of exchange rates may have been omitted. Also, many macroeconomic variables are subject to periodic revisions; therefore, the current vintage data, which have been commonly used in the literature, do not contain the same information as that available to investors at the time of forecast. To address these issues, Guo and Savickas (2005) propose using financial variables, which are broad measures of business conditions and never revised, to predict exchange rates.2 Guo and Savickas find that a measure of U.S. aggregate idiosyncratic volatility (IV) is a strong predictor of the exchange rates of the U.S. dollar against major foreign currencies, especially at relatively long horizons. An idiosyncratic shock to a stock is the part of the stock return that is not explained by asset pricing models. To measure IV, Guo and Savickas first estimate idiosyncratic shocks to all (U.S.) common stocks included in the CRSP (Center for Research in Security Prices) database; they then aggregate the realized variance of idiosyncratic shocks across stocks using the share of market capitalization as the weight. The accompanying chart plots IV from the last quarter of each year (in natural logarithms, solid line) along with one-yearahead changes (December 31 to December 31 of the following year, dashed line) in the Deutsche mark/U.S. dollar rate over the period 1973 to 1998 and the Euro/U.S. dollar rate over the
{"title":"Foreign exchange rates are predictable","authors":"Hui Guo","doi":"10.20955/ES.2005.20","DOIUrl":"https://doi.org/10.20955/ES.2005.20","url":null,"abstract":"Views expressed do not necessarily reflect official positions of the Federal Reserve System. Modern economic theory of foreign exchange rates stipulates that the Deutsche mark/U.S. dollar rate, for example, is equal to discounted future fundamentals—e.g., aggregate income, interest rates, and monetary aggregates—in both the United States and Germany. A substantial portion of the variation in these fundamental macroeconomic variables is predictable across time; therefore, fundamentals should provide important information about future movements in exchange rates. In an influential paper, Meese and Rogoff (1983), however, find that a simple random walk model, in which the forecasted value is the most recent realization, outperforms various forecasting models, including those using economic fundamentals as predictors.1 Meese and Rogoff’s result has inspired numerous empirical investigations of exchange rate predictability, and their conclusion has proven to be strikingly robust after 20 years of fresh data and intensive academic research. In light of seemingly compelling evidence, some recent authors argue that exchange rates are indeed unpredictable—possibly because some shocks have a permanent effect on economic fundamentals. In particular, if people discount the future very little relative to the present, then exchange rates could follow a process close to a random walk. Other economists, however, argue that exchange rates are predictable and that existing empirical studies suffer from various misspecifications. For example, some crucial fundamental determinants of exchange rates may have been omitted. Also, many macroeconomic variables are subject to periodic revisions; therefore, the current vintage data, which have been commonly used in the literature, do not contain the same information as that available to investors at the time of forecast. To address these issues, Guo and Savickas (2005) propose using financial variables, which are broad measures of business conditions and never revised, to predict exchange rates.2 Guo and Savickas find that a measure of U.S. aggregate idiosyncratic volatility (IV) is a strong predictor of the exchange rates of the U.S. dollar against major foreign currencies, especially at relatively long horizons. An idiosyncratic shock to a stock is the part of the stock return that is not explained by asset pricing models. To measure IV, Guo and Savickas first estimate idiosyncratic shocks to all (U.S.) common stocks included in the CRSP (Center for Research in Security Prices) database; they then aggregate the realized variance of idiosyncratic shocks across stocks using the share of market capitalization as the weight. The accompanying chart plots IV from the last quarter of each year (in natural logarithms, solid line) along with one-yearahead changes (December 31 to December 31 of the following year, dashed line) in the Deutsche mark/U.S. dollar rate over the period 1973 to 1998 and the Euro/U.S. dollar rate over the","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"24 1 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":"129817034","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 ach year thousands of high school seniors make the important decision of where to go to college. With tuition at many schools rising faster than the rate of inflation, financing a college education is becoming increasingly challenging. (In fact, in the United States, the growth rate of college costs since 1990 has been, on average, nearly 3 percent higher than the overall inflation rate.) Offers of financial aid—a complex menu of grants, loans, and work-study—vary by school. Indeed, some students may consider a school’s academic attributes and their projected influence on the student’s lifetime earning potential as