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A Quarterly Review of Business and Economic Conditions Vol. 25, No. 4 Productivity Is the Slowdown Due to Retiring of Boomers? President Bullard A Review of 2017’s Key Policy Presentations Fourth Quarter 2017 THE FEDERAL RESERVE BANK OF ST. LOUIS CENTRAL TO AMERICA’S ECONOMY® Shifting Times The Evolution of the American Workplace C O N T E N T S 4 THE REGIONAL ECONOMIST FOURTH QUARTER 2017 | VOL. 25, NO. 4 Shifting Times: The Evolution of the American Workplace PRESIDENT’S MESSAGE 10 The Role of Baby Boomers in Productivity Changes 14 Productivity Is the Slowdown Due to Retiring of Boomers? President Bullard A Review of 2017’s Key Policy Presentations Fourth Quarter 2017 Shifting Times The Evolution of the American Workplace Workers and work have changed dramatically since 1950. Workers are older, more educated and more diverse. Employment opportunities have shifted to higher-skilled occupations. Even jobs that have traditionally required low levels of schooling are employing people with more formal education. Looking for the Positives in Negative Interest Rates By Brian Reinbold and Yi Wen 18 INDUSTRY PROFILE Advanced Manufacturing Vital to Eighth District By Guillaume Vandenbroucke By Charles Gascon and Andrew Spewak Although the Federal Reserve has never used negative interest rates, central banks elsewhere have used them—and continue to use them—to encourage people to shift their investments away from government bonds to something that will do more to stimulate the economy. Director of Research Christopher J. Waller Chief of Staff to the President Cletus C. Coughlin Growth in productivity in the U.S. is noticeably slow these days, as it last was in the 1970s. The baby boomers might be a reason why: They had yet to reach their stride at work in the 1970s, and now they are aging out of the workforce. Deputy Director of Research David C. Wheelock Director of Public Affairs Karen Branding Editor Subhayu Bandyopadhyay Managing Editor Al Stamborski Art Director Joni Williams Please direct your comments to Subhayu Bandyopadhyay at 314-444-7425 or by email at firstname.lastname@example.org. You can also write to him at the address below. Submission of a letter to the editor gives us the right to post it to our website and/or publish it in The Regional Economist unless the writer states otherwise. We reserve the right to edit letters for clarity and length. Vol. 25, No. 4 By Alexander Monge-Naranjo and Juan Ignacio Vizcaino 3 The Regional Economist is published quarterly by the Research and Public Affairs divisions of the Federal Reserve Bank of St. Louis. It addresses the national, international and regional economic issues of the day, particularly as they apply to states in the Eighth Federal Reserve District. Views expressed are not necessarily those of the St. Louis Fed or of the Federal Reserve System. A Quarterly Review of Business and Economic Conditions THE FEDERAL RESERVE BANK OF ST. LOUIS CENTRAL TO AMERICA’S ECONOMY® 16 P.O. Box 442, St. Louis, MO 63166-0442. The Eighth Federal Reserve District includes all of Arkansas, eastern Missouri, southern Illinois and Indiana, western Kentucky and Tennessee, and northern Mississippi. The Eighth District offices are in Little Rock, Louisville, Memphis and St. Louis. By Kevin L. Kliesen By Subhayu Bandyopadhyay and Javed Younas In both developed and developing nations, terrorism destroys life and property. But developing countries suffer more in terms of economic growth, foreign direct investment and trade. ONLINE EXTRA N AT I O N A L O V E R V I E W Economy Absorbs Blows of Hurricanes 12 Impact of Terrorism on Developing Countries Single-copy subscriptions are free but available only to those with U.S. addresses. To subscribe, go to www.stlouisfed.org/publications. You can also write to The Regional Economist, Public Affairs Office, Federal Reserve Bank of St. Louis, Advanced manufacturing requires substantial R&D spending and workers with a high degree of technical knowledge, for which they are paid a wage premium. Such manufacturers have a significant impact on U.S. production and exports. Despite initial forecasts of a sharp slowdown in third-quarter GDP growth because of the hurricanes this summer and fall, the pace of economic activity turned out to be stronger than expected. The fourth quarter is also on track for abovetrend growth. 17 21 First-Time Homebuyers Are Younger, Less Creditworthy By Brian Reinbold and Paulina Restrepo-Echavarria The number of first-time homebuyers has declined over the past 16 years, both in the Eighth District and the rest of the U.S. A closer look at the District finds that these buyers are younger and less creditworthy than those homebuyers nationally. E C O N O M Y AT A G L A N C E www.stlouisfed.org/re DISTRICT OVERVIEW 23 RE ADER E XCHANGE How Fast Will Banks Adopt New Technology This Time? By Drew Dahl, Andrew Meyer and Neil Wiggins To get an idea of how fast the banking industry might embrace new financial technologies—“fintech”—it might be worth looking at how quickly banks entered the internet age with a website almost a generation ago. 2 The Regional Economist | Fourth Quarter 2017 COVER IMAGE © THINKSTOCK / ISTOCK /ZAPP2PHOTO P R E S I D E N T ’ S M E S S A G E A Year in Review S t. Louis Fed President James Bullard, a noted economist and scholar, has been a participant in Federal Open Market Committee (FOMC) deliberations since April 2008. Bullard actively engages with many audiences—including academics, policymakers, business and community organizations, and the media—to discuss monetary policy and the U.S. economy and to help further the regional Reserve bank’s role of being the voice of Main Street. Some of his key policy presentations during 2017 are summarized below, in chronological order. To see all of Bullard’s public presentations, please visit www.stlouisfed. org/from-the-president. Five Macroeconomic Questions for 2017 Jan. 12, 2017: In New York, Bullard discussed key questions related to the overall economy and to the Fed in particular. Bullard said the St. Louis Fed’s recommended policy rate (the federal funds target rate) depends mostly on the safe real rate of return, and such rates are exceptionally low and are not expected to rise soon. “This, in turn, means that the policy rate should be expected to remain exceptionally low over the forecast horizon,” he said. “The new administration’s policies may have some impact on the low-safe-real-rate regime if they are directed toward improving medium-term U.S. productivity growth.” The Role of the Fed’s Balance Sheet for the U.S. Monetary Policy Outlook in 2017 Feb. 28, 2017: Now may be a good time for the FOMC to begin allowing the balance sheet to normalize by ending reinvestment, Bullard said at George Washington University in Washington, D.C. “Adjustments to balance sheet policy might be viewed as a way to normalize Fed policy without relying exclusively on a higher policy rate path,” he said. He also noted that current FOMC policy is distorting the yield curve. “Ending balance President Bullard (left) often travels throughout the St. Louis Fed’s District to share his views on the economy and to listen to the perspectives of others. In September, he visited Dot Foods, the nation’s largest food redistributor, in Mount Sterling, Ill. sheet reinvestment may allow for a more natural adjustment of rates across the yield curve as normalization proceeds and for ‘policy space’ in case balance sheet policy is required in a future downturn,” he said. (The Fed began gradually reducing the size of its balance sheet in October 2017.) rate is likely to be appropriate for this regime over the forecast horizon. “Many future developments could impact this policy path, but the Fed does not need to pre-empt any of them,” Bullard said. Current Growth, Inflation and Price Level Developments in the U.S. Nov. 14, 2017: Inflation has been mostly below the Fed’s 2 percent target since 2012 and is unlikely to return to target anytime soon, Bullard said in Louisville, Ky. “Inflation data during 2017 have surprised to the downside and call into question the idea that U.S. inflation is reliably returning toward target,” he said. If the FOMC is going to hit the inflation target, “it will likely have to occur in 2018 or 2019,” he added. May 26, 2017: In Tokyo, Bullard said that U.S. macroeconomic data have been relatively weak, on balance, since the FOMC met in March and raised the policy rate. For instance, he noted that U.S. inflation and inflation expectations have surprised to the downside in recent months. He also said that even if U.S. unemployment declines substantially further, the effects on U.S. inflation are likely to be small. Regarding the U.S. price level, he said that it “has begun to deviate noticeably from the 2 percent path established in the mid-1990s.” The price level is 4.6 percent below the previously established path. The Path Forward for U.S. Monetary Policy June 23, 2017: In Nashville, Tenn., Bullard said the Fed can wait and see how the economy develops before making any further adjustments to the policy rate. He noted that the U.S. policy rate has been rising while key policy rates abroad have remained fixed. He said the U.S. economy remains in a “regime” of low growth, low inflation and low interest rates, and that the current level of the policy When Will U.S. Inflation Return to Target? Assessing the Risk of Yield Curve Inversion Dec. 1, 2017: In Little Rock, Ark., Bullard said that there is “a material risk of yield curve inversion” over the forecast horizon if the FOMC continues on its present course for raising the policy rate, as suggested in September’s Summary of Economic Projections. Such an inversion—where short-term interest rates exceed long-term interest rates—has helped predict recessions in the past. He noted that yield curve inversion is best avoided in the near term by caution in raising the policy rate. “Given below-target U.S. inflation, it is unnecessary to push normalization to such an extent that the yield curve inverts,” he said. The Regional Economist | www.stlouisfed.org 3 4 The Regional Economist | Fourth Quarter 2017 THINKSTOCK / ISTOCK / KINWUN L A B O R Shifting Times The Evolution of the American Workplace By Alexander Monge-Naranjo and Juan Ignacio Vizcaino hat are the main characteristics of American workers? What types of jobs do they do? Who does what? It turns out that the answers to these questions have been changing, in some cases dramatically. For starters, the basic demographic makeup—age, gender and race—is very different now than it was nearly 70 years ago. Second, the educational levels of workers have been increasing dramatically.1 Third, the occupations or types of jobs employing American workers are very different now relative to what American workers were doing just a few decades ago. In this article, we explore these shifts in the American labor force and workplace. We show that the identity, education and occupations of the average American worker have all been changing. We also show that there are big changes in who does what, especially in the higher-skilled and higher-paying occupations. Overall, the picture emerging from the data is very clear: American workers are older, more educated and more diverse. Because skilled workers are more abundant, the employment opportunities have been shifting to higherskilled occupations, and this movement has taken place for workers of all genders and races. Workers with loweror even middle-level skills are likely to face relatively tougher times because their remaining labor market opportunities are in the lower-skilled occupations. Demographics and Education To characterize American workers over the years, we collected individual level data from IPUMS-USA on the age, gender, race, educational level and current occupation of workers.2 For ease of use, we categorized the nine racial groups in the database into four broader groups: white, black, Asian and other.3 Similarly, for educational levels, we grouped the 11 categories in the data into five broader groups representing the maximum possible level of education attained by these individuals: primary or less (nursery through grade 8), secondary incomplete (grades 9-11), secondary complete (grade 12), college incomplete (one to three years of college), and college complete or more (four or more years of college). The table contains the basic demographic information. A number of salient features are evident. First, female workers almost doubled their share in the labor force; nowadays, they are close to being half of the working population. Similarly, nonwhites as a whole more than doubled their share, accounting for nearly one in four workers. An even more dramatic increment is in terms of schooling levels: In 1950, close to 40 percent of workers had only primary schooling (completed or less); today, the U.S. has only a negligible fraction of workers with such little formal education. On the opposite extreme, from having less than 18 percent of workers with at least some college, the U.S. now has about 60 percent of the labor force with either some college education or a completed college education. A closer inspection of the data reveals that much of the changes took place in the 1970s and 1980s, when the baby boomers entered the labor market. Figure 1 shows the close relationship between the average age of American workers and the fertility rate of previous decades.4 The relatively high fertility rates of the 1950s and 1960s led to an interesting pattern in the age of active workers The Regional Economist | www.stlouisfed.org 5 Characteristics of American Workers: 1950-2015 Gender Race Education Secondary Incomplete Secondary Complete College Incomplete College Complete or More 8.4% Year Average Age Male Female White Black Asian Other Primary or Less 1950 37.7 72.6% 27.4% 90.0% 9.6% 0.3% 0.2% 38.8% 19.3% 24.3% 9.3% 1960 40.1 68.0% 32.0% 89.8% 9.3% 0.6% 0.3% 29.4% 22.3% 28.4% 10.4% 9.6% 1970 39.3 62.9% 37.1% 89.2% 9.5% 0.8% 0.4% 17.3% 21.0% 35.4% 13.4% 12.8% 1980 37.4 57.8% 42.2% 87.7% 9.7% 1.8% 0.8% 8.3% 15.4% 38.4% 19.3% 18.6% 1990 38.3 54.7% 45.3% 83.1% 10.0% 2.9% 4.0% 3.8% 9.2% 33.2% 45.5% 8.2% 2000 40.0 53.6% 46.4% 78.8% 10.1% 3.8% 7.3% 2.9% 7.7% 38.1% 41.6% 9.7% 2010 43.1 52.3% 47.7% 76.8% 10.8% 5.3% 7.1% 2.8% 5.4% 33.8% 46.5% 11.5% 2015 43.5 52.7% 47.3% 74.9% 11.5% 5.9% 7.7% 2.5% 4.8% 32.8% 47.5% 12.3% Farmers and Farm Laborers Laborers SOURCE: IPUMS. FIGURE 2 Workers’ Average Age and Fertility in the U.S. Shifts in the Shares of U.S. Workers across Occupations 44 4 43 3.5 Age 2.5 41 2 40 1.5 39 1 38 0.5 37 1950 1960 1970 1980 1990 2000 2010 0 Average Age of Workers (left axis) Fertility Rate (right axis) SOURCES: For the average age, IPUMS; for the fertility rate, World Bank via FRED. NOTE: Total fertility rate represents the number of children who would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with current age-specific fertility rates. 