Abstract

Despite recent media stories about both labor unions and the potential revitalization of U.S. manufacturing, most current policy discussions about improving business climate to foster manufacturing neglect the role of unions. This, plus the continued decline in U.S. union membership, might lead one to believe that unions matter little for new investment decisions.

This essay argues that, in fact, unions remain an extremely significant factor in decisions by U.S. manufacturers about where they will or will not make new investments. Both unions and manufacturing are discussed in an analysis that distinguishes between new investment at new plants and at existing plants. Two central arguments are presented:

(1) Union success (or lack thereof) in organizing new plants is a reflection, in part, of an intentional strategy by firms to choose locations that have historically not been receptive to unions, in the South and in rural areas. This well-established historical process continues today. That is, unions still make a difference for new investment in manufacturing because they influence where firms decide to open new plants.

(2) Unions also remain relevant for corporate decisions about new investment at existing plants. Many such facilities are hubs of interaction between unionized blue-collar workers and nonunion white-collar workers, including researchers and engineers in research and development labs. To continue this valued interaction at a new nonunion plant, the firm would have to shift white-collar workers, at potentially high cost. The firm might instead consider adding new investment to an existing facility. In this way, the new investment keeps alive a union established long ago.

Through its influence on the ease of labor organizing, policy can therefore influence both the location and the amount of new investment in U.S. manufacturing.

Introduction

With the decline of labor union membership in the United States over recent decades, discussions of policy toward unions usually show up in the back pages of newspapers, if at all. But recently, labor union policy has been front-page news. One major story is that in 2011, the National Labor Relations Board (NLRB) began proceedings to block Boeing, the largest manufacturing exporter in the United States, from opening a billion-dollar plant in South Carolina, due to alleged labor law violations. That the (now-resolved) dispute even made headlines is significant news in an era of supposed union irrelevance.

Another major labor story involves efforts in several states to pass “right-to-work” laws, anti-union statutes that prohibit making union membership a requirement of employment. In December 2012, Michigan, a traditional center of union power, enacted a right-to-work law, joining the ranks of anti-union states in the South that passed such laws over 50 years ago. Indiana did so in February 2012, and Wisconsin enacted a related law for public sector unions in 2011.

Also on the front page are discussions of a potential revitalization of American manufacturing. The automobile industry has been in recovery since the 2009 crisis. General Electric’s (GE’s) “reverse offshoring” of water heater production from China back to Kentucky got substantial media attention, as did Boeing’s rollout of the new fuel-efficient 787 Dreamliner, which it hopes will be a key source of competitive advantage for years to come.

Despite these stories, overall gains in manufacturing have been meager relative to the broad decline of U.S. manufacturing since the 1970s, and many, including President Barack Obama, argue that it should be an important policy priority to promote U.S. manufacturing. For example, a recent presidential report urges improvements in the business climate for manufacturing (President’s Council of Advisors on Science and Technology, July 2012).

It’s telling, perhaps, that this report doesn’t mention unions in its discussion of business climate, consistent with a view that unions are largely irrelevant to corporate decisions about investment in manufacturing. The share of the manufacturing workforce in unions has been in free fall for many years, only 9.6 percent in 2012, compared with 38.9 percent in 1973.¹ This statistic actually understates current union weakness, because factories that are unionized today, to a remarkable degree, are the legacy of union victories over 50 years ago.

These facts might lead one to the view that while unions were relevant to old investment decisions (in locations where unions were established decades ago under a more favorable environment), they matter little for new investment decisions. In discussions of business climate, policymakers and businesspeople think about new investments, of course, not investments made years ago. Given today’s small union membership numbers, it might seem sensible to leave unions out of the discussion about current business climate and new investment.

I believe that this conclusion is ill founded, and in this essay, I will argue that in fact unions remain an extremely significant factor in decisions by U.S. manufacturers about where they will or will not make new investments. To make this point, I discuss both unions and manufacturing, and I present an analysis that distinguishes between new investment at new plants and at existing plants.

I argue first that low union success in organizing new plants is not an accident, but rather a reflection, in part, of an intentional strategy by firms to choose locations that have historically not been receptive to unions, in the South and in rural areas. True, this is an old story, a process that has been going on for decades.² My point is that this process continues today. That is, unions still make a difference for new investment in manufacturing because they influence where firms decide to open new plants.

Second, I argue that unions remain quite relevant for corporate decisions about new investment being considered at existing plants. Many such facilities are significant hubs of interaction between unionized blue-collar workers and nonunion white-collar workers, including researchers and engineers in research and development (R&D) labs. These facilities are old (in some cases 100 years or more!), and unions at them were generally organized just after the National Labor Relations Act of 1935 provided a favorable environment to do so.

If a firm with such a facility were to shift production workers to a new nonunion plant, it would have to shift the white-collar workers as well, if it wants to continue the interactions. It might be costly to break up an existing successful research center, and so the firm might instead consider adding new investment to an existing facility.³ In this way, the new investment keeps alive a union established long ago. Public policy that affects such a firm’s interactions with the incumbent union and its bargaining strength then potentially affects the business climate in which the decision about new investment is made.

To illustrate the continuing relevance of unions to investment decisions, consider again GE’s decision to bring production back from China to its appliance plant in Kentucky. The Kentucky plant is old and has long been union. It is also the headquarters for GE’s appliance business and the R&D center. In public statements, including comments by CEO Jeff Immelt, GE makes explicit the high value it places on having innovation and production at the same location.⁴ GE sustained this co-location by choosing to add new investment to its already unionized plant. However, it is important to emphasize the role recent weakness of unions potentially played in providing a favorable climate for the investment. As part of the deal, the union made a concession that the new workers be paid $10 less per hour than existing workers.⁵ This kind of two-tiered wage structure is anathema to union solidarity, and a concession like this was rarely made in earlier periods when unions were strong.

Consider also the NLRB’s 2011 case against Boeing. Historically, Boeing’s base of production is its heavily unionized facilities in Washington state. (I say “heavily” because even engineers there are in a union.) Boeing has had a rocky relationship with its unions over the years, and strikes are a regular occurrence. In 2010, Boeing began opening a second Dreamliner production line in a South Carolina nonunion plant; “only the third site in the world to assemble and deliver twin-aisle commercial airplanes,” according to Boeing.⁶ CEO Jim McNerney explained that Boeing was doing this because the company was tired of “strikes happening every three to four years in Puget Sound.”⁷ Based on these remarks and others like it, the NLRB filed its case accusing Boeing of an illegal labor practice regarding threats firms can make about how they might respond to strikes.

I offer the Boeing CEO’s expressed motivation for moving to South Carolina as “Exhibit A” for my case that big manufacturers even today are choosing locations to avoid unions. However, company officials have to be very careful about public statements on this issue because these statements have legal ramifications. Hence, for the analysis I will focus on what firms do, rather than on what their officials say. By observing the choices firms make when they decide where to make new investments, I can draw inferences about what matters to them most.

The main work of this paper is an analysis of recent investment behavior by GE, which will serve as “Exhibit B.” Putting GE under the microscope reveals a picture with a great deal of clarity. In the recent period that I look at, whenever GE has built a brand new plant, it has picked a location unlikely to be unionized. And when GE has invested in an existing unionized facility, for the vast majority of new jobs involved, the facility was one with significant R&D presence, and new workers were hired at a lower wage tier than existing employees.

This is a case study of two firms. While these are two very important firms—the two largest manufacturing exporters in the U.S.—as in any case study, there is always an issue of the broader applicability of the results. I believe the insights of this analysis hold more broadly for large U.S. companies in heavy industry, and I give two quick examples to back this up. First, Caterpillar, the construction-equipment manufacturer, is another firm high on the list of top exporters. Union avoidance in this firm’s investment decisions has been very much in the recent news.⁸ Second, if I had included the auto industry in this study (and, in particular, the site-selection decisions of foreign-owned firms), I expect that many of the conclusions would be similar. Foreign automakers in every case have chosen plant locations where they have been able to remain nonunion.

Background

Several key points about firms and unions will aid discussion of the case studies that follow.

1. Unions are organized at the plant level; once established, they seldom disappear.
   Generally speaking, union organization takes place at the plant level, involving a representation election supervised by the NLRB. Once a union gets in a plant and, in particular, is able to negotiate a first contract, it becomes entrenched over time. An NLRB mechanism for decertifying a union does exist, but it affects only a trivial number of cases. In 2005, for instance, unions representing 11,000 workers were decertified, but out of a base of 9 million represented private sector workers, this is a decertification rate of only 0.13 percent.⁹ Hence, once a union becomes entrenched at a plant, it is generally there for good, until the plant shuts down.
2. Unions spread to neighboring establishments, so firms often build new plants in distant areas.
   Unions tend to spill out of organized plants into nearby businesses; that is, to some degree unions are “contagious.” In Holmes (2006), I provide evidence on this point, showing how unions in steel mills, auto plants and coal mines found their way into neighboring grocery stores and health care facilities. If a union can spread from an auto plant to a nursing home down the street, it can likely extend to a neighboring auto plant. Aware of this, firms understand that starting a new nonunion plant generally requires geographic separation from existing unionized plants.
3. Manufacturers may augment existing unionized plants if benefits outweigh costs.
   If a manufacturer invests and adds production worker jobs to an existing unionized plant, the new workers usually join the current union. The manufacturer may make this decision, rather than open a new nonunion facility elsewhere, if the initial site has advantages, like proximity to R&D labs, that offset the disadvantage of being unionized. In this way, a unionization event from many years ago is kept alive.

General Electric

With that as background, I’ll now turn to the meat of the essay where I analyze what key manufacturers are doing. I focus on GE, but I also come back to Boeing.

GE is one of most influential U.S. companies. It is the second largest U.S. manufacturing exporter (after Boeing). It is the third most innovative U.S. firm, measured in terms of patent counts (after IBM and Microsoft).¹⁰ It is at the center of discussions about revitalization of U.S. manufacturing. Immelt is highly visible in this discussion and serves on the Council on Jobs and Competitiveness set up by Obama.

GE is also interesting for my purposes because it has a long history of having both union and nonunion operations. It has long held a reputation of taking a tough stance in dealing with unions. (See the discussion in Meyer (2001), for example.) Here, I take a look at its recent behavior regarding plant openings and new investment.

GE publicizes its new plant openings and investments in an internet series called “GE Reports,” under the category “jobs.”¹¹ I reviewed all announcements in the series published over the four-year period Jan. 1, 2009, to Dec. 31, 2012, and created a data set of new plant openings and expansions. I restricted attention to announcements in which new jobs were added and excluded announcements for GE Capital and GE Corporate, in order to focus on the manufacturing divisions. When multiple expansions occurred at the same location—for example, the appliance factory in Louisville, Ky., mentioned in the introduction had three expansions during this period—I combined the records. After going through 93 announcements and combining information this way, I found 24 locations in which new investment and job growth were announced over the four-year period, with a total of 8,344 new jobs. The 24 locations are listed in Tables 1, 2 and 3.

In constructing the tables, I first categorize locations as new or existing.¹² In my definition of existing, I include brand new buildings and facilities that are part of a larger preexisting GE campus. For example, there is battery factory in Schenectady, N.Y., that was described as new in the announcement, but I classified it as preexisting because it was added to GE’s main campus in Schenectady, which serves as its headquarters location and the site of a number of existing facilities.¹³ Using this classification system, I determined that of the 24 locations receiving new investment, eight were new locations and 16 were existing locations. Table 1 lists the new facilities.

Take a look at the locations of the eight new plants. With one exception, a plant in Michigan discussed below, they are all in locations where unions are weak: two aviation plants in Mississippi, a locomotive plant in Texas, other locations in the South. A partial exception: a non-South location in Colorado, a state where unions are relatively weak. The full exception: GE’s new facility in Michigan, in the Detroit area, a center of union power. But this, as it turns out, is an R&D center, with only white-collar labor;¹⁴ unionization is thus a nonissue.

Of course, union avoidance is only one of many factors considered in a plant location decision. For example, states in the South getting the new plants may have offered better tax incentives than other potential sites in northern states. In fact, GE’s CEO is on record as saying that tax incentives matter in site selection.¹⁵ But this is why GE’s choice to put the R&D center in the Detroit area is interesting. If taxes are the primary consideration and taxes are lower in the South, I might expect the R&D center to be put in the South as well. With a case study of only eight data points, I cannot draw definitive conclusions. Nonetheless, it is striking that a simple theory that GE picks nonunion locations when unions matter gets it right eight out of eight tries. Along with the other evidence from Boeing, it suggests a pattern of behavior.

I next turn to new investment at the 16 locations where GE already had facilities. I classify these plants as “union” or “nonunion” depending on whether the location has workers represented by a union (based on various public sources).¹⁶ The nine nonunion facilities are listed in Table 2, and the seven union plants are listed in Table 3.

Two comments about the nine nonunion facilities. Note first, there is a nonunion GE aviation plant in Michigan. As this is a production facility with blue-collar workers, it might be surprising that it has remained nonunion in Michigan. However, the plant is in western Michigan, where unions are not as strong. Next, note the nonunion GE transportation facility in Grove City, Pa. The plant makes engines for a locomotive plant in Erie, listed in Table 3 in the “union” category. The Erie locomotive plant dates from 1913 and has been a union plant since 1940.¹⁷ The engine plant in Grove City dates from 1971 and has remained nonunion, despite the connection with the union plant in Erie.¹⁸ Apparently, the 85-mile distance between the two locations has been enough to keep the union in Erie out of the Grove City plant.

I now turn to the seven union plants that received new investment, listed in Table 3. The plants are sorted from the highest number of new jobs to the lowest, and I focus on the top three, highlighted in bold. These are the GE energy facility at Schenectady, with 1,200 new jobs, the GE appliance facility in Louisville, with 1,130 new jobs, and the GE transportation facility in Erie, with 610 new jobs. Together they account for the vast majority of new jobs in union plants, 2,940 out of 3,518.

The last three columns of Table 3 reveal interesting facts about these three facilities. First, each is the respective headquarters for its division. Second, each of these three locations has an R&D lab on site.¹⁹ Third, each of the three locations is a successful producer of a large number of patents. I base this on calculations with publicly available U.S. patent data. I extracted all granted patents assigned to GE over the period 2000-11. In the data for each patent, the location of each inventor is provided. The last column of Table 3 reports the number of GE patents over this period with at least one inventor in each of the given locations. ²⁰ Schenectady, the overall GE headquarters, has 4,348 granted patents over the period, while Louisville has 280 and Erie has 351. This is an impressive amount of innovative output.

Earlier, I argued that if production workers are unionized at a facility, the location disadvantage for new investment of the existing union could potentially be offset by beneficial co-location with R&D activity and other white-collar work. I see evidence for this claim in GE’s investment behavior. The vast majority of new investment in unionized facilities has occurred in plants with significant R&D and connections to headquarters.

I consider one last issue for the seven union plants receiving new investment: What is happening to the net number of union jobs at each of the facilities? The “GE Reports” series mentions expansions leading to new jobs to publicize GE’s contribution to U.S. employment, but it doesn’t publicize job cuts through efficiencies or outsourcing. To look at the net effect on union jobs, I use data from the Department of Labor on union membership for each of the union locals at the respective plants.²¹ Membership by local and year are reported in Table 4, and the bottom row tabulates the sum across all seven union plants receiving new investment. Membership at these seven facilities between 2010 and 2011 increased from 7,592 to 8,710 workers, consistent with GE’s message that it is increasing production worker employment at these plants.

However, the recent gain is not enough to offset the fall from 2007. Moreover, these are the selection of union plants getting new investment. I have looked at some of the other large unionized plants not getting new investment, and membership is falling in these plants. One takeaway point is that even though GE is putting some new investment in unionized plants that for historical reasons are connected to headquarters and R&D facilities, this force is not strong enough to offset continual decline of the unionized workforce at GE.

Boeing

Let’s get back to the earlier story about Boeing, where I noted that the NLRB had filed a complaint against Boeing in 2011. The complaint alleged that Boeing had engaged in an unlawful labor practice, by making public statements that it was moving production to a nonunion facility to avoid strikes.²² As a remedy, the acting general counsel sought a court order that Boeing be forced to open the second production line in a union facility in the Washington state area instead of the nonunion facility in South Carolina.

In the end, the issue was resolved by Boeing agreeing to add additional union jobs in Washington state in return for the union dropping the charges, enabling Boeing to go ahead with the South Carolina plant.²³ The story illustrates both kinds of investment highlighted in this essay. First, there is new investment at a location where unions are weak, at a site where Boeing did not have a previous facility. ²⁴ Second, there is new investment at an existing unionized facility, at a site close to Boeing’s R&D infrastructure and other white-collar activity.

The story has two epilogues. In January 2012, Boeing announced that it was closing its entire operations in Wichita, Kan., a unionized facility. (Kansas is not known as a strong union state, but the facility in question dates to 1927, and old facilities in heavy industry are generally union, no matter where they are located.) Many of the jobs were shifted to nonunion facilities in Texas and Oklahoma, some to union facilities in Washington state and some cut altogether. Various news articles report cutbacks in defense spending as the driving factor behind this closure.²⁵ Even so, it is also clear that this decision has implications for the “chess game” of labor management relations going forward, with a longstanding union outpost eliminated and nonunion activity expanded.

The second epilogue is that Boeing’s main union is currently trying to unionize the South Carolina plant.²⁶ Clearly, Boeing has an incentive to try to keep the workers happy enough that they won’t want the union. And it is reasonable to expect that the workers would be familiar with earlier statements by company officials that a nonunion workforce is why Boeing came in the first place. (Public officials in South Carolina have actually reminded the workers on this point. ²⁷) If the South Carolina workers were to vote in the union, they will be giving up the competitive advantage they hold over union workers in Washington state in future competition for new plant investment. Obviously, this situation puts the union in a weak position.

Remarks about Labor Relations Policy

Public policy affects the extent of unions. For example, the 1935 passage of the National Labor Relations Act was followed by a huge surge in the share of unionized workers. (See Freeman 1998.) Think of there being a policy lever, where how high the lever is pushed determines how easy it is for unions to organize in a workplace. For example, in 2009 at the beginning of Obama’s first term, when the Democrats controlled both houses of Congress, there was discussion of the “Employee Free Choice Act,” a bill to allow unions to substitute the secret ballot in an NLRB-supervised election with a system where union organizers collect signed cards from workers.

This policy, called “card check,” would be a significant upward push on the policy lever. (With the new Congress, it is currently not under consideration.) The NLRB recently made administrative rule changes to speed up union representation elections.²⁸ This is an upward push on the lever, because employers have less time to respond. The right-to-work laws recently enacted in Michigan and Indiana push the lever down. In addition to the direct negative effect on unions in these two states, there will likely be a broader negative effect on unions throughout the country. These laws make it harder to collect union dues, and this can potentially lessen the resources available for organizing in other states. For example, when the autoworkers union conducts organizing drives at nonunion auto plants in the South, they are funded by autoworkers’ dues in states like Michigan and Indiana.

Suppose the pro-union organizing policy lever gets pushed up so high that the union gets into Boeing’s new South Carolina plant, and Boeing expects that this will be true for other new plants it might open in the South. Based on the findings above, how will this policy change affect new manufacturing investment?

The analysis above presents evidence that even today, big firms like Boeing and GE are selecting locations to avoid unions. If Boeing were to get a union even in South Carolina, it will have less incentive to shift production from Washington state to South Carolina. Thus, an increase in the policy lever potentially affects where new investment goes within the United States.

In addition to where, the policy lever can potentially affect how much overall new investment there is in this country. If one accepts the proposition that firms choose locations within the United States to avoid unions, then one has to consider the possibility that a change in policy might lead the firm to not invest in the United States. That is, if policy changes so that the firm gets a union no matter where in this country it goes, it might consider investing abroad or not investing at all. In the NLRB case referred to above, the NLRB notes that Boeing has experienced strikes by production workers in 1977, 1989, 1995, 2005 and 2008. In December 2012, Boeing’s engineers union leaders in Seattle said that “the likelihood of a strike is very high,” and though negotiations continued in early 2013, prospects for settlement on a contract remained distant.²⁹ Dealing with strikes on a regular basis can only make Boeing less competitive in the world marketplace, diminishing the returns to new investment.

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Endnotes

1 Statistics on union membership share are based on the Current Population Survey conducted by the U.S. Census Bureau and were obtained from tabulations published at unionstats.com.

2 Fuchs (1962) is an early work arguing for the important role of unions in the migration of industry to the South. See also Holmes (1998) for a discussion of the role of anti-union policies pursued in Southern states.

3 I note two costs in particular. First, key researchers might be unwilling to move. Second, there is much discussion in the economics literature for how R&D centers potentially benefit from knowledge spillovers from other researchers in the vicinity. If the R&D center is moved, it might lose access to these beneficial spillovers.

4 See, in particular, Immelt’s comments in the Harvard Business Review, Immelt (2012). He highlights the Kentucky appliance plant and writes, “Our success on the factory floor rests on human innovation and technical innovation.” He adds, “Engineering and manufacturing are hands-on and interactive … at a time when speed to market is everything, separating design and development from manufacturing didn’t make sense.”

5 In discussing GE’s decision to invest in the Kentucky plant, Immelt writes, “The third element in human innovation is a new model for labor relations. ... The union accepted a lower wage for new hires, we pledged to create new jobs” Immelt (2012). For more on the story, see “G.E. to Add Two New U.S. Plants as Unions Agree on Cost Controls,” New York Times, Aug. 6, 2009.

6 This is how Boeing’s website describes the South Carolina facility. The other two are the Boeing facility in Everett, Wash., and the airbus facility in Toulouse, France.

7 The CEO is quoted in the case document, NLRB Case 19-CA-32431, dated April 20, 2011. The brief also quotes similar comments made by other company officials.

