Digitalization and tax evasion: the moderation effect of corruption

Figures & data

Table 1. Variable definition and sources.

Variables	Definition	Proxy
Dependent variable Tax Evasion	The shadow economy is used as a proxy for tax evasion: Shadow economy as percent of total annual GDP (IMF:(Medina et al., 2018))	TE
Independent variables: Digitalization Adoption index
Digitalization Adoption index	“Worldwide index that measures countries’ digital adoption across three dimensions of the economy: people, government, and business. The index covers 180 countries on a 0–1 scale”. The overall DAI is the simple average of three sub-indexes. Each sub-index comprises technologies necessary for the respective agent to promote development in the digital era: increasing productivity and accelerating broad-based growth for business DAIB, expanding opportunities, and improving welfare for people DAIP, and increasing efficiency and accountability of service delivery for government DAIG. https://www.worldbank.org/en/publication/wdr2016/Digital-Adoption-Index	DAI
Control variables
Inflation	“Inflation, as measured by the consumer price index, reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly. The Laspeyres formula is generally used.” https://www.worldbank.org	INFLAT
Unemployment	“Unemployment refers to the share of the labor force that is without work but available for and seeking employment.” https://www.worldbank.org	UNEMP
Economic freedom	“The Overall index of economic freedom has ten components grouped into four broad categories: Rule of Law; Limited Government; Regulatory Efficiency and Open Markets. The overall economic freedom is scored on a scale of 0 to 100, where 100 represents the maximum freedom.” https://www.heritage.org	ECOFR
Urbanization	“Urban population refers to people living in urban areas as defined by national statistical offices. The data are collected and smoothed by United Nations Population Division”. https://population.un.org/	URBAN

Source: Authors' Calculations.

Table 2. Summary of descriptive statistics of dependent and independent variables included in our model.

	Mean			Std. Dev.			Min			Max
Variables	All	High COR	Low COR	All	High COR	Low COR	All	High COR	Low COR	All	High COR	Low COR
TE	26.75	33.80	19.81	10.57	7.60	8.24	5.40	13.20	5.40	47.90	47.90	43.30
DAI	0.54	0.41	0.67	0.20	0.15	0.15	0.15	0.15	0.24	0.87	0.74	0.87
DAIB	0.60	0.47	0.72	0.21	0.16	0.16	0.13	0.13	0.28	0.97	0.74	0.97
DAIP	0.47	0.30	0.64	0.25	0.18	0.18	0.01	0.01	0.15	0.90	0.70	0.90
DAIG	0.57	0.47	0.66	0.19	0.18	0.17	0.04	0.04	0.27	0.98	0.84	0.98
INFLAT	3.79	6.10	1.51	6.74	8.54	2.89	−1.80	−1.80	−1.50	55.40	55.40	17.50
UNEMP	7.28	6.35	8.20	5.34	4.82	5.69	0.15	0.51	0.15	26.54	25.41	26.54
ECOFR	62.11	55.35	68.76	9.46	6.19	7.11	38.00	38.00	51.00	88.00	71.00	88.00
URBAN	59.91	47.99	71.66	22.62	20.60	17.97	12.39	12.39	17.06	100.00	100.00	100.00

Note: The number of observations is 133 total sample, 66 High-corruption countries, and 67 low-corruption.

Source: Authors' Calculations.

Table 3. Correlation matrix.

	TE	DAI	DAIB	DAIP	DAIG	INFLAT	UNEMP	ECOFR	URBAN
TE	1.000
DAI	−0.7250*	1.000
	0.000
DAIB	−0.7173*	0.9444*	1.000
	0.000	0.000
DAIP	−0.7353*	0.9565*	0.9152*	1.000
	0.000	0.000	0.000
DAIG	−0.5216*	0.8411*	0.6652*	0.6827*	1.000
	0.000	0.000	0.000	0.000
INFLAT	0.3736*	−0.2887*	−0.3161*	−0.2661*	−0.2101*	1.000
	0.000	0.001	0.000	0.002	0.015
UNEMP	0.051	0.052	0.097	0.060	−0.020	−0.021	1.000
	0.562	0.551	0.266	0.491	0.821	0.811
ECOFR	−0.7269*	0.7834*	0.7301*	0.7727*	0.6390*	−0.3395*	−0.009	1.000
	0.000	0.000	0.000	0.000	0.000	0.000	0.923
URBAN	−0.5302*	0.7542*	0.7308*	0.7908*	0.5258*	−0.2014*	0.083	0.6127*	1.000
	0.000	0.000	0.000	0.000	0.000	0.020	0.345	0.000

Source: Authors' Calculations.

