National Economic Trends最新文献

{"title":"The economics of giving","authors":"Rubén Hernández-Murillo","doi":"10.20955/es.2005.24","DOIUrl":"https://doi.org/10.20955/es.2005.24","url":null,"abstract":"In America, charitable giving is a thriving multibillion dollar enterprise, as illustrated in the accompanying chart. Most charitable contributions arise from the generosity of individual donors. In fact, the Giving USA Foundation estimates that individuals gave almost $188 billion to charities in 2004 (a 175 percent inflation-adjusted increase from 1964).1 Generosity is particularly evident after unexpected disasters. In September 2005, for example, the American Red Cross received about $807 million in gifts and pledges earmarked for Hurricane Katrina relief efforts—an increase of about $250 million over total Red Cross contributions during the 2003-04 fiscal year (www.redcross.org/news/ds/hurricanes/katrina_facts.html). Economists analyze the motivations behind individual giving to better understand how contributions are influenced by demographic characteristics, tax policies, and fundraising behavior. A fundamental issue of this analysis is the nature of the benefits that individuals receive when they give to charity. A recent study summarizes two alternative views.2 First, donors may focus on the well-being of charity recipients. In this case, the benefits from giving have a public nature. If the well-being of recipients is tied to the charity’s activities (the provision of disaster relief or the funding of cancer research), donors derive benefits from giving in the same way they derive benefits from public goods such as national defense. That is, a donor cannot exclude anyone else from enjoying the charity’s accomplishments; also, a donor’s enjoyment is not affected by the enjoyment or benefit derived by others. Second, donors may focus on the enjoyment they receive from the act of giving itself—that is, the internal feeling they derive from “doing their share” or “giving back to society.” Donors may also care about public recognition or about signaling wealth status. In these cases, the benefits from giving have a private nature. Individuals derive satisfaction from giving in the same way they derive benefits from consuming other private goods or services, such as clothing or food. These two motives for giving (public and private) have entirely different implications on giving behavior. Donors who experience public benefits look at the overall amount of charitable contributions, and donors who experience private benefits look only at their own contributions. If a donor’s benefits from giving are public, then a total contribution of $100 gives him the same sense that good is being done, even if his own contribution is $10 or $20. On the other hand, if a donor’s benefits from giving are private, a contribution of $20 generates more satisfaction than a contribution of $10, even if the total contribution is $100 either way. Economists predict that, according to the public benefits view, government subsidies to charities that are funded with increased taxes on donors will have no effect on the total contribution. This is because donors will redu","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"5 2 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":"117007452","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":"Reading inflation expectations from CPI futures","authors":"Hui Guo, Kevin L. Kliesen","doi":"10.20955/ES.2005.5","DOIUrl":"https://doi.org/10.20955/ES.2005.5","url":null,"abstract":"Views expressed do not necessarily reflect official positions of the Federal Reserve System. Statements issued by the Federal Open Market Committee (FOMC) at the conclusion of each meeting suggest that inflation expectations matter a great deal to monetary policymakers. Therefore, if expected inflation moves above or below a level that is viewed as optimal, then the FOMC will presumably take action to counter those expectations. Although there are several measures of inflation expectations, a relatively new and potentially useful measure is one based on futures contracts written on the consumer price index (CPI); these have been traded on the Chicago Mercantile Exchange since February 9, 2004. The contracts are based on the CPI for all urban consumers, all items (not seasonally adjusted). Similar to federal funds futures contracts, they have a pricing structure of 100 minus the contracted inflation rate—the three-month change in the CPI ending in the month prior to the expiration of the contract. According to the Chicago Mercantile Exchange, CPI futures can also be used as a derivative product to hedge inflation risk on other types of financial instruments, particularly Treasury inflation-protected securities (TIPS).1 The prices of CPI futures capture market participants’ expectation of future inflation and the associated risk premium. For simplicity, we assume that the latter is negligible. Therefore, 100 minus the contract’s price is approximately equal to the (annualized) expected inflation rate over the contracted period. If investors believe that the realized inflation rate will be lower than implied by the futures price, they will buy CPI futures and thus drive up the price until a new consensus is reached. In the accompanying chart, the solid line plots the average inflation rate implied by the CPI futures. The average inflation rate, which partially smoothes through the seasonal pattern of the future three-month inflation rates (recall that the contracts are written on non-seasonally