8 For a story about Caterpillar closing a union plant in Ontario and transferring jobs to a nonunion plant in newly right-to-work Indiana, see “As Unions Lose Their Grip, Indiana Lures Manufacturing Jobs,” Wall Street Journal, March 18, 2012.

9 This statistic is based on the author’s calculations with the raw NLRB election data. The statistic includes cases where unions were decertified and replaced with an alternative union. Dickens and Leonard (1984) report an analogous estimate with earlier data that is the same order of magnitude.

10 The patent count figure is as reported for 2012 by IFI CLAIMS. The claim about exporting is one regularly made by GE. See, for example, GE Reports.

11 See GE Reports.

12 I use GE’s records in the Million Dollar Directory of Dun and Bradstreet to build a database of GE’s manufacturing plants. I merge this with plant information over the 1987-2010 period in the Toxic Release Inventory published by the Environmental Protection Agency, which can be used to determine when a plant is emitting pollution and is therefore in operation. I combined these data with the GE announcement information to distinguish new and existing plants.

13 See “New York powers up with new GE battery plant,” GE Reports, May 12, 2009.

14 See “GE to bring research center and 1,100 jobs to Michigan,” GE Reports, June 26, 2009.

15 See comments in Immelt (2012).

16 The master 2007-11 GE contract lists all facilities party to the contract that were represented by IUE-CWA, the largest union at GE. I also used government data from the Federal Mediation and Conciliation Service, which publishes information about the location of facilities with expiring union contracts. I resolved ambiguous cases through web searches, including inspection of various websites of local and national unions.

17 See A Brief History of UE Bargaining with GE: Seventy Years of Struggle, United Electrical, Radio and Machine Workers of America (undated manuscript), and GE Transportation BusinessWire news release, “GE Transportation Celebrates 40 Years in Grove City,” Aug. 6, 2011.

18 The age of the plant is based on “GE Transportation Celebrates 40 Years in Grove City.” Given Pennsylvania’s tradition of strong unions, the fact that GE has a nonunion plant there may come as a surprise. Two points are worth noting in addition to the geographic separation with the Erie plant noted in the text. First, the Grove City plant did not exist in the 1940-70 era when labor organizing at plants was easier. Second, it is in a rural area away from other unions.

19 Specifically, each location cited is listed in the Directory of American Research and Technology, 23rd ed., R. R. Bowker, Reed Elsevier, New Providence, N.J., 1998.

20 The patent data report the city and state of a given inventor, but generally not the address. Table 2 reports the count of patents with at least one inventor in the given city and state.

21 The data are the LM Filing Data, published by the Office of Labor-Management Standards at its website. For all but two exceptions, I used the disaggregated membership information in the file, which is useful for separating out membership in the local not in a GE bargaining unit. For the Bucyrus and Madisonville units, only total local membership is available, but this should not be a problem because both appear to represent only GE employees.

22 The complaint is NLRB Case 19-CA-32431, dated April 20, 2011.

23 See, “Union Seeks to Dismiss Complaint Against Boeing,” New York Times, Dec. 9, 2011.

24 For brevity, I am glossing over details. In July 2009, Boeing purchased a supplier plant in South Carolina that already had a union. The plant workers voted to decertify the union in September, and subsequently in October Boeing announced it was going to build the second line in South Carolina.

25 See “Boeing to Shut Wichita Plant, Citing Cuts at Pentagon,” New York Times, Jan. 4, 2012.

26 See “Boeing faces union drive at 787 plant in South Carolina,” Reuters, Oct. 12, 2012.

27 Seattle Times, Oct. 22, 2012, quoted Sen. Jim DeMint, R-S.C., as saying, “It would blow me away if the employees of Boeing here were so foolish as to unionize when that was one of the key reasons that this plant was built.”

28 See “Labor Board Adopts Rules to Speed Unionization Votes,” New York Times, Dec. 11, 2011.

29 See “Boeing’s engineer unions says strike is likely, prepares workers,” Reuters, Dec. 10, 2012. See also “Boeing, engineers set to resume contract talks Wednesday,” Reuters, Jan. 14, 2013.

References

Dickens, William T., and Jonathan S. Leonard. 1984. “Accounting for the Decline in Union Membership, 1950-1980.” Industrial and Labor Relations Review 38: 323.

Freeman, Richard B. 1998. “Spurts in Union Growth: Defining Moments and Social Processes,” in The Defining Moment: The Great Depression and the American Economy in the Twentieth Century.” Michael D. Bordo, Claudia Goldin and Eugene N. White, eds. Chicago: University of Chicago Press, pp. 265-95.

Fuchs, Victor R. 1962. Changes in the Location of Manufacturing in the United States since 1929. Yale University Press.

Holmes, Thomas J. 1998. “The Effects of State Policies on the Location of Industry: Evidence from State Borders.” Journal of Political Economy 106 (August): 667-705.

Holmes, Thomas J. 2006. “Geographic Spillover of Unionism.” Research Department Staff Report 368. Federal Reserve Bank of Minneapolis.

Immelt, Jeffrey R. 2012. “The CEO of General Electric on Sparking an American Manufacturing Renewal.” Harvard Business Review 90 (March): 43-46.

Meyer, Douglas. 2001. “Building Union Power in the Global Economy: A Case Study of the Coordinated Bargaining Committee of General Electric Unions (CBC).” Labor Studies Journal (Spring): 60-75.

President’s Council of Advisors on Science and Technology. 2012. “Report to the President on Capturing Domestic Competitive Advantage in Advanced Manufacturing.” Manuscript.

This Economic Policy Paper was originally published in the December 2012 issue of The Region.

Abstract

Banks are prone to panic-induced runs due to their traditional structure of short-term, unconditional liabilities and long-term, illiquid assets. To avoid systemic crises caused by such panics, governments tend to bail out failing banks. Traditional banking systems thus impose external costs. Three major theoretical benefits are often used to justify a banking system that relies on short-term debt despite these costs: (1) maturity transformation, (2) efficient monitoring of bank managers and (3) facilitation of financial transactions. In a previous paper, we argued that the first two justifications, while seemingly compelling, actually suggest financial arrangements very different from our current system. In this paper, we examine the third justification, that a banking system reliant on short-term debt is essential for the facilitation of transactions. We find, in fact, that this reliance is more costly than generally recognized and, moreover, that socially beneficial financial transactions can and should be provided at less cost and risk by both restricting and broadening the payments system. Transactions should be restricted to institutions that continuously mark to market the value of their assets and issue equity claims to owners. Such accounts should also be broadened to include financial vehicles that are readily available, thanks to advances in information and communication technologies, and possibly quite different from current banks.

Introduction¹

The financial crisis of 2007-08 and consequent Great Recession generated substantial discussion and debate over future banking regulation. Largely absent, however, has been a careful reexamination of whether the beneficial services provided by traditional banks outweigh the inherent financial fragilities of those banks and their associated costs to society.

Three major benefits are usually said to justify traditional bank reliance on short-term debt, the source of their inherent fragility. In a previous article, we assessed—and found wanting—two of these proposed rationales: (1) the benefit of maturity transformation, or creation of long-term financial assets from shorter-term assets and (2) the benefit of efficient monitoring of bank managers, through appropriate alignment of investor incentives. (See Chari and Phelan 2012a.)

Here we discuss the third justification, that traditional banks are beneficial and necessary because they provide payments services essential to the efficient function of modern economies. We conclude that while this rationale was compelling in an earlier historical era—prior to modern advances in information and communication technology that facilitate transactions of all sorts—the necessary services can now be provided through existing financial vehicles that do not rely on traditionally structured, inherently fragile banks.

We begin by briefly reviewing the structural source of traditional bank fragility and proceed to consideration of the necessity of banks, despite this fragility. We then address the main topic of this paper: the argument that banks as currently structured are necessary because their demand deposits facilitate financial transactions. We conclude that the current structure of banks is unduly costly to society and that essential payments services can, with modern information and communication technologies, be provided with less fragile and more efficient financial institutions.

The inherent fragility of banks

In what sense are banks and similar financial institutions fragile? Our previous paper discusses this question in detail; here we provide a synopsis, referring interested readers to the earlier discussion.

The assets of financial institutions are, by and large, financial assets, and claims on them are primarily financial liabilities. Their financial assets consist mainly of conditional promises to deliver dollars at future dates. These assets, such as home mortgages, are often long term and illiquid. Their financial liabilities consist mostly of a variety of obligations to deliver dollars at particular dates, under certain circumstances. Banks in particular have liabilities that are mostly short term and unconditional, such as demand deposits and certificates of deposit.

This reliance on short-term debt makes banks fragile in that they are particularly vulnerable to the risks of insolvency and the possibility of confidence crises. Since bank assets are much longer term and illiquid than their liabilities and because the value of these assets fluctuates, a bank’s net worth also fluctuates a great deal.

The illiquidity of banks’ assets and the demandable structure of their liabilities thus expose banks to crises of confidence. Since a bank typically will not be able to meet the demands of all depositors within a short period of time should they all choose to withdraw, banks are vulnerable to self-fulfilling panics in which depositors withdraw their funds simply because they believe other depositors will do so. This panic is an entirely rational response even if the bank is solvent (though illiquid).

Governments have a strong incentive to intervene to bail out debt holders of banks in order to prevent the entire financial system from failing. Paradoxically, expectations of such bailouts can increase the incidence and depth of financial crises. Once depositors believe that their deposits will be protected in the event of systemic failure, they have less incentive to monitor bank managers, who, in turn, have increased incentive to take on risk, knowing their failures are implicitly insured by taxpayers.

In this way, expectations of bailouts can lead financial systems to rely excessively—from a social perspective—on short-term debt to fund long-term assets. Fragile banking systems thus impose external costs, and regulation may therefore be socially desirable.

Are banks necessary?

The fragility of the banking system together with the reality that such fragility may well lead to occasional massive bailouts compel us to ask why societies would choose regulatory systems that allow financial institutions to fund illiquid assets whose value can fluctuate rapidly with short-term debt and demand deposits.

One could perhaps argue that banks were necessary prior to the electronic information age because no other forms of financial intermediation were feasible. With the advent of high-speed computers and modern communications, however, alternative financial institutions can provide similar services with far less potential for crises. We discuss such alternatives later in this paper.

We now examine the possible social benefits of a financial system in which illiquid assets with volatile values are funded by demand deposits and short-term debt. This cost-benefit analysis facilitates the design of a better regulatory system for banks, clearly a matter of considerable importance.

The previous paper examined two of the three major theoretical justifications for the reliance of the banking system on short-term debt: (1) demand deposits allow banks to engage in socially useful maturity transformation and (2) demand deposits allow for efficient monitoring of bank managers. This paper considers the third major justification: (3) demand deposits facilitate financial transactions.

To anticipate our conclusion, we believe that while all three justifications are compelling, they point us to a financial system very different from the one currently in place. The first two justifications suggest that it is important to have institutions that finance long-term assets with short-term debt, but we have argued that the assets that are so funded should not have close substitutes in publicly traded markets. In this paper, we will argue two main points regarding the usefulness of banks in facilitating transactions. First, we argue that regardless of technology, the social benefit to using banks to facilitate transactions is lower than the private benefit, thus potentially explaining why the historical ubiquity of bank-facilitated transactions does not imply their efficiency. Second, we argue that the necessity of bank-facilitated transactions is much less obvious than it was a century ago, before advances in information and communication technologies allowed us to create very different institutions than we currently have to facilitate transactions.

Our analysis will suggest a framework for thinking about regulatory policy for institutions that facilitate payments. The economic case for regulating such institutions is convincing, given that the failure of the payments system imposes significant external costs. We argue that institutions that facilitate payments should primarily issue equity-like claims such as those issued by standard mutual funds. Current practice hopelessly conflates these two economic cases into a single institution called “banks” and exposes the economy to unnecessary risks and recurrent costly bailouts.

Assessment of the transactions facilitation view

The most obvious service that banks provide now, and have offered throughout their ubiquitous existence, is payments services. Historically, banks have allowed individuals and firms to pay for goods and services through their provision of bank checks and other widely accepted claims. Therefore, those individuals and firms haven’t had to resort to costly barter or specie trade.

Here, we raise the possibility that banks exist because they provide a privately useful function—the facilitation of transactions in a form that pays households interest—but the social usefulness is less than the private usefulness.

The starting point of our assessment involves the central bank and monetary policy. The central bank creates money, which, for simplicity, we will call “cash.” Cash typically earns no interest. Our first key point is that, to the extent that monetary policy is conducted so as to keep inflation—and thus the (nominal) interest rate—inefficiently high, private agents have strong incentives to develop private payments systems to economize on the use of cash. Interest-bearing demand deposits (checking accounts) at banks are one example of such a private payments system. Because of the interest received in such accounts, households and firms will find it advantageous to switch from cash to these private deposits as their means of payment. Clearly, then, there would be private benefits to the introduction of payments systems like checking accounts.

But do these private benefits imply equivalent social benefits? If one household’s use of demand deposits imposed no costs on other households, the answer would be yes. But if use of such demand deposits does indeed impose costs on other households, the net social benefit of demand deposits will be lower and can, in fact, be negative. In the appendix, we present an example economy where these net social benefits from demand deposits are indeed negative, even though each household finds it in its interest to use them (since the private benefits are positive). In Chari and Phelan (2012b), we present a more general model where the net social benefits from interest-bearing means of payments can be either positive or negative, but are nevertheless always less than the private benefits.

The reason one household’s use of demand deposits imposes costs on other households is as follows: Introducing bank-provided payments leads to an expansion of the “means-of-payment” supply, now defined to include both cash and the amount of demand deposits. This higher means-of-payment supply leads to higher prices in the aggregate economy, which reduces the purchasing power of other households’ deposits and cash—but individual firms or households do not take this into account when they choose to use demand deposits over cash. This pecuniary externality (that is, an external cost imposed through prices rather than real resources) can cause households to use deposits instead of cash in cases where they wouldn’t, had they internalized this cost imposed on other households, and this externality implies that net social benefits of demand deposits are lower than net private benefits.

With net private benefits of banking exceeding net social benefits, it is clear that the banking system will be inefficiently large. In the model presented in the appendix, because the net social benefits are negative, not only is the banking system inefficiently large, the optimal size of banks is zero.

The model in the appendix is but a simple example, and the implications from it seem unrealistic. However, we would argue that recent developments in communication technologies and financial innovations may in fact make the model’s implications more than just a hypothetical scenario.

Historically, communication costs and limited development of financial markets have led to the use of systems in which only a fraction of a household’s financial wealth could be used for payments. With improvements in communication and financial markets, however, we can conceive of a world in which each individual can instantaneously access all of his or her financial wealth to make payments. We can also imagine a world in which settlement of transactions is instantaneous. In this world, cash becomes unnecessary, and precisely because cash is unnecessary, there is little or no need for payments systems that arise from the need to economize on cash, that is, arise because monetary policy is setting the inflation rate too high.

In the 1800s, it would have been inconceivable to pay for groceries, for example, by using a debit card associated with one’s mutual fund or stock portfolio (and in doing so, stocks were immediately sold, and the grocery received its settlement while the shopper was still at the counter). But today, this scenario is not far-fetched. In a world with virtually costless communication, banks as specialized providers of transactions services would simply be obsolete. These observations lead us to conclude that the actual importance of banks in the payments system is likely small today and will likely become even smaller in the near future. This is the third and final key point in our assessment of the transactions facilitation view.

What should “banks” look like, if not the traditional but fragile demand-deposit bank? As mentioned in the introduction, alternative financial institutions can provide similar services to the transactions facilitation services that traditional banks offer with far less potential for crises. One such example is the open-end mutual fund. These funds do not owe their shareholders a fixed dollar amount, but instead only the value of their percentage of the fund on the day the shareholder wishes to withdraw. If an unexpected surge of withdrawals occurs, the fund simply sells a sufficient quantity of the fund’s assets and gives the proceeds to the withdrawing shareholders. After this, the remaining shareholders still hold exactly the same assets per share as before. No shareholder gains by being earlier in line than other shareholders. Therefore, a belief that a run will occur cannot cause a run for a mutual fund—the self-fulfilling nature of runs that afflicts banks with demand deposits is thus avoided.

Implications for policy

Banks have been a durable part of the economic landscape for many centuries, and economic theory does explain why it might be efficient to set up institutions that fund long-term assets with short-term debt. Theory also illustrates that it might be optimal for private agents, but undesirable for society at large, to establish such institutions. These competing lessons from economic theory also provide guidance for regulation of such institutions.

As discussed in the earlier paper, both the
maturity transformation and the efficient monitoring views suggest that, given the costs imposed by crises and attendant bailouts, it may be desirable to allow financial institutions to issue short-term debt only if their assets do not have close publicly traded substitutes. Further, to minimize the incentive of governments to bail out institutions if a crisis occurs, such institutions should be separated from the payments system.

Any regulatory system must also take seriously the central role that banks have long played in the payments system. We have argued that this role may well be an artifact of a bygone era. Advances in information and communication technology make it feasible to access a wide array of assets, from stocks in public firms to portfolios of home equity loans, to undertake transactions. We have also argued that payments systems that require the use of demand deposits expose the economy to confidence crises and that it is possible to devise payments systems that do not require the use of debt-like claims, but instead use equity-like claims for transactions purposes.

These considerations suggest that the payments system should be both restricted and broadened. Transactions accounts should be restricted to institutions that mark the value of their assets to market continuously and that issue mutual-fund-like equity claims to owners. Such accounts should be broadened to institutions that are possibly very different from modern-day banks to include institutions such as stock and bond mutual fund companies.

We emphasize that the money market mutual fund as currently structured resembles a bank more than it does a mutual fund and therefore should not be allowed to issue transactions accounts. So, for example, Vanguard’s money market mutual fund (as currently structured) would no longer be allowed to serve as a transactions account, but Vanguard’s 500 Index Fund would.

The framework for regulatory policy implied by our analysis would lead to a banking system that is radically different from the one we currently have. Institutions that issue large amounts of short-term debt relative to their assets would be regulated and required to hold relatively little of their assets in publicly traded securities. The liabilities of such institutions would not serve as means of payment. The payments system would consist of institutions that issue equity claims.

Economic theory tells us that we do need banks. Theory also points us to constructive ways in which we can reform the financial system to make it more efficient and to ensure that crises that affect particular financial institutions do not spill over into the rest of the economy.

Endnotes

References

Chari, V. V., and Christopher Phelan. 2012a. What Assets Should Banks Be Allowed to Hold? Economic Policy Paper 12-3, (May) Federal Reserve Bank of Minneapolis.

Chari, V. V., and Christopher Phelan. 2012b. On the Social Usefulness of Fractional Reserve Banking. Working paper. University of Minnesota.

• Overall, oil production and economic and banking activities continue to expand at a rapid pace in the Bakken area; however, the rate of increase for some indicators has moderated over the past five months.
• Monthly oil production in the Bakken area increased by 3.1 million barrels, or 16 percent, from May to October. During this period, the increase in Bakken oil production represented about 40 percent of the increase in the U.S. total.
• The number of active drilling operations in North Dakota dropped below 200 in recent months as oil companies sought to cut costs and increase efficiency after drilling a number of initial producing wells to meet leasing requirements (N.D. Dept. of Mineral Resources projects drilling rigs will climb above 200 again in 2013). Meanwhile, drilling in Montana picked up recently as oil companies expanded their exploration in the western part of the Bakken.
• Employment levels have continued to expand. The unemployment rate in the Bakken is now 1.8 percent, down from 2 percent in April. However, the number of job postings in the North Dakota part of the Bakken has leveled since June.
• The two most recent quarters of data, from 3/31/2012 to 9/30/2012, show construction and land development loans increasing 64 percent, from $107 million to $176 million, in the Bakken area, while increasing 12 percent in the rest of North Dakota and decreasing 2 percent in the rest of Montana.

The Bakken Oil Boom web page also includes data on oil production, labor markets, and business and banking activity that are updated on a regular basis. In addition, the site highlights fedgazette articles on trends and issues facing the Bakken area.

The Federal Reserve Bank of Minneapolis monitors business and economic conditions throughout its Ninth District, which encompasses Montana, North and South Dakota, Minnesota, northwestern Wisconsin and the Upper Peninsula of Michigan. Data, reports and articles on these states and on the many industries throughout the district can be found on the Bank’s website.

Fifty-eight years ago, Harberger (1954) estimated that the costs of monopoly, which resulted from misallocation of resources across industries, were trivial. Others showed that the same was true for tariffs. This research soon led to the consensus that monopoly costs are of little significance—a consensus that persists to this day.

This paper reports on a new literature that takes a different approach to the costs of monopoly. It examines the costs of monopoly and tariffs within industries. In particular, it examines the histories of industries in which a monopoly is destroyed (or tariffs greatly reduced) and the industry transitions quickly from monopoly to competition. If there are costs of monopoly and high tariffs within industries, it should be possible to see those costs whittled away as the monopoly is destroyed.

In contrast to the prevailing consensus, this new research has identified significant costs of monopoly. Monopoly (and high tariffs) is shown to significantly lower productivity within establishments. It also leads to misallocation within industries: Resources are transferred from high- to low-productivity establishments.

From these histories, a common theme (or theory) emerges as to why monopoly is costly. When a monopoly is created, “rents” are created. Conflict emerges among shareholders, managers and employees of the monopoly as they negotiate how to divide these rents. Mechanisms are set up to split the rents. These mechanisms are often means to reduce competition among members of the monopoly. Although the mechanisms divide rents, they also destroy them (by leading to low productivity and misallocation).

