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Int. J. Production Economics 93–94 (2005) 61–73

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Macroeconomic characteristics and inventory investment:a multi-country study

Attila Chikan�, Erzsebet Kovacs, Tunde Tatrai

Department of Business Economics, Budapest University of Economic Sciences and Public Administration, Veres Palne u.36,

Budapest 1053, Hungary

Abstract

The paper is a new element in a series of studies analyzing macroeconomic inventory behavior by use of multi-

country data. In this paper, seven hypotheses are tested with positive result. These hypotheses include subjects like

relations of inventories with growth and with some other macroeconomic indicators of the use of GDP and the long-

term tendencies of global inventory formations. Multivariate statistical analysis is used for evaluation.

r 2004 Elsevier B.V. All rights reserved.

Keywords: Macroeconomic inventory investment; GDP components; OECD countries; Multivariate statistics; Inventory behavior

1. Introduction

Even though economic analysis of inventorybehavior has a vast literature, very few studieswere published about some of the most elementaryquestions on inventories in an internationalperspective: what conclusions can be drawn fromthe data and experiences of the various countries?What kinds of generalizations can be madeabout inventory behavior of the really differenteconomies?

In some previous papers we have already madesome—perhaps not unsuccessful—attempts tofind answers to a few of the related questions.

e front matter r 2004 Elsevier B.V. All rights reserve

e.2004.06.006

ng author. Tel./fax: +36-1-317-2959.

ss: chikan@mercur.bke.hu (A. Chikan).

(A summary of previous results is given in Chikanand Tatrai, 2003.) Here we continue this with anew methodology: extended multivariate statisticalcalculations are applied.We have used the OECD database, containing

14 of the most developed economies of the world.The following countries were included (from 1968to 1997):

d.

Belgium

Germanya Spain Canada Ireland Sweden Denmark Italy United Kingdom Finland Japan United States France Netherlands

aWest Germany until 1994.

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A. Chikan et al. / Int. J. Production Economics 93–94 (2005) 61–7362

Our analysis is based on annual data, since no

quarterly or monthly data were available in theinventory statistics of OECD. This is of course alimitation, since inventories fluctuate considerablywithin a year. However, we believe that for ourmain objective—to see the long-term tendencies—this data set is acceptable; it does not cause majorjudgment errors.

The main limitation of the research follows fromthe nature of macroeconomic inventory statistics.Many authors call attention to the different andsometimes uncertain contents of calculatingmacroaggregates of inventories in the variouscountries. Because of this we have to be verycautious in the interpretation of results.

2. Research hypotheses

We have established seven hypotheses regardingthe relation of inventory investment to othermacroeconomic indicators. These hypotheses arethe following:

H(1): The range in which characteristic inven-tory investment/GDP ratio varies is differentby countries—as a tendency, lower relative in-ventory investment goes together with highervariation.

H(2): Annual relative inventory investment (ARII)is positively correlated with GDP growth.

H(3): ARII is positively correlated with gross fixedcapital investment.

H(4): ARII is negatively correlated with annualforeign trade balance.

H(5): No general regression model can be appliedin the countries in our sample.

H(6): Over a 30-year period (from 1968 to 1997)timewise differences are more significant thancountrywise differences.

H(7): There is a general tendency of decreasingARII, which overshadows annual fluctuations.

H(1) is based on the observation (see, forexample, Kornai, 1971) that there is a ‘‘normal’’value of many macroeconomic parameters, whichis characteristic to the particular countries in agiven, long period of time and around which theactual values fluctuate.H(2)–H(4) come from earlier studies, especially

from Chikan and Horvath (1999).H(5) is a consequence of what is said at H(1)—

each particular countries have their own charac-teristics and even if H(2)–H(4) hold, i.e. we canestablish macroeconomic rules influencing inven-tory investments, it is highly unprobable that overa longer period significant correlations betweenformation of various parameters in the differentcountries would exist.H(6) and H(7) stem from the meta-hypothesis

that there is a general decreasing tendency ofinventory accumulation in the world, due totechnical and managerial developments, whichtendency comes through only on a rather long run.The above hypotheses are important from the

point of view of understanding the macroeco-nomic role of inventories. They explain therelationship of investments in inventories to theformation of other macroeconomic parametersand to the general path of economic development.A wide range of statistical methods are used to

test these hypotheses.

