The full text on this page is automatically extracted from the file linked above and may contain errors and inconsistencies.
Economic Review ■ Quarter I 1987 Concentration and Profitability in Non-MSA Banking Markets ■ Quarter III 1987 Can Services Be a Source of Export-led Growth? Evidence from the Fourth District by Gary Whalen by Erica L Groshen The Effect of Regulation on Ohio Electric Utilities Identifying Amenity and Productivity Cities Using Wage and Rent Differentials by Philip Israilevich and K.J. Kowalewski by Patricia E. Beeson and Randall W. Eberts Views from the Ohio Manufacturing Index FSLIC Forbearances to Stockholders and the Value of Savings and Loan Shares by Michael F. Bryan and Ralph L Day ■ Quarter II 1987 A New Effective Exchange Rate Index for the Dollar and Its Implications for U.S. Merchandise Trade by Gerald H. Anderson, Nicholas V. Karamouzis and Peter D. Skaperdas How Will Tax Reform Affect Commercial Banks? by Thomas M. Buynak by James B. Thomson ■ Quarter IV 1987 Learning, Rationality, the Stability of Equilibrium and Macroeconomics by John B. Carlson Airline Hubs: A Study of Determining Factors and Effects by Paul W. Bauer A Comparison of Risk-Based Capital and Risk-Based Deposit Insurance by Robert B. Avery and Terrence M. Belton First Quarter Working Papers W orking Paper Notice The Federal Reserve Bank A s of January 1, 1987, we no of Cleveland has changed its longer send method of distribution for the individuals as part of a mass Working Paper series mailing. Our current produced by the Bank's Research Department. Working Papers to Working Papers will be listed on a quarterly basis in each issue of the Economic Review. Individuals m ay request copies of specific Working Papers listed by Papers will be sent free of charge to those who request them. A regular mailing list for Papers, maintained for personal subscribers. Libraries and other organizations m ay request to be placed on a mailing list for institutional completing and mailing the attached subscribers and will automatically form below. receive Working Papers published. ■ 8801 ■ 8802 T o b in ’ s q , In v e s tm e n t and th e En d o g e n o u s A d ju s tm e n t o f Fin a n c ia l S o u rc e s o f W age D isp er s io n : T h e C o n trib u tio n o f In te re m p lo y e r D iffe r S tru c tu re e n tia ls W ithin In d u s try by William P. Osterberg by Erica L. Groshen Please com plete and detach the form below and mail to: Federal Reserve B an k of Cleveland Research D epa rtm en t R O . Box 6387 C leve lan d, O h io 44101 Check item(s) requested. Please send the following Working Paper(s): □ 8801 □ 8802 Send to : Nam e Please print A ddress Working however, will not be as they are E C O N O M I C 1 9 8 7 2 Concentration and Profitability in Non-MSA Banking Markets. Industrial organization economists have traditionally viewed market structure as the primary determinant o f firm conduct and perfor mance. Recently, as barriers to competition in financial services have eroded, this view has been increasingly criticized. Using recent data from a sample o f 191 banks, economist Gary Whalen examines the nature o f the relationship between market structure and bank performance and finds that the traditional view is not supported by the evidence. R E V I E W Q U A R T E R Economic Review 1 is published quar terly by the Research Department of the Federal Reserve Bank of Cleve land. Copies of the issues listed here are available through our Public Information Department, 216/579-2047. Editor: William G. Murmann Assistant Editor: Robin Ratliff Design: Michael Galka Typesetting: Liz Hanna Opinions stated in Economic Review are those of the authors and not necessarily those of the Federal Reserve Bank of Cleveland or of the Board of Governors of the Federal "I The Effect of Regulation on Ohio JL \J Electric Utilities. Previous researchers have neglected to look for a regulatory bias on the rate o f technical change implemented by electric utilities. Economists Philip Israilevich and K.J. Kowa lewski find that regulation has retarded the rate o f technical change experienced by a sample o f Ohio electric utilities over the 1965 to 1982 period. O Views from the Ohio Manu- Ld \ J facturing Index. Interest in U.S. manufacturing trends has heightened the need for more timely data on regional manufacturing production. Recently, a set o f experimental indexes o f manufacturing in Ohio has been developed by the Federal Reserve Bank o f Cleveland. This article introduces the Ohio Manufacturing Index and briefly examines the patterns o f manu facturing growth occurring in the state over this expansion. Reserve System . Material m ay be reprinted provided that the source is credited. Please send copies of reprinted materials to the editor. IS S N 0013-0281 Concentration and Profitability in Non-MSA Banking Markets by Gary Whalen Gary Whalen is an economist at the Federal Reserve Bank of Cleveland. Introduction Until quite recently, industrial-organization econ omists, bank regulators, and the Justice Depart ment shared the view that market structure, that is, the number and size distribution of competitors in a market, is the primary determinant of the con duct and performance of banks operating in that market. More particularly, the traditional structur alist view is that the greater the share of the mar ket controlled by the largest competitors or, alternatively, the higher the market concentration, the greater the likelihood that the firms will be able to agree collusively to raise prices above costs and so earn supranormal or monopoly profits. Concentration and bank profitability have been found to be positively related in a number of empirical studies, and these findings have been interpreted by structuralists as evi dence that their position is correct.1 The presumption that the structur alist view is valid is reflected in the Justice Department’s merger guidelines, which are used by regulators to identify bank acquisitions and mergers likely to have anti competitive effects. In essence, the guidelines generally proscribe bank regulators from approving acquisitions and mergers that would cause market concentration to rise above an assumed critical collusionfacilitating level. In the 1980s, however, a number of legal, regulatory, and technological develop ments and additional theoretical and empirical work have raised questions about the appropri ateness of using the structuralist paradigm as a basis for antitrust policy. In particular, the grow ing importance of potential competitors in an increasingly deregulated environment has been emphasized by critics of the traditional view.2 Other critics have suggested that the positive relationship between concentration and profitability found in previous empirical stud ies may not be attributable to collusion and does not necessarily indicate unidirectional causation running from structure to performance.3They suggest that performance determines market structure rather than the reverse. One author has dubbed this the “efficient structure” hypothesis.4 Superior efficiency, management, or luck cause firms to be profitable and to increase their market share, resulting in market concentration. Market share, a proxy for relative firm efficiency, is thus positively related to profitability. The positive relationship between concentration and profita bility is spurious and simply reflects the correla tion between market share and concentration. 2 For a discussion of these developments and their implications, see McCall and M cFadyen (1986). See also the work on contest- able market theory in Baumol, et al. (1982) and the discussion of the structuralist view in Brozen (1982). 1 See, for example, Rhoades (1982). 3 See Dem setz (19 74) and Smirlock (1985). 4 Smirlock, op. cit. This study represents an attempt to provide additional insight on the nature of the re lationship between market structure and bank per formance. Specifically, the relationship between bank profitability and concentration w ill be exam ined using recent data for a sample of 191 institu tions drawn from non-metropolitan statistical area (MSA) counties in O hio and Pennsylvania. In the following section, some criti cisms of the traditional view will be discussed and previous empirical studies will be briefly reviewed. Next, the data and sample design will be discussed. In the fourth section, the data will be analyzed in several ways. Finally, a summary of the results and conclusions will be presented. I. Problems with the Traditional View The traditional structuralist view reflects several implicit assumptions that appear to be question able. The first is that creating and enforcing tacit collusive agreements is relatively easy. For a col lusive agreement to be stable, participating firms must institute some mechanism to set and adjust price(s) and allocate market shares. This is not a trivial exercise, particularly for banks, which are multiproduct firms selling complex, heterogene ous products and services in a number of differ ent geographic markets. The second is that technological conditions, regulation, other barriers to entry, or the threat of predation allow colluding firms in concentrated markets to disregard potential com petitors. Concentration-related monopoly power and profits can exist and persist only when entry by potential competitors can be effectively pre vented by incumbent firms. In recent work, theo rists have demonstrated that when barriers to market entry and exit are low, or a market is con testable, it is possible to have outcomes approx imating those of perfect competition even if the number of actual competitors is quite small or concentration is high.5 Geographic and product market barriers to competition faced by banks and other financial intermediaries admittedly were formid able prior to the 1980s. Price competition was constrained by interest rate ceilings on deposits and on some types of loans as well. However, this situation has changed dramatically in the past few years. Intrastate and interstate barriers to geo graphic expansion by commercial banks and by savings and loan institutions (S&Ls) have been removed in a large number of states. Remaining barriers have been circumvented in various ways See Baumol, et a!., op. cit. (with loan production offices and nonbanking holding company subsidiaries, for example). The Monetary7Control Act of 1980 and the Gam-St Germain Act of 1982 essentially allow S&Ls to offer all the financial products and services of commercial banks. Largely unregulated nonbank financial companies also now compete aggres sively for both loan and deposit customers of banks. In addition, the increasing sophistication and declining cost of computer and telecommu nications technology have made it possible for financial institutions to compete effectively in a geographic area without an extensive investment in brick and mortar offices. Financial intermediar ies also now are basically free to compete on a price as well as a nonprice basis. These developments have made it much easier for banks and other types of financial -services providers to compete for cus tomers in any given local loan or deposit market. The implication is that market structure may not be the primary determinant of bank performance in the current environment. II. Review of Previous Empirical Studies Comprehensive reviews of structure-performance studies in banking published prior to 1984 have been done by Rhoades (1982) and Gilbert (1984). Although the two authors reviewed many of the same studies, their evaluation of the empirical evidence differs considerably. The former con cluded that the results suggest that bank market structure influences both profit and price perfor mance in the manner predicted by the structural ist paradigm. The latter concluded that the results do not consistently support or reject the hypothe sis that market concentration influences bank per formance. Both concur that where a significant positive concentration impact on prices or profit ability was found, the magnitude of the impact was typically slight. Gilbert emphasizes that the positive impact does not necessarily imply that collusion is the cause. More recent studies of the structureperformance relationship have been done by Burke and Rhoades (1985), Smirlock (1985), Smirlock and Brown (1986), and Whalen (1986). Burke and Rhoades explore the relationship between bank profitability averaged over the 1980-84 interval and the number of bank compet itors faced using a national sample of more than 7600 institutions. First, they calculate and com pare mean profit rates for sample banks operating in 1-bank, 2-bank, 3-bank and 4-bank non-MSA markets and MSA markets and find results con sistent with the traditional structuralist view. The mean profitability of banks in 1-bank markets is significantly greater than the means of the other classifications. Consistent results were found for 3 the other non-MSA markets (that is, mean returns in 2-bank markets are above those in markets with a larger number of competitors, and so on). Burke and Rhoades also explore the relationship between their profitability variable and a binary7 market structure variable (equal to one for MSA banks, equal to zero otherwise) using regression analysis. Additional nonstructural control varia bles are also employed in the regression. Again, the results are in line with the traditional view. The estimated coefficient on the market structure variable is negative and significant, indicating banks operating in urban markets with large num bers of competitors are less profitable than ruralmarket banks facing four or fewer competitors. The authors conclude that the re sults suggest “...banks in monopolistically or oligopolistically structured markets likely pay lower rates on deposits, charge higher rates on loans and services, or both... [suggesting] that out-of market and limited-purpose competitors do not provide effective competition to banks in highly concentrated markets. Such markets are appar ently not contestable probably because barriers to entry exist in real-world markets.”6 Although the results were inter preted by the authors as support for the traditional structure-performance view, alternative explana tions for the findings exist. In particular, the sig nificant differences in mean returns may be largely due to temporary regional differences in economic activity rather than differences in the number of competing banks faced in local markets. Mean returns were calculated for each sample bank over the 1980-84 interval. Over the first three years of this period, the energy and agricultural sectors were booming. As a result, banks located in agricultural and energy-producing states were highly profitable. Coincidentally, many of these states have restrictive geographic branching laws and so have a relatively large number of local markets with few competing banks. Thus, it is possible that local economic conditions rather than the number of competitors are responsible for the observed differences in mean bank profit ability in the sample. In the regression analysis, the authors attempt to control for other factors thought to impact bank profitability. However, several potentially important variables were not included and may