Introduction¹

In standard economic theory, monopoly leads to a welfare loss. This loss stems from a misallocation of resources across industries: Too few goods are produced by the monopolist; too many in other industries. Economic theory had long suggested that this welfare loss exacted high costs from the economy. But modern understanding took a turn when, in a landmark 1954 paper, Arnold Harberger analyzed the quantitative significance of monopoly costs in the United States. Were these costs as high as conventional economic theory suggested? The clear but surprising answer that Harberger provided was no.

Harberger estimated that, contrary to his expectation and to standard theory, the costs of monopoly were quite trivial. “We come to the conclusion that monopoly misallocations entail a welfare loss of no more than a thirteenth of a per cent of the national income. Or, in present values, no more than about $1.40 per capita,” he wrote. “I must confess that I was amazed at this result. ... Monopoly does not seem to affect aggregate welfare very seriously through its effect on resource allocation” (Harberger 1954, pp. 85, 86, 87).

Other economists extended Harberger’s work to estimate costs associated with tariffs, and here, too, the costs were trivial. A consensus quickly developed that Harberger’s conclusion was indeed valid.

Recently, a new literature has taken a different approach to understanding the costs of monopoly. Looking within industries, it examines the histories of industries in which a monopoly is destroyed and the industry transitions quickly from monopoly to competition, as well as the histories of industries that rapidly moved the opposite way, from competition to monopoly. If there are costs of monopoly, those costs should be whittled away as the monopoly is destroyed. Likewise, if an industry is monopolized, costs should be created. In both cases, costs should be apparent when comparing the industry before and after monopolization.

Several industries have been studied with this method, including transportation in the United States and U.S. manufacturing of sugar, iron ore and cement. The historical records of these disparate industries show that there are costs of monopoly and tariffs within industries. In these industries, this new literature has shown that monopoly led to, among other costs, the following:

1. Low productivity at each factory. That is, for any given amount of inputs, monopoly meant that less output was produced than under competition.
2. Misallocation of resources between high- and low-productivity factories. That is, monopoly led to resources (capital, labor, etc.) being transferred from productive factories to unproductive factories. Again, this misallocation occurs within an industry and is different from the misallocation that Harberger (1954) studied.

In sharp contrast to Harberger’s finding, these studies show that the welfare costs associated with monopoly and tariffs are not small. The consequence of cases (1) and (2) above is that industry output could have been produced with fewer inputs. One way to measure the loss, then, is to calculate the value of the “wasted” inputs. The histories of these industries show that as monopoly was destroyed in each, productivity at each factory soared. Doubling of productivities in a few years was common. The value of the wasted inputs was as much as 20 percent to 30 percent of industry value added.

A common theme (or theory) emerges from the histories as to why monopoly led to these costs. When a monopoly is created, “rents” are created.² Conflict emerges among shareholders, managers and employees of the monopoly as they negotiate how to divide these rents among themselves—or, more colloquially, how to “split the spoils.” Mechanisms are set up to split the rents. Although they divide rents, they also destroy them (by leading to low productivity and to misallocation).

As used in this paper, the term “monopoly” means more than the strict definition: an industry with a single producer. One industry mentioned later in the paper was a cartel for 40 years. Conflict over rents emerged between groups in the cartel, firms, workers and managers. In some industries, there were high tariffs (and other forms of protection). This high protection led to strong incentives among groups in the domestic industry to form monopolies. Firms attempted to collude, and workers formed industrywide unions (i.e., monopolies). So, the statement that “tariffs led to large welfare losses” means that tariffs led to incentives to form monopolies and then to actual monopolies, and these monopolies then led to large welfare losses.

A body of literature in the 1960s and 1970s argued that the costs of monopoly and tariffs were not trivial, saying (in essence) that there were costs within industries. This theoretical literature, and why it did little to dent the “Harberger consensus,” is briefly reviewed in Minneapolis Fed Staff Report 468 (Schmitz 2012), on which this policy paper is based. In this policy paper, I discuss historical studies that look at the collapse of monopoly. I describe how the monopolies emerged and how they were destroyed. Then I discuss the mechanisms that were used to split rents and why these mechanisms led to welfare losses.

Monopoly: Its Creation and Destruction

In this section, I introduce some of the industries that have been studied, discussing how monopolies were created in the industries and how they were destroyed. In the section that follows, I discuss the costs of these monopolies.

I discuss four industries: U.S. sugar manufacturing, in particular, sugar manufacturing using sugar beets (Bridgman, Qi and Schmitz 2009, 2012); the U.S. iron ore manufacturing industry (Galdón-Sánchez and Schmitz 2002 and Schmitz 2005); the U.S. cement manufacturing industry (Dunne, Klimek and Schmitz 2010); and U.S. freight transportation by water in the 19th century (Holmes and Schmitz 2001). These papers can be consulted for details that are only sketched here. Other industries will be briefly discussed.

When a monopoly is created, the government often has a hand in the process. This is the case in most of the industries studied, to greater or lesser degrees. In U.S. sugar manufacturing, the government played a central role in creating monopoly. During the Great Depression, sugar manufacturers were permitted, indeed encouraged, by U.S. law to form a cartel.

Many U.S. cartels were created during the Depression (as part of the New Deal), but the New Deal sugar cartel survived much longer than most. For 40 years, from 1934 to 1974, the industry was repeatedly able to renew the U.S. laws that enabled it to operate as a cartel. Soaring world sugar prices in 1974 resulted in the cartel losing political support, and the laws permitting it to operate as a cartel were not renewed.

To describe the government’s role in creating monopoly in the other industries, a useful approach is to first sketch a very simple model, one in the spirit of that in Holmes and Schmitz (1995). Consider an industry where transportation costs are large relative to production costs. If the domestic price is initially set equal to the cost of domestic production, then domestic producers will have a strong incentive to push their price up to the sum of foreign production cost plus the cost of transportation (or tariff) involved in bringing the foreign product to domestic markets.

The incentive to do so is great in this “industry” because, by assumption, transport costs are large relative to production costs. A very large tariff will be an incentive to increase prices, just as a large transportation cost would.

If the transportation or tariff cost is large, then, as in Holmes and Schmitz (1995), assume that groups will make investments to form monopolies. Firms will attempt to collude, and workers to form strong unions. Some groups may succeed. If later on protection is cut, the incentives to make these investments will fall, and the monopolies will weaken (or disappear).

This same logic applies if, rather than a transportation cost advantage, local firms have a production cost advantage.

This simple abstraction is a good representation of both the iron ore and cement manufacturing industries. In the early 1950s, U.S. producers had production cost advantages over foreign producers, and the industries received significant protection.³ Groups invested in creating monopolies. At various times, firms in these industries were charged with trying to collude. The U.S. government investigated the industries for antitrust violations. It is unnecessary to enter into the argument as to how effective collusion was; there is little doubt that very strong, industrywide unions emerged in these industries. Although antitrust laws in the United States made firm collusion difficult, building monopoly unions was easier. Collective bargaining laws enacted by the U.S. government allowed unions to organize all workers in an industry and not be bound by antitrust laws (see Meltzer 1963 and Winter 1963).

The monopolies in these industries—in particular, the strong monopoly unions—lasted for many decades in the post–World War II period. The monopoly unions were able to provide very high wages. For example, by the 1970s, cement workers were paid as much as U.S. autoworkers (who were the highest-paid manufacturing workers). The unions also had very stringent work rules (as described later on).

In the 1980s, the monopolies in these industries weakened or were dissolved. The union in the cement industry dissolved. In the iron ore industry, the union did not disappear, but lost much of its clout. For example, work rules became much less stringent, and plant managers had more control over how to structure plant operations.

Why the weakening of the monopolies in the 1980s? Foreign producers were now threatening to enter local markets. Brazil offered to sell iron ore in Chicago and Cleveland, the heart of the U.S. market, at half the local price. Firms around the world offered to sell cement on the West Coast and Gulf of Mexico at half the U.S. prices.

How could foreign firms offer to sell at such discounts? There are two proximate reasons. First, transportation costs greatly decreased (relative to production costs) in the postwar period. This, by itself, would have meant a weakened incentive for continued investment in keeping monopoly. But, second, the production cost advantage of U.S. producers decreased. This development was, of course, to be expected, as the whole purpose of creating strong unions was to increase wages (and hence costs). The monopolies also led to lower productivity, increasing costs further. But what was striking is that U.S. producers were at a production cost disadvantage.

An obvious question is: Why did the unions (and other groups discussed later on) push wages so high and lower productivity to the point where foreign producers could offer such steep discounts? At least three possibilities come to mind. First, the groups realized that wage demands and work rules would lead to the demise of monopoly, but that this strategy was the best. Second, the groups realized that wage demands and work rules would lead to the possibility of foreign entry, but they expected more government protection than they were able to receive. Many calls for protection were made, and some protection was given, but it was not enough. Third, perhaps the outcome (foreign entry) was not expected. Although I do not know which story best describes the events, the story itself is not important for the issue at hand. The main point is that there are significant costs of monopoly and tariffs.

Monopolies can arise on their own, of course, without the help of government policy. Consider U.S. freight transportation by water in the 19th century. Well before any collective bargaining laws were enacted in the United States, strong unions developed in the port of New Orleans. Groups in transportation had a strong incentive to form monopolies in the port, since much freight went through that port. Many groups, such as warehouse owners, riverboat pilots and longshoremen, were thought to have formed strong monopolies. Evidence of strong monopolies is particularly clear for longshoremen.

The weakening of these monopolies in New Orleans resulted from the development of alternative transportation technologies. New technology—railroads—meant that the returns from these port monopolies were greatly diminished. Investments in sustaining the monopolies waned, and the monopolies were considerably weakened.

Monopoly: Splitting the Spoils (and Destroying Them as Well)

During the period when monopolies in these industries were strong, groups set up mechanisms to split rents. Here I discuss some of the mechanisms used and how they led to the destruction of rents—in particular, to low productivity and misallocation. When monopoly was weakened in these industries, the mechanisms were abandoned, leading to large productivity gains in establishments and to resources being reallocated from low- to high-productivity producers.

Mechanisms Limiting Competition
One mechanism used to split rents was competition-reducing rules. Here I discuss two types that were used: quotas and work rules.

Quotas
In the U.S. sugar industry, the New Deal cartel included factory owners, factory workers, farmers, farm workers and others. As the cartel was established, each of these groups sought to secure (for themselves) as large a share of rents as possible. A major mechanism to split rents was quotas. In the cartel, firms were given quotas—the right to sell a certain amount of sugar each year.

Incumbent farmers also sought, and were successful in acquiring, quotas—the right to grow sugar beet crops on a given number of acres each year. Without these quotas for incumbent farmers, nothing stopped firms from moving the locations of their factories or even using different farmers in the same location. Just as firms in the cartel used firm quotas to limit competition, incumbent farmers wanted quotas to limit competition among themselves (and from other farmers). Without these quotas, there was no way to ensure that incumbent farmers would receive a share of the monopoly profits.

As is often the case, these quota rights (both those of the firms and those of the farmers) could not be sold.⁴ Although the allocation of quotas for acres in 1934 was “efficient,” over time there was a change in the comparative advantage of locations in manufacturing sugar. Hence, there emerged a significant misallocation of resources between factories, with low-productivity factories producing too much sugar and high-productivity factories too little.

As the cartel started in 1934, some of the most profitable or productive (measured as revenue per acre divided by costs per acre) areas to make sugar were in California and Colorado. But after a few decades, these areas were no longer high-productivity areas. The opportunity cost of land in California and Colorado, and of the water used in making sugar in these areas, grew much faster than in other parts of the country, in particular, in Minnesota and North Dakota. By the 1960s, these latter states became the most productive areas in which to make sugar. However, given the mechanisms to split rents (i.e., the quotas), the industry could not increase production in these areas. Once the cartel ended in 1974 and the mechanisms to split rents were abandoned, the share of industry production in Minnesota and North Dakota grew rapidly (and declined rapidly in California and Colorado).

I can estimate the magnitude of the welfare loss due to these mechanisms to split rents (the quotas), that is, from the misallocation of resources within the industry. Recall the introduction to this paper, which mentioned that one way to measure welfare loss is to calculate the value of wasted inputs in producing industry output. In the 1960s, the industry was using land in California, which had high value (or opportunity cost), rather than land in North Dakota, which had much lower opportunity cost. The difference in opportunity costs is a measure of the wasted land input. Not only were the opportunity costs of land much lower in North Dakota, but the opportunity costs of many other inputs—for example, labor and the water used in growing sugar beets—were much lower as well. To calculate the welfare loss in, say, 1965, I imagine “moving” some of the quota allocation from California to North Dakota (keeping industry output fixed) and then calculating the value of the inputs that were wasted by producing in California. At this point, it is easier to estimate the value of the wasted inputs relative to industry profits (rather than relative to value added). The estimates indicate that the losses were roughly 20 percent to 30 percent of industry profits.

Work Rules
In the iron ore and cement industries, those who were in a position to gain from the large transportation costs into local markets, and the protection offered by tariffs, were the factory owners, factory workers and even the local governments (e.g., townships) where factories were located. What mechanisms were used to acquire rents? Local townships placed significant taxes on the production of iron ore and cement. Workers formed very strong unions. Although claims of collusion within both industries have been made, these claims are harder to document than the taxes and union contracts that emerged in these industries.

A major mechanism to split rents was the work rules in union contracts. Among other things, work rules were a way to limit competition among workers. They were structured so that managers could not play workers off each other. Let me briefly discuss these features of work rules and their consequences.

Union contracts split the tasks in plants into groups or categories. Workers were then assigned to one of these groups or categories, that is, given the right to complete tasks in that category. Only the workers in this group could complete the tasks assigned to the group. Very often these distinctions among workers were arbitrary in that a worker in a particular category was able, but not allowed, to complete tasks in many other categories.

Consider an important example. Machine operators were given tasks and repair workers other tasks. Machine operators were prohibited from assisting repair workers in their assigned tasks, even mundane tasks that required no repair expertise, such as getting supplies, holding tools and so on.

In addition to a sharp distinction being made between the tasks of machine operators and the tasks of repair workers, there was also a sharp distinction among tasks assigned to repair workers. Repair workers were grouped into many classifications, as many as 30 in a plant.

These types of work rules dividing work among members of the union are most often called job classification systems. They are similar to the quotas discussed earlier. In particular, work rules are a way to limit competition between workers, just as quotas limited competition between farmers. They ensure that groups of workers receive a share of the monopoly profits. But they also destroy profit, as I now discuss.

What are the negative consequences of such rules? These work rules in the iron ore and cement industries lead not only to overstaffing, but also to idle machinery. When, for example, the Finish Grind Department is down, workers from other departments are not allowed to help restore its machines to operation. Hence, machines are down longer than necessary, and capital productivity suffers. But clearly, energy productivity suffers as well. Fuel is being burned in other parts of the plant, and electricity used, even as the disabled machines are idle and output is not produced. As a result, such rules lead to low total factor productivity.

What is the quantitative significance of work rules? In the 1980s, when the work rules in the iron ore and cement industries were made much less stringent, labor productivity doubled in a few years. Other productivities increased as well. If these increases in productivity can be tied in large part to the relaxing of work rules, then obviously these are big welfare gains. The iron ore and cement manufacturing papers cited earlier argue that most of the productivity gains were due to the relaxing of work rules. Proving such a claim is difficult, but the papers, by looking at both direct and indirect evidence, have marshaled much evidence that this was indeed true.

We can estimate the magnitude of this welfare loss due to these mechanisms to split rents (the work rules), that is, from the low productivity in establishments. Again, one way to measure the welfare loss is by the value of the wasted inputs. With these work rules, machines were down longer than necessary. The energy that was being consumed elsewhere in the plant when output was not produced was a wasted input. The value of this wasted energy was its opportunity cost per unit multiplied by its quantity. The opportunity cost was its price per unit.

Next consider labor. With these work rules, labor input was wasted. For example, a machine operator could not hold a tool for a repair person (who would need to bring in another repair person for such tasks). The value of this wasted input was the opportunity cost of the machine worker’s time multiplied by the amount of time involved.

How much time was wasted in these plants? And what was the opportunity cost of this time? When work rules were changed in these industries, labor productivity doubled in a few years. Half of the workers were able to produce the same output. A rough estimate then suggests a dead-weight-loss-to-industry-value-added ratio of 16 percent to 17 percent. (See Schmitz 2012, pp. 14-15, for details of this calculation.)

In addition to energy and labor, capital was also wasted, as work rules meant that disabled machinery took longer to repair than was necessary. In considering estimating the welfare loss due to wasted energy and wasted capital, I note two things. First, energy productivity and capital productivity both increased significantly with the loosening of work rules, but not to the extent that labor productivity increased. So, not as much was wasted. Second, the price paid for these inputs was likely close to its opportunity cost. Using a dead-weight loss for the wasted capital and energy of a few percentage points (possibly more) of value added, together with the wasted labor estimate of 16 percent to 17 percent of value added, gives an estimate of over 20 percent in total.⁵

As just discussed, work rules in iron ore and cement manufacturing led to low productivity in establishments. But work rules can also lead to misallocation within industry. I will go on to briefly discuss some other industries, arguing that work rules likely led to the same type of misallocation as quotas did in sugar—with low-productivity plants producing too much output and high-productivity plants too little.

The historical studies have shown that monopoly and tariffs can lead to welfare losses within industry on the order of 20 percent. But what about the losses for the entire economy? How many other industries, and of what size, have incurred such losses because of monopoly? I briefly discuss these questions below.

Side Payments Between Groups
Rules to reduce competition (such as quotas and work rules) were an indirect means to split rents between groups. Direct means were also used, whereby some factories would send money to other factories. This was done, for example, in the sugar cartel. These side payments in sugar manufacturing were not lump sum, but involved mechanisms that led to distortions. In particular, the side payments exacerbated the misallocation problem discussed earlier (of having production in California and not North Dakota).

Splitting the Spoils: Other Industries, Other Countries and a U.S. Cost Estimate

In this section, I argue that the mechanisms to split rents just discussed are prevalent throughout industry. Moreover, evidence suggests that these mechanisms have had negative consequences in other industries (and other countries) as well. However, I cannot be sure of their quantitative significance because no studies like those described in the preceding section have been completed for these industries.

Many U.S. industries had significant market power after World War II, first by virtue of the devastation that many countries faced as a result of the war and later because of government protection of U.S. manufacturing. Monopolies emerged; in particular, the postwar years saw the emergence of industrywide unions in the auto, steel, paper, tire, airplane and chemical industries, to name a few.

What mechanisms were used to split rents? The job classification systems discussed earlier are prevalent throughout manufacturing (though for the most part are less stringent today than a few decades ago). Some observers of these industries hold the view that work rules led to low productivity in plants.⁶

Just as they did in the cement and iron ore industries, stringent work rules likely led to low productivity in establishments in many manufacturing industries. In some, stringent work rules led to other types of distortions and losses (which were not seen in the cement and iron ore industries). First, as I suggested earlier, work rules in these industries likely led to misallocation—resources being transferred from high- to low-productivity plants. Second, high wages (and stringent work rules) have likely led to another type of misallocation in industries: a change of technology (in order to escape the wages and work rules).

A similar phenomenon—that is, monopolists splitting (and destroying) rents—occurs in other countries. In Britain, job classification systems (referred to as “job demarcation rules”) are widespread. Demarcation rules are also used in France. In both countries, research suggests that these rules lead to reduced productivity.

I finish this section with a back-of-the-envelope estimate for the within-industry costs of monopoly and tariffs for the United States. This will enable a preliminary stab at the question, are these welfare losses similar in magnitude to Harberger’s losses (0.1 percent of value added), or can I conclude that they may well be significantly larger?

Industries that are known to have strong unions and rigid work rules include mining, utilities, construction, transportation (in particular, airlines and railroads) and parts of manufacturing, in particular, durable manufacturing (steel, airplanes, autos). Assume that work rules had similar negative impacts on productivity in those industries as they did on the industries discussed in detail earlier—again, about 20 percent of industry value added.

Adding together the total value added of these industries thus affected (just over 25 percent of total gross domestic product in 1977) enables an estimate of welfare losses from monopolies and tariffs of roughly 5 percent of GDP (=20 percent loss of 25 percent GDP share). (Further calculation details are in Schmitz 2012.) Again, this calculation is obviously extremely crude, but it does suggest that the losses may well be orders of magnitude larger than Harberger’s estimated losses.⁷  

Costs of Monopoly: Summary and Observations

Research on the theoretical and quantitative significance of monopoly costs has evolved considerably since the mid-1950s, when Harberger’s (1954) influential paper suggested—in contrast to the prevailing view among economists—that in the United States, the costs of monopoly resulting from resource misallocation across industries were actually quite insignificant. This view soon became the dominant consensus among economists. Subsequent research in the 1960s and 1970s sought to establish a convincing counterargument, but was unsuccessful in overturning the prevailing concept of negligible costs.

This paper reviews a new stream of research that uses a different approach to analyzing the costs of monopoly. It examines the costs of monopoly and tariffs within industries rather than across them. In particular, it examines the histories of industries in which a monopoly is destroyed (or tariffs greatly reduced) and the industry transitions quickly from monopoly to competition.

Over considerable time spans and a wide range of industries, this research finds that monopoly exacts high costs in two ways: (1) through misallocation of economic resources between high- and low-productivity factories and (2) by decreased productivity at each factory. The historical studies call the Harberger consensus into question. At least in the industries studied thus far, monopoly and tariffs have led to significant welfare losses, on the order of 20 percent of value added.

A common thread runs through these histories, one that suggests a theory. When a monopoly is created, rents are generated. But the distribution of these rents—splitting the spoils—causes conflict among shareholders, managers and employees of the monopoly. These parties devise mechanisms to split the spoils, but the mechanisms often lead, paradoxically, to the destruction of rents.

