AN INTERINDUSTRY FORECASTING MODEL FOR PRICES AND FACTOR INCOMES FOR THE U. S.

Transcription

1 AN INTERINDUSTRY FORECASTING MODEL FOR PRICES AND FACTOR INCOMES FOR THE U. S. BY M atthew H y le D is s e r t a t io n s u b m itte d to th e F a c u lty o f th e G raduate S chool o f th e U n iv e r s it y o f M a ryla n d in p a r t i a l f u l f i l l m e n t o f th e re q u ire m e n ts f o r th e degre e o f D o c to r o f P h ilo s o p h y 1985

2 T a b le o f C o n te n ts C h a p te r I : O vervie w o f th e M odel \ 1.1 In tr o d u c tio n \ 1.2 S tru c tu re o f th e INFORUM model G 1.3 S urvey o f P re v io u s Work Toward C om plete INFORUM M odel a C h a pte r I I : S t a t i s t i c a l and M o d e llin g S tru c tu re o f GPO I I. 1 The A c c o u n tin g Scheme f o r th e! INFORUM Model I I. 2 The F o re c a s tin g S tru c tu re o f GPO and P ric e s I I. 3 The P r o d u c t- to - In d u s tr y B rid g e T a b le I I. 4 D e s c r ip tio n o f GPO s e rie s Lj* B I I. 5 Summary I I I : L abor Com pensation A C * I I I. l O vervie w I I I. 2 A g g re g a te H o u rly L a b o r Com pensation n I I I. 3 R e la tiv e P ay-r ates i n I n d iv id u a l I n d u s tr ie s C h a p te r IV : R e tu rn to C a p ita l IV. 1 IV. 2 IV. 3 T o ta l R e tu rn to C a p ita l C a p ita l Consum ption A llo w a n c e s N et I n t e r e s t Payments I V. 4 In v e n to r y V a lu a tio n A d ju s tm e n t (IV A ) I V. 5 IV. 6 B u sin e ss T ra n s fe r Payments R e n ta l Income

3 C h a pte r V: I n d ir e c t B usiness Taxes and Government S u b s id ie s SlOO V. 1 I n d ir e c t B u sin e ss Taxes V.2 Government S u b s id ie s ( le s s s u rp lu s o f governm ent e n te r p r is e s ) ^ A \ C h a pte r V I: F o re c a s ts w it h th e M odel V I. 1 The Base F o re c a s t V I. 2 A lt e r n a t iv e F o re c a s t 5-0*0

4 L i s t o f T a b le s Number I I. 2 I I. 3 Name C o m p o s itio n o f 1972 GPO by ty p e o f r e a llo c a t io n Summary o f th e r e s o lu t io n o f s t a t i s t i c a l d is c re p a n c y A g g re g ate d 1972 P r o d u c t- to - In d u s tr y B rid g e ta b le In d u s tr y R e la tiv e P a y -ra te e q u a tio n s IV. 1 I V. 2 IV. 3 IV. 4 IV. 5 IV. 6 IV. 7 C o m p o s itio n ( in p e rc e n ta g e s ) o f th e R e tu rn to C a p ita l In d u s tr y r e t u r n to c a p it a l e q u a tio n s In d u s tr y c a p it a l co nsu m p tio n a llo w a n c e e q u a tio n s N et in t e r e s t f o r 1977 In d u s tr y n e t in t e r e s t paym ent e q u a tio n s In d u s tr y in v e n to r y v a lu a tio n a d ju s tm e n t e q u a tio n s In d u s tr y b u s in e s s t r a n s f e r paym ents e q u a tio n s V. l 1981 D is t r i b u t io n by ty p e o f I n d ir e c t B usiness Taxes V.2a V.2b V.3 V I. 1 V I. 2 V I. 3 V I. 4 V I. 5 V I. 6 Ad V aloreum F e d e ra l E x c is e Taxes D o lla r s p e r p h y s ic a l u n it ta x S e c to ra l i n d ir e c t b u s in e s s ta x e q u a tio n s Base ru n a verage a n n u a l g ro w th r a te s f o r key exogenous v a r ia b le s Base ru n average a n n u a l g ro w th r a te s f o r s e le c te d a g g re g a te v a r ia b le s D is t r i b u t io n o f v a lu e added betw een la b o r, c a p it a l and I n d ir e c t B usiness Taxes D is t r ib u t io n o f v a lu e added f o r s e le c te d in d u s t r ie s A nnual average G rowth R ates o f L a b o r Compens a tio n ( L a b o r), R e tu rn to C a p ita l ( C a p it a l), and C o rp o ra te P r o f i t s ( P r o f it s ) f o r A lt e r n a t iv e Run A verage A nnual G row th R ates f o r S e le c te d A g g re g a te V a ria b le s V I. 7-32

5 C hapter I O verview o f th e Model 1.1 In tro d u c tio n Forecasting factor incom es and prices within an in put-o utp ut fram ew ork i s th e s u b je c t o f t h is s tu d y. The a d d itio n o f t h is work to an existing interin dustry m odel o f th e U. S. economy was a m ajor ste p in c lo s in g th e model w ith re sp e ct to both prices and in c o m e. B e fo re th e work done in th is s tu d y was available, the INFORUM model determ ined consta n t d o lla r ( r e a l) f in a l demands and re a l o u tp u t. Now, i t becomes possible to use endogenous p ric e s and income in t h is d e te rm in a tio n and to translate real p ro d u c t and fin a l demands into nominal values. F u rth e rm o re, th e closed m odel is in t e r n a l l y c o n s is te n t: nom inal GNP e v a lu a te d from th e p ro d u ct sid e w ill equal nom inal GNP calculated from f a c to r incom es. T h e re a re two p o s s ib le approaches to m o d e llin g p ric e s and incom es. One m ethod w o u ld be to use th e in te ra c tio n of dem and and supply f u n c t io n s i n th e d e te rm in a tio n of a p ro d u c t's p r ic e and then c a lc u la te va lu e added as th e d iffe r e n c e between th e v a lu e o f th e o u tp u t and th e v a lu e o f any in te r m e d ia te goods used in th e p ro d u c tio n o f th a t f i n a l p r o d u c t. V a lu e added w o u ld th e n be distributed among its various com ponents such as la b o r, c a p ita l and ta x e s. The a lte r n a tiv e method is to work in th e o th e r d ir e c t io n from incomes to p r ic e s. F ir s t, determ ine th e v a rio u s components o f v a lu e a dded, and th e n o b t a in th e price for each p ro d u c t by u t i l i z i n g th e in p u t-o u tp u t d e f in it io n th a t th e p ric e per u n it o f o u tp u t must equal th e u n it m a te ria l costs p lu s u n it va lu e added. Logically, the two methods are e q u iv a le n t. A shortage o f c a p a c ity, fo r \

6 2 exam ple, t h a t b o o s ts th e price of the p r o d u c t, can e q u a lly well be view ed as in c re a s in g th e p r o f it per u n it. A lth o u g h th e f ir s t method may appear to be a more sim ple approach to d eterm ine p ric e s and incom es, th e re a re tw o re a so n s why i t i s n o t. One i s t h a t th e demand and s u p p ly fu n c tio n s im p ly th a t th e p ric e f o r each p ro d u c t i s a f u n c t io n o f th e prices for o th e r p r o d u c ts. The specification of the demand curve for a p ro d u c t s h o u ld in c lu d e the p r ic e s o f com plem entary and s u b s titu te goods w h ile th e s u p p ly f u n c t io n f o r a p ro d u ct should in c lu d e a l l o f th e in p u t p r ic e s. Since some p ric e s w ill appear in more th a n one demand o r supply fu n c tio n, th e f i r s t method w o u ld r e q u ir e s im u lta n e o u s estimation of e v e ry p r o d u c t's demand and su p p ly fu n c tio n s, an e xtre m ely complex task. In a d d it io n, one o f th e goals of th is m odel is to c re a te th e c a p a b ilit y o f r e p lic a t in g some of th e.ta b le s in th e N a tio n a l Income and Product A ccounts (N IP A ) p ro d u ce d by th e Bureau o f Economic A n a ly s is (BEA). Since th e NIPA do not re p o rt o u tp u t p r ic e s, th e f i r s t method is not p o s s ib le i f c o n s is te n c y with the N IP A is de sire d. However, the BEA does have readily available u n p u b lis h e d data on fa c to r incom e by in d u s t r y - called Gross P ro d u c t O r ig in a tin g (GPO) - th a t is c o n s is te n t w ith i t s GNP a c co u n ts. For th e above re a s o n s, th is s tu d y m odels prices by fo re c a s tin g f a c to r incomes and th e n d e te rm in in g p ric e s. T h is decision im m e d ia te ly presents us w ith another p ro b le m. Prices relate, of c o u rs e, t o p ro d u c ts ; b u t GPO r e la te s to i n d u s t r i e s, d e fin e d as a collection of establishments h a v in g th e same p rim a ry p ro d u ct but p ro d u cin g perhaps numerous secondary p ro d u cts th a t are p rim a ry to o th e r in d u s t r ie s. This n o n - c o n fo r m ity between products and in d u s tr ie s makes " b r id g in g " between th e tw o an im p o rta n t ta s k to be accom plished in t h is s tu d y.

7 3 The s e c to r d e f in it io n s are d ic ta te d by th e co n ve n tio n s used by th e BEA in th e ir u n p u b lis h e d 6P0 d a ta, which is organized in t o s ix t y - fo u r t w o - d ig it SIC in d u s tr ie s. For t h is m odel, th e in d u s tr ie s are aggregated to a s l i g h t l y more compact le v e l o f 42 in d u s tr ie s. The a g g re g a tio n was prim arily in the service sectors where d a ta on o u tp u t and p ric e s is s c a n t. The o r d e r in g i s shown in F ig u re 1-1 : thirty-seven private i n d u s t r i e s, fo u r government s e c to rs and th e re s t o f th e w o rld. GPO f o r each in d u s try can be th o u g h t o f as d iv id e d among la b o r, capital and governm ent, and th e re subdivid e d in to a t o t a l o f th ir te e n components: Labor Compensation 1 ) Wages and S a la rie s 2 ) Wage Supplem ents R eturn to C a p ita l 3 ) Net In te r e s t Payments 4) C o rpora te C a p ita l Consumption Allow ances 5) N oncorporate C a p ita l Consumption Allow ances 6) Business T ra n s fe r Payments 7) C o rpora te P r o f it s 8) P r o p r ie to r Income 9) C o rpora te In v e n to ry V a lu a tio n Adjustm ents 10) N oncorporate In v e n to ry V a lu a tio n Adjustm ents 11) R e nta l Income Taxes and s u b s id ie s 12) I n d ir e c t B usiness Taxes 13) Government S u b sid ie s The deve lopm ent o f te ch n iq u e s f o r fo re c a s tin g th e v a rio u s components by in d u s try c o n s titu te s th e main ta s k o f t h is s tu d y. Opder of, P re s e n ta tio n B e fo re p ro c e d in g, a b r ie f account o f th e c o n te n ts o f th e rem aining work may a id th e reader a t t h is p o in t. I n th e following section, the framework of the entire INFORUM m odel i s d e scrib e d to make c le a r th e overall structure into which this price-income sub-model w i l l be

8 4 F ig u re 1-1 6P0 SIC SECTOR TITLE CODE 1 Farm & A g r ic u lt u r a l S e rvice s Crude Petroleum & N a tu ra l Gas Mi n i ng , C o n tra c t C o n s tru c tio n Food & Tobacco T e x t ile M ill P roducts A pparel and R elated P roducts Paper and A llie d Products P r in tin g and P u b lis h in g 2700 ' 10 Chemical and A llie d P roducts P etroleum and R elated In d u s trie s Rubber & Mi sc P la s tic P roducts L e a th e r and Leath e r P roducts Lumber & Wood P ro d u cts,e x Furn F u rn itu re and F ix tu re s S tone, C la y, & G lass P roducts P rim ary M etal In d u s trie s M etal P roducts Trans Eq + Ord ex M otor V e h ic le s M a ch in e ry, except E le c t r ic a l E le c t r ic a l M achinery M otor V e h ic le s and Equipment In s tru m e n ts and R elated P roducts M isc. M a n u fa ctu rin g in d u s try R a iiro a d s A ir T ra n s p o rta tio n T ru c k in g and O ther T ra n s p o rt 4100,4200,4400,4600, C om m unciations B lank 30 E le c t r ic, Gas, and S a n ita ry W holesale and R e ta il Trade F in a n c ia l & Insurance S e rvice s , Real E s ta te & Com binations Of 6500, H o te ls & R epair (n o t a u to ) 7000,7200, M isc. Business S e rvice s 7300,8100,8400, A uto Repai r M otion P ic tu re s & Amusements M edical & E d u ca tio n a l S e rvices 8000,8200,8300, P r iv a te Households Federal G o v 't E n te rp ris e s 41 S ta te and L o ca l E n te rp ris e s 42 Blank 43 Blank 44 F ederal Government G eneral 45 S ta te and L o ca l G o v 't G eneral 46 Rest o f th e W orld

9 5 in s e rte d. This background is p re s e n te d t o e n a b le th e re a d e r t o u n d e rsta n d better the c o n te x t in which the p r ic e - in c o m e m odel m ust o p e ra te. A b r ie f summary o f p re vio u s INFORUM work on th e development o f p r ic e and income models (a more d e ta ile d d is c u s s io n form s an appendix t o t h is c h a p te r) ends th e c h a p te r. C h a p te r I I deals with the technical but s t a t i s t i c a l l y e s s e n tia l " b r id g in g " r e fe r r e d t o a b o ve : th e transformation of v a lu e added by p ro d u c t to value added by in d u s t r y and vice-versa. In c lu d e d in th e p re s e n ta tio n is a d is c u s s io n o f the s t a t i s t i c a l sources f o r th e d a ta on f a c to r incom es. Chapters I I I - V d e s c rib e th e e s tim a tio n o f th e components o f GPO by in d u s tr y. Labor com pensation is th e t o p ic o f C h a p te r I I I. F irst, i t discusses p r e v io u s work and th e n th e p ro p e r lo n g -ru n im p lic a tio n s and d e s ire d p r o p e rtie s o f th e e q u a tio n s. The strategy for getting la b o r c o m p e n s a tio n by c o m b in in g a g g re g a te nom inal h o u rly la b o r com pensation Chic), sectoral relative w ages, and sectoral h o u rs worked is then p re se n te d. C h a p te r IV concerns th e d e te rm in a tio n o f th e v a rio u s components o f th e " r e tu r n to c a p it a l" : c a p ita l consum ption a llo w a n c e s, n e t interest p a y m e n ts, corporate p ro fits, p r o p r ie t o r in c o m e, b u s in e s s transfer paym ents, in v e n to ry v a lu a tio n a d ju s tm e n ts, and r e n t a l in c o m e. F irs t, the r a t io n a le and fram ework f o r fo re c a s tin g th e t o t a l r e tu rn to c a p ita l (d e fin e d as th e sum o f i t s components) and it s relationship to p ro fit in com e is discussed. Because o f th e s im ila r n a tu re o f p r o f i t income (corporate p ro fits and proprietor in co m e ) and the to ta l return to c a p it a l, th e fram ework o f t h is model is t o fo re c a s t th e t o t a l re tu rn and a l l o f i t s com ponents except for c o r p o r a te p ro fits and proprietor

10 6 incom e. The Last tw o are obtained as r e s id u a ls. N e xt, th e t o t a l re tu rn t o c a p ita l and each component w ith a fo re c a s tin g e q u a tio n has a section which presents any previous work and the current fo re c a s tin g m eth odology. C hapter V shows th e methods employed to fo re c a s t in d ir e c t b u s in e s s taxes snd n ontax paym ents, and government s u b s id ie s. In d ir e c t business ta x e s are s u b d iv id e d in to two components: fe d e r a l e xcise ta x e s and a l l o th e r in d ir e c t b u sin e ss ta xe s. Chapter VI is " th e p ro o f o f th e p u d d in g ": a sample fo re c a s t f o r th e p e rio d is presented. That "b a se " run i s used t o exam ine th e effects on s e c to r a l fa c to r rewards and p ric e s o f an in c re a s e in th e r a te o f grow th o f th e money s u p p ly. Chapter V I a ls o b rin g s th e study to c lo s e with a summary and in d ic a tio n s f o r fu tu r e w ork. 1.2 S tru c tu re o f th e INFORUM model T h is stu d y grew out o f a m ajor e f f o r t w hich began in 1980 to expand th e INFORUM m o d e l. A t t h a t t im e, th e INFORUM model did n o t, in practice, d e te rm in e incom e w ithin the m odel: th e present e f f o r t was undertake n to clo s e th e INFORUM model by d e v e lo p in g a u s a b le structure for integrating p r ic e s, income and th e re a l s id e. Besides t h is s tu d y, th e m ethod o f forecasting p e rs o n a l c o n s u m p tio n expenditures 2 in v e s tm e n t in producer dura b le equipment -j and were re v is e d to in c o rp o ra te a variety of hitherto unused v a ria b le s. In order to capture the importance of fa c to r incomes and the com plexity of a "closed" in te r in d u s t r y m odel, a d e s c r ip tio n o f th e INFORUM m odel i s r e q u ir e d. F u rth e rm o re, th e d e s c r ip tio n w ill h e lp make apparent th e m o tiv a tio n f o r 0)

11 7 t h is s tu d y. The m ajor in t e r r e la t io n s a re g r a p h ic a lly d e p ic te d in F igure 1-2. The model begins w ith th e re a l s id e, on th e to p right p o r t io n o f th e fig u r e * In t h is p o r tio n, a l l f in a l demands and o u tp u ts are measured and forecasted in c o n s ta n t d o lla r u n its. On th e f i r s t it e r a t io n f o r a p a r t ic u la r y e a r, an i n i t i a l guess is made o f personal income and p roduct p r ic e s. Except for g o ve rn m e n t e x p e n d itu r e s, which are e x o g e n o u s ly determ ined, a l l of the other f in a l demands are forecasted with regression e q u a tio n s : p e rs o n a l c o n s u m p tio n expenditures u t i l i z e re la tive prices, real d is p o s a b le incom e and an array of demographic v a r ia b le s ; equipm ent in v e s tm e n t makes use o f th e growth in o u t p u t s, s to c k s o f equipm ent and r e la t iv e in p u t p r ic e s ; in vestm e n t in s tru c tu re s e m p lo y s o u tp u ts, in te r e s t r a te s, demographic v a ria b le s and th e s to c k o f s tr u c tu r e s. E xp o rts and im p o rts b o th use foreign prices relative to domestic prices but exports a ls o re q u ire fo re ig n demand in d ic e s w h ile im p o rts use dom estic demand. Taken to g e t h e r, th e s e detailed sectoral e q u a tio n s give fin a l demand by p ro d u c t. O utput is th e n c a lc u la te d by th e in p u t - o u t p u t id e n tity t h a t requires o u tp u t t o e q u a l th e sum o f o u tp u t f o r in te rm e d ia te use p lu s f in a l demands: (1.1 ) q = Aq + f where f = column v e c to r o f f i n a l demands, q = column v e c to r o f o u tp u ts, A = m a trix o f in p u t-o u tp u t c o e f f ic ie n ts. Those e q u a tio n s a re then so lve d f o r re a l o u tp u t.^

12 INFORUM Framework Only major connections are shown. Numbers indicate numbers of rows or columns *

13 8 Productivity is forecasted with tim e tre n d s and o u tp u ts ; o u tp u t and p r o d u c tiv it y are combined to d e riv e employment by s e c to r. F inally, employment is used in conjunction w ith in d e p e n d e n t la b o r force p r o je c tio n s from th e Bureau o f Labor S tatistics in o r d e r to calculate the unem ploym ent rate. At this ju n c tu r e, th e re a l s id e has com pleted one it e r a t io n Underneath th e in te r in d u s t r y flo w t a b le, in th e m iddle o f th e low er p o r tio n o f th e f ig u r e, is th e p r o d u c t- to - in d u s try ta b le th a t d is tr ib u te s v a lu e added from p ro d u c ts to industries d e fin e d on an e s ta b lis h m e n t basis. A reconciliation is n e c e s s a ry because GPO is re p o rte d on an in d u s try d e f in it io n basis while the real s id e i s based on a p ro d u c t d e fin itio n basis. The essential d e fin itio n a l d iffe r e n c e between an in d u s try and a p ro d u ct s e c to r is th a t an in d u s try may produce more th a n one p ro d u c t. T h e re fo re, an in d u s try s e c to r may be re la te d b ^ d e flq itj.o n t o more than one p ro d u c t o r vice versa. Columns o f the product- t o - i n d u s t r y bridge table sum to product v a lu e added w h ile rows sum to in d u s try GPO. Real p ro d u ct o u tp u ts are d is tr ib u te d by th e ir base year contribu tion to in d u s t r y v a lu e added - th e value added fractio n - th e re b y y ie ld in g re a l va lu e added w eighted o u tp u t (REVAWO):' where v.. is th e b rid g e ta b le c e ll in th e i t ^ row and j 1*1 colum n, and Q* is th e re a l o u tp u t f o r th e j* * 1 p ro d u ct in year t This REVAWO becomes th a n a m ajor v a r ia b le in th e e q u a tio n s f o r nom inal GPO in each inustry. The bridge table is th e n used in reverse to translate n o m in a l GPO by in d u s t r y t o n om in al va lu e added by p ro d u c t.

14 9 More s p e c i f i c a l ly, nom inal GPO by in d u s try in any ye a r is d is tr ib u te d to p ro d u c ts in p r o p o rtio n to each p ro d u c t's c o n tr ib u tio n to th e industry's REVAWO in that year. Value added per u n it o f re a l o u tp u t w ill then be used t o c a lc u la te p ro d u ct p ric e s as e x p la in e d below. C o n s e q u e n tly, th e p r o d u c t - t o - in d u s t r y b r id g e ta b le is a v i t a l li n k between th e re a l and nom inal s id e s o f th e m odel. Chapter I I focuses on t h is lin k. The various pieces of GPO are forecasted using a m ixture of variables com ing from the real sid e and exogenous p o lic y in s tru m e n ts. Sectoral la b o r co m p e n s a tio n u tiliz e s a r e la tiv e wage s tru c tu re ; sectoral e q u a tio n s a re com bined with aggregate values to determ ine h o u rly la b o r com pensation by in d u s try. The re tu rn to capital ( d e fin e d a s th e sum o f d e p r e c ia t io n, n e t interest p a y m e n ts, p ro fit in co m e, b u sin e ss transfer paym ents and in v e n t o r y valuation adjustments) is fore ca ste d w ith changes in output, im port and export shares, u n e m p lo y m e n t, capital-labor ra tio s and a few secto ra l s p e c ific variables. A ll of the com ponents o f th e return to c a p ita l are then s e p a ra te ly com puted by s e c to r e x c e p t for p ro fit in c o m e ; p ro fits and p r o p r ie t o r incom e a re d e te rm in e d as r e s id u a ls. corporate In d ir e c t business ta x e s a re fu n c tio n s o f fe d e ra l e x c is e ta x r a te s, o u tp u t and the s to c k o f s tr u c tu r e s. Chapters I I I - V d e s c rib e these e q u a tio n s. A fte r p a ssin g GPO through th e b r id g e t a b le t o d e te rm in e n o m in a l value added by product, prices are then ca lcu la te d using the in p u t- o u tp u t id e n t it y th a t u n it o u tp u t p ric e e q u a ls u n it m a te r ia l costs p lu s u n it v a lu e added. T his id e n t it y is (1.3 ) p = pa + v where p = row v e c to r o f o u tp u t p r ic e s, and v = row v e c to r o f va lu e added per u n it o f consta n t d o lla r o u tp u t. \o

15 10 As in th e case for real p ro d u c t o u tp u ts / th e p ric e e q u a tio n s are then s o lv e d. W ith th e d e te rm in a tio n o f p ric e s by p ro d u ct and th e f a c t o r incom es by in d u s t r y com pleted, th e p a rt o f th e model nicknamed th e "A cco u n ta n t" ta k e s o v e r. The Accountant uses th e in fo rm a tio n g e n e ra te d by th e real and GPO m o d e ls and computes s e le c te d ta b le s o f th e N a tio n a l Income and P ro d u c t Accounts. In a d d it io n, tran sfer payments, personal and corporate tax payments and some m inor components o f income are computed in o rd e r to o b ta in a c o n s is te n t e s tim a te o f d isp o s a b le p e rs o n a l in co m e. Then a n o th e r iteration of the e n tir e model b e g in s. The re a l sid e i s re s o lv e d u sin g th e re v is e d v a lu e s o f p ric e s and p e rso n a l incom e. GPO by in d u s try and p r ic e are recom puted. Then th e e s tim a te o f d is p o s a b le in co m e is revised. This p ro c e s s co n tin u e s u n t il th e change in o u tp u t fro m it e r a t io n to it e r a t io n is less th a n a specific tolerance l e v e l ; u s u a lly th e to le ra n c e is th a t no product o u tp u t changes by more th a n one p e rc e n t between it e r a t io n s. The result is a fo re c a s t which is co m p le te ly c o n s is te n t. Nominal GNP c a lc u la te d as th e product o f prices and fin a l demands w ill e q u a l nominal GNP as the sum of factor in c o m e s. This consistency flows n a tu r a lly from the in p u t-o u tp u t framework, and can be see by p r e m u ltip ly in g (1.1 ) by p and p o s tm u ltip ly in g (1.3 ) by q t o g iv e (1.1 1) pq = paq + pf and (1.3 1) pq = paq + vq. T a kin g th e d iffe r e n c e between those e q u a tio n s shows 0 = p f - v q, o r th a t (1.5 ) p f = v q. \ \

16 11 The Left s id e o f (1.5) is the p ric e o f each p ro d u ct m u lt ip lie d by th e f i n a l demand f o r th e product or nom inal 6NP on th e p r o d u c t s id e. The r ig h t side is the v a lu e added p e r u n i t o f o u tp u t o f each p ro d u c t m u lt ip lie d by th e o u tp u t o f th e p ro d u ct o r nom inal 6NP from the incom e side. Such c o n s is te n c y i s a v a lu a b le a id to a lo n g -te rm fo re c a s tin g model s in c e i t p ro v id e s a n a tu ra l check w ith in th e model and reduces th e a amount o f ad hoc adjustm e nts to o b ta in a fo re c a s t. 1.3 S urvey o f P re v io u s Work Toward a Complete INFORUM Model In o r d e r to highlight the rationale for the p r e s e n t study, a description of th re e p re vio u s Ph. D. d is s e r ta tio n s w hich have attem pted t o deal w ith p ric e and income d e te rm in a tio n follows. The f i r s t s tu d y was c o n c e rn e d with the forecast of d is p o s a b le incom e, th e second with p r ic e d e te rm in a tio n, and th e th ird with in te g r a te d prices and factor in c o m e s. This section b rie fly d e s c rib e s each s tu d y ; a more d e ta ile d p re s e n ta tio n o f a l l th re e s tu d ie s is in th e appendix t o t h is c h a p te r. B ria n O'Connor in 1973 com pleted h is work on fo r e c a s tin g d is p o s a b le incom e.^ O'Connor fo c u s e d on factor re w a rd s that were com ponents o f d is p o s a b le incom e: la b o r com pensation, n e t in te r e s t payments and p r o f it income (c o rp o ra te p r o f it s were re q u ire d t o o b ta in d iv id e n d s ). Each type o f incom e was prim arily forecasted as a f u n c t io n o f a t r e n d. T h is s p e c ific a t io n meant t h a t those income components were n o t lin k e d t o th e r e a l s id e o f th e m odel. In a d d itio n, f a c to r incomes were not in te g ra te d with p r ic e s. Since t h is study a tte m p ts to in te g r a te th e r e a l, p ric e and income p o rtio n s o f th e INFORUM m odel, th e O'Connor stu d y was n o t used in th e p re se n t w o rk.

17 12 I n 1976, David G itm artin f in is h e d "Forecasting Prices in an In p u t-o u tp u t Framework."^ Gilmartin d e v e lo p e d a specification t h a t allowed demand and supply effects to in flu e n c e p r ic e. P ric e by product was s p e c ifie d as a fu n c tio n o f a d is tr ib u te d la g o f re a l o u tp u t (demand effe ct) and of a distributed Lag of u n it la b o r co sts (s u p p ly effect). For the purposes o f th e present w o rk, th e G ilm a rtin a p p ro a c h c o u ld n o t be u se d. Because th e NIPA does not p u b lis h p ric e s f o r p ro d u c ts, th e re is no guarantee that a n o th e r d a ta so u rce o th e r th a n NIPA for prices would im p ly th e GNP re p o rte d in th e NIPA. M oreover, G ilm a rtin d id not lin k fa c to r incomes w ith p r ic e s, a prim e m o tiv a tio n f o r t h is s tu d y. The la s t e f f o r t was a c c o m p lis h e d by David B e lz e r i n 1978 and is described in "An I n t e g r a t io n o f P ric e s, Wages, and Income Flows in an In p u t-o u tp u t M odel o f th e U n ite d States."^ As it s t i t l e indicates, Belzer's work was an im p o rta n t ste p fo rw a rd sin ce th e INFORUM model had th e c a p a b ilit y to fo re c a s t re a l v a r ia b le s, prices and in com e within a consistent fram ew ork. The s tru c tu re o f th e model was t o a llo w th e re a l s id e to f o r e c a s t, th e n product p ric e s were c a lc u la te d, th e n v a lu e added and fa c to r rewards were calcluated and f in a lly, the prices and d is p o s a b le income were fe d back in t o th e re a l s id e. Though th e work was an important step forw ard, i t s general framework is not d u p lic a te d in this s tu d y. Because o f i t s s iz e and b re a d th, the Belzer m odel r e q u ir e d d a ta from a variety of s o u rc e s, sources that did not use the same d e fin itio n a l c o n v e n tio n s. As a r e s u lt, th e B e lz e r model had many tra n s fo rm a tio n procedures to move data from one d e f in it io n to another d e f in it io n. These procedures added an extra le v e l o f in t r ic a c y to an a lre a d y complex model th a t was d i f f i c u l t t o use. Even th o u g h this m odel was b u ilt with the capacity to run \2>

18 13 s im u lta n e o u s ly with the r e a l s id e, i t s c o m p le x ity and th e r e q u is ite com puter time made th e s im u lta n e o u s r u n n in g o f th e e n t ir e m odel an im p r a c tic a l ta s k. In p r a c tic e, th e sim ultaneous lin k between p ric e s and the real s id e was severe d ; th e re a l s id e was run ove r th e le n g th o f a fo r e c a s t, and th e n th e p ric e model was run u sing th e r e a l v a ria b le s from th e p re v io u s ru n. Those p ric e forecasts were th e n fed back into the real s id e, and th is p ro c e d u re was repeated u n t il th e model converged. In a d d ito n, th e lin k between p ric e s and income was also s e v e re d : real d is p o s a b le in com e was s p e c ifie d exogenously on th e assum ption t h a t th e fe d e ra l governm ent would a d ju s t i t s e x p e n d itu re s in o r d e r to stabilize the o v e r a ll unemployment r a te. T h e re fo re in p r a c tic e, th e model was not clo se d a lth o u g h th e c a p a b ility fo r having income determ ined endogenously was p re s e n t. The abandonm ent o f most o f th e p re v io u s work on income amd p ric e fo rm a tio n o u g h t n o t be viewed as a d is p a ra g e m e n t o f th o s e e ffo rts. Rather, the s e e m in g ly constant f lu x in models re p re s e n ts th e d i f f i c u l t task of constructing forecasting m o d e ls f o r n o m in a l v a lu e s. Each I t version can be viewed as a b a s is f o r fu tu r e im provem ents. In a d d itio n, a l l o f th e p re v io u s s tu d ie s s u ffe re d th e common f a te o f most la rg e scale m odels; th e models worked as long as th e b u ild e rs o p e ra te d th e m. The complexity and dive rsity of th o s e s tu d ie s made th e in t e g r a t io n and u p d a tin g o f th e models a cumbersome ta s k. New c o n c e rn s and m o d e llin g desires c o u ld n o t often be e a s ily im plem ented w it h in th e fram ework o f th e e n t ir e m odel. The new s im p lifie d s tr u c tu r e o ught t o go a lo n g way in th e accom odation o f th e in e x o ra b le fo rc e o f change. One p ro b le m th a t was common to a l l o f these e a r lie r e f f o r t s and to th e p r e s e n t s tu d y is that the b e s t and m ost effe ctive explanatory r

19 14 in s tr u m e n ts o f n o m in a l v a ria b le s are p ro b a b ly o th e r nom inal v a ria b le s. However, th e use o f endogenous n o m in a l variables to forecast factor incom es and p rice s introduces the problem of simultaneanous d e te rm in a tio n in t o th e model because any endogenous n o m in a l variable p re s u p p o s e s the existence of the price i t is supposed t o e v e n tu a lly d e te rm in e. These c o n s id e r a tio n s indicate the practical re sponse o f relying on a mix of endogenous real variables and exogenous p o lic y in s tr u m e n ts, real and n o m in a l, to predict incom es and prices. The efficacy of the e s tim a te d e q u a tio n s is lim ite d by t h is s o lu tio n. D e sp ite th e fo re g o in g d is c u s s io n, th e developm ent o f a nom inal sid e t o an in te g ra te d in te r in d u s tr y fo re c a s tin g model o f th e U n ite d S ta te s is n o t an im p o s s ib le ta s k. In th e fo llo w in g c h a p te rs, t h is re se a rc h e r has t r i e d to combine th e d ic ta te s o f economic th e o ry with the re strictio n s of econometric m o d e llin g in th e specification of th«forecasting e q u a tio n s. \ < r

20 ENDNOTES 1. See Paul D e v in e, "A Cross-Sectional and Tim e Series Analysis of C o n s u m p tio n," u n p u b lis h e d Ph.D dissertation, U n iv e r it y o f M aryland, See Anthony B a rb e ra, "A Study o f th e D e te rm in a n ts o f Factor Demand by I n d u s t r y, " u n p u b lish e d Ph. D. th e s is, U n iv e r s ity o f M aryland, The a c tu a l s o lu tio n is more in v o lv e d th a n taking an inverse. The determination of e q u ip m e n t in v e s tm e n t, im p o rts, in v e n to ry changes and re a l o u tp u t in v o lv e s sim ultaneous r e la tio n s h ip s. An it e r a t iv e procedure is used in th e d e te rm in a tio n o f these re a l v a ria b le 's. 4. The in c lu s io n o f im p o rts in cre a se s th e c o m p le x ity o f th e p ro o f b u t does not change th e lo g ic. Using th e same n o ta tio n as b e fo re, le t A = D + M, D e fin e, where D = 1-0 m a trix f o r dom estic re q u ire m e n ts, and M = 1-0 matrix of require m ents o f im ported goods. r - v e c to r o f p ric e s o f im ported goods, and m = v e c to r o f dom estic demand f o r im p o rte d goods. Then (1.5 ) m = Mq + h, where h = v e c to r o f dom estic demand f o r f i n a l im p o rte d goods. E q uations (1.1 ) and (1.3 ) now become (1.1 *) q=dq + Mq+f-m, and (1.3 * ) p = pd + rm + v. P re m u ltip ly in g by p and p o s tm u ltip ly in g by q y ie ld s ( ) pq s pdq + pmq + p f - pm, and ( ) pq = pdq + rmq + vq S o lv in g f o r vq and s u b s titu tin g (1.5 ) g iv e s th e r e s u lt ( 1.4 1) vq = p ( f - h) - rmq o r th a t Nominal 6NP = va lu e o f demand f o r dom estic goods e v a lu a te d a t dom estic p ric e s le s s th e va lu e o f im p o rte d in t e r m ediate fo re ig n goods e va lu a te d in fo r e ig n p r ic e s. 5. Brian C C o n n o r, "An Incom e S ide t o an In p u t-o u tp u t Model o f th e U n ite d S ta te s," unpublished Ph.D. d is s e r t a t io n, U n iv e r s ity o f M aryland, \G

21 6. D avid G ilm a r tin, "F o re c a s tin g P ric e s in an In p u t-o u tp u t F ra m e w o rk," u n p u b lis h e d Ph.D. d is s e r t a t io n. U n iv e rs ity o f M aryland, David Belzer, "An In te g r a tio n o f P ric e s, Wages and Income Flows in an In p u t- O u tp u t M odel o f the United S ta te s," unpublished Ph.D. d is s e r t a t io n. U n iv e r s ity o f M aryland, 1978.