Table 4. Regression results of digitalization and tax evasion.

	M1	M2	M3	M4
DAI	−23.29***
	(5.72)
DAIB		−22.25***
		(4.91)
DAIP			−22.98***
			(4.64)
DAIG				−3.649
				(4.25)
INFLAT	0.210**	0.176*	0.223**	0.230**
	(0.09)	(0.09)	(0.09)	(0.10)
UNEMP	0.138	0.174	0.144	0.116
	(0.11)	(0.11)	(0.11)	(0.12)
ECOFR	−0.409***	−0.449***	−0.377***	−0.618***
	(0.10)	(0.09)	(0.10)	(0.10)
URBAN	0.021	0.0215	0.0598	−0.0613*
	(0.04)	(0.04)	(0.04)	(0.04)
_cons	61.74***	64.65***	55.47***	69.17***
	(4.90)	(4.57)	(5.24)	(4.81)
N	133	133	133	133
R-sq	0.613	0.623	0.633	0.565
adj. R-sq	0.597	0.608	0.619	0.547
Mean VIF	2.2	1.95	2.32	1.57

Standard errors in parentheses *p < 0.10, **p < 0.05, ***p < 0.01.

Source: Authors' Calculations.

Figure 1. The relationship between digitalization and tax evasion in low corruption and high corruption countries.

Source: Authors' Calculations.

Table 5. Regression results of digitalization and tax evasion in high-corruption and low-corruption countries.

	M5	M6	M7	M8	M9	M10	M11	M12
	High COR	Low COR	High COR	Low COR	High COR	Low COR	High COR	Low COR
DAI	−17.87**	−26.30***
	(8.53)	(7.37)
DAIB			−16.34**	−26.00***
			(7.19)	(6.33)
DAIP					−12.59*	−30.41***
					(7.30)	(6.16)
DAIG							−7.414	−3.24
							(6.51)	(5.17)
INFLAT	0.155	0.152	0.13	0.061	0.174	−0.0477	0.149	0.361
	(0.11)	(0.26)	(0.11)	(0.25)	(0.11)	(0.25)	(0.11)	(0.27)
UNEMP	0.258	−0.0142	0.301	0.0443	0.239	−0.0123	0.213	0.051
	(0.19)	(0.14)	(0.19)	(0.13)	(0.19)	(0.13)	(0.20)	(0.15)
ECOFR	−0.134	−0.399***	−0.197	−0.388***	−0.214	−0.335***	−0.206	−0.532***
	(0.18)	(0.13)	(0.16)	(0.13)	(0.17)	(0.13)	(0.18)	(0.14)
URBAN	0.0569	−0.00255	0.054	−0.00248	0.0594	0.0489	0.0102	−0.101*
	(0.05)	(0.06)	(0.05)	(0.05)	(0.06)	(0.05)	(0.05)	(0.05)
_cons	43.29***	65.03***	47.07***	64.98***	43.97***	59.03***	45.96***	64.81***
	(8.68)	(8.34)	(8.20)	(8.12)	(8.84)	(7.82)	(8.89)	(9.23)
N	66	67	66	67	66	67	66	67
R-sq	0.174	0.589	0.184	0.611	0.155	0.645	0.132	0.506
adj. R-sq	0.105	0.555	0.116	0.579	0.085	0.616	0.06	0.466

Standard errors in parentheses *p < 0.10, **p < 0.05, ***p < 0.01.

Source: Authors' Calculations.

Figure 2. The moderation effect of corruption on the relationship between digitalization and tax evasion (interaction plot).

Source: Authors' Calculations.

Table 6. Testing the moderation effect of corruption on the relationship between digitalization and tax evasion.

