Investigating the Roles of Physical Infrastructure,Financial Development and Human Capital on Economic Growth in Bangladesh

Abstract

This article investigates the dynamic relationship among physical infrastructure, financial development, human capital and economic growth in Bangladesh, employing Autoregressive Distributed Lag (ARDL) bound co-integration and Granger causality test for the period 1985–2019. The study finds a significantly positive long-term impact of physical infrastructure and human capital on economic growth. However, the effect of financial development on growth is found to be negative, and the result suggests that financial development will take place with economic growth. From the policy perspective, this study emphasises increasing investment in physical infrastructure and human capital for Bangladesh to foster long-term economic growth.

Keywords

Physical infrastructure index financial development index human capital index economic growth ARDL

JEL Classification: C01, C10, C22, C52, O11, O40

1. Introduction

This study investigates the impact of physical infrastructure, financial development and human capital on economic growth in the context of Bangladesh. The development of better physical infrastructure, a modern financial system and skilled labour are crucial to keep the current growth momentum of Bangladesh, which is nearly 8 per cent.¹ Although Bangladesh is spending an increasing amount on physical infrastructure through undertaking several mega projects, more investment is required, since the current spending on physical infrastructure standing at 2.8 per cent of the GDP which falls below the target level of infrastructure spending of 5 per cent of the GDP.² Moreover, to sustain long-term growth, skilled human resources are quite important, which is emphasised in both theoretical and empirical literature (Li & Wang, 2018; Lucas, 1988). The share of public spending on education to the GDP was barely 2 per cent in 2019, which is lower than that of India, Bhutan and Nepal.³ As for financial development, the ratio of domestic credit provided by the financial sector to GDP stood at 67.79 per cent in 2019, which was nearly 20 per cent in 1985. It appears that lack of investment in physical infrastructure and human capital might act as obstacles to achieving higher economic growth. In this respect, it is essential to understand the key roles of physical infrastructure, human capital and financial development in enhancing economic growth in the context of Bangladesh.

Generally, infrastructure can be classified into three broad categories: physical infrastructure, financial infrastructure and social infrastructure. Physical infrastructure development means investment in transport, electricity, water and sanitation, etc., financial infrastructure development includes the existence of efficient financial and capital markets, and social infrastructure development indicates investment in education and health services. All three different types of infrastructure have a decisive influence on the economic output. Better physical infrastructure trims down inputs and transaction costs and advances productivity, causing economic growth in the end (Kumari & Sharma, 2017; Meersman & Nazemzadeh, 2017). Efficient financial infrastructure minimises financial intermediation costs and ensures efficient allocation of resources (Xu & Tan, 2020). Finally, human capital, as the measure of social infrastructure development, guarantees the availability of skilled and highly productive labour that contributes to economic growth (Han & Lee, 2020; Ogundari & Awokuse, 2018).

As it seems, the three different types of infrastructure affect economic growth through various channels such as cost, productivity, employment, efficient allocation, the availability of specific resources etc. Taking all these facts into account, it can be easily argued that all three factors have a very important role to play in enhancing economic outputs of a country. However, there is hardly any study that has investigated the combined effects of physical, financial and social infrastructure on economic growth. Moreover, previous studies have mostly dealt with a particular indicator of physical infrastructure and financial development. In this study, we have used physical infrastructure and financial development indices constructed through a principal component analysis (PCA) of different indicators of physical infrastructure and financial development. Therefore, this research fills up two research gaps of the existing literature. It explores the effects of all the three factors—physical infrastructure, financial development and human capital—on economic growth. Rather than using individual indicators of physical infrastructure and financial development, this study constructed PCA based indices of physical infrastructure and financial development.

Given the fact that physical, social and financial infrastructures are complementary to each other, we investigate the role of each of these three different types of infrastructure in economic growth, employing the Autoregressive Distributive Lag (ARDL) bound co-integration approach in the context of Bangladesh for the period 1985–2019. The results show a significantly positive impact of both physical infrastructure and human capital on per capita GDP (GDPP) growth in the long run. However, the impact of financial development on economic growth is found negative, and the result indicates that financial development will take place as growth picks up.

The whole article is organised as follows. Section 2 presents the literature review. In Section 3, the analytical framework of the study is presented. Data, physical infrastructure index (PINFI), financial development index (FINDI), methodology and the estimated models are discussed in Section 4. The effects of financial development, physical infrastructure and human capital are analysed in Section 5. In Section 6, the individual coefficients of each indicator of physical infrastructure and financial development are estimated. Results from Granger causality and Impulse Response Function (IRF) tests are also presented in Section 6. The final section provides the conclusion, with policy recommendations.

2. Literature Review

There are many empirical studies that separately investigated the impacts of physical infrastructure, financial development and human capital on growth. Almost all such studies found a positive impact of physical infrastructure on economic growth. (Batuo, 2015; Fedderke & Bogetic, 2009; Helm, 2009; Zhang & Fan, 2004; Zhang et al., 2015), underlining the positive role of physical infrastructure on economic growth. Studies based on India (Lall, 2007; Sahoo & Ranjan Kumar, 2009) found that physical infrastructure, such as transport and electricity, have a positive influence on economic growth. This means that physical infrastructure leads to economic growth. Pradhan and Bagchi (2013) and Pradhan et al. (2014) found a unidirectional causal impact running from infrastructure to economic growth.

Human capital is considered one of the significant determinants of long-term economic growth, and its role is rightly emphasised in a formal model by Lucas (1988). Investment in human capital, which is acquired through education, on-the-job training and learning by doing, is supposed to increase economic growth, and there are different channels through which human capital enhances economic growth. One of the channels is foreign direct investment; the availability of better human capital induces foreign firms to invest in a country, which, in turn, increases economic outputs (Eaton & Tamura, 1995; Razin et al., 2008). Another channel through which human capital increases economic growth is labour productivity. Greater investment in human capital is thought to augment productivity, and a study by Bils and Klenow (2000) showed a significantly positive long-term relationship between educational achievement and economic growth enforced by the positive connection between human capital and higher productivity. Ifeoma et al. (2013) provided evidence of the greater growth potential of investing a dollar in human capital than in physical capital such as roads and others. Regressing secondary school enrolment as a measure of human capital to economic growth, Ojo and Oshikoya (1995) found a strong, positive long-term effect of human capital investment on economic growth. The same result was also reported by Mankiw et al. (1992). In the context of Bangladesh, Sharif (2013) obtained positive correlation between educational achievement and economic growth using the Engle–Granger co-integration test. Sultana et al. (2019) also provided evidence of the positive effect of human capital on growth. (Hossain et al., 2020) studied human capital development suggesting to provide appropriate IT training to the young Bangladeshi individuals.