less important than the school’s financial aid package. Thus, the way students weight financial aid offers can have a substantial impact on their choice of college. Working with counselors from 510 U.S. high schools, economists Christopher Avery and Caroline Hoxby1 surveyed high-aptitude high school seniors (students likely to gain admission and merit scholarships from selective colleges) to study how students assess financial aid pack ages. In particular, they sought to determine how financial aid characteristics affect the probability that the student will choose a particular school, taking into account individual attributes: SAT score, GPA, legacy status, etc. Avery and Hoxby assert at the outset that there are distinguishing characteristics across financial aid packages that do not necessarily add value. Nevertheless, they find that approxi mately 30 percent of the students in their sample responded strongly to what are arguably trivial distinctions between financial aid packages. The first distinguishing characteristic is whether or not a grant is called a “scholarship.” Clearly, the amount of the grant, not its name, should be what matters. (In fact, the authors note that the amount of a grant is actually negatively correlated to it being designated as a “scholarship.”) Nevertheless, Avery and Hoxby find that students are very responsive to this distinction when deciding which college to attend. Students may consider a named scholarship to be more impressive than an unnamed grant when listed on resumes or job applications—perhaps because scholarship connotes merit-based aid and grant connotes need-based aid. The second characteristic Avery and Hoxby consider is whether or not the grant is front-loaded, meaning the student receives more aid in his or her freshman year than in later years. An example would be a grant that gives $10,000 the first year and $2,000 each of the subsequent three years as opposed to a grant that gives $4,000 each of the four years. Avery and Hoxby find strong student response to front-loading. Potential reasons for students to prefer front-loading are clear: Front-loading better allows students to consider the possibility of transferring to a different school after the first year or two; it gives parents more time to save money toward the total cost of college; and it gives parents the opportunity to ea
{"title":"Financial aid and college choice","authors":"Abbigail J. Chiodo, Michael T. Owyang","doi":"10.20955/ES.2003.20","DOIUrl":"https://doi.org/10.20955/ES.2003.20","url":null,"abstract":"E ach year thousands of high school seniors make the important decision of where to go to college. With tuition at many schools rising faster than the rate of inflation, financing a college education is becoming increasingly challenging. (In fact, in the United States, the growth rate of college costs since 1990 has been, on average, nearly 3 percent higher than the overall inflation rate.) Offers of financial aid—a complex menu of grants, loans, and work-study—vary by school. Indeed, some students may consider a school’s academic attributes and their projected influence on the student’s lifetime earning potential as less important than the school’s financial aid package. Thus, the way students weight financial aid offers can have a substantial impact on their choice of college. Working with counselors from 510 U.S. high schools, economists Christopher Avery and Caroline Hoxby1 surveyed high-aptitude high school seniors (students likely to gain admission and merit scholarships from selective colleges) to study how students assess financial aid pack ages. In particular, they sought to determine how financial aid characteristics affect the probability that the student will choose a particular school, taking into account individual attributes: SAT score, GPA, legacy status, etc. Avery and Hoxby assert at the outset that there are distinguishing characteristics across financial aid packages that do not necessarily add value. Nevertheless, they find that approxi mately 30 percent of the students in their sample responded strongly to what are arguably trivial distinctions between financial aid packages. The first distinguishing characteristic is whether or not a grant is called a “scholarship.” Clearly, the amount of the grant, not its name, should be what matters. (In fact, the authors note that the amount of a grant is actually negatively correlated to it being designated as a “scholarship.”) Nevertheless, Avery and Hoxby find that students are very responsive to this distinction when deciding which college to attend. Students may consider a named scholarship to be more impressive than an unnamed grant when listed on resumes or job applications—perhaps because scholarship connotes merit-based aid and grant connotes need-based aid. The second characteristic Avery and Hoxby consider is whether or not the grant is front-loaded, meaning the student receives more aid in his or her freshman year than in later years. An example would be a grant that gives $10,000 the first year and $2,000 each of the subsequent three years as opposed to a grant that gives $4,000 each of the four years. Avery and Hoxby find strong student response to front-loading. Potential reasons for students to prefer front-loading are clear: Front-loading better allows students to consider the possibility of