A DISEQUILIBRIUM MODEL

OF THE

AUSTRALIAN MANUFACTURING SECTOR

Colin Hargreaves and Melissa Hope

No. 61 - October 1991

ISSN

ISBN

0 157-0188

0 85834 971X

A Disequilibrium Model

of the

Australian Manufacturing Sector

by

Colin Hargreaves,

DirectorEconomic Modelling Bureau of Australia

andDept of Econometrics

University of New England

and

Melissa HopeDept of Economics

University of New England

1. Introduction

While the key assumption in much general equilibrium economics of fully flexible, rapidly

adjusting prices may be very useful theoretically, it has always lacked a certain realism. Thispaper is an attempt to evaluate the merits of the contrasting approach wherein prices are assumedto be fixed and quantities are constrained. These fixed price, quantity constrained models are

identified with the work of Barro and Grossman (1971), Muellbauer and Portes (1978) andBenassy (1986). At the outset we must state however that this is not the ’disequilibrium’economics of Fisher (1986). Fisher would describe these investigations as the study of non-Walrasian equilibria. While Fisher’s stability analysis is rich with possibilities that we would like

to investigate at a later stage, this is a f’trst attempt at changing the standard application ofequilibrium results to continuously changing non-ideal markets.

When markets are at rest in a Walrasian equilibrium, results are obtained fromsimultaneous optimisation in all markets. Here only we consider the labour and goods markets,but account for the possibility that one market may be constrained in some way and then analysethe implications of this for both markets. As we are still working within the equilibriumoptimisation paradigm, the only reason that a market is constrained is that we have assumed fixedprices within each period. Hence we still require a t~tonnement of some kind such that actualquantities traded settle at some market clearing position although this will be different from aWalrasian equilibrium. The question then arises that if one market is not at the Walrasianequilibrium position, what affects does this have for other markets.

For instance, given a certain (fixed) wage level, if ftmas are unable to obtain sufficient

labour to reach their Walrasian equilibrium output, they will employ less labour than the level thatsatisfies the Walrasian equilibrium and so labour will be rationed. This means that the labourmarket will not be able to reach its equilibrium position and so since incomes will be reduced,

this will reduce demand. In a multiplier fashion this then affects production which affects labouragain and so on until a constrained ’non-Walrasian’ equilibrium is reached. The example givenis known as ’Keynesian unemployment’. A good description of the different types ofintermarket affects is given in Cuthbertson and Taylor(1987). The approach leads to theinteresting estimation of ’spillover effects’, that is how strongly the degree of ’disequilibrium’ or’constraint’ on one market affects the other market.

Market constraints entail rationing which affects agents’ trade offers. To model this theapproach used here was originally suggested by Clower (1965) and developed by Benassy (1975to 1986). In this model, the agent is assumed to be unaware of constraints on the market inwhich he is currently operating. Awareness of the constraints on the other market will create an

expressed effective demand or supply lower than would result in a Walrasian equilibrium.However, the key question is what happens in the aggregate. There are two main approaches,that of the ’min condition’ used in Rosen and Quandt (1978) and aggregation by integration

2

(Kooiman, 1984). The ’min condition’ approach assumes that, at any given price, actual tradesare equal to the minimum of demand and supply and hence that the the whole market is either in astate of excess supply or excess demand. The resulting locus of equilibria is on the supply anddemand curves to the left hand side of where the curves cross as shown in Figure 1.

Pricem

Supply

Demand

Q* Quantity

Figure 1: Graphical representation of the ’min’ condition

The aggregation by integration approach assumes enough product differentiation (by

locality or quality for instance) that while some firms may f’md themselves in a position of excess

demand, others may be in a position of excess supply. This market heterogeneity is reflected in a

probability distribution for the proportion of firms in each state. We use the second aggregation

by integration approach as we feel it is more realistic. Also the parameters modelling this market

heterogeneity provide interesting information about the states of the labour and goods markets.

This leads to an aggregate transaction curve as shown in Figure 2.

Priceupply

Figure 2:

,Demand

Q’ Quantity

Graphical representation of the aggregate transaction curve.

One of the features of this approach is that it empirically encompasses the equilibriumapproach as it uses standard explanatory variables to determine a Walrasian equilibrium and thenadds extra variables to explain the deviation from that equilibrium. Hence standard generalequilibrium theory is not ignored but, on the contrary, it is a fundamental building block of thisapproach. This approach does not create a completely different set of explanatory variables likean alternative theory. Rather than testing the relative advantages of one set of explanatoryvariables over another, we test for the relevance of a set of additional variables explaining market

disequilibrium. Another feature of the model is that it neatly introduces a way of using the CAI-Westpac Survey of Industrial Trends to help model the manufacturing sector.

Since the manufacturing sector of Australia is the focus of much public concern, weanalyse the market for manufactured goods. On the whole this entails the concept of a marketsegmented from others as we do not consider the demand or supply positions in other goodsmarkets. On the labour market side, we have to assume not too unreasonably that there is adistinct group of people employed in the manufactt~ng sector and that there is not too muchmobility in and out of this area of employment. However this group is not of course the full setof demanders of manufactured goods and hence if there is a reduced demand for labour in themanufacturing sector we would not expect a proportionate reduction in demand for goods unlessthe whole economy moves in a similar fashion to the manufacturing sector.