Empirical evidence regarding the impact of financial development or improvement in financial infrastructure is quite mixed. Some studies have documented a positive impact, whereas others have reported the opposite. The modern financial system drives economic growth through diversifying risk, promoting innovations and reducing costs. This means that it leads to economic growth because of higher human and physical capital accumulation and technical progress. Studies (Cojocaru et al., 2016; Durusu-Ciftci et al., 2017; Jedidia et al., 2014; Loayza & Ranciere, 2004) have provided empirical support for the positive relation between financial development and economic growth. However, other studies (Easterly & Levine, 2002; Law & Singh, 2014; Rousseau & Wachtel, 2011) have found that financial development negatively affects economic growth. Cecchetti and Kharroubi (2012) argued that the adverse effect of financial development on economic growth could be the result of a crowding-out effect, since the financial sector competes for resources with the other sectors. Moreover, bad credits, adverse selection of loan projects and a weak monitoring and regulatory system of financial markets like Dhaka Stock exchange (DSE) (Ahmed et al., 2020) could dampen the economic output amid rapid financial development. In addition, the effect of financial development is not symmetric over economic regions. The effect on growth is stronger in developed countries (Xu, 2000) but weaker in developing countries (Kar et al., 2011). Therefore, investigating the impact of financial development in the context of a developing country like Bangladesh is quite worthwhile.

From the above literature review, we see that there is a consensus regarding the positive impacts of physical infrastructure and human capital on economic growth. However, little ambiguous agreement exists about the effect of financial development on growth. Most importantly, no study has analysed the simultaneous impacts of physical infrastructure, financial development, and human capital on economic growth. Démurger (2001) investigated the effect of financial development on economic growth, whereas Dwyfor and Christopher (2002) examined the effect of both financial development and human capital. Sahoo and Ranjan Kumar (2009) studied the impact of physical infrastructure on growth in the context of India. Along with physical infrastructure, Mohanty and Bhanumurthy (2018) considered financial development while analysing their influence on economic growth. In the context of Bangladesh, Sharif (2013) and Rahman (2007) separately probed into the effect of human capital and financial development, respectively, on growth. However, our study analyses the effects of physical infrastructure, financial development and human capital together on economic growth in Bangladesh. Therefore, this article makes a unique contribution to the existing literature.

3. The Analytical Framework

In an economy that follows the Cobb–Douglas function, the aggregate output is given by the following simple equation:

Y_{t} = A L_{t}^{α} K_{t}^{β}

(1)

where Y_t is the total output, L_t is labour, K_t is capital, A is the total factor productivity and α and β are the elasticities of labour and capital, respectively. Incorporating physical infrastructure, human capital and financial development as variables of interest, we write the extended Cobb–Douglas function as:

Y_{t} = A L_{t}^{α} K_{t}^{β} Z_{t}^{1 - α - β}

(2)

where Z_t includes physical infrastructure, human capital, financial development and inflation. Expression of the modified Cobb–Douglas function in log-log form yields:

ln Y_{t} = ln A + α_{1} ln L_{t} + α_{2} ln K_{t} + α_{3} ln Z_{t} + ε_{t}

(3)

where ln stands for natural logarithm. On the basis of the augmented Cobb–Douglas function, we specify four models for the purpose of empirical estimation:

\begin{array}{l} lnGDPP = F (lnGCF, lnLF, PINFI, lnINF) \\ lnGDPP = F (lnGCF, lnLF, FINDI, lnINF) \\ lnGDPP = F (lnGCF, lnLF, HCI, lnINF) \\ lnGDPP = F (lnGCF, lnLF, PINFI, FINDI, HCI, lnINF) \end{array}

where lnGDPP is the log of GDPP, lnGCF is the log of gross capital formation (GCF), lnLF is the log of labour force (LF), PINFI is the physical infrastructure index, FINDI is the financial development index, HCI is the human capital index and lnINF is the log of inflation rate. Of the four models specified above, the first model examines the effect of physical infrastructure, along with control variables, on economic growth. The second model estimates the impact of financial development on economic growth. The third model shows the effect of human capital, along with other control variables, on economic growth. The final model incorporates all the three key variables—physical infrastructure, human capital and financial infrastructure, with the control variables—and estimates their effects. Table 1 shows that the impact of lnGCF, HCI, PINFI and lnLF will be positive. Though the effects of FINDI and lnINF are quite ambiguous, we hypothesise that the effect of either on economic growth will be negative.

Table 1.

Expected Signs of Variables

Types	Variables	Expected Sign
Independent variables	PINFI	+
	FINDI	−
	HCI	+
Control variables	lnGCF	+
	lnLF	+
	lnINF	−

Source: The authors.

4. Data and Methodology

For the empirical analysis, annual time series data of Bangladesh from 1985 to 2019 are used. Data on GDP per capita, GCF, LF and GDP deflator, which is used as the measure of inflation, are obtained from the World Development Indicators (WDI). As for the financial variables, data on broad and narrow money are gathered from the database of Bangladesh Bank (BB), and data on the ratio of financial sector credit to GDP are collected from WDI. Data on human capital are extracted from Penn World Table 9.1, where HCI is constructed on the bases of years of schooling and returns to education. Since there exist different indicators of financial development and physical infrastructure, instead of taking different indicators individually, we rather construct two indices—FINDI and PINFI—using PCA. Table 2 shows the data and variable descriptions. Table A3. of appendix shows the summary statistics of variables. Whereas Figure AI-A3. depicts trend of Physical infrastructure index, Financial development index and Human capital index respectively.

Table 2.

Data and Variable Descriptions

Category	Variables	Description	Source
Dependent variable
GDP per capita	lnGDPP	GDP divided by the total number of people	WDI
Independent variables
Human capital index	HCI	Human capital index constructed on the basis of years of schooling and returns to education	Penn World Table (PWT) 9.1
Physical infrastructure index	PINFI
Mobile cellular subscription		Cellular phone subscription per 100	WDI
Road density		Ratio of total road length to total land area of the country.	Bangladesh Economic Review (Ministry of Finance)
Electricity consumption per capita		Electricity consumption per capita (kWh).	WDI
Financial development index	FINDI
Financial sector credit		Financial sector credit to GDP ratio	WDI
Money multiplier		Ratio of broad and narrow money	Bangladesh Bank (BB)
Control variables
Inflation	lnINF	Log of GDP deflator	WDI
Gross capital formation	lnGCF	Log of gross capital formation (annual growth, %)	WDI
Labour force	lnLF	Labour force (total number)	WDI

Source: The authors.

