Significance of technological progress and capital formation to expand foreign direct investment in Bangladesh: Does money circulation matter?

Figures & data

Table 1. Summary of literature

Name of Author	Type of Data, Country & Duration	Methodology	Variables	Results/Findings	Recommendations
(Arvin et al., 2021)	Panel data; G20 countries; 1961–2019	FMOLS, DOLS,GMM and VECM	1) ICT, 2) Trade openness 3) Foreign direct investment 4) Economic growth.	Facilitating collaborative endeavors aimed at augmenting economic liberalization and advancing information and communication technology (ICT) is anticipated to yield a compounding impact, invigorating the economic well-being of G-20 countries in an ever more interlinked worldwide economy.	The contextual analysis of the G-20 economies may provide valuable insights for different countries and conferences.
(Hussain et al., 2021)	Panel data; (India, Pakistan, Bangladesh, and Sri Lanka; 1995–2016	Fully Modified Ordinary Least Square (FMOLS) technique and Panel VECM model.	1)Per capita GDP- 2)FDI 3)Real investment- 4)ICT indicators	The “leapfrogging hypothesis” proposes that developing countries in South Asia will gain both immediate and long-term advantages from greater access to ICTs. The focus also proved that time spent online affects the local per capita income.	Exact outcomes recommend that government ought to design and advance the ICT entrance approaches in the district like other framework venture approaches to give an agreeable zone to the partners in the area.
(Rehman et al., 2021)	Time series data; Pakistan; 1985–2017	ARDL model FMOLS, DOLS and CCR.	ICT, FDI, Trade, Renewable energy, Economic progress.	ICT, commerce, and renewable energy all point to helpful ties to GDP growth in Pakistan, as co-integrating regression analysis shows a positive and considerable correlation between FDI, technical innovation, and trade with GDP growth.	To attract foreign and local investors, Pakistan should enhance infrastructure, create new roads, improve transportation, and use innovative equipment to lower manufacturing costs and boost output.
(Yang & Shafiq, 2020)	Panel Data; Twenty arising Asian countries;2007–2018	The fixed-effects model, along with the robust standard error model	FDI, Capital formation, Money supply, Trade openness, and Inflation	Growth is favorably connected with foreign direct investment (FDI), negatively correlated with inflation, and positively correlated with the money supply rate.	Future research should include other social and political aspects, such as foreign investment, accumulation of capital, liquidity, trade accessibility, and inflation.
(Liu & Wang, 2019)	Time series data; China;January 1997 to October 2017	ARIMA model and GARCH model, ADF stability test,ARCH-LM test	Broad Money Supply (M2)	The growth rate of China’s M2 broad money supply is expected to maintain a relatively stable pace in the upcoming year, with a slight downward trend, which is favorable for controlling inflation.	The national bank might additionally diminish the norm as indicated by the genuine improvement to animate the financial turn of events.
(Wong et al., 2019)	Panel Data; ASEAN-four-plus-one countries; 1995 to 2015.	Cointegration tests, FMOLS and DOLS methods	Government spending, Tax revenue, Broad money, Reserves money, Foreign exchange rate.	In ASEAN nations, long-term links may be shown between FDI, government expenditure, revenues from taxes, broad capital, overall reserves, and changes in foreign exchange rates, with a correlation between FDI, tax revenues, and net reserves.	For the financial approach, government agencies should focus on the sector, which attracts foreign investors to ASEAN countries, like assembling and developing areas. The government should spend more on foundation development to attract international investors.
(Pradhan et al., 2018)	Panel Data; G-20 countries;1990–2014.	A panel co-integration test, A panel Granger causality test,	Mobile phone diffusion, FDI, Financial development, ICT goods imports, and Economic growth.	Factors such as GDP, FD, FDI, and imports of ICT products affect the proliferation or diffusion of mobile phones. Similarly, the widespread adoption of cellular phones, along with developments in finance, FDI, and imports of ICT products, has a causal effect on economic growth per capita.	Promotion of mobile phone diffusion should receive direct encouragement within the economic framework, coupled with the advancement of 5G technology, with the collaboration and support of G-20 governments.
(Latifa et al., 2017)	Panel data; BRICS Country; 2000–2014	OLS with fixed effects, FMOLS, DOLS	1)Composite index of (ICT) 2)International trade 3)Per capital economic growth 4)Foreign direct investment	ICT infrastructure improves FDI, which boosts BRICS economies. ICT attracts international investment and promotes economic growth in these nations, both directly and indirectly. G-20 contextual analysis examples may help nations.	The dataset exclusively encompasses emerging economies; however, it may be applicable to non-industrialized nations, contingent on the availability of information. This presents a prospect for further investigation into this subject matter.