have affected the reported results. In particular, no thrift-presence variable was employed even though S&Ls possessed much the same powers as banks after 1982. Also, a bank-market-share variable was not employed. Burke and Rhoades (1985), p. 11 . As noted above, it has been argued by some that the positive relationship between profitability and market concentration found in empirical studies is spurious and will not be evident if differences in market share are taken into account.7 Finally, it is not clear that the report ed results suggest that potential competition is unimportant. The mean returns used in the t-tests are computed for each market type using all such banks in the sample. That is, banks in each class are pooled regardless of differences in state branching laws. Since differences in bank branch ing restrictions should have an important impact on the intensity of potential competition, the mean-profitability test results do not provide any insight on the potency of this force. In fact, the regression results do provide support for the hypothesis that potential competition is impor tant. Specifically, the two state branching dum mies included in the estimated equation (for unit banking and limited branching states) have posi tive significant coefficients, indicating that bank profitability is higher in states with branching restrictions. Smirlock (1985) uses regression analysis to investigate the profitabilityconcentration relationship using a sample of more than 2,700 banks drawn from unit-banking states in the Tenth Federal Reserve District. The relationship was examined for a single year, 1978. In essence, the study represents an attempt to determine if a positive concentration-profitability relationship remains evident when a bank-marketshare variable is also included in the estimated equation. If it does, it suggests that the traditional view is the correct one. If not, and if the marketshare variable is significant, it suggests that the “efficient structure” hypothesis is correct. The market structure variable used was the three-bankconcentration ratio. The market-share variable is each bank’s share of commercial bank market deposits. Several other additional common con trol variables are also employed. Smirlock concludes that the regres sion results support the efficient structure rather than the traditional concentration-collusion view. Market share is positively and significantly related to profitability even when concentration is includ ed in the estimated equation. However, he finds a significant positive concentration-profitability relationship only when the market-share variable is omitted from the estimated equation. When both are included, the coefficient on the concen tration variable becomes insignificant. I 7 / See the discussion in Smirlock, op. cit., pp. 70 -71 In the later Smirlock and Brown (1986) paper, additional empirical evidence in sup port of the efficient structure hypothesis is pre sented. The same sample of banks is used to esti mate several variants of a profit function. If the traditional concentration-collusion hypothesis is valid, the expectation is that secondary or fringe firms will act as price-setters. Conversely, if the ef ficient structure hypothesis is valid, the fringe firms should act as price-takers. Leading firms may act as price-setters under either hypothesis. The profit function can be, and is, used to test whether a firm is a price-setter or price-taker. The estima tion results indicate that leading firms exhibit price-setting behavior, while secondary “fringe” firms act as price-takers, regardless of market concentration. Further, there is no evidence that collusion increases with market concentration. The study by Whalen (1986) repre sents a simple attempt to examine the relation ship between the number of banks competing in a market and bank profitability for a sample of banks drawn from O hio and Pennsylvania over the 1976-85 interval. The study was designed to provide insight on whether potential competition had become an effective disciplinary force over the past decade. Both states liberalized their bank-branching laws over the period of observa tion. Further, thrifts are an important force in both states, and possessed essentially all the powers of banks after 1982. Thus, barriers to competition were presumably lower at the end of the period than they were at the outset. Following the approach of Burke and Rhoades, sample banks were classified according to the number of competing banks faced in the market. Three classes were created for non-MSA banks: 1-3 competing banks, 4-6 competing banks, and 7 or more competing banks. A separate class was created for MSA banks. Mean returns were calculated for the banks in each class for three subperiods: 1976-78, 1979-81, and 1982-85. If the traditional concentration-collusion hypothesis is valid, the mean profitability of banks operating in highly concentrated markets should be significantly higher than for banks operating in markets with larger numbers of actual competitors in each of the three subintervals. Empirical support for the traditional view was found only in the first time period, be fore relaxation of either state’s bank branching laws and the expansion of S&L asset and liability powers. The findings suggest that the lowering of barriers to actual and potential competition during the last two subintervals largely eliminated any concentration-related impact on bank profitability. Thus, researchers have found sup port for the concentration-collusion hypothesis in only one of the four most recent empirical studies of the structure-performance relationship in bank ing.8 Further, it is not clear that the results of this one supportive study demonstrate that the higher profitability observed in concentrated markets is due to collusion. A deficiency of all of the studies is that the market presence of thrift institutions is not taken into account. III. Sample and Methodology The structure-performance relationship is reex amined in this study, using a sample of 191 nonMSA banks located in O hio and Pennsylvania. Non-MSA banks are studied because potential competition should be relatively weak in such areas, and so the sample is likely to provide evi dence in favor of the concentration-collusion hypothesis— if it is in fact valid. The relationship is investigated over the 1982-84 interval. This period was chosen for several reasons. Bank branching restrictions in both states were liberalized by early 1982. Further, the 1982 Gam-St Germain Act had given S&Ls essentially the same asset and liability pow ers as commercial banks. Both of these develop ments should have intensified potential as well as actual competition in local banking markets in both states. Thus, the sample may indicate if these developments, in conjunction with techno logical changes in the funds-information transfer area, have rendered rural banking markets contestable. The particular banks analyzed were selected in the following way. In each state, all single-market banks in continuous operation over the 1976-85 interval headquartered in non-MSA counties were included. Single-market banks are those with all their offices located within their home office county. The presumption is that nonMSA counties approximate rural banking markets. The sample must be restricted to single-market banks so that market structure can be related to profits earned in that market. The profitability measure employed is annual return on assets (net income after taxes, before securities transactions, divided by average total assets) averaged over the 1982-84 interval. 8 Tw o other interesting studies provide evidence that market con centration need not result in anticompetitive bank performance. Hannan (1979) finds a significant relationship between a potential entrant variable and the rate paid on savings deposits in local markets in Pennsylvania. Shaffer (1982) obtains estimates of the elasticity of bank gross revenue with respect to input prices and concludes that the results indicate that the banking markets he studied are neither perfectly com petitive nor monopolistic. He finds that the coefficient on a concentration variable in his estimated equation is insignificant and concludes that the competitive forces preventing monopolistic conduct were primarily poten tial rather than actual or that the concentration measure did not ade quately proxy actual competition. 5 Mean ROA by Market Concentration Level (Banks only) Market concentration Mean ROA S.D. ROA T-Stat HHI < 1800 (N=62) HHI > 1800 (N=129) 1.179 1.015 0.529 0.621 1.89 HHI < 2 000 (N=71) HHI > 2000 (N=120) 1.171 1.001 0.512 0.635 1.95 HHI < 2 500 (N=104) HHI > 2500 (N=87) 1.116 1.011 0.599 0.591 1.22 HHI < 3000 (N= 133) HHI > 3000 (N=58) 1.101 0.992 0.591 0.606 1.15 HHI < 3500 (N=155) HHI > 3500 (N=36) 1.078 1.023 0.602 0.51 0.575 SOURCE: Author’s calculations, based o n Reports o f Incom e and Condition, Board o f Governors o f the Federal Reserve System; and on Summary o f De posit Data, FDIC. TABLE 1 The deposit data for the sample banks and the non-MSA markets comes from the FDIC Summary of Deposits tape. The deposit data were used to gen erate Herfindahl-Hirschman indexes (H H I) of market concentration for the sample banks, both excluding and including S&Ls.9 Others have used 6 Mean ROA by Market Concentration Level (Banks and S&Ls) Market concentration Mean ROA S.D. ROA T-Stat 1.094 HHI < 1800 (N= 109) HHI > 1800 (N=82) 0.594 0.600 0.70 1.033 HHI < 2000 (N=129) HHI > 2000 (N=62) 1.100 1.001 0.598 0.590 1.09 HHI < 2500 (N=153) HHI > 2500 (N=38) 1.087 0.991 0.599 0.585 0.90 HHI < 3000 (N= 170) HHI > 3000 (N=21) 1.055 1.173 0.618 0.368 -1.27 HHI < 3500 (N= 180) HHI > 3500 (N = ll) 1.061 1.190 0.607 0.368 -1.08 SOURCE: Author’s calculations, based o n Reports o f Incom e and Condition, Board o f Governors o f the Federal Reserve System; and o n Summary o f D e posit Data, FDIC. 9 The H HI index is the sum of the squared market shares of firms competing in a market. The H HI takes on its maximum value of 10,000 in monopoly markets. -I The three-firm-concentration ratio is typically employed. J . W Stated reasons for its use are ease of computation and tendency to exhibit the significant positive relationship between concen tration and profitability predicted by structuralists. alternative concentration measures for various reasons.10 The HHI was employed because this is the measure used by the Justice Department and the bank regulatory agencies in implementing antitrust policy in banking. The relationship between concen tration and bank profitability is investigated in two ways. First, mean returns are calculated for the sample banks after the sample has been split into two concentration categories— “high” and “low”— that are defined in a variety of ways. If the concentration-collusion hypothesis is correct, the mean return of the high-concentration class should be significantly greater than that of the low-concentration class. Since this approach does not con trol for other factors that may impact bank profit ability, regression equations similar to those employed by others are also estimated. The defi nitions of the variables employed in the regres sions appear in the appendix. Specifically, the bank profitability variable was regressed on a measure of bank size, a multibank holding com pany (MBHC) affiliation dummy, a market-size variable, market deposit growth, and the S&L share of total market deposits, in addition to bank market share and market concentration. The traditional view implies that the estimated coefficient on the market-concentration variable should be positive and significant when other independent variables are included in the equation, including a firm market-share variable. The bank-size variable is included to determine if larger banks realize scale econo mies or have diversification opportunities not available to smaller competitors. If size does confer advantages, the sign of the estimated coef ficient should be positive. If MBHC affiliation allows subsidiary banks to realize performance advantages relative to independent competitors, the estimated coeffi cient of the MBHC dummy should be positive. The market-size variable is included because rural markets in the sample vary greatly in size. It has been suggested that this variable prox ies ease of market entry. If this is the case, the ex pected sign of the coefficient should be negative. The market-growth variable is em ployed to proxy the strength of demand for bank ing services in each market relative to supply. Rapid market growth suggests robust demand, and so the estimated coefficient on this variable is expected to be positive. The S&L variable is used to proxy the intensity of nonbank competition in each mar ket. Presumably, the higher the S&L share of mar ket deposits, the greater their competitive impact and the lower the level of bank profitability. Regression Results* Independent variables Equation HB BSize Mkt MG SLS MBHC Constant -.00073 (-0.53) R2 .025 .00027 (1.65) F 1.80 -.00006 (-0.51) -.00815 (-2.71) .1207 (0.82) 1.179 (7.20) .00798 (1.96) -.00341 (-1.81) R2 .045 .00051 (2.75) F 2.47 -.00005 (-0.44) -.00791 (-2.65) .1438 (0.98) 1.012 (7.68) .01682 (2.97) -.00548 .00054 -.00004 (-2.63) R2 .064 (2.93) F 2.87 (-0.31) -.00715 (-2.41) .1732 (1.19) 1.221 (7.58) MSB -.000007 (-0.17) (1) (2 ) -.000122 (3) (- 2 .21 ) *The dependent variable in each equation is bank return on assets averaged over the 1982-84 interval. SOURCE: Author’s calculations, based on Reports o f Incom e and Condition, Board o f Governors o f the Federal Reserve System; and on Summary o f Deposit Data, FDIC. TABLE 3 IV. Results Mean returns for the sample banks, broken down by concentration class, appear in table 1. The concentration measures in table 1 are calculated using only the commercial banks operating in the mar ket. The first dichotomy, using HHI equal to 1800 as the breakpoint, reflects the Justice Department’s definition of a highly concentrated banking mar ket, presumably prone to collusion. The other break downs represent an attempt to determine if there is some higher level of market concentration at which supranormal bank profits become evident. The results do not support the concentration-collusion hypothesis. In particular, for all breakdowns examined, mean profitability is higher for banks in the low-concentration class. T-tests indicate that the observed differences in mean returns are statistically significant for the HHI=1800 and HHI=2000 breakdowns. The results differ somewhat if S&Ls are considered. These results appear in table 2. Once again, for HHI breakdowns up to 2500, mean returns are higher for the low-concentration class than they are for the more concentrated one. When the HHI breakpoint is 3000, mean returns are higher for banks in the more-concentrated class. However, none of the differences in mean returns are statistically significant. Thus, the results do not support the traditional view. The regression results are presented in tables 3 and 4.11 Once again, the concentrationcollusion hypothesis is not supported. Instead, the results mirror those of Smirlock and suggest that the efficient structure view is the correct one. Specifically, whether S&Ls are included in the concentration and market-share calculation or not, the concentration variable has a negative, insignificant coefficient when the market-share variable is excluded from the estimated equation. When a market-share variable is also employed, the concentration-variable coefficient remains negative and becomes significant. The estimated coefficient on the market-share variable is con sistently positive and significant in equations with and without a concentration variable. These results are not sensitive to the concentration measure employed. When the three-firm concentration ratio is used, similar results are obtained, both when thrifts are included and excluded. The reasons for the negative, sig nificant coefficient on the concentration variable in several of the estimated equations are unclear, although a similar result was reported in Smirlock (1985). One possible explanation is that non price competition may be more intense in more concentrated markets and so bank profitability is lower. Another is that managers in more concen trated markets can more easily engage in expense-preference behavior and so bank costs in such markets are higher and profitability is lower.12 Some researchers have suggested that managers in concentrated markets will lim it the amount of risks they take (i.e., choose the “quiet life”) and so could earn lower returns.13 Other n A formal test w as conducted to determine if it w as appro priate to pool the Ohio and Pennsylvania banks. The calcu lated F-statistic w as roughly 0.50, which is well below the critical level, and so pooling w as deemed acceptable. 