4.1 Physical Infrastructure Index (PINFI)

There are various indicators that are classified as physical infrastructure. However, due to the multicollinearity problem, it is not easy to include all the infrastructure variables in the estimation. On the other hand, leaving out some indicators can pose statistical problems of selection bias. For these reasons, PCA is used to create the PINFI. Three major infrastructure indicators are used to construct the PINFI i.e. total road densities, electricity power consumption (kWh per capita) and cellular mobile subscriptions (per 100 people). Correlations among these variables are shown in Table AI of appendix data on electricity power consumption and cellular mobile subscriptions are collected from WDI, while road densities data come from Bangladesh Economic Review, Ministry of Finance, Bangladesh. Since the units of measurement for the data are different, the data are standardised to make them unit-free before constructing the index using PCA. Eigenvalues, the variance of factors and factor loadings of the PINFI are depicted in Table 3. It is seen that the first principal component (PC1) has the largest eigenvalue, which explains nearly 95 per cent of the total variation, whereas the other two components jointly explain only 5 per cent of the total variation. As a result, PC1 is chosen to compute the PINFI.

Table 3.

Construction of Physical Infrastructure Index (PINFI)

Principal Components	Eigenvalues	% Variation	% Cumulative
PC1	2.851780	95.06	95.06
PC2	0.139318	4.64	99.70
PC3	0.008902	0.30	100
	Eigenvectors (factor loadings)
Physical infrastructure	PC1	PC2	PC3
Road density	0.567985	0.753101	0.332013
Mobile cellular subscription	0.590120	−0.091442	−0.802120
Electricity consumption	0.573718	−0.651519	0.496357

Source: The authors.

4.2 Financial Development Index (FINDI)

Due to the scarcity of long time series data on financial indicators in Bangladesh such as market capitalisation, turnover and stock returns, the present study has taken only the bank-based financial proxies for creating financial development index. The two most frequently used indicators of financial development—money multiplier (Bhavani & Bhanumurthy, 2012; Singh & Bhanumurthy, 2014) and financial sector credit as a proportion of GDP (Beck et al., 2000; King & Levine, 1993; Law & Singh, 2014; Liang & Teng, 2006)—are used to measure financial development. Instead of using both indicators individually as the indicators of financial development, we combine both to construct an index FINDI using PCA. The obvious advantage of using a PCA-based index is that it removes the problem of multicollinearity, since Table A2 of appendix shows there is a high correlation (0.89) between the money multiplier and the ratio of financial sector credit to GDP. Eigenvalues, respective variances and factor loadings are shown in Table 4. Since the first principal component (PC1) has the largest eigenvalue, which explains nearly 94 per cent of the total variation, PC1 is selected to compute the FINDI.

Table 4.

Construction of Financial Development Index

Principal Components	Eigenvalues	% Variation	% Cumulative
PC1	1.885278	94.26	94.26
PC2	0.114722	5.74	100
Eigenvectors (factor loadings)
Financial development		PC1	PC2
Money multiplier		0.707107	−0.707107
Financial sector credit		0.707107	0.707107

Source: The authors.

4.3 ARDL Bound Co-integration Approach

The unit root test (Table A4) performed using the augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) methods confirms that some variables are stationary at first difference I(1), whereas others are stationary at level I(0). Given the facts that the order of integration is mixed, that none of the regressors are I(2) and that the sample size is comparatively small—35 yearly observations—ARDL bound co-integration is used to analyse the long-term dynamics among the selected variables. One of the advantages of using ARDL is that it provides both short-term and long-term results. Another thing is that ARDL bound co-integration gives good results with short time series data, whereas for the Johansen co-integration test, a larger sample size is required to produce robust results. Thus, the estimated model is:

\begin{array}{l} Δ {lnGDPP}_{t} = α_{0} + α_{1} {lnGDPP}_{t - 1} + α_{2} {lnGCF}_{t - 1} + α_{3} {lnLF}_{t - 1} + α_{4} {lnINF}_{t - 1} + α_{5} {PINFI}_{t - 1} + \\ α_{6} {FINDI}_{t - 1} + α_{7} {HCI}_{t - 1} + \sum_{i = 1}^{M} γ_{1} Δ {lnGDPP}_{t - i} + \sum_{i = 0}^{M} γ_{2} Δ {lnGCF}_{t - i} + \sum_{i = 0}^{M} γ_{3} Δ {lnLF}_{t - i} + \\ \sum_{i = 0}^{M} γ_{4} Δ {lnINF}_{t - i} + \sum_{i = 0}^{M} γ_{5} Δ {PINFI}_{t - i} + \sum_{i = 0}^{M} γ_{6} Δ {FINDI}_{t - i} + \sum_{i = 0}^{M} γ_{7} Δ {HCI}_{t - i} + φ {ECT}_{t - 1} + u_{t}, \end{array}

(4)

where ∆ stands for the first difference, $α_{i}$ denotes coefficients on lagged level variables, $γ_{i}$ denotes coefficients on lagged first difference variables and $u_{t}$ is the error term. F-statistics (F-stat) provides the evidence of long-term relationship: if F-stat exceeds the upper bound critical value where all variables are I(1), the existence of no long-term relationship $(α_{1} = α_{2} = \dots = α_{n} = 0)$ is rejected, and if it falls below the lower bound critical value where variables are I(0), we fail to reject the null hypothesis of no long-term relationship. If the existence of a long-term relationship is confirmed, then the short-term error correction model is expressed as:

\begin{array}{l} Δ {lnGDPP}_{t} = α_{0} + \sum_{i = 1}^{M} γ_{1} Δ {lnGDPP}_{t - i} + \sum_{i = 0}^{M} γ_{2} Δ {lnGCF}_{t - i} + \sum_{i = 0}^{M} γ_{3} Δ {lnLF}_{t - i} + \sum_{i = 0}^{M} γ_{4} Δ {lnINF}_{t - i} \\ + \sum_{i = 0}^{M} γ_{5} Δ {PINFI}_{t - i} + \sum_{i = 0}^{M} γ_{6} Δ {FINDI}_{t - i} + \sum_{i = 0}^{M} γ_{7} Δ {HCI}_{t - i} + φ {ECT}_{t - 1} + u_{t} \end{array}

(5)

Here, ECT is the error correction term. The coefficient of ECT must be negative; otherwise, the deviations from the equilibrium will not be corrected and the model will move away from long-term equilibrium.

5. Empirical Analysis

5.1 Long-term Results

The evidence for long-term relationship is provided by F-stat, and the results of F-stat are given in Table 5.

Table 5.