(Pradhan et al., 2017)	Panel Data; Asia-Pacific region;2001–2017	Panel data regression,	FDI, ICT, Economic growth.	The discoveries of the review could endorse that ICT ought to be given specific accentuation for its advancement as a crucial foundation of the economy, bringing about the beneficial outcomes of FDI and GDP development. Earlier thoughtfulness regarding advancing the ICT would have both immediate and aberrant positive multiplier consequences for economic growth.	Generalized methods of the moment (GMM), a more advanced board information econometrics methodology, may be used in conjunction with a comparable information design to provide a few innovative findings.
(Mohammed Ershad & Mahfuzul, 2017)	Time series data;Bangladesh; 1972–2014/	VECM model, Error Correction Model (ECM)	Percentage of Broad Money to GDP (BMGDP), Real Interest Rate (RIR) and Annual Per Capital GDP Growth Rate (GRGDP	In the near term, BMGDP consistency is linked to GRGDP. Long-term productivity growth is significantly impacted by monetary policy. As a result, if BMGDP expands at a constant rate, GRGDP will expand at the same rate.	This data may help policymakers improve financial growth, employment creation, and expansion. The government should adopt “the Taylor rule” to enable cash supply to rise steadily, keeping up with economic growth.
(Ahmed et al., 2015)	Panel Data; BANGLADESH; 1972/73–2009/10	Multiple regression models	M1 or M2 money/the deposit interest rate, real interest rate, nominal interest rate, remittance, and bank rate.	Constraints on the availability of currency reduce both the DIR and the IRS. Inflation, real interest rate, bank rate, and remittances (REM) all have a positive effect on Bangladesh’s limited money supply. However, real interest rates, bank rates, and inflation are all far more influenced by the country’s broad money supply.	The controllability of the cash supply by the Bangladesh Bank is constrained by the components of M1 and M2, and the effectiveness of implementing an effective monetary policy would heavily rely on considering the implications of changes in these fundamental components.
(Akbas et al., 2013)	Panel Data; G7 country;1990–2011	Panel unit root test, Panel co-integration test, Causality test- Elena Ivona Dumitrescu and Christophe Hurlin (2012),	Direct capital flows (% GDP) Current account deficit (% GDP Growth rate of gross domestic product (% Total credits (%GDP)	There is just one way in which the current account deficit affects GDP. Researchers only identified a positive, one-way correlation between FDI and economic expansion. Total credits were shown to be correlated to the current account deficit. There is a one-way causal relationship between FDI and TC, and TC also has a one-way causal relationship with CAD.	To help offset the current account deficit, policymakers are counting heavily on FDI. Going forward, governments should prioritise measures that encourage FDI from outside..
(Akbas et al., 2013)	Time series data; Pakistan; 1990– 2011	Augmented Dickey-Fuller Unit Root Test	FDI, Economic growth, Money supply	Growth in the economy naturally attracts a certain amount of foreign direct investment (FDI), and this association is unaffected by monetary policy. Both the exchange rate and exports benefit from an increase in inflationary currency depreciation.	Money supply affects economic growth and FDI, thus policymakers should take it into account. In order to improve Pakistan’s economy in the long term, the government should prioritize spending money on activities like luring international investors and investing in local markets.
(Zafir & Sezgin, 2012)	Time series data; Turkey; 2013 to past 50 year,	Johansen’s Cointegration, Granger causality test	Current account deficits, FDI, Portfolio investment, Public sector debt, Private sector debt	There was a two-way causality between variables of current account deficits, FDI, portfolio investments, and public sector debt, but only one-way between variables of private sector debt.	It is essential to depend on long-term FDI inflows to minimize foreign currency constraints produced by covering continuous shortages; also, the foreign direct investment might bring about economic development in Turkey.
(Kaur et al., 2012)	Time series data; India; 1975–2009	Toda-Yamamoto (T-Y) granger causality technique, Impulse Response Function (IRF)	Current account, Foreign direct investment, Import, Export	FDI has bidirectional causation with imports and exports, as well as a long-term unidirectional causality with the current account. Linear regression shows that FDI has a bigger influence on imports than exports. Finally, FDI supplements domestic investment.	Prioritizing income-generating activities above consumption-focused FDI encourages export-boosting FDI. By paying local workers, investors may balance their gains. Research may also examine how foreign investors’ profit remittances affect the current account deficit.

