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Call for Papers Federal Reserve Ban of Chicago 2002 Conference on Bank Structure and Competition Fourth Quarter 2001 perspectives 2 Electronic bill presentment and payment— Is it just a click away? 17 Liquidity effects in the bond market 38 Countering contagion: Does China’s experience offer a blueprint? 53 Growth in worker quality 75 Index for 2001 Economic . perspectives President Michael H. Moskow Senior Vice President and Director of Research William C. Hunter Research Department Financial Studies Douglas Evanoff, Vice President Macroeconomic Policy Charles Evans, Vice President Microeconomic Policy Daniel Sullivan, Vice President Regional Programs William A. Testa, Vice President Economics Editor David Marshall Editor Helen O’D. Koshy Associate Editor Kathryn Moran Production Julia Baker, Rita Molloy, Yvonne Peeples, Nancy Wellman Economic Perspectives is published by the Research Department of the Federal Reserve Bank of Chicago. The views expressed are the authors’ and do not necessarily reflect the views of the Federal Reserve Bank of Chicago or the Federal Reserve System. Single-copy subscriptions are available free of charge. Please send requests for single- and multiple-copy subscriptions, back issues, and address changes to the Public Information Center, Federal Reserve Bank of Chicago, P.O. Box 834, Chicago, Illinois 60690-0834, telephone 312-322-5111 or fax 312-322-5515. Economic Perspectives and other Bank publications are available on the World Wide Web at http://www.frbchi.org. Articles may be reprinted provided the source is credited and the Public Information Center is sent a copy of the published material. Citations should include the following information: author, year, title of article, Federal Reserve Bank of Chicago, Economic Perspectives, quarter, and page numbers. & chicagofed. org ISSN 0164-0682 Contents Fourth Quarter 2001, Volume XXV, Issue 4 Electronic bill presentment and payment—Is it just a click away? Alexandria Andreeff, Lisa C. Binmoeller, Eve M. Boboch, Oscar Cerda, Sujit Chakravorti, Thomas Ciesielski, and Edward Green This article addresses the following questions about electronic presentment and payment (EBPP) in the business-to-consumer marketplace: Why aren’t electronically presented bills always paid electronically? And, if EBPP does aid in the migration to fully electronic end-to-end payment, what are the barriers to its adoption? 17 Liquidity effects in the bond market Boyan Jovanovic and Peter L. Rousseau The authors find that supply risk in the market for Treasury bills adds between 10 basis points and 40 basis points to the standard deviation of the T-bill interest rate. The risk will probably increase unless the Fed expands the set of assets that it uses to conduct open market operations. Call for Papers Countering contagion: Does China’s experience offer a blueprint? Alan G. Ahearne, John G. Fernald, and Prakash Loungani China did not succumb to the Asian crisis of 1997-99, despite two apparent sources of vulnerability: a weak financial system and increased export competition from the Asian crisis economies. This article argues that both sources of vulnerability were more apparent than real. China’s experience (especially its use of capital controls) does not offer a blueprint for other countries, because other countries would not want to replicate China’s inefficient, non-marketoriented financial system. 53 Growth in worker quality Daniel Aaronson and Daniel Sullivan This article shows that increases in the educational attainment and labor market experience of the U.S. work force have led to an advance in labor productivity of more than 0.2 percentage points per year since the early 1960s. Estimates show, however, some deceleration in the pace of labor quality improvements toward the end of the 1990s. Forecasts call for a continued decline over the remainder of the current decade. Index for 2001 Electronic bill presentment and payment Is it just a click away? Alexandria Andreeff, Lisa C. Binmoeller, Eve M. Boboch, Oscar Cerda, Sujit Chakravorti, Thomas Ciesielski, and Edward Green Introduction and summary This article concerns electronic bill presentment and payment (EBPP) in the business-to-consumer (B2C) marketplace and, more specifically, remote bill payments (as opposed to payments made at the point of sale). B2C EBPP applications are plausibly among the most promising innovations to shift U.S. consumer payments from checks to electronic alternatives. By EBPP, we mean the electronic bill presentment to the consumer and the electronic initiation of payment by the consumer. Some analysts have suggested that electronic delivery of bills will increase the use of electronic payments. Our research attempts to answer the following two questions: Why arent electronically presented bills always paid electronically? And, if EBPP does aid in the migration to fully electronic end-toend payment, what are the barriers to its adoption? While the U.S. continues to lead the world in technological advancements such as the development and widespread use of the computer and Internet technologies, Americans still rely on checks to make most payments. The U.S. has higher check usage per capita than any other industrialized country. Humphrey, Pulley, and Vesala (2000) state that U.S. consumers and businesses write around 20 checks per month. This is more than 2.8 times the number of checks written per person in Canada, France, or the UK and at least 20 times more per person than in Germany, Japan, Italy, Belgium, the Netherlands, Sweden, or Switzerland. In 1999, U.S. businesses and consumers issued a total of 68 billion checks (BIS, 2001). The proportion of check usage is highest for remote payments. Of the 15 billion to 17 billion consumer bills issued every year, over 80 percent are paid by check (Kerr and Litan, 2000). If EBPP were to capture the whole consumer market and convert all check payments into electronic ones, the number of checks written by consumers would be reduced by over 40 percent. 2 Today, most consumers still receive their bills via mail. With todays technology, bills may be presented via the Internet, mobile phone, or personal digital assistant anywhere in the world, allowing for greater convenience to consumers. However, how much, if anything, most consumers are willing to pay for such a service remains unclear. Greater convenience, along with value-added services such as better customer service, account aggregation, and other incentives may be required to achieve the number of consumers necessary for billers to provide EBPP services. In the next section, we explore how most traditional bills are presented and paid. Then, we discuss the various EBPP models and what enhancements to existing payment choices may be necessary for Internetand e-mail-initiated transactions. Next, we consider the various payment options available for EBPP; and finally, we outline barriers to market adoption of EBPP services. Traditional bill presentment and payment Below, we discuss the costs and benefits of the traditional bill presentment and payment process. Industry participants estimate that billers issued between 15 billion and 17 billion consumer bills in 2000. Over 80 percent of these bills were originated by one of four industry groups: finance, insurance, telecommunications, and utilities. Billers and consumers pay $80 Alexandria Andreeff is project leader and Lisa C. Binmoeller is project manager of the Federal Reserve Bank of Chicagos EBPP Leadership Assignment. Eve M. Boboch, assistant vice president, and Thomas G. Ciesielski, vice president in the Financial Services Group, are project directors of the EBPP Leadership Assignment. Oscar Cerda is a regulatory associate economist, Sujit Chakravorti is a senior economist, and Edward Green is senior policy advisor at the Federal Reserve Bank of Chicago. The authors would like to extend a special thank you to Elizabeth Knospe and Ann Spiotto for their legal assistance. 4Q/2001, Economic Perspectives billion annually for bill presentment and payment.1 Internal operations for bill and payment processing cost billers $45 billion yearly, with almost two-thirds ($30 billion) of that going to technology vendors for hardware and software implementation and support. The remaining $35 billion comprises $10.7 billion for postage; $8 billion for account fees at financial institutions; $7.5 billion for insufficient fund fees; $5.7 billion for outsource bill/payment processing; $2.2 billion for check clearing fees; and $0.9 billion for ACH (automated clearing house)/credit card processing and settlement fees (Whaling, 2000a). Bill presentment Traditional consumer bill presentment and payment is a paper-based process that involves presenting the consumer with a paper bill for goods or services previously rendered, which the consumer pays by check. The bill presentment process involves billing operations, such as generating, printing, mailing, and delivery of bills to consumers.2 The cost of processing, printing, and sending bills can range from $.70 to $1.50 (PayAnyBill, 2000). In other words, billers spend between $10.5 billion and $25.5 billion each year to present bills.3 The traditional bill presentment process involves not only the biller generating a periodic report from its billing systems, but also the subsequent notification of the bill to the consumer for goods or services previously rendered. Paper-based billing is an intricate and time-consuming process that involves printing account statements, stuffing them into envelopes (along with appropriate advertising inserts and other marketing material), and sorting for mailing. Depending upon the level of automation, the creation and processing of bills can take anywhere from one to three days (Doculabs, 1999). In the traditional bill payment world, notification and presentment of a bill occur simultaneously when the consumer receives the bill in their mailbox. For most bills today, the post office serves as a notification and presentment network by delivering each statement from each biller individually to each customer. The average bill takes three to five days to reach the consumer. Most billers have streamlined and improved their paper-based billing operations as much as possible. However, the traditional billing process still faces issues of convenience, timeliness, cost, and reliability. Traditional billing does not allow consumers to receive their bills anytime and anywhere. Furthermore, errors in customers addresses (which are common when customers relocate) may lead to significantly longer delivery times. The process of printing bills, assembling bills, and delivering bills is time-consuming Federal Reserve Bank of Chicago and costly to billers since it is resource intensive. Traditional billing also lacks reliability because paperbased billing systems offer no guaranteed delivery mechanism. Furthermore, lost bills result in customer service problems and late fees for consumers. Bill payment The bill payment process involves at least five main participants: the consumer, the consumers financial institution, the biller, its financial institution, and a payment network (see figure 1). The consumer will ultimately use funds on deposit at their financial institution to settle the monetary obligation. In the case of check, debit card, or ACH payments, the consumer will have to deposit the funds into their account before initiating payment to the biller.4 The billers financial institution will present the consumers payment obligation through a payment network that processes checks, ACH payments, credit cards, or debit cards. The consumers financial institution will send funds via the network to the billers financial institution if sufficient funds are in the payees account (or if the payee has a sufficient line of credit). The advantages and disadvantages of each network are well known.5 Electronic bill presentment and payment In contrast to the traditional model, EBPP no longer uses the mail system as a delivery mechanism for bill presentment and payment initiation. Instead, it uses the Internet as a speedier and less expensive delivery infrastructure to present bills electronically.6 With the percentage of U.S. households with Internet access having increased from 26.2 percent to 41.5 percent between December 1998 and August 2000 (U.S. Department of Commerce, 2000), Internet access to bill presentment and payment options is on the rise. Consumer and biller expectations Compared with the traditional bill delivery and payment methods, EBPP seeks to meet or exceed the sometimes competing expectations of consumers and billers. Consumer expectations ■ ConvenienceConsumers do not view the traditional payment methods as overly burdensome and would expect any new bill payment procedure to meet or improve on this convenience before switching. ■ Time/cost savingsConsumers would expect to have costs that are just as low (or even lower) than their current bill paying costs. ■ Control over paymentsAs with the use of checks, with EBPP consumers would expect to have control over the timing and amount of payments. 3 FIGURE 1 Bill payment Consumers Billers Payment network Consumer's financial institution ■ ■ Universal payment mechanismsBecause electronic payments are replacing the check, consumers would expect to have a wide range of electronic payment options that meet their specific payment needs or wishes. Privacy and securityConsumers must be confident that any bill payment process will protect their privacy and funds by securely transferring billing information and payments. ■ ReliabilityConsumers must trust the accuracy of their electronic bills and feel confident that their payments will be delivered accurately and on time. ■ Dispute resolutionConsumers need reliable and accessible customer service options to resolve any questionable transactions. Biller expectations ■ Cost reductionsBillers want a bill creation and payment process that is less costly or, at least, no more costly than the current paper-based billing process. ■ 4 Dispute resolution mechanismBillers require an accessible and cost-efficient dispute resolution procedure. Biller's financial institution ■ Reliable delivery mechanismBillers want a fast and reliable delivery mechanism for both presentment and payment. ■ Ability to up-sell and cross-sellBillers want to employ specialized, targeted marketing techniques, rather than general paper statement stuffers that may not be suitable for each consumer. Control over customer dataBillers want to protect and safeguard their most valuable data. ■ ■ Broad distribution/reachBillers require a broad delivery and payment medium to gain maximum customer use. EBPP presentment models The two primary EBPP models are the biller-direct model and the consolidation/aggregation model. There are a number of variations of the consolidation/ aggregation model, including e-mail-based EBPP, the use of personal financial management software, screen scraping, and scan and pay methods. The notification procedure for both the biller-direct and the consolidation/aggregation models involves the biller notifying the customer of a pending bill, generally via e-mail, and the customer subsequently logging onto either the billers or consolidator/aggregators website (see figure 2). 4Q/2001, Economic Perspectives Biller-direct model In the biller-direct model, once a consumer has enrolled for EBPP services, the biller generates an electronic version of the consumers billing information. The biller may outsource this responsibility using a bill service provider (BSP). These BSPs act as agents on behalf of the biller and provide such services as electronic bill translation, formatting, data parsing, and, at times, hosting the billers website. Next, the biller notifies the consumer of a pending bill, generally via e-mail, and the consumer is directed to log onto each billers website (or to a BSPs website), where the biller presents the consumer with an electronic version of the billing statement (see figure 3). After viewing the online bill, the consumer can initiate payment directly from the website. From the time of enrollment to the time of initiating payment, there are no other parties that come between the biller (or its BSP) and consumer. Thus, the biller (or its BSP) is responsible for interfacing with the customer to enroll, access electronic billing information, and make payments. Biller-direct advantages For the most part, the biller-direct model exceeds the billers basic expectations. It provides the biller with an electronic method of creating and notifying the consumer of pending bills in a cost-efficient manner. It is estimated that the electronic creation of all consumer bills would significantly reduce bill statement production costs. The model also provides a more reliable, faster delivery mechanism for both bill delivery and payment receipt, compared with traditional bill presentment and payment (Kerr and Litan, 2000). Delays and interruptions can have negative effects on the billers cash flow. The model also has the potential to reduce costs through the use of electronic consumer dispute resolution mechanisms. It is estimated that 70 percent of calls to telephone-based customer service centers concern billing statements (IBM, 2000). Online resolution could reduce the labor and overhead costs associated with customer service centers. The biller-direct model is also advantageous for the biller in that it maintains direct contact with the consumer. Since the biller controls how the bill is visually presented through its website, it can maintain its brand identity. The biller-direct model can also lead to better cross-sell and up-sell opportunities. It attempts to integrate bill presentment with specialized, targeted marketing techniques, rather than broad-based advertising that may not apply to all consumers. Since FIGURE 2 Notification Co ns Biller A um Co er ns um Co er ns um er C B Ab m Fro ill bil er bill l Consumer B bill From l n Co er B m su on C Biller C Federal Reserve Bank of Chicago C From l bil er um s on Consumer B From biller C il Ab su C From biller B E-mail Consumer C bill r me er bill From biller A Consumer A bill Biller B bill m Fro l B er m Fro bil Consumer A A C l bil From bille rA bille rB bille rC Consumer C 5 FIGURE 3 Biller-direct presentment Biller A website Consumer A Biller B website Consumer B Biller C website the biller controls the consumers demographic data (captured during the enrollment process) and purchasing habit data (through billing data), it can more easily target different market segments. Finally, since the biller relies on its own (or its BSPs) processing systems, it does not risk third-party integration problems seen in other EBPP models (Doculabs, 1999). In its survey of consumer highvolume billers (over 250,000 bills a month), Gartner Group estimated that by year-end 2000, 74 percent of e-billers would be presenting on their own companys website (Kerr and Litan, 2000). From the consumers perspective, the biller-direct model meets many of the basic expectations seen in the traditional mailing system model. Customers expect EBPP costs that are just as low (or even lower) than their current bill paying costs. Under the billerdirect model, the customer saves on mailing and check writing costs when submitting payments. In addition, biller-direct applications are usually offered free of charge. The majority of consumers perceive the U.S. Postal Service to be a secure, private, and reliable way of transporting bills and payment (Whaling, 2000a). EBPP 6 Consumer C offerings utilizing the biller-direct model use login IDs, passwords, and encryption technology to provide sufficient protection for privacy of consumers bill records, as well as the actual payments. Biller-direct providers are relying not only on the 128-bit RC4 encryption in the consumers browser, but also on additional encryption and secure channels for all messages sent between the biller, financial institution, and consumer. In addition, consumers may be more comfortable establishing EBPP arrangements directly with billers with whom they have existing relationships rather than with unknown third parties. Using the Internet as a reliable delivery mechanism also results in a reduction of potential late fees due to lost mail or misplaced payments.7 Finally, consumers benefit from the biller-direct model from a customer service perspective. Consumers have the ability to access current or real-time information since billers are able to update customer data more frequently. The biller-direct model also allows for simpler and faster dispute resolution online versus current telephone contacts. Market research indicates that direct billers have a tendency to provide better customer service than third parties (Robinson, 2000). 4Q/2001, Economic Perspectives Biller-direct disadvantages The biggest disadvantage of the biller-direct approach is that consumers must take the initiative to visit multiple billers websites to view and pay their bills. Even if some billers use the same BSP, it is currently unlikely that all of a consumers billing information can be received from a single site. This can be time-consuming and potentially confusing if all billers do not use similar processes or interfaces for presenting bills and accepting payments, in addition to the multiple usernames and passwords the consumer must remember. While this model was acceptable to the early adopters, it may be too cumbersome for the broader consumer market. Another disadvantage for consumers utilizing the biller-direct model is that not all billers provide a wide range of electronic payment options for each consumers payment needs. In the traditional model, all consumers have access to one universal payment option, the check.8 For billers, the largest disadvantage to implementing the biller-direct model is that it is expensive to establish, design, host, and maintain an in-house application (Doculabs, 1999). In a survey of highvolume billers building or buying software for inhouse EBPP, first year expenditures in EBPP programs averaged nearly $570,000 (Kerr and Litan, 2000). Consolidation/aggregation model The consolidation model was introduced to address consumers desire to have one destination to access and pay their bills while reducing the cost to billers of implementing EBPP. In this model, the biller sends the customers billing information to a third party called a bill consolidator. The consolidator, operating on behalf of the biller (or the aggregator operating on behalf of the consumer) combines data from multiple billers and consolidates the information at a single destination. Although some consolidators present bills at their own websites, most support the aggregation of bills by consumer service providers (CSPs) such as Internet portals, financial institutions, and brokerage websites (see figure 4). Thick consolidation Two variations of the consolidation model have emerged, thick and thin consolidation. Under thick consolidation, the consolidator maintains both the summary and details of the customers billing information. The customer does not need to have one-on-one contact with the original biller to view the full detail of bills due. Thick consolidation advantages In the thick consolidation model, the biller can offer EBPP services to its customers without having Federal Reserve Bank of Chicago to implement its own costly infrastructure. Smaller billers lacking financial resources can still enter the EBPP arena quickly. In fact, Gartner Group found that 41 percent of current consolidation users cited the ability to start e-billing quickly as the top reason for using the thick consolidation model (Kerr and Litan, 2000). For consumers, having a third party offer EBPP services may be to their advantage if the third party is able to offer numerous electronic payment options not supported directly by the biller. Depending on the consolidation site chosen, convenience may be increased by allowing consumers to access their billing data at popular Internet sites where they can perform other tasks (such as online banking or online shopping). Financial institutions could potentially garner several benefits if they choose to host a bill consolidation site. In contrast to the traditional and biller-direct models, with this model banks are no longer forced into the background performing simple payment processing. Banks entry into hosting an EBPP consolidation site could help drive the use of other online banking services, thus maintaining customer relationships (McPherson, 2001). Thick consolidation disadvantages Under the thick consolidation model, billers generally lose branding, marketing, and cross-selling capabilities as consolidators eliminate direct contact between billers and customers. In addition, billers risk losing control over consumer data once it moves to the consolidator. For consumers, unlike providers offering the biller-direct EBPP model, providers of the third-party consolidation/aggregation model typically charge a fee of approximately $4$12 per month. This is problematic given that 59 percent of consumers are not willing to pay for online bill payment and only 6 percent are willing to pay more than $5 (Kerr and Litan, 2000). In addition, customer service levels may be reduced as consumers are directed to different contacts for different problems. Thin consolidation Under the thin consolidation model, the biller maintains the details of the customers billing information while a summary is forwarded to the consolidator. Customers can view a summary of their bills on the consolidators site, while those desiring to view the details are linked to the original billers website. Thin consolidation advantages The thin consolidation model allows billers a greater opportunity to provide online customer service, cross market products, and gain greater control of their e-billing process, particularly by maintaining control over consumer data (Kerr, 2000). Furthermore, 7 FIGURE 4 Consolidation/aggregation Co ns Biller A um Co er ns um Co er ns um er C B Consumer A s iew A v bills r me ed nsu at Co solid con Ab ill bil l bil l Consumer A bill Biller B Consolidator/ aggregator website Consumer B bill Consumer C bill ill r me su n Co Ab er um s on r me C Biller C B l bil similar to the biller-direct model, the thin consolidation model provides consumers with the perceived security and comfort of knowing that their billing information is stored by the biller, an entity with which they already have an established relationship. Thin consolidation disadvantages While this model may be advantageous for marketing purposes, billers still lose the marketing channel if customers choose not to view the full details of their bills. In addition, the biller must bear the cost of hosting a website where consumers can view the details of their bills. Finally, issues with technology, standards, and security increase between the consolidators and the billers website, as they must be more closely integrated than in the thick consolidation model. Because the consolidation models involve the transmission of more data and the number of parties involved increases, the potential for errors also increases if the industry lacks a universal message standard for data exchange. Several groups are currently working on a universal open standard that would allow billers to present bills to any customer, anywhere, at anytime. Two industry standards have been introduced over the last few years, the Open Financial Exchange (OFX) and Interactive Financial Exchange (IFX). 8 Consumer B Con con sumer soli date C views d bi lls u ns Co ill Cb Consumer B views consolidated bills Consumer C Both of these standards are in their infancy. While OFX has the widest acceptance, it is a very basic standard that supports HyperText Markup Language (HTML) and lacks the depth to support any level of complexity in a billing statement for consumers.9 Meanwhile, IFX is being aggressively pursued by industry workgroups and EBPP software providers. Since IFX is Extensible Markup Language (XML) based, it is more robust than OFX, with mechanisms for richer payment information and customer transaction tracking functionality (West, 2001). However, given that the industry has yet to adopt one standard for data exchange, integration issues in the consolidation/aggregation models persist. Consolidation preferences Larger billers are beginning to show a preference for the thin consolidation model. In its survey of highvolume billers, Gartner Group found that in 1999, 59 percent of billers that adopted the consolidation model used the thick model. In contrast, nearly 63 percent of billers planning to implement EBPP services in the future intended to use the thin consolidation model by year-end 2000. In fact, 64 percent of billers currently utilizing the thick model intended to switch to the thin consolidation model within the next three years. 4Q/2001, Economic Perspectives It was also estimated that 75 percent of high-volume billers will use the thin consolidation model by yearend 2002 (Kerr and Litan, 2000). According to Gartner Group, the consolidation models high fees, lengthy and confusing enrollment, and fragmented customer service have kept enrollment low (Litan, 2000). The challenge inherent in both variations of the consolidation model is the coordination between the different parties involved in the process. Once these issues are resolved, growth of the consolidation model is expected to increase. As the number and type of sites that aggregate multiple bills continue to grow, use of the consolidation model is expected to outpace that of the biller-direct model by 2004. Financial institutions, brokerages, Web portals, and now the U.S. Postal Service have all added EBPP to their online offerings (Kerr, 2000). Although industry experts suspect that consumers will ultimately prefer to have all of their bills presented at one location, the disadvantages of the consolidation models (namely, security, customer service, high fees, and cumbersome enrollment procedures) may perpetuate the use of the biller-direct model. Given the advantages and disadvantages of each model, it is not surprising that many billers currently use both the direct model and variations of the consolidation models. In its 2000 survey of highvolume billers, Gartner Group indicated that by yearend 2000, 74 percent of e-billers would be presenting on their own companys website, while 73 percent would have contracted with third-party bill consolidators (Kerr and Litan, 2000). Alternative consolidation/aggregation models Other variations of the consolidation/aggregation model exist. These mainly consist of differences in bill delivery or creation methods. Next, we provide a brief summary of the alternative aggregation EBPP models. Total bill consolidation This is the only model that currently enables customers to view all their bills at a single point via the Internet. Total bill consolidation providers require customers to redirect their bills to the providers data center, which serves as a lockbox operation where bills are scanned and transformed into electronic format (usually in a portable document format or PDF). After bills are converted to electronic format, they are presented at a consumer service provider (CSP) (Jeffrey, 2000). The primary advantage of the total bill consolidation model is that there is no need for standard systems and there are no complicated issues regarding thick versus thin hosting. In addition, scan and pay Federal Reserve Bank of Chicago companies can provide customers with all of their bills electronically, regardless of whether the biller provides its bills electronically. One of the limitations of this model is the reliance of the bill scanner on printing and mailing checks to the biller after the customer has authorized payment. In fact, it is estimated that over 50 percent of bills presented electronically via the total bill consolidation method are settled with the biller via check (Glossman et al., 2000). In addition, consumers do not receive bills any faster (in some cases more slowly) and the value of the bill is improved little over the paper version, since it has no interactive capabilities. For billers, direct contact with the customer is lost. In fact, the biller may not even be aware that the customer has redirected statements to a total bill consolidator. Screen scraping Screen scrapers are companies that capture customer data from multiple billers websites with the use of customer supplied IDs and passwords. Once captured, the data is presented at the screen scrapers aggregation website or other CSP aggregation website. Screen scraping companies provide the software used for screen-scraping purposes. In the past, screen scraping was primarily a unilateral procedure initiated by the screen scraper acting on behalf of the consumer. A major disadvantage of this model to billers was that they were not involved in the process. Billers were unaware that their customers information had been screen scraped. This raised concerns about security, privacy, and data accuracy, as well as liability and regulatory issues. With resistance to screen scraping fading within the past year, there has been a more collaborative approach between billers and screen scrapers. Major consolidators and major financial institutions have embraced screenscraping technology. Although this approach fulfills a customers desire for aggregation, it has yet to be seen if screen scraping will adequately address the issues of liability, security, privacy, and data accuracy (Gillespie, 2000). One attempt to decrease the use of screen scraping in data aggregation is being headed by the Financial Services Technology Consortium (FSTC). Its goal is to test methods of account aggregation, whereby financial institutions and aggregators work together to facilitate accurate display of customer data without screen-scraping or without the customers surrender of financial institution access codes to third-party aggregators (Gram, 2001). Consumer consolidation model In the consumer consolidation model, electronic bills are delivered directly to the customers desktop. 