THE CAPITAL MARKET AND CONSTRUCTION AS ECONOMIC INDICATORS: THE CASE OF NIGERIA

THE CAPITAL MARKET AND CONSTRUCTION AS ECONOMIC INDICATORS: THE CASE OF NIGERIA Saka, N. Department of Quantity Surveying, Federal University of Technology, Akure, Nigeria. saka_najimu@yahoo.com The capital market and the construction sector are both lead economic indicators that may be used to forecast the behavior of the economy. The study made a comparative appraisal of the efficacy of the capital market and the construction sector as lead indicators of the Nigerian economy. Annualized time series data of indices of the capital market, the construction sector and the Gross Domestic Product (GDP) were extracted from Central Bank of Nigeria (CBN) statistical bulletin volume 16, December 2005. Econometric techniques including unit root test, cointegration test and Granger causality test were used to analyze the data. The unit root test indicated that the data are stationary at second difference of their natural logarithm. The cointegration test indicated that the GDP and construction investment (CNV) and Value of Trade (VOT) are co-integrated. Granger causality test among co-integrating variables indicated that the CNV granger causes or leads the GDP far more than the VOT. The study therefore concluded that the construction sector is a better indicator of the Nigerian economy than the capital market. KEYWORDS: Capital Market, Construction Investment, Econometric Techniques, INTRODUCTION The concepts of forecasting and planning are fundamental to any organization the engage in economic activities whether public or private. the public sector need to forecast and plan the economy to ensure sustainable economic growth, full employment and control of inflation through the implementation of appropriate fiscal and monetary polices (Begg, Dornbusch & Fischer 2000;Samuelson &Nordhaus 2005). In the same vain private firms need to generate economic and business data that improve their ability to make reliable forecast as a basis for production and marketing decision that expand market share and profitability of the firm in a high competitive and globalised economy(mansfield, Allen, Doherty & Weigelt, 2002). In making reliable socio-economic planning in the public sector or business planning in the private firm, planners, economists, statisticians and managers etc invariably employ certain indicators of the economy to build statistical or econometric model that predicts the economy with a certain level of confidence, if the prediction is to be used at all. Economic indicators such as housing starts, new tenders, new contracts, housing starts etc or stock market indexes such price indices, capitalization, number of deals and value of transactions etc are collectively known as lead indicators because they typically go down or up before the economy does. Some Economic indicators that tend to turn simultaneously with the economy such as employment, industrial production, corporate profit and the gross domestic product (GDP) are called coincidental indicators. While retail sales, manufacturers inventories and personal income are said to be lagging indicators because they move or change direction after the economy has changed (Mansfield, et al. 2002).

CIB W065/055 Commissions: Transformation through Construction 2 For most people, the stock market has traditionally been viewed as an indicator or predicator of the economy. Many believe that large decreases in stock prices are reflective of a future recession, whereas large increases in stock price suggest future economic growth. Theoretically, reasons for why stock prices might predict economic activities include the traditional valuation model of stock prices and the wealth effect. The traditional valuation of prices suggests that stock prices reflect expectations about the future economy, and can therefore predict the economy. The wealth effect contends that prices lead economic activity by actually causing what happens to the economy (Comicioli, 1996). While this may be valid for developed economies with sophisticated stock market, it is not without controversy in Nigeria that the stock market can predict the economy reliably. The reasons are not far-fetched; the Nigerian stock market is inefficient and not diversified enough to reflect economic activities in Nigeria accurately. Most Nigerians are oblivious of the stock market as a vehicle for investment and wealth creation (Abudu, Bamidele, Okafor & Adamgbe, 2004). The Construction sector performance is a very important indicator of economy, which is rarely mentioned or used by the private and public firms for forecasting future economic outlook. Housing starts, tender advertisement, number of new contract signed etc are direct indicators of the expectations of the future growth of the economy. Construction facilities investment contributes about 50% to the gross capital formation, which is a direct contribution to the overall growth of the economy (World Bank, 1984). The question of which between the stock market and the construction sector can predict the economy the better has been widely debated. Those who support the stock market s predictive ability argue that the stock market is forward looking and current prices reflect the future earnings potential or profitability of corporations. For those who support the construction sector argue that construction industry contributes to the physical infrastructure of the country which is the driver of rapid economical growth as postulated by Smith (1776) and Keynes (1936) etc. In contrast, stocks transactions are driven by the expansion of the performance of the quoted firms and the national economy and do not directly add to the nation s capital stock (physical infrastructure) at best, they represent changes in the title of the financial assets. Between stock prices and construction investment which is the most reliable lead indicator of the Nigerian economy? The purpose of this paper is therefore, to make a comparative evaluation the performance of stock market and construction sector investment as lead indicators of Nigerian economy and determine the most reliable in the contest of a developing economy like Nigeria. The Nigerian Capital Market The Capital market is a veritable economic barometer for assessing the pulse of the economy. The trend in the number of listed companies, number of listed securities, market capitalization and all share indexes are important indicators which can be used to evaluate the performance of the market (Babalola and Adegbite, 2001). The Nigerian Capital Market started in 1946 with the issuance of securities by the colonial government. The Lagos State Exchange (LSE) was established in 1960 and restructured into the Nigerian Stock Exchange (NSE) in 1977, the same year SEC was established to regulate the market (Ebajemito, et al. 2004). The growth in the total number of listed equities has been impressive given the multitudes of incorporated companies. Listed companies on the NSE rose from 92 in 1984 to 100 in 1988 and further to 195 in 2002 (Odoko, et al. 2004). The increase is traceable to the establishment of the second tier securities market in 1985, the deregulation of interest rate in 1987 and the privatization of some State

