Engineering, Construction and Architectural ManagementEmerald Article: Relationship between the financial crisis of Korean construction firms and macroeconomic fluctuationsSangki Kim, Sanghyo Lee, Jaejun Kim

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Relationship between thefinancial crisis of Koreanconstruction firms and

macroeconomic fluctuationsSangki Kim

Department of Architectural Engineering, Hanyang University, Seoul,South Korea, and

Sanghyo Lee and Jaejun KimDepartment of Sustainable Architectural Engineering, Hanyang University,

Seoul, South Korea

Abstract

Purpose – This study aims to analyze the relationship between the financial crisis of Koreanconstruction firms and macroeconomic fluctuations.

Design/methodology/approach – In this study, current ratio has been used an acting variablefor liquidity ratio, and debt ratio for leverage ratio. GNI (Gross National Income), L (index ofLiquidity), exchange rate, interest, and CPI (Consumer Price Index) were used for themacroeconomic variables. VECM consisted of Crt model and Drt model to analyze therelationship between current ratio and macroeconomic variables, and between debt ratio andmacroeconomic variables, in order to analyze each model through variance decomposition andimpulse response function.

Findings – In Crt model, L is revealed as highly influencing current ratio. In other words, mostfundraising is focused on highly capable financial institutes, investment corporations and publicfunds, since the scale of construction project funds is huge. Such financial sources actually belongto index L (index of Liquidity), but are calculated as current liability in the financial statementsof construction firms, knotting an inverse relationship with current ratio. In Drt model, interest isrevealed as significant against debt ratio. This seems to be because each construction projectneeds to raise substantial funds, and the amount to repay is directly influenced by interestfluctuation.

Research limitations/implications – The collected data are limited, as the time series data ofcurrent ratio and debt ratio were secured based on the financial statements of the most capable 30construction firms in Korea. If the sample companies were divided in future research according toscale, in order to analyze the relation between financial crisis and macroeconomic fluctuation bycompany scale, a more developed result could be obtained.

Practical implications – This study is a useful research to analyze the dynamic relationshipbetween the financial crisis of construction firms and macroeconomic fluctuations. This study can beused to establish a set of countermeasures to apply in the event of macroeconomic fluctuation.

Originality/value – The financial ratios of construction firms are directly used for analysis, makingthis a more practical analysis than studies of the relationship between macroeconomic fluctuations andthe comprehensive indices of construction business.

Keywords Financial crisis, Macroeconomic, Vector error correction model, Republic of Korea,Construction industry

Paper type Research paper

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/0969-9988.htm

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Received 19 April 2010Accepted 23 August 2010

Engineering, Construction andArchitectural Management

Vol. 18 No. 4, 2011pp. 407-422

q Emerald Group Publishing Limited0969-9988

DOI 10.1108/09699981111145844

IntroductionThe state of the construction industry has a close relationship with the state of thenational economy. Put simply, the construction industry plays an important role inleading the national economy, and macroeconomic fluctuations substantially influencethe construction business (Ortalo-Magne and Rady, 2004; Gauger and Snyder, 2003;Pamulu et al., 2007). This can be also seen when one looks at the current globalrecession, and more specifically at the period of inactivity in the construction industryfollowing the subprime mortgage crisis.

With the instability in the global economy following the subprime mortgage crisis,Korea has suffered drastic macroeconomic fluctuations, which have taken the form ofincreased exchange rates and interest rates. This phenomenon has led to a severedepression in the construction industry, causing many construction firms to sufferserious liquidity crises.

Macroeconomic fluctuations influence both demand and supply in the constructionindustry. According to an analysis by the Korean MLTM (Ministry of Land, Transportand Maritime Affairs), the number of unsold apartments reached a record high of160,000 following the subprime mortgage crisis in July 2008. This means that thepurchasing power of consumers has been weakened by the economic depression. Fromthe supply perspective, the profitability of construction firms was sharply worsenedafter the subprime mortgage crisis by sudden increase of lending rates, and thetendency of financial institutions to perform more conservative risk management inthis crisis made their financial troubles more severe. Finally, a serious problemoccurred in cash in and out, leaving the liquidity of construction firms in jeopardy, andliquidations are reportedly increasing.