22 Appendi x S u rve y o f P re v io u s Work Toward a Complete INFORUM model The model developed in t h is th e s is i s n o t th e f i r s t atte m p t to deal w -ith price and incom e d e te r m in a tio n in th e INFORUM project. Three p re v io u s Ph.D. d is s e r ta tio n s have ta c k le d th o s e two areas: one dealt with the d e te r m in a to n o f in c o m e, one with price f o r m a t io n, and one in te g ra te d p r ic e s and incom es. T h is appendix review s th e m ajor results of these s tu d ie s. D uring t h is re v ie w, e x p la n a tio n s w ill be presented as t o why much o f th e p r io r work is not in c o rp o ra te d in t o th e new m odel. O'C o nnor, Incom e Model In 1973, B ria n O'Connor finished the f ir s t a tte m p t t o m odel th e income s id e. The major focus of "An Incom e S id e to an In p u t-o u tp u t M odel o f th e U n ite d States" was to d e v e lo p a method to determine disposable personal in c o m e. T h is accom plishm ent w ould serve to clo se th e INFORUM m odel. At th e same tim e o f th e O 'C onnor s t u d y, tw o o th e r doctoral re se a rch p r o je c ts were underway, m o d e llin g in d u s tr y p ric e s and wages, thereby r e s t r ic t in g the scope of O 'Connor's e f f o r t s. C o n s e q u e n tly, s im p le p ro c e d u re s were d e v is e d t o g e n e ra te an a c tu a l fo re c a s t o f th e income m odel. S e c to ra l la b o r com pensation per em ployee and gross p ro fits were forecasted with tim e tre n d s. The re lia n c e on tre n d s is n o t adequate f o r an in te g ra te d m odel; wages and incom es w ill have no effect on p r ic e s, nor is th e re any in te r a c t io n between th e re a l and nom inal s e c tio n s. The most impressive contribution of the O 'C onnor m odel was th e system to p r e d ic t in d u s try la b o r com pensation by s iz e c la s s. The Bureau

23 o f L a b o r S ta tis tic 's 1965 matrix that related in d u s t r ie s with t h e ir o c c u p a tio n a l r e q u ire m e n ts ( in d u s t r y occupation matirx) was used to derive th o s e labor c o m p e n s a tio n d i s t r ib u t i o n s f o r 54 in d u s tr ie s. In a d d it io n, d is trib u tio n s for r e n t a l in c o m e, proprietor in c o m e, and in te re s t income were also established. Those d istrib u tio n s were combined t o make a more a ccu ra te fo re c a s t o f fe d e ra l p e rso n a l income ta x paym ents. T h is e x e rc is e was not repeated in t h is study f o r two reasons. F irst, the m a in te n a n c e o f th e s e la b o r c o m p e n s a tio n d is tr ib u tio n s re q u ire s th e p r o je c tio n o f th e in d u s try -o c c u p a tio n m a tr ix, an e f f o r t f a r beyond th e scope o f t h is s tu d y. S econdly, th e d is t r ib u t io n o f income by size-class is forecasted as p a r t o f th e determination of p e rs o n a l consum ption e x p e n d itu re s, som ething t h a t was not done a t th e tim e o f th e O'Connor m odel. The O'Connor model was founded on p u b lis h e d d a ta cla ssified on a company basis implying the industry incom es to be independent o f the fo re c a s te d p r ic e s and in p u t-o u tp u t ta b le s u n le ss specific attention is p a id to th e r e c o n c ilia t io n between th e company and p ro d u ct d e f in it io n o f a sector. The p re se n t work developed a procedure to make th e fo re c a s ts o f v a lu e added by e s ta b lis h m e n t based on the BEA's Gross Product O rig in a tin g (GPO) s e rie s correspond to va lu e added by p ro d u ct as d e fin e d by th e 1972 in p u t-o u tp u t ta b le. U n fo r tu n a te ly, com puter lim ita tio n s fo rc e d th e O'Connor model to be ru n in d e p e n d e n tly from th e re a l m odel. In o rd e r to o b ta in a fo r e c a s t, f i r s t th e re a l model was ru n. Then th e income model was ru n u s in g th e previously forecasted variables from the real m odel. Then th e re a l model was re ru n w ith th e new fo re c a s ts o f in c o m e. This procedure was repeated u n t i l the o u tp u t o f th e real s id e c o n v e rg e d betw e en th e V \

24 ite ra tio n s of the two m o d e ls. The e n o rm o u s s trid e s in computer te c h n o lo g y allowed the INFORUM model to be s tru c tu re d to seek a e n tir e s o lu tio n on a ye a r by year b a s is. O verall, the re q u ire m e n ts o f a c o n s is te n t interindustry model d ic t a t e d the developm ent of a new fram ew ork. Though the in d u s try -o c c u p a tio n m a trix approach is a p p e a lin g, th e g e n e ra tio n o f th e d is t r ib u t io n of incomes to forecast PCE made t h is a p p ro a c h an unnecessary d u p lic a tio n. C onsequently, n o th in g from th e O 'C onnor model was u t iliz e d in th e c u rre n t effort. Gilm a r t in P ric e Model David G ilm a r tin completed a th e s is in 1976 entitle d "Forecasting Prices in an In p u t- O u tp u t Framework." G ilm a rtin sought to deve lop a method t o fo re c a s t in d u s try p ric e s a t th e f u ll level of detail of the INFORUM m o d e l. The m odel forecasted prices on a m onthly b a s is. The s p c if ic a t io n o f th e in d u s try p ric e e q u a tio n s in c lu d e d a measure o f costs and a measure o f demand p re ssu re : in effect, the specification was a s im p le reduced form o f th e demand and supply r e la t io n f o r each p ro d u c t. The p ric e e q u a tio n to o k th e form where P1 = a + / v"1 UC1, + ^ w? Q1, + seaso nal dummies t k t - k k t - k P1 = g ro ss o u tp u t p ric e f o r s e c to r i in month t, t (s o u rc e : BLS WPI and CPI data s e rie s ) UC1 == u n it p ro d u c tio n co sts f o r s e c to r i in month t, t ( to be d e fin e d below) Q1 = re a l o u tp u t f o r s e c to r i in month t, t (s o u rc e : FRB m onthly o u tp u t in d ic e s )

25 I and v,w are d is t r ib u t e d Lag w e ig h ts w ith th e sums c o n s tr a in e d to f a ll w ith in a "re a s o n a b le " range. U n it p r o d u c tio n costs were d e fin e d as th e sum o f u n it m a te ria l c o s ts and u n it la b o r c o s ts. T h e re fo re, U C 1 = 5 a.. p1 + WL1 / Q1 - t TJ t t t ' where = in d u s try requirem ent c o e f f ic ie n ts, WL1 t = s e c to ra l wage b ill c a lc u la te d as th e p ro d u ct o f wage ra te s f o r p ro d u c tio n w o rkers and t o t a l hours in each in d u s tr y, (s o u rce : BLS Employment and E a rnings D a ta ). The la g s tr u c tu r e s were e stim a te d w ith p o lyn o m ia l d istribu tions with a u s u a l la g le n g th were twelve m onths f o r th e cost v a r ia b le and tw enty fo u r months f o r th e o u tp u t v a r ia b le. As m entioned above, th e sum o f th e la g w e ig h ts was c o n s tra in e d ; th e lim it s f o r th e cost w e ig h ts were and 1.25 w h ile th e sum o f th e o u tp u t w e ig h ts was between and The e q u a tio n s were e stim a te d over the p e rio d 1954 to December, 1972 with th e p re c is e s t a r t in g p o in t determ ined by th e in d u s try d a ta. F if t y in d u s tr ie s la cked th e r e q u is ite data f o r e s tim a tio n. The G ilm artin a p p ro a ch was n o t duplicated in this s tu d y f o r a variety o f re a s o n s, b o th m e th o d o lo g ic a l and p r a c t ic a l. The model was designed to o p e ra te re c u rs iv e ly in o rd e r to a v o id s im u lta n e ity p ro b le m s in th e s o lu tio n o f o u tp u t p ric e s and m a te ria l p r ic e s : no c u rre n t valu e s o f u n i t cost were in c lu d e d in th e s p e c ific a tio n o f th e p ric e e q u a tio n. The m o n th ly structure is u n d u ly cumbersome f o r a lo n g - te rm model. Benefits from a m o n th ly model decline as the le n g th of th e fo re c a s t increases, especially when th e benefits are weighed against the

26 co m p u ta tio n a l c o s ts embedded in th e d e s ig n. The s p e c ific a tio n of the price e q u a tio n also le a d s t o some problem s. Due to a la c k o f c a p ita l sto cks d a ta, G ilm a r tin e x c lu d e d th e c o s t o f capital fro m th e c o s t v a r ia b le. Labor com pensation com prises a p p ro x im a te ly 60% o f value added which le aves u n a ccounted f o r a rather large portion o f v a lu e a dded. In a d d itio n, th e e m p iric a l r e s u lt th a t th e sum o f th e la g weights on the cost variable did n o t e q u a l one implies that th e share o f p r o f it s rose (fell) when th e sum was g re a te r (less) than o n e. This property led to r is in g p r o f i t shares in in d u s trie s in recession years, an im plication n o t b o rn e o u t by e xp e rie n ce f o r those in d u s tr ie s. M o re o v e r, a n o n z e ro c o e ffic ie n t sum on the output va ria b le indicates increasing or decreasing r e tu r n s t o sca le in th e lo n g -ru n. T h is is a c u rio u s im p lic a tio n f o r a demand v a r ia b le. A more re a s o n a b le s p e c ific a t io n w ould a llo w f o r consta n t re tu rn s t o s c a le in th e lo n g -ru n. M o re o v e r, th e s p e c if ic a t io n excludes m onetary e f fe c ts on in d u s try p r ic e s. G ilm a r tin argued t h a t th e correct effect o f m o n e ta ry policy should be p re s e n t in th e movement o f re a l o u tp u t. M onetary p o lic y ought t o affect fin a l demands v ia interest rate and real balance e f fe c ts. Thus th e model l e f t th e m oney-price lin k to th e c o n s tru c tio n o f th e re a l s id e. T h is m ig h t be a more accurate view of the transmission o f th e lo n g - r u n m o n e y -p ric e relationship. In C h a p te r I I I, t h is rese a rch e r argues th a t th e effects ought to present in th e model and th e s im p le s t and m ost e ffe ctive m o d e llin g method is to have th e e f f e c t o f monetary p o lic y d ir e c t ly im pact on va lu e added. D e sp ite th e s ta te d d e fic ie n c ie s, th e G ilm a r tin approach does have a s tro n g i n t u i t i v e a p p e a l. The p ric e e q u a tio n s a re re d u c e d forms of the

27 u n d e r ly in g demand and s u p p ly f u n c t io n s. The e q u ilib r a t in g fo rc e o f demand and s u p p ly interactions is the preferred m ethod o f m o d e llin g price formation. Abstracting fro m th e id e n tific a tio n p ro b le m s and s p e c ific a t io n errors, the m e th o d o lg ic a l fo u n d a tio n o f th e Gilmartin m odel i s s tro n g. In t h is c o n te x t, th e G ilm a rtin approach could be used t o d eterm ine p ric e s by in d u s try and then compute va lu e added by solving "backwards" the in p u t - o u t p u t price d e fin itio n. The r e s u ltin g va lu e added co u ld th e n be f u r t h e r d iv id e d in t o i t s com ponents by regression e q u a tio n s. U n fo r t u n a t e ly, th e re is a c o m p e llin g argument a g a in s t t h is d e s ig n. One o f th e b a s ic m o tiv a tio n s f o r th e re v is io n o f th e INFORUM model is to fo re c a s t some o f the NIPA ta b le s. However, th e re is no data series for output price s by industry c o n s is te n t with the national accounts. F u rth e rm o re, th e construction o f such a data series would require a series o f in p u t - o u t p u t tables, data which does n o t e x is t. T h e re fo re, th a t n a tu ra l co n s is te n c y o f an in t e r in d u s t r y m odel shown in th e f i r s t chapter is in c o m p a tib le with th e " n a t u r a l" method o f p ric e m o d e llin g g iv e n th e s ta te o f U.S. data collection. The result is that prices w ill be d e te rm in e d more m e chanically w ith th e p ric e d e f in it io n d e p ic te d e a r lie r by e q u a tio n 1.3 : u n it price e q u a ls u n it material costs plus u n it v a lu e added.

28 Be lz er Synthesis: An_Integrated_Income_and_Price_Model A Ph.D. thesis was completed in 1978 by David B elzer e n title d "An In te g r a tio n of P ric e s, Wages and Income Flows in an Input-Output Model of the United S tates." The important ch a racte ristic of the B elzer work is the o v e ra ll scope and breadth of the study. The model was the f i r s t INFORUM attem pt to in te g ra te the real and nominal flow s w ith in the in t e r in d u s tr y s tr u c tu r e. P ric e s, fa c to r rewards, ta x re c e ip ts and d isp o sa b le income were a l l u n ifie d in to a c o n s is te n t model of the U.S. economy. Great care was taken to forecast output prices, fin a l demand d e fla to rs and consumer p ric e s by in d u s try. Most of the major components of value added flowed out of the s o lu tio n of the model. Consequently, the INFORUM model became closed on the income and p ric e side in a c o n s is te n t manner fo r the f i r s t tim e. Since the model was Large in scope, the precise s p e c ific a tio n s o f th e e q u a tio n s are reviewed, where relevant, in the follow ing chapters; a general overview is presented here. The s tru c tu re of the e n tire model is d iffe re n t from the present one. The re a l side in te ra c te d w ith a q u a rte rly price-wage m odel. Q u a rte rly values were annualized in order to ite r a te to a solution. A fter a completed forecast of the real side, the income model computed the v a rio u s components of value added. Real disposable income was calculated and compared with the series used in the fo re c a s t. I f the assumed and c a lc u la te d real disposable incomes were w ith in a specified tolerance Level, the forecast was fin is h e d. I f n o t, the fo re c a s t was rerun w ith the c a lc u la te d real disposable income fed back in to the model. The price-wage model was structured on a q u a rte rly basis in order

29 to capture some of the p ric e dynamics. Prices were calculated as the sum of u n it m aterial costs, u n it labor c o s ts, u n it c a p ita l costs and u n it in d ir e c t business ta x e s. Unit m aterial costs were a mechanistic ca lcu la tion in vo lvin g the input-output c o e fffic ie n t m atrix, the share of imports m a trix, import prices, and domestic p ric e s. U n it labor costs were obtained by d iv id in g wage indices by labor p ro d u ctivity indices. Wage indices were the product of aggregate wage change and s e c to ra l r e la t iv e wages. Changes in the consumer p ric e index (CPI) and the unemployment rate were the key variables in the o v e ra ll e xp la n a tio n of industry wages. Unit cap ital costs were a simple markup over unit labor c o s ts, where the markups were a fu n c tio n of the unemployment rate, changes in output and the output-capital stock ra tio. In d ire ct business taxes were divided in to two categories - ad valorem and a l l other - and fo r e c a s te d w ith two d i f f e r e n t m ethods. Ad va lo re m taxes were p r o p o r t io n a l to p r ic e s and tr e a te d as a d ia g o n a l flo w o f th e in p u t-o u tp u t c o e ffic ie n t m a trix. The remainder were constrained to equal with state and local expenditures. Taking prices and the c o e ffic ie n t m a trix from the fo re c a s t, the income model recalculated product value added. Product value added was then transformed to industry GPO with a procedure described in chapter two of th is s tu d y. Industry labor compensation was computed using the hours data from the real side and the industry wage data from the p ric e s id e. Labor com pensation, d e p re c ia tio n, and in d ire c t business taxes were deducted from the value added. S ectoral p ro p rie to r income used t h a t re m a in d e r, th e number of p r o p r ie t o r s, and h o u rly employee compensation in i t s determ ination. Sectoral corporate p r o fits, business tra n sfe r payments, inventory v a lu a tio n adjustm ents, and net in te re s t

30 payments were obtained as a residual. A few fe a tu re s of the B elzer model have been re ta ine d in the current version the INFORUM model. The method of fo re c a s tin g c a p ita l consumption allowances by constructing a h is to ric a l depreciation series is maintained. The combination of aggregate and re la tiv e wage equations is also preserved though major m odifications to the tim e in te r v a l and s p e c ific a tio n have been enacted. The B elzer price-wage model was a q u a rte rly one. An annual solution in te rva l is used in order to minimize computational burdens and minor in s ta b ility problems. In a d ditio n, data re s tric tio n s constrained Belzer to estimate re la tiv e wage equations and tra n s fo rm a tio n equations th a t related wages fo r production workers to la b o r compensation ra te s f o r a l l em ployees. T h is fram ew ork is unnecessary when the NIPA accounts fo r lab o r compensation and hours worked by employees are u tiliz e d. F in a lly, the d e te rm in is tic approach to price form ation was retained. The co m p le xity of the Belzer model was g reatly sim p lifie d in the development of the model in th is study. The redundant procedure of the re c a lc u la tio n of value added from product prices is eliminated fo r a more straightforw ard approach. A ll of the re q uisite inform ation fo r the computation of industry prices exists upon the determination of a l l the components of GPO. More careful attention is given to the transmission mechanism of changes in the money supply to in d u s try p ric e s over the long-run in the present work. 2 lco

31 \ Chapter I I S ta tis tic a l and Modelling Structure of GPO C o n c e p tu a lly, the procedure fo r fo re c a s tin g fa c to r rewards and p ro d u c t p r ic e s i s a s tr a ig h t fo r w a r d ta s k w it h in an in t e g r a t e d in te r in d u s tr y model. One ju s t fo re c a s ts fa cto r rewards in making each product, sums the rewards fo r each product to o b ta in t o t a l value added, and th e n c a lc u la te s the product p ric e s from the basic in p u t-o u tp u t d e fin itio n th a t price equals un it material costs plus u n it value added Cp = pa + v ). In p ra c tic e, th is task is complicated by the fa ct that the structure of the real side is based on a product d e f in it io n of a sector while the data fo r fa cto r rewards is based on an industry d e fin itio n. The s tr u c tu r e fo r re s o lv in g the problems a r is in g out of the use of both d e fin itio n s in the same model is the topic of th is chapter. As noted before in Chapter one, the co m p lica ting fe a tu re in the c o n s tru c tio n of the model is the product versus industry d e fin itio n. For th is study, a product is a group of s im ila r commodities or se rvice s as d e fine d by a two or th re e -d ig it standard in d u s tria l c la s s ific a tio n (SIC). An in d u s tr y is th e c o ll e c t io n o f a l l p r o d u c in g l o c a t i o n s o r "e s ta b lis h m e n ts " sha rin g the same prim ary product. An establishment's primary product is th a t commodity or s e rv ic e which generates the most sales ( in d o lla r s ). As an example, the cheese industry consists of a ll the establishments where cheese has the largest share of d o lla r volume of s a le s. An industry may produce more than one product, while a product is defined without regard to it s o r ig in of p ro d u c tio n. For in s ta n c e, the cheese in d u s try may inclu d e establishm ents which produce some o th e r products such as ic e cream or b u tte r. In c o n tra s t, in the ice cream product sector, ice cream produced by the ice cream industry is packed in

32 2 w ith ice cream made by the cheese industry. The data on fa c to r rewards or Gross Product O rig in a tin g (GPO) is re p o rte d on an in d u s try basis by the Bureau of Economic Analysis (BEA). Consequently, those data conventions require that the modelling of fa c to r rewards should be done at the industry le ve l. In the inte rest of c la r ity, fa c to r rewards or GPO data w ill always be referred to as "by ind u stry". The BEA also constructs the input-output table fo r the United States. The ta b le is re p o rte d on a p ro d u c t-to -in d u s try b a s is. \ However, th is scheme is u n s a tis fa c to ry as i t confuses the e ffe c ts o f p rim a ry and secondary products. For example, a product-to-industry table w ill include as inputs in to the cheese industry not only the inputs used fo r cheese but a ls o the in p u ts used fo r ice cream made by establishm ents p rim a rily engaged in making cheese. So, i f one asks about the e ffe c t of a ten percent increase in consumer demand fo r cheese, a product-to-industry table w ill show an increase in the use of the in p u ts fo r cheese and the in p u ts f o r ic e cream made in the cheese in d u s try (such as sugar). In order to avoid th is problem, the INFORUM s t a f f " p u r if ie s " the ta b le and tra n s fo rm s i t to a product-to-product ta b le. Therefore the real side of the model is grounded on the product d e fin itio n basis. The f i r s t two sections of the chapter deal with the resolution of the product-to-industry c o n flic t in the model and in a forecast. Section one d e scrib e s an accounting system which connects product output, product value added and GPO by industry. F irs t, an ideal accounting system fo r an input-output model is presented to fa m ilia riz e the readers with the basic system. A fte rw a rd s, the more com plicated system designed to handle c o n flic ts in data sources is presented. Transforming GPO by in d u s try to v a lu e added by p ro d u c t is accomplished w ith the aid of a product- 3^

33 3 to -in d u s try bridge ta b le. The forecasting structure fo r factor rewards by industry and prices by product is the subject of the second s e c tio n. A s h o rt review of the procedures used by other large scale models is also presented and then the lin k in g concept, real value added weighted output (REVAWO), is introduced. The t h ir d and fourth sections of the chapter are concerned with data d e scrip tio n s. The construction of the product-to-industry bridge table is the subject of the t h ir d s e c tio n. The fo u rth s e c tio n deals w ith the s t a t i s t i c a l sources u n d e rlyin g the GPO series. F in a lly, a summary ends the chapter. I I. 1 The Accounting Scheme fo r the INFORUM Model I f the n a tio n a l income and p ro d u c t.a cco u n ts (NIPA) and th e in p u t - o u t p u t ta b le s were alw ays c o n s is te n t, e q u a lly d e ta ile d and up-to-date, the accounting system could be the sim ple one presented in Figure II - 1 fo r a four-product economy. Sales fo r use in other products and sales fo r fin a l demand (the column labeled 6NP) are shown in the f i r s t fo u r rows of the ta b le. The f i f t h row shows the t o t a l value added in making each product while the last three rows (6-8) show the d is trib u tio n of the value added among labor, c a p ita l, and in d ire c t business taxes. U nfortunately, the U.S. national accounts and in p u t-o u tp u t ta b le s are not c o n s is te n t nor e q u a lly d e ta ile d nor u p -to -d a te. A ll of these fa cto rs create various anomalies which fo rc e the accounting system to become more complex than the simple one in fig u re I I - 1. Recalling the discussion from the intro du ctio n, one of the major inconsistencies between the two sources of data is the product versus industry basis of the data. 3 ^

34 4 F ig u re I I 1 Simple Accounting System , Row Intermediate 11 GNP Output ===== 1 Product 1 II I Product 2 II I Product 3 II I Product 4 II I I 5 Value Added I I I! Labor I I Capi ta l I I II 8 Tax I I ; i i i C onsequently, th e re must be a method to reconcile value added by product w ith fa c to r rewards (GPO) by industry. A d d itio n a lly, the NIPA data is not in t e r n a lly c o n s is te n t in th a t GNP as c a lc u la te d as the sum of f in a l demands does not equal the sum of industry GPO. That difference is the o f f ic ia l s ta tis tic a l discrepancy. The accounting system must be capable of handling i t as w e ll. The picture becomes yet more clouded when the problem of updating the in p u t-o u tp u t ta b le is introduced. The publication of the o f f ic ia l U.S. table a rriv e s w ith a lag of a t le a s t fiv e ye a rs. The 1977 ta b le was p u blishe d in the May, 1984 issue of the Survey of Current Business. In order to update the 1972 ta b le to a 1977 basis in 1980, the s ta ff at INFORUM used the NIPA fin a l demands and the production s ta tis tic s from the 1977 Census of Manufacturing. During that process i t became clear that to re c o n c ile the NIPA and production data was an impossible task. both sources must be used, that problem also had to be resolved. Because The most d ire c t solution (and the adopted one) is to introduce an extra s ta tis tic a l discrepancy fo r personal consumption expenditures and fo r investm ent of

35 5 producers durable equipment as fin a l demands. This u n o ffic ia l s ta tis tic a l discrepancy must also be included in the accounting scheme. (The o ffic ia l 1977 ta b le shows even a g re a te r d iffe re n c e from the o rig in a l 1977 NIPA than INFORUM had found necessary.) The accounting scheme to handle the complications discussed above is shown in F ig u re I I - 2. s ta tis tic a l discrepancy. Column H, labeled USD, shows the u n o f fic ia l Its f i r s t entry, a -10 fo r product 1, means that consistency with the NIPA requires a fin a l demand fo r th e ir product which is 10 u n its b ig g e r than can be reconciled w ith production s ta tis tic s fo r the product. In a d ditio n, a column labelled OSD has been added fo r the O ffic ia l S ta tis tic a l Discrepancy. To tra n s m it the e ffe c ts of these two discrepancy columns to the fa c to r income portion of the ta b le, two rows (5 and 6) and two columns (E and F ), la b e le d USD and OSD r e s p e c t iv e ly, have been added to the intermediate flow ta b le. The intermediate flows in these rows and columns are a ll zero, but not the fin a l demands or fa c to r payments. The OSD row has an e n try of 4, the o f f i c i a l s t a t i s t i c a l d isc re p a n c y, in the OSD column. The "u n o ffic ia l" row, USD, has on ly an e n try of +7 in the USD column. T his +7 is the negative of the other e n trie s. Thus the sum of the USD column is zero. C onsequently, the sum of a l l f in a l demands columns (GNP + USD + OSD) excluding the OSD row is GNP as reported in the NIPA. I f we include the OSD row, we get gross product o r ig in a tin g, GPO. Output (Q) fo r each product is derived by adding across the product's row to obtain the to ta l (shown in column J ). Value added fo r the product is computed (in row 8) by subtracting from the output of the product the value of a l l products used in i t s production. For example, the second product had an output of 134; subtracting the value of a ll inputs 100

36 FIGURE I I - 2 Complex A c c o u n tin g System In te rm e d ia te Flows F in a l Demands B uyers A B C D E F G H I J S e lle r s USD OSD GNP USD OSD Q 1 P ro d u ct P ro d u ct P ro d u ct P ro d u ct USD K L 6 7/ 8 OSD V a lu e Added VAA GPO LAB CAP TAX SD Q 10 In d u s tr y In d u s tr y In d u s tr y SD

37 (th e sum of column B from rows 1-4) yields a value added of 34. Note th a t the scheme forces the fin a l demand fo r the s t a t is t ic a l discrepancy " p r o d u c t to equal i t s value added since there are no in te rm e d ia te products used in i t s "production". Value added by product must be a llo c a te d to th e a p p r o p r ia te in d u s tr ie s. T h is is accomplished w ith the p ro du ct-to -in d ustry bridge table (know as the V-matrix) consisting of the intersection of rows w ith columns A-F in figure I I - 2. Looking across a row, the bridge table shows the d is trib u tio n of value added by product fo r an in d u s try w hile moving along a column in d ic a te s the portion of value added made by each industry fo r a product. For instance, row 10 indicates th a t the in d u s try 1 receives from the production of the second product fiv e units of value added. The p o rtio n of column G headed by "VAA" shows th e ^ a lu e added a llo c a te d f o r each industry. VAA is the sum of columns A-F fo r any row. Forexample, industry 1 has a VAA of 85 u n its. Gross product o rig in a tin g re p o rte d in the NIPA is shown in the adjacent column. Thus, industry 1 has a GPO of 93 u n its. Because of the previously mentioned inconsistiency of BEA data sources, VAA w i l l not n e c e s s a rily e q ua l to GPO f o r an in d u s tr y. The sum of a ll the in d u s try differences between VAA and GPO w ill equal the o f f ic ia l s ta tis tic a l discrepancy. F in a lly, the d is trib u tio n of factor rewards fo r each in d u s try (th e G -m a trix) is shown in the re cta n g le formed by the in te rse ctio n of rows with columns I-L. Factor rewards such as labor compensation (LAB), the re tu rn to c a p ita l (CAP), and taxes (TAX) reported in the NIPA are shown in columns I-K. For example, industry 2 has 32 u n its of GPO going to la b o r, 25 u n its to c a p it a l, and 10 u nits fo r taxes. The last column

38 shows the difference between VAA and GPO fo r each ind ustry. For instance, industry 2 has a d iffe re n c e of -3 which is de fine d as the s t a t i s t i c a l discrepancy (SD) f o r th a t in d u s try. Note th a t the t o t a l s ta tis tic a l discrepancy fo r the industries w ill always equal the n egative of the VAA f o r the s t a t i s t i c a l discrepancy "in d u s try ", as shown in the la st row of the ta b le. Both the p ro d u c t-to -in d u s try b rid g e ta b le ( V - m a tr ix ) and th e d is t r ib u t io n of fa c to r incomes by in d u s try (G -m atrix) are u tiliz e d in order to re la te fa c to r income by in d u s try to p ric e s by p ro d u ct. That process is described in the next section. I I. 2 The Forecasting Structure of GPO and Prices T his s e c tio n describes the present forecasting procedure. P rior to th a t d is c u s s io n, a b r ie f treatm ent of c o n tra s tin g s o lu tio n s to th e transform ation from value added to prices is presented. NoPlINfQByQpdels There are two la rg e scale in p u t-o u tp u t model th a t ta c k le t h i s problem : the CANDIDE model of the Canadian economy and the Wharton Industry Forecasting Model. The reader is referred to the chapter I I I of the B e lze r thesis fo r a summary of the two models.1 Both models emphasize constant d o lla r GPO. Constant d o lla r GPO is made a fu n c tio n of real o u tp u t. Then, the constant d o lla r GPO e stim a te is " in f la t e d " by a d e fin itio n of i t own price (PVA), given by

39 8 w ith subscripts denoting tim e. of p ric e b e fo re th a t of GPO. This procedure requires the determ ination Because t h is study is forecasting value added and then determining prices, th is approach was rejected. Belzer Model The in n o v a tio n of the Belzer model (previously discussed in Chapter 1) is th a t no constant d o lla r GPO is necessary in the fo re c a s t. F ir s t, p r ic e s are d e term ined, then product value added is obtained as the difference bewteen nominal output and the in te rm e d ia te use. In d u s try value added is then transformed to GPO in a series of steps characterized by the equation GPO = R * VA, w ith R as a m atrix containing constant s c a la rs to a d ju s t fo r secondary p ro d u c ts, r e d e fin itio n s and the other types of re c o n c ilia tio n s. The m atrix is constructed so no value added is lo s t in the tra n fo rm a tio n : nominal GNP is not altered by the adjustments. The Belzer a lte rna tive was not re p lic a te d in th is study because i t a lso determined prices before determining GPO. INFORUM Model The basic approach used in th is study to forecast GPO by industry and then tra n sfo rm GPO by in d u s try in to prices by product is, in p rin c ip le, quite straightforw ard. The components of GPO are fore ca ste d as in f la t e d sh a re s o f an a r t i f i c i a l constant d o lla r v a r ia b le, re a l value added weighted output (REVAWO). Nominal GPO by industry is then spread through the the p ro d u c t-to -in d u s try bridge table in proportion to each product's

40 9 con tribu tio n to REVAWO. The major Link w ith the real side comes through the use of REVAWO. In th e o ry, REVAWO is a very Loose approxim ation of real gross product o rig in a tin g and is defined as 78 (2.1) REVAWO* = ^ Q* / Q? i 5=1 3 J where v., i s the bridge table c e ll in the i th row and j th J column, i.e. the value added in industry i a ttrib u ta b le to it s production of product j, and Q* is the real output of the j**1 product in year t. j T his d e f in it io n ^ accounts fo r changes in the product mix w it h in an in d u s try s e c to r. The w eighting of the c e lls by the movement in product output re la tiv e to the base year allows products with s ig n ific a n t changes in output to transm it the corresponding impact in to industries which make th a t product. REVAWO is used in the calculation of nominal fa cto r incomes and in the transform ation of factor incomes to product value added. ALL of the components of GPO are forecasted in the same form at. The component's index ( I) in any year is defined as t t. o o (2.2) I ik = (Gik / REVAWO})/(G^/REVAWO..) t For in d u s try i, G ^ is the Level of the kth component of GPO in year t. I t is an entry in the G matrix described e a rlie r. For instance, the Auto industry in 1978 had 26.5 b illio n d o lla rs in labor compensation. Chapters I I I - V d e a l w ith th e methods to fo re c a s t the index fo r the various components. The forecasted Level of any component is then equal to

41 10 (2.3) G * = ( I* )( REVAWOt) (G?./REVAWO?) 1 R I K 1 I K 1 Upon the completion of the calculation fo r a l l the fa c to r incomes, GPO is calculated as the sum of a ll th irte e n components, or t Sectoral gross product o rig in a tin g is the product of REVAWO and a weighted average of fa c to r reward in d ic e s. That the weights are fixed does not im ply th a t the fa c to r shares of income by sector are constant: th e re la tiv e shares depend on the fa cto r indices. The transform ation from GPO by industry to value added by product is then accomplished via the equation Thus, gross product o r ig in a tin g in an in d u s tr y in f u t u r e y e a rs i s d i s t r i b u t e d t o products made by the in d u s try in p ro p o rtio n to each product's c o n trib u tio n to th a t in d u s tr y 's REVAWO in th a t fu tu re year. T h is step preserves the impact of the change in the product mix. A fter t h is step is com pleted, in d u s try p ric e s are c a lc u la te d by using th e d e fin itio n p = pa + v. The method described above may be a p p lie d to the forecast of the o f f i c i a l s t a t i s t i c a l d iscrepancy. In most fo re c a s ts, how ever, th e o f f ic ia l s ta tis tic a l discrepancy is assumed to be zero.

42 11 However, the u n o f f ic ia l s t a t i s t i c a l discrepancy does not use the above system in the fo re c a s t. In each year of a fo re c a s t, the f in a l demand u n o ffic ia l s ta tis tic a l discrepancy fo r each product remains a fixed p ro p o rtio n of the to ta l fin a l demand fo r that product. For instance, i f there was a four percent discrepancy in the constant d o lla r f i n a l demand f o r a u to m ob iles, th a t p ro po rtio n is assumed to hold fo r every year in a fo re c a s t. A fte r the d is t r ib u t io n of re a l u n o f f i c i a l s t a t i s t i c a l d is c re p a n c y is d e te rm in e d, th e nom inal v a lu e o f the s t a t is t ic a l discrepancy fo r each product is calculated by m u ltip ly in g each constant d o lla r discrepancy by i t s p rice (the price is e ith e r the price from the previous ite ra tio n or an i n i t i a l guess). Adding those nominal values over products yield s the aggregate nominal value fo r the u n o ffic ia l s ta tis tic a l discrepancy. That aggregate to ta l is then d is trib u te d among industries in p ro p o rtio n to each in d u s t r y 's base y e a r share o f th e u n o f f i c i a l s t a t i s t i c a l d iscrepancy- For example, i f the Auto repair industry (36) contributed to ten percent of the to ta l u n o ffic ia l s ta tis tic a l discrepancy in the base year, then the Auto repair industry is assumed to re ta in th a t share of the nominal aggregate to ta l in the fo re ca st. Note from figure II- 2 th a t the net e ffe c t on GNP of the u n o ffic ia l s ta tis tic a l discrepancy is zero by construction, so no special assumptions about it s fu tu re values are needed. I I. 3 The Product-to-Industry Bridge Table The p ro d u c t-to -in d u s try bridge table is important fo r two reasons. F irs t, as discussed in the previous two s e c tio n s, t h is ta b le lin k s the re a l side of the INFORUM model, which is based on the product d e fin itio n

43 of a sector, with the factor income portion of the model, which is based on the in d u s try d e fin itio n of a s e c to r. The second reason is that the product-to-industry bridge table is used by the s ta ff of INFORUM to update the in p u t-o u tp u t ta b le before the o f f i c i a l ta b le is p u b lish e d. The o f f i c i a l ta b le s are published approximately every fiv e years, while the GPO series on fa c to r incomes are a v a ila b le on an annual b a s is. Since 1980, the INFORUM model has used a 1977 in p u t-o u tp u t ta b le which was updated from the 1972 o ffic ia l ta b le. This update was accomplished by build in g the product-to-industry bridge table fo r 1972 and then converting i t to a 1977 b a s is v ia the in c o rp o ra tio n of the 1977 GPO in d u s try estimates and an in t ia l INFORUM estimate of output fo r th a t year based on the 1977 Census of Manufacturing production s ta tis tic s. This conversion yielded value added by product fo r 1977 which was used to "balance" the 1977 input-output table. The end r e s u lt of th is work is a p ro d u c t-to -in d u s try bridge table th a t relates the fo rty -s ix GPO industries w ith the s e v e n ty -e ig h t product sectors. Another result is the accounting fo r the o f f ic ia l and u n o ffic ia l s ta tis tic a l discrepancies discussed in section I I. 1. A sm all h y p o th e tic a l example of a b ridge table is given by Figure I I - 3. Rows give the product com position of an in d u s tr y 's value added w h ile columns give the d is t r ib u t io n among in d u s trie s fo r a p a rtic u la r product's value added. The a g ric u ltu re in d u s try has value added of 50 from the harvest of wheat (a primary product) and 3 from the making of ice cream (a secondary product from the m anufacturing product s e c to r). Adjustments outside of primary and secondary products are, in BEA*s terms, simple "re d is trib u tio n s " of value added. Thus any construction performed by a manufacturing establishment's employees (called the force account) is

44 13 Figure II-3 Product Sectors Agri. Cons. Mfg. Industry GPO gpu dcccot A g ricu ltu re II 53 Construction II 80 Manufacturing II II 235 Product Value Added counted as p a rt of the product "construction". For example, from Figure I I - 3, the manufacturing in d u s try had a value added of 5 a ttr ib u te d to construction performed by manufacturing employees. Now we w i l l discuss the construction of the 1972 bridge ta b le, used to convert product value-added to in d u s try GPO. I t u t iliz e d worksheet d e t a i l s fro m BEA's in p u t- o u tp u t s tu d y. Table I I - 1 shows reconci la tio n s by type fo r eleven aggregates of the GPO s e c to rs. The f i r s t column of the table shows the GPO estimates fo r 1972 industry value added, and i t is the equivalent of the row to ta ls from Figure I I - 3. The primary and secondary products re c o n c ila tio n is d isp la yed in colum ns 2 and 3. Column 2 is sim ply the diagonals of Figure I I - 3. Primary products are the 1-0 commodities made by the corresponding GPO in d u s tr ie s. For example, in 1972, a ll of the value-added a ttrib u ta b le to the a c tiv ity of mining by the mining industry is m illio n d o lla r s. Secondary products must be taken in account since industries may produce more than one product. At the 1-0 le v e l, a product may be made by several in d u s trie s. This im plies that the GPO fo r an industry and the value added fo r it s corresponding product w ill not be equal. The mining in d u s try had 71 m illio n d o lla r s of value added from non-m ining products (such as k o

45 TABLE I I - 1 C o m p o sitio n o f 1972 OPO by ty p e o f r e a llo c a t io n ( in m ill In d u s try 1972 PO (1 ) P rim a ry P ro d u c ts (2 ) Secondary P ro d u cts ' (3 ) A c t iv it y Changes (4 ) F orce A ccount (9 ) MSO's (6 ) Space R e n ta l (7 ) S t a t is D is c re p i (8 ) A g r ic u ltu r e (Ag) M in in g (M in ) C o n s tru c tio n (Cons) M a n u fa c tu rin g (M fg) T ra n s p o rta tio n (T ra n s ) U t i l i t i e s ( U t i l ) B Trade S e rv ic e s (S e r) B Government (Oov) 1993B Rest o f th e W orld (Rom) T o ta l o urce : P h ilip R iti# " D e f in it io n s and C o n v e n tio n * o f the 1973 In p u t-o u tp u t S tu d y." Bureau o f Economic A n a ly i lt S t a f f Paper. J u ly 19B0 and u n p u b lished BEA worfc f i l e *.