	M13	M14	M15	M16
	TE	TE	TE	TE
DAI	−11.79*
	(6.55)
COC	5.4	4.886	4.734	−2.741
	(4.63)	(4.55)	(3.39)	(4.60)
COC*C.DAI	−19.36**
	(7.89)
DAIB		−12.41**
		(5.74)
COC*DAIB		−17.31**
		(7.18)
DAIP			−10.66*
			(5.52)
COC*DAIP			−19.34***
			(6.44)
DAIG				−0.783
				(5.32)
COC*DAIG				−5.893
				(7.21)
INFLAT	0.153*	0.128	0.152*	0.178*
	(0.09)	(0.09)	(0.09)	(0.09)
UNEMP	0.132	0.182	0.117	0.187
	(0.11)	(0.11)	(0.11)	(0.12)
ECONOFREE	−0.262**	−0.277***	−0.253**	−0.430***
	(0.11)	(0.10)	(0.10)	(0.11)
URBAN	0.0293	0.0315	0.0529	−0.0424
	(0.04)	(0.04)	(0.04)	(0.03)
_CONS	49.99***	51.46***	46.76***	57.72***
	(5.55)	(5.42)	(5.55)	(5.62)
N	133	133	133	133
R-SQ	0.655	0.666	0.672	0.605
ADJ. R-SQ	0.636	0.648	0.653	0.583

Source: Authors' Calculations.

 Table A1. Data collected for the countries included in study sample.

