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Short term inventory and employment adjustment dynamics in Finnish forest industry
International Journal of Production Economics, 24 ( 1991 ) 129-135
~_2 9
Else, ~er
Short term inventory and employment adjustment dynamics in Finnish forest industry Kalevi Kyl~iheiko Lappeenranta University of Technology, Box 20, SF-53851 Lappeenranta, Finland
I. Introduction This study is conceived as an extension of the major inventory investment studies in explaining short period adjustment interplay between input and finished goods inventories and employment levels in Finnish forest industry.. The data analyzed here are unadjusted fixed price quarterly time series of inventories, value added of industrial production and sales for forest industry ~oh.. 33! and 341 ). The period of analysis runs from the first quarter of 1976 to the second one of i985. Inventory fluctuations are becoming increasingly recognized as a major mechanism by which business cycles are propagated. Recent theoretical and empirical studies have analyzed determinants of inventory investment extensively, although the conclusions drawn are far from a consensus° At least the following seven major factors causing inventory fluctuations can be identified [ ! -7 ]: ( 1 ) changes in the cost of holding inventories, (2) (un)anticipated changes in sales expectation [ 8 ], (3) changes in the cost of other inputs (wages, energy prices etc.), (4) institutional factors, especially taxation [ 9 ], (5) changes in inventory management philosophy, (6) changes in the input and output inventory k~te;actions [ 10], and (7) changes in the desires of managers for "smoothing" the production and employment by means of buffer inventories. This study is focused mainly on the so-called buffer motive (factor (7) in *•he list above), which will be analyzed by utilizing two different the extended Metzler's inventory model, which
makes it possil;le to specify partial adjustment coefficients for inventories. The second model is an extension of the contribution by Brechling, which allows to construct a ec,st-minir~rT~no model of neoclassical employment theory. From this we derive the short ~erm demand for labor and the speeds of adjustmez/) ~ e.2-ilibrium and. estimate the adjustment coefficients by means of tke OLS-method.
2. Posing the question in some e~r~ier studies it has been shown that the inventory turnover rates ( =the value added of production/total inventorle~) of the fores~ sector in Sweden and Finland do not stand up to intonational comparison with FRG, the USA and Japan [7]. It has be,m proposed that one possible explanation could be Scandinavian em~ ployment policy, which prefers ~mooth and hig,~ employment to high inventor, remover rates. On the other hand, however, traditional production smoothing behavior of finished goods inventories has been questioned in many studies, see Kyl~iheiko and Pirttil-~ [ i I ] and especially Abel and Pirttil~i [ 10 ]. In order to clarify connections between employment levels and the input and finished goods inventories of the wood products indust:~j (IS!C 331 ) and the paper and pulp indust~ (ISIC 341 ) the correlation matrix shown in Table 1 of variables concerned has been estimated~ One can conclude that employment levels and different types of inventories are interconnected in a strange way. Firstly, the negative correlation between employees and ihput iilvcn~.':.e~- ;~ high during the whole estimation period and, in fact,
0925-5273/91/$03,50 © 1991 ElsevierSciencePublishers B.V. All fights reserved.
i30 TABLE ! Summa.~' .."fcerre!-_~Ao~sbetween employees and inventories in forest industry
Kylaheiko, Pintil~i [ 12,7 ] where this model and results were first explicated. The starting equao tion ~s as follows: Alt=-~-O"[-O'iOl+ o:
Employees
Input inventories
Finished goods inventories
1976-85 1976-80 19~0-85
-0.74 -0.50 -0.84
-0.18 -0.54 0.01
A/, S~+ j
1976-85 1976- 80 1980-85
+0.04 +0.39 -0.42
It_ t
the connection has strengthened in the 1980z. Although the causal connections remain unclear, one can raise the assumption that this is partly caused by contra-cyclic behavior of input inventories. Perhaps also taxation and price speculation motives can be found behind these correlations (see i. 10,7] ). Secondly, the correlation between employees and finished goods inve~.~tories was quite strong earlier, b,;t disappeared altogether during this decade. This phenomenon thus contradicts the traditional production and employment smoothing hypothesis in Finnish conditions nowadays, but supports it during the seventies. It is also worth mentioning that during the period 19 ]~-80 different types of invent~'ies are positively correlated and during the period t 9~0-85 negatively correlated.
3~ Econometric specification of models and empirical results
i 4.- 0 1 2 [ t _
! -"1- O f 3 1r~t"~ , 12 l ~ , ~ , t 4 ., ~tx4.~,~
(1) where a~, a 4 > 0 and a2, c~3<0 and = inventory investment during period t =long-term sales er:peclations during period t + 1 = inventory level during period t - 1 = real rate of interest during peried t =backlog of orders during period t
RC, B,
Long v~n sales ext)ectations arc specified naively by "static expectations", S~+~ =St Shoe :,;ra~ expectations are included in the r;odel, using a three-fold dummy variable built on the basis of the business barometer of the Central Association of Industry ii~ Finland. This dummy can have value~!, as follows: /t" + 1 when a rise is expected D~ = ~ 0 when the economy is expected to remairJ as befere - 1 when a decline is expected
(
It can be included additively in the ba~.~ceqn. ( 1 ) as follows:
Al,=C~o +alSt +a~L_l
(2)
"1-O~3 R C t 4- otaBt W o~5Dt
where o~5>0. In input inventory equations S, is substituted for Qt ( = value added of production).
