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Short term inventory and employment interplay in Finnish manufacturing industry
International Journal of Production Economics,
26
61
( 1992) 6 l-70
Elsevier
Short term inventory and employment in Finnish manufacturing industry
interplay
Kalevi Kyltiheiko Lappeenranta
University of Technology Box 20, SF-53851
Finland
comparisons made are based on the results obtained by Wilkinson [ 6 1.
1. Introduction Recent inventory studies show that fluctuations in inventory investment are a major mechanism by which business cycles are propagated [ l-61. Much research on inventories has been motivated by the fact that at the aggregate level as well as for most sectors in the economy, the variance of production exceeds the variance of sales. That is against the traditional production smoothing received view. Consequently, the determinants of inventory investment have received much attention in econometric studies. Of particular concern has been the question whether inventory investment responds strongly to interest rates and to credit conditions [ 7,8 1. Recent debate has disregarded, however, many important questions. For example, we do not know empirically whether the firms are using inventories of labor as partial substitutes for inventories of goods [ 9, p. 3471 .l Also the special characteristics of input and works-in-process inventories have been ignored although they together are greater and more volatile than traditionally analyzed finished goods inventories [ 10 ] and [ 11, p. 449 1. Even the differences between industries have been typically overlooked. This paper makes an attempt to clarify interplay between different types of inventories and employment levels in Finnish manufacturing industry in general and in forest and metal industries in particular. The data used are seasonally adjusted time-series from the first quarter of 1976 to the third one of 1988. Some international ‘Ramey [ 131 introduces an interesting approach where inventories are interpreted as factors of production.
0925-5273/90/$03.50
Lappeenranta.
2. Money, employment and inventories - Some preliminary remarks Some theoretical studies have shown that a firm can substitute inventories for other production factors, such as labor and capital [ 12,9,13 1. This production and employment smoothing hypothesis implies a negative correlation between inventory investment and employees. Kyltiheiko and Pirttilti [ 141 suggest that exceptionally high inventory intensities of Scandinavian countries could be partly explained by employment policy, which prefers smooth and high employment to low inventory intensities. This hypothesis implies nonnegative correlations between inventories and employees. Table 1 shows correlations using both quarterly and annual data. The following conclusions can be drawn: ( 1) In manufacturing industry (SIC 3) there has been strong positive correlation, which has strengthened during the 1980s and applies to each type of inventory. This rejects the traditional smoothing hypothesis in Finland (on similar results Abel and PirttilP [ 181. Positive correlations do not, however, necessarily confirm the hypothesis on the existence of a special Scandinavian employment policy but they do not contradict it neither.2 ‘One has to keep in mind that there is a limit on what can be learned from simple bivariate correlations. For example, two shocks that are roughly of equal size and of opposite sign can create close to zero correlation. This does not imply that shocks do not have any impacts.
0 1990 Elsevier Science Publishers B.V. All rights reserved.
62
63 TABLE 2 Summary of correlations between interest rate and inventories in Finnish industry 1976-l 988 Consistent with a priori anticipations
Real rate of interest (quarterly ) 76Ql-82Q4 Manufacturing AII
industry, SIC 3
83Ql-88Q3
(Half-yearly) 76Hl-82H2
83H-88H2
0.08
0.07
0.05
SIC 3 AWI
0.04
0.03
0.11
-0.33
yes
SIC 3 AFI
0.03
-0.07
0.05
-0.32
yes
Metal industry, SIC 37+ 38 AI1
0.20
-0.21
0.27
-0.35
yes
SIC 37+38 AWI
0.02
-0.03
0.14
-0.39
Yes
SIC 37+38 AFI
- 0.09
-0.24
-0.60
yes
Forest industry, SIC 33 I+ 341 AI1 SIC 331+341 AWI SIC 331+341 AFI
-0.14
0.22
no
0.02
0.14
0.08
0.15
no
0.00
0.17
0.11
0.20
no
0.00
-0.04
-0.06
-0.41
yes
AI1= input inventory investment AWI = works in process inventory investment AFI =finished goods inventory investment
(2) In metal industry there are some minor exceptions that may support smoothing behavior as to input inventories, All the other inventories support the main hypothesis. (3 ) Correlation results of forest industry are of great interest. They show that counter-cyclic smoothing behavior of the 1970s has changed into pro-cyclic behavior during the 1980s. Table 2 tackles another controversial subject, the role of user cost of capital in inventory behavior, by presenting the correlations between real rate of interest and different inventories using quarterly and half-yearly data. Starting hypothesis is that desired inventory holdings depend inversely on the financial opportunity cost of holding inventories. Thus implied interest rateinventory linkage represents a channel for counter-cyclic monetary policy measures. Emphasis is put on potential effects of the financial deregulation process in Finland during the 1980s.
Some tentative conclusions concerning Table 2 can be drawn: ( 1) At the aggregate level (SIC 3 ) only finished goods inventory investment seems to react as anticipated. When using half-yearly data works-in-process inventories pass the test, during the 80s. (2) In metal industry all the results are consistent with a priori anticipations. Also the hypothesis concerning the increasing role of monetary factors due to deregulation process is confirmed. (3) As to forest industry, traditional economic logic seems to collapse. Only finished goods inventory investment supports the hypothesis. Curious behavior of input inventories may account for the fact that Finnish timber supply conditions have changed drastically during the 1980s. Selling behavior of nonfarmer forest owners is incalculable. Consequently, companies are forced to behave as if user cost of capital does not matter.