6 The Regional Economist | Fourth Quarter 2017 Children 3 42 Occupational Share in Total Employment FIGURE 1 30% 1950 25% 1980 2015 Craftsmen Service Workers 20% 15% 10% 5% 0% Professional Managers, and Technical Officials, and Proprietors Sales Workers Clerical and Kindred Operatives SOURCE: IPUMS. NOTE: “Clerical and Kindred” includes those occupations whose clerical duties, such as those related to general office work or duties pertaining to the operation of various office machines, take up a majority of the worker’s time or for which the major requirement is the ability to perform the clerical duties. “Operatives” includes those occupations in which duties related to operating and handling machines take up a majority of the worker’s time. over the years. First, average ages tended to increase between 1950 and 1960 as young female workers in the 1950s left the labor force to rear children. Later, however, when the baby boomers’ children entered the labor force in the 1960s, the average age started to decline. Yet, with the lower fertility rates observed since the late 1970s and early 1980s, the average American worker started aging, a trend that has remained up until at least 2015, the last year for which we have data. To be sure, the baby boomers had more formal education than their parents, but the boomers’ education has since been eclipsed by that of their children. It is easy to see why the 1970s and 1980s were years of rapid expansion in the average educational level of American workers. After that, a steady increase in education has been sustained up until 2015, and it is expected to continue. These changes in the educational level of American workers are significant enough that one would expect to see important changes in the structure of the economy, i.e., in the types of occupations in the economy and the types of workers filling those jobs. The data show this vividly. Changes in Work and in Who’s Doing What We now explore the changes in what the American workers do in the marketplace. To this end, we grouped workers into the following nine broad groups,5 ordered by their skill intensity6: professional and technical workers; managers, officials and proprietors; sales workers; clerical and kindred; craftsmen; service workers; operatives (e.g., machine operators); farmers and farm laborers; and laborers.7 Figure 2 shows the shares of workers across the nine broad occupation categories in the data. For ease of presentation, we reported on the data only for the beginning, the middle and the end of the sample period. For each occupation, the first bar in each case corresponds to American workers in 1950, the middle bar corresponds to workers in 1980 and the last bar corresponds to 2015, the most recent year for the data. Figure 2 shows important changes in what American workers do. First, there is a big shift toward professional and technical occupations and toward management. The first group almost tripled its share over all workers between 1950 and 2015, from 8.7 percent to 25.4 percent of all workers. The second group, i.e., the management positions, almost doubled its share, from 8.8 percent to 14.7 percent. Another occupation that expanded is service workers, a finding that is not surprising, given the well-known movement of the U.S. economy toward services and away from agriculture and manufacturing. This movement also explains the significant decline in craftsmen, operatives and farm workers. Beyond these profound changes in the occupations or job types, we observed substantial shifts in the types of workers that are allocated across the different types of jobs. Each of the nine panels of Figure 3 shows the share of workers with different schooling levels in each of the nine broad occupation categories. Obviously, the educational level of the workforce was very different in 2015 relative to that of 1950 and even 1980. Specifically, consider the notable difference in the schooling attainment of workers in professional and technical occupations between 1950 and 2015. In 1950, only half of these workers had completed a college degree. By 1980, those with college degrees already made up 60 percent of these workforces and by 2015 they accounted for 70 percent. In 1950, it was not uncommon to find workers with only a high school diploma in professional positions; in fact, one in 10 of these professional workers had not finished high school, and up to 6 percent of them did not have any secondary FIGURE 3A FIGURE 3B Schooling of Professional and Technical Workers in the U.S. Schooling of Managers 100% 100% 80% 80% 60% 60% 40% 40% 20% 20% 0% 1950 Primary or less Secondary incomplete Secondary complete 1980 2015 College incomplete College complete or more 0% 1950 Primary or less Secondary incomplete Secondary complete 1980 FIGURE 3C FIGURE 3D Schooling of Sales Workers Schooling of Clerical Workers 100% 100% 80% 80% 60% 60% 40% 40% 20% 20% 0% 1950 Primary or less Secondary incomplete Secondary complete 1980 2015 College incomplete College complete or more 0% 1950 Primary or less Secondary incomplete Secondary complete 1980 FIGURE 3F Schooling of Craftsmen Schooling of Service Workers 100% 80% 80% 60% 60% 40% 40% 20% 20% 0% 1950 Primary or less Secondary incomplete Secondary complete 1980 2015 College incomplete College complete or more 0% 1950 Primary or less Secondary incomplete Secondary complete 2015 College incomplete College complete or more FIGURE 3E 100% 2015 College incomplete College complete or more 1980 2015 College incomplete College complete or more SOURCE FOR ALL FIGURES ABOVE: IPUMS. FIGURES IN THIS SERIES ARE CONTINUED ON NEXT PAGE. The Regional Economist | www.stlouisfed.org 7 FIGURE 3G Schooling of Operatives (e.g., Machine Operators) 100% 80% 60% 40% 20% 0% 1950 1980 Primary or less Secondary incomplete Secondary complete 2015 College incomplete College complete or more FIGURE 3H Schooling of Farmers 100% 80% 60% 40% 20% 0% 1950 Primary or less Secondary incomplete Secondary complete 1980 2015 College incomplete College complete or more FIGURE 3I Schooling of Laborers 100% 80% 60% 40% 20% 0% 1950 Primary or less Secondary incomplete Secondary complete 1980 2015 College incomplete College complete or more SOURCE FOR ALL FIGURES ABOVE: IPUMS. 8 The Regional Economist | Fourth Quarter 2017 education at all. Formally or informally, this subset of professional workers must have accumulated technical knowledge on the job. As Figure 3A shows, this group of empiricist professionals had all but disappeared by 1980 and was completely gone in 2015. Even more striking changes can be seen in workers occupying managerial jobs. In 1950, managers were predominantly workers with no formal college education: Individuals who had no more than a high school diploma accounted for more than three in four of American managers. (In 1950, 27.4 percent of managers had only primary education and only 11 percent of them had completed college.) Figure 3B shows the drastic change that has taken place: In 2015, virtually all managers had completed at least secondary education, almost three-fourths of them had some form of college education and 46.4 percent of them had completed at least a college degree. The movement toward higher levels of education can be seen also in all other occupations, albeit to a different extent. In all of them, there is an increasing share of college-educated workers and a decline in workers with primary education only. The main difference across occupations is in the incidence of secondary education (complete and incomplete) and in workers with some college education. For example, while in 1950 virtually no operative worker had any college education, in 2015 more than 30 percent of these operators had some college. It is noteworthy that the agricultural sectors have attracted—or required—workers with higher levels of education. Nowadays, almost 31 percent of these workers have some college education. Notice that similar numbers apply to the group of laborers. Despite some ambiguity in the share of workers who have completed secondary school over the years, all occupations in the country have undergone a process of skill upgrade, namely the movement in which the same form of task, job or occupation is now performed by workers with higher skill levels. 8 This is most evident when looking at the share of college-educated workers performing more and more of all these broadly defined categories of jobs and also when looking at the sharp decline in the share of workers with only primary school completed. This sharp decline appears even among farmers and laborers, a solid majority of whom have traditionally had only a primary school education. Top-Earning Occupations We now look more closely at the managerial and professional occupations, the two occupations that have been expanding at the fastest pace and that are the ones paying the highest salaries. Figure 4 breaks down the composition across gender and race groups for these two broad categories. As the two panels of Figure 4 clearly show, both occupations have traditionally been performed predominantly by white workers and, up until recently, by predominantly white male workers. But that has changed profoundly. In 1950, white males accounted for more than 81 percent of all managers and for 51 percent of all professional and technical workers. Interestingly, the predominance of white males in both groups was even higher in 1960 and 1970, likely reflecting large numbers of younger, highly educated females leaving the marketplace to raise children. But by 2015, white males accounted for about half of the managers and for about 34 percent of professional workers. The entry of highly educated white women is one of the main forces behind this change. From essentially being a rarity in the 1950s and 1960s—and even the 1970s— women in management positions accounted in 2015 for one of every three managers in the U.S. White women accounted for even more of the professional occupations, outnumbering white men in 2015. A second major force of change is the entry of nonwhite workers. Indeed, from virtually being negligible in these two broad groups of higher-paying occupations, nonwhite workers now account for 20 percent of professionals and 15 percent of managers. The rise of women and nonwhite workers in the marketplace can be tied to higher college enrollment rates over time and to reductions in educational and labor market distortions and barriers. In the case of women, some have argued that technological changes favor female skills and that the combination of women’s higher social skills with increased cognitive skills has also played an important role.9 ENDNOTES FIGURE 4A Percent Race and Gender of Managers in U.S. 1 1950 White Male 1960 1970 White Female 1980 Black Male 1990 Black Female 2000 Asian Male 2010 2015 Asian Female FIGURE 4B Percent Race and Gender of Professional and Technical Workers in U.S. 100 90 80 70 60 50 40 30 20 10 0 See Monge-Naranjo. IPUMS-USA, University of Minnesota, www. ipums.org. We discarded individuals whose employment status is unknown or who are unemployed or are not in the labor force, as classified by the variable EMPSTAT codes 0, 2 and 3. Also, see Ruggles et al. 3 In the database, racial categories consist of national origin groups. Beginning in 2000, the race question changed substantially to allow respondents to report as many races as they felt necessary to describe themselves. In earlier years, only one race response was coded. We grouped nine racial categories reported in IPUMS-USA into four broader groups: white (IPUMS-USA: White), black (IPUMS-USA: Black/African American/Negro), Asian (IPUMS-USA: Chinese, Japanese, Other Asian or Pacific Islander) and other (IPUMS-USA: American Indian or Alaska Native, two major races, three or more major races). IPUMS-USA contains separate information on ethnicity, in particular, whether a worker has Hispanic ethnicity. In a future article, we will focus exclusively on the participation of Hispanic workers in the U.S. labor force and in the different occupations. 4 Fertility data come from the World Bank and were obtained via FRED at https://fred.stlouisfed.org. 5 In order for occupations to be comparable across time, we used the 1950 Census Bureau occupational classification. Each of the nine categories groups occupations that are similar in nature according to their three-digit occupational code, the smallest level of desegregation the Census Bureau provides. 