3. I Introducing an inventory model 3.2 Empirical results Recent empirical work is largely based on the flexible accelerator model originally created by Metzler in 1941 and developed by Lovell [5]. The main idea is to take account of the errors of firms when anticipating future sales. Recent development in econometric methodology (e.g. the rational expectations revolution) has sharpened the analysis and resulted in some breakthroughs, see WilkL~zon .t e .I .~or . . a. mcla . . .review, . . . .i ne . present . . model incorporates the lagged inventory stock, anticipated sales, financial factor, backlog of orders, and short term business expectations, see
Because of the different dynamics of the different inventory types input inventories and finished goods invento6es are estimated separately by means of the quarterly data. The OLS estimation results are shown in Table 2. Equations ( I ) and (2) deal with input inventories and eqns. (3) and (4) with finished goods inventories in the wood products (ISiC 33i ) and in the paper and pulp (IS!C 341 ) industries, respectively. The main results can be summarized as follows. First we deal with the input inventories,
f3i TABLE 2 Estimated results of inventory investment, see "t7, p. 83 ] ISIC
Ceo
Q,
St
1,_ ~
~ C,
B,
Dr
n
R2
D-W
-29.15 (3.00)
40
0.44
1.97
- 8.97 (0.34)
40
0.2o
2.30
0.85 (1.68)
-13.96 (0.73)
4f)
0.17
2.14
-0.53 (0.62)
-8.79 (0.33)
40
0.10
2.13
1
331
23 (0.52)
0.17 (2.33)
-0.25 (3.42)
3.60 (1.80)
1.01 (3.61)
2
341
1153 (3.38)
-0.18 (1.Ol)
--0.64 (4.01)
8.09 (1.14)
1.32 (1.77)
3
331
-45 (0.40)
0.06 (1.64)
-o.!a (1.61)
-9.27 (1.93~
4
341
690 (2.53)
-0.09 (2.15)
0.21 (2.00)
9.44 (1.23)
***
-
**
R 2= correlation coefficient D-W = Durbin-Watson value the values in parentheses are t-ratios • =statistically significant at 95% level • • = statistically significant at 99% level • • • = statistically significant at 99.9% level which are better in their statistical properties: ( 1 ) The buffer m o t i v e variable It_ t~ which is o f special interest Ln_ this study,, is the most significant variable. This result strengther, s the earlier tentative correlation result concerning the buffer stock nature o f input inventories.: (2) The partial adjustment coeffici~mt 0~2 of the flexible acceleration m o d e l is statistically significant in both industries. In the paoer industry the rate o f adjustment o f the stock o f inventories is estimated to be at 64% (per quarter). (3) Short term expectations seem to be o f great significance only in the cas,z o f the wood i~dustry. (4) Another expectation:?: determinant, backlog o f orders (Bt), is statistically significant especially in the w o o d scctc, r "-"-u,,~s u p p o s i n g the buffer stock behavior. ( 5 ) The role o f " u s e r cost o f capital", RC,, is m i n o r in both industries. (6) General statistical properties of the models are rather good e.g. the residuals are a;most white noise and all the signs are according to ex ante anticipations. tKyliiheiko and Pirt,tilii [7, p. 83] explain this by means of the capital-intensive nature of the forest industry that favors price-adjustment to production adjustment when market conditions change.
The results obtaiacd from *_he finished goods inventories estimations are poor in their statistical properties and no i~,int c o : ~ e r n i n ~ " s m o o t h ing" strategies is to be found. One interesting feature is to be noticed, however. The sign o f the parameter estimate of the real rate of interest is negative as anticipated and statistically significant. Perhaps deregulafio, process in Firmish financial markets has forced the forest firms to calculate more exactly the opportunity cost o f eapl.tal and forced them also to avoid " s m o o t h i n g " strategies. In other words, financial (and other) cost shocks may give good reasons for firms not to smooth production. This interpretation has partly been confirmed by the observation that the role •fbuffer stocks is more central wheii the t i m e perioa ts 1975-i 98i). This is quite according to the original idea derived from the correlation matrix. 3.3 Introducing a short term employment model In this section a neoclassical e m p l o y m e n t m o d e l is constructed for Finnish forest ;.adustry. The study "~s~ased ~-:= p r e v i o ~ co..-..tr-L,utions of B , ~ , m m g [ i 3 ] and Ball and StCTr [141. The starting point is the standard east-minimizing model where the short term equi~ibrJ,:..m demand
132 for labor is derived. Takin~ into accounl ,~;: ~djustrnent specificat_,_'o~ and qua~eriy employment data enables us to estimate short period equilibrium demand function for labor. The state of technology (the pr~gdaction function ) and existing capital equipment are taken as give~. Only the sho,.t period employment decisions are concemed. Our target is to contrast these ~esu!t~ w~th "he inventory results obtained and clarify the question whether there i.s a~y emi~!oyment smoothing to be found. Iv. the short run output (value added) (Qt) is assumed to be only a function of hours per worke:~ (h,), the number of employees (L,) and a time trend reflecting technology and increased capital. The short period production function is:
Q,=Bt( Lh )'~
(3)
where B = shift parameter depending on the given capital and technology and determining the long run trend of productivity a = par~met_er that determ.i~es t.he sho~ period behavior of productivity Technological progress and increased capital are absorbed by the following exponential trend: B, =Ae p'
(4)
Thus we obtain
Q,=AePt(Lh)'~
(ot, fl, A > 0 )
(5)
Wages per hour actually worked is not an exogenous variable as usual, but instead an U-shaped function of hours worked. Ball and StCyr [ 14 ] assume that an employee receives always the fixed sum, w~*h*, where w~ is the agreed payment per hour for normal hours (or less) and h* is the level of normal working hours. Above normal hours the worker is paid overtime at a wage rate w2. The wage payment system is thus wl ,~*
FiR. i. t~all and StCyr [14, p. 182-3] and Bergo. land and Cappelen [ 15 ] prove that the wage per hour reaches its minimum when hours worked correspond to normal working hours. That is the cost-minimizing optimum. Given the production function (5) and the optimum condition above, the desired number of e~_r_.pl,~wes (L*) is to be found by inve.--tiag the production function (5) ,~ follows:
L*
Q~/'~ --t~l/'~A-l/'~e"~'/~n*,_l --A ~/"eP'/'~h7- ~'
(7)
T,,, io~ interesting to notice that the desired short term level of employees does not depend on the wages at all. In the same way as in typical partial adlustment inventory models also employment adjustment to the desired ~evel takes time. For that reason we introduce an adjustraent scheme as usually: L t =( L~ )~, 0 < 2 < 1 ~ Lt_ 1 \ t t - 1/
(8)
Combining eqn. (8) with eqn. (7) we obtain Lt_L'l = ,(Q~IC~A- ~l!%-n'!"h*- i\~.)