2.1. Basic tendencies of inventory investments over

time

In this section, we analyze the first fourhypotheses set forth. It is a characteristics ofinventory investment (Kornai, 1971, 1980, Chikanand Tatrai, 2003) that they have kind of a norm,which is different in each country, around whichactual inventory investment fluctuates. To furtheranalyze this, we set H(1). In Chikan and Tatrai(2003) we have found that there is a ‘‘clear but nottoo strong’’ positive correlation between inventoryinvestment and growth. This statement was basedon correlation coefficients by countries. Thevalidity of this tendency was checked now usinga different approach (using the time series data asrandom observations) and extended to the analysisof the behavior of components of GDP, to check

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Table 1

Average stock increase/GDP ratio (average RSI) in 14 countries

(increasing order), 1968–1997

Country Average RSI Std. dev. Coefficient

of variation

Sweden 0.00160 0.01284 8.025

Denmark 0.00338 0.00615 1.817

UK 0.00338 0.01284 2.318

Belgium 0.00384 0.00721 1.879

Canada 0.00508 0.00913 1.797

West

Germany

0.00565 0.00899 1.590

US 0.00585 0.00526 0.900

Netherlands 0.00598 0.00758 1.268

Finland 0.00640 0.01609 2.514

France 0.00707 0.01015 1.436

Spain 0.00837 0.00630 0.753

Ireland 0.00857 0.01200 1.400

Japan 0.00918 0.01001 1.090

Italy 0.01230 0.00973 0.791

Total

average

0.00619 0.00989 1.598

30 y

ear

aver

age

of a

nnua

l sto

ck in

crea

se/G

DP

ratio

(R

SI)

.0016 Sweden

.0034 Denmark+UK

.0038 Belgium

.0051 Canada

.0057 W Germany

.0058 US

.0060 Netherlands

.0064 Finland

.0071 France

.0084 Spain

.0086 Ireland

.0092 Japan

.0123 Italy

Annual sto

-5-10-15

Fig. 1. Long term average inventory investmen

A. Chikan et al. / Int. J. Production Economics 93–94 (2005) 61–73 63

some of the earlier results in Chikan and Horvath(1999). Based on our previous results, theH(2)–H(4) hypotheses were set.Analyzing the average stock increase as percen-

tage of GDP (we will use RSI to denote this ratiostanding for Relative Stock Increase) for 14 of themost developed OECD countries in the period1968–1997, we have found that the smallestaverage value belongs to Sweden (0.0016) andItaly has reached the highest number (0.0123)(Table 1). In case of Sweden, UK and Finlandcoefficients of variation (ratio of standard devia-tion to the mean) are above two (they are bold inTable 1), emphasizing big variability of their stockchanges.In general lower average RSI (which denotes the

30-year average of annual relative inventoryinvestment ARII —in each country) goes togetherwith a higher standard deviation, which is aninteresting but not surprising result. This conclu-sion is supported by Fig. 1, which compares dataof annual inventory investment/GDP (ARII) withthe long-term average (RSI).

ck increase/GDP ratio (ARII)

2520151050

t/GDP ratio compared to annual ratios.

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Table 2

Extreme average ARIIs as shown in Fig. 1

Country Extremely low (�) Low (J) High (J) Extremely high (�)

Sweden 1975

Belgium 1969,1974

Canada 1982 1974

West Germany 1969,1970

Finland 1974

Spain 1969,1974

Ireland 1974

Japan 1973 1968,1969,1970,1974,

Italy 1975 1974

A. Chikan et al. / Int. J. Production Economics 93–94 (2005) 61–7364

Fig. 1 should be interpreted as follows: theshadowed boxes include those 15 years’ data foreach country, which are closest to the median(which is represented by the bold line in the box).This way the shadowed boxes show, how inven-tories in the ‘‘characteristic’’ years behaved in eachcountries (how the ‘‘characteristic’’ data arelocated, how frequently decrease of stocks hap-pened, how extended the coefficient of variationwas in the characteristic years). The signs J and �show the ‘‘outliers’’: J means out of 1.5 times theinterquartile range while � stands for values out ofthree times this range. Evidently, low values areprinted on the left, high values appear on the right.