ARDL Bound Test

ARDL Model				F-stat
Model A		lnGDPP = F(lnGCF lnINF lnLF PINFI)		9.034592**
Model B		lnGDPP = F(lnGCF lnINF lnLF FINDI)		13.94632**
Model C		lnGDPP = F(lnGCF lnINF lnLF HCI)		295.0553**
Model D		lnGDPP = F(lnGCF lnINF lnLF PINFI HCI FINDI)		22.01557**
Critical value bounds of F-stat at 10%, 5% and 1% levels
10%		5%		1%
I(0)	I(1)	I(0)	I(1)	I(0)	I(1)
2.525	3.56	3.058	4.223	4.28	5.84

Source: The authors.

Note: ** and * Denote 5% and 10% levels of significance, respectively.

The calculated values of F-stat for models A–D are 9.03, 13.94, 295.06 and 22.02, respectively, which are higher than the upper bound critical value of 5.84 at the 1 per cent level of significance. Therefore, the null hypothesis of no co-integration or long-term relationship is rejected. Since the long-term relationship exists, the results on long-term dynamics are presented in Table 6.

Table 6.

Long-Term Dynamics Results

Variables	Model A	Model B	Model C	Model D
lnGCF	0.647**(2.071)	0.471***(4.684)	0.213*(2.321)	0.326***(3.340)
lnLF	0.044(0.061)	3.527***(10.575)	0.984**(3.515)	−0.266(−0.325)
lnINF	0.353**(2.232)	−0.199***(−3.578)	0.0145(0.188)	0.108***(3.069)
PINFI	0.209**(2.107)	–	–	0.226***(3.338)
FINDI	–	−0.323***(−5.671)	–	−0.093*(−1.862)
HCI	–	–	0.801*(2.229)	1.298**(2.341)
C	−1.534(−0.121)	−61.146***(−10.87317)	−11.328**(−2.591)	2.809(0.208)

Source: The authors.

Note: ***, ** and * Denote 1%, 5% and 10% levels of significance, respectively.

Physical infrastructure proxied by PINFI has a significantly positive effect on economic growth, with coefficients 0.20 and 0.22 in models A and D, respectively. A unit increase in PINFI raises economic growth by approximately 20 per cent. The result is consistent with those of Fedderke et al. (2006), Batuo (2015) and Démurger (2001). This means that in the long run, physical infrastructure development plays a very crucial role in boosting economic growth in Bangladesh. Financial development is found to have a significantly negative impact on economic growth in models B and D, which is in line with the findings of Hye and Islam (2013) for Bangladesh, and the findings support the hypothesised negative relationship outlined at the beginning. Thus, the study does not support the positive relationship between financial development and economic growth in Bangladesh found earlier by Rahman (2004, 2007). Moreover, the effect of financial development is not symmetric over the different economic regions: the effect is supposed to be positive in the developed countries and negative in the developing countries. The negative effect could be the result of a crowding-out effect, since the financial sector competes for resources with the rest of the economy. Moreover, financial development that is plagued by the expansion of bad credits and adverse selection of loan projects is likely to hamper, rather than boost, economic growth. Also, historically, financial expansion in the context of Bangladesh has not been so beneficial, given the fact of gross corruption and unfair practices in the financial sector. In this respect, the negative impact of financial development on economic growth in Bangladesh is quite expected. The contribution of GCF to economic growth is relatively more than that of PINFI; a 1 per cent increase in GCF increases economic growth by 64 per cent in model A and by more than 30 per cent in model D in which PINFI is included. The variations observed in the effect of GCF in models A and D could be due to an omitted-variable bias, since the impact of GCF gets reduced by 50 per cent when HCI and FINDI are included in model D. The positive results indicate that Bangladesh should increase investment in gross fixed capital. Such investment would pay off in the long run through increasing employment and aggregate output, as reflected in the positive effect of GCF. Consistent with the theoretical prediction, human capital has been found to make a significant positive contribution to economic growth; a unit increase in HCI leads to, on average, an increase of more than 80 per cent in economic growth in both models C and D. A similar result in the context of Bangladesh was also reported by Chowdhury et al. (2018). The marginal effect of HCI on economic growth rises from 80 per cent to almost 130 per cent in model D when PINFI and FINDI are added to the model, testifying to the fact that the lower impact of human capital could be the result of omitted-variable bias.

The impact of inflation on economic growth is ambiguous; it is negative in model B but positive in all other models. Theoretical and empirical literature show that the effect of inflation on economic growth is not very straightforward. Moreover, the effect is also not symmetric over economic regions. Easterly and Bruno (1999) argued that the effect of inflation on economic growth is detrimental when inflation exceeds 40 per cent in developing countries. On the other hand, Kremer et al. (2013) presented the threshold effect of inflation, showing that inflation adversely affects economic growth in developing countries if it exceeds the threshold level of 17.2 per cent. However, they also reported that reducing inflation below the threshold level does not enhance economic growth. Historically, inflation rate in Bangladesh has been around 6 per cent, and this rate is quite lower than the threshold level specified above. The significantly positive effect of inflation on economic growth may be due to the shift in income distribution in favour of capitalists, since nominal wages lag behind prices during times of mild inflation. Since capitalists have a higher propensity to save, according to the Kaldorian proposition, higher savings may raise investment and growth in the end.

As for labor force (LF), the effect is very much ambiguous. It is positive in model A but significantly positive in models B and C. However, when all three variables FINDI, PINFI, and HCI are added, the coefficient of lnLF becomes negative, though not significant. The positive result implies that Bangladesh can boost economic growth through the creation and organisation of new employment opportunities. However, it is the negative relation that looks quite interesting in this respect. Perhaps, the negative relation might indicate unskilled labor force growth in Bangladesh, in which case incremental addition to the stock of LF would not contribute much to the growth of GDPP. If the growth in LF is accompanied by unskilled labour and low productivity, then a negative relation might be expected. However, Table 7 shows the short-term results and Table 8 explains the model diagnostics.

Table 7.

Short-term Results

Variables	Model A	Model B	Model C	Model D
∆lnGCF	1.26**(6.71)	−1.58**(10.73)	1.65**(105.52)	0.421**(5.32)
∆lnLF	–	−110**(6.30)	−175.75**(129)	−7.83**(−4.86)
∆lnINF	−0.07**(6.77)	0.21**(8.68)	0.048**(102)	0.14(3.59)
∆PINFI	−1.02*(3.81)	–	–	−0.47*(−2.40)
∆FINDI	–	−0.63**(−3.86)	–	–
∆HCI	–	–	−281.53**(37.00)	–

Source: The authors.

Note: ** and * Refer to significance at 5% and 10%, respectively. The figures in brackets ( ) indicates t-statistics.

Table 8.