A good application of the fixed price, quantity constrained approach can be found inLambert (1988) for the Belgium manufacturing sector. We initially tried applying this modeldirectly to the Australian manufacturing sector but soon found that Lambert had made certainsimplifying assumptions that while suitable for Belgium were not suitable for Australia. Forinstance, he assumes that since Belgian manufactured goods are predominantly exported, there

4

would be no spillover effect on labour supply; this would not apply to Australia. Thus we use

the theoretical approach of Lambert but not his final reduced model. Also Lambert does not take

into account the orders of integration of the variables or use the new cointegration methodology

in econometrics. This is used with interesting results distinguishing the short and long term

effects. Section 2 outlines the theoretical model. Section 3 outlines the ideas of cointegration

and how they relate to our data. Section 4 presents our model estimates. Section 5 shows how

these can be used to analyse the structure of the manufacturing goods and labour markets over

the last twenty years or so and Section 6 presents some final concluding remarks.

2. The General Model

2.1 Notional and effective demands and supplies.

In this model we distinguish notional and effective demands and supplies from actual trades.Notional trades are those that would result if there were no rationing on any markets, ie at aWalrasian equilibrium. These are assumed to be functions of various fundamentals of the

economy as follows -

KS

= notional demand for goods= f( real household disposable income, YHD/CPI,

level of world demand, FORDEM,relative world!Australian export prices, PW/PX,relative import/domestic prices, PM/P)

= notional supply of domestic goods= f( capital stock, K,

relative cost of capital to the price of output, R~,technical progress (time trend), T )

= notional demand for labour= f( capital stock, K,

relative cost of capital to labour, R/W,technical progress, T)

= notional supply of labour= f( real household disposable income, YHD/CPI,

real wages, W/CPI,employment, 1-UNR)

Adopting a log-linear stochastic specification these equations can be written

In ~d = 13d In Zd + UXD

In ~s = 13s in ~Zs + uXS

In [,d = Xd in odd + ULDIn ~s = ~s In qJs + ULS

wherethe parameters are def’med as:

Z, ud are vectors of exogenous variables,[3, ~, are vectors of coefficients and

u denote the error terms, which are all assumed to be jointly normally distributed.

(1.1)

(1.2)

(1.3)

(1.4)

Effective trade offers are the demands and supplies expressed on each market by actors aware ofthe constraints on markets. Since we assume that actual transactions will depart from effective

trades, we only consider the two specifications analysed in Lambert, namely Portes(1977) andIto (1980). Given spillover coefficients represented by 2is we have -

a) Portes

In Xd = In ,~d + 7~ (lnL - In Ls)

In Xs = In ~s + 7xs (InL - In Ld)

In Ld = In ~d + 7LD (lnX - in Xs)

In Ls = In ~s + YES (lnX - In Xd)

(2.1)

(2.2)

(2.3)

(2.4)

b) Ito

In Xd = In ~d + 7xi) (InL - In ~s)

In Xs = In ~s + 7xs (lnL- In Ed)

In Ld = In ~d + ~,m (lnX - In ~s)

in Ls = In ~s + 7is (lnX- In ~d)

(3.1)

(3.2)

(3.3)

(3.4)

The difference between the two is whether the disequilibrium in the other market is measured by

relating actual trades to effective (Portes) or notional (Ito) trades. There is no real theoretical

justification for claiming one over the other but it should be noted that the spillover effects

estimated will be less in the Ito model. All of the "{s should be positive.

Given the notional and effective trades defined by the above, Lambert develops a very

useful theoretical statistical analysis based on the proportions of traders in excess supply and in

excess demand in each market. In this he uses ’tension’ variables which are market indicators of

the degree of disequilibrium on each market. From this analysis he develops the following

equations wherein actual trades are made up from CES-type averages of effective demand and

effective supply.

6

X = [(xd)’PG+ (xS)’PG]"I/pG (4.1)

PG = ~ 1 + (~s)-PGI’I (4.2)

XDUC - (4.3)2s

L = [(Ld)"pL + (Ls)’PL]"I/pL (4.4)

PL = [ 1 + (~s)’PL]"1 (4.5)

LUNR - (4.6)Ls

where: X and L represent respectively the actual levels of manufacturing output and labour,superscripts d and s denote the effective demand and supply of X and L,tildas denote notional demands and supplies,

19’s are measures of structural dispersion on the goods and labour markets,PG and PL are respectively the proportions of firms constrained by demand (that is, there

is an excess supply of goods) and labour (there is an excess demand for labour),DUC is the degree of capacity utilisation andUNR is the unemployment rate.

The CES function provides measures of the structural dispersion of demands and supplies oneach market and the proportions of agents in each market in excess demand and excess supply.These equations lead to four equations for effective demand, i.e.

Xd = X (1- PG)"I/pG (5.1)

Xs = X (PG)"I/pG (5.2)

Ld = L (1- PL)"I/pL (5.3)

Ls = L (PL)"I/pL (5.4)

Using the equations 1.1 to 1.4 and 5.1 to 5.4 to substitute out the notional and effective demandsfrom equations 2.1 to 2.4 one obtains the system

lnX=I( 0y~Yxs)lnPG + 1 (l~xspG 1-PL - ,m)ln(1-pL)+13dln)~d+uxs

lnL=l( Yu~ ) 1__( 1 ,]1nPG 1-0"~xsYm In PG +

(1-PL) + ~,dlnq~cl + ukoPL k,l-ff’h~Yxs )

L-1 ( Y~s )ln(1-PG)+ 1 ( 1 "~lnIn - ~GGL I"0~/LS~/XD)Z ~i-0qx~Y~fl PL + XSlnWS + ULS

(6.1)

(6.2)

(6.3)

(6.4)

where 0 is a parameter that equals zero for Pones specification and one for Ito’s. This provides

a good way of testing between the two alternatives.

