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MAR 2 1 n c *mv pr l D ©rcrram wm w AN ECONOMIC SURVEY Federal Reserve Bank of Atlanta March 1 9 7 7 Federal Reserve Bank of Atlanta Federal Reserve Station Atlanta, Georgia 30303 Address Correction Requested New Tejsts of Banking MarketjLimits Seller Concentration in Banking Markets District Business Conditions Bulk Rate U.S. Postage PAI D Atlanta, Ga. Permit 292 FEATURES: Updating Agricultural Loan D a t a .................................... 31 Southeastern Statistics A summary of data showing the relative econom ic growth of the South has been com pletely revised and is now available. These statistics cover m ajor trends for the six states in the Sixth Federal Reserve District and for the eleven Southeastern states. Changes in Seller Concentration in Banking Markets by B. Frank King This is the third in a series of Federal Reserve Bank of Atlanta W orking Papers. Interested parties may have their name placed on a subscription list for future studies in the series. Both of the above publi cations are available free on request. Please address such requests to the Re search Departm ent, Federal Reserve Bank of Atlanta, Atlanta, Georgia 30303 and include a com plete address with Z IP code to ensure delivery. The Federal Reserve Bank of Atlanta has a promising new technique for obtain ing up-to-date inform a tion of bank loans for agriculture in the South east. Wheat Production to D e c lin e .................................... 37 W heat plantings are down and forecasted produc tion is off sharply. New Tests for Banking Market L i m i t s ......................................... 39 Am ended tests of cluster method and county line definitions support earlier conclusions. Changes in Seller Concentration in Banking M arke ts........................ 41 District Business C o n d itio n s ...................................4 4 Job gains and im proved incom e and consum er spending s h o w e d that the District's econom y strengthened. This infor mation does not include the im pact of the cold weather and natural gas shortage. Director of Research: Harry Brandt Editor: Teresa Wright Wiggins Graphics: Susan F. Pope, Eddie Lee, Jr. Monthly Review, Vol. LXII, No. 3. Free subscription and additional copies available upon request to the Research Department, Federal Reserve Bank of Atlanta, Atlanta, Georgia 30303. Material herein may be reprinted or abstracted, provided this Review, the Bank and the author are credited. Please provide this Bank's Research Department with a copy of any publication in which such material is reprinted. UPDATING AGRICULTURAL LOAIM DATA by Gene D. Sullivan The dearth of current data has always hampered up-to-date analyses of bank loans for agriculture in the Southeast. To hurdle this barrier, the Federal Reserve Bank of Atlanta has recently employed a potentially effective technique. This article explains this method and presents the results of its implementation. In the past, agricultural loan data from com m ercial banks in the Sixth Federal Reserve District have been available only from the sem i annual Report of Call or from irregular special surveys.1 M ore recently, beginning in March 1976, the quarterly Call Report includes agricultural loans. H ow ever, these data are not available for detailed analysis until five months or more after the date of the report. A w eekly report of 32 large com m ercial banks in this D istrict provides detailed inform ation, including that related to agri cultural loans. But these large banks only account for less than five percent (about $75 m illion) of the total volum e of agricultural loans extended by all com m ercial banks. As a result, this inform ation is a poor indicator of agricultural loan activity throughout the District. Every member bank reports data on total loans and deposits w e e k ly ; how ever, detailed categories of loans are not given. But the detailed information in each bank's Report of Call does allow calculation of the proportion or ratio of agricultural to total loans as of a specific Call Report date.2 W eekly reports by banks with relatively high proportions of agricultural loans should, we theorized, be useful current indicators of agricultural credit conditions. So w e identified 62 member banks with agricultural loans of at least 20 percent of total loan volum e, and we com piled w eekly reports of loans and deposits beginning in May 1975. Although 20 'Th e Sixth Federal Reserve D istrict includes all of Alabama, Florida and Georgia and parts of Louisiana, M ississippi and Tennessee. A g ric u ltu ra l loans refer to the sum of loans made to farmers and loans secured by farm real estate. •sTrend analysis of loan volum e for each group yielded the follow ing equations: YATU a = 11,341 + (211.50) YATI_62 = 9,516.3 + (186.83) A percent may seem like a low figure, it is not an accurate indication of the im portance of agricultural lending. By categorizing loans as strictly agricultural, many loans w hich can fall into other categories are disguised. For exam ple, banks make loans to merchants selling farm supplies and equipm ent and to purchas ers, processors and shippers of agricultural products. These are classified as business loans, but these businesses depend prim arily on local agricultural activity. W hen added to the farm loan volum e, the sum could easily account for more than half of all loans at such banks. A m ajority of the loans specifically identified as agricultural w ere made by banks with agricultural loan ratios of .15 or higher, according to our analysis. That list included 93 member banks, compared to 62 with ratios of .20 or higher. W e expected agricultural activity to exert less influence on the w eekly loan volum e reported by the low er ratio (.15) banks. Nevertheless, the advantages of a larger sample that had broader geographic coverage of the D istrict attracted us to explore its use. Data from the two bank groups (i.e., 62 banks and 93 banks) were compared to determ ine what differences were evident in loan and deposit trends during the period extending from May 28 through O ctober 8, 1975. Figure 1 compares the two groups of banks on the basis of the average loan volum e per bank. That average was approxim ately $2 m illion higher for the 93-bank group, showing that the larger banks had lower agricultural loan ratios. Nevertheless, movements