transferring to a different school after the first year or two; it gives parents more time to save money toward the total cost of college; and it gives parents the opportunity to ea","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"1 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":"129859284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Views expressed do not necessarily reflect official positions of the Federal Reserve System. The commodity futures market has changed dramatically over the past five years. The Goldman Sachs Commodity Index (GSCI) rose from 235 at the end of December 2002 to 787 on the last day of May 2008—an average annual commodity price inflation rate of 25 percent. The price of agricultural commodities rose about 15 percent, and energy prices soared almost twice as fast—at 29 percent. Futures market participants normally include commercial hedgers and speculators. Commercial hedgers are firms that produce the commodity or use it in producing goods and services. For example, wheat farmers sell wheat ahead of the harvest to hedge against a falling price at harvest time. On the other side of the market, the bread and pasta producers buy wheat in advance to hedge against the risk of rising prices in coming months (and years). Speculators bring liquidity to the market and are generally believed to make the market more efficient in discovering the equilibrium price. One big change in this market is the growth of index funds that invest in long positions. In 2002, only a small percentage of the long positions were held by such funds. Over the past five years, however, the index funds’ long positions have grown. They now represent a significant share of the investment in commodity futures. The rise of index funds has been accompanied by a rapid rise in the use of derivatives based on commodity price indices. Note, however, that not all of these investors are speculators. Although it is true that they are not hedging risk in the commodity markets, many large investors, including the employee pension funds for the federal government and the state of California, are using the commodity futures index funds to hedge risk in the stock and bond markets. Why the rapid growth in the use of commodity futures to hedge risk in the stock and bond markets? Readers seeking to understand this change are referred to a recent research paper by Gary Gorton and Geert Rouwenhorst in which they develop a data set on commodity futures prices that spans the period from July 1959 to December 2003.1 They analyze the return an investor would have earned on a long position in an equally weighted portfolio of investments in a broad set of commodity futures. They show that such an investment displays the riskreturn characteristics of a similar investment in equities. The most interesting fact they uncover, however, is that the return to commodity futures was negatively correlated with returns in both stocks and bonds. The commodity future returns are positively correlated with inflation, unexpected inflation, and changes in expected inflation. In other words, an investment in commodity futures would have been an effective hedge against the business cycle and inflation risk that had been thought difficult, if not impossible, to hedge. Of course, the history of returns likely would be different if
{"title":"Index funds: hedgers or speculators?","authors":"W. Gavin","doi":"10.20955/ES.2008.17","DOIUrl":"https://doi.org/10.20955/ES.2008.17","url":null,"abstract":"Views expressed do not necessarily reflect official positions of the Federal Reserve System. The commodity futures market has changed dramatically over the past five years. The Goldman Sachs Commodity Index (GSCI) rose from 235 at the end of December 2002 to 787 on the last day of May 2008—an average annual commodity price inflation rate of 25 percent. The price of agricultural commodities rose about 15 percent, and energy prices soared almost twice as fast—at 29 percent. Futures market participants normally include commercial hedgers and speculators. Commercial hedgers are firms that produce the commodity or use it in producing goods and services. For example, wheat farmers sell wheat ahead of the harvest to hedge against a falling price at harvest time. On the other side of the market, the bread and pasta producers buy wheat in advance to hedge against the risk of rising prices in coming months (and years). Speculators bring liquidity to the market and are generally believed to make the market more efficient in discovering the equilibrium price. One big change in this market is the growth of index funds that invest in long positions. In 2002, only a small percentage of the long positions were held by such funds. Over the past five years, however, the index funds’ long positions have grown. They now represent a significant share of the investment in commodity futures. The rise of index funds has been accompanied by a rapid rise in the use of derivatives based on commodity price indices. Note, however, that not all of these investors are speculators. Although it is true that they are not hedging risk in the commodity markets, many large investors, including the employee pension funds for the federal government and the state of California, are using the commodity futures index funds to hedge risk in the stock and bond markets. Why the