Are the informal economy and cryptocurrency substitutes or complements?

Figures & data

Table 1. Variables definitions and sources.

Variable	Definition (observation; mean, std. dev.)	Source
CRYPTO	Percentage of the country’s population using or owning cryptocurrencies, (155; 11.32, 5.99)	Statista Global Consumer Surveys [1]
Informal1	Prevalence of the informal economy, measured via Multiple Indicators Multiple Causes (MIMIC) model-based estimates (as a percentage of official GDP), (155; 24.29, 11.27)	Elgin et al. (2021)
Informal2	Prevalence of the informal sector, measured via Dynamic General Equilibrium (DGE) model-based estimates (as a percentage of official GDP), (155; 21.92, 9.70)	Elgin et al. (2021)
EconFREE	Economic freedom, index values range from 0 to 10; higher values indicate greater economic freedom, (155; 7.36, 0.72)	Fraser Institute [2]
FINglobal	Financial globalization. A subcomponent of KOF globalization index with values ranging from 0 to 100. Higher values indicate greater financial openness, (155; 70.68, 17.55)	Gygli et al. (2019)
GDPpc	GDP per capita (in logs of constant 2015 international $), (155; 9.68, 1.11)	World Development Indicators [3]
FDI	Foreign direct investment, net inflows (% of GDP), (155; 0.62, 2.98)	World Development Indicators [3]
ECONuncertain	Economic uncertainty, measured as the standard deviation of inflation during the last three years, (155; 1.67, 2.23)	Authors’ calculations
EXCHrate	Official exchange rate, local currency units per US dollar. The values represent yearly averages, (155; 861.3, 3739.80)	World Development Indicators [3]
CBindependence	Central bank independence. A subcomponent of the central bank transparency index. The values range from 0 to 1, with higher values suggesting greater bank independence, (143; 0.86, 0.23)	Dincer, Eichengreen, and Geraats (2022)
ISLAND	Dummy variable identifying an island nation, (155; 0.15, 0.36)	Wikipedia [4]
CORRUPTION	Corruption Perceptions Index. The original index was rescaled so that higher values imply greater corruption, (155; 44.17, 19.56)	Transparency International [5]
COLONY	Dummy variable identifying a former British colony, (0.28; 0.45)	Treisman (2007)
REGION	Discrete variable varying from 1 to 7 according to the regional location of a country. Higher values represent countries in the Southern or Eastern hemispheres, (3.03; 1.50).	World Bank [6]

All observations are annual at the country level for the years 2018 to 2021.

[1] https://www.statista.com/statistics/1202468/global-cryptocurrency-ownership/. Accessed 22 September 2022. Information on cryptocurrency users is from 2019 to 2021. For the other variables, the information lags one time period (and, where data is not available, more than one time period).

[2] https://www.fraserinstitute.org/studies/economic-freedom. Accessed 23 September 2022.

[3] https://databank.worldbank.org/source/world-development-indicators. Accessed 22 September 2022.

[4] https://en.wikipedia.org/wiki/List_of_island_countries.

[5] https://www.transparency.org/en/cpi/2021. Accessed 23 September 2022.

[6] https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups. Accessed 19 September 2022.

Table 2. Spillovers from the underground economy to cryptocurrency: baseline models.

Dependent variable: CRYPTO
Model →	(2.1)	(2.2)	(2.3)	(2.4)	(2.5)	(2.6)
Informal1	0.259***	0.189***	0.233***	0.252***	0.190***	0.366***
	(0.093)	(0.040)	(0.066)	(0.059)	(0.026)	(0.083)
GDPpc	0.843	−0.051	0.166	−0.082	−1.526***	−0.985***
	(0.715)	(0.453)	(0.545)	(0.361)	(0.552)	(0.355)
EconFREE	−2.378	−2.659***	−2.196***	−2.216***	−3.137***	1.156
	(0.590)	(0.620)	(0.770)	(0.775)	(0.414)	(1.343)
EXCHrate	0.440**
	(0.170)
FDI		−0.255***
		(0.066)
ECONuncertain			0.270**
			(0.125)
ISLAND				−0.387
				(0.951)
FINglobal					0.148***
					(0.033)
CBindependence						1.455
						(1.811)
Observations	155	155	155	155	155	143
R-squared	0.889	0.892	0.861	0.899	0.935	0.886
F-test (p-value)	0.000	0.000	0.000	0.000	0.000	0.000
Number of countries	53	53	53	53	53	49