The equations for the firm’s notional behaviour are determined from a classical cost minimisationprogramme, i.e.

minimise C = wEd + rKsubject to ~s = 7 [8(e’qT Ed)p + (1- 8) (eY2T K)’P]"lIp,

(7.1)

(7.2)

where:

Y1, Y2 = rates of factor augmenting technical change,

~ = (1 - p)-I is the long run elasticity of substitution,w = the wage rate,r = the user cost of capital,K = the capital stock andT = a time trend standing for technical change.

This leads to the two equations

ln~S =C0K+lnK+(1-~)Y2T+~ln(~)+ uxs

In ~d = c0- In K + ~ in (wr-) + (1- ~)(~. y1)T + ULD

(8.1)

(8.2)

where the Lagrangian multiplier produces

p = the price of manufactured goods.

For the households notional demand for goods is explained by indicators of world and domesticdemand and internal and external competitiveness. Household labour supply was considered to

be a function of employment, income and real wages. These ideas produce the two equations -

In X = Coxd + Clxd In C2xd In FORDEM+ C3xd In PW (9.1)

in L = C01s + Clls In (1-UNR)+ C21s + c31s + ULSt

where the variables are as defined at the beginnIng of Section 2 and in the Data Appendix.

(9.2)

When these notional demands are substituted in the system of equations (6.1 to 6.4) this leads tothe final static model as follows.

Static Model.

lnX=1._~i 1 /ln(1.PG)+ 1_~_( Tx~ /lnPL+Coxd+Clxdln(’~i)PG 1-O~sYx~ PL ~,I-OYxDYLS;

+C2xd InFORDEM+ C3xd1 Pn(p’--~’)+C4xd ln(-P~-)+ Uxd (10.1)

lnX =1-LPG

1 )lnPG + 1 (-1 Yxs )ln(1.-PL)1 - OTLDqtXS PL -0~sxsTt~

+C0K+ In K + (1- o)]t2T + ~ In (~) + uxs

1 1In DUC = In OtDUC + ~1 In PROFIT + -- In PGPG 1 - 0Yxs Yld

1 ’Yxs+ -- in (1-PL) + uDUCPL 1 - OTxs ’](Id

(10.2)

(10.3)

In L - 11 - OTLDTXS PL 1 - OTXSYLD

+C0K+lnK+(1-~)(y2-T1)T+~ln(~)+ ULD (10.4)

PG 1-O"/LSTX~J PL 1-0T-x~i PL

+ C21s C31s In (1-UNR) + ULS

+ C01s + Clls

(10.5)

3. Integration and Cointegration of the Manufacturing Data.

Before moving on to the estimation, we need to say a little about the dramatic change in

econometrics over the period since Lambert estimated his model. We now know that we must be

careful when using variables that are non-stationary as this can lead to very spurious results. For

instance given two totally independent random walks and about 60 observations there is a more

than 50% chance that a test will incorrectly reject the null hypothesis of no association. In fact

for 100 observations the distribution of the correlation coefficient between two independent

random walks is almost uniform between -1 and +1!

For these reasons non-stationary variables are usually differenced to make them

stationary. Normally linear functions of non-stationary variables are non-stationary and hence

the error in such a regression would be non-stationary which completely contradicts the

assumptions made in order carry out inference. However occasionally we are able to find a linear

function of non-stationary variables that is stationary. This implies that the variables are in some

way locked together over time such that errors from the linear relationship are stationary mean-

reverting processes. Hence such processes are identified with long-run equilibria but we should

hasten to add that this is not a ’demand equal to supply type’ equilibria. It is sometimes

described as a system ’at rest’. For instance in other work we found that the log ratio of GDP to

GNE is stationary while each aggregate was non-stationary; we say that these variables are

’cointegrated’. The major breakthough with this analysis is that we can identify clearly distinct

long-run and short-run estimates of models and hence analyse their respective economic

properties.

The analysis of cointegration really started with the seminal paper by Granger(1981).

Since then research in this area has mushroomed greatly and has developed in two distinct

directions. One direction is the single equation regression approach which started with the key

paper by Engle and Granger(1987) which described what is now known as the Engle and

Granger two-step method of testing for cointegration. This uses normal linear regressions to

determine a long-run relation and then uses the error from this as an error-correction mechanism

in a short-run dynamic model. This is the approach used in this paper. The second approach is

the full maximum likelihood approach of Johansen(1988). This approach is based on a full

specification of the the vector time series process providing a joint description of both the short

and the long run. Hargreaves (1990) and Hargreaves and Juselius (1991) show applications of

both approaches to the analysis of money demand.

The first stage of the Engle and Granger approach involves analysing the time series

properties of the variables and looking for long-run cointegrating relations by testing whether the

errors from a linear function of non-stationary variables are stationary. This initially involves

carrying out Dickey-Fuller(1981) or Phillips-Perron(1988) type tests to discover the order of

10

integration of each data series. The order of integration, d, is the number of times that a serieshas to be differenced for the result to become stationary and one talks of series as being I(d).

Economic series are normally either I(0) or I(1) but occasionally they are 1(2). Here we are onlytalking of the stochastic part of the series as deterministic time trends can be simply removed byusing a time trend. It is the stochastic part of the series which contravenes the normal OLSassumptions. Results from applying both of these tests are shown in Table 1.

