The economy of Bangladesh is growing faster. The size of the economy is increasing as well as investment, consumption and growth. The ‘Vision 2041’ has been adopted already in line of ‘Vision 2021’ to provide impetus to the development dream of the nation. Its aim is to end absolute poverty and to be graduated into higher middle-income status by 2031 and eradicate poverty on way to becoming a developed nation by 2041.Vision 2041 has some identified objects: to become a poverty-free middle income country; to develop a skilled and creative human resource; to become a globally integrated regional economic and commercial hub; to be a more inclusive and equitable society. The government of Bangladesh has taken a large range of measures to fulfill vision 2041in which there is no alternative but investment.

In early 1990 government took some policies for the expansion of private sector investment as investment is played as the engine of growth. Government gave priority to poverty alleviation and unemployment reduces in its development policy and in reaching this goal, stronger economic growth and dynamic investment will play crucial role. And also we need huge investment for transition process from an agrarian to industrial economy. That’s why Bangladesh government established the Board of Investment (BOI) by the investment board ACT of 1989 to encourage and facilitate investment in the country. Beside government some donor agencies and NGO have tried to create a favorable investment environment through introducing various economic policies, incentive for investors, promoting privatization.

Not only classical and neoclassical but also Keynes among all economist emphasized on investment strongly for economic development. In theoretically we know that multiplier and accelerator. All economist agreed on the point that the two factors investment and economic growth is closely interconnected. All growth models focused that investment act as dominant factor for promoting growth. According to World Bank GDP growth is higher for those countries which have relatively higher investment ratio.

Objectives of the Study

The main objective of this paper is to find out the long-run relationship between investment and economic growth. Besides this we also aim to:

• To find the bi-directional causal relation between investment and economic growth

• To forecast the tendency of these two variables in future

Data and Methodology of the Study

The analysis is based on annual time series data on current GDP, current investment, current consumption and current net export in local currency (in billion taka) for the period 1984 to 2021. The data was collected from different year’s statistical pocket book of Bangladesh Bureau of Statistics (BBS), Economic Review of Ministry of Finance Division, Bangladesh Export promotion Bureau (BEPB) and World Bank. This paper has taken several modeling issues to confirm the dynamic linkages among the variables. We used Augmented Dickey Fuller (ADF) and Phillips-Perron (PP) test for identifying whether the data set has unit-root problems or not. Then Johansen co-integration test has been applied to examine the long-run relationship among variables and error correction models and Granger causality test has been applied to the short-run dynamics of long-run relationship between GDP and investment. We have used STATA 12 as statistical package for finding causal relation among variables.

There have been various investigations done on nations with shifting constructions to inspect whether investment makes an unambiguous commitment to economic growth. Anwar try to investigate whether there is long-run relationship between GDP and Investment among 90 countries over the period1960-1992. They used unit root, co integration and Granger-Causality method for this. They found that No long-run association between GDP and investment for 25 countries and there is co-integration for 25 countries. Bi-directional causality is found for 10 and unidirectional causality from GDP to investment for 18 and from investment to GDP for 10 countries. Short-run causality is found for 15 and long-run causality for 23 countries. 

Chimobi tries to estimate the relation among economic growth, investment and export in Nigeria considering from 1970-2005 [1]. He used Johansen co-integration and granger causality test. The co-integration result showed no long-run relationship among variables whereas bidirectional causality holds among variables by employing granger causality test. But there is that these relationship was statistically insignificant.

Blomstorm et al. [2] considered 101 countries and fixed investment and economics growth by using granger sims causality test. Their analysis show unidirectional causality from growth and capital formation where economic growth influence more on subsequent capital formation. But causality from fixed investment to economic growth does not very as GDP and investment do.

Raihan and Noman [3] investigate causal link between FDI, domestic investment and economic growth in Bangladesh for the period 1972-2013. In their paper correlation matrix shows that the dependent variable GDP is positively related with all of the independent variables. It can be said that the first difference of GDP growth and its various components do not have unit root problem and the data are stationary with integrated order I(1). OLS estimation confirm that domestic investment affect positively GDP by 70% while the impact of labor is positive but insignificant whereas another factors FDI and trade openness influenced negatively and VECM indicates that there is short-run dynamics to the long-run causality but with a divergence relation exists among all variables.

Chow examined the role of capital formation in agriculture, industry, construction, transportation and commerce in China over the time 1952-1985. In his analysis capital agriculture by 20%, industry by 17%, construction by 26%, transportation by 4% and commerce by 2% and also showed that capital accelerated China’s economic growth by (on an average) 4.5% for 1952 to 1985 whereas GDP growth rate was 6% and capital growth rate 7.6%.

Ershad and Mahfuzul Haque in their analysis they established a relationship between FDI, trade and growth rate of per capita GDP in Bangladesh for the period 1973-2014. Johansen test of co-integration that there is long run relationship among variables at 5% critical values. Dickey Fuller test on first difference with constant and trend with different lag lengths shows that variables contain stationary process and also conducted Augmented Dickey Fuller (ADF) and Phillip Perron (PP). All test bear same result. VECM found that there was long-run causality running from trade and foreign investment to the growth rate of GDP per capita. To prove the validity of analysis they did Jarque Bera test and autocorrelation test.

