A SAM-CGE Demonstration for Indonesia

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A SAM-CGE Demonstration Model for Indonesia: Staticand Dynamic Specifications and ExperimentsG. A. Abbink a; M. C. Braber a; S. I. Cohen aa Erasmus University Rotterdam,

Online Publication Date: 01 September 1995To cite this Article: Abbink, G. A., Braber, M. C. and Cohen, S. I. (1995) 'ASAM-CGE Demonstration Model for Indonesia: Static and Dynamic Specificationsand Experiments', International Economic Journal, 9:3, 15 - 33To link to this article: DOI: 10.1080/10168739500000017

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INTERNATIONAL ECONOMIC JOURNALVolume 9, Number 3, Autumn 1995

A SAM -CGE DEMONSTRATION MODEL FOR INDONESIA:STATIC AND DYNA MIC SPECIFICATIONS AND EXPERIMENTS

G . A. ABBINK. M . C. B RA BE RAND S. I. C O H E NErusrnus Universioj Rotterdam

In this article i t will be demonstrated how a simple static Computable GeneralEquilibrium model of the Indonesian economy can be constructed using the SocialAccounting Matrix as a database. It will be shown that under a few assumptions theconstant labour force and capital stock in the static model can be elaborated 'todynamic specifications. In both static and dynamic versions the effects of productivityincreases are investigated, leading to the conclusion that with respect to income andemployment generating effects innovating sectors are worse off than non-innovatingsectors due to low elasticities of demand. [D 58 ,033 ]1. INTRODUCTION

The empirical application of general equilibrium models can be traced back to1 9 6 0 w h e n oha an sen p r e s e n t e d h i s m u l t i s e c t o r a l m o d e l f o r t h e N o r w e g i a neconom y. Sinc e the mid-sevent ies, the st ream of Co mpu table General Equil ibrium( C G E ) m ode l s ha s g r ow n r e m a r ka b l y , w h i c h m a i n l y c a n be a t t r i bu t e d t o t heavailabili ty of faster and cheaper conlputers and to the renewed recognition of theimp ortan ce of relative prices for the allocation of resources. C G E mod els provedto be very useful tools for the analysis of tax reforms, international trade, resourceal locat ion and income distribut ion. Th e majori ty of CGE models a re appl ied todeveloping countries, there are increasingly more applications to advanced countriesas w ell.'computable General Equil ibrium (CGE) models are especial ly appropriate foroperation within a Social Accounting Matrix framework. A CG E model describes thewhole circular flow of a market economy, while maintaining accounting consistencyboth at the macro level as on the level of individual acto rs. A Soc ial Ac coun tingMatrix (SAM) is a statistical representation of that circular flow, in which everytransaction between the actors distinguished in the system is accounted for.

Another common feature between CGE models and the SAM is the emphasis ondisaggregation. Total production in the SA M is disaggregated by several productionaccou nts, wh ile CG E models distinguish between several types of producers. In thesame way in the SAM consunlption is disaggregated by several household accountswhich can serve to define the different types of consumers in a CG E model. Th edifferent types of labour and capital distinguished in the SAM factor accounts can be

'For a survey for developing countries see Decaluwe and Martens (1988).


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16 G. A. ABBINK, M . C. BRABER AND S . I . COHEN

used to define the different types of labour and capital in a C G E mode l. It is alsopossible to include separate want accounts in the SAM if one wants to introducegood s and services by type of want in the CG E model.

A S AM can be transformed into an econom y-wide model by sub dividing the SAMinto endogenous and exogenous accounts and expressing the transactions betweenendogenous accounts in coefficient form, resulting in the familiar multiplier equationfor the vector of endogenous variables: y = (I - A)" x . A more detailed description ofth is method can be found in Cohen (1988). T h e CG E a p p r o a ch o f fe r s a n o th e ropportunity to transform a SAM into an econonly-wide model without using anysupplementary data. ' Under certain assumptions (Cob b-Douglas p roduction, utilityand aggregation functions) the SAM provides all data necessary to construct a simpleCG E model. Th e objective of this paper is to demon strate the basic links between theprimary form of CGE moclels and the SAM and to show how under a few a ssu n~ ptio nsthe static CGE model can be elaborated to a dynamic one.' T he flexibility of theemployed framework will be demonstrated in terms of analyzing policy simulations inthe static and dynamic forms of the model. Th e applications are demonstrated forIndonesia, a country for which elaborate SAMs are now available and for whichseveral CG E models have been applied for more specific us es 4

The Indonesian economy is character ized by the avai labi l i ty of r ich naturalresource s, in the form of oil. As a result the mining secto r takes in an important placein the industrial structure. Th e ma nufac turing sector is rather underdev elope d incom parison with countries at a similar level of deve lopm ent. If put together, as willbe don e in this article. the mining a nd m anufacturing sector is characterized by a verycapital intensive technology and is dominated by large private and public firms, ofwhich the revenues are mainly accruing to government and higher income groups.Agr icu l tu re i s s t i l l t he l a rges t sec to r and employs the majo r i ty o f the r ap id lyincreasing labour force, which is mainly low-skil led. Th e services sector , whichincludes gove rnme nt services. is the main emp loyer of the high-skilled labour force.