in loan volum e from w eek to w eek were sim ilar.3 A 19.471 T (4.35) 14.635T (3.4419) R2 = .4854 R2 = .3634 where YA TU a and Y A T U 2 = w eekly average volum e of total loans of the 93- and 62-bank groups, respectively; T = the time variable, w hich takes the value of 1 during the first w eek and a successively higher number for each additional w eek through the 20 weeks of the test; R- = the adjusted coefficient of determ i nation, or the percent of the variation in average total loans explained by the time variable; Numbers in parentheses below the coef ficients are T-statistics. G enerally, T-values of 2.0 or higher signify statistically significant relationships among variables. The correlation between the w eekly loan volum e of the two groups was an extrem ely high .9539, where 1.0 represents perfect correlation. So, the group with the lower agricultural loan ratio covered more of the total loan volum e from a broader geographic area and retained the pattern of loan movement displayed by the sm aller sample. Figure 2 compares average total deposits between the two groups of banks. Average deposits of the 93-bank sample are approxi mately $3 m illion above the sm aller group, once again showing the larger size of the lower ratio banks.4 Relatively speaking, how ever, 'Trend analysis of total deposits yielded the follow ing equations: YATD»3 = 18,216 + 34.867T R2 = .6019 (237.77) (5.4520) YA TD ,!l. = 15,391 + 26.024T R2 = .5312 (222.96) a (4.5161) where YATD<t.s and Y A T D 02 represent total deposits per bank in thousands of dollars tor the 93-bank group and the 62-bank group, respectively, and w here all other variables are the same as described in footnote 3. DEPOSITS-AVERAGE PER BANK FOR GROUPS HAVING AGRICULTURAL LOAN RATIOS OF .15 OR ABOVE AND .20 OR ABOVE Mil. $ — .1 5 B an k G roup -20 «■» .2 0 B an k Group TOTAL DEPOSITS -1 8 fit I M l I J I I I I I I I J I I I A I I I S I I LOCATION OF SIXTH DISTRICT MEMBER BANKS WITH AGRICULTURAL LOAN RATIOS OF .15 OR ABOVE I O 1975 J there is less difference between their average deposits than between their average loan volum es. (The correlation of total deposits per bank of the two groups was .9839.) The 93 banks evidently had higher total loan-todeposit ratios than did the group of 62. Figure 2 also compares gross demand deposits between the two bank groups. There is considerably more movem ent from w eek to w eek when time deposits are netted out of the total. H ow ever, the pattern of movement appears more closely related between the two bank groups than is the total deposit series.5 "Trend analysis of gross demand deposits yielded the follow ing equations: Y A G D 93 = 6485.7 + 15.161T (90.630) (2.5378) YAGDo^ = 5578.1 + 12.698T (83.172) A (2.2681) A R" = .2226 R- = .1791 where YAGD93 and YAGDC2 represent gross demand deposits per bank for the 93- and 62-bank groups, respectively, and w here all other variables are the same as described in footnote 3. V The correlation of gross demand deposits between the two groups of banks is .9842. Thus, in the case of both loans and deposits, w e concluded that the selection could be expanded to include all member banks with agricultural loan ratios of .15 or higher, ensuring broader coverage of the agricultural area w hile giving up little of the agricultural character of the data obtained. In January 1976, we updated the selection of banks to reflect entries or exits from the list that reported agricultural loan ratios of .15 percent or higher in two out of the past three years (1973,1974 and 1975). A few banks were removed from the sample because the volum e of agricultural loans as a percentage of total loans fell below the acceptable level, but the number of new banks meeting the selection criteria more than offset the reduction. The new list included 96 m em ber banks. Figure 3 shows their dispersion throughout the Sixth District. W e compared tabulations of w eekly loan and deposit data reported by the 96-bank group with the year-ago levels since January 1976 (see Appendix table). W ere loan movements different from that of all member banks? Figure 4 shows total loan volum e of the 96-bank group plotted in index form and com pared with a sim ilar plot of total loans of all m ember banks.6 Evidence shows loan fiA trend analysis of total loan volum e yielded the follow ing equations: Y IN X meb = 99.972 (106.33) A R2 = .5170 Y IN X 96B = 99.403 + (151.59) R- = .9700 A .95120T + .040267T(4.8321) (4.6338) .12885T + .029291T2 (.93853) (4.8328) A where Y IN X meb a n d Y IN X 96B = the index of total loans of all member banks and of 96 sample member banks, respectively; T 2 = the squared term of the time variable; all other variables are the same as described in footnote 3. growth at the sample banks throughout the period from January 1975 through Decem ber of 1976. In contrast, the total loan volum e of all member banks declined initially and showed extrem ely slow growth during most of 1976. (Note the negative coefficient of the time variable in the equation estimating total loans of all member banks in footnote 6.) The total loan volum e of all member banks and of the 96-bank group had a correlation coefficient of — .0104, a relationship consistent with our expectations. Recession in the national econom y affected general loan activity in 1975, and sluggish growth in business loans lingered into 1976. However, agriculture in the District did not generally share the recession, and lending activity increased briskly, par ticularly during 1976, as agricultural production expanded. So, the 96-bank sample reveals unique behavior in loan volum e that is apparently related to continued vigorous agricultural activity in this District. How accurate has this new loan series been as an indicator of the actual agricultural lending activity reported by all banks? W e compared loan data com piled from the 96-bank sample with the agricultural loan volum e taken from semiannual Call Reports during the coincident period. Since there were only three relevant Call Reports, starting with Decem ber 1974, the comparison is lim ited. Figure 5 shows that total loans of the banks in agricultural areas are sim ilar to the total volume of agricultural loans of all com m ercial banks. The relationship seems closest to the changes in agricultural loans reported by non member banks. If that relationship w ere stable, the new series as an indicator w ould be more valuable since nonmembers extend most of the bank credit for agriculture in the Sixth District. H owever, it is still too early to tell just how reliable the indicator w ill be. W e attributed the increase in loans at sample banks to greater activity in agriculture. But, was that consistent with other agricultural lenders? Did lending activity of other agencies verify that there was brisk agricultural activity in this District? O ur research says yes. Federal Land Banks and production credit associations are cooperatively-owned agri cultural lending agencies that presently account for over 40 percent of the total volum e of AGRICULTURAL LOANS OF ALL COMMERCIAL BANKS COMPARED WITH TOTAL LOANS OF BANKS IN AGRICULTURAL AREAS B il. $ Re !