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R E G I O N A L E C O N O M I S T | J U LY 2 0 0 0 https://www.stlouisfed.org/publications/regional-economist/july-2000/when-politics-makes-no-bedfellows President's Message: When Politics Makes No Bedfellows William Poole In less than six months, American voters will go to the polls to decide who their new president will be. The looming national election raises the question of what role politics plays at the Fed. Some maintain that Federal Reserve monetary policy decisions are political in nature. This charge, in my mind, is a myth. When critics claim that monetary policy decisions are political, they are suggesting that Fed decisions are intended to strengthen the position of one political party or another or to favor one group of people over another. I'm convinced that neither of these points has merit. The Fed's overarching goal is to achieve low and stable inflation. This goal plays no favorites: The benefits of price stability and high employment are shared by all segments of society. Some, however, may argue that, while the Fed's primary goal is in fact impartial, the timing of its actions is not. Could the Fed adjust the timing of its policy actions in the short run to favor one political party or another? In principle, the answer is yes, but in practice the protections built into the Fed's structure make the risk remote. For starters, the backgrounds of Fed policy-makers are varied enough to avoid any predominant political outlook. Members of the Board of Governors are appointed by the president. Given that a governor's term of office is 14 years, at any given time, some governors are typically appointed by a president from one political party and some by a president from the other political party. There's also no consistent pattern in the political affiliation of the governors appointed by any particular president. President Carter, for example, initially appointed Paul Volcker as Board chairman, but President Reagan reappointed him. And President Reagan initially appointed Alan Greenspan as chairman, but President Clinton has twice reappointed him. Among the Reserve Bank presidents, party affiliation is pretty obvious for some—like me—who may have served in a previous position in a particular political administration. I'm not at all sure, however, of the political affiliation of most of my fellow presidents. If you read their speeches, I doubt that you'll find it obvious, either. Members of our boards of directors are also of varied political persuasion. Here again, I really don't know what the political leanings of the board members are. Of course, I can make some guesses, but the issue doesn't really come up. Finally, the Federal Reserve has elaborate provisions in place to prevent political activity by Reserve Bank officers and directors. Fed officials and directors are not allowed to be involved in political campaigns, to engage in candidate fundraising or to take part in overt political activities of any kind. Fed officials also may not serve as advisers—official or unofficial—to political candidates. So who should you vote for this fall to make sure the economy stays on course? The decision is yours. We're staying out of it. R E G I O N A L E C O N O M I S T | J U LY 2 0 0 0 https://www.stlouisfed.org/publications/regional-economist/july-2000/big-fish-small-ponds-large-banks-in-rural-communities Big Fish, Small Ponds: Large Banks In Rural Communities R. Alton Gilbert In the wake of recent changes in bank regulations, large banks have been buying other large banks and smaller regional banks. Federal legislation—the Riegle-Neal Interstate Banking and Branching Efficiency Act of 1994—has permitted bank holding companies to buy banks and other holding companies located throughout the nation since the fall of 1995 and has permitted nationwide branching since June 1997. Prior to this legislation, state regulations set limits on bank branching and interstate banking. Banking consolidation raises questions about the nature of banking services in rural areas. One reason for this concern is that the top managers of the large banks live and work in large urban areas, rather than the rural areas they now serve. Are they interested in providing banking services in rural communities? Can they compete successfully with the community banks located in rural communities? Are large banks becoming the dominant banking organizations in the rural areas where they have established offices? Are the rural offices of large banking organizations located primarily in areas with relatively high population densities, where they can serve relatively large numbers of customers from each office? Or do they also have offices in counties with low population densities? Where Are the Large Banks? In banking studies, various criteria are used for identifying large banking organizations. Several of the recent studies that examine the effects of banking consolidation on lending to small businesses identify large banking organizations as those with total assets in excess of $10 billion.1 This article, which uses deposit data, identifies large organizations as those with total deposits in excess of $10 billion. As the accompanying map shows, most residents of rural areas (counties located outside of metropolitan areas) live in counties where large banking organizations have offices. The rural counties where large organizations do not have offices are clustered in the middle of the nation: in Texas, Oklahoma, Kansas, Nebraska, South Dakota and North Dakota. Nevertheless, in each of these states, except Oklahoma, at least 40 percent of the residents of rural areas live in the counties where large organizations have offices.2 Large Banks Locate, But Don't Dominate, In Rural Areas NOTES: Large banking organizations are those with total deposits of $10 billion or more. Except in the middle of the country, rural counties tend to be served by large banking organizations. In the Western states, large banks tend to dominate rural counties, which is not the case in the rest of the country. SOURCE: Summary of Deposits data Do the Large Banks Dominate? It is not just the presence of large banks that determines their impact on rural communities, but also their shares of deposits at the banking offices in these communities. Are large banking organizations the dominant banks in the rural areas where they have offices? Or do the smaller community banking organizations attract substantial shares of deposits in the rural communities where large organizations have their offices? The map provides some perspective on these questions. The rural counties where large organizations account for half or more of local deposits are concentrated in the Western states. Typically, these states have permitted statewide branching for many