Th e paper is organize d as follows . T he CG E model will be presented in section 2.The der ivat ion of the parameters f rom the SAM and thei r ca lcula ted values arediscussed in section 3. Two demonstrative simulations will be discussed in section 4.In section 5 several dynamic aspects will be introduced in the model. In section 6simu lations are repeated in the context of' the dynam ic model. Finally in section 7som e conc luding remarks will be made.

2. T H E BA SI C MO D E L

'See Dervis, et al. (1982) for a comparative discuss~onof CGE models and SAM-basedplanning models.'A large body of literature is available on SAM-based CGE models. A good description ofthe way in which a CGE model can be linked to the SAM is given in Robinson (1989)5 e e Thorbecke ( I 990) for a discussion of Indonesian CGE models.


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SAM -CGE DEMONSTRATION MODEL 17

In this section we present the core of our general equil ibrium model. I t is ageneral purpose m odel, which contains only a minimal number of equations necessaryto describe the econo mic process. It has the advantage that all parameters needed m aybe principally derived from the SAM . For more specialized applications the modelneeds to be elaborated, in which case supplementary data are required to estimateaddit ional parameters . The bas ic model is presented in Table 1 . Var iables arerepresented by capitals, anlong which the exog enous one s have overbars. Param etersare represented by G reek characters. Th e indices i and j are used to represent sectors,g and h stand for institutions and q for types of labour, the index f is used to representactors abroad.

Every sec to r i s as sumed to ac t as a s ing le r epresen ta t ive p roducer , whoseproduction possibilities are represented by a two-level production function, whichis given in (1). At a first level total output is derived from intermediate deliveriesand an aggregate of labour and capi ta l , which can be cal led real value added.Here, we assume Leont iev technology, i . e . no subs t i tu t ion is poss ible betweeninterm ediate deliverie s and real value added . At a second level smooth substitutionpossibilities exist between c apital and q categ ories of labour. Th e real value addedaggregate follows from a constant returns to scale Cobb-Douglas function in labour(distinguished by skill level) and capital, In ( I ) the function for real value added isgiven in the numerator, while the (fixed) share of real value added is put in thedeno min ator to relate real value added to real gross output. This term is multipliedby the sector price P, to arr ive at gross outpu t in nomlnal term s. Th e sectoralcapital stock is assum ed fixed for the current period, so the volume of sectoral realv a l u e a d d e d a n d o u t p u t c a n i n c r e a s e o n l y t h ro u g h a n i n c r e a s e in s e c t o r a lemployment .

Total nom inal value addeds by sector can easily be derived by subtracting the costof intermediate deliveries (including those which are imported) and indirect taxesfrom the production value, as given in (2). Producers are assumed to m aximize profitsunder perfect competition in product an d labour markets. As a result, in the short runthey will hire employees until wages equal the value of their marginal product, asshown in (3)

Equation (4) shows that the remuneration of capital is the remainder of nominalvalue added after labour is paid its share. Beca use the volume of capital is fixed,remuneration rates have to adjust to equalize them to the value of the marginalproduct of capital. Conse quen tly, again in the short run, remuneration rates of capitalmay differ am ong sectors. Equa tion ( 5 ) states that the labour market is characterizedby full employment by skill level, while the supply of labour by skill type is fixed inthe short run. Wa ges carry the burden of adjustment.

'Nominal value added incorporates besides real value added also a price component whichcan be called value added price. Because nominal value added can be included i n the factordemand equations (3) and (41, resulting from the Cobb-Douglas technology assumption, there isno need to separate price and quantity components explicitly.


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G . A. A B B I N K . M . C. B R A B E R A N D S . 1. C O H E N

Table 1. Th e Static Model~ - - - - - . - - ---Equations

. . . - - - -~

Z, = 1 ,,, Wl,Lq + n,, R j K , + x rl,,3Zs + x q j X j i f h = government11 j c j

61 = Z , - r,/1 Zh - 7 .~1hi'

producing sectorP = 1.0

List of Notations .. .. -. ... -Endogenous variables:

C = consuniption expenditure V = value added1 = investment expenditure W = wage rateL = sectoral labour (quantity) X = value of gross outputP = price index Y = dixposablc incomeR = remuneration rate of capital A = total incomeExogenous variablesE = cxpo rts (quantity)K = capital (quantity)

L = labour supply (quantity)M = competitive imports (quantity)

Cocfficients:a = inputloutput coefficients x = capital inc.distribution sharescp = indirect tax rates T .= transfer ratesy = consumption budget shares o = labour inc. distribution sharesh = labour elasticities 0 1 production p = import shares19 = calibration constant prod.function


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SAM-CGE DEMONSTRATION MODEL 19

Factor remun erations are distributed ov er the institutions in cqilation (6). Totalinstitutional income includes besides factor income also inconie transfers receivedfrom othe r institutions. Inco nle transfers are assum ed to be fixed proportions of totalinstitutional income. A variation on equation (6) is included a s equation (6a ) to allowfor the fact that the total incollie of govern nient coniprises also indirect taxes. Inequation (7 ) transfers paid are subtracted from total institutional incomes to arrive atd i sp o s a b l e i n c on i e. I n s t i t u t i o n s a r e a s su m e d t o b e g r o u p s o f re p r e se n t a t i v ec o n s u m e r s , w h i c h m a x i m iz e a Co bb-D oug las u t i li t y func tion g iven a budge tconstraint. T he first orde r condition of utility max imization implies the fixed budgetshares in equation (8)".