■ - E m em ber ban ks NON-MEMBER BANKS l “AGRICULTURAL” BANKS Figure 6 LOANS OF FARMER COOPERATIVE LENDING AGENCIES AND TOTAL LOANS OF BANKS IN AGRICULTURAL AREAS Jan. 1975 = 100 -1 2 5 Farm Lending Agencies -11 5 •105 “Agricultural” Banks -95 /*l Dec. 31, 1974 June 30, 1975 Dec. 31, 1975 agricultural credit in Sixth D istrict states ($3 billion in 1974). Figure 6 compares the monthly loan volum e (in index form) of these agencies with that of the sample banks since January 1975. Month-to-month fluctuations in loan volum e between the two groups do not precisely co incide, and the loan pay-down period (fall and w inter months) results in a more extreme fluctuation in volum e out standing for cooperative lenders. Yet, for the period w e observed, the upward trend in loan volum e is sim ilar. The index of total loan volum e of these cooperative farm credit agencies and of the 96 member banks had a correlation coefficient of .8764. This is a relatively high figure and emphasizes that the growth in loan volum e at J I I I I I I I I I I I I I I I I I I I I I i J J 1975 DJ J J 1976 D the 96 sample banks, during the period when total loans of all member banks barely grew at all, reflected the behavior of total agricultural loan volum e within the District. W e w ill continue to make further com parisons to determ ine if this sim ilarity is true when conditions in the farm sector change. This new loan series promises to be a useful, quickly available indicator of agricultural credit flowing through the com m ercial banking sector of the Southeast. If it performs as w ell in the future as it has already, it could substantially reduce the lag in detailed knowledge of de velopm ents in bank agricultural lending that continues to exist even though banks have recently begun making quarterly reports. ■ SELECTED FINANCIAL DATA 96 SIXTH DISTRICT MEMBER BANKS WITH AGRICULTURAL LOAN RATIO OF .15 OR ABOVE (in thousands of dollars) ASSETS T o ta l L o ans______ G o v e rn m e n t O b lig a tio n s P e rc e n t C h a n g e D a te _________________ A m o u n t_______ Y r. A go 1-07-76 2-04-76 3-03-76 4-07-76 5-05-76 6-02-76 7-07-76 8-04-76 9-01-76 10-06-76 11-03-76 12-01-76* 1-05-77* NOTE: 1,231,877 (12,832) 1,225,553 (12,766) 1,240,584 (12,999) 1,258,675 (13,111) 1,263,799 (13,165) 1,278,896 (13,322) 1,296,249 (13,503) 1,286,916 (13,405) 1,309,647 (13,642) 1,364,655 (14,215) 1,373,331 (14,306) 1,364,914 (14,368) 1,405,238 (14,792) O th e r S e c u ritie s P e rc e n t C h a n g e Am ount P e rc e n t C h a n g e Y r. Ago_________ A m o u n t + 6.8 210,069 (2,188) + 6.3 215,587 (2,246) + 6.7 236,613 (2,498) + 7.2 249,915 (2,603) + 10.3 246,243 (2,565) + 10.3 240,941 (2,510) + 10.6 235,568 (2,454) + 8.9 240,826 (2,509) + 11.4 228,447 (2,380) + 13.9 237,116 (2,470) + 14.0 239,262 (2,492) + 14.7 233,367 (2,456) + 15.0 238,310 (2,509) + 27.4 + 27.2 + 37.6 + 35.6 + 31.6 + 28.6 + 25.3 + 26.5 + 15.2 + 11.4 + 13.2 + 13.1 + 16.8 Y r. A go 482,692 (5,028) 482,272 (5,024) 474,828 (4,975) 476,562 (4,964) 486,073 (5,063) 491,422 (5,119) 490,373 (5,108) 496,792 (5,174) 496,516 (5,172) 494,638 (5,152) 494,337 (5,149) 491,320 (5,172) 489,244 (5,150) Fed F u n d s S old P e rc e n t C h a n g e A m o u n t_______Y r. A go + 10.3 127,340 (1,326) + 8.8 115,045 (1,198) + 6.5 123,560 (1,287) + 6.2 128,200 (1,335) + 6.2 104,680 (1,090) + 5.4 96,375 (1,004) + 4.5 94,630 (986) + 6.0 67,665 (705) + 6.7 76,360 (795) + 6.6 122,412 (1,275) + 5.0 124,315 (1,295) + 4.8 114,575 (1,206) + 2.8 139,606 (1,470) + 6.8 - 4.0 - 0.5 - 7.0 +10.3 -10.7 -16.4 -38.4 -19.4 +17.9 +19.7 +23.7 + 9.9 N u m b e rs in p a re n th e s e s in d ic a te a v e ra g e p e r b a n k . L IA B IL IT IE S T o ta l D e p o s its G ross D e m a n d D e p o s its N et Dem and D ep o s its T im e & S a v in g s D e p o s its P e rc e n t C h a n g e D a te Am ount Y r. Ago A m ount Am ount 1-07-76 1,974,620 (20,569) 1,957,537 (20,391) 1,987,181 (20,700) 2,023,289 (21,076) 2,016,323 (21,003) 2,018,434 (21,025) 2,049,683 (21,351) 2,027,148 (21,116) 2,025,539 (21,099) 2,093,551 (21,808) 2,108,399 (21,962) 2,092,475 (22,026) 2,157,215 (22,708) + 9.6 740,001 (7,708) 713,706 (7,434) 727,663 (7,580) 741,013 (7,719) 724,270 (7,544) 721,111 (7,512) 735,801 (7,665) 707,841 (7,373) 700,028 (7,292) 734,532 (7,651) 741.447 (7.723) 742.719 (7.818) 786,630 (8,280) 636,455 (6,630) 612,133 (6,376) 622,219 (6,481) 642,282 (6,690) 629,101 (6,553) 621,641 (6,475) 630,368 (6,462) 605,254 (6,304) 608,979 (6,344) 633,923 (6,603) 637,327 (6,639) 638,421 (6,720) 668,027 (7,032) 2-04-76 3-03-76 4-07-76 5-05-76 6-02-76 7-07-76 8-04-76 9-01-76 10-06-76 11-03-76 12-01-76* 01-05-77* + 9.1 + 10.6 + 10.3 + 11.1 + 10.1 + 10.1 + 9.3 + 7.7 + 10.6 + 10.6 + 10.6 + 10.6 N O T E : N u m b e rs in p a re n th e s e s in d ic a te a v e ra g e p e r b a n k . *S a m p le in c lu d e d o n ly 9 5 b a n k s d u rin g re p o r tin g p e rio d . Am ount 1,234,619 (12,861) 1,243,831 (12,957) 1,259,518 (13,120) 1,282,276 (13,357) 1,292,053 (13,459) 1,297,323 (13,514) 1,313,882 (13,686) 1,319,307 (13,743) 1,325,511 (13,807) 1,359,019 (14,156) 1,366,952 (14,239) 1,349,756 (14,208) 1,370,585 (14,427) WINTER WHEAT ACREAGE AND PRODUCTION FOR U. S. AND SIXTH DISTRICT STATES Mil. Acres WHEAT PRODUCTION TO DECLINE by Gene D. Sullivan Farmers have reduced national w in ter w heat plantings by three percent, or nearly 1.9 m illion acres, from the year-ago level. In the District, acreage has remained relatively constant, with increases in Alabam a, Georgia and Louisiana nearly offsetting Tennessee's cutbacks in plantings. Total production is expected to be down sharply in the D istrict as