adjusted data), is simply the average of the outstanding contracts at any point in time. For example, the December 2005 inflation rate is the average of the yields on the March, June, September, and December 2005 contracts; the point plotted for March 2006 is the average of the March 2005 through March 2006 contracts, and so forth. Since CPI futures contracts are written on the same inflation series used for the TIPS, the average inflation rates are analogous to the rates of inflation compensation derived from yield spreads between nominal and inflation-indexed Treasury securities, with some minor adjustments. One potential use of the CPI futures contracts, therefore, is to gauge the future inflation rate relative to the current rate. In 2004, the CPI rose 3.3 percent, the biggest increase in four years. Although a large part of the CPI increase was attributable to the jump in energy prices, it still raises the concern of whether inflation might be headed hig","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"8 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":"115573505","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":"Convergence across states and people","authors":"Riccardo DiCecio, Charles S. Gascon","doi":"10.20955/ES.2008.2","DOIUrl":"https://doi.org/10.20955/ES.2008.2","url":null,"abstract":"Income inequality has been and continues to be a major public policy topic. With respect to U.S. states, the common wisdom is that poorer states tend to grow faster than richer states and, as a result, per capita incomes of poor states and rich states are converging and will continue to converge in the future.1 We argue that such an assessment is quite possibly misleading. We analyze how the distribution of per capita personal income (PCPI), in percentage differences from the U.S. average, evolves over time for the period 1969-2005. We summarize the dynamics with the corresponding long-run distribution. A long-run distribution with a single-peak is consistent with convergence. A long-run distribution with multiple peaks indicates that, in the long-run, there will be groups of states that tend to cluster at different levels of income. The gray line in the chart is the long-run distribution of income across states. The lowest peak corresponds to a PCPI 19.2 percent below the U.S. average. The highest peak corresponds to a PCPI 3.7 percent below the cross-sectional average. In constructing this distribution, the income of any state, regardless of population, is treated the same as any other state. Things change if the PCPI dynamics calculation is weighted by the number of people within each state. The evolution of California’s PCPI will have a larger impact on the shape of the long-run distribution than Iowa’s PCPI dynamics because of California’s relatively larger population. The population-weighted distribution can be interpreted as the long-run distribution across people in the United States. The long-run distribution of income across people (the blue line in the chart) is still twin-peaked, but the low-income peak is much less pronounced. The population-weighted average PCPI is closer to the U.S. average and its standard deviation is 11 percent lower than that of the unweighted distribution. Convergence across people is driven by the fact that states experiencing a decline in their relative income are also losing population share. For example, Ohio in 1969 had the 15th highest income at 8 percent above the national average. By 2005 Ohio lost ground: It occupied the 30th place with a PCPI of 4.5 percent below the national average. At the same time, Ohio’s population declined from 5.35 percent of the total U.S population in 1969 to below 4 percent in 2005. Conversely, states growing rapidly enough to move up in the overall ranking of states’ income were gaining population, contributing to convergence. Colorado was the 22nd state in terms of PCPI in 1969 and climbed to the 9th place by 2005. During the same period, Colorado’s population share increased from 1.1 to 1.6 percent.2 Contrary to previous findings of convergence across states, our finding of a twin-peaked long-run distribution indicates that state incomes will cluster at different levels rather than converge. However, weighting each state by its population produces a nearly single-peaked ","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"299 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":"131475522","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":"Deflation, corrosive and otherwise","authors":"James Bullard, Charles M. Hokayem","doi":"10.20955/ES.2003.17","DOIUrl":"https://doi.org/10.20955/ES.2003.17","url":null,"abstract":"R ecently the Federal Open Market Committee (FOMC) mentioned deflation as a possible risk for the U.S. economy. In the statement released after the May policy meeting, the Committee stated that “the probability of an unwelcome substantial fall in inflation, though minor, exceeds that of a pickup in inflation from its already low level.” Later, Chairman Greenspan spoke about more than just a mild deflation. In comments to the International Monetary Conference in early June, Chairman Greenspan referred to the risk of “corrosive” deflation “that essentially feeds on itself, creates falling asset prices, which in turn brings down levels of economic activity...” What is the main evidence on deflation? Are there corrosive and benign forms? Does economic perfor mance always suffer during periods of sustained deflation? The accompanying table provides some evidence on deflation