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Forecasting industrial bottlenecks
Forecasting industrial bottlenecks An analysis of alternative approaches H.O. Stekler
Thispaper develops a number of approachesfor identtfying those industrial bottlenecks which are likely to occur at high levels of employment and overall capacity utilization. A scenario is constructed which drives the US economy to those levels. This scenario is simulated using the Data Resources Inc. macroeconometric and satellite models. The alternative approaches for identifying bottlenecks include an examination of (i) high growth rates relative to past growth, (ii) high levels of capacity utilization, (iii) increases in prices, and (iv) delivery or lead times which are lengthening. Kqvwords: Industrial
bottlenecks;
Forecasting;
approaches to the aforementioned scenario. The scenarios is simulated using the Data Resources Inc. (DRI) macroeconometric model [ 1] and DRI satellite models. The strength and limitations of each technique are presented. After the methodology and data have been explained the results will be presented and analysed.
Although there have been many studies which have examined issues in capacity utilization there have been few modelling efforts which might identify and/or forecast possible industrial bottlenecks. Since these bottlenecks usually occur when the economy is operating at high levels of employment and overall capacity utilization it is necessary to construct a simulation in which these conditions prevail. Many different scenarios could be constructed to drive the US economy to high levels of employment and capacity utilization. We chose a scenario in which high levels of defence expenditure stimulated the US economy. We use a macromodel and present a series of satellite models for identifying bottlenecks which would equally well have been applied to alternative scenarios such as a boom generated by investment spending. While the techniques presented here have general applicability, the results (ie which industries are likely to be bottlenecks) would differ between scenarios. This paper first explores possible techniques for identifying bottlenecks and then applies these
Identification of bottlenecks An economic bottleneck is a condition that obstructs, slows down or places a constraint on the free flow or operation of the economy. Given this definition the question becomes one of developing operational measures which might show the presence of such constraints or might enable an observer to forecast when such bottlenecks will exist. These constraints would usually exist at the microlevel, and it is at this level that these forecasting techniques must be developed. If a bottleneck existed in a particular sector it would generally be a supply-demand imbalance in that industry and certain economic results would manifest themselves. The appearance of these manifestations would then be prima facie eivdence that the bottleneck existed. Moreover, if these economic factors could be modelled, the forecasts of the particular model would provide information about future possible bottlenecks. The economic results most frequently associated with excess demand conditions are (i) high growth rates relative to past growth; (ii) high levels of capacity utilization; (iii) increases in prices; and (iv) delivery
Industrial College of the Armed Forces, National University, Ft Lesley J. McNair, 6000, USA.
Washington,
Defense DC 20319-
The opinions expressed in this paper are those of the author and are not the views of the National Defense University or the Department of Defense. I wish to thank R. William Thomas for comments on an earlier version of this paper. All remaining errors are, of course, my own responsibility. Final manuscript
received
0264-9993/90/030263-12
14 August
1989.
Simulation
Ltd
263
Forecasting industrial bottlenecks: H.O. Stekler
1
I Distribution of
final
demands
among
industries ------_
/---___
Defence outlays
by
1 I
w
budget
Non-defence
category
Defence
I (Bridge
tables)
(Total)
I A
I [Department
-.
of
Defense
only)
hlacroeconomic forecasting
1
I
Sectoral prices
t Total Industrial
empioyme3t *
by
sector
4
outputs
1 Strategic
and
critical materials requirements (Bureau
Figure 1. Defence
Mines)
economic impact modelling system.
or lead times which are lengthening. In this paper models of these four phenomena will be utilized with the DRI macroeconometric model [ 1] ‘. Projected industrial requirements The first technique uses an input-output model in conjunction with the Defense Economic Impact Modeling System (DEIMS, see Department of Defense [2)) to provide comprehensive and disaggregated measures of the industrial impact of defence spending ie the required industry growth rates to meet the projected demand. This model has been described extensively (Department of Defense [2], Lee and Stekler [ 73) and only the salient characteristics will be mentioned here. Figure 1 describes the model. Basically DEIMS is a series of procedures which translate the data in the five-year defence plan into a ’ Labour market problems might also cause bottlenecks phenomenon is not modelled in this paper.
264
of
but this
vector of final defence demand for each of the 400 commodities represented in the DRI input-output system. Defence expenditures for the five-year defence plan are organized into 50 categories by DEIMS. The data in each of these 50 categories of defence expenditures represent the appropriations which are planned for the next five years. These budget categories represent total obligatory authority (TOA) and not outlays, and all are measured in constant dollars. The first step in the estimation process is to translate these TOA accounts into the pattern of actual outlays. Thus,
Z;i = 1
N$A~_~,
j=
1 . . . 50
(1)
where outlays in year t for the jth category of defence expenditure (Zj) are a weighted average of current and previous years’ appropriations (A) for the category. The weights used vary with each budget category. This procedure is considerably more detailed than published
ECONOMIC
MODELLING
July 1990
Forecasting industrial bottlenecks: H. 0. Stekler
techniques used to estimate the relationships between defence orders and expenditures in the national income and product accounts (Galper and Gramlich [ 51, Galper [4], Lee [ 81). For each year, the 50 element outlay vector is converted into the defence final demand vector by assuming a fixed relationship between the budget category and the final demand for each of the 400 commodities ie each commodity takes a fixed share (b,,) of the outlays in each budget category. Thus, d, = BZ,
where d, is the (400 x 1) vector of final defence demand for each of the 400 commodities, B is a (400 x 50) translation matrix and Z, is the (50 x 1) vector of outlays by budget category. This vector ofdefence final demand is then combined with corresponding vectors of non-defence final demand and the input-output coefficients of the DRI model are used to generate estimates of gross output originating in each industry. From these gross output measures it is possible to calculate the average yearly growth of each industry’s output. If the objective is to determine whether the projected increases were feasible or whether bottlenecks might occur, a possible indicator would be a comparison of the required growth rates with previously observed rates of growth. In the absence of supply side information this comparison of projected growth rates of demand with previously observed rates of output growth might be used as a proxy for excess demand. The rationale is that an industry might not have invested in enough capital equipment to be prepared for such a sudden surge in demand. On the other hand, the previously observed actual growth rates might have been unreasonably low because a recession had occurred and the capacity to meet the increase in demand would in fact be available. Consequently, these growth rate comparisons must be used cautiously; they are obviously subject to interpretation and are demand rather than supply measures of possible bottlenecks.2 Capacity3
A model specifically designed to analyse the possibility that bottlenecks may occur was developed by Feldman,
’ Other information about potential bottlenecks could include capacity utilization rates, capital expansion rates, previous peak levels of output, increases in imports etc. Some of these measures will be analysed below. ‘The literature has discussed the concepts and difficulties associated with measuring capacity and capacity utilization. See for example, Klein and Preston [6]; Perry [ 111; Schnader [ 131 and Morrison [9]. The approach used in this model is based on production functions.
ECONOMIC
MODELLING
July 1990
I
I
Figure 2. The industrial
capacity
monitoring
system.
Salmon and Palmer [3]. The industrial capacity monitoring system (ICMS) ‘has been developed to relate the demand for an industry’s output to its capital expenditure requirements and then relate this to the industry’s potential to produce output’. This model is designed to be a satellite model to the DRI macroeconometric model. In this satellite model (ICMS) the demand for an industry’s output is originally derived, as in the DEIMS case, from the DRI macromodel and input and output system. These estimates are the constant dollar values of output originating in each sector. However, through an iterative procedure these output estimates are then modified so that they are determined simultaneously with the industry’s investment requirements. The input-output tables provide output data for approximately 400 sectors, and an employment model provides labour estimates for the same 400 sectors. However, in order to derive capital stock, investment and capacity estimates, the data at the 400 sector level of detail must be aggregated to the 71 sector level. The satellite model then simultaneously determines potential output, net capital stock, sector investment flows and capacity utilization levels at the 71 sector level of aggregation. The investment flows are then used as inputs into the I-O model which provides another set of output estimates. This iterative process continues until the solution converges on a set of outputs at the 400 sector level which is consistent with the investment flows. A final set of estimates of capacity utilization at the 71 sector level is then obtained from these data.4 The supply side equations of this DRI satellite model (copied from Feldman et al) are presented in the appendix. Figure 2 is a schematic diagram of the models and procedures. 41t should be noted that ICMS assumes that prices are constant. Thus, if demand exceeds the available supply, it is assumed that imports increase.
265
Forecasting industrial bottlenecks: H. 0. Stekler Price and cost pressures
A third possible method of identifying bottlenecks is to identify industries where prices are increasing as a result of demand pressure. Another satellite DRI model, the cost forecasting service (CFS), can be used in this analysis. This model links each product’s specific price model to an overall macroeconomic forecast. Since each commodity’s price is linked to these macroeconomic forecasts, it is possible to determine the price response associated with an increase in demand. The basic equations of this model are of the form: Aln P, = a,, + a, Aln C, + a2 D, C,=b,+b,W,_,+b,
i
miMi.t
(3)
-ki
I=1
+b,
D,=O,-
t j=t
f
ejEj,r-kj
4 O,_i
(5)
i=l
Ot=
$
fiXi.,-ki
(6)
material prices affect intermediate goods prices which in turn influence final goods prices. Consequently, when a particular commodity shows a substantial price increase, this increase may be due either to demand pressures affecting the particular product or to cost pressures passed through from earlier stages of production. Since it is not possible to disentangle these effects we cannot conclusively state that predicted price increases are indicative of bottlenecks. However, the opposite result (ie the absence of price increases) would indicate the absence of bottlenecks throughout all the stages of production. Lead times
Finally, DRI has constructed another set of satellite models designed to predict increases in delivery lead times. If increasing lead times are an indicator of potential bottlenecks, examining these models would be a direct measure of such constraints. However, these models are only available for nine commodities.5 For these commodities estimates of lead times were obtained from regressions in which the independent variables were the measures of market strength (which were explained above). Thus the basic equation is of the form
i=l
where P is the product’s price, C is the product’s production cost, D is a measure of market strength (excess demand), W, Mi and Ej are a labour wage index, material and energy prices indices respectively and the bi represent the fraction of total expenditures attributable to these three components of cost. 0 is the measure of demand for this product, with Xi the index of production of the ith industry which uses the product as an input. Equations (3) (4) and (6) are relatively straightforward. Price changes depend on cost changes and market strength, with cost changes dependent upon the costs of labour, material and energy inputs. Demand for the product (6) is a function of the production of sectors which buy this product as an input. Finally, (5) is supposed to measure the imbalance between supply and demand, with supply ‘defined as a T-term moving average of past market strength’. Under the assumption that desired inventory/sales ratios are held constant, ‘this notion [of supply] is an expectational one in which industry is assumed to keep productive capacity and inventory levels in line with recent demand experiences’ (DRI [I]). These basic equations are sometimes modified to capture asymmetrical effects resulting from differences between weak and strong markets. As a caveat, the model is basically a stage of processing model (see Popkin [ 111) in which raw
266
where D,, the measure as (5) above.
of market
strength,
is defined
Summary
In summary, we will examine four measures which might provide meaningful information about potential bottlenecks. All measures are applied to the same macrosimulation and the results may thus be compared. The first of these measures is strictly demand oriented and involves the comparison of predicted growth rates with historically observed growth rates. The other measures involve the supply side and examine estimates of capacity utilization, prices and lead times.