CIB W065/055 Commissions: Transformation through Construction 3 Owned Enterprises (Englama et al. 2004) perhaps the most important challenge before SEC and NSE is the difficulties of getting more Nigerian firms listed on the NSE. One of the factors responsible for the apathy in stock market by indigenous firms is the aversion to ownership dilution (Odoko et al., 2004). Another factor is the stringent listing requirement of Securities and Exchange Commission. The review of performance in the Nigerian stock market showed that the value of instrument traded on the floor of the NSE rose from N1.5m in 1961 to N388.8m in 1980. By 2002, the value of these instruments has increased to N59, 406.7m this was greatly facilitated by the privatization programme of the government. The market capitalization relative to GDP gives an indication of the potential to raise fund for investment and provide information on prices that guide the allocation of resources. Data shows rising but relatively low rate of market capitalization / GDP ratio. This ratio improved from 9.2% in 1999 to 10.3% and 12.5% in 2000 and 2001, respectively, and by 2003 it peaked as 19.8%. This compares favourable with 18.0% in Ghana in 2000, but unfavourable with 88.6% in Egypt in 1996 (Abudu, et al. 2004). The Construction Sector The construction sector performance is a very important indicator of economy, which is rarely mentioned or used by the private and publics firms for forecasting future economic outlook. Housing starts, plan approvals, tender advertisements, numbers of new contract signed are direct indicators of the expectations of the future growth of the economy. The Construction industry is referred to as capital goods industry because its services and products often constitute the substructure that other economic activities are built upon. The relationship between the construction industry and national economy is very strong construction industry usually accounts for between 3 and 8 percent of developing country s Gross Domestic Product (World Bank 1984). Construction makes considerable contribution to the national economic output of developed and developing countries, it generates employment and incomes (Field & Ofori, 1988). The effect of the construction sector on the national economy is on all levels (Hillebrandt, 2000) and virtually all aspects of our life (Hillebrandt, 1985). The significant impact the sectors makes to economy means that it is linked to virtually all sectors of the national economy( Pietroforte & Bon, 1995; Bon, 2000; Bon, et al.,1999; Pietroforte et al., 2000) this therefore implies that whatever happens to the sector will directly and indirectly affect other industries and ultimately the well being and wealth a nation. The importance of the sector stems from its strong linkages with sectors of the economy (World, 1984). Fluctuations or shocks in construction flow can therefore cause rippling effects in the economy (Chan, 2002) which may have serious repercussions on sectors of the economy. The very strong linkages that the construction sector has with other sectors of economy underlie its significant multiplier effects on the economy and its value as a lead indicator of the economy especially in developing economies. Construction leads the GDP as it is the main buyer from other sectors, it determines the demand for produce and their output activities feed back to the economy (Briscoe, 1988; Ofori, 1990). Construction sector can therefore be used as economy barometer which can be used for measuring, determining or assessing the changes in the economy. This is probably why Governments have use control of investment in the sector as a basis for controlling the direction of the economy through fiscal and monetary policies (Seeley, 1984).

