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In T h i s Is s u e : W a g e s a n d U n e m p lo y m e n t : A State A n a ly sis o f th e P h illip s C u rv e B a n k in g N o te s : B u sin e ss L o a n s in R e c e s s io n D is tric t B u sin e ss C o n d it io n s Fed eral R eserve B a n k O f A tla n ta Fe d e ra l R e s e rve S ta tio n A tla n ta , G e o rg ia 3 0 3 0 3 B U L K R A T E U .S . P O S T A G E - PAID A t la n t a . G e o r g ia A d d re s s C o rre c tio n R e q u e s te d P e r m it N o . 2 9 2 W a g e s a n d U n e m p lo y m e n t : A S t a t e A n a ly s is o f t h e P h illip s C u r v e b y W illia m D . T o a l The concept of a market is basic to our economic system. Demands for goods and services meet suppliers of those goods and services and exchange takes place. The marketplace determines the price at which this exchange occurs— the price that leaves the market with no glut or shortage. W e generally consider the labor market to be one of the more efficiently functioning markets. Here demands for labor meet suppliers; and a wage rate, the price of labor services, is determined when workers are hired. When supply of labor exceeds demand, wages tend to rise more slowly or they may even decline; when labor is scarce, wage rates tend to increase. The most reflective measure of labor supply relative to demand is the unemployment rate. The higher this rate, the greater the quantity of labor supplied exceeds the quantity demanded. Consequently, an efficiently operating labor market would suggest that (other things equal) higher unemployment rates would correspond with slower wage growth and lower rates with faster wage rises.1 This theoretical concept of a labor market has been tested in several countries. The first test was undertaken in the late Fifties in Great Britain by a British economist, A. W. Phillips; this wage/unemployment rate relationship was publicized in the Sixties as the Phillips Curve.2 (Nominal wages instead of real wages were used, however.) Phillips found close ties between the rate of change of wages and the unemployment rate; that is, greater increases in Note: This article presents a preliminary report on the author’s study of subnational wage equations still in progress. Joseph E. Rossman, Jr., was helpful with the early development of the analysis. ’Of c o u rs e la b o r m a rk e ts do h a v e im p e rfe c tio n s a n d a re n o t c o m p le te ly h o m o g e n e o u s. L ab o r d iffe rs by sk ills, e d u c a tio n , in n a te a b ility , a n d o th e r fa c to rs . C o n se q u e n tly , w e sh o u ld n o t e x p e c t th e re la tio n s h ip b e tw e e n w a g e c h a n g e s a n d th e u n e m p lo y m e n t ra te to b e p e rfe c t. S till, th e r e s h o u ld b e s o m e n e g a tiv e re la tio n s h ip b e tw e e n w a g e c h a n g e s a n d th e u n e m p lo y m e n t ra te . 2A. W. P h illip s, “T h e R e la tio n s h ip B etw e e n U n e m p lo y m e n t a n d th e R a te of C h a n g e of M oney W age R a te s in th e U n ite d K ingdom , 1862-1 9 5 7 ,” E c o n o m ic a , 25 (1958), pp. 283-308. Monthly Review, Vol. LX, No. 7. 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. 16 0 JU 1 7 , MONTHLY REVIEW LY 9 5 wages corresponded with lower unemployment rates and conversely. This relationship was extended to the U. S.; studies conducted here in the early and late Sixties roughly verified this type of relationship in this economy. More recently, however, the traditional relation ship between wages and unemployment rates has gone awry. The growth in nominal wages has continued to escalate though the unemployment rate has risen in the late Sixties and early Seventies. In economic terms, we have strayed from the Phillips Curve, or the Phillips Curve has shifted. The question is "W hy?" Recent articles have given us a variety of answers to this question. Changes in labor market composition and expectations about the future rate of inflation are two of the more frequently mentioned explanations. Another group of economists doubts whether, in the long run, any Phillips Curve exists at all; they believe in a natural rate of unemployment from which any deviation will lead to accelerating wage increases or decreases. Another explanation, not precluding but perhaps complementing these others, is the geographic dispersion of labor markets and unemployment. If more than one labor market exists and these markets differ, then changes in the dispersion of unemployment across these markets could shift the national Phillips Curve. Subnational Labor Markets People may migrate from one region to another in response to job availabilities or regional wage differences. This response is not immediate, however, partly because migration costs are involved. Labor markets would seem to exist, then, at a subnational level, whether regional, state, substate, metropolitan area, or city.3 This article will examine labor markets differentiated by geographic divisions to see if supply and demand forces yield a relationship between wage changes and unemployment rates. The most likely geographic division seems to be by state. Labor markets are probably influenced by right-to-work, unemployment insurance, and other state laws that vary widely. In practical terms, data are readily available by state.4 This study seeks answers to the following questions: Do wage-unemployment rate relationships exist at a state level? Do they differ among states and, if so, why? 3O ne c o u ld a rg u e th e p ro p e r g e o g ra p h ic a l re fe re n c e s fo r la b o r m a rk e ts a t s o m e le n g th . S ee, fo r e x a m p le , Lloyd C. R eynolds, T he S tr u c tu re of L ab o r M ark ets (N ew York: H a rp e r & B ro th e rs, 1951). ‘O th e r s tu d ie s h av e e x a m in e d d iff e r e n t ty p e s of lab o r m a rk e ts . S ee, fo r e x a m p le , R ich ard C. M arcis a n d J. David R eed, “J o in t E s tim a tio n of th e D e te rm in a n ts of W ag es in S u b re g io n a l L ab o r M ark ets in th e U n ite d S ta te s : 1 9 6 1 -1 9 7 2 ,” J o u rn a l of R eg io n al S c ie n c e , Vol. 14, No. 2, 1974. FEDERAL RESERVE BANK O ATLANTA F Results In testing these questions, we found a significant relationship between wage changes and unemploy ment rates for fifteen of the forty-five states