years, and large banking organizations established large networks of branches in these states long before the recent legislation that permitted nationwide interstate banking. Therefore, to see the effects of this legislation, it's necessary to look at other regions of the nation. In most of the rural counties outside of the Western states, large banking organizations account for less than half of the deposits in local banking offices.3 So far, then, the Riegle-Neal Act has not led to the domination of banking in rural counties by large organizations. The fact that large organizations have relatively large shares of deposits in the rural counties of the West, however, may portend larger shares at the offices of the large organizations in other regions in the future. Effects of Population Density The incentives for large banking organizations to operate offices in rural areas may depend upon the nature of economic activity in the rural areas. Some of the rural counties that are relatively remote from urban areas have few residents per square mile, whereas other rural areas have population densities close to that in some urban areas. If the minimum level of banking business necessary to be profitable is higher for branches of large banking organizations than for smaller banks, large organizations would tend to locate their offices in the rural areas with relatively high population densities. In that case, low population density would shield the local community banks from entry by large banking organizations. To examine the association between the population density of rural counties and the presence of large banks, it is helpful to divide the states into two groups: those that permitted statewide branching in 1980 and those that prohibited statewide branching at that time. This division is necessary because, in states where banks have only recently been given freedom to establish branches where they wish, large organizations are likely to focus first on the rural counties with relatively high population densities. Therefore, looking at states that have permitted statewide branching for many years may provide more reliable information. In the states that prohibited statewide branching in 1980, large organizations have offices in 95 percent of the rural counties with population densities in excess of 100 persons per square mile, but in only 26 percent of the counties with population densities below 25. A different pattern exists in the states that permitted statewide branching in 1980; large organizations have offices in all of the rural counties of these states that have more than 100 residents per square mile and in about two-thirds of the counties that have population densities less than 25. These observations indicate that low population density is not a barrier to entry by large banking organizations. Will Small Banks Survive? When large banking organizations are given freedom to establish offices wherever they wish, they have the interest and ability to provide banking services in rural communities, including those with relatively low population densities. In most of the rural counties where large organizations have offices, the large organizations as a group hold less than half of the deposits in the local banking offices. Large banks are more dominant in the rural counties of the states that have permitted statewide branching for many years. This contrast indicates that large banks will have a greater presence in rural areas in the future. Judith Hazen provided research assistance. Endnotes 1. Berger, Demstez and Strahan (1999). [back to text] 2. Oklahoma's percentage was 12 as of June 1999. [back to text] 3. See Gilbert (2000) for more details. [back to text] References Berger, Allen N., Rebecca S. Demsetz, and P.E. Strahan. "The Consolidation of the Financial Services Industry: Causes, Consequences, and Implications for the Future," Journal of Banking and Finance (February 1999), pp. 135-94. Gilbert, R. Alton. "Nationwide Branch Banking and the Presence of Large Banks in Rural Areas," Review, Federal Reserve Bank of St. Louis (May/June 2000), pp. 13-28. R E G I O N A L E C O N O M I S T | J U LY 2 0 0 0 https://www.stlouisfed.org/publications/regional-economist/july-2000/fast-lane-slow-lane-or-cruising-speed National and District Overview: Fast Lane, Slow Lane Or Cruising Speed? Kevin L. Kliesen The U.S. economy exhibited considerable strength during the past year: Real GDP rose by 5 percent between the first quarter of 1999 and the first quarter of 2000. This is about 1 to 1.5 percentage points higher than most economists think the economy can grow without spurring inflation. Several reports released during the second half of May and into early June suggest, however, that the effects of higher interest rates, rising energy costs and—perhaps—weaker equity prices are beginning to slow the pace of economic activity somewhat. But considering that the economy grew at a 6.1 percent rate between the second quarter of 1999 and the first quarter of 2000, some slowing was inevitable. The report that garnered the most attention was the May employment report. Though total nonfarm payroll employment rose by 231,000 in May, well short of the 414,000 increase posted in April, private nonfarm payrolls actually fell by 116,000 because of the hiring of 357,000 temporary workers associated with the decennial census. Except for the weakness associated with the recession and the "jobless recovery" of 1990-91, this was the largest percentage decline in private payroll employment since June 1986. Slowdown or Speed Bump? The monthly employment report was puzzling given the exceedingly upbeat surveys of national labor demand. Indeed, the demand for labor regionally (in the Eighth District) remains rather strong according to the latest Beige Book. Anecdotal reports indicate that tight labor markets are still significant for most industries, and the average unemployment rate in the seven states that make up all or parts of the District continues to track below the national average. Nonetheless, some expenditure data suggest a more measured pace of output growth during the second quarter. In particular, despite elevated levels of consumer confidence and ample supplies of credit, consumers apparently will spend at a rate only about one-half to two-thirds of their spectacularly fast 7.7 percent firstquarter growth rate. This pullback appears to be causing some retailers and wholesalers to scale back their orders to factories, although overall, new factory orders outside the defense and aircraft sector—particularly for information and communications equipment—are still piling in and unfilled orders are stacking up. Strengthening foreign growth should also help U.S. manufacturers. Indeed, industrial production registered a healthy gain in May. On the construction