rapid growth in the use of commodity futures to hedge risk in the stock and bond markets? Readers seeking to understand this change are referred to a recent research paper by Gary Gorton and Geert Rouwenhorst in which they develop a data set on commodity futures prices that spans the period from July 1959 to December 2003.1 They analyze the return an investor would have earned on a long position in an equally weighted portfolio of investments in a broad set of commodity futures. They show that such an investment displays the riskreturn characteristics of a similar investment in equities. The most interesting fact they uncover, however, is that the return to commodity futures was negatively correlated with returns in both stocks and bonds. The commodity future returns are positively correlated with inflation, unexpected inflation, and changes in expected inflation. In other words, an investment in commodity futures would have been an effective hedge against the business cycle and inflation risk that had been thought difficult, if not impossible, to hedge. Of course, the history of returns likely would be different if ","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"52 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":"132455553","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":"Cities as centers of innovation","authors":"Rubén Hernández-Murillo","doi":"10.20955/ES.2003.7","DOIUrl":"https://doi.org/10.20955/ES.2003.7","url":null,"abstract":"","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":"127721151","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":"Saving for a rainy day","authors":"T. Garrett","doi":"10.20955/ES.2004.9","DOIUrl":"https://doi.org/10.20955/ES.2004.9","url":null,"abstract":"","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"76 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":"116836475","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. U.S. firms rarely engage in international trade. In 2000, for example, there were 5.5 million firms in the United States; of these only about 4 percent were exporters. And the top 10 percent of these exporters accounted for 96 percent of total U.S. exports. Not surprisingly, goods-producing firms account for the majority of exports (as measured by value). The table shows the distribution of exporting firms among 10 manufacturing industries ranked by their share of total manufacturing employment in 2002. Most manufacturing industries have some firms that export, but the share of those firms in each industry is relatively small, varying between 12 and 38 percent for the larger industries and between 12 and 25 percent for the smaller industries. Furthermore, across all industries, on average, a firm’s foreign shipments represent only a small proportion (never exceeding 21 percent) of total shipments. In manufacturing as a whole in 2002, only 18 percent of firms were exporters and only about 14 percent of total firm shipments were exports. Not only are exporting firms rare, they also stand out in several ways: Studies show that exporting firms are more productive in terms of value-added per worker and total factor productivity and that they ship a higher volume of products. They use more skilled workers, capital, and sophisticated technology than non-exporting firms. They also pay higher wages and are more innovative. (These differences persist even after accounting for firm size and industry type.) On the surface, exporting seems beneficial. So why don’t more firms export? One important distinction may offer a clue: Although the productivity level of exporting firms is higher than that of non-exporting firms, their productivity growth is not—which suggests that high productivity is a requirement for and not a consequence of engaging in international trade. High entry costs for exporting may be a barrier to all but the most efficient firms. At the same time, economists have also found that once firms begin exporting they experience faster growth than non-exporting firms in both employment and output (in both foreign and domestic shipments).1 Andrew Bernard and J. Bradford Jensen, along with coauthors, argue that the higher initial productivity of exporters, combined with higher output and employment growth after entry, suggests an important role for trade liberalization (a reduction of trade barriers) in improving the aggregate productivity of the economy.2 The reason is that a reduction in trade barriers would improve the profits of existing exporting firms and would reduce the initial productivity level necessary for additional firms to enter the export market. This additional entry would, in turn, generate an increased demand for labor and therefore higher wages. Low-productivity non-exporting firms would be forced to exit the industry, and both capital an