6 The occupations with the highest percentages of workers with the top level of education (college or more) are deemed those that are most skill-intense. The top four occupations were the same in 2015 as in 1950. 7 Observations of individuals with unclassified, missing or unknown occupations are discarded. 8 See Costinot and Vogel. 9 See Rendall, Cortes et al. and Hsieh et al. 10 This point is forcefully made by Hsieh et al. 2 100 90 80 70 60 50 40 30 20 10 0 1950 White Male 1960 White Female 1970 1980 Black Male 1990 Black Female 2000 Asian Male 2010 2015 Asian Female SOURCE FOR BOTH FIGURES: IPUMS. Conclusions We explored the substantial shifts in the American labor force and workplace over almost 70 years, showing that the identity, education, race and occupations of the average American worker have all been changing. We documented big changes in the types of jobs being done by American workers and on the assignment of jobs across workers with different educational levels and other characteristics. The data discussed here provide a number of clear lessons. First, American workers are older, better-schooled and much more diverse in terms of race and gender. Second, employment opportunities have shifted to higher-skilled occupations. Third, there has been a generalized process of skill upgrading, as all occupations are employing workers with more formal education. Needless to say, these changes have led to additional challenges for some groups of workers: Those with lower levels of education may be unable to find jobs in occupations that their parents held with much less formal schooling. For those with higher levels of education, they now have heightened competition from more individuals with higher education, including groups that were rarely represented in these ranks in the past, e.g., females and nonwhites. Regardless of how much more challenging labor markets become for everyone, the aggregate productivity is higher when the country takes advantage of the talent of all the demographic groups and not just a subset of them.10 Alexander Monge-Naranjo is an economist at the Federal Reserve Bank of St. Louis. For more on his work, see https://research.stlouisfed.org/ econ/monge-naranjo. Juan Ignacio Vizcaino is a technical research associate at the Bank. REFERENCES Cortes, Guido M.; Jaimovich, Nir; and Siu, Henry. The End of Men and Rise of Women in the HighSkilled Labor Market. Manuscript, 2016. See http:// faculty.arts.ubc.ca/hsiu/work/endofmen_post.pdf. Costinot, Arnaud; and Vogel, Jonathan. Matching and Inequality in the World Economy. Journal of Political Economy, 2010, Vol. 118, No. 4, pp. 747-86. Hsieh, Chang-Tai; Hurst, Erik; Jones, Charles I.; and Klenow, Peter J. The Allocation of Talent and U.S. Economic Growth. Manuscript, 2016. See http:// klenow.com/HHJK.pdf. Monge-Naranjo, Alexander. Workers Abroad Are Catching Up to U.S. Skill Levels. Federal Reserve Bank of St. Louis’ The Regional Economist, Third Quarter 2017, Vol. 25, No. 3, pp. 6-7. Rendall, Michelle. Brain versus Brawn: The Realization of Women’s Comparative Advantage. Manuscript, 2017. See https://sites.google.com/site/ mtrendall/research. Ruggles, Steven; Genadek, Katie; Goeken, Ronald; Grover, Josiah; and Sobek, Matthew. Integrated Public Use Microdata Series: Version 6.0 [dataset]. Minneapolis: University of Minnesota, 2015. See http://doi.org/10.18128/D010.V6.0. The Regional Economist | www.stlouisfed.org 9 D E M O G R A P H I C S Boomers Have Played a Role in Changes in Productivity By Guillaume Vandenbroucke © THINKSTOCK / ISTOCK Productivity 101 A typical measure of productivity is labor productivity, which is gross domestic product (GDP) per worker.2 Figure 1 shows that, in the 1970s, the growth rate of labor productivity was noticeably low.3 This slowdown started in the 1960s, when the growth rate of labor productivity started to decline. The growth rate of labor productivity accelerated between 1980 and 2000. Since 2000, another decline is noticeable. It is interesting to note that the current state of low labor productivity growth is comparable to that of the 1970s and that it results from a decline that started before the 2007 recession. 10 The Regional Economist | Fourth Quarter 2017 FIGURE 1 The Growth Rate of GDP per Worker in the U.S., 1955-2014 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 2012 2009 2006 2003 2000 1997 1994 1991 1988 1985 1982 1979 1976 1973 1970 1967 1964 1961 1958 0 1955 n the 1970s, the U.S. economy experienced a prolonged period of low productivity growth. Nowadays, growth in productivity is once again slower than expected. The causes of these slowdowns have been much debated. The 1970s’ slowdown has often been associated with, among other causes, high energy prices following the 1973 oil price shock, increased antipollution regulations and a decline in the quality of education.1 The current productivity slowdown is often associated with the 2007-08 financial crisis. In this article, I hypothesize that the two slowdowns are related to a single, common factor: the baby boom, that period from 1946 to 1957 when the birth rate increased by 20 percent. This hypothesis is not to say that the baby boom was entirely responsible for these two episodes of low productivity growth. Rather, it is to point out the mechanism through which the baby boom contributed to both. Exactly how much did the baby boom contribute to these slowdowns? The answer to that question is beyond the scope of this article. Percent I SOURCES: Bureau of Economic Analysis and Bureau of Labor Statistics, via FRED. To understand how the baby boom may have contributed to both the 1970s slowdown and the current slowdown, it is worth taking a detour to think about what makes a worker productive. Current thinking is that workers supply “human capital services” to their employer. Sometimes one can refer to “skills” or simply “productivity.” The exact terminology is not critical. What is critical is the theory that young workers have relatively low human capital and that, as they become older, they accumulate human capital. The accumulation of human capital can be achieved in multiple ways. One is simply via experience: Older workers have more human capital, i.e., they know more just because they have done more and have experienced “learning by doing.” Another possibility is that workers go through periods of formal on-thejob training throughout their careers; so, they learn more as they grow older. Human capital is what makes a worker productive: The more human capital, the more output a worker produces in a day’s work. Picture, then, a typical worker’s human capital profile throughout life. A stylized representation of this profile is in Figure 2. In theory, such a human capital profile implies that a worker’s earnings profile should look very similar. This is because, in theory, workers are paid according to their productivity. Interestingly, this is exactly the case in the United States: The data show that the typical earnings profile throughout a worker’s life increases until it reaches a peak, usually a few years before retirement. What, then, does human capital theory tell us about U.S. productivity? Who Is More Productive? Start with a simple example. Suppose that there are only young and old workers. Each young worker produces one unit of a good, while each old worker produces two units since the old worker has more human capital (Figure 2). Suppose now that there are 50 young and 50 old workers. The total number of goods produced is 150 and, therefore, labor productivity is 150/100=1.5. 1 See Cullison. 2 Another measure of productivity is total factor productivity, also called multifactor productivity, which gauges the joint contribution of labor and capital to output, instead of the contribution of labor only, as does labor productivity. 3 The growth rate of productivity in Figure 1 was smoothed to remove frequent variations and to focus on secular changes. 4 The total number of workers is kept constant in this example, but that does not matter. Suppose there were 10 times more workers: 750 young and 250 old. Labor productivity would still be 1.25. 5 The share of people between ages 23 and 33 is a proxy for the share of young people. This does not imply that the old are all the people older than 33. REFERENCE Cullison, W.E. The U.S. Productivity Slowdown: What the Experts Say. Economic Review, Federal Reserve Bank of Richmond, July/August 1989, pp. 10-21. FIGURE 2 Human Capital A Stylized Profile for a Worker’s Human Capital 20 25 30 35 40 45 50 55 60 65 Age SOURCE: Author. NOTE: In theory, a worker’s earnings reflect his or her human capital and should be increasing until the earnings reach a peak shortly before retirement. In the U.S. data, the typical earnings profile of a worker displays this exact pattern. FIGURE 3 The Growth Rate of GDP per Worker and the Share of 23-33-Year-Olds in the U.S., 4.0 39 3.5 37 3.0 35 2.5 33 2.0 31 1.5 Percent 1955-2014 29 1.0 27 25 2012 2009 2006 2003 2000 1997 1994 1991 Share of population ages 23-33 (right axis) 1985 1982 1979 1976 1973 1970 1967 1964 0 1961 Growth rate of real GDP per worker (left axis) 1988 0.5 1958 What do these observations mean for productivity measurement? It is important to realize that, should the theory proposed here be correct, there exists a sense in which the productivity slowdowns (especially in the 1970s) are statistical artifacts, that is, it may be that the productivity of individual workers did not change at all during the 1970s, but that the change in the composition of the labor force caused the slowdown in labor productivity. In a way, therefore, there is nothing to be fixed via government programs. Productivity slows down because of the changing composition of the labor force, and that results from births that took place at least 20 years before. ENDNOTES Guillaume Vandenbroucke is an economist at the Federal Reserve Bank of St. Louis. For more on his work, see https://research.stlouisfed.org/ econ/vandenbroucke. Heting Zhu, a research associate at the Bank, provided research assistance. 1955 Is There a Problem to Be Fixed? If we knew exactly how much human capital each worker has, better measures of productivity could be constructed. This, however, is a difficult endeavor since human capital is not directly observable. The literature devoted to the measurement of human capital is large. Significant progress has been made, but much remains to be learned. Percent But what if there were a larger proportion of young workers? Suppose that there are 75 young and 25 old. The total production would be 125 and, therefore, labor productivity would be 1.25. Thus, the increased proportion of young workers reduces labor productivity as we measure it via output per worker.4 The mechanism just described is exactly how the baby boom may have affected the growth rate of U.S. labor productivity. Look at Figure 3. The blue line represents the growth rate of labor productivity, as in Figure 1. The red line represents the share of people between the ages of 23 and 33 (relative to the population between the ages of 23 and 63).5 An increase in the red line means that the 23-33 population represents a larger share of the U.S. population. The peak circa 1980 is the direct consequence of the baby boom: The U.S. birth rate peaked circa 1960, implying a large share of people in their 20s circa 1980. Note in Figure 3 that the two lines move mostly in opposite directions except during the 2000s. The correlation between the two lines is, indeed, –37 percent. Note also that the share of 23-33 year-olds is increasing since the late 2000s. This can also be viewed as a result of the baby boom: The baby boomers are slowly leaving the 23-63 population, tilting the scale toward the younger population once again. This trend is noticeably less pronounced, however, during the 2000s than it was during the 1970s. Thus, the mechanism discussed here is likely to be a stronger contributor to the 1970s slowdown than to the current one. SOURCES: Bureau of Economic Analysis and Bureau of Labor Statistics, via FRED; and the Human Mortality Database of the University of California, Berkeley, and the Max Planck Institute for Demographic Research (Germany), available at www.mortality.org or www.humanmortality.de. The Regional Economist | www.stlouisfed.org 11 E C O N O M I C D E V E L O P M E N T Trade and Terror: The Impact of Terrorism on Developing Countries By Subhayu Bandyopadhyay and Javed Younas © THINKSTOCK / ISTOCK / MIMADEO E conomists Walter Enders and Todd Sandler defined terrorism as the premeditated use of or threat to use violence by individuals or subnational groups to obtain a political or social objective through the intimidation of a large audience beyond that of the immediate victims. Central to this definition is the widespread sense of vulnerability that individuals or businesses in a venue nation—a country where the violence occurs—must feel. This sense of vulnerability is particularly damaging to trade or foreign direct investment (FDI) because foreign nations always have a choice of conducting business with less-terror-prone nations. The decline in trade and foreign investments compounds the difficulties of developing nations, which suffer a myriad of economic and noneconomic costs associated with the loss of life and property from terror attacks. This article focuses on the economic costs that are imposed by terrorism on developing nations through diminished economic growth, trade and FDI. Terrorism incidents are classified into