64 3. Econometric analysis and estimation of inventory models 3.1 Estimation
of an inventory investment model
This chapter analyzes interplay between money, employment and inventories by estimating partial adjustment coefficients for different inventories and short term employment. A flexible accelerator model of Metzler [ 191 and Love11 [20] is extended in order to take account of (un)anticipated sales, intertemporal prices ( = real rate of interest), input prices ( = unit labor cost and the prices of raw materials) and the rate of capacity utilization.’ All the variables are introduced into the model by means of the concept of the desired level of inventories (on the derivation of the model, see Appendix 1). The expectations formation procedure of sales and real rate of interest consists either of (i) static expectations: RC; =RC,_,
LS:=s,_,,
or of (ii) rational foresight ) s; =s,,
expectations
(with perfect
Al,
= Z,-I,_,= inventory period t
sg
= sales expectations
during
during period t (set up at the end of period t - 1) = passive inventory investment 8 coefficient = active inventory investment or stock n adjustment coeffrlcient I,_, = inventory level during period t - 1 RC; = (expected ) real rate of interest during period t c, = the rate of capacity utilization during period t p: = (expected ) prices of raw materials during period t w: = (expected) hourly wage rate during period t The static and rational expectations inventory models specified above as equations (i) and (ii) were estimated using annual and quarterly data for 1976-l 988. Because different inventories have different adjustment dynamics they were estimated separately.5 The OLS estimation results are shown in Table 3.
3.2 Interpretation
RC; = RC,
investment
of empirical inventory results
‘All time-series data used are based on Industrial Statistics of
This chapter concentrates on the differences between (i) different inventory types, (ii) different industries and (iii) two different subperiods. Some international comparisons are also made. The main results can be summarized as follows: ( 1) The anticipated sales variable (S; ) is statistically significant only in few inventory models. Wilkinson [6] obtained similar results as to inventories of France, Italy, Germany and the UK. The estimates for the unanticipated sales coefficient, (SF -S, ), are very small and statistically insignificant. These results cast some doubt on the production smoothing hypothesis. (2) The buffer motive variables (Z,_, ) and (AZ,_, ) seem to be significant in almost every model, especially when concerning input inventories. This supports the smoothing hypothesis.
Finland or on OECD Main Economic Indicators, details are available from the author. “On derivation of equations see Appendix I. The last lagged term in eqns. (i) and (ii ) represents the combined information of foregone periods and is of great importance.
‘In input inventory equations SC is frequently substituted for Q: ( = value added of production) because of the very nature of production process.
All the other expectations are assumed to be rational. The final regression equations are as follows (see also Wilkinson [ 6 ] :4 AZ,=aO+a,S,_,
+8(S,_,
+a*RC,_,
+a,C,
+a,P,+a,
W,+a&_,
AZ,=a,+a,S,-xZ,_,
a4z=-0 and
(i)
+a,RC,+a3C,
+a4P,+a,W,+a6AZ_, where the expected estimates are
--S,)-nZ,_,
(ii)
a priori signs of parameter
a2,a3,a5<0
65 (3 ) Controversial role of user cost of capital (RC; ) remains open. Looking at the signs of parameter estimates one is tempted to agree with the statement of Love11 [ 2 1 ] : “the probability of obtaining an interest rate coefficient with a negative sign is 50 percent”. Wilkinson’s international results are similar, too. On the other hand, the impact of interest rate has increased during the 1980s especially in metal industry (Table 2 ) . (4) The next set of regressions concern relative input prices. Raw materials prices (P: ) are in Finland (as in Germany and France) statistically significant. The sign of parameter estimate is positive, which supports the pro-cyclic cost shock hypothesis. Anticipated wages ( W: ) do not work as well but even they have got anticipated sign (negative) and some significance as to finished goods inventories. (5) The final variable is the rate of capacity utilization (C,). In Finland (and in Italy) it is unstable and not so significant. One can, however, suggests tentatively that C, works stabilizingly when combined with finished goods inventories. Wilkinson shows that in the UK, France and Germany this variable supports pro-cyclic behavior of inventories. (6 ) The partial adjustment coefficients (n) are extensively studied [ 22,2,8,23 1. The advantage of this paper is its disaggregate character, which does not understate the speed of adjustment. The last column in Table 3 shows the adjustment coefticients obtained. They are positive and almost all significant at the five percent level. The estimates of manufacturing industry (SIC 3) during the period 1976-l 988 range from 0.60 to 0.61 (input inventories), from 0.02 to 0.35 (works-in-process inventories) and from 0.43 to 0.53 (finished goods inventories). When looking at different subperiods one can see that the adjustment speed of finished goods inventories has decreased clearly. International comparison [ 23,6] reveals that Finnish rates of speed of adjustment are high, which may reflect a covariance problem of aggregate inventory studies [ 22 1. General statistical properties of the models are reasonable although some of the R2’s are rather low (as they were also in Wilkinson’s study). All the residuals from the models are not white noise - as evidenced by the
Durbin-Watson and Durbin (h) statistics. At the industry level it is to be noticed that the adjustment speeds of input inventories are lower than the ones of other inventories in both industries.(j When looking at the signs and significance of parameter estimates for forest industry we see that past inventory investment (AZ_ 1) and sales expectations dominate as to input inventories. When finished goods inventories are concerned the counter-cyclic lagged inventory variables are balanced by pro-cyclic raw materials prices and capacity variables. As the correlation matrix (Table 2) suggested, the role of interest rate is negligible. Inventory investment of metal industry depends on all the inventory determinants, (un)anticipated sales excluded. The role of counter-cyclic inventory variables is important as well as the role of pro-cyclic cost of capital. The capacity variable is of high significance, to input inventories and raw materials prices as well as to two other types of inventories. Statistical properties of models are not bad although the E”‘s are rather low. The Durbin-Watson diagnostic shows no evidence of serially correlated residuals. Adjustment speeds of inventories can be compared to employment adjustment mechanism. The idea is to construct a cost-minimizing model that enables to derive short term demand for labor and to calculate the speed of adjustment of employment by means of simple logarithmical estimation procedure (see Brechling [ 241. Since this derivation and estimation has been done in an earlier paper by Kylaheiko [ 25 ] only the main results are appraised here. Table 4 shows the magnitudes of the speed of employment adjustment factor, It is to be seen that in manufacturing industry the speed of labor adjustment /3=0.66, which means 66% of the difference between the desired and actual employment levels is made up during the next quarter. In forest industry the value of /I is higher than in metal industry (0.43>0.30). This points out that the assumption of some lagged employment adjustment cannot entirely be rejected by quarterly data. At annual level one can notice that the speed of adjustment of all 6The estimation of minor forest goods-in-process investment has not been done.