{"title":"U.S. exporters: a rare breed","authors":"Rubén Hernández-Murillo","doi":"10.20955/ES.2007.20","DOIUrl":"https://doi.org/10.20955/ES.2007.20","url":null,"abstract":"Views expressed do not necessarily reflect official positions of the Federal Reserve System. U.S. firms rarely engage in international trade. In 2000, for example, there were 5.5 million firms in the United States; of these only about 4 percent were exporters. And the top 10 percent of these exporters accounted for 96 percent of total U.S. exports. Not surprisingly, goods-producing firms account for the majority of exports (as measured by value). The table shows the distribution of exporting firms among 10 manufacturing industries ranked by their share of total manufacturing employment in 2002. Most manufacturing industries have some firms that export, but the share of those firms in each industry is relatively small, varying between 12 and 38 percent for the larger industries and between 12 and 25 percent for the smaller industries. Furthermore, across all industries, on average, a firm’s foreign shipments represent only a small proportion (never exceeding 21 percent) of total shipments. In manufacturing as a whole in 2002, only 18 percent of firms were exporters and only about 14 percent of total firm shipments were exports. Not only are exporting firms rare, they also stand out in several ways: Studies show that exporting firms are more productive in terms of value-added per worker and total factor productivity and that they ship a higher volume of products. They use more skilled workers, capital, and sophisticated technology than non-exporting firms. They also pay higher wages and are more innovative. (These differences persist even after accounting for firm size and industry type.) On the surface, exporting seems beneficial. So why don’t more firms export? One important distinction may offer a clue: Although the productivity level of exporting firms is higher than that of non-exporting firms, their productivity growth is not—which suggests that high productivity is a requirement for and not a consequence of engaging in international trade. High entry costs for exporting may be a barrier to all but the most efficient firms. At the same time, economists have also found that once firms begin exporting they experience faster growth than non-exporting firms in both employment and output (in both foreign and domestic shipments).1 Andrew Bernard and J. Bradford Jensen, along with coauthors, argue that the higher initial productivity of exporters, combined with higher output and employment growth after entry, suggests an important role for trade liberalization (a reduction of trade barriers) in improving the aggregate productivity of the economy.2 The reason is that a reduction in trade barriers would improve the profits of existing exporting firms and would reduce the initial productivity level necessary for additional firms to enter the export market. This additional entry would, in turn, generate an increased demand for labor and therefore higher wages. Low-productivity non-exporting firms would be forced to exit the industry, and both capital an","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"65 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":"114474410","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}
In 2010, the first of the Baby Boom generation will reach age 65. Many will choose to begin what they hope will be a long and financially secure retirement funded in part by Social Security. Social Security, however, has a looming fiscal problem. Social Security payments to current recipients are funded mainly by taxes levied on current workers. As more and more baby boomers retire, the number of persons receiving Social Security benefits will increase rapidly relative to the number of persons paying taxes to fund those benefits. Accord ing to the Trustees of the Social Security and Medicare Trust Funds, by 2017 Social Security benefit payments will exceed payroll tax revenues and by 2041 all trust fund assets likely will be exhausted.1 The Social Security System’s revenue shortfall mainly reflects a rising elderly dependency ratio: that is, the number of elderly persons (65+ years) relative to the number of working-age persons (20 to 64 years). As shown in the chart, in 1950 there were some 14 persons age 65 and older for every 100 persons between the ages of 20 and 64. By 2000, there were 20; and, as more of the baby boom generation reaches age 65, the ratio will rise to 35 by 2030. Although the coming stampede of baby boomers will cause the dependency ratio to increase sharply after 2010, rising adult life expectancy has been a major reason why the dependency ratio has risen and will continue to rise. The life expectancy of the typical 65year-old man has risen over the years: In 1940, he could expect to live another 12.7 years; by 2005, he could expect to live another 17.1 years; and demographers expect that, by 2030, he could expect to live another 18.7 years. Although the age at which persons are eligible for full Social Security benefits—long fixed at 65—will gradually rise to 67 by 2025, this won’t prevent System revenues from falling short of payments. Declining fertility has also contributed to this rising dependency ratio. In 1950, the U.S. fertility rate was 3.0, meaning the average woman had three children in her lifetime. By 2002, the fertility rate had fallen to 2.0. This decline implies that fewer young persons will enter the labor force to support the growing elderly population. Clearly, the looming Social Security funding crisis largely reflects changing U.S. demographics. The aging baby boom generation, increased adult life expectancy, and declining fertility will rapidly increase the number of retired persons drawing benefits relative to persons paying taxes to fund those benefits. Possible solutions to the problem include policies to (i) slow the growth in the number of retired persons per worker, perhaps by larger and more rapid increases in the age at which persons become eligible for benefits; (ii) otherwise reduce promised benefits; (iii) encourage more immigration of young workers; and/or (iv) substantially raise taxes on current workers. A more radical proposal would replace all or part of the existing system with a syst