The implications of this theory of monopoly costs, and of the empirical findings of high costs, deserve serious consideration by policymakers as they design and enforce antitrust measures. As described earlier in this paper, government policies themselves, such as tariffs and other forms of protection, are an important source of monopoly. This review of recent economic research indicates that the costs of such protectionist policies are considerable and should be fully recognized and appreciated. Furthermore, policy reforms to minimize these costs should be carefully considered.

As for future economic research, a key question is to understand why mechanisms (such as work rules) are used to split rents when they also self-destructively wipe out rents. Why can’t members of the monopoly structure contracts that avoid such large wasted resources? Differences in information? The inability of parties to commit to future actions? Such reasons may well be why mechanisms intended to split rents also destroy them.

Endnotes

1 The author thanks Tasso Adamopoulos, John Asker, Doug Clement, Tom Holmes, Pete Klenow, Sam Kortum, David Lagakos, Justin Pierce, Todd Schoellman and Arilton Teixeira. The views expressed herein are those of the author and not necessarily those of the Federal Reserve Bank of Minneapolis or the Federal Reserve System.

2 In this usage, “rent” is the difference between what a factor of production is actually paid and what it would need to be paid to remain in use; as such, it is a measure of that factor’s monopoly power.

3 Transport costs were large (relative to production costs) in both industries. Also, the iron ore industry received government protection because the U.S. steel industry did. In the cement industry, the U.S. government at various times ruled that foreign producers were dumping in local markets.

4 Why the limit on selling quotas? When the cartel was being proposed, there were, obviously, complaints from farmers in sugar beet and sugar cane areas who did not grow these crops before 1934 (and hence were being left out of the cartel). A reasonable conclusion is that the cartel limited the quota rights so as to limit these complaints (thereby increasing the likelihood that the cartel would be acceptable).

5 In the calculation of the welfare loss due to the wasted labor input, I made no imputation for the value of leisure that might have been enjoyed at the plants (under the work rule regime). For example, perhaps the machine operator prefers standing next to the repair person not holding tools rather than helping out and holding tools. But this brings up other issues: What happens in a general equilibrium model if a large fraction of the population is standing around at work? We are looking at these issues elsewhere.

6 See, for example, Hoerr (1988), who discusses the role of work rules in the U.S. steel industry collapse of the 1980s, and Simberg (2008), who discusses the recent auto industry crisis, lamenting the focus on high wages and not, in his view, the real culprit: work rules and low productivity.

7 Although work rules in the industries discussed earlier have weakened considerably since the 1970s, in other areas, such as education, stringent work rules have grown. Moreover, occupational licensing has grown dramatically in importance, and its impacts on welfare may be important (see Kleiner and Krueger, forthcoming).

References

Bridgman, Benjamin, Shi Qi, and James A. Schmitz, Jr. 2009. “The Economic Performance of Cartels: Evidence from the New Deal U.S. Sugar Manufacturing Cartel, 1934–74.” Research Department Staff Report 437. Federal Reserve Bank of Minneapolis.

Bridgman, Benjamin, Shi Qi, and James A. Schmitz, Jr. 2012. “A Cost of Monopoly: Misallocation of Resources from High to Low Productivity Plants.” Manuscript.

Dunne, Timothy, Shawn Klimek, and James A. Schmitz, Jr. 2010. “Does Competition Spur Productivity? Evidence from the Post WWII U.S. Cement Industry.” Working Paper.

Galdón-Sánchez, José E., and James A. Schmitz, Jr. 2002. “Competitive Pressure and Labor Productivity: World Iron-Ore Markets in the 1980s.” American Economic Review 92(4): 1222–35.

Harberger, Arnold C. 1954. “Monopoly and Resource Allocation.” American Economic Review 44(2): 77–87.

Hoerr, John P. 1988. And the Wolf Finally Came: The Decline of the American Steel Industry. Pittsburgh: University of Pittsburgh Press.

Holmes, Thomas J., and James A. Schmitz, Jr. 1995. “Resistance to New Technology and Trade Between Areas.” Federal Reserve Bank of Minneapolis Quarterly Review 19(1): 2–17.

Holmes, Thomas J., and James A. Schmitz, Jr. 2001. “Competition at Work: Railroads vs. Monopoly in the U.S. Shipping Industry.” Federal Reserve Bank of Minneapolis Quarterly Review 25(2): 3–29.

Kleiner, Morris M., and Alan B. Krueger. Forthcoming. “Analyzing the Extent and Influence of Occupational Licensing on the Labor Market.” Journal of Labor Economics.

Meltzer, Bernard D. 1963. “Labor Unions, Collective Bargaining, and the Antitrust Laws.” Journal of Law and Economics 6(Oct.): 152–223.

Schmitz, James A., Jr. 2005. “What Determines Productivity? Lessons from the Dramatic Recovery of the U.S. and Canadian Iron Ore Industries Following Their Early 1980s Crisis.” Journal of Political Economy 113(3): 582–625.

Schmitz, James A., Jr. 2012. “New and Larger Costs of Monopoly and Tariffs.” Research Department Staff Report 468. Federal Reserve Bank of Minneapolis.

Simberg, Rand. 2008. “Detroit’s Downturn: It’s the Productivity, Stupid.” PJ Media.

Winter, Ralph K., Jr. 1963. “Collective Bargaining and Competition: The Application of Antitrust Standards to Union Activities.” Faculty Scholarship Series. Paper 2176.

This is the only web resource of its kind to collect data on oil production, employment, housing, banking and other key business indicators. Data will be updated regularly. In addition, the site will offer other reports and analyses on the rapidly growing region.

The data on the website offer comparisons between the counties that lie within the Bakken oilfield and the rest of North Dakota and Montana. For example:

• Employment levels are up about 50 percent in the Bakken and are mostly flat in the rest of Montana and North Dakota.
• Average weekly wages are up around 25 percent in the Bakken since 2009; wage growth is up 1.9 percent from third quarter 2009 to third quarter 2011 in the rest of Montana and 4.9 percent in the rest of North Dakota.
• New business establishments have grown by nearly a third in the Bakken, while decreasing slightly in the rest of Montana and North Dakota.

The Federal Reserve Bank of Minneapolis monitors business and economic conditions throughout its Ninth District, which encompasses Montana, North and South Dakota, Minnesota, northwestern Wisconsin and the Upper Peninsula of Michigan. Data, reports and articles on these states and on the many industries throughout the district can be found on the Bank’s website.

Beginning in late 2009, the Greek government had difficulties selling its bonds to private investors, who demanded high interest rates. In May 2010, the European Union (EU) and the International Monetary Fund (IMF) approved a 110 billion euro loan package to the Greek government in return for promises of spending cuts to sharply reduce the Greek public deficit. The plan, negotiated by German Chancellor Angela Merkel and Greek Prime Minister George Papandreou, was intended to cover the borrowing needs of the Greek government through 2013. In spite of this rescue package and another, 130 billion euro, package put together between July 2011 and March 2012, the debt crisis in Greece continues into 2012.

Ireland and Portugal have required similar EU-IMF rescue packages. Cyprus, Italy and Spain have had difficulties selling their bonds. Similar difficulties threaten other members of the European Economic and Monetary Union (EMU)—the countries in the EU that use the euro as their currency, also referred to as the eurozone—like Belgium and France.

In fact, as of April 2012, of the 17 members of the eurozone, only four—Finland, Germany, Luxembourg and the Netherlands—have long-term government bonds with the highest Standard & Poor’s rating, AAA, while the bonds of five countries—Cyprus, Ireland, Italy, Portugal and Spain—have junk ratings, BBB+ or lower. Greek bonds were given the lowest possible rating, CCC, in July 2011, and are currently not rated, but are listed as SD, meaning that the Greek government has selectively defaulted on some issues.

The countries that have suffered debt crises, or are threatened by such crises, got into trouble in different ways. The two crucial common characteristics are that each of these countries is currently experiencing a deep and prolonged recession and each needs to frequently sell large quantities of bonds, either to finance large fiscal deficits or to roll over—and make interest payments on—a large public debt.

We sketch out a theory for analyzing the European sovereign debt crises based on the research of Harold Cole and Timothy Kehoe (1996, 2000) and Juan Carlos Conesa and Kehoe (2012). In this theory, the need to frequently sell large quantities of bonds leaves a country vulnerable to a financial crisis. This vulnerability gives the government the incentive to pay down its debt to a level where such a crisis is not possible. In the event of a deep and prolonged recession, however, the government has a conflicting incentive to “gamble for redemption”—to borrow to smooth government spending, to reduce the debt if the economy recovers and, possibly, to default if the recession continues for too long.

Using this theory, we analyze the various rescue packages and policy interventions made by the EU and the IMF. Policies that result in high interest rates on government bonds and high costs of default provide incentives for a government to reduce its debt. Policies that result in low interest rates and low costs of default provide incentives for a government to gamble for redemption. We conclude that, up until now, policy interventions by the EU and the IMF have encouraged eurozone governments to gamble for redemption. In the theory we present, a government that gambles for redemption is following a policy that is optimal for the citizens of its country. The policy goals of the EU and the IMF may be different from those of the government of an individual country, however, and, to the extent that the EU and the IMF want the government to reduce its debt to avoid a crisis to preserve the stability of the EU, they should adopt policies to discourage the government from gambling for redemption.

Timeline and some data

The Treaty on European Union—signed in Maastricht, Netherlands, on Feb. 7, 1992, and commonly referred to as the Maastricht Treaty—converted the European Community, which then had 12 members, into the European Union. The treaty established four “convergence criteria” as prerequisites for membership in the EMU. One criterion required a country to have an annual public deficit no greater than 3 percent of GDP and a public debt no greater than 60 percent of GDP. Another criterion required the country to participate in the European Exchange Rate Mechanism (ERM)—set up as a voluntary program in 1979—to maintain its exchange rate in a very narrow band around the European Currency Unit (ECU), which eventually became the euro. The other two criteria imposed restrictions on inflation rates and interest rates.

In the process of ratifying the Maastricht Treaty, Denmark and the United Kingdom obtained opt-out clauses from joining the monetary union. All 15 countries that have joined the EU since 1992 were required to join the monetary union. The ERM suffered a major crisis 1992, with a number of countries forced to drop out, and—when the crisis threatened more countries in 1993—the exchange rates bands were widened considerably. The mechanism was restarted in 1999 and is now referred to as ERM II.

Sweden, which joined the EU in 1995, has managed to exploit a legal loophole to avoid adopting the euro: Its accession treaty required Sweden to join the monetary union after meeting the convergence criteria and participating in the ERM II for two years, but it did not explicitly require Sweden to join the ERM II, and it has not done so. The other seven countries in the EU that are not yet in the eurozone are required to go through the process of participating in ERM II and eventually joining the eurozone.

The accompanying table provides a timeline for the major events related to the sovereign debt crises that are ongoing in the eurozone.

Timeline of Events Associated with Eurozone Debt Crises, 2010–2012

European leaders had seen the need to coordinate fiscal policy in a monetary union. In 1997, at the insistence of Germany, they adopted the Stability and Growth Pact (SGP), which imposed financial penalties on countries that violated the convergence criterion that the public deficit not exceed 3 percent of GDP. Nonetheless, when the French and German governments announced that they had violated this deficit limit in 2003, they were not penalized, reducing the credibility of the SGP.

The details differ on how various countries became vulnerable to sovereign debt crises: The Greek government borrowed heavily during Greece’s rapid growth early in the 2000s and employed fraudulent accounting practices to mask its large violations of the 3-percent-deficit rule. The Irish government was in very good fiscal shape until it guaranteed the debts of the six major private banks based in Ireland in 2008. When the housing market collapsed in late 2008 and 2009, leading to the collapse of these banks, the Irish public deficit and debt soared. Portugal took advantage of the low interest rates at which it could borrow after joining the eurozone to build up a large public debt. When Spain was hit by recession in 2008, the government reacted with massive Keynesian stimulus policies, running large public deficits. Although the Spanish public debt started from a very low level, it increased rapidly because of these deficits.

The problems faced by the Italian government stem from conditions very different from those in Spain. Although Italy had low public deficits, it had started with a very high level of debt. Indeed, Belgium and Italy had entered the EMU with public deficits that substantially exceeded the 60-percent-debt rule only because they were deemed to be on a “satisfactory pace” to reducing the public debt to 60 percent of GDP. Yet, in the case of Italy, much of this satisfactory pace was illusory, the product of creative accounting of exactly what constituted government expenditures and government revenues. France employed similar sorts of creative accounting.²

In spite of these differences in initial conditions, Greece, Ireland, Italy, Portugal and Spain (GIIPS) share two crucial characteristics: First, as the data in Figure 1 show, the recoveries from the 2008–2009 recessions in these countries have been nonexistent. Notice that, in Figure 1, the German economy has started to recover in 2010 and 2011, if only weakly, while the GIIPS are still mired in recession. Second, as the data in Figure 2 show, the GIIPS have large borrowing requirements because of high deficits or large debts or both.

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Self-fulfilling debt crises

The need to frequently sell large quantities of bonds leaves the countries vulnerable to self-fulfilling debt crises of the sort analyzed by Cole and Kehoe (1996, 2000) and Conesa and Kehoe (2012). In such a crisis, if investors expect a government to have trouble repaying its debt, they pay a low price at auctions of new government bonds. The resulting low value of the new bond sales makes it difficult for the government to repay the old bonds becoming due, thus justifying the expectation of a crisis. If, however, investors do not expect the government to have trouble repaying its debt, they are willing to pay a high price for new bonds. This expectation too is self-fulfilling.

To understand the reasoning in the model, we start by examining two crucial relations: the government budget constraint—which relates sales of new bonds and payments on old bonds to government expenditures and tax receipts—and the relation between the price that investors pay for bonds and the probability of a sovereign default. We then explain how the government determines its optimal policy and how financial crises can occur.

Government budget constraint
In every time period—which can be a year or longer or shorter, depending on the application—the government’s budget constraint requires that the total of resources collected in the form of tax revenues and receipts from new bond sales be equal to the total of resources spent in the form of government expenditures and transfer payments and coupon payments on outstanding bonds and payments of face value on bonds reaching maturity. To keep things simple, we lump transfer payments into expenditures and coupon payments on outstanding bonds into payments of face value on bonds reaching maturity. We can write the government’s budget constraint as

tax revenues + receipts from new bond sales
= expenditures + payments on bonds becoming due.

Notice that this constraint does not require the government to balance its budget in the traditional sense because it does not require tax revenues to equal expenditures. The difference between expenditures and tax revenues is referred to as the government’s primary deficit. We can rewrite the government’s budget constraint as

primary deficit = expenditures − tax revenues
= receipts from new bond sales − payments on bonds becoming due.

To analyze the evolution of the sovereign debt crises in the eurozone, however, it is more useful to write the government’s budget constraint in a different way as

net borrowing = face value of new bond sales − payments on bonds becoming due
= primary deficit + face value of new bond sales − receipts from new bond sales.

The payments on bonds becoming due are equal to their face value unless the government defaults. Consequently, unless the government defaults, the face value of new bond sales minus the payments on bonds becoming due is equal to the net government borrowing depicted in Figure 2. Because investors discount the future, they pay a price for the bond less than the face value of the bond when it matures. If investors pay a very low price for new bonds, then receipts from new bond sales fall far short of the face value of the bonds, and net borrowing is substantially greater than the primary deficit. We focus on net borrowing because it measures the increase in government debt.

Pricing bonds
Consider an investor whose holdings of the sovereign bonds of a particular country constitute a small fraction of his or her total portfolio. At a bond auction, we assume that this investor is willing to bid a price equal to the expected present discounted value of the return on the bond.³

First consider the case where the investor is sure that the government will not default. Suppose that the government promises to pay back one euro in one year and that the consumer discounts future returns at 2 percent per year. Then the price of the bond is the present discounted value of one euro next year, which is a little more than 98 euro cents,

1/1.02 = 0.98039.

This number is also referred to as the discount factor. The investor multiplies any promised return in one year by the discount factor to obtain its present discounted value. For example, the investor is willing to pay 980.39 euros for a bond paying 1000 euros next year. Another way to think about this is that, if 980.39 euros are invested at 2 percent, then after one year they are worth 1000 euros. For a bond with a longer maturity, the investor multiplies the discount factor by itself N times, where N is the number of years of maturity. For a bond paying 1000 euros in 10 years, for example, the investor would be willing to pay 820.35 euros,

820.35 = (0.98039)¹⁰ x 1000.

Now consider the case where there is a positive probability of default. The difference between the return if there is no default and the return if there is default is referred to as the haircut. Suppose that the probability of a default is 50 percent and that the haircut is 50 percent, which implies that the payoff if there is default is 50 percent. Then the expected return on the bond with a face value of 1000 euros is 750 euros,

expected return = probability of no default × payment if no default
+ probability of default × payment if default
750 = 0.5 x 1000 + 0.5 x 500.

The price of the bond is the expected present discounted value of the return, which in this case is 735.29 euros,

price = discount factor × expected return
735.29 = 0.98039 x 750.

The yield—which is the implicit interest rate on the bond—is 36 percent,

 0.36 = (1 / 0.73529) - 1

because, if 735.29 euros are invested at 36 percent, after one year they are worth 1000 euros. The spread—the difference between the yield and that on a safe bond—is 34 percentage points.

Our example illustrates how an increase in the probability that investors assign to a default results in an increase in the yield on the bond. Figure 3 depicts the yields on government bonds in the GIIPS compared with those in Germany. Notice how spreads on GIIPS bonds increased starting in 2008.

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An attractive feature of our numerical example is that it provides a rationalization for the yields in Figure 3. In the fourth quarter of 2011, for example, German bonds, which can be thought of as a safe investment, had a yield of about 2 percent, while Greek bonds had a yield of about 36 percent, which is the yield if the probability of default is 50 percent and the expected haircut is 50 percent. Of course, there are other combinations of probabilities of default and haircuts that result in this same 36 percent yield.

Optimal government policy and crises
In every time period in the model, the government must decide how much new debt to sell and whether or not to default. We assume that the government is benevolent, in that it values the welfare of consumers, that is, the citizens of the country, who value both private consumption and government expenditures. We also assume that consumers—and consequently the government—value smooth paths of private consumption and government expenditures. Sharp cuts in government expenditures are particularly painful. Defaults are also costly in that they disrupt financial markets, which causes a drop in the GDP of, say, 5 percent—which we refer to as the default penalty—that is available for government expenditures, private consumption and repayment of debt. These assumptions are intuitively appealing and fairly innocuous.

We make a number of other assumptions that are more restrictive to keep the analysis simple. We assume, for example, that tax revenues are a constant fraction of GDP because tax rates are fixed. We also assume that the default penalty is permanent and that, if the government defaults, it is permanently excluded from borrowing. Cole and Kehoe (1996, 2000) model consumers within a country as making private investment decisions, but here—again to keep things simple—we follow Conesa and Kehoe (2012) in having consumers consume all after-tax GDP rather than investing some of it. These assumptions can be relaxed without changing the qualitative results of the model. How much quantitative results change depends on the parameterization, of course, and this is a topic that deserves future research.

A financial crisis is self-fulfilling if the expectation that the government will default causes it to default in a situation where it would otherwise pay for the bonds becoming due. For low levels of debt, self-fulfilling crises are not possible. For higher levels of debt—those above a threshold that we call the upper safe debt limit—self-fulfilling crises are possible. For even higher levels of debt—those above a threshold that we call the upper sustainable debt limit—the government prefers to default rather than pay for the bonds becoming due.

The timing within a period is such that investors decide what price to bid in the auction for new government bonds before the government decides whether or not to default on the old bonds becoming due. Suppose that, before the auction, investors receive some sort of bad news that makes them expect the government to default this period. Under what conditions will this expectation be self-fulfilling? The investors expect that the government will be in default the subsequent period because it is excluded from financial markets. The price that the investors offer for new bonds is the present discounted expected payment in the case of default, which is low or zero. The government can either default or pay for the bonds becoming due. For levels of debt equal to or below the upper safe debt limit, the government prefers to pay for the bonds becoming due and suffer the drop in government expenditures but avoid paying the cost of defaulting. For these low levels of debt, investors will pay a high price, equal to the present discounted face value for new bonds, no matter what the news is. If, however, debt is above the upper safe debt limit, a self-fulfilling crisis occurs if there is bad news. For high levels of debt, those above the upper sustainable debt limit, the government chooses to default even if investors buy the new bonds offered.

The probability that investors assign to receiving bad news in a period is arbitrary.⁴ At the beginning of a period, the bad news arrives or it does not. Notice that, in the bond auction in a period, if bad news had not arrived early in the period, then the bond price depends on the probability of receiving bad news in the next period.

Cole and Kehoe (1996, 2000) call the interval of debt levels above the upper safe limit but equal to or below the upper sustainable limit the crisis zone. If debt is in this zone, a self-fulfilling crisis can randomly occur. Since interest rates are high when the debt being sold is in the crisis zone and the probability of a costly default is positive, a government will optimally choose to run surpluses to run its debt down to the upper safe limit. Once debt reaches the upper safe limit, interest rates drop and the probability of default disappears. Since sharp cuts to government expenditures are painful, however, the government may choose to pay down the debt over a number of periods.

In a quantitative model calibrated to match features of European data, Conesa and Kehoe (2012) show that the upper safe limit is about 120 percent of GDP while the upper sustainable limit is about 210 percent of GDP. These numbers make sense in terms of the numbers currently used by policymakers in Europe, in particular, the need to reduce Greek debt below 120 percent of GDP to eliminate the possibilities of future crisis.⁵

Gambling for redemption

As we have just argued, financial crises and defaults on sovereign debt are costly for a country, and the government of a country that finds itself vulnerable to a self-fulfilling crisis has the incentive to pay down its public debt so that it does not need to frequently sell large quantities of bonds. As Conesa and Kehoe (2012) point out, however, countries that are in deep recessions have an opposite incentive: to cut government spending very slowly and increase the public debt, gambling that a recovery in the economy will lead to a recovery in tax revenues, at which point it can stop increasing the debt. If the country is unlucky and the recession is prolonged, however, the country can find itself more vulnerable to a self-fulfilling debt crisis and ultimately may be forced to default.