On the premise that macroeconomic fluctuations in the vicinity of the constructionindustry can significantly affect the financial risk to construction firms, this study willanalyze the relationship between financial crisis and macroeconomic fluctuationthrough a VECM (Vector Error Correction Model).

Representative indices to present financial crisis are liquidity ratio and leverageratio. In this study, current ratio has been used an acting variable for liquidity ratio,and debt ratio for leverage ratio. GNI, L, exchange rate, interest, and CPI were used formacroeconomic variables. VECM consisted of Crt model and Drt model in order toanalyze the relationship between current ratio and macroeconomic variables, andbetween debt ratio and macroeconomic variables.

Literature reviewTable I presents a summary of previous analyses of the relationship between theconstruction business and macroeconomic variables. Most preceding studies haveanalyzed the manner in which macroeconomic variables influence comprehensiveindexes of the construction business, such as residential investment, non-residentialinvestment, and construction investment.

However, such preceding studies are limited in terms of directly analyzing thefinancial crisis of construction firms, as the indexes of the construction business arevery comprehensive. Therefore, this study is going to analyze the extent to whichmacroeconomic variables actually influence the financial crisis of construction firms,by directly confirming the financial statements of the construction companies andusing them as variables.

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Table II documents previous analyses of the financial ratios of construction firms.Most such preceding studies have developed an appraisal model of construction firms,or actually performed an appraisal.

However, analyses of the relationship between these have been revealed to belacking, in that macroeconomic fluctuations actually influence the financial crisis ofconstruction firms.

Empirical proceduresSelection of variablesThis study has defined the variables of financial crisis as current ratio and debt ratio,to specifically determine the relationship between the financial crisis of Koreanconstruction firms and macroeconomic fluctuations.

To secure series data of the current ratio and debt ratio of construction firms, theircurrent ratio and debt ratio were first calculated through financial statements for eachquarter, followed by the arithmetic meaning of these. The firms are 30 top-rankingcompanies in the Construction Capability Evaluation of Korea. Macroeconomicvariables were secured through the Bank of Korea database. Series data of this paper isquarterly data from 2001 to 2008 (see Table III).

Current ratio is a representative index through which the short-term paymentability of companies is determined by appraising the amount of current assets thatthey have to fill up short-term debt. It can be seen that the higher the current ratio, thebetter the capability for short-term payment:

Current ratio ¼ ðcurrent asset=current liabilityÞ £ 100

Researchers Main contents

Wigren and Wilhelmsson (2007) Examining the relationship between GDP and a broad group ofconstruction and, furthermore, the presence of crowding-outwithin the construction industryPublic infrastructure policies influences on short-run economicgrowth, and slightly on long-run economic growthResidential construction influences on long-run economic growth.

Ortalo-Magne and Rady (2004) Focusing on the drivers for transactions of residential propertiesusing England and Wales as a case studyHousing demand fluctuation takes a considerable role in housingtransactions

Gauger and Snyder (2003) Examining relationships between RFI, money, interest rates, andoutput in pre-deregulation and post-deregulation sub-periodsShort-term interest rate shocks account for much of RFIvariability pre-deregulationAfter deregulation, long-term FHA interest rate shocks betteraccount for RFI movements

Coulson and Kim (2000) Examining the causality and influence of residential and non-residential investment with respect to GDPFinding that residential investment shocks are more important inthe determination of GDP than non-residential investment shocks

Table I.Summary of previous

studies about therelationship between

construction industry andthe macro-economy

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Debt ratio is an index to show the relationship between debt and equity, and it can bedetermined that the lower this ratio is, the sounder a company’s financial structure:

Debt ratio ¼ ðcurrent liability þ long-term liabilityÞ=equity £ 100

GNI (Gross National Income) is the total income of the people of a nation that has beenreceived as remuneration for taking part in production activities for a certain period oftime, and is an index where terms of trade are reflected to measure the actual nationalincome. Real GNI is calculated by adding actual trading loss, according to the changeof trades terms and actual Net Factor Income from the Rest of the World, to real GDP.In most of the preceding studies, national competitiveness has been evaluated usingGDP, but this study has used Gross National Income as a variable to measure thenational economic level, as the global economic situation heavily influences nationalcompetitiveness.

