The influence of information and communication technology on trade in developing countries and partners

Figures & data

Table 1. Summary of E-indexes used to measure ICT adoption.

Index	Measure	Author/organization
Information Society Index	‘Index by which all countries are ranked according to their ability to access and absorb ICT’	IDC
E-Government Readiness Index	‘Measures the capacity of governments to develop & implement the e-government services’	UN
Knowledge Economy Index	‘A broad measure of the overall level of preparedness of a country or region for the knowledge economy’	World bank institute
E-Government Development Index	‘Weighted average of normalized scores on the three most important dimensions of e-Government; Online service index, Telecommunication Infrastructure Index, Human Capital Index’	UNDESA
ICT Development Index	‘To monitor and compare developments in ICT between countries and over time’	ITU
Digital Access Index	‘Measures the overall ability of individuals in a country to access and use ICT’	ITU
Technology Achievement Index	‘Measures the countries’ skills to participate in the network age’	UNDP
Networked Readiness Index	‘Measure the degree of readiness of countries to exploit opportunities offered by ICT’	WEF
Digital Opportunity Index	‘Measures the ICT penetration of households and individuals’	ITU
ICT Opportunity Index	‘Tool to track the digital divide’	ITU
ICT Diffusion Index	‘Weights the distribution of broadband subscribers, personal computers, mobile phones, Internet users, and international Internet bandwidth by economic output’	UNCTAD
Global Innovation Index	‘Capture the multi-dimensional facets of innovation’	INSEAD
Infostates	‘To measure the Digital Divide’	ORBICOM

Figure 1. Three stages in the evolution towards an information society.

Source: International Telecommunication Union (ITU).

Table 2. Reference values obtained through statistical procedures.

Country		ICT access		ICT use		ICT skills
	Fixed Telephone Subscriptions per 100 inhabitants	Mobile Cellular Subscriptions per 100 inhabitants	International internet bandwidth(bit/s) per internet users	Percentage of individuals using internet	Fixed-broadband subscriptions per 100 inhabitants	Secondary gross enrollment ratio	Tertiary gross enrollment ratio
China	4.503945	8.486944	22.69471273	69.5154	4.717612	114.685	56.989555
Hong Kong	4.220251	7.272077	24.37711343	115.746	4.907619	115.6203	93.884581
UAE	3.561657	7.066067	20.19836887	122.021	4.505384	125.6064	57.247469
UK	4.122944	6.643394	23.17327562	129.465	5.332271	127.2098	73.803986
USA	4.409697	6.158995	23.2510588	116.011	4.986517	101.2412	91.037995
Japan	4.011173	6.878227	20.23497791	127.327	4.93776	107.6079	92.151827
Malaysia	2.809439	3.415511	14.48697873	39.8889	2.239938	76.62331	28.314307
India	1.808069	7.728936	22.39701893	25.2635	1.6696	82.76573	32.318649
Germany	4.335863	7.18069	19.20399084	123.158	5.36524	106.333	77.602784
Italy	3.951148	7.371733	14.53184754	86.5465	4.862488	111.2555	79.793409
Spain	3.865332	7.605932	14.1637499	109.795	5.011984	130.8282	102.50572
Netherlands	4.167596	7.205635	14.84054289	133.335	5.283195	139.8593	90.660311
Pakistan	1.510691	8.001449	18.9342263	16.4625	1.648043	47.87871	13.164504

‘Notes: the reference value: mean + 2 * standard deviation’.

‘Source: Computed by the author based on the method explained in ITU (2009).’

Table 3. The indicators and weight in the ICT Development Index.

The indicators and weight in the ICT Development Index
	Weight
ICT access (A)
‘Fixed-telephone subscriptions per 100 inhabitants’ (ai)	Log(x)*0.33/Refvalue
‘Mobile/cellular telephone subscriptions per 100 inhabitants’ (aii)	Log(x)*0.33/Refvalue
‘International internet bandwidth (bit/s) per internet user’ (aiii)	Log(x)*0.33/ Refvalue
ICT Access Sub-Index (A) = (ai + aii + aiii)
ICT use (B)
‘Percentage of individuals using the internet’ (bi)	x*0.5/ Refvalue
‘Fixed-broadband subscriptions per 100 inhabitants’ (bii)	Log(x)*0.5/ Refvalue
ICT use Sub-Index (B) = (bi + bii)
ICT skills (C)
‘Secondary gross enrollment ratio’ (ci)	x/2/(Refvalue)
‘Tertiary gross enrollment ratio’ (cii)	x/2/(Refvalue)
ICT skills Sub-Index (C) = (ci + cii)
Aggregated Index (IDI): (A)*4 + (B)*4 + (C)*2

Table 4. Variable definition and data sources.