front, sales of new and existing homes at both the national and District levels are clearly on a lower trajectory. But because of earlier labor and materials shortages, most builders reportedly have sizable backlogs of unfilled orders to work through. Relatively high interest rates do not appear to be sidetracking nonresidential construction, which is still recovering from the last few years' weakness— particularly at the national level. Is Inflation Accelerating or Downshifting? During the 1970s and 1980s, surging demand growth, accompanied by a spike in oil prices like we have seen during the past year and a half, would have probably led to rapid growth of wages and prices—not to mention a significant boost in inflation expectations. That does not appear to be the case this time around, though, as sharply higher rates of productivity growth have helped firms maintain profit margins without boosting output prices. Confidence in the Fed's ability to maintain low and steady inflation has also helped limit any rise in expected inflation. During the past year, prices (as measured by the deflator for gross domestic purchases) have increased by about 2.25 percent, or by about 1.5 percent when food and energy prices are excluded, a measure often described as core inflation. While these rates are low compared with most post–World War II business expansions, they have nevertheless crept steadily upward since the lows for this expansion were reached in mid-1998. And while CPI inflation was less than 0.5 percent (annualized) during the April-May interval, most forecasters still expect it to accelerate modestly through 2001. This suggests that the risks going forward remain centered on faster rates of inflation, not lower growth. Policy-makers are confident, however, that the cumulative effects of the Fed's recent policy moves—or prospective policy actions—will extend this recordsetting business expansion by more closely aligning the growth of aggregate demand and supply, thereby limiting inflationary pressures. Thomas A. Pollmann provided research assistance. ABOUT THE AUTHOR Kevin L. Kliesen Kevin L. Kliesen is a business economist and research officer at the Federal Reserve Bank of St. Louis. His research interests include business economics, and monetary and fiscal policy analysis. He joined the St. Louis Fed in 1988. Read more about the author and his research. The Regional Economist -July 2000 vuvuvu.stls.frb.org he Federal Reserve is famously tight-lipped about its potential monetary policy moves. In desperate attempts to predict what Fed policy-makers are thinking, market-watchers occasionally resort to rather odd measures. For exam- T Inside the Briefcase: The Aft of Predicting the Federal Reserve B Y W I L L I A M T . G A V I N A N D R A C H E L J . M A N D A L pie, on mornings when the Federal Open Market Committee (FOMC) meets to debate on interest rate policy, the media often focus on the image of Fed Chairman Alan Greenspan carrying his briefcase into the front door of the Federal Reserve Board building.1 If the briefcase is bulging, so the theory goes, it is full of evidence gathered by Greenspan to persuade other members of the FOMC to vote for a higher interest rate target.2 If the briefcase is thin, then markets can relax because no change is likely. Unfortunately for Fed watchers, the size of the briefcase is not always a good predictor of the Fed's actions. A look at the May 2000 FOMC meeting highlights this point. Despite the fact that Greenspan's briefcase that morning was reportedly at its thinnest in years, the FOMC raised its interest rate target by half a percentage point, its largest increase in five years. Therefore, it's not the size of the briefcase that matters, but the type and quality of the information found inside. [5] - What is actually in the briefcase? Mostly, At the_tjme the Fed takes policy action, it is data released by government statistical agencies—information about labor markets, prices, industrial production, capacity utilization, business inventories, factory orders and shipments, etc. Most, if not all, of this information is already in the public domain. Because of the volume of data and its complexity, newsmakers tend to boil it down to just a few statistics, creating a simple picture that can help predict what the Fed -j£' will do. of the year, no Fed forecasts are made public. The public, instead, is left to glean the Fed's views by poring over Greenspan's speeches or analyzing the thickness of his briefcase. Although the public seldom knows the Fed's forecasts of future inflation and GDP growth, it does have access to private sector forecasts. One popular forecast is the Blue Chip consensus, which pools the private forecasts of the country's leading business economists. These economists spend much of their time monitoring the same statistical it does not know, and can only predict, the effects of its decisions r on the future Before trying to predict the Fed's actions, one must first understand the Fed's objective of promoting maximum sustainable growth in the economy. The Fed achieves this objective by supplying just enough money and credit so that the economy will operate at its potential without igniting inflation. However, at the time the Fed takes policy action, it does not know, and can only predict, the effects of its decisions on the future. The information in the Fed chairman's briefcase is used to make these predictions. Because they are important measures of the Fed's beliefs, news agencies would like to report the policy-makers' forecasts, but such forecasts are available to the public only twice a year. Just prior to the FOMC meetings at the beginning of February and July, the Fed is required to provide its official economic forecasts in a report to Congress. For the other six meetings [6] agencies, whose output fills the chairman's briefcase. Because FOMC members and Blue Chip economists all observe the same statistical releases and use similar economic theories to interpret the data, one might guess that their forecasts are highly correlated with each other. The Blue Chip consensus forecasts are released once a month, so there is always at least one new forecast before each FOMC meeting. The question, then, is how useful they are in filling the informational void created by the Fed's silence. " The Fed's Public Forecasts Twice a year, the FOMC members, along with nonvoting Reserve Bank presidents, provide forecasts of nominal gross domestic product (GDP) growth, real GDP growth, inflation and the average level of unemployment. These forecasts are provided The Regional Economist -July 2000 www.stls.frb.org for six-, 12- and 18-month horizons into the future. Of the four variables, real GDP growth and inflation are the ones to focus on because they best capture monetary policy objectives. If