12 13 For a discussion of expense-preference behavior, see Edwards (19 77). The possibility that managers might opt for the “quiet life" in concentrated markets is explored in Heggestad (19 77). Regression Results* Independent variables Equation HS MSS -.000001 ( 0.02) (1 ) (2) .00893 (1.89) BSize Mkt MG SLS MBHC Constant -.00078 (-0.58) R2 .025 .00028 -.00006 (-0.52) -.00816 (-2.60) .1205 (0.82) 1.156 (6.18) -.00288 (-1.66) R2 .00050 (2.70) F 2.42 -.00005 (-0.42) -.00627 (-1.99) .1423 (0.97) 0.979 (6.77) .00052 (2.85) F -.00003 (-0.26) -.00666 (-2.14) .1559 (1.08) 1.234 (6.66) .043 (3) -.000175 (-2.17) .02063 (2.88) -.00489 (-2.51) R2 .062 (1.71) F 1.79 2.79 *The dependent variable in each equation is bank return on assets averaged over the 1982-84 interval. SOURCE: Author’s calculations, based o n Reports o f Incom e and Condition, Board o f Governors o f the Federal Reserve System; and on Summary o f D eposit Data, FDIC. TABLE 4 explanations exist.14 Additional research appears necessary to explain this finding and is beyond the scope of the present paper. V. Summary and Conclusions The empirical results obtained using this sample of non-MSA banks do not support the concentration-collusion hypothesis. That is, a strong positive relationship between market con centration and bank profitability was not detected using either type of statistical analysis. Instead, the findings are in line with those reported in Smirlock (1985). That is, bank market share was found to be positively and significantly related to bank profitability both when concentration was included in the estimated regressions and when it was not. In fact, in equations that included both variables, the concentration variable had a nega tive, significant coefficient, rather than the expected positive one. The fact that the results closely mirror those of Smirlock, despite the much smaller sample size and different time period, with S&Ls excluded and included, lends credence to the view that the efficient structure hypothesis is the correct one. 14 See Smirlock (1985), p. 78, footnote 18. The results suggest that high market concentration is unlikely to lead to collu sion and monopoly profits, at least in states that allow banks some freedom to branch. The im pli cation is that a purely structuralist antitrust policy should be tempered with judgment, particularly in the determination of critical tolerable concen tration levels. APPENDIX DEFINITION OF VARIABLES HB: Herfindahl-Hirschman Index of market con centration, defined using commercial banks only. HS: Herfindahl-Hirschman Index of market con centration, defined using both commercial banks and S&Ls. MSB: Bank share of commercial bank deposits in the market. MSS: Bank share of total bank and thrift deposits in the market. BSIZE: Bank total deposit size. MKT: Total bank and thrift deposits in the market. Heggestad, Arnold J. “Market Structure, Risk and Profitability in Commercial Banking,” vol. 32, no. 4 (September 1977), pp. 1207-16. Journal oj Finance, McCall, Alan S., and James R. McFadyen. “Bank ing Antitrust Policy: Keeping Pace With Change,” vol. 10, no. 1 (Summer 1986), pp. 13-20. Issues in Bank Regulation, Morris, Charles S. “The Determinants of Banking Market Structure,” Working Paper 86-07, Feder al Reserve Bank of Kansas City, September 1986. SLS: S&L share of bank and thrift market deposits. _________“The Competitive Effects of Interstate Banking,” Federal Reserve Bank of Kansas City, vol. 69, no. 9 (November 1984), pp. 3-16. MBHC: Dummy variable equal to one if a bank is a member of a multibank holding company, equal to zero otherwise. All variables, unless other wise noted, are calculated using June 1984 data. Pozdena, Randall J. “Structure and Performance: Some Evidence From California,” Federal Reserve Bank of San Fran cisco, Winter 1986, no. 1, pp. 5-17. MG: Percentage change in total market deposits, 1980-84. REFERENCES Baumol, W illiam J., John C. Panzar, and Robert D. W illig. New York: Harcourt, Brace and Jovanovich, 1982. Contestable Markets and the Theory of Industry Structure, Economic Review, Review, Economic Rhoades, Stephen A. “Structure-Performance Studies in Banking: An Updated Summary and Evaluation,” Staff Study No. 119, Board of Governors of the Federal Reserve System, Washington, D.C., August 1982. Brozen, Yale. New York: Macmillan Publishing Company, Inc., 1982. Shaffer, S. “A Non-structural Test For Competi tion in Financial Markets,” Proceedings of a Conference on Bank Structure and Com peti tion, Federal Reserve Bank of Chicago, April 12-14, 1982, pp. 225-43. Burke, J., and S. Rhoades. “Profits, Potential Com petition and ‘Contestability’ in Highly Concentrated Banking Markets,” unpublished manuscript, Board of Governors of the Fed eral Reserve System, 1985. Sims, Joe, and Robert H. Lande. “The End of Antitrust— Or a New Beginning?” vol. XXXI, no. 2 (Summer 1986). lic Policy, Concentration, Mergers, and Pub Demsetz, Harold. “Two Systems of Belief About Monopoly,” in H. Goldschmid, H. Michael Mann and J. Fred Weston, eds., Boston: Little, Brown and Co., 1974, pp. 164-84. Industrial Concentration: The New Learning, Edwards, Franklin R. “Managerial Objectives in Regulated Industries: Expense-Preference Be havior in Banking,” vol. 85 (February' 1977). Economy, Journal of Political Bulletin, Antitrust Smirlock, Michael, and David Brown. “C ollu sion, Efficiency and Pricing Behavior: Evi dence From the Banking Industry,” vol. XXIV, no. 1 (January 1986), pp. 85-96.' Inquiry, Economic Smirlock, Michael. “Evidence on the (Non)Relationship Between Concentration and Profitabil ity in Banking,” vol. XVII, no. 1 (February 1985). Banking, Journal o j Money, Credit and Gilbert, R. “Bank Market Structure and Com peti tion,” vol. XVI, no. 4, pt. 2 (November 1984). Smirlock, Michael, Thomas Gilligan, and W il liam Marshall. “Tobin’s q and the StructurePerformance Relationship,” December 1984, pp. 1051-60. Hannan, Timothy. “Limit Pricing and the Bank ing Industry7,” vol XI, no. 4 (November 1979), pp. 438-46. Whalen, Gary. “Competition and Bank Profitabil ity: Recent Evidence,” Federal Reserve Bank of Cleveland, November 1, 1986. Journal oj Money, Credit and Banking Banking Journal of Money, Credit and nomic Review, tary, American Eco Economic Commen The Effect of Regulation on Ohio Electric Utilities by Philip Israilevich and K.J. Kowalewski Philip Israilevich is an economist at the Federal Reserve Bank of Chi cago. K .J . Kowalewski is an econ omist at the Federal Reserve Bank of Cleveland. This article was com pleted while M r. Israilevich was an economist at the Federal Reserve Bank of Cleveland. 10 Introduction ties operating under this constraint are not produc During the pioneering days of the electric utility ing electricity as cheaply as they could. Virtually industry, it was believed that utilities were natural all empirical tests of regulatory bias to date have monopolies, meaning that one utility could ser adopted the Averch and Johnson (A-J) model, vice a geographic area more cheaply than any and most have found an overcapitalization bias.2 combination of smaller utilities. More recently, The major challenge to the A-J the economic viability of transferring or wheeling model concerns the nature of the regulatory envi electricity over long distances, the development ronment. Im plicit in the A-J model is a regulator of small-scale generators and efficient w indm ill that constantly monitors capital returns and adjusts and solar power, and the increased use of cogen electricity prices to keep capital returns at “fair” eration have undermined the view of electric util levels. Joskow (1974) argues that regulators are more concerned with nominal electricity prices ities as natural monopolies. Nevertheless, electric utilities continue to be monopolies because regu than with the rate of return on capital. As long as nominal electricity prices do not increase, regula latory agencies, such as the Public Utilities Com mission of O hio (PUCO), give them exclusive tors w ill not actively enforce the rate-of-return constraint, thereby eliminating the source of the rights to produce and distribute electricity in A-J bias. Moreover, utilities face additional con designated markets. straints, such as fuel-cost-adjustment clauses, These regulatory agencies also environmental regulations, strict rules about what attempt to impose profit ceilings on electric utili ties in order to push the price and consumption capital is allowed in the rate base, and the requirement to meet all demand at given electric of electricity away from monopolistic levels and ity prices. When these additional constraints are toward competitive levels. This is accomplished taken into account, the net impact on a utility’s by regulating the rate of return on capital of elec production decisions is not clear. tric utilities. The regulator determines a “fair” rate of return that is sufficient to allow a utility to Atkinson and Halvorsen (1984) de cover its capital costs. With production costs and veloped a generalized cost model that allows for the demand for electricity, this “fair” rate deter the impact of additional regulatory constraints mines the price of electricity. and found empirical evidence of their impact on The impact of this type of regulation on the production decisions of regulated utilities This interpretation of the A - J result is attributed to Baumol and was first described byAverch and Johnson (1962). Klevorick (1970). They argued that this regulation gives utilities the Courville (19 74 ), Spann (19 74 ), Petersen (19 75), Cowing (19 78 ), incentive to overcapitalize, that is, to employ a and Nelson and W ohar (1983), for example, test only for an over capital-labor ratio that is larger than one that m ini capitalization bias against an alternative hypothesis of no bias. O f these mizes costs for a given output level.1 Thus, utili papers, only Nelson and W ohar do not find an overcapitalization bias. 1 2 utility production decisions. However, no one has formally tested the implications of Joskow’s view. The purpose of this paper is to fill this gap by estimating a modified version of Atkinson and Halvorsen’s model. The modifications are of two sorts. The first allows for different regulatory impacts over time as argued byjoskow. The second permits the use of panel data and the estimation of total factor productivity and its components to evaluate more accurately the impact of regulation on the technical change implemented by utilities. The Short-Run Effect of Regulation on Utility Prices Price We find considerable circumstantial evidence consistent with Joskow’s more general regulatory mechanism. However, the estimation results suggest that the impact of regulation in O hio does not completely square with Joskow’s expectation. In opposition to Joskow’s view, we find that these utilities produce electricity with their prevailing technologies more efficiently dur ing the years when Joskow expects regulatory constraints to be more binding. In Joskow’s favor, we find that regulation retards the rate of techni cal change implemented by these utilities to a greater extent during the years when Joskow ex pects tighter regulatory constraints. To our knowl edge, this is the first paper to explicitly estimate a regulators' impact on technical change in the electric utility industry.4 Moreover, this type of inefficiency7is surprisingly large in magnitude. Thus, the emphasis regulators and economists place on efficient production using a given capi tal stock appears to be misplaced; the retardation of technical change implemented by these utili ties appears to be an important source of bias. The first part of this paper reviews the regulatory process and contrasts the A-J and Joskow views. Next, the rate hearing experience in O hio during the 1965 to 1982 period is dis cussed and is found to correspond quite well with Joskow’s view of the regulatory mechanism. The third section describes the empirical results. I. The Regulatory Process --- Demand --- Marginal Revenue Marginal Costs Average Costs SOURCE: Authors. FIGURE 1 The data are a panel sample of the seven major electric utilities in O hio over the period 1965 to 1982.3 O hio utility data were used because of general interest to most residents of the Fourth Federal Reserve District. Also, because these utilities are all privately owned, coalburning plants that are subject to the same regu lator, their technologies should be fairly similar. Tli us, the estimation of a common cost structure for these utilities should yield a smaller potential for specification bias than is true of all previous studies of electric utilities, whose samples include utilities that employ varying technologies and/or face different regulators. 