examined.5 The relationships in these fifteen states fitted the conventional theory except in one case; that is, high unemployment rates were associated with slow growth in wages and low rates with faster wage growth. (Only manufacturing wages were used in these tests. Other tests were run on a more inclusive wage measure, estimated nonfarm wage changes; the results were generally the same. The method used in estimating these relationships and those developed below is described in the Appendix.) In most of these fifteen states, manufacturing wages were more sensitive to unemployment rate changes than when a national Phillips relationship was tested. The only exceptions were Florida and Kansas. To examine whether state wage changes were more responsive to national labor market conditions than to state markets, we substituted the national unemployment rate for each state's unemployment rate. Only five states showed a significant relationship between manufacturing wage changes and national unemployment rates. These results generally support the view that state labor markets are more important than national labor markets in explaining state wage changes. And these results also suggest that subnational labor markets do exist. When a lagged inflation variable was added to each state's wage equation, it had a significant impact on wage changes in over two-thirds of the states examined. This inflation variable was inserted to see the impact of expectations about inflation on wages and the extent to which workers adjust wages to past inflation. The state unemploy ment rate remained a significant influence on wages in most of those cases where it had been before adding the inflation variable. However, this extended version of the Phillips relationship improved substantially the wage equation's explanatory power; on average, over 50 percent of wage changes were explained by this extended equation.6 “A laska, H aw aii, N orth D akota, New H a m p sh ire, a n d V e rm o n t w e re o m itte d b e c a u s e d a ta fo r th e s e s ta t e s w e re n o t a v a ila b le . Only 12 o b s e rv a tio n s w e re a v a ila b le , c o v erin g th e y e a rs from 1951 to 1972. eA v a ria tio n of th e P h illip s re la tio n s h ip in c lu d e s c h a n g e s in th e u n e m p lo y m e n t ra te a s a m e a s u re of e x p e c ta tio n s of c h a n g e s in la b o r m a rk e t c o n d itio n s a lo n g w ith th e u n e m p lo y m e n t ra te its e lf a n d a n in flatio n v a ria b le . W hen th is v a ria b le w a s in c lu d e d in th e s ta t e e q u a tio n s , it w a s s ig n ific a n t fo r th ir te e n s ta te s . T h is v a ria tio n of t h e P h illip s re la tio n s h ip w ill n o t b e c o n s id e re d f u r th e r here, how ever. 17 0 State Variations The most surprising result of this study is how much these Phillips Curve relationships vary among states. In general, the response of wages to unemployment rates was most significant and strongest among the southeastern states (see Map 1). Both the explanatory power and response of wages to unemployment rates appear, according to these tests, to be larger than the response and explanatory power of national wages to the national unemployment rate. The extent of wage changes explained by the unemployment rate in southeastern states was as much as 79 percent in North Carolina and as low as 19 percent in Mississippi and Florida. Adding the inflation factor, the state unemploy ment rate remained significant in explaining wage changes for many southeastern states. In this case, the inflation factor also appeared to be an important influence on wages for some southeastern states. But manufacturing wages seemed to be most sensitive to past price changes in the heavily industrialized midwestern and northeastern states. In fact, the results in some of these states 18 0 suggest that workers are able not only to catch up with but run ahead of inflation. W hy Do State Wage Equations Differ? That Phillips relationships on the state level are not the same as the national Phillips Curve (if one exists) is not surprising. On a subnational level, other options than wage changes are available for labor market adjustment.7 Labor migration provides one such alternative; capital flows, another. Allowing for time, capital will move from areas of labor shortage to areas of surplus in order to maximize its return. The movement of labor and capital across international borders is more difficult than across state borders and is less likely to allow labor market adjustment. Consequently, one might expect migration of labor and capital across state borders to dampen the impact of unemployment rates on state wage 7S e e David E. K aun, "W age A d ju s tm e n t in th e A p p a lac h ia n S ta te s ,” S o u th e rn E co n o m ic Jo u rn a l, Vol. XXXII (O cto b er 1965), p. 227. JU 1 7 , MONTHLY REVIEW LY 9 5 changes. Actually, the test results suggest the opposite. The estimated equations, when significant, show wages responding more sharply to unemployment rates at the state level than the national. However, the lack of a significant relationship between nominal wages and the unemployment rate in two-thirds of the states examined may be related to adjustments through these long-run migration flows. Besides the influence of these long-run ad justments, wage/unemployment rate relationships may vary among states for many other reasons.8 Geography, as noted earlier, seems to be an important factor. But why? One reason seems to be that labor supply conditions vary among states. Labor supply is influenced by many factors, includ ing the size and location of a state. A state bordering on more populous areas may have access to a large outside pool of workers; the wage response to any change in unemployment may then be small because workers can move more easily in or out of the state. Other factors affecting labor supply, particularly manufacturing labor, are the proportions of the labor force in agriculture and low-wage nonmanufacturing industries. The larger these pro portions, the less wages will probably respond to labor market changes. Instead, workers might move between the nonmanufacturing and manufacturing sectors in response to varying labor market condi tions. Hence, the size of a state's agricultural and nonmanufacturing sectors may affect the extent to which manufacturing wages respond to its unem ployment rate. The degree of structural unemployment in a state may also influence labor supply and, therefore, the wage/unemployment rate relationship.9 Structural unemployment is not easily distinguishable from that resulting from education, lack of skills, and other demographic or industry-mix characteristics. However, the more structural unemployment, the more overstated is the employable labor supply, re sulting in sharper wage increases for any unemploy ment rate. Finally, many have argued that the degree of unionization among states can influence labor supply. Consequently, differences in the extent of unionization among states may cause differences in state labor supply. 