9 The biller maintains control of bill details until delivery to the customer is complete. The customer is then able to control and store bills and can integrate them into offline programs, such as personal financial management software. When payments are initiated via a personal financial management software program, the payments are facilitated through a consolidator. As a result, payments may be made via check, ACH, credit, or debit card payments. The major advantage of the consumer consolidation model is that the consumer is able to work offline. One of its less attractive features is that consumers are required to download or purchase special software to view bills rather than using a standard browser (Chandler, 1998). E-mail consolidation In the e-mail-based billing model, companies enable the full detailed billing statement to be sent directly from the biller to the consumer in HTML format. To the consumer, the billing data received resembles a Web page and can contain graphics, advertisements, links to the billers website, links for immediate bill payment, and a link to customer service (Kille, 2001). The major advantage of the e-mail-based model is that consumers are comfortable using e-mail. It also allows for personalized electronic exchanges from biller to consumer (Rini, 2000). As in the biller-direct model, the biller maintains complete control over the bill delivery process. For the consumer, there is no need to download or learn any special software or device. Disadvantages of this mode of bill delivery are that the billing information is not interactive and there is no guarantee that the consumers e-mail post office will properly deliver the e-mail. EBPP payment options The payment mechanisms used by consumers in the EBPP process are essentially the same as those used in a traditional bill payment environment: check, ACH, credit card, and debit card. Consumers often authorize payments online rather than in paper form. These payments are sometimes fulfilled in an electronic fashion; however, many EBPP providers still issue payments via check on behalf of the consumer. Currently, bill payment service providers process an estimated 40 percent of bill payments via check, with less than 60 percent of the remaining bill payments processed electronically, predominantly through the ACH (Whaling, 2000b). Regardless of how the payments are ultimately made, payment instructions are most often provided online in EBPP applicationsintroducing concerns about the privacy of consumers information, the security of payment 10 data being transmitted, and the authentication of parties to the transaction. Privacy of information is one of the most critical issues in an online environment. This is made more difficult by the fact that EBPP providers may be subject to different rules and requirements protecting consumers information, depending on whether the provider is a financial or nonfinancial institution. To make matters more complex, state privacy laws vary greatly in terms of the protection provided to consumers.10 The security of the billing information presented and the payment instructions received are also of concern in an online environment. For an EBPP solution to be effective, it must protect the integrity of billing data presented and payment instructions received through the entire process. Hackers, disgruntled employees, fraudulent billers, or insufficient data security procedures can threaten the security of the process. Also, since EBPP is relatively new, there is little (if any) legal precedent identifying the responsible parties in the event that billing or payment data are compromised. Due to the electronic nature of EBPP transactions, authentication of the parties involved is essential. In a traditional environment, consumers receiving bills via the mail generally assume that these bills are legitimate if they follow the billers conventional format. On the payment side, the legal framework is well established to provide parties to a transaction with protection from fraudlargely based on paper-based signatures. When bills are presented online, the consumer has little way of knowing whether the biller really issued those bills, unless the consumer uses the biller-direct model or has some kind of guarantee of authenticity from the service provider. Conversely, the identity of the consumer must be authenticated to ensure that payment instructions being provided are not being initiated fraudulently. In an online environment, it is unclear what constitutes authenticationparticularly from a regulatory standpoint. Progress is being made in this area, with the approval of the Uniform Electronic Transactions Act (UETA) and its adoption in more than 20 states. The federal Electronic Signatures in Global and National Commerce Act (the E-Sign Act), part of which became effective in October 2000, considers and promotes electronic signatures as an appropriate means of authenticating identity. Per a March 29, 2001, press release, the Federal Reserve Board of Governors is currently modifying Federal Reserve Regulation E, which applies to electronic payments, to reflect certain provisions of the E-Sign Act. 4Q/2001, Economic Perspectives Each of the four primary payment mechanisms used for bill payment has advantages and disadvantages in conjunction with electronic bill presentment. To some extent, consumer choice will drive the popularity of the payment mechanisms. In many electronic transactions, though, the way in which the payment is made is unknown to the consumer. Ultimately, this means that billers and financial institutions preferences for one financial instrument over another could have a significant impact on the mechanism that ultimately dominates EBPP. Check The payment component of EBPP is sometimes accomplished via paper check if a biller participating in an electronic transaction cannot receive electronic payments. In these cases, the consumer often provides electronic payment instructions to the service provider, but the service provider executes the payment by writing a check to the biller. Consumers are often unaware that a check payment has been made to the biller because their portion of the transaction is entirely electronic. Checks provide important benefits in the EBPP process in that they can be used to pay virtually anyoneeven billers that are unable to receive electronic payments. However, there is a unique disadvantage for some billers receiving checks in an EBPP environment. Service providers sometimes consolidate (by biller) multiple consumer payments initiated electronically in a check-and-list format. A biller receiving a check-and-list payment receives a single check payment with a list of the consumers payments included in the lump sum. The biller then must use a manual process to reconcile the payments received in its billing system, which can be labor-intensive and introduces yet another opportunity for error into the reconciliation of bills. Electronic payment options streamline this process, and would seem to be preferable from a billers perspective for use in conjunction with EBPP. From the perspective of payment system efficiency, though, the check-and-list process does reduce the total number of checks that would have to be processed if each consumer paid those bills by check. In addition, the check and list removes authentication issues between the biller and the EBPP service provider; the service provider essentially assumes responsibility for authenticating the identity of consumers initiating payments. ACH ACH is the most common electronic payment option used for consumer bill payments. In a traditional environment, ACH transactions are sometimes Federal Reserve Bank of Chicago perceived negatively by consumers citing a need for greater control over the timing and amount of their bill payments. However, EBPP applications can often provide consumers with greater control over their ACH payments. Consumers have the option to pay bills through one-time ACH credit transactions initiated through their financial institutions, through onetime ACH debit payments authorized online (that is, click to pay), or through the more traditional automated recurring debit transactions (direct debit) authorized online or via paper. Though online bill payment applications appear to remove some of the barriers to consumer use of ACH payments, there are still some obstacles that must be overcome. Many billers (particularly smalland medium-sized organizations) do not initiate or receive electronic payments. The reasons for this are uncertain and require further investigation as discussed in the Barriers to EBPP section of this article. In addition, the consumer enrollment and authentication process for ACH payments is not standardized partly due to the fact that the regulatory environment surrounding ACH debit is still evolving to accommodate online initiation of payments. This poses challenges for those educating consumers about electronic payments and for EBPP providers wanting to offer ACH as a payment option. Lastly, the consumer protections associated with unauthorized ACH transactions are not as robust or well known as they are for other payment options, which may be preventing greater usage. Credit card Credit card payments are another electronic payment option sometimes available to consumers participating in EBPP arrangements. From a billers perspective, credit card transactions are generally more costly than other electronic payment alternatives and are not accepted by all billers, which may limit their use in the electronic payments arena. It is important to note, though, that some credit cards now feature embedded micro chips that store information useful in authenticating the identity of the consumer. If these cards can increase the reliability of authentication of the consumers, billers may be more receptive to them due to the reduced risk that a bill payment transaction will be fraudulent (although it is unclear whether fraud related to consumer bill payment is a significant issue for billers). Credit card usage is prevalent among consumers in an online environment. Since EBPP applications are typically available via the Internet, consumers may pressure more billers to accept credit card payments.11 Some billers that allow consumers to pay their 11 bills via credit card are now charging a special fee for their use. It is not clear whether billers are charging this fee to cover the increased cost of the transaction, to discourage widespread use of credit cards for bill payment, or due to other reasons. If this becomes a common, accepted practice, more billers may be encouraged to accept credit cards for bill payments. Debit card Debit card transactions are also sometimes used for the payment of electronically presented bills. Offline debit cards are most commonly used because these transactions can be processed offline through the credit card networks. Online debit transactions have not been used on a widespread basis, which may be because there is no standard industry model for authenticating consumers identities and for connecting with the ATM networks to obtain the instant verification of funds availability that is needed to process online debits. Online debit card transactions are currently being piloted on the Internet. One such pilot, launched in February 2001 by BillMatrix Corporation in conjunction with Star Systems, Inc., allows consumers to pay their utility bills using a debit card. In addition, NACHA, the Electronic Payments Association, sponsored a pilot which concluded in March 2001 of consumer debit card use on the Internet; the NACHA pilot combined use of the debit card with a digital signature for authentication purposes. The success of both of these pilots remains to be seen, but interested parties are working to ensure that the debit card plays a role in Internet-based payment transactions. Barriers to EBPP In the introduction to this article, we asked the question: What barriers are preventing widespread adoption of electronic presentment and payment? Initial research has uncovered a number of barriers: 1. Lack of incentives for participants, 2. Lack of standards for enrollment and data exchange, 3. Concerns over security and privacy of financial information, and 4. Legal issues surrounding industry regulations, liability, dispute resolution, and consumer protections. Lack of incentives for consumers EBPP services need to save consumers money and time compared with traditional bill payment. Consumers appear reluctant to use EBPP until more of their bills are available electronically. Checks are perceived to be free and relatively easy to use. Industry analysts agree that consumer adoption would grow more rapidly if EBPP services were offered for free 12 or at a fee lower than current costs associated with check payments. Gartner Group reported that a majority of consumers, 59 percent, say they do not want to pay anything for account and bill payment aggregation services, and 51 percent feel other payment types, including checks, cash, and debit cards, are easier to use (Kerr and Litan, 2000). A cumbersome set-up process and long lead time for electronic payments may also need to be addressed to entice further usage. Consumers often have to wait one billing cycle to set up credit card, debit card, or ACH payments for their bills. Some consumers may also experience a three- to five-day delay between the time their account is debited and the merchant is paid. Consumers may not change existing bill payment habits until they perceive a strong value proposition with EBPP. One approach may be to price paper presentment and payment more directly so as to encourage consumers to utilize electronic alternatives. An alternative potential solution may be to attract consumers to adopt electronic payments through financial incentives. Incentives for financial institutions EBPP could result in lost revenue for financial institutions operating retail lockboxes (the service of financial institutions processing remittance information from a post office box and depositing them directly into an account) and check-processing operations. Some institutions struggle with the inherent conflict of reducing check revenue when promoting electronic payment usage. Furthermore, current pricing policies for electronic and check payments may discourage the use of electronic payment alternatives. However, pressure from EBPP providers has resulted in financial institutions entering the marketplace either directly with more user friendly and less costly EBPP options or by partnering with consolidators. In addition, service providers are targeting financial institutions by offering network switches that utilize open architecture for settlement of EBPP transactions. Incentives for billers The initial costs associated with implementation of e-billing programs for high-volume billers are estimated to average $570,000 (Kerr and Litan, 2000). High implementation costs and the need to operate multiple, complex, billing systems concurrently have likely discouraged biller adoption. The lack of standards and the uncertainty surrounding future EBPP solutions introduce additional disincentives for billers considering participation. While 32 percent of all large volume billers (greater than 250,000 bills a month) (Kerr and Litan, 4Q/2001, Economic Perspectives 2000) are presenting electronically, adoption rates drop considerably for small to medium-sized billers for both electronic presentment and payment. However, adoption by the largest billers may generate the critical mass required to convince consumers of the benefits of EBPP because, as noted earlier, the largest billers generate 80 percent of bills. However, the adoption of EBPP has not necessarily led to end-to-end electronic payments. Virtually all consumer bills are paid electronically when they are presented electronically and the consumer has initiated payment online (click and pay). A majority of these payments are completed via the ACH.12 However, consumer-initiated online recurring bill payments that are not presented electronically (primarily the pay-anyone model) are often completed via check. Additional research is needed to investigate if online-initiated check payments are driven by the billers inability to accept electronic payments or if other barriers are also contributing to the use of paper payments in EBPP. Incentives for third-party service providers The dominance of several providers, lack of open systems, and lack of universally accepted standards may serve as deterrents to new entrants. While larger providers may have the financial resources to develop solutions for multiple standards, the lack of standards may limit smaller providers capabilities. It is also unclear if third-party pricing policies for electronic and check payments discourage the use of electronic methods. Lack of standards for enrollment The multitude of models, payment options, and providers require consumers to use various cumbersome, inconsistent enrollment methods to establish EBPP services. The method of enrollment may vary depending on the biller and/or the model. The fragmented enrollment process has historically been a major barrier for the traditional ACH direct payment product. In the direct payment enrollment process, the onus is typically on the consumer to contact each biller to enroll, change, or cancel automatic deductions. This same type of problem is apparent in the initial EBPP enrollment process, where the burden is again on the consumer to search for billers offering EBPP services. The pay-anyone model tries to alleviate this burden by allowing consumers to initiate payments online to anyone regardless of how the bill is received. While this model begins to address some of the barriers to EBPP, it also appears to introduce paper payments into the process. Federal Reserve Bank of Chicago Standards for data exchange The lack of universal message standards for data exchange continues to hamper growth in EBPP. Several standards have been introduced over the last few years, including OFX and IFX. Industry adoption has been slow, and participants continue to use different formats for the exchange of presentment data, hindering interoperability between various provider and biller systems. Security and privacy concerns Consumers are concerned about the security and privacy of the financial information required for the EBPP process. Gartner Group surveyed consumers and determined that of Internet users who do not pay bills online, 52 percent are concerned about privacy and 48 percent are concerned about security and fraud (Barto, 2001). Some security and privacy concerns regarding electronic data transfer for bill presentment include data confidentiality and integrity, billing statement issuer authentication, and nonrepudiation of statements (Whaling, 2000a). Specifically, the issues include the protection of the data that is transferred between biller, service provider, and consumer from being read or modified; verification that the billing statement received by the consumer was sent from the biller or service provider; and proof of the exact contents of the billing statements. Legal issues When the EBPP provider is a financial organization, this raises a number of legal and regulatory considerations that might not be relevant to a typical commercial provider. The question of which states or countrys laws control an Internet relationship is still developing (Spiotto and Mantel, 2000). States have adopted different consumer protection laws, which may be applicable to EBPP services. Consumers may be exposed to differing protection rights and liabilities. The dispute resolution process may vary depending on the players, models, and payment options. The current legal and regulatory environment is still primarily designed for a paper environment. Conclusion In recent years, commercial use of the Internet has changed the way consumers and billers interact. Traditionally, consumers received paper billing statements for products or services rendered; most consumers then forwarded a check to the biller via mail to pay the amount due. As an information delivery channel, the Internet has provided a new alternative 13 for billers and consumers to complete these transactions. Today, some billers are leveraging the Internet to facilitate EBPP, in which billing statements are delivered to consumers and consumers provide bill payment instructions via e-mail or on the Internet. In this article, we addressed two important questions: What barriers are preventing widespread adoption of EBPP?, and Will the electronic delivery of bills increase the use of electronic payments? Industry analysts have heralded EBPP as the killer application enabled by the Internet, and some have claimed that electronic presentment of bills will be the key driver leading to the electronic payment of bills. However, we find that in spite of extremely optimistic predictions of growth for EBPP, actual use of EBPP is estimated at less than 1 percent of consumers electronically paid bills (Kerr and Litan, 2000). Furthermore, upon closer look at the industry, we find that checks are still predominately used to pay consumer bills including bills that are presented electronically. We have uncovered several barriers to greater use of EBPP and electronic payments. The most critical barrier is that key parties have insufficient incentives to use EBPP and/or electronic payments instead of traditional presentment and payment methods. Another inhibiting factor is the lack of standards in several areas of the industry: The enrollment process is inconsistent among service providers, and there are no universally accepted standards for the presentment of bills, thus hindering greater interoperability in the industry. As with other Internet-based applications, security and privacy concerns may be slowing the adoption of EBPP, as are uncertainties and obstacles in the legal and regulatory environment. It is unclear whether increased EBPP usage will truly drive the use of electronic payments among consumersand if so, what their electronic payment method of choice will be. This article identifies the key barriers to EBPP and suggests some areas in which incentives could be provided to encourage greater use of EBPP and electronic payments. Some, but not all, of these areas represent potential opportunities for both the private and public sector in facilitating the migration from traditional paper-based payments to electronic payments. NOTES 1 Admittedly, this cost estimate overstates the real resource cost of bill payment, because some of the cost represents transfers among various participants and third-party providers. 7 Industry participants hope that the Internet will prove reliable; however, there are some reliability concerns relating to e-mail delivery and website hosting. 2 These operations may be done internally by the biller or outsourced to third parties. 8 A consumer does not necessarily need a single electronic payment option, but rather various payment options with flexibility to meet their needs and wishes. 3 This assumes 15 billion bills at $.70 and 17 billion bills at $1.50. The wide range in cost may reflect differences in unit cost for large and small billers and differences in the way the cost estimates were made. 9 As of mid-2000, OFX had achieved broader industry support with the release of OFX 2.0, an XML compliant version. It can be argued that a provider of bills to customers in several states must satisfy the privacy expectations in each state, and the various requirements might conflict. 10 The consumer may have access to overdraft facilities that would allow them to make payment without having funds in their transactions account. 4 This is in part because all four of the major credit card networks limit consumer liability for unauthorized use to zero if proper processing rules are followed. 11 For further information regarding the advantages and disadvantages of each payment mechanism, see Chakravorti, 1997; Chakravorti and Shah, 2001; Federal Reserve Board, 1996; Federal Reserve System, 1997; Flatraaker and Robinson, 1995; Humphrey and Berger, 1990; Humphrey, Kim, and Vale, 1998; and Wells, 1996. 5 6 Cost comparison is done at the margin and does not include transition costs. 14 Consumers may also choose to only view electronically presented bills and write checks rather than initiate online payments. These types of transactions are not currently tracked and are therefore not included in this discussion. 12 4Q/2001, Economic Perspectives REFERENCES Bank for International Settlements, 2001, Statistics on Payment Systems in the Group of Ten Countries: Figures for 1999, Basel, Switzerland: Bank for International Settlements. Bank Network News, 2000, While online debit grows, offlines reign remains, Bank Network News, Vol. 19, No. 5, July 24, p. 5. Barto, George L., 2001, EBPP consumer trends, Gartner Group Interactive, available on the Internet at www3.gartner.com/Init. Federal Reserve System, 1997, Payments primer: Traditional payments, Federal Reserve System Traditional Payments, December, No. 7. Flatraaker, D., and P. Robinson, 1995, Income, costs, and pricing in the payment system, Economic Bulletin, Norges Bank, Vol. 76, pp. 321332. Food Marketing Institute, 1998, EPS Costs, A Retailers Guide to Electronic Payment Systems Costs, Washington, DC: Food Marketing Institute. 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Berger, 1990, Market failure and resource use: Economic incentives to use different payment instruments, in The U.S. Payment System: Efficiency, Risk, and the Role of the Federal Reserve, David B. Humphrey (ed.), Boston: Kluwer Academic Publishers, pp. 4586. Humphrey, David B., Lawrence B. Pulley, and Jukka M. Vesala, 2000, The checks in the mail: Why the United States lags in the adoption of costsaving electronic payments, Journal of Financial Services Research, Vol. 17, No. 1, pp. 1739. Humphrey, David B., Moshe Kim, and Bent Vale, 1998, Realizing the gains from electronic payments: Costs, pricing, and payment choice, Bank of Norway, working paper, No. 1998/1. 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Lawrence, and John Wenninger, 1999, Paying electronic bills electronically, Current Issues in Economics and Finance, Federal Reserve Bank of New York, Vol. 5, No. 1, January. 16 Rini, Nick, 2000, Paperless billing: Whats the payoff?, Wireless Review, No. 18, September 15, pp. 6470. Robertson, Elizabeth, 2000, E-payments: The latest initiatives, TowerGroup, report, September. Robinson, Teri, 2000, Bill presentment and paymentTime to e-pay the bills, InternetWeek, available on the Internet at www.internetweek.com, October 23. Roth, Andrew, 2001, CheckFree says it will use screen scraping, American Banker, March 22. Spiotto, Ann, and Brian Mantel, 2000, Electronic bill presentment and payment, Federal Reserve Bank Chicago, report, June. 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Rousseau Introduction and summary Is money neutral? Most economists would now say that it is not, at least not in the short run. This belief derives partly from the results of studies done decades ago. In their book on monetary history, Friedman and Schwartz (1963) argued that the Federal Reserve may have caused or prolonged the Great Depression by a policy of tight money. And in another study, Phillips (1958) found a negative relation between wage inflation and unemployment. In the two decades that followed, other studies confirmed the view that money growth raises output in the short run. Since the 1970s, however, the Phillips-curve relation seems to have broken down, and money seems to have no clear effect on real interest rates either. Only if we assume that some part of money responds to real variables can we conclude that the exogenous part of money does move interest rates. Evans and Marshall (1998), for example, describe several scenariosidentifying assumptionsunder which some part of the money supply can plausibly be said to move real interest rates. In other words, what we infer about a liquidity effect on interest rates depends on what we believe the Fed reacts to when it sets the money supply. But, if we wish to estimate the liquidity effect on interest rates, or even if we wish to study the interest rate channel of monetary policy, is money the right measure of policy? The rate of interest is the return on bonds, which depends most directly not on the supply of money but on the supply of bonds. Using bonds, one can find a liquidity effect without introducing a host of other variables. Whether we measure money by nonborrowed reserves or more broadly, injections of money are not the same as withdrawals of, say, Treasury bills (T-bills). This is because the Fed sometimes injects money by buying long-term bonds, and this will affect short-term rates less than would a purchase of T-bills. Indeed, table 1 shows that, since 1961, the correlation between Federal Reserve Bank of Chicago the real per capita supply of outstanding Treasury securities (T-secs) and nonborrowed reserves (NBR), which one might expect to be negative, has been slightly positive at .048.1 The table also shows that, at least since 1980, growth in nonborrowed reserves has reduced short-term rates, but not as strongly as a contraction of T-secs. Over the whole period, however, growth in both nonborrowed reserves and T-secs is positively correlated with short-term rateswhich, for nonborrowed reserves, is the wrong sign. These conclusions do not change if we look instead at surprises, as implied in models like Lucas (1990) and Christiano and Eichenbaum (1995). Table 2 presents the correlations of interest rates with surprises in the growth of NBR and T-secs.2 For the 198099 period, surprises to nonborrowed reserves come in with the wrong sign, whereas T-sec surprises have the positive correlation that a liquidity effect implies. Both correlations have the wrong sign for the 196199 period, but the correlation between surprises to growth in the bond supply and real rates is tiny. Tables 1 and 2 suggest that, at least in recent decades, bonds have been the better measure of policy. The remainder of our analysis uses this measure more systematically to estimate the degree of risk that unpredictability in their supply imposes on the investor. A nominal bond carries two kinds of risk. First, its Boyan Jovanovic is a professor of economics at the University of Chicago and New York University, a research associate of the National Bureau of Economic Research (NBER), and a consultant to the Federal Reserve Bank of Chicago. Peter L. Rousseau is an assistant professor of economics at Vanderbilt University and a faculty research fellow of the NBER. The authors thank the National Science Foundation (NSF) for financial help, Fernando Alvarez and Robert Lucas for useful comments, and Timothy Daniels for assistance in obtaining data. Special thanks go to David Marshall and Helen Koshy for many detailed comments on earlier drafts. 17 TABLE 1 Correlations among monthly growth rates Variable 1961–99 1980–99 1-month real return on T-bills and previous month’s real growth in NBR .046 –.063 1-month real return on T-bills and previous month’s real growth in T-secs .137 .107 Real monthly growth in NBR and T-secs .048 –.007 Notes: NBR = real per capita value of nonborrowed reserves; and T-secs = real per capita value of outstanding Treasury securities. Sources: See note 1 on p. 33. real return erodes with inflation, which may be uncertain. Second, unexpected changes in the supply of bonds may cause the value of a bond to change. If a large bond issue causes bond prices to fall, then the return on existing bonds is reduced and the cost of purchase is lowered for investors who are about to buy bonds. The bond issue therefore transfers wealth from existing bondholders to future bondholders. If such issues are not foreseen, they give rise to what we call bond-supply risk. Supply risk can lead to an uncertain price, or (when prices do not clear markets) an uncertain availability. The Fed creates both kinds of risk in the primary market, where it acts as the Treasurys agent in the regular Dutch (that is, single-price) auctions of bonds. The Feds current actions affect not only the current auction-price results, but also the number of bonds that will become available in later auctions. The Fed probably also contributes to price variability in the secondary bond markets, where it conducts open market operations. In this article, we find that bondsupply risk remains as important as ever, even though Fed policy has rendered the price level more and more predictable. The lessons from the data that we highlight are the following: 1. Surprise sales of T-bills raise real rates: Unanticipated (month-to-month) positive shocks in the supply of T-bills or total Treasury securities available to the public have always had a large positive effect on the ex post real returns earned by these instruments. 2. Interest rates and stock returns: Popular wisdom holds that cuts in the federal funds rate raise stock prices. This seems to derive from the view 18 that bonds and stocks are close substitutes so that when the Fed, say, cuts interest rates, stock prices will rise in order to allow priceearnings ratios to rise and, therefore, stock returns to fall. Yet the opposite seems to be true. Overall, if anything, T-bill rates are negatively correlated with stock returns. 3. The decline of Treasury finance: The supply of T-bills has decreased steadily over time, but this has been neutral in its effect on asset prices, including bond prices. 4. Lessons for policy: Supply risk stems from unpredictability in the growth rate of T-secs in the hands of the publicthat is, by the unpredictability of changes in this supply relative to the amount outstanding. But the amount of T-secs has been declining relative to the unpredictable rollover demands for them at auction by foreign monetary authorities and financial institutions. If the inclusion of a broader range of short-term securities in the Feds portfolio were to reduce unpredictability in the growth rate of T-secs, supply risk would decline. This is because the risk would be spread across a wider range of assets, many with deep markets, so that an unexpected change in the Feds overall securities holdings would impact any one of these markets minimally relative to the amount outstanding. In the next section, we provide more detail on how T-bills are sold and how open market operations work. Then, we assess the effects that bond-supply risk has had over the past 80 years on the ex post real returns obtained by purchasing a new three-month T-bill and holding it until maturity and compare them to the effects of bond-supply risk on real stock returns. We then consider the role of supply risk under an investment strategy of purchasing a seasoned three-month T-bill TABLE 2 Correlations among growth-rate surprises Variable 1961–99 1980–99 Unexpected components of: 1-month real return on T-bills and previous month’s real growth in NBR .056 .138 1-month real return on T-bills and previous month’s real growth in T-secs –.008 .142 Real monthly growth in NBR and T-secs –.060 –.118 Sources: See note 1 on p. 33. 