w ell as in the nation in 1977, largely because of unfavorable grow ing conditions that w ill reduce yields and prevent the harvesting of much acreage as grain. W hy has acreage declined? Prices during the five-month period preceding the planting season w ere down an average of 13 percent from last year's level. Furtherm ore, they have continued to fall with each suceeding month ($2.39 per bushel in Decem ber versus $3.41 a year earlier), causing some growers to plan to harvest more acreage for forage rather than for grain. A sharp unforeseen reversal in wheat prices could cause additional acreage to be harvested for grain and mean an upward revision in wheat production by the end of the spring harvest season. Unless there is some m ajor failure in the w orld crop, how ever, it is more likely that even the production currently anticipated w ill contribute further to unused production, ex erting still more dow nward pressure on wheat prices. ■ ■ PRODUCTION 1975 Bil. Bushels 1976 1I Total area seeded for all purposes. 2/ Indicated December 1976 19772 WINTER WHEAT State or Area Area Seeded* for Crop of 1976 1977 1977 as % of 1976 (1,000 Acres) Production: Crop of of 1976 Crop of 19772 1977 as % of 1976 (1,000 Bushels) 200 210 105 3,375 3,150 Florida 30 30 100 660 390 59 Georgia 150 155 103 3,565 3,565 100 55 Alabama 93 65 70 108 1,155 630 Mississippi 220 220 100 5,220 4,840 93 Tennessee 405 373 92 12,395 8,952 72 Louisiana Total, Sixth District States 1,070 1,058 99 26,370 21,527 82 Total, U. S. 57,708 55,845 97 1,566,074 1,438,015 92 June-October Average Prices 1975 1976 ($ per bushel) $3.56 U. S. iTotal area seeded for all purposes ’ Indicated December 1, 1976 $3.08 1976 as % of 1975 87 NEW TESTS OF BANKING MARKET LIMITS by B. Frank King Bank regulatory agencies and the Departm ent of Justice are charged with preventing bank holding com pany acquisitions and mergers that w ill have adverse effects on com petition. To analyze probable com petitive effects of these transactions, relevant geographic and product markets must be defined. In spite of general agreement that a geographic market for a product is an area encompassing buyers and sellers whose pricing, output and purchase decisions are in some w ay insulated from the effects of actions of buyers and sellers outside the area, no w id ely accepted method of defining markets has evolved. An article reporting tests of a method of banking market definition based on clusters of overlapping prim ary service areas of banks appeared in this Review in June 1975.1 This comment reports amended tests of this method of defining banking markets. The results of the 1See Charles D. Salley, "Uniform Price and Banking Market Definition," M onthly Review , Federal Reserve Bank of Atlanta, 60: 86-93 (June 1975). amended tests support the earlier article's conclusions that the cluster method was not clearly superior to the sim pler method of using county lines to define banking markets and that Florida banking markets appear to be geographically small instead of being regional or statewide. In his 1975 study, Charles D. Salley deduced that prices and other measures of perform ance would tend to be more nearly uniform w ithin markets defined by the cluster method than among such markets. He used statistical tests comparing the variation of perform ance variables of banks w ithin Florida banking markets defined by use of the cluster method with variation of perform ance variables among markets defined by that method. The study found a significantly sm aller variation in perform ance measures w ithin market areas. This was interpreted as evidence that banking markets in Florida generally were local and that the cluster method provided relevant banking market definitions. Further tests compared variation in perform ance variables of banks w ithin pseudo-markets delineated sim ply by county lines and variation among markets delineated in the same way. The study found little reason to choose the cluster method over the county-line delineation. The statistical tests used in that study suffer from two flaws that make their results ques tionable. A set of 68 Florida banking markets defined by clusters of primary service areas was used as the criterion for grouping banks in the study's analyses of variance. This set of markets differs considerably from the d efin i tions of Florida banking markets made with the cluster method and most recently accepted by the Board of Governors of the Federal Reserve System in their decisions on the com petitive aspects of bank holding com panies' acquisition applications. For instance, in the 1975 study Broward County was divided into three m arkets: H ollyw ood, Fort Lauderdale-to-Deerfield Beach, and Deerfield Beach. Most recently, the Board of Governors has used market definitions that put banks in H o lly wood in the same market as those in Dade County and treated the area from Hollyw ood to the Palm Beach County line as one market. Another problem arose because the control group of 51 counties (used to test the relevance of arbitrarily defined markets versus those defined by the cluster method) overlapped considerably with the group of markets used to test the relevance of the cluster m ethod.2 Out-of-date market definitions and overlaps between test and control market groups cast some doubt on the earlier study's results. These may explain the lack of difference between the statistical results of tests, using markets defined by the cluster method and of tests using arbitrarily defined markets. Consequently, analyses of variance w ere performed using the new set of grouping criteria, based on the most current market definitions used by the Board. Counties that were also markets defined by the cluster method were removed from the control group, both for the markets used in the 1975 study and for the more current markets. This elim inated 15 counties from the control group used earlier and 20 counties from the current 2Counties are called arbitrarily defined markets because county boundaries were drawn without consideration of their relevance as boundaries, of banking markets. It may, and often does, turn out that by accident county boundaries approximate market boundaries. control group. Analyses of variance w ere run on the resulting groups.3 Results of the rerun come from three sets of com parisons: (1) The market definitions of the 1975 study