and lists periods in which the United States or Japan have experienced three or more years of a declining price level. There are three main episodes: the late 19th century in the United States, the Great Depression in the United States, and Japan since 1999. For each deflationary episode, the table lists the average real GDP growth rate and the average inflation rate based on two popular inflation measures, the GDP deflator and the consumer price index. The table also lists a benchmark average growth rate of GDP for years surrounding the deflation experience, so we can consider whether deflation is associated with lower-than-average growth for the corresponding era or not. Generally speaking, the United States experienced rapid growth during the late 19th century, with GDP growth averaging about 4.0 percent for the period 1876-1900, despite an average deflation of about 1.0 percent. By itself, this suggests that a mild deflation is not necessarily asso ciated with poor economic performance. However, averaging over a long period of time could mask severe distress that may accompany deflation. To address this issue, we examine the subperiods 1876-1879 and 18831885. During the former, GDP growth actually was greater than the bench mark value, despite a rather hefty 3.0 to 4.0 percent annual average decline in the price level. Evidently, this deflationary episode was rather benign. In contrast, the deflationary episode of 1883-1885 was not so benign. GDP growth fell well below the benchmark average for that time. The same is also true at the onset of the Great Depression (1930-1933) and the recent episode in Japan (1999-2002). During the Great Depression, GDP growth averaged a whopping –8.36 percent and inflation was around –6.5 percent. While not as severe, Japan also experienced slow growth during its deflationary episode. As the table shows, U.S. experience with sustained deflation has been limited since the founding of the Federal Reserve in 1914. The results from 1930-1933 are uniformly bad, but deflation may simply have been a by-product of the economic collapse at that time. Ja","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"166 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":"131204040","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":"Public officials and job creation","authors":"T. Garrett, D. Thornton","doi":"10.20955/ES.2004.22","DOIUrl":"https://doi.org/10.20955/ES.2004.22","url":null,"abstract":"Public officials often claim credit for creating jobs through the programs and policies they enact. It is not uncommon to hear, for example, a public official pledging to increase the number of jobs in a particular locality or nationally. Public officials can create jobs in two ways: The first is directly, by creating government jobs. The second is indirectly, by (i) enacting policies that create an economic environment that affects long-run private sector job growth or (ii) using countercyclical fiscal policy to affect short-run private sector job growth.1 How effective have public officials been at creating jobs? The accompanying chart shows the natural logarithm of payroll employment (measured by annual nonfarm payroll employment) from 1946 to 2003, along with the shares of total government, federal government, and state and local government employment. It gives no indication that public officials have created jobs directly. After increasing from 1946 to 1975, total government employment as a percent of payroll employment has trended down. Evidence that public officials create government jobs is even weaker if one considers federal employment. Federal employment as a percent of payroll employment has declined nearly mono tonically over the 1946 to 2003 period, from 5.6 percent in 1946 to 2.1 percent in 2003. Have public officials created jobs indirectly? Again, the chart raises questions about claims they might make. First, consider cyclical variation in payroll employment, as measured relative to a the trend line. With payroll employment expressed in natural logarithms, a constant growth rate is represented by a linear trend. The trend line indicates that payroll employment has grown at an average rate of about 2.1 percent during the post-World War II period. The shaded areas represent years when there was an official recession during at least part of the year. This measure of cyclical variation indicates that the lengths of significant deviations of payroll employment from a 2.1 percent trend line roughly match the lengths of the business cycles, with the exception of the 1960s during the military buildup for the war in Vietnam (armed forces on active duty are excluded from payroll employment). Thus, when it comes to cyclical variation in payroll employment, it seems that the business cycle largely determines the ebb and flow, despite any claims by lawmakers and policymakers that they act to stem the tide. Second, in terms of long-run jobs growth, have policies enacted by public officials affected the average growth rate of payroll employment? Again, the chart suggests that the answer is no. Importantly, there is no indication of a noteworthy break in payroll employment from the 2.1 percent growth path, which is what one would expect if public officials enacted policies that changed the average rate of job growth. The apparent lack of a break from the trend line is especially interesting given the array of national economic policies—changes ","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"96 