4Q/2001, Economic Perspectives with two months until maturity and selling it one month later. Next, we document the recent decline of Treasury finance in the context of the Feds history and show that supply risk is unrelated to this decline. What causes bond-supply risk? Bond-supply risk arises when agents commit funds to the bond market before they know the price at which they will buy the bonds or the price at which they will be able to sell them afterwards. Such risk arises because asset markets are incomplete and, in the sense of Grossman and Weiss (1983), segmented. Some agents and some fraction of their resources are ready to trade in the bond market and this exposes them to the risk that comes from randomness in the supply of bonds. Buyers are in luck when a bond-supply shock is positive because bond prices are then lower than expected and the rate of return is higher than expected. These agents get a good deal, and any real consequences are distributional because the shock has favored some agents at the expense of others. To take part in the bond market, institutions must commit liquid assets to the new-issue and secondary markets. Primary dealers, who make competitive bids in the course of their direct interactions with the New York Fed in the conduct of Treasury auctions, pay for their winning bids when the new bonds are issued on the Thursday following the Monday auctions. Certain depository institutions and other broker/dealers may also pay for their winning bids on the date of issue. Other competitive bidders pay at the time of submission and are either refunded excess balances or called upon to remit additional funds based upon the final auction price and security allocations. A majority of secondary dealers, however, acquire new issues from primary dealers, and presumably pay for them upon delivery, though the bonds trade actively prior to their issue in a when-issued market. Noncompetitive tenders, or offers to purchase bonds at the final auction price, whatever that may be, are paid upfront on the auction day.3 Noncompetitive bids at T-bill auctions are currently limited to $1 million per account, and they have accounted for only 10.4 percent of total auction sales since July 1998.4 Thus, even though many bidders can delay payment until issue, they must be ready to purchase their entire bid if won, and, in the event of an unsuccessful bid, must act quickly to reinvest liquid assets that had been set aside. A closer look at how these markets work shows how the winning bids can become quite uncertain. By supply risk, in some cases, we mean residual supply risk. A large chunk of the demand for T-bills comes from the decisions of foreign financial Federal Reserve Bank of Chicago institutions and international monetary authorities (FIMA) regarding whether to roll over their substantial and various holdings of bonds, and these rollover decisions affect the residual supply that will be available to the remaining traders because they count against the issue quantity stated in the auction announcement. Further, when FIMA make rollovers, they do so at the single auction price as noncompetitive bidders.5 Many individuals also bid noncompetitively, but, as mentioned above, the quantities of such bids are restricted and thus more predictable. All of this means that supply risk can arise at the auction stage, even though the Treasury announces the face value of the T-bills that it intends to issue. Since the public knows only the maturing quantity and not the rollover plans, the randomness in these plans, from the perspective of the dealer, makes the final auction price less predictable. The Fed itself must also decide whether to roll over portions of its own portfolio of maturing bonds at the final noncompetitive price. The securities that the Fed rolls over do not count against the total offered to the public in that weeks auction and, thus, have at best a minimal impact on the final auction price, but they will affect the size of subsequent auctions. For example, if the Fed rolls over only half of the bills that it could have in a given auction, to maintain a constant debt level the Treasury would need to arrange a larger issue for the next week. It seems, however, that the Feds rollovers, at least in recent years, have been quite predictablethe Fed rolls over its entire holdings unless that would exceed its self-imposed limit on individual securities holdings, in which case it redeems enough to meet that limit. The Treasury has changed its usual procedures twice recently with regard to foreign rollovers, and the nature of these changes suggests that it may be trying to reduce supply risk. In early 1999, auction announcements still specified that the Treasury could, at its discretion, issue additional securities for foreign accounts whenever the total of new bids from these sources exceeded their total holdings of maturing bills. Beginning with the T-bill auctions of March 29, 1999, however, the Treasury usually placed an explicit limit of $3 billion on the amount of foreign rollovers that would be counted against the publics total, agreeing to make additional issues automatically if rollover bids were to exceed this amount. This practice became more common as 1999 progressed. The change signaled a more accommodative stance by the Treasury that would have reduced residual supply risk by limiting the degree to which unexpected noncompetitive rollover decisions could affect the final auction price. As of February 1, 2001, however, the Treasury has allowed 19 only $1 billion in total foreign noncompetitive tenders, and that limit cannot be exceeded.6 Foreign institutions seeking to purchase large amounts of T-secs at auctions must now bid competitively. Even though this change might ameliorate disturbances that would impede the systematic paying down of the federal debt, it is also likely to raise residual supply risk. Cammack (1991, p. 110) reports that the Fed and FIMA combined to buy 43 percent of all T-bills that were sold at auction between 1973 and 1984. By examining the press releases of auction results, we have found that this portion has risen to 44.8 percent since mid-1998. The risk associated with rollover decisions exceeds both the spread in the distribution of bids and the time-series variation of the winning bids, because the losing bidders (of which there are more either immediately or in future auctions when the Fed absorbs its limit) must end up holding cash or a lowerreturn substitute. Bond-supply risk and interest rates How much do bond supplies vary from month to month? Figure 1 shows the standard deviation of the monthly per capita real growth of the monetary base, T-bills and T-notes, and all marketable T-secs, including bills, notes, bonds, and certificates of indebtedness since 1920.7 The Treasury quantities reflect securities that are outstanding and in the hands of the public (that is, excluding the Feds holdings).8 The striking feature of figure 1 is the high monthto-month variability of total T-secs in the hands of the public. This variability was particularly high in the early 1940s due to large issues of securities of all maturities to finance the Second World War. We also observe large rolling standard deviations for the T-bills and T-notes subset in the midst of the Depression and again from 1942 to 1947. Interestingly, variability in the supply of T-secs is much larger than that of the monetary base itself, which suggests that a considerable portion of what we call supply risk may have served to stabilize money growth. How strongly do bond-supply surprises affect the real rate of interest? We use the effects of inflation surprises as a standard of comparison and compare the two kinds of risk, first for the entire 192099 period and then for three subperiods. Here is how we proceed: The nominal return at date t on a one-period zerocoupon bond maturing at date t + 1 is ¥1 ´ Rt, t 1 ¦ 1µ , § Pt ¶ 20 FIGURE 1 Standard deviation of monthly growth of T-secs and monetary base percent 12 T-bills and T-notes 9 Marketable T-secs 6 3 0 Monetary base -3 1920 ’30 ’40 ’50 ’60 ’70 ’80 ’90 ’00 Note: Figure displays rolling standard deviations of monthly growth rates of real per capita supplies of T-secs and the monetary base, 1920–99. where Pt is the price of the bond at date t. The ex post real return on this bond is ¥1¨ 1 · ´ © ¸ 1µ , § Pt ª©1 Q t, t 1 ¹¸ ¶ rt, t 1 ¦ where Q t, t 1 is the rate of inflation of goods prices between dates t and t + 1. Rearranging and taking logs, ln(1 rt, t 1 ) ln Pt ln(1 Q t, t 1 ), for any small number F ln 1 F z F Using this, we approximate the above equation by rt, t 1 z it, t 1 Q t, t 1 , where it, t 1 y 1/ Pt 1. Let the superscript e denote an expected value given information from the previous period, which we shall denote It1, so that, for instance, rt,et 1 E \rt, t 1 I t 1 ^ . 9 Let the superscript u denote the surprise component of a random variable so that, for instance, rt, t 1 rt,et 1 rt,ut 1 and so on. Then to a first approximation, 1) rt,ut 1 itu, t 1 Qut, t 1 . The first term is the bond-supply risk and the second is inflation risk. 4Q/2001, Economic Perspectives Now assume a liquidity effect of bond-supply surprises on the price of bonds as in, say, Lucas (1990). That is, assume that 2) itu, t 1 Bgtu1, t , where, once again, gtu1, t is the surprise growth in the number of bonds at t given It1. Substituting into equation 1 leads to 3) rt,ut 1 Bgtu1, t Q ut, t 1 . The notation may suggest that the surprises in the above three variables are formed at different dates and are based on different information sets, but this is not the case. The dependent variable and the regressors all derive from the information set It1 that we describe in note 9 on page 33. To reiterate, at the start of date t, agents know the realization of pt1,t. But the presence of bond-supply risk means that the agents do not know the date t supply of bonds when they form their expectations of Pt and, hence, of it,t+1. This means that they cannot yet know gt1,t, since its realization comes too late to be included in the date t information set. Therefore, in spite of the dating differences in the subscripts, rˆt,ut 1 , gˆ tu1, t , and Qˆ ut, t 1 , are surprises based on the same information set, It1. We estimate equation 3 with the regression 4) rˆt,ut 1 a0 a1 gˆ tu1, t a2 Qˆ ut, t 1 , where rˆt,ut 1 , gˆ tu1, t , and Qˆ ut, t 1 are surprises of the three variables. In practice, we obtain these surprises using de-seasonalized monthly observations as the one-step ahead forecast errors from a set of vector autoregressions (VARs) with a rolling estimation window. To be more precise, the variables in the forecasting equations are: 1. g, the growth rate of real per capita T-secs in the hands of the public, 2. r, the ex post real return on T-bills,10 3. p, the rate of growth of the consumer price index,11 and 4. the ex post real return on the S&P 500.12 Thus, all four variables in the system are dimensionless. We then pool the forecasts and errors from the VARs over the sample period and use them to estimate equation 4. The monthly data represent the highest frequency that is available continuously for the past 80 years of Fed history. The T-bill return is the monthly average of daily rates for the current (that is, on the run) Federal Reserve Bank of Chicago three-month T-bill, from which we subtract realized inflation over the next three months. The returns that we consider first, and the only ones that can be constructed going back to 1920, correspond to an investment strategy of buying the current three-month T-bill and holding it until maturity. Later we consider onemonth holding period returns on seasoned T-bills since 1961. Box 1 describes in detail the methods we used to prepare the data for analysis and to compute the surprises. Supply risk 192099 Using monthly data from January 1920 through December 1999 and forecasting equations with a 36-month rolling window and three lags, figure 2 shows the effects of one-standard-deviation surprises to both the price level and the supply of marketable T-secs available to the public on the annualized ex post real return on T-bills.13 Pooling the surprises across periods, we obtain the following estimates for equation 4 (with t-statistics in parentheses): 5) rˆt,ut 1 .0001 .0274 gˆ tu1, t 1.082 Qˆ ut, t 1 et . (0.60) (7.45) ( 17.31) The superscript u in equation 5 denotes a variables deviation from its one-step-ahead forecast from the rolling VAR. As limited participation models would suggest, gˆ tu1, t raises ex post real T-bill returns because a release of T-bills lowers T-bill prices. This in turn contributes to better-than-expected returns for those who have committed funds to the T-bill market. Unanticipated inflation enters with, essentially, a unit coefficient, which suggests that Qˆ ut, t 1 is indeed a true surprise. To obtain the series plotted in figure 2, we multiply the coefficients gˆ tu1, t and Qˆ ut, t 1 by the centered values of their rolling 12-month standard deviations and compound the result over 12 months to annualize. This measures the effects of the surprises on annualized real T-bill returns.14 The figure indicates that both sources of risk have always mattered, with inflation risk at times quite large, especially at the height of the Great Depression in 1933 and in the year immediately following the end of the Second World War. The relative importance of inflation risk has declined dramatically over the past two decades, however, as the price level has stabilized. The effect of supply risk in three subperiods The method used to construct figure 2 assumes that the seasonal adjustment coefficients applied to the raw data and the responses of the T-bill rate to unexpected inflation and T-sec growth are stable across the 192099 period. One way to examine the 21 BOX 1 Estimating the impact of price and T-sec supply risk on real T-bill returns The methodology underlying figures 29 begins with adjusting the raw data to make the timing of monthly observations consistent across variables. Since the nominal quantity of T-secs in the hands of the public (Xt) is available at the end of each month, while the consumer price index (CPIt) and population (popt) are computed as annualized monthly averages, we derive the real quantity of Treasury securities at the end of month t as: xt 4 s Xt , CPI t 1 CPI t s ( popt 1 popt ) which amounts to averaging the consumption deflator and population across periods to center them with Xt. To approximate the ex post real return on T-bills (rt,t+1) associated with the buy-and-hold strategy discussed in this article, we start with the annualized yields to maturity on three-month (91-day) T-bills that are computed by the Fed as averages of daily yields over the course of a calendar month (Rt) and subtract the annualized inflation rate implied by the change in the CPI over the next three months. Since the CPI is a monthly average and Rt is annualized, we have Before using the series derived above (as well as CPI inflation itself), we de-seasonalize by regressing each on monthly dummy variables and an adequately high-order polynomial in time. We include the time polynomial to reduce the degree to which the estimates of the monthly effects reflect cyclical and trend components. After subtracting the coefficients on the monthly dummy variables from the raw series, we add the mean of the detrended series back in to complete the seasonal adjustment. See Johnston (1984, pp. 2349) for a clear exposition of this method along with its advantages and drawbacks. The VAR equations used to compute the surprises to growth in the supply of T-secs (g) and inflation (p) have the form gt ¤ c g ¤ d Q ¤ f ¤ h s t e k k i 1 1, k t k k i 1 1, k t k i 1 r 1, k t k k i 1 Qt 1, k t k 1, t ¤ c g ¤ d Q ¤ f ¤ h s t e , k k i 1 2, k t k k i 1 2, k tk i 1 r 2, k t k k 1 12 ¨ ¥ ¨¥ CPI CPI ´ 4 ·´ · t 3 t ¸ rt, t 1 z ©1 ¦ Rt ©¦1 µ¶ 1¸¸µµ ¸ 1. © ¦§ CPI t § © ¶ ª ¹ ª ¹ This is the monthly real return that an investor would receive by buying a three-month T-bill and holding it until maturity, assuming that the inflation rate is steady across the three months. The nominal return on the S&P 500 (St) covers an actual calendar month, so we derive an ex post return by subtracting the growth rate of the consumer price index (CPIt), ¨¥ CPI t 1 CPI t ´ · 1¸ , st St ©¦ § CPI CPI µ¶ ª t t 1 ¹ which amounts to computing inflation as the growth in the CPI after averaging across periods. robustness of our results to these assumptions is to repeat the analysis over subperiods. We do this for 192046, 194779, and 198099, and display the results in figures 35. We split the postwar period into pre-1980 and post-1979 segments because of the shift in Fed targeting policy that occurred in 1979. To accommodate the shorter sample periods, we limit 22 i 1 2, k t k 2, t where k is the lag length and t is a linear time trend. The time subscripts refer to the information sets Itk from which the variables derive. To allow the forecasts to reflect recent economic conditions, we allow the VAR samples to roll with time, choosing estimation windows of 36 months (figure 2) or 30 months (figures 39). This implies that each successive onestep ahead forecast and forecast error is computed with an information set that overlaps the previous one in all but the latest and earliest periods. Using the coefficients from the time t regression, we compute the forecasts for time t + 1 as fitted values obtained with the information set from time t. In estimating equation 4, we pool the monthly surprises across the sample period to obtain a single set of regression coefficients. the underlying VAR models to two lags and shorten the length of the estimation periods to 30 months. Table 3 includes regression results for equation 5. Figure 3 reaffirms the importance of inflation risk in the pre-1947 period, including the 1933 and 1946 episodes. The effects of supply risk on T-bill returns rise at these same times and average 0.36 4Q/2001, Economic Perspectives FIGURE 2 Effect of surprises in T-sec supply on T-bill returns, 192099 percentage points 18 46.2% in 1946 15 12 9 Inflation risk 6 3 0 Supply risk -3 1920 ’30 ’40 ’50 ’60 ’70 ’80 ’90 ’00 Note: Figure illustrates effects, in percentage points, of one standard deviation surprises in inflation and growth in the supply of T-secs on annualized real T-bill returns. percent over the 192046 period, but are always less important than the effects of inflation risk, which average 5.31 percent. In figure 4, the narrower scaling reflects the overall decline in inflation risk that occurred from 1947 to 1979, during which it averaged only 1.35 percent. Even though supply risk also fell to 0.12 percent over this same period, the decline is considerably less in percentage terms than that of inflation risk. Figure 5, on the other hand, shows that supply risk has if anything become more important over the past 20 years, averaging to 0.14 percent, while inflation risk has continued to decline, averaging 0.99 percent. By 1980, the Treasury had completed a long-term shift in financing away from T-bonds and into shorterterm T-bills and T-notes (see figure 15 on page 30). It is therefore possible that fluctuations in the quantity of T-bills and T-notes are more precise measures of supply risk for the post-1980 period than the total of outstanding marketable T-secs. To see if this preference shift has influenced our results, we compute supply shocks to T-bills and T-notes only after 1980, and in figure 6 once again display their effects on real T-bill returns. The results are similar to those observed for all T-secs, with average real effects of 0.16 percent and 1.0 percent, respectively. Once again, supply risk grows in relative importance over time. That bond-supply risk, which arises from committing funds to the T-bill market before supply is revealed, should even approach inflation risk in importance is quite striking. After all, if inflation surprises are measured over the entire term of the T-bill, they should affect ex post yields virtually point for point.15 To generate bond-supply risk, however, it is necessary for open market operations or variations in auction quantities to have large effects on interest rates, and this in turn suggests some degree of market segmentation. Otherwise, in the absence of segmentation, investors could offset T-sec supply shocks with transactions in the markets for substitute assets. Bond-supply risk and real stock returns Theory leads us to expect a positive relation between stock returns and real bond returns. If stocks and bonds were perfect substitutes and if they traded TABLE 3 Interest rate regressions for buy-and-hold strategy g = Marketable T-secs g = T-bills and notes 1980–99 1920–99 1920–46 1947–79 1980–99 Constant .0001 (0.60) .0002 (1.04) .0000 (0.01) –.0000 (–0.24) –.0000 (–0.29) gˆtu 1,t .0274 (7.45) .0140 (1.97) .0069 (1.42) .0100 (2.37) .0104 (2.66) Qˆ ut,t 1 –1.082 (–17.31) –.9963 (–9.43) –.3837 (–6.04) –.3446 (–6.54) –.3537 (–6.79) .361 (1.98) .255 (1.19) .092 (1.70) .206 (1.81) .216 (1.77) 919 290 370 205 205 R2/(DW) N u t ,t 1 Notes: The dependent variable is the unanticipated real return on a three-month T-bill, rˆ . The table presents coefficient estimates for equation 5 over the subperiods included in figures 2–6, with T-statistics in parentheses. The R2 and Durbin-Watson (DW) statistics and number of observations (N) for each regression appear in the final two rows. Federal Reserve Bank of Chicago 23 in the same market, their real rates of return would always be equal. In such a world, an open market operation of the Fed or, indeed, any other event that changed the return on bonds would change the return on stocks by the same amount. For example, a cut in the federal funds rate would cause bond prices and stock prices both to rise and the holding rate of return on each asset to fall. The presence of inflation risk on bonds and dividend risk on stocks would, perhaps, weaken the contemporaneous correlation between the ex post real returns on the two assets, but would not eliminate it entirely. One implication of this logic is that if the Feds actions can affect the stock market, we should expect to find a positive correlation between bond returns and stock returns. Surprisingly, we find no evidence of a positive correlation between the two ex post returns. We proceed as we did with T-bill returns, but now the dependent variable in equation 5 is the unanticipated component of the real return on the S&P 500, sˆu: 6) sˆtu, t 1 a0 a1 gˆ tu1, t a2 Qˆ ut, t 1 et . Table 4 presents our findings using surprises from the same VAR models that we used to examine T-bill returns. Interestingly, T-sec surprises never affect real stock returns. Inflation surprises, on the other hand, enter with the expected negative and significant coefficients in the 194779 and 198099 subperiods, FIGURE 3 FIGURE 5 Effect of surprises in T-sec supply on T-bill returns, 192046 Effect of surprises in price level and T-sec supply on T-bill returns, 198099 percentage points percentage points 18 2.0 15 1.6 12 Inflation risk 1.2 9 0.8 Inflation risk 6 0.4 3 0.0 0 T-sec supply risk T-sec supply risk -3 1923 ’27 ’31 ’35 ’39 ’43 ’47 -0.4 1982 Note: Figure illustrates effects, in percentage points, of one standard deviation surprises in inflation and growth in the supply of T-secs on annualized real T-bill returns. ’85 ’88 ’91 ’97 FIGURE 4 FIGURE 6 Effect of surprises in T-sec supply on T-bill returns, 194779 Effect of surprises in supply of T-bills and T-notes on T-bill returns, 198099 percentage points percentage points 5 2.4 4 ’00 1.8 3 Inflation risk 1.2 2 Inflation risk 0.6 1 0.0 0 T-bill and T-note supply risk T-sec supply risk -1 1950 ’55 ’60 ’65 ’70 ’75 Note: Figure illustrates effects, in percentage points, of one standard deviation surprises in inflation and growth in the supply of T-secs on annualized real T-bill returns. 24 ’94 Note: Figure illustrates effects, in percentage points, of one standard deviation surprises in the price level and T-sec supply on annualized real T-bill returns. ’80 -0.6 1982 ’85 ’88 ’91 ’94 ’97 ’00 Note: Figure illustrates effects, in percentage points, of one standard deviation surprises in inflation and growth in the supply of T-bills and T-notes on annualized real T-bill returns. 4Q/2001, Economic Perspectives but with a positive and significant coefficient for 192046. The latter result may be driven by a few extraordinary events, such as the sharp deflation and decline of equity values associated with the Great Depression and the inflation and rising market values of the immediate postwar period. In all, the evidence suggests that the stock market has been relatively unaffected by Fed policy. In table 5, we report contemporaneous correlations among the variables in our VARs (that is, the variables themselves and not their surprises) and for the monetary base over the 192099 period and the three subperiods.16 Once again, links between stock returns and growth in bond supplies are weak and inconsistent across subperiods. For example, correlations between real growth in the T-sec supply and stock returns never exceed 0.05 and have the expected negative sign only for 198099. T-bill returns vary inversely with stock returns in all but the 194779 period, but in all cases the correlations are small. As it turns out, the most consistent correlations are positive ones between growth in T-sec quantities on the one hand and real T-bill returns on the other. This is true for the full 192099 sample period and for all of the subperiods. It is also as we might expect, since more T-secs in the hands of the public require higher interest rates to induce investors to hold them. Since a rise in T-bills and T-notes in the hands of the public usually implies bond sales and, hence, a monetary tightening, it is surprising that growth in the real monetary basea monetary looseningseems to go hand in hand with bond sales (and the higher interest rates that they imply) in all but the 198099 period. To explore this further, we compute the correlations using growth in real per capita nonborrowed reserves, which is probably a closer indicator of policy stance than growth in the monetary base, for 1959 99the period over which we have a series for nonborrowed reserves. We find in this case that a monetary loosening, as measured by growth in nonborrowed reserves, also has an unexpected positive correlation with T-bill returns and T-sec growth, and that this result obtains for both the 195979 and 198099 subperiods.17 This may again reflect important differences between indicators of policy stance that are based on monetary aggregates and our bond supply measures. An alternative measure of real T-bill returns Until now, we have considered the effects of bondsupply risk on T-bill returns under a buy-and-hold strategy. This, of course, is only one strategy that a T-bill investor might follow, as it is easy for an investor to liquidate a T-bill, and in particular after a supply or price shock has been realized. To analyze such a holding strategy, we now estimate equation 5 using surprises to the ex post real one-month holding period return on a seasoned T-bill as the dependent variable. The effects of supply risk should be different under this shorter-term strategy. This is because the investor now faces two sources of supply riskone that occurs just before the bond is purchased and another that occurs over the holding period. A positive shock after commitment but before purchase will lower the bond price and raise the real return, yet a similar TABLE 4 Stock return regressions g g = T-bills and notes 1980–99 = Marketable T-secs 1920–99 1920–46 1947–79 1980–99 –.0014 (–0.55) –.0033 (–0.50) .0014 (0.57) –.0002 (–0.06) –.0008 (–0.19) gˆtu 1,t .1085 (1.20) –.0851 (–0.42) –.0558 (–0.36) –.2390 (–0.76) –.1476 (–0.503) Qˆ ut,t 1 0.2446 (0.16) 5.753 (1.92) –5.571 – (2.81) –6.013 (–1.53) –6.296 (–1.61) .002 (1.76) .014 (1.96) .022 (1.84) .013 (1.78) .014 (1.77) 919 290 370 205 205 Constant R2/(DW) N u Notes: The dependent variable is the unanticipated real return on a one-month investment in the S&P 500 portfolio, sˆt ,t 1 . The table presents coefficient estimates for equation 6, with T-statistics in parentheses. The R2 and Durbin-Watson (DW) statistics and number of observations (N) for each regression appear in the final two rows. Federal Reserve Bank of Chicago 25 TABLE 5 Correlations of real asset returns and real per capita quantities S&P 500 T-bills T-bills and notes T-secs 1920–99 Real return on S&P 500 Real return on T-bills Growth in real T-bills and notes Growth in real T-secs Growth in real monetary base 1.00 –0.034 0.046 0.006 0.067 1.00 0.071 0.110 0.235 1.00 0.669 0.040 1.00 0.142 1.00 1920–46 Real return on S&P 500 Real return on T-bills Growth in real T-bills & notes Growth in real T-secs Growth in real monetary base 1.00 –0.075 –0.022 0.040 0.064 1.00 0.108 0.048 0.161 1.00 0.680 0.067 1.00 0.145 1.00 1947–79 Real return on S&P 500 Real return on T-bills Growth in real T-bills & notes Growth in real T-secs Growth in real monetary base 1.00 0.028 0.040 0.003 0.011 1.00 0.096 0.185 0.470 1.00 0.575 0.017 1.00 0.112 1.00 1980–99 Real return on S&P 500 Real return on T-bills Growth in real T-bills & notes Growth in real T-secs Growth in real monetary base 1.00 –0.032 –0.061 –0.050 0.102 1.00 0.188 0.208 0.064 1.00 0.962 –0.065 1.00 –0.003 shock over the holding period will lower the resale value of the bond. Thus, it is deviations of resale values from investor expectations that were formed prior to purchase that impart risk to the strategy. To derive the equivalent of equation 4 for multiperiod bonds, we again define the cost of such a bond 1 at date t as P units of real consumption. The bonds t nominal return over the holding period is it*, t 1 Pt 1 Pt , Pt where we introduce asterisks to reflect the change from the buy-and-hold investment strategy discussed earlier to the seasoned one-month holding strategy considered here. The ex post real return is again rt,*t 1 it*, t 1 Q*t, t 1 , and as in equation 1, u* t, t 1 r i u* t, t 1 Q . Now we need to be quite precise about the dating of information. Let t 1 z u y the surprise component of a random variable z given It1, and suppose that 26 u ¥ Pt 1 ´ 1 B t 1 gtu*1, t b* t 1 gtu, *t 1 . § Pt µ¶ 7) itu, *t 1 t 1 ¦ The right-hand side of equation 7 is based on the logic behind equation 2. The first term deals with the denominator of the left-hand side; it is the one-stepahead surprise and is the same as in equation 2. The second term deals with the numerator, Pt +1, and is a two-step-ahead surprise to growth in the bond supply. We compute this term as a VAR forecast using I t*1 . Isolating the return surprises on the left-hand side, we have the holding-period analog of equation 3: *u t 1 t, t 1 r z B t 1 gtu*1, t b* t 1 gtu, *t 1 t 1 Qtu,*t 1 , or, roughly, the linear relation that we estimate: 8) u* t, t 1 Monetary base *u t 1 t, t 1 rˆ z a1* t 1 gˆ tu*1,t a2* t 1 gˆ tu, *t 1 a3* t 1 Qˆ ut,*t 1 . The final term in equation 8 is inflation risk over the holding period. Under the buy-and-hold strategy that we considered earlier, we subtracted realized inflation over the three-month term of the T-bill and, assuming monthly 4Q/2001, Economic Perspectives compounding, converted to a monthly return. The result there reflected an average of inflation over the next three months. Here we proceed slightly differently: For the one-month holding strategy, we subtract the one-month inflation rate that corresponds to the actual holding period. Our analysis of one-month investments in seasoned three-month T-bills is limited to 1961 to 1999 the period for which daily secondary market prices on U.S. Treasury securities are available from the New York Fed and the Wall Street Journal.18 Using the composite quote sheets, we collected the annualized yield-to-maturity on the final trading day of the month for the T-bill with closest to 60 days until maturity and then recorded its yield on the final trading day of the next month. We then computed a synthetic annualized 30-day holding period yield as ¨ ¥ 60 ´ · ©1 ¦§ R2 365 µ¶ ¸ ¸ 1, R2,1 © © 1 ¥ R 60 ´ ¸ ¦ 1 µ ª© § 365 ¶ ¹¸ where R2 is the annualized yield-to-maturity on the reference T-bill with approximately 60 days until maturity, and R1 is the annualized yield on the same T-bill a month later. Due to weekends, holidays, and the monthly calendar, we do not always observe prices 30 days apart, so our computation assumes that R1 whenever observed also applies on the 30th and final day of the holding period. This ignores changes in secondary market yields that might arise for a seasoned T-bill over at most a two-day period, but does not generate any systematic bias. We convert to real terms by subtracting Consumer Price Index (CPI) inflation. After again obtaining surprises to T-bill returns, inflation, and growth of the T-sec supply from a series of 30-month rolling VARs with two lags and the S&P 500 return as a control, we use the coefficient estimates from equation 8 to compute the overall effects of supply risk over the course of a month (that is, both pre-purchase and holding period risk) as the square root of product of a3* and the standard deviation of the forecast errors for inflation. We obtained the series of variancecovariance matrices from 12-month rolling samples of the forecast errors. Figure 7 presents our results for the 196179 period, which have been annualized by compounding over 12 months. We report the corresponding estimates for equation 8 in table 6. In figure 7, an inflation surprise of one standard deviation lowers the holding period yield by 1.77 percent on average. Like the results in figures 2 and 5 for the buy-and-hold strategy, inflation risk rises to nearly 4.5 percent in the mid-1970s after fluctuating at about 1 percent to 2 percent throughout the 1960s. Supply risk, though not significant in equation 8, averages .10 percent, which is only slightly smaller than that observed under the buy-and-hold strategy for 194779. Figure 8 and the two other columns of table 6 cover the 198099 period and offer a direct comparison with figures 5 and 6. Whether we use all T-secs in the hands of the public (figure 8) or only T-bills and T-notes (figure 9) in forming gˆ u *, the effects of supply risk on one-month yields are similar to those obtained under the three-month buy-and-hold strategy, averaging .16 percent and .21 percent in figures 8 and 9, respectively. The coefficients on the pre- and post-purchase surprises to growth in the T-sec supply variables also have the expected and opposite signs, but are statistically significant only when T-bills and T-notes are included in gˆ tu*1,t . This differs from the results under the buy-and-hold strategy, where our analysis of pre-purchase risk in isolation showed significant effects of supply surprises for total T-secs as well. Inflation risk is larger on average with the FIGURE 7 Effect of surprises in T-sec supply on one-month T-bill returns, 196179 percentage points 5 4 3 Inflation risk 2 2 Var t 1 rˆt,*tu 1 aˆ1* Var t 1 gˆ tu*1, t * 2 2 aˆ 1 Var t 1 gˆ tu,*t 1 2aˆ aˆ Cov t 1 gˆ * * 1 2 u* t 1, t t 1 , 0 gˆ u* t, t 1 , where the Var(·) terms are variances and Cov(·) the covariance. The effects of inflation risk are the Federal Reserve Bank of Chicago T-sec supply risk -1 1964 ’66 ’68 ’70 ’72 ’74 ’76 ’78 ’80 Note: Figure illustrates effects, in percentage points, of one standard deviation surprises in inflation and growth in the supply of T-secs on annualized real one-month returns on T-bills. 27 TABLE 6 9) Interest rate regressions for the one-month holding strategy u* t 1 t, t 1 sˆ z a1* t 1 gˆ tu*1,t a2* t 1 gˆ tu, *t 1 a3* t 1 Qˆ ut,*t 1 . g = T-bills and notes 1980–99 The results, which we report in table 7, indicate that surprises to gˆ do not generate substantive supply risk for investors Constant .0000 .0001 .0001 who are about to buy the S&P portfolio, (0.17) (1.12) (1.03) but that positive shocks after purchase raise u* ˆ .0046 .0061 .0075 t 1 g t 1,t one-month stock returns for the 198099 (1.02) (1.28) (1.77) period. This runs counter to the standard ˆu* –.0041 –.0064 –.0076 t 1 g t ,t 1 view that stocks lose when the Fed tight(–0.83) (–1.36) (–1.76) ens and gain when the Fed cuts rates. ˆ ut ,*t 1 The lack of significance on the coef–.9644 –.9370 –.9501 t 1 Q (–27.10) (–20.54) (–21.32) ficient for the pre-purchase surprise could simply suggest that the Fed cannot direct.796 .683 .704 R2/(DW) ly and consistently affect the stock market. (2.09) (1.50) (1.50) The positive and significant coefficient on N 196 205 205 the holding period supply shock, on the Notes: The dependent variable is the unanticipated real return on a T-bill rˆ . other hand, is consistent with a policy of The table presents coefficient estimates for equation 8, with T-statistics in passive responses by the Fed to changing parentheses. The R and Durbin-Watson (DW) statistics and number of observations (N) for each regression appear in the final two rows. conditions in other asset markets. For example, when the stock market is surging, the Fed may try to slow it down a bit by injecting bonds and raising interest rates. Since any relationone-month holding strategy than with the buy-andship between Fed policy and the stock market is probably hold. The closeness of the coefficients on the inflation loose, however, the bond sale often seems to have litsurprises to unity is also good news for our specificatle effect, and the market continues to push ahead. tion, as inflation should affect the real return point for point when the time periods for the inflation and The effects of foreseen policy changes: return observations coincide. The secular decline of Treasury finance Next, we again place the unanticipated compou* The relative importance of T-bills and other marnent of the real S&P 500 return (s ) on the left-hand ketable T-secs in the aggregate portfolio has declined side of equation 8 to obtain g = Marketable T-secs 1961–99 1980–99 u* t 1 t ,t 1 2 FIGURE 8 FIGURE 9 Effect of surprises in T-sec supply on one-month T-bill returns, 198099 Effect of surprises in supply of T-bills and T-notes on one-month T-bill returns, 198099 percentage points percentage points 2.4 2.4 1.8 1.8 Inflation risk Inflation risk 1.2 1.2 0.6 0.6 0.0 0.0 T-sec supply risk -0.6 1982 ’85 ’88 ’91 ’94 ’97 ’00 Note: Figure illustrates effects, in percentage points, of one standard deviation surprises in inflation and growth in the supply of T-secs on annualized real one-month returns on T-bills. 28 T-bill and T-note supply risk -0.6 1982 ’85 ’88 ’91 ’94 ’97 ’00 Note: Figure illustrates effects, in percentage points, of one standard deviation surprises in inflation and growth in the supply of T-bills and T-notes on annualized real one-month returns on T-bills. 4Q/2001, Economic Perspectives have become larger relative to the quantity of outstanding T-secs. Figures 10 and 11 Stock return regressions for the suggest that such a trend may be emerging. one-month holding strategy Figure 10 shows that the amount of T-secs g = T-bills in the hands of the public has fallen since g = Marketable T-secs and notes the mid-1990s. Figure 11, on the other 1961–99 1980–99 1980–99 hand, indicates that the standard deviation Constant –.0070 .0042 .0040 of the growth rate surprises (the cause of (–1.89) (1.12) (1.07) supply risk) has increased a little.19 In this u* ˆ section, we document long-run trends in .1020 .3533 .3089 t 1 g t 1,t (0.37) (1.17) (1.12) Treasury financing over the Feds history u* and argue that their effects on supply risk ˆ .2447 .5797 .5693 t 1 g t ,t 1 up until now have probably been small. (0.81) (1.93) (2.03) The size of the bond market can be ˆ ut ,*t 1 –4.153 .6752 .1401 t 1 Q measured by the share of these securities (–1.913) (0.24) (0.05) in the aggregate portfolio. This share will .023 .019 .020 R2/(DW) decline if, because of a policy change, the (1.93) (1.71) (1.73) quantity of Treasury securities made available to the public begins to shrink. The N 196 205 205 share will also decline as more individuals Notes: The dependent variable is the unanticipated real return on the ˆ gain access to instruments other than bank s . S&P 500, The table presents coefficient estimates for equation 9, with T-statistics in parentheses. The R and Durbin-Watson (DW) statistics and deposits for lodging their surplus balances. number of observations (N) for each regression appear in the final two rows. Figures 12 and 13, which include the ratios of federal debt, commercial and corporate debt, and corporate equities to gross doover the postwar period. This should not matter for mestic product and the aggregate portfolio, respecreal interest rates if it is only surprises to the growth tively, indeed show substantial declines in the share of bond supplies that matter. Indeed, most rational of marketable federal debt from its postwar high in expectations models with money and no nominal ri1945. The growing importance of financial assets in gidities specify no real effects for expected changes the U.S. economy and the rapidly rising share of eqin the money or bond supplies. One such change is uity in total finance are also apparent. Figure 14 prothe gradual decline in outstanding T-secs, since this vides additional detail on the rising share of equity in is probably well understood by agents in the bond total business finance, with both the corporate bond and and money markets. But this change may not be neubank lending components of business debt falling to tral, or at least may begin to matter soon if the trend continues. This is because fluctuations in bond supFIGURE 11 plies stemming from rollover risk and other sources TABLE 7 t 1 u* t ,t 1 2 Standard deviation of surprises to monthly growth of T-secs and monetary base FIGURE 10 T-secs in hands of public and monetary base, 196099 percent 5 trillions of 1999 dollars T-bills and T-notes 4 4 3 Marketable T-secs 3 2 T-bills and T-notes 2 1 1 Marketable T-secs 0 1960 M-base 0 1960 ’65 ’70 ’75 ’80 Federal Reserve Bank of Chicago ’85 ’90 M-base ’95 ’00 ’65 ’70 ’75 ’80 ’85 ’90 ’95 ’00 Note: Figure displays rolling standard deviations of surprises to monthly growth rates of real per capita supplies of T-secs and the monetary base, 1960–99. 