3 It is useful to view the regulatory process in two parts: 1) the of setting a utility’s elec tricity price structure, and 2) the that initiate a rate hearing or a review of a utility’s electricity price structure. There is little disagree ment among economists about the first part. Sim ply put, a regulatory agency such as PUCO attempts to maintain a competitive price for elec tricity by regulating the rate of return on a utility’s capital. It establishes a “fair” rate of return ( r), taking into account all of a utility’s production costs and the demand for its electricity, that is consistent with a “fair” level of profit and that is slightly higher than the utility’s cost of capital. The “fair” return or profit on capital is then 7 rr= where is the rate base or the book value of the utility’s net capital stock. The basis for a rate change and, hence, a change in the price of elec tricity, is the difference between this “fair” return on capital and the utility’s accounting return on mechanics B events Br, The seven major electric utilities in Ohio are Ohio Power; Cincin nati Gas and Electric; Cleveland Electric Illuminating; Columbus and Southern Ohio Electric; Dayton Power and Light; Ohio Edison; and Toledo Edison. Over the 1965 to 1982 period, they accounted for about 90 percent of electric power sales in Ohio. 4 Nelson and W ohar (1983) estimated the impact of a rate-of-return constraint on TFP and calculated its impact on technical change as a residual. Israilevich and Kowalewski (1987) argue that this residual is an incorrect estimate of the regulatory impact on technical change. capital ( 7r), which is the difference between the utility’s operating revenues (/?) and its operating costs = Electricity prices are set by the regulator to equate with 7rr on the date of the hearing. If is less than 7 rr, electricity prices are raised, while if is greater than electricity prices are decreased. This mechanism is shown in figure 1, assuming there is only one utility serving the market for electricity. If there were no regulation, the utility would maximize profits (or minimize costs) by equating marginal revenues with mar ginal costs, producing quantity and charging a price Its profits would be If the utility was acting like a perfectly competitive firm, it would maximize profits (and m inim ize so cial costs) by equating the market price, to its marginal costs and to its average costs and would produce the quantity In this case, its profits w ould be zero. Note, however, that at both and production is efficient in the sense that input-factor marginal products are equated to their market prices. A regulator picks some price that is less than but greater than giving the utility a “fair” profit of r) to cover capital costs. At this point, production is inefficient. This is a general description of the price-adjustment mechanism of an electric utility regulator. What brings a utility to a rate hearing and what motivates a regulator are questions de bated by economists. The predominant answers to these questions were influenced by Averch and Johnson. They investigated the optimal response of a cost-minimizing utility in static equilibrium to a “fair” rate of return on capital regulatory con straint. They showed that when the rate of return on capital constraint is binding, and when the “fair” rate of return is larger than the cost of capi tal, a utility has the incentive to overcapitalize; that is, to employ a capital-labor ratio that is larger than the one that minimizes costs for the chosen output level.6 This is called the Implicit in the A-J model is the assumption that the motivating factor behind regulatory action is the rate of return on capital. In the A-J model, the constraint on a utility’s profit-maximization actions is that the actual rate of return on capital earned by a utility is no greater than the “fair” rate. Another assumption is that an active regulator continually monitors util ity returns and pounds on a utility with a “visible hand” to maintain the equality of a utility’s profits (OC):5 n R'OC. n n u n r, Qm Qm(Pm~ ACm). Pm. Pc, Qc. Pm Pc, Pr 12 Pm Qr(Pr- AC Pc, A-J bias. with its “fair” profits. When a utility’s profits are less than its “fair” level of profits, the regulator calls a rate hearing to raise r and, hence, the utili ty’s price of electricity. When a utility’s profits are above the “fair” level, the regulator calls a rate hearing to lower rand the price of electricity. With minor amendments, this view of regulatory behavior predominates in the eco nomics literature, especially in empirical studies of electric utility behavior, with the exception of Joskow (1974).7 Joskow agrees that rate-of-return regulation will give a utility the incentive to employ an inefficient mix of input factors, but he argues that the A-J bias may not always occur. In Joskow’s view, regulators are political institutions whose objective is to m inim ize “conflict and crit icism,” not to keep the rate of return on capital equal to the “fair” rate. One important source of conflict and criticism is an increase in the nom inal price of electricity. Consumers will agitate against increases in electricity prices because they typi cally view these increases as price-gouging. If electricity prices are not increasing, and especially if they are falling, consumers are indifferent to the profits earned by a utility. Thus, Joskow argues that if utilities are able to adjust their pro duction and investment decisions to raise their earned rates of return without raising electricity prices, they will not be thwarted by the regulator. In this case, there may be little A-J bias. O n the other hand, Joskow argues that regulators do not initiate any actions to raise the rate of return on a utility’s capital when it is below the “fair” rate unless requested to do so by the utility. Before a rate increase is granted, the utility will earn a return on capital below the “fair” return. In this case, an A-J bias may appear. Thus, in contrast to the active A-J regulator, the Joskow regulator is passive, adjust ing the rate of return on a utility’s capital only when requested to do so by a utility or by a con sumer advocate. As time passes, earned profits may deviate from “fair” profits if input prices, electricity demand, and other factors change, but the regulator does not initiate a price change to re-equate earned profits with “fair” profits until the next rate hearing. In the meantime, a utility can alter its production and investment decisions in ways opposite to those predicted by the A-J model. The “fair” rate of return is a means to an end (uncontroversial electricity prices), not an end in itself, in Joskow’s view. After reviewing the regulatory experience across the U.S. between the 1950s and early 1970s, Joskow concludes that: Contrary to the popular view, it Operating costs include all noncapital costs of production. 6 does not ap- Actually, Baumol and Klevorick (1970) argue that Averch and Johnson did not prove this as a general result. Note that if there are additional production factors, then the amount of capital relative to these other inputs also will be higher than for the cost-minimizing firm. 7 A slight modification to the A - J regulatory process w as the intro duction of a "regulatory lag"; see, for example, Bailey and Coleman (19 71) and Baumol and Klevorick (1970). The Relationship of Electricity Prices and Sales to the Frequency of Rate Hearings initiated by Electricity Prices (per kilowatt-hour) Current dollars Electricity Sales Billions of kilowatt-hours Rate Hearing Frequency Percent of seven utilities SOURCE: Public Utilities C om m ission o f O h io and Standard and Poor’s Compustat Services, Inc., Utility Com pustat II. FIGURE This regulatory process is therefore ex tremely passive. Regulators take no action regarding prices unless major increases or structural changes are the firms under its jurisdiction. In short, it is the firms themselves which trigger a regulatory rate of return review. There is no “allowed” rate of return that regulatory commissions are continually monitoring and at some specified point enforcing, (p. 298) Because they work in a political en vironment, public utility commissions face other sources of conflict and criticism, which have re sulted in two additional constraints on utility behavior. First, when energy costs increased rapid ly in the mid- 1970s, utilities requested rate hear ings in greater numbers than in the past. This in creased caseload put a large burden on regulatory agencies, who were accustomed to only a few hearings per year. The time lag between the request for a rate hearing and a change in elec tricity prices increased, and many utilities were forced to request another rate hearing immediately after their previous hearing. In order to shorten this lag and to appease utilities, regulators insti tuted fuel-cost-adjustment clauses that permitted utilities to pass on higher fuel costs to consumers without the need for a formal rate hearing. Second, the fossil-fuel generators operating before the mid-1970s emitted a consid erable amount of pollution into the atmosphere. Successful agitation by environmental advocates forced public utility commissions to establish limits on the amount of pollution that utilities could emit. These additional constraints com pli cate the analysis of the impact of a rate-of-return constraint on utility behavior. 2 pear that regulatory agencies have been con cerned with regulating rates of return per se. The primary concern of regulatory com missions has been to keep Firms which can increase their earned rates of return without raising prices or by lowering prices (depending on changing cost and demand characteris tics) have been permitted to earn virtually any rate of return that they can. from increasing. nominal prices Formal regulatory action in the form of rate of return review is primarily triggered by firms attempting to raise the level of their rates or to make major changes in the structure of their rates. The rate of return is then used to establish a new set of ceiling prices which the firm must live with until another regulatory hearing is triggered. General price do not trigger regulatory review, but are routinely approved without formal rate of return review. reductions II. Rate Hearings and Average Costs of O hio Utilities: 1965 to 1982 Some evidence consistent with Joskow’s view of the regulatory mechanism is found in the history of rate hearings in O hio between 1965 and 1982. To put this evidence into perspective, first con sider the behavior of the average price per kilowatt-hour of electricity charged, and the quan tity of kilowatt-hours sold, by the seven major O hio electric utilities (figure 2). For the purposes of this discussion, three distinct periods of different electricity price and consumption behavior can be seen: 1965 to 1968, 1969 to 1975, and 1976 to 1982.8 W ithin 8 Note that the average price shown in figure 2 is not the regulated price, but the ratio of average total revenue for the seven utilities to their average total sales. In general, different consumers face different regulated price schedules, and utilities serving different geographic markets m ay be allowed to charge different prices for the same category of consumer. 1 3 each period, the directions of change in price and quantity were the same for each utility in the sam ple. During the 1965 to 1968 period, the average price of electricity changed very little and electric ity sales rose considerably. During the 1969 to 1975 period, the average annual growth rate of electricity sales slowed, while that of prices increased greatly. Between 1976 and 1982, the electricity sales declined for the first time in O hio’s history, while prices increased at their fast est average annual percentage rate. It is important to note that the average price shown in figure 2 is also the aver age cost of electricity. All regulators, including the PUCO, define the price of capital to be divided by hence equating operating revenues with operating costs. The neoclassical economist’s measure of average cost uses a market price of capital and, hence, the neoclassical measure of average costs can differ from the PUCO’s defini tion. Bemdt and Fuss (1986) argue that a capital price measure such as that used by the PUCO is more appropriate because it is a rental price or user cost of capital and because it controls for changes in capacity utilization. For these reasons, and because it is the measure the PUCO uses and to which utilities respond, the rental price of cap ital is employed in this paper. Figure 2 also shows the percentage of the seven utilities requesting rate hearings in each year. In the first period, utilities rarely re quested rate hearings, and their average costs were falling. This behavior corresponds with Joskow’s first proposition: “During periods of falling average cost we expect to observe virtually no regulatory rate of return reviews” (p. 299). The average price of electricity was also falling during this period, consistent with Joskow’s second proposition: “Dur ing periods of falling average costs we expect to observe constant or falling prices charged by reg ulated firms” (p. 299). Given that there were few rate hearings in this period, it is plausible that utility returns on capital were greater than or equal to the “fair” returns the PUCO would have defined had they been requested to do so.9 Ac cording to Joskow, if actual returns were lower than the “fair” return, then the utilities would have asked for price increases. Hence Joskow’s third proposition: “During periods of falling aver age costs we expect to observe rising or constant (profit maximizing) rates of return” (p. 299). n B, 1 4 During the 1969 to 1975 period, average costs increased slightly, triggering a modest increase in the frequency of hearings, while during the 1976 to 1982 period, average 9 It can never be known whether earned returns were greater than “fair" returns because there were no rate hearings for all firms during these years. costs increased tremendously. Production costs in creased in the late 1960s because of inflation stimu lated by economic policies; they increased very quickly and unexpectedly in the mid- 1970s be cause of inflation engendered by worldwide food shortages and by the Arab oil embargo. For a given electricity price, such increases in operating costs drove utility profits below their “fair” levels. Utili ties promptly responded to these cost increases by requesting electricity price increases that, in most cases, were granted by the PUCO. The fre quency of hearings increased sharply as utilities had trouble keeping up with the effects of the rapid rise in costs. Viewing the 1969 to 1975 period as a transition from a period of falling average costs to one of rising average costs, the modest increase in rate hearings during this period is consistent with Joskow’s fifth proposition: The transition from a period of falling average costs to one of rising average costs for a particular regulated industry w ill at first yield no observable increase in the number of rate of return reviews filed by the regulatory agency, but as cost increases continue more and more rate of return reviews are triggered as firms seek price increases to keep their earned rates of return at least at the level that they expect the commission will allow in a formal reg ulatory hearing, (p. 300) For estimation purposes, the 1965 to 1982 interval was divided into two periods: 1965 to 1973 and 1974 to 1982. Testable hypotheses of the A-J and Joskow views deal with the absolute and relative production inefficiencies of the utili ties in these two periods. The near absence of regulatory hearings in the first period would sug gest, to both Joskow and A-J, that earned rates of return of these utilities were at least as great as “fair” rates of return. Averch and Johnson would argue that earned rates of return were lower than monopoly rates of return and, hence, that the A-J bias should exist in the first period. O n the other hand, Joskow would argue that earned rates of return may have been close to monopoly rates. If this were true, then because m onopoly rates are