(9)
Now we are ready to estimate the structural employment coefficients a ( =the elasticity of net output with respect to labor services) and ;t ( = the speed of adjustment of employment). In order to estimate, natural logarithms are taken: In L, = _2__In A - 2 In h*+ ~ In Q. o/
o/
+ (1-- ~) I" r~ .
2flt OL
(!0)
"I\ Ni.
~vhen h < h* h~
wh=
.. wlh*, h
w2(h'-h* )
wheo_h > h *
h
Fig. 1. The "effective" wage per hour worked.
(6) In this case the relation between wh z~ndh i~ as in
Zlf2=O, then Lt=Lt_ t (static) and if).= 1, then L,=L7 (in. stare adjustment).
133 Modifying equation (10) yields the final estimation equation: lnL~=ao+a~lnQ~+a2lnL,_~+a3t
(!1)
where ao=21n(A-~mh *-~) a2 = 1-2
a~=2/ot
a3 = --).fl/Ot
3.4 Empirical resulgs concerning employment adjustr, ent The model of Ball and ~,_.yr b_s the advantage that all the relevant structural parameters ~o,,fl~ are identifiable after .~ome manipulation. Thus employment adjustment dynamics of firms can be analyzed. Main question is whether Finn:-'s.k. forest industry treats workers mair~!y as a fir:ed element or not. Former betaavior means smoothing strategy. Because of the lack of data forest industry has been estimated as such (iSIC 331 + 341 ) by means of quarterly data during the period 19'76-1985. In order to make some more comparisons we haw." estimat.~,~ ~-~),~,~. ~ . . ~a) also the quarterly equat:ons of the whole Finnish maau~hcturing industry (ISIC 3) and the Finnish metal industry (ISIC 3 7 + 3 8 ) . Dynamic structure has been clarified by means of annual longer period estimations, too. Equations (4 and S~ deal with ~,-o~r ~vo,~,~ -raduets (331) and eqns. (6 and 7) with paper and pulp industries (341). The periods concerned are 1962-1975 and 1976-1987. Equations (8 ~na 9) deal with forest industry and eqas. (10 and I 1 ) with the whole m--_.nafaclmringindvs~:). The method used is standard OLS. The results obtained can be characterized as follows: ( l ) The assumption of some lagged adjustment of employment cannot entirely be rejected by the data. Thus there is something that could be called smoothing employment behavior. (2) The speed of adjustment factor 2 is rather .high, anyway. Using quarterly data 2=0.44, which means 44% of the difference between the desired and actual employment !eve!g i~ made ap during the next quarter, it is iate~,~ sti~:~ ~, no1 ce that the value of 2 is higher in the forest industry than in the metal indust~3' t..~~ .,~, . ~.o o ) , but lo,-er
than in the whole manufacturipg iv_g,~stry (2 = 0.70). Thig tells something impo~-tant about the reactions and smoo,hing abilit~ of firms in different sectors. 3 Ann~aal estimates confirm the idea that partial adjuztment coefficients have increased lately° The .~nly exception is the paper and pulp indtist/y (~qi-is. (7 al,d o°').s "W~,l ................. toor~m~ at the inventory adjustment coefficients of Table 2 it cannot be noticed that the speed of adjustment is considerably higher in the paper and pulp sector than in the wood ~ector. It might be a sign of moderate smoothing behavior. On the other hand, the value of adjustment coefficient is rather low. (3) The signs of employment and inventory adjustment coefficients are in accordance with ex ante anticipations supporting thus the ideas of Section 2. (4) All the estimated models are rather good in their statistical properties in s.~y-'~cof some multicollinearity problems. 4. Conclusions
The purpose of this study was to examine ho~ strong the interplay between inventories and employment levels is in Finnish forest industry. It has been claimed that Scandinavian active employment policy could result in low inventory turnover rates because of buffer stocks. Traditionally this view has been tested by comparing the variance of production with the variance of sales (Abel [ 10] ). Here another route has been chosen. Anyway, it seems that "smoothing" behavior concerning finished goods inventories and employment has lost its importance during the 1980s. One possible reason can be the increasing role of financial factors due to the deregulation of Finnish financial markets. On the other hand, input inventories and employees are strongly negatively correlated. Thus there are still some contraeyclic elements. Drastic changes in the timber supply conditions in Finnish forest indus~y ,.,,ul,, ~,~ one tentative explanation. More theoretical research is needed, however, to conner': input inventory behavior and employment fluc3Someother studies of the author supporl this hypothesis. Also the results of Ball and StCyr [ 1~] in Great B.-hainand B-rgland and Cappelen [ 15] in Norwayare consistent with this idea.
