Will digital financial development affect the effectiveness of monetary policy in emerging market countries?

Figures & data

Table 1. The key research on digital finance.

Authors	Methodology	Conclusion	Type
Snellman et al. (2001)	OLS and learning curve analysis	The expansion of electronic payment creates a lowering effect on the demand for money.	Electronic payment
Tehranchian et al. (2012)	The Rinaldi Model	The influence of monetary policy will be questioned.	Electronic money
Wong and Eng (2020)	New Keynesian Model	P2P calls monetary policy effectiveness into question.	P2P
Muli (2019)	Friedman’s Quantity Demand Theory and Keynes Model	Digital finance negatively influenced money demand.	Digital finance
Wasiaturrahma et al. (2019)	The Error Correction Model (ECM)	Credit cards have a significant negative effect on cash circulation in the long run, while debit cards have a significant positive effect.	Credit cards and debit cards
Obinne et al. (2020)	The ARDL Model	Digital finance had a positive impact on the money supply in Nigeria.	Digital finance
Hasan et al. (2020)	The Interacted Panel VAR model	FinTech adoption generally enhanced monetary policy transmission to real GDP, bank loans, and housing prices.	FinTech

Source: summarized from the above literature.

Table 2. The variation of $m_S^N$ and $m_S^G.$

Narrow money multiplier ($m_S^N)$	Generalized money multiplier ($m_S^G$)
$\raisebox{1ex}{${\partial m}_S^N$}\!\left/ \!\raisebox{-1ex}{$\partial c$}\right.,$ uncertain	$\raisebox{1ex}{${\partial m}_S^G$}\!\left/ \!\raisebox{-1ex}{$\partial c$}\right.<0$
$\raisebox{1ex}{${\partial m}_S^N$}\!\left/ \!\raisebox{-1ex}{$\partial r_E$}\right.<0$	$\raisebox{1ex}{${\partial m}_S^G$}\!\left/ \!\raisebox{-1ex}{$\partial r_E$}\right.<0$
$\raisebox{1ex}{${\partial m}_S^N$}\!\left/ \!\raisebox{-1ex}{$\partial s$}\right.<0$	$\raisebox{1ex}{${\partial m}_S^G$}\!\left/ \!\raisebox{-1ex}{$\partial s$}\right.>0$

Source: Author’s calculation and analysis.

Table 3. The measurement of variables.

Type	Variables		Measurement
Dependent variable	Economic growth (EG)		Gross domestic product (GDP)
Core independent variable	Digital finance (DF)	Digital finance (DF)	Complied digital financial development index
		Coverage index of digital finance (CDF)	The transaction penetration rate
		Use depth index of digital finance UDF	The per capita transaction amount
			The per capita deal count
	Monetary policy (MP)		The RMB loan balance of financial institutions
Control variable	Consumption level (CL)		The total consumption of residents
	Fixed capital formation level (FC)		The total amount of fixed capital formation
	Export level (EL)		Net exports of goods and services
	Fiscal policy (FP)		The general budget expenditure of local finance
	COVID-19		Before 2019 (include 2019), 0. Otherwise, 1

Source: Summarized from 4.1.

Table 4. The summary statistics of variables.