the economy achieves maximum sustainable growth, then unemployment can go no lower. And, by definition, if the Fed achieves its objectives for inflation and real GDP growth, it will have achieved the desired growth in nominal GDP. To construct an official Fed forecast, individual Federal Reserve officials are asked for their economic forecasts prior to the February and July FOMC meetings, based on their judgment about the appropriate policy for the coming year. The projections are then reported to Congress as a range, listing the high and low values for each item, as well as a central tendency, which omits extreme forecasts and is meant to be a better representation of the consensus view. ed higher inflation than the Fed did. This is illustrated by the fact that most of the points lie above the 45-degree line. The period from 1983 to the present has been a period of moderate and falling inflation. Throughout, the Federal Reserve has had a public goal Inside I A Peek the Briefcase GROWTH FORECASTS (1983-2000) Reading the Fed's Mind Comparing February and July Blue Chip forecasts to the Fed's semi-annual forecasts can show how well the Fed's views are captured by the private sector. The Blue Chip forecasts are collected on the first three working days of the month and the information available to private-sector economists is approximately the same as the information available to FOMC members when they make their forecasts. Most important, both groups have the latest information on the price indexes from the Bureau of Labor Statistics and the most recent report on actual GDP from the Bureau of Economic Analysis. The charts on this page show the consensus GDP growth and inflation forecasts for the Fed and Blue Chip economists, taken between 1983 and 2000. The consensus Fed forecast is defined here as the midpoint of the central tendency range. If the Fed and Blue Chip forecasts were exactly the same, they would lie on the 45-degree line shown. As the top chart shows, the forecasts were quite similar and seem to be distributed evenly above and below the 45-degree line. That is, there doesn't seem to be any tendency for the Blue Chip economists to systematically forecast more or less output growth than the Fed does. The same cannot be said of inflation forecasts. As the bottom chart shows, Blue Chip economists usually forecast- INFLATION FORECASTS (1983-2000) • The Fed's twice--a-year public forecasts oi growth cuid J ^ ^ ^ H inflation are highly correlated with the Blue Chip conseJJj^^^H forecasts. So, for the rest of the vear, when the Fed's ' ^ i ^ ^ ^ H are not public, the Blue Chip const?1'1' •.....'" sid^jj^^^H of eliminating inflation. In general, the Fed's forecasts of inflation have been better than the Blue Chip forecasts. However, as inflation became lower in the 1990s, the forecasts have converged, indicating that the private sector has gained confidence in the Federal Reserve's ability to deliver low inflation. So, although the Blue Chip inflation forecasts have not always been good indicators of the Fed's inflation forecasts, they have been better in recent years. [7] When GDP Growth and Inflation when it is far more difficult. Plotting the forecast errors for GDP growth and inflation can give us some graphical insight as to how the Fed reacts to different situations. The figure below shows forecast errors for inflation and real GDP growth. The forecast errors are constructed by subtracting the latest Blue Chip forecast from the relevant quarter's advance report on actual GDP growth. The actual GDP growth rate that the Blue Chip forecasts are compared to in each quarter is based on the most recent estimate available from the government at that time. Because it takes the Bureau of Economic Analysis a good bit of time before it can release an accurate final estimate of quarterly GDP growth, advance estimates are issued in the first month ••n for the second quarter. A positive forecast error in July would indicate that at that point in time, the available GDP growth estimate is above the Blue Chip forecast for the year. The forecast errors should reflect the new information contained in the GDP report. In the case of GDP growth, if the forecast errors are positive, by implication GDP may be growing faster than the estimate of potential. If the forecast errors are negative, by implication GDP is likely to be growing below potential. If both forecast errors are positive-that is, if inflation and growth are both unexpectedly high-then the points will lie in the red region of the figure, indicating the need for a tighter policy. If both forecast Forecast Errors (1994:Q1 to 2000:Ql) errors are negative, indicating surprisingly ""* ow growth and low ...flation, then the points will lie in the green sector, suggesting the need for a looser >olicy. If the points jje in the other two blue quadrants, where one forecast error is positive and the other is negative, there is no clear indication for policy. Since the beginning of -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 ( 1994, GDP growth forecast errors (measured on the Inflation Forecast Error vertical axis) have tended When the forecast errors for inflation and GDP growth have the same sign, that to be positive, mainly is, when the points lie in the green or red quadrants, the implications for monelying above the horizontal tary policy are clear. When they have opposite signs—the blue quadrants—as axis. Generally, the has been the case for most of the time since 1994, there are conflicting signals about what the Fed will do. inflation errors have been negative, with the points mmmmmm^^m^^^m lying to the left of the immediately f ol ig^ffpFYfj^1 'HTO '''H'fdtjfudrT'^'r!™™ ^^^HjfiN^pifi(MMiH^^ t n e m a j o r i t y of the cases, then, the forecast errors have been in the blue These advance estimates contain most of the area, where growth is surprisingly high and information about GDP and are hardest to forecast. Two revisions, preliminary and final, are inflation is surprisingly low. Therefore, since 1994, it has not been clear what, if anything, released in the second and third month after the end of each quarter. For example, advance should be done with monetary policy. In fact, since 1994, the first quarter of 2000 was the first-quarter estimates are issued in April, preliminary estimates are issued in May, and final only instance in which forecast errors for both inflation and GDP growth were positive (that is, estimates are issued in June. Therefore, when advance data for the second quarter is released in the red sector). So we should not be surprised that the Fed would see the need to tighten in July, the available GDP growth estimate for monetary policy. the calendar year includes the final estimate for What Will the Fed Do? The Regional Economist -July 2000 Beyond the