{"title":"Can Social Security survive the baby boomers","authors":"C. Aubuchon, David C. Wheelock","doi":"10.20955/ES.2007.22","DOIUrl":"https://doi.org/10.20955/ES.2007.22","url":null,"abstract":"In 2010, the first of the Baby Boom generation will reach age 65. Many will choose to begin what they hope will be a long and financially secure retirement funded in part by Social Security. Social Security, however, has a looming fiscal problem. Social Security payments to current recipients are funded mainly by taxes levied on current workers. As more and more baby boomers retire, the number of persons receiving Social Security benefits will increase rapidly relative to the number of persons paying taxes to fund those benefits. Accord ing to the Trustees of the Social Security and Medicare Trust Funds, by 2017 Social Security benefit payments will exceed payroll tax revenues and by 2041 all trust fund assets likely will be exhausted.1 The Social Security System’s revenue shortfall mainly reflects a rising elderly dependency ratio: that is, the number of elderly persons (65+ years) relative to the number of working-age persons (20 to 64 years). As shown in the chart, in 1950 there were some 14 persons age 65 and older for every 100 persons between the ages of 20 and 64. By 2000, there were 20; and, as more of the baby boom generation reaches age 65, the ratio will rise to 35 by 2030. Although the coming stampede of baby boomers will cause the dependency ratio to increase sharply after 2010, rising adult life expectancy has been a major reason why the dependency ratio has risen and will continue to rise. The life expectancy of the typical 65year-old man has risen over the years: In 1940, he could expect to live another 12.7 years; by 2005, he could expect to live another 17.1 years; and demographers expect that, by 2030, he could expect to live another 18.7 years. Although the age at which persons are eligible for full Social Security benefits—long fixed at 65—will gradually rise to 67 by 2025, this won’t prevent System revenues from falling short of payments. Declining fertility has also contributed to this rising dependency ratio. In 1950, the U.S. fertility rate was 3.0, meaning the average woman had three children in her lifetime. By 2002, the fertility rate had fallen to 2.0. This decline implies that fewer young persons will enter the labor force to support the growing elderly population. Clearly, the looming Social Security funding crisis largely reflects changing U.S. demographics. The aging baby boom generation, increased adult life expectancy, and declining fertility will rapidly increase the number of retired persons drawing benefits relative to persons paying taxes to fund those benefits. Possible solutions to the problem include policies to (i) slow the growth in the number of retired persons per worker, perhaps by larger and more rapid increases in the age at which persons become eligible for benefits; (ii) otherwise reduce promised benefits; (iii) encourage more immigration of young workers; and/or (iv) substantially raise taxes on current workers. A more radical proposal would replace all or part of the existing system with a syst","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"1 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":"125851517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S tate tax revenue grew markedly during the 1990s as a result of rapid economic growth. Burgeoning tax revenues, the resulting budget surpluses, and rosy revenue forecasts prompted almost every state to enact large permanent tax cuts. Ten states enacted cuts of between 1 and 3 percent of total tax revenues, while 33 states enacted cuts in excess of 3 percent of total tax revenues.1 According to the Center on Budget and Policy Priorities, the tax cuts of the 1990s reduced actual state tax revenues by 8.2 percent from what they otherwise would have been. Nevertheless, actual tax revenues continued to grow throughout the 1990s, thanks to the economic boom. It turned out, however, that states financed permanent tax cuts with the temporary economic boom of the 1990s. In contrast to the 1990-91 recession, when nearly every state raised taxes in response to budget shortfalls, fewer than 20 states have raised taxes since the 2001 recession. And in most cases, the tax increases have focused on relatively narrow and/or shrinking tax bases, such as retail sales and cigarettes.2 Slow economic growth, a weak stock market, an increase in homeland security responsibilities, and a greater reliance on weakening tax bases continue to prolong states’ budget crises. An important question is whether current budget deficits are due entirely to a reduction in revenue, or whether state expenditures have grown at unusually high rates over the past decade. Annual real per capita state expenditures and revenues from 1970 to 2002 are shown in the figure along with recessions as determined by