Simulation of high level defence expenditures: alternative scenarios Methodology
The analysis of potential bottlenecks is an economy which is assumed to be high levels of defence expenditure. For the study it is assumed that peacetime
undertaken for stimulated by the purpose of Department of
‘These commodities are: steel bars and rods, lift trucks, plastic pipe and tubing, pipe fittings, three kinds of metal sheet and strip, copper tubing and fibreglass.
ECONOMIC
MODELLING
July 1990
Forecasting
Table 1. Values of various macroeconomic
variables, baseline and Vietnam-type
industrial bottlenecks:
H.O. Stekler
conflict, with and without a tax increase (%).
Years 1
2
3
4
5
Rate of growth: % real GNP Conflict and no tax Conflict and tax Baseline
6.7 6.3 5.4
4.5 3.7 2.9
2.8 2.4 2.4
2.3 2.4 3.0
1.9 2.3 3.3
Rate of inflation Conflict and no tax Conflict and tax Baseline
5.4 5.4 5.0
6.1 5.9 5.0
7.3 6.4 5.2
7.8 6.5 5.4
7.3 6.3 5.8
Unemployment rate Conflict and no tax Conflict and tax Baseline
1.7 7.9 8.1
6.9 7.3 7.9
7.0 7.4 8.1
7.0 7.5 8.0
7.2 7.6 7.6
Industrial production (growth) Conflict and no tax Conflict and tax Baseline
13.3 12.6 9.4
8.4 7.0 3.5
5.8 5.2 3.5
2.2 2.6 4.2
-0.6 0.2 4.3
Capacity utilization level Conflict and no tax Conflict and tax Baseline
84.1 83.6 81.2
89.3 87.7 82.3
92.2 90.2 83.6
90.6 89.1 84.3
86.0 85.3 84.5
7.0 7.1 6.2
7.8 7.9 6.4
8.0 8.2 6.6
7.8 1.9 6.7
7.4 7.5 6.9
21.5 5.0
16.4 5.0
5.5 5.0
-0.3 5.0
-3.7 5.0
Percentage of GNP taken Conflict and no tax Conflict and tax Baseline Rate of growth Conflict Baseline
by Defense Department
of real Defense Department
expenditures
Defense expenditures were increasing at an annual rate of 5% expressed in real terms. At the same time a Vietnam-size conflict and the associated military expenditure were also imposed upon the economy. The simulations that are presented use the situation prevailing in 1984 as an example. However, the results will be presented in generic terms ie referring to years l-5 of such a simulated situation. It is also assumed that military expenditures for this war will be of the same magnitude in real terms, will have the same time distribution and will display the same procurement distribution as occurred in the actual Vietnam situation. (The methodology for calculating the yearly distribution of outlays is presented in Lee and Stekler [7]). There are three simulations which are relevant for the analysis. The first is a baseline case which assumes that no war occurs. The other two assume a war: in one case there is a personal tax increase equal in magnitude to the increased level of defence expenditure; in the other there is no tax hike. The results for the five years of the conflict are presented in Table 1. Macroeconomic
impacts
The impacts of increased Defense Department spending are consistent with the usual macroeconomic results. ECONOMIC
MODELLING
July 1990
An increase of 1 to 1.5 % in the Defense Department’s share of GNP would stimulate the economy but would also increase inflationary pressures and substantially increase the rate of capacity utilization. The baseline scenario predicts that capacity utilization would be in the mid-80% range, whereas the two alternatives show that capacity utilization could exceed 90%. In fact, the 92.2 % rate predicted under the no tax assumption exceeds the peak utilization rate which occurred in the actual Vietnam build up. Given this potential for bottlenecks, it is necessary to determine which sectors of the economy are most likely to experience bottlenecks.
Identification of bottlenecks: results Projected industrial requirements
As previously noted, using the DRI macro model, the 400 commodity input-output data and DEIMS, it is possible to obtain the projected growth rates in the requirements for these 400 commodities. There are alternative ways of ranking the data. We have chosen to examine those industries for which the Defense Department’s share of output is the largest. Table 2 accordingly presents the projected growth rates for the first decile of commodities ranked on the basis of the 267
2
c
Ammunition expenditure small arms Other ordnance New military facilities Tanks and tank components Shipbuilding and repairing Complete guided missiles Aircraft engines and engine parts Aircraft parts and equipment Radio and TV communication equipment Aircraft Engineering and scientific instruments Measuring and control instruments Small arms ammunition Explosives Non-ferrous forgings Semiconductors Machine tools. metal cuttmg Electronic components not classified elsewhere Industrial trucks and tractors Primary non-ferrous, not classified elsewhere
1984 ranking by defence share of industry output (baseline)
9.3 8.6 7.5 8.8 7.0 5.5 7.2 7.5
8.8 8.4 6.6 6.6 6.7 7.1 6.3 11.3 12.3
9.8 7.2 5.6
69.3
68.9
63.7
61.0
60.5
58.1 55.1
54.2
53.5
48.0 42.0
31.3 29.3
28.1
27.0
26.3
25.6
5-Year compound annual rate of growth
95.7 91.5 89.5
Year I defence share of industry
6.3
13.5
15.8
24.1
14.4 17.0
18.0 18.5
15.3
16.0
20.5 23.4
17.8
18.5
15.2
16.4
17.7
28.2 22.2 19.8
Vietnam-like notax compound annual rate of growth years l-3
1.1
8.4
10.3
13.1
8.0 10.6
9.0 9.3
8.6
8.8
11.4 13.9
9.4
9.4
7.7
7.9
10.0
13.0 11.0 9.5
Vietnam-like compound annual rate of growth years l-5
5.6
13.2
15.0
23.8
13.2 16.4
17.3 17.5
14.9
15.7
20.0 21.9
17.3
17.7
14.9
16.2
17.7
28.2 22.2 23.0
0.0
8.4
10.0
13.1
7.4 10.4
9.0 9.3
8.4
8.5
11.1 12.9
9.1
9.1
7.6
8.2
10.0
13.0 11.0 10.9
Vietnam-like with-tax compound annual rate of growth’ years l-5
baseline
conrinurd
0.3
5.9
12.1
-21.1
8.5 14.2
36.9 - 3.2
17.7
0.8
10.5 -6.6
- 5.0
2.7
1.8
Il.3
6.3
10.1 19.4
9.7
on p, 269
Historical growth rates 5 previous years
takes largest share of output: 5 years of simulation,
Vietnam-like with-tax compound annual rate of growth” years l-3
Table 2. Industrial impact of a Vietnam-type war: projected growth rates of 40 commodities for which Defense Department and war scenarios with and without tax increases.
$
7.9 3.4
4.2 2.5 0.7 3.6 6.6
6.8
6.2 3.3 4.0 5.4 5.0 2.8 1.4
5.1 3.1 4.8
Il.9
17.8
17.1
16.8 16.7
16.7
16.3
14.9
14.5 14.1
13.9 13.6 13.3 13.3 13.3
13.1
12.1 12.0
5.6 8.6
10.3
8.1 8.1 8.3 6.7 5.4
9.1 7.9
7.1
3.4 5.5
7.1
4.1 5.4 5.4 3.3 1.4
6.5 3.2
6.8
6.8
10.8
10.7
6.4
3.6 -5.4
4.4
9.1
1.3 0.0
9.0
6.4
1.9
15.0 10.7
1.9 7.8
Vietnam-like compound annual rate of growth years 1-5
12.4
5.9
Vietnam-like notax compound annual rate of growth years l-3
6.6 7.7
9.4
9.5 8.1 7.1 5.6 2.8
6.9 5.8
9.6
10.3
8.4
6.3 0.0
7.8
10.0
13.0
3.8 11.5
from the year
Vietnam-like with-tax compound annual rate of growth” years l-3
from the values of the year 1 no war scenario as the base. If they had been calculated activity would have been higher in year 1 if there had been a war.