Briefcase Knowing the forecasts and the latest information about the economy—that is, the contents of the briefcase—is not enough to predict what the FOMC will do with policy. Policy will change when GDP growth and inflation stray from the Fed's objectives. Since the Fed does not set specific objectives for these measures, people use the forecasts and the deviations of actual inflation and growth from the forecasts as tea leaves for reading the Fed's objectives. For example, the best long-term forecast of real GDP is the trend growth in potential GDP. This measure aligns closely with the Fed's objective to keep the economy growing along its potential. If actual GDP is above potential GDP, economists will tend to believe that it is unsustainable and expect the Fed to adopt a tighter policy in order to prevent future inflation. The Fed's long•. term forecast for inflation is often treated as an - ^ estimate of the Fed's policy objective because monetary policy is the primary determinant of inflation in the long run. If inflation comes in above this forecast, people will expect the Fed to tighten policy. If the Fed does not tighten policy, people will tend to believe that the Fed's inflation objective has risen. Sometimes, monetary policy is relatively straightforward. For example, if output and inflation are both coming in above expectations, then FOMC members and the marketplace are likely to agree that the Fed must tighten policy. Similarly, if inflation and GDP growth are both coming in below expectations, then the market should not be surprised to see the Fed ease its policy stance. The more difficult cases occur when GDP growth and inflation surprise us in opposite directions. If growth is weaker than expected and inflation turns out to be surprisingly high, there will be tension between those who want to fight inflation and those who want to stimulate growth. Conversely, with surprisingly high growth and unexpectedly low inflation, some will want to raise interest rates because they fear that the rapid growth is not sustainable, and that a failure to tighten policy now will lead to higher inflation down the road. Others will note that inflation is below expectations, so why not wait for more information before changing the policy stance. This dilemma is discussed in the sidebar at left. To Watch the Briefcase or Not? Private sector forecasts, such as the Blue Chip consensus forecasts, are useful summaries of incoming information about the economy and good proxies for the Fed's forecasts. One lesson to draw from this is that we can have a pretty good idea of what is "in" the briefcase, making the veil of secrecy shrouding FOMC meetings a bit more transparent. It is important to note, however, .-''' that since 1994, knowing what is in the brief1 case has not been much help in predicting policy actions because the incoming data point the Fed in different directions. Ordinarily, good news on high growth would suggest the need for a more restrictive policy. At the same time, the good news on low inflation would suggest that an easier policy stance might be preferred. Only recently, in the first quarter of the year 2000, has the message become consistent: The combination of unexpectedly high GDP growth and surprisingly high inflation indicates a need for tighter policy. William T. Gavin is a vice president and Rachel J. Mandal is a research associate at the Federal Reserve Bank of St. Louis. [9] ENDNOTES 1 In fact, CNNfn.coTi updates its "Eyes on the Fed"sertion with commentary and pictures of the chairman's briefcase on tr.e mornings of FOMC meetings at <http://cnnfn.com/news/specic.ls/ eyes_on_fed/>. 2 The FOMC consists of the Board of Governors of the Federal Reserve System (seven governors) and five of the Federal Reserve Bank presidents. The president of the New York Fed is the vice chairman of the committee and always votes. The other four voting positions rotate among the other 11 Reserve Bank presidents. R E G I O N A L E C O N O M I S T | J U LY 2 0 0 0 https://www.stlouisfed.org/publications/regional-economist/july-2000/the-american-economy-producing-more-with-less The American Economy: Producing More with Less? Adam M. Zaretsky Without a doubt, the 1990s was the decade of the American worker. Between 1990 and 1999, labor productivity—that is, output per hour of all persons working—in the nonfarm business sector grew at an average rate of 1.9 percent per year. That was half of a percentage point higher than the average growth rate in the 1980s. What could cause such an increase? Either improvements in technology or changes in the production process that would enable workers to get more done in the same amount of time or the same amount done in less time. In either case, the end result is the same: more output per hour from workers, which is good for everyone. Why? Because higher productivity in a particular sector of the economy—or in the economy as a whole— means resources that can then be put to use elsewhere are freed up, which drives economic growth and leads to higher wages and income. The U.S. economy excelled at this task in the 1990s. More-Productive Workers or More Jobs? What change might have caused the increase in productivity growth in the 1990s? This question is easier asked than answered. The data do show, however, that while productivity was growing around 1.9 percent a year during the decade, employment at private nonfarm businesses was also growing an average of 1.9 percent a year. One might mistakenly believe, then, that productivity was rising each year only because more workers (mixed with more capital) were on the job, and not because technological changes enabled workers to become better at what they were doing. In other words, it was simply more jobs, not more-productive workers, driving the growth. This conclusion is wrong for a fundamental reason related to the difference between increases in production and increases in productivity. If a firm hires more workers and, consequently, produces more output, production has increased, but not necessarily productivity. Increases in productivity occur only when the current work force is able to produce more output in the same amount of time, not just when more workers show up at the plant (along with additional capital) and then produce more output. Thus, for productivity growth to have increased half of a percentage point between the 1980s and 1990s, an improvement in either technology or the production process must have occurred. In other words, a change must have ensued that enabled workers to become better at what they do.1 Another way to see this is to look at employment and productivity growth rates across the two decades. In the 1980s, private, nonfarm employment grew an average of 2 percent each year—marginally faster than in the 1990s—while productivity