134 TABLE3 Estimateo ~est~ltsof employment adjustment 1SIC 1
2
a2
a3
-~
~
n
Rz
D-W
331+341 1975-1985
a0 1.12 (1.12)
aj 0.26 (2.01)
0.56 (3.86)
-0.005 (2.75)
0.44
1.69
38
0.64
1.70
3 1976-1985
0.60 (0.48)
0.58 (3.53)
0.30 (2.01)
-0.006 (3.50)
0.70
1.21
38
0.54
i.92
*
***
***
2
4
5
37+38 i976-i985
0.65 (9.75)
0.18 c'1.271
0.72 (5.49)
-0.002 (1.!2)
0.28
!.56
38
0.78
1.85
331 1962-i975
- 1.73 (3.01)
9.46 (9.12)
0.54 (3.32)
-0.0!5 (5.58)
0.46
i.00
i4
0.95
1.12
331 1976-1~.~7
- 1.41 (3.93)
0.5! (9.03)
0.44 (6.49)
-0.028 (11.97)
0.56
1.10
12
0.99
3.18
***
***
***
6
7
***
341 1962-1975
0.54 (0.37)
0.13 (1.49)
0.62 (2.44)
0.003 (0.40)
0.38
2.92
14
0.98
? 41
341 !976-1987
-1.18 (0.81)
0.29 (3.38)
0.73 (3.24)
-0.0i4 (3.71)
0.27
0.93
12
0.77
2,34
***
***
0.71 (4.35)
-0.008 (1.97)
0.29
1.9~
14
0.93
1.53
***
*
0.55 (4.37)
-0.03 (6.76)
0.45
1.13
12
0.97
2.91
***
***
***
8
331+341 1962-1975
-1.17 (1.15)
0.29 (4.~3) ***
9
331+341 ~976-1987
-1.07 (1.44)
0,40 (5.69) ***
10
11
3 i962-1975
-2.38 (2.97)
0.47 (6.16)
0.64 (8,26)
--0.0~19 (3.9i)
0.36
0.77
!4
0.99
1.52
3 1976-1987
-3.67 (4.74)
0.70 (10.10)
0.50 (5.86)
-0.032 (i0.30)
0.50
0.71
12
0.97
2.62
***
***
***
***
R 2= correlation coefficient D-W = Durbin-Watson value the value in parentheses are t-ratios * =statistically significant at 95% level * • = sta'Sstically significant at 99% level * • • =statistically significant at 99,9% level t u a t i o n s w i t h e a c h o t h e r . P e r h a p s " r a t i o n a l exp e c t a t i o n r e v o l u t i o n " a n d , say, t h e i m p l i c i t c o n tract theory o f e m p l o y m e n t can help to u n d e r s t a n d t h e s e e m p i r i c a l a n o m a l i e s in t h e n e a r future. References
Akhtar, M.A., 1983. Effects of ;aterest rates and inflation on aggregate inventory in J:stment in the United States. Amer. Econ. Rev., June 319-328.
2
Blinder, A.S., 1986. Can the production smoothingmodel of inventory behavior be saved? Quart. J. Econ.: 431453. 3 Hansen, A., 1989. Inventories, inflation~ a~d pace expectations. J. Post Keynesian Econ., Spring: ,'~ 9-459. 4 Irvine, F.O., t981. Merchant wholesale inventory investments and the cost of capital. Amer. Econ. Rev., ."~aT: 23-29. 5 Love!!, M., 1961. Manufacturers' inventories, sale3 expectations, and the acceleration pr/nciple. Econometrica, July: 293-314.
135 6
Wilkinson, M., 1989. Aggregate inventory behavior in large European economies. European Econ. Rev., January: 181-194. 7 Kyliiheiko, K. and Pirttilii, T., 1988. Short-term invento,'.,' dynamics ir~ zkz F'.'nn~sbforest industry. Eng. Costs Prod. Econ., 15: 81-85. 8 Blanchard, O., 1983. The production and inventory behavior of the American automobile industry. J. Political Econ.: 365-400. 9 Kanniainen, V. and Heruesniemi, H., 1989. The cost of holding iiicentories, and the demand for labor and capite, under corporate taxation, Discussion Papers No. 273, Dept. of Economics, University of Helsinki. 10 Abe!, I. and Pirttilii, T., 1989. Input and output inventory interactions - A czmparzfive study of the ;:~nnish
11 12
13 14
15
and Hungarian m,~nufact~fing industry. Researct'~ Report 11 ] 1989, Lappeenranta University of Technology. Kylfiheiko, K. and ?L, ttdi~, ";., 1987. Econometric analysis of invento~ investment and the role of short-terra expectations. Eng. Costs Pr~.,6. Ecv~, 12: 293-298. Kyliiheiko, K. and Pirttilii, T., 1985. Interest rates, inflation, and inventory investment: Some Finnish experiences. Eng. Costs Prod. Econ., 9: 259-266. Brechling, F.P.R., 1965. The relationship between output and employment ~n British n~anufact~5-..g. Rev Econ. Stud., July. Ball, R.J. and StCyr, E.B.A., 1966 St, oft term employmerit functions in British manufacturing industry. Rev. Econ. Stud., Jt~ly: i79-2G7. Bergland, H. and Cappelen, A, 198!~ Produktivet och ~;ys_~e]~e*~,~~I,dl;strin. Statistisk Sentralbyr~, Oslo.