One can see that lower average RSI usually goestogether with higher fluctuation (except Finlandand France). Five countries do not have outliers,the other nine countries have one or two extremevalues (see Table 2), except Japan, where therewere five (extremely) high inventory investmentyears—all in a seven year period between 1968 and1974 which were, as data show, rather ‘‘rocky’’years of the Japanese economy—though it must bementioned that even these ‘‘extreme’’ values wouldbe within the normal range in many othercountries. Seven of the nine countries had ex-tremely high inventory investment ratio (ARII) in1974, which we remember as the year of the first oilshock.

Beside the graphical presentation, ARIIs can becharacterized by means and standard deviations ofthe 14 countries (Appendix A). Using the separa-tion of the total time period to two subperiods

(1968–1983 and 1984–1997), as in Chikan andTatrai (2003), we found that there are bigdifferences between the subperiods. The dividingline between the two subperiods was drawn at1984, because this year has proved to have specialcharacteristics in previous statistical analyses, seefor example McConnell and Perez-Quiros (2000).These characteristics do not play a role in ourcurrent study but for sake of comparability wekept the original subperiods. The first 16 years canbe characterized by a higher relative stock increasewith smaller variability than the one experienced inthe second subperiod. Values of coefficient ofvariation higher than two highlight those countrieswhere the year-by-year values are not concentratedaround their means. Six countries are enhanced inAppendix B. Average stock increase in Finlandvaried extremely around the mean between 1984and 1997. Italy, Japan and Spain were character-ized by the most stable inventory level. (This resultcorresponds to those shown in Fig. 1.) Interest-ingly, the three countries (Sweden, UK andFinland) which have had high coefficients ofvariations in the total period (Table 1) have notshown the same behavior in the two subperiods:while the variation was high in both subperiods inSweden and the UK, Finland had low variation inthe first round and extremely high in the secondone (Appendix B).The data in Appendix B support H(7) and

reflects the result in Chikan and Tatrai (2003):both the average relative inventory investment andtheir country-wise variation is decreasing in time.

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A. Chikan et al. / Int. J. Production Economics 93–94 (2005) 61–73 65

The fact that the relative standard deviation hasincreased in some countries reflects that lowerinventory investment goes together in these caseswith a relatively smaller decrease of variation,which is economically explainable.

It is interesting to note, that the variation ofaverage RSI by countries is far bigger in the firstsubperiod than in the second one—this supportsthe result in Chikan and Tatrai (2003), accordingto which inventory investment behavior of thedeveloped countries is getting more and moresimilar. In fact, it may also be added by analyzingTable 2, that the extreme values are all from thefirst period—which is in correspondence with theabove.

3. Correlation of inventory investments with other

GDP components

To analyze the effects of different macroeco-nomic structures on inventory investment, wewanted to see the connection between them andthe various other components of the use of GDP.The following variables were handled (all inpercentage of the annual GDP volume):

Table 3

Significanta corr

Correlations

Indices

STOCK—GR G

STOCK—GFC

STOCK—GOV

STOCK—PRIV

STOCK—EXP

STOCK—IMP

STOCK—FTB

aSignificance

STOCK

inventory investment GFC gross fixed capital formation GOV governmental consumption PRIV private consumption EXP exports

elation coefficients of standardized indices across cou

30 years Positive

1968–1997 1968–1983

DP 0.531 0.459

+ 0.469 0.451

�0.517

— �0.266

�0.265

�0.085

(p ¼ 0:083)+ �0.218

below 0.05.

IMP

ntries and time

1984–19

0.537

0.320

0.073

(p ¼ 0:310.213

imports

FTB foreign trade balance GRGDP annual growth rate of GDP

To find the connection between real-time valuesof STOCK and the other variables we used againthe data of the same 14 countries for the sameperiod as in the previous section.We have checked three hypotheses. These

hypotheses are all related to the role of inventoriesin the adjustment of the economy. We believe thateven though macroeconomic inventory behaviorof a country is the consequence of effects of atremendous number of micro-level decisions, theadjustment needs of the economy come throughthis flow of effects.H(2) calls attention to the fact, that a bigger

growth rate has a destabilizing effect on theeconomy (it changes proportions of variouscomponents of the economy), which results in ahigher inventory investment—because of the needof adjustment.H(3) tells us, in connection with H(2) that, as a

tendency, increase of fixed capital and stocks havea parallel move, both connected to growth.H(4) is also connected logically to H(2), and it

tells us that short term adjustment of an economycan go on by adjusting either foreign trade orinventories.Variables were standardized by countries and