Equ ation (9) gives the equilibrium con dition for the product mark et. Prices carryt h e b u r d e n o f a d j u s t m e n t . Be c a u se w e a r e p r i m a r i ly i n t e r e s te d i n d o m e s t i ctransactions we kept the relations with tlie rest of the world as simple as possible.Qua ntities of exports and com petitive imports are fixed. Foreign prices are assunicdto be equal to domestic prices to keep the model homogeneous in all priccs. W eassume that investment goods are supplied by one capital goods producing sectoronly, as stated in equation (9a). W e assumed a 'classical' closure7 0f the model, s o anequation for investment can be omitted because total investment follows as a residual,from the oth er equations.

Because our model can only determine relative prices we have to apply a pricenormalization rule. In equation (10) tlie price of ag ric ~~ lti lres chosen as numeraire, soall oth er price and nom inal variables arc defined relative to agriculture. Th erefo re.on e should be aw are that an increase i n disposable inco mc does not imply that incomchas increased in an absolute sense, but only that incomc has increased relative to theprice of agriculture.

3. PARAMETERIZATION O F THE M ODELA Social Accounting Matrix gives a consistent representation of the money flowswhich are the nominal counterpart of all real transactions bctween the agents in a n

economy. In Tablc 2 the SA M for Indonesia for 1975, which s crvcs a the database forour CGE model, is shown. The SAM is an adapted version of tlie SAM published byBiro Pusat Statistik Indone sia (1982). In Ta blc 2 we distinguish between tlirec factorof product ion accounts , e ight inst i tu t ional accounts ( i .e . s ix household groups,corporate business and government) , a capitlil account, foils production activitiesaccounts and a rcst of the world (RO W ) account.

Assuming that the SAM represents a benchmark equil ibr ium, i ts cel ls can beviewed as realizations of the variables of the model. T o distinguish between quantity

'Although i t may seem unrealistic to assume the snnie behnviour for government ;is forhouseholds, i n this context government behnviour conies down to fixed expenditure shares.which is not an unusual assumption.'See for a discussion on closure Dewatripont and M~chcl 1987).


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Table 2 Social Accounting Matrix for Indonesia, 1975 - -18

ROW-. .--493

-

1 2 3 4 5 6 7 8 9LOWN HlSK CAP1L OWN FARM NFUP NFLO URUP URL(

- - - .- - -5.FARM6.NFUP7.NFLO 14 13 3 28.URUP 322 545 3269.URLO 745 84 652

. - 2 2 73 47IO.COMP 38 10

59 ' 39 2 5 6 40 2612. ITAX13. CAPA 1 332 -39 49 22 229 X X

--

TOTAL--4163106380973731615642

141216841682

Source: Riro Pusc~t mri.srik 111dorle.sirr1982)

- - . -- -2422 467 346 882 534 698351 68 105 196 351 301268 61 61 I60 221 298268 53 60 I31 233 222

. -

-. .- -- .-I4.AGRI II5.INDUI6.TRADI7.SEKV

-

18.ROW- . - -

TOTAL

71 1.- --

4163 1063 8097-

373 1 615 642 1412 1684 1682~- . . - -- .


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Table 3. Social Accounting Matrix Expressed in CGE Model Variables18ROW TOTAL

1-3FACTORS 4-1 1INSTITUTIONS12

INDIRECTTAXES

14-17

ACCOUNT

a,,,.,(1 - C, d,,,.)VjFACTORS

SUBTOTALFACTORS

INSTITUTIONS. .. ..INDIRECTTAXES

CAPITAL 1 residual residual I I

RO W f q w q q f j R , E , %zhTOTAL


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22 G. A . A B B I N K , M . C . BRABEK A N D S . 1. C O H E N

and nominal variables prices and wages can be expressed as indices, which are allunity in the benchm ark equ ilibrium . Th is implies that the quantity variables in themodel (factor services, products) arc expressed in units which can not be converted towell-defined physical units as tons or barrels. Product units (units of factor services)are then dcfincd as the amount of a good (factor ) that in equilibrium gives a reward ofone currency unit in any of its possible uses.

In Table 3 we expressed the SAM as a set of relations between the variablesinclude in the model presented in Table I as an illustration of thc relationship betweenthe SAM and the CGE mod el. An additional row is included for the (sub ) total ofsectoral value added. T he three factors of production are low skilled man pow er, high-skilled ma npo we r and capital. Agricultural labo urers, production w orkers, clerical,sales and service workers are classified as low-skilled labour (account I in table 2).The fac tor high-ski l led labour (2) consists of prof'essionals. managers and non-civilians. Gr oss operating surplus is assu m e to be the remu neration of the l21ctorcapital (3). Factors receive income payments from production activities, which inTable 3 are related to the factor demand equations stated in equations (3) and (4). Inthe colunin factor incomes are distributcd i n fixed proportions to domestic institutionsand the rest of the world. T he parameters (ofq and x, represent the share of factorpayments flowing to abroad.