29 their lowest levels in recent years. The market for commercial paper has also grown rapidly over the past three decades, but it remains a small part of total finance. (See boxes 2 and 3 for descriptions of how we constructed the series for outstanding corporate equities and the components of outstanding debt that are presented in these figures.) Figure 15, which provides a breakdown of marketable Treasury securities by type, shows that longterm bonds dominated government finance between 1915 and 1960, but that medium-term T-notes and short-term T-bills have risen to preeminence more recently. These shifts suggest that a broad measure of government bond activity, such as the sum of all marketable Treasury securities in the hands of the public, may be best for evaluating the effects of supply shocks related to the Feds open market policies over the long term, but that the quantities of T-bills and T-notes might be more relevant in recent years. These considerations more precisely explain our choices of variables for quantifying supply risk earlier in this article. Interestingly, and in keeping with most rational expectations models, we found in most cases that the choice of supply variable did not matter. Figures 12 and 13, when combined with the effects of changes in the supply of T-secs presented in figure 2, suggest that the decline in the share of these securities in the aggregate portfolio has had little effect on the distribution of rtthe real return on T-bills. This stands in sharp contrast to the implications that such a decline would have in the limited participation model of Alvarez et al. (2001), in which the interest rate effects of monetary injections depend inversely on the fraction of agents that take part in the bond market. FIGURE 12 FIGURE 14 Federal and business securities as share of GDP, 191599 Corporate financing in the 20th century ratio of securities to GDP percent 3.0 100 Marketable Treasury securities 2.0 80 Corporate equities 60 40 1.0 Corporate bonds Corporate equities Commercial paper 20 Business debt Bank loans 0.0 1915 ’25 ’35 ’45 ’55 ’65 ’75 ’85 ’95 0 1900 ’10 ’20 ’30 ’40 ’50 ’60 ’70 ’80 FIGURE 13 FIGURE 15 Outstanding federal and business securities, 191599 Marketable Treasury financing, 191599 percent percent 100 100 80 80 ’90 ’00 Other Corporate equities T-bonds 60 60 T-notes 40 40 Business debt 20 20 T-bills Marketable Treasury securities 0 1915 30 ’25 ’35 ’45 ’55 ’65 ’75 ’85 ’95 0 1915 ’25 ’35 ’45 ’55 ’65 ’75 ’86 ’95 4Q/2001, Economic Perspectives BOX 2 Estimating the market value of outstanding corporate equity To estimate the market value of outstanding corporate equity, we extend the Federal Reserve Boards Flow of Funds series (table L.4) backwards, using the available data on capitalization for the New York Stock Exchange (NYSE), the regional exchanges, and overthe-counter (OTC) markets. We work backwards not from 1945 (which is when the Flow of Funds data series begins) but, rather, from 1949 because the closest overlapping observations of OTC activity are for 1949. The Flow of Funds reports $117 billion for outstanding corporate equities in 1949, which we divide into the value of NYSE-listed firms, the value of firms listed exclusively on The American Stock Exchange (AMEX) and the regional exchanges, and the value of firms traded exclusively in OTC markets. Friend (1958) estimates the sum of NYSE and regional capital in 1949 at $95 billion. We know from the Center for Research on Securities Prices (CRSP) database that NYSE capitalization was $68 billion. This implies a regional capitalization of $27 billion and OTC capital of $22 billion in 1949. Assuming that the capitalizations of NYSE listed and regionally listed firms are proportional to their transaction values, which are available from various issues of the Annual Report of the Securities and Exchange Commission for 193549, we multiply NYSE capital by the ratio of regional to NYSE transactions to approximate movements in capitalization on the regional exchanges. We then adjust the resulting regional series to match the $27 billion that we estimate for 1949. To estimate regional capital for 192034, we observe that the ratio of regional to NYSE transaction value was steady at 0.18 for 193550 and again use NYSE capital to derive regional capital from 1920. The OTC market presents a double-counting problem. Friend estimates that, in 1949, 25 percent of quoted OTC issues were also listed on a registered exchange. Our measure of OTC capital must exclude such firms. To derive estimates for 192049, we use Friends counts of the number of OTC-quoted firms over a three-month window surrounding three benchmark dates in 1949, 1939, and 1929. There were 5,300 such OTC firms in 1949, of which 75 percent were not listed on registered exchanges. The median market value of these unlisted firms was $2.4 million. Therefore, we approximate exclusive OTC capital at $9.54 million (.75 ´ 5,300 ´ÿ $2.4) in 1949. Assuming that the real median size of unlisted OTC firms did not change over 192049, we next use the gross domestic product (GDP) deflator to convert the median size into nominal terms at the other benchmark dates. Next, we observe that the $9.47 million for 1949 is too small by a factor of 2.3, given our comparable estimate from the Flow of Funds, and adjust the OTC benchmark Federal Reserve Bank of Chicago estimates by this factor. Finally, we interpolate between the benchmarks to obtain an annual OTC series for 192949. To obtain OTC capital for 192028, we continue to assume that capital on the exchanges is proportional to relative transaction values. Since we know NYSE capitalization and now have estimates for the regional and OTC markets in 1929, we can estimate the share of the OTC in total market value in 1929. Using Friends (1958, p. 109) estimates of this share for 1926 and 1920, we can estimate OTC capital for these years given the values of NYSE capitalization from CRSP and our earlier estimates of regional capital. We interpolate between the benchmarks once again to obtain OTC capital for 192029. FIGURE 2A Estimates of outstanding equity, 190099 share of GDP 1.8 1.5 1.2 Flow of funds and backcast 0.9 0.6 0.3 CRSP 0.0 1900 ’10 ’20 ’30 ’40 ’50 ’60 ’70 ’80 ’90 ’00 By adding NYSE, regional and OTC capitalizations, we obtain a series for total market value for 192049 that is consistent with the Flow of Funds in the sense that the two segments coincide in 1949. Our final estimates of equity capital outstanding, displayed in figure 2A, are obtained by splicing our series with the Flow of Funds in 1945. The figure also includes the series for equity capital that would result from the use of CRSP (192599) and our NYSE listings (190024) data alone. The importance of equities that were not listed on the NYSE from the end of the First World War to the start of Nasdaq in 1971, as depicted by the vertical distance between the black and colored lines in the figure, is considerable. Since we wish to use market value from 1900 in figures 12 and 13, for the purpose of computing the share of equity in total finance, we ratio splice the value of NYSE capital for 190020 (obtained from individual issues of the New York Times Co.s The Annalist, Dana & Companys Commercial and Financial Chronicle, the New York Times, and Bradstreets) to our result for 192099. 31 BOX 3 Estimating the market value of business debt We define U.S. business debt as the value of outstanding commercial and industrial bank loans, corporate bonds, and commercial paper. For 194599, book values for loans and corporate bonds are from the Flow of Funds (table L.4, lines 5 and 6, respectively). For 190044, the book value of outstanding corporate bonds is from Hickman (1952) and that of bank loans is from the Federal Reserve Boards AllBank Statistics. Since bank loans are reported in the latter source as June 30 figures, we average across years for consistency with the calendar-year basis of the Flow of Funds. For commercial paper, the outstanding amount for 197093 is available from the FRED database of the Federal Reserve Bank of St. Louis. We carry this series to the present using the quantity of open market paper from the Flow of Funds (table L.4, line 2). We extend the series backwards to 1959 using the Federal Reserve Boards Banking and Monetary Statistics (Board of Governors, 1976b, pp. 717719). These quantities include paper placed both directly (that is, finance company) and by dealers. For 1919 58, we have a continuous series for dealer-placed paper only, again from Banking and Monetary Statistics (1976b, pp. 714717; 1976a, pp. 465467), which we ratio-splice to the later series. The splice leads to what is likely to be an overestimate of outstanding commercial paper by 1918 due to the rapid growth of directly placed paper between the mid1920s and 1941. For example, Greef (1937, p. 118) presents a figure of $874 million for outstanding commercial paper in 1918, while the spliced series would imply a total of $4.2 billion. Since we do not have the data on finance paper that would be required to reconcile these series, we have chosen to simply use Greefs figures before 1931, the point at Conclusion Bond-supply risk normally contributes between 10 basis points and 40 basis points to movements in the real rate of interest on T-bills. The effect has shown no tendency to decline over the past half century. The Fed will find it harder and harder to push this risk to zero because the gradual paying down of the federal debt has meant that it has become harder to expand T-bill issues to accommodate unexpectedly large rollover demands from foreign sources. Further, so long as the Fed uses the secondary market for Treasury securities as its chief means of conducting open market operations, shocks to the supply of these securities to the public will persist. If the supply of outstanding Treasury securities indeed does continue 32 which the outstanding totals from both series differ the least in percentage terms. Prior to 1918, Greef (1937, pp. 5759) provides estimates of the volume of commercial paper trading in 1907 and 191216. Assuming four- to six-month maturities, we then estimate the amount of commercial paper outstanding at 5/12 of the trading volume, and assume constant growth between the benchmarks of 1907 and 1912. We apply the same growth rate to 190006 to complete the series. From the above, it should be clear that the commercial paper series is not very reliable prior to 1931. Since we do not perform any econometric analysis with this series, however, and it turns out to be a small portion of total debt finance in any case during this period, we consider the inclusion of the totals in figures 1214 to be useful. To build a market value series, we include both commercial paper and bank loans, due to their short maturities, at their book values. We then convert outstanding corporate bonds from par values to market values using the average annual yields on Moodys AAA-rated corporate bonds (from Moodys Investors Service for 191998 and Hickmans high grade bond yields, which line up precisely with Moodys, for 190018). To determine market value, we let rt be the bond interest rate and compute the weighted average rt* ¤ ¤ t 1 (1 E )t i rt i . t i (1 E ) i 1885 t i 1885 We choose d = 10 percent to approximate the growth of new debt plus retirements of old debt and multiply the book value of outstanding corporate rt* bonds by the ratio rt to obtain their market value. its decline, an increase in the use of other debt instruments for open market operations will reduce the supply-risk for the group as a whole. We also find that despite the challenges to implementing monetary policy that are imposed by supply risk, the Fed has been managing this risk well. This is clear from observing that the variability of the monthly growth rate of the T-bill supply has not changed much in recent years. The bond and stock markets also show a lack of comovement that is hard to explain unless one assumes that the markets are segmented. Characterizing the nature of such segmentation is an endeavor in which we are actively engaged. 4Q/2001, Economic Perspectives NOTES Tables 1 and 2 both deal with ex post real returns of investors who purchase three-month U.S. Treasury bills in the secondary market with two months remaining until maturity and sell them a month later. We obtain monthly nonborrowed reserves from the FRED database of the Federal Reserve Bank of St. Louis, and describe other data sources in the text and note 8. an absence of bond-supply risk. Therefore, It1 contains insufficient information to forecast Pt perfectly. We compute surprises to T-secs and nonborrowed reserves as one-step ahead forecast errors from a series of rolling bivariate vector autoregressions (VARs) with four lags and a 30-month estimation window. 11 The Consumer Price Index, which we also use to deflate the Tsec quantities, is that for all urban consumers from the U.S. Bureau of Labor Statistics. 1 2 Noncompetitive bidders who specify a bank account for direct debit under the Treasury Direct investment plan also do not pay for their bills until the issue date. 3 We compute this figure as the average share of accepted noncompetitive bids in the total face value of T-bills sold at each weekly auction of 13-week and 26-week T-bills from July 30, 1998, through April 5, 2000. Press releases of auction results are available at the Bureau of the Public Debts website, www.publicdebt.treas.gov. 4 Before November 1998, marketable Treasury securities were auctioned in a discriminatory fashion, with the highest bidders receiving their requested quantities in full at the tendered price subject to a maximum of 35 percent of the total quantity auctioned (this 35 percent rule is still in effect). Noncompetitive bidders received their requests in full at prices based on a weighted average of accepted competitive bids. Both auction systems, discriminatory and single-price, generate some degree of supply risk. 5 Foreign bids are now restricted to $200 million or less per account, and are filled from smallest to largest until the $1 billion total limit is reached. The size of foreign bids will be restricted to $100 million or less as of January 1, 2002. 6 We compute the standard deviations using a 12-month rolling window and then apply the HodrickPrescott filter to each series before plotting. 7 The quantities of outstanding marketable Treasury securities are end-of-month observations from individual issues of the Annual Report of the Secretary of the Treasury for 192031, the Board of Governors of the Federal Reserve Systems Banking and Monetary Statistics (1976a, pp. 868873; 1976b, pp. 509511) for 193270, and individual issues of the U.S. Department of the Treasurys Monthly Statement of the Public Debt of the United States thereafter. To compute the quantity in the hands of the public, we subtract the Feds holdings from Banking and Monetary Statistics (1976a, p. 343; 1976b, pp. 485487) for 193270 and from individual issues of the Federal Reserve Bulletin for 192031 and 197199. 8 The monetary base is from the FRED database of the Federal Reserve Bank of St. Louis for 193699, with M1 from the Friedman and Schwartz (1970, table 1, pp. 458) ratio, spliced to the M0 aggregate for 192035. The information set It1 consists of the realized inflation rate from t1 to t (that is, pt1,t), the real T-bill return from t1 to t (that is, rt1,t), the real return on the S&P 500 portfolio from t1 to t, and the growth in the bond supply from t2 to t1 (that is, gt2,t1). In other words, thinking of date t as February and date t1 as January, and so on, when we commit funds to the bond market before any February auction, we know the return on the S&P 500 and the inflation rate for January, and the growth of the bond supply in December. We do not include the growth of bond supply in January, however, because that would imply knowledge of Pt and 9 Federal Reserve Bank of Chicago Nominal secondary market interest rates on three-month T-bills are from the FRED database for 193499 and Board of Governors (1976a) for earlier years. 10 Nominal calendar-month returns on the S&P 500 assume the reinvestment of dividends and are from worksheets underlying Wilson and Jones (2001). 12 As we show in a later section, the governments maturity preferences have shifted considerably over time, but these shifts in themselves did not introduce risk in the total supply of securities available to the public. Thus, focusing on supply shocks to a single instrument such as T-bills over the long term would overemphasize variations in the maturity structure of government finance that were not shocks but rather just substitutions of one maturity for another. For this reason, we work primarily with the total of marketable Treasury securities in the hands of the public rather than a narrower quantity measure such as T-bills alone. 13 Figure 2 does not span the full 192099 period because observations are lost in accommodating the lag length of the VAR, in constructing the initial estimation window, and in computing the initial and final rolling standard deviations of the forecast errors. We also lose observations early in the sample when making similar computations for figures 39. 14 In this section, however, we measure inflation over only the first month of the T-bill term and then assess its effect on the three-month real yield. Even here we obtain coefficients on the inflation surprises that are close to unity for the 192099 period and the 192046 subperiod, though the coefficients are considerably below unity for 194779 and 198099. 15 Since an adequate breakdown of Treasury securities into its Tbill and T-note components is not available on a monthly basis prior to 1932, the correlations that include T-bills and T-notes in the two upper panels of table 5 begin in 1932 rather than in 1920. 16 The correlations of nonborrowed reserves for 195979 are .110 with the S&P 500, .216 with T-bill returns, .071 with T-bill and T-note quantities, and .091 with T-sec quantities. For 198099, the respective correlations are .104, .134, .068, and .087. Correlations of the real monetary base for 195979 are .096 with the S&P 500, .461 with T-bill returns, .010 with T-bill and T-note quantities, and .094 with T-sec quantities. The correlations of nonborrowed reserves with real T-bill returns differ from those reported in table 1 because contemporaneous rather than leading relationships are considered here. In addition, the return measure in table 1 reflects a one-month yield on a seasoned T-bill rather than the return to the buy-and-hold strategy considered here. 17 We obtained the secondary market quotes for 196186 from the master microfilm reels that are on deposit at the New York Feds Department of Public Information. Quote sheets for 198796 are available at their website (www.ny.frb.org.) We collected quotes for 199799 from individual issues of the Wall Street Journal. 18 We compute surprises to T-secs and the monetary base as onestep-ahead forecast errors from a series of rolling bivariate VARs with four lags and a 30-month estimation window. In this figure and all others in this section, we apply the Hodrick-Prescott filter to our data series before plotting them. 19 33 REFERENCES Alvarez, Fernando, Robert Lucas, and Warren Webber, 2001, Interest rates and inflation, American Economic Review, Papers and Proceedings, Vol. 91, No. 2, pp. 219225. Board of Governors of the Federal Reserve System, 2000, Flow of Funds Accounts, Fourth Quarter, Washington, DC. , 1976a, Banking and Monetary Statistics, 19141941, Washington, DC. , 1976b, Banking and Monetary Statistics, 19411970, Washington, DC. , 1959, All-Bank Statistics, United States, Washington, DC. , 192099, Federal Reserve Bulletin, Washington, DC, various issues. Bradstreet Co., 192025, Bradstreets, New York, various issues. Cammack, Elizabeth B., 1991, Evidence on bidding strategies and the information in Treasury bill auctions, Journal of Political Economy, Vol. 99, No. 1, pp. 100130. 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Friend, Irwin, 1958, The Over-the-Counter Securities Markets. New York: McGraw Hill. Greef, Albert O., 1937, The Commercial Paper House in the United States, Cambridge, MA: Harvard University Press. Grossman, Sanford, and Laurence Weiss, 1983, A transactions-based model of the monetary transmission mechanism, American Economic Review, Vol. 73, No. 5, pp. 871880. Hickman, W. Braddock, 1952, Trends and cycles in corporate bond financing, National Bureau of Economic Research, occasional paper, No. 37. Johnston, J., 1984, Econometric Methods, third edition, New York: McGraw Hill. Lucas, Robert E., Jr., 1990, Liquidity and interest rates, Journal of Economic Theory, Vol. 50, No. 2, pp. 237264. New York Times Co., The, 191325, The Annalist: A Magazine of Finance, Commerce, and Economics, New York, various issues. , 18971928, The New York Times, New York, various issues. Phillips, A. W., 1958, The relation between unemployment and the rate of change of money wage rates in the United Kingdom, 18611957, Economica, Vol. 25, No. 100, pp. 283299. Standard and Poors Corporation, 2000, Compustat, database, New York. 4Q/2001, Economic Perspectives U.S. Department of Commerce, Bureau of the Census, 1975, Historical Statistics of the United States, Colonial Times to 1970, Washington, DC: Government Printing Office. U.S. Department of the Treasury, Bureau of the Public Debt, 19752000, Monthly Statements of the Public Debt of the United States, Washington, DC: Government Printing Office, various issues. U.S. Securities and Exchange Commission, 193549, Annual Report of the Securities and Exchange Commission. Washington, DC: Government Printing Office, various issues. University of Chicago, Center for Research on Securities Prices, 1999, CRSP, database, Chicago. U.S. Department of the Treasury, 191731, Annual Report of the Secretary of the Treasury on the State of Finances, Washington, DC: Government Printing Office, various issues. Federal Reserve Bank of Chicago Wilson, Jack W., and Charles P. Jones, 2001, An analysis of the S&P 500 index and Cowles extensions: Price indexes and stock returns, Journal of Business, forthcoming. 35 CALL FOR PAPERS May 8-10, 2002 38th ANNUAL CONFERENCE ON BANK STRUCTURE AND COMPETITION FEDERAL RESERVE BANK OF CHICAGO Financial Market Behavior and Appropriate Regulation over the Business Cycle The Federal Reserve Bank of Chicago invites the submission of research and policyoriented papers for the 38th annual Conference on Bank Structure and Competition to be held May 8-10, 2002, at the Fairmont Hotel in Chicago. Since its inception, the conference has aimed to encourage an ongoing dialogue on current public policy issues affecting the financial services industry. Although we are interested in papers related to the conference theme, we are most interested in high-quality research addressing public policy and the financial services industry. he theme of the 2002 conference will be an evaluation recovery from the recession was unusually slow. Deposit of the changing condition and behavior of financial flows were also disrupted, and as the economy recovered institutions, and the appropriate regulatory response, some institutions found it more difficult than others to raise over the business cycle. Over the past decade, we have seen core deposit funds using traditional means. As we proceed unprecedented economic expansion, yet events of the past through the current slowdown, there are reasons to believe year suggest that the business cycle with its associated that lending and deposit-taking institutions may react differ booms and recessions has not been eliminated. As we move ently than in the past. Banks are more geographically diversified, through the business cycle, it is important to understand how offer a wider range of financial products, and are better able financial firms respond to changing credit conditions and market to hedge risk, all of which may make them more resilient to demands. It is also important to understand the forces driving adverse economic shocks. More generally, the current financial financial firm behavior and to evaluate the alternative policy sector is characterized by extensive securitization, increased options available to regulators across the business cycle. risk-management options, and a broad network of nonbank n In addition to their importance over the cycle, the events of financial alternatives, such as mutual funds, commercial September 11 underscore the importance of appropriate policy paper, and deeper markets for debt securities. Many of the functions provided by these alternatives previously resided responses during a crisis. There is need for an immediate response to allow financial markets to continue to function efficiently, followed by a longer-term response to address the adverse impact the shock could have on the financial industry. In the early 1990s, financial institutions responded to the almost exclusively in commercial banks. Although this new network of intermediation is growing rapidly, it has never been tested by recession and is not subject to the same level of regulation as commercial banks. It remains an unanswered question as to whether these changes have economic slowdown by adjusting their credit standards and, made the new financial system more or less susceptible to as a result, credit availability. A "credit crunch" ensued, and the business cycle and economic shocks. Given the impact of the business cycle on the economic condi tion of financial firms, and the resulting impact on the economy, what is the appropriate regulatory and supervisory response over the business cycle? Some argue that policy should be countercyclical: more stringent during upturns and more accommodating during downturns. Examination standards, it is argued, need to be relaxed during downturns to avoid exacerbating an already bad situation. It has even been argued that bank capital requirements should vary inversely with the cycle. In essence, bank regulation and supervision should be used as a macroeconomic tool to help smooth the business cycle. Others have argued that sound regulatory and supervisory policy regarding credit quality and bank condition must be based on established standards that are invariant to the business cycle. The prompt-corrective-action provisions and least-cost-resolu tion requirements introduced under the Federal Deposit Insurance Corporation Improvement Act (FDICIA) are consistent with this view. These provisions were designed to at least partially limit supervisory discretion and forbearance when banks encounter financial difficulties. However, the effective conflict between FDICI A provisions that eliminate supervisory discretion and a desire to smooth the cycle via supervisory tools? What are the advantages or disadvantages of having responsibility for macroeconomic policy and bank regulation combined within a single agency? The 2002 conference will focus on these and related questions. However, we are also interested in other financial topics including, but not limited to: ■ Bank capital standards (particularly the proposed Basel Accord) ■ Measuring and managing risk (particularly for transnational/globaI financial services companies) ■ Alternative approaches to dealing with financial crises ■ Financial industry consolidation ■ The efficacy of the Gramm-Leach-Bliley Act ■ Fair lending issues and predatory pricing issues ■ The implications of technology on bank delivery systems (for example, Internet banking) and payment innovations. Continuing the format of recent years, the final session of the conference will feature a panel of industry experts who will ness of these provisions has recently come into question, and many argue that the restrictions are not—and perhaps should not be—binding on supervisors. discuss the purpose, structure, problems, and proposed changes associated with an important and topical banking regulation. Past topics discussed at this session include bank antitrust analysis, capital regulation, the role of government These financial and regulatory issues raise a number of public policy questions. Are financial firms more resilient to economic shocks today than in the past? Have deregulation and advances in portfolio management made them better prepared for downturns? How are credit crunches initiated—are they a supply or a demand phenomenon? How do changing credit standards affect targeted or fair lending? Do firms attempt to manage reported earnings over the business cycle? What problems and opportunities does this create? Are smaller financial firms at a competitive disadvantage at raising funds during economic slowdowns? As conditions deteriorate, how good are supervisors at identifying problem banks? How successful have "early warning models" been for this objective? sponsored enterprises (GSEs), optimal regulatory structures, and the appropriate role of the lender-of-last-resort. Proposals for this session are also welcome. If you would like to present a paper at the conference, please submit four copies of the completed paper or a detailed abstract (the more complete the paper the better) with your name, address, affiliation, telephone number, and e-mail address, and those of any coauthors, by December 21, 2001. Correspondence should be addressed to: Conference on Bank Structure and Competition Research Department Do current capital requirements, and perhaps regulation in Federal Reserve Bank of Chicago general, exacerbate business cycles? Do proposals aimed at increasing the role of market discipline diminish or further 230 South LaSalle Street Chicago, Illinois 60604-1413 aggravate this procyclicality? What are the advantages or disadvantages of having the new Basel Capital Accord incor porate a countercyclical component? Should regulation and supervision be used as an additional tool to smooth the business cycle? What problems does this create? Is there an inherent For additional information contact: Douglas Evanoff at 312-322-5814 (devanoff@frbchi.org), or Portia Jackson at 312-322-5775 (portia.jackson@chi.frb.org) Countering contagion: Does Chinas experience offer a blueprint? Alan G. Ahearne, John G. Fernald, and Prakash Loungani Introduction and summary China did not succumb to the Asian crisis of 199799. In this article, we discuss why China was not swept into the crisis despite two potential sources of vulnerability, a weak financial sector and increased export competition from the Asian crisis countries. Next, we discuss the role strong external accounts and capital controls played in countering contagion. We conclude by outlining the continuing risks to Chinas outlook, in an attempt to assess whether China is likely to avoid future crises. Table 1 shows Chinas relatively strong growth performance throughout the crisis. As shown in column 1, Chinas real gross domestic product (GDP) grew nearly 8 percent in 1998, a year in which the median real GDP contraction among economies in the region was 6 percent.1 Chinas slowdown in growth during 1999, shown in the second column, was modest. Another indicator of Chinas stability amidst the Asian crisis was its success at maintaining the peg between the Chinese renminbi and the U.S. dollar.2 Chinas ability to counter contagion was very much in doubt almost from the onset of the crisis, with the question Is China next? being actively debated.3 Many observers pointed to financial sector vulnerabilities as the most likely reason for the crisis to spread to China. It was generally accepted that problems with Chinas financial system were far worse than in other regional economies (see, for example, Lardy, 1998a, 1998b). In addition, as indicated by the third column of table 1, Chinas banking system was similar in size to that in the rest of Asia. In particular, the median ratio of bank loans relative to GDP was 93 percent in Malaysia, almost identical to the ratio in China. Nevertheless, despite the large banking system and prevalence of bad loans and other institutional weaknesses, China avoided a crisis during 199799. Why was this the case? We argue that the reason is 38 the absence of a credit culture in China. In a marketoriented system, pressures generally force rapid adjustment when institutions are, or are perceived to be, insolvent; these mechanisms do not operate fully in China. For example, banks can continue to operate regardless of balance-sheet weaknesses, because of the governments support. A second source of vulnerability for China that was commonly mentioned was through increased export competition as a result of the sharp real depreciations of the currencies of the Asian crisis countries. However, this channel of contagion also did not prove to be very strong. The reason, in our view, is that the trade relationship between China and other Asian economies is far less adversarial than is generally assumed. Chinese exports were affected by the reduced income of Asian economies, but much less so by the relative price effects from the depreciations of other currencies. We present evidence, based on an estimation of aggregate trade equations for Asian economies, that a modest impact on China from the depreciations elsewhere in Asia is what one should have expected. By contrast, the robustness of industrial country growth over the crisis period (excluding Japan, of course) continued to anchor Chinese export growth. We also Alan G. Ahearne is an economist at the Board of Governors of the Federal Reserve System; John G. Fernald is a senior economist at the Federal Reserve Bank of Chicago; and Prakash Loungani is assistant to the director (External Relations) at the International Monetary Fund (IMF). The authors are grateful to numerous colleagues at the Federal Reserve Board, Federal Reserve Bank of Chicago, and the IMF for helpful comments and conversations about aspects of this article. They particularly thank Christoph Duenwald, Hali Edison, Dale Henderson, Jeremy Mark, David Marshall, and John Rogers. They thank Oliver Babson and Jon Huntley for helpful research assistance. The views expressed are those of the authors and should not be attributed to the IMF. 4Q/2001, Economic Perspectives TABLE 1 China and other Asian economies: Selected indicators (percent) Real GDP growth, 1998 China Hong Kong Indonesia Korea 7.8 –5.4 Real GDP growth, 1999 Bank loans/ GDP, 1996 Current account/ GDP, 1996 Total debt/ reserves, 1966 Short-term BIS bank claims/ reserves, 1996 7.1 3.6 92.7 162.4 0.9 –1.7 130.5 — 25.1 — –13.1 0.8 55.4 –3.4 607.0 187.6 –6.5 10.9 61.5 –4.4 420.4 198.3 Malaysia –7.3 5.7 93.4 –4.4 137.4 41.4 Philippines –0.6 3.4 49.0 –5.4 414.7 77.1 Singapore 0.5 5.7 96.0 15.2 — — Taiwan 4.6 5.4 143.7 3.9 31.1 21.4 –10.9 4.3 100.5 –7.9 322.9 121.1 Thailand Notes: The first two columns show year-over-year growth rates for 1998 and 1999. The remaining columns show indicators for 1996, and hence are not affected by the crisis itself. Debt figures for offshore banking centers (Hong Kong and Singapore) are not shown, as they are not comparable with data from other economies due to the large size of external claims and liabilities. Sources: All GDP figures are taken from IMF International Financial Statistics. Bank loans data and data for foreign exchange reserves are also from IMF International Financial Statistics. Current account data are from FRB INTL databases. Total debt figures and shortterm BIS bank claims data are from the joint BIS-IMF-OECD-World Bank Statistics on External Debt. present evidence from disaggregated export