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Effective Use of Economic Policy Instruments
Copyright © IFAC Dynamic Modelling and Control of f'.iational Economies , Edinburgh, lI K. 1989
EFFECTIVE USE OF ECONOMIC POLICY INSTRUMENTS M. Damiani* and L. Panattoni** *Univers it~
of Perugia, Perugia, Italy **IBN Scimtific Centre , Pisa, Italy
Abstract. The use of an econometric model for policy simulations is based on the analysis of the effects which shocks on exogenous variables (instruments ) cause on selected endogenous variables ( objectives ) , In the standard simulation procedures these effects are examined by shocking the instruments one at a time. In this paper we try to approach the problem from the more general point of view of the simultaneous use of several instruments, In order to suitably combine the shocks we make use of the concept of effectiveness of the single instruments, that is on their ability to drive the economic system, as described by the model, towards more satisfa('tnry solutions. In order to be able to quantify this criterion it is necessary to give a value to the variations induced by these shocks on each objective, by means of the idea of equivalent deviations. By supposing that for each instrument the (per cent) shock the policymaker a priori decided to use in the simulation exercise has been given, the effectiveness of the instrument can be measured by the amount of the variations caused on the objectives. Following this approach an algorithm has been implemented in which the shocks to be actually used are obtained by suitable modifications of the initially chosen ones, in the sense that a greater shock is given to the more effective instrument. Some experiments have been performed with a medium size model of the Italian economy, in order to quantify the gain resulting from the use of the modified shocks and the results are here shown. Keywords. Economic simulation; econometric models; policy selection rules; policy instruments effectiveness.
INTRODUCTION
which, although remaInIng in an optimization framework, try to closely follow the decision making process by means of an interactive algorithm, in such a way that this process is no more a black-box process. Some of these algorithms have also the advantage of requlflng information about the policymaker's preferences of a lower degree of complexity than those based on the classical optimal control approach (Panattoni[ 1988 J). In this paper we suggest a method which has the double advantage of making use of some concept of optimality, with refcrence to the policymaker's preferences, and of remaining in the more transparent simulation framework . We tried to generalize the standard approach based on successive single shocks to the instruments by allowing the policymaker to combine all these shocks into a unique simulation, following a criterion based on the concept of instruments effectiveness. It must be pointed out that this method is conceptually not too different from the interactive optimization algorithm proposed by Geoffrion et al. [1972J and discussed in Panattoni [1988], and in particular that it requires the same kind of information about the policymaker's preferences.
The standard simulation approach to the evaluation of the effects of policy actions, based on the use of a macroeconometric model, consists in practice in shocking an exogenous instrument over the chosen time horizon and looking then at the effects on the This exercise is eventually endogenous objectives, repeated for several instruments and some conclusions on their relative effectiveness are drawn from the comparison of the results. Control theorists have tried to rationalize this approach by allowing a contemporaneous use of several instruments in an optimization framework based on the maximization of a suitable objective function, However this approach to the solution of the economic policies selection problem has been criticized from several points of view. Among other critiques, it has been argued that such abstract mathematical techniques fail to provide much insight into why the resulting instrument path is optimal. This usually implies that the policymaker is often not confident of the results and hesitates to actually use them. For this reason some methods have been recently proposed, see e,g. Wallenius [1982J for a survey,
259
260
1\1. Dalllialli alld L. l'allalloni
In order to be able to somehow quantify the advantages of the use of the proposed approach instead of the standard one, they have been used in some experiments relevant to an econometric model of the Italian labor market and the results are here presented and discussed.
THE SIMULATION PROCEDURE Our starting hypotheses are that there is a policymaker who wants to use an econometric model as an aid in the policy selection process . He starts from a baseline simulation, given by the solution of the econometric model when the instruments assume some initial values x o , shocks these values and computes the effects by looking at the values the objectives assume in the corresponding model solution . He has also an idea of how much these instruments can be varied in practice and can quantify this information by specifying for each instrument an initial shock I5xk • We make also the assumption that so me flexibility is attached to the use of the various instruments, namely that these shocks are not fixed quantities, and that the policymaker is ready to increase or to decrea se them (that is to use more or to use less the relevant instrument) according to the higher or lower effectiveness of the inst ruments in steering the solution of the econometric model towards more satisfying values for the objectives. The problem then arises of devising an operative criterion to suitably evaluate the effecti ve ness of each instrument. A poss ible way to quantify this criterion is to give a value to the variations induced by each shock on the complete set of objectives. This clearly implies the use of some information about the policymaker's preferenccs and from our point of view a suitable way is the one based on the idea of equivalent improvements with reference to the initial solution: fixed an arbitrary variation 6y; for a reference objective, the policymaker is asked to indicate , for all the objectives, the variations 6Yj ,j i , which for him ha ve the sa me importance as 6y;, in practice the tradeoff between the j-th and the i-th objectives. Follo wi ng our assumption that for each instrument a positive quantity I5x k has been fixed, which gives the absolute value of the (per cent) shock the policymaker a priori decided to use in the simulation exercise, the effectiveness of the instrument can be measured by the amount of the variations caused on the objectives, each variation being measured in term s of the corresponding equivalent improvement. If we have then n objectives, a measure of the effectiveness of the initial I5xk on the k-th instrument is given by