Series Descriptions Period Frequency

Crt Current ratio 2001:1-2008:4 QuarterlyDrt Debt ratio 2001:1-2008:4 QuarterlyRGNIt Real Gross National Income 2001:1-2008:4 QuarterlyLt L (index of Liquidity) 2001:1-2008:4 QuarterlyErt Exchange rate 2001:1-2008:4 QuarterlyIt CD (Certificate of Deposit) rate 2001:1-2008:4 QuarterlyCPIt Consumer Price Index 2001:1-2008:4 Quarterly

Table III.Variables anddescriptions

Researchers Main contents

Yee and Cheah (2006) Examining the strategic performance of 61 large internationalengineering and construction firms from the regions of NorthAmerica, Europe, and East AsiaFinding that there is no significant correlation between firm size andprofitabilityFinding that firm size has some influence on generic strategies, aslarge firms tend to adopt either a broadly targeted or a non-relateddiversification strategy

Huang and Stoll (2001) Examining a different potential path for exchange rate effects, namelythe effect of exchange rate variability on a stock’s liquidityFinding that the impact of exchange rate volatility on market liquidityis not a conduit by which stock values are affected

Pamulu et al. (2007) Evaluating financial ratios in the Indonesian construction industryFinding that the Indonesian firms are financially sound, where profitsand returns generated from construction works are still satisfactory

Kangari et al. (1992) Presenting a quantitative model based on financial ratios to assess thefinancial performance and grade of a construction company, and itschances of business survivalDeveloping the model for the following financial ratios: current ratio,total liabilities to net worth, total assets to revenues, revenues to networking capital, return on total assets, and return on net worth

Table II.Summary of previousstudies about financialratios of constructionfirms

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L is an index to grasp the overall liquidity in Korea. Preceding studies use M2 as avariable to stand for money supply in the market, but this study has used it as ananalysis variable, determining that L standing for liquidity actually reflects the moneysupply in the market in the current credit-economy.

The exchange rate is an index to present the international relations of a nation. Inthe recent global financial crisis, Korea’s economic situation was revealed to have aclear inverse relationship with the exchange rate. This means that the exchange rateheavily influences the national economy due to the global economic system, andeventually influences the construction industry, which is in a close relationship withthe national economy, so this study uses this as an analysis variable.

Interest is significantly influential on both the supplier and user sides of theconstruction business. In other words, the interest rate has a very close relationshipwith the construction business, since it is a factor in the loans used by suppliers forproject funds and in the house-buying loans for users. Generally, the interest rate forlending is connected with the CD rate, so this study has used the CD rate as an analysisvariable to represent the interest rate variables.

Consumer price index (CPI) is an important index to estimate price fluctuation,through which we can measure the actual changes in purchasing power. Therefore, it isused as an analysis variable of this study, as it is determined to be in a relationshipwith the construction business as an economic variable to indirectly assess thepurchasing power of users in the construction industry.

Unit root testUnit root test is based on the fact that the characteristic root of a non-stationaryprocess is 1, the unit root, when it is expressed in an autoregressive model(Yt ¼ aþ bYt21 þ 1t). The fact that a unit root exists means that its time series iscurrently unstable. When traditional quantitative analysis is done with non-stationary

SIC (Schwartz Information Criteria)Model Lag 0 Lag 1 Lag 2 Lag 3

Crt model 221.68276 230.35556 * 228.18354 226.94404Drt model 220.99299 230.03076 * 228.42980 227.68471

Table V.Lag specification results

for cointegration tests

Level 1st differencingVariables t-statistic p-value t-statistic p-value

Crt 21.369363 0.8501 24.940794 0.0021Drt 21.437343 0.8292 28.885609 0.0000RGNIt 20.946197 0.9373 24.827699 0.0028Lt 21.473198 0.8172 24.271325 0.0106Ert 1.427381 1.0000 24.532614 0.0057It 21.781923 0.6891 24.186726 0.0129CPIt 22.486935 0.3318 25.487258 0.0006

Note: Paraphrase indicates the signification number of lags chosen based on SIC (SchwartzInformation Criteria)

Table IV.Tests for unit roots

(AugmentedDickey-Fuller tests)

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series data, a phenomenon of spurious regression can be found, in which variablesappear to be in close relation though they are not in reality.