Variables		Mean	Std.Dev.	Min	Max	Observations
EG	overall	23877.760	20358.370	611.500	1.08e + 05	310
	between		19401.650	1152.390	80119.940	31
	within		7000.202	−3169.373	51744.720	10
DF	overall	207.665	87.299	16.220	377.730	310
	between		24.066	175.164	269.199	31
	within		84.017	18.655	316.195	10
CDF	overall	186.924	26.958	147.242	259.433	310
	between		80.849	29.892	288.112	31
	within		26.958	147.242	259.433	10
UDF	overall	203.998	81.219	23.537	291.882	310
	between		89.190	6.760	400.400	31
	within		34.964	153.374	291.995	10
MP	overall	31409.160	82.268	−1.757	312.403	310
	between		26377.850	405.050	145169	31
	within		10493.210	−1.40e + 04	72573.170	10
CL	overall	8491.699	7036.197	142.700	34097.100	310
	between		6738.755	276.354	28761.210	31
	within		8632.642	542.600	39657.500	10
FC	overall	12876.350	8414.599	1066.164	33110.740	310
	between		4602.961	4892.891	23037.720	31
	within		2404.023	2209.888	20863.790	10
EL	overall	−774.446	4713.683	−9973.600	47744	310
	between		3482.889	−8554.330	10440.320	31
	within		3231.347	−1.23e + 04	36529.230	10
FP	overall	4905.492	2782.281	705.910	17297.850	310
	between		2438.998	1154.504	12251.590	31
	within		1402.013	−633.701	9951.748	10
COVID-19	overall	0.8	0.400	0	1	310
	between		0	0.8	0.8	31
	within		0.400	0	1	10

Source: Authors’ processing in Stata16.

Table 5. Estimated results of the spatial autoregressive model.

Variables	Model
	(1)	(2)	(3)	(4)	(5)
Constant	10154.95 (4.23) ***	2873.862 (1.75) *	−3162.782 (-2.36) **	−3117.015 (-2.31) **	−3062.806 (-2.41) **
DF	−17.664 (-4.51) ***	−27.942 (-8.13) ***	−35.082 (-11.72) ***	−35.339 (-11.79) ***	−35.636 (-12.93) ***
DF × MP	0.001 (29.90) ***	0.001 (11.25) ***	0.001 (16.86) ***	0.001 (16.89) ***	0.0005 (10.86) ***
CL		1.118 (16.55) ***	0.661 (7.12) ***	0.662 (7.15) ***	0.629 (7.57) ***
FC			0.783 (10.92) ***	0.771 (10.62) ***	0.453 (5.98) ***
EL				−0.029 (-1.00)	−0.069 (-2.56) ***
FP					1.817 (8.51) ***
W × EG	0.345 (5.93) ***	0.456 (9.06) ***	0.535 (12.23) ***	0.539 (12.31) ***	0.420 (9.77) ***
R^2	0.721	0.920	0.946	0.945	0.959
Observations	310	310	310	310	310

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

Source: Authors’ processing in Stata16.

Table 6. Initiative measures of central banks.

Central Bank	Measures
US Federal Reserve	Cut federal funds rate by 50 basis points. Added additional liquidity of more than 3 trillion dollars.
Bank of Japan	Issued statement to increase asset purchase to stimulate liquidity. Provided bank liquidity with 43 billion dollars.
People Bank of China	Injected more than $240 billion in the economy to increase liquidity.
Bank of England	New lending facility among larger firms. Cut interest rate to 0.1%.
European Central Bank	Announced 750 billion Euros asset-purchase program.
Central Bank of Russia	Increase 4 billion dollars to induce economy.
Reserve Bank of Australia	56 billion dollars to stimulate liquidity.

Source: concluded from Baber (2020).

Table 7. Estimation results after introducing COVID-19.

Variables	Model
	(6)	(7)	(8)	(9)	(10)
Constant	−54.785 (-0.04)	−3745.852 (-2.83) ***	−1296.049 (-0.97)	−540.079 (-0.35)	1782.843 (1.26)
DF	−31.221 (-10.46) ***
CDF		−37.3061 (-12.74) ***		−32.525 (-10.28) ***
UDF			−30.904 (-10.73) ***		−27.475 (-9.51) ***
DF × MP	0.0005 (11.62) ***
CDF × MP		0.0005 (10.34) ***		0.0005 (11.17) ***
UDF × MP			0.0005 (13.07) ***		0.0005 (13.99) ***
CL	0.662 (8.02) ***	0.628 (7.59) ***	0.593 (7.08) ***	0.662 (8.04) ***	0.666 (8.04) ***
FC	0.496 (6.56) ***	0.441 (5.84) ***	0.379 (4.80) ***	0.488 (6.47) ***	0.440 (5.67) ***
EL	−0.061 (-2.30) **	−0.069 (-2.57) ***	−0.062 (-2.24) ***	−0.061 (-2.32) ***	−0.054 (-2.00) **
FP	1.500 (6.63) ***	1.855 (8.67) ***	1.748 (8.20) ***	1.519 (6.68) ***	1.440 (6.67) ***
COVID-19	−1210.619 (-3.69) ***			−1244.655 (-3.82) ***	−1531.549 (-4.90) ***
W × EG	0.318 (6.25) ***	0.437 (9.87) ***	0.356 (7.89) ***	0.33 (6.32) ***	0.253 (5.16) ***
R^2	0.958	0.957	0.956	0.964	0.964
Observations	310	310	310	310	310