the National Bureau of Economic Research.3 The aggregate state budget deficit is clearly seen at the far right of the figure and is much greater than the deficit associated with the 1990-91 recession. Also, as shown, the growth in real per capita expenditures during the 1990s was not greater than that of earlier decades. In fact, the average annual growth in real per capita state expenditures from 1992 through 2000 was 1.4 percent, compared with 2.5 and 2.3 percent in non-recession years during the 1980s and 1970s, respectively. However, revenue and expenditure data for the past three years reveal that expenditure growth did not slow in the wake of decreasing tax revenues. Real per capita state revenue fell by 0.2 percent in 2000, 1.9 percent in 2001, and 0.7 percent in 2002, whereas real per capita expenditures rose by 1.3 percent, 3.4 percent, and 1.3 percent, respectively. While this scenario occurred during other recessionary periods, as shown in the figure, state budget surpluses prior to this recent recession were smaller than those prior to earlier recessions, thus increasing the chances that a reduction in revenue would lead to a budget deficit. States might have underestimated how volatile their revenue sources would prove to be in the face of a recession. States began relying on capital gains and income from stock options and bonuses as a growing source of tax revenue (compare
{"title":"State budget crises: cause and effect","authors":"T. Garrett","doi":"10.20955/ES.2003.27","DOIUrl":"https://doi.org/10.20955/ES.2003.27","url":null,"abstract":"S tate tax revenue grew markedly during the 1990s as a result of rapid economic growth. Burgeoning tax revenues, the resulting budget surpluses, and rosy revenue forecasts prompted almost every state to enact large permanent tax cuts. Ten states enacted cuts of between 1 and 3 percent of total tax revenues, while 33 states enacted cuts in excess of 3 percent of total tax revenues.1 According to the Center on Budget and Policy Priorities, the tax cuts of the 1990s reduced actual state tax revenues by 8.2 percent from what they otherwise would have been. Nevertheless, actual tax revenues continued to grow throughout the 1990s, thanks to the economic boom. It turned out, however, that states financed permanent tax cuts with the temporary economic boom of the 1990s. In contrast to the 1990-91 recession, when nearly every state raised taxes in response to budget shortfalls, fewer than 20 states have raised taxes since the 2001 recession. And in most cases, the tax increases have focused on relatively narrow and/or shrinking tax bases, such as retail sales and cigarettes.2 Slow economic growth, a weak stock market, an increase in homeland security responsibilities, and a greater reliance on weakening tax bases continue to prolong states’ budget crises. An important question is whether current budget deficits are due entirely to a reduction in revenue, or whether state expenditures have grown at unusually high rates over the past decade. Annual real per capita state expenditures and revenues from 1970 to 2002 are shown in the figure along with recessions as determined by the National Bureau of Economic Research.3 The aggregate state budget deficit is clearly seen at the far right of the figure and is much greater than the deficit associated with the 1990-91 recession. Also, as shown, the growth in real per capita expenditures during the 1990s was not greater than that of earlier decades. In fact, the average annual growth in real per capita state expenditures from 1992 through 2000 was 1.4 percent, compared with 2.5 and 2.3 percent in non-recession years during the 1980s and 1970s, respectively. However, revenue and expenditure data for the past three years reveal that expenditure growth did not slow in the wake of decreasing tax revenues. Real per capita state revenue fell by 0.2 percent in 2000, 1.9 percent in 2001, and 0.7 percent in 2002, whereas real per capita expenditures rose by 1.3 percent, 3.4 percent, and 1.3 percent, respectively. While this scenario occurred during other recessionary periods, as shown in the figure, state budget surpluses prior to this recent recession were smaller than those prior to earlier recessions, thus increasing the chances that a reduction in revenue would lead to a budget deficit. States might have underestimated how volatile their revenue sources would prove to be in the face of a recession. States began relying on capital gains and income from stock options and bonuses as a growing source of tax revenue (compare","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128107724","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 chorus from Travis’s 1947 song about the plight of coal miners might ring true for someone looking at average hourly earnings (AHE) of production and nonsupervisory workers. By this measure, as shown in the chart, the pay for an hour of work fell in real terms by 3 percent between 1975 and 2006. Is the average worker actually receiving less per hour of work today than 31 years ago? The answer is likely no. In fact, an alternative measure of compensation, national labor income per hour, increased 44 percent during this period. What accounts for these conflicting results and why do we conclude that the average worker’s real compensation per hour has increased since the mid-1970s? Both