CIB W065/055 Commissions: Transformation through Construction 4 ECONOMETRIC METHODOLOGY This study uses a set of econometric techniques to determine the best economic indicator between the stock market and construction. These set of econometric techniques includes unit root test, Johansen cointegration test, and Granger causality test. Unit root tests: In most econometric analysis the unit root test or test for stationarity is always the first major step. The first procedure is to test for unit root or to check if the data are stationary. A series is said to be stationary if it displays the tendency of returning to its mean value and fluctuates around it within a more or less, constant range, i.e. it has variance (Harris, 1995). This step is very important because if non stationary variables are not identified and used in the model, it will lead to a problem spurious regression (Granger and Newbold, 1974). The number of times the data have to be differenced to become stationary is the order of integration. If a series is differenced one time to be become stationary, it is said to be integrated of order I (0). Several tests are available for testing the order of integration. The study adopted the most common procedures of Dickey Fuller (DF), Augmented Dickey fuller (ADF). The optimal lag lengths are chosen based on the Pairwise Final Prediction Error (FPE), which has received wide acceptance among time series analysts (McClave, 1978). Once, the variables are integrated of the same order, it implies that the differenced variables can be expressed in a mathematical form. A vector autoregression (VAR) model can be utilized to present the relationship, and causality among the variables must exist in at least one direction (Granger and Newbold, 1986). This can be tested using Granger causality procedure. Johansen cointegration test: The Granger causality test, pioneered by Granger and Newbold (1986), is used to determine the Granger causality relationships between construction stocks, stock market and the economy. Two variables are said to be cointegrated i.e. they exhibit a long-run equilibrium relationship, if they share a common trend. If cointegration exists between the two variables, causality must exist in at least one direction, either unidirectional or bidirectional (Granger and Newbold, 1986; Granger 1988). Engle and Granger (1987) pointed out that a linear combination of two or more non-stationary series may be stationary. If such a stationary linear combination exists, the nonstationary time series are said to be cointegrated. The stationary linear combination is called the cointegrating equation and may be interpreted as a long-run equilibrium relationship among the variables. The purpose of the cointegration test is to determine whether groups of non-stationary series are cointegrated or not. The presence of a cointegrating relation forms the basis of the Vector Error Correction (VEC) specification. EViews the econometric package used for this study implements VAR-based cointegration tests using the methodology developed in Johansen (1988, 1991 & 1995). Granger causality: Test Correlation does not necessarily imply causation in any meaningful sense of that word. The econometric graveyard is full of magnificent correlations, which are simply spurious or meaningless. The Granger (1969) approach to the question of whether X causes Y is to see how

CIB W065/055 Commissions: Transformation through Construction 5 much of the current Y can be explained by past values of X and then to see whether adding lagged values of Y can improve the explanation. Y is said to be Granger-caused by X if it helps in the prediction of Y, or equivalently if the coefficients on the lagged are statistically significant. Note that two-way causation is frequently the case; X Granger causes Y and Y Granger causes X. It is important to note that the statement X" Granger causes "Y does not imply that Y is the effect or the result of X. Granger causality measures precedence and information content but does not by itself indicate causality in the more common use of the term. The data There are five variables in this model, Construction Investment (CNV) Construction Sector (CNS) No of Deals (NOD) Value of Transaction (VOT) and Gross Domestic Product (GDP). All the data used in this study are extracted from Central Bank Nigeria (CBN) Statistical bulletin December 2005, Volume 16 and National Bureau of Statistics (NBS) Abstract of Statistics for relevant years. Operationalisation definition of variables i. Construction Investment (CNV); this is the total expenditure on the building of new constructed facilities within a country in a given year; this entry in the national account also includes money expended on the maintenance of these facilities. ii. Construction Sector (CNS); this is the measure of market value of all economic activities within the construction sector during a given year. iii. Gross Domestic Product (GDP); this is a measure of the market value of all final goods and services produced in a country within a given year. iv. No of Deals (NOD): This is the total number of agreements in the capital market for the purchase or sale of securities within the year. v. Value of Transactions (VOT); this is the aggregate monetary value of all securities (bonds and stocks) bought or sold in the capital market within the year. RESULT Table 1: Presents the descriptive statistics for CNS, CNV, GDP, NOT and VOT series. The Jarque-Bera P value indicates that all the series are normally distributed. Table 1: Descriptive Statistics CNS CNV GDP NOD VOT Mean 28682.61 44252.12 2959790. 181051.9 32311.01 Median 8019.100 14633.83 701472.9 42074.00 850.3000 Maximum 215786.1 205121.0 14894454 1021967. 262935.8 Minimum 1532.000 2957.000 50749.09 10014.00 215.0000 Std. Dev. 51832.54 56268.83 4180049. 292784.0 69737.83 Skewness 2.734851 1.434353 1.541334 1.956578 2.471655 Kurtosis 9.552226 4.063355 4.324949 5.611299 7.906116 Jarque-Bera 75.88470 9.750204 11.72743 23.05382 50.52739 Probability 0.000000 0.007634 0.002841 0.000010 0.000000 Sum 717065.1 1106303. 73994760 4526299. 807775.2 Sum Sq. Dev. 6.45E+10 7.60E+10 4.19E+14 2.06E+12 1.17E+11 Observations 25 25 25 25 25