inventory
results
Industry (SIC)
3
3
3
3
3
3
3
3
3
Estimation
Period and data
1976Q21988Q2
1976Q31988Q2
1976Q31988Q2
1976Q2198204
1983Ql198842
1976Q2198244
1983Ql198842
1976Q31982Q4
1983Ql1988Q2
TABLE 3
(i) SK
(i) SK
(ii) RK
(i) SK
(ii) RK
(ii) RK
(i) SK
(i) SK
(ii) RK
type
Equation
A FI
A FI
AWI
AWI
A II
A II
A FI
AWI
A II
Dependent variable
22
26
22
27
22
27
48
48
49
n
15339 (2.74) * 439 (0.04)
10336 (2.43)
203 (0.05)
12923 (1.29)
*41.25 (0.72)
7772 (1.73)
1299 (0.38)
2321 (0.63)
a0
(0706)
-0.16 (2.47) * 0.19 (1.92) *
-0.09 (0.91)
-0.12 (0.38)
-0.66 (1.65)
-0.05 (2.46) ** 0.16 (1.58)
-0.22(Q) (0.84)
S: (8:)
0.07 (0.72)
0.01 (0.12)
0.25 (3.10) **
-
-
0.17 (3.38) *** 0 (0.07)
-
(s:-S,) -0.60 (3.32) *** -0.35 (2.47) ** -0.43 (2.76) *** -1.17 (3.39) t** - 1.20 (3.92) *** -0.48 (2.45) * -0.54 (2.23) * -0.89 (3.88) *** -0.58 (2.90) **
I,-,
111 (3.58) ***
$5)
(2Yl) ** -75 (1.98) * -15 (0.621)
(21568) ** 169 (2.90) tt
1.3 (0.08)
(0!644)
RC:
f’:’
(0.;:)
;o’ (3.09) ***
;:.87)
-
-
-154 (3.04) ***
(t.26)
;;.38, *
;7 (1.87) *
(l.67)
*
(0.946)
-35 (0.31)
-94 (2.04) ** 100 (1.44)
(0.689, (y.84)
t
(0.9429)(;.79)
C,
(2!53) * -
(Of71)
$62)
(1?35)
$02 *
-
0.1 (0.05)
W:
)
-0.72 (4.60) *** -0.79 (4.92) *** -0.49 (2.69) *** -0.91 (4.38) *** - 1.14 (4.87) *** -0.64 (3.05) *** - 1.03 (3.57) *** -0.73 (2.93) *** -0.71 (3.15) ***
AI_,
0.53
0.57
0.32
0.53
0.48
0.44
0.34
0.35
0.29
6,
2.97
1.85
2.02
2.46
2.35
2.49
1.81
2.07
2.06
D-W
0.58
0.89
0.54
0.48
1.25
1.17
0.43
0.35
0.60
x
67
68 TABLE
4
Estimated
B
results
(see
[ 25, p. 131)
of employment
adjustment
lSlC3 1976QI987Q4
FOREST 1976Q21987Q4
METAL 1976Q2I987Q4
SIC 3 1962-75 (annual
0.66
0.43
0.30
0.36~
)
branches studied has increased clearly. As noticed in Section 2, employment adjustment is becoming more rapid just like inventory adjustment does. When comparing the inventory adjustment speeds to that of employment one can see that in manufacturing industry input and finished goods inventories adjust about as rapidly as employment does. Works-in-process inventories are more sluggish. In forest industry input inventories and employment adjust simultaneously, but finished goods inventories are much more rapid ( 1.02 > 0.43 ). This phenomenon could be a sign of nonsmoothing inventory policy and perhaps of some active employment policy.’ In metal industry employment adjustment during the 1980s has been more rapid than finished goods and work-in-process inventory (0.97> 0.73 and 0.70), not to mention input inventory adjustment (0.97 > 0.40). This could be a sign of “hiring or firing” employment policy. 4. Concluding remarks This paper analyzed interplay between inventories and employment. Evidence from many countries [ 6, p. 183; 2,181 suggests that production is typically more unstable than final sales. This casts doubt on the traditional smoothing hypothesis. It has also been claimed that firms can use inventories of labor as partial substitutes for inventories of goods. Our bivariate correlations between inventory investment, employees and real rate of interest suggested that there has been all the time strengthening positive relationship between in‘This sluggish employment adjustment could be explained by modern labor market theories, e.g., “insider-outsider” theory or “efficient contract” theory, see Lindbeck and Snower
1261.
SIC 3 1976-87 (annual)
FOREST 1962-75 (annual)
FOREST 1976-87 (annual)
METAL 1962-75 (annual
0.50
0.29 <
0.45
0.69 <
)
METAL 1976-87 (annual
)
0.97
ventories and employees. This is against the production-smoothing hypothesis. As to the role of user cost of capital and inventories, a tentative evidence suggested that metal industry inventory investment has responded to the changes of capital cost. Regression analysis confirmed this remark. Forest industry did not respond at all. This could be due to changes in timber supply conditions. Next step was constructing a flexible accelerator model, which suggested that counter-cyclic inventory determinants dominated input inventories. As to works-in-process and finished goods inventories, the role of the pro-cyclic variables (expected sales, interest rate, raw materials prices and capacity utilization) came strengthened. Metal industry behaved more pro-cyclically than forest industry. Comparing the adjustment speeds of inventories to the ones of employment revealed that metal industry is utilizing “hiring or firing” employment policy combined with nonsmoothing inventory policy. Forest industry employment policy has been more conservative and inventory policy more according to the smoothing hypothesis. References
Akhtar, M.A., 1983. Effects of interest rates and inflation on aggregate inventory investment in the United States. Amer. Econ. Rev., June. Blinder, A., 1986. Can the production smoothing model of inventory behavior be saved? Quart. J. Econ., 3: 43 l453. Chikan, A., Kalotay, K. and Paprika, 2.. 1986. Macroeconomic factors influencing inventory investment - An international analysis. In: A. Chikan, (ed.), Inventory in Theory and Practice. North-Holland Amsterdam, pp. 55-71. Irvine, F.O., 198 I. Retail inventory investment and the costs ofcapital. Amer. Econ. Rev., September: 633-648.