Conesa and Kehoe (2012) modify the Cole-Kehoe model so that the country finds itself in an unexpected recession, where GDP is, say, 10 percent lower than its otherwise constant level.⁶ This is meant to correspond to the situation in Europe in 2008. In every period there is a constant probability—say 0.2, that is, one in five—that the economy will recover. With this stochastic process, which is like flipping a biased coin with the probability of heads being the probability of recovery, the expected waiting time for a recovery is a number of periods equal to the reciprocal of the probability of recovery. If, for example, the probability of an economic recovery is 0.2 per year, then, at any time where a recovery has still not occurred, the expected waiting time for a recovery is 1 / 0.2 = 5 years.

To understand gambling for redemption, consider first the case where self-fulfilling debt crises are not possible because, for some reason, the probability of bad news is zero. Then, because it wants to smooth expenditures as much as possible, a government would optimally choose to borrow when it is in recession at a high bond price equal to the present discounted face value, planning to pay back when the economy recovers. Like a gambler at a roulette wheel who keeps doubling his bet, the government is gambling that the recession will not continue for too long. Unlike the gambler, the government is doing something beneficial while it is gambling. It is smoothing government expenditures, something that the citizens of its country value.

If the recession does go on, there are two possibilities for the equilibrium outcome, depending on the costs of default: If the costs of default are high, the government will borrow less and less each period until its debt converges to an upper limit above which investors know that the government would default. If the costs of default are lower, the government will optimally choose to default after a finite number of periods, borrowing in the period before default at a price equal to the present discounted expected value of the face value if there is a recovery in the next period and the payoff in default if there is no recovery. This is not a self-fulfilling crisis: Investors and the government correctly anticipate default if there is no recovery. The only uncertainty is whether the economy will recover or not.

Consider now the general case where self-fulfilling crises are possible but where the economy is also in a recession from which it might recover. The government faces conflicting incentives. Various outcomes are possible and reasonable, depending on the values of parameters. The government could optimally choose either to pay down its debt to the upper safe limit or to borrow still more, running up its debt, gambling for redemption. The optimal choice depends on the costs of default, the probability of a crisis and the probability of recovery from recession.

Cristina Arellano (2008) argues that defaults can also occur when GDP is low enough. In her model, countries borrow large amounts in booms because interest rates are low because debt is below the upper safe limit. When a recession hits, however, the same amount of debt may be above the new upper safe limit, and interest rates rise, making it costly to roll over the debt. For a sufficiently large drop in GDP, a level of debt that is safe if GDP is high can be above the upper sustainable limit if GDP is low, in which case the government now prefers to default.

Analyzing EU and IMF policy and extending the model

We can use our theory to evaluate the impact of policies followed by the EU and the IMF. Any policy that decreases the price that a country receives for its bonds (that is, increases the yields that it pays), or increases the costs of default, provides the government with incentives to reduce its debt to exit the crisis zone. In contrast, any policy that increases bond prices (lowers the yields), or lowers the costs of default, provides the government with incentives to gamble for redemption.

The rescue packages listed in the timeline stopped self-fulfilling crises in Greece, Ireland and Portugal. They also provided credit to countries at lower interest rates than the yields presented in Figure 3. These policies can be interpreted as encouraging gambling for redemption. It is worth pointing out, however, that the rescue packages also explicitly required austerity measures, even if these requirements were later violated, especially in the case of Greece.

One policy that very clearly encourages gambling for redemption is the European Central Bank’s Securities Market Program (SMP). The SMP buys bonds of countries whose bond prices fall too low. By propping up their bond prices and keeping yields low, the SMP reduces incentives to pay down the debt and escape the crisis zone. Similarly, the ECB’s policy of reducing its repo rate and relaxing collateral constraints to encourage banks to buy government bonds with high yields drives up the price of bonds and encourages gambling for redemption.

Another policy that may have encouraged gambling for redemption was the 50 percent haircut on Greek bonds planned at the European Summit in July 2011 to be imposed on private investors, principally private banks in the EU. By labeling the haircut voluntary, the EU intended to eliminate some costs of default, such as triggering credit default swaps (CDSs), securities that pay the buyer in the event of a default. EU leaders thought that triggering CDSs would be very disruptive to the financial system, both inside and outside Greece. Greece had already reached a debt level that it could not hope to repay, but planning “voluntary” haircuts on Greek bonds signaled other troubled governments that such a reduction in the costs of default might be available for them.

By March 2012, it was clear, however, that this sort of “voluntary” haircut was not feasible, mostly because courts would not rule out claims on CDSs. Greece ended up imposing a much larger haircut, negotiating with the majority of bond holders and enforcing the settlement on the rest of bond holders by appealing to CACs (collective action clauses). There are currently doubts about the legality of this move, however, because the CACs were inserted into the bond contracts retroactively. A challenge for Europe is how to best design restructuring procedures for countries that might follow Greece into default while minimizing adverse incentives for other countries.⁷

While our theory provides an appealing explanation of why the threat of sovereign debt crises in Europe has been going on for so long, it leaves open a couple of major questions. We can use our theory to understand the behavior of leaders of countries threatened by debt crisis, like George Papandreou in Greece, but it does not help us understand the behavior of EU leaders like Angela Merkel of Germany and Nicolas Sarkozy of France, who have struggled to provide rescue packages. It may that they too have been gambling for the redemption of the eurozone itself, rather than their national economies. Merkel and Sarkozy may have believed that the only thing that will pull the eurozone out of the danger of debt crises is a vigorous economic recovery from the recession, and they are just trying to hold the EMU together until that happens. It would be useful to develop a model of this.⁸

It is also clear that the institutional design of the EMU—in particular, the mechanisms to enforce fiscal discipline, like the Stability and Growth Pact—is inadequate. European leaders are currently struggling to come up with a better institutional design, and it would be worth developing a theory of the optimal design of the EMU.

A related question is why sovereign debt crises like those in Europe do not currently threaten countries like Japan, the United Kingdom and the United States. These countries, like those in the eurozone, have large public debts and have suffered from the recent recession. Thomas Sargent (2012) presents a provocative narrative arguing that a key difference in the United States is that the central government has the power to raise substantial resources through taxation, a power the EU lacks. Another crucial difference is that each of these countries, unlike the eurozone countries, has its own currency whose value can fluctuate freely in response to changing economic conditions. This too is worthy of further research.

Endnotes

1 We thank Tito Cordella, Isabel Correia, Patrick Kehoe, Narayana Kocherlakota, David Levine, Thomas Lubik, Fabrizio Perri, and Pedro Teles for helpful discussions. We also thank Jose Asturias, Wyatt Brooks, and Laura Sunder-Plasssmann for excellent research assistance. The data presented in the figures are available here. The views expressed herein are those of the authors and not necessarily those of the Federal Reserve Bank of Minneapolis or the Federal Reserve System.

2 See Paul De Grauwe (2009) for a discussion of the politics of violations of the Maastricht Accords.

3 We assume that the bond holdings are a small fraction of the portfolio because this implies that the investor behaves as if he or she were risk-neutral, justifying expected-present-discounted-value pricing. If the bond holdings are a large fraction of the portfolio and the investor is risk-averse, then he or she would pay less, to compensate for the riskiness of the bond. We ignore any liquidity services provided by bonds and alternative assets, which can complicate the pricing relation in minor ways.

4 Cole and Kehoe (1996, 2000) model this news shock as what economic theorists call a sunspot, a random variable that affects the equilibrium only through investors’ expectations. The value of bad news is arbitrary and can vary over time, which would account for fluctuations in the spreads in Figure 3. The arbitrary nature of exactly what constitutes bad news is how the model captures what finance ministers refer to when they complain about their country’s bonds being at the mercy of the financial markets.

5 Whether this gives us more confidence in the quantitative properties of the model or more confidence in European policymakers is an open question.

6 To keep things simple, we assume that GDP does not have a growth trend. If GDP is 100 before the recession, it falls to 90 during the recession. A recovery is a return to 100. If there is a default during the recession, GDP falls another 5 percent, to 85.5. A recovery now only increases GDP to 95. It is easy to convert the model to one in which the economy is growing at a constant rate and in which neither the qualitative results nor the quantitative results change. In a more complicated model, the shock could affect the growth trend. Mark Aguiar and Gita Gopinath (2006) argue that shocks to growth rates have stronger effects on default incentives than do changes in levels.

7 David Benjamin and Mark Wright (2009) and Pablo D’Erasmo (2011) provide a theory for renegotiation between a government and a representative of the bond holders. They argue that it is worth delaying restructuring until countries have low default risk and high output because those are times when mutually beneficial outcomes can be obtained more easily. Their results imply that renegotiation is particularly difficult now when many Eurozone countries are still deep in recession and where there is substantial uncertainty about the future.

8 Arellano and Yan Bai (2012) argue that a reason for a lender—and the EU itself has become a major lender to troubled countries though the European Financial Stability Facility (EFSF) and the ECB’s SMP and repurchase agreements—to be lenient with a subset of borrowers in default is to avoid other defaults from other borrowers.

References

Mark Aguiar and Gita Gopinath (2006), “Defaultable Debt, Interest Rates and the Current Account,” Journal of International Economics, 69, 64–83.

Cristina Arellano (2008), “Default Risk and Income Fluctuations in Emerging Economies,” American Economic Review, 98, 690–712.

Cristina Arellano and Yan Bai (2012), “Linkages across Sovereign Debt Markets,” Federal Reserve Bank of Minneapolis.

David Benjamin and Mark Wright (2009), “Recovery before Redemption: A Theory of Delays in Sovereign Debt Renegotiations,” Centre for Applied Macroeconomic Analysis, Working Paper 2009-15, Australian National University.

Harold L. Cole and Timothy J. Kehoe (1996), “A Self-Fulfilling Model of Mexico's 1994–95 Debt Crisis,” Journal of International Economics, 41, 309–330.

Harold L. Cole and Timothy J. Kehoe (2000), “Self-Fulfilling Debt Crises,” Review of Economic Studies, 67, 91–116.

Juan Carlos Conesa and Timothy J. Kehoe (2012), “Gambling for Redemption and Self-Fulfilling Debt Crises,” Federal Reserve Bank of Minneapolis Staff Report 465.

Pablo D’Erasmo (2011), “Government Reputation and Debt Repayment,” Working Paper, University of Maryland.

Paul De Grauwe (2009), “The Politics of the Maastricht Convergence Criteria,” VoxEU, 15 April 2009.

Thomas J. Sargent (2012), “United States then, Europe now,” Nobel Prize Lecture, New York University.

Banks are vulnerable to self-fulfilling panics because their liabilities (such as demand deposits and certificates of deposit) are short term and unconditional, and their assets (such as mortgages and business loans) are long term and illiquid. To prevent wider financial fallout from such panics, governments have strong incentive to bail out bank debtholders. Paradoxically, expectations of such bailouts can lead financial systems to rely excessively—from a societal perspective—on short-term debt to fund long-term assets. Fragile banking systems thus impose external costs, and regulation may therefore be socially desirable.

In light of this fragility and cost, we examine two of the major theoretical benefits from the reliance of the banking system on short-term debt: (1) maturity transformation and (2) efficient monitoring of bank managers. We argue that while both justifications may be compelling, they point us to financial regulations very different from the ones currently in place. These theoretical justifications suggest that the assets funded by banks should not have close substitutes in publicly traded markets, as is currently the case.

Introduction¹

The enormous direct and indirect costs of rescuing banks and related financial institutions during the financial crisis of 2007-08 generated widespread policy debate on future banking regulation, resulting in part in the Dodd-Frank Act of 2010. Largely absent from these discussions was a careful reexamination of the services that banks provide and whether they are sufficient justification for the inherent financial fragilities of banks and the societal costs of this fragility.  

The inherent fragility of banks

In what sense are banks and similar financial institutions fragile? Answering this question requires understanding the balance sheets of financial institutions more generally. The assets of financial institutions are, by and large, financial assets, and claims on them are primarily financial liabilities. They hold few tangible assets such as land, buildings or machinery. Their financial assets consist mainly of promises to deliver dollars at future dates (and perhaps then only under certain circumstances). Among such assets are stocks, bonds and the notes on mortgages and business and consumer loans, as well as an array of more exotic financial instruments such as derivatives. Likewise, their financial liabilities consist mostly of a variety of obligations to deliver dollars at particular dates, under certain circumstances.

Some types of financial institutions, particularly banks, have liabilities that are mostly short term and unconditional. For example, banks issue demand deposits, which promise to pay a fixed amount of money whenever a depositor demands a withdrawal. Likewise, banks issue certificates of deposit (CDs), which promise to pay a fixed amount of money at a particular (usually very near-term) date. Typically, banks rely on the rolling over of CDs, when they mature, into new CDs. In this sense, a failure to roll over a short-term CD can be thought of as equivalent to a withdrawal from a deposit account.

In Figure 1, we display the distribution of firms—financial and nonfinancial—by their ratio of short-term debt to total assets, for all publicly traded companies. This figure shows that financial firms typically have much higher levels of short-term debt relative to total assets than nonfinancial firms. For example, 80 percent of financial firms have a ratio of short-term debt to total assets greater than 40 percent, while only 10 percent of nonfinancial firms do (see vertical dashed line).

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In Figure 2, we display the distribution of firms by their ratio of short-term debt to short-term assets, again for all publicly traded financial and nonfinancial firms. Firms that have ratios greater than 1 are using short-term debt to finance holdings of long-term assets. The figure shows that nonfinancial firms are far less likely than financial firms to use short-term debt to finance their holdings of long-term assets—less than 20 percent of nonfinancial firms depend on short-term debt, while roughly 60 percent of financial firms do so.

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The bulk of these financial firms with high levels of short-term debt are legally structured as banks. From an economic standpoint, the financial firms we focus on in this policy paper are those with high levels of short-term debt, and for convenience we’ll refer to them all as banks.

This reliance on short-term debt makes banks fragile in the sense that they are particularly vulnerable to the risks of insolvency and the possibility of confidence crises. Since bank assets are usually much longer term than their liabilities and since the value of these assets fluctuates, a bank’s net worth (its assets less its liabilities) also fluctuates a great deal.

Furthermore, a bank’s assets are typically illiquid. A bank cannot easily and quickly sell its portfolio of small business loans, home equity loans or jumbo mortgages, for instance, to satisfy an unexpected surge of withdrawals from demand deposit accounts. The illiquidity of banks’ assets and the demandable structure of their liabilities thus expose banks to crises of confidence. Since a bank typically will not be able to meet the demands of all depositors within a short period of time should they all choose to withdraw, banks are vulnerable to self-fulfilling panics in which depositors withdraw their funds simply because they believe other depositors will do so.

This panic is an entirely rational response even if the bank is solvent (though illiquid). If a depositor suspects that other depositors will withdraw their funds in the near future, it is rational for that depositor to rush to the bank and withdraw his or her deposits before other depositors can do so. But since this logic holds for all depositors, banks are subject to self-fulfilling panics in which the belief in a run causes the run.

In the midst of such self-fulfilling panics, governments have a strong incentive to intervene to bail out debt holders of banks in order to prevent the entire financial system from failing. Paradoxically, expectations of such bailouts can increase the incidence and depth of financial crises because once depositors believe that their deposits will be protected by general tax revenues in the event of potential systemic failure, they have less incentive to monitor the risk-taking proclivities of bank managers.

Bank managers, in turn, have increased incentive to take on risk, knowing their failures are implicitly insured—taxpayers (not banks themselves) bear the full consequences of this risk-taking. Given that the banking system rationally chooses to take on risk, the possibility of insolvency of the banking system increases, and the need for bailouts rises.

In this way, expectations of bailouts can lead financial systems to rely excessively—from a societal perspective—on short-term debt to fund long-term assets. Fragile banking systems thus impose external costs, and regulation may therefore be socially desirable.

The benefits of banks

Why then do we allow banks—financial institutions that fund long-term assets with short-term debt—to operate? One answer is historical: The very ubiquity of banks throughout history suggests that they serve a valuable function. But the world has much changed since the invention of banks. In particular, the range of economic activities that can be funded through publicly traded assets has expanded enormously. For instance, in the not-too-distant past, publicly traded mortgage-backed securities did not exist. In light of these changing circumstances, it is worth examining how long-term assets should be funded and the role banks should play in funding such assets.

Given that we have already discussed the weaknesses or costs of such financial institutions, we’ll now use economic theory to examine the possible social benefits of having a financial system in which illiquid assets with volatile values are funded by demand deposits and short-term debt. This cost-benefit analysis allows us to ask how modern economic theory can be used to design better regulatory systems for banks. This issue is clearly of pressing importance given the central role that banks are argued to have played during the recent financial crisis. This issue is also of central importance given the importance of banks to financial systems throughout the world.

We examine the role of banks and the role of regulation by considering, in turn, two of the major theoretical justifications for the reliance of the banking system on short-term debt:

• Demand deposits allow banks to engage in socially useful maturity transformation.
• Demand deposits allow for efficient monitoring of bank managers.

We argue that while both justifications may be compelling, they point us to financial regulations very different from the ones currently in place. Specifically, they suggest that while it may be important to have institutions that finance long-term assets with short-term debt, the assets that are so funded should not have close substitutes in publicly traded markets.

Our logic is as follows: The economic case for regulating such institutions at all is due to the external costs they impose. But any such regulation should also, to the extent possible, preserve the benefits that economic theory suggests they convey. We argue that these benefits exist only when these institutions hold assets which do not have close substitutes that are traded in public markets. Indeed, our analysis suggests that bank assets having close publicly traded substitutes destroys the possible social benefit of banks under the maturity transformation view and reveals the social benefit to be zero (or small) under the efficient monitoring view. Thus, institutions that issue short-term debt should be allowed to hold only a limited amount of publicly traded assets.

Theoretical justifications of banking

We now provide closer consideration of the two primary theoretical justifications for a banking system that relies on short-term debt. We first describe and then assess the maturity transformation rationale, and then we turn to the notion that demand deposits permit efficient monitoring of bank managers, again starting with a description followed by our assessment.

Maturity transformation
Banks are often said to perform a miracle of financial alchemy referred to as maturity transformation. They are thought to hold long-term assets that yield a high rate of return and finance these assets with short-term claims (such as deposits) that pay a higher rate of return than what those depositors could earn if they invested directly in short-term assets (such as very short-term bonds).

Such a form of financial alchemy implies that those who roll over their short-term claims until the maturity of the long-term asset will necessarily receive a lower rate of return than if they had directly held the long-term asset. In effect, maturity transformation is a redistribution of resources from those who roll over their short-term claims to those who redeem such short-term claims early. Why would this financial alchemy be socially desirable?

Diamond and Dybvig (1983) developed a model in which such maturity transformation is desirable and possible. Furthermore, they showed that the arrangement that delivers such maturity transformation resembles banks in the sense that (at least conceptually) households get access to their funds whenever they want to, so that the liabilities of banks resemble demand deposits, while the assets of banks are long term and illiquid. (See Appendix A-1 for a simplified technical description of the model.) The basic idea in their model is that the redistribution of resources associated with maturity transformation is desirable because such redistribution allows for insurance. If people are not sure whether they will need resources only at the maturity of long-term assets or at a date prior to maturity, they may wish to insure themselves by accepting a lower return at maturity in exchange for a higher return should they instead desire their funds sooner.

An assessment of the maturity transformation view
While Diamond and Dybvig’s model provides important insights into the possibility of maturity transformation, the mechanism rests on an increasingly implausible feature of the model: nonbank financial institutions do not also hold assets such as the mortgages and mortgage-backed securities typically held by banks as part of their transformation role [a point originally made by Jacklin (1987)]. Put simply, if it is possible for households to hold the long-term assets of banks outside the banking system, the ability of banks to perform maturity transformation is destroyed, along with the social benefit from doing so. (For technical discussion of this point, see Appendix A-2.)

As we show in Appendix A-2, if people can hold long-maturity assets outside the banking system, they have strong incentives to do so. If they happen to not need funds prior to maturity, they can obtain higher returns than under maturity transformation since they are not subsidizing those who need their funds sooner. If they happen to need their funds sooner, they can simply sell their holdings of these long-term assets to customers of the banking system who do not need their funds until maturity. Further, these customers will be willing to sell to them. Recall that such bank customers were promised relatively low returns for funds held long term inside the banking system and relatively high returns if they needed their funds earlier. But for these customers, the event they were insuring against—that they would need their funds early—did not occur. After they know this, they have strong incentives to trade with those outside the banking system in order to achieve higher returns. Analysis of the Diamond-Dybvig model under this alternative assumption—that the assets typically held by banks are also traded in public markets—demonstrates that banks cannot provide the social benefit associated with maturity transformation.

Recent data on holdings of U.S. financial institutions show that, indeed, market trading of such assets is increasingly common.

Figure 3 shows mortgages and mortgage-backed securities held by bank and bank-like entities in the United States relative to their stock of financial assets (typically known as bank credit). Note that such assets constitute almost 50 percent of bank financial assets over the past decade.

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Figure 4 shows the total outstanding stock of mortgages and mortgage-backed securities held by banks and bank-like entities relative to the total stock of mortgages. Note that these banks and bank-like entities hold only about 40 percent of the total outstanding stock of mortgages over the past decade, and that share has declined steadily since the late 1970s. That is, institutions that are very different from banks now hold a very significant and rising share of the total outstanding stock of mortgages. Such institutions do not fund their assets with short-term debt.