Table 1 for variable details. The estimates are based on panel least squares estimation. All the specifications include, but are not reported, a constant, regional, and year dummies. Standard errors, reported in parentheses, are robust against panel-specific heteroskedasticity and autocorrelation. *** p < 0.01, ** p < 0.05, * p < 0.1.

Table 3. Spillovers from the underground economy to cryptocurrency: accounting for possible reverse causality.

Dependent variable: CRYPTO
Model →	(3.1)	(3.2)	(3.3)	(3.4)	(3.5)	(3.6)
Informal1	0.248***	0.217**	0.242**	0.235***	0.296***	0.317***
	[0.055–0.441]	[0.023–0.411]	[0.046–0.437]	[0057–0.413]	[0.111–0.482]	[0.125–0.509]
	(0.098)	(0.099)	(0.100)	(0.091)	(0.095)	(0.098)
GDPpc	−0.116	−1.082	−1.145	−0.925	−1.742*	−0.466
	(0.839)	(0.790)	(0.779)	(0.797)	(1.019)	(0.736)
EconFREE	−0.009	−0.002	0.398	−0.396	−0.611	0.449
	(0.717)	(0.685)	(0.760)	(0.739)	(0.756)	(0.805)
EXCHrate	0.605***
	(0.211)
FDI		−0.308***
		(0.074)
ECONuncertain			0.176
			(0.166)
ISLAND				1.179
				(0.903)
FINglobal					0.106**
					(0.049)
CBindependence						0.309
						(1.763)
Observations	155	155	155	155	155	143
R-squared	0.411	0.399	0.375	0.378	0.382	0.443
Number of countries	53	53	53	53	53	49
First stage F-stat	14.45	13.94	12.97	13.55	15.05	12.12
Underidentification test^a	0.000	0.000	0.000	0.000	0.000	0.000
Overidentification test^b	0.584	0.614	0.654	0.877	0.299	0.766
Anderson-Rubin Wald test^c	0.060	0.103	0.069	0.067	0.003	0.005

Table 1 for variable details. Each specification uses instrumental variables to predict the values of Informal1. The instruments used are COLONY, REGION, and CORRUPTION.

Standard errors, reported in parentheses, are robust against clustering effects at the income per capita level of a country and panel-specific heteroskedasticity. Squared brackets contain 95% confidence interval. All the specifications include a constant and year dummies, but are not reported. *** p < 0.01, ** p < 0.05, * p < 0.1.

a.The numbers are the p-values of the Kleibergen-Paap rk LM statistics. The null hypothesis is that the first-stage equation is underidentified.

b.The numbers are the Hansen J statistics. The null hypothesis is the joint test that all instruments are valid.

c.Anderson-Rubin Wald test. It tests the null hypothesis that the coefficients of the endogenous regressors in the structural equation are jointly equal to zero and overidentifying restrictions are valid.

Table 4. Spillovers from the underground economy to cryptocurrency: robustness check with an alternate measure of the underground economy.

Dependent variable: CRYPTO
Model →	(4.1)	(4.2)	(4.3)	(4.4)	(4.5)	(4.6)
Informal2	0.314***	0.217***	0.261***	0.271***	0.218***	0.373***
	(0.064)	(0.047)	(0.070)	(0.055)	(0.027)	(0.078)
GDPpc	0.734	−0.115	−0.149	0.014	−1.593***	−0.809**
	(0.700)	(0.484)	(0.560)	(0.365)	(0.612)	(0.361)
EconFREE	−1.940***	−2.354***	−1.859***	−2.457***	−3.001***	0.425
	(0.642)	(0.629)	(0.653)	(0.672)	(0.402)	(1.096)
EXCHrate	0.378**
	(0.168)
FDI		−0.250***
		(0.065)
ECONuncertain			0.314***
			(0.118)
ISLAND				−0.189
				(1.127)
FINglobal					0.149***
					(0.036)
CBindependence						1.197
						(1.612)
Observations	155	155	155	155	155	143
R-squared	0.892	0.883	0.896	0.908	0.933	0.878
F-test (p-value)	0.000	0.000	0.000	0.000	0.000	0.000
Number of countries	53	53	53	53	53	49

See Table 2.