8S o m e of th e s e fo rc e s a re s k e tc h e d by W illiam P. A lb re c h t, Jr., in “T h e R e la tio n s h ip B etw een W age C h a n g e s a n d U n e m p lo y m e n t in M etro p o lita n a n d In d u s tria l L abor M ark e ts,” Yale E co n o m ic E ssay s, Vol. 6 (1969), pp. 315-320. . . s o m e g ro u p s h av e p e rs is te n tly h ig h u n e m p lo y m e n t th a t te n d s to b e of lo n g d u ra tio n . . . . S u c h u n e m p lo y m e n t is o fte n re fe rre d to a s s tru c tu ra l. . . . S tru c tu ra l u n e m p lo y m e n t r e p re s e n ts im p e rfe c t la b o r m a rk e t a d ju s tm e n t a s a re s u lt of s o m e b a rrie r to t h e m o b ility of r e s o u rc e s .” E co n o m ic R e p o rt of th e P r e s id e n t, U n ited S ta te s G o v e rn m e n t P rin tin g O ffice, W ash in g to n : 1975, pp. 89-90. FEDERAL RESERVE BANK OF ATLANTA Differences in labor demand related to differences in industry mix can also affect the wage/unemploy ment rate relationship. Those states with more industries producing for a national market may show wage changes more closely related to national than state unemployment rates. At the same time, industry mix and unionization should show much the same influence since certain industries, such as steel and automaking, are more likely to be union ized than others. Consequently, it may be impossiable to separate the impact of unionization and industry mix on state Phillips Curve relationships. The findings of this study do lead us to some tentative observations, however, about geographic differences in Phillips Curves. The southeastern states, in general, have the most significant and sensitive wage/unemployment rate relationships. Beyond geographic location, what other important influences explain this? Are there any characteristics distinctive to this region that may account for this clustering? The one striking feature of both southeastern states and those other states where wage/unem ployment rate relationships were significant was that they were generally the least unionized. O f the fifteen states with significant wage/unemployment rate relationships, only Kentucky and New Jersey had a degree of unionization approaching the national average (31 percent of the labor force); most of the other thirteen states were far below that. On the other hand, most of the heavily union ized states (except New Jersey) had insignificant Phillips Curves (see Map 2). To draw a firm con clusion from this bit of evidence is difficult since, as mentioned, the extent of unionization may be related to industry mix. Nevertheless, there does seem to be some evidence to warrant making the following observation: Those states which are less unionized appear to have the most significant Phillips Curves. This observation, though only tentative, has not been supported by some other empirical studies.10 One other point is worth noting. The extent of unionization seems to correlate closely with those states in which wages are most responsive to the lagged inflation variable. Past price increases appear to have had the largest impact on wages in the heavily unionized midwestern and northeastern states. On the other hand, in none of the lessunionized southeastern states were wage changes nearly as sensitive to past inflation rates. This would suggest at the least that the stronger bargaining position of labor in the more heavily unionized states can apparently adjust wages to past inflation 10S e e A lb re c h t’s s tu d y of m e tro p o lita n a re a P h illip s C urves, op. c it., pp. 318-319. 19 0 rates more readily and are, therefore, better able to maintain real wages. Conclusion and Implications Many economists have long considered Phillips' expressed relationship between wage changes and the unemployment rate too simplistic. Just two years after Phillips wrote his famous article, one study pointed out that the distribution of unem ployment between various markets in an economy, as well as the level of unemployment itself, could influence aggregate wage changes and shift the aggregate Phillips Curve .11 Unemployment disper sion and its impact on aggregate wage changes have been examined in several contexts— by labor force groups, by industrial sectors, and by geographic regions (as this article does ).12 A more complete analysis might consider all aspects of unemployment ” R ichard G. Lipsey, "T h e R elatio n B etw een U n e m p lo y m e n t a n d th e R ate of C h an g e of M oney W age R a te s in th e U nited K ingdom , 1862-1951: A F u rth e r A n a ly sis,” E c o n o m ic a , F e b ru a ry 1960, p. 19. 12S ee, fo r e x am p le, G eo rg e L. Perry, “ C h a n g in g L abor M ark ets a n d In fla tio n ," B ro o k in g s P a p e rs on E co n o m ic 10 1 dispersion, but this was clearly beyond the scope of this study. The tentative results obtained show Phillips-type relationships existing in one-third of the states examined. Previous studies have not always confirmed subnational wage equations; in fact, some have argued they do not exist.13 This study, however, suggests that forces of labor supply and demand do operate at the state level and affect state wage changes, supporting the claim of sub national labor markets. The results also show substantial differences in state Phillips Curves, with southeastern states gen erally having the most significant and responsive wage equations. Some evidence, though not con clusive, suggests that the extent of unionization may influence these interstate differences. Whatever the A ctivity, 3:1970, pp. 411-441; S an fo rd V. B erg a n d T h o m as R. D alton, “ S e c to ra l E m p lo y m en t a n d S h ifts in th e A g g re g a te P h illip s C u rv e ,’’ S o u th e rn E co n o m ic J o u rn a l, Vol. 41, April 1975, pp. 593-601; G. C. A rchibald, “T he P h illip s C urve a n d th e D istrib u tio n of U n e m p lo y m e n t," A m erican E co n o m ic R eview , P a p e rs a n d P ro c e e d in g s, Vol. LIX, May 1969. 