69
8 9
IO
11
12 13
14
18
19 20
21
22
23
24
25
26
Rubin, L.S., 1979. Aggregate inventory behavior: Response to uncertainty and interest rates. J. Post Keynesian Econ., Winter: 201-2 11. Wilkinson, M., 1989. Aggregate inventory behavior in large European economics. European Econ. Rev., January: 181-194. Kyllheiko, K. and Pirttils, T., 1985. Interest rates, inflation, and inventory investment: Some Finnish experiences. Eng. Costs Prod. Econ., 259-266. Hansen, A., 1989. Inventories, inflation, and price expectations. J. Post Keynesian Econ., Spring: 439-459. Blinder, A., 1982. Inventories and sticky prices: More on the microfoundations of macroeconomics. Amer. Econ. Rev., June: 334-348. De Leeuw, F., 1982. Inventory investment and economic instability. Survey Current Business, December: 23-31. Blinder, A., 198 I. Retail inventory behavior and business fluctuations. Brookings Paper Econ. Activity, 2: 443-505. Abel, A.S., 1985. Inventories, stock-outs and production smoothing. Rev. Econ. Stud.: 283-293. Ramey, V., 1989. Inventories as factors of production and economic fluctuations. Amer. Econ. Rev., June: 338359. KylPheiko, K. and Pirttill, T., 1988. Short Term Inventory Dynamics in the Finnish Forest Industry, Engineering Costs and Production Economics, 8 I-85. Abel, I. and Pirttill. T., 1989. Input and output inventory interactions - A comparative study of the Finnish and Hungarian manufacturing industry. Research Report I 1/ 1989, Lappeenranta University of Technology. Metzler, L.A., 194 1. The nature and stability of inventory cycles. Rev. Econ. Statist., August. Lovell, M., I96 1. Manufacturers’ inventories, sales expectations, and the acceleration principle. Econometrica, July: 293-3 14. Lovell, M., 1976. Comments on “Inventory Behavior in durable goods manufacturing” by Martin Feldstein aqd Alan Auerbach. Brookings Papers Econ. Activity, 2: 397401. Nguyen, S.V. and Andrews, S., 1989. Stage-of-fabrication inventory behavior in durable goods manufacturing industry. J. Post Keynesian Econ., Summer: 561-588. Feldstein, M. and Auerbach, A., 1976. Inventory behavior in durable goods manufacturing: The target - adjustment model. Brookings Papers Econ. Activity, 2: 35 l396. Brechling, F.P.R.. 1965. The relationship between output and employment in Britich manufacturing. Rev. Econ. Stud., July. Kylsheiko, K., 1990. Short term inventory and employment adjustment dynamics in Finnish forest industry. Research Report 2 I/ 1990, Lappeenranta University of Technology. Lindbeck, A. and Snower, D., 1989. The Insider-Outsider Theory of Employment and Unemployment. Cambridge, MA.
27
Hernesniemi, H., 1989. Factors affecting inventory levels in firms (in Finnish). Res. Inst. Finnish Econ., 55.
Appendix 1. Derivation of the flexible accelerator inventory model We used the flexible accelerator model originally created by Metzler and developed by Love11 and Blinder [ 2 1. This model assumes that each firm has desired level of inventories (I? ) and that it can adjust its inventories only partially towards this level within any one period because of the positive adjustment cost (*active inventory investment, AZ: ). Since the firm cannot exactly anticipate its future sales there will be some sales forecasting errors, which can be partly adjusted by means of inventory changes (=-passive inventory investment, AZ7 ). Eqn. ( I ) for inventory investment can now be written in the form: hl,=I,-I,_,
=Al:+Ay
(1)
The idea of partial adjustment as follows:
can be formalized
Az?=n(z:-I,_,) Correspondingly, adjustment is AI: =e(s:
(2) the idea of passive inventory
-S,)
(3)
Combining eqns. (2 ) and (3 ) into eqn. ( 1) yields the starting equation [27, p. 301: ~,=n(rp-II_I)+e(S~-S,)
(4)
Next step is to specify the desired level of inventories. This can be understood by means of Arrowian buffer and transaction motives and costminimizing logic (as in Kyl5heiko_Pirttil%, [ 7 ] ): I,D=a,+a,S~+cr,RC~+~u,c, + a‘$P; + a, w: + a&_,
(5)
The interpretation of symbols and signs can be found from Section 3.1: Inserting eqn. ( 5 ) into eqn. (4) yields final inventory investment: ~,=~((Y,+cY,S:+CY~RC~+(Y~C~+(Y~P~ (6) +cu, w;+(YgAz,-,
-z,-,)+s(sy-s,)
70 =7m!yo
+
+xa,s; +tys;
mi2
--LIT,)-XI,_,
A&=ao+a,S,_,
RC: + 7ra3 C, + TWY~, P:
+a, W,+a6Al_,
=a,+a,ST+B(S:-S,)-n~,_,
+a, W; +a6AI-,
-&)-RI,_,
+a3C,+a4P,
+QRC,_,
+no!s w; + m&I_,
+a,RC:+a,C,
+lqs,_,
AI,=a,,+a,S,-nI,_,
+a,P:
+a,P,+a, (6)
where a, = na,, Vi. Expectation formation procedure is specified by using two alternatives: (i) X; =X,_ , (static expectations) (ii) XT =X, ( = rational expectations with perfect foresight ) Expected sales (SF ) and interest rate (RC:‘ ) are specified by both alternatives. All the other expectations are assumed to be rational. Now we can write our final regression equations:
(i) +a,RC,+a,C,
w,+a,AI_,
(ii)
Drastic effects of different speeds of adjustment can be derived easily as follows: (a) 8= n= 0 (neither passive nor active adjustment) (b) 6=n=
1 (perfect adjustment)
Applying (a) and (b) in eqn. (4) above yields: Az,=I,-I,_,
=o
AZ,=],-I,_,
=I?-I,_,
(4a) +s:-s,
(4b)
26
61
( 1992) 6 l-70
Elsevier
Short term inventory and employment in Finnish manufacturing industry
interplay
Kalevi Kyltiheiko Lappeenranta
University of Technology Box 20, SF-53851
Finland
comparisons made are based on the results obtained by Wilkinson [ 6 1.