*
(I)
where the equivalent improvement of each objective is weighted by the corresponding (impact or delayed) multiplier with respect to the considered instrument. It is interesting to note that in the already mentioned interactive method for multi-criteria optimisation of Geoffrion et al. [1972J the quantities wk are interpreted as proportional to the partial derivatives of an implicit preference function W with respect to the objectives, so that the use of a gradient algorithm for its maximisation is made possible, in an optimal control fr a mework. In practice the expression (I) gives the total number of equivalent improvements from the initial state produced by the shock I5xk to the k-th instrument. This information could also be sufficient for the policymaker in order to use more the more effective instruments by suitably modifying the values of the relevant shocks following a subjective criterion. However, as we want to make some evaluations of the differences arising from usi ng or not the information resulting from (I) in simulation experiments and an actual policymaker is not available, we tried to reason ab ly represent his behaviour by implementing an algorithm in which the actually used shocks, 8Xb arc obtained from the initially chosen ones, I5xk , on the basis of the quantities evaluated in (I), according to the empirical formula
]
(2)
where Iwl is the mean value of Iwkl, 6w the difference between its ma xi mum a nd minimum values and a is an empirical amplifying factor whose value ranges between o and 2: a = 0 doesn't change the absolute values of the initial choices of the shocks and a = 2 , under the assumption of a mean value Iwl equidistant from the maximum and minimum Wk , modifies the shocks in such a way that it doubles the most effective and set to zero the less effective onc. About formula (2) it mu st be once more stressed that it has none but operational me a ning and other similar expressions could be devised for the same purpose of allowing comparison experiments.
THE ECONOMETRIC MODEL The model employed for simulation experiments refers to the real sector of Italian economy and was originally designed to test so me assumptions on t he behaviour of firms operating within oligopolystic markets. In the current version of the model, the labour market block has been improved according to recent contributions by Sylos Labini [1987]. In this context, the theoretical assumptions include keynesian unemployment caused by lack of demand (.in accordance with the hypothesis of dominating non-competitive markets built into the model) and technological unemployment of ricardian type, caused by labour-saving innovations. The former phenomenon has been widely analysed and accepted in current
Flleui\'e Cse of Economic I'olin· Instrumellts models, while for the latter we refer the reader to Sylos Labini [1987]. However, we point out here that our theoretical scheme is at variance with other eclectic models (such as, for instance, the onc proposed by Malinvaud [1977], in th a t it couples to keynesian unemployment the unemployment due to dynamic processes of substitution of labour by machines, rather than classical unemployment from too high wages. The present vers ion of the model consists of six blocks: dem a nd, s upply , prices, labour ma rket, foreign trade a nd income d istribu tion. The total number of equations in the model is 62, while 25 is the number of stoc hastic equations. In the following we will present a brief analysis of the main relationships characterising the model. Of particular importa nce in the dem a nd block is the introduction of indu s tri al inves tments as a function of the degree of unu sed capacity and of the difference between the profit a nd interest rates. The proposed specification is d ifferent from t he neoclassical onc in the way it evaluates the inOuen ce of the monetary in terest rate. We assume tha t the latter va ri a ble is releva nt as a determining factor of t he net profits and /or as a n indicator of the relative co nve nience of real ve rsus finan cial investments, rather th a n as a component of the cost of capital services. In the su ppl y block, we have introduced an equ at ion for the industria l added value where the foreign demand and the indi vidu al components of the dem an d of the public and private scctors a ppear as determinin g factors. Furthermore, thc differen ce between the rates of growth of domestic a nd import prices appears as a me as ure of s ubstitutabi lity between domest ic and forei g n s upply. To ex pla in the trend in the added va lue of pri vate se rvices, we have further assumed th at t he latter st ric tly depend s on the industria l growth. This is in line with the kaldorian hy pothesis of the ma nufacturin g sector as engine of growth of the entire eco nomic syste m. In the labour market block onc equation for the total hours worked and o nc for industrial dependent employmcnt have bcc n introd uced. In both equations we have allowed for inco me cffects a nd for both s ho rt and medium term price effects. The first price effect, represented by the d ifference betwee n unit labour cost and w holesale prices, constitutes an indicator of the conve nience of redu cing (in a bsolu te terms) labou r input. Indeed , a reduced translation of la bour cost on prices determines, coeteris paribus, a compression of profit m a rgin s a nd therefore induces production restructuring in order to reduce labour requirem ents. Furthermore, wc have introduced the ratio of wages to machine prices with a time lag of two-three years, in the assumption that s uch variable represents an incenti ve for the replacement of la bour by instrumental goods. This s ubs titution process is assu med to occur in time rather tha n a lon g a n isoquant a nd thus it exhibits different features from those of the static and reversible substitution of neocl assica l models. From empirical tests, we have observed a certain degree of stickiness in the yearly adjustment of effective to desired values for the industrial dependent
261