Variables/indicators	Unit/Proxy	Definition	Sources
Exports	‘Exports of goods and services (current US$)’	‘The goods and services which are produced in one country and is sold to another country’	‘World development indicators’
Imports	‘Imports of goods and services (current US$)’	‘The goods and services which are produced in the foreign country and are sold in the resident country’	‘World development indicators’
Exchange Rate	‘Real effective exchange rate index (2010 = 100)’ ‘Official exchange rate (LCU per US$, period average)’	‘The value of the currency of a specific country in terms of another country’	‘World development indicators and FRED’
Tariffs	‘Tariff rate, applied, weighted mean, all products (%)’	‘A duty or tax which is paid on a certain class of exports or imports’	‘World development indicators’
Gross Domestic Product	‘GDP (constant 2015 US$)’	‘Refers to the monetary value of all finished goods and services made within a country during a specific period.’	‘World development indicators’
Per capita income	‘GDP per capita (constant 2015 US$)’	‘Measure of the amount of money earned per person in a nation.’	‘World development indicators’
Inflation	‘Consumer price index (2010 = 100)’	‘Refers to the increase in the prices over a given period of time.’	‘World development indicators’
Gross Fixed Capital Formation	‘Gross capital formation (% of GDP)’	‘Comprises all the fixed assets minus the disposals by resident producers.’	‘World development indicators’
Fixed Telephone subscriptions	‘Fixed telephone subscriptions (per 100 people)’	‘Refers to the sum of active number of analogue fixed telephone lines, voice-over-IP (VoIP) subscriptions, fixed wireless local loop (WLL) subscriptions, ISDN voice-channel equivalents and fixed public payphones.’	‘World development indicators’
Mobile Cellular subscriptions	‘Mobile cellular subscriptions (per 100 people)’	‘Subscriptions to a public mobile telephone service that provide access to the PSTN using cellular technology.’	‘World development indicators’
Fixed broadband subscriptions	‘Fixed broadband subscriptions (per 100 people)’	‘Subscription to the high-speed internet access to the public.’	‘World development indicators’
Mobile cellular subscriptions	‘Mobile cellular subscriptions (per 100 people)’	‘Subscriptions of mobile advertising data speeds of 256 kbit/s or greater.’	‘World development indicators’
International bandwidth	‘International bandwidth; in Mbit/s’	‘The transmission or rate of data from one part of world to the other.’	‘ITU’
Internet users	‘Individuals using the Internet (% of population)’	‘The total number of users of the internet from any region or locality.’	‘World development indicators’
Secondary school enrollment	‘School enrollment, secondary (% gross)’	‘The percentage of the total enrollment in the in secondary education institutions to the population’	‘World development indicators’
Tertiary school enrollment	‘School enrollment, tertiary (% gross)’	‘The percentage of the total enrollment, in post-secondary institutions to the population.’	‘World development indicators’

Table 5. Descriptive statistics for export demand function.

Variables	Mean	Median	Maximum	Minimum
EXPORTS	26.06398	27.37298	28.63285	24.61773
IDI	4.972492	5.211605	3.700862	3.513723
GCF_PCGDP	3.698720	3.700862	3.842890	3.513727
GRTH_PCGDP	2.049996	2.094884	2.612700	0.749775
OER	1.958598	1.932736	2.153939	1.565112
REER	99.46759	96.76413	129.9704	70.52476
TARIFF	2.128490	1.945678	3.502802	0.000000

Table 6. Covariance matrix of export demand function.

	EXPORTS	IDI	GCF_PCGDP	GRTH_PCGDP	OER	REER	TARIFF
EXPORTS	28.20482	1.599678	−0.1595	0.205287	−5.55695	8.8517	−0.58328
IDI	1.599678	5.657746	−0.1047	−0.00472	−1.5688	−1.84695	−1.22984
GCF_PCGDP	−0.1595	−0.1047	0.065926	0.027651	0.108686	−0.13017	0.047053
GRTH_PCGDP	0.205287	−0.00472	0.027651	0.350042	−0.00637	−1.198	0.022442
OER	−5.55695	−1.5688	0.108686	−0.00637	3.886528	−2.78653	0.72807
REER	8.8517	−1.84695	−0.13017	−1.198	−2.78653	178.2493	−1.63569
TARIFF	−0.58328	−1.22984	0.047053	0.022442	0.72807	−1.63569	0.757317

Table 7. Unit root test-summary statistics for export demand function.

Variable	Levin, Lin & Chu t	Im, Pesaran & Shin W-stat	ADF – Fisher Chi Square	PP – Fisher Chi Square	Hadri-Z-stat	Stationarity
Null Hypothesis: Panels contain unit roots
Exports	2265.54***	−2.39757***	46.6360***	49.1626***	10.8352***	I(0)
IDI	−8.81536***	−5.90941***	89.2526***	235.539***	13.2696***	I(0)
GCF_PCGDP	−2.19835**	−1.87478**	35.5465*	38.3160*	6.79773***	I(0)
GRTH_PCGDP	6.69622	−3.11522***	61.0185***	91.5075***	1.84878***	I(0)
OER	−1.75262**	−1.47931*	32.7000	30.2108	8.65133***	I(0)
REER	−2.12396**	−1.97119**	40.2748**	31.5420	6.50965***	I(0)
TARIFF	−2.65107***	−0.15216	25.5956	47.8961***	11.1823***	I(0)

Note: (*) indicates significance at 10%, (**) indicates significance at 5%, (***) indicates significance at 1%.