Therefore, the stability of a time series must first be determined in order to analyzeit, and for this purpose the unit root test exists.

The unit root test can take different forms, such as the DF Procedure proposed byDickey and Fuller (1979), the ADF (Augmented Dickey-Fuller) by Said and Dickey, andthe PP Procedure by Phillips and Perron. This study has chosen the ADF (AugmentedDickey-Fuller) Procedure, which is widely used to confirm the stability of time seriesdata, for the unit root test:

DY t ¼ aþ gYt21 þXp

i¼1

diDYt2i þ jt

Null hypothesis H 0 : 0, H 1 :, 0 was examined with DF-t statistic figures through ADFprocedure.

First of all, the null hypothesis that all variables have a unit root cannot be denied asa result of the ADF unit root test for the level variable that log-transformed eachvariable, so it is determined to have unit root.

However, as a result of the ADF unit root test with first-order difference values oftime series variables, the hypothesis that it has unit root was possibly completelydenied, at a 5 per cent significance level (see Table IV).

Cointegration testAlthough individual variables introduced for analysis may be unstable, the linearintegration of variables can be stable. In other words, using order difference variablesof the data can lead to the loss of significant data on the long-term relationship betweenvariables, so the Vector Auto Regression Model (VARM) is recommended when it isdetermined there is no cointegration relation among the data, and the Vector ErrorCorrection Model (VECM) is recommended when such relationship exists. Both theADF Procedure and the Johansen Procedure can be used to determine the existence ornon-existence of such cointegration. Gonzalo (1994) reviewed several ways to estimate

Model Null hypothesis Test statistic 0.05 critical value

Crt model r ¼ 0 * 176.9047 103.8473r # 1 * 87.98231 76.97277r # 2 * 54.09552 54.07904r # 3 33.36429 35.19275r # 4 19.05465 20.26184r # 5 6.888093 9.164546

Drt model r ¼ 0 * 181.6168 103.8473r # 1 * 93.35180 76.97277r # 2 * 57.39974 54.07904r # 3 * 35.99874 35.19275r # 4 16.06210 20.26184r # 5 5.030946 9.164546

Note: Significant at 5 per cent level – r is cointegration rankTable VI.Cointegration test results

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Figure 1.Variance decomposition –

Crt model

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the cointegration vector, and verified that the Johansen Procedure by MaximumLikelihood Estimation is better than any other method (Gonzalo, 1994). Therefore, thisstudy performs a cointegration test using the Johansen Procedure. However, one studyhas said that this can deny the null hypothesis that too short time difference displaysno cointegration, while too long a time difference weakens the verification. Therefore,this study has determined 1 as the appropriate lag order, using lag order selectioncriteria based on SIC (see Table V).

As a result of the cointegration test performed in this manner, the null hypothesisthat there is no cointegration at 5 per cent significance level for Crt model can bedenied, since at least three cointegrations were revealed. In addition, the nullhypothesis that there is no cointegration at 5 per cent significance level for Drt modelcan also be denied, as at least four cointegrations exist (see Table VI).

Empirical resultsThe VECM procedure mentioned earlier was used to appraise the relationship betweencurrent ratio and macroeconomic variables and, between debt ratio andmacroeconomic variables. VECM is a way to solve some, but not all, of theproblems of VARM, and is related with the concept of cointegration (Eagle andGranger, 1987). Most economic variables are unstable time series, but those seriesvariables can have long-term balanced relationships in the event that such unstableones have a relationship of cointegration, being able to test their dynamic structuralrelation.