13M arcis a n d R eed, op. c it., pp. 205-206. JU 1 7 , MONTHLY REVIEW LY 9 5 reason for these differences, these subnational Phillips Curves and their variation implies that national wage/unemployment rate relationships are probably very unstable unless unemployment dispersion or distribution is also considered. As pointed out in an earlier study, subnational Phillips Curves are very difficult to aggregate into a stable national curve.14 Although the notion of a simple national curve 14S e e L ipsey, op. c it., p p. 16-19. is appealing, it is naive. Much evidence, including the experience of the past few years, disproves such a simple relationship. This analysis indicates that the uneven impact of the business cycle in different regions and states could be at least partially re sponsible for the unstable national Phillips Curve, in other words, diverse subnational labor markets (as observed by the tested state Phillips Curves) act with the uneven geographic impact of changes in economic activity to affect national wage changes. The simple Phillips Curve concept does not consider these factors, ■ Appendix Correlation and regression analyses were used to estimate several forms of Phillips Curve equations for the fortyfive states for which data were available. Annual data from 1961 to 1972 were used. This allowed only twelve observations, an obvious limitation. However, the lack of seasonally adjusted monthly or quarterly data precluded using these alternatives. Work is presently underway reestimating several of the equations with quarterly data using seasonal dummy variables; this will sharply increase the number of observations available. The forms of the equations estimated are as follows: W f = a +b 1 U Rj 1 W t = a + b r r rr + cP u -1 s UR, w t = a + b u F + c P - - i + d i uR; percent change from previous year in manufacturing average hourly earnings in state i. 1 = reciprocal of the annual average unem UR; ployment rate for state i. p* ru -1 percent change from previous year in the s U. S. Consumer Price Index lagged one year. where W ; 1 A — — = c h a n g e in t h e r e c i p r o c a l o f t h e a n n u a l a v e r a g e u n e m p l o y m e n t r a t e i n s t a t e i. a ,b ,c ,d = e s tim a t e d c o n s t a n t a n d re sp o n se co e f fic ie n t s . The reciprocal form of the unemployment rate variable was used, as in many studies of the national Phillips Curve. It is generally believed that the negative relationship between percentage wage changes and the unemployment rate is not linear, hence, the reciprocal form of the unemployment rate variable. Tests were also run using the direct unemployment rate; however, the reciprocal form generally gave better results. Tests were also run using lagged values of the unemployment rate variable. But, except for isolated cases, the current value of the unemployment rate proved more satisfactory. In the cases referred to in FEDERAL RESERVE BANK O ATLANTA F the text where the impact of national labor market conditions on state wage changes was estimated, the national unemployment rate was simply substituted for the state unemployment rate. In the simple Phillips Curve case, the equation took the following form: Wf = a + b where UR, _1_ UR, the reciprocal of the annual average unemployment rate for the U. S. The second equation listed gives an enlarged version of the Phillips Curve with a price variable added. Price data generally do not exist on a state level. Hence, the U. S. Consumer Price Index was used for each state's wage equation. This assumes, realistically enough, that the rate of inflation is very similar in all regions and states of the country. The price variable is lagged one year. It is assumed that inflationary expectations rather than inflation itself affect wage changes; inflationary expectations are roughly estimated by the previous year's actual inflation rate. (This is admittedly a very rough approximation.) The third equation adds a variable for changes in unemployment rates which are assumed to measure labor market expectations (see footnote 5 in text). All the equations described were reestimated using U. S. Department of Commerce wage and salary data for nonfarm industries standardized for number of nonfarm wage and salary workers. These data provided broader coverage than the U. S. Department of Labor series on manufacturing average hourly earnings used in the rest of the study. The results, though somewhat less significant, were generally unchanged with this reestimation. State unemployment rate data were based on the old place-of-work methodology and not on the new place-of-residence estimating methodology. A sufficient back series of these data is not as yet available. Statistical results are not reported in detail here. The accompanying maps give some summary results. Detailed regression equations are available upon request. 111 BANKING STATISTICS Bil. $ DEPOSITS** Total - 4 0 -3 6 - -3 2 * ________ 40 _____ AJ Net Demand 36 — 14 A / - - 2 4 10 A/ — 18 - 2 0 AJ Time - 8 — 14 -5 10 Savings -2 Z rj /V — A/ A/ I I I I I I I I I I I D IS T R IC T I I I I I I I II I I 1974 1975 *F ig u res a re fo r th e la s t W ed n e sd a y of e a c h m o n th **Daily a v e ra g e fig u re s LATEST MONTH PLOTTED: MAY SIXTH I i i i i i i i i i i 1973 I 6 B A N K IN G N D T E 5 B u s in e s s L o a n s in R e c e s s i o n C o m m e r c ia l a n d In d u s tria l L o a n s | | J u n e 1 9 7 4 -Ju n e 1975 t~ A nnual % change 1 J u n e 1 9 6 9 -Ju n e 1970 — .a re i_ 3 a ------r r 3 TJ C Z ------T - _ I O o -C £ — re 0) cn [J 10 fc r -1 0 — -2 0 * T ra n s p o rta tio n , C o m m u n ic a tio n , a n d P u b lic U tilities N ote: D ata g a th e re d from la rg e D is tric t b a n k s. 112 JU 1 7 , MONTHLY REVIEW LY 9 5 A comparison of slack periods in commercial-andindustrial loan activity at Sixth District banks during the current