1. Introduction Recent inventory studies show that fluctuations in inventory investment are a major mechanism by which business cycles are propagated [ l-61. Much research on inventories has been motivated by the fact that at the aggregate level as well as for most sectors in the economy, the variance of production exceeds the variance of sales. That is against the traditional production smoothing received view. Consequently, the determinants of inventory investment have received much attention in econometric studies. Of particular concern has been the question whether inventory investment responds strongly to interest rates and to credit conditions [ 7,8 1. Recent debate has disregarded, however, many important questions. For example, we do not know empirically whether the firms are using inventories of labor as partial substitutes for inventories of goods [ 9, p. 3471 .l Also the special characteristics of input and works-in-process inventories have been ignored although they together are greater and more volatile than traditionally analyzed finished goods inventories [ 10 ] and [ 11, p. 449 1. Even the differences between industries have been typically overlooked. This paper makes an attempt to clarify interplay between different types of inventories and employment levels in Finnish manufacturing industry in general and in forest and metal industries in particular. The data used are seasonally adjusted time-series from the first quarter of 1976 to the third one of 1988. Some international ‘Ramey [ 131 introduces an interesting approach where inventories are interpreted as factors of production.
0925-5273/90/$03.50
Lappeenranta.
2. Money, employment and inventories - Some preliminary remarks Some theoretical studies have shown that a firm can substitute inventories for other production factors, such as labor and capital [ 12,9,13 1. This production and employment smoothing hypothesis implies a negative correlation between inventory investment and employees. Kyltiheiko and Pirttilti [ 141 suggest that exceptionally high inventory intensities of Scandinavian countries could be partly explained by employment policy, which prefers smooth and high employment to low inventory intensities. This hypothesis implies nonnegative correlations between inventories and employees. Table 1 shows correlations using both quarterly and annual data. The following conclusions can be drawn: ( 1) In manufacturing industry (SIC 3) there has been strong positive correlation, which has strengthened during the 1980s and applies to each type of inventory. This rejects the traditional smoothing hypothesis in Finland (on similar results Abel and PirttilP [ 181. Positive correlations do not, however, necessarily confirm the hypothesis on the existence of a special Scandinavian employment policy but they do not contradict it neither.2 ‘One has to keep in mind that there is a limit on what can be learned from simple bivariate correlations. For example, two shocks that are roughly of equal size and of opposite sign can create close to zero correlation. This does not imply that shocks do not have any impacts.
0 1990 Elsevier Science Publishers B.V. All rights reserved.
62
63 TABLE 2 Summary of correlations between interest rate and inventories in Finnish industry 1976-l 988 Consistent with a priori anticipations
Real rate of interest (quarterly ) 76Ql-82Q4 Manufacturing AII
industry, SIC 3
83Ql-88Q3
(Half-yearly) 76Hl-82H2
83H-88H2
0.08
0.07
0.05
SIC 3 AWI
0.04
0.03
0.11
-0.33
yes
SIC 3 AFI
0.03
-0.07
0.05
-0.32
yes
Metal industry, SIC 37+ 38 AI1
0.20
-0.21
0.27
-0.35
yes
SIC 37+38 AWI
0.02
-0.03
0.14
-0.39
Yes
SIC 37+38 AFI
- 0.09
-0.24
-0.60
yes
Forest industry, SIC 33 I+ 341 AI1 SIC 331+341 AWI SIC 331+341 AFI
-0.14
0.22
no
0.02
0.14
0.08
0.15
no
0.00
0.17
0.11
0.20
no
0.00
-0.04
-0.06
-0.41
yes
AI1= input inventory investment AWI = works in process inventory investment AFI =finished goods inventory investment
(2) In metal industry there are some minor exceptions that may support smoothing behavior as to input inventories, All the other inventories support the main hypothesis. (3 ) Correlation results of forest industry are of great interest. They show that counter-cyclic smoothing behavior of the 1970s has changed into pro-cyclic behavior during the 1980s. Table 2 tackles another controversial subject, the role of user cost of capital in inventory behavior, by presenting the correlations between real rate of interest and different inventories using quarterly and half-yearly data. Starting hypothesis is that desired inventory holdings depend inversely on the financial opportunity cost of holding inventories. Thus implied interest rateinventory linkage represents a channel for counter-cyclic monetary policy measures. Emphasis is put on potential effects of the financial deregulation process in Finland during the 1980s.
Some tentative conclusions concerning Table 2 can be drawn: ( 1) At the aggregate level (SIC 3 ) only finished goods inventory investment seems to react as anticipated. When using half-yearly data works-in-process inventories pass the test, during the 80s. (2) In metal industry all the results are consistent with a priori anticipations. Also the hypothesis concerning the increasing role of monetary factors due to deregulation process is confirmed. (3) As to forest industry, traditional economic logic seems to collapse. Only finished goods inventory investment supports the hypothesis. Curious behavior of input inventories may account for the fact that Finnish timber supply conditions have changed drastically during the 1980s. Selling behavior of nonfarmer forest owners is incalculable. Consequently, companies are forced to behave as if user cost of capital does not matter.