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

Source: Authors’ processing in Stata16.

Table 8. Decomposition results of digital financial development index.

Variables	Model
	(11)
	Total effect	Direct effect	Indirect effect
DF	−46.095 (-5.94) ***	−31.357 (-9.92) ***	−14.738 (-3.08) ***
DF × MP	0.001 (9.84) ***	0.0005 (11.72) ***	0.0002 (4.24) ***
CL	0.970 (6.79) ***	0.663 (8.24) ***	0.306 (3.64) ***
FC	0.743 (5.78) ***	0.508 (6.92) ***	0.235 (3.37) ***
EL	−0.089 (-2.26) **	−0.061 (-2.31) **	−0.028 (-1.96) **
FP	2.188 (6.45) ***	1.499 (7.92) ***	0.690 (3.72) ***
COVID-19	−1784.041 (-4.24) ***	−1233.824 (-3.79) ***	−550.218 (-3.90) ***

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

Source: Authors’ processing in Stata16.

Table 9. Decomposition results of digital financial development from subindex Level.

Variables	Model
	(12)			(13)
	Total effect	Direct effect	Indirect effect	Total effect	Direct effect	Indirect effect
CDF	−48.964 (-5.74) ***	−32.689 (-9.73) ***	−16.275 (-3.04) ***
UDF				−36.961 (-5.96) ***	−27.500 (-9.09) ***	−9.461 (-2.82) ***
CDF × MP	0.0008 (9.70) ***	0.0005 (11.28) ***	0.0003 (4.26) ***
UDF × MP				0.0007 (10.2) ***	0.0005 (13.96) ***	0.0002 (3.66) ***
CL	0.988 (6.67) ***	0.664 (8.26) ***	0.325 (3.60) ***	0.889 (7.28) ***	0.666 (8.29) ***	0.224 (3.46) ***
FC	0.745 (5.68) ***	0.500 (6.83) ***	0.245 (3.35) ***	0.603 (5.28)	0.450 (5.99) ***	0.152 (3.00) ***
EL	−0.091 (-2.28) **	−0.061 (-2.33) ***	−0.030 (-1.97) **	−0.072 (-1.98) **	−0.054 (-2.00) *	−0.018 (-1.72) ***
FP	2.259 (6.32) ***	1.519 (7.18) ***	0.7404 (3.65) ***	1.914 (7.26) ***	1.436 (7.13) ***	0.478 (3.81) ***
COVID-19	−1868.59 (-4.41) ***	−1268.829 (-3.93) ***	−599.761 (-3.94) ***	−2059.709 (-5.40) ***	−1549.583 (-4.98) ***	−510.126 (-3.76) ***

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

Source: Authors’ processing in Stata16.

Table 10. Spatial panel model form test.