Model Diagnostics

Diagnostic Tests	Model A	Model B	Model C	Model D
Serial correlation	0.503[0.734]	1.846[0.213]	0.919[0.392]	0.801[0.575]
Normality test	4.949[0.084]	0.070[0.966]	0.224[0.894]	1.522[0.467]
Heteroscedasticity test	0.326[0.982]	0.862[0.623]	2.094[0.211]	0.788[0.674]
Stability	Yes	Yes	Yes	Yes

Source: The authors.

Notes: ***, ** and * Denote 1%, 5% and 10% levels of significance, respectively.

Coeff is estimated coefficients.

The figures in [ ] are the p-values.

For model A, ARDL (4, 4, 3, 0, 3) is selected based on AIC; for model B, ARDL (4, 3, 3, 3, 1) is selected based on AIC; for model C, ARDL (1, 5, 5, 5, 4) is selected based on adjusted R²; and for model D, ARDL (5, 1, 1, 1, 1, 0, 0) is selected based on SIC.

As seen, the effect of GCF is positive both in the short and long run. As shown in the long run, the influence of inflation on economic growth is quite ambiguous, alternating between positive and negative among the models. In contrast to the long-term relationship, the effect of PINFI, FINDI and HCI are negative in the short run. Therefore, the contribution of GCF to economic growth is more than that of physical infrastructure, human capital and financial development. Compatible with the theoretical argument, the coefficient of the ECT is negatively significant in all models, reflecting the fact that the system will converge to long-term equilibrium.

Several diagnostic tests, such as serial correlation test, Jarque–Bera normality test and heteroscedasticity test, have been employed to check the robustness of the estimated models. The results of Table 8 shows that the models do not suffer from serial correlation and heteroscedasticity and that the residuals are normally distributed. The stability of the models has been measured by the cumulative sum (CUSUM) and the cumulative sum of squares (CUSUMSQ). It can be easily seen that both CUSUM and CUSUMSQ test statistics do not exceed the bounds of the 5 per cent level of significance, confirming that the estimated models are stable over time (Figures 1–4).

Figure 1.

Source: The authors.

Figure 2.

Source: The authors.

Figure 3.

Source: The authors.

Figure 4.

Source: The authors.

5.2 Estimating the Coefficients of the Constructed Indices

So far, the long-term impacts of the two indices PINFI and FINDI have been estimated. However, these indices are constructed through PCA of several indicators of physical infrastructure and financial development. Now, it would be rather quite interesting to observe the contribution of each of the indicators of PINFI and FINDI, since such results would inform us about the relative effects of several indicators and help policymakers prioritise the most appropriate indicators on the basis of relative contributions to economic growth. Three indicators—road density, mobile cellular subscription and electricity consumption—are used to construct the PINFI. To estimate the long-term coefficients of each of these indicators, the estimated long-term coefficient of PINFI from the ARDL model is multiplied with the factor loading (PCA), considering the importance of different components of physical infrastructure. Two indicators—money multiplier and financial sector credit—are used to make the FINDI. A similar procedure is applied to estimate the long-term coefficient of money multiplier and financial sector credit. The estimated coefficients are shown in Table 9.

Table 9.

Long-term Coefficients of Selected Infrastructure and Financial Development Indicators

Infrastructure Development Indicators			Financial Development Indicators
Variables	Coefficients(Model A)	Coefficients(Model D)	Variables	Coefficients(Model B)	Coefficients(Model D)

	0.119	0.128		−0.228	−0.066
Road density			Money multiplier
Mobile cellular subscription	0.123	0.133	Financial sector credit	−0.228	−0.066
Electricity consumption	0.120	0.130

Source: The authors.

It is noted that the long-term coefficients of PINFI are 0.209 and 0.226 for model A and model D, respectively, and the long-term coefficients of FINDI are −0.323 and −0.093 for model B and D, respectively. The factor loadings 0.568, 0.590 and 0.574, respectively, of the three infrastructure indicators road density, mobile cellular subscription and electricity consumption are multiplied with the coefficients of PINFI, and the coefficient of FINDI is multiplied with the factor loadings of 0.707. The results show that a unit change in each of the three physical infrastructure indicators causes around a 12–13 per cent change in economic growth. The interesting thing is that the contributions of each of the indicators are quite similar; they almost equally affect economic growth in Bangladesh. A similar scenario is also noted in the case of FINDI. In line with the negative long-term impact of financial development on economic growth, the effects of either indicators of FINDI are also negative. Both money multiplier and financial sector have an equal negative long-term influence on economic growth in Bangladesh. This breakdown of the long-term impact of individual indicators of PINFI and FINDI confirms that the long-term impact of individual physical infrastructure indicators is greater than that of financial development indicators.

6. Further Robustness Check

6.1 Granger Causality Test

So far, we have checked the long-term relationship among economic growth, physical infrastructure, human capital, financial development, inflation and labour. Now the question is, which causes what? More specifically, we need to determine exactly how the causal relationship runs between, for example, economic growth and human capital. Does human capital cause economic growth, or does economic growth cause human capital, or does it work both ways? The detection of the direction of causal impact is of great importance, since it particularly tells us about the potential source of causation and how it takes form. For this purpose, the Granger causality test put forward by Toda Yamamoto (1995) is applied to see the causal relationship between the variables of interest. To test the existence of Granger causality, the following vector autoregressive (VAR) model is applied:

\begin{array}{l} {lnGDPP}_{t} = α_{10} + \sum_{i = 1}^{k} α_{1 i} {lnGDPP}_{t - i} + \sum_{i = k + 1}^{d_{max}} α_{2 i} {lnGDPP}_{t - i} + \sum_{i = 1}^{k} β_{1 i} Z_{t - i} + \sum_{i = k + 1}^{d_{max}} β_{2 i} Z_{t - i} + u_{1 t} Z_{t} \\ = γ_{10} + \sum_{i = 1}^{k} γ_{1 i} Z_{t - i} + \sum_{i = k + 1}^{d_{max}} γ_{2 i} Z_{t - i} + \sum_{i = 1}^{k} δ_{1 i} {lnGDPP}_{t - i} + \sum_{i = k + 1}^{d_{max}} δ_{2 i} {lnGDPP}_{t - i} + u_{2 t} \end{array}

(6)

Z_{t} = γ_{10} + \sum_{i = 1}^{k} γ_{1 i} Z_{t - i} + \sum_{i = k + 1}^{d_{max}} γ_{2 i} Z_{t - i} + \sum_{i = 1}^{k} δ_{1 i} {lnGDPP}_{t - i} + \sum_{i = k + 1}^{d_{max}} δ_{2 i} {lnGDPP}_{t - i} + u_{2 t}

(7)

Here, Z includes other variables, such as physical infrastructure, human capital, financial development, labour and inflation. One of the key advantages of this model is that it fits into all types of series—I(0), I(1) and I(2). Therefore, the VAR model is fitted into level variables regardless of the order of integration. In the above model, VAR is of the (k + d_max) order, and the coefficients on the last lagged vector d_max are not taken into consideration. The Akaike information criterion (AIC) and Schwartz–Bayesian criterion (SBC) are used to select the optimal lag, which is 4 in this case. Therefore, the hypothesis that physical infrastructure, human capital, inflation, labour and financial development do not Granger-cause economic growth would mean , and that GDPP does not cause physical infrastructure, human capital, inflation, labour and financial development would mean .