Table 1

Variable Augmented Dickey-Fuller Phillips-Perron Test Order of

Levels 1st. Levels 1st. Integration

digerence O!fference

ln(X) -0.2505 -3.5427 * -8.1448 I(1)

In(PG) -0.81927 -3.2667 * -8.4254 I(1)

ln(PL) -1.404 -3.2279 * -6.5751 I(1)

In(RYHD) 0.59585 -3.4485 -0.30512 - 19.444 I(1)

ln(FORDEM) -0.32628 -4.8348 * -7.7012 I(1)

ln(PW/PX) - 1.6690 -3.1954 * -7.227 I(1)

ln(PM/P) -0.74626 - 1.8416 * -5.9324 I(1)

In(PROFIT) -3.5716 -10.942 I(O)

In(R/P) -0.69888 -4.1012 -0.83611 -7.9612 I(1)

In(R/W) -0.99949 -3.7781 * -8.1308 I(1)

In(L) -1.9158 -2.7145 * -6.1551 I(1)

In (DUC) -2.1054 -3.1809 -2.0817 -5.6087 I(1)

In(K) -0.95027 -2.5427 - 1.3576 -6.4589 I(1)

ln(1-UNR) -1.8752 -3.2616 -1.9301 -5.3480 I(1)

ln(W/CPI) -1.2422 -3.7910 -2.1321 -10.287 I(1)

From this it can be seen that all the variables are integrated of f’trst order, I(1), except Profit; thisis because we used the definition of Profit used by Lambert which is a differenced variable.

These results mean that the t-statistics in simple OLS regressions would not have standard tdistributions; even if one has cointegration, one has to find much larger t-statistics than normalfor them to be significant. This means that we immediately have to depart from the route takenby Lambert as he took no account of the orders of integration of the variables. We do not havehis data but it would be very surprising if all his variables were I(0).

11

Having determined the orders of integration, one then looks for cointegrating relations

between the variables by using OLS on the static model given in equations (***). When this was

done we found that the constrained nonlinear model did not have stationary errors and so we had

to look at each equation, one by one. We did not wish to introduce too many variables from

outside the model as the point of this model is just to look at the degree of disequilibrium and the

spill-over effects between the labour and goods markets. Hence we just used the variables listed

above. However no cointegrating relationship could be found for any of the equations except for

the demand for goods where a good simple relationship was to excess demand on the labourmarket (PL) and foreign demand, i.e.

log(X) = 0.035 log(PL) + 0.3489 log(FORDEM) + ECMt (11)

(7.10) (19.6)

The Phillips-Perron test statistic was -4.31 and hence lower than the critical value of -3.45 whichmeans that one rejects the null hypothesis of a non-stationary error term and so we accept that

there is cointegration. All the results given in this paper were found by using the SHAZAMstatistical package (White, 19"*). This implies that in the long run demand for manufacturedgoods is dependent on foreign demand and the degree of disequilibrium on the labour market;interestingly we found that domestic real household disposable income was not significant in this

relationship and neither were relative 9rices of imported to domesticly produced goods and worldprices to export prices.

12

4. Model Estimates

Having found such a long run relationship, this was then applied to a model found from simply

differencing equations 10.1 to 10.5 and adding the lagged ECM from equation 1 1 into the

demand for goods equation, 10.1. Our first results gave estimates of 0 nowhere near 0 or 1 and

so like Lambert we compared the likelihoods under each and thereby chose the Portes

formulation for effective demands and supplies. The resulting dynamic model is -

AlnX=1-- Aln (1-PG)+ YXD AlnPL +Coxd+ClxdAln@~I)+C2xdAlnFORDEMPG PL

+ C3xd &in + C4xd &In + eXD ECMt_l + Uxd (12.1)

Aln(~ = 1_~_ AlnPG + Yxs Aln(1._PL)+OtlAlnPROFiT +CoxsPG PL

+~ Aln (~) + Uxs

Aln DUC = Coduc + Oq Aln PROFIT + 1__ Aln PGt + 1 &In (1-PL) + UducOG PL

(12.2)

(12.3)

Aln(~-)-TI~ AlnPG + l__Aln(1._PL)+C0K+~Aln(_~)+ ULDPG OL

(12.4)

Aln L = YLS Aln (l_PG)+1--~- &in PL + C01s + Clls A@~I)+ C21s A(~I)PG OL

+ C31s Aln (1-UNR) + ULS (12.5)

Log-Likelihood Function = 1020.538

Tables 2 and 3 show the results from estimating the model initially. The results look very

promising in that all the spillover effects are significant and apporximately one. The Po andpL

parameters are not dissimilar to those found by Lambert. However the rest of the model is not so

successful; these are mostly the parameters on variables that explain notional demands and

supplies. Real household disposable income is positvely related to goods demand but the relative

price variables and foreign demand are insignificant which may be reasonable in a short term

13

Table 2

Parameter Coefficient t-ratio

36.31 35.96

42.54 40.87

Yxs 1.053 18.450.944 44.171.050 117.5

YLs 0.955 160.1

-0.00015 -0.222

COXD 0.0099 9.926

C lXD 0.052 3.329

C 2XD 0.0003 0.639

C 3XD 0.0002 0.586

C 4XD -0.0001 -0.358

Coxs 0.012 24.09

-0.0025 -1.999-0.0025 - 1.831

COd.c 0.040 - 1.202

COLD -0.0014 -1.492

COLS 0.004 0.912

C ILS 0.050 3.368

c2ts -0.0003 -0.269

C 3LS 0.0051 1.398

Table 3

Equation Demand Supply of Capacity Demand for Supply of

for Goods Goods Utilisation Labour Labour

DW 2.374 1. 875 1.297 1.408 2.094

model. Foreign demand was significant in explaining long term movements in goods demandwhile there income was not. The error-correction mechanism is not statistcially significant inexplaining the short term and yet we feel that it is still worth retaining this in the model as it ties

14

down the levels in the long run. When we look at the supply of goods, we find that profit issurprisingly negative and significantly so while the ratio of the user cost of capital to prices isinsignificant. Given the optimisation framework used here, this last cr parameter is identified

with the elasticity of substitution between capital and labour. A value of one would have implieda Cobb-Douglas production function. Here in this short term model, the value is essentiallyzero.