versus more current market definitions; (2) the definitions of the 1975 study versus a control group of 36 Florida counties that are not also markets in that set of defini tions; and (3) current definitions versus a control group of 31 Florida counties that are not also markets in the current set of defini tions. Current market definitions generally are more relevant as grouping criteria than those used in the 1975 study. The probability that within-m arket variation in perform ance measures is equal to among-market variation is usually less when current definitions are used. Thus, more uniform perform ance characteris tics prevail in the currently defined markets. H owever, the differences are not great. Markets used in the 1975 study are clearly superior on the basis of uniform ity of prices paid for time and savings deposits— the set of perform ance variables in w hich the uniform ity expectation is most certainly justified. Results using cur rently accepted market groupings are superior on each of three rates of return measures and on two of the five efficiency and risk measures. Eliminating overlap between the markets defined by the cluster method and those defined by county lines does not change the reported results of the 1975 study. Neither set of markets defined by the cluster method was clearly superior to the county pseudo-markets. The cluster-method definitions showed better results on price measures and one of the three rates of return m easures; the control groups were superior for the other two rates of return measures. This rerun of the tests used in the 1975 study does not substantially change its con clusions. Banking markets in Florida still appear to be local. Local areas delineated as banking markets by outlining clusters of overlapping primary service areas seem to be relevant markets. H owever, other local areas delineated by county lines also appear to be relevant markets by the same criterion. ■ :!An analysis giving a fuller explanation of the differences between Salley's markets and current markets and a more extensive discussion of results and their implications is available on request. CHANGES IIMSELLER CONCENTRATION IN BANKING MARKETS by B. Frank King This article summarizes a staff analysis that may interest those in the economics and banking professions, as well as others. The analysis and conclusions are those of the author. Studies of this kind do not necessarily reflect the views of the Federal Reserve Bank. The complete study is available as part of a series of Federal Reserve Bank of Atlanta Working Papers. Single copies of this and of other studies are available upon request to the Research Department, Federal Reserve Bank of Atlanta, Atlanta, Georgia 30303. In making decisions on bank mergers and bank holding com pany acquisitions, banking agencies and the Departm ent of Justice often rely on im plicit assumptions about the deter minants of concentration in banking markets. Yet, the theoretical literature on this subject is sparse and the em pirical literature contains only one study. Even this study tests only a sim ple relationship in a very small sample of markets. Consequently, the study of concentra tion change in banking markets offers an op portunity to develop evidence relevant to policy decisions and, additionally, to provide evidence of concentration determinants across markets w here technology, entry barriers and basic firm -custom er relationships are sim ilar. This study develops hypotheses about deter minants of concentration in an industry charac terized by steeply declining cost at small scale, m inim um efficient output at small size, no diseconom ies of large-scale production, strict regulatory entry barriers, closeness of firm -custom er relationships and a w ider variety of output by large firm s than by small. The hypotheses developed are that relatively large banks become entrenched, i.e., that there is less concentration change in markets that begin with high concentration, that greater econom ic growth fosters changes in concentration and that the effects of growth in causing greater changes in concentration dim inish as the size of the market increases. The study tests these hypotheses over 262 markets in the states of the Sixth Federal Reserve District between 1960 and 1970. Each of these markets contained at least three banks in 1970. The model used was an ordinary least squares regression model. The test indicated that there was entrenchm ent, that growth mitigated the effects of entrenchm ent and that growth had more influence in small markets than in large. Attempts to relate the existence of different types of branching law to changes in concentration were not particularly successful. The test results provide further em pirical basis for regulators' tendency to look askance at concentration increasing mergers in markets with high concentration and poor growth prospects. H ow ever, they indicate that regulators should pay particular attention to this type of merger in large markets w here the concentration-reducing effects of growth tend to be weak. In addition, the results indicate that factors not tested in this study also have important influences on concentration change. These factors probably include profit rates, advertising intensity and variability of market growth. ■ SIXTH DISTRICT STATISTICS Seasonally Adjusted (All data are indexes, unless indicated otherw ise.) Latest Month 1976 One Month Ago Two Months Ago One Year Ago SIXTH D ISTR IC T Unemployment Rate (Percent of Work Force)*** . . . . Dec. Average Weekly Hours in Mfg. (H rs.) . Dec. INCOME AND SPEN DIN G Dec. Oct. Oct. Oct. 145.4 230 219 198 144.5 191 173 219 141.3 190 165 201 132.5 224 203 206 . Nov. . Nov. . Nov. 875 767 154.5 840 753 149.2 806 777 148.8 827 752 138.1 Dec. Dec. Dec. Dec. Dec. Dec. Dec. 107.4 98.5 99.1 97.8 96.4 95.3 99.4 106.9 104.4 97.7 89.3 91.4 98.8 104.7 110.1 96.3 110.2 82.1 105.4 107.6 114.1 118.7 107.2 119.3 61 107.1 97.8 98.3 97.5 95.7 94.1 99.3 106.6 102.3 97.0 89.0 90.5 97.7 103.3 108.9 95.7 110.0 81.9 105.2 107.8 114.4 118.0 107.4 119.1 58 106.7 97.3 98.5 97.4 95.4 94.4 99.1 106.3 103.8 95.8 88.5 88.6 96.7 103.2 108.4 93.3 109.7 82.0 104.6 107.8 113.6 117.7 106.9 118.6 56 106.4 96.6 98.6 97.3 96.0 96.6 97.0 105.5 103.3 94.1 87.6 90.9 92.4 96.7 104.1 91.8 109.4 86.4 102.7 106.6 114.1 117.4 106.6 117.6 51 Dec. 7.3 7.6 7.6 8.8 Dec. Dec. Nov. Nov. Nov. Nov. Nov. Nov. Nov. Nov. Nov. Nov. 3.9 40.5 174 205 144 73 85.3 149.7 148.0 132.2 145.5 121.1 148.2 126.8 164.2 152.6 167.7 133.4 140.8 105.8 107.3 165.9 262.2 148.5 4.1 40.6 310 166 451 70 85.2 149.1 147.9 130.3 143.8 121.1 148.2 129.3 165.1 151.4 166.4 134.5 134.9 104.8 107.4 166.1 262.6 147.3 3.8 40.3 174 168 179 75 87.5 150.4 148.7 127.3 147.5 123.9 147.4 130.4 166.8 153.7 164.6 134.3 143.6 104.9 108.9 164.6 262.8 152.8 4.5 40.8 151 132 166 74 89.0 147.5 149.0 133.7 145.0 133.0 144.2 130.2 160.8 144.7 146.0 140.4 147.0 102.2 114.5 147.0 231.8 141.1 a n k s ............................ . Dec. .............................................. . Dec. 287 226 284 224 282 222 272 229 a n k s ............................ ............................................. ........................................ 243 202 370 243 204 363 239 199 368 229 202 310 M anufacturing IncomeFarm Cash R eceipts . . . C r o p s ........................................ Livestock ............................. Instalm ent Credit at B an ks' . . . . One Month Ago 6.4 40.4 Two Months Ago One Year Ago 6.6 40.5 7.6 40.4 FIN A N CE AND BANKING 318 252 360 309 251 342 308 251 346 275 235 288 149.8 318 149.8 264 148.9 197 130.8 310 110.1 99.9 111.7 62.7 75 109.7 99.8 111.3 62.9 73 109.2 99.3 110.8 63.4 77 109.6 95.8 111.8 70.0 86 9.4 40.8 9.4 40.7 9.6 40.7 11.6 40.4 302 269 385 306 268 391 300 264 380 288 252 325 134.6 310 135.8 208 129.9 132 126.9 329 103.2 96.4 105.9 72.8 58 103.3 96.0 106.1 74.2 56 103.1 95.1 106.2 74.1 51 101.9 94.3 104.9 75.6 65 5.8 40.2 6.3 40.5 6.1 39.8 8.3 40.9 259 204 446 257 211 436 258 200 442 250 196 376 162.6 188 158.2 164 159.5 191 144.3 197 106.3 101.6 107.2 105.1 63 106.2 100.9 107.2 104.4 57 106.2 100.9 107.2 104.6 52 105.9 100.5 107.0 105.1 57 7.4 41.0 8.2 41.1 7.7 41.5 7.3 42.0 268 233 294 255 229 289 249 227 301 265 215 258 Manufacturing I n c o m e - ............................ Dec. Farm Cash R e c e ip t s ........................................Oct. 146.1 161 142.9 138 141.5 252 137.1 139 EM PLOYM ENT Nonfarm E m p lo y m e n t .................................. Dec. M anufacturing ..............................................Dec. N o n m a n u fa c tu rin g ...................................... Dec. C o n s t r u c t io n ..............................................Dec. Farm Employment ........................................Dec. 107.8 100.0 111.6 106.4 42 107.1 99.3 110.9 102.2 44 106.8 99.2 110.5 102.5 46 106.5 99.6 109.8 102.8 52 Member Bank L o a n s ........................................Dec. Member Bank D e p o s it s .................................. Dec. Bank D e b i t s * * ................................................... Nov. FLORIDA INCOME EM PLO YM ENT AND PRODUCTION Nonfarm E m p lo y m e n t ............................. . M anufacturing ........................................ . Nondurable G o o d s ............................. . F o o d ......................................................... . ............................................. . Te xtiles Apparel ............................................. . Paper ................................................... Printing and Publishing . . . C h e m i c a l s ........................................ . Durable G o o d s .................................. . Lb r., Woods Prods., Furn. & Fix, Stone, Clay, and G lass . . . . Prim ary M e t a l s ............................. • Fabricated M e t a l s ....................... • M a c h i n e r y ........................................ . Transportation Equipment . N o n m a n u fa c tu rin g .................................. . Construction .................................. Transportation ............................. . Trade ................................................... . F in ., in s., and real est. . . . S e r v i c e s ............................................. . Federal Government . . . . . State and Local Government • Farm E m p lo y m e n t........................................ . Unemployment Rate . (Percent of Work Force) . . . . Insured Unemployment (Percent of Cov. E m p .) ....................... . Average Weekly Hours in Mfg. (H rs.) . Construction C o n t r a c t s * ....................... . R e s id e n t ia l................................................... All O t h e r ......................................................... . Cotton C o n s u m p tio n * * ............................ . Petroleum Production*/** . M anufacturing Production . . . . Nondurable G o o d s .................................. . Food ................................................... . Te xtiles .............................................. Apparel .............................................. . Paper ................................................... . Printing and Publishing . . . C hem icals ........................................ Durable G o o d s ........................................ . Lumber and W o o d ....................... . Furniture and F ixtures . . . . Stone, C lay, and G lass . . . Prim ary M e t a l s ............................ . Fabricated M e t a ls ....................... . N onelectrical M achinery . . . Electrica l M achinery . . . . Transportation Equipment . Dec. Dec. Dec. Dec. Dec. Dec. Dec. Dec. Dec. Dec. Dec. Dec. Dec. Dec. Dec. Dec. Nov. Oct. Dec. Nov. Nov. Nov. Nov. Nov. Nov. FIN A N CE AND BANKING Loans* All Member B Large Ban ks Deposits* All Member B Large Banks Bank Deb its*/** Latest Month 1976 . Dec. . Dec. M anufacturing I n c o m e - ............................ Dec. Farm Cash R e c e ip t s ........................................Oct. EM PLOYM ENT Nonfarm E m p lo y m e n t ..................................Dec. M anufacturing ............................................. Dec. N o n m a n u fa c tu rin g ........................................Dec. C o n s t r u c t io n ............................................. Dec. Farm E m p lo y m e n t............................................. Dec. Unemployment Rate (Percent of Work Force)*** . . . . Dec. Average Weekly Hours in Mfg. (H rs.) . Dec. FIN AN CE AND BANKING Member Bank L o a n s ........................................Dec. Member Bank D e p o s i t s .............................Dec. Bank D e b i t s * * ................................................... Nov. GEORGIA INCOME M anufacturing I n c o m e * .............................Dec. Farm Cash R e c e ip t s ........................................Oct. EMPLOYM ENT Nonfarm E m p lo y m e n t .................................. Dec. M anufacturing ............................................. Dec. N o n m a n u fa c tu rin g ...................................... Dec. C o n s t r u c t io n ............................................. Dec. Farm Employment ........................................Dec. Unemployment Rate (Percent of Work F o r c e ) .......................Dec. Average W eekly Hours in Mfg. (H rs.) . Dec. FIN AN CE AND BANKING Member Bank L o a n s ........................................Dec. Member Bank D e p o s i t s .............................Dec. Bank D e b i t s * * ................................................... Nov. LOUISIANA INCOME M anufacturing Incom e2 .............................Dec. Farm Cash R e c e ip t s ........................................Oct. EM PLOYM ENT Nonfarm E m p lo y m e n t .................................. Dec. M anufacturing ..............................................Dec. N o n m a n u fa c tu rin g .......................................Dec. C o n s t r u c t io n ..............................................Dec. Farm Employment ........................................Dec. Unemployment Rate (Percent of Work Force)*** . . . . Dec. Average Weekly Hours in Mfg. (H rs.) . Dec. FIN AN CE AND BANKING Member Bank L o a n s * ..................................Dec. Member Bank D e p o s it s * ............................ Dec. Bank D eb its*/** ............................................. Nov. M IS SISS IP P I INCOME . Dec. 148.0 216 148.8 207 146.3 232 134.6 204 EM PLO YM ENT Nonfarm Employmenl M anufacturing Nonm anufacturing Construction . . . . . Dec. Dec. Dec. Dec. Dec. 111.3 100.8 116.0 121.6 58 110.9 100.2 115.6 121.7 56 1 lCf.6 100.1 115.3 121.7 54 108.8 99.4 113.0 123.7 64 Latest Month 1976 Unemployment Rate (Percent of Work F orce)*** . . . . Dec. Average W eekly Hours in Mfg. (H rs.) . Dec. One Month Ago Two Months Ago i ' ! 39.8 i 37 39.7 ,5 n8 7 40.7 290 286 M 270 One Month Ago Two Months Ago 105.6 96.2 110.4 84.0 61 104.8 94.7 109.9 82.9 59 104.1 94.0 109.3 81.9 60 104.6 94.9 109.5 92.1 63 280 235 321 281 234 306 284 235 320 276 228 254 Latest Month 1976 One Year Ago EM PLOYM ENT 6.0 39.9 FIN AN CE AND BANKING Member Bank L o a n s * ...................................Dec. Member Bank D e p o s it s * .............................Dec. Bank Deb its*/** ..............................................Nov. One Year Ago 296 250 306 Nonfarm E m p lo y m e n t .................................. Dec. M anufacturing ..............................................Dec. N o n m a n u fa c tu rin g .......................................Dec. C o n s t r u c t io n .............................................. Dec. Farm Employment ........................................Dec. Unemployment Rate (Percent of Work F o r c e ) ....................... Dec. Average Weekly Hours in Mfg. (H rs.) . Dec. 11^ Zb/ TE N N E S S E E M anufacturing I n c o m e - .............................Dec. Farm Cash R e c e ip t s ........................................Oct. 143.0 169 140. 0.5 18 86 134.9 227 *For Sixth D istrict area only; other totals for entire six states ***Season ally adjusted data supplied by state agencies. Note: 130.6 138 FIN A N CE AND BANKING Member Ban k L o a n s * ...................................Dec. Member Bank D e p o s it s * .............................Dec. Bank D ebits*/** ..............................................Nov. **D aily average basis fP re lim in a ry data r-Revised N.A. Not available All indexes: 1967 = 100, except mfg. income, employment, and retail sales, 1972 = 100. Sources: M anufacturing production estimated by this Bank; nonfarm. mfg. and nonmfg. emp.. mfg. income and hours, and unemp., U .S. Dept, of Labor and cooperating state agencies; cotton consum ption. U .S. Bureau of Census; construction contracts. F. W. Dodge Div.. McGraw-Hill Information System s Co.; pet. prod., U .S. Bureau of M ines; farm cash receipts and farm emp.. U .S.D.A. Other indexes based on data collected by this Bank. All indexes calculated by th is Bank. ’ Data have been bench marked and new trading day factors and seasonal factors computed using December 31, 1974 and June 30, 1975 Report of Condition data as bases. ♦Partially estim ated •M anufacturing Income data has been rebenchmarked to the most recent U .S. Dept, of Commerce m anufacturing income series. DEBITS TO DEMAND DEPOSIT ACCOUNTS Insured Commercial Banks in the Sixth District (In Thousands of Dollars) Percent Change Dec. 1976 Nov. 1976 Dec. 1975 Dec. 1976 From Nov. Dec. 1976 1975 Dothan Selma STANDARD METROPOLITAN STA TIST IC A L A R E A S2 Birm ingham . . Gadsden . . . . H untsville . . . M o b il e ...................... Montgomery . . Tuscaloosa . . . Dec. 1976 Nov. 1976 Dec. 1975 . . . . 298,979 118,472 272,646 109,453 237,989 105,324 + 10 + 8 +26 + 12 +21 +17 5,676,415 126,604 473,154 1,552,640 1,009,018 291,516 + 12 + 0 + 11 + 8 + 17 + 16 +21 + 10 + 16 + 9 +36 + 19 + 16 + 16 + 16 + 2 +28 + 8 Bradenton . . . Monroe County O c a l a ....................... St. Augustine . . St. Petersburg T a m p a ....................... 280,953 115,186 247,545 57,762 1,379,186 2,913,898 217,879 90,318 220,827 52,286 1,174,767 2,571,064 184,049 99,269 240,567 50,702 1,126,511 2,637,691 +29 +28 + 12 + 10 + 17 + 13 +53 + 16 + 3 + 14 +22 + 10 + 10 -1 3 + 2 + 11 + 12 + 8 1,138,451 572,896 971,905r 510,010 1,013,432 511,906 + 17 + 12 + 12 + 12 + 11 + 10 Athens ....................... Brunsw ick . . . D a lt o n ....................... Elberton . . . . G ainesville . . . G r i f f i n ....................... LaG range . . . Newnan . . . . R o m e ....................... Valdosta . . . . 219,543 138,477 259,775 39,082 225,853 99,610 52,816 67,704 202,946 147,338 192,575 114,914 231,545 33,244 210,484 84,034 48,814 63,910 175,089 125,762 195,852 135,800 204,612 39,837 195,302 81,675 42,908 68,776 280,258 118,780 + 14 +21 + 12 + 18 + 7 + 19 + 8 + 6 + 16 + 17 + 12 + 2 +27 - 2 + 16 +22 +23 - 2 -28 +24 + 12 + 4 +22 + 14 + 14 +14 + 15 +15 - 5 +11 Abbeville . Bunkie . Hammond New Iberia Plaquem ine Thibodaux . . . . . . 27,836 17,939 103,502 112,836 34,555 72,258 20,420 21,272 104,192 lll ,0 1 7 r 31,268 70,907 23,826 18,009 97,395 109,923 21,194 70,079 +36 -1 6 - 1 + 2 +11 + 2 +17 - 0 + 6 + 3 +63 + 3 +10 - 8 - 2 +14 + 1 + 0 Hattiesburg . . L a u r e l ....................... Meridian . . . . Natchez . . . . PascagoulaMoss Point . . Vicksburg . . . Yazoo City . . . 