Inspection type		Spatial Panel Autocorrelation Tests
		(14)	(15)	(16)	(17)	(18)	(19)
Residual spatial autocorrelation test	GLOBAL Moran MI	0.775 (0.000)	0.774 (0.000)	0.775 (0.000)	0.799 (0.000)	0.812 (0.000)	0.802 (0.000)
	GLOBAL Geary GC	0.348 (0.000)	0.349 (0.000)	0.346 (0.000)	0.324 (0.000)	0.313 (0.000)	0.318 (0.000)
	GLOBAL Getis-Ords GO	−13.803 (0.000)	−3.784 (0.000)	−13.795 (0.000)	−14.235 (0.000)	−14.451 (0.000)	−14.289 (0.000)
	Moran MI Error Test	2.997 (0.000)***	2.991 (0.007)	2.995 (0.003)	3.400 (0.003)	3.440 (0.009)	3.412 (0.009)
	LM Error (Burridge)	1408.155 (0.000)	1404.278 (0.000)	1406.657 (0.000)	1486.638 (0.000)	1532.181 (0.000)	1497.949 (0.000)
	LM Error (Robust)	2718.663 (0.000)	2669.109 (0.000)	2385.253 (0.000)	2986.429 (0.000)	3019.075 (0.044)	2625.448 (0.044)
Dependent variable spatial autocorrelation test	LM Lag (Anselin)	175.69 (0.000)	185.105 (0.0000)	132.251 (0.000)	207.7 (0.000)	216.190 (0.000)	159.490 (0.000)
	LM Lag (Robust)	1486.197 (0.0001)	1449.936 (0.000)	1110.846 (0.000)	1707.491 (0.0001)	1703.083 (0.000)	1286.990 (0.000)
Generalized spatial autoregressive test	LM SAC (LMErr + LMLag_R)	2894.353 (0.000)	2854.214 (0.000)	2517.504 (0.000)	3194.129 (0.000)	3235.264 (0.000)	2784.938 (0.000)
	LM SAC (LMLag + LMErr_R)	2894.353 (0.000)	2854.214 (0.000)	2517.504 (0.000)	3194.129 (0.000)	3235.264 (0.000)	2784.938 (0.000)

Note: () is the p value.

Source: Authors’ processing in Stata16.

Table 11. Estimated results of the dynamic spatial panel model.

Variable	Model
	(20)	(21)	(22)	(23)	(24)	(25)
Constant	1059.066 (2.95) ***	490.497 (1.32) *	933.059 (2.99) ***	279.291 (0.78)	949.780 (2.19) **	206.388 (0.586)
EGt-1	1.408 (11.50) ***	1.317 (10.84) ***	1.349 (12.28) ***	1.275 (11.76) ***	1.479 (11.25) ***	1.360 (10.17) ***
W × EG	0.016 (6.53) ***	0.016 (6.05)	0.016 (6.20) ***	0.016 (5.38) ***	0.013 (5.33) ***	0.014 (5.32) ***
DF	−25.432 (−9.04) ***	−22.572 (−7.14) ***
CDF			−25.296 (−7.70) ***	−19.733 (−4.97) ***
UDF					−19.985 (−7.83) ***	−18.254 (−7.20) ***
DF × MP	0.0003 (6.69) ***	0.0003 (6.33) ***
CDF × MP			0.0004 (6.68) ***	0.0004 (6.47) ***
UDF × MP					0.0003 (6.07) ***	0.0003 (6.29) ***
CL	YES	YES	YES	YES	YES	YES
FC	YES	YES	YES	YES	YES	YES
EL	YES	YES	YES	YES	YES	YES
FP	YES	YES	YES	YES	YES	YES
COVID-19	NO	YES	NO	YES	NO	YES
Wald	2114.490	1732.929	1850.761	1514.668	2166.033	1783.786
Raw Moments R^2 Adj	0.9194	0.9273	0.9272	0.9319	0.9119	0.9219
Engle LM ARCH Test	95.273***	94.951***	94.265***	94.124***	95.448***	95.575***

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

Source: Authors’ processing in Stata16.

Baber, H. (2020). Spillover effect of Covid19 on the global economy. Transnational Marketing Journal, ), 8(2), 177–196. https://doi.org/10.33182/tmj.v8i2.1067

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