Pearson’s correlation coefficients were calculated

periods

Negative

97 1968–1983 1984–1997

�0.570 �0.438

�0.293 �0.384

�0.334

5)

�0.136

�0.167 �0.149

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A. Chikan et al. / Int. J. Production Economics 93–94 (2005) 61–7366

for the total 30-year period and the two sub-periods (Table 3). Owing to the lack of stronglinear tendency in the investigated period timeseries data were used together as random observa-tions.

In case of those correlations, which werehigher in the 30-year period than in any ofthe subperiods, we have put a ‘‘+’’ sign(STOCK–GFC and STOCK–FTB), while in caseof STOCK–PRIV ‘‘�’’ denotes that the long termcorrelation is weaker than the ones in thesubperiods.

Table 3 shows all the three hypotheses(H(2)–H(4)) true, both for the 30-year period andthe two subperiods. In addition, it shows:

Table 4

Significant variables in countrywise regression models

Country PRIV GOV IMP

B �57.955

(13.195)

J �37.120

(3.8089)

C �110.331

(23.269)

FR �68.079

(9.041)

IT

NL

SP

US

D �71.361 45.624

(10.408) (9.084)

FI 34.83

(14.896)

WG

UK �134.298

(21.524)

SW �249.603

(51.339)

IR

Standard errors are in brackets.

�

a rather stable negative correlation between thetwo consumption variables and inventories;

�

a different behavior of the foreign trade vari-ables in the two subperiods: the correlation ofinventories is negative with both variables in thefirst subperiod while positive in the secondsubperiod (the correlation coefficient for theexport is not significant).

It is interesting to note that the sign andmagnitude of the variables are usually the samein the total period and the two subperiods (whichshows the stability of correlations). There are twoexceptions: exports and imports show differentbehavior in the two subperiods. This result

GRGDP GFC R2 DW

0.417 1.635

0.779 0.992

0.434 1.521

0.677 2.056

7.65 0.424 2.348

(1.715)

21.887 0.460 1.636

(4.568)

21.571 0.518 2.101

(4.004)

20.102 0.365 1.811

(4.864)

0.642 1.505

16.902 0.380 1.221

(6.347)

58.219 �31.816 0.686 1.248

(9.453) (13.734)

53.779 0.595 1.579

(11.638)

115.796 �121.87 0.521 1.722

(36.635) (59.108)

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Table 5

Summary of factor models

Output 1968–1983 1984–1997 1968–1997

K–M–O test 0.408 0.454 0.454

A. Chikan et al. / Int. J. Production Economics 93–94 (2005) 61–73 67

indicates that inventories may be more correlatedwith the balance of foreign trade than with its twocomponents, but this is still just a conjecture.

The above results can be used as benchmarks forregression models for various countries.

Total variance explained 70% 66% 67%

4. Regression models

The correlation analysis was followed by calcu-lation of regression models, for cross-checking thevalidity of H(2)–H(4).

The regression models were fit by stepwiseprocedure to estimate increase of inventories/GDP in case of all the 14 countries. Stepwiseregression is achieved by choosing the sequence ofvariables to introduce regressors one at a time toincrease R2 as quickly as possible. The mostimportant regressor is introduced first. The samesix independent variables as in the previous sectionwere used for each country. Export ratio is theonly variable not involved in any of the regressionequations. Table 4 presents the significant vari-ables in countrywise models.

Analyzing our data for 30 years, there areextremely different country-specific models, whichsupport H(5) i.e. that no general regression modelcan be found:

�

Stock increase in eight countries can be esti-mated by one explanatory variable. Four typesof models give significant results for twocountries. Belgium and Japan, Canada andFrance, Italy and Netherlands, Spain and USare described by the same explanatory variables.In spite of the numerical differences signs arestable and supportive to the findings in Table 3.