The ins t i t u t i ons a re d i s t i ngui shed by s ix ho~~scholdroups (accounts 4 to 9) ,c o r po r a t e bus i ne s s ( 10 ) a nd gove r nm c n t ( 1 1 ) . F or gove r nm e n t a n a dd i t i ona la c c oun t i s i nc l udc d t o r e c o rd i nd i r ec t t a xe s ( 12 ) . H o i ~ s e h o l d s r e c l a s s i fi e daccording to the s ta tus 0 1 ' the head of the housc.l iold into landowners (4). farmwo rkers (51, rural non-ag ricultural lower clash households (6 ), rural non-ag riculturalupper c lass househ olds (7 ), urban lower class households (8 ) and urban upp er classhouseho lds (9 ). Incom ings by institution are the institutions' share of factor incom eand income transfers from uthcr inst i tut ions as specified in equat ion (6). In thecolumn income transfers are recorded as outgoings of institiltions. they are calcillatedas constant fractions r,, 0 1 lotal income Z,,, some institutions also pay a fraction r,, oftheir income to institutions abroad. Other outgoings are consumption exp enditures,which in rows 14-17 are related to disposable income Y,, using constant budget sharesx,, equation (8)) . Disposable income is dcl 'incd as total income after payment ofincome transfers: ( I - ,Eqrq,, T,,,)Z,, ecluation (7 )) . Sav ings can be calculated as aresidual.

The capital account (1 3) has investment expenditure in the column, investnlentgoods are assumed to be supplied by the sector industry and construction (row 15)on ly. T he sou rces of investable funcls, savings by institutions (column s 4-1 I ) andne t sav ings f rom abroad (co lumn 18) . a rc p laced in the row. The produc t ionact ivi t ies or sectors are disaggergatcd by four sectors: agricul ture (14), industry(incl. mining ) and construct ion ( IS ). t rade L I I I ~r;i~isport 16) and governmcnt ands e r v i c e s ( 1 7 ) . T h e o u t g o i n g s of t h e p ro d u c t io n a c t i v i t ie s a r e p a y m e n t s f o rintermediate deliveries by oth er domestic s cctor s (rows 14- 17) and by the rest of thew o r ld ( n o n - c o m p e t i t i v e im p o r t s , r o w 1 8 ) . p a y m e n t s o f i nd i r e c t t a xe s t o t he


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government (row 12) and factor paymen ts (row s 1 -3). It can be seen in table 3 thatthe act ivi ty columns incorporate the defini t ion of nominal value added (equation(2)). Because price indices are equal to 1 in the benchmark equilibrium, the priceratio Pi Pj can be omitted in the SAM . Sectoral demand as stated in equation (9)can be found in the activity rows, which record the incomings of the activities. Th eincomings compr i se r ece ip t s fo r in t e rmedia te goods de l ive red to o ther sec to r s( co lum ns 14-17) , consumpt ion good s de l ive red to ins t itu t ions ( co lum ns 4 -1 l ) ,expor ted goods m inus compet i t ive impo r t s (co lumn 18) and inves tmen t goods(column 13).

Th e RO W accou nt (18) records the transactions with the rest of the world . In therow the payments to the ROW can be found (which are receipts from the point ofview of the ROW ) and in the column the receipts from the RO W are given. Th ebalance of current transactions with the ROW is given at the intersection of the ROWcolum n with the capital accoun t row. As the capital account of the balan ce ofpayments mirrors the current account, this cell also gives the net inflow of foreigncapital.

Ta ble 3 is also very useful in demonstrat ing how the parameters can be derivedfrom benchm ark values of nominal variables. Th e fact that all pr ice variables areexpressed as indices makes i t easy to calcula te parameters f rom the SAM forrelat ion s between q uanti ty variables as well . Th e main features oS the derivedparameters wi l l be d iscussed br ief ly , the parameter values can be found in theapp end ix. Th e inputloutput parameters aii' pi and cpi can be calculated by simplydividing the appropriate rows in the production activities column by their columntotals. It has been found that agriculture has the largest backward linkag es, while thelinkages of services are relatively low . T he amount of imports and indirect taxes perunit of final product is small, only in industry and construction the share of imports issignificant: 0.159 .

Th e labou r elasticities A(lican be calculated by dividing the Sactor payments in theactivity colum ns by the value added sub total. Elasticities for low-skilled labour arehighest in agriculture and trade, services have relatively high high-skilled labourelasticities, while industry and construction is the most capital intensive sec tor. T hecalibration constant in the production function p, is calculated by rewriting equation( 1 ) as:

By defining prices and the low-skilled wage rate as indices, as described ab ove, X j / Pi


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24 G . A . A B B I NK . M . C. BRABER A N D S. I . COHEN

is equal to X, and L,, is equal to \ J , *L,,, which are both given in the SA M.' For high-skilled labour and capital the same approach could be chosen in the static niodel, butfor reasons of dynaniization we used a different method.