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":"132864393","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":"How well do wages follow productivity growth","authors":"R. Anderson","doi":"10.20955/ES.2007.7","DOIUrl":"https://doi.org/10.20955/ES.2007.7","url":null,"abstract":"Over long periods of time, increases in “real” wages—that is, wages adjusted for changes in consumer prices—reflect increases in labor productivity. Economists now widely agree that labor productivity growth increased in the mid-1990s and remains at an elevated pace—at least relative to its anemic pace between 1973 and the mid-1990s. Numerous studies have traced the cause of the productivity acceleration to technological innovations in the production of semiconductors that sharply reduced the prices of such components and of the products that contain them (as well as expanding the capabilities of such products). The impact of more rapid productivity growth on wages continues to be a topic of widespread economic research. Numerous news articles have discussed the apparent failure of wages to increase in line with productivity. Less appreciated, perhaps, is that the productivity acceleration has been accompanied by important changes in the way businesses compensate their employees. Of particular importance is the increased use of “variable pay,” that is, compensation tied either to the performance of individual employees or to the business’s overall performance, including end-ofyear bonuses, “cash awards,” profit sharing, and stock options. The chart compares labor productivity in the nonfarm business sector to two measures of real labor compensation: average hourly earnings for non-supervisory and production workers (AHE) in the upper panel, and total compensation per hour in the lower panel. AHE measures the typical, scheduled hourly wage plus legally required benefits but excludes variable pay—overtime, bonuses, shift premiums, and employer benefits. Total compensation, in contrast, includes variable pay. Increases in these compensation series track productivity quite closely through 1999. Beginning in 2000, however, AHE falls increasingly below productivity and increases little after 2003. Total compensation remains close until 2003, but does not follow 2003’s uptick in productivity growth (behavior which remains a topic for future research). Economists long have noted that focusing on AHE rather than total compensation yields an inaccurate picture of labor compensation due to the omission from AHE of employer-provided benefits. The trend toward increased use of variable pay provides an additional reason for focusing on broader compensation measures. But, why has more of labor compensation become variable pay? And why has this trend widened since 2000? One reason, perhaps, is that the character of the productivity acceleration changed circa 2000. Prior to that date, studies have suggested that the more important effect was an increasing ratio of capital to labor (capital deepening) as businesses substituted relatively less expensive information technology and communication equipment for labor. Since 2000, some studies suggest that the more important factor has been a re-engineering of business practices, which has increased the “skill bias” in ","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"74 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":"132801550","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":"Color me beige","authors":"Howard J Wall","doi":"10.20955/ES.2002.5","DOIUrl":"https://doi.org/10.20955/ES.2002.5","url":null,"abstract":"When making monetary policy decisions, members of the Federal Open Market Committee (FOMC) want to know as much as possible about current and future economic conditions. Unfortunately, most economic data are reported with a lag of one month or more. Neverthe less, some nonfinancial economic data are available every week, such as initial unemployment claims, auto and steel production, and electricity consumption. Private forecasters use such data to update their estimates of currentquarter gross domestic product (GDP) through their knowledge of how the industrial production number is constructed from weekly data on electricity, autos, and steel. The Fed supplements real-time analysis of the data construction process in two distinct ways. First, it runs a large macro econometric model that uses past trends to project likely economic outcomes from alternative policy choices. Second, the regional Federal Reserve Banks conduct surveys to gather qualitative information about economic conditions in each district. Prior to every FOMC meeting, this anecdotal survey information is compiled into what is known as the Beige Book. The information provided by the Beige Book adds value in two ways. First, business cycle fluctuations are now thought to be more heterogeneous across regions and sectors than they used to be. Hence, one hears references to a “rolling recession” that bottoms out in different regions at different times. State and regional data, however, are much less complete than national data. In this void, the Beige Book can help identify the current regional focal point of such a rolling downturn. Second, for some one-time events, macroeconometric models are not reliable guides because history has not recorded a pattern for how the economy is likely to respond. Examples of such events include the surge in computer and software investment that preceded Y2K and the terrorist attacks on September 11, 2001. The best way to infer the likely consequences of such events is to talk with business leaders to discern