data that the crisis did little to disrupt ongoing changes in Chinas market share in different product markets and regions. In contrast to these two sources of vulnerability, Chinas external accounts looked favorable compared with the rest of Asia, as shown by columns 4 to 6 of table 1. China runs current account surpluses (about 1 percent of GDP in 1996, but closer to 3 percent in 1997 and 1998), and its total debt relative to reserves was lower than in most Asian economies. Measures of short-term debt relative to reserves looked particularly favorable for China, as shown by column 6.4 Hence, the mainstream view was that the strength of Chinas external fundamentals could preclude a financial panic or country run as investors sought to pull funds out of an economy, akin to a self-fulfilling bank run.5 Furthermore, some argued that Chinas capital controls would prevent a destabilizing speculative attack on the Chinese renminbi. We share the view that external sector strength helped to counter contagion. However, we make a somewhat different argument for the potential role of Chinas capital controls in contributing to stability: Regardless of their other (often adverse) effects, capital controls prevented Chinese financial institutions from borrowing excessively abroad and, hence, helped keep the external fundamentals strong. In the following two sections, we spell out in greater detail our arguments for why financial sector weaknesses and increased trade competition Federal Reserve Bank of Chicago were not as damaging to China as expected. Next, we discuss the role of external sector strength and capital controls in Chinas ability to avoid a crisis. Then, we provide evidence on an increased country risk premium for China since the crisis and discuss risks facing China over the next few years. China’s financial sector Understanding Chinas resilience requires some understanding of why the crisis was so virulent elsewhere. Although the underlying cause or causes remain controversial,6 explanations tend to fall into one of two broad categories: 1) weak fundamentals in the affected economies, or 2) a self-fulfilling financial panic (also known as multiple equilibria). In this sectionand the two that followwe argue that neither explanation would point to an imminent crisis in China. In a market-oriented system, pressures generally force rapid adjustment when institutions are, or are perceived to be, insolvent; these mechanisms do not operate fully in China. Hence, despite serious fundamental weaknesses in Chinese enterprises and financial institutions, a crisis need not develop. Perhaps the most common view is that the Asian crisis reflected fundamental macroeconomic and microeconomic weaknesses in the most affected economies. Externally, large short-term external borrowingespecially when used to finance current account deficitsleft the economies vulnerable to capital flow reversals. Domestically, inadequately supervised and 39 capitalized banks made excessively risky loans to poorly governed firms.7 The story is typically some variant of the following (the emphasis differs across analysts, and not all aspects of the story are relevant for every economy in Asia). Widespread moral hazard existed because financial institutions were poorly regulated and companies had little accountability to shareholders. As a result, corporations borrowed heavily to invest in risky projects, financed by loans from banks that, in turn, borrowed excessively (and unhedged) from abroad. At the same time, foreign creditors were willing to lend large amounts to banks and corporations in these economies: The region had a strong track record for economic performance, and the borrowers were often state-owned banks and corporations which, the lenders thought, had implicit or explicit sovereign guarantees. Hence, risky investments were financed through excessive leverage, and especially through excessive short-term, unhedged external borrowing. These fundamental weaknesses left economies vulnerable to crisis from several directions. First, consider external pressures. The large short-term external borrowingespecially when used to finance current account deficitsleft the economies dependent on sustained short-term capital flows. If these flows slowed or reversed for any reason (for example, because of changes in monetary policy in industrial countries, changes in perceptions of the riskiness of these loans, or a run on the country), then the economy and the currency were vulnerable. Reversal of inflows contributed to slower growth of the real economy as a result of the need to reduce current account deficits; the reversal also contributed to downward pressure on the exchange rate peg, which might prove to be unsustainable. A substantial depreciation, in turn, weakened banking systems because of the unhedged currency exposure. Second, consider domestic forces. Poor supervision of banks, particularly those with inadequate capital, led to excessively risky bank lending. If the risks turn out badly, then banks might find themselves without enough capital to make new loans, or even insolvent. In addition, excessive leverage by corporations meant that if risky or speculative projects (office buildings and other real estate investments, say, or a hightech semiconductor factory) did not pay off, then firms might not have sufficient cash flow to pay workers and suppliers, let alone to repay their creditors. If they could not repay loans, then bad loans in the banking sector again would contribute to a banking crisis. Are these considerations relevant for China? The external considerations are probably not of great 40 concern to China, since its external debt is relatively small in proportion to GDP. As noted earlier, Chinas short-term external bank debt relative to reserves is among the lowest in Asia. We return to these external considerations in the next section when we consider speculative attacks and the role of capital controls in China. Domestically, however, it is often argued that China looks very similar to some of the frontline crisis economies, with poorly regulated banks making policy loans to inefficient, over-leveraged state enterprises.8 Chinas central bank, the Peoples Bank of China, has undertaken a widely publicized campaign to improve financial supervision and the operations of the banking system. In the meantime, however, Chinese banks continue to operate with an enormous overhang of bad loans. In May 2001, for example, the Bank of China (one of Chinas largest state banks) announced that nearly 30 percent of its loan book was nonperforming, even after a large chunk of nonperforming loans had been transferred to an asset management company. Western observers generally estimate that the proportion of nonperforming loans in the banking system as a whole may be even higher. In most Asian economies, policymakers postcrisis concern with banking-sector health reflects not only long-run concerns about the allocation of capital, but also short-run concerns. In particular, poor bank health can lead to a credit crunch, as banks reduce lending even to viable nonfinancial firms. This credit crunch exacerbates the real effects of the crisis. For example, banks may lose the funding base (deposits) with which to make loans; and even if they have the funding, they may not have adequate capital to make loans. In addition, creditors (depositors and foreign lenders) may lose confidence in financial institutions, leading to fund withdrawals or even bank runs. These short-term concerns are probably not relevant for China: Banks can and will continue to lend even if loans go bad. That is, it is unlikely that in order to restore their profitability, Chinese banks will be forced to cut back on other loans. First, it is fairly clear that the Chinese government continues to guarantee bank depositswhich are, after all, primarily held in state banks. Hence, depositors continue to have faith in the banking system, and the deposit base remains sound. Second, if a severe credit crunch begins to impinge on the real economy, Chinese authorities can in essence order the banks to lend. In the first quarter of 1998, for example, bank loans grew particularly slowly amid reports that banks were concerned about loan quality and amid reports of a credit crunch; in the second half of the year, loans grew very quickly amid 4Q/2001, Economic Perspectives reports of new loans to state enterprises in order to maintain growth. In other words, despite substantial moves in recent years to make the banking system more competitive and commercially oriented, neither the Chinese authorities nor anyone else believes the banking system is fully commercially oriented or operates independently from the government. Hence, Chinese banks can continue to operate even with substantially negative net worth. percent change Trade linkages between China and the Asian crisis economies 10 From the onset of the crisis, fears kept surfacing in the financial press that devaluations of the Asian crisis currencies would have a substantial adverse impact on Chinese exports and, therefore, ultimately trigger a Chinese devaluation. Often, the impression given was that China was locked in mortal combat for market share with other Asian economies. However, the evidence to support such an adversarial view of trade linkages between China and the other Asian economies is hard to come by. In this section, we present evidence that, over the last 20 years, changes in real exchange rates have not been the primary determinant of export growth for the major Asian exporters. Instead, the most important determinant has been demand from major trading partners (mostly the industrialized countries, particularly the United States). Industrial country demand and the effects of structural changes are likely to have outweighed exchange rate fluctuations as determinants of Chinas export growth.9 Figure 1 shows strikingly that exports by China and by other Asian economies tend to move together. The figure shows export growth (measured in dollar values) to the world from China (including Hong Kong) and from the rest of developing Asia. Both panels use trading partner statistics. Fernald (1999) argues that it makes economic sense to combine data for China and Hong Kong even in the period preceding formal unification, since many goods use Chinese labor and Hong Kong management and distribution skills. It makes statistical sense to use trading-partner statistics to avoid double-counting Chinese and Hong Kong exports.10 The similarity in export growth between China and other Asian economies suggests that common factorssuch as growth in developed economies, movements in the world price of key exports such as semiconductors, and movements in the yendollar ratewere probably more important determinants of Asian exports than competition with China. Discussions of Chinas export performance tend to Federal Reserve Bank of Chicago FIGURE 1 Exports from greater China and developing Asia 40 World imports from China/HK (net of internal China/HK trade) 30 20 World imports from developing Asia (excl. China and Hong Kong) 0 -10 1979 ’83 ’87 ’91 ’95 ’99 Note: Exports are measured by trading partner imports. Source: IMF, Direction of Trade Statistics. emphasize factors peculiar to China, such as economic reform initiatives, rapid investment, or tax incentives. However, these discussions appear to miss the prevalence of common shocks. A closer look at the changes in Asian export growth also does little to support the adversarial view of trade linkages. Consider the U.S. market for these countries exports. In 1989, China accounted for about one-tenth of total exports to the United States from the major Asian exporters (excluding Japan). By 1993, Chinas share had risen to one-quarter of all exports from these countries. However, contrary to popular perceptions, this gain in market share did not come about at the expense of the labor-intensive so-called ASEAN-4 economies (comprising four major economies of the Association of Southeast Asian Nations, Indonesia, Malaysia, the Philippines, and Thailand). Instead, China displaced the newly industrialized economies (NIEsKorea, Singapore, and Taiwan) in industriessuch as apparel, footwear and household productsthat these more advanced economies were relinquishing. This is a healthy, rather than disturbing, development. It mimics an earlier period, when the NIEs moved into the industries relinquished by a more advanced Japan. Even over the more recent period, 1994 to 2000, we have seen virtual stability in export shares of the three Asian groups (China, the NIEs, and the ASEAN-4), both at the aggregate level and in key industries. This stability of export shares holds in the United States, Japan, and many major European markets. To the extent that there have been small gains in Chinas export shares, these have continued to come largely by displacing the NIEs. The significant real 41 depreciations of the currencies of the Asian crisis economies have not had the dramatic impact on market shares that we would have expected if exchange rate movements were a strong factor behind export growth. Next, we substantiate these arguments, beginning with a look at the disaggregated export data and then presenting estimates of aggregate trade equations. United States have been scaled up so that they add up to 100. As shown in column 1 of table 2, in 1989 China and Hong Kong together accounted for about onequarter of total exports to the United States from the three groups. By 1993, Chinas share had increased to one-third. Mainland China alone nearly doubled its share of the U.S. market, helped perhaps by the real depreciation of the renminbi over this period. The ASEAN-4 group also increased its market share, but by a smaller magnitude than that for mainland China. Correspondingly, the NIEs share of the U.S. market fell from 59 percent to 44 percent. There is, therefore, some evidence of competitionshifts in market shareamong the three groups over the period 1989 to 1993. By contrast, the period between 1993 and 1997 is far more tranquil. The shares of China and the ASEAN-4 inch up over this period at the expense of the NIEs. The Asian crisis, and the associated sharp real depreciations in the currencies of many Asian economies, did not lead to any dramatic changes in market shares: The relative stability that characterized the period 1993 to 1997 continued through 2000. It may be argued that the evidence presented in table 2, which was for an aggregate of all industries, masks changes in market shares in particular industries. Our analysis of data for the 48 industries that make up the aggregate shows that is not the case. In table 3, we show examples of our analysis for two Export competition in particular markets and industries For this analysis,11 we classified the Asian economies we consider into one of three groups: 1) China (China and Hong Kong), 2) the NIEs (Korea, Singapore, and Taiwan), and 3) the ASEAN-4 (Indonesia, Malaysia, the Philippines, and Thailand). We begin by examining the export performance of these three groups in different geographical regions and industries for evidence of export competition, defined as shifts in market share across the three groups.12 In particular, we want to see if Chinas market share has increased markedly in a particular region or industry.13 Competition in the U.S. market Our analysis is based on three-digit industry level data published by the U.S. Department of Commerces Bureau of Economic Analysis (BEA). The numbers in table 2 provide the market shares of the three groups in the U.S. market in 1989, 1993, and 1996 2000. The total exports of these economies to the TABLE 2 Export shares of selected Asian economies in the U.S. market Economy 1989 1993 1996 1997 China 24 33 34 37 China 13 25 29 HK 11 8 5 NIEs 59 44 Korea 22 Singapore Taiwan 1998 1999 2000 39 40 40 32 34 35 36 5 5 5 4 41 38 36 36 36 14 13 12 11 13 14 10 10 11 10 9 8 7 27 20 17 16 16 15 15 ASEAN-4 17 23 25 25 25 24 24 Indonesia 4 4 5 5 4 4 4 Malaysia 5 8 10 9 9 9 9 Philippines 3 4 4 5 6 5 5 Thailand 5 7 6 6 6 6 6 100 100 100 100 100 100 100 90 126 180 199 211 234 276 Total (China + NIEs + ASEAN–4) Total (US$ billions) Source: U.S. Department of Commerce, Bureau of Economic Analysis. 42 4Q/2001, Economic Perspectives key industries, industry 213 (computers, peripherals, and semiconductors) and industry 400 (apparel, footwear, and household products). For industry 213 (table 3, panel A), mainland Chinas market share rose from essentially zero in 1989 to 7 percent in 1997; however, over half of this increase appears to have come at the expense of Hong Kong. When the two are combined, their market share increases only slightly over the period. The share of the ASEAN-4 increases somewhat more substantially, with a corresponding fall in the share of the NIEs. In the period since the onset of the Asian financial crisis, both China and the ASEAN-4 have continued to gain market share at the expense of the NIEs. The story in the case of industry 400 is a bit different (table 3, panel B). Here, China does experience a big increase in market share between 1989 and 1997, from 36 percent to 62 percent, with the bulk of this increase occurring between 1989 and 1993. The share of the ASEAN-4 also increased over the period, with the change being more substantial in the earlier part of the period than the latter. Since the onset of the crisis, there has been virtual constancy in market shares. In summary, our analysis of competition in the U.S. market leads to three conclusions:14 ■ Over the period 1989 to 1993, China did gain market share in many markets. In contrast, the period 1993 to 1997 is characterized by relative stability in market shares across the three groups. ■ Chinas gains have come largely at the expense of the NIEs rather than the ASEAN-4. ■ The Asian crisis has not led to any dramatic changes in market shares across the three groups. Aggregate export equations We also estimated standard aggregate export equations for the nine Asian economies, that is, equations expressing real export growth as a function of real income growth of the major trading partners and real exchange rate changes. The estimates show the dominance of income effects over relative price effects. This suggests that Chinese export growth should not have been expected to slow dramatically as long as overall growth among its trading partners remained robust, despite the price effects from the depreciations of the Asian crisis currencies. The data used in the estimation are annual, and extend from 1973 to 1996. To obtain sufficient degrees of freedom, we pool the data for all economies and run a panel vector autoregression (VAR) with three variables: real export growth, real income growth among major trading partners, and real exchange rate growth. We include country fixed effects in all regressions. Federal Reserve Bank of Chicago We also conducted a test of whether China could be pooled with the other economies and could not reject the hypothesis that it could. Since our sample period has been characterized by many structural changes (for example, the opening up of Chinas economy) and changes in exchange rate regimes, there may be concern that the parameters of the export equations may not be stable over time. We reestimated the equation over two subsample periods, 1973 to 1985 and 1986 to 1996, and found the conclusions to be fairly similar to those for the full sample period. Figure 2 shows the estimated response of export growth to standard-sized (one standard deviation) increases in each of the three sources of shocks in the panel VAR, going out four years after the shock.15 As expected, an increase in income growth among trading partners leads to an increase in a representative Asian economys export growth. There is a strong contemporaneous, and statistically significant, impact, which dissipates over the next few years. A depreciation in the currencies of major trading partners has the predicted adverse impact on export growth in the representative economy; however, the impact is weak. Table 4 presents the variance decomposition of real export growth. As shown, income effects account for a much larger percentage of the variance than relative price effects. For instance, at the one-year horizon, income growth accounts for 20 percent of the variance, compared with 2 percent for real exchange rate changes. (Not surprisingly, shocks to exports themselves show the largest dynamic response in figure 2 and account for the largest share of the variance.) In sum, since in the case of China, overall demand remained high (with strength in the United States and Europe countering weakness among Asian partners), export growth remained quite robust despite the drag from the depreciation of many Asian currencies. External sector strength and capital controls One view of the Asian crisis, most clearly associated with Sachs and Radalet (1998), is that the Asian crisis reflects financial panic, akin to self-fulfilling bank runs on the affected economies. However, Chinas external fundamentals were more favorable than in most Asian economies, making it plausible that China would not have been subject to such a run. At the end of 1998, China had about $150 billion in total reserves less gold. This compared with gross nominal external debt of $146 billion at the end of 1998, of which less than $32 billion was short-term debt to foreign banks. Given Chinas reserves, its sizable external debt remained manageable. 43 TABLE 3 Export shares for Asian economies in the U.S. market, selected industries A. Industry 213 (computers, peripherals, and semiconductors) Economy 1989 1993 1996 1997 1998 1999 2000 China 7 7 8 10 12 14 15 China 0 3 5 7 9 12 13 HK 7 4 3 3 2 2 2 NIEs 72 68 65 60 55 53 52 Korea 21 16 18 16 13 17 18 Singapore 31 29 28 24 22 18 16 Taiwan 20 23 19 20 20 18 18 ASEAN-4 21 25 27 29 33 33 33 Indonesia 0 0 1 1 1 1 1 Malaysia 12 15 15 15 16 17 17 Philippines 5 6 5 8 10 10 10 Thailand 4 4 6 5 6 5 5 100 100 100 100 100 100 100 1997 1998 1999 2000 Total B. Industry 400 (apparel, footwear, and household products) Economy 1989 1993 1996 China 36 55 60 62 62 63 64 China 18 41 47 50 49 51 52 HK 18 14 13 12 13 12 12 NIEs 52 26 17 15 15 15 15 Korea 27 13 7 6 7 7 7 3 2 1 1 1 1 1 Taiwan 22 11 9 8 7 7 7 ASEAN-4 12 19 23 23 23 22 21 Indonesia 3 6 8 8 8 8 8 Malaysia 2 3 4 4 4 3 2 Philippines 3 5 5 5 5 5 5 Singapore Thailand Total 4 5 6 6 6 6 6 100 100 100 100 100 100 100 Source: U.S. Department of Commerce, Bureau of Economic Analysis. China is often cited as an example of a country using capital controls successfully and avoiding a destabilizing currency attack.16 Chinas controls take various forms, including restrictions on foreign borrowing by Chinese entities, restrictions on portfolio outflows by Chinese citizens and inflows by foreigners, and a ban on futures trading in the renminbi. (Note that a major reversal in capital flowsan apparent panicneed not reflect a situation of multiple equilibria. It may reflect an informational revelation: The fundamentals of these economies are in bad shape.) 44 As already noted, China does not have a marketoriented financial system; the government uses controls of various sorts, including capital controls, to limit the ability of market forces to operate.17 Because of their role in Chinas repressed financial system, capital controls probably did play a role in limiting Chinas vulnerability. Without capital controls, for example, it seems likely that many investors would have tried to invest abroad for precautionary reasons, removing resources from the state banks.18 In addition, without a freely accessible onshore futures market,19 4Q/2001, Economic Perspectives FIGURE 2 Impulse responses of exports to various shocks 0.08 0.06 0.04 Export shock 0.02 0 Income Exchange rate -0.02 -0.04 1 2 3 4 Note: Lines show responses over time of Chinese exports to shocks to income of their trading partner, their trade-weighted real exchange rate, and exports themselves. financial stability, by helping to keep Chinas fundamentals strong. Chinese financial institutions suffer moral hazard problems that are at least as severe as those in other countries: Financial institutions are inadequately regulated and supervised, and they bear little responsibility for losses. Had they been allowed full access to international capital markets, they would have sought to borrow far more from abroad than was optimal from a social perspective. (Until the October 1998 default of the Guangdong International Trust and Investment Corp., foreign lenders generally considered Chinese borrowers to have implicit or explicit guarantees from the state and were therefore willing to lend large amounts at favorable rates.) Thus, regardless of their other effects, capital controls could have helped keep Chinas external fundamentals sound, thereby helping to ward off the worst aspects of the crisis. Risks to China it is difficult to speculate against the future value of the renminbi, and controls on outflows make it harder for Chinese investors to convert their renminbi if they expect the currency to weaken. But Chinas financial system is not a system that other countries would want to emulate in order to avoid crisis. Just as a tourniquet on a bleeding arm is no substitute for proper medical care, controlseven if they have contributed to Chinas stabilityhave serious costs. First, controls are often leaky. They may work well enough to prevent financial crisis for a time, but people always have an incentive to find ways around the rules. Moreover, this evasion potentially contributes to further problems in the form of corruption and fraud, such as capital flight through underreporting exports or overreporting imports. Second, the controls may work too well, placing unacceptable limits on a countrys opportunities for growth and prosperity. For example, Eichengreen (1999, p. 6) points out that North Koreas financial system is immune from crises because it is subject to such draconian controls. However, these harsh controls also help explain the countrys extreme poverty. Fortunately for China, as noted earlier, capital and other controls are not the whole story behind its resilience. Chinas financial system may be weak, but many of the economys fundamentals look far stronger than in the front-line crisis economies. In particular, China has relatively low external debt relative to GDP, large foreign-exchange reserves, a current-account surplus, and continuing sizable inflows of foreign direct investment (FDI). And much of the non-state sector remains vibrant. Nevertheless, there is an indirect channel by which capital controls could well have contributed to Federal Reserve Bank of Chicago That China successfully dodged the bullet is not to say that foreign financial market participants were unconcerned about Chinas vulnerability during the crisis. In the first part of this section, we assess indicators of foreign perceptions of China. The evidence suggests that the risk premium attached to China by foreign investors did indeed increase during the crisis. Since then, Chinas risk premium appears to have returned to near pre-crisis levels, partly reflecting the Chinese governments efforts to step up the pace of structural reforms. In the second part of this section, we present some conjectures on the outlook for China. Several measures showed an increased China risk premium during 1997 and 1998. Stock prices The top panel of figure 3 shows stock indices in Shanghai and Hong Kong.20 After stock exchanges opened in Shanghai and Shenzhen in the early 1990s, China maintained separate classes of shares for domestic residents (so-called A shares) and foreigners (B or H shares). Foreigners could not buy the domestic-only shares; domestic residents could neither purchase the foreign-only shares, nor, given Chinas capital TABLE 4 Variance decomposition of Asian export growth Income Exchange Exports rate 1 20 2 78 2 20 5 75 3 25 5 70 Step 45 account restrictions, generally invest legally in assets abroad.21 The Shanghai foreign shares have sometimes tracked the domestic shares, sometimes Hong Kongs Hang Seng index, and sometimes neither. From late 1996 until October 1997, the foreign and domestic Shanghai shares (the black and gray lines) generally tended to move together. (Although not shown, domestic and foreign share prices in Shenzhen generally move similarly to their counterparts in Shanghai.) Following sharp declines in the Hong Kong stock market in October 1997, Shanghai foreign share prices followed the Hang Seng down. Indeed, in the second half of 1998, Shanghai foreign shares underperformed relative to the Hang Seng. Domestic share prices, by contrast, remained largely unaffected by the crisis and, as of early 1999, were close to their October 1997 levels. Since the dividend stream is the same for the foreign and domestic classes of shares, the most plausible interpretation for the divergence is an increase in the return required by foreign investors. This increase in returns could reflect an increase in the risk-free real rate, an increase in the risk premium, or both. An even more striking way to see this divergence is to look at the average relative price paid by foreigners in the three markets, shown in the bottom panel of figure 3. Although at times there have been wide differences across marketsfor example, Hong Kong shares in 1994 and 1995 traded near parityby mid1998, foreigners in all three markets typically paid less than one-fifth the price paid by Chinese residents for the corresponding share. Thus, China is unlike most markets with investment restrictions, where foreigners typically pay a premium. The most plausible reason for the pricing difference is that Chinese investors have lower required rates of return, reflecting their lack of access to alternative investments. Their main alternative is bank deposits, since financial markets remain poorly developed, and Chinese capital controls make it difficult to invest overseas. Bank deposits tend to pay interest rates below world levels. In addition, Chinese investors may have a low equity premium because stocks offer one of the few opportunities available to diversify their investments. As noted earlier, the Asian financial crisis appeared to raise the risk premium demanded by foreign investors. Fernald and Rogers (2001) estimate how much required returns must have widened, given data on earningsprice ratios (the inverse of typically quoted priceearnings ratios) and dividendpayout ratios in China. In particular, they calibrate the standard Gordon (1962) pricing formula, which says that 46 P = D/(r g), where P is the price, D is the current dividend, r is the investors expected return, and g is the growth rate of dividends. Everything except r is the same for foreign and domestic investors. Fernald and Rogers rearrange this formula to show that the difference in expected returns is: ´ ¥ D´ ¥¥ E ´ ¥ E´ ¦ µ . µ ¦ µ ¦ E ¶ § § P ¶ Foreign § P ¶ Domestic µ¶ rForeign rDomestic ¦ § The dividend-payout rate D/E for listed stocks averaged about 0.5 from 1993 to 1997. The 1997 peak in relative prices was around one-half (larger in Hong Kong, smaller in Shanghai). With earnings price ratios of about 0.05 for foreign shares and 0.025 for domestic shares, this equation implies that FIGURE 3 Foreign and domestic equity prices in China A. Stock indices in Shanghai and Hong Kong July 1997=100 400 Shanghai B 300 200 Shanghai A 100 Hang Seng 0 1993 ’94 ’95 ’96 ’97 ’98 ’99 ’00 ’01 ’02 B. Relative price paid by foreigners 2.0 Hong Kong 1.6 1.2 Shenzhen 0.8 0.4 Shanghai 0.0 1993 ’94 ’95 ’96 ’97 ’98 ’99 Notes: Average prices for foreign-only shares relative to prices for corresponding domestic-only shares, using capitalization (domestic plus foreign shares) weights. In Shanghai and Shenzhen, foreign and domestic shares trade on the same exchange. For Hong Kong H shares, the corresponding domestic share trades in Shanghai. Foreign prices are converted into Chinese renminbi. Source: Fernald and Rogers (2001). 4Q/2001, Economic Perspectives the difference in expected returns was only about 1.25 percent. By mid-1998, the earningsprice ratios had risen to about 0.1 for foreign shares and 0.025 for domestic shares, implying a difference in expected returns of 3.75 percent. Hence, the Asian crisis widened the difference in expected returns by about 2.5 percentage points. This equation does not tell us whether domestic required returns fell or foreign required returns rose. However, domestic share prices changed little, while foreign share prices fell sharply. Hence, much of the movement presumably reflected an increase in the return required by foreign investors. Other financial market evidence The top panel of figure 4 shows the forward price of renminbi from the offshore nondeliverable forward market. This market offers one direct (though somewhat illiquid and, hence, imperfect) way to hedge renminbi exposure, and prices may reflect either expected currency depreciation or a currency risk premium. Until Hong Kongs stock market crashed in late October 1997, the forward market priced in little change in the value of the renminbi at all horizons. After late 1997, however, the forward price rose sharply, pricing in a considerable likelihood of devaluation; the forward price remained high, on balance, until early 2000, as sluggish exports and reduced capital inflows led many foreign investors to conjecture that China might devalue the renminbi. Since then, strong export growth, a pickup in inflows of foreign portfolio equity capital, and an increase in the value of FDI contracts have contributed to a narrowing of the premium.22 Finally, the bottom panel of figure 4 shows the widening of the yield spread between Chinese sovereign debt and U.S. Treasuries during the crisis, using a dollar-denominated Chinese government bond due in 2006. The spread widened from under 100 basis points to a high of around 400 basis points in September 1998. In early 1999, spreads stood at around 250 basis points, but declined to roughly 150 basis points in the latter half of 1999 and remained at these levels through much of 2000. Spreads narrowed even further in 2001, as government measures to restructure the economy seemingly boosted foreign investors confidence in China. Given the higher value of FDI contracts signed in 2000, increased capital inflows seem likely. Of course, China does not rely on foreign capital in a macroeconomic sense. That is, China has a currentaccount surplus and, hence, is a net exporter of capital (taking the form, especially, of central bank purchases of U.S. Treasuries and other foreign exchange assets). Therefore, even if foreign investment were to Federal Reserve Bank of Chicago decline, China could in principle offset the direct effect on domestic investment by reducing its investments abroad (for example, by converting its investments in U.S. Treasury bonds into investments in, say, factories in China). Nevertheless, FDI has played an important role in improving Chinas economy. One direct benefit of FDI is that foreign firms provide new products, improved technology, and examples of a reengineered employeremployee relationship (see Rosen, 1999). A second, indirect benefit of FDI is the support it provides to the dynamic non-state sector (see Fernald and Babson, 1999). Gross inflows of foreign capital allow the non-state sector to bypass domestic intermediated channels, and hence allow profitable investments that otherwise would not be made. As a result, increased FDI would tend to make enterprise restructuring less difficult. Downsizing state-owned enterprises requires destroying existing jobs and laying off workers, which is socially and politically much easier if new jobs are being created. Outlook for China We argued above that the acceleration of economic reform in China since the crisis has contributed to improved foreign-investor sentiment toward China. In part, the reform program has been stepped up in order to prepare the economy to meet the challenges of international competition following Chinas entry to the World Trade Organization (WTO). Now, we briefly discuss these reforms, including government efforts to restructure Chinas financial and stateowned enterprise sectors, and outline risks to the outlook for China. Many of these risks stem from factors that threaten to slow the pace of economic reforms, which in turn would likely depress inflows of foreign capital. Since the crisis, the Chinese government has intensified efforts to reform the major state-owned commercial banks that dominate the banking system. These efforts include cleaning up Chinese banks mountains of bad loansin large part a legacy of directed lending under central planningby transferring about $170 billion of bad loans to asset management companies. These entities, in turn, are expected to write off about one-third of the transferred bad loans through debtequity exchanges. The assets underlying the remainder of the bad loans will eventually be sold via auction.23 China has also made some progress in reforming the state enterprise sector. Many small firms have been privatized or shut down, while larger firms have shed some surplus labor. However, reform has been hindered by concerns about possible social unrest. The 47 FIGURE 4 Financial market evidence of Chinas risk A. Forwards non-deliverable forwardsa renminbi/$ 10.0 9.5 1-year 9.0 3-month 8.5 Spot 8.0 Q3 Q4 1997 Q1 Q2 Q3 Q4 Q1 Q2 Q3 1999 Q4 Q1 Q2 Q3 2000 Q4 Q1 Q2 2001 Q2 Q3 1998 Q4 Q1 Q2 Q3 1999 Q4 Q1 Q2 Q3 2000 Q4 Q1 Q2 2001 1998 Q3 B. Yield spreads yield spreadb (percentage points) 5 4 3 2 1 0 Q3 Q4 1997 Q1 Q3 a Rates from offshore forward market, where all transactions are settled in U.S. dollars based on the value of the renminbi. Government bonds relative to U.S. Treasuries. Source: Reuters. b development of a functioning social welfare system to provide laid-off workers with unemployment benefits, pensions, and health insurance is still in its early stages. The problem of surplus labor is even more acute in rural areas. Increased pressures on the agriculture sector following WTO entry may exacerbate the already large differential between urban and rural incomes. The threat of resulting massive rural-to-urban migration may cause Chinas leadership to slow the reform process, not least because mass urbanization will require massive fiscal spending. The fiscal costs 48 of resolving the banks bad loans, reforming the state enterprises, and financing a new social welfare system also raise concerns about whether the reform process is fiscally sustainable. In addition, if restructuring proves too painful, less reform-minded policymakers may come to power and reverse the reform agenda, perhaps as early as 2002, when the current set of leaders step down. Another risk is that reform may be slowed by Chinas inadequate infrastructure for commercial transactions, especially its accounting and legal systems, including the lack of a suitable framework for corporate bankruptcy. 