46 crushed gravel and cut stone, which are manufactured products). A primary and secondary product m a trix was used to d is t r ib u t e to in d u s trie s the value added of the 1-0 se cto rs in the same p ro p o rtio n in which to ta l output of the product was d is trib u te d.^ The value added reconciled because of secondary products is generally very s m a ll, u s u a lly less than 2% of GPO. Scrap m etal is a p e culiar secondary product. framework, scrap metal is a separate product s e c to r. In the 1-0 accounting I t is d is tr ib u te d th ro u gh o u t a l l of the GPO sectors. Since scrap metal is a small product sector, the value added is a ttrib u te d to the ten la rg e st producers, nine in the manufacturing sectors and railroads. C olum n 4, la b e lle d " a c t i v i t y c h a n g e s ", shows tw o ty p e s o f re c o n c ilia tio n s ; those due to SIC r e c la s s ific a tio n s and those due to a c t i v it y re d e fin itio n s (except force account construction, which is shown separately). There were only two re c la s s ific a tio n s to be accom plished. One was Veterinary Services (SIC 0772) which is included in A griculture in th e GPO d a ta b u t in M e d ic a l S e rv ic e s a t th e 1-0 l e v e l. T h is re c la s s ific a tio n is shown by the addition of 684 m illio n d o lla rs of value added in the A griculture industry row, and the corresponding negative 684 m illio n d o lla rs in part of the entry in the Services row. S im ila rily, O il and Gas Well D r illin g (SIC 1380) was moved from C o n structio n at the 1-0 le v e l t o Crude P e tro le u m and N atural Gas in the GPO scheme. Both movements involve actual data on value added from the BEA work f ile s. For the second type of " a c tiv ity change", a c tiv ity re d e fin itio n s are employed by the BEA to achieve homogenous products. For instance, a ll of the value added stemming from restaurant a c t i v it y in the Hotel in d u s try (GPO s e c to r 34) is moved from the Service industry in Table I I 1 back to )

47 13 the r e ta il trade sector since that is the "home" of E ating and D rin king places (SIC 58). The sum of the a c tiv ity changes column is zero. One e s p e c ia lly large re d e fin itio n involves construction work done by employees not in the construction industry (the force a cco u n t). This is shown in column 5. At the GPO le v e l, that construction (which includes maintenance and repair) is not reported as part of the construction sector as i t is at the 1-0 le v e l, but is included in the sector in which the a c tiv ity took place (e.g. the 5 in the manufacturing row in Figure I I - 3 ). The largest force account re d e fin itio n s were fo r maintenance and repair of highways by s ta te and local government employees and construction of o il and gas rig s and pipelines. The to ta l amount redefined was about 11% of prim ary value added m illio n dollars - fo r the construction sector. Again, the column sum of the force account column is zero. The source fo r these re d e fin itio n s is "D e fin itio n s and Conventions of the 1972 In p u t-o u tp u t Study, 1980."^ This document l i s t s a l l of th e r e d e fin itio n s - over 150 fo r the fo rce account alone - and the output redefined. Since the data on the r e d e fin itio n s was o u tp u t, the value added fo r the r e d e fin itio n s was assumed to be d is trib u te d in the same proportion as output.^ Column 6 portrays the adjustment fo r m anufacturers' sales o ffic e s (MSO's) which are included in the wholesale tra d e in the GPO but are d is trib u te d throughout the manufacturing product sectors. The value added generated by a MSO is, fo r the most p a rt, labor compensation. The Censtj 2l_W holesale_trade of 1972 has p a y ro ll data fo r MSO's by fo u r d ig it wholesale SIC ( i.e. the 's ). Appendix E of t h is Census gave the correspondence between the wholesale SIC sectors and the manufacturing SIC sectors. In most cases, a single wholesale SIC matched many manufacturing ^ 3

48 16 SICs. In those cases, the p a y ro ll was a llo c a te d on the basis of the d is trib u tio n of the to ta l value of shipments among those m anufacturing SICs that corresponded with the wholesale SIC. A f t e r a c c o u n tin g f o r the these fa c to r s, only space re n ta l and s t a t i s t i c a l discrepancy remain to be re c o n c ile d. U n fo rtu n a te ly, no s u f f ic ie n t ly d e ta ile d data source exists to allocate space rental which, as a p ro d u c t, is in Real e s ta te, to the in d u s trie s "p ro d u c in g " i t. Therefore, the a llo ca tion of space rental was combined w ith the resolution of the s ta tis tic a l discrepancy. At th is p o in t, the difference between industry GPO (Column 1 of Table I I - 1 ) and th e sum of va lu e added from prim ary p ro d u cts, secondary products, a c tiv ity changes, force accounts and manufacturing sales o ffic e s (columns 2-6) would give the to ta l discrepancy fo r each in d u s try. Table I I - 2 shows t h is fig u r e fo r each of the aggregated industry sectors from Table I I - 1. A p o sitive to ta l discrepancy meant that the GPO reported fo r th a t in d u s try in 1972 was more than the sum of the value added allocated (VAA) fo r th a t industry in the same year. For example, the A g ric u ltu re in d u s try had 1130 m illio n d o lla rs more of GPO than the sum of value added allocated to the products i t made. From Table I I - 2, one can see th a t the m anufacturing industries had the largest surplus of GPO while the Finance industry had the largest s h o rtfa ll. The reason th a t the finance sector had the largest s h o rtfa ll is due to the treatment of space rental in the in p u t-o u tp u t ta b le of the BEA. The re n ta l a c t i v it ie s of a ll industries are redefined to the Real estate industry (33) by the BEA. Consequently, the Real e s ta te in d u s try (33) w i l l have a l l of the value added from rental a c tiv ity performed anywhere in the economy.

49 17 Table I I - 2 Summary of the resolution of s ta tis tic a l discrepancy GPO Space Stat less VAA Rental Disc Agri culture Mining Construction Manufacturing Transportati on U t ii it ie s Trade Finance Services Government Rest of the World Total In o rd e r to m in im iz e th e t o t a l number o f in d u s t r ie s w ith a d isc re p a n c y, space re n ta l was d istrib u te d from the Real estate industry (33) to any industry with a s h o rtfa ll of value added. This a llo ca tio n was done at the Level of 46 industries and the results are summarized in Table II - 2. Therefore the U t ilit y industry (28) received an a llo c a tio n of 1427 m illio n d o lla r s in space re n ta l to reduce it s discrepancy to zero. The same type of adjustm ent was also done fo r every in d u s try th a t had a s h o r t f a l l o f value added a llo c a te d. C onsequently, the A g ric u ltu re in d u s try (1) received 1130 m illio n d o lla rs of space r e n ta l and two governm ent in d u s t r ie s (F e d e ra l e n te r p r is e s and S ta te and Ico a l enterprises) were a llo c a te d a t o t a l of 1607 m illio n d o lla rs to o ffs e t th e ri s h o r tfa ll. W h ile th e same r e a llo c a t io n o f space re n ta l was done fo r the in d u strie s in the transportation sector and the manufacturing s e c to r, the table obscures some of the d e ta ils. In each case, those aggregates in the ta b le had some industries with surpluses and some w ith s h o rtfa lls, so the

50 space rental th a t was allocated represents the t o t a l s h o r t f a ll of those in d u s tr ie s. For example, the transportation sector has three industries: Railroads (25, A ir T ra n s p o rta tio n (26, and Other tra n s p o rta tio n (2 7 ). R a ilroa d s had no s h o r t f a ll, A ir tra n s p o rta tio n had a s h o rtfa ll of 469 m illio n d o lla rs and Other tra n s p o rta tio n had a su rp lu s of 520 m illio n d o lla rs. Consequently, the f i r s t entry in the transportation row in Table I I - 2 is the d iffe re n c e between the 469 and 520 and the second entry in th a t row is the space rental allocated to the A ir transportation industry. S i m ila r il y, the m anufacturing s e cto r had two in d u s trie s w ith a s u rp lu s, eleven w ith a s h o rtfa ll, and eight where GPO equalled VAA. The to ta l s h o rtfa ll fo r those eleven industries was m illio n d o lla rs, the amount equal to the space rental allocated to those in d u strie s. A fte r these adjustm ents were com pleted, then th e m a te r ia l f o r product-to-industry bridge table were at hand. The actual construction of the bridge table was a four-step process. F irs t, the value added fo r each prim ary product was allocated to it s corresponding GPO in d u stry. Second, adjustments to the value added were made fo r secondary products were done as p re v io u s ly described. T h ird, that value added fo r each industry was adjusted fo r a c tiv ity changes, the fo rc e account, m anufacturing s a le s ' o f f ic e s, and space r e n ta l The re su ltin g bridge table is summarized in Table. As noted in se ctio n one, the s t a t i s t i c a l d is c re p a n c y f o r each industry is counted in the framework as a "fa c to r" reward, so i t is placed in the <5 m atrix. For instance, the s ta tis tic a l discrepancy of the mining industry (846 m illio n do llars) is taken out of the m in in g 's in d u s try row in the bridge table and regarded as a "payment", to the fa c to r " s ta tis tic a l d iscrepancy" in the G m atrix. At th is jun cture, the accounting framework

51 T a b le 11-3 ( b i l l i o n s o f d o lla r s ) A f. m in. I n d u s tr ie * Con* A]> Min. Con* MfB I. 34 T ran** U t i l Trade 0. IB F in * S arv Oov, RDh T o ta l VA P ro d u cts T ran*. d fg U t i l T ra d * O. 07 O. 60 O O O B. B7 F in * Oov* OPO B ar ROM T o ta l O B

52 19 resembles the complex system pictured in Figure II- 2 and described in the f i r s t section of th is chapter. T his balanced p ro d u c t-to -in d u s try bridge ta b le was then used to update the 1977 input-output m atrix. Independent o utput estim ates from the 1977 Census were used to create a vector of product outputs. Each c e ll along an 1-0 column was moved by the growth in o u tp u t. The rows, which represent GPO ind u strie s, were adjusted to conform to the 1977 BEA reported values fo r GPO by industry. The re su ltin g column sums were then taken as the new estimates of value added by product consistent with the NIPA. The new v e c to r of value added was used to help rebalance th e input-output m atrix fo r I I. 4 Description of GPO series In 1962, the BEA began to develop measures of the in d u s tria l o rig in of gross national product. This data is compiled f o r 64 two d ig i t SIC in d u s tr ie s. For each industry, there are series fo r th irte e n components of GPO: wages and s a la r ie s, wage supplements, net in te r e s t payments, corporate capital consumption allowances, noncorporate capital consumption allowances, in d ire c t business taxes, corporate p r o fits, proprietor income, business tra n s fe r payments, corporate inventory valuation adjustments, noncorporate in v e n to ry v a lu a tio n a d ju s tm e n ts, r e n t a l incom e, and government subsidies. Though the data is assembled fo r 64 in d u s trie s, the BEA fe e ls th a t not a ll of the series are of publishable q u a lity fo r a ll of the components, but a ll are available upon request. The July issue of the Survey of C urrent Business contains the annual updates and revisions to a ll of the components at various levels of industry d is a g g re g a tio n. The

53 t u fo llo w in g d e s c rip tio n of how the BEA makes these series is included here because of the central role they play in th is study and of the fragmentary BEA descriptions.^ The la rg e s t component o f GPO is labor com pensation (wages and s a la rie s p lu s wage supplements) which comprise approximately 60% of GNP from The sources of wages and s a la rie s are s ta te Unemployment Insurance agencies and the federal Office of Management and Budget (0MB). The state agencies report to the Department of Labor to ta l employment and t o t a l p a y ro ll by three d ig it SIC industries on a qu arterly basis. These re p o rts do not cover a l l wage supplements, so the BEA estim ates th e rem ainder from several other sources. Wage supplements include a va rie ty of programs: contributions to social insurance, h e alth p la n s, l i f e and u n em p lo ym e n t in s u r a n c e, and w orkm an's c o m p e n sa tio n. B usiness contributions to wage supplements are derive d from IRS ta b u la tio n s of business tax returns while government contributions are taken from the 0MB f o r fe d e r a l em ployees and from Census surveys of s ta te and lo c a l governments fo r th e ir employees. The return to capital is the second largest component of value added w ith a 30% share of GPO. The return to ca p ital consists of net inte rest payments, co rp ora te and noncorporate c a p ita l consumption allow ances, c o rp o ra te p r o f i t s, p r o p r ie to r income, business tra n s fe r payments, corporate and noncorporate in v e n to ry v a lu a tio n adjustm ents and re n ta l income. A ll of the components share the same data source, the annual IRS S tatistics_of_incom e. Most of these components are not tabulated with the BEA d e fin itio n s in mind, and th e re fo re some additional adjustments are requi red. The f i r s t problem w ith a l l IRS data on in d u s tr ie s is th a t th e

54 21 in d u s trie s are d e fin e d, not as a c o lle c tio n of establishments, but as a c o lle c tio n of companies. Thus p ro fits earned by the chemical d iv is io n of a s te e l company appear as p r o f it s in th e steel industry. To deal with th is problem, the BEA constructs a matrix depicting the d is trib u tio n of a company's employment among industries defined on an establishment basis. Income of a p a r tic u la r typ e, say p r o f i t s, is then a llo c a te d among a company's ind u strie s in the same proprotion as i t s employees. A ll income components are allocated to the appropriate tw o -d ig it SIC d ig it industries on th a t basis. This procedure is unnecessary fo r p ro p rie to r income and noncorporate c a p ita l consumption allow ances since few proprietors own establishm ents in d if fe r e n t in d u s trie s. T h e re fo re, the noncorporate series come d ire c tly from IRS tabulations. More adjustments are needed fo r net in te re s t payments. Net inte rest payments are defined as the sum of net monetary in te re s t paid by business and net imputed in te re s t paid. Net monetary in te re st paid is reported by the IRS by in d u stry. Net imputed in te re s t is imputed in te r e s t paid less imputed in te re s t received. The values are calculated from Federal Reserve Board and Federal D eposit Insurance Corporation data. Imputed in te re st received is estimated as the value of free banking se rvice s received by a ll in d u s trie s. Business tra n sfe r payments arise from consumer bad debts, losses due to th e ft and payments fo r personal in ju ry. The IRS is the source of data fo r bad debts w h ile the other two have a va rie ty of sources to allocate those payments on a industry basis. Inventory valuation adjustments (IVA) are made by in d u s try. Due to th e widespread p ra c tic e of FIFO ( f i r s t - i n, f i r s t - o u t ) accounting by business, inventories are valued at o rig in a l c o s t. For the expenditure

55 22 side of the national accoounts, the relevant investment in inventories is the change in inventories measured at the average price of the period, not the prices of the preceding period. So during times of in fla tio n, the use of FIFO underestimates the value of inventories, and thus allows recorded p r o f it s to o v e rs ta te the le v e l of economic p r o fits. The BEA constructs IVA to a d ju s t fo r th a t bias in reported p r o f it s in order to measure economic p r o fits. The primary source is Census establishment information on the book value of inventories and wholesale prices from the Bureau of Labor S ta tis tic s. Rental income applies only to the real estate ind u stry. For tenant occupied housing and mobile homes, rental income comes d ir e c t ly from the Annual Housing Survey. Owner-occupied housing requires an adjustment fo r imputed rent. For th is adjustment, the BEA combines estim a te s fo r to ta l u n its and mean contract rent from the Annual Housing Survey and internal estimates of the value of rental durables. The industry allocation of in d ire ct business taxes and nontaxes can be d iv id e d in t o th re e separate c a te g o rie s. Federal excise taxes and custom duties are id e n tifia b le by product. The w in d fa ll p r o f it s tax and the chemical cleanup (Superfund) ta x are d e fin e d by the BEA as excise ta x e s. P roperty taxes are a llo c a te d on an in d u s try basis by Census e s ta b lis h m e n t data on p ro p e rty taxes p a id. For o th e r ye a rs, these payments are moved using IRS depreciable assets and adjusted to conform to the appropriate to ta l. The remainder of the taxes, mainly sales taxes and various license fees, are e ither assigned to wholesale and r e ta il trade or are allocated to the industries on the basis of sales. Only a few industries receive government subsidies. The assignment o f the su b sid ie s to p a r tic u la r in d u s trie s is made on the basis of a r \

56 23 s p e c ific program or le g is la tio n. Housing is the la rg e s t b e n e fic ia ry of fe d e ra l subsidies; A griculture (1 ), Water transportation (27), Railroads (25) and A ir c a r r ie r s (26) receive the rem ainder. The su b sid ie s are somewhat o f f s e t by th e s u rp lu s e s g e n e ra te d from many government e n te rp ris e s. L o tte r ie s, o ff tra c k b e ttin g, highw ay t o l l s, p u b lic u t i l i t i e s and the Tennessee Valley Authority account fo r well over half of a ll government surpluses. I 1.5 Summary This chapter has dealt with the modelling structure of GPO. Because the real side of the model is based on a product d e f in it io n b asis w hile th e GPO data is reported on an in d u s try d e f in it io n b a s is, a sp e cia l s tru c tu re is re q uire d to "b rid g e " the gap between the two ty p e s o f d e fin itio n s. A framework fo r tra n slatin g GPO by industry in to value added by product was describ e d. The procedures fo r forecasting the index fo r each component of GPO - an im portant p a rt of the s tru c tu re - w i l l be described in chapters I I I - V. The next chapter deals with the forecast of labor compensation, the largest component of GPO f o r most in d u s tr ie s. s 's.

57 ENDNOTES 1. David B e lze r "An Integration of Prices, Wages, and Income Flows in an In p u t-o u tp u t Model of th e U n ite d S ta t e s, " u n p u b lis h e d Ph.D d is s e rta tio n, U niversity of Maryland, For a more detailed account of the CAND IDE model see M.C. McCraken, An_Overvi^w_of_CANDIDE_model_ I/O, CANDIDE project Paper No. 1, Economic Council of Canada, fo r the In te rd e p a rtm e n ta l Committee (O ttawa, Inform ation Canada, 1973). The Wharton model is described in Ross Preston, Jhe_Wharton_Annual_and_ In d u s tf o re c a s ti_ n g _ M o d e il. (P h ila d e lp h ia, Economics Research U nit, U niversity of Pennsylvannia, 1972). 2. As the name im plies, real value added weighted output im p lie s no changes in in d u s try prices. This can easily seen by taking the column ' sums im plied by equation (2.1). t 46 t 0 t 0 46 R ecalling th a t the column sums are the base year value added, t t 0 0 Prices are calculated by m ultiplying the dual of the c o e ffic ie n t m atrix by value added per u n it of real output. For any product sector, th is ra tio is t t t t O O 0 0 VA / Q = (Q / q ) (VA- / Q.) = VA. / Q. J J J J J J Thus, the e n tire vector of value added per u n it of re a l output would remain unchanged; prices remain unaltered. 3. The m a trix was an aggregated ve rsio n of the m a trix reported in P h ilip M. R itz, "The Input-O utput S tru c tu re of the U.S. Economy, 19 72," Survey_of Current Business v o l. 59, no. 2 (February 1979) p P h ilip M. R it z, " D e f in it io n s and C o n v e n tio n s o f th e 1972 Input-Output Study," Bureau of Economic Analysis S ta ff Paper, July A few of the force account re d e fin itio n s used the BEA work f ile s. 6. The changes involved in the 1976 benchmark revisions have yet to be documented in w r itte n form. This s e c tio n is based on te le p h o n e conversations w ith the individuals responsible fo r the components. s 3

58 Appendi x The f o l l o w i n g pages c o n t a in a c o m p le te l i s t i n g o f th e p r o d u c t - t o - in d u s t r y b r id g e ta b le f o r Each GPO in d u s try is represented: product sectors w ith any type ot r e a llo c a tio n are snown. The columns are the type of re a llo c a tio n ; rows correspond to product sectors. S ta tis tic a l discrepancy is the la s t (78) product s e c to r. The d e f i n i t i o n s o f th e columns are the same as Table II- 1 w ith the ot manufacturing sales o ffic e s (Mfg Branch). Total value added (V A) is the sum ot th e columns along a row: i t is the entry updated to 1977 and used in the bridgetable fo r the model. A ll values are in m illio n s of d o lla r s. Farms and a g r ic u ltu r a l se rvice s - GPO in d u s try 1 - provides an example o t the ta b le. Seven product sectors have value added a ttrib u ta b le to farm and a g ric u ltu re establishments. The farm in d u s try does some ot it s own tru c k in g, hence the secondary product. The a c tiv ity changes arise from a v a r ie t y ot accounting conventions used by the BEA. For instance, the largest a c tiv it y change m illio n - is the s h iftin g ot v e te rin a ry se rvice s from the medicine sector and industry to a g ricu ltu re services. Other causes fo r a c tiv ity changes a ris e from the re s a le ot anim als and s u p p lie s, landscaping services, and crop and livestock services performed in wholesale trade establishments. Space rental is included because ot a dearth ot value added in th a t sector.

59 OPO 8ECT0R 1 FARM li AGRICULTURAL SERVICES P rim a ry Bacondary A c t iv it y F o re * Mffl Spaca T o ta l P ro d u c t S ecto r P roducts P rod ucts Changes A ccount Branch R a n ta l V A 1 AGRICULTURE. FORESTRY. FISHERY FOOD fc TOBACCO TRUCKING. rtjy PAS8 TRAN8IT WHOLESALE TRADE O RETAIL TRADE REAL E8TATE BU8INES8 SERVICES MEDICINE. EDUCATION. NPO TOTALS OPO 8ECT0R 2 CRUDE PETROLEUM 8c NATURAL 0A8 P rim ary 8acondary A c t iv it y Forca Mfy Spaca T o ta l P ro d u c t 8 e c to r P roducts P roducts Changes Account Branch R a n ta l V A 9 NATURAL 0A8 EXTRACTION CRUDE PETROLEUM CONSTRUCTION PETROLEUM REFINING OTHER NONFERROUS METALS O METAL PRODUCTS ENGINES AND TURBINES NIPA STATISTICAL DISCREPANCY TOTALS OPO 8ECT0R 3 MINING P rim ary 8acondary A c t iv it y Forca Mfg Space T o ta l P ro d u c t S ecto r P roducts P rod ucts Changes Account Branch R e n ta l V A 2 IRON ORE MININO NONFERROUS MET ALB MININO COAL MININO NON-METALLIC MININO CONSTRUCTION OTHER CHEMICALS PETROLEUM REFINING STONE. CLAY. GLASS NIPA STATISTICAL DISCREPANCY TOTALS

60 ( OPO SECTOR 4 CONTRACT CONSTRUCTION P rim a ry Bacondary A c t iv it g Fore a H ffl 8paca T o ta l P ro d u c t 8 a c to r P rod ucts P ro d u cts Changas Account Branch R a n ta l V A 1 ACRI CULTURE, FORESTRY. FISHERY NON-METALLIC MININC B CONSTRUCTION COMMUNICATIONS SERVICES WHOLESALE TRADE f 60 RETAIL TRADE OWNER-OCCUPIED HOUSING ( % 69 HOTELBi REPAIRS EXC AUTO BUSINES3 SERVICES TOTALS i OPO SECTOR 9 FOOD «i TOBACCO, { P r i u r y Secondary A c t iv it y Fore a Hfg Spaca T o ta l P ro d u c t S actor P roducts P rod ucts Changas Account Branch R a n ta l V A 1 AGRICULTURE, FORESTRY. FISHERY t a CONSTRUCTION FOOD «< TOBACCO B ia PAPER O ( 19 AGRICULTURAL FERTILIZERS HER CHEMICALS PETROLEUM REFINING RUBBER PRODUCTS PLASTIC PR0DUCT METAL PRODUCTS o AGRICULTURAL MACHINERY SERVICE INDUSTRY MACHINERY ( 74 SCRAPS AND USED O TOTALS v c (

61 < ( ( ( OPO SECTOR 6 TEXTILE M ILL PRODUCTS... P rim a ry 8«condary A c t iv it y F o re * Hfg 8paco T o ta l P ro d u c t 8 «c to r P rod ucts P rod ucts Changes Account Branch R o n ta l V A 8 CONSTRUCTION TEXTILE8. EXC. KNITS KNITTING APPAREL. HOU8EHOLO TEXTILES PAPER PRINTING & PUBLISHING * OTHER CHEMICALS RUBBER PRODUCTS PLASTIC PRODUCT SHOES AND LEATHER BTQNE. CLAY. GLASS METAL PRODUCTS INSTRUMENTS B MISC. MANUFACTURING REAL EBTATE B CRAP8 AND USED TOTALS GPO SECTOR 7 APPAREL AND RELATED 1PR0DUCT8 P rim a ry Bscondary A c t iv it y Forco Mfg Bpaco T o ta l P ro d u c t 8 o c to r P rod ucts P rod ucts Changes A ccount Branch R e n ta l V A 8 CONSTRUCTION TEXTILF8. EXC. KNITS , KNITTING APPAREL. HOUSEHOLD TEXTILES B. 13 PAPER PRINTINC b PUBLISHING OTHER CHEMICALS RUBBER PR0DUCT PLASTIC PRODUCTS SHOES AND LEATHER FURNITURE METAL PRODUCTS METALWORKING MACHINERY OTHER TRANSP. EQUIP INSTRUMENTS MISC. MANUFACTURING a. 63 REAL ESTATE TOTALS I (

62 OPO 8ECT0R 8 PAPER AND ALLIED PR0DUCT8 9 \ n P rim a ry Secondary A c t iv it y Forco Mfg Space T o ta l P ro d u c t S ecto r P rod ucts P ro d u cts Changes A ccount Branch R e n ta l V A 8 CONSTRUCTION SO FOOD * TOBACCO TEXTILES! EXC. KNITS APPAREL* HOUSEHOLD TEXTILE H) PAPER PRINTING * PUBLISHING OTHER CHEMICALS RUBBER PRODUCTS PLASTIC PRODUCTS LUMBER FURNITURE a. 24 STONE* CLAY, 0LA METAL PRODUCTS METALWORKINQ MACHINERY SPECIAL INDUSTRY MACHINERY MISC MIN-ELECTRICAL MACH C0MPUTER OTHER OFFICE EQUIPMENT SERVICE INDUSTRY MACHINERY COMMUNIC EQ. ELECTRONIC COMP MISC ELECTRICAL EO INSTRUMENTS MISC. MANUFACTURING REAL ESTATE SCRAPS AND USED TOTALS GPO SECTOR 9 PRINTINO AND PUBLISHING P rim a ry Secondary A c t iv it y F o re * Mfg 8pace T o ta l 8 P ro d u c t S ecto r P rod ucts P ro d u cts Changes A ccount Branch R e n ta l V A CONSTRUCTION TEXTILE8. EXC. KNITS APPAREL. H0U8EH0LD TEXTILE PAPER PRINTING fc PUBLISHING OTHER CHEMICALS METAL PRODUCTS METALWORKING MACHINERY SPECIAL INDU8TRY MACHINERY MISC NON-ELECTRICAL MACH OTHER OFFICE EQUIPMENT I instrum ents MISC. MANUFACTURING REAL ESTATE SCRAPS AND USED TOTALS

63 ( QPO SECTOR 10 CHEMICAL AND ALLIED PRQDUCT8 P rim a ry 8ocondary A c t iv it y F o re * M*b 8pac» T o ta l P ro d u c t 8 «c to r P ro d u c t* P rod ucts Changes A ccount Branch R e n ta l V A 6 CRUDE PETROLEUM CONSTRUCTION FOOD li TOBACCO TEXTILE8. EXC. KNIT APPAREL. HOUSEHOLD TEXTILES PAPER O PRINTING fc PUBLI8HINQ AGRICULTURAL FERTILIZERS OTHER CHEMICALS > PETROLEUM REFINING RUBBER PRODUCTS PLA8TIC PRQDUCT TONE. CLAY. QLA FERROUS METAL OTHER N0NFERRQU8 METALS METAL PR0DUCT METALUORKINO MACHINERY SPECIAL INDUSTRY MACHINERY MISC NON-ELECTRICAL MACH SERVICE INDUSTRY MACHINERY ELEC INDL APP Sc DISTRIB EQ HOUSEHOLD APPLIANCE MOTOR VEHICLES INSTRUMFNT MISC. MANUFACTURING TOTALS GPO SECTOR 11 PETROLEUM AND RELATED INDU8TRIE8 P rim ary Secondary A c t iv it y F o re * Mfg 8paco T o ta l 7 P ro d u c t S e cto r P roducts P ro d u cts Changes Account Branch R o n ta l NEW-METALLIC MINING V A CONSTRUCTION FOOD li TOBACCO TEXTILES, EXC. KNITS APPAREL. HOUSEHOLD TEXTILES PAPER AGRICULTURAL FERTILIZERS OTHER CHEMICALS PETROLEUM REFINING RUBBER PRODUCTS PLASTIC PRODUCTS STONE, CLAY. 0LAS METAL PR0DUCT NIPA STATISTICAL DISCREPANCY TOTALS

64 OPO SECTOR 12 RUBBER <i MISC. PLASTIC PRODUCTS P rin a rg Secondary A c t iv it y F o re * Mfg Space T o ta l P ro d u c t B a c to r P ro d u c ts P ro d u c ts C bangis A ccount Branch R a n ta l V A B CONSTRUCTION FOOD * TOBACCO TEXTILES* EXC. KNITS KNITTING APPAREL* HOUSEHOLD TEXTILES PAPER PRINTING fc PUBLISHING OTHER CHEMICALS PETROLEUM REFININO RUBBER PRODUCTS PLA8TIC PRODUCTS SHOES AND LEATHER LUMBER FURNITURE 0. IS T0NE* CLAY* 0LA OTHER N0NFERR0U8 METALS METAL PR0DUCT C0N8TR* MINING* OILFIELD EQ METALWORKING MACHINERY SPECIAL INDUSTRY MACHINERY HI8C ELECTRICAL EQ TV SET8* RADIOS* PHONOGRAPHS MOTOR VEHICLES AEROSPACE OTHER TRANSP. EQUIP INSTRUMENTS MI8C. MANUFACTURING o TOTALS OPO SECTOR 13 LEATHER AND LEATHER PRODUCTS P rim a ry Bacondary A c t iv it y Fore a Mfg 8paca T o ta l P ro d u c t B o c to r P ro d u c ts P ro d u c ts Changas A ccount Branch R a n ta l V A 8 CONSTRUCTION TEXTILE8# EXC. KNITS APPAREL* HOUSEHOLD TEXTILE PRINTING I t PUBLISHING OTHER CHEM1CAL RUBBER PRODUCTS PLA8TXC PRODUCTS SHOES AND LEATHER STONE* CLAY* 0LAS INSTRUMENTS MISC. MANUFACTURING NIPA 8TATI8TICAL DISCREPANCY T0TAL

65 I. ( GPO 8ECT0R 14 LIMBER fc MOOD PRODUCTS. EX FURN ( P rim a ry Secondary A c t iv it y F o re * Mfg Space T o ta l P ro d u c t S octo r P roducts P ro d u cts Changss Account Brancb R e n ta l V A y 8 CONSTRUCTION PAPER OTHER CHEMICALS > 30 PLASTIC PRODUCTS LUMBER S3 FURNITURE S4. V 34 STONE. CLAY. CLASS S3 FERROUS METALS sa METAL PRODUCTS (' 43 MOTOR VEHICLES OTHER TRANSP. EQUIP MISC. MANUFACTURING ( 63 REAL ESTATE TOTALS * «GPO SECTOR 13 FURNITURE AND FIXTURE8 ( P rim a ry Secondary A c t iv it y F o re * Mfg 8pac«T o ta l P ro d u c t S octor P roducts P ro d u cts Changes Account Branch R o n ta l V A a CONSTRUCTION TEXTILES. EXC. KNITS is APPAREL. HOUSEHOLD TEXTILES ( 13 PAPER O PRINTING * PUBLISHING V SS LUMBER SO RUBBER PR0DUCT8 PLASTIC PR0DUCT S S3 FURNITURE T0NE. CLAY, GLA METAL PRODUCTS O AGRICULTURAL MACHINERY O C0N8TR. MININO. OILFIELD EQ fc. 33 METALWORKING MACHINERY S. 34 MISC NON-ELECTRICAL MACH COMPUTERS SERVICE INDUSTRY MACHINERY COMMUNIC EQ. ELECTRONIC COMP ELEC INUL APP * DISTRIB EQ O HOUSEHOLD APPLIANCES MISC ELECTRICAL EQ TV SET8.RADIOS. PHONOGRAPHS ' 43 MOTOR VEHICLES AEROSPACE INSTRUMENTS MISC. manufacturing TOTALS O'

66 c ( c ' t o I ( OPO SECTOR 16 STONE. CLAY. * CLASS PRODUCTS P ro d u c t S acto r P rim a ry P ro d u c t* Secondary P ro d u cts A c t iv it y Changas F orce Account M#g Branch Bpaca R a n ta l T o ta l V A 7 NON-METALLIC MINING CONSTRUCTION B9. 10 TEXTILF8. EXC. KNXT PAPER PRINTING fc PUBLISHING OTHER CHEM1CAL PETROLEUM REFINING RUBBER PRODUCTS PLASTIC PR0DUCT H0E8 AND LEATHER LUMBER STONE, CLAY. GLASS FERR0U8 METAL METAL PRODUCTS O AGRICULTURAL MACHINERY METALWORKI NO MACHINERY HI8C NON-ELECTRICAL MACH O HOUSEHOLD APPLIANCE MI8C ELECTRICAL EO MOTOR VEHICLES AEROSPACE INSTRUMENTS MISC. MANUFACTURING TOTALS I c

67 ( GPO SECTOR 17 PRIMARY METAL INDUSTRIES P ro d u c t S eetor P ria w ry P roducts 8ocondary P ro d u cts A c t iv it y Changes F orco A ccount Mfg Branch 8paco R e n ta l T o ta l V A 8 CONSTRUCTION PAPER PRINTINQ * PUBLISHING AGRICULTURAL FERTILIZERS OTHER CHEMICALS PETROLEUM REFINING PLASTIC PRODUCTS FURNITURE STONE. CLAY, GLASS O FERROUS METALS COPPER OTHER NONFERROUS METALS METAL PRQDUCT AGRICULTURAL MACHINERY CONSTR. MINING. OILFIELD EQ METALWORKING MACHINERY PECIAL INDUSTRY MACHINERY MISC NON-ELECTRICAL MACH OTHER OFFICE EQUIPMENT SERVICE INDU8TRY MACHINERY COMMUNIC EQ. ELECTRONIC COMP ELEC INOL APP t DISTRIB EQ MISC ELECTRICAL EQ MOTOR VEHICLES AEROSPACE OTHER TKANSP. EQUIP INSTRUMENTS MISC. MANUFACTURING GAS UTILITY REAL ESTATE CRAP8 AND USED TOTALS I

68 GPO SECTOR 18 METAL PRODUCTS P rim ary Secondary A c t iv it y Forco Mffl Space T o ta l P ro d u c t 8 o c to r P ro d u c t* P rod ucts Changes A ccount Branch R e n ta l V A 8 CONSTRUCTION APPAREL. HOUSEHOLD TEXTILES PAPER PRINTINO V PUBLISHINO OTHER CHEMICALS O RUBBER PR0DUCT SO PLASTIC PR0DUCT LUMBER FURNITURE BTONE, CLAY, CLASS FERR0U8 METALS COPPER OTHER N0NFERR0U8 METALS METAL PRODUCTS ENGINES AND TURBINES AGRICULTURAL MACHINERY O CONSTR. MINING, OILFIELD EQ METALWORKING MACHINERY SPECIAL INDUSTRY MACHINERY MISC NON-ELECTRICAL MACH C0MPUTER I HER OFFICE EQUIPMENT SERVICE INDUSTRY MACHINERY CQMMUNIC EQ, ELECTRONIC COMP ELEC INDL APP * DISTRIB EQ O HOUSEHOLD APPLIANCES MISC ELECTRICAL EQ MOTOR VEHICLE AEROSPACE OTHER TRANSP. EQUIP INSTRUMFNTS MI8C. MANUFACTURING SCRAP8 AND USED 308. o: TOTALS