Country	TE	DAI	DAIB	DAIP	DAIG	INFLAT	UNEMP	ECOFR	URBAN	AGE	High or Low Corruption (Median-0.24)
Albania	27.8	0.61	0.62	0.46	0.74	−0.4	15.42	66	58.42	12.96	High
Algeria	32.9	0.43	0.50	0.42	0.38	6.4	10.2	50	71.46	6.01	High
Angola	40.6	0.34	0.41	0.13	0.46	30.7	7.2	49	64.15	2.26	High
Armenia	36.7	0.62	0.71	0.48	0.67	−1.4	17.62	67	63.08	10.98	High
Australia	11.8	0.71	0.77	0.78	0.59	1.3	5.71	80	85.8	15.14	Low
Austria	7.4	0.86	0.88	0.87	0.85	0.9	6.01	72	57.9	18.91	Low
Bahamas	27.6	0.53	0.74	0.50	0.35	−0.3	12.7	71	82.83	6.88	Low
Bahrain	15.6	0.79	0.75	0.84	0.77	2.8	1.38	74	89.09	2.34	Low
Bangladesh	26.6	0.37	0.36	0.19	0.57	5.5	4.35	53	35.08	5.14	High
Belarus	39.2	0.59	0.74	0.65	0.39	11.8	5.84	49	77.66	14.42	High
Belgium	16.9	0.78	0.85	0.73	0.76	2	7.83	68	97.92	18.37	Low
Benin	44.9	0.22	0.33	0.15	0.20	−0.8	2.52	59	46.23	3.24	High
Bhutan	20.6	0.44	0.47	0.37	0.50	3.2	2.42	60	39.43	5.84	Low
Bosnia and Herzegovina	28.9	0.60	0.68	0.47	0.64	−1.6	25.41	59	47.52	15.08	High
Botswana	25.6	0.47	0.48	0.47	0.47	2.8	17.62	71	67.93	3.96	Low
Brazil	34.6	0.68	0.68	0.55	0.82	8.7	11.6	57	86.04	8.3	High
Brunei	30.3	0.63	0.69	0.53	0.66	−0.3	8.47	67	76.99	4.3	Low
Bulgaria	24	0.63	0.69	0.62	0.57	−0.8	7.58	66	74.33	20.43	Low
Burkina Faso	34	0.24	0.28	0.15	0.27	0.4	5.6	59	28.13	2.41	Low
Burundi	36.4	0.26	0.31	0.06	0.42	5.6	1.53	54	12.39	2.14	High
Cameroon	29.3	0.30	0.28	0.15	0.46	0.9	3.47	54	55.18	2.73	High
Canada	12.3	0.69	0.79	0.69	0.60	1.4	7	78	81.3	16.46	Low
Cape Verde	28.6	0.43	0.49	0.43	0.38	−1.4	12.1	67	64.78	4.49	Low
Central African Republic	35.9	0.15	0.32	0.01	0.11	4.9	4.25	45	40.62	2.83	High
Chad	39.9	0.23	0.29	0.06	0.34	−0.8	1.89	46	22.68	2.45	High
Chile	16.9	0.76	0.82	0.56	0.89	3.8	6.74	78	87.42	10.88	Low
Colombia	30	0.64	0.67	0.48	0.76	7.5	8.69	71	80.11	7.94	High
Costa Rica	21.4	0.66	0.68	0.68	0.63	0	8.6	67	77.74	8.97	Low
Croatia	23.6	0.65	0.75	0.58	0.61	−1.1	13.1	59	56.4	19.62	Low
Cyprus	26.7	0.68	0.82	0.77	0.44	−1.4	12.95	69	66.88	13.12	Low
Czech Republic	12.3	0.72	0.86	0.66	0.65	0.7	3.95	73	73.57	18.52	Low
Democratic Rep. of the Congo	44.5	0.21	0.18	0.05	0.40	2.9	4.35	46	43.31	3.02	High
Denmark	12.1	0.79	0.92	0.90	0.56	0.3	5.99	75	87.64	19.42	Low
Dominican Republic	27.5	0.50	0.52	0.37	0.60	1.6	7.28	61	79.44	6.69	High
Ecuador	31.3	0.57	0.68	0.39	0.63	1.7	4.6	49	63.53	6.76	High
Egypt	30	0.53	0.49	0.38	0.71	13.8	12.41	56	42.73	5.15	High
El Salvador	39.1	0.50	0.56	0.40	0.55	0.6	4.42	65	70.5	7.93	High
Equatorial Guinea	35.6	0.19	0.38	0.13	0.04	1.4	8.24	44	71.14	2.51	High
Estonia	20.9	0.83	0.85	0.80	0.85	0.1	6.76	77	68.56	19.06	Low
Ethiopia	27	0.27	0.27	0.15	0.40	6.6	2.17	52	19.87	3.47	High
Fiji	24.4	0.46	0.54	0.38	0.47	3.9	4.32	59	55.24	5.14	Low
Finland	11.4	0.81	0.92	0.83	0.67	0.4	8.82	73	85.28	20.79	Low
France	12.2	0.75	0.77	0.73	0.76	0.2	10.04	62	79.92	19.28	Low
Gambia	45.3	0.36	0.40	0.29	0.39	7.2	9.24	57	59.92	2.64	High