6.1
2.4 1.2
S-year compound annual rate of growth
18.8 18.7
Year 1 defence share of industry output
“These rates of growth are calculated would have been lower, for economic
Electronic tubes Plating and polishing Machine tools, metal forming Primary metal production not classified elsewhere Non-ferrous castings not classified elsewhere Metal ores mining not classified elsewhere Primary zinc Steam engines and turbines Optical instruments and lenses Non-ferrous rolling and drawing not classified elsewhere Non-metallic mineral production not classified elsewhere Wafches and clocks Cold finishing, steel shapes Aluminium castings Primary aluminum Copper ore mining Footwear cut stock Electrical industry apparatus not classified elsewhere Iron and ferro alloy ores mining Metal heat treating Average rate of growth (40)
1984 ranking by defence share of industry output (baseline)
Table 2 (continued)
2.3
3.3 - 1.0
- 0.3
8.0 5.8 - 10.2 - 5.1 0.2
-2.6 - 6.4
-1.7
6.6
16.8
- 13.7 -19.0
4.6
6.1
~ 14.9
-8.0 2.2
Historical growth rates 5 previous years
I war scenario as the base, the growth rates
6.8
2.1 5.1
6.2
4.7 5.6 4.6 2.6 1.4
5.5 1.8
6.2
6.7
5.7
2.1 -5.4
4.4
6.1
7.9
1.4 7.5
Vietnam-like with-tax compound annual rate of growth” years I-5
Forecasting
industrial bottlenecks:
Table 3. Distribution
H.O. Stekler
of capacity utilization,
52 industries, assuming normal investment. three scenarios, five year average, years 3 and 5. Conflict tax
Conflict no tax
Base Level of capacity utilization (%)
Five-year average
Year 3
Year 5
Five-year average
Year 3
Year 5
Five-year average
Year 3
Year 5
Less than 70 70-79.9 80-89.9 90-94.9 95-99.9 100 or more
10 14 14 7 2 5
10 16 12 6 3 5
9 13 17 4 4 5
7 17 14 7 2 5
7 14 14 9 3 5
11 12 19 3 3 4
8 18 13 7 1 5
7 18 14 6 2 5
11 14 17 3 3 4
Defense Department’s share of output ie the top 40 commodities.6 Among these 40 commodities the Defense Department’s share of output ranges from over 90% of ammunition and ordance to around 12% of output of products such as electric industrial apparatus, iron and ferroalloy ores mining and metal heat treating. Among the top 14 commodities are the items usually classified in the defence sector ie tanks, aircraft, ships, missiles, arms etc. The remaining products are the industrial items required to produce these weapons or are contained in the defence systems. For the baseline case, which assumes a 5% real growth of defence expenditure, the average compound annual growth rate of these 40 commodities for the five years is expected to be 6.1%. Only 12 of these industries are expected to have growth rates of less than 5 % per year. Similarly, in the event of a Vietnamtype war during the five-year period, the projected growth rates of these 40 commodities are higher, averaging 7.1% per year without a tax increase and 6.8 % even with the tax surcharge. Moreover, with the rapid build up of the defence effort between years 1 and 3, the growth rates for those years are substantially higher. Even with a tax increase, more than half of the commodities display double digit rates of growth between years 1 and 3. The feasibility of these growth rates or the converse, the possibility of bottlenecks, remains to be determined. The last column of Table 2 displays the growth rates that each industry achieved during the year l-3 period. A comparison of the required with the historically observed growth rates shows that, in many instances, the former exceed the latter.’ Does this necessarily mean that bottlenecks would exist in the year l-3 time frame? 6Alternatively, the industries could have been ranked by projected growth rates. If this ranking had been used, about half of the commodities for which Defense Department demand is the largest would have been included among the 40 commodities which were projected to have the highest growth rates. ‘This is especially true for the year l-3 rates for the war scenarios.
270
As a caveat, since our generic simulations used 1984 as an actual starting point, it must be remembered that the 1981-84 period included a severe recession which severely retarded the growth of many of these industries. Second, the presence of negative growth rates over a historical period may indicate either that capacity utilization is low and that growth potential exists or the possibility that exit from the affected industry has occurred and the capacity is no longer available. These remarks suggest that our attention should focus not only on the required rates of growth but also on the availability of capacity to produce that output and/or of imports to meet excess demand. We shall turn to the availability of capacity measures shortly. Unfortunately, we cannot, as yet, address the issue of import availability. To summarize, it is possible to derive projected growth rates for 400 commodities. Analyses of these data only permit a comparison of required growth rates with historically observed rates. Projected capacity estimates are missing but, at the present time, they cannot be generated at the same disaggregated level of detail for which the input-output projections are available. Thus the projected growth rates can, at best, only suggest where bottlenecks might occur. We now turn our attention to questions concerning the availability of sufficient capacity to meet the required demand.
Capacity
Before providing the capacity figures for the various industries under the three alternative simulations it should be noted that the industry groupings of the industrial capacity monitoring system (ICMS) are not identical with those of the input-output system. In fact the 400 commodities of the input-output system are aggregated into 71 two- and three-digit SIC industries in ICMS. In addition, the current version of ICMS only contains equations for the 52 industries comprising the manufacturing sector of the economy. Table 3 presents the distribution of capacity utilization
ECONOMIC
MODELLING
July 1990
Forecasting
rates for the three scenarios (base with 5 % real defence expenditure increase and a Vietnam-type war with and without a tax increase). The capacity utilization rates are presented for the average of the entire five-year period and for the third and fifth years individually. On the assumption that investment behaviour in each industry will continue as it has in the past, four or five industries are clearly expected to display bottlenecks. The capacity utilization rates for these industries exceed 100% either for the entire period or for one of the specific years.8 Depending on the scenario an additional one to four industries have utilization rates which exceed 95 OK9 In the prior 12-year period there was only one industry, food, in which the average capacity utilization exceeded 95 %. Thus, about 10 % of the 52 industries may be expected to develop some degree of capacity constraint under any of the three scenarios. These capacity utilization figures were based upon two fundamental assumptions: first that the relationship between imports and demand would not change; second that the investment behaviour that has characterized these industries will prevail in the future. If the increased demands were met by an increase in the share of imported goods, the estimates of capacity utilization would be biased upward. We recognize this possible bias but could not correct for this in the simulations. On the other hand it is entirely conceivable that industry officials might believe that the increase in output attributable to Defense Department growth (either for a peacetime build up or for a limited war) was not likely to be sustained. In that event, investment behaviour might be altered and new capacity might not be added. It is important to determine what capacity constraints might develop if investment behaviour were altered. An extreme assumption is that no new capacity would be added during the entire period and that capital would remain at the original level. lo Under this assumption, if output growth were to continue at the projected levels, significant capacity
sThe number
of industries which display high levels of capacity utilization may change from scenario to scenario and year to year for two reasons. Output may change or investment and the level of capacity may be affected. Either might be influenced by the government policies which have been selected. 9 The industries with projected rates over 95% are food, miscellaneous textiles, miscellaneous fabricated textile products, other furniture and fixtures, rubber and miscellaneous plastics, glass, stone and clay, other transport equipment, instruments and supplies, and paperboard. lo It is recognized that this assumption is heroic, especially since output and investment flows were determined simultaneously. If there were no net investment, output flows in other industries would decline and capacity utilization would decrease. Nevertheless, this procedure is useful in bounding the problem.
ECONOMIC
MODELLING
July 1990
industrial bottlenecks:
H.O. Stekler
constraints would appear in the third and fifth years. For example, even under the base case in the third year, 25 % of the industries would be operating at more than 95 % of capacity. In the other two scenarios, 35 % or more of the industries are projected to have some types of capacity constraints, if this extreme assumption holds. These results clearly suggest the need under all scenarios to have an adequate rate of growth of investment expenditures to avoid potential capacity constraints. Alternatively, increased imports would be required. Up to this point our analysis has focused on the entire set of 52 manufacturing industries. What is the condition of the 40 input-output industries for which the Defense Department accounted for the largest share of output? These 40 industries could not be mapped one for one with any of the 52 two- and three-digit SIC industries. Instead, they constituted only parts of the more aggregated industries. For the purpose of this analysis one of the 52 two - (three-) digit SIC manufacturing industries was classified as a defence industry if it contained any of the 40 inputoutput industries. This was true no matter what proportion of the aggregate output was attributable to those industries. Of the 52 manufacturing industries 17 contained at least one of the 40 commodities for which the Defense Department took the largest share of output. Table 4 presents the distribution of capacity utilization ratios for those 17 industries. Although showing a slightly higher potential for bottlenecks, the distribution of these ratios is not substantially different from those of the other 35 industries. Two or three of could have significant capacity the 17 industries” constraints if normal investment prevailed, while over 50% would display this characteristic if no investment occurred. As a caveat it must be remembered that data for these 52 manufacturing industries are even more highly aggregated than are the figures in the input-output tables. Thus it is possible that a particular commodity might be in short supply while there was excess capacity in the remaining portions of the industry. This is a problem that we cannot resolve with the data and models currently available. We therefore turn our attention to possible techniques or methods for identifying particular commodities which might be in short supply.
” The industries which could have bottlenecks are stone and clay, other transport equipment and instruments and supplies. The mapping from the input-output industries to these three industries is as follows: non-metallic minerals, those not classified elsewhere are in stone and clay, shipbuilding is in other transport and engineering and scientific instruments, measuring and control instruments and watches are all in the aggregated instrument industry.
271
Forecasting
industrial bottlenecks:
Table 4. Distribution three scenarios.
of capacity utilization,
Base Level of capacity utilization (%)
Year 3
Less than 70 70-79.9 80-89.9 90-94.9 95-99.9 100 or more
4 5 5 1 0 2
H.O. Stekler 17 defence industries,
Conflict no tax
Conflict tax
Year 5
Year 3
Year 5
Year 3
Year 5
3 5 5 1 1 2
2 5 5 3 0 2
5 3 6 0 0 3
2 7 5 1 0 2
5 4 5 0 1 2
Assuming no investment
Less than 70 70-79.9 80-89.9 90-94.9 95-99.9 100 or more
Price
2 4 7 2 0 2
2 1 3 3 3 5
2 2 4 2 4 3
2 2 0 4 3 6
2 2 4 3 3 3
2 2 0 4 4 5
and cost pressure
As indicated, estimates of the expected price response of commodities to an increase in demand can be obtained from the cost forecasting model developed by DRI. That model estimates the price responses of more than 200 commodities. The three scenarios described above are again used to provide the inputs to the DRI satellite model. For each scenario the 200 commodities, which included both consumer and producer items, were then ranked according to the size of the price increases. For illustrative purposes the 50 commodities which are expected to have the largest price increases by the third year of the simulation of the no tax conflict scenario are listed in Table 5. The most interesting finding, apart from the magnitude of the price increases, is that virtually all commodities on the list are industrial products. An item by item comparison with the products identified by the inputoutput demand analysis which are of importance to the Defense Department will not be made. However, some similarities are obvious. Machine tools, aluminium, steel and non-ferrous products appear on both lists ie Tables 2 and 5. Since the techniques for constructing the two lists were dissimilar, when an item appears on both lists it is more likely that the particular commodity might become a bottleneck. It should also be noted that 14 of these commodities (identified for the conflict no tax secenario) would not have been among the 50 products which showed the largest price increases in the base case. In the base case some consumer products and building (construction) items were among the 50 commodities showing the largest price increases. The 14 items which appear
272
Table 5. List of SO commodities with largest price increases by the end of year 3 in no tax increase scenario.’ Commodity
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
Index for year 1 = 100
Explosives* Oil well casing alloy: steel Aluminium plate Sheet coils (firestock) Ball and roller bearings Oil well casing: carbon steel Titanium sponge Copper and brass mill shapes Structural metal products Electrode: graphite Gas compressors Copper base scrap* Tttanium mill shapes Reinforcng bars Air compressors Metal commercial furniture* Copper cathode: imported Wire rods Reinforced concrete pipe Mill shapes Miscellaneous chemical preparation* Copper cathode: domestic* Magnesium, pig ingot Standard malleable iron castings* Primary non-ferrous shapes except precious metals Aluminium sheet: siding coil Pumps Closed die forgings: carbon steel Railroad equipment Non-current carrying wiring devices* Fabricated structural steel for bridges Machine shop products Electronic hardware Transformers and power regulators* Lead, pig: common Construction Special industrial machinery and equipment Carbon mechanical tubing* Metal forming machine tools Steel foundries* Nickel carbide sheets Aluminium base scrap* Fabricated structural metal products* Metal cutting machme tools Sand, gravel, crushed stone Mining machinery parts Atmosphere generators. endothermic Hot rolled steel strip* Bolts. nuts, screws and rivets* Natural gas
a For explanation
of asterisk
141.9 139.4 137.7 137.4 136.1 136.0 135.8 134.8 134.1 133.8 133.3 133.3 132.7 132.5 132.3 131.9 131.8 131.6 131.5 131.5 131.5 131.3 130.7 130.6 130.3 130.3 130.0 129.1 129.7 129.7 129.7 129.3 129.1 128.9 128.1 128.6 128.2 128.0 128.0 121.9 127.7 127.6 127.6 121.2 127.0 126.6 126.5 126.5 126.4
see text.