~_2 9
Else, ~er
Short term inventory and employment adjustment dynamics in Finnish forest industry Kalevi Kyl~iheiko Lappeenranta University of Technology, Box 20, SF-53851 Lappeenranta, Finland
I. Introduction This study is conceived as an extension of the major inventory investment studies in explaining short period adjustment interplay between input and finished goods inventories and employment levels in Finnish forest industry.. The data analyzed here are unadjusted fixed price quarterly time series of inventories, value added of industrial production and sales for forest industry ~oh.. 33! and 341 ). The period of analysis runs from the first quarter of 1976 to the second one of i985. Inventory fluctuations are becoming increasingly recognized as a major mechanism by which business cycles are propagated. Recent theoretical and empirical studies have analyzed determinants of inventory investment extensively, although the conclusions drawn are far from a consensus° At least the following seven major factors causing inventory fluctuations can be identified [ ! -7 ]: ( 1 ) changes in the cost of holding inventories, (2) (un)anticipated changes in sales expectation [ 8 ], (3) changes in the cost of other inputs (wages, energy prices etc.), (4) institutional factors, especially taxation [ 9 ], (5) changes in inventory management philosophy, (6) changes in the input and output inventory k~te;actions [ 10], and (7) changes in the desires of managers for "smoothing" the production and employment by means of buffer inventories. This study is focused mainly on the so-called buffer motive (factor (7) in *•he list above), which will be analyzed by utilizing two different the extended Metzler's inventory model, which
makes it possil;le to specify partial adjustment coefficients for inventories. The second model is an extension of the contribution by Brechling, which allows to construct a ec,st-minir~rT~no model of neoclassical employment theory. From this we derive the short ~erm demand for labor and the speeds of adjustmez/) ~ e.2-ilibrium and. estimate the adjustment coefficients by means of tke OLS-method.
2. Posing the question in some e~r~ier studies it has been shown that the inventory turnover rates ( =the value added of production/total inventorle~) of the fores~ sector in Sweden and Finland do not stand up to intonational comparison with FRG, the USA and Japan [7]. It has be,m proposed that one possible explanation could be Scandinavian em~ ployment policy, which prefers ~mooth and hig,~ employment to high inventor, remover rates. On the other hand, however, traditional production smoothing behavior of finished goods inventories has been questioned in many studies, see Kyl~iheiko and Pirttil-~ [ i I ] and especially Abel and Pirttil~i [ 10 ]. In order to clarify connections between employment levels and the input and finished goods inventories of the wood products indust:~j (IS!C 331 ) and the paper and pulp indust~ (ISIC 341 ) the correlation matrix shown in Table 1 of variables concerned has been estimated~ One can conclude that employment levels and different types of inventories are interconnected in a strange way. Firstly, the negative correlation between employees and ihput iilvcn~.':.e~- ;~ high during the whole estimation period and, in fact,
0925-5273/91/$03,50 © 1991 ElsevierSciencePublishers B.V. All fights reserved.
i30 TABLE ! Summa.~' .."fcerre!-_~Ao~sbetween employees and inventories in forest industry
Kylaheiko, Pintil~i [ 12,7 ] where this model and results were first explicated. The starting equao tion ~s as follows: Alt=-~-O"[-O'iOl+ o:
Employees
Input inventories
Finished goods inventories
1976-85 1976-80 19~0-85
-0.74 -0.50 -0.84
-0.18 -0.54 0.01
A/, S~+ j
1976-85 1976- 80 1980-85
+0.04 +0.39 -0.42
It_ t
the connection has strengthened in the 1980z. Although the causal connections remain unclear, one can raise the assumption that this is partly caused by contra-cyclic behavior of input inventories. Perhaps also taxation and price speculation motives can be found behind these correlations (see i. 10,7] ). Secondly, the correlation between employees and finished goods inve~.~tories was quite strong earlier, b,;t disappeared altogether during this decade. This phenomenon thus contradicts the traditional production and employment smoothing hypothesis in Finnish conditions nowadays, but supports it during the seventies. It is also worth mentioning that during the period 19 ]~-80 different types of invent~'ies are positively correlated and during the period t 9~0-85 negatively correlated.
3~ Econometric specification of models and empirical results
i 4.- 0 1 2 [ t _
! -"1- O f 3 1r~t"~ , 12 l ~ , ~ , t 4 ., ~tx4.~,~
(1) where a~, a 4 > 0 and a2, c~3<0 and = inventory investment during period t =long-term sales er:peclations during period t + 1 = inventory level during period t - 1 = real rate of interest during peried t =backlog of orders during period t
RC, B,
Long v~n sales ext)ectations arc specified naively by "static expectations", S~+~ =St Shoe :,;ra~ expectations are included in the r;odel, using a three-fold dummy variable built on the basis of the business barometer of the Central Association of Industry ii~ Finland. This dummy can have value~!, as follows: /t" + 1 when a rise is expected D~ = ~ 0 when the economy is expected to remairJ as befere - 1 when a decline is expected
(
It can be included additively in the ba~.~ceqn. ( 1 ) as follows:
Al,=C~o +alSt +a~L_l
(2)
"1-O~3 R C t 4- otaBt W o~5Dt
where o~5>0. In input inventory equations S, is substituted for Qt ( = value added of production).