180,731 96,458 157,143 70,573 172,154 91,009 144,026 76,510 173,481 92,259 144,711 72,156 + + + - + + + - 5 9 2 +12 +14 + 9 +14 174,465 103,985 63,223 174,686 113,104 50,400 175,709 100,178 59,556 - 0 - 8 +25 - 1 + 4 + 6 + 0 +17 + 0 B ristol* . . . . Johnson City . . Kingsport . . . 305,240 176,154 475,451 284,706 161,258 416,212 162,396 188,652 406,064 + 7 + 9 + 14 +88 - 7 + 17 +62 - 0 . . 128,858,415 110,962,136r 107,918,789r + 16 + 19 +15 13,871,145r 34,029,940r 34,247,723 ll,5 2 0 ,6 4 5 r 4,383,771r 12,908,912r +23 +20 +10 +23 +16 +15 +17 +21 + 14 2,466,319r 419,714 298,322 5,886,403 2,471,944 410,262 284,158 5,851,607 +40 +23 + 18 +28 +40 +26 +24 +29 +30 + 5 + 11 +25 536,160 13,108,452 2,404,577 847,387 636,267 920,862 5,767,211 1,642,780 442,791 9,523,345r 2,121,914 711,472 499,458 1,258,839 4,851,274 1,328,669 707,964 9,365,594 2,070,209 800,688 685,291 998,707 4,967,292 1,268,165 +21 +38 + 13 + 19 +27 -2 7 + 19 +24 -2 4 +40 + 16 + 6 - 7 - 8 + 16 +30 - 1 +21 +21 +25 - 5 + 4 + 9 + 11 Albany . . . . Atlanta . . . . Augusta . . . . Columbus . . . Macon ....................... Savannah . . . 259,358 27,873,781 970,714 648,917 969,642 1,526,590 238,166 25,213,287 881,072 567,114 846,471 1,403,835 229,296 23,685,771 654,963 529,256 881,400r 1,292,972 + 9 + 11 + 10 + 14 + 15 + 9 + 13 + 18 +48 +23 + 10 + 18 + 9 + 16 +23 + 13 + 3 +34 402,012 2,460,704 556,967 462,745 6,908,735 380,967 2,021,575 462,626 391,179 6,312,605 345,818 2,035,494 458,869 318,366 6,356,194 + 6 +22 +20 + 18 + 9 + 16 +21 +21 +45 + 9 + 12 + 3 + 14 + 19 + 9 Biloxi-Gulfport Jackson . . . . 423,028 2,409,785 366,266 2,212,785r 344,569 2,020,269 + 15 + 9 +23 + 19 +21 +22 Chattanooga . . Knoxville . . . N ashville . . . . 1,574,771 1,996,080 6,308,486 1,321,009 1,771,730 5,580,338 1,338,255 1,702,443 4,973,005 + 19 + 13 + 13 + 18 + 17 +27 + 6 + 11 + 15 181,684 161,519 142,697 + 12 +27 + 17 OTHER C E N T ER S A n n is t o n ....................... Year to date 12 mos. 1976 from 1975 6,122,973r 138,351 497,730 1,557,993 1,173,393 299,446 3,464,586 515,221 352,853 7,539,416 . . . . . . . . . . Dec. 1976 From Nov. Dec. 1976 1975 6,885,558 138,707 550,798 1,690,351 1,370,107 347,286 Bartow-LakelandWinter Haven . Daytona Beach Ft. LauderdaleHollywood . . Ft. Myers . . . G ainesville . . . Ja ckso n ville . . MelbourneTitusville-Cocoa M i a m i ....................... Orlando . . . . Pensacola . . . Sarasota . . . . Tallahassee . . Tampa-St. Pete . W. Palm Beach . Alexandria . Baton Rouge Lafayette . . Lake C harles New Orleans Percent Change Year to date 12 mos. 1976 from 1975 •Changes reflect structural changes in series. 'D is tric t portion only. 2Conforms to SMSA definitions as of December 31, 1972. . . . . . . . . . . . . 1ST R IC T TOTAL Alabama . . . . 15,697,091 F l o r i d a ..................... 42,286,511 Georgia . . . . . 37,729,712 Lo uisian a1 . . . . 13,938,323 M ississip p i1 . . 4,795,779 Tennessee1 . . . . 14,410,999 12,776,869 35,189,300 32,561,941r 11,360,325 4,137,737 11,892,617 5 6 9 8 + 13 +24 + 10 +21 + 9 + 12 4 +20 +16 + 9 +19 DISTRICT BUSIN ESS CONDITIONS 1972=100 - Seas. Adj. Nonfarm Employment Unemployment Rate* *Seas. adj. figure; not an index Latest plotting: December, except mfg. prod., constr. contracts and retail sales, November, and farm cash receipts, October. Tha District's economy continued to strengthen in December. Job gains brightened the labor market. There was more improvement in income and consumer spending. Construction contracts rose in value. Bank lending increased, completing a strong fourth-quarter advance. Farmers received higher prices but suffered severe damage from cold weather. January's harsh weather and the resulting gas shortage adversely affected much of the region. Cold weather in January has reduced production and employment in major industries in Georgia, Alabama and Tennessee. Nonfarm jobs grew mod erately in December and there was a substantial de line in the unemployment rate. Large employment gains in machinery, metal, apparel and chemical industries boosted the manufacturing sector; non manufacturing jobs grew at a slower pace because trade and federal government job declines offset job gains in services, construction, utilities, and state and local government. Factory hours dropped. Manufacturing income continued to rise in De cember but will probably show a decline in Jan uary. Retail sales growth spurted in November; the increase over a year ago was nearly 12 percent. Department store sales have made equally strong yearly gains, capped by three consecutive monthly increases through November. Weakness in auto registrations may reflect shortages of more popular models. Extensions of commercial bank consumer installment credit continued to rise. Construction contract values increased moderate ly in December. A sharp gain in nonresidential con tracts concentrated in Florida overcame moderate but widespread weakness in the residential sector. All states in the region recorded declines in resi Note: dential contracts. Deposit inflows and new loan commitments were up sharply at savings and loan associations in January, w hile mortgage rates eased. Bank lending rose in December, completing a strong fourth-quarter advance. Large bank loans to businesses in durable goods manufacturing, w hole sale and retail trade, and services were especially strong. Member banks moderately reduced their holdings of U. S. government securities during De cember but left their tax-exempt holdings un changed. Deposit growth remained strong as a large seasonal demand deposit inflow was accom panied by a moderate advance in time and savings deposits. Prices received by farmers rose slightly in De cember, and preliminary data indicate a large in crease in January. Prices increased for broilers, eggs and cotton, as did prices for citrus fruit and vegetables, reflecting the impact of subfreezing temperatures in Florida. Demands for livestock feeds are up sharply because of the unusually harsh winter. Hay shortages may force marketings of livestock and lower prices for grass-fed cattle. Farm cash receipts during November were higher than the year-ago pace. Data on which statements are based have been adjusted whenever possible to eliminate seasonal influences.
@FedFRASER