two broad categories, “domestic” and “transnational.” Domestic incidents are ones in which the perpetrators, victims and damaged properties belong to the venue nation. In contrast, transnational terrorism involves different nationalities. The table reports data for total terrorism, domestic terrorism and transnational terrorism incidents and associated fatalities and injuries for the 12 most-terrorism-prone countries in the world and for the world as a whole.1 These 12 nations account for almost 79 percent of global terrorist incidents. It is also notable that most of these nations are developing countries. It is understandable that developing nations are more vulnerable to terrorism 12 The Regional Economist | Fourth Quarter 2017 because they are unlikely to have the resources to adequately fight terrorism. This problem is often compounded by corruption, poor governance, and lack of proper judicial systems or rule of law in these nations. Such institutional shortcomings breed discontent in the population, which in turn can spur terrorism. Notice that almost 87 percent of global terrorist incidents are domestic (12,899 out of a total of 14,820). Therefore, the vast majority of damages due to terrorism are borne exclusively by the citizens of the venue country. The associated rise in security costs and loss in productivity of the workforce—through damages to labor and capital—are likely to reduce national income. Transnational incidents, although less numerous, have significant economic implications, especially through loss in trade and FDI. Transnational incidents involve foreign citizens and therefore garner international press attention. Such publicity makes foreign nations less willing to do business with a terrorism-prone nation, leading to less trade and FDI. Growth Effects A 2004 study by economists Brock Blomberg, Gregory Hess and Athanasios Orphanides used a sample of 177 nations (developed and developing) over the period of 1968 to 2000 to estimate the effect of terrorism on growth rates of gross domestic product (GDP). They found that transnational terrorism has rather modest effects on the economy, reducing per capita GDP growth by 0.048 percent in a given year. A 2009 paper by Todd Sandler and his co-author Khusrav Gaibulloev highlighted the differences between developed and developing nations by dividing a sample of 42 Asian nations into seven developed and 35 developing nations. They did not find any significant adverse effect on growth for developed nations. However, an additional transnational terrorist incident (per million people) reduced an affected developing nation’s growth rate by around 1.4 percentage points. Foreign Direct Investment Greater terrorism in a developing nation raises the risk for foreign investors of not being able to get the returns to their investments in the future. Such investors will look for safer alternate nations to invest in. Economists Alberto Abadie and Javier Gardeazabal investigated this issue in a paper published in 2008 and found that there is substantial diversion of FDI from a venue nation of terrorism to alternate terror-free nations. One standard deviation increase in the risk of terrorism in a particular nation can reduce the country’s net FDI position by approximately 5 percent of its GDP. This is a huge potential loss in capital formation for any nation, but it is especially hard on a developing nation that seeks to use foreign investments to fuel its growth. A 2014 paper by economists Subhayu Bandyopadhyay, Todd Sandler and Javed Younas focused on a sample of 78 developing countries from 1984 to 2008. They found that a one standard deviation increase in domestic terrorist incidents per 100,000 people reduces net FDI by between $323.6 million and $512.9 million for the average sample country, while the comparable reduction in the case of transnational terrorist incidents is between $296.5 million and $735.7 million. They also found that foreign aid can substantially mitigate terrorism-related FDI damages due to greater aid flows. ENDNOTE Terrorism Incidents and Casualties Summed over the Period 2001-2012 Terrorism incidents Domestic Terrorism fatalities Terrorism injuries 15,066 2,737 6,693 14,075 191 407 843 4,371 9,855 2,229 3,614 7,909 78 621 1,716 820 2,995 985 788 2,786 21 20 186 842 2,164 1,680 712 1,829 1,498 92 305 181 810 1,707 2,450 708 1,537 2,307 91 146 126 Russia 722 1,884 3,901 670 1,655 3,654 21 191 214 Philippines 702 862 2,280 621 779 1,960 51 66 239 Colombia 620 1,000 2,171 540 896 1,939 37 47 181 Israel 546 738 3,585 482 551 2,772 42 170 798 Nepal 323 439 713 282 411 607 27 8 69 Turkey 321 292 1,149 264 192 809 32 50 143 Yemen 313 648 685 261 573 627 42 59 52 14,820 33,910 62,651 12,899 26,135 52,179 1,296 6,894 9,273 Country Terrorism incidents Total Terrorism fatalities Terrorism injuries Pakistan 3,043 7,282 India 2,438 Thailand 1,027 Nigeria Somalia World (167 countries) Transnational Terrorism Terrorism Terrorism incidents fatalities injuries SOURCE: Global Terrorism Database. NOTES: Afghanistan, Iraq, Syria, and West Bank and Gaza are not included due to warlike/civil conflict situations there. Total terrorism incidents and casualties include incidents and casualties from domestic and transnational terrorism and from those terrorism incidents that cannot be unambiguously categorized into either of the two categories. International Trade Terrorism raises the costs of doing business across national borders. For example, shipping costs will rise if shippers have to buy insurance to cover possible damages in the ports of terrorism-prone nations. In turn, such costs are passed on to the consumers in the form of higher prices, which will tend to reduce both exports and imports of terror-affected nations. Consider a pair of developed nations. Based on the table, which clearly shows that the most terror-prone nations are developing nations, we would not expect terrorism to be a significant deterrent to trade between this developed country pair. On the other extreme, consider a pair of developing nations—and to make the case clear, consider a pair from the top 12 nations in the table. For this pair, a good exported by one nation and imported by the other suffers potential risks in transportation in both nations. This will contribute to higher trade costs and prices and be a significant deterrent to trade. A 2004 paper by economists Volker Nitsch and Dieter Schumacher found that a doubling in the number of terrorist incidents over the period 1960 to 1993 is associated with a decrease in bilateral trade among 200 countries by about 4 percent. There is evolving literature on this issue, with some papers finding more modest effects of terror on trade. Among other reasons, this may be due to changes in a nation’s production patterns in response to terror-related disruptions. For example, if terror disproportionately disrupts an importcompeting domestic industry in a developing nation, that nation may be forced to turn to imports for the good in question, thus raising rather than reducing trade. 1 The data are drawn from the Global Terrorism Database (GTD) online, which records domestic, transnational and other terrorist incidents that cannot unambiguously be placed into either of the two categories (National Consortium for the Study of Terrorism and Responses to Terrorism, 2014). For this table, we have summed data over the period 2001-2012. REFERENCES Abadie, Alberto; and Gardeazabal, Javier. Terrorism and the World Economy. European Economic Review, January 2008, Vol. 52, No. 1, pp. 1-27. Bandyopadhyay, Subhayu; Sandler, Todd; and Younas, Javed. Foreign Direct Investment, Aid, and Terrorism. Oxford Economic Papers, January 2014, Vol. 66, No. 1, pp. 25-50. Blomberg, S. Brock; Hess, Gregory D.; and Orphanides, Athanasios. The Macroeconomic Consequences of Terrorism. Journal of Monetary Economics, July 2004, Vol. 51, No. 5, pp. 1007-32. Enders, Walter; and Sandler, Todd. The Political Economy of Terrorism, Second Edition. New York: Cambridge University Press, 2012. Gaibulloev, Khusrav; and Sandler, Todd. The Impact of Terrorism and Conflicts on Growth in Asia. Economics and Politics, November 2009, Vol. 21, No. 3, pp. 359-83. National Consortium for the Study of Terrorism and Responses to Terrorism (START). Global Terrorism Database. 2014. See www.start.umd.edu/gtd. Nitsch, Volker; and Schumacher, Dieter. Terrorism and International Trade: An Empirical Investigation. European Journal of Political Economy, June 2004, Vol. 20, No. 2, pp. 423-433. Sandler, Todd. The Analytical Study of Terrorism: Taking Stock. Journal of Peace Research, March 2014, Vol. 51, No. 2, pp. 257-71. Conclusion We have discussed some of the economic costs of terrorism. There are myriads of other costs like destruction of infrastructure, flight of skilled workers (brain drain) and diversion of funds to counterterrorism (compared to funding of health, education, etc.). A comprehensive discussion of these costs is beyond the scope of this article. However, a greater understanding of terrorism-related damages can help governments and multilateral organizations (e.g., United Nations, World Bank) to better direct scarce resources to mitigate terrorism-related costs. Subhayu Bandyopadhyay is an economist at the Federal Reserve Bank of St. Louis, and Javed Younas is an associate professor of economics at American University of Sharjah, United Arab Emirates. Research assistance was provided by Rodrigo Guerrero, a senior research associate at the Bank. For more on Bandyopadhyay’s work, see https://research. stlouisfed.org/econ/bandyopadhyay. The Regional Economist | www.stlouisfed.org 13 M O N E T A R Y P O L I C Y Looking for the Positives In Negative Interest Rates By Brian Reinbold and Yi Wen T he 2007 global financial collapse resulted in central banks around the world taking unprecedented action to combat weak aggregate demand in both consumption and investment. In the United States, the Federal Reserve implemented a zero-interest-rate policy, slashing the federal funds rate to the range of 0-0.25 percent beginning in late 2008. It was seven years later before the Fed raised rates—and then it was just by 25 basis points. Today, the target for the fed funds rate stands at a range of 1.25-1.50 percent.1 Although the U.S. has never used negative interest rates (NIR), many other industrial nations have implemented them to spur their economies and continue to use them. For example, Denmark, Japan, Hungary, Sweden, Switzerland and the entire euro area have implemented negative nominal interest rates. The nominal interest rate in the entire euro area has been negative since 2014. Among this group of countries, Switzerland has the lowest level, at 75 basis points below zero. (See the figure.) The use of negative interest rates raises three important questions for monetary theory. First, given the widely held doctrine of the zero lower bound on nominal interest rates, how is a negative interest rate policy possible? Second, if an NIR is possible, will it effectively stimulate aggregate demand? Finally, is it desirable to keep the nominal interest rate very low for so long? This article addresses these questions. Different Countries, Different Rates In general, the overnight lending rate on loans and deposits from a central bank to commercial banks is called a policy rate. In the U.S., this rate is the federal funds rate. This overnight lending rate is a key 14 The Regional Economist | Fourth Quarter 2017 economic tool for central bankers as it can be used to adjust the cost of borrowing, which influences real economic activity. For example, since the Fed’s lending (or deposit) rate directly translates into shortterm government bond yields (e.g., through open market operations), low interest rates incentivize others to shift investment from low-yielding government bonds to moreproductive investments.2 The interest rate for the euro area, set by the European Central Bank (ECB), is the overnight deposit rate that banks receive. In Sweden, the official policy rate is the repo rate, which is the rate of interest at which banks can borrow or deposit funds at the Riksbank for a period of seven days. Normally, the overnight deposit rate is 0.75 percentage points lower than the repo rate, and the overnight lending rate is 0.75 percentage points higher than the repo rate at the Riksbank. The monthly average is reported here. Japan’s policy rate is the overnight deposit rate on excess reserve balances. Switzerland’s central bank does not set a target interest rate but instead sets a target range based on the three-month Libor (London Interbank Offered Rate) for threemonth interbank loans in Swiss francs. The reported policy rate in the figure is the midpoint of this range. The policy rate set by Denmark’s central bank is the rate charged on certificates of deposit. The certificates of deposit are sold on the last banking day of the week and typically mature one week later. The rate reported for Hungary is the overnight lending rate on deposits, analogous to the Federal Reserve’s policy rate, the fed funds rate. Conventional Wisdom Conventional monetary theory always assumed that the policy rate cannot go below zero because an individual would not pay, in theory, to lend out his or her own money. Instead, people would hoard cash to prevent nominal rates from falling below zero. Since the policy rate is closely linked to the rate of return on short-term government bonds, the bond yield is also assumed bounded below by zero—the nominal rate of return on cash. When the zero lower bound is reached, this situation is referred to by economists as a liquidity trap, the point