Khan [4] paper was slightly different from another all papers. In their analysis they took both private and public investment for 24 developing countries. Their finding show that jointly private and public investment plays larger effect in economic growth than only public investment do.

Blin and Ouattara [5] carried out a study in which foreign direct investment and trade openness took in economic growth in Malaysia during 1975-2005. This paper try to understand the effect of FDI and trade openness by using bound test method. Their empirical result show trade openness enhances economic growth which was statistically significant. And also foreign direct investment play great role in Malaysian growth.

Pham Mai Anh developed two VAR model that used to find out whether export led growth or investment led growth which was the driving force to boost up Vietnam economy by taking four variables such as GDP, investment, export and productivity. This paper prove that investment affect positively in GDP, export and productivity in one VAR model of investment led growth whereas export was unused as exogenous variable that has bigger effect on investment, GDP and productivity in the another VAR model of export led growth. But between these two model investment was determined a strong factor for boost up Vietnam’s economic growth over the past two decades [6-7].

Miankhel et al. examined dynamic relation between export, FDI and GDP by adopting vector error correction model for six countries like Chile, India, Mexico, Malaysia, Pakistan and Thailand. Their paper give an evidence that export enhance growth in South Asia But in Pakistan GDP is such a variable that enhance all factors [8].

Mahanad adopted a time series data for the period in Qatar foreign direct investment as percentage of GDP and economic development as measured by gross domestic product per capita between FDI and GDP per capita for investigation causality relation. He has taken several methods Augmented Dickey Fuller (ADF) test, Johansen co-integration, Granger Causality test and Jarque Bera normality test and also robust empirical finding drawn from Johansen co integration test to confirm long-run association between FDI and GDP. Granger causality test shows that there is a bidirectional causality between FDI and GDP for one, two and 3 (three) years lags. And vector error correction mechanism indicates short-run causality between FDI and GDP. That means Qatar’s development boosted up by the inflows of foreign direct investment to the country [9,10].

Analytical Structure, Econometric Analysis and Findings

Unit Root Test: Empirical work based on time series data assumes that underlying time series is stationary because regressing non-stationary time series on another non-stationary time series may be spurious indicating no relationship though it contains high R²value. That’s why we have conduct Dickey and Fuller test where assuming that the error term is uncorrelated. So they have developed a test where error term are correlated, known as the Augmented Dickey-Fuller (ADF) test. This test is conducted by adding the lagged values of the dependent variable with random walk model with drift or drift around a stochastic trend. Phillips-Perron provide an alternative test for the unit roots that is robust to a wide variety of stochastic processes form disturbance term. The null hypothesis in ADF and PP tests is non-stationary.

The ADF test is based on the estimation of following regressions:

m ∆Y_t = β₁+µY_t-1+α_i∑∆Y_t-i+є_t……… I = 1

(1)

m ∆Y_t = β₁+β₂t+µY_t-1+α_i∑∆Y_t-i+є_t……… I = 1

(2)

Where, є_t is the white noise error term and ∆Y_t-1 = (Y_t-1-Y_t-2), ∆Y_t-2 = (Y_t-2-Y_t-3), etc. m is the number of lags chosen by Schwarz information criterion. Equation 1 and 2 indicates content with no trend and constant around deterministic trend respectively. In each case the null hypothesis is that the coefficient of Y_t-1, is zero that is the time series is non-stationary. The alternative hypothesis is that the coefficient is less than zero, that is, time series is stationary. The result of the ADF test has further been justified by Phillips-Perron test.

We found that all variables in the Table 1 (LnGDP, LnCons, LnInv and LnExp) are highly significant because test statistic is greater than 1% critical value. So we can come to a conclusion that our every variables are stationary at level that is a pre-condition of Granger causality test. We also can run johansen Co-integration here, because all variables have the same integrated order.

Table 1: Unit Root Test (ADF and PP) at Level

Johansen Tests for Co-Integration and Error Correction 

If it is found that the individual time series in Eq. 1 and 2 are integrated order zero i.e., I (0) and the next step is to examine the co-integration among the series (Table 2). 