�

1Adjusted R2 is given for multiple models.2Autocorrelation occurs for Japan, Denmark, Finland and

West Germany at 90% confidence.3Principal Component Analysis was conducted.4Low value of Kaiser–Meyer–Olkin is a warning of weak

Stock increase can be explained by two inde-pendent variables for Denmark, Finland, Ger-many and the UK. These regression modelsconsist of different variables; the only similarityis the number of variables. Compared to Table3, import variables show ‘‘irregular’’ behavior intwo cases, and GFC in one case.

latent structure behind the variables. The KMO measure of

� sampling adequacy is an index for composing the magnitudes of

the deserved correlation coefficients to the magnitudes of the

The Swedish model involves three variables toexplain the stock increase.

partial correlation coefficients (SPSS, 1994). This test result

� shows that the suitability of our sample for factor analysis is

There is no statistically valid linear model forIreland.

�

There are both + and � signs for the GFCvariable. Where the sign is + (Spain, USA),there is no other variable in the regressionequation. Where the sign is �, one morevariable, the GDP growth comes in. This meansthat increase of stocks can move adversely tofixed capital investment, but only in case if GDPgrowth counterbalances this effect.

�

Higher growth rate increases inventories inevery case.

Coefficients of determination1 are above 36%.Durbin�Watson test values support to acceptregression models, residuals are not auto-corre-lated2 for 9 countries.

5. Factor analysis

Applying stepwise regression procedure wecould exclude high multicollinearity of explana-tory variables involved in the models, but we hadto face limited coefficients of determination incountrywise models explaining stock increase.Instead of fitting further regression models factoranalysis3 was applied to identify smaller number offactors influencing the seven standardized vari-ables.Based on the moderate correlations over the 30-

year period Kaiser–Meyer–Olkin test4 values were

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Table 6

Rotated component matrix coefficients

Variable 1968–1997 components 1968–1983 components 1984–1997 components

1 2 1 2 1 2

PRIV �0.595 �0.653 �0.699

GOV �0.637 0.247 �0.539 0.695 �0.673

GFC 0.741 –0.449 0.713 �0.338 0.658 �0.481

STOCK* 0.792 0.738 �0.249 0.763

EXP �0.215 0.938 �0.136 0.946 0.967

IMP 0.918 0.932 0.881

GRGDP 0.817 0.822 0.790

Component Plot in Rotated Space

Component 1

1.00.50.0-0.5-1.0

Com

pone

nt 2

1.5

1.0

0.5

0.0

-0.5

zrstock

zimpzexp

zgrgdp

zgfc

zpriv

zgov

Fig. 2. Loading plot for 1968–1997.

Table 7

Final cluster centers in five dimensions

Cluster (members)

1 (170) 2 (136) 3 (97)

ZGOV 0.450 0.343 �1.054

ZGFC �0.931 0.313 1.076

ZGRGDP �0.850 0.333 1.024

ZIMP 0.327 0.157 �0.567

ZSTOCK �0.554 �0.139 1.081

A. Chikan et al. / Int. J. Production Economics 93–94 (2005) 61–7368

below 0.5 in case of both the total period and thetwo subperiods. Extracting two components totalvariance explained percentages were over 66% inall the three cases (Table 5).

Varimax rotation was conducted to improveinterpretation of factors. Rotated componentmatrices5 of z-score variables can be comparedfor different time periods in Table 6.

(footnote continued)

limited. However, it does not say the factor analysis results are

not valid, since this only means that the long time series does

not show definite linear character.50.2 was the limit to print coefficients in the rotated matrix.

Factor 1 describes simultaneously two poles inthe total period and for the sub periods as well(Fig. 2). ‘‘Consumption’’ is measured on the leftside with negative coordinates. Stock, capitalinvestment and GDP growth rate variables corre-late strongly and positively with Factor 1.Factor 2 influences export and import ratios

positively, and moderate negative correlation ispresent for gross fixed capital investment.

6. Cluster analysis

We wanted to answer the following questions onthe basis of the multivariate analysis:

�

Time wise differences or countrywise differencesare dominant over these 30 years?

�

Is it possible to separate three types of sub-

periods, which are characterized by increasing,decreasing and stable stocks?