at which further monetary injections do not stimulate the economy because people opt to hoard all cash available instead of investing or spending it. So, further monetary injections by the government would only end up hoarded by people or the banking system instead of being lent out and circulated in the economy. In monetary theory, this situation of low circulation is also called zero velocity of money because money is not circulated in the economy. However, if there are costs for people or institutions to hoard cash, then it is possible for banks to charge depositors by offering a negative interest rate. This means that depositors need to pay to have banks hold cash for them, or commercial banks must pay to have the central bank keep their deposits. In this case, the nominal deposit rate can go negative without getting into the liquidity trap. Of course, how negative the nominal interest rate can go depends on the costs of holding cash in hand. In other words, negative nominal interest rates are possible because there are costs to holding cash, especially for large What the Model Shows Researchers Feng Dong and Yi Wen recently created a theoretical model with costs of holding cash to capture the negative interest rate phenomenon as seen in the figure. They showed that when aggregate demand for investment and consumption is extremely weak, it is optimal for central banks to implement negative interest rates.4 This policy would potentially reduce the cost of borrowing and stimulate investment spending. In addition, these authors showed that the competitive interest rate on bank loans may move more than one-for-one with changes in the expected inflation rate, in contrast to the conventional wisdom. The conventional wisdom holds that given total bank deposits, a 1 percent increase in the expected inflation rate would induce a one-for-one increase in the nominal interest rate on bank loans to keep the lender indifferent between lending and not lending. However, this conventional wisdom fails to take into account the adverse general-equilibrium effect of inflation on total deposits. If total deposits decline as a result of the inflation increase, the competitive nominal interest rate would increase more than the increase in the expected inflation rate to keep the lender just as well off. Indeed, we know that people opt to hold less cash when the inflation rate is expected to be high. This implies that there is less money to be deposited into the banking system. So the nominal interest rate on bank loans has to increase more than the anticipated increase in inflation for profitmaximizing banks to break even. In this case, the correct definition of the real interest rate is no longer the difference between the nominal interest rate and the expected inflation rate, but something else. This means that under negative-interest-rate policy, the conventionally defined real interest rate (by the Fisherian relationship, Nominal Interest Rate ≈ Real Interest Rate + Inflation) tends to overestimate the level of the real interest rate (namely, the real interest rate may be more negative than the conventional Fisherian principle suggests). Not So Far-Fetched, After All Negative interest rates may seem ludicrous since why would an individual buy a government bond with a negative yield, but this is what a central bank would like you to think. The central bank’s goal is to incentivize agents to shift investments away from government bonds to something more productive economically, thus stimulating the economy. ENDNOTES 1 2 3 4 As of Federal Open Market Committee meeting in December 2017. The overnight rate is the interest rate at which a depository institution lends funds to another depository institution (short term), or the interest rate the central bank charges a financial institution to borrow money overnight. The rate increases when liquidity decreases (when loans are more difficult to come by) and decreases when liquidity increases (when loans are more readily available). The Federal Reserve influences the overnight rate in the United States through its open-market operations. For example, selling government bonds can increase the bond yield and the overnight rate because these sales reduce the money supply to the economy. Hence, the overnight rate and bond yield move together. For example, it is costly to build and secure a large private vault by private individuals or corporations, and such facilities yield no gains in normal times. See Dong and Wen. REFERENCE Dong, Feng; and Wen, Yi. Optimal Monetary Policy under Negative Interest Rate. Working Paper No. 019A, Federal Reserve Bank of St. Louis, May 2017. Yi Wen is an economist at the Federal Reserve Bank of St. Louis, and Brian Reinbold is a research associate there. For more on Wen’s work, see https://research.stlouisfed.org/econ/wen. Central Banks’ Policy Interest Rates 2 Euro area Switzerland Sweden Denmark Japan Hungary 1 Percent corporations.3 The central bank can also require (by law) large corporations to keep their cash, savings and loans in the banking system when a negative interest rate policy is implemented. The same argument applies to commercial banks that deposit their cash in the central bank. If the effective returns to cash go negative, then short-term yields of government bonds can also go negative, suggesting that there is still demand for government-issued debt even if it pays a negative interest rate. This means that the lower bound of the nominal interest rate is not zero, but lower than zero, if there are costs of holding (hoarding) cash. So long as the negative interest rate falls short of reaching its lower bound determined by the cost of holding cash, conventional monetary policies remain as effective as in the case of positive interest rates. 0 –1 Jan.’14 May ’14 Sept.’14 Jan.’15 May ’15 Sept.’15 Jan.’16 May ’16 Sept.’16 Jan.’17 May ’17 Sept.’17 SOURCES: European Central Bank, Riksbank, Denmark Nationalbank, Swiss National Bank, Bank of Japan, Central Bank of Hungary, Haver Analytics, Bloomberg, World Bank, Trading Economics. The Regional Economist | www.stlouisfed.org 15 N A T I O N A L O V E R V I E W Probabilities of Different Levels of Inflation 0.8 0.7 By Kevin L. Kliesen T wo major hurricanes hit the U.S. mainland in August (Harvey) and September (Irma).1 Given the population and economic significance of the impacted regions, most forecasters immediately downgraded prospects for the U.S. economy’s growth of real gross domestic product (GDP) in the third quarter. Although the hurricanes reduced U.S. employment in September, employment subsequently recovered in October. Despite initial forecasts of a sharp slowdown in third-quarter real GDP growth, the pace of economic activity turned out to be stronger than expected. Forecasters continue to see above-trend real GDP growth in the fourth quarter, bolstered by the burst in economic activity that normally occurs during the recovery and rebuilding phase after natural disasters. Economic Effects of Natural Disasters Typically, natural disasters disrupt activity in three key ways. First, disasters destroy lives, property and other factors of production. These are termed direct losses. These losses reduce the region’s and, if large enough, the nation’s wealth and tend to adversely affect productivity, income and profits in the short term. Second, indirect losses occur as a result of the disaster’s direct losses. These indirect losses include disruptions to the supply chain, upending the efficient distribution of goods and services, as well as lost sales and increased costs for businesses. Some of these losses (e.g., restaurant meals or airline services) can never be made up. Finally, natural disasters eventually trigger a rebound in economic activity, as structures, furniture, appliances and vehicles are repaired or replaced. For example, U.S. auto sales rose sharply in September and remained at a high level in October. 16 The Regional Economist | Fourth Quarter 2017 0.6 Probability Economy Bounces Back from Hurricanes 0.57 0.5 0.37 0.4 0.3 0.2 0.05 0.1 0.0 July ’12 Jan. ’13 July ’13 Jan. ’14 July ’14 Jan. ’15 Price Pressures Measure (inflation above 2.5 percent) Deflation probability (inflation below 0 percent) July ’15 Jan. ’16 July ’16 Jan. ’17 July ’17 0.00 Inflation between 1.5 and 2.5 percent Inflation between 0 and 1.5 percent SOURCE: Federal Reserve Bank of St. Louis. This chart plots the four St. Louis Fed Price Pressures Measures (PPM). Each series measures the probability that the personal consumption expenditures price index (PCEPI) inflation rate over the next 12 months will fall within a certain bucket. The four buckets are as follows: below 0 percent, between 0 and 1.5 percent, between 1.5 and 2.5 percent, and above 2.5 percent. For example, the probability for the above 2.5 percent bucket (“Price Pressures Measure”) is 0.05, which indicates there is a 5 percent probability inflation will exceed 2.5 percent over the next 12 months. All data for this article are as of Dec. 1. Developing Momentum Despite the hurricane-spawned disruptions, U.S. real GDP accelerated at a 3.3 percent annual rate in the third quarter. The second estimate was modestly stronger than the advance estimate. The advance estimate of 3 percent was very close to the St. Louis Fed’s Economic News Index (ENI) estimate, which had predicted third-quarter growth of 2.9 percent. With the hurricanes in the rearview mirror, the near-term outlook for the economy is brightening. Business surveys, such as the purchasing managers reports and the national homebuilders survey, indicated high levels of activity in September and October. Importantly, business-capital expenditures continue on an upward trajectory. Likewise, consumer confidence continues to trend higher, reflecting record-high stock prices and healthy labor market conditions. Indeed, the unemployment rate fell to 4.1 percent in October, its lowest level since December 2000. Wage gains have also picked up, albeit at a sluggish pace. Importantly, labor productivity growth is finally beginning to accelerate, which would be a catalyst for stronger wage and real GDP growth. Another factor helping to bolster the U.S. economy is the improving global economic outlook, which has triggered an upswing in U.S. exports. At the same time, the construction industry has slowed, mostly because of slowing in the multifamily and commercial segments. Housing sales have slowed, but homebuilders generally report that this reflects supply shortages (e.g., labor and lots) rather than a softening in demand. The St. Louis Fed’s ENI predicted on Dec. 1 that real GDP will increase at a 3.1 percent rate in the fourth quarter. Inflation Developments The effects of Hurricane Harvey were notable because it affected the heart of the nation’s petrochemical industry on the Gulf Coast. As refineries, pipelines and chemical production facilities shut down, prices of gasoline, diesel fuel and petroleum-based products like resins and plastics rose appreciably; price increases were passed along to consumers and producers to varying degrees. However, as production returned to normal, these supply shortages abated and prices retreated accordingly. Likewise, Hurricane Irma roared through Florida, disrupting its important tourism and agricultural industries. Food price increases were already on the upswing since fall 2016, and Irma may put additional upward pressure on them. The recent fires in northern E C O N O M Y A T A G L A N C E All data as of Dec. 1. REAL GDP GROWTH PERCENT 4 2 0 –2 Q3 ’12 ’16 All Items, Less Food and Energy 2 0 –2 ’17 2.75 October ’12 ’13 ’14 ’15 ’16 ’17 RATES ON FEDERAL FUNDS FUTURES ON SELECTED DATES 1.70 5-Year 2.50 1.60 10-Year 2.25 1.50 05/03/17 10/20/17 06/14/17 11/01/17 07/26/17 1.40 20-Year 2.00 1.30 1.75 1.20 1.50 1.10 1.00 1.25 0.90 Nov. 24 ’13 ’14 ’15 ’16 0.80 ’17 NOTE: Weekly data. C I V I L I A N U N E M P L O Y M E N T R AT E 1st-Expiring Contract 3-Month 6-Month 12-Month CONTRACT SETTLEMENT MONTH I N T E R E S T R AT E S 4 10 10-Year Treasury 9 3 8 PERCENT 7 6 5 2 Fed Funds Target 1 1-Year Treasury 4 3 ’12 October ’13 ’14 ’15 ’16 0 ’17 October ’13 ’14 ’15 ’16 ’17 NOTE: On Dec. 16, 2015, the FOMC set a target range for the federal funds rate of 0.25 to 0.5 percent. The observations plotted since then are the midpoint of the range. U.S. AGRICULTURAL TRADE AVERAGE LAND VALUES ACROSS THE EIGHTH DISTRICT 90 Exports 75 60 Imports 45 30 Trade Balance 15 0 ’12 September ’13 ’14 ’15 ’16 NOTE: Data are aggregated over the past 12 months. ’17 YEAR-OVER-YEAR PERCENT CHANGE A third major hurricane, Maria, ravaged Puerto Rico. Because U.S. GDP and employment data do not include economic activity from Puerto Rico, this article does not discuss the potential economic effects stemming from Maria on the U.S. economy. ’15 I N F L AT I O N - I N D E X E D T R E A S U RY Y I E L D S P R E A D S PERCENT 1 ’14 CPI–All Items NOTE: Each bar is a one-quarter growth rate (annualized); the red line is the 10-year growth rate. BILLIONS OF DOLLARS ENDNOTE ’13 PERCENT CHANGE FROM A YEAR EARLIER 4 1.00 Kevin L. Kliesen is an economist at the Federal Reserve Bank of St. Louis. Brian Levine, a research associate at the Bank, provided research assistance. See http:// research.stlouisfed.org/econ/kliesen for more on Kliesen’s work. CONSUMER PRICE INDEX (CPI) 6 PERCENT California may be another source of additional pressures on food price inflation. Despite the uptick in food and energy prices, the personal consumption expenditures price index was up in October by only 1.6 percent from a year earlier. Still, the rise in crude oil prices in October and November suggests that inflation could drift higher in the fourth quarter. Nonetheless, inflation expectations remain stable, perhaps reflecting the expectation of further tightening actions by the Federal Open Market Committee in 2018, which would be expected to help stanch rising price pressures. As of late November, the St. Louis Fed’s inflation forecasting model continues to see a low probability of headline inflation exceeding 2.5 percent over the next 12 months. 