Germany	10.7	0.84	0.87	0.78	0.87	0.5	4.12	74	77.22	21.29	Low
Ghana	33.2	0.45	0.42	0.39	0.55	17.5	5.45	63	54.75	3.03	Low
Greece	25.4	0.61	0.71	0.68	0.42	−0.8	23.54	53	78.39	21.13	Low
Guatemala	43.4	0.52	0.57	0.33	0.67	4.4	2.58	62	50.32	4.59	High
Guinea	31.8	0.21	0.13	0.15	0.34	8.2	4.33	53	35.46	2.89	High
Guinea-Bissau	29.1	0.26	0.30	0.10	0.38	1.5	2.97	52	42.53	2.72	High
Guyana	24.8	0.36	0.57	0.21	0.29	0.8	13.69	55	26.48	5.97	High
Haiti	47.9	0.25	0.31	0.12	0.32	11.5	13.86	51	53.4	4.73	High
Honduras	42.6	0.43	0.51	0.27	0.50	2.7	6.73	58	55.81	4.44	High
Hungary	20.5	0.69	0.77	0.65	0.65	0.4	5.12	66	70.78	18	Low
Iceland	11.6	0.74	0.97	0.82	0.42	1.7	2.98	73	93.74	14.07	Low
India	19	0.51	0.50	0.23	0.80	4.9	5.51	56	33.18	5.79	High
Indonesia	21.6	0.46	0.42	0.41	0.54	3.5	4.3	59	53.99	5.52	High
Ireland	9.7	0.66	0.83	0.65	0.50	0	8.37	77	62.74	13.15	Low
Israel	17.6	0.79	0.77	0.74	0.85	−0.5	4.8	71	92.26	11.47	Low
Italy	20.6	0.77	0.75	0.68	0.87	−0.1	11.69	61	69.86	22.23	Low
Jamaica	31.7	0.50	0.58	0.42	0.49	2.4	13.19	68	55.1	8.6	Low
Japan	11.1	0.84	0.76	0.84	0.91	−0.1	3.1	73	91.46	26.59	Low
Jordan	14.9	0.55	0.50	0.57	0.58	−0.8	15.28	68	90.51	3.78	Low
Kazakhstan	37.7	0.67	0.60	0.57	0.84	14.5	4.96	64	57.26	6.92	High
Kenya	26	0.45	0.57	0.20	0.59	6.3	2.76	58	26.1	2.18	High
Kuwait	19.4	0.64	0.73	0.67	0.50	3.2	2.16	63	100	2.24	High
Kyrgyzstan	30.6	0.50	0.61	0.35	0.54	0.4	7.21	60	35.94	4.36	High
Laos	24.3	0.26	0.34	0.17	0.27	1.6	0.67	50	33.74	3.91	High
Latvia	18.8	0.73	0.77	0.71	0.71	0.1	9.64	70	68.02	19.68	Low
Lebanon	28.9	0.57	0.67	0.56	0.49	−0.8	6.26	60	88.27	6.52	High
Lesotho	28.5	0.29	0.30	0.25	0.32	6.6	24.3	51	27.32	4.84	Low
Liberia	38.3	0.24	0.30	0.14	0.29	8.8	3.08	52	50.25	3.2	High
Lithuania	21	0.79	0.80	0.75	0.83	0.9	7.86	75	67.37	18.97	Low
Luxembourg	8.7	0.86	0.94	0.87	0.77	0.3	6.29	74	90.46	14.04	Low
Malawi	33.9	0.26	0.39	0.07	0.32	21.7	5.79	52	16.51	2.65	High
Malaysia	28.8	0.69	0.55	0.64	0.87	2.1	3.44	72	74.84	6.2	Low
Maldives	25	0.51	0.64	0.58	0.31	0.5	6.12	54	38.95	3.86	High
Mali	33.4	0.29	0.28	0.22	0.37	−1.8	7.6	57	40.78	2.52	High
Malta	20.1	0.86	0.94	0.79	0.84	0.6	4.69	67	94.48	19.38	Low
Mauritania	30.8	0.34	0.38	0.24	0.39	1.5	10.31	55	51.96	3.12	High
Mauritius	21.5	0.62	0.63	0.57	0.65	1	6.81	75	40.91	10.44	Low
Mexico	28.8	0.60	0.63	0.44	0.74	2.8	3.86	65	79.58	6.87	High
Moldova	38.2	0.61	0.70	0.55	0.57	6.4	4.19	57	42.51	10.42	High
Mongolia	15.2	0.54	0.65	0.35	0.61	0.7	7.24	59	68.3	3.92	High
Morocco	30.3	0.56	0.60	0.42	0.65	1.6	9.3	61	61.36	6.54	Low
Mozambique	39.5	0.25	0.26	0.17	0.33	17.4	3.38	53	34.93	2.9	High
Namibia	24.5	0.38	0.50	0.34	0.32	6.7	23.35	62	47.96	3.7	Low
Nepal	34	0.37	0.35	0.25	0.50	8.8	3.05	51	18.94	5.6	High
Netherlands	9.1	0.84	0.91	0.80	0.81	0.3	6.01	75	90.64	18.37	Low
New Zealand	10.1	0.71	0.77	0.79	0.56	0.6	5.08	82	86.4	15	Low
Nicaragua	34.1	0.46	0.50	0.37	0.50	3.5	3.9	59	58.09	4.88	High
Niger	38	0.16	0.24	0.06	0.18	1.7	0.51	54	16.29	2.58	High