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Table 8

Summary of cluster results

Country Number of years (and

sign of stock changes) in

the three clusters

Nonsignificant variables

(p-value of F-test)

Number of subperiods Character of subperiods

1969–1997

(1) (2) (3) (4) (5)

B 6(+) 16(0) 7(�) 4 +0�0

C 16(0) 12(�) 1(+) 6 0�+�0�

D 9(+) 10(0) 10(�) 4 +0�0

Fi 19(0) 7(+) 3(�) IMP (p ¼ 0:110) 9 0+0+0+0�0

Fr 12(+) 16(�)1(0) 4 +�0�

WGa 5(+) 10(0) 11(�) 7 +0�0�0�

Ir 7(+) 15(0) 7(�) STOCK (p ¼ 0:086) 5 +0+0�

It 8(�) 6(+) 15(0) IMP (p ¼ 0:568) 10 �+�+�+�+�0

J 6(+) 7(0) 16(�) 5 +0�0�

Nl 6(+) 11(�)12(0) 3 +�0

Sp 4(+) 8(0) 17(�) 5 +0+0�

Sw 10(0) 5(+) 14(�) 10 0+0+0�+�0�

UK 9(+) 5(0) 15(�) 4 +0+0�+�

US 12(+) 12(�) 5(0) 4 +�+�+�+�+�0

aThere are 26 observations for West Germany till 1994.

A. Chikan et al. / Int. J. Production Economics 93–94 (2005) 61–73 69

�

Are these subperiods similar in length and orderfor all of the countries? If the answer to thesequestions would be yes, this could lead tointeresting conclusion regarding business cycles.

Nonhierarchical (k-means) cluster analysis wasconducted to classify year-by-year data for the 14countries using five6 standardized variables (GOV,GFC, STOCK, IMP, GRGDP). Final cluster centervalues are given in Table 7. Cluster 1 can becharacterized by very low gross fixed capitalinvestment, growth of GDP and relative stockincrease. On the other hand this group reached thehighest government consumption and import ratio.Cluster 3 is on the top concerning gross fixed capitalinvestment, GDP and stock increase. Cluster 2 is inbetween according to the five variables.

Focusing on stocks, Cluster 1 is identified by‘‘minus’’, Cluster 2 is mentioned as ‘‘zero’’, andCluster 3 is signed by ‘‘plus’’ in the next section.Membership list of clusters in time wise grouping is

6Based on the F-test these five variables (out of the original

six) were significant for most of the countries. Export is

excluded, since it was not significant. Descriptive statistics of

these variables can be found in the Appendix (Table A).

Fourteen missing data were in this analysis.

presented in Appendices C and D. Countriesappeared in all clusters during 1969–1997.7 Thisindicates the high variability of the variablesinvolved. Stock increase was higher than the clusteraverage in all countries in 1973, and lower than thecountry average between 1992 and 1996. Starting theinvestigated period with relevant increase of stocksand ending with below the average changes for mostof the countries leads to the conclusion that timewise differences are bigger than countrywise ones—i.e. H(6) is held. It can be added: Appendix C and Dshows that four subperiods can be well identifiedover the 30-year period:

�

in 1969–1974 most countries are in the largestock increase cluster,

�

in 1975–1980 most countries are in the middlestock increase cluster,

�

the 1981–1990 period shows a mixed picture, � from 1991 the overwhelming majority (in most

years all) the countries are in the low stockincrease cluster.

Appendix D shows that the vast majority ofcountries behave according to the general tendency,

71968 is the base year.

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Cluster centers

COUNTRY

SPSW

BIT

JNL

FIWG

USFR

DIR

UKC

3

2

1

0

-1

-2

highest ARII

lowest ARII

Fig. 3. The highest and lowest stock changes at cluster centers

over 30 years.

A. Chikan et al. / Int. J. Production Economics 93–94 (2005) 61–7370

except in the early 1980s, though even these years atleast half of the countries are in the dominant range.

Analyzing the tendency of stock changes for1969–1997 in details, significant stock increase wastypical for 10 countries in the first years, but thelength of this period was different. Seven countriesare characterized by decreasing stocks in thesecond subperiod.

Table 8 shows a summary of classificationresults according to countries and time. Columnsof the table have the following meaning:

�

Column (2) shows cluster results. Classifyingyears into three groups, the number of years incertain clusters is given. Membership of clustersis not proportional, in general decreasing orstable periods cover higher number of years.Sign of stock changes in the cluster center is inbrackets. Relevant increase or decrease valuesare given with + or –, and 0 stands fornonsignificant changes.