In the dyna mic m odel new investment has to be added to the existing capital stock,so capital has to be expressed in the same physical u nits as investm ent. Th e way inwhich this is done will be treated in detail in section 5 : here we will give only thebenchmark values of E l . These are 10541.22 for agriculture, 13552.40 for industryand construc tion, 6894 .12 for trade and transport and 3 1 12.25 for services.

In a similar way, in the dynamic niodel low-skil led and high-skil led labourh a v e t o a d d u p t o t o ta l l a b o u r s u p p l y . T h u s h i g h - s k i ll e d l a b o u r h a s to b eexpressed in the same uni ts as low-ski lled labour . T o obta in th is result , it i sassumed that a unit of high-skil led labour earns four t imes the wage of a low-skil led worker in the benchmark period, which is a reasonablc assunlption in theIndonesian context . Th is implies that in the benchm ark L,, is equal to W , / 4 * Lzl .The resulting calibration constants are close to unity for agriculture and trade andt r a n sp o r t, n e a r l y 3 f o r s e r v i c e s a n d s l i g h t ly a b o v e o n e h a l f f o r i n d u s t r y a n dconstruction.

T h e p a r a m e t e r s w h i c h d e s c r i b e t h e d i s t r i b u t io n o f f a c t o r i n c o m e o v e rhouseholds, w ,,and n,,,an be calculated by dividing the cells in the factor accountcolun ins by the column totals. On the basis of the calcula ted parameters it can bec o n c l u d e d t h a t l o w - s k il l e d l a b o u r i n c o m e a c c r u e s n i a in ly t o l o w i n c o m ehouseho lds N ~ L Iandow ners . High-ski l lcd labour incom e goes mainly to non-a g r i c u l tu r a l u p p e r i n c o m e g r o u p s . Re c i p i e n t s o l' c a p i t a l i n c o m e a rc n i ai n lylandown ers and f irms.

T he transfer rates the ratio of incom e transfers to total incom e,' are calculatedby dividing the income transfer matr ix T ~ , , ,, by the respective column totals Z,,.Transfers are dominated by tlows of funds from firms to households and governnient,for which t ransfer ra tes are 0 .122 and 0.377 r espec t ive ly . Budge t shares a r ecalculated by dividing consunlption expenditures in the inst i tut ions columns bydisposable income or (1 - Ex%,, r,,,)Z,,. The largest share of income is spent onagricultural products, on which rural households spend even more than half of theirincom e. T he gove rnm ent spen ds ncarly half oC its incom e on services, which includ ethe activities of civil servants.

4 . STAT IC EXPERIMEN TSIn this section we demonstrate the functioning of the model by simulating twosupply impulses to economic growth. Th e outcomes of thcse experiments are of aWote that according to equation (3 ) in the benchmark equilibriunl \v , *L, , is equal to L,?V,,

which can be found in table 3."Total income 1s defined as primary income plus income transfers received


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general nature based on the restrictive assumptions of the model. They can give aninsight in the implications of the v arious assump tions and form a gu idance to futureelaborations of the model, but should be cautiously interpreted as being specific toIndonesia, because of the generality of the assumptions and the absence of qualitativestructural characteristics of the Indonesian economy in the CGE model. The twoexperim ents are: (1) 10% productivity increa se in industry and con struction; and (2 )10% productivity increase in services.

Experiment 1: 10% productivity increase in industry and con struction."'The efficiency parameter I? of the production function in industry is increased by

10 percent. The resul t i s a 8.78% increase in the volum e of output . Ho we ver,because dem and is inelastic," the favourable volume-effect is reversed by a -10.01%price decreas e, resulting in a decre ase of value added in industry in relation to othersectors . S o a l l factors emp loyed in the sec tor wi th the increased product iv i tyundergo a decline in their income. Th e increased productivity makes m ore than fivepercent of the labour employed in industry redundant, while the owners of capitalsee their remunerat ion reduced by 5 .35% , because of the decl ined value of themarginal product of capital. Be cau se a relatively large share of the national capitalstock is installed in industry, total remuneration of capital falls with value addedin industry. Th e wage rate of high-skilled labour increases sl ightly relat ive to thelow-skil led wage rate, 0.42% versus 0.21%, because very little high-skilled labouris employed in industry.

The change in factor incomes is not large enough to bring about a large changein income distribution: there is a slight decrease in income for all groups varyingfrom -0 .65% for landow ners to -0 .14% for farm work ers . Th e fa l l in nominalincom e does not mean that welfare has decreased, however. Because the consum erprice index has decreased by more than the fal l in nominal income, purchasingpower has increased for all household group s. Th e welfare increase of the differentgroups i s conf i rmed by a +2.5% increase in real G D P (average increase in thevolum e of value added).

Experiment 2: 10% productivity increase in services.A s i n t h e a b o v e e x p e r i m e n t t h e e f f i c i e n c y p a r a m e t e r o f t h e p r o d u c t i o n"'Only the main results will be shown here, tables containing elaborate simulation outputcan be obtained from the authors."Aggregate demand consists of demand categories with a (negative) unitary(consumption and investment) and with a zero elasticity of demand (intermediate deliveries,exp orts, competitive imports ). As a consequence, the aggregate demand elasticity liesbetween - 1 and 0.