Table A1. Income classifications and regional distribution of sample countries.

Country	Income class	Region	Country	Income class	Region
Argentina	UME	LAC	Pakistan	LME	South Asia
Australia	HIE	EAP	Peru	UME	LAC
Austria	HIE	W. Europe	Philippines	LME	EAP
Belgium	HIE	W. Europe	Poland	HIE	W. Europe
Brazil	UME	LAC	Portugal	HIE	W. Europe
Canada	HIE	N. America	Romania	UME	EECA
Chile	HIE	LAC	Russia	UME	EECA
China	UME	EAP	Saudi Arabia	HIE	MENA
Colombia	UME	LAC	Singapore	HIE	EAP
Czechia	HIE	W. Europe	South Africa	UME	ESA
Denmark	HIE	W. Europe	South Korea	HIE	EAP
Dominican Republic	UME	LAC	Spain	HIE	W. Europe
Egypt	LME	MENA	Sweden	HIE	W. Europe
Finland	HIE	W. Europe	Switzerland	HIE	W. Europe
France	HIE	W. Europe	Thailand	UME	EAP
Germany	HIE	W. Europe	Turkey	UME	EECA
Greece	HIE	W. Europe	UAE	HIE	MENA
Hungary	HIE	W. Europe	UK	HIE	W. Europe
India	LME	South Asia	USA	HIE	N. America
Indonesia	LME	EAP	Vietnam	LME	EAP
Ireland	HIE	W. Europe
Israel	HIE	MENA
Italy	HIE	W. Europe
Japan	HIE	EAP
Kenya	LME	ESA
Lithuania	HIE	W. Europe
Malaysia	UME	EAP
Mexico	UME	LAC
Morocco	LME	MENA
Netherlands	HIE	W. Europe
New Zealand	HIE	EAP
Nigeria	LME	WCA
Norway	HIE	W. Europe

N = 53. The number of countries in specific models varies due to missing data.

This table uses information from United Nations World Economic Situation and Prospects 2022 annexure.

UME = upper-middle income economy; HIE = high income economy; LME = lower middle income economy; LIE = low income economy.

LAC = Latin America and the Caribbean; EAP = East Asia and Pacific; MENA = Middle East and North Africa; ESA = Eastern and Southern Africa; WCA = West and Central Africa; W. Europe = Western Europe; and EECA = Eastern Europe and Central Asia.

Table A2. Correlation matrix of key variables.

	CRYPTO	Informal1	Informal2
CRYPTO	1.00
Informal1	0.60	1.00
Informal2	0.57	0.99	1.00

See Table 1 for variable details.

N = 143.

Elgin, C., M. A. Kose, F. Ohnsorge, and S. Yu, 2021. Understanding Informality. CERP Discussion Paper 16497, Centre for Economic Policy Research, London.

 Google Scholar

Gygli, S., F. Haelg, N. Potrafke, and J. -E. Sturm. 2019. “The KOF Globalisation Index- Revisited.” The Review of International Organizations 14 (3): 543–574. doi:10.1007/s11558-019-09344-2.

 Web of Science ®Google Scholar

Dincer, N., B. Eichengreen, and P. Geraats. 2022. “Trends in Monetary Policy Transparency: Further Updates.” International Journal of Central Banking 18 (1): 331–348.

 Web of Science ®Google Scholar

Treisman, D. 2007. “What Have We Learned About the Causes of Corruption from ten Years of Cross-National Empirical Research?” Annual Review of Political Science 10: 211–244. doi:10.1146/annurev.polisci.10.081205.095418.

 Web of Science ®Google Scholar