Table 2: Lag Selection Order Criteria 

A set is said to be co-integrated if a linear combination of their individual integrated series -I (d) is stationary. This procedure needs an estimation of the co-integration regression equation:

lnGDP_t = α+β₁lninvt_t+β₂lncon+β₃lnnex_t+u_1t

(3)

lninvt_t = θ+δ₁lnGDP_t+δ₂lncon+δ₃lnnex_t+u_2t

(4)

If the residual, u_t, from the regression are I (0), then variables are co-integrated and interrelated with each other in the long run. If the series are found co-integrated, then we construct standard Granger causality test by augmenting with an appropriate error correction term derived from the co-integration:

k k k k

∆lnGDP_t = Ḃ₀+∑ρ_i ∆lnGDp_t-1+∑σ_i∆lninvt_t-1+

∑ϑ_i∆lncon_t-1+∑ζ_i∆lnnex_t-1+ʎ₁ ECT_t-1+е_1t

i = 1 i = 1 i = 1 i = 1

(5)

k k k k

∆lninvt_t = Ḃ₀+∑π_i ∆lnGDp_t-1+∑θ_i∆lninvt_t-1+

∑µ_i∆lncon_t-1+∑Ω_i∆lnnex_t-1+ʎ₂ ECT_t-1+е_2t

i = 1 i = 1 i = 1 i = 1

(6)

Table 3 shows co-integration result by johansen test. In the upper panel rank ‘o’ specify that null hypothesis indicates that there is no co-integration. The trace statistic is larger than the critical value implying rejection of null hypothesis. So there is long-run relationship among the variables or they are moving together in the long-run. Rank 1 indicates that null hypothesis is there is one co-integration. But the trace statistic is smaller than the 1% critical value with rank 1impyling acceptance of null hypothesis. Now we need to do Vector Error Correction Model (VECM).

Table 3: Johansen Tests for Co-integration

Table 4 indicates results for D_lngdp and D_lninv. And also indicating that the sign of error correction term is negative and also significant as p value is less than 5%. So we can understand that there is long-run causality running from investment to economic growth. We also checked the short-term causality of investment and GDP with three lags to investment and GDP. And here lag was selected by AIC and SIC. We found that investment lag can influence GDP that was highly significant and vice-versa. But in the short-run lag GDP do not affect GDP significantly. Further lag investment and lag GDP also cause investment.

Table 4: Vector Error Correction Model (VECM)

The Granger Causality Test

The Granger causality concept is employed within a bivariate Vector Auto-Regression (VAR) framework to check up the relationship between investment and economic growth. According to the Granger Causality approach a variable Y is caused by X if Y can be predicted better by the lagged values of Y and X than lagged values of Y alone assuming that both X and Y are stationary. In a two variable framework, the test is based on the following pair of regressions:

m              n

lnGDP_t = а+∑α_i lnGDPt−i+∑ βi lninvt t−i+є_t

i = 1 i = 1

(7)

m              n

lninvt_t = ь+∑ µi lninvt t−i+∑ δi lnGDP_t−i+ѵ_t

i = 1 i = 1

(8)

Where, U_1t and U_2t are white noise error term and it is assumed that the disturbances U_1t and U_2t are uncorrelated. And m and n are the number of lags to be specified. Equation 7 postulates that current GDP is related to past values of itself as well as past values of investment and Equation 8 postulates that current investment is related to past values of GDP and past values of itself. From the above equation four cases may be happened:

• Unidirectional causality from investment to GDP indicating investment causes GDP if the estimated coefficient on the lagged investment in Equation 7 are statistically different from zero i.e., H₀ = 0 and H₁≠0 and the estimated coefficient on the lagged GDP in Equation 8 is not statistically different from zero i.e., H₀≠0 and H₁ = 0

• Unidirectional causality from GDP to investment indicating GDP causes investment if the estimated coefficient on the lagged investment in Equation 7, 8 is not statistically different from zero i.e., H₀≠0 and H₁=0 and the estimated coefficient on the lagged GDP are statistically different from zero i.e., H₀ = 0 and H₁≠0 

• Feedback and bilateral causality is suggested when the sets of investment and GDP coefficients are statistically significantly different from zero in both regressions

• Finally independence is suggested when the sets of investment and GDP coefficient are not statistically significant in both the regressions

From the Table 5, we can conclude that investment Granger causes GDP and also GDP granger causes investment that is highly significant at 1% level at lag 2, 4, 6 and 8. It gives clear message that bidirectional causality from investment to GDP. The selection of lagged term is an important issue since the direction of causality may depend critically on the number of lagged terms included. If we use too few lags we will omit potentially valuable information contained in the more distant lagged values.

Table 5: Causal Relationship Between Investment and GDP

Recommendation

• Post investment difficulties needs to be identified and resolved immediately 

• Uninterrupted power supply including electricity, gas, coal, oil must be ensured to increase investment. Power supply may be subsidies for the new investors

• Need to ensure efficient investment-friendly infrastructure and the complexity of transporting goods needs to be resolute quickly

• Effective policies need to be adopted to attract foreign investment

• Modern economic zones need to be established with incentive based investment opportunities

• Need to create favorable investment environment promoting privatization with ensuring labor security

The above analysis comes to end with the findings that there is a strong bidirectional causality between investment and economic growth, that means investment accelerates economic growth and economic growth also influences investment. In Bangladesh there are 65% of the people are capable to work. If we want to utilize their labor force in a productive way we must need to increase investment. More importantly Bangladesh government has a vision to set this country as a ‘Digital Bangladesh’, there is no alternative but investment to achieve this target. Although the economy of Bangladesh is growing faster, the size of the economy is increasing, investments are also increasing but to achieve SDGs by 2031 and vision 2041 investments need to be expanded to further level.

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