12 10 8 6 4 2 0 –2 –4 –6 –8 –10 Quality Farmland Ranchland or Pastureland 2016:Q3 2016:Q4 2017:Q1 2017:Q2 2017:Q3 SOURCE: Agricultural Finance Monitor. On the web version of this issue, 11 more charts are available, with much of those charts’ data specific to the Eighth District. Among the areas they cover are agriculture, commercial banking, housing permits, income and jobs. To see those charts, go to www.stlouisfed.org/economyataglance. The Regional Economist | www.stlouisfed.org 17 I N D U S T R Y P R O F I L E Advanced Manufacturing Is Vital across Nation, Including Eighth District By Charles S. Gascon and Andrew Spewak Manufacturing has been one of the nation’s largest and most productive sectors dating back to the Industrial Revolution, and that remains true today despite a long-term decline in employment.1 s technological progress continues to alter the landscape of the economy, a subset of manufacturing industries known as “advanced manufacturing” serves as a critical source of growth as these products drive productivity gains throughout the economy. In some sense, all manufacturing is “advanced” because it requires specific knowledge and use of modern technology. However, we refer specifically to the advanced manufacturing sector as industries in which research and development spending exceeds $450 per worker and at least 21 percent of jobs require a high degree of technical knowledge.2 These two metrics quantify the high level of development, design and technical work that is needed to initially develop advanced products. Thirty-five manufacturing industries outlined in the North American Industry Classification System (NAICS) qualify as advanced. Among the largest U.S. advanced manufacturers are companies that produce electronics, motor vehicles and fuel. The table displays the largest advanced manufacturing firms, based on revenue, in the nation and the Eighth Federal Reserve District.3 A company is classified as a manufacturing firm if its main business purpose is to produce goods, regardless of how much it engages in the actual production of those goods. Consider Apple: Its purpose is to produce electronics, so it is a manufacturing firm even though it contracts production to other suppliers and has many employees developing software. Similarly, there are two types of manufacturing 18 The Regional Economist | Fourth Quarter 2017 employees: production workers, who physically make the goods, and nonproduction workers, who work in other occupations that include administrative, professional, technical and management positions. We restrict our analysis to advanced manufacturing for two reasons. First, these industries are more productive than the rest of manufacturing. Although they historically have employed only about 45 percent of manufacturing employees, their output makes up to 53 percent of manufacturing output. Second, there exists a wage premium for advanced manufacturing employees. The average employee in these industries earns about 40 to 50 percent more than the average private sector worker, depending on the data source. As of 2016, the wage premium for nonproduction workers compared with private sector workers is 72 percent, and the premium for production workers is 7 percent.4 In contrast, workers in non-advanced manufacturing sectors earn essentially the same wage as other private sector workers. In this article, we will examine advanced manufacturing’s long-term shifts, its current state and its impact on the Eighth District economy. © THINKSTOCK / ISTOCK Largest Advanced Manufacturing Firms by Revenue National Eighth District 1 Apple (3342) Emerson Electric (335) (St. Louis, Mo.) 2 Johnson & Johnson (3254) MilliporeSigma (3254) (St. Louis, Mo.) 3 Gilead Sciences (3254) Energizer Holdings (3359) (St. Louis, Mo.) 4 Intel (3344) Hillenbrand (3339) (Batesville, Ind.) 5 Cisco Systems (3342) American Railcar Industries (3365) (St. Charles, Mo.) 6 General Motors (3361) Esco Technologies (3345) (St. Louis, Mo.) 7 General Electric (335) FutureFuel (3251) (Clayton, Mo.) 8 Amgen (3254) Kimball Electronics (3344) (Jasper, Ind.) 9 Pfizer (3254) Escalade (3399) (Evansville, Ind.) Exxon Mobil (3241) Sypris Solutions (3363) (Louisville, Ky.) 10 SOURCE: Compustat. NOTE: Firm location is based on the location of the headquarters, which is self-reported by the corporation. Company NAICS code in parentheses. All data are from December 2016 unless otherwise noted. MilliporeSigma data are from Sigma-Aldrich in December 2014; since then, Sigma-Aldrich has been bought out and merged into MilliporeSigma. National Advanced Manufacturing From January 1997 to the end of the Great Recession in June 2009, advanced manufacturing lost over 2 million employees. The biggest losses were in computer electronics manufacturing, which lost 720,000 jobs, and primary metals manufacturing, which lost 450,000. As a share of private employment, advanced manufacturing employment fell from 7.5 percent to 4.9 percent during this period. During the recovery from June 2009 Regional Employment Advanced manufacturing is especially vital to the Eighth District economy: The sector employs 7 percent of private sector workers and generates 11 percent of private output.5 As Figure 1 shows, both the employment share and growth since the recession exceed the national averages. Among District states, the employment FIGURE 1 Advanced Manufacturing Employment Employment Growth (2009-2017) 40% 35% 30% 25% 20% 15% 10% 5% 0% –5% –10% 0% Kentucky Tennessee Louisville Indiana Eighth District Missouri Memphis St. Louis U.S. Mississippi Arkansas 2% Little Rock 4% 6% Share of Private Employment (2017) 8% 10% 12% SOURCES: Bureau of Labor Statistics and authors’ calculations. This figure shows the advanced manufacturing employment share in March 2017 versus the growth of advanced manufacturing employment from the end of the recession in June 2009 until March 2017. Areas to the right of the vertical line have a higher employment share than the nation. Areas above the horizontal line have experienced faster employment growth than the nation. Areas in the top-right quadrant are the best-performing, as both the share and growth exceed the national averages. NOTE 1: Due to nondisclosure at the county level for some industries over time, estimates for the Eighth District advanced manufacturing sector are calculated as the sum of data for the entirety of all District states except Illinois. We excluded Illinois from our calculations since most of Illinois’ economic activity stems from the Chicago area, outside the District. The other District states are Arkansas, Indiana, Kentucky, Mississippi, Missouri and Tennessee. NOTE 2: In calculating employment for each metropolitan statistical area (MSA), we estimated nondisclosed four-digit North American Industry Classification System (NAICS) industries by projecting the MSA employment data using the employment growth rate of the MSA’s largest county. If the data were also nondisclosed in the largest county, we used the state growth rate. If the state data were also missing, we used the growth rate of the corresponding three-digit NAICS industry. FIGURE 2 Advanced Manufacturing Wages 25% Real Wage Growth (2009-2017) to March 2017, advanced manufacturing employment increased 6 percent, but the share fell to 4.5 percent. (See Figure 1.) Despite gains in recent years, employment in advanced manufacturing has fallen over 30 percent since 1997. Yet, that is not necessarily an indication of weakness in the sector. From 1997 to 2015, real output increased by over 50 percent due to gains in labor productivity. In 2015, advanced manufacturing was 40 percent more productive than the private sector as a whole. Similarly, advanced manufacturing remains the largest U.S. exporter. In 2016, advanced manufacturing accounted for 60 percent of the dollar value of exports, down slightly from 68 percent in 1997, but up from 2014. Moreover, wages in advanced manufacturing are high, with the average worker making over $1,600 per week. Wages are highest in computers and electronics manufacturing, at $2,300, and chemical manufacturing, at $1,900. Real (inflation-adjusted) wages have grown 11 percent since the recession, with the largest gains in computers and electronics manufacturing. Today, the average advanced manufacturer makes $1.53 for every $1 that the average private sector worker makes. (See Figure 2.) Most advanced manufacturing jobs are in large metropolitan statistical areas (MSAs). Employment is highest in Los Angeles, which has 232,000 employees, followed by Chicago, with 143,000, and New York, with 132,000. These three MSAs account for 9 percent of advanced manufacturing employment nationwide. While the total number of employees is smaller, as a share of private employment, advanced manufacturing is most heavily concentrated in Midwestern MSAs. The share is highest in Battle Creek, Mich. (the main product being autos), followed by Wichita, Kan. (airplanes), and Columbus, Ind. (machinery). 20% Louisville Arkansas 15% 10% Little Rock Tennessee Memphis 5% 0% $1.10 U.S. Kentucky Eighth District Indiana Missouri $1.15 $1.20 $1.25 $1.30 $1.35 $1.40 $1.45 Mississippi St. Louis $1.50 $1.55 $1.60 Wage Premium SOURCES: Bureau of Labor Statistics and authors’ calculations. Analogous to Figure 1, this figure shows the wage premium for advanced manufacturing workers in March 2017 versus real wage growth from the end of the recession in June 2009 until March 2017. Areas to the right of the vertical line have a higher wage premium than the nation. Areas above the horizontal line have experienced faster real wage growth than the nation. Areas in the top-right quadrant are the best-performing, as both the wage premium and growth exceed the national averages. The apparent negative relationship in the figure is due to the limited number of observations presented. A sample of all 50 states indicates a modest positive correlation between wage growth and wage premiums. NOTE 1: The wage premium is calculated as the amount of money the average advanced manufacturing employee earns for every $1 earned by the average private sector employee. NOTE 2: Due to nondisclosure at the county level for some industries over time, estimates for the Eighth District advanced manufacturing sector are calculated as the sum of data for the entirety of all District states except Illinois. We excluded Illinois from our calculations since most of Illinois’ economic activity stems from the Chicago area, outside the District. The other District states are Arkansas, Indiana, Kentucky, Mississippi, Missouri and Tennessee. NOTE 3: Due to nondisclosure at the county level for some industries over time, wage estimates are based off the 3-digit NAICS industries 325, 327, 331, 333, 334, 335, 336 and 339. The Regional Economist | www.stlouisfed.org 19 In the Eighth District, advanced manufacturing has a relatively large presence, mostly due to a high concentration of automotive manufacturing employment. However, the wage premium for advanced manufacturing employees, while significant, is smaller regionally than nationally. Likewise, though real wages are growing positively in the Eighth District, the pace of growth lags behind the national average. Auto production accounts for 39 percent of the advanced manufacturing jobs in Eighth District states. The auto industry’s share of these jobs is highest in Indiana, Kentucky and Tennessee. share is largest in Indiana, Kentucky and Tennessee. Among the District’s four largest MSAs (St. Louis, Mo.; Memphis, Tenn.; Louisville, Ky.; and Little Rock, Ark.), the employment share is highest in Little Rock. Since the end of the recession, advanced manufacturing employment in the District states has grown 23 percent, outpacing the national rate considerably. That translates to 139,000 new jobs in the District states. Employment growth has been fastest in the eastern portion of the District: Kentucky, Tennessee and Indiana are growing substantially more rapidly than the rest of the District states. Among the MSAs, Louisville has experienced the fastest employment growth since 2009, at 29 percent, followed by Memphis, at 10 percent. Auto Manufacturing in the District Auto manufacturing has a significant presence regionally, employing 39 percent of advanced manufacturing workers, and has driven the bulk of advanced manufacturing’s growth. On net, 90 percent of new advanced manufacturing jobs since 2009 are automotive. Among District states, auto manufacturing employment as a share of advanced manufacturing employment is largest in Indiana, Kentucky and Tennessee. Among the MSAs, the auto employment share is largest in Louisville, at 37 percent, and Memphis, at 15 percent. Recall from Figure 1 that these areas also experienced the fastest growth in advanced manufacturing employment. The Regional Impact District productivity in the sector mirrors the nation. Advanced manufacturing in 2015 was 36 percent more productive than the overall private sector, with the most 20 The Regional Economist | Fourth Quarter 2017 © THINKSTOCK / ISTOCK /ZAPP2PHOTO productive subsector being transportation equipment manufacturing. Advanced manufactures are a larger component of trade for the District than