employment. This phenomenon can be ascribed to costs of adjustment as well as to institutional constraints. The employment in the private service sector is explained in the model by the sectorial product and by the lagged value of the total dependent employment except that of private services. Indeed , it has been observed that in the Itali a n experience the increase in service employment has been accelerated after periods of decreasing of industrial employment. This phenomenon of dynamic tra nsfer can be due to a residu a l role of the private services sector. Fin a lly, we have tested the hypothesis that mass unemployment in It a ly be ca used by the slowing down of the economic growth , the increase in labour supply (particularly female labour) and indus trial restructuring (substitution of la bo ur by mac hines). In the income dist ribution block, we have adopted in the equation for wages a specification of Phillips-Lipsey type featuring , besides the un employment rate , the index of consumer prices. This s hould account for the current indexing mech a nis ms as we ll as for trade union negotiations aiming a t inOation rate reco very. We have es timated in the price block an equation for wholesa le prices whe reb y it has been verified that the application by oligopolystic firm s of a mark-up on the cost of labour a nd raw materials (imported in the Ita lian case) is indepe nd ent from Ouctuation s in aggregate dem a nd but co nstra ined by forei g n competition. Finally, in the foreign trade block, we have es timated two equations allowing a n analysis of the dependence of It a lian economy on the interna tional context: imports s how a high degree of elast icity to income growth, while expo rts result to be s tro ng ly conditioned by foreign dem a nd and, to a lesser extent, by the competitivity of Italia n firms on the in ternat iona l market. To conclude, we have to stress th at t he dynamic structure of the model wc have built requires for a policy evaluation a medium-long term a nal ys is of the consequences of different politica l measures, since some impact effects ma y be incre ased or completely offset by th e lagged effects. This h as several co nsequences. For insta nce the problem of a n in verse relations hip between rea l wages and unemployme nt ca n be reconsidered by taking past trend s in wages, prices of fi nis hed products and ma chines into account. This means th at the d y namic features of our model render a static ana lysis of the trade off impliCit in Phillips' curve la rgely irrelevant. Therefore, to evaluate in o ur si mulation experiments the effectiveness of, say, a wage policy in changing the rate of unemployment s hort a nd medium term gains have to be considered . In fact, an increase in wages can have impact effects a nd lagged effects going in opposite directions: the positi ve effects of a n increased demand for consumption goods can be balanced, through the falling profit rates, by reduced in vestmcnt ince ntives. In the medium term, an increase of machine prices lower than that of wages will res ult in a more efficient use of labour resources , with negative consequences on employment levels.
262
M. Damiani alld L. Panattoni THE DESIGN OF THE EXPERIMENTS
The main goal of our simulation experiments with the above described econometric model was the evaluation of the improvements in the model solutions when the initial shocks on the instruments are modified, as previously described, to better exploit their effectiveness. For this purpose several simulations have been performed for a five years horizon, from 1978 to 1982, chosen inside the sample period in order to avoid problems arising from the need of assigning values to the exogenous variables. All these exercises were substantially based on the comparison of three different model solutions: i) a baseline solution obtained by assigning the historical values to the instruments, ii) a standard simulation solution, in which the instruments have been shocked by some initially chosen values OX. and iii) an effective simulation solution, in which the shocks have been computed according to (2). The simulations have been performed following two different operational procedures. In the first case we used all together the instruments from 1978 to 1982 and evaluate their effectiveness on all the objectives contemporaneously by means of a single simulation for the whole period. From now on this procedure will be referred as fixed horizon simulation. In the second case we tried to follow more closely the actual behaviour of a policymaker who wants to use econometric models as an effective aid in policy selection, and we made the hypothesis that at each year he uses the simulations just to choose the values of the current instruments, but nevertheless he wants to see how the alternative policies affect the objectives over a given length horizon (in our exercises we used a time horizon of four years) . So, for example, we start by considering the instruments at 1978, we evaluate their effectiveness on the objectives up to 1981 to be used for the computation of the effective shocks according to formula (2); then we repeat the procedure for the period 1979-1982, and so on up to the final simulation for the years 1982- I 985. Clearly in each simulation only the values computed for the first year are retained for the final comparisons. We will call this procedure as the moving horizon simulation. In our simulation exercises we assumed as primary target the reduction of the unemployment rate and, subordinately, the control of the inflation rate and the growth of the domestic product and of the industrial productivity:. in practice we tried to achieve, through an expansion of the aggregate demand, a partial absorption of the unemployment avoiding at the same time a worsening of the trade deficit. In order to quantify the priority we gave to the reduction of the unemployment rate, rather than to the other objectives, we assumed equivalent improvements equal to I for it and equal to 3 for the others (note that all the objectives are expressed as growth rates). In order to hit these targets a policy oriented to increase the public expenditure and to stimulate the
private investment, resulting in the desired increase of the aggregate demand, has been adopted. The control variables were long term interest rate, expenditure for public goods, public investment (both in billion of liras at constant prices) and public sector employment (in thousands) and the initially chosen shocks were 20%, 5%, 10% and 4% of the historical values, respectively.