consistent with efficient production, there may have been very little A-J bias in the first period. Indeed, as Joskow argues in his seventh proposi tion, production may have been very efficient in the first period because reducing costs would have contributed to higher earned rates of return that were not taken away by regulators: During periods of falling or constant nom inal average costs firms have an incentive to produce efficiently since all profits may be kept as long as prices stay below the level established by the regulatory7com mission in the last formal rate of return review, (p. 303) The high frequency of hearings in the 1974 to 1982 period suggests that earned rates of return for these utilities were lower than “fair” rates of return for most of the period; that is, tt Because these earned rates were even further away from monopolistic rates of return, Joskow would argue that it is more likely there are inefficiencies of the A-J type in the second period. His proposition eight says: "During peri ods of rising average costs A-J type biases may begin to become important” (p. 304). He does not exclude the possibility that firms may con tinue to try to be as efficient as they were in the first period in order to earn greater than “fair” rates of return. However, he argues: Unless the direction of the cost path can be changed, however, the continuous inter action of firms and regulators in formal regu latory hearings, resulting from the necessity to raise output prices, is exactly the situa tion for which the A-J type model (with some modifications) would hold. I would therefore expect that it is under this situa tion of continuously rising output prices, triggering rate of return reviews that the A-J type models and the associated results are most useful, (p. 304) Thus, Joskow would argue that utilities would try to organize their production more efficiently in the first period than in the second period. His concept of production effi ciency includes the static notion of employing currently available production inputs in the leastcost way for any given level of output (that is, employing the least-cost combination of inputs along a given isoquant) and the dynamic notion of investing in more productive capital over time (that is, investing in productive capital to push the family of isoquants toward the origin). Averch and Johnson deal only with the static notion of productive inefficiency because their model ana lyzes a static equilibrium. They would argue that the amounts of this static inefficiency are the same in both periods because they assume a reg ulator who maintains the earned rate of return on capital at its “fair” rate. The distinction between the static and dynamic notions of production efficiency is important. When a public utility commission conducts a rate hearing, it pays attention only to the static notion of production efficiency. Indeed, most models of regulatory impact deal only with the static notion. However, it is conceivable that regulation also affects the rate of technical change implemented by utilities; if regulation biases the amount of capital employed by a utility, it may also bias the type of capital employed. Regulatory impacts on overall inefficiency' and on the rate of technical change are estimated below. < n r. III. Empirical Evidence About the A-J and Joskow Views The A-J and Joskow views are examined using a modified version of the Atkinson and Halvorsen (1984) generalized long-run cost-function ap proach with capital ( ), labor and fuel ( as inputs.10 Atkinson and Halvorsen argued that the long-run neoclassical cost-function approach is incorrect for a regulated firm because it assumes the firm is m inim izing costs in a per fectly competitive world constrained only to pro duce a given level of output.11 When a firm is subject to a number of regulatory constraints, as is generally true today, firms view all input prices differently from their market or rental prices. The exact specification of these nonmarket or “shadow” prices depends on the exact form of the additional constraints. Atkinson and Hal vorsen approximated these shadow prices by simple proportional relationships with market prices; that is, the shadow price of input *= where is its market price and is a con stant. Thus, the generalized cost function is simply the neoclassical cost function with substituted for Instead of m inim izing longrun actual costs, a utility is assumed to minimize long-run costs by equating the marginal cost of each input with the amount of the input used. The modifications made to the Atkinson and Halvorsen approach are 1) the inclusion of time variables to accommodate panel data and to permit the estimation of total factor productivity ( and its returns to scale and pure technical change components, and 2) the distinction between the 1965 to 1973 and 1974 to 1982 time periods. is measured as the change in the cost of production not due to changes in in put prices, and reflects the overall productivity of all inputs rather than the productivity of a single input such as labor. The neoclassical approach to the measurement of assumes an optimal dis tribution of production resources in a firm, which is an inappropriate assumption for regulated electric utilities. The generalized-cost-function approach yields an estimate of that is con sistent with regulated behavior. The most impor tant variable for examining Joskow’s view on productivity behavior is the pure technical change component of Gollop and Roberts (1981), among others, argue that this component is a better measure of productivity than K kiPi, (L), Pt F) kt i: P P* Pt. shadow shadow TFP) TFP TFP TFP TFP. TFP. See Israilevich and Kowalewski (1987) for complete details about the data, the specification and estimation of the shadow-cost model, and the results. n utilities. Nevertheless, some authors, for example Gollop and Roberts (19 8 1, 1983), use the neoclassical approach to study electric 15 The distinction between the two periods is made by estimating separate coeffi cients for them. This allows the production deci sions, as well as the degree of regulatory con straint, to differ between the two periods.12 The coefficients measure the degree to which the neoclassical first-order con ditions are not satisfied and, hence, serve to test for production input biases. If all equal one, then shadow prices equal market and rental prices, and regulation does not affect production deci sions; actual, not shadow, long-run costs are m in imized. If the for all inputs except capital equal one, then there is only an overcapitaliza tion bias. If any other do not equal one, regardless of the value for capital, then the A-J view is rejected. The results of estimating the model over the 1965 to 1982 period show that both and are separately and jointly statisti cally different from one at better than a 5 percent significance level in both periods.13 Thus, pro duction efficiency is rejected, and the neoclassical cost-function approach for regulated firms employed by Gollop and Roberts (1981) and others is inappropriate for this sample. Moreover, these results reject the A-J view over the whole sample; regulation affects the efficient utilization of all production inputs by these utilities. Another test of the A-J view, and a test of the implications of Joskow’s view, is whether production inefficiencies resulting from regulation differ in the 1965 to 1973 and 1974 to 1982 periods. The A-J view is that the inefficiencies should be the same in each period, while the Joskow view is that there should be greater ineffi ciencies in the second period than in the first. Two approaches are taken here. In the first, the differences in and are examined. The A-J view is not rejected if the difference in between the two periods is insignificantly differ ent from zero and both equal one. If the suggest greater inefficiencies in the second period than in the first, then the Joskow view is not rejected. The test results show that the A-J view is rejected at better than 0.5 percent, and the differences in the and coefficients between the two periods are significantly different from zero at better than 5 percent. However, the Jos kow view is also rejected, because the differences and coefficients, second period in the kt kt kt kt kt kK 16 kF kK kF kK kF ki kK kK 12 -1 O JL ^ kF kK kF kF kK A test of similar production behavior in the two periods was convincingly rejected. For technical reasons, only two of the three mated. The to one, and only estimated. kF minus the first, are significantly negative; to not reject Joskow, this difference should have been positive. Unfortunately, due to technical reasons related to the specification of the cost function, the sources of the differences in these coeffi cients cannot be identified. The second approach examines estimates of the differences in total and dynamic inefficiency due to regulation between the two periods. The full cost of regulation and, hence, the magnitude of the inefficiencies created by regulation, cannot be estimated because there is no evidence to suggest how the utilities would have organized their production had regulation not existed over the sample period. O f course, it is impossible to know how these utilities would have behaved without regulatory constraints. For example, the activities of production and distribu tion might have been separated, different amounts of capital might have been employed, and different technologies might have been chosen.14 Hence, it is impossible to know what these firms’ cost functions and associated returns to scale and productivities would have been. “Instantaneous” inefficiency esti mates can be computed, however. A total ineffi ciency measure compares actual utility costs pre dicted by the estimated model with the actual costs predicted by the model, but with and set equal to one in both periods. That is, cur rent production costs for actual levels of output, which are generated by current capital, labor, and fuel inputs; production techniques; and regulatory constraints, are compared with the costs generated with the same input levels and production tech niques and for the same actual output levels, but without the regulatory constraints. This estimate, also examined by Atkinson and Halvorsen, mea sures the shift in the cost curve due to regulation. An estimate of the dynamic notion of inefficiency can be obtained by examining the technical change experienced by these utilities with and without regulation. Technical change is defined here as the negative of the derivative of total costs with respect to time, holding all other factors constant. It is a function of a constant term, shadow input prices, output (returns to scale), and time, and it shifts the position of a firm’s average cost curve over time. As above, technical change with regulation is that im plied by the estimated model; technical change with out regulation is that im plied by the estimated model, but with and set equal to one. The difference does not have a real-world coun- kK k, and kt can be esti- coefficient on the price of labor is normalized k F, for capital and fuel, respectively, are "| / Under the current regulatory environment, the production and _L x. distribution of electricity must be handled by each utility. Moreover, the transferal of electric power across state lines is also impeded. terpart or explanation, but it does indicate the direction of regulatory bias. These inefficiency estimates provide mixed evidence about Joskow’s view. The differ ence in the total inefficiency measure between the two periods is the opposite of Joskow’s expec tation. Instead of greater total inefficiency in the second period, when Joskow expects regulatory constraints to be binding, our estimates show greater total inefficiency in the first period, when Joskow expects regulatory constraints to be less binding. In the first period, the total inefficiency varies between 26 percent and 49 percent and averages 36 percent. In the second period, it var ies between 16 percent and 19 percent and aver ages 17 percent. This difference in total ineffi and ciency squares with the differences in between the two periods described earlier. An interesting feature of these total inefficiency estimates is their large magnitude in the first period. Atkinson and Halvorsen find much smaller inefficiency losses (9.0 percent) in kK kF Estimated Technical Change Technical change in percentage points, average over firms Year 1965 1966 1967 1968 1969 1970 1971 1972 0.3 -0.1 -0.3 -0.6 -1.0 -1.2 -1.4 -1.7 -2.0 -3.4 -3.6 -3-6 -3.8 -3.8 -4.0 -4.2 -4.4 -4.6 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 Average over year 1965-1973 1974-1982 -0.9 -3-9 their cross-section sample, which includes two of our firms.15 However, the Atkinson and Hal vorsen result captures only the static portion of total inefficiency costs because the authors do not use time variables in their cost equation. Our estimates include the dynamic inefficiency costs and, hence, are more representative of the total costs of regulation. The difference between the Atkin son and Halvorsen result and ours suggests that the dynamic inefficiency may be quite large. Indeed, we find that regulation retarded the growth of technical change, on average, by 0.3 percentage point per year in the first period and by 0.4 percentage point per year in the second. This is an important result, and one that has been neglected by economists and regulators alike. Regulation not only affects the efficient utilization of existing production inputs, but it also affects the implementation of efficient capital and man agement techniques over time. Unlike our total inefficiency estimates, our dynamic inefficiency estimates support Joskow’s view. The behavior of technical change over time also confirms Joskow’s view. Table 1 shows the technical-change estimates over the whole period, averaged over all firms for each year. As Joskow argues, the rate of technical change is lower in the second period, when he ex pects regulatory constraints to be more binding. The most notable characteristic about these technical-change estimates is their strong downward trend.16 Starting at 0.3 percent in 1965, the annual average rate of technical change drops steadily each year to -4.6 percent in 1982. This rather uniform decline, except around 1973 and 1974, when period one ends and period two begins, is due to dominant estimated time trends in each period. That shadow input prices have little influence on technical change is not surprising, because electricity production offers little opportunity for input substitution in the short and medium runs. The time trend cap tures the effects of pure technical change embod ied in the capital investments of these utilities and may be additional evidence in favor of Jos kow’s seventh proposition. Although this is not conclusive proof of Joskow’s seventh proposition, because we do not know the nature of the capital investments made in these and earlier periods, it at least does not contradict it. SOURCE: Authors. *1 JL 4T -1 Z ' A strong downward trend in the rates of technical change 1 experienced by utilities also w as found by Nelson and Wohar 0 I * is likely that our estimates are more accurate for Ohio (1983), Gollop and