CIB W065/055 Commissions: Transformation through Construction 6 Table 2 presents the correlation matrix of the series the matrix shows that all the series are of high correlation with the GDP with value ranging between 91.5% and 97.28%. NOD has the highest correlation of 97.28% to the GDP while CNS has the lowest correlation of 91.58% to the GDP. Table 2: correlation matrix of GDP, VOT, CNS, CNV and NOD. GDP VOT CNS CNV NOD GDP 1 0.9345 0.9153 0.9461 0.9728 VOT 0.9345 1 0.9836 0.8840 0.9798 CNS 0.9153 0.9836 1 0.8947 0.9509 CNV 0.9461 0.8840 0.8947 1 0.9166 NOD 0.9728 0.9798 0.9509 0.9167 1 The results of Dickey Fuller (DF) and Augmented Dickey Fuller (ADF) tests are summarized in Table 3. The test of hypothesis of stationarity is performed at the usual 1%, 5%, and 10% significance levels with the natural logarithm of the data. The associated critical values for the tests are presented in table 4. The results from the DF test indicate that none of the data series are stationary. Based on ADF test, all the first differenced data are stationary except for LVOT. However, at the second differencing, all the data series are stationary at 1% and 5% significance level. Table 3: unit root test result with log of series DF test at level ADF test in first difference ADF test in second difference Series Constant With trend Constant With trend Constant With trend LCNS -0.453-1.683-3.672** -4.632*** -5.264*** -5.227*** LCNV 0.285-1.764-3.888*** -4.323** -6.376*** -2.254 LNOD -0.058-1.786-3.770*** -3.817** -6.016*** -5.849*** LVOT -0.399-1.273-1.735-2.442-3.930** -4.126** LGDP -0.142-2.087-3.561** -3.433* -5.264*** -5.227*** Figures marked with *, ** and *** denote rejection of the hypothesis at the 10 %, 5% and 1% levels Table 4: test critical values DF test at level ADF test in first difference ADF test in second difference Critical Constant With trend Constant With Constant With trend value trend 1% -2.680-3.770-3.788-4.668-3.788-4.468 5% -1.958-3.190-3.012-3.733-3.012-3.645 10% -1.608-2.890-2.646-3.310-2.646-3.262