69 OPO 8ECT0R 19 TRANS EQ ORD EX MOTOR VEH P ro d u c t S octo r P rim a ry P ro d u c t* 8ocondary P roducts A c t iv it y C h tn g tt For co Account Mfg Branch Bpaco R o n ta l T o ta l V A 8 CONSTRUCTION APPAREL. HOUSEHOLD TEXTILES PRINTING * PUBLISHING PLASTIC PR0DUCT LUMBER FURNITURE STONE. CLAY, GLASS FERROUS METALS OTHER N0NFERR0U8 METAL METAL PRODUCTS ENGINES AND TURBINES AGRICULTURAL MACHINERY C0N8TR. MINING. OILFIELD EQ METALWORKING MACHINERY SPECIAL INDUSTRY MACHINERY MISC NON-ELECTRICAL MACH O COMPUTERS SERVICE INDUSTRY MACHINERY COMMUNIC EQ. ELECTRONIC COMP ELEC INDL APP It DISTRIB EQ MI8C ELECTRICAL EQ MOTOR VEHICLES AEROSPACE HIP8. BOATS OTHER TRANS. EQUIP INBTRUTCNTS MISC. MANUFACTURING CRAPS AND USED TOTALS

70 OPO SECTOR 20 MACHINERY. EXCEPT ELECTRICAL P r im r y Secondary A c t iv it y F orco Mfg 8pace T o ta l P ro d u c t S ecto r P roducts P ro d u cts C h «ngn A ccount Branch R e n ta l V A a CONSTRUCTION APPAREL. HOUSEHOLD TEXTILES PAPER OTHER CHEMICAL RUBBER PROOUCT PLASTIC PR0DUCT LUMBER : FURNITURE T0NE. CLAY, 0LAS FERROUS METALS COPPER OTHER N0NFERR0U8 METALS METAL PRODUCTS ENGINES AND TURBINES AGRICULTURAL MACHINERY C0N8TR, MINING. OILFIELD EG METALWORKING MACHINERY PECIAL INDUSTRY MACHINERY MISC NON-ELECTRICAL MACH COMPUTERS OTHER OFFICE EQUIPMENT SERVICE INDUSTRY MACHINERY COmUNIC EQ. ELECTRONIC COMP ELEC INDL APP fc DISTRIB EQ HOUSEHOLD APPLIANCES MISC ELECTRICAL EQ MOTOR VEHICLES AER08PACE SHIPS, BOATS OTHER TRANSP. EQUIP IN8TRUTCNT MI8C. MANUFACTURING REAL ESTATE SCRAPS AND USED TOTALS

71 ( < c ( ( GPO SECTOR 21 ELECTRICAL MACHINERY ( - P rim ary Secondary A c t iv it y F o re * N f Space T o ta l P ro d u c t S ecto r P ro d u c t* P rod ucts Change* A ccount Branch R o n ta l V A a CONSTRUCTION I 10 TEXTILES. EXC. KNIT PAPER PRINTING ti PUBLISHING K 16 OTHER CHEMICAL SO PLASTIC PRODUCTS FURNITURE 0. ( BTQNEi CLAY. GLA FERROUS METALS i 26 COPPER OTHER NONFERROUS METALS METAL PRODUCTS ENGINE8 AND TURBINES METALWORKING MACHINERY ( 33 SPECIAL INDUSTRY MACHINERY MI8C NON-ELECTRICAL MACH COMPUTERS OTHER OFFICE EQUIPMENT SERVICE INDUSTRY MACHINERY ( 38 CQMMUNIC EQ. ELECTRONIC COW* ELEC INUL APP & DISTRIB EQ HOUSEHOLD APPLIANCE MI8C ELECTRICAL EQ TV SETS.RADIOS. PHONOGRAPHS MOTOR VEHICLES AEROSPACE OTHER TRANSP. EQUIP INSTRUMENTS MISC. MANUFACTURING REAL ESTATE SCRAPS AND USED I TOTALS < L c c

72 0 OPO SECTOR 22 MOTOR VEHICLES AND EQUIPMENT P rim ary Secondary A c t iv it y Forco Hfg Bpaca T o ta l P rod uct S ector P roducts P rod ucts Changes Account Branch R o n ta l V A 8 CONSTRUCTION APPAREL. HOUSEHOLD TEXTILES OTHER CHEMICALS RUBBER PR0DUCT PLASTIC PRODUCTS LUMBER FURNITURE o STONE. CLAY. GLASS FERROUS METALS COPPER OTHER NONFERROUS METALS METAL PRODUCTS ENGINES AND TURBINES AGRICULTURAL MACHINERY CONSTR. MINING, OILFIELD EG METALWORKING MACHINERY MISC NON-ELECTRICAL MACH COMPUTERS * SERVICE INDUSTRY MACHINERY COMMUNIC EQ, ELECTRONIC COMP ELEC INDL APP * DI8TRIB EQ HOUSEHOLD APPLIANCES MISC ELECTRICAL EQ TV 8ET8. RADIOS. PHONOORAPH MOTOR VEHICLES AEROSPACE O BHIP8. BOATS OTHER TRAN8P. EQUIP INSTRUMENTS o MISC. MANUFACTURING REAL ESTATE CRAP8 AND USED T0TAL

73 OPO SECTOR 23 INSTRUMENTS AND RELATED PRODUCTS P rim ary 8acondary A c t iv it y Forco Mfg 8p«ca T o ta l P ro d u c t S a cto r P roducts P ro d u cts Changss A ccount Branch R o n ta l V A 8 CONSTRUCTION TEXTILES. EXC. KNITS PAPER PRINTINO * PUBLISHING OTHER CHEMICAL RUBBER PR00UCT SO PLASTIC PRQDUCT SI 8H0E8 AND LEATHER O FURNITURE T0NE. CLAY. 0LA HETAL PRODUCTS CQNSTRj MINING* OILFIELD EQ METALWORKING MACHINERY SPECIAL INDUSTRY MACHINERY MISC NON-ELECTRICAL MACH COMPUTERS OTHER OFFICE EQUIPMENT SERVICE INDUSTRY MACHINERY COMMUNIC EQ. ELECTRONIC COMP ELEC INDL APP It DISTRIB EQ HOUSEHOLD APPLIANCES MI8C ELECTRICAL EQ TV SETS. RADIOS. PHONOGRAPHS MOTOR VEHICLES AEROSPACE INSTRUMENTS MISC. MANUFACTURING REAL E8TATE TOTALS

74 GPO SECTOR 24 HISC. MANUFACTURING INDUSTRY P rim a ry Secondary A c t iv it y F ore s Mfg Spaco T o ta l P ro d u c t S e c to r P ro d u c ts P ro d u c ts Changes A cco unt B ranch R e n ta l V A e CONSTRUCTION TEXTILES. EXC. KNITS APPAREL* HOUSEHOLD TEXTILES PAPER PRINTING PUBLIBHINO OTHER CHEMICALS RUBBER PRODUCTS PLA8TIC PR0DUCTB SHOES AND LEATHER LUMBER FURNITURE STONE. CLAY* 0LA FERROUS METALS OTHER NONFERROUS METALS METAL PR0DUCT METALWORKINO MACHINERY SPECIAL INDUSTRY MACHINERY HISC NON-ELECTRICAL MACH OTHER OFFICE EQUIPMENT SERVICE INDUSTRY MACHINERY COMMUNIC EQ* ELECTRONIC COMP ELEC INDL APP fc DISTRIB EQ MISC ELECTRICAL EQ AEROSPACE HIP8* BOATS OTHER TRANSP. EQUIP INSTRUMENTS MISC. MANUFACTURINO REAL ESTATE TOTALS n o OPO SECTOR 29 RAILROADS P rim a ry Secondary A c t iv i t y F orce Mfg Space T o ta l P ro d u c t S e c to r P ro d u c ts P ro d u c t* Changes A cco un t Branch R e n ta l V A 8 CONSTRUCTION OTHER TRANSP. EQUIP. 0. ' RAILROADS TRUCKING* HUY PASS TRANSIT AIR TRANSPORT EATING ti DRINKING PLACE HOTELS) REPAIR8 EXC AUTO SCRAPS AND USED TOTALS

75 * GPO SECTOR 26 AIR TRANSPORTATION P rim a ry Secondary A c t iv it y F orca Mfg Spaca T o ta l P ro d u c t 8 a c to r P ro d u c ts P ro d u c ts Changes A ccount Branch R a n ta l V A B CONSTRUCTION O AIR TRANSPORT RETAIL TRADE' EATING It DRINKING PLACES REAL ESTATE HOTELS! REPAIRS EXC AUTO BU8INEB8 SERVICES MOVIES AND AMUSEMENTS O TOTALS * OPO SECTOR 27 TRUCKINO AND OTHER TRANSPORTATION P rim a ry Secondary A c t iv it y Forca Mfg Spaca T o ta l P ro d u c t 8 a c to r P ro d u c ts P ro d u c ts Changes A ccount Branch R a n ta l V A 1 AGRICULTURE, FORESTRY. FISHERY e CONSTRUCTION RAILR0AD o: TRUCKINO. HUY PASS TRANSIT WATER TRANSPORT AIR TRANSPORT PIPELINE TRANSPORTAION SERVICES WHOLESALE TRADE RETAIL TRADE EATINO fc DRINKINO PLACES BUSINESS SERVICES NIPA STATISTICAL DISCREPANCY TOTALS GPO SECTOR 28 C0MMUNCIATI0N8 P rim a ry Secondary A c t iv it y F orca Mfg Spaca T o ta l P ro d u c t S a c to r P ro d u c ts P ro d u c ts Changas A ccount Branch R a n ta l V A 8 CONSTRUCTION COMMUNICATIONS SERVICES REAL EBTATE TOTALS OPO SECTOR 30 ELECTRIC. GA8. AND SANITARY U T IL IT IE S P rim a ry Secondary A c t iv it y Forca Mfg Spaca T o ta l P ro d u c t 8 a c to r P ro d u c ts P ro d u c ts Changas Account Branch R a n ta l V A 6 CRUDE PETROLEUM CONSTRUCTION AGRICULTURAL FERTILIZERS PETROLEUM REFININO O FERROUS METALS ELECTRIC U T IL IL IT IE S 17B S A8 U T ILIT Y MATER AND SANITATION TOTALS 264 T P'> "9313.

76 OPO SECTOR 31 WHOLESALE AND RETAIL TRADE P rim a ry Bacondary A c t iv i t y Fores Mfg Space T o ta l P ro d u c t 8» c to r P ro d u c ts P ro d u c ts Changes Account Branch R e n ta l V A 1 AGRICULTURE. FORESTRY. FISHERY CONSTRUCTION FOOD & TOBACCO TEXTILES. EXC. KNITS KNITTING APPAREL. HOUSEHOLD TEXTILES PAPER PRINTING <t PUBLISHING AGRICULTURAL FERTILIZERS OTHER CHEMICALS PETROLEUM REFINING RUBBER PRODUCTS PLASTIC PRODUCTS 0. * SHOES AND LEATHER LUMBER d FURNITURE STONE. CLAY. GLASS FERROUS METALS COPPER OTHER N0NFERR0U8 METALS METAL PRODUCTS ENGINES AND TURBINES AGRICULTURAL MACHINERY CONSTR.MINING. OILFIELD EQ METALWORKINO MACHINERY PECIAL INDUSTRY MACHINERY o MISC NON-ELECTRICAL MACH COMPUTERS OTHER OFFICE EQUIPMENT ERVICE INDUSTRY MACHINERY CQMMUNIC EQ. ELECTRONIC COMP ELEC 1NDL APP St DI8TRIB EQ HOUSEHOLD APPLIANCES MISC ELECTRICAL EQ TV SET8.RADIOS. PHONOGRAPHS MOTOR VEHICLES AEROSPACE SHIPS. BOATS OTHER TRANSP. EQUIP INSTRUMENTS MISC. MANUFACTURING WATER TRANSPORT WHOLESALE TRADE RETAIL TRADE EATINO * DRINKING PLACES HOTELS! REPAIRS EXC AUTO BUSINESS SERVICES AUTOMOBILE REPAIRS MOVIES AND AMUSEMENTS TOTALS

77 OPO SECTOR 32 FINANCIAL <t INSURANCE SERVICES P rim a ry Secondary A c t iv it y F o re * h ffl 8paco T o ta l P ro d u c t S s c to r P ro d u c ts P ro d u c ts Changss A ccount Branch R o n ta l V A 62 FINANCE * INSURANCE REAL ESTATE BUSINESS SERVICES TOTALS OPO 8ECT0R 33 REAL ESTATE * COMBINATIONS OFF P rim a ry 8ocondary A c t iv it y Fores H fg Bpacs T o ta l P ro d u c t 8 o c to r P ro d u c ts P ro d u c ts Changss A ccount B ranch R s n ta l V A 63 REAL ESTATE OWNER-OCCUPIED HOUSING N IPA.8TA TI8TIC A L DISCREPANCY TOTALS GPO SECTOR 34 HOTELS Si REPAIR (NOT AUTO) P rim a ry Secondary A c t iv it y Fores Mfg Spacs T o ta l P ro d u c t S s c to r P ro d u c ts P ro d u c ts Changss Account Branch R o n ta l V A 8 CONSTRUCTION RETAIL TRADE EAT I NO It DRINKING PLACE REAL ESTATE HOTELS! REPAIR8 EXC AUTO BUSINESS SERVICES T0TAL GPO SECTOR 33 MISC. BUSINESS SERVICES P rim a ry 8ocondary A c t iv it y Fores Hfg Spacs T o ta l P ro d u c t S o c to r P ro d u c ts P ro d u c ts Changss A ccount Branch R o n ta l V A 8 CONSTRUCTION WHOLESALE TRADE RETAIL TRADE REAL ESTATE HOTELS! REPAIRS EXC AUTO BUSINESS SERVICES TOTALS

78 GPO SECTOR 36 AUTO REPAIR P rim a ry Secondary A c t iv it y F o rce Mfg Space T o ta l P ro d u c t 8 e c to r P ro d u c t* P ro d u c ts C h a n g n A ccount Branch R e n ta l V A 8 CONSTRUCTION RETAIL TRADE AUTOMOBILE REPAIRS TOTALS OPO 8ECT0R 37 MOTION PICTUREB li AMUSEMENTS P rim a ry Secondary A c t iv i t y F o rce Mfg Space T o ta l P ro d u c t 8 e c to r P ro d u c t* P ro d u c ts Changes A cco unt Branch R e n ta l V A 8 CONSTRUCTION RETAIL TRADE EATING * DRINKINO PLACE HOTELSj REPAIR8 EXC AUTO MOVIES AND AMUSEMENT NIPA-STATISTICAL DISCREPANCY TOTALS OPO 8ECT0R 38 MEDICAL V EDUCATIONAL SERVICES P rim a ry Secondary A c t iv it y F o rce Mfg Space T o ta l P ro d u c t S e c to r P ro d u c ts P ro d u c ts Changes A cco unt Branch R e n ta l V A 8 CONSTRUCTION RETAIL TRADE EATINO * DRINKINO PLACES HOTELS! REPAIRS EXC AUTO MOVIES AND AMUSEMENTS MEDICINE* EDUCATION* NPO TOTALS OPO SECTOR 39 PRIVATE HOUSEHOLDS P rim a ry Secondary A c t iv it y F orce Mfg Space T o ta l P ro d u c t S e c to r P ro d u c ts P ro d u c ts Changes A cco unt Branch R e n ta l V A 72 DOMESTIC SERVANTS TOTALS OPO 8ECT0R 40 FEDERAL GOVERNMENT ENTERPRISES P rim a ry Secondary A c t iv it y F orce Mfg Space T o ta l P ro d u c t 8 e c to r P ro d u c ts P ro d u c ts Changes A cco unt Branch R e n ta l V A 8 CONSTRUCTION EATING Si DR INKINO PLACE REAL ESTATE FED Si SScL GOVT ENTERPRISES TOTALS

79 OPO SECTOR 41 8TATE li LOCAL ENTERPRISES P rim a ry Secondary A c t iv it y F o re * Mfg Space T o ta l P ro d u c t 8 a c to r P ro d u c ts P ro d u c ts Changos A cco unt B ranch R a n ta l V A B CONSTRUCTION STONE* CLAY# OLASS RAILROADS TRUCKING. HWY PASS TRANSIT WATER TRANSPORT AZR TRANSPORT COMMUNICATIONS SERVICES ELECTRIC U T IL IL IT IE WATER AND SANITATION RETAIL TRADE EATINO ti DR INKINO PLACES FINANCE It INSURANCE REAL.ESTATE AUTOMOBILE REPAIR MOVIES AND AMUSEMENTS FED * 8 * L OOVT ENTERPRISES TOTALS O OPO SECTOR 44 FEDERAL GOVERNMENT INDUSTRY P rim a ry 8ocondary A c t iv it y F o rco Mfg Spaca T o ta l P ro d u c t S e c to r P ro d u c ts P ro d u c ts Changes A cco unt B ranch R a n ta l V A 8 CONSTRUCTION GOVERNMENT INDUSTRY TOTALS OPO 8ECT0R 49 STATE AND LOCAL GOVERNMENT INDUSTRY P rim a ry Secondary A c t iv i t y F o rca Mfg Spaca T o ta l P ro d u c t S e c to r P ro d u c ts P ro d u c ts Changes A cco unt Branch R a n ta l V A 8 CONSTRUCTION GOVERNMENT INDUSTRY TOTALS OPO SECTOR 46 REST OF THE WORLD P rim a ry Secondary A c t iv it y F orca Mfg Spaca T o ta l P ro d u c t S e c to r P ro d u c ts P ro d u c ts Changes A ccount B ranch R a n ta l V A 79 REST OF THE WORLD INDUSTRY TOTALS

80 Chapter I I I Labor Compensation I I I. 1 Overview Labor or employee compensation, as noted before,- is the s in g le la rg e s t component of value added, approximately 60% of GNP. The size im plies a greater impact on re la tiv e prices and in fla tio n than any other component of value added. Moreover, personal income is fundam entally composed of labor income. T h e re fo re, two of the links w ith the real s id e, p ric e s and personal income, are c lo s e ly tie d w ith employee compensation. As a re s u lt, the performance of the model as a whole w ill hinge on the development of a s a tis fa c to ry procedure fo r forecasting to ta l labor compensation by industry. Employee compensation is composed of two components, wages and s a la r ie s, and wage supplements. Consequently the hourly compensation rate w ill exceed the hourly wage rate. For the purposes of th is stu d y, the term "p a y -ra te " w i l l re fe r to the hourly labor compensation fo r employees. The s tru c tu re fo r fo re c a s tin g employee compensation combines aggregate and s e c to ra l p a y -ra te equations to provide predictions of sectoral pay-rates. Two aggregate equations fo r the m anufacturing and non-manufacturing pay-rates are combined with sectoral re la tiv e pay-rate e s tim a te s to determ ine h o u rly employee compensation by in d u s try. R e la tiv e s e c to ra l p a y-ra te s are d e fine d as the in d u s try p a y -ra te re la tiv e to either the manufacturing or non-manufacturing pay-rate. The re s u ltin g measures of s e c to ra l p a y-ra tes are adjusted fo r changes in 76

81 employee p ro du ctivity (from the real side of the model) and are therby converted in to indices that can be used with real value added weighted output (REVAWO) to forecast to ta l employee compensation as was described in the f i r s t section of the previous chapter. The th e o re tic a l s p e c ific a tio n and re s u lts o f e s tim a tin g th e aggregate equations are reported in section I I I. 2. These equations are "assigned" the task of transm itting the appropriate long-run e ffe c ts of monetary p o lic y and changes in productivity into prices. In a d ditio n, the rationale fo r re je ctin g a P hillips-curve sp e cifica tio n is presented. The to p ic of se ctio n I I I. 3 is the form and e s tim a tio n o f th e s e c to ra l equations. The dependent v a ria b le s in these equations are indices of re la tiv e hourly employee compensation: in d u s try p a y-ra te re la tiv e to the appropriate aggregate measure. Each in d u s try 's re la tiv e p a y -ra te is a ffe c te d by the unemployment rate, the in fla tio n rate and that in d u stry's share of to ta l employment. From the p e rsp e ctive of the m odeller, th e r e la t iv e p a y -ra te approach o ffe rs some p ra c tic a l advantages over the d ire c t method of estim ating average pay-rate by industry. In d ire c tly forecasting hourly compensation by industry, one is also im p lic it ly fo re c a s tin g r e la t iv e p a y-ra te s among in d u s trie s. Rather than discovering, by ca lcu la tion, the im p lic a tio n s of a set of equations fo r r e la t iv e p a y -ra te s, the r e la t iv e p a y-ra te approach allow s d ire c t c o n tro l over the re la tiv e pay-rate ch a racteristics. Another advantage the two-stage framework o ffe rs the modeller is a "d iv is io n of labor." Important long-run macro properties can be imposed on aggregate equations more easily than on a large number of industry equations. Not only is the data more conveniently used at the aggregate 77

82 L e ve l, but the im p lic a tio n s of the c o e ffic ie n ts fo r a fo re c a s t o f in fla tio n are more readily discernible fo r the aggregate equations. The in d u s try equations are then free to concentrate on the impact of change in Labor markets on re la tiv e compensation rates, both over the cycle and in trend. For a model with many equations, th is d iv is io n of Labor is very im portant in b u ild in g the model since i t greatly s im p lifie s the task; instead of monitoring many equations and tra c in g t h e ir o v e ra ll e ffe c t s, only a few equations need be s c ru tin iz e d to get reasonable macro-economic properties. The fin a l point in favor of the re la tiv e approach is that i t is has proven to be a re lia b le forecasting to o l. The previous v e rs io n of the INFORUM model u t iliz e d th is approach w ith success. The r e la tiv e pay-rate concept fo r forecasting employee compensation presented in th is chapter is based on the previous work accomplished by David B elzer and. p described in the th ird chapter of his thesis But t h i s study is not a re p lic a tio n of B e lz e r's work. Major differences in th is study occur in the s p e c ific a tio n of the aggregate p a y-ra te equations and in the d e fin itio n of sectoral pay-rates. Belzer used a sp e cifica tio n fo r the aggregate equations th a t was very s im ila r to a P h illip s curve. For reasons discussed below, the P hillips-curve sp e cifica tio n is rejected and an a lte rna tive sp e cifica tio n is developed which allow s monetary variables to a ffe ct in fla tio n over the long-run. At the industry le ve l, Belzer was constrained by data considerations to use r e la t iv e wage equations fo r production workers and then transform those wages to employee com pensation f o r a l l em ployees. That transformation procedure added another level of complexity to the model. This study is not bound by the same data considerations that constrained 78

83 B e lz e r, so th a t the focus can be on pay-rates by in d u s try fo r a lt employees, thereby sim plifying the structure of the INFORUM model. I I I. 2 Aggregate Hourly Labor Compensation This section describes the specification and estim ation results fo r the two aggregate pay-rate equations. The requirem ents of long-term fo re c a s tin g mandated th a t the specifications d iffe r considerably from those most commonly encountered in the lite ra tu re of wage determination. F irs t, the rationale fo r th is divergence w ill be developed. During th is exposition, the general requirements and constraints fo r the equations sh o u ld become a p p a re n t. Then, th e e s tim a tio n r e s u lts fo r the manufacturing sector and the non-manufacturing sector w ill be presented. In order that the INFORUM model give plausible answers to long-term policy questions, the model must be constructed with certain appropriate long-run properties in mind. examples of th is requirement. The aggregate pay-rate equations are prime The two equations, in essence, determine th e le v e l o f employee compensation throughout the e n tire model. Employee compensation is the s in g le la rg e s t determ inant of in d u s try p ric e s and personal income, two im portant lin k s w ith the real side. T h e re fo re, the c r ite r io n of reasonable lo n g -ru n e f f e c t s is v e ry important in th is instance. Four basic long-run properties should be present in the forecast of the INFORUM model. F irs t, real wage and pro du ctivity growth ought to be re la te d : real wages. increases in p ro d u c tiv ity are passed on, in p a rt, to higher Second, the growth of prices must re fle c t the growth in the money supply. More s p e c ific a lly, in the long-term, the level of p ric e s 79

84 should r e f le c t the movement of the money supply divided by real GNP. Third, unemployment rates in the remote past should not have, as they do in the conventional P h illip s curve s p e c ific a tio n, the same impact on cu rre n t re a l wages as do more recent unemployment rates. Fourthly, changes in tax rates (income or FICA) should be able to a ffe c t the gross, as w ell as the net, pay-rate. When taken together, the four properties indicate that the rate of change in the nominal hourly pay-rate should equal the in f la t io n ra te plus the economy-wide p ro d u c tiv ity growth rate plus any other factors (such as the e ffe c t of ta x e s ). equation with the four properties is r Then the general form of a p a y-ra te (3.1) W= p + prod + f( u,t,z ) where W = growth in the nominal pay-rate, p = in fla tio n rate, prod = growth in p ro d u ctivity, u = growth in the unemployment rate, t = growth in a tax variable, and z = growth in other variables. From ( 3.1 ), the im plem entation of the f i r s t property is clear. The c o e ffic ie n t fo r p ro d u c tiv ity growth should be one: a two p e rcen t increase in p ro d u c tiv ity is transm itted as two percent increase in the nom inal h o u rly p a y - r a te. Note th a t t h is p r o p e rty means th a t p ro d u c tiv ity is " n e u tra l" w ith respect to fa cto r shares: a change in p ro du ctivity should not a ffe c t la b o r's or c a p it a l's share of income. 80

85 However, the rationale and implementation procedure fo r the other three variables do not jump out from (3.1) as did the p ro d u c tiv ity argument. Each other ch a racteristic requires a re la tiv e ly short discussion. Ef fget of _Money Any attempt to model the growth of p rice s in the lon g -ru n must address the issue of the in flu e n c e of money on in fla tio n. Without a mechanism allowing growth in the appropriate measure of money to a ffe c t the growth in the p ric e le v e l, the model is severely lim ited in the macro questions i t can tackle. The method employed here is to enter the money supply d ire c tly into the aggregate compensation equations. Those equations are not stru cu tra l equations but reduced forms of the actual transmission process. One remarkable* feature of the U.S. economy is the v irtu a l constancy of the v e lo c ity of money as defined-as the r a tio of nominal GNP to M2 over the la s t twenty years. This s ta b ility is shown by Figure III.1. In order to compare the two common measures of v e lo c ity - the v e lo c ity of Ml and M2 - the v e lo c itie s are shown as indexes of th e ir 1977 values. W hile the M1 v e lo c ity of money has increased at a steady rate, the M2 ve lo city has no trend and has not fluctuated by more than eight percent from i t s low est to i t s highest va lu e s. Coupling th is fa ct with the quantity theory of money implies that the long-run in fla tio n ra te ought to approximately equal the difference between the growth in money supply and the growth in real GNP. From , the average annual growth rates fo r M2, real GNP and the GNP deflator were 7.84 (M2), 3.30 (re a l GNP) and 4.54 (GNP d e f la t o r ). Thus the in fla tio n rate implied by the long-run constant v e lo c ity, 4.57, d iffe rs from the a ctu a l ra te of

86 FIGURE I I I. I ' V IV l in d o i o f GNP / Ml too» IV 3 - in d» s o f 0NP / MS ( lo O ) OATS IV l IV2 IS IS B too IS IS I 8 2

87 by 0.03 per year, not a bad prediction fo r so simple a model. The design of the precise mechanism which allows the money supply to influence in fla tio n has two possible general s o lu tio n s. One method m ight be to attem pt to r e p lic a te the actual transm ission process of monetary growth to price growth. This translates in to allow ing changes in the money supply to a ffe c t real fin a l demands which, in tu rn, a ffe c t output. In the case of an increasing money supply, th is increase would tra n s la te in to a higher demand fo r real o u tp u t. But constraints on physical capacity might prevent producers from meeting the demand fo r t h e ir p ro du cts. Excess demand cannot be s a tis fie d ; market pressures force prices upwards. In fla tio n is the re su lt. U n fo rtu n a te ly, th is chain of events is fo rg e d w ith two weak m odelling lin k s. on fin a l demands. The f i r s t weak Link is the e ffe c t of the money supply The INFORUM project has had l i t t l e success in such endeavors except in the few sectors which are s e n sitive to in te re s t rates, such as construction of single fam ily housing and new autom obile sales. These sectors are not large enough to have the overall re q uisite e ffe c t on f in a l demand. A more serious d i f f i c u l t y is the "c o n tro l" problem : th e re is no guarantee th a t, even i f the e ffe c t could be m odelled, the d e sire d in flu e n c e on prices would re s u lt. The correct size of the e ffe c t fo r a p a rtic u la r demand of any product is not easy to evaluate w ith in the context of a large inte rin d u stry model. The other weak lin k is the capacity constraint. Physical capacity is d i f f i c u l t to q u a n tify. In order to use a measure of p h ysica l capacity in a forecast, one must forecast that measure as well not an easy task. F in a lly, one would have to discover how capacity pressures a ffe c t prices. Within the inte rin d u stry model, prices are determined by 83

88 d e fin itio n from the ra tio of nominal value added to real output. Do the capacity constraints serve to decrease real output or to increase value added? And even i f some effects on real output were found, i t would be d i f f i c u l t to en sure th a t they produced p la u s ib le macro-economic responses to changes in the money supply. There is another possible capacity c o n s tra in t, the c o n s tra in t of human c a p i t a l. The increase in f in a l demands caused by monetary expansion w ill increase the demand fo r la b o r, e s p e c ia lly labor w ith those s k i l l s a lre a d y in demand and th e re fo re already employed. Additional output cannot be produced u n t il the workers can be found. Therefore, wages of employed workers are bid up. Increases in wages are then e v e n tu a lly passed onto p ric e s. But note: in th is scenario, an increase in money a ffe c ts wages before i t a ffe c ts e it h e r o u tp u t, employment or p ric e s. A ll of the preceding discussion points to the d ir e c t in c lu s io n o f money in t o the d e te rm in a tio n o f a g g re g a te compensation rates to create quasi-reduced- form equations. In the lite ra tu re of wage determination, there are two instances of e m p iric a l work estim ating the quasi-reduced-form equation indicated by the above a n a ly s is. In 1972, in "A N e o -c la s s ic a l Approach to th e Determination of Prices and Wages fo r the Canadian Economy,' Agarwala et al presented a small model of in fla tio n and wage d e te rm in a tio n fo r the Canadian economy.^ The innovation of the paper was the point that since prices and wages are only two aspects of in f la t io n in the Long-run, re lia n c e on a s t r i c t P h illip s curve fo rm u la tio n fo r a fo re c a s t of in fla tio n is unwarranted. As a consequence, the money wage ought to be determined as the product of the re a l wage and the price level where real wages are a function of p ro du ctivity and the degree of unionization 8k

89 and prices are estimated as a fu n ctio n of Lagged growth in money and unemployment. The other instance of money in a wage equation can be found in Michael Wachter's "The Changing C y c lic a l Responsiveness to Wage I In fla tio n ".^ Although the main focus of the paper was to investigate the s h if t in g of the P h illip s curve over tim e, Wachter did te s t the effectiveness of an aggregate demand va ria b le in a wage equation by estim ating a v a rie ty of equations w ith e ith e r changes in prices or changes in M1. Wachter found that there was not much difference in the two types o f equations except th a t the influence of the monetary variable was less than that of the changes in the nonfarm GNP deflator.^ The reduced influence of M1 was a ttrib u te d to two fa c to rs : the lag structure and use of Ml instead of another monetary measure.^ In the INFORUM model, money w ill a ffe c t price s v ia a four-step process. The f ir s t step is to allow some measure of monetary growth to in flu e nce dire ctly the growth in aggregate pay-rates. Quite naturally, pay-rates influence the level of employee compensation (step 2) which, in tu rn, affects value added (step 3). Finally, value added determines prices via the equation p = pa + v. The excess growth in the money supply - the growth in money th a t exceeds the growth of real GNP - is fu lly passed through to pay-rates. Because the monetary influence on in fla tio n is a long-run e ffe c t, the v a ria b le should enter w ith a d is trib u te d lag where the sum of the coefficients for the lag equals one. This d is trib u te d lag w ill allow short-run fluctuations in the ra tio of M2 to GNP. Note th a t the inclusion of a long-run monetary effect obviates the need fo r a long-run price e ffe c t on pay-rates. This bypasses the 85

90 complex issue of sim u lta n e ity bewteen wages and prices: does price growth cause wage growth or is the reverse true? Causation is not an issue here; the structure of the model imposes the solution that wage growth "causes" p rice growth. However, to the exte nt th e re is a short-run e ffe c t of in fla tio n on pay-rates, a co st-o f-livin g variable might be included in the lis t of other variables fo r equation (3.1 ). Unemployment The dispute over the existence of the P h illip s curve has yet to be re so lve d. A recent survey of the infiation-unem ploym ent tra d e o ff lite ra tu re by Santomero and Seater^ concludes that serious th e o re tic a l and methodological d iffic u ltie s destroy any foundation to the P h illip s curve. The lack of micro-economic underpinings, ignorance over the formation of expectations and flaws in empirical technique (i.e. serial correlation and simultaneous equation bias) lead the authors to proclaim "th e re appears to be no long-run tra d e o ff between in fla tio n and Q unemployment." In contrast to th is conclusion, the recent works of Eckstein and Girola, and Sachs, estimate a Phillips-type relationship fo r the U.S. economy over an 87-year period. Both papers deal with the simultaneous equation bias by in c lu d in g a p ric e equation in the f o r m u la t io n o f th e P h i l l i p s curve; s e r ia l c o r r e la tio n and m u litco llin e a rity seemed to pose no d iffic u lty. Though the authors use diffe ren t estimation techniques, the same general result is apparent: a long-run tradeoff between in fla tio n and changes in aggregate demand does e x is t but the influence of changes in aggregate demand on in fla tio n has 1 1 dirmm shed.11 86

91 The relevant issue for this study is not whether or not a P h illip s curve may be estim ated but the appropriateness of the P h illip s curve specification for Long-term analysis. In p a rtic u la r, the conventional s p e c ific a tio n has an u n s a tifa cto ry long-run im p lic a tio n fo r wage determination: unemployment rates from d is ta n t and near past have an equal impact on the level of real wages. To see th is property, let us examine a simple version of the PhiLlips-curve relation such as (3.2) Wt - Wt-1 = a + b Ut + c (Pt - P ^, where subscripts refer to the time period and W,U, and P are logarithm s of wages, the unemployment rate and the price level. The solution of the difference equation is T (3.3) WT = WQ + at + b u j + c (PT - PQ) J= 0 Consider the case in which Py = pq so that Wy - WQ is the change in real wage. Then, T (3.4) W. - wn = a T + b t U. u j=0 j According to (3.4 ), the change in the wage over the period can be decomposed in to a tre n d te rm, and the unw eighted sum of past unemployment rates. Two serious implications are imbedded in equation (3.4 ): f i r s t, there is an inexorable change to wages as implied by the constant term (a ), and secondly, real wage change depends on the y Q iiti9tl d_sum f unemployment ra te s. Both im p lic a tio n s are 87

92 inappropriate for Long-term forecasting. The inclusion of a constant term into the specification introduces an unwanted trend: wages w ill always change by, at least, that constant term. For example, imagine th a t an economy is on the verge of the steady state as envisioned by Ricardo, M ill and o th e r c la s s ic a l economists. Furthermore, imagine that there w ill be no unemployment and no change in the money supply, a true nirvana for a ll. One would expect no change in real wages, but a model u t iliz in g equation (3.2) would forecast a constant change in wages equal to the constant term. Perhaps the constant term is attempting to capture the e ffe c t of p ro d u c tiv ity change. equation. However, productivity change is easily introduced into such an F o rtun ate ly, a simple so lu tio n to th is problem is the suppression of the constant term in the estimation. Equation (3.4) also shows th a t the conventional PhiLlips-curve specification implies an inappropriate long-run a ffe c t of unemployemnt on real wage changes. The undesirableness stems from the indiscriminate treatm ent of past unemployment rates: unemployment from ten years ago has the same impact on the current wage rate as the contem porary unemployment ra te. illu s tra te the po int. Another hypothetical example should serve to Suppose there are two economies id e n tic a l in alm ost every respect except th a t one experiences some sm all, very infrequent stochastic shocks. Imagine the unemployment h is to rie s of those economies are depicted by Figure I I I. 2. The only difference between the two situations is a slight disturbance over the in te rv a l t 1 to t ^. F in a lly, assume fo r whatever reason, that both economies have the same fin a l level of prices at time T. From equation (3.3 ), the change in real wages would d if f e r between the two economies by the 88

93 FIGURE I I I. 2 Graph of Hypothetical Example UN i i j *1 *2 T Time d iffe re n ce in the unemployment history regardless of the length of the in te rv a l, real wages w ill d iffe r whether the in te rv a l (t-j^ t^ ) is two weeks, two years or two decades ago. Short-run deviations from the past should not have an equal impact in the determ ination of long-run variables as recent influences. The undesirable characteristic is due solely to the sp e c ific a tio n : i t is not inherent to the tra d e o ff re la tio n s h ip. The flaw can be avoided by using lagged va lu es of the unemployment ra te or the unemployment rate as a f ir s t difference. For example, substituting the change in the unemployment rate into equation (3.2) yields (3.2*) Wt - wt _1 = a + b(ut - U ^ ) + c(pt - ) 89