4Q/2001, Economic Perspectives Another concern raised by observers is that imports to China might surge following WTO entry as China reduces trade barriers, possibly leading to pressure on the currency and balance of payments. Our view is that the tariff reductions promised in Chinas protocol of accession to the WTO are unlikely to lead to a substantial rise in the overall level of imports, because Chinas average tariff rates are already relatively low, after having been cut sharply during the 1990s. Likewise, the overall incidence of non-tariff barriers fell significantly during the 1990s. In certain highly protected sectors, however, notably agriculture and automobiles, barriers are set to fall steeply, and competitive pressures in these sectors are likely to increase substantially as a result of WTO accession. Observers have also questioned whether massive capital flight could put pressure on the currency and balance of payments, given evidence that Chinas capital controls can be easily evaded. Errors and omissions are, indeed, large, averaging a $16 billion outflow from 1995 to 2000. However, this is roughly an order of magnitude smaller than international reserves. With continuing current account surpluses and inflows of FDI, outflows would have to rise very sharply before foreign exchange reserves would begin to fall substantially and there would be irresistible pressure on the currency. Given the weakness of Chinas banking system, observers have expressed concern about Chinese banks ability to compete with foreign banks following Chinas WTO entry.24 If depositors were to shift large amounts of funds from domestic banks to foreign banks, many domestic banks might face a liquidity crisis. If the government is then called upon to rescue these banks with central bank funds, it may face the undesirable choice of seeing an increase in inflation or a substantial slowdown in growth (as banks are unable to extend new loans and are forced to call in outstanding ones). Conclusion China avoided the emerging market crisis of 199799 but, given the risks to the outlook, how likely is it that China will avoid future crises? Chinese authorities appear aware of the risks, but the problems are inherently difficult. China is attempting to balance conflicting concernsa desire for short-run stability and growth (which tends to slow reforms) versus a need for long-run improvements in the allocation of resources (which requires that reforms keep moving forward). Federal Reserve Bank of Chicago Would Chinese banks operate more soundly if they had adequate capital? The U.S. savings and loan problem highlighted the moral hazard problems of financial institutions with low net worth and access to deposit insurance. In 1998, Chinese authorities announced a 270 billion renminbi ($33 billion) program to recapitalize the banks. However, before Chinese banks can operate on a fully commercial basis. China needs to reduce the need to make policy loans (through enterprise reform), provide banks with experience and skill in assessing loans on commercial grounds, and ensure that banks are transparent and accountable. These are necessarybut obviously difficultsteps before Chinese policymakers can successfully recapitalize the banks or otherwise try to solve the underlying problems of inherited bad loans of the banking system. Chinese authorities certainly appear to recognize the need for these steps and have made substantial progress in recent years in training bankers and examiners and in increasing the accountability of banks. But progress is slow. Suppose, for example, that Chinese banks were successfully recapitalized, so that they would meet capital adequacy standards under the best of accounting systems. Major state-owned employers would still need loans to pay wages and pensions. In principle, the government could move these quasi-fiscal operations onto the official budget. However, financial instruments (including taxes but also bond markets) remain underdeveloped, so such a move is likely to happen later rather than sooner. In addition, inherent incentive problems could remain. In the United States, there were clear incentives for the owners of poorly capitalized savings and loans to engage in risky behavior; more capital would have mitigated these incentives. For Chinese banks, however, the issue is the incentives faced by bank managers (rather than owners). Individual managers may continue to have incentives to make loans to, say, friends or powerful politicians.25 In sum, China now faces the very difficult task of sequencing, that is, of trying to move from a non-commercial banking system where market mechanisms do not fully work to a viable commercial banking system where incentives are appropriate. The transitional stagewhere controls have been lifted but incentives remain inappropriateholds significant dangers, as was evident in the Asian crisis economies. 49 NOTES The strong output performance was surprising and appears in part to have reflected substantial increases in investment in infrastructure and by state enterprises (including strong inventory investment), the latter apparently financed by substantially faster lending by the four major banks. Hence, the increase in growth may have been at the expense of previously announced enterprise and bank reform. Numerous observers have also questioned the reliability of the output data, particularly given the clear political commitment at that time to an 8 percent growth target. Given long-noted problems with Chinese statistics (see Borensztein and Ostry, 1996, for instance), most analysts tend to interpret Chinese statistics as indicating trends, even if levels, or even exact growth rates, are uncertain. The concern is that the biases in the statistics are not constant, so that the economy might have weakened in 1998 despite the reported growth. 1 The terms renminbi and yuan are often used interchangeably to refer to Chinas currency. (Technically, the renminbi is the currency; the yuan is the unit of account.) We use the term renminbi throughout this article. 2 See, for instance, Dornbusch (1998), Manning (1999), and Butler and Palmer (1997). 3 To obtain consistent data across economies, we measure shortterm debt from creditor data, using short-term bank claims by BIS reporting banks. 4 5 See the discussion in Sachs and Radelet (1998), p. 5. 6 See Kochhar, Loungani, and Stone (1998) and Berg (1999). See, for example, IMF (1998), Goldstein (1998), and Krugman (1998). 7 See, for example, Lardy (1998a, 1998b), McGraw-Hill Companies, Business Week (1998), Rennie (1998), and Harding (1998). 8 Chinese export growth has also been helped by structural reforms of the exchange and trade system, as detailed in Cerra and Dayal-Gulati (1999). Examples include allowing local governments and exporting enterprises to retain a proportion of foreign exchange receipts, eliminating mandatory export and import planning, and opening up the economy to foreign direct investment. Despite occasional reversals, the overall trend has been to reduce the role of central planning in Chinas foreign trade. 9 See Arora and Kochhar (1995) for a comprehensive discussion of size and source discrepancies in bilateral trade statistics between China and its main industrial-country trading partners. 10 11 Some of the analysis here is an updated version of section 3 in Fernald, Edison, and Loungani (1999). See Leamer and Stern (1970, Chapter 7) for a good discussion of export share analysis. The effects of export competition can be reflected not just in changes in export shares but also in export prices or profit margins. We intend to explore these other effects in future work. 12 Note that by focusing on shares in particular markets, we are stacking the deck in favor of the export-competition view. A country may have its share in a particular market decline without necessarily experiencing a decline in the level of its exports to that market. It may also be losing market share in one market but gaining it in another. Furthermore, some changes in shares may be deliberate. Several Asian economies have shifted production toward components that are then assembled in China for export. 13 50 These shifts tend to increase Chinas export shares and decrease the shares of other economies, without having an adverse effect on these other economies. These shifts were most pronounced in Hong Kong (whose statistics we have combined with Chinas) and Taiwan, but to some extent affected the statistics of other Asian economies as well. 14 A less detailed examination of the data for the European and Japanese markets suggests broadly similar conclusions. The results were not sensitive to the ordering of the VAR. 15 See, for example, Lardy (1998a) and Stiglitz (1998). 16 17 See Gordon and Li (2001) for a discussion of financial repression in China. 18 Of course, that capital account liberalization improves opportunities for risk diversification is one of its important benefits. Eichengreen et al. (1998) provide a comprehensive review of the benefits and potential costs of capital account liberalization, arguing that ... with appropriate safeguards, orderly and properly sequenced capital account liberalization and the broader financial liberalization of which it is part are not only inevitable but clearly beneficial. 19 As discussed below, there is an offshore nondeliverable forwards (NDF) market in Hong Kong, where all transactions take place in U.S. dollars, based on the underlying value of the renminbi. Given the nonconvertibility of the underlying currency, the existence of the NDF market does not bring much pressure onto the renminbi. 20 This material draws heavily on Fernald and Rogers (2001). See Fernald and Rogers for an expanded discussion of the market and additional references. 21 In February 2001, China announced and began to implement plans to allow domestic Chinese residents to legally purchase the (much cheaper) foreign shares. The discussion here concerns the period before that date, although it should be noted that many Chinese investors were illegally purchasing B shares. The spike in B-share prices in 2001 reflects the opening of the market to domestic investors. 22 According to press reports, China raised about $21 billion on foreign equity markets in 2000 by selling off parts of the countrys largest state enterprises. This figure is roughly double the amount raised in each of the preceding three years. Contracted FDI inflows soared roughly 50 percent in 2000 compared with a year earlier, although actual FDI inflows rose only 1 percent. 23 For discussions of how to deal with the bad loans of the banking system, see Lardy (1998b) and Bonin and Huang (2001). 24 At present, foreign banks are not allowed to conduct local-currency business with domestic Chinese entities. Under the anticipated terms of Chinas WTO accession agreement, however, these restrictions will be lifted. In particular, following WTO accession, foreign banks will be allowed to conduct local-currency business with Chinese firms after two years and with retail customers after five years. 25 China has undertaken a high-publicity anti-corruption campaign. One feature of this campaign is its focus on the financial sector, evidenced by the arrest of several high-profile business and bank executives. See, for example, Dow Jones (1999a, 1999b) and Faison (1999). 4Q/2001, Economic Perspectives REFERENCES Arora, Vivek, and Kalpana Kochhar, 1995, Discrepancies in bilateral trade statistics: The case of China, International Monetary Fund, paper on policy analysis and assessment, No. PPAA/95/10, June. Berg, Andrew, 1999, The Asia crisis: Causes, policy responses, and outcomes, International Monetary Fund, working paper, No. 99/138, October. Bonin, John P., and Yiping Huang, 2001, Dealing with the bad loans of the Chinese banks, Journal of Asian Economics, Vol. 12, pp. 197214. Borensztein, Eduardo, and Jonathan Ostry, 1996, Accounting for Chinas growth performance, American Economic Review, May, pp. 224228. Butler, Steven, and Brian Palmer, 1997, Slow boat in China: Its economy slows, its banks are wobbly, is China next?, U.S. News and World Report, December 15. Cerra, Valerie, and Anuradha Dayal-Gulati, 1999, Chinas trade flows: Changing price sensitivities and the reform process, International Monetary Fund, working paper, No. 99/01, January. Diamond, Douglas, and Phillip Dybvig, 1983, Bank runs, liquidity, and deposit insurance, Journal of Political Economy, Vol. 91, pp. 401419. Dornbusch, Rudi, 1998, Is China next?, Financial Times, August 4. Dow Jones, Inc., 1999a, China newspaper says 1998 corruption could pass $10 billion, wire service report, January 8. , 1999b, Chinese bank executive given 10-year prison term for corruption, wire service report, January 13. Eichengreen, Barry, 1999, Safeguarding Prosperity in a Global Financial System: The Future International Financial Architecture, Washington, DC: Institute of International Economics. Eichengreen, Barry, and Michael Mussa, with Giovanni DellAriccia, Enrica Detragiache, Gian Maria Milesi-Ferretti, and Andrew Tweedie, 1998, Capital account liberalization: Theoretical and practical aspects. International Monetary Fund, occasional paper, No. 172. Federal Reserve Bank of Chicago Faison, Seth, 1999, China points finger at culprit of the week, New York Times, January 13, p. A8. Fernald, John G., 1999, China and Hong Kong: Reconciling bilateral trade statistics, Federal Reserve Bank of Chicago, manuscript. Fernald, John G., and Oliver Babson, 1999, Why has China survived the Asian Crisis so well? What risks remain?, in Financial Market Reform in China: Progress, Problems, and Prospects, Baizhu Chen, J. Kimball Dietrich, and Yi Feng (eds.), Boulder, CO: Westview Press, pp. 5585. Fernald, John G., Hali Edison, and Prakash Loungani, 1999, Was China the first domino? Assessing links between China and the rest of emerging Asia, Journal of International Money and Finance, Vol. 18, No. 4, August, pp. 515535. Fernald, John G., and John Rogers, 2001, Puzzles in the Chinese stock market, Review of Economics and Statistics, forthcoming. Goldstein, Morris, 1998, The Asian Financial Crisis: Causes, Cures, and Systemic Implications, Washington: Institute for International Economics. Gordon, M., 1962, The Investment, Financing, and Valuation of the Corporation, Homewood, IL: Irwin. Gordon, Roger H., and Wei Li, 2001, Government as a discriminating monopolist in the financial market: The case of China, Journal of Public Economics, forthcoming. Harding, James, 1998, China: SurveyTighter controls increase the pain, Financial Times, November 16. International Monetary Fund, 1998, The Asian Crisis: Causes and cures, Finance and Development, Vol. 35, June, p. 2. Johnston, R. Barry, 1998, Sequencing capital account liberalization, Finance and Development, Vol. 35, December, p. 4. Kochhar, Kalpana, Prakash Loungani, and Mark Stone, 1998, The East Asian crisis: Macroeconomic developments and policy lessons, International Monetary Fund, working paper, No. 98/128, September. 51 Krugman, Paul, 1998, What happened to Asia? conference paper, available on the web at http:// web.mit.edu/krugman/www/DISINTER.html. Lardy, Nicholas, 1998a, China and the Asian crisis, Foreign Affairs, Vol. 77, No. 4, pp. 7888. , 1998b, Chinas Unfinished Economic Revolution, Washington, DC: Brookings Institution. Leamer, Edward, and Robert Stern, 1970, Quantitative International Economics, Boston: Allyn and Bacon, chapter 7. Manning, Robert A., 1999, The coming China crash?, IntellectualCapital.com, February 19. McGraw-Hill Companies, The, 1998, Can China avert a crisis?, Business Week, March 16. ONeill, Mark, 1998, China: No hurry to make yuan fully convertible, South China Morning Post, December 1. Reuters, 1999a, China warns foreign banksNo lending without state approval, wire service report, January 14. , 1999b, China vows to improve accuracy of statistics, wire service report, January 14. 52 , 1999c, Full textChina central bank governors speech, wire service report, January 27. Rennie, David, 1998, Street protests as Chinese are bled of their savings, Telegraph Group, London, November 19. Rosen, Daniel H., 1999, Behind the Open Door: Foreign Enterprises in the Chinese Marketplace, Washington, DC: Institute for International Economics. Sachs, Jeffrey, and Steven Radalet, 1998, The onset of the East Asian financial crisis, National Bureau of Economic Research, working paper, No. 6680. Stiglitz, Joseph, 1998, Second-generation strategies for reform for China, address given at Beijing University, July 20, available on the Internet at www.worldbank.org/html/extdr/extme/ jssp072098.htm. Ta Kung News Group, 1998a, Chinas policy on Asian financial crisisInterviewing Dai Xianglong, governor of the Peoples Bank of China, Ta Kung Pao, November 4, p. 8, reported as FBIS document, No. FTS19981109000541. , 1998b, Central bank to boost euro asset holdings, Ta Kung Pao, December 13, p. A2, reported as FBIS document, No. FTS19990101000613. 4Q/2001, Economic Perspectives Growth in worker quality Daniel Aaronson and Daniel Sullivan Introduction and summary Improvements in worker quality due to changes in the distribution of education and work experience are among the key determinants of the economys potential rate of growth. The rate of such improvements is thus of substantial interest to monetary and fiscal policymakers concerned with maintaining balance between aggregate supply and demand. It also is of importance to officials charged with planning for the future of programs such as Social Security, whose projected financial condition is highly sensitive to assumptions about long-term economic growth. In this article we provide new estimates and forecasts of the rate of improvement in worker quality. Consistent with previous research, we find that changes in the distribution of workers education and work experience levels account for a significant portion of the growth in labor productivity. In particular, of the 2.7 percent average growth rate in labor productivity since 1965, we find that about 0.22 of a percentage point is attributable to the growth of labor quality. We also find that this contribution has fluctuated significantly over the last 35 years. For instance, as recently as the late 1980s and early 1990s, improvements in worker skill levels were adding about 0.40 percentage points per year to the growth of output. However, by the end of the 1990s, this figure had fallen to about 0.18 percentage points. Our forecasts call for a further decline to about 0.05 percentage points by 2010. The recent figures represent the combined effects of two long-running demographic trends and a partially offsetting business-cycle effect. The two demographic trends are the continuing increase in the education levels of the labor force and the movement of workers toward experience levels associated with higher wages and productivity. A major factor in the latter trend has been the aging of the Baby Boom generation, many of whom are now in their peak earnings years. Federal Reserve Bank of Chicago The positive effects of demographic change are partially offset by what has recently been the relatively faster employment growth of low-education and lowexperience workers, the typical pattern in a businesscycle expansion. Our forecast of a declining growth contribution from worker quality derives from two sources. First, we expect a slight decline in the rate of educational gains. Second, and more important, as the decade progresses, a significant portion of the Baby Boom generation will move beyond the highest earnings years that most workers experience in their early 50s. Indeed, by the end of the decade, the leading edge of the Baby Boom will be at an age associated with lower than average wage rates. At the same time, the age ranges associated with maximum wages and productivity will become populated with the smaller cohorts born in the late 1960s and early 1970s. As a result, the change in experience levels will turn from a positive to a negative factor for worker quality growth. We also examine the gap in labor quality between the employed and the pool of available workers, those who, while not working, currently report that they want a job. We find that available workers typically have predicted wages and productivity that are 15 percent to 20 percent lower than the employed. Over the course of a business cycle expansion, most potential workers with higher skill levels become employed, which tends to expand this gap. The long business cycle expansion that began in the early 1990s is particularly notable in pushing the gap in quality between the employed and the pool of available workers to nearly 23 percent, its highest level in our data. Daniel Aaronson is a senior economist and Daniel Sullivan is a vice president and senior economist at the Federal Reserve Bank of Chicago. 53 The compositional changes in the labor force we study can obscure the effects of changes in labor supply and labor demand on the price of an hour of constant quality labor. This may make it more difficult to evaluate macroeconomic theories that have implications for the behavior of real wages. Thus, we provide new estimates of real wage growth adjusted to a constant quality of labor hour. The resulting series is modestly more procyclical than unadjusted real wage growth measures. We also construct an unemployment rate for human capital that accounts for the fact that the loss in labor input associated with unemployment is greater when the affected worker is at a higher skill level. The resulting measure is between 0.5 and 1.0 percentage point lower than the standard civilian unemployment rate that counts all members of the labor force equally. The importance of labor quality has been made clear by the development of human capital models, which relate productivity and wage rates to characteristics such as education and work experience. The last 35 years have seen several major shifts in the distribution of such characteristics in the labor force, most notably the increase in the share of college-educated workers and the influx of relatively inexperienced women and Baby Boomers in the 1970s. In addition, the nature of the skills learned through formal education and on-the-job training has changed, with, in particular, a tremendous increase in workers with computer skills in the 1980s and 1990s. Human capital models quantify the extent to which these transformations have caused the growth of total labor input to differ from that of raw hours worked. This difference is known as worker quality growth. It is positive when total labor input is growing faster than the raw total of hours worked. As we show in this article, fluctuations in labor quality growth have had a significant impact on trends in output growth. Thus, by quantifying the expected future gains in labor quality, we can improve forecasts of potential output growth. In addition, quantifying past gains in labor quality is vital to producing productivity growth estimates that constitute a meaningful measure of our economys progress. Indeed, the simple observation that accurate measurement of productivity is critically dependent on using correctly measured outputs and inputs has been the root of a long literature on growth accounting that dates back to influential work by Solow (1957), Denison (1962), and Jorgenson and Griliches (1967) and has been updated and revised by Jorgenson and his coauthors (1987, 1999, 2000) and the U.S. Department of Labor, Bureau of Labor Statistics (BLS) (1993), among others.1 One recent prominent example is Young 54 (1995), who argues that input growth explains all the extraordinary output growth in East Asia in the 1970s and 1980s. This is very much in the spirit of Jorgenson and Griliches (1967) and Jorgenson, Gallup, and Fraumani (1987), who argue that proper measurement of inputs should result in estimates of total factor productivity growth that are close to zero.2 By more clearly articulating the trends in worker quality, this article also contributes to improved productivity measurement. Our measure of labor quality relies on the basic empirical implications of human capital models. Workers invest in productivity-increasing skills through formal education and on-the-job training. Moreover, in long-run competitive equilibrium, firms hire additional labor until workers marginal productivity coincides with their wage rate. This allows us to infer the effects of worker characteristics on productivity, which are not directly observable, from their effects on predicted wages, which can be estimated from cross-sectional data. We use such wage function estimates to value additional years of education, experience, and other forms of human capital. Applying these value estimates to the changing distributions of human capital indicators yields estimates of the growth in average worker quality. The critical assumption underlying our approach is that workers wage rates are equal to their marginal productivity, a basic implication of the competitive model of labor markets. There are, of course, models of the labor market in which wages are not equal to marginal products. For example, if firms discriminate against women or minority groups or if unions or firms exercise market power, wage rates may differ from productivity.3 In addition, Spence (1973) argues that firms use education and other observable human capital variables as a signal of unobservable worker ability. This can lead workers to invest in education even when it provides no actual increase in productivity. Finally, implicit contract models of the type studied by Lazear (1979) suggest that in order to induce higher effort and investment in skills, firms defer a portion of workers compensation until later in their careers. This leads to wages being below productivity early in workers careers and above productivity in later years. While not denying the relevance of these alternative models of the labor market in some contexts, we are nevertheless comfortable relying on the competitive model to provide at least a good first approximation to the growth in worker quality. An enormous number of empirical studies of the labor market have found competition to be a useful framework. In contrast, there is less evidence for the widespread relevance of the 4Q/2001, Economic Perspectives alternative models. Moreover, some direct support for the link between human capital and aggregate growth emerges from recent studies using international macroeconomic data. Though some early research found little correlation between changes in human capital and output growth, studies by Heckman and Klenow (1997), Topel (2000), and Krueger and Lindahl (1999) show that macro and micro estimates of the return to education are similar once adequate account is taken of measurement error. A bigger concern is that the available data on worker characteristics only begin to scratch the surface in explaining the determinants of wages and productivity. For instance, the productivity increase associated with a college degree must depend on the program of study, the quality of the institution, and a myriad of other factors. But typical data sources merely record whether a worker has any college degree. Similarly, the productivity increase associated with a year of work experience will vary with the nature of the work, how much time is devoted to training, and other factors. But data sources do not typically include such information. Indeed, almost all research on worker quality growth is based on proxies for years of work experience derived from the difference between a workers age and their years of formal education. Unobserved differences in time out of the labor force due, for example, to raising children will lead such proxies to differ from actual experience. The existence of unmeasured differences in worker characteristics will not greatly bias estimates of worker quality growth if the distributions of such characteristics around their means remain relatively fixed over time. In that case, changes over time in mean years of schooling and age are reasonable proxies for the overall improvement in productivity due to education and work experience. But systematic changes in unmeasured characteristics can lead to biases. For example, women tend to spend more time out of the labor force than men. So the ongoing increase in female labor force participation could lead to progressively greater overestimation of the average level of labor market experience by the usual proxy measure. In addition, some researchers suggest that the quality of education has changed systematically over time, which could cause growth in true education to differ from that suggested by increases in years of schooling. In particular, Bishop (1989) attributes a significant portion of the post-1973 slowdown in measured productivity growth to deterioration in the quality of education as evidenced by declining test scores. More recently, we suspect the greatly increased use of computers in the work place has raised the quantity of onthe-job training associated with a year of work Federal Reserve Bank of Chicago experience, which could also bias estimates of worker quality growth. Other recent work on labor quality includes Ho and Jorgenson (1999) and BLS (1993). Our methodology and data differ somewhat from these papers, as we discuss in a later section. This leads to some differences in estimates of labor quality growth. However, the broad contours of our results agree reasonably well with earlier work for periods in which our results overlap. Together, our research and the earlier work show that growth in labor quality has fluctuated in important ways over time. The quarter century after World War II was a period of especially rapid gains in worker skill levels. Indeed, Ho and Jorgenson describe the period from 1948 to 1968 as the golden age of labor quality growth. During this period, they estimate that rapid expansion of secondary and post-secondary education caused labor quality growth to average nearly 1 percent per year. As noted above, this means that changes in the composition of the labor force caused total labor input to grow nearly 1 percentage point more rapidly than total hours. However, with the flood of inexperienced Baby Boomers and female workers into the labor force in the 1970s, labor quality stagnated. Then, after 1980 as the Baby Boomers aged and educational attainment soared to new heights, labor quality accelerated again, to a growth rate of about 0.5 percent per year, about half of that seen in the 1950s and 1960s. Our estimates indicate that labor quality continued to advance in the last few years, but at a yet slower rate of only about 0.27 percentage points per year. Our forecasts call for annual growth to decline further to about 0.07 percentage points by 2010. Given the standard assumptions of constant returns-to-scale production and cost minimization, the contribution of labor input to output growth is the product of the growth rate of labor input and labors share in production costs. The latter figure is approximately two-thirds. Thus, our estimates for labor quality growth imply effects on real output growth of 0.18 percentage points late in the 1990s and 2000, declining to about 0.05 percentage points by 2010.4 In the next section of this article, we review some of the broad trends in human capital accumulation that underlie our estimates of labor quality growth. In the following two sections, we discuss our methodology and our detailed results. Trends in human capital accumulation Here, we document some of the broad trends in human capital accumulation that underlie estimates of worker quality growth. These are the increases in educational attainment, fluctuations in the age 55 population of 17 year olds, the number of new high school diplomas granted in the late 1990s was 7 percentage points lower than it was in the early 1970s. Only when general equivalency diplomas (GEDs) are added to the totals do recent rates match earlier levels. However, Heckman and Cameron (1993) have shown that GED holders typically possess considerably less Education human capital than high school graduates. Thus, the U.S. levels of formal education have expanded recent data in figure 1 should be regarded as showing greatly over the last century. We can see this in figure an overall deterioration in the fraction of new labor 1, which shows the increase in high school and colmarket entrants with the skills typically associated lege graduation rates since 1870.5 During this period, with secondary school completion.6 high school graduation went from a rarity to the norm. College graduation rates, however, have continAs the figure shows, a good part of that transformaued to increase, though not without some significant tion occurred during the early part of the twentieth fluctuations. Indeed, after an especially rapid advance century, a period Claudia Goldin and Lawrence Katz during the Vietnam War era, growth in new college (Goldin 1998, Goldin and Katz, 1999a) argue was degrees granted actually lagged the growth in 22 year formative for American education. College attendance olds until the mid-1980s, when it turned up again. and graduation rates also rose rapidly during the More recently, growth accelerated in the early 1990s, twentieth century. Increases in college graduation and by the end of the last decade graduation rates rates were especially rapid after WWII with the introwere back to the trend line established in the postduction of the GI Bill and increased growth in federal WWII period. funding of higher education. But even before WWII, Increasing graduation rates have led to a corregrowth in college graduation rates was impressive. sponding increase in the percentage of workers with Enrollment rates quadrupled between 1940 and 1970 high school and college education. In 1964, the bebut also tripled between 1910 and 1940. This pre-WWII ginning year for the data we use for this study, less expansion in education levels was unique to the U.S.; than 58 percent of workers had completed high school education did not expand at such rates in other counor had a GED. By 2000, this figure was over 90 pertries until several decades later. These trends likely cent. In 1964, less than 12 percent of workers had increased potential output growth in the U.S. during college degrees. By 2000, more than 28 percent did. the early twentieth century relative to other develThere were also healthy increases over the same perioped nations that were slower to invest in education. od in the share of workers with at least some college More recently, the growth in the rate of high education and with post-graduate education. school completion has stalled. Indeed, relative to the Though still significant, the growth in average levels of education has slowed since FIGURE 1 its high point in the early 1960s and late Educational attainment, 18702000 1970s. This can be seen in figure 2, which plots the five-year moving average of the percent 100 annual change in the percentage of workers GED with high school and college degrees.7 The figure indicates that the increase in high 80 school graduation rates has fallen relatively steadily from around 1.7 percentage points 60 per year at its peak in the early 1970s to only about 0.1 percentage points the last several 40 years. Increases in college graduation rates College have also declined over time, but the drop High school 20 has been smaller. Between 1970 and 1975 and again between 1978 and 1983, the in0 crease in the share of college workers peaked 1870 ’80 ’90 1900 ’10 ’20 ’30 ’40 ’50 ’60 ’70 ’80 ’90 ’00 at a rate of about 0.8 percentage points per Source: Authors’ calculations based on data from U.S. Department of Commerce, year. Since the mid-1980s, the advance has Bureau of the Census (1975) and U.S. Department of Labor, Bureau of Labor Statistics, Current Population Survey, 1964–2000. been relatively stable at about 0.4 percentage points per year. distribution, and the rising fraction of female workers. We also note some other changes in the nature and composition of the work force that may affect the growth of human capital but are not usually included in analysis of labor quality. 