RESULTS In order to evaluate the gain resulting from the use of formula (2) to compute the shocks on the instruments, we first measured the differences between the values of the objectives obtained from the standard (a = 0) and from two effective simulations (a = I and a = 2) and their baseline values, using for each one the relevant equivalent improvement as unit of measure. Then we built up a global indicator by averaging these measures over all the objectives for each year. Table I displays these quantities, together with their mcan values over the simulation period. The values shown in this table mean , for example, that, having assumed equal to one the equivalent improvement of the unemployment rate for all the years 1978-1982, the use of a = 2 in the fixed horizon simulation yields a mean global improvement on all the objectives which is equivalent to an average reduction of 1.09 units of the unemployment rate each year. It must be pointed out that all the displayed values have been obtained by shocking the instruments with the same total amount and that they differ only for the way in which this quantity has been distributed among the instruments. TABLE 1 a=O Lh.
a=1 m.h.
Lh.
a=2 m.h.
1978 1979 1980 1981 1982
1.26 0.66 0.75 0.76 0.72
1.12 0.59 0.76 0.81 1.51
1.18 0.70 0.86 0.90 0.90
0.98 0.52 0.76 0.86 2.31
1.09 0.76 0.97 1.0 I 1.07
mean
0.83
0.96
0.91
1.09
0.98
It is also evident that the improvements resulting from the fixed horizon simulations, for a #- 0, are globally higher than those obtained with the moving horizon simulations and that this better performance is entirely due to the last year values. This depends on the fact that, contrary to the latter, the former are not conditioned by the values the objectives assume in the years immediately subsequent the simulation period. From our experiments it came also out that the fixed horizon approach generates a higher instruments instability. For these reasons we concluded that the moving horizon simulation is actually the more realistic
Effecti\'e Use of Economic Polio' Instruments one and in the following, for brevity sake, we will refer only to it. In Table 2 the gains for eaeh single objective, over the whole simulation period, are shown, still measured in equivalent improvements of each one. To have actual variations we obviously sho uld triplicate the values relevant to inflation, GDP and productivity. TABLE 2
Inflation GDP Unemployment Prod uctivity
a=O
a=1
a=2
0.14 0.98 2.32 0.72
0.17 1.15 2.40 0.82
0.21 1.31 2.48 0.91
Figure I shows in detail for each objective the time paths of the differences between the values resulting from simulations in wh ich the instrumcnts have been shocked, with different values of the parameter a, and the baseline solution (circles refer to a = 0, stars to a = I and crosses to a = 2). It is interesting to note the difference between the behaviour of the most important objective (unemployment) and the behaviour of the others: the first shows a continuously increasing improvement over the time, while the others have the maximum improvement just at the beginning of the simulation period. Furthermore for all the four objectives an increase of the value of the parameter Ca = 0) provides worse results in the first year (for the unemployment in the second year too), while better values are obtained only in the subsequent years. This behaviour clearly depends on the dynamic characteristics of the econometric model and in particular on the fact that the delayed multipliers, up to four years, of the objectives with respect to the instruments are often equal in size and opposite in sign to thc impact ones. As a final poin t we report some results on the effectiveness of the used instruments: table 3 displays the percentage of the total gains for each year due to the single instruments and their average va lue over the whole simulation period, while in figure 2 the time paths for the four instruments are shown (the meaning of the symbols is the same as figure I , with the addition of the historical values, indicated by the full circles).
263 TABLE 3
Interest Rate
Govern. Expend.
Govern. Invest.
Public Empl.
1978 1979 1980 1981 1982
36.9 40.7 46.9 62.4 52.8
22.8 16.5 13.2 2.0 13.4
25.0 26.9 26.9 28.1 23 .4
15.3 15.9 13.0 7.5 10.4
Mean
48 .0
13.6
26.0
12.4
From the values of the table and from the relative positions of the four lines in the figure it is clear that the shock initially assumed for the interest rate is the most effective and those for the Government expenditure on goods and for the public employment are the less effective ones: in fact, in the first case, as a increases the instrument assumes values which move away from the historical ones, while for the other two an increase in a brings their values back. The shocks on Government investment have a mean effectiveness and their initial values are not influenced too much by the values given to the parameter a .
REFERENCES Geoffrion A.M., J .S. Dyer and A . Feinberg (1972). An Interactive Approach for Multi-Criterion Optimization. Management Science, 11, 357-368. Malinvaud E. (1977). The Theory of Unemployment Reconsidered. Basil Blackwell, Oxford. Panattoni L. (1988). Interactive Preference Elicitation in Macroeconomic Decision Models. Journal of Economic Dynamics and Control, g, 109-116. Sylos Labini P. (1987). The Theory of Unemployment, too, is Historically Conditioncd . BNL Quarterly Review, ill, 379-435. Wallenius, H. (1982). Optimizing Macroeconomic Policy, Review of Approaches and Applications. European Journal of Operations Research, 10/3, 221-228.
1\'1. Damiani and L. Pan
264 "N
INTEREST RATE
GDP
GOV. EXPENDITURE ON GOODS
'" q
.,'"
., "
/'
INFLATION
1980
"HI7& 19B1
1982
197!!!