was growing only 1.4 percent a year on average—half of a percentage point slower than in the 1990s. With employment growth remaining basically unchanged between the decades, and average productivity growth jumping half of a percentage point, it's logical to conclude that a change ensued that enabled workers to become better at what they do. So is this the end of the story? Not exactly. Stellar Performer Economists know that actually measuring productivity is extremely difficult, especially when the economy is broken into its major sectors—manufacturing and nonmanufacturing. Of the two, manufacturing is the easier sector to work with because firms in this sector produce concrete, physical output that can be counted. The task should be simple then: 1) count all the output, and 2) count the number of hours the workers spent producing the output. Productivity at nonmanufacturing firms, on the other hand, is more difficult to gauge because these firms do not produce physical, concrete output that can be counted. For example, how should the output of a nurse, a teacher or—here's a scary thought—an economist be measured? The best that analysts can do is to try to value the amount of time these workers spend producing their services and then use this figure as an estimate of the value of their output. Although not exactly precise, it beats guessing. Because of this measurement predicament, the Bureau of Labor Statistics does not publish productivity data for nonmanufacturing firms; instead, it focuses more on the business sector (the whole economy minus government), the nonfarm business sector and the manufacturing sector, which is part of the other two. In the 1990s, the manufacturing sector was the stellar performer in terms of productivity growth. Between 1990 and 1999, this sector's labor productivity grew at an average rate of 4 percent a year—clearly outperforming the rate for the nonfarm business economy as a whole, as the accompanying figure shows. What the chart does not show, however, is that manufacturing's productivity growth rate in the 1980s averaged only 2.6 percent per year, which itself is certainly nothing to sneeze at. The more important point, though, is the jump in the rate from 2.6 percent to 4 percent between the 1980s and 1990s. Figure 1 The Increasingly Productive American Worker U.S. Productivity in the 1990s By the end of the 1990s, manufacturing sector productivity was 45 percent higher than at the start of the decade. Over the same period, productivity in the overall economy—minus farming and government—was only 20 percent higher. Can manufacturing firms really churn it out that much faster? Apparently, even though the manufacuring productivity data are somewhat exaggerated. SOURCE: Bureau of Labor Statistics In this case, there is no confusing higher productivity with higher production. Output was increasing, not because more workers were on the job, but because the workers were becoming more productive. In the 1990s, employment at manufacturing firms, as also illustrated by the chart, was actually lower by the end of the decade than at the beginning. In fact, while manufacturing productivity growth was increasing 4 percent a year during the 1990s, employment at these firms was falling an average of 0.5 percent each year. Fewer and fewer workers were producing more and more goods. Statistics Can Mislead Case closed? Not really, even though manufacturing is the "easy" sector to measure productivity in. The problem is that counting the output and hours—especially the hours—at manufacturing firms is not always as straightforward as it seems. When the BLS collects the information about the number of hours people are working at manufacturing firms, it counts only the hours of those who are actually on the payrolls at the firms. If these were the only people working for manufacturers, then there would be no discrepancy. But they're not. Manufacturing companies, like many other firms, hire temporary workers who do not appear on their books, but instead on the books of the temporary employment agencies supplying them (which are classified as nonmanufacturing firms). In other words, more people than the BLS is counting are producing the manufacturing output. Therefore, the numbers the BLS reports for manufacturing productivity are slightly exaggerated because the bureau is undercounting the true number of people working at these plants. How exaggerated are the data? Economists Marcello Estevão and Saul Lach tackled just this question in two recent studies. To answer the question, Estevão and Lach first had to determine how many manufacturing workers were in fact employed by temporary agencies. They estimated that manufacturing firms actually employed around 890,000 uncounted temporary agency workers, which adds to the reported 18.5 million manufacturing workers. While not a tremendous amount overall, the 890,000 figure is far from insignificant. When Estevão and Lach then recalculated the productivity numbers and included these uncounted workers, they found that the official manufacturing productivity growth figures were overstated by about half of a percentage point per year. In other words, including all of the workers lowered average manufacturing productivity growth in the 1990s from 4 percent to about 3.5 percent per year. Continuing to Crank It Out Productivity growth in the manufacturing sector still outpaced the average rate for the economy in the 1990s, although the gap between the two is narrower than at first believed. The discrepancy in the manufacturing productivity growth data, however, does not occur in the nonfarm business productivity numbers because temporary workers are included in these data. In any case, the bottom line is that workers actually produced more with less during the last decade. Paige M. Skiba provided research assistance. Endnotes 1. Saying that workers are better at what they do needs not imply that they are more skilled or educated. It could mean that the capital they work with is more advanced, which subsequently makes the workers more productive.