Source: Author’s analysis.

Table 8. Kao cointegration test for export demand function.

	Statistic	p value
Modified Dickey-Fuller t	−3.9592	0.0000***
Dickey-Fuller t	−3.7076	0.0001***
Augmented Dickey-Fuller t	−2.2620	0.0118**

Note: (*) and (**) show a 5% significance level; (***) indicates significance at the 10%, 5%, and 1% levels, respectively.

Source: Authors’ own computations.

Table 9. Short run results (Wald test results) for export demand function.

Variable	F-statistic	p value	Conclusion
IDI	0.201246	0.6540	Insignificant
GCF_PCGDP	0.529510	0.4672	Insignificant
GRTH_PCGDP	3.150430	0.0767*	Significant
OER	144.5698	0.0000***	Significant
REER	4.971866	0.0263**	Significant
TARIFF	6.839732	0.0093***	Significant

Note: (*) and (**) show a 5% significance level; (***) indicates significance at the 10%, 5%, and 1% levels, respectively.

Source: Authors’ own computations.

Table 10. Descriptive statistics for import demand function.

Variables	Mean	Median	Maximum	Minimum
IMPORT	26.29777	27.19826	28.57263	24.37293
IDI	4.972492	5.211605	8.542679	−0.791823
GCF_PCGDP	3.698720	3.700862	3.842890	3.513723
GRTH_PCGDP	2.049996	2.094884	2.612700	0.749775
OER	1.958598	1.932736	2.153939	1.565112
REALGDP	29.11463	29.11728	30.31442	27.65803
TARIFF	2.128490	1.945678	3.502802	0.000000
CPI	89.56379	86.50932	128.1094	40.44026

Table 11. Covariance matrix of import demand function.

	IMPORT	IDI	GCF_PCGDP	GRTH_PCGDP	OER	REALGDP	TARIFF	CPI
IMPORT	3.292112	1.894675	0.035595	−0.1479	−0.98282	1.518444	−0.71302	19.95872
IDI	1.894675	5.657746	−0.1047	−0.00472	−1.5688	0.51231	−1.22984	42.02764
GCF_PCGDP	0.035595	−0.1047	0.065926	0.027651	0.108686	0.048721	0.047053	−0.38746
GRTH_PCGDP	−0.1479	−0.00472	0.027651	0.350042	−0.00637	−0.15811	0.022442	−2.73093
OER	−0.98282	−1.5688	0.108686	−0.00637	3.886528	−0.47127	0.72807	−3.89029
REALGDP	1.518444	0.51231	0.048721	−0.15811	−0.47127	1.979322	−0.23974	9.118399
TARIFF	−0.71302	−1.22984	0.047053	0.022442	0.72807	−0.23974	0.757317	−11.6064
CPI	19.95872	42.02764	−0.38746	−2.73093	−3.89029	9.118399	−11.6064	688.129

Table 12. Unit root test-summary statistics for import demand function.

Variable	Levin, Lin & Chu t	Im, Pesaran & Shin W-stat	ADF – Fisher Chi Square	PP – Fisher Chi Square	Hadri-Z-stat	Status
Null Hypothesis: Panels contain unit roots
Imports	1.53092	−2.21511**	47.0160***	46.7419***	10.9054***	I(0)
IDI	−8.81536***	−5.90941***	89.2565***	235.539***	13.2696***	I(0)
GCF_PCGDP	−2.19836**	−1.87478**	35.5465	38.3160*	6.79773***	I(0)
GRTH_PCGDP	6.69622	−3.11520***	61.0185***	91.5075***	1.84878***	I(0)
OER	−2.69704***	−1.36073*	32.1623	30.2108	8.65133***	I(0)
REALGDP	−6.73293***	−2.02512**	40.5554**	34.7429	13.4855***	I(0)
TARIFF	−4.76341***	−2.42931***	46.5232***	47.8961***	11.1823***	I(0)
CPI	−1.38139*	2.83470	40.9704**	35.9514*	12.9851***	I(0)

Note: (*) and (**) show a 5% significance level; (***) indicates significance at the 10%, 5%, and 1% levels, respectively.

Source: Authors’ own computations.

Table 13. Kao cointegration test for import demand function.

	Statistic	p value
Modified Dickey-Fuller t	−3.0775	0.0010***
Dickey-Fuller t	−3.5377	0.0002***
Augmented Dickey-Fuller t	−1.5676	0.0585*

Note: (*) and (**) show a 5% significance level; (***) indicates significance at the 10%, 5%, and 1% levels, respectively.