recession and during the economic slow down of 1969-70 yields several interesting results, some surprising, some not. (Mid-1974 to mid-1975 was used to mark the current period; mid-1969 to mid-1970 delineates the comparison period. This permits direct comparison of data which are not seasonally adjusted.) As would be expected, business loan activity has been much weaker during the current recession than during its predecessor. During the four quarters since mid-1974, District business loan activity has declined by 10 percent; it had increased by 5 per cent in the four quarters from mid-1969 to mid1970. During the current recession, moreover, busi ness loan activity decelerated sharply, with rates of + 2, — 1 , — 5, and —6 percent between the third quarter of 1974 and the second quarter of 1975. In the 1969-70 slowdown, by comparison, declines occurred in only two of the four quarters, and then the declines were small. The fact that business loans have declined much more markedly at key District banks in this recession than in the previous one is to be expected for several reasons. First, the current recession has been much more severe, both nationally and in the District, than in 1969-70. The demand for bank credit by businesses is closely related to the activity of those businesses themselves, especially their de mands for inventory finance and working capital. Second, banks have generally been more concerned with building liquidity and improving the quality of their loan portfolios this time, much more than in 1969-70. Accordingly, banks have been much less aggressive as lenders and have brought their interest rates down rather reluctantly. Third, busi nesses have moved increasingly to convert their short-term bank debt into long-term bond liabilities. Such a conversion is typical in a recession, but rec ord calendars in the corporate bond market testify to the recent strength of this tendency in the cur rent recession. Looking at the major categories of large banks lending to businesses during these two recession periods, however, a more surprising result emerges. Two of the hardest-hit sectors in the current re cession, according to production and employment data, have been construction and durable goods manufacturing. Yet District bank lending to heavy manufacturers and contractors are the only loan categories which have not declined more during the 1974-75 recession than in 1969-70. Loans to con tractors declined both times; but the 10 -percent decline in 1974-75 was not as severe as the 18-per cent drop posted in 1969-70. Most of the difference T o ta l C o m m e rc ia l a n d I n d u s tr ia l L o a n s o/o c h a n g e N. S. A. - - t r T = 5 -5 r - -1 0 I III IV 1969 I I II 1970 N ote: D ata g a th e re d from III IV 1974 I I 1975 la rg e D istric t b a n k s. in these two numbers was concentrated in compar ing the third calendar quarters of 1969 and 1974. In durable goods manufacturing, bank loans increased at about 10 percent during the 1969-70 period and 8 percent during the current period. In five other broad categories of business bor rowers, as evidenced by the chart on the opposite page, the 1974-75 drop-off has been much more severe than in 1969-70. Nondurable manufacturing firms— dominated by textiles in the Sixth District— and the service industry showed substantial drops, but the wholesale and retail trade categories showed the largest comparative declines in 1974-75, com pared with no growth during the previous recession. The remaining sectors— transportation, communica tion, and public utilities— posted a small decline during each of the recessions. W IL L IA M N . C O X , III An e a rlie r a n a ly s is by Sally Loving, c o o rd in a to r of fin a n c ia l s ta tis tic s , w a s h e lp fu l in p re p a rin g th is N ote. FED ERAL RESERVE BANK O ATLANTA F 13 1 S ix t h D is t r ic t S t a t is t ic s S e a s o n a lly A d ju s te d (A ll d a ta a r e i n d e x e s , u n le s s in d i c a t e d o t h e r w is e .) L a t e s t M onth 1975 O ne M onth Ago Tw o M onths Ago One Year Ago U n e m p lo ym e n t R a te (P e rc e n t of W ork F o r c e ) ......................... M ay Avg. W e e k ly H rs. in M fg. (H rs .) . . . M ay S IX T H D IS T R IC T IN C O M E A N D S P E N D IN G M a n u fa c tu rin g P a y ro lls F a rm C a sh R e c e ip ts . . . A p ril . A p ril , A p ril 172.7 172 227 165 170.1 224 391 177 168.2 214 308 188 In s ta lm e n t C re d it at B a n k s * (M il.$ ) . May . M ay 628 640 . M ay . M ay 129.9 108.0 107.6 103.6 9 9.4 5 52 r 629 r 522 604 175.3 173 188 179 68 6 579 E M P L O Y M E N T A N D P R O D U C T IO N N o n fa rm E m p l o y m e n t ............................... M a n u fa c tu rin g ............................................ N o n d u ra b le G o o d s ............................... F o o d ............................................................... T e x t i l e s .................................................. A p p are l .................................................. Paper ........................................................ P rin tin g and P u b lis h in g . . C h e m i c a l s ............................................ D u ra b le G o o d s ...................................... L b r ., Wood P ro d s ., F u rn . & F ix .. Sto n e , C la y , and G la s s . . . P r im a ry M e t a l s ............................... F a b ric a te d M e t a l s ......................... M a c h i n e r y ............................................ T ra n sp o rta tio n Eiquip m ent N o n m a n u fa c t u r in g ...................................... C o n s t r u c t i o n ...................................... . . . . . . . . . . . . . . . . . . . . . , M ay M ay M ay M ay M ay M ay M ay M ay M ay May M ay May May M ay M ay M ay M ay M ay May May May Ju n e S e r v i c e s .................................................. F e d e ra l G o v e rn m e n t . . . . S ta te and L o c a l G o v e rn m e n t F a rm E m p lo y m e n t ............................................ U n e m p lo ym e n t R a te May (P e rc e n t of W ork F o rc e ) . . . . In su re d U n e m p lo ym e n t (P e rc e n t of C ov. E m p . ) ......................... . May Avg. W e e k ly H rs. in M fg. (H rs .) . . . May C o n s tru c tio n C o n tra c ts* - ......................... . M ay R e s i d e n t i a l ......................................................... . May A ll o t h e r ............................................................... . M ay C otto n C o n s u m p t i o n * * ............................... . A p ril M a n u fa c tu rin g P ro d u c tio n . . . . . A p ril N o n d u ra b le G o o d s ..................................... . A p ril Food ........................................................ . A p ril T e x t i l e s .................................................. . A p ril A p p a rel .................................................. . A p ril Paper ........................................................ . A p ril P r in tin g and P u b lis h in g . . . A p ril . A p ril . A p ril . A p ril . A p ril F u rn itu re and F ix t u r e s . . . A p ril S to n e , C la y , and G la s s . . P rim a ry M e t a l s ......................... . A p ril . A p ril F a b ric a te d M e ta ls . . . . . A p ril N o n e le c tric a l M a ch in e ry . A p ril E le c tr ic a l M a c h in e ry . . T ra n sp o rta tio n E q u ip m e n t . A p ril 105.7 124.0 106.1 108.5 94.1 115.8 101.4 129.8 107.7 106.7 104.5 9 7.3 101.7 104.6 123.7 105.4 108.9 9 4.4 116.6 102.9 105.0 125.0 105.7 109.5 9 4.0 116.6 103.9 146.9 98.7 137.6 127.0 123.5 134.5 149.9 155.1 105.6 143.9 7 8.5 148.9 97.6 137.6 132.6 123.4 134.3 150.0 153.9 105.4 143.3 79.1 150.4 97.5 138.4 136.4 123.6 135.1 150.3 154.3 106.0 143.5 78.5 10 .6 2 130.3 107.5 105.9 104.5 9 5.2 101.1 12 .0 121.0 121.8 0 9.7 6 .8 39.1 182 134 228 58 140 .4 142.5 135.0 135.6 118.2 132.3 125.6 159.5 137.9 139.5 115.8 137.5 101.1 111.2 148.8 240.7 12 .0 2 10 .2 6 .9 3 8.7 163 133 191 54 139.7 142.5 135.8 135.9 117.7 132.1 126.3 160.6 135.0 129.3 114.0 134.0 101.4 111.3 150.5 2 26.2 122.5 10 .1 6 .7 38.6 224 131 316 53 141.4 144.6 135.6 1 36.9 120.9 133.7 127.3 159.7 136.6 126.2 117.1 142.3 102.7 112.6 153.8 227 .8 121.8 134.6 119.3 116.2 106.0 114.4 115.0 114.6 132.3 109.2 123.2 111.2 133.1 114.7 133.5 164.7 108.7 140.0 153.9 128.2 138.9 153.9 153.6 104.2 137.2 7 8 .4 r . May , M ay 270 251 . May 22 2 193 300 267 250 219 192 3 08 r 276 255 O ne M o nth Ago O ne Year Ago 10.2 3 8 .4 9 .2 3 9.0 Tw o M o n th s Ago 4 .6 4 0 .5 267 214 294 251 206 2 60 F IN A N C E A N D B A N K IN G M em b er B a n k L o a n s ............................................ M ay M em b er B a n k D e p o s i t s ................................M ay B a n k D e b i t s * * .........................................................M ay 269 218 300 2 65 2 16 309r F L O R ID A M a n u fa c tu rin g P a y ro lls ................................May F a rm C a s h R e c e i p t s ............................................ A p ril 212 178.1 3 09 180 .7 2 49 184 .5 169 M ay M ay M ay M ay Ju n e 149 .5 117 .3 155.7 1 45 .8 7 7.0 149 .2 117 .5 155.3 1 5 6 .9 7 2.7 149 .8 117 .2 156.1 162.1 7 0 .9 157 .0 1 28 .6 162.4 2 08 .5 8 3 .4 r M ay M ay 12.3 3 9.3 3 9 .0 10.7 3 9 .9 5 .4 4 0.1 M em b er B a n k L o a n s ......................... M em b er B a n k D e p o sits . . . . B a n k D e b i t s * * ...................................... M ay M ay M ay 2 94 248 317 288 2 40 303 301 2 42 3 09 309 247 301 M a n u fa c tu rin g P a y ro lls . . . F a rm C a sh R e c e i p t s ......................... M ay A p ril 159.1 188 154.7 202 149.1 2 18 1 66 .5 181 M ay M ay M ay M ay Ju n e 124.7 9 9.5 136.2 1 22 .4 103.7 124.3 9 8.7 136 .0 123.5 103 .9 125.1 9 7 .8 1 37 .6 128 .2 103.7 1 31 .0 M ay M ay 9 .7 3 9 .2 10.5 3 8 .7 11.5 3 7 .9 4 .1 4 0 .0 M ay M ay M ay 252 195 352 248 195 381 250 191 353 266 196 327 M ay A p ril 163 .0 131 166 .2 239 1 69 .4 181 156 .6 170 M ay M ay M ay M ay Ju n e 119 .6 1 05 .4 1 20 .9 107 .4 123.7 107 .6 7 4 .8 121.0 10 .6 0 M ay M ay 7 .6 3 8.2 M ay M ay M ay 179 .0 EM P LO YM EN T N o n fa rm E m p lo y m e n t U n e m p lo y m e n t R a te (P e rc e n t of W ork F o rc e ) . . Avg. W e e k ly H rs. in M fg. (H rs .) 12.2 F IN A N C E A N D B A N K IN G EM P LO YM EN T 4 .7 2 .2 40.1 228 219 237 76 147.8 1 47.9 134.6 145.8 137.7 136.0 136.1 157.5 147.8 150.9 150.1 154.9 108.1 128.6 147.6 242.7 127.6 F IN A N C E AN D B A N K IN G Loan s* A ll M e m b er B a n k s ............................... La rg e B a n k s ............................................ D e p o sits* A ll M em b er B a n k s ............................... La rg e B a n k s ............................................ B a n k D e b its * / * * ...................................... L a t e s t M onth 1975 274 257 219 193 303 215 186 285 170.6 233 185.2 193 N o n fa rm E m p lo y m e n t . . . . M a n u fa c tu rin g ................................ N o n m a n u fa c tu rin g . . . . C o n s t r u c t i o n ................................ F a rm E m p lo y m e n t ......................... U n e m p lo y m e n t R a te (P e rc e n t of W ork F o rc e ) . . A vg. W e e k ly H rs. in Mfg. (H rs .) 112.2 139 .6 1 47 .2 93. r 6 F IN A N C E AN D B A N K IN G M em b er B a n k L o a n s ......................... M em b er B a n k D e p o sits . . . B a n k D e b i t s * * ...................................... EM P LO YM EN T N o n fa rm E m p lo y m e n t . . . . M a n u fa c tu rin g ................................ N o n m a n u fa c t u r in g ......................... C o n s t r u c t i o n ................................ F a rm E m p lo y m e n t ................................ U n e m p lo y m e n t R a te (P e rc e n t of W o rk F o rc e ) . . Avg. W e e k ly H rs. in M fg. (H rs .) 122.6 10 2.8 7 2.6 120 .2 1 07 .2 122.9 105.5 7 5.7 8 .1 118 .9 108 .4 7 8 .6 r 6 .1 3 8.5 8 .4 3 9 .5 3 9 .8 246 206 249 253 207 261 261 207 2 59 2 55 189 2 29 M ay A p ril 196.9 173 195.5 233 193 .0 215 197 .5 197 M ay M ay M ay M ay Ju n e 126.3 119 .4 129.5 117 .8 5 9.6 126.4 118 .4 130.1 125 .9 6 3.5 127 .5 119 .5 131.2 135 .0 5 8.3 1 31 .8 1 34 .5 130 .6 151.1 6 9 .9 r F IN A N C E A N D B A N K IN G M em b er B a n k L o a n s * . . . . M em b er B a n k D e p o sits* . . . B a n k D e b i t s * / * * ...................................... A LA B A M A M IS S IS S IP P I . May 178.8 193 171.2 204 EM P LO YM EN T N o n farm E m p lo y m e n t M a n u fa c tu rin g . . N o n m a n u fa c tu rin g C o n s tru c tio n . . 