Let us assume that simple integral calculus was done for every element of variables’vector Y ¼ ðY 1;t; · · · ;Yk;tÞ’. Vector Y can be expressed in this manner, as thefollowing VAR(p) model:

Yi ¼ A1Yt21 þ · · · þ ApYt2p þ 1t

Here, Y i is a variable vector of ðk £ 1Þ, and Ai is the coefficient matrix of ðk £ kÞ, and 1t

is the vector of the white noise items of ðk £ 1Þ. Such a VAR(p) model can betransformed into the following:

Variance decompositionof Crt period

Crt

(Figure 1a)RGNIt

(Figure 1b)Lt

(Figure 1c)Ert

(Figure 1d)It

(Figure1e)CPIt

(Figure 1f)

1 75.48901 2.072769 9.841733 0.820846 0.696667 11.078972 66.34041 4.008656 19.37330 2.190350 1.032941 7.0543473 61.14643 3.385973 25.53029 2.087648 1.539753 6.3099094 56.97855 3.238257 28.25996 2.370908 2.515941 6.6363835 54.82201 3.149002 29.54185 2.508900 3.172011 6.8062266 53.36494 3.015120 30.48225 2.639762 3.698536 6.7993957 52.30391 2.868468 31.18221 2.699213 4.103353 6.8428398 51.51120 2.770639 31.62599 2.736058 4.416774 6.9393449 50.95152 2.710795 31.91409 2.763927 4.643529 7.016134

10 50.53114 2.664812 32.14231 2.788392 4.814148 7.059203

Table VII.Variance decomposition– Crt model

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Figure 2.Variance decomposition –

Drt model

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DY i ¼ PYt21 þXp21

i¼1

GiDYt2i þ 1t

Here, DYi is ðk £ 1Þ vector of first-order difference values for variables, while Gi and Pare the coefficient matrix of ðk £ kÞ. Since matrix P contains information about thelong-term balanced relationship of VAR model variables, P is often called a long-runequilibrium matrix. Since DYi and 1 were assumed to be stationary, PY t21 must bealso. Matrix P must have linearly independent items for PY t21 to be stationary, sinceYt21 is a vector of non-stationary variables. The rank of P is the numbers of linearlyindependent items, which are the same as the numbers of cointegration. According toGranger representation theorem, the rank of P is less than that of the cointegratedvariables. In other words, there are ðk £ rÞ matrixes a and b which make everyelements of P ¼ ab‘ and b‘Ytb‘Yt stationary. Therefore, Granger representationtheorem is presented in the following formula:

DY i ¼ ab‘Yt21 þXp21

i¼1

GiDYt2i þ 1t

Matrix b consists of cointegrated vectors, and matrix a can be translated into weightto cointegrated vector elements. In other words, the previously mentioned formula canpresent the short-term and dynamic character of cointegrated variables to restore theirlong-term balanced relationship, which is called a Vector Error Correction Model.

In this study, the result of variance decomposition and impulse response wasdescribed ten quarters out of the total 32 quarters, because there was no extremefluctuation after ten quarters.

As a result of variance decomposition to measure the relative significance of eachvariable by decomposing them in proportion to the rate of contribution to shock, first ofall the current ratio is reduced to about 50 per cent at the tenth quarter as its influencebecomes reduced, when total fluctuation up to the fourth quarter of 2008 is assumed tobe 100. In other macroeconomic variables the influence of L is relatively the biggest,which increases with the passage of time to reach about 32 per cent by the tenthquarter (see Figure 1 and Table VII).