CIB W065/055 Commissions: Transformation through Construction 7 Table 5 reports the Eigenvalue, the associated likelihood ratio statistics (also referred to as the trace test statistics) and the hypothesized number of cointegrating equations of Johansen cointegration tests performed between VOT, GDP, NOD, CNV and CNS. The corresponding critical value for rejection of the cointegration test at 5% significance level is also presented. A trace test indicates cointegrating equation at 5% level. There is no unique cointegrated relation between these variables except between GDP and CNV as well as GDP and VOT. This means that there is a cointegrating relation between them and they are dependent on each other. Since cointegration cannot indicate the direction of Granger causality among the variables. Pairwise Granger causality test is used to determine the direction of causality. Table 5: Johansen cointegration test result (Log) VARIABLES Hypothesized No. of CE(s) Eigenvalue Trace Statistic 5 Percent Critical Value 1 Percent Critical Value LGDP and LCNS None 0.406716 13.21947 15.41 20.04 At most 1 0.028319 0.689478 3.76 6.65 LGDP and LCNV None ** 0.610241 34.01481 15.41 20.04 At most 1 ** 0.378150 11.40135 3.76 6.65 LGDP and LNOD None 0.405492 12.18703 15.41 20.04 At most 1 0.009801 0.226534 3.76 6.65 LGDP and LVOT None ** 0.544060 20.47123 15.41 20.04 At most 1 0.099369 2.407166 3.76 6.65 *(**) denotes rejection of the hypothesis at the 5 %( 1%) level The number of Lags in the Granger causality model is set at 2, 3, 4, 5, 6 and 7 even though test is run to search for the optimal lag structure. Table 6, presents the probability values of the Granger causality test for logarithmic second differenced of the variable. Gross Domestic Product granger causes the CNV and VOT at all lags tested though the causal effects running from the GDP is stronger toward the CNV than the VOT. The CNV granger causes the GDP at lags 2, 3, 6 and 7 while the VOT granger cause the GDP at lags 3, 4 and 7. The CNV therefore granger causes the GDP at more lags than the VOT showing that the CNV leads the GDP than the VOT. Table 6: pairwise Granger Causality test(p-values) Variables 2lags 3lags 4lags 5lags 6lags 7lags LGDP LCNV 0.001*** 0.000*** 0.000*** 0.000*** 0.000*** 0.003*** LCNV LGDP 0.002*** 0.005** 0.160 0.363 0.001*** 0.006** LGDP LVOT 0.043** 0.004*** 0.001*** 0.005** 0.000*** 0.016** LVOT LGDP 0.165 0.023** 0.088* 0.184 0.380 0.018** *(**) and *** denotes rejection of the null hypothesis at the 10 %,( 5%) and 1% level

CIB W065/055 Commissions: Transformation through Construction 8 Discussion The pair wise Granger causality test indicates the direction of causality between the data series. LVOT leads LGDP by 3years while LCNV leads LGDP by 2years; this is a very clear indication that the construction sector has more direct linkages with the economy than the capital market. This is not surprising, as Abudu et al (2004) have observed that the Nigerian capital market is not deep, diversified or efficient as to be able to accurately lead or predict the Nigerian economy. similarly it is also very apparent from the granger test that the LGDP granger causes both the LCNV and the LVOT the strength of the causality running from LGDP is however stronger on LCNV than LVOT. this is another empirical evidence to support the stronger linkage between the economy and construction than between the economy and the capital market. CONCLUSIONS The result concludes that the Construction Sector is a better indicator of the economy than the capital market because from the analysis it shows that the relationship between Gross Domestic Product and Capital Market is inferior to that which exists between Gross Domestic Product and Construction Sector. Government must therefore increase direct spending in the construction sector enhance the development of physical infrastructure of the country which is the driver of rapid economical growth. Construction investment stimulates the economy and help in poverty alleviation, as construction industry would provide gainful employment for a barge number of skill and unskilled workers. The public and Private sector can forecast future economic outlook by considering a number of construction sector performance indicators such as plan approvals, housing starts, tender advertisement, and number of new contract signed etc. REFERENCES Abudu, M, Bamidele, A, Okafor,P.N and Adamgbe,E.T(2004), an overview of Financial markets in Nigeria.In Nnanna, O.J, Englama,A. and Odoko, F.O.(Eds) Financial markets in Nigeria. Abuja: CBN Pp 28-41. Babalola, J.A. And Adegbite, M.A. (2001). The Performance Of The Nigerian Capital Market Since Deregulation In 1986. CBN Economic and Financial Review Vol. 39 March No. 1. Begg, D., Fischer, S. and Dornbusch, R. (2000).Economics (6 th Edn) London: McGraw Hill Book Company Bon, R. (2000). Economic Structure and Maturity: Collected papers in input output modeling and Applications. London: Ashgate, Aldershot. Bon, R., Birgonul, T. and Ozdogan, I. (1999). An input output analysis of the Turkish construction sector, 1973-1990: A note, Construction Management and Economics, 17, 543-51. Briscoe, G. (1988). The economics of the construction industry London: Mitchell Chan, S. (2002). Response of selected economic indicators to construction output shocks: the case of Singapore, Construction Management and Economics 20, 523-33

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