[back to text] References Estevão, Marcello M., and Saul Lach. "Measuring Temporary Labor Outsourcing in U.S. manufacturing," NBER Working Paper No. 7421 (November 1999). _________. "The Evolution of the Demand for Temporary Help Supply Employment in the United States," NBER Working Paper No. 7427 (December 1999). Segal, Lewis M., and Daniel G. Sullivan. "The Growth of Temporary Services Work" Journal of Economic Perspectives (Spring 1997), pp. 117-36. R E G I O N A L E C O N O M I S T | J U LY 2 0 0 0 https://www.stlouisfed.org/publications/regional-economist/july-2000/making-connections-in-greenville-mississippi Community Profile: Making Connections in Greenville, Mississippi Stephen P. Greene Greenville, Miss., wants to build bridges—the kind that people drive over and the kind that drive people closer together. This city on the Mississippi River hopes that these new connections will make its economic future as fertile as the surrounding Delta soil. Located in one of the poorest areas of the country, Greenville has seen much of the nation prosper in recent years. While the party raged on in other places, Greenville experienced little of the boom. The area, nevertheless, has managed to endure. A diverse mix of industries—manufacturing, agriculture, retail and medical services—has helped to keep the local economy in check. "We don't have real strong swings one way or another in economic activity," says Tommy Hart, director of the Economic Development District of Washington County. Hart and other local officials believe that Greenville has a few hurdles to overcome before reaching its potential. Among these challenges are: constructing two new bridges across the Mississippi River, and achieving unity among local economic development agencies and among the residents themselves to improve racial relations. Playing All the Hits The Highway 82 bridge that connects Greenville to Lake Village, Ark., is, in the words of Greenville Mayor Paul Artman, "kind of scary to cross. It's a functionally obsolete two-lane bridge and the most hit bridge on the Mississippi River." By "hit," Artman is referring to the roughly 50 barges that have crashed into the bridge over the past three decades. The curvature of the river near the bridge has led to these occasional navigational difficulties, which, in turn, have resulted in damage to the bridge. Congress has appropriated money for the first phase of a new four-lane bridge about 3,000 feet south of the current one. Artman says the new bridge should be completed by 2006. "Creating a safer daily crossing will allow our markets to expand," says Hart. "The other side of the river is very important to us. Many residents on both sides cross that bridge each day to go work. We can't let that river become a wall." A much more ambitious addition to the area's inadequate road network is in the early planning stages and probably is at least 10 years away from fruition. More national in scope, this project calls for stretching Interstate 69, which currently runs from Canada to Indiana, all the way to Mexico. At a still unspecified location north of Greenville, a bridge would be needed to span the "NAFTA Highway" across the river. Hart says this project is sorely needed because Greenville is one of only three cities in the nation that has a population of at least 50,000 and no direct interstate access. (About 65,000 people live in Washington County.) Quest for Unity In June of 1999, Greenville hosted its first Regional Developers' Showcase. The agenda was filled with seminars, industrial tours, site visits and quality of life presentations. Although business prospects did emerge from the event, the real success was the cooperative environment that made it possible to begin with. Artman says that fragmentation has historically been a problem among the area's development agencies, but the showcase brought together 16 entities, including the Economic Development District, Chamber of Commerce, Convention and Visitors Bureau and local utility companies. A second showcase is planned for October. In addition, the main city and county development agencies now hold quarterly meetings to discuss how to work together on projects. A more complicated and ingrained type of fragmentation in Greenville concerns race relations. According to the most recent Census Bureau statistics, blacks make up just under 60 percent of the population in Greenville; whites, slightly more than 40 percent. Despite these numbers, the concentration of wealth in Greenville lies with a small percentage of whites, whereas most black residents face an opposite set of circumstances. "To some extent, racism is still alive here," says Harry Bowie, president of the Delta Foundation, a community development foundation based in Greenville that operates several for-profit businesses in the region and also provides loans for low-income minority clients. The organization has created more than 6,000 jobs in the area since 1980. Even though most outward examples of discrimination have been relegated to the history books, Bowie says that subtle forms may still have a hold on the region. "I think that most bankers do try to be fair, but there are still vestiges of discrimination that occur in the access to capital," Bowie says. "How conscious it is, I don't know. I'm not trying to judge individuals because I know many of them, and they are very good and decent people." Mayor Artman says that racial discrimination must be overcome in order for Greenville to make real progress. "We are attempting to tackle that problem by being very open and honest about it," he says. "There is a problem, we recognize it, and we are trying to solve it." A related problem is the existence of what Artman calls "a dual school system." Minorities overwhelmingly make up the city's low-rated public schools, while white children predominantly attend the private schools. Artman says that the racial conditions and school system quality are two factors that Greenville must defend itself against when trying to attract new businesses. One step the mayor has taken to confront the racial problem is to hold monthly harmony luncheons at the Salvation Army. A free lunch is served, and all residents are encouraged to attend and discuss issues that still divide those in the area. A cooperative approach is also being used to bring higher education to Greenville. Currently, the closest community college to Greenville is 25 minutes away, and the nearest four-year university is 45 minutes away. But a new Higher Education Center is almost completed in the south part of town. Three schools—Mississippi Valley State University, Delta State University and Mississippi Delta Community College—will offer courses in the building. Students will be able to earn two-year, four-year and graduate degrees, as well as partake in job training programs and continuing education classes. Solid Ground What has allowed Greenville and nearby towns to survive for many decades is still a vital part of the local economy today—the land. The area is a major producer of cotton, soybean, rice, corn and catfish, which has risen to be the No. 2 crop—after cotton—thanks to technological advances in aquaculture. "Even though some people tend to discount it, agriculture still has a large impact on this area," says Joyce Franklin, vice president of The Jefferson Bank, which specializes in farm loans. "Of course, the number of people that farms employ has been cut down because of mechanization." Greenville also is the state's largest river port. This industry was hit hard in the 1980s because