Variance decompositionof Drt period

Drt

(Figure2a)RGNIt

(Figure 2b)Lt

(Figure2c)Ert

(Figure2d)It

(Figure 2e)CPIt

(Figure 2f)

1 69.67369 3.683850 1.314355 11.37041 6.132278 7.8254112 42.44677 1.312428 1.276396 10.03933 27.07196 17.853123 36.66184 1.170295 2.511171 9.435442 33.36202 16.859244 33.79517 1.147514 3.557822 8.151722 39.74339 13.604395 33.21245 1.037218 4.114633 7.435770 43.42748 10.772456 33.03484 0.873901 4.242721 7.015484 45.85847 8.9745787 33.05537 0.755895 4.241711 6.828415 47.27549 7.8431198 32.94707 0.678845 4.248101 6.691939 48.30973 7.1243199 32.82080 0.631942 4.289021 6.568038 49.12688 6.563317

10 32.71472 0.594275 4.333422 6.452377 49.82294 6.082266

Table VIII.Variance decomposition– Drt model

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Figure 3.Impulse response

function – Crt model

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When the total change of debt ratio from the first quarter of 2001 to the fourth quarterof 2008 was presented as 100, the influence of debt ratio change on debt ratio itself untilthe tenth quarter dropped to about 32 per cent. In contrast, interest continuedincreasing after surging in the second quarter, having an influence of about 49 per centrelative to the fluctuation of the debt ratio at the tenth quarter (see Figure 2 andTable VIII).

IRF (Impulse Response Function) is used to determine the manner in which aspecific variable influences other variables when it is shocked by 1 standard deviation.First of all, in IRF of Crt model, the influence of RGNIt shock on Crt has increased from0.003 per cent in the first quarter to 0.007 per cent to the second quarter, and maintainsa nearly uniform level thereafter. Lt shock was more influential than macroeconomicvariables on Crt, from about 20.008 per cent in the first quarter to 20.025 per cent inthe fifth quarter, and maintains a nearly uniform level thereafter. The influence of Ert

shock on Crt was about 0.002 per cent in the first quarter, began to have a negativeinfluence from the second quarter, increased to about 20.008 per cent in the sixthquarter, and kept a nearly uniform pattern thereafter. The influence of It shock on Crt

was about 0.002 per cent in the first quarter, began to have a negative influence fromthe second quarter, increased to about 20.011 per cent in the seventh quarter, andmaintained a nearly uniform level thereafter. The influence of CPIt shock was about0.009 per cent in the first quarter, increased to about 0.012 per cent in the eighthquarter, and maintained a nearly uniform influence thereafter. In conclusion, the shockof RGNIt and CPIt had a positive influence on Crt for ten quarters, while the shock of Lt,Ert, and It almost had a negative influence on Crt for ten quarters (see Figure 3 andTable IX).

Seen from IRF of Drt Model, the shock of RGNIt on Drt was about -0.007 per cent inthe first quarter, began to have a positive influence from the third quarter as 0.007 percent, which has decreased as time flows, finally reaching about 0.004 per cent in thetenth quarter. The shock of Lt and Ert was about 0.004 per cent in the first quarter forDrt and about 0.004 per cent for Lt, and about 0.012 per cent for Ert, keeping a positiveinfluence for the whole period. The shock of It was more influential than othermacroeconomic variables on Drt. The shock of It influenced Drt by about 0.008 per centin the first quarter, began soaring from the second quarter, and reached 0.049 per centin the tenth quarter. The shock of CPIt on Drt was about 0.010 per cent in the firstquarter, maintaining a positive influence for nearly the whole period of time.

Response of Crt periodCrt

(Figure 3a)RGNIt

(Figure 3b)Lt

(Figure 3c)Ert

(Figure 3d)It

(Figure 3e)CPIt

(Figure 3f)

1 0.023237 0.003850 20.008390 0.002423 0.002232 0.0089022 0.026206 0.007701 20.016966 20.005885 20.003757 0.0071553 0.029550 0.006493 20.022771 20.005585 20.005812 0.0092874 0.029447 0.007235 20.024389 20.007195 20.008839 0.0113525 0.030365 0.007400 20.024982 20.007384 20.009696 0.0117166 0.030448 0.006980 20.025599 20.007762 20.010429 0.0114377 0.030407 0.006536 20.025913 20.007617 20.010797 0.0117228 0.030332 0.006590 20.025826 20.007585 20.010989 0.0120949 0.030419 0.006734 20.025732 20.007598 20.010966 0.012137

10 0.030467 0.006739 20.025773 20.007637 20.010945 0.012035

Table IX.Impulse response – Crt

model

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Figure 4.Impulse response

function – Drt model

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In conclusion, the shock of every variable’s positive influence on Drt overall, and that ofIt on Drt, were the most influential (see Figure 4 and Table X).