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In this sense, the ability of banks to achieve maturity transormation is severely limited by the holdings of close substitutes by other institutions. Figure 5 shows bank loans for commercial and industrial purposes relative to the stock of financial assets (again, typically known as bank credit) held by banks. Such bank loans may well not have close publicly traded substitutes. But they also make up only one-quarter of bank assets. This perspective suggests that if we view maturity transformation as the principal reason for the existence of banks, the United States could do very well with a banking sector just a quarter the size it now is.

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Efficient monitoring
Over the past 25 years, a theoretical literature has emerged arguing that short-term debt and the associated possible runs on financial institutions may in fact be an efficient way of allowing markets to incorporate information that investors have about returns on financial assets. [See Chari and Jagannathan (1988), Calomiris and Kahn (1991), Diamond and Rajan (2001) and Zetlin-Jones (2011).]

The basic idea is that some investors often have access to information about the returns on financial assets and that this information is valuable to other investors and financial markets as a whole. Efficient arrangements will then have to provide such investors with incentives to obtain and act on this information, and those incentives may require market mechanisms that provide higher returns to those investors compared with others. These differential returns can be efficiently provided through “bank runs” in which early withdrawers (“sophisticated” investors who have relevant information through their close monitoring of managers) get higher returns than late (“uninformed”) withdrawers. [See Appendix A-3 for a simplified technical discussion, using the Calomiris-Kahn model (1991).]

One way of implementing the efficient arrangement outlined above is to think of assets being provided and being funded by demand deposits. The demand deposits allow all investors to withdraw their assets at will. In normal times (when sophisticated investors receive favorable signals about the bank’s returns), all investors wait to withdraw their assets at maturity and receive the same return. In crisis times (when sophisticated investors get unfavorable signals), large withdrawals occur. Those who choose to withdraw early receive a higher rate of return than those who wait until maturity. The observation that investors who withdraw early get a higher return resembles a bank run and therefore creates a crisis for the bank.

An assessment of the efficient monitoring view
The efficient monitoring view of the social usefulness of banks rests crucially on the idea that it is difficult to set up alternative methods of compensating sophisticated investors and/or efficient managers. But, in fact, this assumption may be unrealistic. For example, one could easily imagine various kinds of equity claims as well as combinations of equity and debt that could provide sophisticated investors with appropriate incentives to monitor the activities of managers. One could also imagine compensation contracts for managers that are tied to the market valuation of their assets. Such mechanisms are widely used in firms whose claims are traded in public markets.

On the one hand, to the extent that close substitutes to the assets held by banks are traded in public markets, the marginal value of having banks fund these projects as well is likely to be small since these assets would likely be funded by public markets even if banks did not exist. On the other hand, the costs of banks—in the form of crises and their associated bailouts (as well as the changes in bank risk-taking behavior induced by the expectation of bailouts)—are clearly quite large.

In this sense, under both the efficient monitoring and the maturity transformation views, banks provide significant social value only when the assets they hold do not have close substitutes that are traded in public markets. Bank assets having close publicly traded substitutes destroys the possible social benefit of banks under the maturity transformation view and reveals the social benefit to be zero (or small) under the efficient monitoring view.

Finally, the efficient monitoring view also poses a severe challenge to those who view bailouts as necessary for the proper functioning of financial markets. Note that under the efficient monitoring view, crises do occasionally occur, and, at the time of the crisis, if governments were particularly concerned about the well-being of unsophisticated investors, bailouts of all investors would be considered desirable. But if such bailouts are anticipated, sophisticated investors have no incentive to monitor banks when such monitoring is costly. Absent such monitoring, either valuable projects will not be undertaken or, given the expectations of large transfers from the government, inefficient projects will be undertaken.

Implications for policy

To answer the question “Do we need banks?” we first need to answer the question “What are banks?” Guided by the data, we have chosen to think of banks as institutions that hold long-term, illiquid, volatile assets and issue large amounts of short-term debt to finance these assets. Given the possibility of bank crises, this institutional arrangement seems puzzling on the face of it. Nevertheless, we need to take seriously the observation that such institutions have been a durable part of the economic landscape for many centuries and, arguably, have played a significant role in the industrial revolutions the world has been fortunate enough to experience.

Both the maturity transformation and the efficient monitoring views suggest that it may desirable to fund long-term assets that have no close substitutes in publicly traded markets with short-term debt. Both views also suggest that the social value of funding long-term assets that have close publicly traded substitutes with short-term debt is small. Given the costs imposed by crises and attendant bailouts, both views suggest that it may be desirable to allow these institutions to issue short-term debt only if their assets do not have close publicly traded substitutes. Otherwise their benefits are either zero or negligible.

These implications suggest policies that are dramatically different from those undertaken by most bank supervisors or various forms of international regulation under the Basel agreements. Current supervisory systems encourage banks to hold assets with close substitutes for publicly traded assets by giving such assets lower risk ratings and by requiring less equity capital to back up these assets. These supervisory systems encourage the banking system to hold publicly traded assets that are risky in the full knowledge that if the returns on these assets are poor, governments will step in to bail out short-term debt holders.

The framework for regulatory policy implied by our analysis would lead to a banking system that is radically different from the one we currently have. Institutions that issue large amounts of short-term debt relative to their assets would be regulated and required to hold relatively little of their assets in publicly traded securities.

This new system would not eliminate all crises. Indeed, we have argued that bank runs may well be an essential ingredient of an efficient economic system. In all likelihood, however, the crises of this new system would be much smaller and less costly than those we have experienced in the recent past. One reason: The volume of assets backed by short-term debt would be dramatically smaller under our proposed system than under the current system.

The economic theories explored in this paper suggest we do need banks. These theories also point us to constructive ways in which we can reform the financial system to make it more efficient and to minimize the spillover costs imposed on the broader economy by crises that affect particular financial institutions.

Endnote

1 The authors thank Narayana Kocherlakota, Dick Todd and Kei-Mu Yi for useful comments and Doug Clement for editorial assistance. V. V. Chari thanks the National Science Foundation for supporting the research that led to this paper.

References

Calomiris, Charles W., and Charles M. Kahn. 1991. The role of demandable debt in structuring optimal banking arrangements. American Economic Review 81 (3): 497–513.

Chari, V. V., and Ravi Jagannathan. 1988. Banking panics, information, and rational expectations equilibrium. Journal of Finance 43 (3): 749–61.

Diamond, Douglas W., and Philip H. Dybvig. 1983. Bank runs, deposit insurance, and liquidity. Journal of Political Economy 91 (3): 401–19.

Diamond, Douglas W., and Raghuram G. Rajan. 2001. Liquidity risk, liquidity creation, and financial fragility: A theory of banking. Journal of Political Economy 109 (2): 287–327.

Farhi, Emmanuel, Mikhail Golosov and Aleh Tsyvinski. 2009. A theory of liquidity and regulation of financial intermediation. Review of Economic Studies 76 (3): 973–92.

Jacklin, Charles J. 1987. Demand deposits, trading restrictions, and risk sharing. In Contractual Arrangements for Intertemporal Trade, ed. Edward C. Prescott and Neil Wallace. Vol. 1 of Minnesota Studies in Macroeconomics. Minneapolis: University of Minnesota Press.

Wallace, Neil. 1988. Another attempt to explain an illiquid banking system: The Diamond and Dybvig model with sequential service taken seriously. Federal Reserve Bank of Minneapolis Quarterly Review 12 (Fall): 3–16.

Zetlin-Jones, Ariel. 2011. Efficient financial crises. Working paper. University of Minnesota.

In this note, we demonstrate and analyze the inability of standard neoclassical models to generate accurate estimates of the fiscal multiplier (that is, the macroeconomic response to increased government spending). We then examine whether estimates can be improved by incorporating recently developed theory on demand-induced productivity increases into neoclassical models. We find that neoclassical models modified in this fashion produce considerably better estimates, but they remain unable to generate anything close to an accurate value of the fiscal multiplier.

Introduction¹

There is a vast empirical literature studying the “fiscal multiplier,” the response of macroeconomic variables to an increase in government spending. A summary of this literature is that the multiplier—defined as the response of gross domestic product to an exogenous change in government expenditures—is somewhere in the range of 0.7 to 1.0 (see Hall 2009 for a discussion of the empirical findings) and perhaps even higher in extraordinary times like ours with a very low nominal interest rate and a very high unemployment rate. Put more concretely, if the federal government were to increase spending by $1 billion, GDP would increase by between $700 million and $1 billion.

Government expenditure provides a variety of goods and services (public roads, national representation and the like), many of which benefit a wide range of people regardless of who provided the revenue to pay for them and thus are unlikely to be privately provided. Policymakers also view public expenditures as a fiscal tool to affect macroeconomic variables such as output and employment. This function has particular relevance during recessions, when policymakers seek mechanisms that can restore employment and economic growth to healthy levels.

To assess the usefulness of government expenditure as a fiscal tool, we need to weigh the costs of fiscal expansion that arise because of the higher taxes that are eventually required against the benefits generated from higher government spending in terms of higher output. This requires explicit models, suitable for policy analysis, that faithfully capture the relevant features of the economy, and this realism can be judged by seeing whether a given model is capable of generating the fiscal multiplier seen in the data.

At least until recently, the workhorse of both macroeconomics and public finance has been the real business cycle model, also referred to as the standard neoclassical model, or SNM. However, this model of the economy is not able to deliver realistic results for the fiscal multiplier—that is to say, the multiplier values the SNM predicts are close to 0, nowhere near the estimates provided by actual data (again, between 0.7 and 1). In other words, according to the SNM, an increase in government spending would have no discernible effect on economic output, as it will be accompanied by sharp reductions in consumption and investment. Clearly, something is missing from the SNM.

Another type of macroeconomic model, referred to as New Keynesian, is able to yield a multiplier in the range of the empirical literature.² But at the same time, these New Keynesian models generate other unrealistic results, such as a decline in the markup ratio of price over cost when output rises, and a dramatically procyclical labor share. The data show the opposite: Markups tend to rise during economic booms and decline in recessions (again, Hall 2009), while labor share tends to shrink in expansions. Unfortunately, then, these models also fail to faithfully reproduce the macroeconomy and so are of limited use to economists or policymakers in designing fiscal policies.

Other models that may be helpful in generating a realistic multiplier use frictions in labor markets to generate involuntary unemployment, but these have yet to be fully developed.

Seeking better results

In this note, we briefly explore the implications for the fiscal multiplier of a family of models developed in some of our recent research (Bai, Ríos-Rull and Storesletten 2011, Dyrda, Kaplan and Ríos-Rull 2011 and Dyrda and Ríos-Rull 2012) that, while squarely in the neoclassical tradition, imply changes in some of the main properties of neoclassical models.

The impact of an increase in government expenditure on output depends on several factors, and key among these is the extent to which government spending replaces or “crowds out” private consumption and investment. If government spending simply takes the place of private spending that would have occurred in its absence, then there will be no net effect on total output, and the multiplier is 0.

However, a multiplier of 1 results when increased government spending does not induce a reduction in private consumption and investment, but rather adds to it. But, of course, increases in government expenditure have to be paid for eventually, and the effects of this burden depend on the duration of the increase in expenditures and on the form of taxation. Throughout this note, we will explore the effects of short-lived additional public expenditures that are paid for in a very efficient manner, with lump-sum taxes.³

Our research suggests that models in the neoclassical tradition can produce higher estimates of the fiscal multiplier via two channels. Dyrda, Kaplan and Ríos-Rull (2011) demonstrate the importance of adjusting neoclassical models to increase the response of hours worked to temporary changes in wages. Model parameters of this responsiveness—that is, the change in labor supplied by workers when wage rates rise or drop temporarily—should be consistent with new evidence about how household size changes in response to macroeconomic change. In contrast to the traditional notion that people can be identified with households, our data analysis reveals that people’s living arrangements change often in ways that are partly synchronized with the business cycle: During recessions, households take in more members as young adults move into (or delay departure from) their parents’ home. Seniors may also move in with middle-aged relatives, and unrelated singles may form group households.

More importantly, in terms of the fiscal multiplier, Bai, Ríos-Rull and Storesletten (2011) articulate a neoclassical model in which movements in productivity are not the result of technological shocks, but of greater willingness to spend or higher demand. As we will see, this explanation gives solid theoretical rationale for macroeconomic expansion when government spending increases.

In the next section, we describe reasons behind the inability of the SNM to generate realistic values of the fiscal multiplier. We then move to a brief discussion of our findings about fluctuations in household size that affect the responsiveness of hours worked to changes in wages (the “Frisch elasticity”) and how that affects the fiscal multiplier. Finally, we discuss neoclassical models with “demand-enhanced” productivity based on our earlier and ongoing work (Bai, Ríos-Rull and Storesletten 2011 and Dyrda and Ríos-Rull 2012) and the extent to which these modifications to the SNM can deliver values of the fiscal multiplier closer to the empirically documented range. Along the way, we make some observations about what could be capable of generating fiscal multiplier values in the actual empirical range without drastic departures from the neoclassical tradition.

The failure of the standard neoclassical model

Two features of the SNM are central to its estimates of the fiscal multiplier; both concern household decisions about how many hours they’ll work. And both ultimately lead to the model’s inability to realistically predict the fiscal multiplier.

The first is that the number of hours worked is the outcome of choices that households make in response to wages and interest rates. That hours worked respond to wage rates is rather intuitive, but households also adjust labor in response to interest rates because, among other things, interest rates affect the value of the present relative to the future. Through so-called substitution effects, hours worked are then affected.

The model’s second key feature: The two inputs essential to producing economic output—capital and labor—are subject to diminishing returns, and each is paid its marginal product (the market value of the last unit of output it contributes). So, the more units of labor or capital that are added to the economy, the less additional output will be obtained. Therefore, as the supply of capital increases, its marginal product (the interest rate) decreases. The same applies to hours worked and the wage.

These features, and their complex interaction with other economic factors, mean that increasing the number of labor hours in order to increase economic output—the mechanism through which a fiscal multiplier works in the neoclassical world—ultimately requires that both consumption and investment decrease in the short term.⁴

But to estimate the net effect of a fiscal multiplier, the question is, How large are these drops? That is, to what degree will the positive impact of government expenditures on economic output—through increased labor supply—be negated by the short-term decreases in consumption and investment?

To answer this question quantitatively, economists feed specific parameter values into the model’s equations. The values are selected to match key statistics in the U.S. economy. So, government expenditure is usually set at 16 percent of GDP, a typical figure for the United States. Investment and private consumption are about 19 percent and 65 percent of GDP, respectively. Labor income is roughly two-thirds of total GDP. The nominal interest rate is 4 percent. Another important statistic is the “intertemporal rate of substitution,” a term economists use for people’s willingness to forgo consumption now in order to consume in the future; this is usually set at 0.5. And people are assumed to work 30 percent of their nonsleeping time. All of these parameter values are widely agreed upon by macroeconomists.

The only controversial number in this model is the one that describes the willingness of people to work longer than usual if the wage is temporarily high—again, the Frisch elasticity. (See Chetty, Guren, Manoli and Weber 2011 for a discussion of the debate among economists as to its magnitude.) Although disputable, a very conventional value used in such models is 0.7. The value is calculated using a married couple as the notion of household (see Heathcote, Storesletten and Violante 2010).

With these parameter values, the model predicts that a temporary increase in government expenditures of 1 percent of GDP will have very little net impact. It results in just a 0.023 percent increase in GDP. Why so little effect? The primary cause is a dramatic drop in investment. A 1 percent increase in government expenditures leads (according to this model) to investment levels falling from 19 percent of GDP to 18.04 percent, wiping out 96 percent of the 1 percent boost in government spending.

Consumption also drops, but just slightly, falling from 65 percent to 64.98 percent of GDP. And, indeed, the increase in government spending barely increases the number of labor hours supplied by households, from 30 percent of total available time to 30.01 percent. Put in more practical terms, the neoclassical model predicts that if government boosts public spending by 1 percent of GDP for three months, the average adult would work no more than 10 minutes longer.

First candidate for a bigger multiplier:
A higher Frisch elasticity

How can the model achieve better results, closer to actual estimates of the impact of a fiscal multiplier (again, in the range of 0.7 to 1, rather than the 0.023 just obtained)? The lynchpin appears to be labor’s response to an increase in wages, the Frisch elasticity. A higher figure would have dramatic influence over the model’s estimate of the fiscal multiplier.

As mentioned above, a conventional if debatable value used for Frisch elasticity is 0.7. But our recent work, Dyrda, Kaplan and Ríos-Rull (2011), makes the case for a higher estimate. The gist of our argument is based on the fact that standard measurements of the Frisch elasticity of labor assume that households are stable in that they keep the same characteristics over time (for instance, that married people stay married). But, of course, many people are not “stable” in this sense; young people move in and out of their parents’ home, some people divorce and become two households and the like.

In our work, we show that those “unstable people” display a higher Frisch elasticity than the 0.7 figure based on a stable population. In addition, we argue that the concept of the household itself is not set in stone, and we document that the size of households is larger in recessions as many people move in with family or friends to bear the harder times. When they do so, they work even fewer hours than they would normally. According to our calculations, properly accounting for unstable people and their movements in and out of households changes the Frisch elasticity of the economy as a whole from 0.7 to 1.1.

When we apply this higher value of the Frisch elasticity of labor to the standard neoclassical model, we obtain a higher multiplier.⁵ Indeed, the multiplier goes up by 31 percent. Unfortunately, this improvement is less impressive than it sounds: Given the low initial value of 0.023, a 31 percent increase yields very little: a prediction of just 0.029—still nowhere close to empirical measurements in the range of 0.7 to 1.

Why standard neoclassical models fail

Neoclassical models contain two “first-order conditions” (or mathematical requirements) that together determine the response of workers to increases in government expenditures. Understanding these conditions helps explain why neoclassical models generate such low estimates of the fiscal multiplier.

One of these conditions—the “intratemporal”—links variables such as working hours, consumption levels and wage rates in the here-and-now: “today.” So, the “intratemporal first-order condition” says what all hourly workers know: Consumption by a household is partially determined by the prevailing wage rate times the number of hours that household members work at that wage.

But because labor in neoclassical models is paid its marginal product and is also subject to diminishing returns, prevailing wages in the economy as a whole will decline as the labor force grows, discouraging households from offering more labor hours. To increase working hours (in order to generate more output) as wages decline, a fall in consumption is necessary.

But to increase output in a neoclassical model, a fall in consumption isn’t enough. The other first-order condition—the “intertemporal”—must also be satisfied. This condition refers to the way variables interact over time—that is, between the here-and-now and the future: “today” and “tomorrow.” So in this context, this condition mathematically links the ratio of hours worked “today” and “tomorrow,” on one hand, with the ratio of wages today and wages tomorrow, multiplied by tomorrow’s interest rate, on the other hand.

Because the intratemporal condition has just indicated that wage rates are declining, the only way the intertemporal condition can generate an increase in the first ratio (working hours today over working hours tomorrow) is to significantly increase interest rates tomorrow. Because interest rates are the marginal productivity of capital (just as labor is paid its marginal product) and capital’s marginal productivity is higher when there’s less of it, then the only way to get a high interest rate is to decrease the amount of capital. This means that investment “today” has to be low.

So, the bottom line (of neoclassical models with these two conditions) is that to obtain the increase in working hours necessary for an increase in GDP, both consumption and investment must fall. But as we’ve seen in our model simulations, only investment fell significantly; consumption barely budged. Therefore, the models fail to generate a realistic increase in working hours or—thereby—a rise in GDP.

Other economic models exist in which the intratemporal first-order condition is not necessary. New Keynesian models with rigid wages, for example, operate without it. In this class of models, firms are unwilling to hire more workers at the prevailing wage. Increased government expenditures increase profits for firms, and that induces them to hire more workers at the current fixed wage. The number of hours worked then rises.

Another type of model holds that firms and workers expend considerable and costly effort in seeking good fits. These search models of unemployment hold that there are always willing workers, but they are costly to find. In this class of models, the intratemporal first-order condition is absent, but a zero-profit condition on firms applies. Increases in government expenditures may provide firms with incentives to look for more workers, and this might provide a satisfactory mechanism for generating a realistic multiplier.

Can government expenditures increase wages?

As we’ve seen, the neoclassical model is unable to generate realistic estimates for the fiscal multiplier, in large part because decreasing returns to labor—the diminishing additional output from each additional hour of work—mean that workers face diminished wages as government expenditures increase. Is there a different way to design a model so that government expenditures could increase wages instead of reducing them? If so, that would certainly help to increase hours worked.

In fact, this is what New Keynesian models with rigid prices do. As government expenditures increase, firms in these models are required to deliver the goods or increase prices or both. Since some firms cannot adjust prices, they need to hire more labor to deliver the goods. To induce households to provide more labor hours, wages have to go up. However, fixed prices and higher wages imply that profits and markups fall as a result of the increase in government expenditures.

Again, Hall (2009) provides a very nice description of the issue and of the lack of evidence for a drop in markups when government expenditures rise. Let us add that the behavior of labor share, which is slightly countercyclical in the data, is also at odds with the prediction of New Keynesian models with fixed prices.

In fact, greater potential lies in a model in the neoclassical tradition, with a twist.

A second candidate: The shopping model

In a recent paper, Bai, Ríos-Rull and Storesletten (2011) built a modified neoclassical model that goes a significant way toward generating a realistic multiplier. This model allows any increase in expenditures (which we refer to as demand) to increase productivity and, with it, to increase wages. The innovation in this model is that generating output requires not only inputs of production, but also the active participation of the purchasers of goods and services.