Source: Authors’ own computations.

Table 14. Short run results (Wald test results) for import demand function.

Variable	F-statistic	p value	Conclusion
IDI	15.43702	0.0001***	Significant
GCF_PCGDP	9.420133	0.0023***	Significant
GRTH_PCGDP	1.859915	0.1734	Insignificant
OER	1.911373	0.1676	Insignificant
REALGDP	156.3945	0.0000***	Significant
TARIFF	18.89265	0.0000***	Significant
CPI	0.815488	0.3671	Insignificant

Note: (*) and (**) show a 5% significance level; (***) indicates significance at the 10%, 5%, and 1% levels, respectively.

Source: Authors’ own computations.

Table 15. Empirical results of export demand function.

Dependent variable: EXPORTS
Method: Least Squares
Date: 07/21/23 Time: 21:04
Sample: 1 31
Included observations: 31
Variable	Coefficient	Std. Error	t-Statistic	Prob.
IDI	0.542540	0.080317	6.754980	0.0000
GCF_PCGDP	−0.069377	0.906470	−0.076535	0.9396
GRTH_PCGDP	−0.021176	0.165413	−0.128021	0.8992
OER	−1.278344	0.620923	−2.058781	0.0505
REER	−0.013910	0.009721	−1.430971	0.1653
TARIFF	−0.275000	0.105050	−2.617806	0.0151
C	29.13893	4.614528	6.314606	0.0000
R-squared	0.978120	Mean dependent var		27.06398
Adjusted R-squared	0.972650	S.D. dependent var		1.370744
S.E. of regression	0.226690	Akaike info criterion		0.065211
Sum squared resid	1.233318	Schwarz criterion		0.389014
Log likelihood	5.989234	Hannan-Quinn criter.		0.170763
F-statistic	178.8179	Durbin-Watson stat		1.073665
Prob(F-statistic)	0.000000

Table 16. Empirical results of import demand function.

Dependent variable: IMPORT
Method: Least Squares
Date: 07/22/23 Time: 13:15
Sample: 1 31
Included observations: 31
Variable	Coefficient	Std. Error	t-Statistic	Prob.
IDI	−0.126025	0.107521	−1.172096	0.2532
GCF_PCGDP	0.828724	0.442402	1.873239	0.0738
GRTH_PCGDP	0.218416	0.092012	2.373772	0.0263
OER	0.602126	0.319779	1.882942	0.0724
REALGDP	2.014685	0.365734	5.508605	0.0000
TARIFF	−0.167367	0.056219	−2.977026	0.0067
CPI	−0.007453	0.006145	−1.212825	0.2375
C	−34.77089	10.40457	−3.341887	0.0028
R-squared	0.993906	Mean dependent var		26.92777
Adjusted R-squared	0.992051	S.D. dependent var		1.373603
S.E. of regression	0.122463	Akaike info criterion		−1.144382
Sum squared resid	0.344934	Schwarz criterion		−0.774321
Log likelihood	25.73793	Hannan-Quinn criter.		−1.023752
F-statistic	535.8982	Durbin-Watson stat		1.713830
Prob(F-statistic)	0.000000

Table 17. White’s heteroscedasticity test.

White’s Heteroscedasticity Test
Null Hypothesis: Homoscedasticity
	Chi Sq	Probability
Export demand function	30.42	0.29
Import demand function	31.00	0.41

Source: Authors’ own computations.

Table 18. Multicollinearity test.

	EXPORTS	IDI	GCF_PCGDP		GRTH_PCGDP	OER	REER	TARIFF
EXPORTS	1.000000	0.12663	−0.116969		0.065334	−0.53075	0.12483	−0.12620
IDI	0.126634	1.00000	−0.171433		−0.003353	−0.33455	−0.05815	−0.59413
GCF_PCGDP	−0.116969	−0.17143	1.000000		0.182023	0.21471	−0.03797	0.21058
GRTH_PCGDP	0.065334	−0.00335	0.182023		1.000000	−0.00545	−0.15166	0.04358
OER	−0.530755	−0.33455	0.214715		−0.005459	1.00000	−0.10586	0.42437
REER	0.124839	−0.05815	−0.037971		−0.151664	−0.10586	1.00000	−0.14078
TARIFF	−0.126204	−0.59413	0.210583		0.043588	0.42437	−0.14078	1.00000
	IMPORT	IDI	GCF_PCGDP	GRTH_PCGDP	OER	REALGDP	TARIFF	CPI
IMPORT	1.00000	0.43901	0.076405	−0.137775	−0.2747	0.594844	−0.45157	0.41933
IDI	0.43901	1.00000	−0.171433	−0.003353	−0.33455	0.153092	−0.59413	0.67356
GCF_PCGDP	0.07640	−0.17143	1.000000	0.182023	0.2147	0.134873	0.21058	−0.05752
GRTH PCGDP	−0.13777	−0.00335	0.182023	1.000000	−0.00545	−0.189952	0.04358	−0.17596
OER	−0.27476	−0.33455	0.214715	−0.005459	1.0000	−0.169913	0.42437	−0.07522
REALGDP	0.59484	0.15309	0.134873	−0.189952	−0.16991	1.000000	−0.19581	0.24707
TARIFF	−0.45157	−0.59413	0.210583	0.043588	0.4243	−0.195813	1.00000	−0.50841
CPI	0.419334	0.673563	−0.057526	−0.175960	−0.07522	0.247073	−0.50841	1.000000

Source: Authors’ own computations.