Table 10.

VAR Granger Causality Test

	Chi-square	Prob.	Direction of Causality
PINFI does not Granger-cause lnGDPPlnGDPP does not Granger-cause PINFI	15.042*25.097*	0.00460.0000	BidirectionalPINFI  lnGDPP
FINDI does not Granger-cause lnGDPPlnGDPP does not Granger-cause FINDI	7.13015.686***	0.12920.0035	UnidirectionallnGDPP  FINDI
HCI does not Granger-cause lnGDPPlnGDPP does not Granger-cause HCI	14.728*35.857*	0.00530.0000	BidirectionalHCI  lnGDPP

Source: The authors.

Note: ***, ** and * Denote significance at the 1%, 5% and 10%, respectively.

The results reported in Table 10 show a bidirectional causal effect between economic growth and physical infrastructure development. This means that not only does physical infrastructure development Granger-cause economic growth but also economic growth induces more investment in physical infrastructure. In both cases, the hypothesis of no Granger causal impact is rejected at the 1 per cent level of significance. The bidirectional causal impact could be the result of the fact that as the economy develops, it would need more investment in infrastructure development to sustain its growth.

As for financial development, the relationship runs from economic growth to financial development. This means higher economic growth causes financial development; the result is similar to that of Hossain et al. (2017). We have already seen that the impact of financial development on economic growth is negative, and such a negative contribution to growth could suppress the development of the financial system. This means higher economic growth might encourage financial system development, suggesting that in the case of Bangladesh demand factors are the key drivers behind financial development, rather than supply factors. Evidence of bidirectional causal relation is also found between human capital and economic growth, which is compatible with the findings of Rahman (2011). Higher human capital contributes positively to economic growth, as evidenced by the above empirical result. Since an increasing level of human capital indicates the availability of skilled and productive human resources, investment in human capital can boost long-term growth. In turn, higher growth can encourage a country to invest more in human capital.

6.2 Impulse Response

To understand further the dynamic relationship among economic growth, physical infrastructure, financial development and human capital, we estimate VAR, which is utilised to derive impulse response function (IRF). The standard VAR model is given as:

X_{t} = c + \sum_{j = 1}^{k} B_{1 j} X_{t - j} + φ_{t}

(8)

where X_t is a vector of the endogenous variables—economic growth, financial development and human capital—c is the vector of intercepts, B is the coefficient matrix and $φ_{t}$ is the vector of error term or residuals. Since IRF is sensitive to the ordering of endogenous variables, we put the variables in the following order: physical infrastructure, human capital, financial development, inflation, GCF, LF and economic growth, following Goyenko and Ukhov (2009). The reason for placing economic growth at the end of the list is to obtain stronger statistical power (Goyenko & Ukhov, 2009). Figure 5 shows the response of economic growth or lnGDPP to a unit standard deviation innovation in PINFI, FINDI and HCI, and the response is traced for a period of 10 years.

Figure 5.

Source: The authors.

It is seen that economic growth is positively influenced by shocks to physical infrastructure development. In 10 years of time, physical infrastructure increases economic growth up to a standard deviation of 0.40. However, FINDI generates a negative impact, decreasing economic growth over the 10 years, with a standard deviation of 0.35, and the result is consistent with the earlier result of long-term negative effect of financial development on economic growth. The finding of the positive long-term impact of human capital noted earlier is also confirmed by IRF. Human capital creates a positive impact, increasing economic growth, with a standard deviation of 0.07, in 10 years of time. As it turns out, most of the responses are consistent with both the theoretical arguments and earlier results of the long-term positive impact of PINFI, negative impact of FINDI and positive impact of HCI.

7. Conclusion and Policy Recommendations

So far, we have examined the impact of physical infrastructure, human capital and financial development on economic growth in Bangladesh using data covering the period 1985–2019. The study enables us to derive the following conclusions. First, both ARDL and IRF results show that physical infrastructure has a positive impact in the long run. This means that Bangladesh can boost its long-term economic growth by investing in physical infrastructure. The positive result is reinforced further by the bidirectional causal relationship between physical infrastructure and economic growth, indicating that higher investment in infrastructure leads to economic growth and higher growth induces more investment in infrastructure. Second, the impact of financial development is negative both in the short run and long run, as confirmed by the ARDL and IRF results. The Granger causality test shows that economic growth leads to financial development, rather than financial development causing economic growth. Third, investment in human capital pays off in the long run, since the effect of human capital on growth is positive in the long run, and there are increasing returns from investment. The causality test reveals that both human capital and economic growth causes each other, suggesting that higher human capital causes growth, which in turn stimulates more investment in human capital.

Given the empirical evidence, the study proposes the following policy recommendations. Bangladesh can reap higher returns from investment in human capital. The empirical evidence lends support to this assertion, since a unit increase in human capital increases economic growth by more than 80 per cent in the long run. Thus, Bangladesh, with its huge population bulge, can immensely gain from incremental spending on education, which ultimately gets embodied as human capital. Further, investment in human capital will boost economic growth, augmenting labour productivity. Bangladesh should emphasise more investment in physical infrastructure development as well. This policy suggestion is underscored by the following two critical observations: (a) Bangladesh needs to spend 5 per cent of its GDP on physical infrastructure to compensate for the deficit in physical infrastructure; and (b) Empirical evidence shows that in the long run, a unit increase in infrastructure development causes, on average, an increase of more than 20 per cent in economic growth. Thus, infrastructure development should be prioritised in the coming years, as it would enable Bangladesh to keep itself on the trajectory of sustained long-term growth. The unidirectional causal impact from economic growth to financial development implies that financial development would respond passively to economic growth; this means that financial development would follow economic growth.

Finally, it is worthwhile to mention that the findings of the article should not override its limitations. One of the limitations is that while constructing physical infrastructure and financial development indices, we have considered a few indicators while leaving out others that could also have been considered. This limitation is reinforced by the lack of availability of data for different indicators of physical infrastructure and financial development. Another limitation is that the study has not considered governance or institutional factors, which are of relevance in the context of Bangladesh. This issue opens up the possibility of further research in which institutional factors could be taken into account.