Roberts (19 8 1), and Gollop and Jorgenson (1980), all because our sample includes only Ohio firms, which are fairly of whom used samples that ended in the 1970s. Thus, the results report similar in a number of important respects, as mentioned earlier. ed here confirm these earlier findings for the late 1970s and early 1980s. 17 18 IV. Summary and Conclusions Electric utility regulators attempt to maintain a competitive price for electricity by adjusting the rate of return on a utility’s capital. At first blush, this price-setting scheme appears sensible. It seems reasonably efficient to allow utilities to pass along operating costs and cover their cost of capital. However, potentially serious problems with this type of regulation relate to consumer reactions to price increases and to the types of incentives given to utilities. First, price increases may lower the consumption of electricity, which may reduce earned rates of return below “fair” rates and trigger a price increase, which in turn may lower consumption and trigger another price increase, and so on. That is, the proper response to falling utility profits because of lower demand may not be to raise prices. Second, utilities may be able to ef fect price increases by overcapitalizing, which inflates their rate base. Indeed, rate increases lower the risk of capital investment below the risk level of unregulated industries, clearly giving utilities the incentive to overcapitalize. This poten tial bias was recognized by Averch and Johnson, and many empirical studies that adopted their model found an overcapitalization bias. Finally, the ability to pass along operating cost increases that originated from pro ductivity declines suggests that utilities may not have the incentive to raise productivity. This dynamic source of inefficiency was recognized by Joskow, who also argued that the regulatory7 mechanism is more complicated than that assumed by Averch and Johnson. This paper is, to our knowledge, the first to test the A-J view against Joskow’s more general view. Using a modified version of the generalized long-run cost function derived by Atkinson and Halvorsen (1984) and a sample of the seven major electric utilities in O hio over the 1965 to 1982 period, substantial evidence is found against the A-J view. However, the evi dence is not wholly in agreement with Joskow’s view, either. The circumstantial rate hearing evi dence is consistent with Joskow’s view of the regulatory mechanism, but our estimation results do not wholly confirm the implications Joskow draws from his regulatory mechanism. Two sets of results imply that regulatory constraints were more binding during the years in which Joskow expects them to be less binding. Nevertheless, in accordance with Joskow’s view, we find that regu lation substantially retards the rate of technical change experienced by these utilities, and the retardation is greater when Joskow expects regu lation to be more binding. This is the first dem onstration of a regulatory impact on technical change. It clearly suggests that regulators ought to pay closer attention to the incentives they give utilities to innovate.17 A reconciliation of these findings is difficult. They may suggest that the circumstantial rate hearing evidence is not closely correlated with the degree of regulatory constraint. Utilities may have been constrained in the 1965 to 1973 period by the possibility or fear of a rate hearing that would eliminate the above “fair” returns they were currently earning. Another possibility is that fre quent rate hearings in the 1974 to 1982 period pre vented utilities from artificially fattening their rate bases. That is, given the incentive to overcapital ize, utilities were prevented from taking advantage of the regulatory system by frequent and accurate regulatory review. In this case, the price of elec tricity may have remained close to competitive levels, where production, though different from monopolistic levels, nonetheless is efficient. The poor technical-change performance between 1974 and 1982 may be the primary cause of the greater rate-hearing frequency, and not the reverse. Or Joskow may be correct, and utili ties were simply lax about maintaining efficient production in the first period, or they anticipated future regulatory constraints and took actions to fatten their rate bases while they had the oppor tunity.18 Clearly, there is much to learn about the impact of regulation on utility performance. -I “ “7 1 The poor technical-change performance also m ay be due to / increased investment in nuclear power plants. M any of these plants were cancelled after the mid-1970s, but they diverted managerial attention and funds aw ay from conventional power-generation capital investments. -1 Q JLO Jo sk o w can also be defended by arguing that our inefficiency measures are incorrect. A s w as mentioned earlier, it can never be known how utilities would have behaved without regulation. Without this knowledge, any inefficiency measure can be faulted. Never theless, the estimated change in kF and is hard evidence against Jo sk o w ’s view . kK between the two periods REFERENCES Atkinson, Scott E., and Robert Halvorsen. “Para metric Efficiency Tests, Economies of Scale, and Input Demand in U.S. Electric Power Generation,” vol. 25, no. 3 (October 1984), pp. 647-62. International Economic Review, Gollop, Frank M., and MarkJ. Roberts. “Environ mental Regulations and Productivity Growth: The Case of Fossil-Fueled Electric Power Gen eration,” vol. 91, no. 4 (August 1983), pp. 654-74. Journal of Political Economy, Averch, Harvey, and Leland L Johnson. “Behavior of the Firm under Regulatory Constraint,” vol. 52, no. 5 (December 1962), pp. 1052-69. __________“The Sources of Economic Growth in the U.S. Electric Power Industry,” in T.C. Cow ing and R.E. Stevenson, Eds., New York: Academic Press, 1981. Bailey, Elizabeth E., and Roger D. Coleman. “The Effect of Lagged Regulation in an AverchJohnson Model,” vol. 2, no. 1 (Spring 1971), pp. 278-92. Israilevich, Philip, and K. J. Kowalewski. “A Test of Two Views of the Regulatory Mechanism: Averch-Johnson and Joskow,” Federal Reserve Bank of Cleveland, (forthcoming). Baumol, W illiam J., and Alvin K. Klevorick. “Input Choices and Rate-of-Return Regulation: An Overview of the Discussion,” vol. 1, no. 2 (Autumn 1970), pp. 162-90. Joskow, Paul L. “Inflation and Environmental Concern: Structural Change in the Process of Public Utility Price Regulation,” vol. 17 (October 1974), pp. 291-327. Berndt, Ernst R., and Melvyn A. Fuss. “Productivity Measurement with Adjustments for Variations in Capacity Utilization and Other Forms of Temporary Equilibrium,” vol. 33, no. 1 / 2 (October/November 1986), pp. 7-30. Nelson, Randy A., and Mark E. Wohar. “Regula tion, Scale Economies, and Productivity in Steam-Electric Generation,” vol. 24, no. 1 (February American Economic Review, The BellJournal oj Econom ics and Management Science, The BellJournal of Economics and Management Science, metrics, Journal of Econo Christensen, Laurits R., and Dale W. Jorgenson. “U.S. Real Product and Real Factor Input, 19291967,” ser ies 16, no. 1 (March 1970), pp. 19-50. The Review of Income and Wealth, Courville, Leon. “Regulation and Efficiency in the Electric Utility Industry,” vol. 5, no. 1 (Spring 1974), pp. 53-74. The BellJournal of Economics and Management Science, Cowing, Thomas G. “The Effectiveness of Rate-ofReturn Regulation: An Empirical Test Using Profit Functions,” in M. Fuss and D. McFadden, Eds., Amsterdam: North Holland Publishing Company, 1978. Production Economics: A Dual Approach to Theory and Application, Fare, Rolf, and James Logan. “Shephard’s Lemma and Rate of Return Regulation,” vol. 12 (1983), pp. 121-25. ters, Economics Let Gollop, Frank M., and Dale W. Jorgenson. “U.S. Productivity Growth by Industry, 1947-1973,” in J.W. Kendrick and B.N. Vaccara, Eds., National Bureau of Economic Research, Chicago: University of Chicago Press, 1980. in Income and Wealth, Studies Productivity Mea surement in Regulated Industries, Working Paper Law atid Economics, Economic Review, TheJournal of International 1983), pp. 57-79. Petersen, H. Craig. “An Empirical Test of Regula tory Effects,” vol. 6, no. 1 (Spring 1975), pp. 111-26. The Bellfoum al of Economics and Management Science, Spann, R.M. “Rate of Return Regulation and Effi ciency in Production: An Empirical Test of the Averch-Johnson Thesis,” vol. 5, no. 1 (Spring 1974), pp. 38-52. The BellJournal of Economics and Management Science, 1 9 Views from the Ohio Manufacturing Index by Michael F. Bryan and Ralph L. Day Michael F. Bryan is an economist and Ralph L . Day is an economic analyst at the Federal Reserve Bank of Cleveland. The authors gratefully acknowledge the assistance of Diane Smith, Nannette Thompson, and Frances Davis of the Federal Reserve Bank of Cleveland's Data Services Department, w ho compiled the electric power consumption data used in this study. 20 A Preview Economists and other observers are closely exam ining the manufacturing sector these days, fearing that America’s industrial base is disappearing. Cer tainly, the steady decline in the proportion of total jobs in manufacturing, as shown in figure 1, supports this view. However, a more careful look reveals that manufacturing’s overall share of real national output has remained essentially unchanged since 1950.1 Ohio and U.S. Manufacturing Employment Thousands, seasonally adjusted A more reasonable worry, it would seem, is the uneven regional distribution of manu facturing growth that is obscured by nationally aggregated data. Unfortunately, the information used by analysts to evaluate regional manufactur ing output has been limited to quinquennial cen sus data and, when available, annual survey data. Lack of timely regional data prompted the establishment of regionally based production indexes by the Federal Reserve Banks of Atlanta, Boston, Dallas, and San Francisco.2 The Federal Reserve Bank of Cleveland has recently developed a monthly manufacturing production index for the state of O hio— the O hio Manufac turing Index (OM I). The OMI is an experimental index of real output by O hio manufacturers that is derived from state-level manufacturing employ ment and electric power consumption data. The OMI tracks manufacturing output at the two-digit standard industrial classification (SIC) level of aggregation, beginning in January 1979 and end ing in December 1986. The methodology and pro cedures used to develop the index are outlined in the technical appendix that follows this article. I For an overview of developments in the U .S . manufacturing sec tor, see Michael F . Bryan, "Is Manufacturing Disappearing?" Economic Commentary, Federal Reserve Bank of Cleveland, Ju ly 15, 1985; and Patricia E. Beeson and Michael F . Bryan, “ The Emerging Ser vice Econom y," Economic Commentary, Federal Reserve Bank of Cleve land, June 15, 1986. 2 Regional production indexes produced by the Federal Reserve Banks of Boston and Atlanta have been discontinued, primarily due to budget reductions. In 1984, O hio firms represented 6.3 percent of the nation’s manufacturing output, making O hio the third-largest manufacturing state, trailing only California (11.0 percent) and New York (7.4 percent) in manufacturing prominence.3 Manufacturing Output Index, 1982 = 100 SOURCE: Federal Reserve Bank o f Cleveland and Board o f G overnors o f the Federal Reserve System. FIGURE 2 Despite this size, the cyclical pat terns of O hio’s manufacturing output remain large lv unseen and are often thought to mirror national manufacturing trends. Yet, evidence from the OMI suggests that important differences exist between U.S. and O hio manufacturers, particularly within individual industries. In this article, we Distribution of Manufacturing Output by State, 1984 (ten largest manufacturing states, nominal dollars) Distribution o f Output State United States 1. California 2. New York 3. O H IO 4. Texas 5. Illinois 6. Michigan 7. Pennsylvania 8. N. Carolina 9. New Jersey 10. Indiana Value A dded (m illion s $ ) Share o f Nation Durable N ondurable (%)* (%) 983,560 __ 11.0 7.4 57.6 68.1 53.7 42.4 108,373 72,361 62,346 55,556 55,246 53,069 51,725 36,682 36,543 33,762 6.3 5.6 5.6 5.4 5.3 3.7 3.7 3.4 68.3 49.9 56.1 75.8 56.2 38.7 43-3 70.3 31.9 46.3 31.7 50.1 43.9 24.2 43.8 61.3 56.7 29.7 *Durable-goods manufacturing is defined to include SICs 24, 25, and 32-39. SOURCE: 1984 Annual Survey o f Manufactures, Bureau o f the Census. introduce the OMI and discuss the new perspec tive it provides of manufacturing trends in Ohio. I. A View of the Forest Manufacturing employment in O hio reached a peak of 1.4 m illion workers in March 1979. At that time, manufacturing industries employed more than 30 percent of the state’s workers. Since 1979, however, manufacturing employment in O hio has fallen by more than 20 percent. In recent months, it was roughly 1.1 m illion workers, or about 20 percent of O hio’s civilian work force (figure 1). As in the nation, O hio’s manufacturing sector has failed to register significant employ ment growth in nearly three years. However, because the relationship between employment and output is not constant over time, due to changes in productivity and to the substitution of capital for labor, inferences about the manufacturing sector drawn exclusively from a labor perspective can be misleading. Unlike employment, real manufac turing output in Ohio, as measured by the OMI, has been rising throughout most of the current economic expansion (figure 2). Between the recessionary trough occurring in the fourth quarter of 1982 and the fourth quarter of 1986, real m anu facturing output in the state rose 34.7 percent. Manufacturing output at the national level grew at a slower pace over the period, 30.4 percent.4 Differences between U.S. and O hio manufacturing output trends arise principally from two related sources. First, the level of real output per worker (labor productivity) and the growth rate of labor productivity are greater in O hio than in the rest of the country. Furthermore, the O hio manufacturing business cycle tends to be more sharp than the national cycle, a conse quence of the state’s concentration of durablegoods manufacturing. For example, 1984 census data show that O hio workers produced roughly 8 percent more real manufacturing output per worker than is produced nationally. Between 1982 and 1984, the rate of growth in labor productivity for O hio manufacturers was roughly 20 percent, compared with only a 16 percent gain for the nation.5 More- 3 Output estimates are based on value added. 4 comparable because of differences in methodology. How ever, The U .S . and Ohio manufacturing indexes m ay not be perfectly many of the data sources and the fundamental structure of the indexes are the same. 5 These productivity estimates are based on real value added per worker. Value added and employment data come from the Survey of Manufactures. Nominal value-added estimates were deflated using national price deflators supplied by the U .S . Department of Commerce. 