N
PRODUCTIVITY
~
1979
1980
19B1
1982
~
GOV. INVESTMENT
V
~
§
§~'~77.~-1~'~7'~--1~"~O~~19~'-1~-'~'.2
8
~
~
PUBLIC EMPLOY M[1\'T
~
§
. q
8
UNEMPLOYMENT 19 78
Fig 1 - Time paths of the gains on the objectives
Hil7S
1980
1981
1982
~9 78
1979
1980
Fig 2 - Time paths of the instruments
1981
1952
EFFECTIVE USE OF ECONOMIC POLICY INSTRUMENTS M. Damiani* and L. Panattoni** *Univers it~
of Perugia, Perugia, Italy **IBN Scimtific Centre , Pisa, Italy
Abstract. The use of an econometric model for policy simulations is based on the analysis of the effects which shocks on exogenous variables (instruments ) cause on selected endogenous variables ( objectives ) , In the standard simulation procedures these effects are examined by shocking the instruments one at a time. In this paper we try to approach the problem from the more general point of view of the simultaneous use of several instruments, In order to suitably combine the shocks we make use of the concept of effectiveness of the single instruments, that is on their ability to drive the economic system, as described by the model, towards more satisfa('tnry solutions. In order to be able to quantify this criterion it is necessary to give a value to the variations induced by these shocks on each objective, by means of the idea of equivalent deviations. By supposing that for each instrument the (per cent) shock the policymaker a priori decided to use in the simulation exercise has been given, the effectiveness of the instrument can be measured by the amount of the variations caused on the objectives. Following this approach an algorithm has been implemented in which the shocks to be actually used are obtained by suitable modifications of the initially chosen ones, in the sense that a greater shock is given to the more effective instrument. Some experiments have been performed with a medium size model of the Italian economy, in order to quantify the gain resulting from the use of the modified shocks and the results are here shown. Keywords. Economic simulation; econometric models; policy selection rules; policy instruments effectiveness.
INTRODUCTION
which, although remaInIng in an optimization framework, try to closely follow the decision making process by means of an interactive algorithm, in such a way that this process is no more a black-box process. Some of these algorithms have also the advantage of requlflng information about the policymaker's preferences of a lower degree of complexity than those based on the classical optimal control approach (Panattoni[ 1988 J). In this paper we suggest a method which has the double advantage of making use of some concept of optimality, with refcrence to the policymaker's preferences, and of remaining in the more transparent simulation framework . We tried to generalize the standard approach based on successive single shocks to the instruments by allowing the policymaker to combine all these shocks into a unique simulation, following a criterion based on the concept of instruments effectiveness. It must be pointed out that this method is conceptually not too different from the interactive optimization algorithm proposed by Geoffrion et al. [1972J and discussed in Panattoni [1988], and in particular that it requires the same kind of information about the policymaker's preferences.
The standard simulation approach to the evaluation of the effects of policy actions, based on the use of a macroeconometric model, consists in practice in shocking an exogenous instrument over the chosen time horizon and looking then at the effects on the This exercise is eventually endogenous objectives, repeated for several instruments and some conclusions on their relative effectiveness are drawn from the comparison of the results. Control theorists have tried to rationalize this approach by allowing a contemporaneous use of several instruments in an optimization framework based on the maximization of a suitable objective function, However this approach to the solution of the economic policies selection problem has been criticized from several points of view. Among other critiques, it has been argued that such abstract mathematical techniques fail to provide much insight into why the resulting instrument path is optimal. This usually implies that the policymaker is often not confident of the results and hesitates to actually use them. For this reason some methods have been recently proposed, see e,g. Wallenius [1982J for a survey,
259
260
1\1. Dalllialli alld L. l'allalloni
In order to be able to somehow quantify the advantages of the use of the proposed approach instead of the standard one, they have been used in some experiments relevant to an econometric model of the Italian labor market and the results are here presented and discussed.
THE SIMULATION PROCEDURE Our starting hypotheses are that there is a policymaker who wants to use an econometric model as an aid in the policy selection process . He starts from a baseline simulation, given by the solution of the econometric model when the instruments assume some initial values x o , shocks these values and computes the effects by looking at the values the objectives assume in the corresponding model solution . He has also an idea of how much these instruments can be varied in practice and can quantify this information by specifying for each instrument an initial shock I5xk • We make also the assumption that so me flexibility is attached to the use of the various instruments, namely that these shocks are not fixed quantities, and that the policymaker is ready to increase or to decrea se them (that is to use more or to use less the relevant instrument) according to the higher or lower effectiveness of the inst ruments in steering the solution of the econometric model towards more satisfying values for the objectives. The problem then arises of devising an operative criterion to suitably evaluate the effecti ve ness of each instrument. A poss ible way to quantify this criterion is to give a value to the variations induced by each shock on the complete set of objectives. This clearly implies the use of some information about the policymaker's preferenccs and from our point of view a suitable way is the one based on the idea of equivalent improvements with reference to the initial solution: fixed an arbitrary variation 6y; for a reference objective, the policymaker is asked to indicate , for all the objectives, the variations 6Yj ,j i , which for him ha ve the sa me importance as 6y;, in practice the tradeoff between the j-th and the i-th objectives. Follo wi ng our assumption that for each instrument a positive quantity I5x k has been fixed, which gives the absolute value of the (per cent) shock the policymaker a priori decided to use in the simulation exercise, the effectiveness of the instrument can be measured by the amount of the variations caused on the objectives, each variation being measured in term s of the corresponding equivalent improvement. If we have then n objectives, a measure of the effectiveness of the initial I5xk on the k-th instrument is given by