Table 19. Autocorrelation test.

Autocorrelation test
	Export demand function	Import demand function
Dwstat	1.07	1.71

Source: Authors’ own computations.

Table 20. Specification test

Specification test
	Export demand function	Import demand function
Ovtest	F = 20.70 Prob > F = 0.0000	F = 16.96 Prob > F = 0.0000

Source: Authors’ own computations.

Table

Variable	Levin, Lin & Chu t	Im, Pesaran and Shin W-stat	ADF – Fisher Chi Square	PP – Fisher Chi Square	Hadri-Z-stat
Exports	60.8990	−8.82075***	126.289***	162.860***	2.20075**
GCF_PCGDP	−6.23891***	−9.06520***	129.599***	236.925***	−1.15509
GRTH_PCGDP	2.41055	−8.83222***	138.791***	242.062***	1.92023**
IDI	1.91437	−8.48256***	121.771***	343.228***	10.6241***
OER	−9.16466***	−9.33075***	124.016***	161.899***	−0.44389
REER	−7.85213***	−1.97119**	40.2748**	31.5420	−0.15662
TARIFF	−10.1231***	−14.0945***	203.548***	391.909***	3.22301***

Table

Variable	Levin, Lin & Chu t	Im, Pesaran and Shin W-stat	ADF – Fisher Chi Square	PP – Fisher Chi Square	Hadri-Z-stat
Exports	52.5922	−19.8154***	301.319***	376.902***	3.46906***
GCF_PCGDP	−12.4962***	−17.5736***	264.968***	306.275***	−0.68600
GRTH_PCGDP	−6.32566***	−15.7977***	236.140***	243.959***	1.46200*
IDI	−7.93038***	−19.6924***	299.199***	313.822***	2.22645**
OER	−16.4243***	−17.4048***	254.598***	274.086***	7.37328***
REER	−16.0507***	−18.2825***	277.181***	333.534***	−1.50643
TARIFF	−12.6383***	−20.5667***	296.217***	221.048***	1.62236

Table

Variable	Levin, Lin & Chu t	Im, Pesaran and Shin W-stat	ADF – Fisher Chi Square	PP – Fisher Chi Square	Hadri-Z-stat
Imports	−250.883***	−79.4023***	171.560***	170.380***	4.86202***
GCF_PCGDP	−13.7414***	−14.4815***	214.489***	236.925***	−1.15509
GRTH_PCGDP	−11.7476***	−15.3181***	229.255***	242.062***	1.92023**
IDI	−24.0695***	−25.1457***	339.906***	343.228***	10.6241***
OER	−13.3032***	−12.0571***	161.974***	161.899***	−0.44389
CPI	−5.33001***	6.64239***	101.539***	106.259***	5.66368***
REALGDP	−0.81305	−5.42897***	83.4083***	89.0165***	4.22672***
TARIFF	−23.6899***	−23.7356***	337.049***	391.909***	3.22301***

Table

Variable	Levin, Lin & Chu t	Im, Pesaran and Shin W-stat	ADF – Fisher Chi Square	PP – Fisher Chi Square	Hadri-Z-stat
Imports	−291.443***	−105.032***	338.592***	345.665***	4.23756***
GCF_PCGDP	−27.5223***	−27.3669***	384.570***	306.275***	−0.68600
GRTH_PCGDP	−24.5557***	−25.1214***	338.333***	243.959***	1.46200*
IDI	−27.4331***	−30.3545***	410.279***	313.822***	2.22645**
OER	−25.2332***	−23.2099***	326.075***	274.086***	7.37328***
CPI	−19.9276***	−18.4528***	267.843***	321.540***	−0.98452
REALGDP	−13.5476***	−16.4226***	243.316***	279.050***	1.85900**
TARIFF	−32.6677***	−33.6984***	410.950***	221.048***	1.62236*