ConclusionAs can be seen from the depression in the construction business following the recentsubprime mortgage crisis, various macroeconomic fluctuations significantly influencethe construction industry, including their financial conditions, possibly leading to theworst-case scenario of non-payment. However, previous research in this area has beenlimited in terms of analysis of the influence of macroeconomic variables on the actualfinancial state of construction firms, as they were performed on the fluctuations ofindices relating to comprehensive construction business, such as residentialinvestment, non-residential investment, construction investment, etc. Therefore, thepurpose of this study is to analyze the influence of macroeconomic variables aboutfinancial states of construction firms in a more practical manner, by investigating therelationship between the financial crisis of Korean construction firms andmacroeconomic fluctuations through VECM.

To confirm the financial crisis of construction firms, the representative financialcrisis indexes of Liquidity ratio and Leverage ratio were used. There are many detailedindexes for Liquidity ratio and Leverage ratio, and this study has employed currentratio for liquidity ratio, and debt ratio for leverage ratio. To secure series data ofcurrent ratio and debt ratio of construction firms, current ratio and debt ratio were firstcalculated through financial statements for each quarter, which was followed bydetermining their arithmetic meaning. The firms studied are the biggest 30 of the mostcapable 50 construction firms in Korea. GNI (Gross National Income), L, exchange rate,interest, and CPI (Consumer Price Index) were used for macroeconomic variables.

This paper has defined VECM between current ratio and macroeconomic variablesas Crt model, and between debt ratio and macroeconomic variables as Drt model.

Looking at Impulse Response Function of Variance Decomposition in Crt model, L isrevealed as highly influencing current ratio. In other words, most fundraising isfocused on highly capable financial institutes, investment corporations and publicfunds, since the scale of construction project funds is huge. Such financial sourcesactually belong to index L, but are calculated as current liability in the financialstatements of construction firms, knotting an inverse relationship with current ratio.

Response of Drt periodDrt

(Figure 4a)RGNIt

(Figure 4b)Lt

(Figure 4c)Ert

(Figure 4d)It

(Figure 4e)CPIt

(Figure 4f)

1 0.028702 20.006600 0.003942 0.011595 0.008515 0.0096192 0.031610 20.003579 0.006267 0.017226 0.033018 0.0259663 0.038663 0.007039 0.013132 0.020560 0.043088 0.0275504 0.035865 0.007101 0.016045 0.016021 0.048942 0.0181025 0.036230 0.005333 0.015758 0.014632 0.048188 0.0081046 0.036085 0.002488 0.013933 0.014435 0.047797 0.0064857 0.037244 0.002415 0.013312 0.015644 0.047823 0.0089898 0.037464 0.003072 0.013648 0.015977 0.048669 0.0110669 0.037470 0.003677 0.014174 0.015843 0.049063 0.010862

10 0.037234 0.003571 0.014282 0.015508 0.049087 0.009865

Table X.Impulse response – Drt

model

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Looking at Variance Decomposition and Impulse Response Function of Drt model,interest is revealed as significant against debt ratio. This seems to be because eachconstruction project needs to raise substantial funds, and the amount to repay isdirectly influenced by interest fluctuation.

This study is limited in its terms of its data collection, as it has secured time seriesdata of current ratio and debt ratio based on the financial statements of the mostcapable 30 construction companies in Korea. If the sample companies are divided infuture research according to scale to analyze the relationship between financial crisisand macroeconomic fluctuation for each scale of company, a more developed resultcould be obtained.

References

Coulson, N. and Kim, M. (2000), “Residential investment, non-residential investment and GDP”,Real Estate Economics, Vol. 28 No. 2, pp. 233-47.

Dickey, D. and Fuller, W. (1979), “Distribution of the estimators for autoregressive time serieswith a unit root”, Journal of the American Statistical Association, Vol. 74 No. 366,pp. 427-31.