nationally. They make up 70 percent of the dollar value of District state exports to the world, above the 1997 share of 64 percent. Average weekly advanced manufacturing wages in the District are generally below the U.S. average. However, nominal wages are lower throughout the private sector in the Eighth District, mostly because of the District’s lower cost of living.6 Figure 2 shows that the District’s wage premium, which accounts for differences in cost of living, also tends to fall below the U.S. average. This result is largely due to the fact that nonproduction workers, who garner higher wages than production workers, constitute a smaller proportion of the sector’s workforce in the District compared to the nation. Of the District MSAs and states, only Mississippi’s wage premium of 54 percent exceeds the national average. Among the four MSAs, the premium is highest in St. Louis, at 51 percent. Likewise, real wage growth in the District, while positive, is slow. Of the states, only Arkansas real wages are growing more quickly than the national average. Among the MSAs, real wages are growing fastest in Louisville and Little Rock, at 20 percent and 9 percent, respectively. Sector Still Significant Advanced manufacturing employment as a share of private employment has steadily declined over the years, but the sector remains a significant cog in the U.S. economy. Advanced manufacturing accounts for 7 percent of private output and 60 percent of the dollar value of U.S. exports. Charles Gascon is a regional economist, and Andrew Spewak is a senior research associate, both at the Federal Reserve Bank of St. Louis. For more on Gascon’s work, see https://research. stlouisfed.org/econ/gascon. ENDNOTES 1 2 3 4 5 6 See Kliesen and Tatom. See Muro et al. Headquartered in St. Louis, the Eighth Federal Reserve District includes all of Arkansas and parts of Illinois, Indiana, Kentucky, Mississippi, Missouri and Tennessee. In our analysis we exclude Illinois; see endnote 5 for more information. The Quarterly Census of Employment and Wages (QCEW) and Occupational Employment Statistics (OES), both from the Bureau of Labor Statistics, report industry-level wages. The advanced manufacturing wage premium is estimated to be 53 percent (QCEW) and 42 percent (OES). The OES provides estimates for both nonproduction and production occupations. Throughout the rest of the article, we will use QCEW data, as they are better suited for time series and regional analysis. When available, we have tested the robustness of our results using the OES data. Due to nondisclosure at the county level for some industries over time, estimates for the District’s advanced manufacturing sector are calculated as the sum of data for the entirety of all District states except Illinois. We excluded Illinois from our calculations since most of that state’s economic activity stems from the Chicago area, which is outside the District. See Coughlin, Gascon and Kliesen for more information on the relationship between cost of living and income. REFERENCES Coughlin, Cletus; Gascon, Charles; and Kliesen, Kevin. Living Standards in St. Louis and the Eighth Federal Reserve District: Let’s Get Real. Federal Reserve Bank of St. Louis Review, Fourth Quarter 2017, Vol. 99, No. 4, pp. 377-94. Kliesen, Kevin; and Tatom, John. U.S. Manufacturing and the Importance of International Trade: It’s Not What You Think. Federal Reserve Bank of St. Louis Review, January/February 2013, Vol. 95, No. 1, pp. 27-49. Muro, Mark; Rothwell, Jonathan; Andes, Scott; Fikri, Kenan; and Kulkarni, Siddharth. America’s Advanced Industries: What They Are, Where They Are, and Why They Matter. The Brookings Institution, February 2015. O V E R V I E W First-Time Homebuyers Appear to Be Younger, Less Creditworthy in Eighth District The Eighth Federal Reserve District is composed of four zones, each of which is centered around one of the four main cities: Little Rock, Louisville, Memphis and St. Louis. By Brian Reinbold and Paulina Restrepo-Echavarria FIGURE 1 Total Number of First-Time Homebuyers States in Eighth District F irst-time homebuyers are essential to the dynamics of the housing market by allowing current homeowners to trade up. The number of first-time homebuyers decreased between 2000 and 2011, and then started slowly increasing again. (See Figure 1.) There are many possible reasons why this happened, such as rising rent and home prices, rising student debt and tightening credit standards. Have there been fewer first-time homebuyers in the Eighth Federal Reserve District? In this article, we study the number and some characteristics of firsttime homebuyers in the Eighth District1 and see how they compare to those at the national level. We used the Federal Reserve Bank of New York Consumer Credit Panel/Equifax to estimate the number of first-time homebuyers. The FRBNY Consumer Credit Panel (CCP) consists of detailed credit-report data, updated quarterly, for a unique longitudinal panel of individuals and households beginning in 1999. It provides information on various forms of debt, including student loans, auto loans and mortgages. The CCP is a nationally representative 5 percent random sample of individuals in the United States with a Social Security number and credit report.2 We took a 10 percent random sample of the CCP dataset, so we have a 0.5 percent nationally representative sample. In other words, we have 1,347,520 unique records for this sample in 2016, out of approximately 269,504,000 individuals in the U.S. with a Social Security number and a credit report. A drawback of the CCP dataset is that it only includes homebuyers who finance 400,000 9,000,000 350,000 8,000,000 300,000 7,000,000 6,000,000 250,000 5,000,000 200,000 4,000,000 150,000 3,000,000 100,000 2,000,000 50,000 1,000,000 0 USA D I S T R I C T 2000 AR 2002 IL 2004 2006 IN 2008 KY 2010 MO 2012 2014 MS TN 2016 0 USA SOURCES: Federal Reserve Bank of New York Consumer Credit Panel/Equifax and authors’ calculation. NOTE: Some parts of these states lie outside of the Eighth District. their purchase with a mortgage; it excludes all cash purchases. However, it is likely that most first-time homebuyers finance their home. Following work by Jessica Dill and Elora Raymond, we used the CCP to estimate the number of first-time homebuyers.3 We took the year of the oldest mortgage on file for individuals within the dataset to determine the first time they obtained a mortgage. This analysis does not consider individuals who transitioned back to renting and then purchased a home later on.4 Figure 1 is a plot of the total number of first-time homebuyers from 2000 to 2016 by each state in the Eighth District5 and the whole U.S. The number of first-time homebuyers decreased significantly since 2000. The decline bottomed out around 2011 and 2012 for the U.S. and most states in the District. From 2000 to 2011, the rate of decline for these District states is similar to the 76 percent decline nationwide. Indiana and Illinois experienced the sharpest decline during this period, each falling about 80 percent, while Arkansas had the smallest decline, dropping about 70 percent. The number of first-time homebuyers bottomed out in 2011 for the nation and most states in the Eighth District. Since 2011, the number of first-time buyers nationally has increased 34 percent. The growth rates since 2011 for Missouri and Tennessee exceeded the nation’s at 46 and 54 percent, respectively. The rates in Arkansas, Illinois and Indiana are in line with the nation’s. However, the rate remains flat in Kentucky, while the rate in Mississippi has actually declined 22 percent since 2011. Figure 2 plots the median credit score of first-time homebuyers at the date of purchase. As we can see, credit worthiness appears to be of lesser importance in the states of our District throughout the whole period of 2000 to 2016; the combined credit score is lower than the national level. The Regional Economist | www.stlouisfed.org 21 FIGURE 2 Median Risk Score of First-Time Homebuyers at Date of Purchase ENDNOTES 730 1 States in Eighth District 720 710 2 3 700 4 States in Eighth District 690 USA 680 670 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 SOURCES: Federal Reserve Bank of New York Consumer Credit Panel/Equifax and authors’ calculation. NOTE: Some parts of these states lie outside of the Eighth District. 5 FIGURE 3 REFERENCES Total Number of First-Time Homebuyer by Birth Year 35,000 250,000 200,000 25,000 20,000 150,000 15,000 100,000 10,000 USA States in Eighth District 30,000 50,000 5,000 0 2000 2002 1985 District 2004 2006 1975 District 2008 2010 1985 USA 2012 2014 2016 0 1975 USA SOURCES: Federal Reserve Bank of New York Consumer Credit Panel/Equifax and authors’ calculation. NOTE: Some parts of these states lie outside of the Eighth District. Qualitatively, however, the District and national trends behave the same. Credit requirements eased from 2003 to 2006, corresponding to the time of the housing bubble. When the housing bubble burst, credit significantly tightened as lenders increased credit worthiness requirements. As we mentioned, credit scores in the District follow a similar trajectory but began to increase a year earlier than the national trend. After the sharp increase, the District and national trends flattened out in 2009. Although increasing over the last several years, the number of first-time homebuyers is still much lower than the pre-2007 level, suggesting that tightened lending standards have been a headwind for first-time homebuyers. Did this decline affect age groups differently? Figure 3 shows the total number of 22 The Regional Economist | Fourth Quarter 2017 Headquartered in St. Louis, the Eighth District includes all of Arkansas and parts of Illinois, Indiana, Kentucky, Mississippi, Missouri and Tennessee. See van der Klaauw and Lee. See Dill. According to the U.S. Department of Housing and Urban Development, a first-time home buyer is “an individual who has no ownership in a principal residence during the 3-year period ending on the date of purchase of the property.” Therefore, an individual who buys their first home, then becomes a renter and finally purchases a home three or more years later would be considered a first-time home buyer again. See HUD. The sample includes individuals from the entire state, not just those from the parts of the state that belong to the District. first-time homebuyers who were born in 1975 and 1985 for the U.S. and the District, from 2000 to 2016. The number of first-time homebuyers for those born in 1975 peaked in the early 2000s, when they were in their late 20s, while the number of first-time homebuyers for those born in 1985 has remained more constant since 2010. For those born in 1975, the total number of first-time homebuyers fell precipitously after age 30, while the number for those born in 1985 remained fairly constant after 2010. These results suggest that demand by first-time buyers is more spread out for later generations. From these data, we can conclude that the number of first-time homebuyers in the District states has a trend which is very similar to the national level and that credit requirements are somewhat looser in the District. Dill, Jessica; and Raymond, Elora. Are Millennials Responsible for the Decline in First-Time Home Purchases? Federal Reserve Bank of Atlanta: Real Estate Blog, May 20, 2015. See http://realestateresearch.frbatlanta.org/ rer/2015/05/are-millennials-responsible-forthe-decline-in-first-time-home-purchases. html. Dill, Jessica; and Raymond, Elora. Are Millennials Responsible for the Decline in First-Time Home Purchases? Part 2. Federal Reserve Bank of Atlanta: Real Estate Blog, July 1, 2015. See http://realestateresearch.frbatlanta.org/ rer/2015/07/are-millennials-responsible-forthe-decline-in-first-time-home-purchasespart-2.html. U.S. Department of Housing and Urban Development. HUD HOC Reference Guide, Nov. 7, 2012. See https://archives.hud.gov/offices/hsg/ sfh/ref/sfhp3-02.cfm. van der Klaauw, Wilbert; and Lee, Donghoon. An Introduction to the FRBNY Consumer Credit Panel. Federal Reserve Bank of New York, Staff Report No. 479, November 2010. Paulina Restrepo-Echavarria is an economist, and Brian Reinbold is a research associate, both at the Federal Reserve Bank of St. Louis. For more on Restrepo-Echavarria’s work, see https://research.stlouisfed.org/econ/restrepoechavarria. R E A D E R A S K A N E X C H A N G E E C O N O M I S T ASK AN ECONOMIST LETTERS TO THE EDITOR Don Schlagenhauf has been an economist at the Federal Reserve Bank of St. Louis since 2015. His research interest is in macroeconomics and policy, with emphasis on housing. He enjoys baseball. Don was born in St. Louis and has been a lifelong Cardinals fan. In fact, he is a season ticket holder for the Cards spring training. For more on his research, see https:// research.stlouisfed.org/econ/schlagenhauf. These letters pertain to articles in our Third Quarter issue (stlouisfed.org/ publications/regional-economist/third-quarter-2017). The first letter is about the article Quantitative Easing: How Well Does This Tool Work? Dear Editor: I agree with you on the point that QE should not be repeatedly used in the future as a monetary policy because (1) purchasing private bonds is too influential to the firms’ financial health, which may result in economic biases; and (2) public sentiment can no longer be more optimistic than it was from Q: How did consumer borrowing change after the Great Recession? 