56 4Q/2001, Economic Perspectives FIGURE 2 Five-year moving average of change in percentage with high school and college education average annual change in percentage 2.0 High school graduates 1.6 1.2 0.8 College graduates 0.4 0.0 1968 ’72 ’76 ’80 ’84 ’88 ’92 ’96 ’00 Source: Authors’ calculations based on data from U.S. Department of Labor, Bureau of Labor Statistics, Current Population Survey, 1964–2000. Such fluctuations in the growth of average educational levels can occur for two reasons. First, younger workers entering the labor force are constantly replacing older workers reaching retirement age. The former have historically had more education. Second, some of those in the age ranges typically associated with working choose to acquire more education, often while continuing to work part or full time. Figures 3 and 4 shed some light on the importance of new entrants replacing retiring workers for the increase in education levels. Figure 3 plots the difference in high school and college graduation rates between those near the end of their careers (55 to 59 year olds) and those near the beginning of their careers (25 to 29 year olds). As the graph shows, in the 1960s, there was a more than 30-percentage-point difference between the high school graduation rates of older and younger workers. Likewise, the expansion of college graduation rates in the 1970s led to a more than 15percentage-point difference between the college graduation rates of older and younger workers. These large gaps between workers entering and leaving the work force were a major factor behind the rapid growth of average educational attainment during those periods. But those differences, and their resulting implications for labor quality growth, had all but disappeared by 2000. This is one of the factors underlying the slower growth in average education levels in the 1990s seen in figure 2. The size of cohorts entering and exiting the labor force also drives fluctuations in the growth of educational attainment. When the flow into the labor market of younger, more highly educated workers is Federal Reserve Bank of Chicago faster than the flow out of the labor market of older, less highly educated workers or vice versa, the average educational level increases more rapidly for a given gap in the two groups educational attainment. These flows are, of course, largely determined by changes over time in the size of birth cohorts. Figure 4 provides an indication of how cohort sizes have varied since the mid-1960s. Specifically, the colored line shows the percentage of the working age (1869) population made up of 25 to 29 year olds and the black line shows the percentage of 55 to 59 year olds. The figure shows that the fraction of the workingage population accounted for by early-career workers rose from around 10 percent in the early 1960s to nearly 14 percent in the mid-1980s. A big part of that rise, which acted to increase the growth of average educational attainment, was the Baby Boom generation reaching working age. The first members of that generation reached age 25 around 1970 and its last members reached age 25 a few years after the peak in the share of early-career workers. Since the mid-1980s, the share of early-career workers has dropped to around 10.5 percent, which has contributed to the slower pace of growth in average educational attainment. Census Bureau projections call for the 2529 share to stabilize this decade at around 10 percent. The share of late-career workers fluctuated somewhat less, dropping from around 8 percent in the early 1960s to a minimum of about 6.5 percent in the mid1990s, before starting to rise again. Projections are for it to continue to rise to nearly 10 percent by 2010. The increase in the size of retiring cohorts is a positive for the growth in average educational attainment, but given the currently small gap in educational attainment shown in figure 3, it is only a small positive. Of course, many workers continue to acquire formal education until quite late in their lives. Moreover, workers with greater formal education tend to live longer and to remain more firmly attached to the labor force. Thus, even without additional school attendance, the average educational attainment of a cohort of workers can rise as the less educated workers tend to drop out of the labor force more quickly. The combined effects of additional education and the greater tendency for the less educated to drop out of the labor force induces a significant increase over time in the average educational attainment of workers from a given birth cohort. For example, figure 5 follows the cohort born between 1941 and 1945. It shows that the fraction with college degrees increased between 1970, when they were aged 25 to 29, and 2000, when they were 55 to 59. The colored line shows the proportion of the whole cohort with 57 FIGURE 3 FIGURE 4 Difference in worker graduation rates: Age 2529 minus 5559 Population of working age in 2529 and 5559 age range percent percent 40 14 High school graduates 30 25–29 12 20 10 10 College graduates 8 0 -10 1964 ’68 ’72 ’76 ’80 ’84 ’88 ’92 ’96 ’00 Source: Authors’ calculations based on data from U.S. Department of Labor, Bureau of Labor Statistics, Current Population Survey, 1964–2000. college degrees, while the black line is limited to those working in the given year. As the graph shows, the increase in the share reporting a college education has been quite significant. When this cohort was between the ages of 25 and 29 in 1970, only 16.5 percent reported having a college education. Thirty years later, the share was 25.5 percent. When we limit the samples to those who were working, the increase in the percentage was even greater, going from about 19.3 percent to about 29.3 percent. As the gap in educational attainment between younger and older workers narrows, the increasing educational attainment of middle-aged workers is becoming a bigger factor in the growth of educational levels. Work experience Workers labor market experience is a second important determinant of skill levels. Until they reach their early fifties, workers wage rates and, thus by inference, their productivity, tend to increase with age. These increases presumably reflect skills learned over time in the labor market. As a rough indication of the trends in labor market experience, figure 6 shows the average age of workers between 1890 and 2000. Consistent with greater life expectancies and lower birth rates, the average age of workers grew from 35 at the turn of the twentieth century to over 40 at its apex in the mid-1960s. However, starting in the late 1960s, the first of the large Baby Boom cohorts entered the labor force, causing the average age to drop for the next 20 years, reaching a bottom at around 37.5 as the last of the Baby Boomers entered the labor force in the early 1980s. As we show later, this drop in experience levels partially offsets the labor quality 58 55–59 6 1960 ’70 ’80 ’90 ’00 ’10 Source: Authors’ calculations based on data from U.S. Department of Labor, Bureau of Labor Statistics, Current Population Survey, 1964–2000. improvements arising from the tremendous gains in formal education during the 1970s. Since the early 1980s, the aging of the Baby Boom cohort has helped to push the average age of the working population back to about 39.5, roughly where it was in the early 1970s. Sex composition The final, major and easily quantifiable change in the composition of the labor force is the rise of female workers, particularly during the second half of the twentieth century. Though the gap has been narrowing in recent decades, women tend to earn lower wages than men of the same age and educational level. The competitive labor market framework that we employ in this article implies that this malefemale wage gap reflects differences in productivity, rather than discrimination or some other factors.8 One interpretation is that the error in approximating labor market experience by age minus years of education minus six is greater for women than men, who generally spend less time not in the work force or attending school. Thus among groups with the same level of measured experience, women will tend to have less actual experience. Indeed, the wage gap between men and women is significantly smaller at low levels of experience. Under this interpretation, it is not that women are intrinsically less productive than men, but rather that they have less actual labor market experience than men of the same age and education level. Still, given their lower wage, an increasing share of women in the labor force lowers the growth of labor quality below that expected on the basis of age and education. 4Q/2001, Economic Perspectives FIGURE 6 FIGURE 5 College graduate rate: Population and workers born 194145 Average age of the labor force, 18902000 years 42 percent 32 40 Workers 28 38 24 Population 36 20 16 1970 34 1885 ’95 ’05 ’15 ’25 ’35 ’45 ’55 ’65 ’75 ’85 ’95 ’05 ’75 ’80 ’85 ’90 ’95 ’00 Source: Authors’ calculations based on data from U.S. Department of Labor, Bureau of Labor Statistics, Current Population Survey, 1964–2000. Beginning after WWII, the share of female workers rose from less than 25 percent to about 38 percent in 1970 to close to 47 percent by 1990, before flattening out during the last decade. Given their lower wage rates, rapid increases in the share of female workers, such as occurred in the 1950s to 1980s, tended to hold down the growth in labor quality. This negative effect on worker quality has diminished as the share of women has grown more slowly over the last decade. Other factors While most analyses of labor quality, including the basic estimates we present below, are based exclusively on the trends in education, age, and sex composition that we have just discussed, there are good reasons to think that other factors may also play a role. We have already noted that crude measures of years of schooling or labor market experience may fail to fully capture the accumulation of human capital when educational or work experiences differ across workers and time. One change that we wish to highlight is the increasing computerization of the workplace, which may be leading to more on-the-job investment in skills per year of labor market experience. The 1980s and 1990s saw an explosion in the use of computer skills. Among workers age 18 to 64, roughly 25 percent used a computer in 1984. That fraction grew to 37.4 percent in 1989, 46.6 percent in 1993, and 60.5 percent in 1997.9 At first glance, this seems like an obvious improvement in the skill level of the work force. Kreuger (1993) even attempts to quantify the rate of return to using a computer at work by estimating the gap in wages between otherwise similar workers who report that they do or do not use a computer. However, DiNardo and Pischke (1997) Federal Reserve Bank of Chicago Source: Authors’ calculations based on data from U.S. Department of Commerce, Bureau of the Census (1975) and U.S. Department of Labor, Bureau of Labor Statistics, Current Population Survey, 1964–2000. argue that similar apparent rates of return are associated with using a pencil or sitting in a chair at work. In other words, computer usage may be associated with higher wages because it is correlated with unmeasured ability. On average, workers with more ability tend to have jobs that require sitting or using a computer (or pencil) at work. Therefore, the extent to which computer usage has added to labor quality is subject to a great deal of uncertainty. Thus, it would be difficult to implement a labor quality measure incorporating computer usage, even if such data were available in a general enough form. Nevertheless, the fact that there has been such a sharp increase in an important form of on-the-job training highlights the fact that simple years of labor market experience is a rather crude measure of the human capital acquired while working. Finally, there have been a number of other trends in labor force composition that may have implications for labor quality. One is the steady increase in the minority and immigrant portions of the working population over the last 30 years. The latter is discussed extensively in work by Borjas (1999). Because minorities and immigrants tend to have lower wages than natives and whites, these trends may have had an impact on worker quality growth. A second trend is the fall in the share of married workers from around 75 percent in the mid-1960s to around 60 percent in 2000. Research on the productivity implications of marriage, and how those vary by gender, is discussed in Gray (1997). Finally, the share of those working part time has fluctuated over the years. This may have implications for productivity, since part-time workers tend to earn lower wages.10 59 Methodology In this section, we discuss our methodology for summarizing the effects of the changes outlined above in an overall measure of labor quality growth. Data Our labor quality estimates are derived from the Bureau of Labor Statistics Current Population Survey (CPS). The CPS, the source for such well-known statistics as the unemployment rate, is a monthly, nationally representative survey of approximately 50,000 households conducted by the Census Bureau. Importantly for our purposes, it collects basic demographic data, such as age, race, sex, and educational attainment, as well as data on labor market status. Participating households are surveyed for four months, ignored for the following eight months, and then surveyed again for four more months. Those households in the fourth and eighth months of their participation are known as the outgoing rotation groups (ORGs) and are asked some additional questions that allow construction of an hourly wage measure. Moreover, the micro data from the ORGs are collected in easily accessible form. The major advantage of the ORG files is the large sample sizes (150,000 households per year) that are available. However, the data only go back to 1979, a relatively short period for examining labor quality. Therefore, we base most of our results on the CPS data for March of each year, which are available from 1962. The advantage of the March data is that they are supplemented with additional questions about income, weeks worked, and usual hours worked per week in the previous calendar year. Using data since 1975, we can compute an hourly wage as annual earnings in the last year divided by the product of usual weekly hours and number of weeks worked in the last year. Prior to the 1976 survey, data on usual weekly hours are not available; but there are data on hours worked in the week prior to the survey that we can use to construct a wage measure. Although the March CPS files are available starting in 1962, education is missing in the 1963 survey. So we begin our analysis with the 1964 data. A disadvantage of the March data relative to the ORG files is the smaller sample sizes (50,000 households per year). However, we find that after 1980 when we can compute measures based on both data sources, the March data yield a series that, while somewhat more variable, is quite close to that based on the ORGs. Statistical methodology In order to compute an index of labor quality based on the trends surveyed in the last section, we 60 need to evaluate the impact of such variables on productivity, or on what is assumed to be the same thing, wages. We do this by estimating linear regression models that relate the natural logarithm (log) of workers wage rates to their education, age, sex, and other characteristics. The estimated coefficients are the predicted effects of worker characteristics on wages and productivity. Combining the estimated regression coefficients with the micro data on education, experience, and sex yields a predicted average wage. Using the same regression coefficients to compute the predicted average wage in adjacent years isolates the portion of aggregate wage growth that is due to changes in worker characteristics, the definition of labor quality growth. One could in principle use a single, fixed regression model to evaluate worker characteristics in all years. However, because there have been major changes over time in the valuations associated with worker characteristics, we choose to allow the regression coefficients to vary over time. Moreover, to compute our index, we adopt a chain-weighting procedure that bases the growth in the index from one year to the next on the geometric average of the growth rates in worker quality obtained using regression coefficients in the two years. In more detail, the first step in the construction of our index is to estimate for each year regression models for the log wage of the form log Wit Sit B t ¤E C ¤F E H 4 4 j 1 j it jt j 1 it j it jt Fi Gt X it E t F it , where log Wit is the log hourly wage of worker i in year t, Sit is a vector of education variables, Eitj is estimated labor market experience raised to the jth power, Fit is an indicator variable that takes the value one if the worker is female, and Xit is a vector of other background variables that might affect wages, including, race, marital status, and whether the worker is employed part time.11 We also include an interaction between the quartic polynomial in experience and the female indicator to allow for different rates of return to experience between genders. We do this because, as we discussed in the last section, the only available measure of work experience is potential (maximum) experience, computed as age minus years in school minus six. Since women have more career interruptions than men, they also have larger deviations between actual and potential experience. The interaction terms account for this difference by allowing a different rate of return to potential experience across genders. 4Q/2001, Economic Perspectives In each year in our sample, we classify individuals into five education categories: less than high school, high school graduate, some college, college graduate, and post-graduate. However, a complication arises because a 1992 redesign of the CPS changed the educational attainment question from one on the number of years of education to one on type of degree completed. This change caused a significant break in the fractions of our sample in the five education categories. We employed two methods for dealing with this problem. First, we followed the method described in Jaeger (1997) for optimally matching CPS education questions pre- and post-1992. Second, for the construction of 1991 to 1992 labor quality growth rates, we collapsed the responses into three more easily comparable categories: less than high school, high school graduates (including some college), and college graduates (including post-graduates). We then use these three categories to compute labor quality growth for 1991 to 1992 and the five categories for all other years. The next step of the calculation is to compute weighted averages of predicted wages for workers in the CPS based on coefficients for education (at), experience (bjt), femalemale differential in value of experience (gjt), and female (ft) estimated from alternative years of data ¥ Wˆits exp ¦ Sit B s § ¤ 4 j 1 Eitj C js ¤FE H 4 j 1 i j it js ´ Fi G s µ , ¶ where the s superscript on Wˆits denotes that the predicted wage is computed using coefficients estimated from year s data. The weights given to different individuals vary for two reasons. First, the CPS is a probability sample in which different individuals have different probabilities of being sampled. In order to form consistent estimates of population quantities, the Census Bureau provides weights that undo the probability sampling in expectation. Using these weights would allow us to consistently estimate the average predicted wage for all workers in a given year. However, our interest is not in the average worker, but rather in the average hour worked. Those who work more hours are, therefore, more important for this average than those who work fewer hours. Thus, we base our weights on the product of the usual CPS weight and hours worked by the individual. That is, the averages of the Wˆits are based on w it wit hit / wit hit for i person i in year t, where wit is the usual CPS weight and hit is the number of hours worked. Finally, for each year t, we identify growth in average labor quality with the growth in average predicted wages relative to year t1 using a common ¤ Federal Reserve Bank of Chicago set of rates of return to value human capital characteristics in the two years. We compute such growth using rates of return estimated using year t1 data, dQt0 ¤ w Wˆ / ¤ w it t 1 it ˆ t 1 it 1W it 1 i , i and using year t data, dQt1 ¤ w Wˆ / ¤ w it i ˆ it 1W it 1 t it t . i Both of these ratios are estimates of the growth in average wages that is attributable to improved worker quality; they differ from one only because of changes from t1 to t in the distribution of education, experience, and sex. Since it is arbitrary whether we use rates of return based on estimates from year t or t1, we emulate the strategy of a Fisher ideal index by taking the geometric average of the results based on year t and t1 regression coefficients. Thus, the final estimate of worker quality growth in year t can be expressed as 1/ 2 dQt dQt0 s dQt1 . An overall index can be formed by chaining together the above growth rates from an arbitrary base level in some year. Relative to an index based on a single, fixed vector of rates of return, the advantage of the above is that it allows for varying rates of return. Alternative measures of labor quality Our method of measuring labor quality differs to some extent from that used by earlier researchers. BLS (1993) provides a detailed account of its methodology, along with those of Jorgenson et al. (1987) and Denison (1985). Below, we briefly describe the BLS (1993) methods and those of Ho and Jorgenson (1999), an updated version of Jorgenson et al. Ho and Jorgenson split the working population into 168 possible cells, partitioned by sex, age ranges, education, and self-employment status. They then compute changes in hours worked and compensation per hour for each cell. In addition to using data from the decennial Census of Population and the CPS, their measures of compensation include imputations of the value of nonwage compensation that they derive from the National Income and Product Accounts. In this framework, the growth in total labor input is a Tornqvist index or weighted average of the change in log hours in the various cells, where the weights are given by the average share of total compensation attributable to the cell in the two years. The growth in their labor quality index is defined as the difference 61 between this total labor input growth and the growth in raw labor hours worked. Ho and Jorgensons disaggregation of workers into many cells allows for substantial flexibility in measuring the distribution of labor services across types of workers. However, their method assumes that all workers in a given cell have equal levels of human capital, which our regression analysis shows not to be the case. At the same time, mean wages must be estimated based on some relatively small samples. Thus, we prefer our approach, which in a sense allows for a separate cell for each individual worker in the CPS, but derives wage estimates from a standard wage regression. This provides the maximum possible flexibility, while keeping the number of estimated parameters relatively low. Since fringe benefits and the value of social insurance make up a significant fraction of total labor compensation, we are sympathetic to Ho and Jorgensons attempts to account for them in their wage measures. However, splitting measures of total nonwage compensation obtained at high levels of aggregation between different classes of workers based on factors such as age and education is inherently arbitrary. The fundamental problem is that none of the sources of data on wages by demographic characteristics contain information on the value of nonwage compensation. Thus, we find it most sensible to stick to wage and salary compensation for which there is solid data. The methodology developed by the BLS (1993) uses a combination of a regression approach to estimating the effect of worker characteristics on wages that is similar to ours and a Tornqvist index computed on a number of discrete worker cells based on characteristics that resembles Ho and Jorgensons methodology. Relative to Ho and Jorgenson, the major difference is that rather than estimating average wage rates for a large number of cells, the BLS estimates cell means using a regression model. This eliminates the problem of cell means based on small numbers of observations, but the use of a constant growth rate for all workers in a cell still represents what we think is an unnecessary constraint relative to our less restrictive analysis. A potential strength of the BLS methodology is its use of a special data set containing records from the 1973 CPS matched to Social Security Administration work history files. This allows the BLS to compute actual work experience for this group of workers. Moreover, based on a regression model estimated using this sample of 25,000 workers, the BLS imputes actual experience for workers in all time periods. The BLSs imputation of actual work experience data addresses one of the most serious shortcomings of CPS data for measuring labor quality. However, 62 patterns of labor force participation change over time, so it is not clear that those imputations are particularly helpful for data separated significantly in time from 1973. Moreover, such imputations may do little other than to provide an interpretation of why certain variables affect wages. For instance, if marital status affects the level of actual experience relative to potential experience, then marital status may be useful for predicting wages even if wages in reality only depend on education and actual work experience. Consistent with this possibility, the BLS approach forces the dependence of wages on such factors to be through their effect on experience. However, it is also possible that factors such as marital status have a direct effect on wages in addition to any effect on work experience. In such cases, the alternative results discussed below, in which we include such variables in the calculation of the quality index, may better capture their effects on worker quality growth. Differences in methodology may not be particularly critical to estimates of labor quality. Where samples overlap, our results are broadly similar to those of Ho and Jorgenson (1999) and the BLS (1993). However, as we discuss below, our estimated series appear to be somewhat less variable and to align in a more reasonable way with the business cycle. Forecasting labor quality growth In this article, we also provide forecasts of labor quality growth for the rest of this decade. These are necessarily somewhat speculative, relying on the extrapolation of certain trends in population, educational attainment and labor force participation. We do not attempt to forecast changes in the regression coefficients used to value education and experience. We simply rely on the coefficients estimated in the last year of our actual data. These are applied to simulated populations of workers defined by age, education, race, and sex. In constructing these simulated populations for the rest of this decade, we start with the middle population projections made by the U.S. Census Bureau. These show the likely number of U.S. residents by one-year age group, sex, race, and Hispanic ethnicity. We combine this information with a statistical model predicting educational attainment on the basis of such characteristics to obtain a simulated population broken down by educational levels as well. That is, each cell of the Censuss projected population is divided into separate cells corresponding to different levels of education. The breakdown of the population into these sub cells is determined by the statistical model to be described below. The final step in the construction of our simulated population is to project what fraction of 4Q/2001, Economic Perspectives the people in these simulated populations will be in the labor force. We do this also on the basis of a statistical model. We then use the 1999 regression coefficients relating wages to worker characteristics to compute the growth in predicted wages due to the changing distribution of age, education, and sex among the labor force participants in the simulated populations. Given that the Census Bureau has good information on the population of individuals not yet of working age as well as birth rates, they are in a good position to forecast the growth and changing composition of the working-age population over the next decade.12 These translate fairly straightforwardly into projections for the component of labor quality based on labor market experience. Given that the average age of workers is projected to rise over the decade by about a year and a half to a little over 42 years, one might expect the contribution of work experience to boost labor quality growth over the decade. However, the contribution of labor market experience to worker quality growth depends on the whole distribution of experience levels, and as the decade progresses, the large Baby Boom cohorts move beyond their peak earnings years, which actually contributes to slowing labor quality growth. The forces underlying our projections of the effect of experience on worker quality are illustrated for men in figure 7.13 The upper panels of the figure show the change in the proportion of people of a particular age. The leading edge of the Baby Boom stands out in the graphs. In 2001, the cohort born in 1947, which was much larger than that born in 1946 is 54 years old. Thus the first panel of the figure, which covers the change from 2000 to 2001, has a large spike at age 55. Birth cohorts continued to grow until the early 1960s. Thus, in the first panel there are generally positive changes in the proportion of the population aged 40 to 52. Of course, the sum over all ages of the changes in the proportion of the population accounted for by each age is zero, so there are always ages that are declining in their share of population. For instance, in the case of the panel corresponding to the 2000 to 2001 transition, most of the ages between 25 and 40 have decreasing shares of the population. The top panel graphs in figure 7 also show the relative wage rate associated with each age. These are obtained from a log wage regression like those described above, except that the quartic in experience was replaced with a quartic in age. The coefficients of the quartic are used to compute wages for each age level. The plots show the percentage difference between this predicted wage and the overall mean wage based on the base years distribution of ages. Thus, Federal Reserve Bank of Chicago if the value shown for a given age is 0.1, that age is associated with wages that are 10 percent above average. The graph shows that male workers generally earn higher than average wages between their midthirties and late fifties. The peak age for wages is in the early fifties. The contribution to worker quality will be greatest when there is a positive correlation between the change in the proportion at the age and the relative wage deviation. That is, quality grows fastest when there are large increases in the proportion of workers at the age levels associated with high relative wages and large decreases in the proportion of workers at ages associated with low relative wages. The bottom panels in figure 7 show the product of the change in the proportion at the age and the relative wage associated with the age. The overall contribution of the changing age distribution to worker quality is approximately the sum of those products. For the 2000 to 2001 transition, the positive products in the bottom panel substantially outweigh the negatives, leading to a positive contribution to worker quality growth from the changing age distribution. A big part of that contribution comes from the effects of the leading edge of the Baby Boom. The age group (55 year olds) associated with the big increase in proportion of the labor force is one that also has a high relative wage. This implies a sizable positive contribution to labor quality growth. The panels on the right in figure 7 show what happens by the end of the decade. In 2010, the oldest Baby Boomers will actually be at an age (63) associated with below-average wages. This, combined with the movement of the smaller birthdearth generation into the peak earnings years, swings the overall labor quality growth contribution of the age distribution to negative territory. Forecasting the effects of changes in education requires making additional forecasts of educational attainment and labor force participation. We make these forecasts based on statistical models estimated using the ORG files for the years 1992 to 1999. The detailed methodology is described in box 1. The approximate effects of our educational projections on labor quality growth are shown in figure 8. This figure has the same layout as figure 7, except that instead of showing the impact of changes in the age distribution, it shows the impact of changes in the educational distribution. The top panels show the projected change in the fraction of the population with each of the five educational levels and the relative wage rate associated with those levels. The contribution to labor quality growth is the product of the change in 63 FIGURE 7 Effects of experience on male worker quality Change in male age distribution 2000–01 and male relative wages 2001 relative wage change in percent 0.6 Relative wage 0.3 0.0 Change in age distribution -0.3 -0.6 15 20 25 30 Change in male age distribution 2009–10 and male relative wages 2010 35 40 45 age 50 55 60 65 change in percent 0.40 0.6 0.20 0.3 0.00 0.0 -0.20 -0.3 -0.40 -0.6 70 relative wage 0.40 Relative wage 0.20 0.00 Change in age distribution -0.20 -0.40 15 20 25 30 35 40 45 age 50 55 60 65 70 Contribution to male quality growth 2000 to 2001 Contribution to male quality growth 2009 to 2010 contribution to quality growth contribution to quality growth 0.10 0.10 0.05 0.05 0.00 0.00 -0.05 -0.05 -0.10 -0.10 15 20 25 30 35 40 45 age 50 55 60 65 70 15 20 25 30 35 40 45 age 50 55 60 65 70 Source: Authors’ calculations based on data from U.S. Department of Labor, Bureau of Labor Statistics, Current Population Survey, 1964–2000. proportion with the relative wage and is shown in the bottom panels. The sum of these contributions approximates the contribution of increasing education to worker quality. Figure 8 shows that between 2000 and 2001, the shares of workers with less than a high school education and exactly a high school education were both falling. In contrast, the shares with some college, a college degree, and post-graduate education were all rising. Figure 8 also shows the relative wage rates associated with the different levels of education. These range from negative 45 percent for high school dropouts to positive 85 percent for those with post-graduate education. Clearly, there is a strong positive correlation between relative wages and growth in population share. This positive correlation is reflected in the mainly positive contribution estimates shown in the bottom left panel. The sum of these contributions is substantially positive. 