14 1 IN C O M E M a n u fa c tu rin g P a y ro lls . . . F a rm C a s h R e c e i p t s ......................... EM P LO YM EN T . May . May 118.8 107.5 124.0 129.2 72.3 118.4 106.6 123.8 129.6 73.0 118.5 105.8 124.3 131.2 74.6 122.4 118.4 124.2 142.2 7 0.4 N o n fa rm E m p lo y m e n t . . . . M a n u fa c tu rin g ............................... N o n m a n u fa c t u r in g ......................... C o n s t r u c t i o n ................................ F a rm E m p lo y m e n t ................................ JU 1 7 , MONTHLY REVIEW LY 9 5 O ne M onth Ago L a t e s t M onth U n e m p lo y m e n t R a te (P e rc e n t o f W o rk F o rc e ) . . A vg. W e e k ly H rs. in M fg. (H rs .) O ne Year Ago Tw o M o nths Ago O ne M onth Ago L a t e s t M onth Tw o M o nth s Ago O ne Year Ago E M P LO YM EN T . M av . May 7.4 3 8.9 8 .4 3 8.8 8.5 38.1 3.2 3 9.8 M em ber B an k Lo an s* . . . . M em b er B a n k D e p o sits* . . . B a n k D e b i t s * / * * ...................................... . May 262 218 257 248 217 259 266 217 255 171.6 197 167.0 244 221 125.5 107.5 135.5 1 38.4 129.0 119.3 1 34 .4 135.8 7 8 .9 r 8 .5 3 9.5 8 .9 3 9.2 9.6 3 6.3 4 .1 4 0 .3 277 223 244 274 291 224 276 261 203 274 . M ay . Ju n e 177.3 186 268 125 .4 107.1 135.5 137.2 . M ay . M ay 256 173.5 158 . M ay . May E m p lo y m e n t N o n m a n u fa c tu rin g C o n s tru c tio n 125.1 107.3 135.0 130.7 . May . M ay N o n farm F IN A N C E A N D B A N K IN G U n e m p lo ym e n t R a te 8.6 8.0 8.6 6 8 8 TEN N ESSEE F IN A N C E A N D B A N K IN G M a n u fa c tu rin g P a y r o l l s ......................................M ay F a rm C a s h R e c e i p t s ............................................A p ril • F o r S ix th D is t ric t a re a o n ly ; o th e r to ta ls fo r e n tir e s ix s ta te s * * D a ily a v e ra g e b a s is t P r e lim in a r y d a ta r-R e v ise d 20 2 258 N .A . N ot a v a ila b le Note: All indexes: 1967 = 100. S o u rce s : M a n u fa c tu rin g p ro d u ctio n e s tim a te d by t h is B a n k ; n o n fa rm , m fg. an d n o n m fg . e m p ., m fg . p a y ro lls an d h o u rs, an d u n e m p ., U .S . D ept, o f L a b o r an d co o p e ra tin g s ta te a g e n c ie s ; cotto n c o n su m p tio n , U .S . B u re a u of C e n s u s ; c o n s tru c tio n c o n t ra c t s , F . W. Dodge D iv ., M cG ra w -H ill In fo rm atio n S y s te m s C o .; fa rm c a s h re c e ip ts an d farm e m p ., U .S .D .A . O th e r in d e x e s based on d a ta c o lle c te d by th is B a n k . A ll in d e x e s c a lc u la te d by t h is B a n k , 'D a ta b e n ch m a rk e d to Ju n e 1971 R e p o rt o f C o n d itio n . D e b it s to D e m a n d D e p o s it A c c o I n s u r e d C o m m e r c i a l B a n k s in t h e S i x t h D i s t r i c t (In Th o u sa n d s of D o lla rs ) P e r c e n t C h an g e M ay 1975 from May 1975 A p ril 1975 May 1974 A pr. 1975 M ay 1974 ST A N D A R D M ET R O P O L IT A N S T A T IS T IC A L A R E A S ' B irm in g h a m . . . G ad sd e n . . . . H u n ts v ille . . . . M o b i l e ......................... M o ntg o m ery . . . T u sc a lo o sa . , . . 5 ,6 5 4 ,7 2 4 104,785 381,091 1 ,4 41 ,4 46 7 7 4 ,5 4 5 2 9 2 ,6 8 9 5 ,8 15 ,7 46 107,2 92 4 02 ,4 0 9 1 ,5 1 0 ,2 2 9 8 6 0 ,1 7 0 2 7 4 ,7 8 9 4 ,7 9 0 ,8 4 6 112,073 4 0 3 ,8 5 9 1 ,3 21 ,1 31 7 3 0 ,7 0 4 260 ,3 31 8 7 4 ,4 1 8 4 5 8 ,0 3 6 9 0 9 ,5 8 9 5 4 3 ,2 37 1 ,8 33 ,8 01 4 3 2 ,4 1 3 2 3 7 ,8 2 0 5 ,0 6 5 ,4 7 9 - 3 + 23 + 7 8 4 3 ,8 2 9 4 5 3 ,2 9 2 - 4 -1 6 + 4 + + + 2 ,1 97 ,9 21 4 7 7 ,6 9 5 2 8 5 ,1 1 7 5 ,0 61 ,6 61 1 ,9 76 ,0 33 4 0 0 ,6 7 8 2 8 2 ,6 4 5 5 ,6 0 5 ,4 8 7 —17 - 9 -17 + - 7 + -16 - 5 + + - 3 4 3 5 ,2 5 9 7 ,1 7 0 ,5 0 8 1 ,6 74 ,6 52 530 ,0 95 5 7 5 ,6 6 9 1,2 29 ,8 32 4 ,2 7 5 ,9 3 8 1 ,1 39 ,0 69 4 7 8 ,9 7 3 7 ,7 56 ,1 51 1,7 29 ,6 27 5 11 ,6 59 6 10 ,1 6 2 1 ,0 14 ,9 88 4 ,6 7 0 ,5 7 0 1 ,2 68 ,6 76 4 6 6 ,3 6 7 7 ,5 17 ,5 73 1 ,6 19 ,7 76 5 30 ,7 49 592 ,7 22 1 ,0 09 ,1 32 4 ,2 8 7 ,0 7 5 1 ,3 52 ,4 58 - 9 - 3 + 4 + - - 7 - 5 + 3 - 3 + -1 6 + 3 + + + - A lb a n y ......................... A t l a n t a ......................... A u g u s t a ......................... C o lu m b u s . . . M aco n ......................... Savannah . . . . 1 9 2 ,1 82 . 1 9,93 6,67 1 6 6 4 ,2 6 4 4 8 6 ,2 3 7 8 4 5 ,7 2 0 . 1 ,0 6 1 ,4 1 8 191 ,4 76 2 3 ,1 9 1 ,3 7 2 6 6 0 ,0 4 3 5 0 3 ,3 2 4 86 2 ,5 8 0 1 ,0 2 2 ,5 8 8 2 1 9 ,8 8 0 1 9 ,7 1 1 ,3 3 2 6 7 4 ,3 9 9 5 1 8 ,6 9 6 8 9 5 ,0 9 5 6 4 5 ,4 0 4 A le x a n d ria . . . B a to n R o uge L a fa y e tte . . . . L a k e C h a rle s New O rle a n s . . . 3 2 2 ,2 5 3 1 ,8 9 4 ,0 6 9 4 0 3 ,0 7 1 2 9 6 ,6 6 0 5 ,5 5 1 ,5 5 6 3 1 6 ,6 8 2 1 ,9 9 5 ,5 2 6 4 2 1 ,3 2 7 29 1 ,0 5 0 5 ,7 6 9 ,5 0 2 295 ,8 41 1 ,7 9 9 ,2 3 5 325 ,5 21 2 8 0 ,1 0 7 5 ,3 0 7 ,8 9 0 + - 5 - 4 + - 4 2 9 5 ,4 5 7 1 ,7 0 5 ,8 4 0 3 1 2 ,7 6 8 1 ,7 1 0 ,0 5 0 2 6 0 ,5 23 1 ,7 8 4 ,1 8 4 - 1 ,2 54 ,6 22 1 ,4 92 ,2 63 4 ,3 2 1 ,9 5 0 1 .2 8 3 ,4 1 0 1 ,5 7 7 ,5 6 8 4 ,7 0 3 ,6 7 8 1 ,5 3 0 ,7 0 0 2 ,0 6 9 ,1 6 6 4 ,0 8 0 ,6 5 4 122 ,6 84 121,924 125,657 B a rto w -L a k e la n d W in te r H a ven D ayto n a B e a c h . . F t. L a u d e rd a le H o llyw o od . . . . F t. M yers . . . . G a in e s v ille . . . Ja c k s o n v ille . . . M elbourneT itu s v ille - C o c o a ......................... M iam i O r l a n d o ......................... P e n s a c o la . . . S a ra so ta . . . . T a lla h a s s e e . . . . T a m p a -S t. Pete. . W. P a lm B e ac h . . . B ilo x i- G u lfp o rt . . Ja c k s o n ......................... C h atta n o o g a . . K n o x v ille . . . . N a s h v ille . . . . . . . . . . . 5 5 -10 - 0 6 + 15 + 22 6 + 14 12 + 8 1 8 0 -10 8 8 12 8 1 2 1 0 12 6 1 21 2 6 2 8 0 1 -10 6 + 0 —13 - 6 -1 4 + 1 + 5 + 1 - 2 + 6 - 3 - 6 + 0 - 2 - 6 + 8 + 4 2 2 - 6 0 2 5 8 + 1 )T H E R C E N T E R S A n n isto n 2 -t-64 + 67 + 9 + 5 + 24 + + 5 + + 30 + 32 + + + 13 - 4 + 17 + 10 6 11 11 -1 8 -2 8 + 6 -11 - 8 6 - + 15 2 + 7 'C o n fo rm s to S M S A d e fin itio n s a s of D e c e m b e r 31, 1972. - D is tric t p o rtio n o n ly , r-revised F ig u re s fo r so m e a re a s d iffe r s lig h tly from p re lim in a ry fig u re s p u b lis h e d in " B a n k FEDERAL RESERVE BANK O ATLANTA F Year to d a te 5 m o s. 1975 M ay fro m 1974 197.4 M ay 1975 fro m A p ril 1975 M ay 1974 A p r. 1975 1 8 9 ,5 17 8 2,55 1 1 9 5 ,7 19 9 1 ,7 9 4 2 1 4 ,4 6 8 8 1,27 1 -10 B ra d e n to n . . M onroe C o u n ty O c a l a ......................... S t. A u g u stin e S t. P e te rs b u rg h Tam pa . . . . 