94 S olving fo r and le t t in g Pt = Pq gives (3.4*) WT - wq = at + bcut - UQ) Note that the change in real wages is now a ttrib u ta b le to a time tre nd and the overall change in the unemployment rate, not the unweighted sum of past unemplyment rates as implied by the conventional Phi H i ps-curve specification. gmployer and Employee.Taxes The impact of socia l secu rity and personal income taxes upon compensation rates rests on a positive e la s itic ity of the labor supply with respect to compensation. A p o s itiv e e la s t ic it y im plies th a t an increase in taxes w ill increase the rate of before-tax compensation because of a reduction in the quantity of labor supplied at any given p a y -ra te. The e l a s t i c i t y is an e m p iric a l is s u e ; income and substitution effects of a tax change work in opposite d ire c tio n s. For an in d iv id u a l, a decrease in a tax rate reduces the gap between the gross and net earnings. The influence of a higher real wage on the work - leisure decision has a dual e ffe ct. The increased opportunity cost of leisure (substitution effect) induces a s h ift from leisure towards work. On the other hand, income has grown, permitting the individual to expand consumption of a ll goods including leisure. Consequently, the o v e ra ll effect is theoretically ambiguous. Empirical evidence supporting a positive e la s tic ity of labor is not overwhelming. Rosen, in a short review of the lite r a tu r e ^ notes that 90

95 the general results are that prime age males are re la tiv e ly in s e n s itiv e to small changes in wages while the supply of hours worked of other groups are much more sensitive. Rosen cautions th a t the re s u lts are probably lower bounds since the studies neglected to investigate the effect of tax changes on retirement decisions, and investment in human c a p ita l. A study by Hausman performed after the Rosen survey, confirms those general results of the survey though Hausman finds a large income effect for prime age males. Extending the analysis from the individual response to a tax change to the aggregate response does not s im p lify the s itu a tio n. James 1 L. Gwartney and Richard Stroup * point out that aggregate real disposable income does not change by the same amount of the tax change. The tax change does not, in it s e lf, a lte r the technical production p o s s ib ilitie s of the economy. For instance, i f a tax cut is coupled w ith an equal reduction in the p rovision of public goods and services, th e precise «e ffe c t depends on the p riv a te valuation of the p u b lic goods. I f c itiz e n * s va lu a tio n of the foregone public goods exceeds the value of the a v a ila b le p riv a te goods, then real "psych ic" income d e c lin e s. Therefore, the income effect reinforces the substitution effect and the quantity of labor supplied w ill increase. Of course, the opposite e ffe c t is po ssible: the p riva te goods a v a ila b le may be valued more highly than the foregone public goods, thereby increasing aggregate real income and the income and s u b s titu tio n e ffe c ts w ill move in opposite directions, thus having an ambiguous effect on the supply of labor. However, th is analysis leads to an a.p rio ri, expectation of a positive e la s tic ity of the labor supply with resipect to social se cu rity tax changes. Social se cu rity is a tra n sfe r program: tax revenues 91

96 co llected from workers are simply disbursed to r e t ir e d persons. Therefore, real aggregate income is not altered by changes fn the tax rate, especially i f the taxpayers view the change as a guid_prg..guo fo r fu tu r e b e n e fits. From the above an alysis, a zero change in real aggregate income implies no income effect fo r the economy. Since the s u b s titio n effect is the only remaining e ffe ct, the overall response is unambiguous: the overall supply of labor w ill be reduced in response to an increase in the tax ra te. A reduced supply of labor i» p tie s an increase in the before -tax pa y-rate. The precise magnitude of the increase depends on the amount of the labor supply s h if t and the slope of the demand for labor function. One may note th a t the preceding analysis im p lic itly assumes that the b e n e fits are p ro p o rtio n al (or viewed as p ro p o rtio n a l) to th e c o n trib u tio n s. I f th a t is not tru e, as has been the case in recent years where tax rates were increased and b e n e fits reduced, then taxpayers and workers may not view the change as an equitable trade-off. The main point of th is discussion is to show that social security tax changes can affect the before-tax pay-rate. ra te s. Some research indicates there is some tax e ffe c t on compensation Gordon, in a study covering the period ^, found that 4 employees s h ifte d forward fourteen percent o f t h e ir taxes w h ile employers s h ifte d backwards to employees more than the amount of the 16 tax. Belzer found that changes in the social security tax rate were not evenly borne by employees and employers: employers shifted back to employees. Moreover, Belzer also found an extremely weak Link between changes in hourly pay-rates and changes in the e ffe ctive income tax rate. The Link was so weak that the income tax variable was dropped. 92

97 on_ and_r esults_for_the_p resent_m odel Using the same convention in defining a ll variables in terms of growth rates, the preceding discussion implies the specification should resemble (3.5) W- prod = b,j M(t) + b^ U + b^ tax + b^ Z where W= growth in the nominal pay-rate, prod = growth in productivity, M(t) = a distributed lag of the growth in M2 to real GNP, U = growth rate in the unemployment rate, tax = growth in a tax rate, and Z = other variables.. Equation (3.5) embodies a ll of the desired long-run properties discussed above. Because the growth in productivity is subtracted from the growth in the nominal pay-rate, a ll changes in productivity w ill be d ire ctly transfered into pay-rate changes. As long as the sum of the c o e ffic ie n ts fo r the d is trib u te d lag equals one, any excess growth in the money supply w ill be eventually transmitted into pay-rates. Aggregate pay-rate equations were specified fo r the manufacturing sector and the non-manufacturing sector. Further breakdowns of the manufacturing sector into durable and nondurable manufacturing or the nonmanufacturing sector in to various subcategories such as services, transportation and government proved to be unmanageable. For some of

98 those cate go ries, i t was not possible to implement the desire long-run properties with any measure of success. Since one of the rationales for the aggregate equations is to allow fo r simple implementation of the desired long-term p ro p e rtie s, only equations fo r manufacturing and nonmanufacturing sectors were used. The a v a ila b ility and quality of data helped to determine the actual choice of variables in the specification of the two aggegrate pay-rate equations. Hourly employee compensation indices were constructed for a ll secto rs, in clu d in g the m a n u fa ctu rin g and n o n -m a n u fa ctu rin g aggregates. Total compensation of employees is published by the Bureau of Economic Analysis on a two d ig it SIC code basis. Total compensation equals wage and salary payments, employer and employee contributions to social insurance, and to various private insurance and b e n e fit plans. Dividing the to ta l compensation series by total hours worked by f u ll and p a r t- tim e employees p u b lish e d in ta b le 6.12 of NIPA gave hourly 17 compensation rates. The rates were constructed over the period 1955 to 1980 and normalized to equal unity in the base year of the model, A s lig h t d i f f i c u l t y a ris e s in the a p p ro p ria te measure of p ro d u c tiv ity fo r both equations. Id e a lly, labor p r o d u c tiv ity is measured as real net product per hour. U n fo rtu n a te ly, there is no series on the d iv is io n of real_gnp between the m a n u fa ctu rin g and non-manufacturing sectors. Therefore, productivity is measured as real GNP less real m ilita ry spending per c iv ilia n job. To avoid introdu cing short-run cyclical effects, the actual variable in both equations is the percent change in the three year average. The monetary variable is the growth in the ra tio of M2 to real GNP. M2 is used because its velocity has been more stable than the velocity 3b

99 of Ml. Import prices - especially o il - have an im portant impact on the cost of liv in g. As seen w ith the creation of OPEC, large exogenous price shocks can be experienced in the United States. This in d ica te s th a t some measure of import prices ought to be included in the pay-rate equations. Import prices are exogenous in the model, so no confusing feedback loop is introduced by th e ir use. Exchange rate fluctuations and the use of imports as intermediate goods create a high c o rre la tio n between the nominal price of imports in the U.S. and U.S. prices: the correlation coefficient between the NIPA im port d e fla to r and the PCE deflator is approximately 0.8. However, exchange rate fluctuations are caused, in part, by d iffe re n tia l rates of in fla tio n between the tra d in g co u n trie s. This leads to the adopted practice of adjusting the growth in import prices for domestic in fla tio n by subtracting the growth in M2 to real GNP ra tio from the growth in import prices. What is the best way to measure unemployment? Since the characteristics of the labor force have been changing over the past decades, presumably the overall unemployment rate is not capturing those e ffe c ts : an unemployment of six percent in 1960 meant more prime age males were looking fo r jobs than did a six percent rate in Gordon 1 9 and others have trie d to adjust the unemployment rate for the changing characteristics of the labor force with a variety of measures. However, a lt of those measures, including the unemployment rate of males twenty fiv e and over - the "prime age male" rate - cannot, at present, be generated by the INFORUM model. Therefore, the o v e ra ll unemployment rate is used. In te s ts, the d iffe re n ce in performance between the overall unemployment rate and the "prime age male" unemployment was 95

100 negligible. The tax variable is the growth in the statutory social security tax ra te paid by the employer and the employee. for a d iffe re n tia l impact fo r a given increase; This specification allows an one percentage point increase in the tax rate from fiv e to six percent w i l l have a larg e r impact on gross hourly labor compensation than an increase from ten to eleven percent. The estimated equation fo r manufacturing hourly compensation rates is depicted in Figure I I I. 3. The fiv e year d is trib u te d lag of the growth in the M2 to GNP ra tio is used to implement the desired long-run property of monetary growth on in fla tio n. constrained to lie on a Line and to sum to one. The lag coefficients were Approximately h a lf of the e ff e c t is tra n sm itte d through the f i r s t two years of the Lag structure with the remaining half occuring in the remaing three years of the Lag. Even though a ll of the c o e ffic ie n ts are constrained, the s ta t is t ic s in d ic a te th a t a ll of the variables (taken as a whole) are important in the explanation of the dependent variable. Social security tax rate changes have a small and p o s itiv e e ffe c t on hourly compensation ra te s. Since the dependent variable includes employer and employee contributions to social insurance, a c o e ffic ie n t of less than one indicates that Less than a ll of the increase is shifted forward in to gross pay-rates. The precise magnitude w ill depend on the size of the tax rates. According to these estim ates fo r example, an increase from a 13.5 percent tax rate to 14 percent w ill raise gross pay-rates by 0.24 percent but reduce net pay-rates by about 0.35 percent. F in a lly, changes in real import prices have a moderate positive 96

101 FIGURE I I I. 3 M anufacturing Hourly Pay-rate Equation Dependent Variable (DEP) = Mfg. HLCt % % PR0Dt Equati on DEP = * % (M2/GNP). C3.91 *) * % (M2/GNP)? (7.27*) * % (H2/GNPV, (7.15*) " t _ * % (N2/GNPV. (4.66*) ' t_ * % (M2/GNP) c (2.57*) t * ( % (Social Security Tax Rate) ) ( 1.20) ~ * * ( % (Import DFL) - % (M2/GNP). 1) (1.89) " * ( X (Import DFL).. - % (M2/GNP)* O (2.16*) * 1 ' t_2 Period of estimation: R2 = RBAR2 = DW = AAPE = = percentage change t s ta tis tic s in parathensis, * sig n ificica n t at the 5% level ** significant at the 10% level 97

102 effect on compensation growth. Summing the c o e ffic ie n ts on the two va ria b le s in d ica te s an e la s tic ity of hourly compensation to changes in import prices of approximately 18 percent. For example, a doubling of the im po rte d p ric e of o il w ill y ie ld a 18 % growth in the hourly compensation of the manufacturing secto r, a ll other influences held constant. Conspicuous by th e ir absence are an intercept and the unemployment rate. No intercept is estimated for the reasons stated above, otherwise the long-run transmission process fo r the excess growth in the money supply is augmented by the value of the in te rc e p t. The growth in unemployment was tested but i t came in the equation w ith a p o s itiv e sign. Forecasting purposes and in tu itio n indicate a positive influence to be an unreasonable e ffe c t: the p o s itiv e c o e ffic ie n t w i l l cause compensation to increase during a recession w hile a boom w ill force compensation to decline. Therefore, increases in the unemployment ra te would serve to increase the average pay-rate of the manufacturing sector in a forecast. Given the constraints imposed on the equation from economic theory and forecasting purposes, the overall f i t of the equation is good. The independent va ria b le s explain 71% of the v a ria tio n in the dependent variable, 55% after adjustment fo r degrees of freedom. A ll but two of the c o e ffic ie n ts are s ig n ific a n t at the five percent level, with the remaining s ig n ific a n t at the ten percent confidence le v e l. S e ria l c o r r e la t io n is p resen t but e f f o r t s to c o rre c t fo r i t w ith the Hildreth-Lu procedure did not appreci ablely change the re s u lts. The la s t d e s c rip tiv e s t a t is t ic, the AAPE, represents the average annual absolute percentage error. The AAPE is re la tive ly large due in part to 98

103 the v o la t ilit y and magnitudes of the series. However, when the AAPE is calculated using the actual and predicted levels implied by the equation of the manufacturing p a y-ra te, the AAPE is reduced from fifty - fo u r percent to approximately three (2.65) percent. P lots of the im plied le ve ls from the estimated equation and the actual levels are portrayed in Figures III.4 A and III.4 B. The values predicted by the equation are shown by the dotted lin e s, actual values by the s o lid lin e s. The e q u a tio n cap ture s most of the tu r n in g p o in ts but c o m p le te ly underpredi cts the boom years of 1968^1971. The equation does not have many short-run cyclical va ria b le s, a fa ct which may account fo r th a t mi ss. Rather than duplicate the specification used fo r the manufacturing s e cto r, a new approach is used to model the monetary tra nsm ission process fo r the nonmanufacturing sector. Excess money growth is s t i l l passed through to hourly compensation but by the in c lu s io n of a lag of the manufacturing compensation growth. Figure I I I. 5 portrays the r e s u ltin g e q u a tio n. The dependent v a r ia b le is the growth in nonmanufacturing pay-rate less the growth in productivity. Growth in the manufacturing pay-rate less productivity growth enters with a lag s tru c tu re of two years. The c o e ffic ie n ts are a ll positive, lie on a straight lin e and are constrained to equal u n ity. This co n stra in t m aintains the long-run property of money in the model. The growth in unemployment rate has the expected sign and has a significant in flu e n ce in the determination of nonmanufacturing compensation. Social security tax changes have a s lig h tly larger effect on the nonmanufacturing sector than the manufacturing one, an e ffe c t tha t might be explained by the 99

104 FIGURE I I I. 4A A c tu a l p e rc e n t change in m a n u fa c tu rin g p a y -ra te le s s tl»«g ro w th in p r o d u c t iv it y and M2 to r e a l CNP vs P re d ic te d p e rc e n t changes OATE ACTUAL PREDIC m s s ZS * IS f IS A-P * O O I O. 62* O , S IS * IS + IS A-P * FIGURE I I I. 48 A c tu a l le v e l in m a n u fa c tu rin g p a y -ra te v» P re d ic te d le v e l DATE ACTUAL PREDIC 60 IS * IS S IS * IS IS IS

105 FIGURE I I I. 5 N on-m anufacturing H ourly P a y-ra te Equation Dependent Variable (DEP) = % Non-Mfg HLCt % PROD^ Equati on DEP = * C % (Mfg. HLC) - % PROD.) (5.01 ) * ( % (Mfg. HLC). - % PROD*.) (1.80**) ~ M * ( % (Mfg. HLC)? - % PROD. O (1.96 ) * ( %Unemployment Rate ) ( **) * ( %(Social Security Tax Rate.).) ** * t (1.82 ) * ( % (Social Security Tax Rate 1)) (2.52 ) t_1 Period of estimation: R2 = RBAR2 = DW = AAPE = = percentage change t s ta tis tic s in paranthesis, * significant at the 5% level, ** sign ifica nt at the 10% level.

106 Larger share of workers in the nonmanufacturing sector who are more sensitive to FICA taxes. An intercept is also suppressed fo r the same reason as before. Compared to the other aggregate equation, the nonmanufacturing performs very w ell. The f i t as measured by the adjusted R^ is good (0.8110) with s e ria l c o rre la tio n less of a problem. The AAPE for the estimated equation is approximately thirteen percent which is reduced to 1.3 percent when the calculation is performed fo r the level of pay-rates instead of the growth in pay-rates less p ro d u c tiv ity change. Figures I I I. 6 a and I I I. 6 b show the p lo ts of actual le ve ls versus predicted Levels im plied by the estimated equation. The p lo ts show th a t the equation syste m a tica lly misses the period from 1970 through A dummy variable fo r the Nixon wage-pri ce freeze was tested but i t was found to be in s ig n ific a n t. Excepting that one in te rv a l, the equation tracks the major turning points. I I I. 3 Relative Pay-Rates in Individual Industries The approach applied here in determining compensation by in d u stry is to focus on rela tive pay-rates by industry. By focusing on sectoral pay-rates, long-run trend influences and short-run cyclical e ffe c ts can be e a s ily modelled. Pay-rates by industry are obtained as the product of the aggregate rate and the re la tive sectoral pay-rate. In th is study, re la tiv e pay-rates are a ffecte d by a s e c to ra l s p e c ific v a ria b le, the share of to ta l employment accounted for by that industry, and by several economy-wide v a ria b le s : the growth in the personal consumption expenditures deflator, the unemployment rate, and 102

107 FIGURE I I I. 6A A ctual growth in non-manufacturing pay-rate leas tha growth in p ro d u c tiv ity vs. P redicted Growth DATE ACTUAL PREDIC MISS IS * IS + IS A-P a O O O * I O. 20 IS * IS IS A-P a a FIOURE I I I. 6B Laval o f tha non m anufacturing pay ra ta vs. P redicted le v e l DATE ACTUAL PREDIC IS * IS US IS * IS >

108 the proportion of teenagers in the overall c iv ilia n labor force. Before discussing the precise s p e c ific a tio n and re s u lts of the s e c to ra l pay-rate equations, a short review of the work on "wage" determination by industry is presented. Relatiye.,pay7 rates, i n_ other. studi es The f i r s t work which u tiliz e d the r e la tiv e wage framework was 1 8 published by Michael Wachter in Wachter developed a model in which m a n u fa ctu rin g in d u s tr ie s are d iv id e d in to two groups: a high-wage, noncompetitive sector and a low-wage, com petitive sector. The high-wage in d u s trie s attempt to maintain a wage premimum over the other industries. Employers in the high-wage in d u s trie s m aintain a la b o r queue from which they h ire. As aggregate demand increases, low-wage industries lose workers to the higher wage industries, fo rc in g the low-wage industries to bid up wages. Since the high-wage industries h ire from a queue, there is no need for them to raise wages u n til the queue becomes unacceptably sm all. Therefore, low e rin g le v e ls of unemployment w i l l narrow the wage s tr u c tu r e w h ile in c re a s in g unemployment widens the gap between the high and low-wage in d u s trie s. This suggest Wachter1s main hypothesis that high-wage industries are positively affected, i f at a l l, by flu c tu a tio n s in aggregate demand w h ile low-wage in d u s tr ie s are n e g a tiv e ly c o rre la te d w ith the unemployment rate. Em pirical te s tin g of th is model was accom plished w ith in th e framework of a Cobb-Douglas function for the level of wages :

109 (3.6 ) W = a Vb1 Ub2 W*b3 a,b1,b3 > 0, b2<0, where V i s value added, U is aggregate unemployment and W* is a proxy fo r wages paid in other industries. Some manipulation and the a d d itio n of a d is trib u te d lag on prices and unemployment to capture some of the dynamics of the wage-price spiral yields, (3.7) Log W / W* = log ag + log V / V + a^ Time * 3 Ut-k + X>4 Pt-k + a5 KW + e k=0 k=0 (where KW is a dummy for the Korean War) Wachter estimated the equation fo r the period using annual data fo r nineteen tw o -d ig i t SIC manufacturing sectors. He constrained the lag structure on prices and unemployment to be identical across a ll s e c to rs, and the r e s u lts confirm ed h is h y p o th e s is. V In d u s trie s w ith high measures of concentration and unionization rates were positively correlated with the unemployment rate. David Belzer m odified W achter's model in to a s tr u c tu r e more accomodating to the needs of long-term in te rin d u s try fo re ca stin g.^ Belzer postulated a general model for the wage of an industry as (3.8) RWt = g( log P, U, S) + (1-a) RW^ 105

110 with RW referring to the re lative pay-rate, P to a d is trib u te d lag of the growth in the CPI, U to unemployment and S a vector of industry specific variables. Working with Bureau of Labor S ta tis tic s series on h o u rly ea rnin gs in d ic e s fo r production workers, Belzer tested a specification derived from equation 3.8 fo r one hundred tw o -d ig it SIC in d u s trie s. C oefficients fo r the lagged dependent variable frequently f e ll outside the zero to one range, thereby implying d e s ta b liz a tio n in th e lo n g -ru n. 2 1 The equation was modified by dropping the lagged dependent variable and a ne gotiations va ria b le which had a n e g lib le 22 influence on re la tive wages. The fin a l form of equation was 2 (3.9) RWt = aq + a(j P + a2 UNEM25t _k + a3 TIME + a4 log EMP k=0 where P = distributed lag on in fla tio n with the weights specified as 0.5,0.33 amd 0.17 for t to t-2, UNEM25 = unemployment rate for males 25 years and older, and EMP = industry's share of to ta l employment. The tim e tr e n d was in c lu d e d to a llo w fo r " t a r g e t 1' wage d iffe re n tia ls to change overtime and to capture s h ifts in the s k i l l mix of the labor force. The change in employment served three purposes: to account fo r short-run changes in earnings due to overtime, to re fle ct short-run labor supply in e la s tic ity for an in d u s try, and to represent the e ffe c ts of a changing employment mix within an industry. In order to economize on degrees of freedom, weights were sp e cifie d fo r the lag on prices; experimentation gave the pattern. Belzer found that twice as many sectors had negative price terms as 106

111 23 had positive price terms. However/ he noted that the magnitudes of the p o s itiv e c o e ffic ie n t s were much greater than the negative ones. A dditionally, cotlin e arity between the unemployment terms and prices may have meant the influence of prices were moderated by the e ffe c t of unemployment. The unemployment rate was the most powerful influence in the equations. A positive sign on the unemployment ra te im plie s th a t as unemployment increases the wages in that industry f a ll (or do not rise as quickly) as the average wage or as unemployment decreases, wages increase more quickly than the average wage. This procyclical behavior was most evident in the te x tile and service in d u s trie s, a ll b a s ic a lly non-union, low-wage sectors. Strongly unionized industries - such as motor v e h ic le s, s te e l, and tru c k in g - had n e g a tiv e sign s on th e unemployment ra te, thus in d ic a tin g th a t th e ir wages (re la tive to the average) are "protected" during downturns in economic a c t iv it y. Time tr e n d e f f e c t s were la rg e s t in the m a n u fa c tu rin g, m ining and transportation sectors, a result attributed to union aggressiveness at the bargaining t a b l e. ^ S u p ris in g ly, the sector s p e c ific term, the change in ind ustry employment, performed very weakly. Only twelve sectors out of the hundred had s ta tis tic a lly sign ifica nt signs. Seven of the sectors exhibit a positive coefficient indicating the overtime or demand fo r high-wage labor effect. The major focus of the remaining studies on r e la t iv e wages, however, has been the influence of differen t factors at a specific point in tim e. Moreover, a primary concern has been the influence and extent of concentration, e ith e r in d u s tria l or la b o r u n io n, on the wage structure. 107

112 The arguments presented in those studies concerning the effect of industry concentration on wage determination can be condensed into three types. F irs t, the high correlation between p r o fits and concentration indicate that highly concentrated industries divide the monopoly p ro fits w ith lab or. Second, highly-concentrated industries can ty p ic a lly pass on th e ir costs to th e ir customers, thereby reducing resistance in those in d u s tr ie s to la rg e wage in c re a s e s. F in a lly, a high degree of concentration and strong labor unions w ith in an in d u stry is a common occurence, giving unions more power to raise wages. While the effect of industrial concentration on value added exists, i t is not necessary to use as an explanatory variable in a forecasting context because the time p r o file of industry concentration probably resembles e ith e r a constant term or a slow moving trend. Thus, any effect of market structure is lik e ly to be indistinguishable from other v a ria b le s. Furthermore, a recent cross-sectional study comparing the effects of concentration and unionization on wage d iffe re n tia ls for 1958 and 1967 concludes th a t, i f changes in labor quality are included, then market power was found to be correlated with high wages in 1958 but not 25 in As a consequence, no e x p lic it influence of market power was included in the re la tive pay-rate equations. Specification and Results for the Present Modgl This study adapted the Belzer methodology with some m odifications. A major m o d ifica tio n involves the d e fin itio n and construction of the dependent variable. Because of lim ita tio n on data a v a ila b ility, Belzer used hourly earnings indices fo r production workers provided by the 108

113 Bureau of Labor sta tis tic s. In his forecast, the model adjusted fo r the compensation o1 lonproduction workers with regression equations. In the present study, and hours wor ' combining NIPA series on to ta l employee compensation d by a ll employees, average hourly earnings indices by tw o-digit SIC iustries fo r a ll employees are computed. This method etim i nates an> Other moc leed fo r further adjustment of the forecasted variables, /ic a tio n s came in the s p e c ific a tio n of the r e la tiv e pay-rate equa present model (3.10) RW* = on. The specification for a particular industry in the 2 ^ PC2Qt + b2 Pt + 2 UN^ J + b4 EMPSHARE w ' e RW = industry hourly employee compensation rate re la tive to the appropriate aggregate pay-rate, PC20 = teenage share of the labor force, UN = inverse of the overall unemployment rate, 3SHARE = industry's share of hourly adjusted employment, and P = rate of growth of the PCE deflator. This specifica government sec One notew i s th a t the a. to a particula variable canno on was used fo r thirty-seven private sectors and eig h t rs over thy feature of the relative wage or pay-rate formulation r io r i sign on any coefficient of a variable not specific industry can be e ith e r p o s itiv e or negative since a possibly raise or lower a ll relative pay-rates within an industry aggre te. For instance, the teenage share of the labor force (PC2Q) is inc An increasing ided as a crude measure of the s k ill of the labor force, o rtio n of teenagers in the labor force has opposing 109

114 in flu e n c e s : a larger share of relatively unskilled labor is available while more highly skilled labor becomes more scarce. Consequently, the c o e ffic ie n t accompanying PC20 may have a positive effect fo r industries requiring skille d labor and a negative influence in the tower s k ille d i ndustries. This reasoning extends to the unemployment and in fla tio n variables. S trongly unionized in d u s trie s w ill have a positive sign, since th e ir pay-rate w ilt not decline (re la tive to the average) during an increase in unemployment. Conversely, nonunionized sectors, where wages are more responsive to labor market conditions, should e xh ibit a negative sign since an increase in unemployment w ill increase the supply of a v a ila b le labor and depress th e ir wages re la tiv e to the average. In fla tio n, measured as a distributed tag of the growth in the PCE d e fla to r w ith weights of 0.5, 0.33 and 0.17, can either raise or lower an industry's pay-rate depending on the union strength in n e g o tia tin g e s c a la to r clauses. To add to the s t a b ilit y of the model, the unemployment rate is entered as its inverse. The ensuing nonlinear relationship permits the unemployment rate to have a diffe ren t impact on an industry's pay-rate depending on overall economic conditions. A given one percent change in the unemployment rate w ill have a larger impact when the re cip ro ca l of that rate is smalt rather than large. Industry share of to ta l employment is the only sector specific variable. I t is included to capture the demand conditions w ith in an in d u s tr y, but i t also may have e ith e r sign. C ontraction in some industries works by laying o ff the low-wage workers, thus d riv in g up average pay-rate fo r the remaining employees, which increases th e ir 110

115 pay-rate re lative to the average. Expansion in those industries occurs by increasing the employment of low-wage workers, thereby lowering average pay-rate relative to the aggregate. In those in d u s trie s, the expected sign of the c o e ffic ie n t is negative. On the other hand, the in d u s trie s which, to expand have to a t t r a c t w orkers from o th e r in d u s trie s, would e x h ib it a p o s itiv e coe fficient on the unemployment rate variable. The results of the estim ation procedure are displayed in Table I I I. 1. The second column shows the results fo r the teenage share of the labor force (PC20). O ve ra ll, PC20 is a strong explanatory variable, significant in twenty-seven of the th irty -e ig h t of the private secto rs, and f iv e of e ig h t in the pu blic sectors. Among the th ir t y - e ig h t industries in the private sector, twenty have a positive coefficient fo r PC20 and eighteen have negative ones. As expected, the high* ski lie d in d u s trie s such as Autos (2 2 ), Mining (3 ), Communications (28) and U tilitie s (30) have strong positive influences, while A g ric u ltu re (1 ), Wholesale and R eta il trade (31) and most of the finance and service sectors have a th e ir relative pay-rate reduced as the teenage share of the civlia n labor force increases. The performance of PC20 is, in part, due to the high correlation with a time trend. The inverse of the unemployment rate was not so strong a variable as Belzer found. Twelve sectors had the same sign throughout the la g ; nine of these had negative c o e ffic ie n ts. Of these nine, two were low-wage sectors, Agriculture (1) and Wholesale and R e ta il (31) tra d e. Another anomaly is the positive coefficient fo r the the Transportation equipment manufacturing sector excluding m otor v e h ic le s (1 9 ), a

116 TABLE I I I - l INDUSTRV RELATIVE PAY-RATE EQUATIONS SECTOR INTERCEPT PC20 UN UN(T-l) UN<T-2) PRICE AMPSHR R8Q RBRSQ AAPE D-W 34 hotels (7. 807) ( ) (1. 322) (0. 931) (1.230) (-0.470) C ) MISC. BUS ( ) ( ) (0. 229) (1. 610) ( ) ( ) ( ) AUTO REPAIR ( ) (1. 163) ( ) 0. US (0. 987) (1. 129) ( ) ( ) AMUSEMEN1S (4. 603) (-3.344) (0. 473) (-0.326) (0. 082) ( ) ( ) SB HEALTH * EOU ( ) (2. 471) ( ) ' (1. 103) ( ) ( ) (1. 819) PERS. SERV (4. 039) ( ) (0. 196) (0. 242) (-0.999) (0.036) (0. 798) FED. ENT (6. 133) ( ) ( ) (1. 317) (0. 104) ( ) ( ) S&L ENT (6. 247) (8. 260) ( ) 0: 037 (0. 227) (1. 700) ( ) ( ) O FED IND (3. 386) (1. 126) ( ) (1. 247) (0. 241) -1. B97 ( ) ( ) S&L IND ( > (2. 123) ( ) (2. 187) (1. 730) ( ) (0. 991) FED. CIV (1. 480) 48 FED. MIL ( ) (-1.033) (3 498) ( ) ( ) (2.381) f ( ) (0. 937) ( ) ( ) ( ) o o o o ( ) S&L EDUC ( ) (0. 044) (0. 723) (2. 286) (1. 733) ( ) (3.871) SVL OTH (3. 932) (4.016) ( ) (1. 181) 0. n o (1. 170) ( ) (-1.386) WAGE RELATIVE TO NONMANUFACTURING WAGE

117 ( { 8ECT0R INTERCEPT PC20 17 PR I. METALS < ) 18 FAB. METALS ( ) 19 TRANS EQ (5. 093) 20 NOW-ELECT ( ) 21 ELECT. MACH ( ) 22 AUTOS (7. 127) 23 INSTRUMENTS ( ) 24 MISC. MFG (4. 836) 23 RAILROADS (8. 493) 26 AIR TRAN (14.773) 27 OTHER TRANS (8. 080) 28 COfMUN (9. 184) 30 UTILITIES ( ) 31 TRADE (13.229) 32 FIN. 8ERV ( ) ( ) ( ) (0. 471) (3.474) (1. 613) (3. 370) ( ) (1.194) ( ) (0. 737) ( ) (3. 638) (6. 523) (-3.947) ( ) TABLE I I I - l INDUSTRY RELATIVE PAY-RATE EQUATIONS UN U N (T -l) UN( T -2 ) PRICE AMP8HR RSQ RBR8Q AAPE D-W (1.686) ( ) (0. 744) (2. 045) (-2.450) ( ) ( ) (-1.230) ( ) ( ) (0. 347) ( ) (-2.841) ( ) (0. 151) ( ) ( ) 0*. 067 (0. 312) (0. 704) (0. 538) (0. 829) (1. 108) (1. 799) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) (1. 491) ( ) (3. 084) (0. 535) (4. 417) (1. 868) ( ) ( ) (-3.682) ( ) 0, 006 (0. 205) ( ) (1. 970) (7. 290) (1. 015) ( ) (1. 649) ( ) (5. 001) ( ) ( ) (6. 320) ( ) (3. 620) (3. 973) ( ) ( ) ( ) ( ) (2. 065) (-1.729) -O. 098 ( ) (0. 277) (2. 332) (-4.978) (2. 063) ( ) (2. 339) ( ) ( ) ( ) (-4.720) (0. 065) I ( ( ( ( ( ( ( { ( ( ( V ( ( 33 REAL ESTATE (9. 772) ( ) (1.739) ( ) (0. 309) (0. 042) (4. 026)

118 ( TABLE I I I - l INDUSTRY RELATIVE PAY-RATE EQUATIONS SECTOR INTERCEPT PC20 UN UN(T-l) UN(T -2) PRICE AMPSHR RSQ RBRSQ AAPE D-W i 1 AGRIC <7. 614) ( ) ( ) ( ) ( ) (3. 969) ( ) ( 2 CRUDE PETRO (9. 073) (0. 229) ( ) ( ) ( ) ( ) (2. 939) i 3 MININO (S. 748) (2. 243) (0. 248) ( ) ( ) (6. 389) (3. 627) < 4 CONS (8. 794) (1. 238) (-1.179) (1. 984) (1. 296) (-3. B99) ( ) i 9 FOOD ( ) 6 TEXTILES (21.113) (2. 084) (7.096) ( ) (2. 176) ( ) (1. 961) (0. 118) ( ) ( ) (4.990) ( ) (7. 14B) I i 7 APPAREL (3. 174) ( ) ( ) (3. 604) ( ) (0. 626) (2. 168) PAPER ( ) 9 PUBLISH (4. 687) 10 CHEMICALS ( ) (4. 389) -O. 299 ( ) (7. 862) ( ) ( > ( ) ( ) (2. 491) ( ) ( ) (1.419> (1. 463) (-1.019) ( ) (0. 701) ( ) ( ) ( ) it S f 11 PETRO. REFIN ( ) ( ) (0. 721) ( ) ( ) (9. 190) ( ) RUBBER ( ) 13 LEATHER (6. 838) 14 lumber (7. 999) 19 FURNITURE _ (8. 842) ( ) (0. 190) (S. 234) ( ) ( ) ( ) ( ) ( ) (2. 093) (3. 766) (0. 136) (3. 228) (0. 124) (0. 063) ( ) ( ) ( ) (-0.480) (7. 206) (0. 917) ( ) (2. 070) (1. 889) (2. 760) f j \ 1 i u 16 STOfC, ETC (9. 987) (1. 421) (0. 120) (-1.091) ( ) (1. 633) ( ) &?!