Table

Author (s), time period, countries	Variables and source	Objective/methodology	Major findings
Abelinsky & Hilbert Time period: 1995–2008 Countries: 122	Variables: Telecommunication quantity, quality, mobile telephony, internet services, bilateral trade, regional trade agreements, GDP, population, currency, language, distance, colony, legal origin, border, world bank governance, paved roads. Source (s): WDI, CEPII and ITU.	Objective: To investigate whether there is a presence of any differential effect of ICT quantity (subscriptions) and quality (bandwidth) on international trade for both the developing as well and developed countries. Methodology: The BB methodology with the inclusion of time dummies in a fixed effects framework.	Found a significant and positive correlation between ICT quality as well as quantity and exports. The study also concluded that in the developing countries, the quality of data speed is more important as compared to the number of ICT subscriptions.
Kurihara, Y., & Fukushima, A. (2013). Time period: 2005 and 2010 Countries: 58	Variables: bilateral trade volume (export plus import), GDP, distance, number of Internet hosts Sources: Direction of Trade Statistics, World Factbook (CIA), IMF, Auroral Rays (http://chihuahua.s171.xrea.com/).	Objective: to study the relationship between the Internet effect and international trade. Methodology: gravity model	The study shows a strong relationship between networks and international trade.
Ahmad et al. (2011) Time period: 1980 to 2008 Countries: 36	Variables: Bilateral trade flows (exports), Real gross domestic product, Endowment, Distance, Exchange Rate, Border, Locked, Infrastructure Source (s): World Bank, World Development Indicators	Objective: to investigate and examine the effect of ICT Infrastructure on trade in Malaysia Methodology: The gravity model, pooled OLS, Random Effects Model (REM) and Fixed Effects Model (FEM).	The results shows that some of ICT components including mobile subscribers, fixed-line telephone subscribers, personal computers and internet users are significantly and positively related to the value of trade between Malaysia and its trading partners.
Salmani et al. (2013) Time period: 1990 to 2011 Countries: 135 developing countries	Variables: GDP, population, distance, exports and imports, Internet variable Source (s):WDI,	Objective: to examine the effect of internet on the notion of international trade in terms of service in developing countries. Methodology: unbalance panel data approach and modified gravity model	The study concluded that the internet users per hundred people have a positive impact on international trade in terms of services.
Time period: 1998–2007 Countries: 28 African countries	Variables: ICT infrastructure, Institutional quality, Educational Attainment, Trade Source (s): ITU, WDI, African Development indicators, UNCTAD	Objective: to investigate how information and communications technology (ICT) infrastructure impacts on intra-African trade Methodology: PLS-based structural equation analysis	The results concluded that telecom infrastructure had a direct as well as indirect impact on trade while educational attainment indirectly influence intra-African trade
Bojnec and Fertö (2009) Time period: 1995–2003 Countries: OECD countries.	Variables: GDP growth, population, number of Internet users, real GDP, growth in the number of Internet users, distance, and language. Source (s):WDI, CEPII.	Objective: examine the effect of the internet users on the bilateral trade in OECD countries Methodology: gravity model	The study concluded that internet stimulates manufacturing exports of the OECD countries.
Choi (2010) Time period: 1990–2006 Countries: 151	Variables: Service trade (export and import), GDP, Internet, population, financial depth Source: WDI, IMF	Objective: to investigate the impact of Internet on service trade Methodology: pooled ordinary least squares regression, a fixed-effects model, and a panel GMM	The results of the showed that the increase in Internet users per hundred people increased total service trade (export and import)
Chu and Guo (2019) Time period: 2001 to 2017 Countries: 11	Variables: growth rate of exports (imports) growth rate of total trade, telephone users, mobile phone users, internet users, growth rate of real GDP, population, ratio of fixed capital formation to GDP, ratio of government spending to GDP Source (s):WDI	Objective: to examine the effect of ICT on trade between PRC and ASEAN countries. Methodology Hausman test	The results showed that internet and cellular phones positively impacted the imports as well as the bilateral trade of ASEAN countries from PRC.
Choi & Yi, 2009 Time period: 1995–2007 Countries: 93	Variable: GDP, Internet, investment, Government, inflation. Source: IMF, UNCOMTRADE, UNDESA, UNESCO, UNPAN, WITSA, UIS, ITU	Objective: to how internet effects growth Methodology: pooled OLS, random effects, fixed effects, time fixed effects and panel GMM regressions.	The study found evidence that internet plays a significant and positive role in enhancing economic growth
Clarke and Wallsten (2006) Time period: Mid-late 1990s and early 2000s Countries: 179	Variables: results from regressions of total exports, exports to high-income countries, and exports to low-income countries on Internet use and additional control variables suggested in the literature on trade openness. Source: (ICTs), [ITU]	Objective: investigates the effect of the Internet on export behavior. Methodology: Cross-Sectional Gravity Model, standard gravity model specification	The study concluded that the developing countries tend to export more to countries with high internet penetration.