A few examples can help to illustrate this mechanism, and perhaps the best illustrations are from service industries. The tourism industry needs tourists, restaurants need diners, hotels need guests, hospitals need patients, movies have to be seen, advice has to be heard, pedicures need toes, and so on. In our model, consumption and investment require the active participation of consumers and firms. Without spenders, there is no output. Productivity goes up when the contribution of buyers (consumers, firms and the government) increases. It is the buyers—not producers—who are ultimately responsible for increased productivity by exerting more—but unmeasured—effort to use the economy’s productive capacity more intensely.

In the model, these ideas are implemented by requiring consumers not only to pay for the goods, but also to find them, a disagreeable task that is costly in terms of utility (just as is work in the standard model for those who prefer leisure). An increase in consumption can be achieved through both an increase in labor that raises the potential output of the economy, and an increase in search effort that allows households to find more output, thus making the economy operate at higher capacity.

Firms stand ready to produce, with capital and labor, but output occurs only when consumers find the firms and generate demand for that output. The search efforts of consumers are not measured in GDP, and the higher output is imputed to higher productivity.

While the spirit of this “shopping economy” is neoclassical—prices are flexible, people and firms are restricted only by technology, markets clear—the aggregate production function with constant productivity (a traditional workhorse of the SNM) does not hold.

In the shopping economy, as in all models, there is a budget constraint that requires households to pay for what they (and the government) purchase. An additional constraint, unique to this economy, requires households to search in order to find consumption goods. The more search effort consumers expend, the more goods will be found and, thus, the higher output will be. Consequently, output can increase even if there are no changes in measured inputs (since search effort itself is unmeasured).

In our paper, we use the modern concept of competitive search that achieves an optimal allocation and thus guarantees that the model has a unique prediction. However, we include all forms of hassle associated with searching for consumption goods, such as receiving worse service in restaurants at capacity, a lengthy wait in emergency rooms on Saturday nights, not getting the right options or color when buying a car and so on.

All of these hassles are greater when the economy is in an expansion, generating higher productivity as a result of higher demand. During recessions, hassles diminish—parking spaces, shopping lines, waiting times all decline; clerks and salespeople stand idle as they wait for customers. This dynamic applies to firms, as well, since they have to search for investment goods. Purchasing departments and shopping professionals need to find the right type of capital goods, a task that is clearly less costly during recessions.

The shopping economy model holds potential for generating a fiscal multiplier more in line with the empirical estimates. This is because increased government spending in this model generates higher productivity, and that may generate higher wages, in contrast to the SNM. To gauge this potential, we added a government sector financed with lump-sum taxation to the model in Bai, Ríos-Rull and Storesletten (2011).⁶

As in the SNM, an increase in government expenditures induces an increase in hours worked.⁷ But unlike in the SNM, productivity goes up, which can potentially increase wages, or at least slow down their reduction. Productivity increases because now people and firms have to look harder to find goods since higher government spending has raised effective demand.

When the relevant parameters are applied to the shopping model, it generates much larger effects than did the SNM. An increase in government expenditures of 1 percent of GDP (from 16 percent of the average value of GDP to 17 percent) now yields a fiscal multiplier of 0.172, over seven times that generated by the SNM. This results from both a 0.07 percent increase in productivity and a 0.09 percent increase in hours worked. By comparison, the SNM generates no increase in productivity and a 0.034 percent increase in hours worked.

The main difference between the shopping economy and the SNM is in investment, with the reduction being much smaller in the shopping economy. Interestingly, consumption in the shopping economy falls by more than in the SNM—from 65 percent to 64.94 percent—and investment falls from 19 percent to 18.22 percent. As in the SNM, an increase in the Frisch elasticity to 1.1 also increases the multiplier, albeit by a small amount (from 0.172 to 0.187).

These fiscal multiplier estimates are still a far cry from the values between 0.7 and 1 found in the empirical literature. One way for models of the neoclassical tradition to produce a higher value would be to have “time to build” features to avoid wild oscillations in investment.⁸ For example, we could pose a requirement that investment projects take a long time to both plan and implement. Therefore, in any period, only a fraction of the current investment is chosen, the rest being the outcome of previous decisions. Similarly, in the current period, decisions are made about future investment. With this formulation, we would obtain some rigidity in investment, not because markets do not work well, but because of technological—indeed, engineering—reasons. Short-lived increases in government expenditures would not in this case disrupt investment very much, and a swift increase in government expenditures would imply a larger multiplier. The exact calculations would require a specification of how to absorb the losses of firms that are required to secure the investment goods. In this scenario, a much larger drop in consumption is likely. This is a topic that deserves more attention.

The multiplier in a recession

So far we have looked at the multiplier in average times, when the economy is operating normally. But “normal times” are not when policymakers consider using government expenditures as a tool to expand output. Leading researchers (Christiano, Eichenbaum and Rebelo 2010, for instance) postulate that in certain circumstances, such as when the nominal interest rate is 0 percent, New Keynesian models with fixed prices give rise to large fiscal multipliers, suggesting that such a policy tool would be particularly potent under such conditions.

To analyze the multiplier in recessions, we need to “engineer” a recession in our models. In neoclassical contexts, recessions occur because of poorer-than-average technology or because of preference changes that induce people to work less. In this exercise, we first consider shocks to preferences in both SNMs and shopping models that make work less agreeable. We set the sizes of the shocks so that GDP shrinks by 1 percent, and we use a high Frisch elasticity of 1.1.⁹

In the SNM, a 1 percent drop in output is generated via a 1.5 percent reduction in hours worked. However, consumption barely changes (it goes down just 0.02 percent), while the bulk of the reduction in output is accommodated by lower investment. In actual recessions, investment typically falls by more than consumption, but in a less extreme manner. In the shopping economy, a similar 1 percent output drop comes from a drop not only in labor, but also in productivity, which falls by 0.4 percent.

Now that we’ve engineered a recession in the model economies, we can consider an expansionary policy that increases government expenditures the same amount in both. We find that the fiscal multipliers are higher than in normal times, as theory has suggested, but barely so. In the SNM, the multiplier is up to 0.0298 relative to 0.0296 in normal times. In the shopping economy, it is 0.1916 in a recession, up from 0.1871 in normal times. The gains in the shopping economy come from productivity (0.07 percent) and from labor (0.12 percent); in the SNM, the gains are all from labor.¹⁰

The shopping economy allows for an additional type of recession: a shock to the cost of search—the willingness of people to put up with the hassle needed to find goods and services. An increase in search costs thus acts as a shock to demand and generates a recession. Such a recession compounded with the expansionary policy generates the largest multiplier found in these exercises, a value of 0.221.

This quick review of multipliers during recessions generated by neoclassical models arrives at the same destination: The fiscal multiplier is almost 0 for the SNM and about 0.2 for the shopping economy. The reason these models predict such similar multipliers, whether the economy is in a recession and not, is clear. Neoclassical models assume that there are no market frictions, such as wages that don’t adjust quickly or prices that don’t change right away. Therefore, in these sorts of models, recessions are generated because people simply choose to work less. Therefore, the models respond in recessions very much the same as they do in normal times: The model economy operates optimally.

Conclusions

This note shows that neoclassical models of the business cycle have serious shortcomings that limit their ability to evaluate the effect of changes in government expenditures. State-of-the-art extensions can increase the predicted values for the fiscal multiplier, but these estimates are still much smaller than those measured empirically.

Many researchers have embraced New Keynesian models in which the central mechanism generating large fluctuations is the assumption that prices are fixed, so firms and workers are unable to adjust (nominally denominated) contracts. While this may be fruitful, we think that some of its counterfactual implications (specifically, on price-to-cost markups and labor share) and the lack of solid theoretical explanation for why prices are fixed justify the exploration of models with neoclassical flavor, but with enough frictions so as to allow for recessions as situations with idle resources. We think that such models should include frictions in labor markets that make many households work fewer hours than they would like and consume less than desired because of difficulties in borrowing.

Models with these features in addition to those provided by our shopping economy, in which increases in government expenditures boost productivity, may be capable of yielding multiplier values consistent with those in the empirical literature. These models are the subject of our ongoing work.

Endnotes

1 The authors thank Doug Clement and Carolyn Wilkins for their editorial comments and Fabrizio Perri for discussions of theory. The views expressed herein are those of the authors and not necessarily those of the Federal Reserve Bank of Minneapolis or the Federal Reserve System.

2 For example, Drautzburg and Uhlig (2011) quantify the effect of the American Recovery and Reinvestment Act of 2009 using the benchmark New Keynesian model developed by Smets and Wouters (2003) with additional frictions. They yield short-run multipliers around 0.52. Leeper, Traum and Walker (2011), using Bayesian techniques, show that New Keynesian models with sticky prices and wages can generate multipliers around 1.

3 Unlike income taxes, for example, that not only make people poorer but also provide an incentive to substitute away from consumption goods and into leisure, which is not taxed, lump-sum taxes are not distortionary and do not provide such a disincentive to work.

4 See the appendix for a technical explanation of this conclusion.

5 Doing so requires an adjustment in the value of parameter θ (see appendix) to ensure that the amount of hours worked on average is 30 percent.

6 For simplicity, we have assumed that the government has a pile of projects in a drawer and is exempted from having to search for goods (a trivial simplification). Also, it is very easy to model similar search frictions for the government by being explicit about the part of measured government expenditures that are used for search purposes rather than providing strictly useful goods.

7 Both because people are poorer and because the reduction in investment increases the rate of return, propelling a wealth effect and a (intertemporal) substitution effect.

8 “Time to build” is an expression used by Kydland and Prescott (1982), referring to multiperiod construction as a fundamental characteristic of most economies and one that helps explain macroeconomic fluctuations. As they put it: “That wine is not made in a day has long been recognized by economists.”

9 Let’s for now ignore the fact that in this type of recession, a benevolent government should do nothing: The economy is optimal on its own.

10 Another type of recession, one due to a short-lived shock that reduces the household’s relative preference for current consumption. It turns out that a recession of 1 percent in output via a reduction in the willingness to consume generates wild oscillations in consumption and investment. In the SNM with the high Frisch elasticity of 1.1, consumption drops by half(!) and investment goes up two and a half times. The behavior of the shopping economy is more subdued: Consumption falls from 65 percent of GDP to 59.9 percent, and investment goes from 19 percent to 23.1 percent. These recessions are clearly uninteresting, and for what it is worth, the multipliers are not very different from those in normal times: 0.030 in the SNM and 0.195 in the shopping economy.

References

Bai, Y., J.-V. Ríos-Rull and K. Storesletten. 2011. Demand Shocks as Productivity Shocks. Working paper, University of Minnesota.

Chetty, R., A. Guren, D. Manoli and A. Weber. 2011. Are Micro and Macro Labor Supply Elasticities Consistent? A Review of Evidence on the Intensive and Extensive Margins. American Economic Review Papers & Proceedings 101 (3): 471-75.

Christiano, L., M. Eichenbaum and S. Rebelo. 2010. When Is the Government Spending Multiplier Large? Mimeo, Northwestern University.

Drautzburg, T., and H. Uhlig. 2011. Fiscal Stimulus and Distortionary Taxation. Working Paper 17111, National Bureau of Economic Research.

Dyrda, S., G. Kaplan and J.-V. Ríos-Rull. 2011. Business Cycles and Household Formation: The Micro versus the Macro Labor Elasticity. Mimeo, University of Minnesota.

Dyrda, S., and J.-V. Ríos-Rull. 2012. Expansionary Policies when Demand Increases Productivity. Mimeo, University of Minnesota.

Hall, R. 2009. By How Much Does GDP Rise if the Government Buys More Output? Brookings Papers on Economic Activity 2: 183-231.

Heathcote, J., K. Storesletten and G. Violante. 2010. The Macroeconomic Implications of Rising Wage Inequality in the United States. Journal of Political Economy 118 (4): 681-722.

Kydland, F. E., and E. C. Prescott. 1982. Time to Build and Aggregate Fluctuations. Econometrica 50 (6): 1345-70.

Leeper, E. M., N. Traum and T. B. Walker. 2011. Clearing Up the Fiscal Multiplier Morass. Working Paper 17444, National Bureau of Economic Research.

Smets, F., and R. Wouters. 2003. An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area. Journal of the European Economic Association 1(5): 1123-75.

The classes, which begin March 20, are part of a course offered to undergraduates at the George Washington University School of Business. Viewings at the Minneapolis Fed will be held in the evening on the same day as the lectures, after which visitors will have the opportunity to ask questions and discuss content with Minneapolis Fed economists and staff.

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Abstract

In this paper, we explore the impact of the Great Recession on economic inequality and redistribution in the United States. We analyze many sorts of inequality (in earnings, disposable income, consumption expenditures and wealth) for different sections of the economic distribution.

Here we highlight three central findings.

• In 2010, the bottom 20 percent of the U.S. earnings distribution was doing much worse, relative to the median, than in the entire postwar period. This is because their earnings (including wages, salaries, and business and farm income) fell by about 30 percent relative to the median over the course of the recession. This lowest quintile also did poorly in terms of wealth, which declined about 40 percent.


• Redistribution through taxes and transfer programs reached historically high levels in 2010. As a result, spending power, captured by disposable income and consumption expenditures on nondurables, of this same lowest 20 percent did not significantly change relative to other economic groups during the recession.


• Although government redistribution protected households from fully bearing the impact of an earnings decline, households that experienced such a decrease nonetheless endured sizable drops in disposable income and drops in consumption expenditures.

Introduction¹

Although there is little doubt that the Great Recession constituted a watershed for overall business cycle dynamics in the United States, the jury is still out on its distributional consequences. Did economic inequality change significantly during the recession? If so, which dimensions—income earnings, wealth and consumption—saw the largest changes? And what impact did government policies, such as taxes and transfer programs, have over this time period on both inequality and economic well-being?

Analyses focused on the first two years of the downturn seem to find no increase in economic inequality; indeed, some report a decline. For example, a recent comprehensive volume (Jenkins et al. 2011) that analyzes income distribution in 21 Organisation for Economic Co-operation and Development (OECD) countries (including the United States) across the Great Recession sees “little change in household income distributions in the two years following the downturn.” Heathcote et al. (2010b) and Petev et al. (2011) study inequality in consumption expenditures in the United States up until 2009 and also find little change (if anything, they find a decline).

A longer-term view, however, suggests that high levels of unemployment and the large drop in housing prices, both of which started during the Great Recession but persisted well after, might have had longer-term adverse distributional consequences. In particular, the recession may have left a significant fraction of the U.S. population with very little wealth (due to the fall in asset prices) and poor labor market prospects (due to high unemployment).

The goal of this paper is to paint a more complete picture of the distributional impact of the Great Recession, including more recent data from 2010 and part of 2011. Most importantly, this paper considers inequality in a wide array of variables, such as earnings, disposable income, consumption expenditures and wealth, and looks at inequality for all of these variables at different sections of the economic distribution.

Our first finding is that during and after the Great Recession, the bottom of the U.S. earnings distribution has fallen dramatically. This is the result of historically high unemployment and nonparticipation. In terms of earnings, the bottom 20 percent of the U.S. population has never done so poorly, relative to the median, during the whole postwar period. We also show that this group experienced rapidly declining wealth.

Despite this, we find that inequality in disposable income and consumption did not increase at either the top or bottom of the distribution, confirming the findings of other studies. In other words, the same bottom 20 percent of the earnings distribution that fared so poorly during the Great Recession in terms of earnings and wealth is in pretty much the same relative position in terms of disposable income and consumption in 2010, after the recession officially ended, as it was in 2006, before the start of the recession.

Such a divergence of trends in earnings and disposable income at the bottom of the distribution is unprecedented in U.S. history, and we show that it is mainly due to government transfers and taxes, as opposed to private components of unearned income.

We conclude our study using panel analysis (i.e., following a specific set of households through time) to better assess the role of government taxes and transfers. This allows us to distinguish between the experience of a given section of the income distribution (e.g., the bottom 20 percent of the distribution, whose members change each period) and the experience of a fixed group of households (e.g., those households that were at the bottom 20 percent of the distribution in 2006 but whose position may have changed by 2010. If the “Smiths,” say, were in the bottom fifth in 2006, we use panel analysis to understand where the Smiths ended up later on).

Our main finding is that although the bottom 20 percent of the earnings distribution experienced constant disposable income or consumption expenditures despite earnings losses, individual households that face earnings losses and enter the bottom 20 percent group do suffer significant losses in disposable income and small losses in consumption.

Our main substantive conclusion is that government redistribution in the Great Recession was at historical highs and partially shielded households from experiencing large declines in disposable income and consumption expenditures. The same households, though, have experienced losses in net wealth, and this might make them more vulnerable to further or more persistent earnings declines in the future.

We believe our analysis provides useful data to inform the policy debate about whether or not, looking forward, the government should take a more aggressive role in providing assistance for households that experience earnings losses.

Income inequality in U.S. recessions: Some historical perspective

We start our analysis by putting the Great Recession in historical perspective, in particular by comparing the patterns of income inequality in the Great Recession with patterns of inequality in previous recessions.

For this analysis, our data source is the March supplement of the Current Population Survey (CPS), an annual survey of about 60,000 households selected to represent the U.S. civilian noninstitutional population. The longest series that is comparable, for the purpose of our analysis, starts with the March 1968 sample (which refers to 1967 calendar year) and ends with the survey collected in March 2011, which covers incomes of the calendar year 2010. Because of our interest in the recession, which mostly affected households in labor markets, we select only those households with at least one member between the ages of 22 and 60 years.

Throughout this paper, we focus mostly on two simple measures of inequality: the 50/20 ratio and the 95/50 ratio. These are ratios of percentiles in the economic distribution. For example, the 50/20 ratio for income is the ratio of median income (the “50”) to the income of the richest household in the bottom fifth of the income distribution (the “20”). The 95/20 ratio for earnings is the ratio of the lowest-earnings household in the top 5 percent of the earnings distribution (the “95”) to the median earnings figure.

These ratios have two advantages over other inequality indicators. First, as ratios of variables, they are easy to translate directly into inequality magnitudes and inequality changes. The second advantage is that they concisely capture inequality at the bottom and at the top of the distribution, respectively.

In terms of income measures, we first focus on three measures of household resources. The first is earnings, which includes wages, salaries, and business and farm income from all household members. The second is total income, which includes all sources of household income, including not only earnings, but also interest, dividends, rents, private transfers (such as alimony and child support) and government transfers (such as Social Security, unemployment insurance and welfare). The last measure is disposable income, which subtracts tax liabilities from total income.² To account for different household sizes, we divide all three measures of household income by the number of “adult equivalents” in the household.³

Figures 1 and 2 report the evolution, from 1967 to 2010, of the 95/50 ratio and of the 50/20 ratio for these three measures of household resources.

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Figure 1 confirms the finding, highlighted by several authors (e.g., Piketty and Saez 2003), that inequality at the top of the distribution has increased substantially during the 1980s and the 1990s. It also shows that there was an increase in earnings inequality during the Great Recession. This increase was due to the fact that median earnings per adult equivalent fell quite substantially from 2008 to 2010 (from around $26,300 to roughly $24,700), while earnings of the 95th percentile have been more stable (about $89,170 to $88,640).

Notice also that the increase in earnings inequality resulted in a rise in inequality in total income but not disposable income, suggesting that taxes reduced the differential impact of the recession on the top and on the median. Overall, though, the changes in inequality at the top of the distribution were small compared with changes at the bottom, as seen in Figure 2.

The first feature of Figure 2 that we want to highlight is an extraordinary fall of the bottom of the earnings distribution. This is captured by the 50/20 ratio, which increased sharply during the recession from roughly 2.7 to nearly 3.5. Note that the 50/20 ratio rises in all recessions, which are, by definition, periods of increasing unemployment. Higher unemployment raises the fraction of households with no or very low earnings, and this causes the 20th percentile of the earnings distribution to fall relative to the median, thereby raising the 50/20 ratio.⁴

But note that while unemployment in 2010 was slightly below its postwar historical high,⁵ the 50/20 ratio in the same year was well above its previous historical high, reaching almost 3.5, while in previous recessions it never exceeded 3. This suggests that the cause for the high inequality at the bottom is not just unemployment but also nonparticipation in the labor market.

A second feature seen in Figure 2 is that the 50/20 ratios in both total income and disposable income have much lower levels and, during the Great Recession, experienced much smaller increases than the 50/20 ratio in earnings. Indeed, despite the substantial increase in earnings inequality, inequality in disposable income was about the same in 2010 as in 2003.

This lack of change is quite remarkable; in all previous U.S. recessions, with the exception of that in 1973, disposable income inequality at the bottom increased. Constant inequality in disposable income during recessions has been experienced in some European countries (Sweden, for example; see Domeji and Flodén 2010), but it is unusual in the United States. This suggests that mechanisms like private or government transfers played an important role in mitigating the effect of the Great Recession on inequality in disposable income. The next section investigates the impact of such mechanisms in greater detail. 

Income inequality in the Great Recession: Getting to the bottom of it

First we look more closely into the large increase in earnings inequality at the bottom of the distribution, and then we identify more precisely the causes of the divergence between inequality in earnings and in disposable income.

Earnings are the product of hours worked and wages per hour, and the CPS provides data on hours worked per household. Following a similar analysis to that done by Heathcote et al. (2010a) for previous recessions, Figure 3 plots average real earnings, average hours worked and average disposable income for the bottom 20 percent and the middle 10 percent of the earnings distribution. In both panels, all statistics are “normalized” (or mathematically set) to 1 in 2008.

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Three features of this figure are quite striking.

The first is that the increase in earnings inequality was the result of a large absolute (and not relative) fall of earnings at the bottom of the distribution. The top panel shows that earnings at the bottom fell more than 30 percent (in real terms) from 2008 to 2010, while the bottom panel shows only a moderate 5 percent earnings fall for the middle.

The second is that the sharp fall in earnings at the bottom can be attributed largely to the decrease in total hours worked, which fell by 25 percent, and not to a possible change in hourly wage rates.