*
(I)
where the equivalent improvement of each objective is weighted by the corresponding (impact or delayed) multiplier with respect to the considered instrument. It is interesting to note that in the already mentioned interactive method for multi-criteria optimisation of Geoffrion et al. [1972J the quantities wk are interpreted as proportional to the partial derivatives of an implicit preference function W with respect to the objectives, so that the use of a gradient algorithm for its maximisation is made possible, in an optimal control fr a mework. In practice the expression (I) gives the total number of equivalent improvements from the initial state produced by the shock I5xk to the k-th instrument. This information could also be sufficient for the policymaker in order to use more the more effective instruments by suitably modifying the values of the relevant shocks following a subjective criterion. However, as we want to make some evaluations of the differences arising from usi ng or not the information resulting from (I) in simulation experiments and an actual policymaker is not available, we tried to reason ab ly represent his behaviour by implementing an algorithm in which the actually used shocks, 8Xb arc obtained from the initially chosen ones, I5xk , on the basis of the quantities evaluated in (I), according to the empirical formula
]
(2)
where Iwl is the mean value of Iwkl, 6w the difference between its ma xi mum a nd minimum values and a is an empirical amplifying factor whose value ranges between o and 2: a = 0 doesn't change the absolute values of the initial choices of the shocks and a = 2 , under the assumption of a mean value Iwl equidistant from the maximum and minimum Wk , modifies the shocks in such a way that it doubles the most effective and set to zero the less effective onc. About formula (2) it mu st be once more stressed that it has none but operational me a ning and other similar expressions could be devised for the same purpose of allowing comparison experiments.
THE ECONOMETRIC MODEL The model employed for simulation experiments refers to the real sector of Italian economy and was originally designed to test so me assumptions on t he behaviour of firms operating within oligopolystic markets. In the current version of the model, the labour market block has been improved according to recent contributions by Sylos Labini [1987]. In this context, the theoretical assumptions include keynesian unemployment caused by lack of demand (.in accordance with the hypothesis of dominating non-competitive markets built into the model) and technological unemployment of ricardian type, caused by labour-saving innovations. The former phenomenon has been widely analysed and accepted in current
Flleui\'e Cse of Economic I'olin· Instrumellts models, while for the latter we refer the reader to Sylos Labini [1987]. However, we point out here that our theoretical scheme is at variance with other eclectic models (such as, for instance, the onc proposed by Malinvaud [1977], in th a t it couples to keynesian unemployment the unemployment due to dynamic processes of substitution of labour by machines, rather than classical unemployment from too high wages. The present vers ion of the model consists of six blocks: dem a nd, s upply , prices, labour ma rket, foreign trade a nd income d istribu tion. The total number of equations in the model is 62, while 25 is the number of stoc hastic equations. In the following we will present a brief analysis of the main relationships characterising the model. Of particular importa nce in the dem a nd block is the introduction of indu s tri al inves tments as a function of the degree of unu sed capacity and of the difference between the profit a nd interest rates. The proposed specification is d ifferent from t he neoclassical onc in the way it evaluates the inOuen ce of the monetary in terest rate. We assume tha t the latter va ri a ble is releva nt as a determining factor of t he net profits and /or as a n indicator of the relative co nve nience of real ve rsus finan cial investments, rather th a n as a component of the cost of capital services. In the su ppl y block, we have introduced an equ at ion for the industria l added value where the foreign demand and the indi vidu al components of the dem an d of the public and private scctors a ppear as determinin g factors. Furthermore, thc differen ce between the rates of growth of domestic a nd import prices appears as a me as ure of s ubstitutabi lity between domest ic and forei g n s upply. To ex pla in the trend in the added va lue of pri vate se rvices, we have further assumed th at t he latter st ric tly depend s on the industria l growth. This is in line with the kaldorian hy pothesis of the ma nufacturin g sector as engine of growth of the entire eco nomic syste m. In the labour market block onc equation for the total hours worked and o nc for industrial dependent employmcnt have bcc n introd uced. In both equations we have allowed for inco me cffects a nd for both s ho rt and medium term price effects. The first price effect, represented by the d ifference betwee n unit labour cost and w holesale prices, constitutes an indicator of the conve nience of redu cing (in a bsolu te terms) labou r input. Indeed , a reduced translation of la bour cost on prices determines, coeteris paribus, a compression of profit m a rgin s a nd therefore induces production restructuring in order to reduce labour requirem ents. Furthermore, wc have introduced the ratio of wages to machine prices with a time lag of two-three years, in the assumption that s uch variable represents an incenti ve for the replacement of la bour by instrumental goods. This s ubs titution process is assu med to occur in time rather tha n a lon g a n isoquant a nd thus it exhibits different features from those of the static and reversible substitution of neocl assica l models. From empirical tests, we have observed a certain degree of stickiness in the yearly adjustment of effective to desired values for the industrial dependent
261