The capacity utilisation equation is the worst equation which may be simply because themeasure used for capacity utilisation was a fairly crude measure taken from the CAI-Westpacsurvey (see data appendix). However there are no really good measures available and this seemdappropriate as it was found from questionnaire results that produced the tension variables, PLand PG, and so they may be reasonably consistent. This measure was also specific to the

manufacturing sector whereas other measures available were not manufacturing sector specific.Apart from PG and PL, the only explanatory variable in this equation is profit and again one

would not have expected a negative coefficient here. This equation also had a poor DurbinWatson coefficient. The demand for labour is essentially only explained by the spillover effects.The coefficient on the relative cost of labour to capital, ~, is inappropriately negative and not

significant. Again the Durbin-Watson coefficient is not very good. Finally the supply of labourequation has, as explanatory variables, income, real wages and employment. While income issignificant, the other variables are not.

Clearly the specification of notional demand needs to be improved but this was not themajor aim of this paper which was to look at spillover effects and the degree of disequilibrium onthe goods and labour markets. Given the above results, normal practice would lead one to startremoving variables that were insignificant. However on doing this, one by one, the othercoefficients on the notional demand variables would sometimes change quite drastically. Giventhis we decided to try imposing certain theoretical concepts. Firstly Carmichael and Dews (1987)found a unit elasticity of substitution between capital and labour and hence we imposed the valueof one on ~. Also Lambert assumed that the spillover effect from the goods market to labour

supply was zero. While we tried these and other restraints, the model as a whole did notimprove. The major result however was that even when one removed all other variables exceptthe spillover effects, the ~, coefficients all remained about one and the p’s were in the thirties andall of these remained statistically significant. The implication of this is that when modelling the

manufacturing sector one needs to take account of quantity constraints and spillover effectsbetween the goods and labour markets.

15

5. Estimated Effective and Notional Supplies and Demands ofGoods and Labour

Given these estimates of the heterogeneity and spillover effects, Lambert derives the followingformulae to derive notional and effective demand and supply and the goods and labour markets.

Xd (1-PG)’I/PG (13.1)X -

Xs.~ _ PG -1/9G(13.2)

. l/PG -Yxs/PL~ = PG (1-PL)

which can be used to calculate the following ratios -

the percentage of firrns experiencing an excess demand for goods,

the percentage of firms experiencing an excess supply of goods,

the percentage margin of profitable capacity.

(13.3)

Using our ~, and 9 values we obtain the graphs in figures 3 to 5.

16

Ratio

14

12

10

Effective Demand:Actual Output

Effective Supply : Actual Output

Notional Supply : Actual Output

Figure 3." The ratio of the latent variables in the goods market to actual level of output produced.

Ratio

1.08

1.07

1.06

1.05

1.04

1.03

1.02

1.01

1.00

0.99

0.98 I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I

75 76 77 78 79 80 81 82 83 84 85 86 87 88 89

Figure 4: The ratio of effective demand to effective supply in the goods market.

17

Output

130~0

120O0

110~0

900O

700O

Actual Output

~ ................Effective Demand

/] ......... Effective Supply

Notional Supply : .- // v

75 76 77 78 79 80 81 82 83 84 85 86 87 88 89

Figure 5: Output expressed in effective, notional and actual levels.

The same analysis can be conducted for the labour market, with the excess demand and supplyratios being expressed as:

Ld .lipL-L- = (I’PL) (13.1)

LS

L = PL -119L (13.2)

E .yLD/PG .119L~ = PG (1-PL) (13.3)

and then we have

the percentage of firms experiencing an excess supply of labour,

the percentage of firms experiencing an excess demand of labour,

the percentage excess of notional demand to current employment.

These lead to the following graphs in Figures 6 to 9.

18

Ratio

1.125

i.I00

1.075

1.050

1.025

Effective Demand : Actual Labour

Notional Demand : Actual Labour

A

75 76 77 78 79 80 81 82 83 84 85 86 87 88 89

Figure 6." The ratio of latent labour demand variables to the actual level of labour employed.

Ratio

0.40

0.35

0.30

0.20

0.15

Labour Demand : Supply

Figure 7." The ratio of effective demand to effective supply in the labour market.

0.1075 76 77 78 79 80 81 82 83 84 85 86 87 88 89

19

Labour

1350

1300

1250

1200

1150

II00

1050

100075 76 77

Actual Labour

78 79 80 81 82 83 84 85 86 87 88 89

Figure 8." Labour expressed in effective, notional and actual terms.

Labour

10000-

9OO0

8OO0

71300-

60(K)

5OOO

40O0

3~

Effective Supply

75 76 77 78 79 80 81 82 83 84 85 86 87 88 89

Figure 9." The effective supply of labour (Note that this is graphed separately to the other latentvariables because of the different scales involved).