3. I Introducing an inventory model 3.2 Empirical results Recent empirical work is largely based on the flexible accelerator model originally created by Metzler in 1941 and developed by Lovell [5]. The main idea is to take account of the errors of firms when anticipating future sales. Recent development in econometric methodology (e.g. the rational expectations revolution) has sharpened the analysis and resulted in some breakthroughs, see WilkL~zon .t e .I .~or . . a. mcla . . .review, . . . .i ne . present . . model incorporates the lagged inventory stock, anticipated sales, financial factor, backlog of orders, and short term business expectations, see
Because of the different dynamics of the different inventory types input inventories and finished goods invento6es are estimated separately by means of the quarterly data. The OLS estimation results are shown in Table 2. Equations ( I ) and (2) deal with input inventories and eqns. (3) and (4) with finished goods inventories in the wood products (ISiC 33i ) and in the paper and pulp (IS!C 341 ) industries, respectively. The main results can be summarized as follows. First we deal with the input inventories,
f3i TABLE 2 Estimated results of inventory investment, see "t7, p. 83 ] ISIC
Ceo
Q,
St
1,_ ~
~ C,
B,
Dr
n
R2
D-W
-29.15 (3.00)
40
0.44
1.97
- 8.97 (0.34)
40
0.2o
2.30
0.85 (1.68)
-13.96 (0.73)
4f)
0.17
2.14
-0.53 (0.62)
-8.79 (0.33)
40
0.10
2.13
1
331
23 (0.52)
0.17 (2.33)
-0.25 (3.42)
3.60 (1.80)
1.01 (3.61)
2
341
1153 (3.38)
-0.18 (1.Ol)
--0.64 (4.01)
8.09 (1.14)
1.32 (1.77)
3
331
-45 (0.40)
0.06 (1.64)
-o.!a (1.61)
-9.27 (1.93~
4
341
690 (2.53)
-0.09 (2.15)
0.21 (2.00)
9.44 (1.23)
***
-
**
R 2= correlation coefficient D-W = Durbin-Watson value the values in parentheses are t-ratios • =statistically significant at 95% level • • = statistically significant at 99% level • • • = statistically significant at 99.9% level which are better in their statistical properties: ( 1 ) The buffer m o t i v e variable It_ t~ which is o f special interest Ln_ this study,, is the most significant variable. This result strengther, s the earlier tentative correlation result concerning the buffer stock nature o f input inventories.: (2) The partial adjustment coeffici~mt 0~2 of the flexible acceleration m o d e l is statistically significant in both industries. In the paoer industry the rate o f adjustment o f the stock o f inventories is estimated to be at 64% (per quarter). (3) Short term expectations seem to be o f great significance only in the cas,z o f the wood i~dustry. (4) Another expectation:?: determinant, backlog o f orders (Bt), is statistically significant especially in the w o o d scctc, r "-"-u,,~s u p p o s i n g the buffer stock behavior. ( 5 ) The role o f " u s e r cost o f capital", RC,, is m i n o r in both industries. (6) General statistical properties of the models are rather good e.g. the residuals are a;most white noise and all the signs are according to ex ante anticipations. tKyliiheiko and Pirt,tilii [7, p. 83] explain this by means of the capital-intensive nature of the forest industry that favors price-adjustment to production adjustment when market conditions change.
The results obtaiacd from *_he finished goods inventories estimations are poor in their statistical properties and no i~,int c o : ~ e r n i n ~ " s m o o t h ing" strategies is to be found. One interesting feature is to be noticed, however. The sign o f the parameter estimate of the real rate of interest is negative as anticipated and statistically significant. Perhaps deregulafio, process in Firmish financial markets has forced the forest firms to calculate more exactly the opportunity cost o f eapl.tal and forced them also to avoid " s m o o t h i n g " strategies. In other words, financial (and other) cost shocks may give good reasons for firms not to smooth production. This interpretation has partly been confirmed by the observation that the role •fbuffer stocks is more central wheii the t i m e perioa ts 1975-i 98i). This is quite according to the original idea derived from the correlation matrix. 3.3 Introducing a short term employment model In this section a neoclassical e m p l o y m e n t m o d e l is constructed for Finnish forest ;.adustry. The study "~s~ased ~-:= p r e v i o ~ co..-..tr-L,utions of B , ~ , m m g [ i 3 ] and Ball and StCTr [141. The starting point is the standard east-minimizing model where the short term equi~ibrJ,:..m demand
132 for labor is derived. Takin~ into accounl ,~;: ~djustrnent specificat_,_'o~ and qua~eriy employment data enables us to estimate short period equilibrium demand function for labor. The state of technology (the pr~gdaction function ) and existing capital equipment are taken as give~. Only the sho,.t period employment decisions are concemed. Our target is to contrast these ~esu!t~ w~th "he inventory results obtained and clarify the question whether there i.s a~y emi~!oyment smoothing to be found. Iv. the short run output (value added) (Qt) is assumed to be only a function of hours per worke:~ (h,), the number of employees (L,) and a time trend reflecting technology and increased capital. The short period production function is:
Q,=Bt( Lh )'~
(3)
where B = shift parameter depending on the given capital and technology and determining the long run trend of productivity a = par~met_er that determ.i~es t.he sho~ period behavior of productivity Technological progress and increased capital are absorbed by the following exponential trend: B, =Ae p'
(4)
Thus we obtain
Q,=AePt(Lh)'~
(ot, fl, A > 0 )
(5)
Wages per hour actually worked is not an exogenous variable as usual, but instead an U-shaped function of hours worked. Ball and StCyr [ 14 ] assume that an employee receives always the fixed sum, w~*h*, where w~ is the agreed payment per hour for normal hours (or less) and h* is the level of normal working hours. Above normal hours the worker is paid overtime at a wage rate w2. The wage payment system is thus wl ,~*
FiR. i. t~all and StCyr [14, p. 182-3] and Bergo. land and Cappelen [ 15 ] prove that the wage per hour reaches its minimum when hours worked correspond to normal working hours. That is the cost-minimizing optimum. Given the production function (5) and the optimum condition above, the desired number of e~_r_.pl,~wes (L*) is to be found by inve.--tiag the production function (5) ,~ follows:
L*
Q~/'~ --t~l/'~A-l/'~e"~'/~n*,_l --A ~/"eP'/'~h7- ~'
(7)
T,,, io~ interesting to notice that the desired short term level of employees does not depend on the wages at all. In the same way as in typical partial adlustment inventory models also employment adjustment to the desired ~evel takes time. For that reason we introduce an adjustraent scheme as usually: L t =( L~ )~, 0 < 2 < 1 ~ Lt_ 1 \ t t - 1/
(8)
Combining eqn. (8) with eqn. (7) we obtain Lt_L'l = ,(Q~IC~A- ~l!%-n'!"h*- i\~.)