employment. This phenomenon can be ascribed to costs of adjustment as well as to institutional constraints. The employment in the private service sector is explained in the model by the sectorial product and by the lagged value of the total dependent employment except that of private services. Indeed , it has been observed that in the Itali a n experience the increase in service employment has been accelerated after periods of decreasing of industrial employment. This phenomenon of dynamic tra nsfer can be due to a residu a l role of the private services sector. Fin a lly, we have tested the hypothesis that mass unemployment in It a ly be ca used by the slowing down of the economic growth , the increase in labour supply (particularly female labour) and indus trial restructuring (substitution of la bo ur by mac hines). In the income dist ribution block, we have adopted in the equation for wages a specification of Phillips-Lipsey type featuring , besides the un employment rate , the index of consumer prices. This s hould account for the current indexing mech a nis ms as we ll as for trade union negotiations aiming a t inOation rate reco very. We have es timated in the price block an equation for wholesa le prices whe reb y it has been verified that the application by oligopolystic firm s of a mark-up on the cost of labour a nd raw materials (imported in the Ita lian case) is indepe nd ent from Ouctuation s in aggregate dem a nd but co nstra ined by forei g n competition. Finally, in the foreign trade block, we have es timated two equations allowing a n analysis of the dependence of It a lian economy on the interna tional context: imports s how a high degree of elast icity to income growth, while expo rts result to be s tro ng ly conditioned by foreign dem a nd and, to a lesser extent, by the competitivity of Italia n firms on the in ternat iona l market. To conclude, we have to stress th at t he dynamic structure of the model wc have built requires for a policy evaluation a medium-long term a nal ys is of the consequences of different politica l measures, since some impact effects ma y be incre ased or completely offset by th e lagged effects. This h as several co nsequences. For insta nce the problem of a n in verse relations hip between rea l wages and unemployme nt ca n be reconsidered by taking past trend s in wages, prices of fi nis hed products and ma chines into account. This means th at the d y namic features of our model render a static ana lysis of the trade off impliCit in Phillips' curve la rgely irrelevant. Therefore, to evaluate in o ur si mulation experiments the effectiveness of, say, a wage policy in changing the rate of unemployment s hort a nd medium term gains have to be considered . In fact, an increase in wages can have impact effects a nd lagged effects going in opposite directions: the positi ve effects of a n increased demand for consumption goods can be balanced, through the falling profit rates, by reduced in vestmcnt ince ntives. In the medium term, an increase of machine prices lower than that of wages will res ult in a more efficient use of labour resources , with negative consequences on employment levels.
262
M. Damiani alld L. Panattoni THE DESIGN OF THE EXPERIMENTS
The main goal of our simulation experiments with the above described econometric model was the evaluation of the improvements in the model solutions when the initial shocks on the instruments are modified, as previously described, to better exploit their effectiveness. For this purpose several simulations have been performed for a five years horizon, from 1978 to 1982, chosen inside the sample period in order to avoid problems arising from the need of assigning values to the exogenous variables. All these exercises were substantially based on the comparison of three different model solutions: i) a baseline solution obtained by assigning the historical values to the instruments, ii) a standard simulation solution, in which the instruments have been shocked by some initially chosen values OX. and iii) an effective simulation solution, in which the shocks have been computed according to (2). The simulations have been performed following two different operational procedures. In the first case we used all together the instruments from 1978 to 1982 and evaluate their effectiveness on all the objectives contemporaneously by means of a single simulation for the whole period. From now on this procedure will be referred as fixed horizon simulation. In the second case we tried to follow more closely the actual behaviour of a policymaker who wants to use econometric models as an effective aid in policy selection, and we made the hypothesis that at each year he uses the simulations just to choose the values of the current instruments, but nevertheless he wants to see how the alternative policies affect the objectives over a given length horizon (in our exercises we used a time horizon of four years) . So, for example, we start by considering the instruments at 1978, we evaluate their effectiveness on the objectives up to 1981 to be used for the computation of the effective shocks according to formula (2); then we repeat the procedure for the period 1979-1982, and so on up to the final simulation for the years 1982- I 985. Clearly in each simulation only the values computed for the first year are retained for the final comparisons. We will call this procedure as the moving horizon simulation. In our simulation exercises we assumed as primary target the reduction of the unemployment rate and, subordinately, the control of the inflation rate and the growth of the domestic product and of the industrial productivity:. in practice we tried to achieve, through an expansion of the aggregate demand, a partial absorption of the unemployment avoiding at the same time a worsening of the trade deficit. In order to quantify the priority we gave to the reduction of the unemployment rate, rather than to the other objectives, we assumed equivalent improvements equal to I for it and equal to 3 for the others (note that all the objectives are expressed as growth rates). In order to hit these targets a policy oriented to increase the public expenditure and to stimulate the
private investment, resulting in the desired increase of the aggregate demand, has been adopted. The control variables were long term interest rate, expenditure for public goods, public investment (both in billion of liras at constant prices) and public sector employment (in thousands) and the initially chosen shocks were 20%, 5%, 10% and 4% of the historical values, respectively.
RESULTS In order to evaluate the gain resulting from the use of formula (2) to compute the shocks on the instruments, we first measured the differences between the values of the objectives obtained from the standard (a = 0) and from two effective simulations (a = I and a = 2) and their baseline values, using for each one the relevant equivalent improvement as unit of measure. Then we built up a global indicator by averaging these measures over all the objectives for each year. Table I displays these quantities, together with their mcan values over the simulation period. The values shown in this table mean , for example, that, having assumed equal to one the equivalent improvement of the unemployment rate for all the years 1978-1982, the use of a = 2 in the fixed horizon simulation yields a mean global improvement on all the objectives which is equivalent to an average reduction of 1.09 units of the unemployment rate each year. It must be pointed out that all the displayed values have been obtained by shocking the instruments with the same total amount and that they differ only for the way in which this quantity has been distributed among the instruments. TABLE 1 a=O Lh.
a=1 m.h.