64 3. Econometric analysis and estimation of inventory models 3.1 Estimation
of an inventory investment model
This chapter analyzes interplay between money, employment and inventories by estimating partial adjustment coefficients for different inventories and short term employment. A flexible accelerator model of Metzler [ 191 and Love11 [20] is extended in order to take account of (un)anticipated sales, intertemporal prices ( = real rate of interest), input prices ( = unit labor cost and the prices of raw materials) and the rate of capacity utilization.’ All the variables are introduced into the model by means of the concept of the desired level of inventories (on the derivation of the model, see Appendix 1). The expectations formation procedure of sales and real rate of interest consists either of (i) static expectations: RC; =RC,_,
LS:=s,_,,
or of (ii) rational foresight ) s; =s,,
expectations
(with perfect
Al,
= Z,-I,_,= inventory period t
sg
= sales expectations
during
during period t (set up at the end of period t - 1) = passive inventory investment 8 coefficient = active inventory investment or stock n adjustment coeffrlcient I,_, = inventory level during period t - 1 RC; = (expected ) real rate of interest during period t c, = the rate of capacity utilization during period t p: = (expected ) prices of raw materials during period t w: = (expected) hourly wage rate during period t The static and rational expectations inventory models specified above as equations (i) and (ii) were estimated using annual and quarterly data for 1976-l 988. Because different inventories have different adjustment dynamics they were estimated separately.5 The OLS estimation results are shown in Table 3.
3.2 Interpretation
RC; = RC,
investment
of empirical inventory results
‘All time-series data used are based on Industrial Statistics of
This chapter concentrates on the differences between (i) different inventory types, (ii) different industries and (iii) two different subperiods. Some international comparisons are also made. The main results can be summarized as follows: ( 1) The anticipated sales variable (S; ) is statistically significant only in few inventory models. Wilkinson [6] obtained similar results as to inventories of France, Italy, Germany and the UK. The estimates for the unanticipated sales coefficient, (SF -S, ), are very small and statistically insignificant. These results cast some doubt on the production smoothing hypothesis. (2) The buffer motive variables (Z,_, ) and (AZ,_, ) seem to be significant in almost every model, especially when concerning input inventories. This supports the smoothing hypothesis.
Finland or on OECD Main Economic Indicators, details are available from the author. “On derivation of equations see Appendix I. The last lagged term in eqns. (i) and (ii ) represents the combined information of foregone periods and is of great importance.
‘In input inventory equations SC is frequently substituted for Q: ( = value added of production) because of the very nature of production process.
All the other expectations are assumed to be rational. The final regression equations are as follows (see also Wilkinson [ 6 ] :4 AZ,=aO+a,S,_,
+8(S,_,
+a*RC,_,
+a,C,
+a,P,+a,
W,+a&_,
AZ,=a,+a,S,-xZ,_,
a4z=-0 and
(i)
+a,RC,+a3C,
+a4P,+a,W,+a6AZ_, where the expected estimates are
--S,)-nZ,_,
(ii)
a priori signs of parameter
a2,a3,a5<0
65 (3 ) Controversial role of user cost of capital (RC; ) remains open. Looking at the signs of parameter estimates one is tempted to agree with the statement of Love11 [ 2 1 ] : “the probability of obtaining an interest rate coefficient with a negative sign is 50 percent”. Wilkinson’s international results are similar, too. On the other hand, the impact of interest rate has increased during the 1980s especially in metal industry (Table 2 ) . (4) The next set of regressions concern relative input prices. Raw materials prices (P: ) are in Finland (as in Germany and France) statistically significant. The sign of parameter estimate is positive, which supports the pro-cyclic cost shock hypothesis. Anticipated wages ( W: ) do not work as well but even they have got anticipated sign (negative) and some significance as to finished goods inventories. (5) The final variable is the rate of capacity utilization (C,). In Finland (and in Italy) it is unstable and not so significant. One can, however, suggests tentatively that C, works stabilizingly when combined with finished goods inventories. Wilkinson shows that in the UK, France and Germany this variable supports pro-cyclic behavior of inventories. (6 ) The partial adjustment coefficients (n) are extensively studied [ 22,2,8,23 1. The advantage of this paper is its disaggregate character, which does not understate the speed of adjustment. The last column in Table 3 shows the adjustment coefticients obtained. They are positive and almost all significant at the five percent level. The estimates of manufacturing industry (SIC 3) during the period 1976-l 988 range from 0.60 to 0.61 (input inventories), from 0.02 to 0.35 (works-in-process inventories) and from 0.43 to 0.53 (finished goods inventories). When looking at different subperiods one can see that the adjustment speed of finished goods inventories has decreased clearly. International comparison [ 23,6] reveals that Finnish rates of speed of adjustment are high, which may reflect a covariance problem of aggregate inventory studies [ 22 1. General statistical properties of the models are reasonable although some of the R2’s are rather low (as they were also in Wilkinson’s study). All the residuals from the models are not white noise - as evidenced by the
Durbin-Watson and Durbin (h) statistics. At the industry level it is to be noticed that the adjustment speeds of input inventories are lower than the ones of other inventories in both industries.(j When looking at the signs and significance of parameter estimates for forest industry we see that past inventory investment (AZ_ 1) and sales expectations dominate as to input inventories. When finished goods inventories are concerned the counter-cyclic lagged inventory variables are balanced by pro-cyclic raw materials prices and capacity variables. As the correlation matrix (Table 2) suggested, the role of interest rate is negligible. Inventory investment of metal industry depends on all the inventory determinants, (un)anticipated sales excluded. The role of counter-cyclic inventory variables is important as well as the role of pro-cyclic cost of capital. The capacity variable is of high significance, to input inventories and raw materials prices as well as to two other types of inventories. Statistical properties of models are not bad although the E”‘s are rather low. The Durbin-Watson diagnostic shows no evidence of serially correlated residuals. Adjustment speeds of inventories can be compared to employment adjustment mechanism. The idea is to construct a cost-minimizing model that enables to derive short term demand for labor and to calculate the speed of adjustment of employment by means of simple logarithmical estimation procedure (see Brechling [ 241. Since this derivation and estimation has been done in an earlier paper by Kylaheiko [ 25 ] only the main results are appraised here. Table 4 shows the magnitudes of the speed of employment adjustment factor, It is to be seen that in manufacturing industry the speed of labor adjustment /3=0.66, which means 66% of the difference between the desired and actual employment levels is made up during the next quarter. In forest industry the value of /I is higher than in metal industry (0.43>0.30). This points out that the assumption of some lagged employment adjustment cannot entirely be rejected by quarterly data. At annual level one can notice that the speed of adjustment of all 6The estimation of minor forest goods-in-process investment has not been done.