only on the alternative scenario list are designated by an asterisk in Table 5. These are items whose prices rose substantially more than the average in response to an increase in demand. A comparison of the products showing the largest prices increases for the conflict simulations with and without a tax increase is also possible. This comparison (which is not presented here) showed that 43 of the 50 items were common to
ECONOMIC
MODELLING
July 1990
1
‘.
Forecasting industrial bottlenecks: H.O. Stekler
both lists. There was no obvious pattern in the items which were excluded from one list but on the other. Thus it would be very difficult to suggest where bottlenecks might be eliminated if demand pressures were reduced by imposing a tax increase. Nevertheless, it is only through analyses of these types that bottlenecks for particular commodities might be identified. Lead times Although lead time estimates can be considered a direct measure of commodity bottlenecks, DRI has developed such models for only nine commodities. The lead times associated with the three scenarios for these nine commodities are presented in Table 6. The results indicate that the lead times in both wartime scenarios are generally higher than they are in the base case. However, the lead times for only two of these commodities increase by as much as four weeks in any of the scenarios. These two commodities are lift trucks and steel sheet and strip. Lift trucks are classified in the industrial trucks and tractor industry, which is listed among the industries for which Defense Department demand is most important. Moreover, according to the input-output projections in all three scenarios, this industry is on the list of commodities for which the Defense Department accounts for the largest share of output. However, the demand projections had suggested an extraordinary rate of demand growth only for the first three-year period of the two alternative scenarios. Thus the increased lead times for the entire period are somewhat surprising. This methodology for identifying bottlenecks is still undeveloped. Nevertheless, it may provide useful information about potential bottlenecks. In concept, the methodology is similar to the capacity utilization approach presented earlier based on the assumption of no new investment. Moreover, import responses are also not modelled in this approach.
Conclusion This paper has examined four different techniques which might be used in modelling efforts to identify future potential industrial bottlenecks. The first, the industrial growth rates required to meet the projected demand, is quite detailed but is exclusively demand oriented. It is possible to determine whether the growth rates are high by historical standards but not whether there is sufficient capacity to produce the output. The second method examines projected capacity utilization data, but this model is very aggregated and does not provide sufficient detail about particular commodities. In examining the projected price behaviour of specific commodities it is possible to determine
ECONOMIC
MODELLING
July 1990
Table 6. Production lead times for particular products, three scenarios (weeks).
Commodity
Base Year 1
Steel bars and rods 5.4 Lift trucks 11.1 Plastic pipe and tubing 3.9 Pipe fittings 3.8 Aluminium sheet and strip 6.9 CoDDer and &ass sheet and strip 5.9 Steel sheet and strio 5.5 Copper tubing 4.9 ’ Fibreglass 5.6
Conflict
Conflict tax
3
5
3
5
3
s
6.3 14.2
7.0 18.1
7.2 18.1
7.0 24.7
6.9 17.6
7.3 24.5
4.2 4.6
4.1 6.0
4.4 5.0
4.2 6.8
4.3 4.9
4.3 7.2
8.1
8.4
8.7
8.4
8.3
8.4
6.8 7.0 6.2 5.6
7.2 10.4 6.8 5.6
7.1 9.9 7.3 5.8
7.4 15.9 7.6 5.7
7.0 9.6 7.0 5.6
7.4 16.1 7.4 5.9
commodities which would show the largest price increases. However, it is not possible to distinguish between demand pull pressures (indicative of possible bottlenecks) and cost push factors resulting from problems at earlier stages of production. While the lead time models are also at the commodity level, they are few in number. Moreover, these models assume that no new investment will occur. When these techniques were applied to the alternative simulations some commodities or industries were identified as potential bottlenecks by several approaches. Thus several of the products where the largest share of output went to the Defense Department showed both high growth rates and were parts of aggregated industries where capacity utilization was expected to be high. Similarly, steel and aluminium products, machine tools etc were among the products showing the highest projected price increases and also expected to have large growth rates. The fact that some of these products are identified by the alternative models is significant because the modelling methodologies are quite disparate. This would tend to increase the likelihood that a product might become a bottleneck. The modelling of industrial bottlenecks is still an infant art and the techniques are still quite rudimentary; but the procedures presented here should provide a start in improving our forecasting abilities. It is more imperative than ever to note that further research is required to solve many of the remaining problems.
References 1 2
3
Data Resources Inc. (DRI), Cost Forecasting Service (CFS), National Price and Wage Modeling, mimeo, nd. Department of Defense, Defense Purchases: An Introduction to DIEMS, Office of the Assistant Secretary of Defense/Acquisition and Logistics, 1985. S. J. Feldman, M. A. Salmon and K. Palmer, An
273
Forecasting industrial bottlenecks: H.O. Stekler Approach to Ecaluating Industry Bottleneck Potential: The Industrial Capacity Monitoring System, Data Resources Inc., 1984, mimeo. H. Galper, ‘The impact of the Vietnam War on defense spending: a simulation approach’, Journal of Business, Vol 42, No 4, 1969, pp 143-155. H. Galper and E. Gramlich. ‘A technique for forecasting defense expenditures’, Review qf Economics and Statistics, Vol 50. No 2, 1968, pp 143- 155. L.R. Klein and R.S. Preston, ‘Some new results in the measurement of capacity utilization’, American Economic Review, Vol 57, 1967. pp 34-58. D. Lee and H.O. Stekler, ‘Modelling high levels of defense expenditures, a Vietnam-like case’, Journal of Policy Modeling, Vol 9, 1987, pp 437-453. M. Lee, ‘Impact, patterns, and duration of new orders for defense products’, Econometrica, Vol 38, No 1, 1970,
9
10
11 12
13
pp 153-164. Catherine J. Morrison, ‘Primal and dual capacity utilization: an application to productivity measurement in the US automobile industry’, Journal ofBusiness and Economic Statistics, Vol 3, No 4 1985, pp 312-324. N.I. Nadiri and S. Rosen, A Disequilibrium Model of Demand.for Factors of Production. National Bureau of Economic Research, No 99. New York, 1974. George L. Perry, Capacity in manufacturing’. Brookings Papers on Economic Activity, NO 3, 1973, pp 701-742. J. Popkin. ‘Consumer and wholesale prices in a model of price behavior by stage of processing’, Reriew of Economics and Statistics, VoI 56, 1974 pp 4966501. M.H. Schnader. ‘Capacity utilization’, in F.J. Fabozzi and H.I. Greenfield, eds, The Handbook of Economic and Financial Measures, Dow Jones-Irwin, Homewood. IL, 1984, pp 74-104.
Appendix DRI’s capacity model Forecasts of sector output demand are generated by the Data Resources interindustry model. Given this demand framework, analysing capacity constraints requires the development of a supply side model. Equations (7-12) describe the basic structure of the model employed.12 Q;,
j=l
=
Aoe”~‘+“‘f2 ( K”Ujr)Z’L$ Mj”l3 to73
KY; = B,jQ;f’BIK;;(Bf) j = 1 to 73 $
(7)
( W/RP)$s3’
i = plant, equipment
= K,, - ( 1 - hj)K:i_
,
(9)
~;~plrmr+ K;ie4uipmenr= ~7, U, = Q$/Q;
(8)
historically
(10) Q; = QT,= Qj,
(11)
Uj, = 1 when Qg = Q; Q$ = f (I-O
model solution)
hj
= constant dollar output actually transacted in industry j at time t = constant dollar output supplied by industry j Qj”l at time t, Q; = Qii, when Qf; is produced = constant dollar output of industry j demanded Q,9 at time t = potential output of industry j at time t Q,pI = net utilized capital stock (plant and KYJ, equipment) in industry j at time t = net capital stock (plant and equipment) in K;, industry j at time t = man hours of production workers in industry Ljr j at time t = constant dollar materials consumed by Mjr industry j at time t ( W/RP)j, = ratio of average hourly wage rate of production workers ( W) to the rental price ofcapital (RP) by type ofcapital i in industry j at time t Z, , Z,, Z, = shares of industry output accruing to capital, labour and materials respectively t = time which proxies for trends in technology
(12) By taking natural logarithms, Equations (7) and (8) are linearized to make them amenable to estimation:
where
piJr
Qjr
= gross investment by industry j in capital good type i at time t = economic depreciation rate for typ i capital good in industry j
In Qj, = In A, + a, t + a2 t2 + Z, In (K”U,,) + Z,ln + Z,ln
Mj,
(13)
In K$ = In b,j + B, In Qi + B,ln KY:_ I
+ B,ln (W/RP):, 12The theoretical underpinnings of the model are set out in a book by N.I. Nadiri and S. Rosen [lo].