Lh.
a=2 m.h.
1978 1979 1980 1981 1982
1.26 0.66 0.75 0.76 0.72
1.12 0.59 0.76 0.81 1.51
1.18 0.70 0.86 0.90 0.90
0.98 0.52 0.76 0.86 2.31
1.09 0.76 0.97 1.0 I 1.07
mean
0.83
0.96
0.91
1.09
0.98
It is also evident that the improvements resulting from the fixed horizon simulations, for a #- 0, are globally higher than those obtained with the moving horizon simulations and that this better performance is entirely due to the last year values. This depends on the fact that, contrary to the latter, the former are not conditioned by the values the objectives assume in the years immediately subsequent the simulation period. From our experiments it came also out that the fixed horizon approach generates a higher instruments instability. For these reasons we concluded that the moving horizon simulation is actually the more realistic
Effecti\'e Use of Economic Polio' Instruments one and in the following, for brevity sake, we will refer only to it. In Table 2 the gains for eaeh single objective, over the whole simulation period, are shown, still measured in equivalent improvements of each one. To have actual variations we obviously sho uld triplicate the values relevant to inflation, GDP and productivity. TABLE 2
Inflation GDP Unemployment Prod uctivity
a=O
a=1
a=2
0.14 0.98 2.32 0.72
0.17 1.15 2.40 0.82
0.21 1.31 2.48 0.91
Figure I shows in detail for each objective the time paths of the differences between the values resulting from simulations in wh ich the instrumcnts have been shocked, with different values of the parameter a, and the baseline solution (circles refer to a = 0, stars to a = I and crosses to a = 2). It is interesting to note the difference between the behaviour of the most important objective (unemployment) and the behaviour of the others: the first shows a continuously increasing improvement over the time, while the others have the maximum improvement just at the beginning of the simulation period. Furthermore for all the four objectives an increase of the value of the parameter Ca = 0) provides worse results in the first year (for the unemployment in the second year too), while better values are obtained only in the subsequent years. This behaviour clearly depends on the dynamic characteristics of the econometric model and in particular on the fact that the delayed multipliers, up to four years, of the objectives with respect to the instruments are often equal in size and opposite in sign to thc impact ones. As a final poin t we report some results on the effectiveness of the used instruments: table 3 displays the percentage of the total gains for each year due to the single instruments and their average va lue over the whole simulation period, while in figure 2 the time paths for the four instruments are shown (the meaning of the symbols is the same as figure I , with the addition of the historical values, indicated by the full circles).
263 TABLE 3
Interest Rate
Govern. Expend.
Govern. Invest.
Public Empl.
1978 1979 1980 1981 1982
36.9 40.7 46.9 62.4 52.8
22.8 16.5 13.2 2.0 13.4
25.0 26.9 26.9 28.1 23 .4
15.3 15.9 13.0 7.5 10.4
Mean
48 .0
13.6
26.0
12.4
From the values of the table and from the relative positions of the four lines in the figure it is clear that the shock initially assumed for the interest rate is the most effective and those for the Government expenditure on goods and for the public employment are the less effective ones: in fact, in the first case, as a increases the instrument assumes values which move away from the historical ones, while for the other two an increase in a brings their values back. The shocks on Government investment have a mean effectiveness and their initial values are not influenced too much by the values given to the parameter a .
REFERENCES Geoffrion A.M., J .S. Dyer and A . Feinberg (1972). An Interactive Approach for Multi-Criterion Optimization. Management Science, 11, 357-368. Malinvaud E. (1977). The Theory of Unemployment Reconsidered. Basil Blackwell, Oxford. Panattoni L. (1988). Interactive Preference Elicitation in Macroeconomic Decision Models. Journal of Economic Dynamics and Control, g, 109-116. Sylos Labini P. (1987). The Theory of Unemployment, too, is Historically Conditioncd . BNL Quarterly Review, ill, 379-435. Wallenius, H. (1982). Optimizing Macroeconomic Policy, Review of Approaches and Applications. European Journal of Operations Research, 10/3, 221-228.
1\'1. Damiani and L. Pan
264 "N
INTEREST RATE
GDP
GOV. EXPENDITURE ON GOODS
'" q
.,'"
., "
/'
INFLATION
1980
"HI7& 19B1
1982
197!!!
N
PRODUCTIVITY
~
1979
1980
19B1
1982
~
GOV. INVESTMENT
V
~
§
§~'~77.~-1~'~7'~--1~"~O~~19~'-1~-'~'.2
8
~
~
PUBLIC EMPLOY M[1\'T
~
§
. q
8
UNEMPLOYMENT 19 78
Fig 1 - Time paths of the gains on the objectives
Hil7S
1980
1981
1982
~9 78
1979
1980
Fig 2 - Time paths of the instruments
1981
1952