2 1 over, evidence from the OMI indicates that O hio’s leading growth industries generally have above-average labor productivity. As a result, slightly slower rates of growth in total manufac turing employment since 1982 generated some what greater real manufacturing output gains for O hio manufacturers than for U.S. manufacturers. Distribution of the Ohio Manufacturing Sector by Industry, 1984 (durable-goods industries in CAPITALS) Industry Importance Industry (SIC)____________ Qhio share in To Ohio (% ) To U.S. ( % ) of U.S. ( % ) the U.S. 1. TRANSPORTATION 17.8 EQUIPMENT (37) 2. FABRICATED 12.6 METALS (34) 3. NONELECTRICAL 11.5 MACHINERY (35) 4. PRIMARY 9.7 METALS (33) 5. Chemicals and 8.9 Allied Products (28) 6. ELECTRICAL 8.8 MACHINERY (36) 7. Food and Kindred 7.7 Products (20) 8. Rubber and 5.5 Plastics (30) 9. Printing and 4.9 Publishing (27) 10. STONE, CLAY, 3.6 AND GLASS (32) 11. Paper and Allied 2.6 Products (26) 12. INSTRUMENTS 1.7 Remaining Manufacturers 4.7 11.6 9.7 3 6.9 11.6 1 11.4 6.4 3 4.3 14.3 1 9.6 5.9 5 11.2 4.9 5 10.0 4.9 6 3.5 10.0 1 6.8 4.6 6 2.8 8.1 2 4.2 4.0 8 4.1 2.6 17 13.6 2.2 — SOURCE: 1984 Annual Survey o f Manufactures, Bureau o f the Census. TABLE 2 O hio’s manufacturing recovery was also preceded by a contraction that occurred ear lier and was more severe than that experienced nationally. To illustrate, O hio’s last manufacturing recession may be more accurately viewed as a combination of two recessions. Between the first quarter of 1979 and the third quarter of 1980, manufacturing output in O hio declined by slightly over 15 percent—about three times the percentage drop felt at the national level (5.2 percent). O hio’s second manufacturing contrac tion began in the third quarter of 1981, and by the fourth quarter of 1982, manufacturing produc tion had fallen 12.6 percent, compared with a 10.7 percent decline over the same period for all U.S. manufacturers. The relatively sharp business cycle experienced by O hio manufacturers reflects the state’s industrial composition (table 1). In the latest survey year, 1984, durable-goods manufac turing represented 68.3 percent of the state’s total manufacturing output. O hio is not the most durable-goods-intensive state of the 10 largest manufacturing states— Michigan’s durable-goods share was 75.8 percent in 1984 and Indiana’s share was 70.3 percent. However, the relative size of durable-goods manufacturing is considerably greater in O hio than is the case nationally, where durable-goods manufacturing accounted for only 57.6 percent of the 1984 total. Michigan’s dependence on durablegoods production is primarily a consequence of the automobile industry’s dominance in that state (representing about 36 percent of its manufactur ing output in 1984), while O hio’s durable-goods sector is more broad-based. For example, in 1984, O hio’s manufacturing output was distributed among five important durable-goods and one nondurable-goods industry (table 2). The state’s largest manufacturing industry was transportation equipment, representing 17.8 percent of its over all manufacturing production, compared with a contribution of only 11.6 percent at the national level. Following transportation equipment were the fabricated metals (12.6 percent), nonelectrical machinery (11.5 percent), primary metals (9.7 percent), chemicals (8.9 percent), and electrical machinery (8.8 percent) industries. In 1984, O hio led all states in out put for two durable-goods industries, fabricated metals and primary metals, and for one nondurable-goods industry, rubber and plastics. In addition, O hio manufacturers were the second-leading producers of stone, clay, and glass products and the third-leading producers of transportation equipment and nonelectrical machinery, all durable-goods industries. Historically, durable-goods pro ducers have suffered more pronounced businesscycle swings than nondurable-goods producers; a phenomenon, it would seem, that is not yet clearly understood (figure 3)- One view is that changes in the economic climate, which are accompanied by fluctuations in income and interest rates, result in intertemporal substitutions by consumers. Because durable goods, by definition, involve a longer consumption horizon than nondurable goods, these intertemporal substitutions are more keenly felt in the consumer durables market. A possibly complementary view, from the perspective of the firm, is that changes in the desired capital stock, such as those arising from changes in consumer demand, generate exaggerated swings in net investment. This “acceleration principle” implies that the more “durable” the capital stock, the more pronounced Ohio Durable and Nondurable Goods Index, 1982 =100 generates roughly a 1.0 percent decrease in manufacturing output.6 Indeed, the 6 percent plunge in the value of the dollar between June and September 1986 was probably welcomed by O hio’s manufac turers, as the OMI showed five consecutive month ly advances between July and December 1986, and increased 2.3 percent in the final quarter, com pared with only a 0.8 percent increase nationally. From the broad perspective, then, O hio’s manufacturing economy seems to be char acterized by a rather pronounced cycle, resulting from the combined influence of a large concentra tion of durable-goods manufacturers and a relative ly high and growing level of productivity. II. A View of the Trees SOURCE: Federal Reserve Bank o f Cleveland. FIGURE 3 the production cycle for capital goods. Beyond its business-cycle implica tions, O hio’s industrial mix probably makes the state’s manufacturing sector more vulnerable to pressure from foreign rivals, and implies that O hio’s manufacturing economy is more sensitive to international trade fluctuations than is the national manufacturing economy. A recent analy sis of the impact of exchange-rate movements on manufacturing revealed that a 10 percent increase in the value of the dollar generates about a 0.8 percent decrease in U.S. manufacturing output, whereas in Ohio, a similar exchange-rate increase At the industry7level, differences between the O hio and national manufacturing economies are more striking. In some industries, the perfor mance of O hio’s manufacturers between 1979 and 1986 exceeded national growth rates, and in a few cases, such as chemicals and fabricated metals, O hio’s growth has been impressive. Other industries, including paper, printing, electrical machinery, and stone, clay, and glass manufactur ing, have lost ground relative to the rest of the country since 1979. It is not the intention of this analy sis to discuss each industry7in detail, and only the state’s largest industries have been singled out for comment. Industries that are not expressly consid ered in this section are presented in figures 4h through 4o at the end of the article. • Transportation Equipment Transportation Equipment Index, 1982 = 100 SOURCE: Federal Reserve Bank o f Cleveland and Board o f G overnors o f the Federal Reserve System. Transportation equipment manufacturing, tradi tionally a pivotal industry7in the national business cycle, was hit particularly hard by the recessions of the 1980s. The ensuing expansions, however, allowed transportation manufacturers in the U.S. and O hio to surpass the output peaks established in 1979 (figure 4a). Over the expansionary7period span ning the fourth quarter of 1982 and the fourth quarter of 1986, transportation equipment output in the U.S. grew 48.2 percent. Over the same period, this industry’s growth rate in O hio was 50.4 percent, making transportation equipment production one of O hio’s fastest-growing man ufacturing industries in recent years. Indeed, evi dence from the OMI suggests that transportation 6 See C B O Staff Working Paper, "The Dollar in Foreign Exchange and U .S . Industrial Production,” December 1985; and A m y Durrell, Philip Israilevich, and K .J . Kowalewski, "Will the Dollar's Decline Help Ohio Manufacturers?" Economic Commentary, Cleveland, August 15, 1986. Federal Reserve Bank of 2 3 Fabricated Metals Index, 1982 = 100 SOURCE. Federal Reserve Bank o f Cleveland and Board o f Governors o f the Federal Reserve System. FIGURE 24 4 B equipment production has generated about 25 per cent of the state’s manufacturing output growth since 1982 and may currently represent more than 20 percent of its manufacturing economy. There are a number of reasons that O hio’s transportation equipment producers have expanded rapidly since 1982. For one, motor ve hicle production, the fastest-growing component in the transportation field in this decade, repre sents a larger share of transportation equipment output in O hio (about 70 percent) than it does nationally (about 48 percent). It would seem that motor vehicle production also contributed to Nonelectrical Machinery Index, 1982 = 100 SOURCE: Federal Reserve Bank o f Cleveland and Board o f Governors o f the Federal Reserve System. O hio’s relatively severe decline in real transporta tion equipment output between 1979 and 1982. Despite some strength since 1983, production of aircraft, railroads, and ships changed little between 1980 and 1985. These industries are significantly less important to the state’s manufacturing economy than they are to the national economy. In addition, real output per worker in transportation equipment production is roughly 15 percent greater in O hio than in the U.S., and the rate of growth in labor productivity for transportation equipment workers between 1982 and 1984 was about 28 percent, compared with 19 percent nationally. Another contributing factor to O hio’s recovering transportation equipment industry7has been the establishment of a Japanese auto plant, and its supporting suppliers, in the state. Honda, which began producing in O hio in 1982, currently assembles more than 145,000 cars there annually, generating roughly $650 m illion in annual manufacturing output.7 • Fabricated Metals Fabricated metals has been a growth industry in O hio’s manufacturing economy (figure 4b). Although the state’s fabricated metals manufac turers experienced approximately the same con traction as national manufacturers did over the 16 quarters between 1979 first quarter and 1982 fourth quarter (-25.6 percent versus -26.5 percent nationally), the recovery of fabricated metals pro duction in O hio has been stronger than the pace set nationally (40.0 percent over the 16 quarters ending in 1986 fourth quarter, compared with 32.3 percent for the nation). Again, some of O hio’s improve ment in fabricated metals production can be traced to a decided productivity advantage for the state. In 1984, real output per worker in fabri cated metals was about 21 percent greater in O hio than in the U.S., and the state’s growth rate of productivity in this industry exceeded the U.S. rate (roughly 22 percent versus 14 percent). Industrial mix also appears to be a contributing factor to O hio’s success in the fabri cated metals area. About one-third of the state’s fabricated metals production occurs in the forging and stampings field, whereas nationally this indus try7represents only about 18 percent of the fabri- 7 These estimates assume domestic content of 50.0 percent, on an average 1985 new-car cost of $8,845. Not all of the U .S . content is captured in Ohio, as some domestic suppliers are located outside the state. See Michael F. Bryan and Michael W . Dvorak, “American A u to mobile Manufacturing: It's Turning Japanese," Economic Commentary, Federal Reserve Bank of Cleveland, March 1 ,1 9 8 6 . Primary Metals Index, 1982 = 100 1801--------- O hio manufacturers rely heavily on the produc tion of metalworking machinery, an industry dependent on durable-goods demand and one that has been under pressure in recent years from foreign competition. Approximately 20 percent of O hio’s nonelectrical machinery involves the pro duction of metalworking machinery, more than twice the national incidence. Surprisingly enough, the national nonelectrical machinery industry is heavily dom i nated by computer manufacturing, which gener ates roughly 25 percent of the nation’s nonelec trical machinery output, but which accounts for only about 7 percent of the nonelectrical machinery output in Ohio. Computer production, which set a blistering pace early in this decade, has slowed appreciably since 1984. • Primary Metals sol— I— I— I— i— I— I— l—-I 1979 1980 1981 1982 1983 1984 1985 1986 SOURCE: Federal Reserve Bank o f Cleveland and Board o f G overnors o f the Federal Reserve System. FIGURE 4D cated metals output. The forging and stampings industry generates much of its demand from pro duction of consumer durables, particularly motor vehicles which, as stated earlier, have been important contributors to the current economic expansion. At the national level, the fabricated metals industry has been dominated by the pro duction of structural metals, which are used primarily in construction—an industry that has not fared as well as consumer durables during the recovery to date. • Nonelectrical Machinery Although the recovery in O hio’s nonelectrical machinery industry' has been slightly greater than that experienced nationally (figure 4c), produc tion of nonelectrical machinery in the state suf fered a sharper decline during the recessions of 1980 to 1982. Between 1979 first quarter and 1982 fourth quarter, O hio nonelectrical machin ery production was off 27.8 percent versus a decline of only 8.6 percent nationally. In this industry, at least, differences in productivity and productivity growth rates are not a major factor in industrial growth rate differ ences between the U.S. and Ohio. Here, the differ ences in national and Ohio industry performance are probably related to the mix of industries within the nonelectrical machinery category. O hio is the largest producer of primary7metals in the nation, as a result of its heavy concentration of steel and iron makers. And, as is true nation ally, the performance in O hio’s primary metals industry7has failed to regain the ground lost since 1979 (figure 4d). Data from the OMI indicate that at year-end 1986, O hio primary7metals makers w7ere producing at only about 68 percent of their average 1979 output. O hio’s experience in the primary7 metals area has been virtually identical to the nation’s, even though real output per wrorker in this industry is apparently greater in O hio than in the U.S. (about 23 percent more in 1984). • Chemicals and Allied Products In the U.S., the chemicals and allied products industry7means drugs ( more than 22 percent compared with 5 percent in O hio), but in O hio it means soaps (34 percent versus 18 percent nationally). The patterns outlined by the OMI suggest that, despite similar performances between 1979 and 1985, O hio chemicals produc ers substantially outpaced the nation last year (figure 4e). During the current expansion (end ing in the fourth quarter of 1986), the growth rate of the chemicals industry7nationally w7as 28.5 per cent, w7hich is wrell below the 45.2 percent advance registered for Ohio. Differences in productivity between O hio and U.S. manufacturers are also influential in this industry7; real output per w7orker in O hio was 19 percent greater than for workers nationally, and the growth rate of productivity in O hio between 1982 and 1984 exceeded the nation’s (33 percent versus 25 percent). 