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26 C. A . ABBINK. M. C . B R A B E l i A N D S. I . C O H E N

funct ion is increased by 10 percent . Th e resul t ing product ion increase in these r v i c e s e c t o r i s +9 .1 7 % , w h i c h i s o n l y s l i g h t l y h i g h e r t h a n t h e i n c r e a se i nindustry production in the above simuli it ion. Ho we ver, in this simulat ion thereare hardly any spill over effects to other sectors, in which the increase in productiondo es not excee d 0.2%. Th is is du e partly to the fact that in Indon esia the servicesector is rather small and partly to the lack of backward linkage s. Th e resultingincrease in real GDP is less than one half of the increase in the industry-experiment:+1.19%.

Aga in , as a result of inelast ic dem and , the value adde d of the sector wi th theproduct iv i ty increase fa l l s re la t ive to the sectors wi thout product iv i ty increase( -2 .5 2% ), a l though the fa l l i s less than in the abo ve exp er im ent . Be cau se themajor i ty o f h igh- sk il l ed worker s i s employed in the se rv ice sec to r , t he de creasein value added is det r imental for thei r re la t ive wage ra te , i t decreases wi th -0 . 2 1 % . T h e d e c re a s e in t h e r em u n e r a t io n f o r t h e h i g h - sk i l le d l e a d s t o aworsening of the income posit ion for non-farm upper income groups (both ruraland urban: -0.58% and -0.45% respectively) , these groups consist for a large partof high-skil led workers. But as the consumer price index has decreased by evenmore, welfare of these income groups has increased also, so the increase in realG D P reaches a ll households .

5. T H E D Y N A MI C MO D E LTh e mod el can be made dy nam ic by upclating the variables which are exogeno us

in the current year. T he updating can be based on behavioural relationships, h istoricalgrowth rates or just ex oge nou s fixation. Here the updating will be restricted to thetwo factors of production: capital and the labour force, to keep the effects of thedynam ization on the simulation results traceable. Cha nges in exports and imports andin parameters are easily introduced.W h e n w e w a n t t o u p d a t e c a p i t a l s t o c k w e e n c o u n t e r t w o p r o b l e m s t h a tcannot be solved by using the SAM on ly . F i rs t. for investmen t to be added tocapi ta l s tock, both var iables have to be expressed in the sam e unit . How everthe SA M does not g ive such informat ion. Second , there is a need to know howinves tme nt is al located by dest ination in the benchm ark perio d. Both problenlsare solvable by s imply assuming that capi ta l s tock grows in every sector a t thesa m e rate (5%) in the benchmark ycnr . Be caus e it is also assumed that capitaldepreciates at 5% a year , this i luplies that nat ional capital stock is defined a s tentimes benchm ark gross investment. I f i t is also assumed that all rates of return tocapital are equal i n the benchmark year , investment is distr ibuted over the sectorsin the same proportions as the operating surplus. I n later years sectoral investmentshares, and as a result sectoral growth rates, change as a result of differences insec to ra l p ro fi t r a t es . T he upda t ing o f the cap i t a l s tock can be desc r ibed asfollows.

Investment of the previous period is added at the end of that year to the capital


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SAM-CGE DEMONSTRATION MOD EL 27

stock of the current period. The invtstment goods are distributed over usingsectors in proportions which are a function of the ratio of sectoral profitability toaverage profitability PRj / A P R . Sectors which show high profitability in thecurrent period will increase their share in total investment. Their capital stock andtheir production should increase faster than for other sectors, their relative pricesshould decline and so should their profit rates. In this way profits above or belowthe average will be eliminated in the long term through competitive investment in acompetitive market.

The sectoral investment shares 5: are defined in equation (1 1):

(3)"sj = APR

AP R

The benchmark investment shares c which are calculated from the sectoral sharesin total operating surplus: R*Kj / 4 R*K,, are 0.3091 for agriculture, 0.3974 for .industry and construction, 0.2022 for trade and 0.0913 for services. '

Sectoral profit rates P R , are defined in (12) as the return to invested (orfinancial) capital and has to be distinguished from the value of the marginalproduct of (physical) capital R,. In the numerator R, Kj is just the operating surplusand the second term is financial depreciation. The denominator gives thereplacement value of the capital stock, which is assumed to be equal to the amountof invested capital. Capital is valued at price PI , with k being the index of thecapital producing sector in the economy, which happens to be the sector of industryand construction.

The average profit rate follows from ( 1 3):

Ki ,AP R = xi--* PR;C , j K , P ,


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28 G . A. ABBINK, M. C . BRABER A ND S . I . C O H E N

In (14 ) total investment exp end iture is distributed ov er the secto rs of destination

In (15) new investment is added to the capital stock and depreciation is subtracted.Note that the investment outlays have to be divided by the price of investment goodsto determine the investment in physical units.