The third striking feature of this figure—evident in the top panel—is that the sharp fall in hours worked and earnings for the bottom 20 percent of households did not result in a parallel decline in disposable income.

Taken together, these three facts suggest that government and private support for unemployed individuals played a major role in muting the impact on disposable income of lower earnings and employment during the Great Recession.

In Table 1, we analyze more precisely which components of disposable income were most important in mitigating the fall in earnings of the bottom 20 percent. To do so, we look at the increase in the 50/20 ratio when we add to earnings each of the individual components that constitute “disposable income.”

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The table’s first column simply identifies each component, one by one. The second column reports the change (from 2006 to 2010) in the 50/20 ratio for the income measure that includes earnings plus the respective component in the first column. The third column is simply the second column minus the increase in the 50/20 ratio for earnings alone: The smaller the number, the more that particular disposable income component prevents a rise in inequality at the bottom. (For instance, Social Security payments, with a -0.08 impact, plays a greater role in preventing a rise in 50/20 disposable income inequality than does rental income, with a -0.04 impact.)

The rows are ranked according to the third column; the first row reduces the inequality increase the most. Not surprisingly, unemployment benefits contribute greatly to the lack of increase in disposable income inequality as do taxes (most likely through the Earned Income Tax Credit program). Overall, the table suggests that government programs, as opposed to other non-earned-income categories like interest or dividends, are an important factor in explaining why increased earnings inequality did not translate into an increase of disposable income inequality during the Great Recession.

Consumption inequality during the Great Recession

Previous research on inequality (e.g., Blundell and Preston (1998) and Krueger and Perri (2006)) has suggested that the distribution of consumption expenditures, not of income, gives greater insight into the distribution of household well-being. Financial markets permit consumption expenditures that are more closely related to a household’s lifetime resources (sometimes referred to as the “permanent income” of the household). Consumption, therefore, is a better indicator of the well-being of the household.

This logic might also be relevant in evaluating the distributional impact of the Great Recession, for two reasons.

One reason is that current consumption better reflects expectations about future income prospects than do current earnings—an individual who expects to lose his/her job may well reduce expenditures even when she/he is still employed.

We have just established that during the Great Recession, inequality in disposable income did not increase because government transfers like unemployment insurance supported disposable income of low-earnings households. But if shocks to earnings are persistent and transfers to low-income households have limited duration, then the permanent income of some low-earnings households will fall, and so we would expect to see a drop in consumption expenditure, despite stable disposable income.

The other reason consumption expenditures might be a better indicator of distributional changes is related to the fact that the defining event of the Great Recession was the large fall of asset prices, particularly of housing.

Consider two households with the same income but very different shocks to the value of their wealth. Looking only at income would not inform us about distributional changes between them, but looking at consumption would, as the households would adjust their consumption in response to changes in their net wealth. More concretely, when housing prices fall, households feel less wealthy and spend less—even when their salaries and other income streams do not change.

For these reasons, understanding the evolution of consumption distribution during the Great Recession may shed light on the impact of the recession. Here we present household-level consumption data from the Consumer Expenditure (CE) Interview Survey. The CE Survey is a rotating panel of households that are selected to be representative of the U.S. population. Each quarter the survey reports, for the cross section of households interviewed (about 6,000), detailed demographic characteristics for all household members, detailed information on consumption expenditures for the three-month period preceding the interview and information on income, hours worked and taxes paid over a yearly period. The most recent data available are from the first quarter of 2011.

The statistics we present track closely those analyzed earlier. We start with inequality at the top, captured by the 95/50 ratio, together with inequality in earnings and disposable income (from the CE sample). Figure 4 reports measures of inequality in expenditures on nondurable goods (labeled “Nondurable Consumption”) and inequality in expenditures on nondurables plus a few durables such as cars and furniture (labeled “Nondurable Consumption+”).⁶

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Overall, the figure suggests that despite some swings in the inequality measures, the Great Recession did not significantly change inequality in consumption for households at the top of the distribution.

Notice how, in the initial phase of the recession, inequality in both consumption measures seemed to fall; this might be simply due to the large fall in purchases of durables that took place in the middle of the recession. If many consumers stop purchasing durables, fewer large consumption expenditures are recorded and, hence, inequality at the top falls. Consistent with this hypothesis is the fact that by the end of 2010, inequality in expenditures at the top returned to the same level as in 2006.

Figure 5 reports inequality at the bottom, the 50/20 ratio. The plot lines for earnings and disposable income mimic closely those observed for CPS and, perhaps not surprisingly, given the steady path of disposable income over these years, inequality in consumption barely moves during the Great Recession. Overall, Figures 4 and 5 suggest an overall stability of consumption inequality over the course of the Great Recession.

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In the last part of this section, we investigate the issue further by focusing, as we did in Figure 3, on the bottom 20 percent of the earnings distribution. Figure 6 plots, for the bottom 20 percent of the earnings distribution in each quarter, average earnings and disposable income, average nondurable and nondurable+ consumption and average total net wealth, all normalized to 1 in 2007q4.

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First, notice that earnings and disposable income behave very similarly to the corresponding CPS series in Figure 3. Nondurable consumption tracks disposable income closely and does not seem affected by the fall in earnings. In contrast, both the average wealth and the nondurable+ series for this group fall considerably.

It would no doubt be difficult—and certainly it is outside the scope of this paper—to establish a causal link between the fall in earnings and in wealth of this group. Nevertheless, the figure suggests that the bottom 20 percent of the earnings distribution in 2010 was a very different group than it was in 2007. The bottom 20 percent groups in 2010 and 2007 had the same disposable income, but both earnings and wealth of the 2010 group were 40 percent lower. In absolute terms, this means that the average wealth of the bottom 20 percent fell from around $80,000 in 2007 to a little over $50,000 in 2010. The lower wealth is particularly important, as it makes this group more vulnerable if government support for low-earnings households were to cease.

A panel analysis

The cross-sectional data analyzed in the previous sections show that while earnings for the bottom 20 percent of households fell dramatically over the Great Recession, disposable income in the same group was virtually constant. However, these cross-sectional data do not necessarily tell us how individual households are faring over time, since the group of households in the bottom 20 percent changes each year; some previously higher-earnings households move into the bottom 20 percent, and some households that were previously in the bottom 20 percent move out of it.

In this section, we use panel data from the Panel Study of Income Dynamics (PSID) to study the importance of two components of the bottom 20 percent group: (1) changes in income and expenditures of households that stay in the group, and (2) changes in the composition of the group.⁷

The PSID is the longest-running representative household panel study in the United States. The PSID data sets provide a wide variety of information on geographic location, income, employment, wealth and expenditures for many households that are followed, after 1996, at a biannual frequency. We concentrate our analysis on the 2007 and 2009 surveys (which provide data on 2006 and 2008) to study distributional dynamics since the start of the Great Recession.

Panel (a) of Table 2 (labeled “Bottom 20 percent of earnings”) shows the results, for the PSID, of the same cross-sectional analysis we did above on the CPS and CE. In particular, we look at a series of statistics for a particular group: the bottom 20 percent of the earnings distribution.

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In Panel (a), we see that earnings for the bottom 20 percent rose slightly between 2006 and 2008, by about the same amount as in the cross-sectional data in Figure 3. Disposable income rose as well. Panels (b), (c) and (d) of the table decompose, or separate, these changes into changes of the households that stay in the group (“Stayers,” Panel d), which make up about 75 percent of the group, and the difference in income between the group that entered the bottom 20 percent in 2008 (“In-switchers,” Panel b) and the group that left the bottom 20 percent in 2008 (“Out-switchers,” Panel c).

What we want to highlight is that while disposable income for the bottom 20 percent as a whole rose slightly between 2006 and 2008, this increase is due only to a change in the group’s composition. The panel analysis allows us to control for these compositional effects and shows instead that, on average, households in the bottom 20 percent of the earnings distribution in 2008 actually suffered a significant decline in disposable income.

Putting this in actual dollar figures might make the point still clearer: While the bottom 20 percent of households experienced an average disposable income increase of $228, this was simply because the 2008 income of those shifting into the group was higher than the 2006 income of those who left the bottom earnings quintile. Households in the bottom 20 percent in both 2006 and 2008 had a $159 disposable income decline, on average, and those who experienced such a severe drop in earnings that they moved to the bottom quintile had, on average, $12,236 less income to spend in 2008 than they had two years earlier.

Consumption of in-switchers also fell slightly, although interestingly, the drop in consumption has not been as large as the drop in disposable income. The relatively high average wealth of in-switchers ($70,146 in 2006) may have provided sufficient resources for them to smooth their consumption.

We find this panel analysis very instructive, as it reveals that looking at simple cross-sectional measures of inequality is not enough to assess the full distributional impact of the Great Recession. The panel analysis suggests that although government redistribution policies—taxes, unemployment insurance and others—have provided an important cushion against the effect of earnings declines on disposable income and consumption, they have not fully shielded households’ disposable income from these earnings fluctuations.

This further suggests that the Great Recession could have indeed had major redistributive effects at the bottom of the distribution. As panel data become available on the 2009-10 period, in which earnings of the bottom 20 percent fell dramatically (seen in Figure 3), it will be especially important to monitor the disposable income and consumption of households that moved into the bottom 20 percent in 2008 and remained there for the remainder of the Great Recession. For those that remain in the bottom 20 percent, their depleted wealth may not have been enough to prevent persistently low earnings from impacting consumption and welfare.

Conclusions

This paper provides an empirical analysis of inequality and redistribution during the Great Recession.

On one hand, we find that redistribution (through taxes and transfers) from high-earnings to low-earnings households in the United States was at its historical high, which possibly explains the calls by some for cutbacks in government programs that provide such assistance. On the other hand, we provide evidence that households that experience a severe earnings loss also face a large loss in disposable income and a loss in consumption, and that low-earnings households have become, during the course of the Great Recession, more vulnerable due to large losses in wealth.

This analysis should help inform future policy action regarding the extent of social insurance. For example, it could assist in assessing the consequences of extending, or curtailing, the duration of unemployment insurance benefits.

Endnotes

1 The authors thank Doug Clement and Kei-Mu Yi for valuable comments.

2 The CPS does not provide data for disposable income for all years in the sample. Therefore, we compute disposable income figures with TAXSIM, a widely used tax simulation program provided by the National Bureau of Economic Research. In years for which we have disposable income from the CPS, summary measures of disposable income in the CPS are very similar to our measures.

3 Following the commonly used OECD scale, the number of “adult equivalents” in a household is a weighted sum of household members in which the first adult is given a weight of 1, each additional adult has a weight of 0.7 and each member under the age of 17 has a weight of 0.5.

4 For more on how unemployment affects the dynamics of inequality over the business cycles, see Castañeda et al. (1998).

5 The U.S. unemployment rate in 2010 was 9.6 percent, just under the postwar high of 9.7 percent in 1982.

6 Specifically, the nondurable expenditures category includes expenditures on food and beverages, utilities and fuels, education, medical supplies, clothing and personal care, reading and transportation services. The nondurable+ category adds to this purchases of cars, furniture, jewelry and durable entertainment goods.

7 To see this more precisely, let’s define the following relation:

$$\overline{Y}_{B20}(t) - \overline{Y}_{B20}(t-1) = \alpha\left(\overline{Y}_{B20}^{Stay}(t) - \overline{Y}_{B20}^{Stay}(t-1)\right)$$
$$+ (1-\alpha)\left(\overline{Y}_{B20}^{In}(t) - \overline{Y}_{B20}^{Out}(t-1)\right)$$
where ~\overline{Y}_{B20}(t)~ is the average income measure of the bottom 20 percent of the earnings distribution in period t, ~\alpha~ represents the share of households that stay in the bottom 20 percent, ~\overline{Y}_{B20}^{In}(t)~ is the average income of the households that enter the bottom 20 percent at time t (and were not in the bottom 20 percent in period t-1) and ~\overline{Y}_{B20}^{Out}(t-1)~ is the average income of the households that were in the bottom 20 percent at t-1 and exited the group at time t. The equation highlights that observed changes in the cross-sectional data ~\overline{Y}_{B20}(t) - \overline{Y}_{B20}(t-1)~ are driven both by changes in income/expenditures of households that stay in the group (the term ~((\overline{Y}_{B20}^{Stay}(t) - \overline{Y}_{B20}^{Stay}(t-1)))~) and by changes in composition of the group (the term ~(\overline{Y}_{B20}^{In}(t) - \overline{Y}_{B20}^{Out}(t-1))~).

8 The 14 percent figure is derived by adding (a) the disposable income loss of the stayers (2 percent) times their numerical share of the group (75 percent) and (b) the disposable income loss of the in-switchers (50 percent) times their weight in the group (25 percent).

References

Blundell, R., and I. Preston. 1998. Consumption Inequality and Income Uncertainty. Quarterly Journal of Economics 113 (2): 603-40.

Castañeda, A., J. Díaz-Giménez and J. V. Ríos-Rull. 1998. Exploring the Income Distribution Business Cycle Dynamics. Journal of Monetary Economics 42 (August): 93-130.

Domeij, D., and M. Flodén. 2010. Inequality Trends in Sweden 1978-2004. Review of Economic Dynamics 13 (1):179-208.

Heathcote, J., F. Perri and G. Violante. 2010a. Unequal We Stand: An Empirical Analysis of Economic Inequality in the United States, 1967–2006. Review of Economic Dynamics 13 (1): 15-51.

Heathcote, J., F. Perri and G. Violante. 2010b. Inequality in Times of Crisis: Lessons from the Past and a First look at the Current Recession. VoxEU.

Jenkins, S., A. Brandolini, J. Micklewright and B. Nolan. 2011. The Great Recession and the Distribution of Household Income. Working paper. Fondazione Rodolfo de Benedetti.

Krueger, D., and F. Perri. 2006. Does Income Inequality Lead to Consumption Inequality? Evidence and Theory. Review of Economic Studies 73 (March): 163-93.

Petev, I., L. Pistaferri and I. Saporta Eksten. 2011. Consumption and the Great Recession: An Analysis of Trends, Perceptions and Distributional Effects. In Analyses of the Great Recession, D. Grusky, B. Western and C. Wimer (eds.) forthcoming.

Piketty, T., and E. Saez. 2003. Income Inequality in the United States, 1913-1998. Quarterly Journal of Economics 118 (1): 1-39.

Hang was named president and CEO of the Wilder Foundation, a nonprofit health and human service agency, in 2010 and was formerly its director of Children and Family Services, having spent most of her career working to improve the lives of those who are disadvantaged. She holds a bachelor’s degree in psychology from Brown University and a master’s degree in public affairs from the University of Minnesota. She also serves on the boards of the John S. and James L. Knight Foundation, Twin Cities LISC and Minnesota Philanthropy Partners. She received the Ann Bancroft Dream Maker Award in 2009 for supporting women and girls and was a Minneapolis-St. Paul Business Journal 40 under 40 honoree in 2010.

The Minneapolis Fed has a nine-member board of directors. Three directors represent the interests of banking in the district and are elected by banks that are members of the Federal Reserve System. The other six represent the general public, which includes business, agriculture, labor and consumers. Of these, three are elected by member banks and three are appointed by the Board of Governors of the Federal Reserve System in Washington, D.C. Hang was appointed to the Minneapolis board by the Board of Governors.

The responsibilities of directors are broad, ranging from overseeing the general operations of the Minneapolis Fed to reporting on district economic conditions. This information helps prepare the Minneapolis Fed president for participation in Federal Open Market Committee meetings, where decisions are made about monetary policy.

As one of the 12 Federal Reserve banks, the Federal Reserve Bank of Minneapolis contributes to a variety of Federal Reserve System functions, including operation of a nationwide payments system, distribution of the nation’s currency and coin, supervision and regulation of member banks and bank holding companies, and serving as a fiscal agent for the U.S. Treasury. Additionally, the president of the Minneapolis Fed serves as a member of the Federal Open Market Committee, the monetary policymaking arm of the Federal Reserve’s Board of Governors. Together with its branch in Helena, Mont., the Minneapolis Fed serves the Ninth Federal Reserve District, which includes Minnesota, Montana, North and South Dakota, 26 counties in northwestern Wisconsin and the Upper Peninsula of Michigan.

Kurokawa has been with the bank since 1979. He became a director in 1987 and was named president in 2008. Kurokawa has been active in a number of business and civic organizations in the community, including the Chamber of Commerce and Agriculture, Montana Microbusiness Program, Missouri Valley Development Corp., Great Northern Development Corp., and the Montana Bankers Association. He also served as an alderman for the city of Wolf Point.

Branch directors help oversee the operations of the Helena office and contribute their perspectives on regional economic conditions. This information helps prepare the Minneapolis Fed president for participation in Federal Open Market Committee meetings, where decisions are made about monetary policy.

As one of the 12 Federal Reserve Banks, the Federal Reserve Bank of Minneapolis contributes to a variety of Federal Reserve System functions, including operation of a nationwide payments system, distribution of the nation’s currency and coin, supervision and regulation of member banks and bank holding companies, and serving as a fiscal agent for the U.S. Treasury. Additionally, the president of the Minneapolis Fed serves as a member of the Federal Open Market Committee, the monetary policymaking arm of the Federal Reserve’s Board of Governors. Together with its branch in Helena, Mont., the Minneapolis Fed serves the Ninth Federal Reserve District, which includes Minnesota, Montana, North and South Dakota, 26 counties in northwestern Wisconsin and the Upper Peninsula of Michigan.

Palmer has been with Range Financial Corporation and Range Bank since 1998. He is chairman of the advisory board for Northern Michigan University College of Business and the Catholic Diocese of Marquette finance committee. He serves on the board of the Negaunee City Police Retirement Fund and the advisory board for the Upper Peninsula Medical Center. Palmer is also a member of the Marquette County Economic Club.

The Minneapolis Fed has a nine-member board of directors. Three directors represent the interests of banking in the district and are elected by banks that are members of the Federal Reserve System. The other six represent the general public, which includes business, agriculture, labor and consumers. Of these, three are elected by member banks and three are appointed by the Board of Governors of the Federal Reserve System in Washington, D.C. Palmer was elected to the Minneapolis board by member banks.

The responsibilities of directors are broad, ranging from overseeing the general operations of the Minneapolis Fed to reporting on district economic conditions. This information helps prepare the Minneapolis Fed president for participation in Federal Open Market Committee meetings, where decisions are made about monetary policy.

As one of the 12 Federal Reserve banks, the Federal Reserve Bank of Minneapolis contributes to a variety of Federal Reserve System functions, including operation of a nationwide payments system, distribution of the nation’s currency and coin, supervision and regulation of member banks and bank holding companies, and serving as a fiscal agent for the U.S. Treasury. Additionally, the president of the Minneapolis Fed serves as a member of the Federal Open Market Committee, the monetary policymaking arm of the Federal Reserve’s Board of Governors. Together with its branch in Helena, Mont., the Minneapolis Fed serves the Ninth Federal Reserve District, which includes Minnesota, Montana, North and South Dakota, 26 counties in northwestern Wisconsin and the Upper Peninsula of Michigan.

Mary K. Brainerd was named chair of the board of directors for 2012. She has been a member of the board since 2008 and was deputy chair for 2010 and 2011. Brainerd is president and chief executive officer of HealthPartners in Minneapolis. 

Randall J. Hogan was named deputy chair of the board of directors for 2012. He has been a member of the board since 2010. Hogan is chairman and chief executive officer of Pentair, Inc., in Minneapolis. 

Minneapolis Fed directors are selected to represent a cross section of the Ninth District economy, including consumers, industry, agriculture, the service sector, labor and commercial banks of various sizes. The Federal Reserve Bank of Minneapolis Board has nine members. Commercial banks that are members of the Federal Reserve System elect three bankers and three nonbankers. The Federal Reserve Board of Governors in Washington, D.C., appoints three additional nonbankers and designates the board's chair and deputy chair from among its three appointees.

The responsibilities of directors are broad, ranging from overseeing the general operations of the Minneapolis Fed to reporting on district economic conditions. This information helps prepare the Minneapolis Fed president for participation in Federal Open Market Committee meetings, where decisions are made about monetary policy.

The Federal Reserve Bank of Minneapolis is one of 12 regional Reserve Banks that, with the Board of Governors in Washington, D.C., make up the Federal Reserve System, the nation’s central bank. The Federal Reserve Bank of Minneapolis is responsible for the Ninth Federal Reserve District, which includes Montana, North and South Dakota, Minnesota, northwestern Wisconsin and the Upper Peninsula of Michigan. The Federal Reserve Bank of Minneapolis participates in setting national monetary policy, supervises numerous banking organizations, and provides a variety of payments services to financial institutions and the U.S. government.

Because business transactions in tribal jurisdictions are not necessarily governed by state law, lenders often face uncertain rules and higher risk doing business in Native communities when tribes do not have a developed legal infrastructure governing commercial activity, Woodrow testified. Consequently, loans and other business deals are made at higher costs or not made at all, she said.

“To address this, the Federal Reserve Bank of Minneapolis has provided substantial assistance to tribes across the country that choose to adopt the Uniform Law Commission’s Model Tribal Secured Transactions Act, a comprehensive template law substantially similar to state law but specifically tailored for tribal environments,” Woodrow said. “We have also helped facilitate several state-tribal compacts that enable tribes that have adopted the model act to utilize these states’ UCC filings systems for the filing of liens under tribal law. These arrangements complete the tribes’ secured transactions systems in a manner that offers certainty and reliability for lenders and borrowers, while at the same time preserving tribal sovereignty and jurisdiction.”

The Minneapolis Federal Reserve Bank’s Ninth District includes more than 40 Indian reservations. For more information on the Bank’s work in Indian Country, see Woodrow’s complete statement and the Indian Country Currents web page.