One can see the dramatic effect of the resources boom in 1981/2 triggered by the oil price shock.Whereas over most of the sample there appears to be Keynesian unemployment, the nominalwage shock created a situation of an excess supply of labour and excess demand for goods, that

2O

is classical unemployment. In the period before this, we note that actual labour did not followthe rise in notional and effective demands. Partial indexationmay have kept real wages downand there may have been some labour dishoarding. Participation rates also fell from 1980 to

1982.Over the latter period one finds an increasing gap between effective and actual demand

and supply for labour and actual labour and the reverse on the goods markets. This may reflectthe Accord keeping real wages down and causing a labour for capital substitution. In this latter

period one also notes the effect of the 1985 sharp depreciation of the Australian dollar increasingcompetitiveness and hence raising demand over the following year.

6. Conclusion

This paper has taken the framework developed by Lambert (1988) for a disequilibrium quantityconstrained model of the Belgian manufacturing industry and applied it to the Australianmanufacturing sector. While the model could not explain the notional demands and supplies in

each market, it found quite significant spillover effects and heterogeneous disequilibrium effects.In the long run, manufacturing output related strongly to Foreign Demand and the heterogeneityof the labour market. The latter implies that there are significant long-term gains to be obtainedfrom making the labour market more efficient and hence reducing frictional unemployment.In the short run, there were strong income effects, the capital -labour substitution appeared to bezero and relative prices to imports and on the export market had little effect. This may bepartially due to using quarterly as opposed to annual data and only one time lag. Developing the

dynamics of this model is one possible direction for future research.We have however managed to show that it can be useful moving away from the normal

general equilibrium framework and considering fixed-price quantity constrained models. Wehave found distinct heterogeneity on both the goods and labour markets and shown that there aresignificant spillover effects between the markets. This approach produces some interestingconcepts to help understand the manufacturing sector as shown by the graphs of effective andnotional demand.

21

REFERENCES.

Barro R.J. and H.I. Grossman (1971), "A general Disequilibrium Model of Income andEmployment", American Economic Review, vol 61, pp82-93.

Benassy, J-P., (1975), Neo-Keynesian Disequilibrium Theory in a Monetary Economy",Review of Economic Studies, Volume 42, pp. 503-528.

Benassy J.P., (1977), "On Quantity Signals and the Foundations of Effective Demand Theory",Scandinavian Journal of Economics, vo179 pp147-168.

Benassy, J-P., (1980), "Developments in Non-Walrasian Economics and the Microeconomicfoundations of Macroeconomics", in W. Hildenbrand (ed.), Advances in EconomicTheory: Invited Papers in the Fourth World Congress of theEconometrics Society,Cambridge University Press, Cambridge, pp 121-146.

Benassy J.P., (1982), The Economics of Market Disequilibrium, Academic Press, New York.

Benassy J.P., (1986), Macromonics: An Introduction of the Non-Walrasian Approach,Academic Press, Orlando.

Carmichael, J., and N. Dews, (1987),"The Role and Consequences of Investment in RecentAustralian Economic Growth", RDP 8704, Reserve Bank of Australia, Sydney.

Clower, R., (1965), "The Keynesian Counterrevolution: A Theoretical appraisal", in F. Hahnand F. P. R. Brechling, The Theory of Interest Rates, London, Macmillan, pp. 103-125.

Cutherbertson, K., and M. P. Taylor, (1987), Macroeconomic Systems, Basil Blackwell,Oxford.

Dickey, D. A., and W. A. Fuller, (1981), "Likelihood Ratio Statistics for Autoregressive TimeSeries with a Unit Root", Econometrica, Volume 49 pp1057-72

Engle R. F. and C. W. J. Granger, (1987), "Cointegration and Error Correction:Representation, Estimation and Testing", Econometrica, Vol. 55, pp251-276.

Fisher, F. M., (1984), Disequilibrium Economic of Equilibrium Processes, CambridgeUniversity Press, Cambridge.

Hargreaves, C.P. (1990) "Modelling Money Demand in Australian Economy Wide Models",paper presented to the first Australasian Economic Modelling Conference, June, andsubmitted for publication.

Hargreaves, C.P. and K. Juselius (1991), "Long Run Relations in Austalian Monetary Data",paper presented at this conference and to be published in "Macroeconomic Modelling ofthe Long Run", edited by Colin Hargreaves and to be published by Edward Elgar, early1992.

Ito, T. (1980), ’"Methods of Estimation for Multi-Market Disequilibrium Models",Econometrica, 48, p97-125.

Johansen, S. (1988), "Statistical Analysis of Cointegration Vectors", Journal of EconomicDynamics and Control, Vol. 12., 231-254.

22

Kooiman P., (1984), "Smoothing the Aggregate Fix-Price Model and the Use of BusinessSurvey Data", The Economic Journal, vol 94, pp899-913.

Lambert J.P., (1988), Disequilibrium Macroeconomic Models: Theory and Estimation ofRationing Models Using Business Survey Data, Cambridbe University Press,Cambridge.

Muellebauer J., R. Portes, (1978), "Macroeconomic Models with Quantity Rationing", TheEconomic Journal, vol 88, pp788-821.

Perron, P., (1988), "Trends and Random Walks in Marcoreconomic Time Series", Journal ofEconomic Dynamics and Control, Volume 12, pp297-332.

Portes,R. (1977), "Effective Demand and Spill-Over Effects in Empirical Two-MarketDisequilibrium Models", Harvard Institute of Economic Research, Discussion Paper No595, Harvard University, Cambridge.