their plans. The Beige Book provides a concise compendium of such a survey. Because it is based purely on anecdotal information, there are many reasons to question the usefulness of the Beige Book in assessing economic conditions. For example, it may partly reflect the biases of the economists who compile it, or policymakers may use only the anecdotes that are consistent with the views they already have. Recently, economists within the Federal Reserve System have tried to assess the Beige Book as an indicator of present and future economic activity. A study from the Minneapolis Fed found that the Beige Book has been an accurate predictor of real growth in the current quarter.1 They also found, however, that the Beige Book did not improve upon private sector forecasts of real growth. Their conclusion is that the Beige Book’s value is not as a forecaster of economic activity, but in providing insight and context not found in formal forecasting mode","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"135 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133016876","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":"Stock market dispersion and unemployment","authors":"Hui Guo","doi":"10.20955/ES.2007.5","DOIUrl":"https://doi.org/10.20955/ES.2007.5","url":null,"abstract":"Views expressed do not necessarily reflect official positions of the Federal Reserve System. According to the definition used in government statistics, a person who is actively looking for a paying job but is unable to find one is considered to be unemployed. Some positive level of unemployment always exists because (i) firms continually adjust the size of their work force in response to changing business conditions and (ii) it takes time for an unemployed worker to find a new job. It is common to use the unemployment rate—the number of unemployed workers divided by the total civilian labor force—to gauge labor market conditions, and this measure is closely watched by monetary policymakers as well as financial market participants. The unemployment rate usually rises during business recessions and falls during business expansions; and many economists, e.g., Lilien (1982), argue that sectoral shifts account for a large portion of the cyclical variation in unemployment.1 The underlying premise is as follows: When an economy is hit by an adverse shock, e.g., a sharp increase in crude oil prices, then production resources—including labor—will move from more adversely affected sectors to less adversely affected sectors. Because of the presence of industry-specific skills and the time-consuming nature of the job search, the process of transferring workers across industries tends to be slow and involves spells of unemployment. Therefore, an increase in intersectoral shifts leads to higher unemployment by increasing the amount of labor reallocation. Loungani, Rush, and Tave (1990) suggest that stock market dispersion is a good proxy for the volume of intersectoral shifts.2 Intuitively, because stock prices are equal to expected discounted future cash flows, when stock prices in a sector go up (down), the sector is likely to experience increased (decreased) cash flows and thus demand more (less) labor input in the future. Consistent with Lilien’s (1982) conjecture, Loungani, Rush, and Tave document a significantly positive relation between stock market dispersion and the future unemployment rate using data for the period 1926-87. In the accompanying chart, I replicate Loungani, Rush, and Tave’s main finding for the period 1964:Q1 to 2006:Q4. The solid line is the log stock market dispersion, which is measured by the value-weighted average realized variance of idiosyncratic shocks to all common stocks included in the CRSP (Center for Research in Security Prices) database; and it is lagged by one year.3 The dashed line is the change in the unemployment rate from its level one year ago. As hypothesized, the two variables tend to move in the same direction, with a correlation coefficient of 0.27. In particular, stock market dispersion appears to provide a good explanation for the movement of the labor market in the past few years. After the spectacular run-up in the second half of 1990s, the prices of information technology stocks collapsed in the year 2000. S","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"2007 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129661669","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":"Subject to revision","authors":"Abbigail J. Chiodo, Michael T. Owyang","doi":"10.20955/ES.2002.14","DOIUrl":"https://doi.org/10.20955/ES.2002.14","url":null,"abstract":"T he overall state of the economy is often judged by economic statistics such as inflation, unemployment, and, of course, gross domestic product (GDP). Many of these economic statistics undergo substantial revisions. This is especially true for GDP, which is revised twice in the first three months after its initial release. In the month after each quarter, the Bureau of Economic Analysis releases an advance estimate of GDP. In the two subsequent months, the BEA updates this estimate with preliminary and then final estimates. The initial estimates garner quite a bit of attention in the financial world, but how well do they reflect the true state of the economy? How well do they predict final GDP? The advance estimate of GDP is calculated with incom plete data from the quarter including business inventories, housing, retail sales, and automobile sales. The preliminary estimate is released a month later and incorporates more