119 high-wage and unionized industry. I t is possible that the c o llin e a rity with the price variable moderates those perverse effects. On the oth er hand, summing the coefficients indicates that Autos (22), Non-electrical machinery (20) and other tra n sp o rta tio n (27) - mainly trucking and U tilitie s (30) have a positive net effect of unemployment. The service sectors and p u b lic education (49) have the expected net negative sign for unemployment. Column 6 of Table 3.1 displays the effect of in fla tio n on r e la tiv e compensation. The lag stru ctu re was the same as that used by Belzer. As Belzer found, the negative coefficients dominated the p o s itiv e ones by a tw o -to -o n e m a rg in, 33 to 13. Again, the th irte e n sectors benefitting from escalator clauses gain at a much higher rate than the lo se rs. Autos (2 2), Railroads (25) and Communications (28) led those sectors with large, sign ificant coefficients. The other sectors which did not always have s ta tis tic a lly significant variables, included a ll of the se rvice, government and trade sectors exhibiting the response of Low-wage sectors. In a stark contrast w ith B e lze r's study, the sector s p e c ific v a ria b le of the secto ral share of employment showed a s ig n ific a n t influence on re la tiv e p a y-ra te. T h irty of fo r ty - s ix sectors had a s ig n ific a n t e ffe c t; twenty-seven sectors had a negative influence, nineteen had a p o s itiv e one. in tra in d u s try employment mix. The v a ria b le seems to cap ture the Air Transportation (26) and Real Estate (33) led the l i s t of in d u s trie s of high-wage demand p u l l. Not s u p ris in g ly, the trade and service sectors expand and contract with low-wage employees. The discrepancy between th is study and the e a rlie r one by Belzer might be traced to the different data sources of hourly 115

120 employee compensation: Belzer mixed estimates from the Bureau of S ta tis tic s and the Bureau of Economic Analysis while th is study soley on BEA estimates. Additionally, Belzer excluded non-prod workers in his equations while this study includes those workers. ^abor rhied ction In order to show the variety of the sectors and to give the ^ader a feel for the d iffic u ltie s in the estim ation process, the f 0i. wing four pages contain plots fo r eight sectors. The sectors range from manufacturing industries to the public sector. The f ir s t two sectors portrayed are the Mining (3) and Textii * (6) in d u s trie s. M ining, which excludes crude petroleum and natural ^ gas d r illin g, starts fa r below the manufacturing aggregate and ends *b0ve the average. Good escalator clauses insulated miners very welt f* 0,1 the ravages of in fla tio n. On the other hand. Textiles had a slight h ecl*ine over the quarter century. An increased supply and use of unsi,' H(.ed labor as measured by the teenage share of the labor force (PC2n^ ' ] and te x tile 's share of to ta l employment (EMPSHARE) help explain the deci The next pair equations are some of the be st, but for diff 6rent reasons. Primary Metals (17) e x h ib it co u n te rcyclica l behavin r but mostly a strong upward trend captured by PC20 and EMPSHARE. s *h al c o rre la tio n is not much of a problem and the adjusted 4C a Sood Conversely, Fabricated Metal Products (18) has a low adjus c o e ffic ie n t of determination of less than 35 percent. Fabrieaten Products has one of the most stable relative pay-rate series: Pe. live pay-rates bounce within a tig h t band between 1.00 and 0.97, so ther l i t t l e variation to explain. less than half a percent! 6 is Yet, on average, the equation misc* 5eS by U t i l i t e s (30) and Real E state te (33) show s im ila r histori- bistro^s. 116

121 RELATIVE PAY RATE EQUATION FOR MINING (3) DATE ACTUAL PREDIC MISS IS * IS + IS A-P ? O S I SO I IS * IS + IS A-P i O. 839 * O O RELATIVE PAY RATE EQUATION FOR TEXTILES (6) DATE ACTUAL PREDIC MISS IS * IS IS A-P * I. 03! j t ? t \ j ] W * I. 01 I ;r I t A I oo IS * IS + IS A-P *

122 RELATIVE PAY RATE EQUATION FOR PRIMARY METALS ( 1 7 ) DATE ACTUAL PREDIC M ISS IS * IS + IS A -P * / oo«c o o ) \ t IS * IS + IS A -P * O. 861 O. 90S R E LA TIV E PAY RATE EQUATION FOR FABRICATED METALS DATE AC1UAI PREDIC M ISS IS» IS + IS A -P * I. 0 0 I I o. o t- * I / IS * IS + IS A -P *

123 ' RELATIVE PAY RATE EQUATION FOR UTILITIES <30) r! ACTUAt PREDIC M ISS IS * IS + IS A -P * I r *! " B t V ! f ; " ]: t i"' i ! i r j i S j \ i j j ) i IS * IS + IS A-P * O. 869 O O RELATIVE PAY RATE EQUATION FOR REAL ESTATE DATE ACTUAL PREDIC MISS IS * IS + IS A-P O I t SO I IS * IS + IS A-P 119

124 " 3p ' RELATIVE PAY RATE EQUATION FOR MILITARY (48) DATE ACTUAL PREDIC MISS IS» IS + IS A-P * > * S y v 6? O IS * IS + IS A-P * O O RELATIVE PAY RATE EQUATION FOR PUBLIC EDUCATION (49) f DATE ACTUAL PREDIC MISS IS * IS + IS A-P * * p I d :h V V, IS * IS + IS A-P *

125 U tilitie s are strongly countercylical and reached a peak in The equation tra c ts the zig-zag upward trend with EMPSHARE and PC20 doing most of the work. Real estate shows the huge boom of the recent past, with burgeoning commissions increasing the re la tiv e pay-rate. The industry share of employment captures the growth very w ell. The last pair of plots p o rtra y two government sectors th a t have been in the p o licy s p o tlig h t. M ilita ry personel (48) has one of the strongest growth trends; th is trend was required by the s h ift to the volunteer army. In te re s tin g ly, the share of employment and teenage share of the labor force have opposite effects. R elative compensation of teachers in public schools follows the baby boom generation through school with a peak in 1972 and a downslide th e re a fte r. The series is s lig h tly countercyclical with the coefficient of EMPSHARE mirroring the "brain drain" from education with a positive coe fficient. Overall, the aggregate variables coupled with the industry variable do a reasonable job explaining the va ria tio n in re la tiv e hourly labor compensation by industry. Coupling these equations with the aggregate functions gives long and sho rt-run credi tabi I i ty to the forecast of employee compensation. Summary A dual approach for forecasting employee compensation is discussed in t h i s c h a p te r. As a consequence, c a re fu l s c ru tin y to the specification of the aggregate pay-rate equations in re la tio n to the long-run implications of wage determination and in fla tio n is warranted. The result is that the conventional formulation of the P h illip s curve is 121

126 eschewed and the Long-run influence of money on in fla tio n is implemented d ire ctly into the equations. A beneficial side effect is the avoidance of the problem of simultaneity between prices and wages. 122

127

128 Chapter IV Return to Capital The term "return to capital" is used here to mean value added not paid to labor in wages or b e n e fits, or to government in taxes, or received from the goverment in subsidies. Thus, the return to capital is composed of nine GPO categories: net interest payments, corporate c a p ita l consumption allowances, noncorporate c a p ita l consumption allowances, corporate p ro fits, proprietor income, business transfer payments, corporate inventory valuation adjustments, noncorporate inventory valuation adjustments, and rental income. The conglomeration, as a whole, has comprised about 30% of GNP for the last th irty years, a share which has remained fa irly constant. Despite its relatively constant share of value added, the mix of the re tu rn to ca p ita l has changed dram atically over the past th irty years. Table 4.1 displays the components* share of return to.capital at ten year in te rv a ls from 1950 to Though corporate p r o fit and p ro p rie to r income have declined dramatically in importance, they are s t i l l nearly h a lf the to ta l. On the opposite side, net in te re s t payments and depreciation have taken up the gap le ft by p ro fits. Net interest has been the most rapidly growing component of the return to c a p ita l; i t s r e la tiv e size has increased e ig h tfo ld since i Depreciation allowances grew rapidly u n til the 1970's and have levelled o ff to about t h ir t y percent of the return to capital in s h ift is not solely due to increased borrowing and investment; This net in te re s t and depreciation are also accounting phenomena and are untaxed flows. Government legislation on taxation may a ffe c t the amount and \^Q>

129 2 Table IV - 1 Composition (in percentages) of the Return to Capital Q Net Interest Depreciation a) Corp. b) Non-Corp P rofit Income a) Corporate b) Proprietor ' Rental Income Business Trans Inv. Va I. Ad j. a) Corp. b) Non-Corp timing of both net interest payments and depreciation allowances. Firms may have some latitude to "offset" taxable p rofit income by accelerating debt or depreciation. The choice between debt and equity financing, or between corporate or non-corporate organization, strongly influences the distribution among it s components but has l i t t l e e ffe c t on the to ta l return to c a p ita l. In order not to have the to ta l return to capital dependent on our modelling of these choices, the to ta l return is forecasted f i r s t. This procedure allows us to obtain one component as a residual. Since p ro fit income is half of the return to capital, modelling both would be redundant; forecasting the return to capital, in essence, determines corporate p ro fits and proprietor income. Therefore, fo re c a s tin g equations for the return to capital and a ll of its components except for

130 3 corporate p ro fits and proprietor income are developed; profit-type income is determined as a residual. The fir s t section of this chapter deals with the to ta l return to c a p ita l and i t s role as a s ta b iliz in g agent in a forecast. Also included in the f ir s t section is a discussion of previous work on the return to capital. Section 2 concerns the specification and estimation of depreciation equations. For the remaining components of the return to capital, the specification of the equations is different from that of the t o t a l re tu rn or d e p re c ia tio n equations. Total return and depreciation are each forecasted by sector w ith o u t reference to aggregate controls. Though sectoral data is available for net interest payments, business transfer payments and inventory valuation adjustment, each of those components is forecasted in the aggregate. The aggregate value is then d is trib u te d throughout the industries on the basis of sectoral share equations. This procedure was used because in each case, variables that were too good to pass-up were available at the aggregate le v e l but not a t the sectoral level. For example, the stock of inventories is a major determinant of inventory valuation adjustments but t h is v a lu e is not a v a ila b le by holder in the fo re c a s t. Consequently, better forecasts could be obtained by spreading macro to ta ls than by using sectoral equations. Sections 3-5 describe this procedure for each component. 6. Rental income is the topic of the section

131 4 IV.1 Total Return to Capital The return to capital is composed of two q u a lita tiv e ly d iffe re n t types of components; those that are relatively insensitive to movement in economic a ctivity and others that are more v o la tile. Net in te re st and depreciation are "creatures" of the past; both are predetermined by past investment and financing decisions and are relatively unaffected by current economic conditions. Rental income seems to retain a steady ten percent of the return to c a p ita l, regardless of the state of the economy. Conversely, the other components - p ro fit income, business transfer payments, and inventory valuation adjustments - are much more sensitive to current economic a c tiv ity. P ro fits, the largest component of a ll nine, are closely tied to the business cycle: p ro fits soar during a boom and plummet with a recession. This movement is a key s ta b iliz in g force in the model. In the beginning of a boom, profits rise more quickly than the other components (e xcep t, p o s s ib ly, in ve n to ry v a lu a tio n adjustm ents) or labor compensation. Approximately half of the increase in corporate profits is taxed away by the government. Of the remaining amount, only that portion returned to individuals as dividends finds its way to personal income. In addition, the recipients of dividends are in high marginal ta x brackets and have high marginal propensities to save, fu rth e r withdrawing purchasing power from the economy. Consequently, the percentage of p r o f it s reaching personal income is less than the percentage of labor compensation reaching personal income. In addition, the increase in profits w ill increase in fla tio n which decreases real

132 5 income. Thus, the size of disposable income relative to to ta l GNP is reduced, thereby restraining the growth in share of personal consumption expenditures in GNP. The restraint on personal consumption expenditures dampens the overall growth in fin a l demands, and the boom subsides. An analagous reaction occurs in times of slack a c tiv ity. During a recession, p r o f it s, and therefore in f la t io n, drop more quickly than labor compensation. The decline in real disposable income is stemmed, thereby averting a continuing recession. The upshot is th a t even though the re turn to c a p ita l is the variable to be forecasted, any sub stan tial deviations from the trend w i l l be caused by the movements in p r o fit income. Consequently, the specification of a return to capital equation must resemble th a t of a p r o fits equation in it s c y c lic a l components. Before turning to the industry specification used in th is study, a review of the relevant work in the explanaton and forecasting of the return to capital is presented with a focus on p ro fit income. P rofits and. the Return, to^capital in other models N either the Wharton annual industry model nor the B ro o k in g s ' q u a rte rly in d u stry model have regression equations fo r the return to capital or corporate p ro fits. The Wharton model has price equations and A determines corporate profits as a residual in the aggregate. Brookings also determines corporate p ro fits as residual but at a sectoral level.^ In the Brookings model, prices are determined, and then by working backward from-the definition of price shown in chapter I, nominal gross product o rig in a tin g is computed by sector. P ro p rie to r income is forecasted in fir s t- d iffe r e n c e form as a function of changes in labor

133 6 compensation and the change in nominal gross product o rig in atin g less the o th e r components of value added. Corporate p ro fits are then determined as a residual. The general approach of Wharton and Brookings model is prohibited by the structure of the INFORUM model; fo r the reasons presented in Chapter I, the determination of value added for the model must precede that of price. Lawrence O ffic e r, in a quarterly model of the Canadian economy, eschews the residual approach and directly estimates a ll the components of value added.^ A ll the components by sector are added together to obtain value added. Officer assumes a modified markup procedure fo r p ric in g behavior on the p art of the firm, implying p ro fits are determined as a percentage of total value added. The same specification is used for both corporate profits and entrepreneurial income, where PI^ = the level of corporate profits or entrepreneurial income in millions of dollars, VAt = nominal value added, WTt = a four quarter distributed lag of wage rates, PRT^ = productivity, measured as real value added per employee. j = 0 The f ir s t difference in value added is included to capture short-run deviations from the long-run markup rate. Productivity is also included as a cyclical term. a squeeze on profits. Wages are expected to enter with negative signs as Because profit income is a determinant of current value added, two stage least-squares was used to estimate the equations

134 by sector. O ffic e r obtained good f i t s but his formulation has drawbacks as well as advantages. On the positive side, the change in value added as arr e x p la n a to ry v a r ia b le is u s e fu l, because changes, especially unexpected ones, in output and value added are more lik e ly to a ffe c t p r o fits than contractual aggreements such as labor compensation. While the change in value added captures short-run c y c lic a l behavior, the inclusion of the productivity term is an attempt to measure the long-run c y c lic a l behavior of p r o fits, a sensible m odification. Perhaps the productivity of c a p ita l, as embodied as the c a p ita l to labor ra tio, might be a more effective variable. On the negative side, the assumption th a t value added (VA) is already known is ina ppropria te fo r the s tr u c tu re of t h is model. M oreover, O f f ic e r 's s p e c ific a tio n re la tes the level of corporate p ro fits, to among other varaibles, wage rates. During a boom, p ro fits and wages both increase but the magnitudes of the changes are d iffe re n t; p ro fits are more v o la tile. Even though wages and p ro fits both increase, the shares of income going to labor and ca p ita l w ill change. This suggests the dependent variable ought to be corporate p ro fits as a share of output. In a d d itio n, the d iffe r in g u n its of measurement on the v a ria b le s makes the interpretation of the coefficients d if f ic u lt. For instance, though the equation explains the level of nominal corporate p r o f it s, real p ro d u c tiv ity is used as one of the additive variables. F in a lly, c o llin e a rity between the p ro d u c tiv ity and wage terms can be expected. The previous version of the INFORUM price-income model b u ilt by David Belzer^ forecasted the return to ca p ita l in the w age-price

135 8 q u a rte rly submodel. Belzer forecasted the return to capital as markup (m) over labor compensation or, p X w L + r K r K wl ul w L where p = price index of real GPO, X = real GPO less indirect business taxes, w = hourly pay-rate of labor L = hours worked by labor, r = rate of return per unit of capital, K = units of capital Starting with a CES production function, Belzer derived^ the fo llo w in g basic long-run formulation, r K w (4.1) log ~ = aq + a. log a-, Time w L r The time trend served to measure the bias in te ch nica l change; a positive value indicates labor-saving technical change while a negative value shows capital-augmenting technical change. However, demand effects and deviations of actual labor productivity from the cost-minimizing level of productivity could have disrupted the long-run formulation. In addition, there was no allowance for short-run wage dynamics. Therefore, equation (4.1) was m odified by Belzer to allow for those short-run effects,

136 9 (4.2) Log (p X / w L)^ = ag + Log (w /r)t + a2 Time + a^ Demand + a^ Log (L /L)^ + a5 Log (wt /wt _1) where L* was the "equilibrium" Labor input fo r X^. The dependent variable was defined as the return to capital without in v e n to ry v a lu a tio n adjustm ent (IVA) which was excluded on the assumption that business's ignored the effect of IVA on p ro fits. Actual implementation of equation (4.2) Led to the dropping of the wage-rental ra tio and the growth in the wage rate variable. The demand variable was measured as the re a l ou tpu t to real c a p ita l stock r a tio fo r the manufacturing sectors. For the manufacturing sectors, demand was represented as either the unemployment rate or the three year growth in the moving average of real sectoral output. Equilibrium hours (L*) was approximated by a "kinked" time trend and output; two time trends were in the equation with one starting in 1958, the other in Given the obstinancy of the return to c a p ita l data to curve f i t t i n g, the Belzer form ulation gave good results. On the whole, the e q u a tio n perform ed b e tte r fo r m a n ufa ctu rin g s e c to rs than f o r nonmanufacturing sectors. The wholesale and re ta il trade sectors could not be adequately f i t by this specification. The change in the WPI was in c lu d e d fo r both s e c to rs, d r a s t ic a lly improving the f i t. The productivity variable (L* / L) seemed to give the most trouble with the largest incidence of perversely large positive signs, implying that in a forecast labor producitivty gains accrued to capital not labor. The Belzer form ulation has some strong advantages. F ir s t, i t linked the s p e c ific a tio n of the return to c a p ita l v a ria b le w ith a

137 1 0 cost-minimizing production function. Secondly, the formulation includes a proxy measure of technical progress, the time trend. Finally, the specification did the best job of f it t in g the data of any reviewed or encountered. Despite the impressive re s u lts, the Belzer specification was not carried over to th is study. of the dependent variable. The major objection is to the form ulation Labor compensation in the denominator puts th e.e n tire burden of forecasting GPO on labor com pensation. The pay-rate equations would be, in e ffe c t, forecasting the level of the return to capital. This is not a good method to minimize the ris k of fo re c a s tin g erro rs since an error in a pay-rate forecast leads to more error in the same direction elsewhere. Another fa u lt is the neglect of the sta b iliza tio n function fo r the model th a t p ro fit income can provide. The sectoral demand measures may be thought of as im p lic itly providing that function, but the importance of the s ta b ilizin g influence merits e x p lic it attention. The remaining research on p r o fit income or the return to capital has focussed almost exclusively on the ro le of a d ve rtisin g and market s tr u c tu r e on p r o f i t a b i l i t y. W ithin an in te rin d u s try framework, advertising and market structure are not apt to prove useful v a ria b le s. One of the major purposes fo r advertising is to lure customers from one brand or manufacturer to another brand. For example, the ad vertisin g campaign promoting M iller beer aims to lure beer drinkers from the other brands along w ith a ttra c tin g new beer consumers. To th a t end, an advertising variable makes l i t t l e sense at th is le ve l of aggregation, since a l l the same competing firm s are included in the industry d e fin itio n. Even i f an advertising variable were s ig n ific a n t, such a

138 11 variable would be l i t t l e help in forecasting because i t would have to be forecasted it s e lf. Four reasons - two th e o re tic a l and two em pirical - lead one to expect that a market structure variable such as the concentration ra tio would be ine ffe ctive. The underlying ju s tific a tio n fo r such a variable is that high levels of concentration (however defined) cause high levels of p r o fita b ility. On the theoretical side, there is no consensus as to whether the coexistence of high p ro fits and high levels of concentration imply that direction of causation. C ritics argue that concentration may be the re s u lt of high p r o fits, not the reverse, i f economies of scale are present.^ In a d d itio n, the use of the concentration ra tio as a measure of in d u s try concentration has also been questioned. The four-firm or eight-firm concentration ra tio only provides inform ation about the top four or eight firms in the industry without including the re m ainin g firm s in the in d u s tr y. For in s ta n c e, a f o u r - f i r m concentration ra tio of 0.80 shows that no firm outside the top four can have twenty percent of sales or capacity. But the r a tio cannot say whether there are two or two hundred firms in the rest of the market. Economic theory indicates that average p r o fits per firm would behave d iffe r e n tly w ith fringe of two firms than with a fringe of two hundred firm s. Besides the th e o re tic a l o b je ctio n s, two more e m p iric a lly based c r it ic is m s demonstrate the in e ffe ctive n e ss of the a p p lica tio n of concentration ratios to return to capital equations. At the level of aggregation fo r the model - two d ig it SIC - the concentration ratio may have l i t t l e e ffe c t. For example, the steel ind ustry may be highly co n ce n tra te d but the prim ary metal in d u s try is p ro b a b ly less

139 1 2 concentrated. Louis and Frances Espositio in a study published in 1977^ estimated p r o fita b ility as a function of the concentration r a tio equations at the four, three and two d ig it SIC categories. They found that the s ta tis tic a l significance of the concentration ra tio rapidly diminished as the level of aggregation increased. F in a lly, the time series of a concentration ra tio fo r an industry is quite lik e ly to resemble a constant or a time tre nd. Consequently, any e ffe c t of concentration would probably be captured by the intercept or trend term. Fqrecasting the Return to Capital. in_the present stu dy The re a l side of the INFORUM model in conjunction w ith the product-to-industry bridge generates real value added weighted output (REVAVIO) by industry. This fact suggests that the appropriate dependent variable fo r forecasting purposes is the real return of capital per unit of REVAWO. The numerator, the real return to ca p ita l, is obtained by dividing the return to capital in nominal terms by an aggregate price d e fla to r, the GNP d e fla to r. Using any price measure to forecast an element of value added - a determinant of prices - introduces the issue of sim ultaneity between prices and value added in a forecast. Appendix A of th is chapter demonstrates that the ite ra tiv e s o lu tio n handles the simultaneity problem. By combining elements of microeconomic theory and the dictates of long-run fo re c a s tin g, the fo llo w in g in d u s try s p e c ific a tio n was estimated: \ V n

140 13 Return to Cap (4.3) Log bq + ^ QTRD + b. DFL * REVAWO K - + b, UN L b^ Time + b$ SPEC where DFL * aggregate output deflator, QTRD = deviation in "normal" output defined as the difference between real output in the current period and the moving average of real output of the previous three years, output, K = industry capital stock in real dollars, L = hourly adjusted employment for that industry, UN = to ta l unemployment rate, and SPEC = an optional variable specific to the particular industries (see below) The d e via tio n in the trend in output term is meant to capture the effect of the unanticipated growth in output on p ro fits. Therefore, its coefficient is expected to be positive. The capital-labor ratio is included as a proxy for the u tiliz a t io n of c a p ita l. Id e a lly, when the u tiliz a tio n of capital is increased by, say, overtime fo r labor or m ultiple s h ifts, then the capital-labor ra tio should decline and capital's return should increase since i t is becoming more productive. Consequently, the expected sign on th is va ria b le is negative. The ca p ita l stock is generated by the INFORUM investment equations and attempts to measure the actual level of the physical stock

141 14 o of equipment.0 From the sign of the capital-labor ra tio, one might be tempted to make an inference concerning the e la s tic it y of substitution between capital and labor. In fa c t, for a perfectly competitive ind ustry with only two fa c to rs of production and constant returns to scale, one can show that the sign on the c o e ffic ie n t of the c a p ita l-la b o r ra tio is determined by the e la s tic ity of su b s titu tio n.^ In that situation, an industry with an e la s tic ity of substitution between c a p ita l and labor th a t is less than one w ill experience a decrease in capital's share of GPO with an increase in the c a p ita l-la b o r r a tio. An e la s tic it y of s u b s t it u t io n th a t is g re a te r than one and an in cre a se in the capital-labor ra tio combine to increase cap ital's share of GPO. However in th is study, there are three "fa c to rs " of GPO production - labor, c a p ita l, and in d ire c t business taxes - not two, thus rendering invalid the hypothesized relationship. A simple example w ill demonstrate th is r e s u lt. Suppose th a t, due to a tax increase, a perfectly competitive industry experiences an increase in the indirect business tax share of GPO. That increase means that either ca p ita l's share of GPO or labor's share of GPO must change regardless of the e la s tic it y of s u b s titu tio n between c a p ita l and labor. That does not imply however, any change in the return to capital per un it of REVAWO. The in c lu s io n of in d ire c t business taxes as a separate fa c to r of production severs the lin k between the e la s tic ity of s u b s titu tio n, the c a p ita l-la b o r r a tio and ca p ita l's share of GPO. In order to generate the s ta b iliz in g influ ence of p ro fits, the inverse of the unemployment rate is included. accentuate the asymmetry of the influ ence. The inverse is used to Accordingly, the sign is

142 15 expected to be positive; a fa llin g unemployment rate w ill increase p ro fits * Technical change and the influence of market structure ( if any) is captured by the time trend. Consequently, it s c o e ffic ie n t can have e ither sign since technical change could either enhance or reduce capital's share of income. The industry variable is ta ilo re d to the sp e cific needs of a sector. The in itia l specification called for a foreign trade variable: either import share of total output, export share of to ta l output, or the net trade share of total output. However, these variables had to be dropped from the specifications for most of the sectors because of wrong signs or insignificance. For instance, a postive sign associated with the import share of output for the te x tile industry im plies that the real return to capital for the te x tile industry is increased as a result of an increased use (measured as a share of domestic output) of imported te xtile s. There are a few sectors where the impact of foreign trade had a s ig n ific a n t influence in the expected d ire c tio n on the return to capital: Mining (3 ), Publishing and p rin tin g (9 ), Fabricated metal products (18) and Autos (22). These sectors retained the foreign trade measures. Mining has the net trade share; Autos have the import share; Printing and publishing, and Fabricated metal products have the export shares. In a d d itio n, Transportation equipment except autos has the growth in m ilitary and space expenditures on equipment, since i t is the major producer of a ircra ft, missiles and rockets. Petroleum refining and natural gas (11) had the characteristic of a negative to ta l return to capital for and Huge "paper" losses were incurred, as the vertically integrated o il companies shifted profits from refining to d r illin g. By doing so, the companies could

143 \ TABLE IV - 2 INDU8TRY RETURN TO CAPITAL EQUATIONS INTERCEPT QTRD KLR INVUN TIME DUM SPEC R8Q RBRSQ AAPE D-VJ 1 FARMS (8. 3 1) ( ) ( ) (0. 42) (1. 79) ( ) BB CRUDE PETROLEUM ( ) (0. 4 6) (1. 0 9) ( ) (9. 96) 9. 9B1 ( ) MININO <6. 8 3) (1.6 2 ) (1. 22) ( ) (-2. 66) ( ) (2. 29) CONSTRUCTION ) (2. 0 9) (0. 21) ( ) (4. 69) 0. B B FOOD AND TOBACCO (3. 91) -O. 016 (-1. 19) (0. 9 9) -O. 226 ( ) -O ) TEXTILEB ( ) ( ) (0. 19) (1. 93) ( ) B B APPAREL (9. 3 8) (1. 14) (0. 09) (0. 91) (-0. 36) PAPER <7. 92) 0. O il (1. 9B) (1. 03) ( ) (-1. 92) PUBLISHINO ( ) (2. 97) (0. 39) (-0. 29) -O. 014 (~0. 81) (2. 20) CHEMICALS ( ) (1. 11) (3. 0 7) (-0. 79) (-6. 19) PETROLEUM REFINING* ( ) (-1. 11) (0. 71) (1. 94) (-0. 17) (-2. B l) RUBBER ( ) ( *. 0 3 ) (9. 18) (3. 20) -O. ( ) * N e g a tiv e v a lu e * f o r th e r e tu r n to c a p it a l f o r t h is s e c to r p re vente d th e use o f lo g a rith m s. In s te a d * th e dependent v a r ia b le is th e r e a l p u rc h a s in g power o f c a p it a l p e r u n it o f r e a l o u tp u t.

144 TABLE IV - 2 INDUBTRY RETURN TO CAPITAL EQUATIONS INTERCEPT QTRD KLR INVUN T ire DUN 8PEC R8Q RBRBQ AAPE D-W 13 LEATHER (1. 43) <1. 30) <0. 52) <0. 72) < 0. 61) LUMBER O (1. 45) <0. 50) <-0. 40) <0. 56) <2. 12) FURNITURE O B (0. 83) <0. 62) «-l. 44) <-0. 29) <0. 89) T0NE ETC (6. 35) <1. 49) <-1. 21) <-2. 50) <-0. 10) PRIMARY METALB (2. B7) <-3. 62) <-5. 18) <3. 02) <3. 89) IB FAB. METALB (4. 94) <2. 59) <1. 09) <1. 33) ) <2. 62) TRANS E0. B <5.79) <4. 47) <3. 50) <-3. 96) <-3. 79) <-0. 61) O NON-ELECTRICAL MACH <7. 32) <-0. 41) <-0. 38) <0. 80) <-0. 57) ELECT. MACH (2. 31) <0. 73) <-0. 92) <0. 61) <0. 59) AUTOS <-0. 30) <3. 18) <1. 47) <1. 47) <1.00) <1. 94) INSTRUMENTS O. 990 O <13. 33) <-1. 23) <3. 21) <7. 42) <-7. 29) MISC. MFO. IND <5. B7) <0. 41) <1. 94) <0. 67) <-2. 36) RAILR0AD <0.82) <-0.06) <-1. 84) <0. 44) <1. 16)

145 on J x"' TABLE IV - 2 INDUSTRY RETURN TO CAPITAL EQUATIONS INTERCEPT QTRD KLR INVUN TIME DUM SPEC RSO RBRBQ AAPE D-W 26 AIR TRANSPORTATION <9.0 7) <4. 43) <1.42) < 2. 66) <-3. 39) B TRUCK I NO ETC < 8.9 1) <0. 3 3) <-0. 8B) <0. 49) <-0. OB) COMMUNICATIONS <13.92) <0. 20) <-0. 12) <2. 01) <-2. 92) U T IL IT IE S <13. 33) <1. 92) < ) <-2. 99) <-0. 69) TRADE <30. 96) <0. 74) <3. 79) <0. 19) <-7. 00) PIN. BERV > O < 9.0 3) <0. 19) <1. 14) <2. 06) <-1. 68) B REAL ESTATE <24. 03) <0. 82) <-1. 42) <0. 28) <4. 09) O HOTELS <14.39) <2. 8 6) <-6. 31) <-1. 99) <6. 46) MISC. BUS. 8ER B <23.24) <-2. 12) <-3. 17) <1. 77) <0. 08) AUTO REPAIR <40. SB) <1. 30) <0. 30) <-1. 12) <0. 79) AMUSEMENTS O <6.0 7) <1. 67) < - t. 0 7) <-0. 22) <0. B l) HEALTH AND EDUC <22. 32) <0. 76) < ) <4. 17) <>6. 96)

146 16 o ffs e t those p r o fits against o il depletion allowances. To deal with these negative numbers, the dependent variable for th is sector was not in logarithms but in normal shares..table IV - 2 disp lays the re s u lts of the sectoral estim ation. Before turning to the results of any equation, one should note th a t a low Rdoes not necessarily indicate an unsatisfactory equation. For instance, i f the dependent variable is constant, the R is zero but the e q u a tio n 's a b ilit y to p redict the to ta l return to capital when given REVAWO is p e rfe c t. For th is reason, the R& and the th re e oth e r d e s c rip tiv e s ta tis tic s are also calculated fo r the to ta l return and are reported underneath th e ir values fo r the estimated equation. The equations, fo r the most p a rt, f i t w e ll and there is l i t t l e evidence of serious serial correlation. Overall, the time trend and the in v e rs e of the unemployment ra te seem to be the most forceful influences. The domination of negative coefficients (23 to 14) on the time trend in d ic a te s that technical change has been operating to lower the return to capital per unit of REVAWO. Instruments (23), Mining (3), Rubber (12), and Chemicals (10) have the strongest negative trends while Crude Petroleum (2 ), Primary metals (17) and C onstruction (4) move against the negative trend with the most strength. C ollinearity between the trend and the c a p ita l-la b o r ra tio may be the cu lp rit may serve to induce the negative time tre n d s. For tw e n ty -fo u r s e c to rs, the coefficients fo r the two variables have opposite signs. C o n ve rse ly, the unemployment rate comes in w ith the desired positive effect in twenty-two cases although only eight of those sectors have sig n ifica n t c o e ffic ie n ts at the ten percent le v e l. Yet, those tw enty-two sectors w ill exert an important sta b ilizin g influence on the

147 17 entire model during a forecast. Textiles (6 ), Autos (2 2 ), E le c tric a l Machinery (21) and F in a n c ia l S ervice s (32) have the strongest sta b ilizin g influence. However, the unemployment rate comes in with the undesired negative e ffe c t fo r f i f t e e n in d u s tr ie s of which seven have s ig n if ic a n t c o e ffic ie n ts at the ten percent level. There are two possible reasons that may explain the negative coefficients. One is that the stab ilizin g argument fo r the unemployment rate assumes th a t an in d u s try 's price re fle c ts market co n ditio n s; upswings in demand increase price while downswings serve to reduce p r ic e. For two i n d u s t r i e s, A ir tra nspo rta tion (26) and U tilitie s (30), that assumption is not true for the estimation period. Both industries were tig h tly regulated over that period, thereby restraining the p ro fit movements in both industries. In fa c t, price regulation may serve to move p r o fits c o u n te r-c y c lic a lly. For example, during a surge in economic a c tiv ity, the price of inputs fo r an e le c tric or gas u t ilit y may rise but the legal price th a t the u t i l i t y may charge remains unchanged u n til the appropriate regulatory agency approves the rate increase. Until that rate increase goes in to e ffe c t, the u t i l i t y ' s p r o fits w ill s u ffe r. I f there is a sufficient time lag between the rate request and the subsequent approval by the regulatory agency, the rate hike may be approved during the economic downturn in the cycle, thus improving p ro fits. The second reason may be found in in d u s trie s th a t use a large proportion of raw materials in th e ir production process. Generally the prices of raw materials tend to fluctuate more over the business cycle than the prices of the finished products that embody them. This implies th a t those in d u s trie s w ill experience a p r o fit "squeeze" during an

148 18 upswing and a p ro fit boom during the downturn. The above reason may explain the negative coefficient fo r the Food and Tobacco (5), Furniture (15) and Stone, Clay and Glass (16) industries. The ca p ita l-la b o r ratio occurs with the expected negative sign for sixteen in d u s trie s while tw enty-one in d u s tr ie s have a p o s itiv e in flu e n c e. Six industries with positive coefficients are s ta tis tic a lly significant at the ten percent level while five sectors had negative and sign ifica nt coefficients. The unexpected positive signs for the capital to labor ra tio are offset by negative time trends in seventeen of the twenty-one sectors. Without exception, the impact of this variable appears to be rather small. F in a lly, the least effective variable is the deviation from normal output term; i t is only significant at the 10% level in nine instances. At le a s t, the c o e ffic ie n t s are only negative fo r eight sectors. Col lin e a rity between the unemployment rate and the output term may cause th is result. The sectoral specific variables are a ll of the correct sign, and except fo r autos, are a ll significant. The share of imports fo r autos was retained in order to maintain more credible forecasts. Upon inspection of the ca p ita l share s e rie s, three sectors had dummy va ria b le s included in the equation to adjust fo r extraordinary circumstances. Both the Crude petroleum (2) and Mining (3) sectors experienced a reversal in a downward trend in ca p ita l's share of value added at the same time OPEC became dominant in the crude o il market. Consequently, a non-opec intercept dummy equal to one in years prior to the emergence of OPEC in 1973 and zero thereafter is included fo r both sectors. A g ric u ltu re (1) had a huge jump in its return to capital in

149 due to the Russian grain deal. Since th is deviation is a one-time occurrence, or at Least is an unpredictable one, the year was "dummied ou t", thereby improving the estimates of the other coefficients. To give some idea of the task involved in f i t t i n g reasonable fo re ca stin g equations to the return to cap ital, plots of eight sectors are presented in the next fo u r pages. The p lo ts show a c tu a l cu rre n t-p rice Levels of the return to capital and the predicted levels. While three of the eight sectors have l i t t l e varia tion from a trend in th e ir le ve ls - Wholesale and R etail tra d e, Real Estate (in b illio n s instead of m illio n s of d o lla rs ) and Misc. Business Services - the others have a more v o la tile nature. Agriculture (1) f it s the levels well except for a two large misses in the last two years; the average miss is 12.7 percent. The plo t of the Mining (3) sector reflects the s h ift to coal because of the o il embargo. The p lo ts of the Primary M etal (17) and Autos (22) sectors experience large swings and the equations, while not actually predicting the magnitude of the change, do capture the turning points. One fin a l point should be mentioned b e fore tu r n in g to the discussion of the components of the return to capital. The forecasting specification fo r the return to capital reported above was not the only s p e c ific a tio n that was tested. During the testing of the entire model, i t became apparent that the the tw in goals of equations th a t f i t the data w ell and of equations tha t had reasonable forecasting properties were not necessarily compatible. More than four general types of e q u a tio n s were estim ated w ith each type having a t le a s t five permutations. The f ir s t type which had it s dependent v a ria b le defined as the re tu rn to ca p ita l per u n it of REVAWO was rejected because the

150 ACTUAL LEVELS OF RETURN TO CAPITAL VERSUS PREDICTED LEVELS FOR AGRICULTURE ( 1 ) DATE ACTUAL IS * PRED1C IS B ISO B B IS * IB * IS B. 798 ACTUAL LEVELS OF RETURN TO CAPITAL VERSUS PREDICTED LEVELS FOR HININO (3 ) DATE ACTUAL PREDIC IB IS « IS IS * * * *

151 ACTUAL LEVELS OF RETURN TO CAPITAL VERSUS PREDICTED LEVELS FOR PRIMARY METALS (1 7 ) DATE ACTUAL PREDIC IS « IS « ACTUAL LEVELS OF RETURN TO CAPITAL VERSUS PREDICTED LEVELS FOR AUTOS (22) DATE ACTUAL PREDIC IS B S

152 ACTUAL LEVELS OF RETURN TO CAPITAL VERSUS PREDICTED LEVELS FOR AIR TRANSPORTATION (2 6 ) DATE ACTUAL PREDIC IB IB B B B B B BO 26B IB I15B ACTUAL LEVELB OF RETURN TO CAPITAL VERSUS PREDICTED LEVEL8 FOR WHOLESALE AND RETAIL TRADE (3 1 ) DATE ACTUAL IS BO IB * PREDIC * * * * * B. 93B B \STC>

153 X* REAL ESTATE (3 3 ) ACTUAL LEVEL8 OF RETURN TO CAPITAL VERSUS PREDICTED LEVELS FOR REAL ESTATE (3 3 ) DATE ACTUAL PREDIC IS « « ACTUAL LEVELS OF RETURN TO CAPITAL VERSUS PREDICTED LEVELS FOR MSC. BUSINESS SERVICES (3 9 ) DATE ACTUAL PREDIC 18 IS B IS B B9 \ T \