1 94,913 1 3 5 ,3 08 2 4 1 ,0 3 5 4 3 ,8 6 0 9 7 7 ,4 7 3 2 ,3 1 9 ,6 1 3 2 1 4 ,2 83 1 25 .3 98 2 5 1 ,7 7 2 4 6 ,2 9 6 1 ,1 0 6 ,0 8 8 2 ,4 5 6 ,8 6 6 2 2 5 ,1 0 8 9 6 ,5 9 8 1 9 6 ,7 2 4 5 4 ,7 1 8 1 ,0 90 ,2 77 2 ,1 3 9 ,9 8 5 - 9 + - 4 - 5 A th e n s . . . . B ru n s w ic k . . D alto n . . . . E lb e rto n . . . G a in e s v ille . . G riffin . . . . L a G ra n g e . . . N ew n a n . . . . R o m e ......................... V a ld o sta . . . 1 72 ,3 83 1 19 ,9 97 1 77 ,0 22 2 7 ,9 0 9 1 6 0 ,1 89 7 5,74 3 4 0 ,3 3 7 4 4 ,1 5 6 1 62 ,1 52 1 1 6 ,6 34 1 6 9 ,4 88 1 24,973 1 8 0 ,9 2 4 2 8 ,5 0 0 176 ,8 55 7 7 ,5 3 8 3 8,34 5 50,241 1 6 5 ,6 20 1 18,224 1 69 ,4 74 1 0 4 ,6 39 192 ,7 99 2 3,95 2 151 ,8 97 9 1 ,1 0 3 4 5 ,8 6 9 55,351 161 ,6 07 1 13 ,4 46 A b b e v ille . . B u n k ie . . . . Ham m ond . . New Ib e ria . P la q u e m in e . T h ib o d a u x . . 1 8,530 1 6,73 0 1 16 ,3 95 9 5 ,4 4 4 2 7 ,5 6 0 6 8 ,5 0 8 1 9,44 9 1 7,70 8 117 ,8 47 88,38 1 2 9,41 1 74,271 18,172 1 5,15 4 100 ,0 67 7 0 ,9 8 8 2 6 ,9 5 5 4 4 ,4 0 7 . . 1 47 ,8 66 7 6 ,2 5 0 132 ,6 24 5 3 ,3 8 9 1 49 ,2 10 8 5 ,6 6 3 1 36 ,4 88 6 2 ,9 8 8 1 44 ,4 18 9 2,90 3 135 ,7 05 6 0 ,8 9 5 . . 186 ,7 95 7 5,69 0 4 2 ,7 0 6 152 ,6 26 8 0 ,5 7 9 5 4,65 0 168 ,0 36 9 3 ,8 4 3 5 1 .0 1 4 B ris to l . . . . Jo h n so n C ity K in g sp o rt . . . 1 46 ,4 50 1 70 ,1 30 3 13 ,7 61 148,181 1 8 1 ,9 29 3 2 6 ,6 3 6 1 46,255 1 81 ,7 74 3 0 3 ,6 3 0 M ay 1975 D o than S e lm a + 18 - 7 + 9 + + . P e rce n t C h a n t* Year to d ate 5 m os. 1975 from 1974 . . . . . . . . H a ttie sb u rg . L a u re l . . . . M e rid ia n . . N a tch e z . . P a sc a g o u la M o ss P o in t V ic k s b u rg . Y a zo o C ity . S T R IC T T O T A L . . . . . . . . 9 1 ,8 1 1 ,4 0 9 9 8 ,2 7 7 ,6 2 8 r 9 0 ,6 6 2 ,8 3 4 A la b a m a . . . F lo rid a . . . . G eo rg ia . . . . L o u is ia n a * . . M is s is s ip p i. . Ten n essee: . . 1 1 ,8 6 5 ,5 5 4 2 8 ,4 9 3 ,6 8 4 2 7 ,4 6 5 ,0 5 8 . 1 0,16 5,28 0 . 3 ,5 7 7 ,2 8 7 . 1 0 .2 4 4 ,5 4 6 1 2 ,3 8 2 ,3 7 5 r 1 0 ,6 2 3 ,5 6 7 3 0 ,0 2 8 ,9 7 2 r 2 8 ,2 8 7 ,3 5 1 3 0 ,6 7 1 ,9 5 4 2 6 ,6 0 1 ,4 2 6 1 0 ,5 7 6 ,3 0 5 9 ,5 8 0 ,3 9 4 3 ,5 9 4 ,9 4 0 3 ,6 4 8 ,9 1 3 1 1 ,0 2 3 ,0 8 2 1 1 ,9 2 1 ,1 8 3 - 3 8 + -12 2 - -1 3 + 40 + 23 + 14 + 9 + 3 + 7 + 5 6 1 -20 -22 -12 -10 - 5 6+ 8+ 8 + 2 + 2 + 1 - 4 + 15 + 19 - 2 - 8 -1 3 - 2 + 17 + 7 - 9 + 5 + 8 - 2 -1 7 -12 + 5 -12 - 1 4 -12 -20 - 1 5 - 2 + 0 + 3 - 1 + 3 + 7 - 5 + 2 + 9 - 6 + 10 + 2 8 - 1 + 16 + 30 + 8 + 34 + 33 - 6 + 2 + 24 - 8 + 54 + 6 0 - 1 + 2 + 12 -11 - 1 8 - 3 - 3 - 2 + 1 -12 + 3 -1 5 +2 2 + 11 + 5 - 6 -1 9 -12 -22 - 1 6 - 0 - 1 + 0 + 13 - 6 - 6 - 2 - - 4 '1 - 4 + 12 - 5 + 1 -10 + 3 - 4 + 6 - 0 - 2 - - 7 7 + + 7 -14 + 19 + 7 + 17 + 4 + 3 0 D e b its an d D ep o sit T u rn o v e r " by B o a rd o f G o v e rn o rs of th e F e d e ra l R e s e rv e S y s te m . 15 1 D is t r ic t B u s in e s s C o n d it io n * S e a s. a d j. fig u re ; n o t a n in d e x L a te s t p lo ttin g : May, e x c e p t m fg. p ro d u c tio n a n d fa rm c a s h re c e ip ts , A pril. Several sectors of the Southeastern economy have improved noticeably. Employment increased amid other hopeful signs in labor markets. Incomes of manufacturing employees grew, and consumer spending seemed to strengthen. The value of construction contracts was marginally higher. Rising livestock prices and falling feed costs improved the agricultural outlook. A weak loan demand is causing banks to purchase securities. Total nonfarm employment rose in May for the first time in 1975, while the unemployment rate registered its first decline in 11 months. Manu facturing jobs, stimulated by strength in nondur ables, continued to pick up despite a weak durable goods sector. The average manufacturing worker put in a longer workweek and received a fatter pay check. Moderate job gains in most nonmanufactur ing industries offset continuing heavy losses in construction, halting the recent slide in total non manufacturing employment. Consumers appeared to spend more freely, as the prolonged erosion of total consumer instalment indebtedness to banks eased substantially during May. Increasing extensions of automobile credit in combination with lower repayments were primarily responsible, although personal loans outstanding also rose. Home repair and modernization credit was essentially unchanged, while other consumer goods credit outstanding continued to decline. In comes of persons employed in manufacturing increased in both April and May. New car registra tions fell slightly in April and stood over one quarter below the year-ago level. The value of residential construction contracts gained slightly in May for the third month in suc cession. Accompanying this improvement were continued record deposit inflows at savings and loan associations and slight declines in mortgage rates. The value of nonresidential contracts was also up slightly, on the strength of a large contract for an electric power plant near Birmingham. Prices of agricultural products turned upward slightly in May. Sharply rising livestock, cotton, and citrus prices were primarily responsible for an in crease largely offset by declining grain and soybean prices. Preliminary data for June indicate that live stock prices have continued their upward trend, with broiler prices advancing most rapidly. Broiler placements have increased in response to an im proved outlook which also reflects lower feed costs. Indicated wheat production is one-eighth higher than a year ago, but lower prices have reduced the value of the crop. Ample rainfall created excellent growing conditions for crops and pastures through mid-June. Loan demand at commercial banks has shown no signs of reviving. Business loans declined over the mid-June corporate tax payment date in contrast to the usual seasonal increase in borrowing. Banks are continuing to add sizable amounts of government securities to their portfolios in an attempt to expand earning assets. Stronger deposit growth has been centered around net demand deposits and passbook savings accounts. N Data on which statem ote: ents are based have been adjusted w henever possible to elim inate seasonal influences. 16 1 JU 1 7 , MONTHLY REVIEW LY 9 5