Footnotes

Acknowledgement

The authors acknowledge Mr Md. Monirul Islam, Assistant Professor, Bangladesh Institute of Governance and Management (BIGM), for his valuable suggestions.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

1

Bangladesh Economic Review (2019).

2

Seventh Five-Year Plan, Bangladesh.

3

World Development Indicators (WDI).

ORCID iDs

Faroque Ahmed

Md. Jamal Hossain

Mohammad Tareque

Appendix A

Table A1. Co-relationship Between Selected Infrastructure Indicators

Physical Infrastructure Variables	Road Density	Mobile Cellular Subscription	Electricity Consumption
Road density	1
Mobile cellular Subscription	0.943892	1
Electricity consumption	0.862398	0.970260	1

Source: The authors.

Table A2. Co-relationship Between Selected Financial Development Indicators

Financial Development Variables	Money Multiplier	Financial Sector Credit
Money multiplier	1
Financial sector credit	0.885278	1

Source: The authors.

Table A3. Summary Statistics of the Variables

Statistics	lnGDPP	lnGCF	lnLF	lnINF	PINFI	FINDI	HCI
Maximum	1.954839	2.480872	18.05981	2.951948	1.880095	1.820885	2.059684
Minimum	−1.573659	0.426550	17.16473	−1.860993	−3.288946	−2.623568	1.380054
Mean	1.032495	2.011156	17.65580	1.685560	−2.86E-11	6.34E-17	1.704711
Std. deviation	0.831201	0.427663	0.262726	0.759154	1.713375	1.393100	0.213615
Skewness	−1.603539	−2.008115	−0.276791	−2.792834	−0.609125	−0.310175	0.179323
Kurtosis	5.382772	7.438821	1.906312	14.96812	1.901341	1.731876	1.771894
Observations	35	35	35	35	35	35	35

Source: The authors.

Table A4. Unit Root Test Result

Variables	Augmented Dickey–Fuller Test		Phillips–Perron Test		Order of Integration
Variables	Intercept	Intercept and Trend	Intercept	Intercept and Trend
lnGDPP	−5.709052***	−3.454772	−2.555714	−4.424294**	I(0)
lnLF	−2.145554	−1.934894	−1.686094	−0.841113	–
lnGCF	−2.140158	−1.526627	−0.650004	−1.356464	–
lnINF	−1.933652	−4.206470*	−4.066998**	−4.393381**	I(0)
HCI	1.216854	−1.856427	1.174319	−1.867615	–
FINDI	−1.517939	−1.688029	−1.465013	−1.688029	–
PINFI	−2.581053	−0.240311	−2.581053	−0.044406	–
lnGDPP	−5.877970***	−7.092466***	−8.734058***	−8.549755***	–
lnLF	−1.629425	−7.161905***	−1.635107	−1.851275	I(1)
lnGCF	−3.973095**	−4.792937**	−3.973095**	−3.930240*	I(1)
lnINF	−2.790539	−2.634316	−17.66765***	−19.47855***	–
HCI	−5.023179***	−5.166697**	−5.034082***	−5.166697**	I(1)
FINDI	−4.220067**	−4.333253**	−4.271852**	−4.385138**	I(1)
PINFI	−5.382027***	−7.314619***	−5.482587***	−7.629510***	I(1)

Source: The authors.

Note: ***, ** and * Denote significance at 1%, 5% and 10% levels, respectively.

References

Ahmed

Islam

M. M.

Islam

M. M.

(2020). Analyzing volatility of Dhaka Stock Exchange ( DSE ) with historical events around breakpoints: ICSS algorithm approach. European Journal of Scientific Research (EJSR) , 156(3), 262–274.

Batuo

M. E.

(2015). The role of telecommunications infrastructure in the regional economic growth of Africa. The Journal of Developing Areas , 49(1), 313–330.

Beck

Levine

Loayza

(2000). Finance and the sources of growth. Journal of Financial Economics , 58(1–2), 261–300.

Bhavani

T. A.

Bhanumurthy

N. R.

(2012). Financial access in post-reform India . OUP Catalogue. Oxford University Press.

Bils

Klenow

P. J.

(2000). Does schooling cause growth? American Economic Review ,90(5), 1160–1183. https://dx-doi-org-s.web.bisu.edu.cn/10.1257/aer.90.5.1160

Cecchetti

S. G.

Kharroubi

(2012). Reassessing the impact of finance on growth (Working Paper). BIS.

Chowdhury

Mohammed

Nezum

Shamim

(2018, June). Human capital development and economic growth in Bangladesh. Journal of World Economic Research , 7(2), 52–63.

Cojocaru

Falaris

E. M.

Hoffman

S. D.

Miller

J. B.

(2016). Financial system development and economic growth in transition economies: New empirical evidence from the CEE and CIS Countries. Emerging Markets Finance and Trade , 52(1), 223–236.

Démurger

(2001). Infrastructure development and economic growth: An explanation for regional disparities in China? Journal of Comparative Economics , 29(1), 95–117. https://dx-doi-org-s.web.bisu.edu.cn/10.1006/jcec.2000.1693

10.

Durusu-Ciftci

Ispir

Serdar M.

Yetkiner

(2017). Financial development and economic growth: Some theory and more evidence. Journal of Policy Modeling , 39(2), 290–306.

11.

Dwyfor

Christopher

(2002). Human capital and financial development in economic growth: New evidence using the translog production function. International Journal of Finance and Economics , 140, 123–140.

12.

Easterly

Bruno

(1999). Inflation crises and long-run growth . The World Bank.

13.

Easterly

Levine

(2002). It’s not factor accumulation: Stylized facts and growth models . Banco Central de Chile.

14.

Eaton

Tamura

(1995). Bilateralism and regionalism in Japanese and US trade and direct foreign investment patterns . National Bureau of Economic Research.

15.

Fedderke

J. W.

Bogetic

(2009). Infrastructure and growth in South Africa: Direct and indirect productivity impacts of nineteen infrastructure measure . Manuscript, DPRU Conference.

16.

Fedderke

J. W.

Perkins

Luiz

J. M.

(2006). Infrastructural investment in long-run economic growth: South Africa 1875–2001. World Development , 34(6), 1037–1059.

17.

Goyenko

R. Y.

Ukhov

A. D.

(2009). Stock and bond market liquidity: A long-run empirical analysis. Journal of Financial and Quantitative Analysis , 44(1), 189–212.

18.

Han

J.-S.

Lee

J.-W.

(2020). Demographic change, human capital, and economic growth in Korea. Japan and the World Economy , 53, 100984.

19.

Helm

(2009). Infrastructure investment, the cost of capital, and regulation: An assessment. Oxford Review of Economic Policy , 25(3), 307–326.