�

Column (3) informs about the classificationpower of variables. Import does not play asignificant role in the classification for Finlandand Italy. Stocks are only slightly decreasing inIreland for 1969–1997.

�

Column (4) describes countrywise changes. Stockchanges in certain countries were very frequentfor 1969–1997 supporting again the thesis thatthe second subperiod is characterized by lowerinventory investment but a higher volatility of it,

therefore, the numbers of subperiods varybetween 3 and 11. The number of subperiods ishigher in the case of countries, in which neighboryears are not in the same cluster. The Nether-lands is the only country with three subperiods.Belgium and Denmark follow the same patternwith four subperiods. All the other countriesshow several up and down runs.

�

Column (5) presents the character of subper-iods. ‘‘Subperiod’’ here is used to identifyconsecutive years with the same sign of stockchanges.

Fig. 3 presents the highest and lowest stockchanges at cluster centers. Countries are arrangedin increasing order according to the highest values.If one takes a look at the first column of Table 8and at Fig. 3, it is quite sensible that:

�

there are more years of stock decrease thanstock increase in most countries;

�

the deviations upwards from the average,however, can be usually much higher than thedownwards deviations.

7. Conclusion

In this phase of our research a lot of computa-tions have been made and we believe thatsignificant results were achieved—but perhapseven more new questions can be raised. Nowonder that all the seven hypothesis set have beenvalidated: they were well based in previousanalyses.We got new results in the relationship between

the structure of GDP use and inventory invest-ment. These results can be useful in analyzingbusiness trends and economic policies: the effectsof trends and policy measures can be tested. Ourresults show that even though there is a general(but not very fast) decrease of inventory invest-ment relative to GDP in the leading countries ofthe world, each countries have their own particularcharacteristics of inventory phenomena, whichleads to the statement that no general regressionmodel can be found to explain inventory behaviorin different countries.

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A. Chikan et al. / Int. J. Production Economics 93–94 (2005) 61–73 71

However, some generalized statements can bemade regarding the correlation between inven-tories and other macroeconomic parameters:

�

Inventory investments are higher in periods ofhigher growth.

�

Fluctuation of inventory investments and in-vestments in fixed assets correlate.

�

Inventory investments correlate negatively withforeign trade balance—which seems to indicatethat inventory change and foreign trade are twoalternative ways of adaption.

These findings help in analyzing and forecastingmacroeconomic behavior and policy alternatives(like saying to policy makers, for example that

speeding up growth of a country will lead with ahigh probability to larger inventories).The result that on the long-run countrywise

differences are smaller than timewise differencesshows kind of an effect of globalization: namely,over a longer period of time, each countries areobeying the same general trend.All in all, it is quite clear that even though the

relative size of inventory investment has definitelydeclined in the past decades—mainly due to moresophisticated inventory management techniquesand more integrated inter-company relations(supply chains)—inventories did not loose theirimportant role as indicators of economic fluctua-tions and tendencies.

Appendix A. Descriptive statistics of five standardized variables for 1968–1997

N

Minimum Maximum Mean Std. dev.

GOV

417 0.073 0.296 0.178 0.045 GFC 417 0.137 0.364 0.214 0.041 GRGDP 403 0.952 1.269 1.097 0.053 IMP 417 0.026 0.880 0.290 0.152 STOCK 417 �11.184 20.686 1.000 2.608

Appendix B. Descriptive statistics of average RSI 1968–1983 and 1984–1997

Mean68–83

St. dev.68–83

C.V.68–83

Mean84–97

St. dev.84–97

C.V.84–97

Sweden

0.00549 0.01534 2.792 �0.00286 0.00616 �2.157

Denmark

0.00454 0.00578 1.273 0.00205 0.00630 3.073

United Kingdom

0.00449 0.00951 2.120 0.00211 0.00473 2.243

Belgium

0.00671 0.00809 1.206 0.00056 0.00371 6.654

Canada

0.00639 0.01120 1.752 0.00357 0.00520 1.455 West Germany 0.00783 0.01056 1.348 0.00249 0.00362 1.455 United States 0.00644 0.00568 0.882 0.00517 0.00460 0.890 Netherlands 0.00778 0.00866 1.113 0.00391 0.00524 1.340 Finland 0.01095 0.01857 1.696 0.00119 0.00990 8.345