Eagle, R. and Granger, C. (1987), “Co-integration and error correction: representation, estimation,and testing”, Econometrica, Vol. 55 No. 2, pp. 251-76.

Gauger, J. and Snyder, T. (2003), “Residential fixed investment and the macroeconomy: hasderegulation altered key relationships?”, Journal of Real Estate Finance and Economics,Vol. 27 No. 3, pp. 335-54.

Gonzalo, J. (1994), “Five alternative methods of estimating long-run equilibrium relationships”,Journal of Econometrics, Vol. 60, pp. 203-33.

Huang, R. and Stoll, H. (2001), “Exchange rates and firms’ liquidity: evidence from ADRs”,Journal of International Money and Finance, Vol. 20 No. 3, pp. 297-325.

Kangari, R., Farid, F. and Elgharib, H. (1992), “Financial performance analysis for theconstruction industry”, Journal of Construction Engineering and Management, Vol. 118No. 2, pp. 349-61.

Ortalo-Magne, F. and Rady, S. (2004), “Housing transactions and macroeconomic fluctuations:a case study of England and Wales”, Journal of Housing Economics, Vol. 13 No. 4,pp. 287-303.

Pamulu, M., Kajewski, S. and Betts, M. (2007), “Evaluating financial ratios in constructionindustry: a case study of Indonesian firms”, Proceedings of the 1st InternationalConference of European Asian Civil Engineering Forum, p. E-158.

Wigren, R. and Wilhelmsson, M. (2007), “Construction investments and economic growth inWestern Europe”, Journal of Policy Modeling, Vol. 29 No. 3, pp. 439-51.

Yee, C.Y. and Cheah, C. (2006), “Fundamental analysis of profitability of large engineering andconstruction firms”, Journal of Management in Engineering, Vol. 22 No. 4, pp. 203-10.

Further reading

Brown, R., Durbin, J. and Evans, J. (1975), “Techniques for testing the constancy of regressionrelationships over time”, Journal of the Royal Statistical Society, Vol. 37 No. 2, pp. 149-92.

Edum-Fotwe, F., Price, A. and Thorpe, A. (1996), “A review of financial ratio tools for predictingcontractor insolvency”, Construction Management and Economics, Vol. 14 No. 3,pp. 189-98.

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Elyamany, A., Basha, I. and Zayed, T. (2007), “Performance evaluating model for constructioncompanies: Egyptian case study”, Journal of Construction Engineering and Management,Vol. 133 No. 8, pp. 574-81.

Granger, C. (1969), “Investigating causal relations by econometric models and cross-spectralmethods”, Econometrica, Vol. 37 No. 3, pp. 424-38.

Granger, C. and Newbold, P. (1974), “Spurious regressions in econometrics”, Journal ofEconometrics, Vol. 2, pp. 111-20.

Green, R. (1997), “Follow the leader: how changes in residential and non-residential investmentpredict changes in GDP”, Real Estate Economics, Vol. 25 No. 2, pp. 253-70.

Horrigan, J. (1965), “Some empirical bases of financial ratio analysis”, The Accounting Review,Vol. 40 No. 3, pp. 558-68.

Ocal, M., Oral, E., Erdis, E. and Vural, G. (2007), “Industry financial ratio-application of factoranalysis in Turkish construction industry”, Building and Environment, Vol. 42 No. 1,pp. 385-92.

Swanson, N. and Granger, C. (1997), “Impulse response functions based on a causal approach toresidual orthogonalization in vector autoregressions”, Journal of the American StatisticalAssociation, Vol. 92 No. 437, pp. 357-67.

Toda, H. and Phillips, P. (1993), “Vector autoregressions and causality”, Econometrica, Vol. 61No. 6, pp. 1367-93.

Yee, C.Y. and Cheah, C. (2006), “Interactions between business and financial strategies of largeengineering and construction firms”, Journal of Management in Engineering, Vol. 22 No. 3,pp. 148-55.

Corresponding authorSanghyo Lee can be contacted at: siegfried_sun@hotmail.com

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