20.

Hossain

Biswas

Nasif Hossain

Md.

Arnab Kumar

(2017). Financial sector development and economic growth in Bangladesh: A factor analysis based causality approach. International Journal of Economics and Finance , 9(8), 229. https://dx-doi-org-s.web.bisu.edu.cn/10.5539/ijef.v9n8p229

21.

Hossain

Hamja

M. A. A.

Ahmed

Arafat

K. M.

(2020). Exploring the behavior of app developers and the future of digital Bangladesh. European Journal of Social Sciences (EJSS) , 59(3), 255–264.

22.

Hye

Q. M. A.

Islam

(2013). Does financial development hamper economic growth: Empirical evidence from Bangladesh. Journal of Business Economics and Management , 14(3), 558–582. https://dx-doi-org-s.web.bisu.edu.cn/10.3846/16111699.2012.654813

23.

Ifeoma

Mba

E. I.

Ogbuabor

J. E.

Ikpegbu

C. H.

(2013). Human capital development and economic growth in Nigeria. Journal of Economics and Sustainable Development , 4(18), 48–53.

24.

Jedidia

K. B.

Boujelbène

Helali

(2014). Financial development and economic growth: New evidence from Tunisia. Journal of Policy Modeling , 36(5), 883–898.

25.

Kar

Nazlıoğlu

Ağır

(2011). Financial development and economic growth nexus in the MENA countries: Bootstrap panel granger causality analysis. Economic Modelling , 28(1–2), 685–693.

26.

King

R. G.

Levine

(1993). Finance, entrepreneurship and growth. Journal of Monetary Economics , 32(3), 513–542.

27.

Kremer

Bick

Nautz

(2013). Inflation and growth: New evidence from a dynamic panel threshold analysis. Empirical Economics , 44(2), 861–878.

28.

Kumari

Sharma

A. K.

(2017). Physical & social infrastructure in India & its relationship with economic development. World Development Perspectives , 5, 30–33.

29.

Lall

S. V.

(2007). Infrastructure and regional growth, growth dynamics and policy relevance for India. The Annals of Regional Science , 41(3), 581–599.

30.

Law

S. H.

Nirvikar

(2014). Does too much finance harm economic growth? Journal of Banking & Finance , 41, 36–44.

31.

Wang

(2018). Growth channels of human capital: A Chinese panel data study. China Economic Review , 51, 309–322.

32.

Liang

Teng

J. Z.

(2006). Financial development and economic growth: Evidence from China. China Economic Review , 17(4), 395–411. https://dx-doi-org-s.web.bisu.edu.cn/10.1016/j.chieco.2005.09.003

33.

Loayza

Ranciere

(2004). Financial development, financial fragility, and growth . The World Bank.

34.

Lucas

R. E.

(1988). On the mechanics of economic development. Journal of Monetary Economics ,22(1), 3–42. https://dx-doi-org-s.web.bisu.edu.cn/10.1016/0304-3932(88)90168-7

35.

Mankiw

N. G.

Romer

Weil

D. N.

(1992). A contribution to the empirics of economic growth. The Quarterly Journal of Economics , 107(2), 407–437.

36.

Meersman

Nazemzadeh

(2017). The contribution of transport infrastructure to economic activity: The case of Belgium. Case Studies on Transport Policy , 5(2), 316–324.

37.

Mohanty

R. K.

Bhanumurthy

N. R.

(2018). Analyzing the dynamic relationship between physical infrastructure, financial development and economic growth in India. Asian Economic Journal , 33(245), 1–27.

38.

Ogundari

Awokuse

(2018). Human capital contribution to economic growth in Sub-Saharan Africa: Does health status matter more than education? Economic Analysis and Policy , 58, 131–140.

39.

Ojo

Oshikoya

(1995). Determinants of long-term growth: Some African results. Journal of African Economies , 4(2), 163–191.

40.

Pradhan

R. P.

Arvin

M. B.

Norman

N. R.

Bele

S. K.

(2014). Economic growth and the development of telecommunications infrastructure in the G-20 countries: A panel-VAR approach. Telecommunications Policy , 38(7), 634–649.

41.

Pradhan

R. P.

Bagchi

T. P.

(2013). Effect of transportation infrastructure on economic growth in India: The VECM approach. Research in Transportation Economics , 38(1), 139–148.

42.

Rahman

(2004). Financial development-economic growth nexus: A case study of Bangladesh. Bangladesh Development Studies , 30(3–4), 113–128.

43.

Rahman

Md. H.

(2007). Financial development: Economic growth nexus in Bangladesh. SSRN Electronic Journal . https://dx-doi-org-s.web.bisu.edu.cn/10.2139/ssrn.1310460

44.

Rahman

Md. M.

(2011). Causal relationship among education expenditure, health expenditure and GDP: A case study for Bangladesh. International Journal of Economics and Finance , 3(3). https://dx-doi-org-s.web.bisu.edu.cn/10.5539/ijef.v3n3p149

45.

Razin

Sadka

Tong

(2008). Bilateral FDI flows: Threshold barriers and productivity shocks. CESifo Economic Studies , 54(3), 451–470.

46.

Rousseau

P. L.

Wachtel

(2011). What is happening to the impact of financial deepening on economic growth? Economic Inquiry , 49(1), 276–288.

47.

Sahoo

Kumar

Ranjan

D. (2009). Infrastructure development and economic growth in India. Journal of the Asia Pacific Economy , 14(4), 351–365.

48.

Sharif

(2013). Human resource development and economic growth in Bangladesh: An econometric analysis. Journal of Business Market Management , 5(7), 133–144.

49.

Singh

Bhanumurthy

N. R.

(2014). Infrastructure development and regional growth in India. In Ghosh

A. N.

Karmakar

(Eds.), Analytical issues in trade, development and finance (pp. 321–341). Springer.

50.

Sultana

Moniruzzaman

Shamsuddin

Tareque

(2019). Endogenous growth model of a labour-abundant and land-scarce economy. Journal of Social and Economic Development , 21(2), 309–328.

51.

(2000). Financial development, investment, and economic growth. Economic Inquiry , 38(2), 331–344.

52.

Tan

(2020). Financial development, industrial structure and natural resource utilization efficiency in China. Resources Policy , 66, 101642.

53.

Zhang

Fan

(2004). How productive is infrastructure? A new approach and evidence from rural India. American Journal of Agricultural Economics , 86(2), 492–501.

54.

Zhang

Skitmore

Jiang

(2015). Sustainable infrastructure projects in balancing urban–rural development: Towards the goal of efficiency and equity. Journal of Cleaner Production , 107, 445–454.