inventory
results
Industry (SIC)
3
3
3
3
3
3
3
3
3
Estimation
Period and data
1976Q21988Q2
1976Q31988Q2
1976Q31988Q2
1976Q2198204
1983Ql198842
1976Q2198244
1983Ql198842
1976Q31982Q4
1983Ql1988Q2
TABLE 3
(i) SK
(i) SK
(ii) RK
(i) SK
(ii) RK
(ii) RK
(i) SK
(i) SK
(ii) RK
type
Equation
A FI
A FI
AWI
AWI
A II
A II
A FI
AWI
A II
Dependent variable
22
26
22
27
22
27
48
48
49
n
15339 (2.74) * 439 (0.04)
10336 (2.43)
203 (0.05)
12923 (1.29)
*41.25 (0.72)
7772 (1.73)
1299 (0.38)
2321 (0.63)
a0
(0706)
-0.16 (2.47) * 0.19 (1.92) *
-0.09 (0.91)
-0.12 (0.38)
-0.66 (1.65)
-0.05 (2.46) ** 0.16 (1.58)
-0.22(Q) (0.84)
S: (8:)
0.07 (0.72)
0.01 (0.12)
0.25 (3.10) **
-
-
0.17 (3.38) *** 0 (0.07)
-
(s:-S,) -0.60 (3.32) *** -0.35 (2.47) ** -0.43 (2.76) *** -1.17 (3.39) t** - 1.20 (3.92) *** -0.48 (2.45) * -0.54 (2.23) * -0.89 (3.88) *** -0.58 (2.90) **
I,-,
111 (3.58) ***
$5)
(2Yl) ** -75 (1.98) * -15 (0.621)
(21568) ** 169 (2.90) tt
1.3 (0.08)
(0!644)
RC:
f’:’
(0.;:)
;o’ (3.09) ***
;:.87)
-
-
-154 (3.04) ***
(t.26)
;;.38, *
;7 (1.87) *
(l.67)
*
(0.946)
-35 (0.31)
-94 (2.04) ** 100 (1.44)
(0.689, (y.84)
t
(0.9429)(;.79)
C,
(2!53) * -
(Of71)
$62)
(1?35)
$02 *
-
0.1 (0.05)
W:
)
-0.72 (4.60) *** -0.79 (4.92) *** -0.49 (2.69) *** -0.91 (4.38) *** - 1.14 (4.87) *** -0.64 (3.05) *** - 1.03 (3.57) *** -0.73 (2.93) *** -0.71 (3.15) ***
AI_,
0.53
0.57
0.32
0.53
0.48
0.44
0.34
0.35
0.29
6,
2.97
1.85
2.02
2.46
2.35
2.49
1.81
2.07
2.06
D-W
0.58
0.89
0.54
0.48
1.25
1.17
0.43
0.35
0.60
x
67
68 TABLE
4
Estimated
B
results
(see
[ 25, p. 131)
of employment
adjustment
lSlC3 1976QI987Q4
FOREST 1976Q21987Q4
METAL 1976Q2I987Q4
SIC 3 1962-75 (annual
0.66
0.43
0.30
0.36~
)
branches studied has increased clearly. As noticed in Section 2, employment adjustment is becoming more rapid just like inventory adjustment does. When comparing the inventory adjustment speeds to that of employment one can see that in manufacturing industry input and finished goods inventories adjust about as rapidly as employment does. Works-in-process inventories are more sluggish. In forest industry input inventories and employment adjust simultaneously, but finished goods inventories are much more rapid ( 1.02 > 0.43 ). This phenomenon could be a sign of nonsmoothing inventory policy and perhaps of some active employment policy.’ In metal industry employment adjustment during the 1980s has been more rapid than finished goods and work-in-process inventory (0.97> 0.73 and 0.70), not to mention input inventory adjustment (0.97 > 0.40). This could be a sign of “hiring or firing” employment policy. 4. Concluding remarks This paper analyzed interplay between inventories and employment. Evidence from many countries [ 6, p. 183; 2,181 suggests that production is typically more unstable than final sales. This casts doubt on the traditional smoothing hypothesis. It has also been claimed that firms can use inventories of labor as partial substitutes for inventories of goods. Our bivariate correlations between inventory investment, employees and real rate of interest suggested that there has been all the time strengthening positive relationship between in‘This sluggish employment adjustment could be explained by modern labor market theories, e.g., “insider-outsider” theory or “efficient contract” theory, see Lindbeck and Snower
1261.
SIC 3 1976-87 (annual)
FOREST 1962-75 (annual)
FOREST 1976-87 (annual)
METAL 1962-75 (annual
0.50
0.29 <
0.45
0.69 <
)
METAL 1976-87 (annual
)
0.97
ventories and employees. This is against the production-smoothing hypothesis. As to the role of user cost of capital and inventories, a tentative evidence suggested that metal industry inventory investment has responded to the changes of capital cost. Regression analysis confirmed this remark. Forest industry did not respond at all. This could be due to changes in timber supply conditions. Next step was constructing a flexible accelerator model, which suggested that counter-cyclic inventory determinants dominated input inventories. As to works-in-process and finished goods inventories, the role of the pro-cyclic variables (expected sales, interest rate, raw materials prices and capacity utilization) came strengthened. Metal industry behaved more pro-cyclically than forest industry. Comparing the adjustment speeds of inventories to the ones of employment revealed that metal industry is utilizing “hiring or firing” employment policy combined with nonsmoothing inventory policy. Forest industry employment policy has been more conservative and inventory policy more according to the smoothing hypothesis. References
Akhtar, M.A., 1983. Effects of interest rates and inflation on aggregate inventory investment in the United States. Amer. Econ. Rev., June. Blinder, A., 1986. Can the production smoothing model of inventory behavior be saved? Quart. J. Econ., 3: 43 l453. Chikan, A., Kalotay, K. and Paprika, 2.. 1986. Macroeconomic factors influencing inventory investment - An international analysis. In: A. Chikan, (ed.), Inventory in Theory and Practice. North-Holland Amsterdam, pp. 55-71. Irvine, F.O., 198 I. Retail inventory investment and the costs ofcapital. Amer. Econ. Rev., September: 633-648.