154 ENDNOTES 1. Ross Preston, Jhe_Wharton_Annual_and_Industr^_Forecating_ModeI, (P h ila d e lph ia : Economics Research Unit, U niverisity of Pennslyvannia, 1972) 2. Michael McCarthy, "A nalysis of Non-Wage Income Components," Jhe 22JslD9S-!32^liA ed. James Duesenberry, G. Fromm, "L. Klein arid E. Kuh. (Chicago: *Rand McNally, 1969) pp Lawrence O ffic e r, Sugei^- R elati>onshigs_in_the_canadian_economy, (East Lansing, Michigan: Michigan State U niversity, 1972) pp David Belzer, "An Integaration Of Price, Wage and Income Flows in an In p u t-o u tp u t Model of the U n ite d S ta te s," u n p u b lis h e d Ph.D. dissertation, University of Maryland, See Belzer, Ib iji., p for a complete derivation. 6. For a concise description of the controversy see Almarin P h illip s, "A C ritiq u e of E m p iric a l Studies of the R elation Between Market Structure and P ro fita b ility," Journal_of_Industrial_Econonncs. Vol. 24, No. 4, June 1976, pp F rancis E sp o sito and L o u is E s p o s ito, "A gg reg ation and the Concent ra ti on-prof ita b i lit y Relationship," Southern Economic Journal, Vol. 44, No. 2, October 1977, pp " 8. See Anthony Barbera, "A Study of the Determinants of Factor Demand by Industry," unpublished Ph.D. d isse rta tio n, U n iv e rs ity of Maryland, A ty p ic a l proof can be found in R.G.D. A lle n, Macrp-Economic_ Theory, (New York City; St. Martin's Press, 1967) pp. 48-4*9 10. In fa c t, CCA does not quite equal accounting depreciation but the difference is less than one percent of the to ta l. C apital consumption allowances contain depreciation of employees' autos reimbursed by business, unrepairable damage caused by accidents to fixed c a p ita l, and depreciation of mining exploration, shafts and w ells, while accounting depreciation does not. On the other hand, accounting depreciation includes am ortization of intangible assets, depreciation of film s and assets of foreign branches, and allowances fo r a u d it but c a p ita l consumption does not. 11. Gary Fromm and Lawrence K le in, "The Complete Model: A F irs t Approximati on," Ihe_Brooki ngs_quarterix.econometric_model_of _the_uni ted States, ed. James Duesenberry, G. Fromm, L. Klein and E. Kuh. (Chicago: Rand McNally; 1965) pp McCarthy, og_cit pp

155 13. The residual sector had the growth in investment and the current level of investment as explanatory variables. 14. Preston, og_cit pp See Belzer, og_cit, pp See Clopper Almon, Margaret Buckler, Lawrence Horwitz and Thomas Rei mbold, 1985^ ld i0^y 2-2 sts_of_the_american_econom^, (Lexington, Mass. : Lexington Books, 1974) pp Belzer op c it pp The fourth section of Chapter I I has more d e ta il. 19. P rio r to the adopting of the Belzer specification, a log-linear function was tested w ith actual depreciation regressed on syn th e tic de p re ciation. Though the equations f i t w ell, they had inappropriate forecasting properties: e la s tic itie s ranging from a low of fo r Instruments to a high of for Crude petroleum were estimated. Only a handful of sectors had e la s tic itie s close to unity. Similar results were obtained fo r a linear function. 20. The s ta tis tic s are computed by transform ing the e rro rs from the equation to errors in terms of the levels of depreciaton. 21. Belzer, og_ci.t p Brian O'Connor, "An Income Side to an Input-O utput Model of the U nited S ta te s," (u np u blishe d Ph.D. d is s e rta tio n. U n ive rsity of Maryland, 1973) p Belzer, og^cit p Ibid p Ibjd, p The savings rate of p ro p rie to rs is assumed to equal the overall personal savigs rate: personal savings divided by disposable income less transfers to foreigners. 27. Net recepients of interest payments are not creating value added by convention. The value added necessary to make the net interest payments is created elsewhere. 28. O ffic e r, 2 _ c it, pp Belzer, og_cit pp Belzer, og_cit pp. 252

156 31. O'Connor/ og_cit. p Belzer/ og_cit. p. 250 \< \o

157 1 APPENDIX IV-A Allowing the return to capital to be a linear fu n ctio n of a price d e fla to r im plies th a t a p o rtio n of value added by product is also a function of the price variable. Naturally, two issues sprin g'from the form ulation. F irs t, given that the model is solved ite ra tiv e ly for any forecast year, w ill value added by product converge to a solution? S econdly, i f a stable s o lu tio n e xists fo r value added, w ill th a t solution be independent of the f i r s t guess of prices? Since product p ric e s are a lin e a r fu n c tio n of value added, i f value added is independent of the in it ia l prices, then fin a l prices w ill converge and be independent of the in it ia l prices. Consequently, the analysis w ill focus on the behavior of value added over the ite ra tio n s of a forecast year. Though the equations in the price model are estim ated at the industry level, the model may, fo r convienence in th is appendix, be thought to forecast the return to capital by product. A ll variables are defined in terms of products, not industries. Let v = row vector (1xn) of to ta l value added per unit of real output, p = row vector of prices, d = row vector of labor compensation and indirect business taxes per unit of real output, w = column vector (nxd of output shares of total output, such that the output deflator DFL = pw, s = row vector of the real capital per unit of real output, and A = direct requirements matrix. \< w

158 2 Adopting the convention th a t subscripts refer to ite ra tio n s, the interindustry price d e fin itio n is CD Pk = pk A vk or (2) pk = V ( CI-A)"1. Total value added for an ite ra tio n is (3) vk = d + = d + v ^ d - A ) 1ws. Solving equations (2) and (3) for p^ yields, k (A) Pk = ^ d(w s < I-A )~ V 1 + (w s (I-A ) 1) k" 1DFL0 s From (4 ), independence and convergence w ill occur i f a ll of the elements -1 k-1 of (w s (I-A) ) move towards zero as k approaches in fin ity. In order to show th a t a ll of those elements go to zero as k approaches i n f i n it y, the concept of a l-norm of a matrix (or vector) needs to be introduced. The l-norm of a m atrix (B) is the largest column sum of the absolute values of its elements or n B!) = max ^ One can show that i f B <1 then a ll of the elements of B w ill go to zero as k approaches in fin ity. 2 Using Schwarz's inequality that b c < b c, where b and c are arbitrary vectors, we now have

159 3 (5) 11 w s (I-A ) 1 < H u ll 11 s(i-a) Since ^ w = 1 and w is a column vector, <6) w = 1. Recalling that in the base year a ll prices are one by d e fin itio n, (7) p = 1 = v(i-a ) ^, where 1 is the unit vector. Since v > s for every product, i t follows that (8) s<i-a> 1 < v(i-a)~1 = 1, and (9) *U -A )- 1 < 1. Substituting the results of (6) and (9) into equation (5) yields.sk 1 (10) llws <I-A)- 1 < H u ll sci-a)"1, or (11) hs(i-a )_1 < <1) (1) = 1. For the base year of the model, s ( I- A ) = 0.905, so a ll of the -1 elements of ws(i-a) matrix do go to zero as k approaches in fin ity. Pushing the analysis fu rth e r, suppose that value added by product was a fun ction of product p ric e s not an aggregate in d e x, would forecasted value added s t i l l converge and e x h ib it independence? In order to answer th is question, the d e fin itio n of value added must be changed to \ ^

160 4 (3*) vk = d + Pj^ ^ S, where S is a diagonal matrix with the diagonals equal to the real return to capital per unit of real output. Solving the system as in the previous case, the*analogous equations to (3) and (4) are k <* > Pk» ^ + (S(I-A)"1) k 1p0 s j=1 Convergence and independence depend on the Movement of the elements in the S(I-A)* ^ matrix. The 11S11 <1 since i t is, by d e fin itio n, a diagonal matrix of real shares. However, ( I- A ) II > 1 since the diagonals of (I-A) are typ ica lly greater than one. Consequently, there i s no guarantee of convergence, because there is no guarantee th a t a ll the elements of -1 S(I-A) are less than one. Whether those elements are less than one w i l l depend on the share of the real return to capital by product (S). As a practical matter, the shares fo r Agriculture (1), Real estate (63), and the Rest of the world (75) are large enough to insure th a t a ll of the elements are not less than one. The d iffe re n ce in the two results about s ta b ility stems from the use of the weights fo r the aggregate price index. In the d e fla to r instance, the output shares modify the impact of a large share of the return to capital. When the return to capital is a function of product p ric e s, the weights are in essence, unity, which w ill allow no damping effect of repeated m u ltiplication.

161 5 NOTES 1. For a proof see Clopper Aim on, Ma t r i x_methods_i n_economi cs, (Reading, Mass.: Addison-Wesley Publishing Co., 1967), p or Alpha Chang, Fundamental^Methods.of_Mathematical_Econoini.cs, (New York: McGraw-Hill, 1967), p For a proof, see Almon, Ibid.. or Evan Nering, Element a r^_lj nea r_ Algebra, (PhiIdelphia: U.B. Saunders Co., 1974), p

162 APPENDIX IV - B For the purposes of forecasting to ta l depreciation, investment in stru ctu re s by industry is required to be consistent with the estimates reported by NIPA. The NIPA reports purchases of stru ctu re s by type of structures (Tables 5.4 and 5.5). For certain sectors, there is a direct lin k between purchases by type of structures and industry purchases of structures. Table B1 displays the in d u s trie s and the NIPA purchase types. Table B1 Industry Agriculture (1) Railroads (25) Communications (28) U tilitie s (30) NIPA Category Farms RaiI roads Telephone and Telegraph Electric lig h t and Power, Gas Investment in plants and stru ctu re s is a va ila b le in the Annual. f r manufacturing sectors. The other sectors have the construction data reported in a v a rie ty of sources. The Bureau of Labor S tatistics in 1979 published Cagita^Stock^Esti.mates which containe d investment in structures by industry for The in d u s trie s not displayed in Table B1 had two problems to be corrected before those s e rie s could be used in the e s tim a tio n : V'VQ,

163 Groug Indus tr i es Table B2 NIPA de finition s 2 Crude Petro. (2) Mining (3) A ll manufacturing (sectors 5-24) Construction (4) Air Trans. (26) Other Trans- (27) Trade (31) Fin. Serv. (32) Real Estate (33) Misc. Bus. (35) Auto Repair (36) Amusements (37) Health & Educ. (38) Petroleum Pipelines Petroleum and natural gas Mining exploration, shafts, and wells Industrial Commercial, Religous Educational, Hospitals and In s titu tio n a l consistency with the NIPA estimates and extensions of the data through From the d e fin itio n s of the types of structures in the NIPA, a group of in d u s trie s can be linked to a group of NIPA co n stru ctio n cate go ries. For instance, the two in d u s trie s, Crude Petroleum and Natural Gas (2 ), and Mining (3), did a ll of the construction portrayed by the NIPA group of types. Petroleum pipelines, Petroleum and natural gas, and Mining e x p lo ra tio n, shafts and w e lls. Table B2 shows the concordance between the industry categories and NIPA structure types. The in d u s try series were scaled so the sum of industry group equaled the to ta l for the NIPA group. Regressions were estimated w ith the industry estimates as the dependent variables and the NIPA group as the independent variable. Those estimated equations are shown in Table B3. Estimates fo r were obtained from sim ulation of the equations over that period.

164 C0NS2 = Table B * NIPA GR0UP2 (7.867) (6.324) RBSQ = DW = CONS3 = C0NS4 = CONS5 = C0NS6 = C0NS7 = C0NS8 = C0NS9 = C0NS10 C0NS11 CONS12 C0NS13 C0NS14 C0NS15 C0NS16 C0NS17 C0NS * NIPA GR0UP2 (0.252) (5.606) RBSQ = DW = * NIPA GR0UP4 (**2.083) (20.208) RBSQ = DW = * NIPA GR0UP3 (0.706) (12.086) RBSQ = DW = * NIPA GR0UP3 (-2.100) (13.672) RBSQ = DW = * NIPA GR0UP3 (-2.100) (13.672) RBSQ = DW = * NIPA GR0UP3 (-0.218) (18.326) RBSQ = DW = * NIPA GR0UP3 (-0.061) (8.660) RBSQ = DW = = * NIPA GR0UP3 (-3.219) (28.111) RBSQ = DW = * * NIPA GR0UP3 C2.684) (8.498) RBSQ = DW = * NIPA GR0UP3 (-3.149) (9.768) RBSQ = DW = * NIPA GR0UP3 (-1.326) (11.831) RBSQ = DW = * NIPA GR0UP3 (1.463) (6.762) RBSQ = DW = s * NIPA GR0UP3 (-2.065) (9.820) RBSQ = DW = * NIPA GR0UP3 (2.410) (9.201) RBSQ = DW = = * NIPA GR0UP3 (3.090) (8.440) RBSQ = DW = * NIPA GR0UP3 (-0.473) (17.513) RBSQ = DW = 1.299

165 Table 63 continued C0NS19 = (-0.767) * (9.420) NIPA 6R0UP3 RBSQ = DW = C0NS20 = (-3.803) * (20.533) NIPA GROUP3 RBSQ = DU = C0NS21 = (-4.095) * (25.258) NIPA GROUP3 RBSQ = DW = C0NS22 = (1.008) * (5.083) NIPA GR0UP3 RBSQ = DW = C0NS23 = (-3.822) * (15.210) NIPA GR0UP3 RBSQ = DW = C0NS24 = (-1.719) * (9.651) NIPA GR0UP3 RBSQ = DW = C0NS26 = (-2.421) * (10.546) NIPA GR0UP4 RBSQ = DW = C0NS27 = (3.217) * (6.703) NIPA GR0UP4 RBSQ = DW = CONS31 = (2.369) * (24.354) NIPA GR0UP4 RBSQ = DW = C0NS32 = (-2.063) * (13.248) NIPA GR0UP4 RBSQ = DW = C0NS33 = (-2.136) * (10.585) NIPA GR0UP4 RBSQ = DW = CONS35 = (-1.313) * (9.183) NIPA GR0UP4 RBSQ = DW = CONS36 = (-2.122) * (13.108) NIPA GR0UP4 RBSQ = DW = C0NS37 = (-2.120) (10.749) NIPA GR0UP4 RBSQ = DW = CONS38 = (3.004) * (19.636) NIPA GR0UP4 RBSQ = DW = 0.723

166 Chapter V Indirect Business Taxes and Government Subsidies The government piece of value added is the subject of th is chapter. I t comes in two sorts: (1) indirect business taxes and nontax charges, and (2) government subsidies. In d ire c t business taxes and nontax charges comprise nine percent of GNP; government subsidies, a meager two-tenths of a percent. Since neither component is large, simple forecasting procedures are developed fo r both. In d ire c t business taxes are, for the most part, composed of federal excise taxes, property taxes, franchise fee s, and state and local sales taxes. Because of data imitations at the industry le v e l, in d ir e c t business taxes by industry are divided in to two categories: federal excise taxes and a ll other taxes. Federal excise taxes are forecasted as functions of output or personal consumption expenditures depending on the legislated tax base. Since the residual category of a ll other taxes is dominated by sales and property taxes, i t is made a function of output and the capital stock of the industry. The f i r s t section describes the estim ation and forecasting structure for ind irect business taxes. Section 2 describes government subsidies and the rationale for exogenously specifying them throughout a forecast. V.1 Indirect Business Taxes In d ire c t business taxes and nontax accruals are tax lia b ilitie s th a t are in c u rre d by business and o th e r l i a b l i l i t i e s th a t are "convenient" to treat as taxes. Indirect business taxes include excise, 'S-CO

167 2 property, sales, black lung, and hazardous waste taxes, and the w indfall p r o fits tax on crude o i l. Nontaxes include such categories as rents, ro ya ltie s, fin e s and fees paid by business to the government. The d is t r ib u t io n of in d ir e c t business taxes and nontaxes by type of lia b ilit y is shown in Table V-1 State and local l i a b i l i t i e s have the largest share of the to ta l. Sales and property taxes share the lead fo r the larg est single component. Federal excise taxes are the next in size, followed by custom duties. As the Table V-1 shows, there is a wide d iv e rs ity of sources fo r in d ire c t business tax receipts. The d istrib u tio n of taxes by industry is available from the BEA upon request. The best method of modelling th is component by industry might be to develop equations fo r the major categories of these taxes. However, data re s tric tio n s block t h is approach. F irs t, on the state and local level, the id e n tifica tio n of a sing le s ta tu to ry tax rate over a ll of the ju r is d ic t io n s would be extremely d i f f i c u l t. For example, in order to obtain a property tax rate fo r the U.S. data fo r property taxes by l o c a li t y would be necessary. Secondly, the task of constructing and forecasting the appropriate tax base by category would also be extremely d i f f i c u l t. A f i n a l c o n s id e ra tio n is th a t the BEA must perform a great deal of prorating to obtain annual tax payments by category fo r each in d u s try. The data, therefore, have a considerable margin of error. As a consequence of the above considerations a sim plified approach is employed here. Sectoral d e ta il fo r federal excise taxes is q u ite r e lia b le and is ava ila ble from the BEA. Consequently, excise tax equations are estimated fo r those in d u s trie s where excise taxes are re la tive ly important such as Communications (28), and Trade (31). Since

168 3 Table V D istribution by Type of Indirect Business Taxes (B illio n s of Dollars) Indirect business taxes and nontax accruals Federal (23.3%) Excise taxes (17.6%) Custom duties 8.59 ( 3.4%) Nontaxes 5.80 ( 2.3%) State and Local (76.7%) Sales taxes (36.0%) Property taxes (29.9%) Motor vehicle licences 2.62 ( 1.0%) Severence taxes 7.54 ( 3.0%) Other taxes 6.82 ( 2.7%) Nontaxes ( 4.1%) Source: Table 3.4, urve _of_current_busj.nes, July, the residual tax receipts by ind ustry represent, fo r the most p a rt, sales and property taxes, they are forecasted as a function of output and the capital stock which is intended as a proxy for property values. The above approach is diffe rent from that used in other large scale models. For example, the Wharton industry model had equations fo r only indirect business tax receipts in the aggregate.^ Industry d e ta il was re ta in e d in the Brookings q u a rte rly model by combining aggregate 2 equations by major categories w ith industry share equations. \ The industry share equations were a function of the sector's share of output and share of c a p ita l consumption allowances. In the Brookings model, capital consumption allowances were included as a proxy fo r property values. Belzer modified the Brookings specification to the dictates of the INFORUM model.^ Equations in the aggregate were developed fo r state and

169 4 local general taxes, and property taxes. Share equations by industry were developed to distribute the to ta l for property and sales taxes with the shares being a function of a time trend and with industry share of output. Excise taxes were estimated fo r the major products and were d ire c tly allocated to the a p p ro p ria te in d u s try to com plete the calculation of sectoral indirect business taxes. -f2 IS stin g _E xcj>se- Taxes Federal excise taxes are attributed to approximately tw o-third s of the in d u s trie s : 21 of the 37 sectors have them. However, in most cases, those taxes do not amount to more than fiv e percent of a l l in d ire c t business tax l i a b i l i t i e s fo r a sector. Only seven sectors - Food and Tobacco (5), Petroleum re fin in g (11), Rubber products (1 1 ), Motor vehicles (22),, A ir transportation (26), Communication (28), and Trade (3 1 ), have excise tax l i a b i l i t i e s over fiv e percent of th e ir in d ire c t business taxes. For those seven sectors, excise tax equations have been estimated. There are two types of excise ta x e s : ad valorem and those specified as dollars per physical u n it. Three sectors have excise taxes le g is la te d in ad valorem terms, Motor Vehicles (22), Air Transportation (26), and Communications (28). For each sector, a synthetic excise tax series is calculated as the product of the statutory tax rate and the in d u s try 's output. The logarithm of the synth e tic ta x s e rie s is regressed against the logarithm of the actual series. Note that the synthetic tax series is not expected to equal the actual tax re c e ip ts, an appropriate forecastin g property would be th a t the e la s tic ity of actual receipts to synthetic receipts should be u n ity. In a fo re c a s t.

170 5 the unitary e la s tic ity would imply that any growth in the synthetic tax series would be transm itted to actual tax re c e ip ts ; a ten percent growth in the synthetic series would mean a ten percent increase in the actual tax receipts. Table V^2A displays the re s u lts fo r those three e q u a tio n s. Each equation has an e la s tic it y of about u n ity. Any variation from one is caused by the use of an approximation of the actual tax base. The sectors which have taxes set in dollar terms per physical unit have th e ir equations displayed in Table V-2B. These sectors have the logarithm of actual tax re ce ip ts as a function of a variable in real terms - either personal consumption expenditures, output/ or im ports. Again, we would lik e to find and do fin d that the e la s tic itie s are again close to u n ity ; a given increase in the tax base boosts the tax receipts by the same percentage. One equation to note is the one fo r tobacco. The e la s tic ity for the rate base is as close to unity as can be expected. Overall, both sets of equations f i t reasonably w ell with good long-run forecasting properties. A special set of sectors have taxes resembling excise taxes: petroleum (2 ), Mining (3 ), Chemicals (10) and Real estate (33). Crude The windfall p ro fits tax on crude petroleum extraction f a lls mainly on the Crude petroleum (2) industry though a small portion is passed on to the recipients of royalty income in the Real estate (33) sector. The black lung tax fa lls on coal mining, and the chemical cleanup (Superfund) tax f a lls mainly on the Chemical (10) in d u stry. A ll of the taxes are specified d iffe r e n tly : windfall p ro fits approximate an ad valorem tax on a portion of the price, Superfund tax is legislated per physical unit and the black lung tax is s p lit into an ad valorem part and a physical

171 6 Table V - 2A Ad Valoreum Federal Excise Taxes ctor_and_iaxable_item Isyation_R gsu.lt s 22 Motor Vehicles Tax = * TRUCK (-1.80) (4.67) RBAR = D-W = Air Transportation Tax « * Fly (-2.91) (2.19) RBAR2 = D-W = Telelphone use Tax = * Talk - (-1.59) (1.51) RBAR = D-W = 2.05 t values fo r the slope coefficients are calculated for the hypothesis that the coefficient is different from one. Variable definitions (a ll variables are in logarithms) TRUCK = synthetic excise tax fo r truck chassis obtained by taking the excise tax rate m ultiplied by nominal output of the motor vehicle industry, FLY = synthetic excise tax fo r personal a ir travel obtained by the excise tax m ulitplied by the nominal output of the air transportation industry, TALK = s y n ':n r.;c, u te le p h o n e use obtained by multiplying the excise tax rate by the nominal output of the communications industry, 3.0

172 7 Table V - 2B Dollars per physical unit tax l 9y tion_results 5 Alcohol 5 Tobacco 11 Gasoline 12 Tires 31 Custom Duties Tax = * PCEALC (-4.52) (1.33) RBAR = D-W = Tax = * PCETOB. (-5.87) (0.43) RBAR = D-W = Tax = * Q11 (0.60) (0.50) RBAR = D-W = Tax = * Q12 (-3.35) (2.69) RBAR = D-W = 1.03 Tax = * IMP77$ (-6.96) (0.42) RBAR = D-W = t values for the slope coefficients are calculated fo r the hypothesis that the coefficient is differen t from one. Variable definitions (a ll variables are in logarithms) PCEAL = personal consumption of alcohol, on and o ff premises, in 1977 dollars, PCETOB = personal consumption of tobacco in 1977 do lla rs, Q11 = output in 1977 dollars for the petroleum refining industry, Q12 = output in 1977 dollars for tire s, IMP72 = to ta l merchandise imports in 1977 dollars

173 8 tax. No equations could be estimated for any of these taxes because a ll became effective in 1980 or Therefore, a stop-gap procedure is to m a in ta in the r a tio of tax receipts to nominal output in the la s t observed year for the legislated duration of the tax. Non-exci se ind jr^ct_ta x eguatjons The s e c to ra l forecastin g equation fo r the remaining in d ire c t business taxes is given by Remaining IBT Nom. Cap. Stock (5.1) Log = a + b log c Time Nominal Output Nominal Output where Nom. Cap. Stock = nominal capital stock defined as the same capital stock used in the depreciation equations in Chapter IV. Nominal capital stock is used as a proxy for property values. The time trend is included to capture any trend in s h iftin g tax bases or tax rates. Letting IBT stand fo r remaining indirect business taxes, K fo r the capital stock and Q for output, the solution of (5.1) for IBT is, (5.2) IBT = Kb Q1~b (ea + c Time) Therefore, the e la s tic ity of the remaining ind irect business taxes with respect to output is equal to 1-b. Table V-3 presents the results for that set of equations. For the most p a rt, the specification works well for the residual categories of indirect business taxes. The nominal ca p ita l stock to nominal output

174 'L o t, TABLE V - 3 SECTORAL INDIRECT BUSINE68 TAX 1EQUATIONS SECTOR NUMBER SECTOR NAME INTER CEPT CAPITAL STOCK TO OUTPUT TIME RSQ RBSQ D-W AAPE 1 AGRICULTURE ( ) (1.2933) ( ) CRUDE PETROLEUM (4.8619) ( ) ( ) MINING ( ) (2.3009) ( ) CONSTRUCTION ( ) (1.0766) ( ) FOOD AND TOBACCO ( ) ( ) ( ) * TEXTILE ( ) ( ) ( ) APPAREL ( ) ( ) ( ) e PAPER ( ) ( ) ( ) PUBLISHING ( ) ( ) ( ) CHEMICALS ( ) ( ) ( ) PETROLEUM REFIN ( ) ( ) ( ) RUBBER ( ) ( ) ( )

175 ( b o - e TABLE V - 3 SECTORAL INDIRECT BU8INE88 TAX EQUATION8 SECTOR SECTOR INTER CAPITAL 8T0CK NUMBER NAME CEPT TO OUTPUT TIME R8Q RBSQ D-W AAPE 13 LEATHER < ) ( ) ( ) LUMBER ( ) ( ) ( ) FURNITURE ( ) ( ) ( ) T0NE* ETC ( ) ( ) ( ) PRIMARY METALS ( ) <4.2934) ( ) IB FAB. METAL ( ) ( ) ( ) TRAN8 EO ( ) ( ) ( ) MIN-ELECT. MACH «-l. 9877) ( ) (O. 7070) ELECT. MACK ( ) ( ) ( ) AUTG ( ) ( ) -O ( ) INSTRUMENTS ( ) ( ) ( ) MI8C. MFO. 1N ( ) ( ) ( ) RAILROADS ( ) (-O. 8940) ( ) a 23

176 < t TABLE V - 3 SECTORAL INDIRECT BUBINESS TAX EQUATIONS SECTOR SECTOR INTER CAPITAL STOCK NUMBER NAME CEPT TO OUTPUT TIME RSQ RBSQ D-U AAPE 36 AIR TRANSPORTATION ( ) ( ) ( ) TRUCKING ETC ( ) ( ) ( ) COMMUNICATIONS ( ) (1.3849) ( ) UTILITIES ( ) ( ) ( ) TRADE C -l. 9764) ( ) ( ) FIN. 8ERV ( ) ( ) ( ) REAL ESTATE ( ) ( ) > < ) HOTELB ( ) (0.3931) ( ) MISC. BUS. SER ( ) ( ) ( ) AUTO REPAIR ( ) ( ) ( ) 0. B AMUSEMENTS ( ) ( ) ( ) HEALTH AND EDUC < ) ( ) O ( ) I

177 9 r a tio is s ig n ific a n t fo r tw enty-six sectors while the time trend is significant for twenty-eight sectors. generally in the acceptable range. The signs of the coefficients are Three sectors have a negative e la s t ic it y between the ca p ita l stock and indirect business taxes, but only Crude petroleum (2) has a significant coefficient. Property taxes are a major p o rtio n of th is residual in d irect business tax fo r Crude petroleum and are probably "counted" in the Real estate (33) sector. In a forecast, the two sectors with insignificant coefficients - Railroads (25) and E le c tric a l machinery (21) - have those coefficients set to zero. V.2 Government Subsidies (less surplus of government enterprises) Government subsidies are the smallest component of GPO, comprising less than two-tenths of a percent of GNP. Government subsidies consist of two pieces, actual subsidies from legislated programs and surpluses earned by any government enterprises. Seven private industries receive federal subsidies: Agriculture (1 ), Transportation equipment except a u to s ( 1 9 ), R a ilro a d s (2 5 ), A ir tr a n s p o r ta tio n (2 6 ), Other transportation (27), Financial services (3 2), and Real estate (33). Real estate receives slig h tly less than half (46%) of a ll subsidies for a variety of programs such as rental supplements, rural housing g rants, and in te re s t supplements. Maritime interests receive subsidies in two sectors: building ships (sector 19) and operating them (se ctor 27). Railroads (25) and Agricultural (1) receive direct payments, while the insurance industry (32) has federal backing of some flo o d insurance payments.

178 10 Surplus from government enterprises is a s lig h tly misleading t i t l e, since government enterprises can be run at an operating d e fic it so that the "surplus" may be negative. At the federal le v e l, "surpluses" are created by the Postal Service, Commodity Credit Corporation, Federal Housing Administration, Tennesseee Valley A u th o rity, Federal Deposit Insurance C orporation and the Federal Savings and Loan Insurance Corporation. State and local governments generate surpluses from an even more d is p a rite group of a c tiv itie s : u t ilit ie s, to lls and parking fees, liquor stores, public tra n s it, a ir and water te rm in a ls, housing and urban renewal, state lo tte rie s, and o ff-tra ck betting. Government s u b s id ie s, and to a s lig h t degree surpluses, are p o lit ic a l programs s u b je c t to fre q u e n t change in the ru le s of e l i g i b i l i t y and methods of administration which do not lend themselves to modelling work. Consequently, no equations are specified fo r th is small component of valued added. Government subsidies are specified as a constant share of REVAWO fo r each sector for forecasting purposes.

179 ENDNOTES 1. Ross Preston, The_Wha rton_annua I_and_Indust rx_ orecating_modelz (Philadelphia: Economics Research Unit, U niverisity of Pennslyvannia, 1972). 2. Michael McCarthy, "A nalysis of Non-Wage Income Components," The Brookings Model: Some_Further Results, ed. James Duesenberry, G. Fromm, L. Klein and *E. Kuh. (Chicago: Rand McNally, 1969) pp David Belzer, "An Integration Of Price, Wage and Income Flows in an In p u t- O u tp u t Model of the U nite d S ta te s," un pu b lish e d Ph.D. dissertation, University of Maryland, 1978, pp

180 UASE RUN TABLE VI 1-7. FORECAST CONTROLS 8< RESULTS I G ross N a tio n a l P ro d u c t* GNPZ Sum o f VA by c a te g o ry : S t a t i s t i c a l d is c re p a n c y L a b o r co m p e n satio n I n d ir e c t b u s in e s s ta x e s S u b s id ie s R e tu rn to c a p it a l N et in t e r e s t Corp. c a p i t a l consump. a llo w N oncorp. cap. consump. a llo w B u s in e s s t r a n s f e r paym ents C o rp o ra te p r o f i t s B P r o p r ie t o r income Corp. in v e n to r y v a lu a t io n a d j N oncorp. in v e n. v a lu a t io n a d j a R e n ta l incom e G ross N a tio n a l P ro d u c t D e fla to r COMPENSATION PER MAN-HOUR INDEXES M a n u fa c tu rin g N o n -m a n u fa c tu rin g ' LABOR PRODUCTIVITY (ONP/JOB8) ENERGY PRICE INDEXES D om estic cru d e o i l ( * / b b l ) F o re ig n c ru d e o i l ( / b b l ) FINANCIAL VARIABLES AAA C o rp o ra te bond r a t e C om m ercial p aper r a te M2 ( b i l l i o n s o f c u r r e n t * ) R a tio o f M2 to r e a l GNP R a tio o f M2 to n o m in a l ONP S a v in g s r a te G ross N a tio n a l P ro du ct< 1977* PCE R e s id e n tia l s t r u c t u r e s N o n -re s id e n fc ia l s tr u c t u r e s P ro d u c e rs ' d u ra b le equipm ent BO In v e n to ry change E x p o rts o f goods & s e rv ic e s Im p o rts o f goods fc s e rv ic e s Government P u rch a se s F e d e ra l D e fen se N o n -d e fe n se S ta te and lo c a l B E d uca t io n O th e r Unemploym ent r a te S pending r a le O o vt t r a n s f e r share o f income F e d e ra l d e f i c i t. NIPA

181 BASE RUN TABLE V I- 8. PRICE IN0EXE5 AND FINANCIAL VARIABLES IM P LIC IT DEFLATORS ( ) oss N a tio n a l P ro d u c t P e rs o n a l c o n su m p tio n e x p e n d itu re s R e s id e n tia l s tr u c t u r e s N o n - r e s id e n tia l s tr u c t u r e s P ro d u c e rs ' d u ra b le equipm ent E x p o rts * m erchandise Im p o rts * m e rcha n d ise F e d e ra l d e fe n se F e d e ra l n o n -d e fe n s e S ta te & lo c a l e d u c a tio n S ta te & lo c a l o th e r g o v t B COMPENSATION PER MAN-HOUR INDEXES M a n u fa c tu rin g N o n -m a n u fa c tu rin g LABOR PRODUCTIVITY (0N P/J0B8) ENERGY PRICE INDEXE8 D om estic c ru d v o i l ( t / b b l ) F o re ig n cru d e o i l ( * / b b l ) SO FINANCIAL VARIABLES AAA C o rp o ra te bond r a t e Com m ercial p aper r a t e M ortgage r a te In t e r e s t r a t e on F e d e ra l debt Average r a t e p a id by B&L g o vt A verage r a t e re c e iv e d by 6&L g o v t R eal r a te o f in t e r e s t (ex a n te ) S M2 ( b i l l i o n s o f c u r r e n t * ) R a tio o f M2 to r e a l ONP R a tio o f M2 to n o m in a l GNP > OO a v in g s r a t e F e d e ra l s u rp lu s o r d e f i c i t * NIPA S o c ia l in s u ra n c e fu n d s O th e r fu n d s

182 BASE RUN TABLE VI -9. GNP. NNPi NATIONAL INCOME* PERSONAL INCOME < 1.7 ) G ross N a tio n a l P ro d u c t B. 71 C a p ita l co n su m p tio n a llo w a n c e s w ith c a p it a l co n su m p tio n a d j. : N et N a tio n a l P ro d u c t I n d ir e c t b u s in e s s ta a and n ontan l i a b i l i t y B u s in e s s t r a n s f e r paym ents I t S t a t i s t i c a l d is c re p a n c y : S u b s id ie s le s s c u r r e n t s u rp lu s o f g o v t e n te r p r is e s : N a tio n a l Income : C o rp o ra te p r o f i t s w ith IVA and a c a p it a l co n su m p tio n a d j. N et in t e r e s t C o n tr ib u tio n s f o r s o c ia l in s u r Wage a c c ru a ls le s s d is b u rs e m e n ts : O ovt t r a n s f e r paym ents to p erson P e rs o n a l in t e r e s t income OB P e rs o n a l d iv id e n d income B u s in e s s t r a n s f e r paym ents E r r o r P e rs o n a l Income B B 7.13 ADDENDEA FOR CHK r e n t a l incom e w /o cca y r iic a \ cca. r i c a y r l y r i

183 BASE RUN TABLE V I PERSONAL :0ME - SOURCES AND DISPOSITION <2. 1) Qa '-'* P e rs o n a l Income Wag* and s a la r y d is b u rs m e n ts O th e r la b o r income P r o p r ie t o r s ' incom e w. Farm Nonfarm IVA&CCADJ R e n ta l income o f p e rs o n s hi. CCADJ D iv id e n d * P e rs o n a l in t e r e s t incom e T ra n s fe r paym ents F e d e ra l S ta te j»nd lo c a l B u s in e s s t r a n s f e r paym ents : -P e rs c o n t r ib to s o c ia l in s u ra n c e E r r o r - : P e rs o n a l ta x and n o n ta x paym ents F e d e ra l incom e ta x e s : D is p o s a b le Income P e rs o n a l O u tla y s C onsum ption e x p e n d itu re s In t e r e s t p a id by consum ers to b u s in e s s e s P e rs o n a l t r a n s f e r payments to fo r e ig n e r s ( n e t) «: P e rs o n a l S a v in g s ADDENDA: D is p o s a b le Income ( $ )i T o ta l P e r c a p ita P o p u la tio n (m id -p e rio d * m illio n s ) P e rs o n a l s a v in g s as % o f d is p o s a b le p e rs o n a l incom e ( le s s in t e r e s t p a id to b u s in e s s and tr a n s f e r paym ents to f o r e ig n e r s ) T o ta l ta x e s / P e rs o n a l income F e d e ra l D e f ic it * NIPA DI72RL TRASHR - T r a s fe r sh a re o f income BPENDR - S pending r a te OB RO

184 BASE HUN TABLE VI FEDERAL GOVERNMENT RECEIPTS & EXPENDITURES ( 3. 2 ) i*?ao 19B RECEIPTS P e rs o n a l ta x end n o n - ta i r e c e ip ts C o rp o ra te p r o f i t s ta x I n d ir e c t b u s in e s s ta s and n o n ta x a c c ru a ls C o n tr ib u tio n s f o r s o c ia l in s u ra n c e BI EXPENDITURES P u rchases o f Goods and S e rv ic e s N a tio n a l d e fe n se C om pensation o f em ployees O th e r B N ondefense C om pensation o f em ployees O th e r T r a n s fe r Paym ents To p e rsons O ld age b e n e fits H o s p ita l b m e d ic a l Unem ploym ent R e tire m e n t: Fed c iv It RR V e t l i f e in s u r< workmen comp. M il i t a r y r e tir e m e n t V e te ra n s b e n e fits Food stam ps O th e r S B To fo r e ig n e r s G r a n ts - in - A id to S&L G ovt N e t I n t e r e s t P a id I n t e r e s t p a id I n t e r e s t re c e iv e d S u b s id ie s le s s C u rre n t B u rp lu s o f G ovt E n te rp r is e s S u rp lu s o r D e f i c i t (-># NIPA S o c ia l in s u ra n c e fu n d s O th e r fu n d s D ebt o f F e d e ra l G overnm ent D ebt fro m F e d e ra l lo a n s B