274
Lj,
Q; = f (I-O
(14)
model solution)
ECONOMIC
MODELLING
July
1990
Thispaper develops a number of approachesfor identtfying those industrial bottlenecks which are likely to occur at high levels of employment and overall capacity utilization. A scenario is constructed which drives the US economy to those levels. This scenario is simulated using the Data Resources Inc. macroeconometric and satellite models. The alternative approaches for identifying bottlenecks include an examination of (i) high growth rates relative to past growth, (ii) high levels of capacity utilization, (iii) increases in prices, and (iv) delivery or lead times which are lengthening. Kqvwords: Industrial
bottlenecks;
Forecasting;
approaches to the aforementioned scenario. The scenarios is simulated using the Data Resources Inc. (DRI) macroeconometric model [ 1] and DRI satellite models. The strength and limitations of each technique are presented. After the methodology and data have been explained the results will be presented and analysed.
Although there have been many studies which have examined issues in capacity utilization there have been few modelling efforts which might identify and/or forecast possible industrial bottlenecks. Since these bottlenecks usually occur when the economy is operating at high levels of employment and overall capacity utilization it is necessary to construct a simulation in which these conditions prevail. Many different scenarios could be constructed to drive the US economy to high levels of employment and capacity utilization. We chose a scenario in which high levels of defence expenditure stimulated the US economy. We use a macromodel and present a series of satellite models for identifying bottlenecks which would equally well have been applied to alternative scenarios such as a boom generated by investment spending. While the techniques presented here have general applicability, the results (ie which industries are likely to be bottlenecks) would differ between scenarios. This paper first explores possible techniques for identifying bottlenecks and then applies these
Identification of bottlenecks An economic bottleneck is a condition that obstructs, slows down or places a constraint on the free flow or operation of the economy. Given this definition the question becomes one of developing operational measures which might show the presence of such constraints or might enable an observer to forecast when such bottlenecks will exist. These constraints would usually exist at the microlevel, and it is at this level that these forecasting techniques must be developed. If a bottleneck existed in a particular sector it would generally be a supply-demand imbalance in that industry and certain economic results would manifest themselves. The appearance of these manifestations would then be prima facie eivdence that the bottleneck existed. Moreover, if these economic factors could be modelled, the forecasts of the particular model would provide information about future possible bottlenecks. The economic results most frequently associated with excess demand conditions are (i) high growth rates relative to past growth; (ii) high levels of capacity utilization; (iii) increases in prices; and (iv) delivery
Industrial College of the Armed Forces, National University, Ft Lesley J. McNair, 6000, USA.
Washington,
Defense DC 20319-
The opinions expressed in this paper are those of the author and are not the views of the National Defense University or the Department of Defense. I wish to thank R. William Thomas for comments on an earlier version of this paper. All remaining errors are, of course, my own responsibility. Final manuscript
received
0264-9993/90/030263-12
14 August
1989.
Simulation
Ltd
263
Forecasting industrial bottlenecks: H.O. Stekler
1
I Distribution of
final
demands
among
industries ------_
/---___
Defence outlays
by
1 I
w
budget
Non-defence
category
Defence
I (Bridge
tables)
(Total)
I A
I [Department
-.
of
Defense
only)
hlacroeconomic forecasting
1
I
Sectoral prices
t Total Industrial
empioyme3t *
by
sector
4
outputs
1 Strategic
and
critical materials requirements (Bureau
Figure 1. Defence
Mines)
economic impact modelling system.
or lead times which are lengthening. In this paper models of these four phenomena will be utilized with the DRI macroeconometric model [ 1] ‘. Projected industrial requirements The first technique uses an input-output model in conjunction with the Defense Economic Impact Modeling System (DEIMS, see Department of Defense [2)) to provide comprehensive and disaggregated measures of the industrial impact of defence spending ie the required industry growth rates to meet the projected demand. This model has been described extensively (Department of Defense [2], Lee and Stekler [ 73) and only the salient characteristics will be mentioned here. Figure 1 describes the model. Basically DEIMS is a series of procedures which translate the data in the five-year defence plan into a ’ Labour market problems might also cause bottlenecks phenomenon is not modelled in this paper.
264
of
but this
vector of final defence demand for each of the 400 commodities represented in the DRI input-output system. Defence expenditures for the five-year defence plan are organized into 50 categories by DEIMS. The data in each of these 50 categories of defence expenditures represent the appropriations which are planned for the next five years. These budget categories represent total obligatory authority (TOA) and not outlays, and all are measured in constant dollars. The first step in the estimation process is to translate these TOA accounts into the pattern of actual outlays. Thus,
Z;i = 1
N$A~_~,
j=
1 . . . 50
(1)
where outlays in year t for the jth category of defence expenditure (Zj) are a weighted average of current and previous years’ appropriations (A) for the category. The weights used vary with each budget category. This procedure is considerably more detailed than published
ECONOMIC
MODELLING
July 1990
Forecasting industrial bottlenecks: H. 0. Stekler
techniques used to estimate the relationships between defence orders and expenditures in the national income and product accounts (Galper and Gramlich [ 51, Galper [4], Lee [ 81). For each year, the 50 element outlay vector is converted into the defence final demand vector by assuming a fixed relationship between the budget category and the final demand for each of the 400 commodities ie each commodity takes a fixed share (b,,) of the outlays in each budget category. Thus, d, = BZ,
where d, is the (400 x 1) vector of final defence demand for each of the 400 commodities, B is a (400 x 50) translation matrix and Z, is the (50 x 1) vector of outlays by budget category. This vector ofdefence final demand is then combined with corresponding vectors of non-defence final demand and the input-output coefficients of the DRI model are used to generate estimates of gross output originating in each industry. From these gross output measures it is possible to calculate the average yearly growth of each industry’s output. If the objective is to determine whether the projected increases were feasible or whether bottlenecks might occur, a possible indicator would be a comparison of the required growth rates with previously observed rates of growth. In the absence of supply side information this comparison of projected growth rates of demand with previously observed rates of output growth might be used as a proxy for excess demand. The rationale is that an industry might not have invested in enough capital equipment to be prepared for such a sudden surge in demand. On the other hand, the previously observed actual growth rates might have been unreasonably low because a recession had occurred and the capacity to meet the increase in demand would in fact be available. Consequently, these growth rate comparisons must be used cautiously; they are obviously subject to interpretation and are demand rather than supply measures of possible bottlenecks.2 Capacity3
A model specifically designed to analyse the possibility that bottlenecks may occur was developed by Feldman,
’ Other information about potential bottlenecks could include capacity utilization rates, capital expansion rates, previous peak levels of output, increases in imports etc. Some of these measures will be analysed below. ‘The literature has discussed the concepts and difficulties associated with measuring capacity and capacity utilization. See for example, Klein and Preston [6]; Perry [ 111; Schnader [ 131 and Morrison [9]. The approach used in this model is based on production functions.
ECONOMIC
MODELLING
July 1990
I
I
Figure 2. The industrial
capacity
monitoring
system.
Salmon and Palmer [3]. The industrial capacity monitoring system (ICMS) ‘has been developed to relate the demand for an industry’s output to its capital expenditure requirements and then relate this to the industry’s potential to produce output’. This model is designed to be a satellite model to the DRI macroeconometric model. In this satellite model (ICMS) the demand for an industry’s output is originally derived, as in the DEIMS case, from the DRI macromodel and input and output system. These estimates are the constant dollar values of output originating in each sector. However, through an iterative procedure these output estimates are then modified so that they are determined simultaneously with the industry’s investment requirements. The input-output tables provide output data for approximately 400 sectors, and an employment model provides labour estimates for the same 400 sectors. However, in order to derive capital stock, investment and capacity estimates, the data at the 400 sector level of detail must be aggregated to the 71 sector level. The satellite model then simultaneously determines potential output, net capital stock, sector investment flows and capacity utilization levels at the 71 sector level of aggregation. The investment flows are then used as inputs into the I-O model which provides another set of output estimates. This iterative process continues until the solution converges on a set of outputs at the 400 sector level which is consistent with the investment flows. A final set of estimates of capacity utilization at the 71 sector level is then obtained from these data.4 The supply side equations of this DRI satellite model (copied from Feldman et al) are presented in the appendix. Figure 2 is a schematic diagram of the models and procedures. 41t should be noted that ICMS assumes that prices are constant. Thus, if demand exceeds the available supply, it is assumed that imports increase.
265
Forecasting industrial bottlenecks: H. 0. Stekler Price and cost pressures
A third possible method of identifying bottlenecks is to identify industries where prices are increasing as a result of demand pressure. Another satellite DRI model, the cost forecasting service (CFS), can be used in this analysis. This model links each product’s specific price model to an overall macroeconomic forecast. Since each commodity’s price is linked to these macroeconomic forecasts, it is possible to determine the price response associated with an increase in demand. The basic equations of this model are of the form: Aln P, = a,, + a, Aln C, + a2 D, C,=b,+b,W,_,+b,
i
miMi.t
(3)
-ki
I=1
+b,
D,=O,-
t j=t
f
ejEj,r-kj
4 O,_i
(5)
i=l
Ot=
$
fiXi.,-ki
(6)
material prices affect intermediate goods prices which in turn influence final goods prices. Consequently, when a particular commodity shows a substantial price increase, this increase may be due either to demand pressures affecting the particular product or to cost pressures passed through from earlier stages of production. Since it is not possible to disentangle these effects we cannot conclusively state that predicted price increases are indicative of bottlenecks. However, the opposite result (ie the absence of price increases) would indicate the absence of bottlenecks throughout all the stages of production. Lead times
Finally, DRI has constructed another set of satellite models designed to predict increases in delivery lead times. If increasing lead times are an indicator of potential bottlenecks, examining these models would be a direct measure of such constraints. However, these models are only available for nine commodities.5 For these commodities estimates of lead times were obtained from regressions in which the independent variables were the measures of market strength (which were explained above). Thus the basic equation is of the form
i=l
where P is the product’s price, C is the product’s production cost, D is a measure of market strength (excess demand), W, Mi and Ej are a labour wage index, material and energy prices indices respectively and the bi represent the fraction of total expenditures attributable to these three components of cost. 0 is the measure of demand for this product, with Xi the index of production of the ith industry which uses the product as an input. Equations (3) (4) and (6) are relatively straightforward. Price changes depend on cost changes and market strength, with cost changes dependent upon the costs of labour, material and energy inputs. Demand for the product (6) is a function of the production of sectors which buy this product as an input. Finally, (5) is supposed to measure the imbalance between supply and demand, with supply ‘defined as a T-term moving average of past market strength’. Under the assumption that desired inventory/sales ratios are held constant, ‘this notion [of supply] is an expectational one in which industry is assumed to keep productive capacity and inventory levels in line with recent demand experiences’ (DRI [I]). These basic equations are sometimes modified to capture asymmetrical effects resulting from differences between weak and strong markets. As a caveat, the model is basically a stage of processing model (see Popkin [ 111) in which raw
266
where D,, the measure as (5) above.
of market
strength,
is defined
Summary
In summary, we will examine four measures which might provide meaningful information about potential bottlenecks. All measures are applied to the same macrosimulation and the results may thus be compared. The first of these measures is strictly demand oriented and involves the comparison of predicted growth rates with historically observed growth rates. The other measures involve the supply side and examine estimates of capacity utilization, prices and lead times.