France

0.01230 0.00952 0.773 0.00109 0.00678 6.248

Spain

0.01074 0.00697 0.649 0.00566 0.00373 0.660 Ireland 0.01108 0.01318 1.189 0.00570 0.00954 1.673 Japan 0.01379 0.01144 0.830 0.00391 0.00258 0.660 Italy 0.01474 0.01141 0.774 0.00952 0.00595 0.625

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Appendix C. Timewise classification for 1969–1997 (Five standardized variables: GOV, GFC, STOCK,

GRGDP, IMP)

Year

Clusters (STOCK) No. of countries

1(�)

2(0) 3(+)

69

1 13 14 70 1 1 12 14 71 4 10 14 72 2 12 14 73 14 14 74 3 11 14 75 1 9 4 14 76 8 6 14 77 9 5 14 78 12 2 14 79 10 4 14 80 1 12 1 14 81 7 6 1 14 82 8 6 14 83 9 5 14 84 7 6 1 14 85 9 5 14 86 11 3 14 87 9 5 14 88 7 6 1 14 89 4 10 14 90 6 8 14 91 11 3 14 92 14 14 93 14 14 94 14 14 95 13 13 96 13 13 97 11 2 13 Total 170 136 97 403

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A. Chikan et al. / Int. J. Production Economics 93–94 (2005) 61–73 73

Year:

Appendix D. Dominant stock changes

69 70 7

1 72 73 74 75 76 77 78 79 80 81 8 2 83 84 85 86 87 88 89 90 91 9 2 9 3 94 95 96 97

B

+ + + + + + 0 0 0 0 0 0 � � � � � � � � 0 0 � � � � � � �

C

+ 0 0 + + + + + 0 0 + 0 0 � � 0 � � 0 0 0 � � � � � � � �

D

+ + + + + + 0 + 0 0 0 � � 0 � 0 0 0 � � � � � � � � � � 0 FI + + + + + + + 0 0 0 0 0 0 0 0 0 0 � 0 0 0 0 � � � � � � �

FR

+ + + + + + 0 + + 0 0 0 0 0 � � � � � � 0 � � � � � � � �

G

+ + + + + 0 � 0 0 0 0 0 � � � � � � � � � 0 0 � � � na na na IR + + 0 + + + 0 0 + 0 0 0 0 0 0 0 � � � � � � � � � � � � 0 IT + + 0 0 + + 0 + + 0 + + 0 0 0 0 0 � � � � � � � � � � � �

J

+ + + + + + 0 0 0 0 0 0 � � � � � � � 0 0 0 0 � � � � � �

N

+ + + + + + 0 0 0 0 0 0 � � � � � � � � 0 0 � � � � � � �

SP

+ + + + + + + + + 0 0 0 � 0 � � � � � 0 0 0 0 � � � � � �

SW

+ + + + + + + + 0 0 0 0 0 � 0 0 0 0 0 0 0 0 � � � � � � �

UK

+ + + + + 0 0 0 0 + + 0 � � � � � 0 0 + 0 0 � � � � � � �

US

0 � 0 0 + 0 0 0 + + + 0 + � 0 + 0 0 0 0 0 � � � � � � � �

Dominant

Stockchanges

+ + +

+ + + 0 0 0 0 0 0 � � � � � � � � 0 0 � � � � � � �

%

93 86 7 1 86 100 79 64 57 64 86 71 86 50 5 7 64 50 64 79 64 50 71 57 79 1 00 1 00 100 100 100 85

na: Not available.

%: The proportion of countries in the dominant range.

References

Chikan, A., Horvath, C., 1999. A multi-country analysis of

macroeconomic inventory behavior. International Journal

of Production Economics 59 (1–3), 1–11.

Chikan, A., Tatrai, T., 2003. Developments in global inventory

investment. International Journal of Production Economics

81–82, 13–26.

Kornai, J., 1971. Anti-Equilibrium. North-Holland, Amsterdam.

Kornai, J., 1980. The Economics of Shortage. North-Holland,

Amsterdam.

McConnell, M., Perez-Quiros, G., 2000. Output fluctuations in

the United States: What has changed since the early 1980s?

The American Economic Review 9 (5), 1464–1476.

SPSS, 1994. SPSS Professional Statistics 6.1. Marija J. Norusis/

SPSS Inc., Chicago.