185 BASE RUN TABLE V I OUTPUT BY PRODUCING SECTOR ( * ) AGRICULTURE*FORESTRY*FISHERY MINING IRON ORE MINING SB NONFERROUS METALB MINING COAL MINING NATURAL GAS EXTRACTION IB. 50 IB CRUDE PETROLEUM NON-METAt-LIC MININQ B B B e CONSTRUCTION NON-DURABLES FOOD & TOBACCO TEXTILES. EXC. KNITS KNITTINO APPAREL. HOUSEHOLD TEXTILEB PAPER O l PRINTING St PUBLISHING B AGRICULTURAL FERTILIZERS OTHER CHEMICAL IB 17 PETROLEUM REFINING FUEL O IL RUBBER PRODUCTS IB PLASTIC PRODUCTS SHOES AND LEATHER OB DURABLES B4 B LUMBER FURNITURE STONE# CLAY* GLASS FERROUS METAL COPPER B OTHER N0NFERR0U8 METALS METAL PRODUCTS NON-ELEC MACHINERY B ENGINES AND TURBINES AGRICULTURAL MACHINERY CONSTR* MiNINO* O ILFIELD EQ IB IB METALWORKING MACHINERY SPECIAL INOUSTRY MACHINERY MISC NON-ELECTRICAL MACH COMPUTERS B BO OTHER OFFICE EQUIPMENT SERVICE INOUSTRY MACHINERY ELECTRICAL MACHINERY COMMUNIC EQ* ELECTRONIC COMP ELEC INDL APP * DISTRIB EQ HOUSEHOLD APPLIANCES ELEC LIGHTING St WIRING EQ TV SETS. RADIOS*PH0N0GRAPH IB TRANSPORTATION EQ IB MOTOR VEHICLES AEROSPACE SHIPS. BUAT OTHER TRAN8P. EQUIP

186 BASE RUN TABLE V I-1 2. OUTPUT BY PRODUCING SECTOR < *) INSTRUMENTS 48 MISC. MANUFACTURING TRANSPORTATION 49 RAILROADS 30 TRUCKING, HWY PASS TRANSIT 31 WATER TRANSPORT 32 AIR TRANSPORT 53 PIPELINE 34 TRANSPORTAION SERVICES B IB U T IL IT IE S 55 COMMUNICATIONS SERVICES 56 ELECTRIC U T IL IL IT IE S 57 GAS U T ILIT Y 38 WATER AND SANITATION B WHOLESALE TRADE RETAIL TRADE 61 EATINO & DRINKING PLACES lo l FINANCE & INSURANCE REAL ESTATE 64 OWNER-OCCUPIED HOUSING B. 02 SERVICES 63 HOTELSi REPAIRS EXC AUTO 66 BUSINESS SERVICE8 67 AUTOMOBILE REPAIRS 68 MOVIES ANO AMUSEMENTS 69 MEDICINE. EDUCATIONNPO FED & SfcL GOVT ENTERPRISES 71 NON COMPETITIVE IMP0RT8 72 DOMESTIC SERVANTS 73 UNIMPORTANT INDUSTRY 74 SCRAP AND USED 75 REST OF THE WORLD INDUSTRY 76 GOVERNMENT INDUSTRY 77 INFORUM STAT. DISCREPANCY 78 NIPA STAT. DISCREPANCY B

187 BASE RUN TABLE V I ADJUSTED EMPLOYMENT (M ILLIO N S OF JOBS) TOTAL PRIVATE SECTOR JOBS AGRIC. MINING* CONSTRUCTION AGRICULTURE( 1 ) CRUDE O IL Si GAS ( 9-6 ) MINING ) CONSTRUCTION ( 8 ) NON-DURABLE GOODS FOOD. TOBACCO ( 9 ) TEXTILES (1 0 ) KNITTING (1 1 ) APPAREL St HHLD TEXTILES (1 2 ) PAPER 0 3 ) PRINTING (1 4 ) AGRICULTURAL FERTILIZER (1 9 ) OTHER CHEMICALS (1 6 ) PETROLEUM REFINING (1 7 ) RUBBER * PLASTIC PROD ( ) FOOTWEAR Si LEATHER (2 1 ) DURABLE GOODS LUMBER '2 2 ) FURNITURE (2 3 ) STONE. CLAY Si GLASS (2 4 ) IRON & STEEL (2 9 ) NON-FERROUS METALS ( ) METAL PRODUCTS (2 8 ) ENGINES St TURBINES (2 9 ) AGRICULTURAL MACHINERY (3 0 ) METALWORKING MACHINERY (3 2 ) SPECIAL INO MACH (3 3 ) MISC NONELEC MACH ( ) COMPUTERS. OFFICE EQ ( ) ERVICE INDUSTRY MACH (3 7 ) COMMUNIC EQ. ELECTRON COMP (3 8 ) ELEC APP Si DISTRIB EQ (3 9 ) HOUSEHOLD APPLIANCEB (4 0 ) ELEC L1UHT Si WIRINO EQ (4 1 ) TV SETS-RADIOS. PHONOGRAPH (4 2 ) MOTOR VEHICLES (4 3 ) AEROSPACE (4 4 ) HIPS Sc BOATS (4 9 ) OTHER TRAN8P EQ (4 6 ) INSTRUMENTS (4 7 ) MISC MANUFACTURING (4 8 ) TRANSPORTATION RAILR0AD8 (4 9 ) AIR TRANSPORT (9 2 ) TRUCKING. OTH TRANS( ) B B IB B B IB

188 BASE RUN TABLE VI HOURLY ADJUSTED EMPLOYMENT (MILLIONS OF JOBS) B U T IL )T IE S 45 COMMUNICATIONS SERVICES (53) 46 ELECTRIC U T IL IT IE S (5 6 ) 47 OAS, WATER fc SANITATION ( ) O O WHOLESALE * RETAIL TRADE ( ) FINANCE. INSURANCE. REAL EST. 49 FINANCE & INSURANCE (6 2 ) 30 REAL ESTATE (6 3 ) SERVICES 51 HOTELS! REPAIR8 EXC. AUTO (65) 52 BUSINESS SERVICE8 (6 6 ) 53 AUTO REPAIR (6 7 ) 54 MOVIES & AMUSEMENTS (6 8 ) 35 MEDICINE. EDUC. NPO (6 9 ) DOMESTIC SERVANTS C IV IL IA N GOVERNMENT FEDERAL DLFENSE FEDERAL NON-DEFENSE STATE & LOCAL EDUCATION STATE 8t LOCAL OTHER FEDERAL GOVT ENTERPRISES 6TATE & LOCAL OOVT ENTERPRISES TOTAL C IV IL IA N JOBS TOTAL C IV IL IA N EMPLOYMENT MULTIPLE JOB HOLDERS MILITARY JOBS C IV IL IA N UNEMPLOYMENT RATE LABOR PRODUCTIVITY GNP / C IV IL IA N J0B8 ( ONP OOVT) / PRIVATE JOBS , U IB

189 BABE RUN TABLE V I DOMESTIC PRODUCERS' PRICES BY SECTOR <1977»100> AGRICULTURE,FORESTRY.FISHERY MININO 2 IRON ORfc MINING NONFERROUS METALS MININO COAL MININO B NATURAL GAS EXTRACTION CRUDE PETROLEUM NON-METALLIC MININO e CONSTRUCTION NON-DURABLES 9 FOOD Si TOBACCO TEXTILES. EXC. KNITS KNITTING APPAREL. HOUSEHOLD TEXTILES PAPER PRINTING & PUBLISHING OB 15 AGRICULTURAL FERTILIZERS OTHER CHEMICALS PETROLEUM REFINING FUEL O IL B B RUBBER PRODUCTS PLASTIC PR0DUCT SHOES AND LEATHER DURABLES 23 LUMBER FURNITURE STONE. CLAY. QLA8S FERROUS METALS COPPER OTHER NONFERROUS METALS METAL PRODUCTS NON-ELEC MACHINERY ENGINES AND TURBINES AGRICULTURAL MACHINERY CONSTR. MINING. O ILFIELD EQ METALWORKING MACHINERY SPECIAL INDUSTRY MACHINERY MISC NONELECTRICAL MACH S COMPUTERS OTHER OFFICE EQUIPMENT SERVICE INDUSTRY MACHINERY ELECTRICAL MACHINERY OO * COMMUNIC EQ. ELECTRONIC COMP ELEC INDL APP & D I8TR IB EQ HOUSEHOLD APPLIANCES ELEC LIGHTING 8c WIRING EQ TV SETS. RADIOS. PHONOGRAPHS TRANSPORTATION EQ 43 MOTOR VEHICLE AEROSPACE SHIP8. B0AT OTHER TRAN8P. EQUIP

190 BASE RUN TABLE V I DOMESTIC PRODUCERS' PRICES BY SECTOR ( = ) INSTRUMENTS MISC. MANUFACTURING B TRANSPORTATION RAILROADS TRUCKINO, HWY PA8S TRAN8IT MATER TRANBPORT AIR TRANSPORT PIPELINE TRAN8P0KTAI0N SERVICES B U T IL IT IE S COMMUNICATIONS SERVICES ELECTRIC U U L IL IT IE B GAS U T ILIT Y MATER AND SANITATION ; B. 2B MHOLESALE TRADE RETAIL TRADE EATINO * DRINKING PLACES FINANCE fc INSURANCE BB REAL ESTATE OWNER-OCCUPIED HOUSINO B IB SERVICES HOTELSj REPAIRS EXC AUTO BUSINESS SERVICES AUTOMOBILE REPAIRS MOVIES ANO AMUSEMENTS MEDICINE, EDUCATION, NPO B FED * S«L OOVT ENTERPRISES NON COMPETITIVE IMPORTS DOMESTIC SERVANT8 UNIMPORTANT INDUBTRY 8CRAP AND USED REST OF THE WORLD INDUSTRY GOVERNMENT INDUSTRY INFORUM 6TAT. DISCREPANCY NIPA STAT. DISCREPANCY ISO B BO B IBB B IM P LIC IT DEFLATORS < ) G ross N a tio n a l P ro d u c t P e rs o n a l co n s u m p tio n t i p t n d i t u r a t 127. B B COMPENSATION PER MAN-HOUR INDEXES M a n u fa c tu rin g Non->nanuf ac t u r in g

191 BASE RUN TABLE V I LABOR COMPENSATION BY INDUSTRY, BILLIO N ALL INDUSTRIES FARM 81 AGRICULTURAL SERVICES MINERALS CRUDE PETROL. * NAT. 0A MINING B B CONTRACT CONSTRUCTION NON-DURABLES FOOD it TOBACCO TEXTILE M ILL PR0DUCT APPAREL AND RELATED PRODUCTS PAPER AND ALLIED PRODUCTS PRINTING AND PUBLISHING CHEMICAL AND ALLIED PRODUCTS B PETROLEUM AND RELATED INDUSTRI ? RUBBER * HISC PLASTIC PRODUCTS LEATHER AND LEATHER PR0DUCT DURABLES LUMBER fc WOOD PRODUCTS. EX FURN FURNITURE AND FIXTURES STONE. CLAY. & GLASS PRODUCTS PRIMARY METAL INDUSTRIES METAL PRODUCTS TRANS EQ + ORD EX MOTOR VEH MACHINERY. EXCEPT ELECTRICAL ELECTRICAL MACHINERY MOTOR VEHICLES AND EQUIPMENT INSTRUMENTS AND RELATED PROD M I8C. MANUFACTURING IND TRANSPORTATION RAILROADb AIR TRANSPORTATION TRUCKING AND OTHER TRANSPORT COMMUNICATIONS ELECTRIC. GA8. AND SANITARY WHOLESALE AND RETAIL TRADE F IN. IN 8UR. REAL E8TATE FINANCIAL fc INSURANCE SERVICE REAL ESTATE * COMBINATIONS OFF SERVICES HOTELS & REPAIR<NOT AUTO) B MISC. BU81NE88 SERVICE AUTO REPAIR MOTION PICTURE8 * AMU8EMENT MEDICAL & EDUCATIONAL 8ERV1CE PRIVATE H0U8EH0LD GOVERNMENT IB FED. GOV'T ENTERPRISES STATE * LOCAL GOV'T ENTERPRISES IB FED OOV'T GENERAL ADMINI8T BTATE & LOCAL GENERAL ADMINIST REBT OF THE WORLD

192 BASE RUN TABLE V INDEXES OF AVERAQE HOURLY COMPENSATION ALL INDUSTRIES 1 FARM S< AGRICULTURAL SERVICES MINERALS 2 CRUDE PETROL. * NAT. OAB MINING CONTRACT CONSTRUCTION NON-DURABLES 3 FOOD b TOBACCO TEXTILE M ILL PRODUCTS I APPAREL AND RELATED PR0DUCT PAPER AND ALLIED PR0DUCT PRINTINO AND PUBLISHING CHEMICAL ANO ALLIED PRODUCTS PETROLEUM AND RELATED 1NDUBTRI RUBBER & MISC PLA8TIC PRODUCTS LEATHER ANO LEATHER PRODUCTS DURABLES 14 LUMBER & WOOD PRODUCTS.EX FURN FURNITURE AND FIXTURES STONE. CLAY, fc 0LAS8 PRODUCTS PRIMARY METAL INDUSTRIES IB METAL PRODUCTS TRANS EQ + ORD EX MOTOR VEH MACHINERY. EXCEPT ELECTRICAL ELECTRICAL MACHINERY MOTOR VEHICLEB AND EQUIPMENT INSTRUMENTS AND RELATED PROD MIBC. MANUFACTURING IND TRANSPORTATION 23 RAILROADS AIR TRANSPORT ATION TRUCKING AND OTHER TRANSPORT B COMMUNICATIONS ELECTRIC, GAS. AND SANITARY WHOLESALE AND RETAIL TRADE F IN. 1N8UR. REAL ESTATE 32 FINANCIAL & INSURANCE 8ERVICE REAL E6TATE fc COMBINATIONS OFF ERVICE8 34 HOTELS & REPAIR(NOT AUTO) MISC. BUBINE8B SERVICES I AUTO REPAIR MOTION PICTURES & AMUSEMENTS MEDICAL * EDUCATIONAL BERV1CE PRIVATE H0U8EH0LD GOVERNMENT 40 FED. GOV'T ENTERPRISES TATE & LOCAL GOV'T ENTERPRISES FED GOV'T GENERAL ADMINIST STATE * LOCAL GENERAL ADMINIST , ' ? ; B REST OF THE WORLD

193 BASE RUN TABLE V I TOTAL RETURN TO C A P IT A L BY INDUSTRY, B IL L IO N * A LL IN D U S TR IE S FARM & AGRICULTURAL SERVICES M INERALS CRUDE PETROL, fc NAT. OAS M IN IN G CONTRACT CONSTRUCTION N0N-DU RA8LE FOOD Sc TOBACCO T E X T IL E M IL L PRODUCTS APPAREL AND RELATED PRODUCTS PAPER AND A L L IE D PRODUCTS P R IN T IN G AND P U B LIS H IN G CHEMICAL AND A L L IE D PRODUCTS PETROLEUM AND RELATED IN D U 8TR I RUBBER Sc MISC P LA S T IC PRODUCTS LEATHER ANO LEATHER PRODUCTS DURABLES LUMBER Sc WOOD PRODUCTS. EX FURN FURNITURE AND FIX TU R E S STONE. CLAY. Si OLASS PRODUCTS B PRIMARY METAL IN D U S TR IE S METAL PRODUCTS TRANS EQ ORD EX MOTOR VEH MACHINERY. EXCEPT ELECTR IC AL E LE C TR IC A L MACHINERY MOTOR V E H IC LE S AND EQUIPMENT INSTRUMENTS AND RELATED PROD MISC. MANUFACTURING IN D TRANSPORTATION RAILROADS A IR TRANSPORTATION TRUCKING ANO OTHER TRANSPORT COMMUNICATIONS B E LE C TR IC. GAS. AND SANITARY WH0LE8ALF AND R E T A IL TRADE F IN. INSUR. REAL ESTATE F IN A N C IA L Sc INSURANCE SERVICES REAL ESTATE Sc COMBINATIONS OFF SERVICES B HOTELS Sc REPAIR (NOT AUTQ) MISC. B U E IN E 8S S E R V IC E AUTO REPAIR MOTION PICTURES S< AMUSEMENTS MEDICAL S. EDUCATIONAL SERVICES REST THE WORLD f T V

194 BASE RUN TABLE V I CORPORATE PROFITS. B IL L IO N * 19B A LL INDUSTRIES B FARM & AGRICULTURAL SERVICES MINERALS 45. S3 6B B SB CRUDE PETROL. 8c NAT. GAS M IN IN G B s CONTRACT CONSTRUCTION NON-DURABLES FOOD St TOBACCO IB T E X T IL E M IL L PRODUCTS B 3. IB APPAREL AND RELATED PRODUCTS PAPER AND A LLIE D PRODUCTS PR IN TIN G AND PUBLISHING CHEMICAL AND A LLIE D PRODUCTS IB PETROLEUM AND RELATED INDUSTRI RUBBER it MISC PLASTIC PRODUCTS LEATHER AND LEATHER PRODUCTS DURABLES LUMBER it WOOD PRODUCTS. EX FURN FURNITURE AND FIXTURES STONE. CLAY. & GLASS PRODUCTS PRIMARY METAL INDUSTRIES t METAL PRODUCTS TRANS EQ + ORD EX MOTOR VEH MACHINERY. EXCEPT ELECTRICAL ELECTRICAL MACHINERY MOTOR VEHICLES AND EQUIPMENT INSTRUMENTS AND RELATED PROD MISC. MANUFACTURING IND TRANSPORTATION RAILROADS A IR TRANSPORTATION TRUCKING AND OTHER TRANSPORT COMMUNICATIONS ELECTRIC. GAS. AND SANITARY WHOLESALE AND R ETAIL TRADE F IN. INSUR. REAL ESTATE F IN A N C IA L & INSURANCE SERVICES REAL ESTATE fc COMBINATIONS OFF SERVICES HOTELS «c REPAIR (NOT AUTO) MISC. BUSINESS SERVICES AUTO REPAIR MOTION PICTURES «. AMUSEMENTS MEDICAL it EDUCATIONAL SERVICES B REST OF THE WORLD

195 BASE RUN TABLE V I PROPRIETORS' INCOME BY INDUSTRY, B IL L IO N * A LL INDUSTRIES FARM Sc AGRICULTURAL SERVICES MINERALS CRUDE PETROL. Sc NAT. GAS M IN IN G O CONTRACT CONSTRUCTION NON-DURABLES FOOD Sc TOBACCO T E X T IL E M IL L PRODUCTS APPAREL AND RELATED PRODUCTS B PAPER AND A LLIE D PRODUCTS P R IN T IN G AND PUBLISHING CHEMICAL AND A LLIE D PRODUCTS PETROLEUM AND RELATED INDUSTRI RUBBER Sc MISC PLASTIC PRODUCTS LEATHER AND LEATHER PRODUCTS DURABLES LUMBER Sc WOOD PRODUCTS# EX FURN FURNITURE AND FIXTURES STONE* CLAY. Sc GLASS PRODUCTS PRIMARY METAL INDUSTRIES IS METAL PRODUCTS TRANS EQ + ORD EX MOTOR VEH MACHINERY. EXCEPT ELECTRICAL ELECTR IC AL MACHINERY MOTOR VEHICLES AND EQUIPMENT INSTRUMENTS AND RELATED PROD MISC. MANUFACTURING IND TRANSPORTATION RAILROADS 2 6 A IR TRANSPORTATION O TRUCKING AND OTHER TRANSPORT , COMMUNICATIONS ELE C TR IC. GAS. AND SANITARY WHOLESALE AND RETAIL TRADE F IN. INSUR* REAL ESTATE F IN A N C IA L Sc INSURANCE SERVICES REAL ESTATE Sc COMBINATIONS OFF 4. S SERVICES HOTELS S< REPAIR (NOT AUTO) MISC. BUSINESS SERVICES AUTO REPAIR MOTION PICTURES Sc AMUSEMENTS MEDICAL Sc EDUCATIONAL SERVICES REST OF THE WORLD

196 BASE RUN TABLE V I-2 0. NET INTEREST PAYMENTS BY INDUSTRY, B ILLIO N * B ^0 ALL INDUSTRIES FARM Sc AGRICULTURAL SERVICES IB MINERALS CRUDE PETROL. Sc NAT. GAS M ININO B CONTRACT CONSTRUCTION B. 57 NON-DURABLES FOOD Sc TOBACCO T E X T ILE M IL L PRODUCTS APPAREL AND REbATED PRODUCTS PAPER AND A LLIE D PRODUCTS PR IN TIN G AND PUBLISHING CHEMICAL AND ALLIE D PRODUCTS PETROLEUM AND RELATED INDUSTRI RUBBER Sc MISC PLASTIC PRODUCTS LEATHER AND LEATHER PRODUCTS B DURABLES LUMBER Sc WOOD PRODUCTS. EX FURN FURNITURE AND FIXTURES STONE, CLAY, Sc GLASS PRODUCTS PRIMARY METAL INDUSTRIES METAL PRODUCTS TRANS EQ + ORD EX MOTOR VEH MACHINERY. EXCEPT ELECTRICAL ELECTRICAL MACHINERY MOTOR VEHICLES AND EQUIPMENT INSTRUMENTS AND RELATED PROD MISC. MANUFACTURING IND B TRANSPORTATION RAILROADS A IR TRANSPORTATION TRUCKING AND OTHER TRANSPORT COMMUNICATIONS ELECTRIC, GAS. AND SANITARY WHOLESALE AND R ETAIL TRADE F IN. INSUR. REAL ESTATE F IN A N C IA L Sc INSURANCE SERVICES REAL ESTATE Sc COMBINATIONS OFF , B SERVICES HOTELS Sc REPAIR (NOT AUTO) MISC. BUSINESS SERVICES AUTO REPAIR MOTION PICTURES Sc AMUSEMENTS MEDICAL Sc EDUCATIONAL SERVICES REST OF THE WORLD

197 BASE RUN TABLE V I CORPORATE CAPITAL CONSUMPTION ALLOWANCE, B IL L IO N * 19B B3 19B4 19B5 19B A LL IN D U S TR IE S FARM * AGRICULTURAL SERVICES M INERAL CRUDE PETROL. It NAT. OAS M IN IN G CONTRACT CONSTRUCTION NON-DURABLES FOOD It TOBACCO T E X T IL E M IL L PRODUCTS APPAREL AND RELATED PRODUCTS PAPER AND A L L IE D PRODUCTS P R IN T IN G AND P U B LIS H IN O CHEMICAL AND A L L IE D PRODUCTS PETROLEUM AND RELATED INDUSTRI RUBBER l< M ISC P LA S T IC PRODUCTS LEATHER AND LEATHER PRODUCTS DURABLES LUMBER It WOOD PRODUCTS. EX FURN FURNITURE AND FIX TU R E S STONE* CLAY. It GLASS PRODUCTS PRIMARY METAL IN D U S TR IE S IB METAL PR0DUCT TRANS EQ * ORD EX MOTOR VEH MACHINERY, EXCEPT ELECTRICAL ELE C TR IC A L MACHINERY MOTOR V E H IC LE S AND EQUIPMENT INSTRUMENTS AND RELATED PROD M ISC. MANUFACTURING IND TRANSPORTATION RAILROADS A IR TRANSPORTATION TRUCKING ANO OTHER TRANSPORT COMMUNICATIONS E LE C TR IC. GAS. AND SANITARY WHOLESALE AND R E T A IL TRADE F IN. INSUR, REAL ESTATE F IN A N C IA L It INBURANCE SERVICES REAL ESTATE It COMBINATIONS OFF SERVICES HOTELS It REPAIR (NOT AUTO) M ISC. BUSINESS SERVICES AUTO REPAIR MOTION PICTURES It AMUSEMENTS M EDICAL It EDUCATIONAL SERVICES REST OF THE WORLD

198 BASE RUN TABLE V I NONCORPORATE CAPITAL CONSUMPTION ALLOWANCE, B IL L IO N ALL INDUSTRIES B FARM & AGRICULTURAL SERVICES MINERALS CRUDE PETROL. St NAT. GAB MINING BO CONTRACT CONSTRUCTION NON-DURABLES FOOD b TOBACCO TEXTILE M ILL PRODUCTS APPAREL AND RELATED PRODUCTS OB B 9 PAPER AND ALLIED PRODUCTS PRINTING AND PUBLISHING CHEMICAL ANO ALLIED PRODUCTS B PETROLEUM AND RELATED 1NDUSTRI RUBBER St hisc PLASTIC PRODUCTS LEATHER AND LEATHER PRODUCTS DURABLES LUMBER & WOOD PRODUCTS. EX FURN FURNITURE AND FIXTURES STONE, CLAY. St GLASS PRODUCTS PRIMARY METAL INDUSTRIES IB METAL PRODUCTS OB 19 TRANS EO + ORD EX MOTOR VEH MACHINERY. EXCEPT ELECTRICAL ELECTRICAL MACHINERY MOTOR VEHICLES AND EQUIPMENT INSTRUMENTS AND RELATED PROD. MISC. MANUFACTURING IND TRANSPORTATION RAILROADS AIR TRANSPORTATION OB 27 TRUCKING ANO OTHER TRANBPORT COMMUNICATIONS ELECTRIC. GAB. AND BAN1TARY O WHOLESALE AND RETAIL TRADE F IN. INBUR, REAL ESTATE * FINANCIAL & INSURANCE SERVICES REAL ESTATE Si COMBINATIONS OFF OB SERVICES HOTELS & REPAIR(NOT AUTO) MISC. BUSINESS SERVICE AUTO REPAIR MOTION PICTURES Sc AMUSEMENTS I MEDICAL t. EDUCATIONAL SERVICES REST OF "rhe WORL D

199 BASE RUN TABLE V I BUSINESS TRANSFER PAYMENTS, B IL L IO N * ALL INDUSTRIES FARM Sc AGR ICULTURAL SERVICES MINERALS 0 OS CRUDE PETROL. * NAT. GAS MINING CONTRACT CONSTRUCTION NON-DURABLES FOOD & TOBACCO TEXTILE M ILL PRODUCTS APPAREL ANO RELATED PRODUCTS PAPER AND ALLIED PRODUCTS PRINTING AND PUBLISHING CHEMICAL ANO ALLIED PRODUCTS PETROLEUM AND RELATED INDU8TRI RUBBER & MISC PLASTIC PRODUCTS LEATHER ANO LEATHER PRODUCTS DURABLES LUMBER & WOOD PRODUCTS.EX FURN FURNITURE AND FIXTURES STONE* CLAY. & GLASS PRODUCTS PRIMARY METAL INDUSTRIES METAL PRODUCTS TRANS EQ + ORD EX MOTOR VEH MACHINERY. EXCEPT ELECTRICAL ELECTRICAL MACHINERY MOTOR VEHICLES AND EQUIPMENT INSTRUMENTS ANO RELATED PROD MISC. MANUFACTURING IND TRANSPORTATION RAILROADS AIR TRANSPORTATION TRUCKING ANO OTHER TRANSPORT OO 28 COMMUNICATIONS ELECTRIC. GAS. AND SANITARY WHOLESALE AND RETAIL TRADE F IN. INSUR. REAL ESTATE FINANCIAL & INSURANCE SERVICES REAL ESTATE & COMBINATIONS OFF SERVICES HOTELS & REPAIR(NOT AUTO) MISC. BUSINESS SERVICES AUTO REPAIR MOTION PICTURES & AMUSEMENTS MEDICAL h EOUCATIONAL SERVICES REST 0 * THE WORLD

200 BASE RUN TABLE V I CORPORATE IVA BY INDUSTRY. B IL L. * A LL IN D U S TR IE S I u > FARM Sc AGRICULTURAL SERVICES 2 CRUDE PETROL. Sc NAT. OAS 3 MINING 4 CONTRACT CONSTRUCTION 5 FOOD Sc TOBACCO 6 T E X T IL E h I L L PRODUCTS 7 APPAREL AND RELATED PRODUCTS 6 PAPER AND A L L IE D PRODUCTS 9 P R IN T IN G AND P U B LIS H IN G 10 CHEMICAL ANO A L L IE D PRODUCTS 11 PETROLEUh AND RELATED INDUSTRI 12 RUBBER Sc MISC P LA S T IC PRODUCTS 13 LEATHER ANO LEATHER PRODUCTS 14 LUMBER it WOOD PRODUCTS, EX FURN 15 FURNITURE AND FIXTU R ES 16 STONE, CLAY, Sc GLASS PRODUCTS 17 PRIMARY METAL IN D U STR IES 18 METAL PRODUCTS 19 TRANS EQ + ORD EX MOTOR VEH 2 0 MACHINERY, EXCEPT ELECTRICAL 21 ELE C TR IC AL MACHINERY 2 2 MOTOR VEH IC LES AND EQUIPMENT 2 3 INSTRUMENTS AND RELATED PROD. 2 4 M ISC. MANUFACTURING IND. 2 5 RAILROADS 2 6 A IR TRANSPORTATION 2 7 TRUCKING ANO OTHER TRANSPORT 2 8 COMMUNICATIONS 3 0 ELECTR IC, GAS. AND SANITARY 31 WHOLESALE AND R E T A IL TRADE 3 2 F IN A N C IA L L INSURANCE SERVICES 3 3 REAL ESTATE Sc COMBINATIONS OFF 3 4 HOTELS Sc REPAIR (NOT AUTO) 3 5 MISC. BUSINESS SER VIC E8 3 6 AUTO REPAIR 3 7 MOTION PICTURES Sc AMUSEMENTS 3B MEDICAL Sc EDUCATIONAL SERVICES 4 6 REST OF THE WORLD SB IB IS

201 BASE RUN TABLE V I NON-CORPORATE IVA BY INDUSTRY, B IL L. * I B ALL INDUSTRIES FARM 8c AGRICULTURAL SERVICES 2 CRUDE PETROL. S' NAT. GAS MINING CONTRACT CONSTRUCTION FOOD & TOiiACCO TEXTILE M ILL PRODUCTS APPAREL AND RELATED PRODUCTS 8 PAPER AND ALLIED PRODUCTS 9 PRINTING AND PUBLISHING CHEMICAL AND ALLIED PRODUCTS PETROLEUM AND RELATED INDUSTRI RUBBER h MISC PLASTIC PRODUCTS LEATHER AND LEATHER PRODUCTS LUMBER & WOOD PRODUCTS,EX FURN FURNITURE AND FIXTURES STONE, CLAY, fc GLASS PRODUCTS PRIMARY METAL INDUSTRIES IB METAL PRODUCTS TRANS EQ + ORD EX MOTOR VEH MACHINERY, EXCEPT ELECTRICAL ELECTRICAL MACHINERY MOTOR VEHICLES AND EQUIPMENT INSTRUMENTS AND RELATED PROD O MISC. MANUFACTURING IND RAILROADS AIR TRANSPORTATION TRUCKING AND OTHER TRANSPORT COMMUNICATIONS ELECTRIC, GAS. AND SANITARY WHOLESALE AND RETAIL TRADE FINANCIAL & INSURANCE SERVICES REAL ESTATE Sc COMBINATIONS OFF HOTELS 8c REPAIR (NOT AUTO) MISC. BUSINESS SERVICES AUTO REPAIR O MOTION PICTURES 8< AMUSEMENTS MEDICAL 8< EDUCATIONAL SERVICES REST OF THE WORLD

202 BASE RUN TABLE V I GOVERNMENT SUBSIDIES TO BUSINESS. B ILL IO N * B ALL INDUSTRIES FARM & AGRICULTURAL SERVICES MINERALS 2 CRUDE PETROL. & NAT. GAS 3 MINING 4 CONTRACT CONSTRUCTION NON-DURABLES 5 FOOD & TOBACCO 6 TEXTILE M ILL PRODUCTS 7 APPAREL ANO RELATED PRODUCTS B PAPER AND ALLIED PRODUCTS 9 PRINTING AND PUBLISHING 10 CHEMICAL AND ALLIED PRODUCTS 11 PETROLEUM AND RELATED INDUBTRI 12 RUBBER & MISC PLASTIC PRODUC18 13 LEATHER AND LEATHER PRODUCTS DURABLES -O O BO LUMBER & WOOD PRODUCTS. EX FURN 15 FURNITURE AND FIXTURES 16 STONE. CLAY. fc GLASS PRODUCTS 17 PRIMARY METAL INDUSTRIES 18 METAL PRODUCTS 19 TRANS EO ORD EX MOTOR VEH -O O O. 58 -O. 69 -O. 80 -O MACHINERY. EXCEPT ELECTRICAL 21 ELECTRICAL MACHINERY 22 MOTOR VEHICLES AND EQUIPMENT 23 INSTRUMENTS AND RELATED PROD. 24 MISC. MANUFACTURING IND. TRANSPORTATION 25 RAILROADS 26 AIR TRANSPORTATION 27 TRUCKING ANO OTHER TRANSPORT O O I. 57 -O O. 13 -O O. 14 -O O O. 20 -O O. 23 O COMMUNICATIONS 30 ELECTRIC. GAS. AND SANITARY 31 WHOLESALE AND RETAIL TRADE F IN, INSUR. REAL ESTATE FINANCIAL & INSURANCE SERVICES IB REAL ESTATE & COMBINATIONS OFF B3 SERVICES 34 HOTELS fc REPAIR(NOT AUTO) 35 MI8C. BUSINE8S SERVICES 36 AUTO REPAIR 37 MOTION PICTURES & AMUSEMENTS 38 MEDICAL * EDUCATIONAL SERVICES 40 FEDERAL GOVERNMENT ENTERPRISES STATE * LOCAL GOVT. ENTERPRISES REST OF THE WORLD

203 BASE RUN TABLE V I 27. INDIRECT BUSINESS TAXES BY INDUSTRY, B IL L IO N * B BB A LL IN D U S TR IE S FARM 8c AGRICULTURAL SERVICES MINERALS CRUDE PETROL. 8c NAT. 0A M IN IN G CONTRACT CONSTRUCTION NON-DURABLES FOOD 8c TOBACCO T E X T IL E M IL L PRODUCTS APPAREL AND RELATED PRODUCTS PAPER AND A L L IE D PRODUCTS P R IN T IN G AND P U B LIS H IN G CHEMICAL AND A L L IE D PRODUCTS PETROLEUM AND RELATED INDUSTRI RUBBER MISC P LA S T IC PR0DUCT LEATHER ANO LEATHER PRODUCTS DURABLES LUMBER 8c MOOD PRODUCTS. EX FURN FURNITURE AND FIXTUR ES STONE. CLAY. 8i OLASS PRODUCTS PRIMARY METAL IN D U STR IES METAL PRODUCTS TRANS EO + ORD EX MOTOR VEH MACHINERY. EXCEPT ELECTRICAL E LE C TR IC A L MACHINERY MOTOR VEH IC LES AND EQUIPMENT INSTRUMENTS AND RELATED' PROD M ISC. MANUFACTURING IND TRANSPORTATION RAILROADS A IR TRANSPORTATION TRUCKING ANO OTHER TRANSPORT COMMUNICATIONS ELECTR IC. GA8. AND SANITARY WHOLESALE AND R E T A IL TRADE F IN, 1NSUR. REAL ESTATE F IN A N C IA L 8c INSURANCE SERVICES REAL ESTATE 8c COMBINATIONS OFF SERVICES HOTELS 8c REPAIR (NOT AUTO) M ISC. B U S IN E S 8 SERVICES AUTO REPAIR MOTION PICTURES 8c AMUSEMENTS M EDICAL 8c EOUC AT IO N AL SERVICES PRIVATE HOUSEHOLDS GOVERNMENT FED. GOV'T ENTERPRISES STATE 8c LOCAL G O V 'T ENTERPRISES 4 4 FED G O V 'T GENERAL ADM IN18T. 4 5 STATE 8t LOCAL GENERAL ADMIN18T. 46 REST OF THE WORLD OO B

204 M2 GROWTH 107. TABLE V I FORECAST CONTROLS?< RESULTS G ross N a tio n a l P ro d u ct# GNPZ Sum o f VA by c a te g o ry : S t a t i s t i c a l d is c re p a n c y L a b o r com p e n satio n I n d ir e c t b u s in e s s ta x e s S u b s id ie s R e tu rn to c a p it a l N et in t e r e s t C orp. c a p i t a l consump. a llo w N oncorp. cap. consump. a llo w B u s in e s s t r a n s f e r payments C o rp o ra te p r o f i t s P r o p r ie t o r income Corp. in v e n to r y v a lu a t io n a d j N oncorp. in v e n. v a lu a t io n a d j R e n ta l income B G ross N a tio n a l P ro d u c t D e fla to r COMPENSATION PER MAN-HOUR INDEXES i M a n u fa c tu rin g N o n -m a n u fa c tu rin g LABOR PRODUCTIVITY (GNP/JOBS) ENERGY PRICE INDEXES ) D om estic c ru d e o i l ( t / b b l F o re ig n c ru d e o i l ( * / b b l ) FINANCIAL VARIABLE AAA C o rp o ra te bond r a te B C om m ercial p aper r a te M2 ( b i l l i o n s o f c u r r e n t * ) R a tio o f M2 to r e a l QNP R a tio o f M2 to n o m in a l GNP S a v in g s r a le G ross N a tio n a l P ro d u c t* 1977$ PCE R e s id e n tia l s t r u c t u r e s N o n - r e s id e n tia l s tr u c t u r e s P ro d u c e rs ' d u ra b le equipm ent In v e n to ry change E x p o rts o f goods & s e rv ic e s Im p o rts o f goods & s e rv ic e s Governm ent P u rch a se s F e d e ra l D efense N o n -d e fe n se S ta te and lo c a l E d u c a tio n O th e r Unemploym ent r a te Spending r a t e O ovt t r a n s f e r sh a re o f income F e d e ra l d e f i c i t * NIPA