of the grain embargo against the former Soviet Union, and the action had a lasting effect on the town. Still, the river remains vital to the local economy, with about 250,000 tons of cargo shipped out of the port annually. Other key industries affecting the economy include: Manufacturing: Greenville is home to about 90 manufacturing and processing plants, including Fruit of the Loom and Uncle Ben's rice. Parts of the northern sections of the county are designated as a federal empowerment zone, giving companies that locate there federal tax incentives. Greenville, however, did see two manufacturers leave town last year, resulting in the loss of around 280 jobs. Hart says those cuts were offset by expansions in other businesses and the addition of new businesses like Sewell Products Inc., a Virginia-based company that opened a new bleach plant in downtown Greenville last year. Retail: Within the next year, about a half-million square feet of new retail space will open in Greenville, including a Home Depot, Eckerd pharmacy, and Wal-Mart Supercenter. Gaming: Gambling has been legal in Greenville since 1993. Two floating casinos currently operate downtown. The mayor calls the casinos' effect a "mixed-bag." Although gaming employs nearly 900 residents, most of the jobs pay low wages and offer few benefits. Furthermore, Mississippi is chock-full of casinos, particularly in Tunica near Memphis, thus limiting the amount of people willing to travel to Greenville to gamble. Greenville has many bridges to build in the coming years. Some will be completed faster than others; some will require the power of the mind rather than the power of machines to construct. Thanks to a diverse economic base and the willingness to admit the need for improvement, residents of Greenville can at least start building from a solid foundation. Greenville, Miss., by the numbers Population 42,042 Labor Force 17,308 Unemployment Rate 7.9% Per Capita Personal Income $16,720 Top Five Employers Hospitals: King's Daughters Hospital 1,204 Delta Regional Medical Center Greenville Public School District 1,100 Fruit of the Loom 975 Casinos 865 Farm Fresh Catfish 550 National and District Data Selected indicators of the national economy and banking, agricultural and business conditions in the Eighth Federal Reserve District Commercial Bank Performance Ratios first quarter 2000 U.S. Banks by Asset Size $100 million$300 million less than $300 million $300 million$1 billion less than $1 billion $1billion$15 billion 1.36 1.29 1.22 1.41 1.29 1.50 1.40 1.33 3.94 4.63 4.61 4.65 4.62 4.61 4.62 3.53 0.97 0.83 0.88 0.73 0.82 0.98 0.90 1.01 1.67 1.37 1.39 1.50 1.43 1.98 1.72 1.64 ALL Return on Average Assets* Net Interest Margin* Nonperforming Loan Ratio Loan Loss Reserve Ratio Net Interest Margin* Return on Average Assets * 1.23 1.26 1.14 1.19 1.05 1.02 1.01 1.36 1.24 1.26 1.26 1.24 1.33 1.31 1.26 1.31 0 .25 .50 .75 1 1.25 4.08 4.04 4.12 4.14 3.91 3.83 3.91 4.32 3.93 4.04 4.42 4.53 4.05 3.75 4.16 4.15 Eighth District Arkansas Illinois Indiana Kentucky Mississippi Missouri Tennessee 1.50 1.75 2 3 percent 3.50 Nonperforming Loan Ratio 0.85 0.58 0.99 0.95 Illinois 1.20 Indiana 0.81 0.88 Kentucky Mississippi Missouri 0.87 1.01 .5 .6 .7 .8 .9 1 1.1 Tennessee 1.23 1.2 1.3 4.50 5 5.50 6 1.33 1.38 1.26 1.28 1.25 1.33 1.23 1.33 1.29 1.41 1.38 1.44 1.34 1.37 1.38 1.42 Arkansas 0.70 0.69 0.69 0.68 4 Loan Loss Reserve Ratio Eighth District 1.09 1.06 less More than than $15 billion $15 billion 1.4 1.5 percent 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 First Quarter 1999 First Quarter 2000 NOTE: Data include only that portion of the state within Eighth District boundaries. SOURCE: FFIEC Reports of Condition and Income for all Insured U.S. Commercial Banks *Annualized data [16] For additional banking and regional data, visit our web site at: http://www.stls.frb.org/fred/data/regional.html. 1.8 1.9 2 The Regional Economist July 2000 ■ www.stls.frb.org Regional Economic Indicators Nonfarm Employment Growth year-over-year percent change first quarter 2000 Goods Producing total mfg cons 2.2% 2.3 0.8 1.5 2.7 1.2 1.4 1.5 –0.8% 0.6 –0.9 0.9 0.6 –0.9 –2.2 –0.1 5.7% 6.0 2.6 1.2 4.6 –1.1 7.6 2.6 United States Arkansas Illinois Indiana Kentucky Mississippi Missouri Tennessee 1 Construction 2 Service Producing 1 3 Transportation and Public Utilities 2 govt 1.9% 2.1 0.8 1.7 2.6 2.0 1.7 2.8 tpu fire3 services trade 2.7% 2.5 0.1 –0.3 3.8 4.0 0.3 0.5 1.2% 3.5 0.4 0.7 1.8 –0.1 1.0 0.2 3.9% 2.7 1.9 2.4 4.2 3.7 2.2 2.8 1.7% 2.9 0.4 1.6 2.5 –0.1 1.7 1.1 Finance, Insurance and Real Estate Unemployment Rates Exports percent year-over-year percent change United States Arkansas Illinois Indiana Kentucky Mississippi Missouri Tennessee I/2000 IV/1999 I/1999 4.1% 4.6 4.3 3.1 4.0 5.4 2.6 3.5 4.1% 4.2 4.2 3.0 4.1 5.1 2.9 3.8 4.3% 4.8 4.2 3.1 4.7 5.1 3.5 4.2 United States 1.8 –1.0 – 4.7 Arkansas –0.8 1.8 Illinois Indiana 9.3 4.8 2.4 Kentucky 9.6 1.8 –3.1 Mississippi Missouri –0.2 5.2 –14.3 3.3 3.5 Tennessee –20 –15 –10 –5 0 1999 first quarter 5 10 1998 fourth quarter Housing Permits Real Personal Income year-over-year percent change in year-to-date levels year-over-year percent change –1.4 –3.4 –4.9 6.3 22.4 0 5 10 4.5 2.2 2.3 3.0 Tennessee 15 20 25 30 percent 1999 0 1 2 1999 All data are seasonally adjusted. [17] 3.8 1.7 Mississippi Missouri 9.0 –3.6 2000 4.5 2.7 Kentucky 3.0 3.9 4.0 1.7 Indiana – 20.7 4.0 2.6 Illinois 15.4 4.4 2.7 Arkansas –3.4 – 2.3 –5.4 –30 –25 –20 –15 –10 –5 3.2 United States 10.6 9.7 15 3 3.7 4 1998 5 Major Macroeconomic Indicators Real GDP Growth Consumer Price Inflation percent percent 8 4.0 7 3.5 6 all items, less food and energy 3.0 5 4 3 2 1 2.5 2.0 all items 1.5 0 1995 96 97 98 99 1.0 1995 00 NOTE: Each bar is a one-quarter growth rate (annualized); the green line is the 10-year growth rate. 96 97 98 99 00 (May) NOTE: Percent change from a year earlier Civilian Unemployment Rate Interest Rates percent 6.5 percent 8 10-year 6.0 t-bond 7 5.5 fed funds target 6 5.0 5 4.5 three-month t-bill 4 4.0 3.5 1995 96 97 98 99 3 1995 00 (May) 96 97 98 99 00 (May) NOTE: Except for the fed funds target, which is end-of-period, data are monthly averages of daily data. Farm Sector Indicators U.S. Agricultural Trade Farming Cash Receipts billions of dollars 40 billions of dollars 115 35 110 exports 30 105 25 100 imports 20 crops 95 15 90 10 trade balance 5 0 1995 96 97 98 99 livestock 85 80 1995 00 (Mar.) NOTE: Data are aggregated over the past 12 months. Beginning with December 1999 data, series are based on the new NAICS product codes. 96 97 98 99 00 (Feb.) NOTE: Data are aggregated over the past 12 months. U.S. Crop and Livestock Prices index 1990-92=100 145 135 crops 125 115 105 95 livestock 85 75 1986 87 88 89 90 91 92 93 [18] 94 95 96 97 98 99 00 (May)
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