69
8 9
IO
11
12 13
14
18
19 20
21
22
23
24
25
26
Rubin, L.S., 1979. Aggregate inventory behavior: Response to uncertainty and interest rates. J. Post Keynesian Econ., Winter: 201-2 11. Wilkinson, M., 1989. Aggregate inventory behavior in large European economics. European Econ. Rev., January: 181-194. Kyllheiko, K. and Pirttils, T., 1985. Interest rates, inflation, and inventory investment: Some Finnish experiences. Eng. Costs Prod. Econ., 259-266. Hansen, A., 1989. Inventories, inflation, and price expectations. J. Post Keynesian Econ., Spring: 439-459. Blinder, A., 1982. Inventories and sticky prices: More on the microfoundations of macroeconomics. Amer. Econ. Rev., June: 334-348. De Leeuw, F., 1982. Inventory investment and economic instability. Survey Current Business, December: 23-31. Blinder, A., 198 I. Retail inventory behavior and business fluctuations. Brookings Paper Econ. Activity, 2: 443-505. Abel, A.S., 1985. Inventories, stock-outs and production smoothing. Rev. Econ. Stud.: 283-293. Ramey, V., 1989. Inventories as factors of production and economic fluctuations. Amer. Econ. Rev., June: 338359. KylPheiko, K. and Pirttill, T., 1988. Short Term Inventory Dynamics in the Finnish Forest Industry, Engineering Costs and Production Economics, 8 I-85. Abel, I. and Pirttill. T., 1989. Input and output inventory interactions - A comparative study of the Finnish and Hungarian manufacturing industry. Research Report I 1/ 1989, Lappeenranta University of Technology. Metzler, L.A., 194 1. The nature and stability of inventory cycles. Rev. Econ. Statist., August. Lovell, M., I96 1. Manufacturers’ inventories, sales expectations, and the acceleration principle. Econometrica, July: 293-3 14. Lovell, M., 1976. Comments on “Inventory Behavior in durable goods manufacturing” by Martin Feldstein aqd Alan Auerbach. Brookings Papers Econ. Activity, 2: 397401. Nguyen, S.V. and Andrews, S., 1989. Stage-of-fabrication inventory behavior in durable goods manufacturing industry. J. Post Keynesian Econ., Summer: 561-588. Feldstein, M. and Auerbach, A., 1976. Inventory behavior in durable goods manufacturing: The target - adjustment model. Brookings Papers Econ. Activity, 2: 35 l396. Brechling, F.P.R.. 1965. The relationship between output and employment in Britich manufacturing. Rev. Econ. Stud., July. Kylsheiko, K., 1990. Short term inventory and employment adjustment dynamics in Finnish forest industry. Research Report 2 I/ 1990, Lappeenranta University of Technology. Lindbeck, A. and Snower, D., 1989. The Insider-Outsider Theory of Employment and Unemployment. Cambridge, MA.
27
Hernesniemi, H., 1989. Factors affecting inventory levels in firms (in Finnish). Res. Inst. Finnish Econ., 55.
Appendix 1. Derivation of the flexible accelerator inventory model We used the flexible accelerator model originally created by Metzler and developed by Love11 and Blinder [ 2 1. This model assumes that each firm has desired level of inventories (I? ) and that it can adjust its inventories only partially towards this level within any one period because of the positive adjustment cost (*active inventory investment, AZ: ). Since the firm cannot exactly anticipate its future sales there will be some sales forecasting errors, which can be partly adjusted by means of inventory changes (=-passive inventory investment, AZ7 ). Eqn. ( I ) for inventory investment can now be written in the form: hl,=I,-I,_,
=Al:+Ay
(1)
The idea of partial adjustment as follows:
can be formalized
Az?=n(z:-I,_,) Correspondingly, adjustment is AI: =e(s:
(2) the idea of passive inventory
-S,)
(3)
Combining eqns. (2 ) and (3 ) into eqn. ( 1) yields the starting equation [27, p. 301: ~,=n(rp-II_I)+e(S~-S,)
(4)
Next step is to specify the desired level of inventories. This can be understood by means of Arrowian buffer and transaction motives and costminimizing logic (as in Kyl5heiko_Pirttil%, [ 7 ] ): I,D=a,+a,S~+cr,RC~+~u,c, + a‘$P; + a, w: + a&_,
(5)
The interpretation of symbols and signs can be found from Section 3.1: Inserting eqn. ( 5 ) into eqn. (4) yields final inventory investment: ~,=~((Y,+cY,S:+CY~RC~+(Y~C~+(Y~P~ (6) +cu, w;+(YgAz,-,
-z,-,)+s(sy-s,)
70 =7m!yo
+
+xa,s; +tys;
mi2
--LIT,)-XI,_,
A&=ao+a,S,_,
RC: + 7ra3 C, + TWY~, P:
+a, W,+a6Al_,
=a,+a,ST+B(S:-S,)-n~,_,
+a, W; +a6AI-,
-&)-RI,_,
+a3C,+a4P,
+QRC,_,
+no!s w; + m&I_,
+a,RC:+a,C,
+lqs,_,
AI,=a,,+a,S,-nI,_,
+a,P:
+a,P,+a, (6)
where a, = na,, Vi. Expectation formation procedure is specified by using two alternatives: (i) X; =X,_ , (static expectations) (ii) XT =X, ( = rational expectations with perfect foresight ) Expected sales (SF ) and interest rate (RC:‘ ) are specified by both alternatives. All the other expectations are assumed to be rational. Now we can write our final regression equations:
(i) +a,RC,+a,C,
w,+a,AI_,
(ii)
Drastic effects of different speeds of adjustment can be derived easily as follows: (a) 8= n= 0 (neither passive nor active adjustment) (b) 6=n=
1 (perfect adjustment)
Applying (a) and (b) in eqn. (4) above yields: Az,=I,-I,_,
=o
AZ,=],-I,_,
=I?-I,_,
(4a) +s:-s,
(4b)