The second variable in which dynamics are introduced is the supply of labour:both the level of the total labour force and its distribution over the skill types ch ang eover time. T he total labour force L,,,, is defined as the sum of low-skilled and high-skilled labour:

The growth rate of the total labour force is exogenous:

We assumed the growth rate g~ to be 0,025.T he distribution of labour over both ski ll types changes endogen ously . Th e

workers' choice between low-skilled and high-skilled employment depends on therelative wages and relative education costs. T he ratio between the two types can bewritten for the long term as follows:


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SAM-CGE DEMONSTRATION MODEL

where D, epresents the cost of education of type q and 5, the share of costs ofeducation q charged to the user; E is the supply elasticity and K is just a calibrationconstant. By introducing the costs of education in the labour supply equation it ispossible to analyze the consequences of changes in the costs of education charged tothe student on econom ic welfare. T o keep things simp le, we assum e in this paper thatthe students cost of higher education is four times the cost of lower education. Th esubstitution elasticity between high-skilled and low-skilled labour is assumed equal to1 , the calibration constant can be calculated from the be nchm ark as L,/L, =15.664.

In the short term the low-skilled/ high skilled ratio is assumed to react with adistributed lag to change s in the wag e rate:

Th e adjustment parameter 8 s assumed to be equal to 0.25.

6. DYNAM IC EXPERIMENTSWith the dynamic model we simulated the basic time path from 1975 to 1980.

The base time path is the sequence of temporary equilibria under unchanged policies.T o get an idea of changes that take place over time in the base time path w e willfirstly discuss the rates of growth between 1979 and 1980. Th e growth of GDP is4.40% pe r annum. Th e largest contribution to this growth comes from the sectorindustry, its real value added grows at 5.37%. T he good performance of industry ismainly the result of the increased demand for investment goods (+6.73%), of whichindu stry is the only supplier. Th is leads to higher profit rates for industry and a fasteraccumulation of capital in this sector (+5.89%). As a result of increased supply , theprice of industry has already begun to declin e by 0.27% in 1980.

The distribution of income changes, although only slightly, in favour of the higherincome groups, which can be traced back to the deteriorated position of low-skilledlabo ur in the facto r market. T he sectors which employ mainly low-skilled workers,agriculture and trade, grow at a lower rate than other sectors: 4.12% and 3.78%respectively. With the dyn am ic model we als o repeated the simu lation s of section 4starting in 1975. The results of static simulations, which take into account the effectsfor 1975 only, and of the dynamic simulations are rather similar in the case of theproductivity increase in services (experiment 2). T he largest difference appe ars in thedecline of the wage rate of the high-skilled. Beca use potential suppliers of high-skilled labour are discouraged by the low er wage, they quit the education system andstart sup plying themselves a s low-skilled labour. Th e supply of high-skilled labour


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30 G . A . ABBINK, M . C. BRABER AND S . I . COHEN

drops by -0.83%, which in comparison to the stat ic simulation reduces the wagedecrease needed to adjust to the decreased demand with a ful l percentage point to-0.74%.

In experiment 1 substantial differences are found between the results of dynamicand static simulations. T he effect on GDP is niore than twice as large i n the dynamicsimulation: 6.1% as compared to 2.5% in the static simula tion. Th is can be explainedby the decline in the price index of the industrial sector by 9.14%, which makes i tpossible to buy more capital goods at a given level of savings. Th erefo re capital isaccumulated fas ter than in the base time path. Th is effect has been reinforced by thefact that an increased inflow of foreign savings puslies up total investment expenditureby 5.59%.

Th e per fo rmance o f the sec to r indus t ry and cons t ruc t ion has improved ascom pared to the static simulation. This can be attributed to both deniand and sup plyfactors. Dem and for the sector's output has increased resulting from the increase intotal investm ent. On the other hand thc supply of tlie sector is reduce d, because thelow pro f it r a t e l eads to a decrease in inves tmen t in th i s sec to r . Th us cap i t a lacc un ~u latio n s lower than in other sectors and so is the growth of output. Cornbinedwith the inelasticity of deniand, the reduced supply has a favourable effect on valueadded . Ho wev er, although the decrease in value addcd is reduced in comparison withthe static simulation, value added generated in industry and construction is still lowerthan in other sectors. So , also in the dynamic siniulation the effect of an increase inindustrial productivity leads to a reduced iniportancc of industry and construction inthe dornestic economy.

7. CONCLUDING REMAR KSIt has been demonstrated how a sim ple static CGE model can be constructed using

a one period SAM as database. We also showed that untler a few assumptions thisstatic model can be elaborated to a dynam ic one. Th e dynaruic elcnients in the currentpaper were restricted to labour supply and capital accuniulntion only.

The dynaniization of labour supply proved to have only a limited effect on thesimu lation results. T o a large extent this can be explaine d by the fact that labou rmarket adjustment takes place through the education system and thus reacts with alag. Ho we ver, adjustment can take place more directly if actors start supplying niorehours if their wage rate increases or i f non-participants decide to supply their skills onthe labour market in reaction to a wag e increasc. Future analys is would require morcflexible specification of labour supply responses to wage ch anges in the medium term.