Simulation of high level defence expenditures: alternative scenarios Methodology
The analysis of potential bottlenecks is an economy which is assumed to be high levels of defence expenditure. For the study it is assumed that peacetime
undertaken for stimulated by the purpose of Department of
‘These commodities are: steel bars and rods, lift trucks, plastic pipe and tubing, pipe fittings, three kinds of metal sheet and strip, copper tubing and fibreglass.
ECONOMIC
MODELLING
July 1990
Forecasting
Table 1. Values of various macroeconomic
variables, baseline and Vietnam-type
industrial bottlenecks:
H.O. Stekler
conflict, with and without a tax increase (%).
Years 1
2
3
4
5
Rate of growth: % real GNP Conflict and no tax Conflict and tax Baseline
6.7 6.3 5.4
4.5 3.7 2.9
2.8 2.4 2.4
2.3 2.4 3.0
1.9 2.3 3.3
Rate of inflation Conflict and no tax Conflict and tax Baseline
5.4 5.4 5.0
6.1 5.9 5.0
7.3 6.4 5.2
7.8 6.5 5.4
7.3 6.3 5.8
Unemployment rate Conflict and no tax Conflict and tax Baseline
1.7 7.9 8.1
6.9 7.3 7.9
7.0 7.4 8.1
7.0 7.5 8.0
7.2 7.6 7.6
Industrial production (growth) Conflict and no tax Conflict and tax Baseline
13.3 12.6 9.4
8.4 7.0 3.5
5.8 5.2 3.5
2.2 2.6 4.2
-0.6 0.2 4.3
Capacity utilization level Conflict and no tax Conflict and tax Baseline
84.1 83.6 81.2
89.3 87.7 82.3
92.2 90.2 83.6
90.6 89.1 84.3
86.0 85.3 84.5
7.0 7.1 6.2
7.8 7.9 6.4
8.0 8.2 6.6
7.8 1.9 6.7
7.4 7.5 6.9
21.5 5.0
16.4 5.0
5.5 5.0
-0.3 5.0
-3.7 5.0
Percentage of GNP taken Conflict and no tax Conflict and tax Baseline Rate of growth Conflict Baseline
by Defense Department
of real Defense Department
expenditures
Defense expenditures were increasing at an annual rate of 5% expressed in real terms. At the same time a Vietnam-size conflict and the associated military expenditure were also imposed upon the economy. The simulations that are presented use the situation prevailing in 1984 as an example. However, the results will be presented in generic terms ie referring to years l-5 of such a simulated situation. It is also assumed that military expenditures for this war will be of the same magnitude in real terms, will have the same time distribution and will display the same procurement distribution as occurred in the actual Vietnam situation. (The methodology for calculating the yearly distribution of outlays is presented in Lee and Stekler [7]). There are three simulations which are relevant for the analysis. The first is a baseline case which assumes that no war occurs. The other two assume a war: in one case there is a personal tax increase equal in magnitude to the increased level of defence expenditure; in the other there is no tax hike. The results for the five years of the conflict are presented in Table 1. Macroeconomic
impacts
The impacts of increased Defense Department spending are consistent with the usual macroeconomic results. ECONOMIC
MODELLING
July 1990
An increase of 1 to 1.5 % in the Defense Department’s share of GNP would stimulate the economy but would also increase inflationary pressures and substantially increase the rate of capacity utilization. The baseline scenario predicts that capacity utilization would be in the mid-80% range, whereas the two alternatives show that capacity utilization could exceed 90%. In fact, the 92.2 % rate predicted under the no tax assumption exceeds the peak utilization rate which occurred in the actual Vietnam build up. Given this potential for bottlenecks, it is necessary to determine which sectors of the economy are most likely to experience bottlenecks.
Identification of bottlenecks: results Projected industrial requirements
As previously noted, using the DRI macro model, the 400 commodity input-output data and DEIMS, it is possible to obtain the projected growth rates in the requirements for these 400 commodities. There are alternative ways of ranking the data. We have chosen to examine those industries for which the Defense Department’s share of output is the largest. Table 2 accordingly presents the projected growth rates for the first decile of commodities ranked on the basis of the 267
2
c
Ammunition expenditure small arms Other ordnance New military facilities Tanks and tank components Shipbuilding and repairing Complete guided missiles Aircraft engines and engine parts Aircraft parts and equipment Radio and TV communication equipment Aircraft Engineering and scientific instruments Measuring and control instruments Small arms ammunition Explosives Non-ferrous forgings Semiconductors Machine tools. metal cuttmg Electronic components not classified elsewhere Industrial trucks and tractors Primary non-ferrous, not classified elsewhere
1984 ranking by defence share of industry output (baseline)
9.3 8.6 7.5 8.8 7.0 5.5 7.2 7.5
8.8 8.4 6.6 6.6 6.7 7.1 6.3 11.3 12.3
9.8 7.2 5.6
69.3
68.9
63.7
61.0
60.5
58.1 55.1
54.2
53.5
48.0 42.0
31.3 29.3
28.1
27.0
26.3
25.6
5-Year compound annual rate of growth
95.7 91.5 89.5
Year I defence share of industry
6.3
13.5
15.8
24.1
14.4 17.0
18.0 18.5
15.3
16.0
20.5 23.4
17.8
18.5
15.2
16.4
17.7
28.2 22.2 19.8
Vietnam-like notax compound annual rate of growth years l-3
1.1
8.4
10.3
13.1
8.0 10.6
9.0 9.3
8.6
8.8
11.4 13.9
9.4
9.4
7.7
7.9
10.0
13.0 11.0 9.5
Vietnam-like compound annual rate of growth years l-5
5.6
13.2
15.0
23.8
13.2 16.4
17.3 17.5
14.9
15.7
20.0 21.9
17.3
17.7
14.9
16.2
17.7
28.2 22.2 23.0
0.0
8.4
10.0
13.1
7.4 10.4
9.0 9.3
8.4
8.5
11.1 12.9
9.1
9.1
7.6
8.2
10.0
13.0 11.0 10.9
Vietnam-like with-tax compound annual rate of growth’ years l-5
baseline
conrinurd
0.3
5.9
12.1
-21.1
8.5 14.2
36.9 - 3.2
17.7
0.8
10.5 -6.6
- 5.0
2.7
1.8
Il.3
6.3
10.1 19.4
9.7
on p, 269
Historical growth rates 5 previous years
takes largest share of output: 5 years of simulation,
Vietnam-like with-tax compound annual rate of growth” years l-3
Table 2. Industrial impact of a Vietnam-type war: projected growth rates of 40 commodities for which Defense Department and war scenarios with and without tax increases.
$
7.9 3.4
4.2 2.5 0.7 3.6 6.6
6.8
6.2 3.3 4.0 5.4 5.0 2.8 1.4
5.1 3.1 4.8
Il.9
17.8
17.1
16.8 16.7
16.7
16.3
14.9
14.5 14.1
13.9 13.6 13.3 13.3 13.3
13.1
12.1 12.0
5.6 8.6
10.3
8.1 8.1 8.3 6.7 5.4
9.1 7.9
7.1
3.4 5.5
7.1
4.1 5.4 5.4 3.3 1.4
6.5 3.2
6.8
6.8
10.8
10.7
6.4
3.6 -5.4
4.4
9.1
1.3 0.0
9.0
6.4
1.9
15.0 10.7
1.9 7.8
Vietnam-like compound annual rate of growth years 1-5
12.4
5.9
Vietnam-like notax compound annual rate of growth years l-3
6.6 7.7
9.4
9.5 8.1 7.1 5.6 2.8
6.9 5.8
9.6
10.3
8.4
6.3 0.0
7.8
10.0
13.0
3.8 11.5
from the year
Vietnam-like with-tax compound annual rate of growth” years l-3
from the values of the year 1 no war scenario as the base. If they had been calculated activity would have been higher in year 1 if there had been a war.