Clarke (2008) Firm-level data, mostly from small and medium-sized enterprises. 1999, 2002 and 2005	Variables: exports, internet users, internet hosts, entry restrictions for ISPs, population, area, GDP per capita, member of WTO, average tariff, political openness, remoteness. Source: Business Environment and Enterprise Performance Survey (BEEPS)	Objective: to examine whether internet access appears to impact the export performance of low and middle-income economies of Eastern Europe and Central Asia. Methodology: OLS, 2SLS, Cross-Sectional Gravity Model	The study concluded that there is a strong correlation between exporting and the internet access at the enterprise level of low and middle-income economies of Eastern Europe and Central Asia.
Demirkan et al. (2009) Time period: 1962 Countries: 175	Variables: GDP, internet, distance, border, language, colonial rel, colonizer. Source (s): U.N. Commodity Trade Statistics Database	Objective: to determine whether physical and cultural distance matters in how ICT affects bilateral trade flows Methodology: gravitational model	The results showed that greater bilateral trade flows tend to occur between countries which have higher Internet use.
Time period: 1995–1999 Countries: 31	Variables: Internet, financial depth, GDP, population, distance, language, adjacency. Source: WDI	Objectives: To determine whether the Internet has significantly affected international service provision in practice Methodology: Gravity model	The results indicated that development of internet has already begun facilitating increased exports of services to the United States.
Freund and Weinhold Time period: 1998–1999 Counties: 56	Variables: GDP, population, distance, adjacency, language, link, FTA, internet hosts. Source: WDI	Objectives: To determine whether the Internet has significantly affected international service provision in practice Methodology: Gravity model	Internet creates a global exchange for goods, thereby reducing market-specific sunk costs of exporting.
Kneller and Timmis (2016) Time period: 1998 and 2008, Countries: 100	Variables: fixed broadband, proportion of exporting firms, exports, sales, age, employment etc. Source: International Trade in Services Inquiry (ITIS)	Objectives: to find the impact of broadband use on the firm-extensive margin of UK service exports. Methodology: ADSL	The study found that there is a significant and positive effect of broadband use on the extensive margin of UK service exports.
Lin (2015) Time period: early 1990s to the year 2006 Countries: 200	Variables: log of trade (export) flow, gross domestic product (GDP) per capita and population, distance, language, border, Internet users per 100 people and dummies for various variables. Source: WDI	Objective: to investigate the impact of Internet use on the value of bilateral exports. Methodology: two-step system GMM method	The study concluded that the internet has a significant and positive effect on bilateral trade flow.
Liu and Nath (2013) Time period: 1995 to 2010 Countries: 40	Variables: growth of telecom investment, international Internet bandwidth, Internet subscriptions and hosts per 100 people, share of total exports/imports in GDP, per capita GDP, and pop growth Source (s): WDI, GDF, ITU	Objective: To examine and investigate whether ICT effects exports and imports in emerging markets Methodology: panel least square (PLS) method	The results showed that Internet subscriptions and hosts have a significant and positive impact on export and imports shares in emerging markets.
Time period: 1999 Countries: 62	Variables: value of exports, income, population, distance, language, transport infrastructure, technology, and knowledge Source (s): Statistics Canada, WDI, CIA, UNDP	Objective: to examine the impact of technology on trade for different levels of development in countries. Methodology: Gravity model	The study concluded that technology can be considered as a barrier to trade for those countries which have lower endowment levels.
Mattes et al. (2012) Time period: 1995–2007 Countries: 29	Variables: Exports, distance, border, language, GDP, transport, ICT development index. Source (s): IMF Dots, WDI, CEPII, ITU.	Objective: to assess the impact of ICT on the notion of international trade Methodology: Gravity model	ICT does have a significant and positive impact on EU trade.
Meijers (2014) Time period: 1990 until 2008 Countries: 162	Variables: per capita GDP log, Growth rate of per capita GDP, openness ratio Trade, Per capita internet use, Inflation, GCF, General government final consumption expenditure, Secondary School Enrollment, population, Land area, Number of telephone lines and Mobile cellular subscribers per 100 people Source (s):ITU, WDI	Objective: to examine the relationship between trade, internet use and economic growth. Methodology: simultaneous equation estimation to address the endogeneity issue	The study concluded that there is a positive and significant role of internet use to openness.
Michael (2011). Time period: 2003–2007. Countries: 162	Variables: Imports and exports of services, GDP per capita, Exchange rate, distance, labor force, number of internet users. Source (s): United Nations Services Trade Database, UN, WDI, World bank	Objective: to identify the major determinants of service trade in Africa Methodology: Gravity model	The results showed that service trade in Africa is significantly and positively influenced by its GDP per capita, labor force, internet facility, colonial history and common language as compared to other variables.