(9)
Now we are ready to estimate the structural employment coefficients a ( =the elasticity of net output with respect to labor services) and ;t ( = the speed of adjustment of employment). In order to estimate, natural logarithms are taken: In L, = _2__In A - 2 In h*+ ~ In Q. o/
o/
+ (1-- ~) I" r~ .
2flt OL
(!0)
"I\ Ni.
~vhen h < h* h~
wh=
.. wlh*, h
w2(h'-h* )
wheo_h > h *
h
Fig. 1. The "effective" wage per hour worked.
(6) In this case the relation between wh z~ndh i~ as in
Zlf2=O, then Lt=Lt_ t (static) and if).= 1, then L,=L7 (in. stare adjustment).
133 Modifying equation (10) yields the final estimation equation: lnL~=ao+a~lnQ~+a2lnL,_~+a3t
(!1)
where ao=21n(A-~mh *-~) a2 = 1-2
a~=2/ot
a3 = --).fl/Ot
3.4 Empirical resulgs concerning employment adjustr, ent The model of Ball and ~,_.yr b_s the advantage that all the relevant structural parameters ~o,,fl~ are identifiable after .~ome manipulation. Thus employment adjustment dynamics of firms can be analyzed. Main question is whether Finn:-'s.k. forest industry treats workers mair~!y as a fir:ed element or not. Former betaavior means smoothing strategy. Because of the lack of data forest industry has been estimated as such (iSIC 331 + 341 ) by means of quarterly data during the period 19'76-1985. In order to make some more comparisons we haw." estimat.~,~ ~-~),~,~. ~ . . ~a) also the quarterly equat:ons of the whole Finnish maau~hcturing industry (ISIC 3) and the Finnish metal industry (ISIC 3 7 + 3 8 ) . Dynamic structure has been clarified by means of annual longer period estimations, too. Equations (4 and S~ deal with ~,-o~r ~vo,~,~ -raduets (331) and eqns. (6 and 7) with paper and pulp industries (341). The periods concerned are 1962-1975 and 1976-1987. Equations (8 ~na 9) deal with forest industry and eqas. (10 and I 1 ) with the whole m--_.nafaclmringindvs~:). The method used is standard OLS. The results obtained can be characterized as follows: ( l ) The assumption of some lagged adjustment of employment cannot entirely be rejected by the data. Thus there is something that could be called smoothing employment behavior. (2) The speed of adjustment factor 2 is rather .high, anyway. Using quarterly data 2=0.44, which means 44% of the difference between the desired and actual employment !eve!g i~ made ap during the next quarter, it is iate~,~ sti~:~ ~, no1 ce that the value of 2 is higher in the forest industry than in the metal indust~3' t..~~ .,~, . ~.o o ) , but lo,-er
than in the whole manufacturipg iv_g,~stry (2 = 0.70). Thig tells something impo~-tant about the reactions and smoo,hing abilit~ of firms in different sectors. 3 Ann~aal estimates confirm the idea that partial adjuztment coefficients have increased lately° The .~nly exception is the paper and pulp indtist/y (~qi-is. (7 al,d o°').s "W~,l ................. toor~m~ at the inventory adjustment coefficients of Table 2 it cannot be noticed that the speed of adjustment is considerably higher in the paper and pulp sector than in the wood ~ector. It might be a sign of moderate smoothing behavior. On the other hand, the value of adjustment coefficient is rather low. (3) The signs of employment and inventory adjustment coefficients are in accordance with ex ante anticipations supporting thus the ideas of Section 2. (4) All the estimated models are rather good in their statistical properties in s.~y-'~cof some multicollinearity problems. 4. Conclusions
The purpose of this study was to examine ho~ strong the interplay between inventories and employment levels is in Finnish forest industry. It has been claimed that Scandinavian active employment policy could result in low inventory turnover rates because of buffer stocks. Traditionally this view has been tested by comparing the variance of production with the variance of sales (Abel [ 10] ). Here another route has been chosen. Anyway, it seems that "smoothing" behavior concerning finished goods inventories and employment has lost its importance during the 1980s. One possible reason can be the increasing role of financial factors due to the deregulation of Finnish financial markets. On the other hand, input inventories and employees are strongly negatively correlated. Thus there are still some contraeyclic elements. Drastic changes in the timber supply conditions in Finnish forest indus~y ,.,,ul,, ~,~ one tentative explanation. More theoretical research is needed, however, to conner': input inventory behavior and employment fluc3Someother studies of the author supporl this hypothesis. Also the results of Ball and StCyr [ 1~] in Great B.-hainand B-rgland and Cappelen [ 15] in Norwayare consistent with this idea.