6.1
2.4 1.2
S-year compound annual rate of growth
18.8 18.7
Year 1 defence share of industry output
“These rates of growth are calculated would have been lower, for economic
Electronic tubes Plating and polishing Machine tools, metal forming Primary metal production not classified elsewhere Non-ferrous castings not classified elsewhere Metal ores mining not classified elsewhere Primary zinc Steam engines and turbines Optical instruments and lenses Non-ferrous rolling and drawing not classified elsewhere Non-metallic mineral production not classified elsewhere Wafches and clocks Cold finishing, steel shapes Aluminium castings Primary aluminum Copper ore mining Footwear cut stock Electrical industry apparatus not classified elsewhere Iron and ferro alloy ores mining Metal heat treating Average rate of growth (40)
1984 ranking by defence share of industry output (baseline)
Table 2 (continued)
2.3
3.3 - 1.0
- 0.3
8.0 5.8 - 10.2 - 5.1 0.2
-2.6 - 6.4
-1.7
6.6
16.8
- 13.7 -19.0
4.6
6.1
~ 14.9
-8.0 2.2
Historical growth rates 5 previous years
I war scenario as the base, the growth rates
6.8
2.1 5.1
6.2
4.7 5.6 4.6 2.6 1.4
5.5 1.8
6.2
6.7
5.7
2.1 -5.4
4.4
6.1
7.9
1.4 7.5
Vietnam-like with-tax compound annual rate of growth” years I-5
Forecasting
industrial bottlenecks:
Table 3. Distribution
H.O. Stekler
of capacity utilization,
52 industries, assuming normal investment. three scenarios, five year average, years 3 and 5. Conflict tax
Conflict no tax
Base Level of capacity utilization (%)
Five-year average
Year 3
Year 5
Five-year average
Year 3
Year 5
Five-year average
Year 3
Year 5
Less than 70 70-79.9 80-89.9 90-94.9 95-99.9 100 or more
10 14 14 7 2 5
10 16 12 6 3 5
9 13 17 4 4 5
7 17 14 7 2 5
7 14 14 9 3 5
11 12 19 3 3 4
8 18 13 7 1 5
7 18 14 6 2 5
11 14 17 3 3 4
Defense Department’s share of output ie the top 40 commodities.6 Among these 40 commodities the Defense Department’s share of output ranges from over 90% of ammunition and ordance to around 12% of output of products such as electric industrial apparatus, iron and ferroalloy ores mining and metal heat treating. Among the top 14 commodities are the items usually classified in the defence sector ie tanks, aircraft, ships, missiles, arms etc. The remaining products are the industrial items required to produce these weapons or are contained in the defence systems. For the baseline case, which assumes a 5% real growth of defence expenditure, the average compound annual growth rate of these 40 commodities for the five years is expected to be 6.1%. Only 12 of these industries are expected to have growth rates of less than 5 % per year. Similarly, in the event of a Vietnamtype war during the five-year period, the projected growth rates of these 40 commodities are higher, averaging 7.1% per year without a tax increase and 6.8 % even with the tax surcharge. Moreover, with the rapid build up of the defence effort between years 1 and 3, the growth rates for those years are substantially higher. Even with a tax increase, more than half of the commodities display double digit rates of growth between years 1 and 3. The feasibility of these growth rates or the converse, the possibility of bottlenecks, remains to be determined. The last column of Table 2 displays the growth rates that each industry achieved during the year l-3 period. A comparison of the required with the historically observed growth rates shows that, in many instances, the former exceed the latter.’ Does this necessarily mean that bottlenecks would exist in the year l-3 time frame? 6Alternatively, the industries could have been ranked by projected growth rates. If this ranking had been used, about half of the commodities for which Defense Department demand is the largest would have been included among the 40 commodities which were projected to have the highest growth rates. ‘This is especially true for the year l-3 rates for the war scenarios.
270
As a caveat, since our generic simulations used 1984 as an actual starting point, it must be remembered that the 1981-84 period included a severe recession which severely retarded the growth of many of these industries. Second, the presence of negative growth rates over a historical period may indicate either that capacity utilization is low and that growth potential exists or the possibility that exit from the affected industry has occurred and the capacity is no longer available. These remarks suggest that our attention should focus not only on the required rates of growth but also on the availability of capacity to produce that output and/or of imports to meet excess demand. We shall turn to the availability of capacity measures shortly. Unfortunately, we cannot, as yet, address the issue of import availability. To summarize, it is possible to derive projected growth rates for 400 commodities. Analyses of these data only permit a comparison of required growth rates with historically observed rates. Projected capacity estimates are missing but, at the present time, they cannot be generated at the same disaggregated level of detail for which the input-output projections are available. Thus the projected growth rates can, at best, only suggest where bottlenecks might occur. We now turn our attention to questions concerning the availability of sufficient capacity to meet the required demand.
Capacity
Before providing the capacity figures for the various industries under the three alternative simulations it should be noted that the industry groupings of the industrial capacity monitoring system (ICMS) are not identical with those of the input-output system. In fact the 400 commodities of the input-output system are aggregated into 71 two- and three-digit SIC industries in ICMS. In addition, the current version of ICMS only contains equations for the 52 industries comprising the manufacturing sector of the economy. Table 3 presents the distribution of capacity utilization
ECONOMIC
MODELLING
July 1990
Forecasting
rates for the three scenarios (base with 5 % real defence expenditure increase and a Vietnam-type war with and without a tax increase). The capacity utilization rates are presented for the average of the entire five-year period and for the third and fifth years individually. On the assumption that investment behaviour in each industry will continue as it has in the past, four or five industries are clearly expected to display bottlenecks. The capacity utilization rates for these industries exceed 100% either for the entire period or for one of the specific years.8 Depending on the scenario an additional one to four industries have utilization rates which exceed 95 OK9 In the prior 12-year period there was only one industry, food, in which the average capacity utilization exceeded 95 %. Thus, about 10 % of the 52 industries may be expected to develop some degree of capacity constraint under any of the three scenarios. These capacity utilization figures were based upon two fundamental assumptions: first that the relationship between imports and demand would not change; second that the investment behaviour that has characterized these industries will prevail in the future. If the increased demands were met by an increase in the share of imported goods, the estimates of capacity utilization would be biased upward. We recognize this possible bias but could not correct for this in the simulations. On the other hand it is entirely conceivable that industry officials might believe that the increase in output attributable to Defense Department growth (either for a peacetime build up or for a limited war) was not likely to be sustained. In that event, investment behaviour might be altered and new capacity might not be added. It is important to determine what capacity constraints might develop if investment behaviour were altered. An extreme assumption is that no new capacity would be added during the entire period and that capital would remain at the original level. lo Under this assumption, if output growth were to continue at the projected levels, significant capacity
sThe number
of industries which display high levels of capacity utilization may change from scenario to scenario and year to year for two reasons. Output may change or investment and the level of capacity may be affected. Either might be influenced by the government policies which have been selected. 9 The industries with projected rates over 95% are food, miscellaneous textiles, miscellaneous fabricated textile products, other furniture and fixtures, rubber and miscellaneous plastics, glass, stone and clay, other transport equipment, instruments and supplies, and paperboard. lo It is recognized that this assumption is heroic, especially since output and investment flows were determined simultaneously. If there were no net investment, output flows in other industries would decline and capacity utilization would decrease. Nevertheless, this procedure is useful in bounding the problem.
ECONOMIC
MODELLING
July 1990
industrial bottlenecks:
H.O. Stekler
constraints would appear in the third and fifth years. For example, even under the base case in the third year, 25 % of the industries would be operating at more than 95 % of capacity. In the other two scenarios, 35 % or more of the industries are projected to have some types of capacity constraints, if this extreme assumption holds. These results clearly suggest the need under all scenarios to have an adequate rate of growth of investment expenditures to avoid potential capacity constraints. Alternatively, increased imports would be required. Up to this point our analysis has focused on the entire set of 52 manufacturing industries. What is the condition of the 40 input-output industries for which the Defense Department accounted for the largest share of output? These 40 industries could not be mapped one for one with any of the 52 two- and three-digit SIC industries. Instead, they constituted only parts of the more aggregated industries. For the purpose of this analysis one of the 52 two - (three-) digit SIC manufacturing industries was classified as a defence industry if it contained any of the 40 inputoutput industries. This was true no matter what proportion of the aggregate output was attributable to those industries. Of the 52 manufacturing industries 17 contained at least one of the 40 commodities for which the Defense Department took the largest share of output. Table 4 presents the distribution of capacity utilization ratios for those 17 industries. Although showing a slightly higher potential for bottlenecks, the distribution of these ratios is not substantially different from those of the other 35 industries. Two or three of could have significant capacity the 17 industries” constraints if normal investment prevailed, while over 50% would display this characteristic if no investment occurred. As a caveat it must be remembered that data for these 52 manufacturing industries are even more highly aggregated than are the figures in the input-output tables. Thus it is possible that a particular commodity might be in short supply while there was excess capacity in the remaining portions of the industry. This is a problem that we cannot resolve with the data and models currently available. We therefore turn our attention to possible techniques or methods for identifying particular commodities which might be in short supply.
” The industries which could have bottlenecks are stone and clay, other transport equipment and instruments and supplies. The mapping from the input-output industries to these three industries is as follows: non-metallic minerals, those not classified elsewhere are in stone and clay, shipbuilding is in other transport and engineering and scientific instruments, measuring and control instruments and watches are all in the aggregated instrument industry.
271
Forecasting
industrial bottlenecks:
Table 4. Distribution three scenarios.
of capacity utilization,
Base Level of capacity utilization (%)
Year 3
Less than 70 70-79.9 80-89.9 90-94.9 95-99.9 100 or more
4 5 5 1 0 2
H.O. Stekler 17 defence industries,
Conflict no tax
Conflict tax
Year 5
Year 3
Year 5
Year 3
Year 5
3 5 5 1 1 2
2 5 5 3 0 2
5 3 6 0 0 3
2 7 5 1 0 2
5 4 5 0 1 2
Assuming no investment
Less than 70 70-79.9 80-89.9 90-94.9 95-99.9 100 or more
Price
2 4 7 2 0 2
2 1 3 3 3 5
2 2 4 2 4 3
2 2 0 4 3 6
2 2 4 3 3 3
2 2 0 4 4 5
and cost pressure
As indicated, estimates of the expected price response of commodities to an increase in demand can be obtained from the cost forecasting model developed by DRI. That model estimates the price responses of more than 200 commodities. The three scenarios described above are again used to provide the inputs to the DRI satellite model. For each scenario the 200 commodities, which included both consumer and producer items, were then ranked according to the size of the price increases. For illustrative purposes the 50 commodities which are expected to have the largest price increases by the third year of the simulation of the no tax conflict scenario are listed in Table 5. The most interesting finding, apart from the magnitude of the price increases, is that virtually all commodities on the list are industrial products. An item by item comparison with the products identified by the inputoutput demand analysis which are of importance to the Defense Department will not be made. However, some similarities are obvious. Machine tools, aluminium, steel and non-ferrous products appear on both lists ie Tables 2 and 5. Since the techniques for constructing the two lists were dissimilar, when an item appears on both lists it is more likely that the particular commodity might become a bottleneck. It should also be noted that 14 of these commodities (identified for the conflict no tax secenario) would not have been among the 50 products which showed the largest price increases in the base case. In the base case some consumer products and building (construction) items were among the 50 commodities showing the largest price increases. The 14 items which appear
272
Table 5. List of SO commodities with largest price increases by the end of year 3 in no tax increase scenario.’ Commodity
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
Index for year 1 = 100
Explosives* Oil well casing alloy: steel Aluminium plate Sheet coils (firestock) Ball and roller bearings Oil well casing: carbon steel Titanium sponge Copper and brass mill shapes Structural metal products Electrode: graphite Gas compressors Copper base scrap* Tttanium mill shapes Reinforcng bars Air compressors Metal commercial furniture* Copper cathode: imported Wire rods Reinforced concrete pipe Mill shapes Miscellaneous chemical preparation* Copper cathode: domestic* Magnesium, pig ingot Standard malleable iron castings* Primary non-ferrous shapes except precious metals Aluminium sheet: siding coil Pumps Closed die forgings: carbon steel Railroad equipment Non-current carrying wiring devices* Fabricated structural steel for bridges Machine shop products Electronic hardware Transformers and power regulators* Lead, pig: common Construction Special industrial machinery and equipment Carbon mechanical tubing* Metal forming machine tools Steel foundries* Nickel carbide sheets Aluminium base scrap* Fabricated structural metal products* Metal cutting machme tools Sand, gravel, crushed stone Mining machinery parts Atmosphere generators. endothermic Hot rolled steel strip* Bolts. nuts, screws and rivets* Natural gas
a For explanation
of asterisk
141.9 139.4 137.7 137.4 136.1 136.0 135.8 134.8 134.1 133.8 133.3 133.3 132.7 132.5 132.3 131.9 131.8 131.6 131.5 131.5 131.5 131.3 130.7 130.6 130.3 130.3 130.0 129.1 129.7 129.7 129.7 129.3 129.1 128.9 128.1 128.6 128.2 128.0 128.0 121.9 127.7 127.6 127.6 121.2 127.0 126.6 126.5 126.5 126.4
see text.