25 Chemicals and Allied Products Index, 1982 = 100 ufacture of communications equipment. This com pares with only about a 12 percent share in Ohio. Moreover, electrical components used in the production of computers, namely semiconduc tors, are much more important to national electri cal machinery manufacturing than to manufactur ing in O hio (about 26 percent versus 9 percent). O hio’s electrical machinery7m anu facturing industry relies primarily on the manufac ture of appliances. Although the household appli ance industry has been relatively healthy in recent years, its growth pales in comparison to the gains felt in the communications and com pu ter fields. • Rubber and Plastics SOURCE: Federal Reserve Bank o f Cleveland and Board o f G overnors o f the Federal Reserve System. FIGURE 4 E • Electrical Machinery At the national level, electrical machinery7produc tion enjoyed a boom between 1982 fourth quar ter and 1984 third quarter because of an enor mous increase in the output of communications equipment and electronic components (figure 40- These industries manufacture products essen tial to the skyrocketing telecommunications field. But O hio’s experience in electronic equipment manufacturing has been unimpressive, rising only to its pre-recession levels. At the national level, one-third of the electrical machinery industry involves the man Electrical Machinery Index, 1982 = 100 SOURCE: Federal Reserve Bank o f Cleveland and Board o f G overnors o f the Federal Reserve System. Plastics has supplanted rubber as the dominant component of the rubber and plastics industry in Ohio, and the OMI appears to reflect this transi tion (figure 4g). The rubber and plastics industry7 has enjoyed growth in both O hio and the nation over the present expansion, but O hio’s expe rience has been more volatile. The sharp cycle here is probably a result of O hio’s rubber-makers, whose production follows the often-turbulent for tunes of the transportation equipment industry. O hio seems to be shedding its dependence on rubber production. In 1977, O hio’s rubber and plastics industry was dom i nated by rubber-makers (54 percent versus 46 percent in plastics). Yet, within six years the roles were reversed, as rubber-makers accounted for only 39 percent of the state’s output in the rubber and plastics industry. III. An Overview The OMI and its subindexes are a product of ongoing research at the Federal Reserve Bank of Cleveland. It is therefore important to emphasize that these indexes are experimental and may not be wholly7reliable from month to month, or within some industries. The structure of the indexes and the data used in their construction are subject to revisions. Future revisions may be especially large between 1984 and 1986, over which period the productivity assumptions were intentionally conservative. With these caveats noted, the pat terns traced by the index make sense in light of O hio’s manufacturing mix and differences in pro ductivity levels and growth rates. The state’s m anu facturing cycle tends to be sharper than that expe rienced at the national level. Industry-level data show7that O hio manufacturers are recovering the transportation equipment output lost in the last recession, as a result of the state’s active motor vehicles industry7. Rubber and Plastics Index, 1982 = 100 SOURCE: Federal Reserve Bank o f Cleveland and Board o f Governors o f the Federal Reserve System. FIGURE 4G Indeed, the demand for consumer durables in this decade probably accounts for much of the growth experienced by O hio manufacturers since 1982, such as that experienced by O hio’s fabri cated metals producers. In addition, many of these recover ing industries are characterized by relatively high and rising productivity levels, which in part explains why the growth of O hio manufacturing production since 1982 exceeds the national expe rience, despite slightly more modest gains in manufacturing employment. Unfortunately, not all manufactur ing industries in the state have improved their position relative to the rest of the country. O hio manufacturing growth in recent years appears to be most prominent in industries w7hose futures are regarded by many as uncertain. However, O hio has lost ground in manufacturing fields that are considered growth industries nationally, such as printing and publishing, and electrical machinery manufacturing. Technical Appendix — Methodology for the Ohio Manufacturing Index (O M I) A number of production index methodologies have been proposed. The procedure chosen for the construction of the O hio Manufacturing Index (O M I) involves a m inim um of time to produce and has been showTi to be relatively accurate for the Texas economy (see Fomby [1986]). The OMI is structurally similar to the regional produc tion indexes produced at other Federal Reserve Banks and is virtually identical to that produced by the Federal Reserve Bank of Atlanta (see Stroebel [1978]).1 We begin by assuming that O hio manufacturers are profit maximizers who operate in a competitive market. If we further assume that O hio manufacturers are subject to a two-factor (labor and capital) linear homogeneous produc tion function (constant returns to scale), we can use Euler’s theorem to show7that: (1) l ) + k where is manufacturing output measured by value added, and are the unit price of labor and capital inputs, respectively, and and are the industry’s employment of labor and capital. Equation 1 can be algebraically manipulated to yield the more complex, but eas ily estimable, time series: (2) ( ( + ( k ) ( t) for = where are the factor shares for labor (Z ) and capital ( ) inputs, are the output ratios for inputs in period and represents the level of inputs in period The O hio Manufacturing Index uses fixed shares of labor and capital, but allows for monthly productivity increases by a factor Specifically, the output ratios are adjusted monthly such that: (3) + C, where represents the number of months that have elapsed since the last survey of O hio manu facturers. The productivity factor is defined by: VA = (P L {P K \ VA PL PK L K VAt = P,L/VA) VA/L)t Lt P K/VA VA/K)t K, = X (S i Oiti i L,K, Si K Ol t t, it t. Cr Oi t = Oit n(\ n cf = (4) m r v * j i mI LVAo /‘ o J n), i<!> 4> w7here and o are tw7o survey years and is the monthly interval separating the tw7o surveys. Input productivity factors since 1984, for w7hich data do not yet exist, wrere assumed to be equal to the av erage productivity factor between 1978 and 1984.2 I The Sixth District Manufacturing Production Index uses man-hours to measure labor inputs, while the OM I uses employment levels. In addition, the Sixth District Index seasonally adjusts the computed indexes, while the OM I seasonally adjusts the factor inputs prior to index construction. 27 Percentage Share of Labor and Capital For Ohio Manufacturers Labor (% ) Capital (% ) Manufacturing 40.3 59.7 Durable-Goods Manufacturing Nondurable-Goods Manufacturing 44.0 31.9 56.0 68.1 Food and Kindred Products (20) Apparel and Other Textile Products (23) Lumber and W ood Products (24) Furniture and Fixtures (25) Paper and Allied Products (26) Printing and Publishing (27) Chemicals and Allied Products (28) Rubber and Miscellaneous Plastic Products (30) Stone, Clay, and Glass Products (32) Primary Metals Industries (33) Fabricated Metal Products (34) Machinery, Except Electrical (35) Electric and Electronic Equipment (36) Transportation Equipment (37) Instruments and Related Products (38) 24.9 75.1 43.2 44.0 46.2 46.1 41.5 56.8 56.0 53.8 53.9 58.5 19.7 80.3 45.2 54.9 43.2 43.8 45.5 50.1 56.8 56.2 54.5 49.9 38.0 40.9 62.0 59.1 44.6 55.4 Industry (SIC)________________________ ( n - 32), APPENDIX The fixed factor shares (5 ,) were estimated using Ohio manufacturing data from the 1984 Survey of Manufactures. The share of labor ( L) was calculated as the ratio of the total manufacturing payroll to the value added in manufacturing in nominal dollars. The share of capital ( was derived by: (5) The factor shares are reported in table 1 of this technical appendix. The output ratios were calculated for the survey years 1978, 1983, and 1984 and for the census year 1982. The labor output ratio ( is real value added to total employment. The cap ital output ratio ( is similarly constructed, using electric power consumption as a proxy for the employment of capital.3 S SK) SK = 1 - St. Ol) Ok) 2 (n - Description of the Data and Procedures SOURCE: 1984 Annual Survey o f Manufactures, Bureau o f the Census. TABLE 1 The OMI was produced for 15 twodigit SIC industries and for the durable-goods, nondurable-goods, and total manufacturing aggregates (appendix table 1). Five manufactur ing industries are not reported because of con straints on the data: tobacco products (21), textile mill products (22), petroleum and coal products (29), leather and leather products (31), and other miscellaneous manufacturing (39). Fortunately, these five industries are relatively small contribu tors to the O hio economy, representing only about 2 percent of this state’s value added in 1984. The OMI and components are available monthly 96) and quarterly both seasonally adjusted and nonseasonally adjusted. Index values are reported on a 1982 = 100 basis. • The O hio Manufacturing Index and the durable- and nondurable-goods aggre gates represent a summation of the industry-level indexes, weighted according to share of real value added in 1984. • O hio manufacturing value added and payroll data are available for the cen sus year 1982 and for the survey years 1978, 1983, and 1984. • Value added was deflated using national price deflators for these two-digit indus tries, supplied by the U.S. Department of Commerce. • Monthly employment data in O hio by two-digit industrial classifications were supplied by the U.S. Bureau of Labor Statistics and the O hio Bureau of Employment Services. • O hio electric power, measured in kilowatt-hours, is used as a proxy' for capital use. Electric power data were collected by twodigit SIC codes by the Data Services Department of the Federal Reserve Bank of Cleveland.4 The data include self-generated electric power. The monthly timing of electric power consumption data is not exact and tends to overlap between months. For this reason, electric power data are entered into the OMI as a three-month moving average. • The input series are indepen dently seasonally adjusted using the X-11 ARIMA adjustment procedure. In many industries, this period is associated with little or no growth in factor productivity. Consequently, this assumption may be unrea- listically low. Without firm data to the contrary, however, a conservative approach seemed appropriate. 3 Virtually all regional and national industrial production indexes employ electnc power data to approximate capital usage. See Moody (1974) for a justification of this procedure. 4 A short description of electrical consumption data sources used in this study is available from the authors upon request. H. Food Production Index I. Apparel and Other Textiles Index, 1982 = 100 Index, 1982 = 100 J. Lumber and Wood Products K. Furniture and Fixtures Index, 1982 = 100 Index, 1982 = 100 L. Paper and Allied Products M. Printing and Publishing Index, 1982 = 100 Index, 1982 = 100 SOURCE: Federal Reserve Bank o f Cleveland and Board o f G overnors o f the Federal Reserve System. N. Stone, Clay, and Glass O. Instruments and Related Products Index, 1982 = 100 Index, 1982 = 100 160 170 160 — — 140 140 120 J O h io • 120 — A\ N \ ~ _ 130 — _____ 130 _ /s _ / 150 / United States \ V "\ . 110 — / 100 — J 8 0 __ / O h io V A / ________ _— ■ United States \ -------- p \ 90 — \ v A jT / / 7 0 — __ / 60 — 90 ........... L ... 1 .........L ......... 1....... 1 1 ........._L...... . 50 1979 1980 1981 1982 1983 1984 1985 1986 ____ 1 1 ........1 SOURCE: Federal Reserve Bank o f Cleveland and Board o f Governors o f the Federal Reserve System. FIGURES 4N.0 Technical Appendix References Fomby, Thomas B. “A Comparison of Forecast ing Accuracies of Alternative Regional Pro duction Index Methodologies,” vol. 4, no. 2 (April 1986), pp. 177-186. Journal of Business and Economic Statistics, Moody, Carlisle E. “The Measurement of Capi tal Services by Electrical Energy7,” vol. 36, no. 1 (February71974), pp. 45-52. Oxford Bulletin of Economics and Statistics, Stroebel, F.R. “Sixth District Manufacturing Index, Technical Note and Statistical Supple ment,” Federal Reserve Bank of Atlanta, 1978. Sullivan, Brian P. "Manufacturing Capacity7New Texas Index Assesses Utilization,” Federal Reserve Bank of Dal las, September 1975. iness Review, Bus 1 ..........1 1 !... ..... 1979 1980 1981 1982 1983 1984 1985 1986 Economic Commentary Alternative Methods for Assessing Risk-Based Deposit-Insurance Premiums James B. Thomson 9/15/86 Monetarism and the M l Target William T. Gavin 10 / 1/8 6 Debt Growth and the Financial System John B. Carlson 10/15/86 Competition and Bank Profitability: Recent Evidence Gary Whalen 1 1 / 1/86 Is the Consumer Overextended? K.J. Kowalewski 11/15/86 Labor Cost Differentials: Causes and Consequences Randall W. Eberts and Joe A. Stone 12/ 1/86 The Thrift Industry: Reconstruction in Progress Thomas M. Buynak 6/ 1/86 The Emerging Service Economy Patricia E. Beeson and Michael F. Bryan 6/15/86 Domestic Nonfinancial Debt: After Three Years o f Monitoring John Carlson 7/1/86 Equity, Efficiency, and Mispriced Deposit Guarantees James B. Thomson 7/15/86 Target Zones for Exchange Rates? Owen F. Humpage and Nicholas V. Karamouzis 8/ 1/86 Will the Dollar’s Decline Help Ohio Manufacturers? Amy Durrell, Philip Israilevich, and KJ. Kowalewski 8/15/86 Implications o f a Tariff on Oil Imports Gerald H. Anderson and KJ. Kowalewski 9 / 1/86 The Changing Nature o f Our Financial Structure: Where Are W e Headed? Where Do W e Want To Go? Karen N. Horn 12/15/86 Loan-Quality Differences: Evidence from Ohio Banks Paul R. Watro 1/1/87 Is the U.S. Pension-Insurance System Going Broke? Thomas M. Buynak 1/15/87 Should W e Intervene in Exchange Markets? Owen F. Humpage 2/1/87 The Decline in U.S. Agricultural Exports Gerald H. Anderson 2/15/87 The Japanese Edge in Investment: The Financial Side William Osterberg 3/1/87 Debt-Deflation and Corporate Finance Jerome S. Fons 3/15/87 Requirements for Eliminating the Trade Deficit Owen F. Humpage 4/1/87 3 1 Economic Review 3 2 Quarter I 1986 The Impact of Regional Difference in Unionism on Employment Quarter III 1986 Exchange-Market Intervention: The Channels of Influence by Edward Montgomery by Owen F. Humpage The Changing Nature of Regional Wage Differ entials from 1975 to 1983 Comparing Inflation Expectations of Households and Economists: Is a Little Knowledge a Dangerous Thing? by Lorie D. Jackson Labor Market Conditions in Ohio Versus the Rest of the United States: 1973-1984 by James L Medoff by Michael F. Bryan and W illiam T. Gavin Aggressive Uses of Chapter 11 of the Federal Bankruptcy Code by Walker F. Todd Quarter II 1986 Metropolitan Wage Differentials: Can Cleveland Still Compete? by Randall W. Eberts and Joe A Stone The Effects of Supplemental Income and Labor Productivity on Metropolitan Labor Cost Differentials Quarter IV 1986 Disinflation, Equity Valuation, and Investor Rationality by Jerome S. Fons and W illiam P. Osterberg by Thomas F. Luce The Collapse in Gold Prices: A New Perspective Reducing Risk in Wire Transfer Systems by Eric Kades by EJ. Stevens “Don’t Panic”: A Primer on Airline Deregulation by Paul W. Bauer