Source: Authors Self Investigation

Figure 1. Correlations Heatmap.

Table 2. Variables details

Variables name	Short form	Log Form	Variable Dimension
Foreign Direct Investment	FDI	LFDI	Dependent Variable
Broad Money (Money circulation)	BM	LBM	Independent Variables
Gross Capital Formation	GCF	LGCF
Technology (Fixed Telephone Subscription)	TEC	LTEC

Sources: WDI, 2023, IFS, 2023.

Table 3. Summary statistics

	(2)	(3)	(4)	(5)
Variables	Mean	Sd	Min	Max
LFDI	17.73	3.052	11.41	21.76
LBM	26.95	2.229	23.34	30.54
LGCF	26.56	2.181	21.30	30.02
TEC	0.390	0.283	0.0701	0.932

Table 4. Principal component analysis

Eigenvectors (loadings):
Variable	PC 1	PC 2	PC 3	PC 4	PC 5
LFDI	0.444981	−0.011883	0.882926	0.149193	0.005774
LBM	0.460018	−0.264113	−0.303228	0.427220	−0.666456
LGCF	0.457050	−0.341824	−0.280482	0.241112	0.733114
TEC	0.406375	0.886404	−0.203746	0.061939	0.061627

Table 5. Unit root test

Variable	ADF Test		KPSS Test		Remark
	Level	1^st Diff.	Level	1^st Diff.
LFDI	−0.055	−5.522***	−1.431	−4.523***	Stationary at I(1)
LBM	2.195	−3.380***	1.811	−7.319***	Stationary at I(1)
LGCF	−1.518	−6.512***	−1.491	−6.921***	Stationary at I(1)
TEC	−3.052**		−3.7121**		Stationary at I(0)

Table 6. Structural breaks (ZA unit root test)

Variables	ZA statistic	Break	1%	5%	10%	Decision
LFDI	−3.088***	1997	−5.34	−4.93	−4.58	Break Exist
LBM	−3.784***	1989	−5.34	−4.93	−4.58
LGCF	−4.114**	1991	−5.34	−4.93	−4.58
TEC	−3.11*	2001	−5.34	−4.93	−4.58

Note: The significance levels are denoted as *** for 1%, ** for 5%, and * for 10%.

Table 7. Optimal lags selection

Lag	LogL	LR	FPE	AIC	SC	HQ
0	−119.3404	NA	0.009238	6.667050	6.841203	6.728447
1	94.85615	370.5022*	2.07e-07*	−4.046279*	−3.175512*	−3.739293*
2	107.5433	19.20219	2.56e-07	−3.867206	−2.299827	−3.314632
3	121.5198	18.13161	3.11e-07	−3.757825	−1.493832	−2.959662

*Indicates 5% level of significance.

Table 8. Bound co-integration test

Test Statistic	Value	K
F- Statistic	2.929	3
Critical Value Bounds
Significance level	I(0)	I(1)
10%	2.45	3.52
5%	2.86	4.01
1%	3.74	5.06

Table 9. ARDL output

Variable	Coefficient	Std. Error	t-Statistic	Prob.*
LFDI(−1)	0.72	0.10	6.99	0.00
LGCF	4.36	1.66	2.62	0.01
LGCF(−1)	−3.53	1.11	−3.19	0.00
LBM	−6.76	2.53	−2.67	0.01
LBM(−1)	5.88	2.26	2.60	0.01
LTEC	1.55	0.58	2.69	0.01
C	8.76	6.02	1.46	0.15
R-square	0.93
Adjusted R-square	0.91
S.E. of regression	0.87
Sum squared resid	26.98
Log likelihood	−50.99
F-statistic	74.93
Prob(F-statistic)	0.00
Durbin-Watson stat	2.06

Table 10. Short and Long Run Dynamics

Short Run Dynamics or Co-integrating Form
Variable	Coefficient	Std. Error	t-Statistic	Prob.
D(LGCF)	4.36	1.66	2.62	0.01
D(LBM)	−6.76	2.53	−2.67	0.01
D(LTEC)	1.55	0.58	2.69	0.01
CointEq(−1)	−0.28	0.10	−2.72	0.01
Cointeq = LFDI - (2.9669*LGCF −3.1289*LBM + 5.5434*LTEC + 31.2393)
Long Run Coefficients
LGCF	2.97	2.96	1.00	0.32
LBM	−3.13	3.10	−1.01	0.32
LTEC	5.54	2.60	2.13	0.04
C	31.24	25.21	1.24	0.22

Table 11. Residual diagnostics tests

Breusch-Godfrey Serial Correlation LM Test: H0= No serial Correlation
F-statistic	0.72	Prob. F(2,34)	0.50
Obs*R-squared	1.74	Prob. Chi-Square(2)	0.42
Heteroskedasticity Test: Breusch-Pagan-Godfrey: H0= No Heteroskedasticity
F-statistic	1.71	Prob. F(6,36)	0.15
Obs*R-squared	9.54	Prob. Chi-Square(6)	0.15
Scaled explained SS	8.78	Prob. Chi-Square(6)	0.19

Figure 2. Stability test: CUSUM and CUSUMsq tests.

Table 12. Granger causality test

Null Hypothesis:	F-Statistic	Prob.	Decision
LBM ≠ LFDI	4.62	0.04	Bidirectional
LFDI ≠ LBM	4.48	0.04
LGCF ≠ LFDI	1.16	0.29	No causality
LFDI ≠ LGCF	1.63	0.21
LTEC ≠ LFDI	4.07	0.05	Unidirectional
LFDI ≠ LTEC	0.30	0.59
LBM ≠ LGCF	74.61	0.00	Bidirectional
LGCF ≠ LBM	13.42	0.00
LTEC ≠ LGCF	2.04	0.16	Unidirectional
LGCF ≠ LTEC	3.01	0.09
LTEC ≠ LBM	3.37	0.07	Unidirectional
LBM ≠ LTEC	1.99	0.17

Note: The symbol “≠” refer the statement of null hypothesis assumption “does not Granger cause.”

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Data availability statement

Data will be available based on request.