only on the alternative scenario list are designated by an asterisk in Table 5. These are items whose prices rose substantially more than the average in response to an increase in demand. A comparison of the products showing the largest prices increases for the conflict simulations with and without a tax increase is also possible. This comparison (which is not presented here) showed that 43 of the 50 items were common to
ECONOMIC
MODELLING
July 1990
1
‘.
Forecasting industrial bottlenecks: H.O. Stekler
both lists. There was no obvious pattern in the items which were excluded from one list but on the other. Thus it would be very difficult to suggest where bottlenecks might be eliminated if demand pressures were reduced by imposing a tax increase. Nevertheless, it is only through analyses of these types that bottlenecks for particular commodities might be identified. Lead times Although lead time estimates can be considered a direct measure of commodity bottlenecks, DRI has developed such models for only nine commodities. The lead times associated with the three scenarios for these nine commodities are presented in Table 6. The results indicate that the lead times in both wartime scenarios are generally higher than they are in the base case. However, the lead times for only two of these commodities increase by as much as four weeks in any of the scenarios. These two commodities are lift trucks and steel sheet and strip. Lift trucks are classified in the industrial trucks and tractor industry, which is listed among the industries for which Defense Department demand is most important. Moreover, according to the input-output projections in all three scenarios, this industry is on the list of commodities for which the Defense Department accounts for the largest share of output. However, the demand projections had suggested an extraordinary rate of demand growth only for the first three-year period of the two alternative scenarios. Thus the increased lead times for the entire period are somewhat surprising. This methodology for identifying bottlenecks is still undeveloped. Nevertheless, it may provide useful information about potential bottlenecks. In concept, the methodology is similar to the capacity utilization approach presented earlier based on the assumption of no new investment. Moreover, import responses are also not modelled in this approach.
Conclusion This paper has examined four different techniques which might be used in modelling efforts to identify future potential industrial bottlenecks. The first, the industrial growth rates required to meet the projected demand, is quite detailed but is exclusively demand oriented. It is possible to determine whether the growth rates are high by historical standards but not whether there is sufficient capacity to produce the output. The second method examines projected capacity utilization data, but this model is very aggregated and does not provide sufficient detail about particular commodities. In examining the projected price behaviour of specific commodities it is possible to determine
ECONOMIC
MODELLING
July 1990
Table 6. Production lead times for particular products, three scenarios (weeks).
Commodity
Base Year 1
Steel bars and rods 5.4 Lift trucks 11.1 Plastic pipe and tubing 3.9 Pipe fittings 3.8 Aluminium sheet and strip 6.9 CoDDer and &ass sheet and strip 5.9 Steel sheet and strio 5.5 Copper tubing 4.9 ’ Fibreglass 5.6
Conflict
Conflict tax
3
5
3
5
3
s
6.3 14.2
7.0 18.1
7.2 18.1
7.0 24.7
6.9 17.6
7.3 24.5
4.2 4.6
4.1 6.0
4.4 5.0
4.2 6.8
4.3 4.9
4.3 7.2
8.1
8.4
8.7
8.4
8.3
8.4
6.8 7.0 6.2 5.6
7.2 10.4 6.8 5.6
7.1 9.9 7.3 5.8
7.4 15.9 7.6 5.7
7.0 9.6 7.0 5.6
7.4 16.1 7.4 5.9
commodities which would show the largest price increases. However, it is not possible to distinguish between demand pull pressures (indicative of possible bottlenecks) and cost push factors resulting from problems at earlier stages of production. While the lead time models are also at the commodity level, they are few in number. Moreover, these models assume that no new investment will occur. When these techniques were applied to the alternative simulations some commodities or industries were identified as potential bottlenecks by several approaches. Thus several of the products where the largest share of output went to the Defense Department showed both high growth rates and were parts of aggregated industries where capacity utilization was expected to be high. Similarly, steel and aluminium products, machine tools etc were among the products showing the highest projected price increases and also expected to have large growth rates. The fact that some of these products are identified by the alternative models is significant because the modelling methodologies are quite disparate. This would tend to increase the likelihood that a product might become a bottleneck. The modelling of industrial bottlenecks is still an infant art and the techniques are still quite rudimentary; but the procedures presented here should provide a start in improving our forecasting abilities. It is more imperative than ever to note that further research is required to solve many of the remaining problems.
References 1 2
3
Data Resources Inc. (DRI), Cost Forecasting Service (CFS), National Price and Wage Modeling, mimeo, nd. Department of Defense, Defense Purchases: An Introduction to DIEMS, Office of the Assistant Secretary of Defense/Acquisition and Logistics, 1985. S. J. Feldman, M. A. Salmon and K. Palmer, An
273
Forecasting industrial bottlenecks: H.O. Stekler Approach to Ecaluating Industry Bottleneck Potential: The Industrial Capacity Monitoring System, Data Resources Inc., 1984, mimeo. H. Galper, ‘The impact of the Vietnam War on defense spending: a simulation approach’, Journal of Business, Vol 42, No 4, 1969, pp 143-155. H. Galper and E. Gramlich. ‘A technique for forecasting defense expenditures’, Review qf Economics and Statistics, Vol 50. No 2, 1968, pp 143- 155. L.R. Klein and R.S. Preston, ‘Some new results in the measurement of capacity utilization’, American Economic Review, Vol 57, 1967. pp 34-58. D. Lee and H.O. Stekler, ‘Modelling high levels of defense expenditures, a Vietnam-like case’, Journal of Policy Modeling, Vol 9, 1987, pp 437-453. M. Lee, ‘Impact, patterns, and duration of new orders for defense products’, Econometrica, Vol 38, No 1, 1970,
9
10
11 12
13
pp 153-164. Catherine J. Morrison, ‘Primal and dual capacity utilization: an application to productivity measurement in the US automobile industry’, Journal ofBusiness and Economic Statistics, Vol 3, No 4 1985, pp 312-324. N.I. Nadiri and S. Rosen, A Disequilibrium Model of Demand.for Factors of Production. National Bureau of Economic Research, No 99. New York, 1974. George L. Perry, Capacity in manufacturing’. Brookings Papers on Economic Activity, NO 3, 1973, pp 701-742. J. Popkin. ‘Consumer and wholesale prices in a model of price behavior by stage of processing’, Reriew of Economics and Statistics, VoI 56, 1974 pp 4966501. M.H. Schnader. ‘Capacity utilization’, in F.J. Fabozzi and H.I. Greenfield, eds, The Handbook of Economic and Financial Measures, Dow Jones-Irwin, Homewood. IL, 1984, pp 74-104.
Appendix DRI’s capacity model Forecasts of sector output demand are generated by the Data Resources interindustry model. Given this demand framework, analysing capacity constraints requires the development of a supply side model. Equations (7-12) describe the basic structure of the model employed.12 Q;,
j=l
=
Aoe”~‘+“‘f2 ( K”Ujr)Z’L$ Mj”l3 to73
KY; = B,jQ;f’BIK;;(Bf) j = 1 to 73 $
(7)
( W/RP)$s3’
i = plant, equipment
= K,, - ( 1 - hj)K:i_
,
(9)
~;~plrmr+ K;ie4uipmenr= ~7, U, = Q$/Q;
(8)
historically
(10) Q; = QT,= Qj,
(11)
Uj, = 1 when Qg = Q; Q$ = f (I-O
model solution)
hj
= constant dollar output actually transacted in industry j at time t = constant dollar output supplied by industry j Qj”l at time t, Q; = Qii, when Qf; is produced = constant dollar output of industry j demanded Q,9 at time t = potential output of industry j at time t Q,pI = net utilized capital stock (plant and KYJ, equipment) in industry j at time t = net capital stock (plant and equipment) in K;, industry j at time t = man hours of production workers in industry Ljr j at time t = constant dollar materials consumed by Mjr industry j at time t ( W/RP)j, = ratio of average hourly wage rate of production workers ( W) to the rental price ofcapital (RP) by type ofcapital i in industry j at time t Z, , Z,, Z, = shares of industry output accruing to capital, labour and materials respectively t = time which proxies for trends in technology
(12) By taking natural logarithms, Equations (7) and (8) are linearized to make them amenable to estimation:
where
piJr
Qjr
= gross investment by industry j in capital good type i at time t = economic depreciation rate for typ i capital good in industry j
In Qj, = In A, + a, t + a2 t2 + Z, In (K”U,,) + Z,ln + Z,ln
Mj,
(13)
In K$ = In b,j + B, In Qi + B,ln KY:_ I
+ B,ln (W/RP):, 12The theoretical underpinnings of the model are set out in a book by N.I. Nadiri and S. Rosen [lo].
274
Lj,
Q; = f (I-O
(14)
model solution)
ECONOMIC
MODELLING
July
1990