RosenH.S., Quandt R.E., (1978), "Estimation of a Disequilibrium Aggregate Labour Market",Review of Economics and Statistics, vol 60, pp371-379.

23

DATA APPENDIX,

All data is derived from non-seasonally adjusted, quarterly series’, unless otherwise stated. Inthe case where monthly data was used, the last observation for each respective quarter was usedto obtain a quarterly series. The following list provides a breif description of each variable usedto estimate the model and the source from which the series was obtained.

1. X: Manufacturing Product at 1984-85 prices, $m. Source: Australian Bureau of StatisticsCatalogue Number (ABS Cat.) 5222.0.

2. L: Total number of person employed in the manufacturing sector, thousands. Source: obtainedon request from the Australian Bureau of Statistics.

3. PG: The proportion of manufacturing fm’ns experiencing an excess supply of their product,non-seasonally adjusted quarterly data. Source: The CAI-Westpac Survey of Industrial Trends.For this indicator of excess supply of goods, question number 3 was used. It asks: What singlefactor is most limiting your ability to increase production? - None, orders, material finance,labour, capacity or other. In this case, responses that cited orders as being the most limitingfactor to production increases were employed to indicate an excess supply of goods.

4. PL: The proportion of manufacturing fLrms experiencing an excess demand of labour. Source:Again, question three from the CAI-Westpac survey was used. The appropriate response relatingto labour being the most limiting factor to increases in production.

5. DUC: The degree of capacity utilisation.Source: Question 2 from the CAI-Westpac survey.This question asks manufacturing firms: At what level of capacity utilisation are you working? -Above normal, normal for your firm, or below normal. DUC was calculated to be the "netbalance" from this question (that is: above minus below - these net balances are reported by theCAI-Westpac survey).

6. PW: The world price level for manufactured goods, $A. Source: The $US index of worldmanufactured goods export prices, as calculated in the United Nations Monthly Bulletin ofStatistics (the series, however, was obtain from the Australian Treasury, on request). The serieswas converted to $A by using the end of quarter $US/$A exchange rate (Source: The Bulletin,The Reserve Bank of Australia).

7. PX: An export price index for manufactured goods, 1989-90 prices, monthly data. Source:ABS Cat. 6405.0

8. PM: An import price index for manufactured goods, 1984-85 prices. Source: This idex wasconstructed from data obtained form relating to the value and volume of manufactured imports,on request from the Australian Treasury (originally, these series were taken from the QuarterlyBalance of Payments). The ratio of these two series was then taken in order to obtain a priceindex for manufactured imports. This was done as a long term series could not be obtainedelsewhere. However, the caluclated series was compared with the import price index formanufacturing (ABS Cat. 6414) with little difference being apparent.

9. P: Price index for articles produced by manufactures, monthly, 1984-85 prices. Source: ABSCat. 6412.0

10. FORDEM: An indicator of foreign demand for manufactured goods, quantum index. Source:United Nations Monthly Bulliten of Statistics (obtained from the Australian Treasury).

11. YHD: Household disposable income, $m. Source: dX database, TSS directory, NationalAccounts, identifier NODQ.UC_HDI#.

24

12. CPI: Consumer Price Index, 1980-81 prices. Source: dXdatabase, TSS directory, Prices,identifier RJDQ.UI81SM00900A.

13. W: Non-farm wages, salaries and supplements per wage and salary earner. Source: dXdatabase, TSS directory, National Accounts, identifier NODQ_UCWSS_WSE

14. PROFIT: an index used to represent short mn changes in profitability conditions, calculatedas; PROFITt- (P.X/W.L)t

(P.X/W.L)t.1

15. R: Ten year Treasury Bonds, monthly, percent. Source: The Bulletin, Reserve Bank ofAustralia.

16. RHOL: The difficulty experienced by manufacturing f’u’ms in obtaining labour. Source: CAI-Westpac survey. Question 4(a) was used for this indicator. It asks manufacturing fLrms: Do youfind it harder, easier of the asme as it was three months ago to get (a) labour? The response usedis calculated as the "net balance" (that is: harder minus easier).

17. UNR: The aggregate unemployment rate, percent. Source: obtained on request from theReserve Bank of Australia.

18. K: Manufacturing fLrms capital stock, $m. Source: This series had to be estimated usingannual data as there is no quarterly estimates of the captial stock published in Australia. This wasdone using a method suggested by Walter and Dippelsman.1 It uses the approach that, for anyquarter, t, the capital stock can be calculated as:

Kt = (1-dt)Kt-1 + (I--~) Itwhere: K is the net capital stock, in 1984-85 prices, $ million (the annual databeing abtained from ABS Cat. 5221.0),

d is the depreciation rate for each quarter and is calculated (using annualdata) as:

d= - I + D + I/2 /4 ,

I is fixed capital expenditure, 1984-85 prices, $ million (quarterly datafrom ABS Cat. 5626.0 and annual from ABS Cat. 5221.0) and

D is captial consumption, 1984-85 prices, $ million, annual (ABS Cat.5221.0).

1 Walter, R., and R. Dippelsman, (1986), "Estimates of Depreciation and Capital Stock Within Australia",Australian Bureau of Statistics Occassional Paper 1985/3, Canberra.

WORKING PAPERS IN ECONOMETRICS AND APPLIED STATISTICS

25

~o~ ~ia2_o!~ ~o~. Lung-Fei Lee and William E. Griffiths,No. I - March 1979.