2008. On the other hand, I believe that people’s faith in QE positively worked at least in the past. In the analyses with Japan and Canada, you did not mention exchange rates. However, both Japanese yen and Canadian dollars significantly A: Following the run-up in household debt during the early 2000s, changed during the past decade. I also studied international economics consumers have been steadily reducing their overall debt level and learned that Canadian transports with the U.S. remarkably increased (i.e., deleveraging) since the Great Recession ended in June 2009. after US-Can FTA (1989), and its economy became more reliant on the U.S. The ratio of household debt to personal income peaked in the mid- economy. Likewise, Japanese trade volumes and its stock prices are reacting 2000s at nearly 1.2, and it has declined to about 0.9 in the second in accordance to JPY-USD exchange rates. quarter of 2017. However, looking at aggregate data tells us only part of the story. To better understand the run-up in debt and subsequent deleveraging, Carlos Garriga, Bryan Noeth and I studied patterns in mortgage Therefore, the fact that Canadian real GDP boosted without QE is explained by 1) its reliance on US economy, and 2) large fluctuations in exchange rates. By the way, nominal GDP in U.S. dollars shows completely different trends. debt, credit card debt, auto loans and student loans held by differ- The growths from 2008 to 2015 are: Canada 0.24 percent, Japan –13.06 percent ent age groups between 1999 and 2013.1 and U.S. 23.11 percent. Obviously, the biggest change in borrowing over that period has been mortgage debt. In the early 2000s, average mortgage debt Emi Igawa, Nagoya, Japan increased among all age groups, but especially for younger households. In 1999, homeowners with the largest mortgage debt (about $60,000 in 2013 dollars) were around 45 years old. In 2008, peak mortgage debt (about $117,000) occurred for those around age 42. Despite large deleveraging after the recession (particularly among those younger than 60), average mortgage debt remained higher in 2013 than in 1999. For the other types of debt, the general patterns we found were: • Credit card debt also increased, primarily for those older than 30, and then began to decline after 2008. Unlike other types of debt, average credit card debt in 2013 was below its 1999 level for most age groups. • Auto debt also rose between 1999 and 2008, but dropped across all age groups after the recession. Auto debt then rebounded in 2013. • Student debt, on the other hand, consistently grew from 2005 to 2013 for all age groups. For those over 50, the rise is likely due to parents or grandparents taking on loans or co-signing for relatives. Having debt is not necessarily bad, as it allows individuals to make up for the mismatch between income and consumption The second letter comments on the article titled Household Participation in Stock Market Varies Widely by State. Dear Editor: I think the methodology in this analysis is very flawed, and a wide variety of conclusions could, therefore, be drawn. Our household falls within the key household income group discussed. We do all of our savings within tax-deferred retirement vehicles and have substantial savings, with about 75 percent in equities. We never report dividends because we own no equities outside the tax-deferred accounts; so, we are a reason that they report low participation in the stock market. So an alternative explanation of the data shown in this paper is that the people in the states with high stock market participation rates are investing in tax-inefficient vehicles and could benefit from financial advice to put more or all of their savings into tax-deferred plans. Between Roth and Regular IRAs, 401(k)s, and 403(b)s, there is no reason for anybody making less than $200k per year to have ANY taxable stock dividends. We may have a retirement crisis, but it is not because people are not expenditures earlier in life; consumers just need to be prudent with buying stocks outside of tax-deferred accounts. the amount of debt they take on. By studying debt patterns, how- Raymond D’Hollander, Fayetteville, N.Y., an engineer ever, we hope to gain a better understanding of the tipping point between manageable debt and debt levels that expose consumers to excessive risk. 1 Garriga, Carlos; Noeth, Bryan; and Schlagenhauf, Don E. Household Debt and the Great Recession. Federal Reserve Bank of St. Louis Review, Second Quarter 2017, Vol. 99, No. 2, pp. 183-205. We welcome letters to the editor, as well as questions for “Ask an Economist.” You can submit them online at www.stlouisfed.org/re/ letter or mail them to Subhayu Bandyopadhyay, editor, The Regional Economist, Federal Reserve Bank of St. Louis, P.O. Box 442, St. Louis, MO 63166-0442. The Regional Economist | www.stlouisfed.org 23 P.O. Box 442 St. Louis, MO 63166-0442 CHANGE SERVICE REQUESTED The Regional Economist: 100 Issues and Counting I n January 1993, the first issue of the Regional Economist debuted. The three articles focused on health insurance, the business cycle and exports from our District. The editor then was James Bullard, now president and CEO of the St. Louis Fed. The issue you are reading is the 100th of this quarterly publication. Much has changed over the past 25 years—the magazine is bigger, readership has mushroomed (thanks largely to our web presence), the topics span from the local to the international, and all articles are now written by our economists (but still in layman’s language). To prepare RE for its next 100 issues, we’re introducing some changes in the coming year: • Articles will be published online (www.stlouisfed.org/re) as they are finished—one every two weeks or so. This will ensure that they don’t become outdated while waiting for the next quarter’s release. We think online readers will also appreciate the one-article-at-a-time approach. (Print subscribers will continue to receive this magazine—with all of the articles—in their mailbox four times a year.) • The online version of RE will be redesigned to reflect our new approach of continuous publishing. Check it once in a while to see what’s new. (Readers who already subscribe to receive an email when a new issue is published will receive in the future an email when each new article is posted. Sign up for this email newsletter at www.stlouisfed.org/subscribe/re.) • The print version of RE will also be redesigned—the first new look since 2008. We hope you like the changes. Subhayu Bandyopadhyay, Editor The Regional Economist F E D E R A L A Quarterly Review of Business and Economic Conditions Income Inequality It’s Not So Bad in the United States Exports to China District Tops Nation in Growth of Shipments A Quarterly Review of Business and Economic Conditions Vol. 16, No. 4 October 2008 Vol. 25, No. 2 THE FEDERAL RESERVE BANK OF ST. LOUIS CENTRAL TO AMERICA’S Second Quarter 2017 President Bullard Let’s Start Trimming Fed’s Balance Sheet ECONOMY ® Industry Profile Growth in Tech Sector Returns to Glory Days APRIL 2003 R E S E R V E B A N K O F S A I N T L O U I S The Regional Economist A Quarterly Review of Business and Economic Conditions A Winning Combination? Vol. 17, No. 1 Economics and Sports El Dorado Promise Free College Education Rejuvenates Arkansas Town January 2009 The Federal reserve Bank oF sT. louis A Quarterly Review of Business and Economic Conditions Split Decisions How Marriage and Motherhood Affect Women’s Wages CONSUMER CONFIDENCE What Do They Tell SURVEYS Us? Nation COMMUNITY PROFILE Starkville, Miss., a Stark Contrast to State’s Image WWW.STLOUISFED.ORG China’s Econo mic Data An Accu Deficits, Debt INNOVATION The District vs. the Community Colleges and Looming Disaster Reform of Entitlement Programs May Be the Only Hope rate Reflectio n, or Just Smoke and Mirrors? ECONOMY AT A THE REGIONAL GLANCE ECONOMIST FOURTH QUARTER All data as of Dec. 1, 2017. REAL GDP GROWTH 4 2 0 Q3 ’12 ’13 ’14 ’15 ’16 PERCENT CHANGE FROM A YEAR EARLIER 4 PERCENT VOL. 25, NO. 4 CONSUMER PRICE INDEX 6 –2 | ’17 CPI–All Items All Items, Less Food and Energy 2 0 –2 October ’12 ’13 ’14 ’15 ’16 ’17 NOTE: Each bar is a one-quarter growth rate (annualized); the red line is the 10-year growth rate. I N F L AT I O N - I N D E X E D T R E A S U RY Y I E L D S P R E A D S 2.75 1.70 5-Year 2.50 1.60 10-Year 2.25 PERCENT RATES ON FEDERAL FUNDS FUTURES ON SELECTED DATES 1.50 1.30 1.75 1.20 1.50 1.10 10/20/17 11/01/17 07/26/17 1.40 20-Year 2.00 05/03/17 06/14/17 1.00 1.25 0.90 Nov. 24 1.00 ’13 ’14 ’15 ’16 0.80 ’17 NOTE: Weekly data. C I V I L I A N U N E M P L O Y M E N T R AT E 1st-Expiring Contract 3-Month 12-Month I N T E R E S T R AT E S 10 4 9 10-Year Treasury 3 8 7 PERCENT PERCENT 6-Month CONTRACT SETTLEMENT MONTH 6 5 2 Fed Funds Target 1 1-Year Treasury 4 3 ’12 October ’13 ’14 ’15 ’16 0 ’17 October ’13 ’14 ’15 ’16 ’17 NOTE: On Dec. 16, 2015, the FOMC set a target range for the federal funds rate of 0.25 to 0.5 percent. The observations plotted since then are the midpoint of the range. U . S . A G R I C U LT U R A L T R A D E AVERAGE LAND VALUES ACROSS THE EIGHTH DISTRICT 90 BILLIONS OF DOLLARS 75 60 Imports 45 30 Trade Balance 15 0 ’12 September ’13 ’14 ’15 ’16 NOTE: Data are aggregated over the past 12 months. ’17 YEAR-OVER-YEAR PERCENT CHANGE Exports 12 10 8 6 4 2 0 –2 –4 –6 –8 –10 Quality Farmland Ranchland or Pastureland 2016:Q3 2016:Q4 2017:Q1 2017:Q2 2017:Q3 SOURCE: Agricultural Finance Monitor. U.S. CROP AND LIVESTOCK PRICES 140 INDEX 1990-92=100 120 Crops Livestock 100 80 60 40 ’02 September ’03 ’04 ’05 ’06 ’07 ’08 ’09 ’10 ’11 ’12 ’13 ’14 ’15 ’16 ’17 COMMERCIAL BANK PERFORMANCE RATIOS U.S. BANKS BY ASSET SIZE / THIRD QUARTER 2017 All $100 million$300 million Less than $300 million $300 million$1 billion Less than $1 billion $1 billion$15 billion Less than $15 billion More than $15 billion Return on Average Assets* 1.08 1.10 1.07 1.13 1.10 1.17 1.15 1.07 Net Interest Margin* 3.15 3.87 3.86 3.85 3.85 3.77 3.80 3.01 Nonperforming Loan Ratio 1.17 1.02 1.06 0.89 0.96 0.82 0.87 1.25 Loan Loss Reserve Ratio 1.27 1.36 1.38 1.30 1.33 1.10 1.18 1.29 R E T U R N O N AV E R A G E A S S E T S * NET INTEREST MARGIN* 1.17 1.12 1.33 1.33 1.10 .40 .60 Third Quarter 2017 .80 3.60 3.68 Indiana Kentucky 3.90 3.77 1.10 Mississippi 3.83 3.85 1.12 1.08 Missouri 1.15 1.07 Tennessee 1.00 .20 3.56 3.47 Illinois 1.15 1.14 .00 4.11 4.14 Arkansas 0.98 1.00 0.97 3.73 3.70 Eighth District 1.00 1.20 1.40 PERCENT 3.48 3.46 3.40 3.33 0.0 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 Third Quarter 2016 Third Quarter 2017 N O N P E R F O R M I N G L O A N R AT I O 0.79 L O A N L O S S R E S E RV E R AT I O 1.09 .20 .40 Third Quarter 2017 .60 .80 1.18 1.17 Kentucky 0.95 Mississippi 1.06 1.25 1.31 Missouri 0.81 0.84 .00 0.71 0.74 Indiana 1.22 0.82 0.69 1.11 1.16 Illinois 0.81 0.60 1.08 1.11 Arkansas 1.02 1.03 1.09 0.70 1.09 1.14 Eighth District 0.94 0.83 Third Quarter 2016 1.00 1.02 1.09 Tennessee 0.99 1.20 Third Quarter 2016 NOTE: Data include only that portion of the state within Eighth District boundaries. SOURCE: Federal Financial Institutions Examination Council Reports of Condition and Income for all Insured U.S. Commercial Banks. * Annualized data. 1.40 PERCENT .00 .20 .40 Third Quarter 2017 .60 .80 1.00 1.20 Third Quarter 2016 For additional banking and regional data, visit our website at: https://fred.stlouisfed.org. 1.40 REGIONAL ECONOMIC INDICATORS N O N FA R M E M P L O Y M E N T G R O W T H / T H I R D Q U A RT E R 2 0 1 7 YEAR-OVER-YEAR PERCENT CHANGE United States Eighth District † Arkansas 2.1% Total Nonagricultural 1.4% 1.2% Natural Resources/Mining 8.4 0.5 Construction 2.6 Manufacturing Trade/Transportation/Utilities Illinois Indiana Kentucky Mississippi Tennessee 0.8% 1.8% 1.6% 0.4% 1.5% –1.6 3.7 1.6 –5.3 2.5 6.5 8.3 1.1 3.2 –1.0 4.8 6.6 –1.7 –2.4 NA 0.8 0.7 2.6 –0.2 1.5 –0.3 0.4 1.6 0.3 0.4 0.5 1.2 –0.6 –0.3 1.5 1.4 0.8 1.6 –2.1 –1.4 –2.5 0.7 –6.6 5.5 –6.0 –4.9 –0.2 Financial Activities 1.9 2.2 0.1 2.6 3.0 2.1 0.4 2.7 1.5 Professional & Business Services 2.9 2.3 3.9 1.8 1.8 5.2 –1.5 3.5 2.0 Educational & Health Services 2.2 1.5 3.8 0.8 2.6 0.8 2.6 1.7 0.9 Leisure & Hospitality 1.8 1.8 3.8 0.9 0.2 0.4 1.3 3.8 3.3 Other Services 1.3 1.3 3.8 0.3 2.7 3.6 –0.3 0.5 1.4 Government 0.3 0.7 –0.6 –0.7 2.6 –0.3 1.3 2.1 1.4 Information 1.5% Missouri † Eighth District growth rates are calculated from the sums of the seven states. For the Construction category, data on Tennessee are no longer available. Each state’s data are for the entire state even though parts of six of the states are not within the District’s borders. U N E M P L O Y M E N T R AT E S EIGHTH DISTRICT ADJUSTED GROSS CASINO REVENUE* II/2017 III/2016 United States 4.3% 4.4% 4.9% Arkansas 3.5 3.4 4.0 Illinois 4.9 4.7 5.8 Indiana 3.5 3.3 4.4 Kentucky 5.4 5.1 5.0 Mississippi 5.2 5.0 5.8 Missouri 3.8 3.9 4.8 Tennessee 3.2 4.1 4.8 MILLIONS OF DOLLARS III/2017 800 750 700 650 600 550 500 450 Mississippi Indiana Illinois Missouri 400 350 300 2009:Q1 2010:Q1 2011:Q1 2012:Q1 2013:Q1 2014:Q1 2015:Q1 2016:Q1 2017:Q1 * NOTES: Adjusted gross revenue = Total wagers minus player winnings. Native American casino revenue is not included. In 2003 dollars. SOURCE: State gaming commissions. HOUSING PERMITS / THIRD QUARTER R E A L P E R S O N A L I N C O M E / S E C O N D Q U A RT E R YEAR-OVER-YEAR PERCENT CHANGE IN YEAR-TO-DATE LEVELS YEAR-OVER-YEAR PERCENT CHANGE 6.0 1.3 7.7 14.8 7.2 33.1 20.2 2017 10 0.8 1.1 0.2 Missouri 0.9 1.9 Tennessee 13.2 15 2.4 0.5 0.6 Mississippi 19.1 5 1.8 Kentucky –9.8 0 1.1 Indiana 8.6 6.2 0.2 Illinois 12.2 0.3 1.5 1.5 Arkansas 15.2 15.5 –15 –10 –5 1.3 1.4 United States 20 25 30 2016 All data are seasonally adjusted unless otherwise noted. 35 40 PERCENT 2.6 0 .50 2017 1.0 1.5 2.0 2.5 2016 NOTE: Real personal income is personal income divided by the PCE chained price index. 3.0