Nath and Liu (2017) Time period: 1995 to 2010 Countries: 40	Variables: Telecom investment growth, Internet Bandwidth, Internet subscriptions, Internet hosts, Per capita GDP growth, Population growth Source (s):WDI, GDF,	Objective: to examine the effects of ICT on international trade in the emerging markets Methodology: fixed effects panel data model	The study found that internet subscriptions and hosts tend to have a significant and positive effect on both exports and imports in the emerging markets.
Ozcan (2018) Time period: 2000–2014 Countries: 35	Variables: GDP, population, durance, ICT, border, language, colony, RTA, landlocked, island. Source (s): DOTS, IMF, WDI, CEPII, ITU, WTO.	Objective: to analyze the impacts ICT on trade between Turkey and its trading partners Methodology: augmented version of the gravity model	The study concluded that ICT is said to have a positive and significant impact on both Turkish trade to its trading partners.
Time period: 2000–2014 Countries: 120	Variables: Bilateral exports, Internet users, GDP, Bilateral distance, colony, border, language. Source (s):CEPII, UN Comtrade, World Bank.	Objective: to examine the impact of internet use on the aggregate trade flow. Methodology: structural gravity model using up to date PPML estimation technique	The results showed that internet use increased trade in countries where the level of ICT is same
Time period: 2004–2013 Countries: 55	Variables: bilateral exports, Internet users, Mobile phone subscriptions, Broadband subscriptions, GDP, Bilateral distance Source (s): IMF, WDI, ITU, World bank, CEPII.	Objective: to examine the impact of Internet, mobile phone, and broadband usage on bilateral flows for high-income, low- and middle-income economies Methodology: dynamic gravity model and panel data	The results showed that the effects of ICT use are sig. for trade flows between high-income countries and from high-income to low- and middle-income countries
Soeng (2020) Time period: 1996 to 2017 Countries: 84 trading partners of Cambodia	Variables: ICT per-capita income, population, foreign direct investment, exchange rate, trade freedom, Generalized System of Preference, border Source (s): IMF, UN, WDI, Heritage Foundation, Cambodia’s Ministry of Commerce, UNCTAD	Objective: to investigate the impact of ICT on the goods export performance in Cambodia. Methodology: Hausman-Taylor method	Results concluded that there is an impact and role of ICT in promoting the goods exports of Cambodia.
Tang (2006) Time period: 1960 to 2000 Country: US	Variables: GDP, GDP per capita, IT, tariff, distance, language, NAFTA, English, Imports Source: ITU	Objective: to determines how different forms of telecommunications affects the imports of differentiated goods in USA Methodology: fixed-effect model approach	Adoptions of fixed line telephones, mobile phones, and internet connection in the exporting countries have significant impact on US imports of differentiated goods
Timmis (2012) Time period: 1990–2010 Countries: 38 OECD countries	Variables: Internet Users, Broadband Connections, Any Fixed Line Connection, % Broadband Connections Mobile Phones, Computers, Telephone Lines, Bilateral aggregate export, GDP, distance, border, language, colony regional trade agreements, currency unions, Source (s):ITU, COMTRADE, CEPII	Objective: to investigate the relationship between the Internet technologies and trade in goods Methodology: gravity framework	The study found that due to an increase in adoption within country pairs, there was little significant effect on trade
Vemuri and Siddiqi (2009) Time period: 1985–2005 Countries: 62	Variables: Per capita GDP, distance, Contiguity, common language, colonial links, ICT, internet availability Source (s): IMF, ITU, CEPII, UN database.	Objective: to examine the effect of ICT and internet commercialization on the volume and intensity of international trade Methodology: Hausman-Taylor instrumental variable approach	The study found a positive and significant impact of ICT infrastructure and the availability of Internet on intl trade.
Time period: 2001 Countries: 93	Variables: Exports, Internet Users/Hosts, Entry Restrictions for ISPs, Monopoly for Data Lines/ISPs/Leased Lines, Population, Area, GDP per Capita, Oil Exporter, Political Openness, Remoteness. Source (s):COMTRADE, ITU, WDI, WTO	Objective: to examine whether the use of internet affects the export behavior Methodology: OLS, 2SLS.	The study concluded that internet access stimulated exports from poor countries to rich countries
Wheatley and Roe (2005) Time period: 1995–2003 Countries: 79	Variables: trade, GDP, population, distance, language, free trade association, internet penetration. Source (s): Foreign Agricultural Trade of the United States database of the Foreign Agricultural Service of the United States Department of Agriculture, Internet Software Corporation’s (ISC) survey, WDI, WTO, CIA	Objective: to examine the impacts of the internet on agricultural trade. Methodology: gravity model	The study concluded that internet augmented imports of various commodities to the United States
Xing (2018) Countries: 21 developing- and least-developed countries and 30 OECD countries.	Variables: trade, distance, language, colony, telephone, cellphone, broadband, internet security, internet user, shipping cost, GDP per capita Source (s):WDI, CEPII, OECD, INSEAD, WEF	Objective: to investigate the effect of Internet & e-commerce adoption on bilateral trade Methodology: augmented gravity model with fixed effects	The results showed that better access to the ICT and e-commerce adaption stimulated bilateral trade flows at various levels

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Data availability statement

Supportive data will be provided upon request by the corresponding authors.