Norway	13.1	0.80	0.88	0.81	0.72	3.5	4.68	71	81.49	16.59	Low
Oman	18.4	0.65	0.68	0.65	0.63	1.1	3.27	67	82.5	2.31	Low
Pakistan	31.7	0.40	0.47	0.16	0.57	3.8	3.78	56	36.23	4.32	High
Papua New Guinea	29.1	0.34	0.55	0.09	0.38	6.7	2.53	53	13.05	3.31	High
Paraguay	32.3	0.54	0.62	0.37	0.64	4.1	5.26	62	61.03	6.06	High
Philippines	35.4	0.49	0.57	0.44	0.47	1.3	2.69	63	46.47	4.76	High
Poland	20.4	0.69	0.76	0.68	0.63	−0.7	6.16	69	60.18	16.34	Low
Portugal	17.1	0.79	0.76	0.73	0.87	0.6	11.07	65	64.09	21.16	Low
Qatar	16.8	0.71	0.76	0.76	0.60	2.7	0.15	71	99.01	1.11	Low
Republic of the Congo	43.7	0.31	0.36	0.22	0.35	3.2	9.85	43	66	2.61	High
Romania	23.8	0.64	0.65	0.57	0.72	−1.5	5.9	66	53.9	17.42	Low
Russia	38.4	0.74	0.71	0.70	0.82	7	5.56	51	74.16	13.88	High
Rwanda	28.5	0.43	0.42	0.19	0.67	7.2	1.11	63	17.06	2.77	Low
Saudi Arabia	14.8	0.67	0.68	0.72	0.60	2.1	5.65	62	83.4	3.11	Low
Senegal	43.3	0.35	0.39	0.23	0.43	0.8	6.7	58	46.3	3.07	Low
Sierra Leone	36.6	0.27	0.20	0.20	0.42	10.9	4.59	52	41.23	2.99	High
Singapore	10.5	0.87	0.85	0.80	0.96	−0.5	4.08	88	100	9.77	Low
Slovakia	13.2	0.69	0.76	0.67	0.64	−0.5	9.67	67	53.81	14.55	Low
Slovenia	20.2	0.72	0.86	0.63	0.65	−0.1	8	61	54.02	18.48	Low
South Africa	27.3	0.64	0.69	0.50	0.73	6.6	26.54	62	65.34	5.12	Low
South Korea	22.7	0.86	0.75	0.84	0.98	1	3.65	72	81.56	13.34	Low
Spain	21.3	0.77	0.78	0.67	0.84	−0.2	19.64	69	79.84	18.91	Low
Sri Lanka	36.6	0.48	0.44	0.38	0.61	4	4.24	60	18.31	9.77	High
Suriname	35.3	0.49	0.52	0.62	0.32	55.4	7.17	54	66.04	6.71	High
Swaziland	41.6	0.32	0.46	0.20	0.28	7.8	22.72	60	23.46	3.99	High
Sweden	10.9	0.83	0.94	0.86	0.70	1	6.99	72	86.85	19.84	Low
Switzerland	5.4	0.82	0.89	0.89	0.69	−0.4	4.92	81	73.74	18.23	Low
Tajikistan	38.7	0.32	0.42	0.24	0.32	6	6.9	51	26.85	2.96	High
Tanzania	47.1	0.34	0.28	0.17	0.57	5.2	2.08	59	32.33	2.56	High
Togo	31.6	0.25	0.37	0.14	0.24	1.3	2.95	54	40.63	2.83	High
Trinidad and Tobago	31.7	0.59	0.64	0.57	0.55	3.1	3.21	63	53.25	10.01	Low
Tunisia	35.6	0.56	0.61	0.46	0.60	3.6	15.51	58	68.35	7.83	Low
Turkey	29.1	0.63	0.68	0.43	0.79	7.8	10.84	62	74.13	8.05	Low
Uganda	29.4	0.34	0.32	0.14	0.56	5.4	1.83	59	22.62	1.9	High
Ukraine	43	0.54	0.67	0.47	0.47	13.9	9.35	47	69.15	15.89	High
United Arab Emirates	22.7	0.82	0.78	0.80	0.89	1.6	1.64	73	85.96	0.99	Low
United Kingdom	9.7	0.76	0.90	0.80	0.59	1	4.81	76	82.89	18.16	Low
USA	5.9	0.75	0.78	0.73	0.73	1.3	4.87	75	81.86	15.03	Low
Vietnam	13.2	0.52	0.59	0.43	0.54	2.7	1.85	54	34.51	6.82	High
Zambia	40.4	0.34	0.33	0.18	0.52	17.9	10.88	59	42.44	2.07	High
Zimbabwe	46.5	0.33	0.43	0.21	0.35	−1.5	5.24	38	32.3	2.86	High

Sources: https://www.worldbank.org, Medina et al., 2018, https://www.heritage.org, https://population.un.org.

Medina, L., Schneider, F., & Fedelino, A. (2018). Shadow economies around the world: What did we learn over the last 20 years? International Monetary Fund.

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