T he capital accum ulation-effect can lead to niore significant differences betwee nstatic and dyn am ic sim ulatio ns if tlie price of capital cha nge s substantially. In thatcase more investment goods can be bought for a given amount of money. Althoughthe results of static and dynaniic sinlulations differ in magnitude the main conclusionremains the same, namely that a productivity increase leaves the innovating sectorworse off in compai-ison with the non-innovating sectors. This phenomenon has been


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SAM-CGE DEMON STRATION MODEL 3 1

observed for the period 1975 -1980 by Poot et al. (1984), wh o found that in traditionalindustries the replacement of old-fashioned technologies by more efficient techniquesled to a substantial decrease in employm ent du e to dem and constraints. Th is outcom ecan be explained by the low demand elasticities, which resulted from consumptiondetermined by constant budget shares and f rom f ixed quant i t i es of export s andimpo rts. Future analysis would require more flexible dem and specifications to beused in combination with elasticities obtained by empirical estimation.

Another reason why innovating entrepreneurs are worse off than less innovatingentrepreneurs is the fact that we assumed that all firms in a sector can be described byone representative firm, which implies that all firms are innovators or none of themare. Ho wev er if som e firms are innovators while others are not, the form er can gain atthe expen se of the latter. In the Indon esian context, where the modern large-scaleenterprises are the innovators, this implies that small-scale enterprises will ~ ~ n d e r g odeterioration in their term s of trade. Modelling the distinction between inno vatingand non-innovating firms within a specific sector, as well as other differentiationsbetween firm s within a sector will be very impo rtant refinements. Su ch type ofmicro-simulation models, as in Eliasson (1985) and van Tongeren (1993), wouldimply a departure from the as su n~ pti on f the representative firm typical of generalequilibrium models.

APPENDIXTable A.1. Input-Output Parameters

INDU2.- - .--

0.03700.17090.096 10.0 1240.01790.1593

TRAD3-

0.07290.07 190.01810.04580.00990.0637

SERV4

Table A.2. Production Function P arametersAGRI INDU TRAD SERV

I 2 3 4


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32 G . A . ABBINK, M. C . BRABER A ND S . I . C O H E N

-. .- .-

1 L O W N2 FARM3 N F U P4 N F L O5 U R U P6 U R L O7 C O M P- 8 GOV. . -

Table A.3. Factor Incom e Distribution Param eters

- . -

L O W N1

000

0.0120

0.0010

0.011

Table A.4 Income Transfer Rates-- -- - -.F A R M NFUP NFLO U R U P U R L O

2 3 4 5 6C O M P

7G O V

8

Table A.5 Budget Shares.L O W N F A R M N F U P NFLO UKUP URLO C O M P G O V

I 2 3 4 5 6 7 8Y I ~ 0.665 0.766 0.557 0.634 0.341 0.434 0.000 0.001Y2h 0.096 0.1 12 0.169 0.141 0.224 0.187 0.000 0.125Y3h, 0.074 0.100 0.098 0.115 0.141 0.185 0.000 0.111Y4 h 0.074 0.087 0.097 0.094 0.149 0.138 0.000 0.439

REFERENCESBiro Pusat Statis t ik Indonesia (Indonesian Central Bureau of Statis t ics) , Social

Account ing Matr ix lndonesiu 1975, Vol . I & 11, Jakarta: Biro Pusat StatistikIndonesia, 1982.


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Cohen, S. I., "A Social Accounting Matrix Analysis for the Netherlands", DeEconornist, Sunlmer 1988, 253-272.Decaluwe, B. and Martens A., "CGE Modelling and Developing Econonlies: AConcise Empirical Survey of 73 Applications to 26 Countries", Journal ?f'Policy

Modelling, Winter 1988, 529-568.Dervis, K ., de Melo, J . and Robinson, S., G e r ~ e r a l q ~ r il ib r iu r nM o d e l s f o rDevelopmerzt Policy, New York: Cambridge University Press, 1982.Dewatripont, M. and Michel, G., "On closure rules, homogeneity and dynamics inapplied general equilibrium models", Journal of Development Econotnics, June1987, 65-76.Eliasson, G., The Fi nn and Firzunciul Markets in the Swedish Micro-to-M acro Mo del,Stockholm: Almqvist& Wicksell International, 1985.Poot, H., Kuyvenhoven, A. and Jansen, J., Industrialisation arzd trade in In(k,nesia,Yogyakarta: Gadjah Mada University Press, 1990.Robinson, S., "Multisectoral Models," in H.S. Chenery and T.N. Srinivasan, eds.,Handbook of Development Ecorzotnics, Amsterdam: North-Holland, 1989.Thorbecke, E., Adjustment, Growth crrzd Incorne Distribution rn Indonesia, Paris:OECD Development Center, 1990.Van Tongcren, F. W., Corporutes irz nr~Econorny-)vide Model, a MicrositnulationApproach, PH.D-Thesis, Rotterdam: Erasmus University, 1993.

Mailirzg Address: Professor S. I. Coherz, Erasr7zus U niv ers ity Ro tterdu rn. P .0.Box1738, 30 00 DR. Rottercl~rrn, HE NETH ERL AND S.

A SAM-CGE Demonstration for Indonesia

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