64 Figure 8 also shows that the effects of education on worker quality will change only slightly by 2010. Our predictions indicate that the rate at which the share of high school dropouts is shrinking will decline by about half from 0.29 percentage points to 0.14 percentage points, and that there will be a similar sized decline in the rate at which the some college group is growing. Given the large negative relative wage of dropouts, the former effect has a bigger impact on labor quality growth. Thus, as the decade progresses, we predict a slightly smaller increase in labor quality growth from improving educational attainment. Results Worker quality Figure 9 displays our estimate of 1964 to 2000 labor quality growth for the working population based on trends in the educational, experience, and sex 4Q/2001, Economic Perspectives BOX 1 Forecasting trends in educational attainment and labor force participation This section describes the statistical models that we use to forecast trends in educational attainment and labor force participation in order to forecast the growth of labor quality. We begin by forecasting educational attainment as a function of age, birth year, sex and race. We then forecast labor force participation on the basis of those variables and educational attainment. Let pitj Prob < yit j > j 1, ..., 5 be the probability that the ith worker in year t has educational level j, where j=1 is less than high school and j = 5 is more than college and let qitj Prob ¨ª yit r j yit r j 1·¹ j 2, ..., 5 be the probability that the worker reaches at least level j given that he reached level j = 1. We fit statistical models to predict the qitj and then recover the pitj from pitj q j k 2 1 qitj 1 . Specifically, the q j are it k it predicted on the basis of logistic regression models of the form log qitj 1 qitj ¤D B ¤D C a it b it ja a jb xit H j , b where the Dita and Ditb are indicator variables for the person being age a and born in year b, respectively, and xit is a vector of additional control variables. Models for qitj are estimated using all those in the ORG files from 1992 to 1999 that had education of at least level j 1 and met an age requirement of 18 for the high school, 19 for the some college, 22 for the college, and 26 for the post-graduate models. The idea behind this model is that there is a typical lifetime pattern of the probability of completing another level of schooling. For instance, the probability of completing high school or the equivalent rises very rapidly up to age 20, then increases only slowly with age. According to the model, cohorts born in different years follow the same basic time pattern, but at a uniformly higher or lower level in terms of the log odds. Models for high school, some college, and college are estimated separately for the eight sex by race combinations without any additional controls (xit variables). Samples of nonwhite workers with college degrees become somewhat small, however. So distributions of workers. The black line uses the CPS March supplements. The colored line uses the ORG files. As we mentioned earlier, the ORG begins in 1979 (so growth rates begin in 1980). As the ORG-based measure is based on three times more data, the yearto-year variability of ORG-based labor quality growth is lower.14 However, the general trends are very similar; Federal Reserve Bank of Chicago models for post-graduate education are estimated separately for men and women with race indicators included as controls. For each population cell defined by age, birth year, sex and race, the estimated model is used to predict the fraction in each year with each of the five levels of educational attainment. Our estimation samples yield birth year coefficients (bjb) corresponding to birth years for which there are individuals in the appropriate age range in the ORG files. But as the projection period progresses, we also need cohort coefficients for workers born too soon to be in the ORG files. For instance, no one born in 1990 is included in our ORG files. Thus we have no data from which to estimate the tendency for that cohort to complete different educational levels. However, by 2010, many such individuals will be in the labor force. For each race and sex combination, we forecast these additional cohort coefficients on the basis of a linear regression on year of birth using the last 15 coefficients up to, but not including, the last one estimated. (The last one estimated is based on only one year of data and thus, especially for minority races, small sample sizes.) This admittedly ad hoc procedure extrapolates recent trends in educational attainment. We chose 15 years because most of those trends appeared fairly close to constant over that period. Results are not sensitive to extrapolating based on the last 10 or last 20 birth year coefficients. The above procedure yields forecasts of the distribution of age, sex, and educational attainment. These can be used to obtain forecasts of labor quality for the whole population. However, to obtain forecasts of labor quality for workers only, we also need to forecast labor force participation. We do that using a model with the same form as above, except that educational attainment becomes an additional control variable. As with educational attainment, cohort coefficients for birth years too late to yield workers in our data sets are forecast from a linear regression on birth year using the last 15 coefficients up to, but not including, the last one estimated. For each year out to 2010, this procedure yields a forecast of the population of cells defined by age, race, sex, educational attainment, and labor force participation. Thus, we can construct a forecast for labor quality in those years. from 1980 to 2000, labor quality grew 0.50 percent per year according to the ORG and 0.43 percent per year according to the March supplements. Furthermore, since 1990, the ORG and March growth rates are also similar0.48 percent and 0.42 percent per year, respectively. Therefore, the use of one data set over another has little effect on any of our inferences. 65 There are several notable features of figure 9. First, labor quality is somewhat countercyclical; peaks in the data occur near the trough of recessions in November 1970, March 1975, November 1982, and March 1991. This is consistent with firms reacting to economic downturns by first dismissing low-quality workers, resulting in an increase in the aggregate quality of the working population (but not the full population). As hiring heats up during expansions, workers of lower productivity find employment more readily and labor quality drops. Typically, toward the end of expansions, we might expect to see labor quality growth slow even further, as the pool of available human capital is drained. This seems to have happened somewhat in the 1990s but not during the 1980s expansion. The extraordinary increase in educational attainment during the 1970s and 1980s offset any cyclical effect from the declining pool of high human capital. This brings us to the second notable feature of the data: the deceleration, acceleration, and deceleration of labor quality over the last three decades. During the late 1960s and 1970s, labor quality grew by approximately 0.2 percent per year. This coincides with the beginning of the post-1973 productivity slowdown, which lasted for two decades.15 But the slowdown in labor quality did not last long. Beginning in the early 1980s, the U.S. experienced a sizable acceleration in labor quality growth, rising to 0.4 percent per year from 1979 to 1987 and close to 0.6 percent per year from 1988 to 1995. Since 1995, labor quality has decelerated to 0.27 percent per year, although there was a mild upturn (0.36 percent per year) from 1997 to 1999, including a 0.38 percent rate of growth in 1999. In 2000, labor quality growth fell to 0. Figure 9 also shows our projections of labor quality growth. As described above, these are based FIGURE 8 Effects of education on worker quality Change in educational distribution 2000–01 and relative wages 2001 change in percent Change in educational distribution 2009–10 and relative wages 2001 relative wage 1.0 0.5 change in percent 1.0 1.0 0.5 0.5 1.0 0.5 relative wage relative wage 0.0 0.0 0.0 0.0 chg. in educational distribution chg. in educational distribution -0.5 -1.0 1 2 3 education level relative wage 4 -0.5 -0.5 -1.0 -1.0 5 Contribution to quality growth 2000 to 2001 -0.5 -1.0 1 2 3 education level 5 Contribution to quality growth 2009 to 2010 contribution to quality growth contribution to quality growth 0.18 0.18 0.12 0.12 0.06 0.06 0.00 0.00 -0.06 -0.06 -0.12 -0.12 -0.18 4 -0.18 1 2 3 4 education level 5 1 2 3 4 education level 5 Source: Authors’ calculations based on data from U.S. Department of Labor, Bureau of Labor Statistics, Current Population Survey, 1964–2000. 66 4Q/2001, Economic Perspectives FIGURE 9 Labor quality growth CPS March vs. ORG percent 1.2 0.8 March ORG 0.4 0.0 -0.4 1960 ’65 ’70 ’75 ’80 ’85 ’90 ’95 ’00 ’05 ’10 Note: Dashed line indicates projections. Source: Authors’ calculations based on data from U.S. Department of Labor, Bureau of Labor Statistics, Current Population Survey, 1964–2001. on projections of the labor force. Thus, they are free from any variation due to changes in the level of unemployment. As can be seen, they decline smoothly as the decade progresses. By 2010, they are down to only 0.07 percent, much below the average of the previous 35 years. Figure 10 decomposes the growth of overall labor quality into contributions due to education, experience, and gender. The overall trends in education and experience that we saw in figures 2 to 5 are readily apparent. The improvement in education attainment in the 1970s and 1980s was the sole positive contributor to labor quality growth, adding 0.54 percent per year to labor quality growth between 1965 and 1985. The slowdown in education, particularly the lack of further progress in reducing the fraction of high school dropouts, resulted in a decelerating education component of labor quality growth of 0.30 percent per year after 1985 and 0.18 percent per year after 1995.16 The modest increase from 1997 to 1999 is the main contributor to the pickup in overall labor quality observed in that period. Offsetting the big increases in education during the 1970s and 1980s was the drop in work experience resulting from the entry of the Baby Boomers. This cut 0.34 percentage points per year off labor quality growth from 1965 to 1980. However, as those workers moved into age ranges associated with higher relative earnings after 1980, the contribution of labor market experience averaged a positive 0.09 percentage points per year. The increase in female labor force participation has little overall impact on our labor quality growth estimates, cutting only about 0.03 percentage points per year over the full period. This small effect is Federal Reserve Bank of Chicago partly due to a somewhat arbitrary accounting choice. That is, we include the effects of the femaleexperience interactions in the contribution attributable to experience. This means that the only wage gap between men and women that goes into the calculation of the female contribution is that corresponding to zero years of experience. Since the malefemale wage gap is relatively minor for new labor market entrants, the corresponding implications for worker quality are also minor. Given our preferred interpretation of the wage gap, our accounting convention seems appealing. However, some might argue to include the interactions of the female and experience terms in the female category. Doing so would make the contribution of the female component of the index substantially more negative. The contribution of the experience component would be correspondingly more positive. The forecasts of the individual components show that the forecast decline in quality growth over this decade is largely due to the contribution of experience becoming negative by mid-decade. The forces underlying this projection were discussed in detail in the last section. The positive contribution to growth from increasing education levels is also forecast to decline slightly. Given our definition of the female contribution, it is unimportant to the projections. Figure 11 provides an indication of how our results change with the inclusion of an expanded list of human capital variables available in the CPS. These are race, genderrace, marital status, gendermarital status, and full-time work status. As we discussed, on the one hand, there are reasons why such variables might affect workers productivity or be correlated with unmeasured variables that affect productivity, and, thus, FIGURE 10 Quality growth due to education, experience, gender percent 1.5 Education 1.2 0.9 0.6 0.3 0.0 Female -0.3 Experience -0.6 1960 ’65 ’70 ’75 ’80 ’85 ’90 ’95 ’00 ’05 ’10 Source: Authors’ calculations based on data from U.S. Department of Labor, Bureau of Labor Statistics, Current Population Survey, 1964–2001. 67 ought to be included in a measure of worker quality. On the other hand, it is possible that the correlation of such variables with wages may be due to labor market discrimination or other reasons unrelated to productivity, in which case they ought not to be included in a measure of labor quality. While the patterns look quite similar, growth in the expanded labor quality index is lower, particularly in the late 1970s and early 1980s. This suggests that more work might be warranted to investigate the source of correlation between these variables and wages. The detailed reasons for the growth in the extended index being below the one based only on education, experience, and sex are shown in figure 12. Race has evidently had little impact and, since 1976, the same is true for full-time/part-time status. But steady drops in marriage rates have consistently been a drag on the extended measure of labor quality growth. Table 1 presents growth rates in labor quality by gender, industry, and region. For comparison, the top row presents overall trends. The results are split into business cycle periods with the first column presenting the average growth rate over the full 35-year period. Rows 2 and 3 stratify the sample by sex. Since 1965, female labor quality has grown faster than male labor quality by 0.15 percent per year.17 There has been a remarkably stable 0.13 percentage point per year difference in growth rates between the two sexes for most of the sampleexcept the 1980s, when the difference expanded to 0.26 percent per year. Productivity analysts have been concerned with explaining differences in productivity growth across industries and regions. Our results show that some of those differences reflect differences in the growth rates of labor quality. Table 1 shows that labor quality has grown fastest in agriculture, durable and nondurable manufacturing, and transportation, communication, and public utilities (TCPU) over the last 35 years. Lagging industries include retail trade and construction. All industries follow the general overall trend of lower growth in the late 1960s and 1970s, followed by accelerating growth during the 1980s and early 1990s. However, somefor example, nondurable manufacturing and constructionexperience more variable labor quality growth, while othersfor example, services and governmentgrow more steadily. Over the last five years, labor quality growth has been particularly strong in durable manufacturing and weakest in mining, agriculture, construction, and services. Finally, labor quality in the south (East South Central and South Atlantic) and east (Mid Atlantic and New England) has grown quickly since 1965, while the west (Mountain and Pacific) has lagged behind. Since 1995, the midwestern region has experienced the fastest labor quality growth and the western region the slowest. FIGURE 11 FIGURE 12 Effects of including other human capital variables Quality effects due to race, married, full-time status Labor quality growth of nonworkers The results above provide evidence on labor quality growth of the working population. However, in light of the rise in employment-to-population ratios over the last five years and the ensuing concern about the size of the pool of available workers, we are also interested in evaluating quality growth trends among those who are available and interested in jobs, but not currently at work. Therefore, in this section, we report results for the quality of the potential work force. Figure 13 presents one view of this. The colored line represents the labor quality growth of workers percent percent 0.50 1.2 Basic human capital variables Full-time 0.8 0.25 0.4 0.00 0.0 -0.4 1964 ’68 ’72 ’76 ’80 ’84 ’88 ’92 ’96 Source: Authors’ calculations based on data from U.S. Department of Labor, Bureau of Labor Statistics, Current Population Survey, 1964–2001. 68 Race -0.25 Add race married, full time Married ’00 -0.50 1964 ’68 ’72 ’76 ’80 ’84 ’88 ’92 ’96 ’00 Source: Authors’ calculations based on data from U.S. Department of Labor, Bureau of Labor Statistics, Current Population Survey, 1964–2001. 4Q/2001, Economic Perspectives TABLE 1 Labor quality growth, by gender, industry, and region 1964–00 1964–71 1972–77 1978–86 1987–94 1995–00 All workers 0.33 0.15 0.23 0.40 0.58 0.22 Men Women 0.40 0.55 0.23 0.37 0.36 0.49 0.49 0.75 0.59 0.72 0.25 0.30 Construction Durables Nondurables TCPU Wholesale trade Retail trade Services FIRE Government Agriculture Mining 0.21 0.43 0.46 0.39 0.31 0.11 0.32 0.34 0.34 0.45 0.41 0.12 0.12 0.27 0.03 0.28 -0.10 0.20 0.27 0.03 0.25 0.46 0.07 0.24 0.16 0.40 0.21 –0.13 0.44 0.16 0.38 0.77 –0.45 0.27 0.61 0.60 0.63 0.07 0.18 0.44 0.30 0.48 0.27 0.94 0.46 0.52 0.83 0.61 0.58 0.41 0.42 0.76 0.39 1.01 0.90 0.06 0.63 0.30 0.19 0.43 0.15 0.06 0.13 0.44 –0.06 –0.37 New England Mid Atlantic East North Central West North Central South Atlantic East South Central West South Central Mountain Pacific 0.37 0.39 0.34 0.36 0.46 0.53 0.36 0.19 0.20 0.21 0.26 0.11 –0.11 0.39 0.60 0.18 0.37 –0.09 0.33 0.34 0.06 0.31 0.51 0.38 0.35 –0.34 0.07 0.36 0.39 0.32 0.37 0.49 0.43 0.56 0.46 0.29 0.74 0.64 0.54 0.53 0.65 0.79 0.45 0.34 0.52 0.07 0.21 0.52 0.48 0.17 0.47 0.05 0.00 –0.01 Notes: TCPU is transportation, communication, and public utility. FIRE is financial, insurance, and real estate. Source: Authors’ calculations based on data from U.S. Department of Labor, Bureau of Labor Statistics, Current Population Survey, 1964–2001. and the black line represents labor quality growth of the entire labor force. The latter includes workers plus those who are unemployed (that is, searching for work).18 Not surprisingly, the graphs line up quite well since most of the labor force is employed. The most important differences arise during recessions when hoarding of higher-skilled workers leads to a more rapid increase in the labor quality of the working than the unemployed. A more comprehensive view of the quality of available workers is presented in figure 14. Here, we link two groups togetherthose unemployed (searching) and those not in the labor force (not searching) but who want a joband refer to them as the group of available workers.19 Figure 14 reports the ratio of the labor quality of workers to the labor quality of available workers.20 For example, if the ratio is 1.18, it implies that the labor quality of the employed is 18 percent higher, on average, than the quality of the average available worker. If the ratio increases, the quality of workers is growing faster than the quality of available workers. If the ratio decreases, the quality of available workers is growing faster than the quality of workers. Federal Reserve Bank of Chicago From 1965 to the early 1990s, the labor quality ratio remained roughly flat, bouncing between 1.15 (during recessions) and 1.20 (at the end of expansions).21 However, in the last five years, the labor quality of workers has risen steadily faster than the quality of available workers. By 2000, an average employed worker had 21.5 percent higher human capital than an average available worker; this is just off the highest that this ratio has been over the 35-year sample period which was reached in 1999. The jump in the last five years is due to relative changes in both education and work experience between the two groups. Among the employed, high school and college graduation rates have increased by 0.5 percentage points and 1.7 percentage points, respectively, since 1995, but, among available workers, high school graduation rates have dropped by 0.5 percentage points and college graduation rates have increased by only 0.7 percentage points. Furthermore, the average age of a worker has increased by 0.9 years, but the average age of an available worker has dropped by 0.8 years. Moreover, it seems likely that the ratio of labor qualities shown in figure 14 underestimates the true ratio. As we have noted, the reported information on 69 FIGURE 13 FIGURE 14 Quality growth of workers vs. labor force Ratio of labor quality of employed workers to available workers percent ratio 1.2 1.3 All workers 0.8 Labor force 1.2 0.4 1.1 0.0 -0.4 1964 ’68 ’72 ’76 ’80 ’84 ’88 ’92 ’96 ’00 Source: Authors’ calculations based on data from U.S. Department of Labor, Bureau of Labor Statistics, Current Population Survey, 1964–2001. education and age barely begins to summarize the differences between workers skill levels. There are many differences in the quality of education, actual labor market experience, and the like that are unmeasured in the CPS. It seems likely that those in the pool of available workers would also have a worse distribution of such characteristics. This would lead to the ratios in figure 14 being underestimated. Somewhat more speculatively, the business-cycle-related swings in the quality ratio may also be underestimated. Related measures In addition to being an input into forecasts of longrun output growth and improved measures of productivity, such as in Basu, Fernald, and Shapiro (2000), our labor quality measures can also be used to adjust measures of wage growth to better reveal fluctuations in the price of a raw hour of labor and to provide a more comprehensive measure of the extent of unemployed labor resources. Fluctuations in standard measures of aggregate wage growth confound the effects of changes in the price of a constant unit of labor with changes in worker quality. In particular, some portion of the increase in measured wage rates reflects changes in the distribution of education and work experience rather than actual changes in the price of a constant unit of labor services. Using our measure of labor quality growth, however, it is straightforward to adjust measures of aggregate wage growth to reveal fluctuations in the true price of labor services. Such a measure could help to clarify the nature of business cycles. For instance, simple equilibrium business cycle models can only account for the relatively substantial fluctuations in hours worked if the real wage is significantly procyclical. 70 1.0 1964 ’68 ’72 ’76 ’80 ’84 ’88 ’92 ’96 ’00 Source: Authors’ calculations based on data from U.S. Department of Labor, Bureau of Labor Statistics, Current Population Survey, 1964–2001. One measure of the price of raw labor is shown in figure 15. The black line is just the standard growth rate in real hourly compensation in the business sector. 22 The colored line is our adjusted measure obtained by subtracting our measure of labor quality growth. As we have noted already, on average, labor quality grew by roughly 0.33 percent per year over the last 35 years. However, the cyclical nature of labor quality growth implies the most important labor quality adjusted differences in compensation growth occur in or near recessions. For example, in March 1992, real hourly compensation growth increased 2.0 percent, but 0.9 percent of this gain was due to improvements in the quality of the labor force. Therefore, the price of raw labor increased only 1.1 percent over the previous March. Over the last four years, real hourly compensation has grown about 2.2 percent; the labor quality adjusted growth rate is 1.9 percent. Thus, adjusting for labor quality growth does make real wage growth appear somewhat more procyclical. The adjustments to the price of labor reported in figure 15, while noticeable, do not greatly increase the extent to which real wages appear to be procyclical. Thus, they do not greatly change the evidence on the plausibility of simple equilibrium business cycle models. However, as noted above, it is possible that our results, which rely only on a few observable worker characteristics, underestimate the effects of worker quality in masking procyclical wage growth. For instance, Solon et al. (1994) find a larger compositional effect using longitudinal data that control for changes in unobserved worker differences. Our labor quality growth measures can also help to clarify the cost of unemployed labor resources. 4Q/2001, Economic Perspectives As we have seen, the characteristics of the labor force have changed significantly over the last 35 years, with educational levels improving at varying rates and typical labor market experience levels first falling then rising. Using the cross-sectional relationship between these characteristics and wage rates allows us to infer that the pace of improvement in worker quality has varied over time from a low of around 0.15 percent per year between 1964 and 1971 to a peak of around 0.58 percent per year between 1987 and 1994. In the period since 1995, we find that worker quality improvement had slowed to about 0.27 percent per year. Largely because the Baby Boom generation will be moving beyond their peak earnings years, we forecast that worker quality improvement will slow further over the remainder of this decade to a rate of about 0.07 percent per year. In addition to such long-run variation in the pace of worker quality improvement, we observe shorterrun fluctuations associated with the business cycle. In particular, average worker quality improvement tends to be especially rapid during downturns, as those with lower predicted wages are more likely to become unemployed or leave the labor force. Conversely, as more and more workers are drawn into the labor force over the course of a long expansion, there is a tendency for worker quality growth to slow. A related finding is that the gap in predicted quality between the employed and the pool of available workers widens over the course of an expansion. That pattern was particularly pronounced over the course of the long expansion that began in the early 1990s, with the gap between the average predicted wage rate for workers and that for the pool of available workers reaching an all-time high of 23 percent in 1999. Correcting for variation over time in average worker quality implies a modestly more procyclical pattern to real wage growth. It also shows that FIGURE 15 FIGURE 16 Growth of price of raw labor private hourly compensation deflated by CPI Unemployment rate adjusted for labor quality Because workers differ significantly in their levels of human capital, the standard unemployment rate, which counts every member of the labor force equally, does not fully capture variation in the level of unutilized labor resources. In particular, there is a greater loss of output when high-productivity workers are unemployed than when low-productivity workers are unemployed. Figure 16 shows an alternative measure of unemployed human capital based on our labor quality estimates that does allow for differences in worker productivity. This is estimated by computing the unemployment rate of the March CPS labor force, weighted by our gauge of labor quality. The colored line in figure 16 shows our qualityadjusted unemployment rate. In general, accounting for human capital accumulation reduces the unemployment rate by 0.5 to 1.0 percentage points. Most recently (March 2001), the CPS unemployment rate drops from 4.4 percent to 3.6 percent, after labor quality adjustments are included. These figures indicate that because higher skilled workers are less likely to be unemployed, the standard unemployment measure overestimates the fraction of human capital that is not being utilized. Conclusion percent percent 12 4 Nonfarm business 2 6 0 -2 CPS unemployment rate 9 Nonfarm business adjusted for LQ -4 1964 ’68 ’72 ’76 CPs unemployment rate adjusted for LQ 3 ’80 ’84 ’88 ’92 ’96 Note: The real compensation measure is the nominal hourly compensation measure reported in the Bureau of Labor Statistics’ productivity report deflated by the CPI. Source: Authors’ calculations based on data from U.S. Department of Labor, Bureau of Labor Statistics, Current Population Survey, 1964–2001. Federal Reserve Bank of Chicago ’00 0 1964 ’68 ’72 ’76 ’80 ’84 ’88 ’92 ’96 ’00 Note: CPS unemployment rate and unemployment rate adjusted for labor quality are computed for March of each year. Source: Authors’ calculations based on data from U.S. Department of Labor, Bureau of Labor Statistics, Current Population Survey, 1964–2001. 71 depending on the state of the business cycle, the rate of unemployment of total human capital is between 0.5 and 1.0 percentage points lower than the standard civilian unemployment rate that counts all members of the labor force equally. Finally, while our current findings provide substantial insight into the determinants of long-term productivity growth, it should be recognized that the measures of worker characteristics on which our work is based are quite crude. Levels of formal schooling and years of potential labor market experience only begin to scratch the surface in predicting productivity and wage rates. While we find that including in our analysis additional characteristics such as race, marital status, and part-time status led to only modest changes in our conclusions, even these characteristics likely fail to capture the full range of human capital determinants that may be evolving in ways significant for productivity growth. Extending the analysis to other data sources that contain a richer characterization of the determinants of workers wages may be a priority for future research. NOTES 1 A recent innovation has been the use of longitudinal firm-level data to measure not only the factors that underlie productivity growth but also the extraordinary amount of heterogeneity and persistence in productivity growth across manufacturing firms. See Bartelsman and Doms (2000) and Hulten (2000) for recent surveys. 2 Topel (2000) criticizes this line of research for, among other reasons, the difficult measurement issues involved, including the complexity of distinguishing capital and labor returns in a simple CobbDouglas framework and the limitations of human capital measures. 3 See Altonji and Blank (2000) for a review of the labor market discrimination literature and Lewis (1986) for a review of the union literature. 4 That is, two-thirds of labor quality growth rates of 0.27 percentage points and 0.07 percentage points equals approximately 0.18 percentage points and 0.05 percentage points of output growth. New high school and college graduates are compared to, respectively, the population of 17 year olds and 23 year olds. These figures were obtained from the National Center for Education Statistics website at http://nces.ed.gov/ and the U.S. Department of Commerce, Bureau of the Census (1975). 5 6 Unfortunately, the data we use to construct our measures of labor quality do not distinguish between high school graduates and GED holders. Given the more rapid growth in the latter, this may imply some overestimation of the rate of quality improvement. 7 Because of the lack of strict comparability between the 1991 and 1992 figures, this change is left out of the moving averages. For the five years effected, the average is based on only four years of data. 8 See Altonji and Blank (2000) for a review of the literature. 14 The standard deviation of the 1980 to 2000 annual growth rates is 0.27 for the March data and 0.16 for the ORG data. Our March estimates are actually slightly less variable than those of Ho and Jorgenson. For comparable years in our samples (1965 to 1995), the standard deviation of Ho and Jorgensons annual measure is 0.37 percent versus 0.31 percent for ours. We use the quality series reported in table B2 of Ho and Jorgenson. 15 Bishop (1989) argues that a drop in labor quality, as measured by test scores, explains much of the productivity slowdown during the 1970s. 16 As we noted earlier, growth in educational attainment of older workers has been particularly strong in the last decade. The labor quality of workers aged 50 to 59 increased by 0.72 percent per year during 1996 to 2000. By comparison, over the same period, labor quality of workers in their thirties grew 0.28 percent per year, and labor quality of workers in their forties fell by 0.22 percent per year. 17 Note that labor quality growth for both men and women is higher than for workers overall. This reflects the negative effects on overall quality growth of a growing fraction of female workers. 18 Because unemployed workers have no hours worked, our labor quality measure is weighted by CPS population weights only. For comparison purposes, the colored line also is weighted by CPS population weights. 19 Prior to 1976, the CPS does not ask about wanting a job. Therefore, we include the unemployed from 196475 and the unemployed plus those not in the labor force who are available for work from 1976 to the present. ¤ Wˆ / ¤ Wˆ where the numerator is weighted by w h / ¤ w h and the denominator is weighted by w h / ¤ w h . The ratio is computed as 0 it 20 workers 0 it available it it it it workers These are computed from the October supplements of the Current Population Survey. 9 10 See Aaronson and French (2001). All of the results in this paper are very robust to some common differences with other papers, including controlling for state of residence, using age instead of potential experience, and eliminating government, self-employed, and private household workers from the sample. 11 Of course, changes in immigration policy could make a significant difference to that composition. 12 The forces determining the contribution of work experience to labor quality for women are very similar. 13 72 it it it it available 21 Educational attainment gains were fairly similar across groups between 1964 and 1995. The rate of high school graduation of employed workers grew from 58 percent to 90 percent, a gain of 32 percentage points, and from 41 percent to 75 percent among available workers, a gain of 34 percentage points. Likewise, college graduation rates grew from 12 percent to 26 percent among the employed and 3 percent to 11 percent among available workers. 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Moser Third Quarter 2-12 The value of using interest rate derivatives to manage risk at U.S. banking organizations Elijah Brewer, William E. Jackson, and James T. Moser Third Quarter 49-66 ECONOMIC CONDITIONS Growth in worker quality Daniel Aaronson and Daniel Sullivan Fourth Quarter 53-74 INTERNATIONAL ISSUES Evidence of the North-South business cycle Michael A. Kouparitsas First Quarter 46-59 The credit risk-contingency system of an Asian development bank Robert M. Townsend and Jacob Yaron Third Quarter 31-48 Countering contagion: Does China’s experience offer a blueprint? Alan G. Aheame, John G. Femald, and Prakash Loungani Fourth Quarter 38-52 REGIONAL ISSUES Are the large central cities of the Midwest reviving? William A. Testa Second Quarter 2-14 Polycentric urban structure: The case of Milwaukee Daniel P. McMillen Second Quarter 15-27 Private school location and neighborhood characteristics Lisa Barrow Third Quarter 13-30 MONEY AND MONETARY POLICY The crisis of 1998 and the role of the central bank David A. Marshall First Quarter 2-23 Central banking and the economics of information Edward J. Green Second Quarter 28-37 Electronic bill presentment and payment—Is it just a click away? Alexandria Andreeff, Lisa C. Binmoeller, Eve M. Boboch, Oscar Cerda, Sujit Chakravorti, Thomas Ciesielski, and Edward Green Fourth Quarter 2-16 Liquidity effects in the bond market Boyan Jovanovic and Peter L. Rousseau Fourth Quarter 17-35 To order copies of any of these issues, or to receive a list of other publications, please telephone (312)322-5111 or write to: Federal Reserve Bank of Chicago, Public Information Center, P.O. Box 834, Chicago, IL 60690-0834. Federal Reserve Bank of Chicago 75
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