data from the last month of the quarter. Even final GDP is subject to annual revisions, which have resulted in changes to prior GDP growth rates by more than 1.5 percentage points.1 Economists Karen Dynan and Douglas Elmendorf report that, from 1968 to 2001, the average revision of GDP growth from the advance to the final estimate was 0.67 percentage points. During the same period, revisions around peaks and troughs of the business cycle varied greatly. Near business cycle peaks, revisions were—on average—similar in magnitude to those during the rest of the business cycle. Near troughs, however, estimates were revised quite a bit more. When it comes to detecting the end of a recession, therefore, current GDP estimates may not be the best indicator. The magnitude of the revisions to GDP makes it unclear whether or not the most recent recession will conform to the rule of thumb that a recession includes at least two consecutive quarters of negative GDP growth. Advance and preliminary GDP estimates for the third quarter of 2001 were –0.4 percent and –1.1 percent, respectively. Final GDP growth was revised down to –1.3 percent. Fourth quarter numbers were revised upward by 1.5 percentage points from the advance (0.2 percent) to the final estimate (1.7 percent). These revisions make it increasingly likely that the third quarter of 2001 was the only quarter in the recession with negative growth. Revisions aside, from 1978 to 1991, 88 percent of the time the advance estimate correctly established the direction of quarterly change in real GDP growth.2 Since total revisions do not tend to change the direction of the estimates, the initial numbers may be helpful when determining the direction in which GDP is heading, if not by how much. However, advance and preliminary estimates of GDP around business cycle turning points may be less accurate measures of output. One may take heart, though, that revisions to GDP appear to have gotten smaller (see accompanying figure) during two extended expansions.","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"2002 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":"129676208","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":"Ringing in the new year with an investment bust","authors":"Kevin L. Kliesen","doi":"10.20955/ES.2004.29","DOIUrl":"https://doi.org/10.20955/ES.2004.29","url":null,"abstract":"Views expressed do not necessarily reflect official positions of the Federal Reserve System. Ringing In the New Year with an Investment Bust? Real outlays by firms for nonresidential capital equipment and software (E&S) plunged 9 percent in 2001, a recession year; it was the largest decline since 1958 and the third largest since World War II. In an attempt to kick-start business investment, President Bush signed legislation in March 2002 that, among other things, allowed firms to immediately expense (depreciate) 30 percent of the cost of E&S purchased between September 10, 2001, and September 11, 2004, and put into service before January 2005. In subsequent tax legislation signed in May 2003, this partial expensing provision was raised to 50 percent and the purchase date was moved back to December 31, 2004. An increase in the depreciation allowance for capital goods increases the present value of the firm’s deductions for tax purposes, which, all else equal, lowers the cost of capital. Accordingly, when the partial expensing provision reverts to its original level on January 1, 2005, the present value of the depreciation deduction will be less—and the cost of capital will be higher—than what it was on December 31, 2004. Although other factors were also probably at work, the recent growth of investment expenditures suggests that firms responded to this incentive, albeit with a lag. From 2001:Q4 to 2003:Q1, real E&S investment fell at about a 1 percent annual rate; however, in the second quarter of 2003, real E&S investment surged at an 11 percent annual rate and has since increased at a 14.5 percent annual rate through the third quarter of 2004. With the expiration of the partial expensing provision fast approaching, some forecasters believe that many firms still plan to shift a portion of their planned capital expenditures from 2005 into 2004. If these expenditures are significant, then we would expect to see an upsurge in business investment in the final quarter of 2004 and then a drop-off in the first quarter of 2005 (or later). Recent surveys compiled by the National Association of Business Economics and the Federal Reserve Bank of Philadelphia suggest that some firms have already shifted, or plan to shift, some of their capital outlays from 2005 into 2004.1 In August, forecasters expected a much larger slowdown in the growth of business fixed investment (BFI), from about 11 percent in 2004:Q4 to 6.5 percent in 2005:Q1.2 Since then, as seen in the table, forecasters have concluded that there will be both a smaller burst in investment spending in the fourth quarter and less of a lull in the first quarter. Even though forecasters repeatedly changed their assessment of the relative strength of investment spending in 2004:Q4 and 2005:Q1—as viewed by the difference between the projected growth rates of real BFI in 2004:Q4 and 2005:Q1—they do not foresee such a swing in real GDP growth. This pattern is consistent with the fact that business investme","PeriodicalId":305484,"journal":{"name":"National Economic Trends","volume":"13 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":"115328362","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}