the AHE and the national labor income series are adjusted for inflation. However, AHE is adjusted using the consumer price index for all urban wage earners and clerical workers (CPI-W), while national labor income per hour is adjusted using the personal consumption expenditures (PCE) implicit price deflator. To calculate the purchasing power of an hour of work, it is more appropriate to use the PCE implicit price deflator to adjust for inflation because this index better reflects the basket of goods and services actually consumed. Contrary to the CPI-W, which assumes that the same basket of goods and services is purchased for several years, the PCE deflator is calculated using expenditures from the current and preceding period. After applying the PCE deflator, AHE show an 11 percent increase rather than a 3 percent decrease between 1975 and 2006. Another difference in the construction of the two data series is that national labor income per hour includes not only wages and salaries, but also fringe benefits. Given the importance of benefits to a worker’s standard of living, we think many would disagree with the use of the label “fringe.” The benefits of employer contributions to worker’s pension and insurance funds and to government social insurance are included in national labor income per hour, but are not in the AHE series.1 These benefits have become a larger share of worker compensation over time, rising from 14 percent in 1975 to 19 percent in 2006. Once the AHE data are adjusted to include estimated benefits per hour and the PCE deflator is applied, the calculated increase in real wages and benefits reaches 16 percent between 1975 and 2006. Without question, the 16 percent increase in average hourly earnings following the two adjustments described above remains far short of the 44 percent increase in national labor income per hour. What accounts for the remaining difference is unclear. Part of the difference is likely due to the fact that the AHE is restricted to production and nonsupervisory workers. What is clear, however, is that the average worker is receiving more in 2006 for “sixteen tons” than 31 years ago.
{"title":"What do you get for \"Sixteen Tons\"?","authors":"Cletus C. Coughlin, Lesli S. Ott","doi":"10.20955/es.2007.29","DOIUrl":"https://doi.org/10.20955/es.2007.29","url":null,"abstract":"The chorus from Travis’s 1947 song about the plight of coal miners might ring true for someone looking at average hourly earnings (AHE) of production and nonsupervisory workers. By this measure, as shown in the chart, the pay for an hour of work fell in real terms by 3 percent between 1975 and 2006. Is the average worker actually receiving less per hour of work today than 31 years ago? The answer is likely no. In fact, an alternative measure of compensation, national labor income per hour, increased 44 percent during this period. What accounts for these conflicting results and why do we conclude that the average worker’s real compensation per hour has increased since the mid-1970s? Both the AHE and the national labor income series are adjusted for inflation. However, AHE is adjusted using the consumer price index for all urban wage earners and clerical workers (CPI-W), while national labor income per hour is adjusted using the personal consumption expenditures (PCE) implicit price deflator. To calculate the purchasing power of an hour of work, it is more appropriate to use the PCE implicit price deflator to adjust for inflation because this index better reflects the basket of goods and services actually consumed. Contrary to the CPI-W, which assumes that the same basket of goods and services is purchased for several years, the PCE deflator is calculated using expenditures from the current and preceding period. After applying the PCE deflator, AHE show an 11 percent increase rather than a 3 percent decrease between 1975 and 2006. Another difference in the construction of the two data series is that national labor income per hour includes not only wages and salaries, but also fringe benefits. Given the importance of benefits to a worker’s standard of living, we think many would disagree with the use of the label “fringe.” The benefits of employer contributions to worker’s pension and insurance funds and to government social insurance are included in national labor income per hour, but are not in the AHE series.1 These benefits have become a larger share of worker compensation over time, rising from 14 percent in 1975 to 19 percent in 2006. Once the AHE data are adjusted to include estimated benefits per hour and the PCE deflator is applied, the calculated increase in real wages and benefits reaches 16 percent between 1975 and 2006. Without question, the 16 percent increase in average hourly earnings following the two adjustments described above remains far short of the 44 percent increase in national labor income per hour. What accounts for the remaining difference is unclear. Part of the difference is likely due to the fact that the AHE is restricted to production and nonsupervisory workers. What is clear, however, is that the average worker is receiving more in 2006 for “sixteen tons” than 31 years ago.","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":"128191065","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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