134 TABLE3 Estimateo ~est~ltsof employment adjustment 1SIC 1
2
a2
a3
-~
~
n
Rz
D-W
331+341 1975-1985
a0 1.12 (1.12)
aj 0.26 (2.01)
0.56 (3.86)
-0.005 (2.75)
0.44
1.69
38
0.64
1.70
3 1976-1985
0.60 (0.48)
0.58 (3.53)
0.30 (2.01)
-0.006 (3.50)
0.70
1.21
38
0.54
i.92
*
***
***
2
4
5
37+38 i976-i985
0.65 (9.75)
0.18 c'1.271
0.72 (5.49)
-0.002 (1.!2)
0.28
!.56
38
0.78
1.85
331 1962-i975
- 1.73 (3.01)
9.46 (9.12)
0.54 (3.32)
-0.0!5 (5.58)
0.46
i.00
i4
0.95
1.12
331 1976-1~.~7
- 1.41 (3.93)
0.5! (9.03)
0.44 (6.49)
-0.028 (11.97)
0.56
1.10
12
0.99
3.18
***
***
***
6
7
***
341 1962-1975
0.54 (0.37)
0.13 (1.49)
0.62 (2.44)
0.003 (0.40)
0.38
2.92
14
0.98
? 41
341 !976-1987
-1.18 (0.81)
0.29 (3.38)
0.73 (3.24)
-0.0i4 (3.71)
0.27
0.93
12
0.77
2,34
***
***
0.71 (4.35)
-0.008 (1.97)
0.29
1.9~
14
0.93
1.53
***
*
0.55 (4.37)
-0.03 (6.76)
0.45
1.13
12
0.97
2.91
***
***
***
8
331+341 1962-1975
-1.17 (1.15)
0.29 (4.~3) ***
9
331+341 ~976-1987
-1.07 (1.44)
0,40 (5.69) ***
10
11
3 i962-1975
-2.38 (2.97)
0.47 (6.16)
0.64 (8,26)
--0.0~19 (3.9i)
0.36
0.77
!4
0.99
1.52
3 1976-1987
-3.67 (4.74)
0.70 (10.10)
0.50 (5.86)
-0.032 (i0.30)
0.50
0.71
12
0.97
2.62
***
***
***
***
R 2= correlation coefficient D-W = Durbin-Watson value the value in parentheses are t-ratios * =statistically significant at 95% level * • = sta'Sstically significant at 99% level * • • =statistically significant at 99,9% level t u a t i o n s w i t h e a c h o t h e r . P e r h a p s " r a t i o n a l exp e c t a t i o n r e v o l u t i o n " a n d , say, t h e i m p l i c i t c o n tract theory o f e m p l o y m e n t can help to u n d e r s t a n d t h e s e e m p i r i c a l a n o m a l i e s in t h e n e a r future. References
Akhtar, M.A., 1983. Effects of ;aterest rates and inflation on aggregate inventory in J:stment in the United States. Amer. Econ. Rev., June 319-328.
2
Blinder, A.S., 1986. Can the production smoothingmodel of inventory behavior be saved? Quart. J. Econ.: 431453. 3 Hansen, A., 1989. Inventories, inflation~ a~d pace expectations. J. Post Keynesian Econ., Spring: ,'~ 9-459. 4 Irvine, F.O., t981. Merchant wholesale inventory investments and the cost of capital. Amer. Econ. Rev., ."~aT: 23-29. 5 Love!!, M., 1961. Manufacturers' inventories, sale3 expectations, and the acceleration pr/nciple. Econometrica, July: 293-314.
135 6
Wilkinson, M., 1989. Aggregate inventory behavior in large European economies. European Econ. Rev., January: 181-194. 7 Kyliiheiko, K. and Pirttilii, T., 1988. Short-term invento,'.,' dynamics ir~ zkz F'.'nn~sbforest industry. Eng. Costs Prod. Econ., 15: 81-85. 8 Blanchard, O., 1983. The production and inventory behavior of the American automobile industry. J. Political Econ.: 365-400. 9 Kanniainen, V. and Heruesniemi, H., 1989. The cost of holding iiicentories, and the demand for labor and capite, under corporate taxation, Discussion Papers No. 273, Dept. of Economics, University of Helsinki. 10 Abe!, I. and Pirttilii, T., 1989. Input and output inventory interactions - A czmparzfive study of the ;:~nnish
11 12
13 14
15
and Hungarian m,~nufact~fing industry. Researct'~ Report 11 ] 1989, Lappeenranta University of Technology. Kylfiheiko, K. and ?L, ttdi~, ";., 1987. Econometric analysis of invento~ investment and the role of short-terra expectations. Eng. Costs Pr~.,6. Ecv~, 12: 293-298. Kyliiheiko, K. and Pirttilii, T., 1985. Interest rates, inflation, and inventory investment: Some Finnish experiences. Eng. Costs Prod. Econ., 9: 259-266. Brechling, F.P.R., 1965. The relationship between output and employment ~n British n~anufact~5-..g. Rev Econ. Stud., July. Ball, R.J. and StCyr, E.B.A., 1966 St, oft term employmerit functions in British manufacturing industry. Rev. Econ. Stud., Jt~ly: i79-2G7. Bergland, H. and Cappelen, A, 198!~ Produktivet och ~;ys_~e]~e*~,~~I,dl;strin. Statistisk Sentralbyr~, Oslo.