Howard E. Doran and Rozany R. Deen, No. 2 - March 1979.

NoZean ~ Za~ ~a~~n an ~£aco~ @nnan Made!.William Griffiths and Dan Dao, No. 3 - April 1979.

D.S. Prasada Rao, No. 5 - April 1979.

~ed2/: ~ Diar_~ o~ :~o~a a~ -~ZcLe.n~. Howard E. Doran,No. 6 - June 1979.

Re-~ Req~ ~oxi~. George E. Battese andBruce P. Bonyhady, No. 7 - September 1979.

Howard E. Doran and David F. Williams, No. 8 - September 1979.

D.S. Prasada Rao, No. 9 - October 1980.

~ ~o2~ - 1979. W.F. Shepherd and D.S. Prasada Rao,No. I0 - October 1980.

~o~~ Neq~~an~ RL0ka. W.E. Grlffiths andAnderson, No. II - December 1980.

£o~-O~-~it ~e~tin ~he ~r~x~xceo~ R~. Howard E. Doranand Jan Kmenta, No. 12 - April 1981.

~0%oZ O,’uiea, d~onP_Qa,e~d.~ D~. H.E. Doran and W.E. Griffiths,No. 13 - June 1981.

~ia~ Week~ Wo~ RaZe. Pauline Beesley, No. 14 - July 1981.

$o~ ~ata. George E. Battese and Wayne A. Fuller, No. 15 - February1982.

!)e~. H.I.. Tort and P.A. Cassidy, No. 16 - February 1985.

26

H.E. Doran, No. 17- February 1985.

J.W.B. Guise and P.A.A. Beesley, No. 18 - February 1985.

W.E. Griffiths and K. Surekha, No. 19- August 1985.

9nieru1~3r~m~ ~n~. D.S. Prasada Rao, No. 20 - October 1985.

H.E. Doran, No. 21- November 1985.

~e-YeaZ ga~ ~ tAe~az~ ~oxie/. William E. Griffiths,R. Carter Hill and Peter J. Pope, No. 22 - November 1985.

William E. Griffiths, No. 23 - February 1986.

2qx~ ~u~ ~aia~ ~ Doi~. George E. Battese andSohail J. Malik, No. 25 - April 1986.

George E. Battese and Sohail J. Malik, No. 26 - April 1986.

~o~. George E. Battese and Sohail J. Malik, No. 27 - May 1986.

~er~ ~nxwq22n ~aaduc~ ~un~ end 2ea2i Deia on ~o~ ~:Uun,,o,.George E. Battese, No. 28- June 1986.

Nu~. D.S. Prasada Rao and J. Salazar-Carrillo, No. 29 - August1986.

~u~ ~ex~ ~n ~n~ g~in an ~R(1) g~ ~oxie!. H.E. Doran,W.E. Griffiths and P.A. Beesley, No. 30 - August 1987.

William E. Griffiths, No. 31 - November 1987.

Chris M. Alaouze, No. 32 - September, 1988.

G.E. Battese, T.J. Coelli and T.C. Colby, No. 33- January, 1989.

27

Tim J. Coelli, No. 34- February, 1989.

9~ Ze ~ ~co~-~ide ~ed~. Colin P. Hargreaves,No. 35 - February, 1989.

William Griffiths and George Judge, No. 36 - February, 1989.

~~ o~ ~~ ~o~ Dunin~ D~. Chris M. Alaouze,No. 37 - April, 1989.

~pp/i~ Ze ~o22_a ~ ~ru~. Chris M. Alaouze, No. 38 -July, 1989.

Chris M. Alaouze and Campbell R. Fitzpatrick, No. 39 - August, 1989.

Do~. Guang H. Wan, William E. Grlffiths and Jock R. Anderson, No. 40 -September 1989.

o~ Y~mzed ~eae~ 0~. Chris M. Alaouze, No. 41 - November,1989.

~ ~Ae~ and Yitru~ ~. William Griffiths andHelmut L~tkepohl, No. 42 - March 1990.

Howard E. Doran, No. 43 - March 1990.

Howard Doran, No. 45 - May, 1990.

Howard Doran and Jan Kmenta, No. 46 - May, 1990.

9~ 9ani~ and ~nZmw%neiio~tz/ ~aiaea. D.S. Prasada Rao andE.A. Selvanathan, No. 47 - September, 1990.

~canomi~ ~ oi U%e ~/~ o~ A/eu~ ~ng2zm~. D.M. Dancer andH.E. Doran, No. 48- September, 1990.

2S

D.S. Prasada Rao and E.A. Selvanathan, No. 49 - November, 1990.

~ L% ~ gconamix~. George E. Battese,No. 50 - May 1991.

W~ @~o~ ~n~ ~ozun. Howard E. Doran,No. 51 - May 1991.

YeaZg~ Non-Ne~ ~o~. Howard E. Doran, No. 52 - May 1991.

~~ ,~o~v~. C.J. O’Donnell and A.D. Woodland,No. 53 - October 1991.

~ar~c~y~ $ec~. C. Hargreaves, J. Harrington and A.M.Siriwardarna, No. 54 - October, 1991.

Demand ~n ~ gcona.~-Wale ~:Colin Hargreaves, No. 55 - October 1991.

2.0. T.J. Coelli, No. 57- October 1991.

Barbara Cornelius and Colin Hargreaves, No. 58 - October 1991.

Barbara Cornelius and Colin Hargreaves, No. 59 - October 1991.

Duangkamon Chotlkapanich, No. 60 - October 1991.