Data and estimation methodology 1- Data

The basis concern of our study is to explore the relationship between public debt, economic growth, and income inequality with the transmission channel of corruption. We use annual data from 1996-2017 relating to BRI countries. The BRI is a newly established economic block and consist of countries from Asia, Europe, Africa, Oceania, and Latin America (see Appendix for list of countries). More than seventy countries are included in BRI. Due to data limitation, our sample restrict to only sixty countries.

Our main variables of interest are public debt and GDP growth. The corruption indices are sourced from Kaufmann et al. (2013) and Transparency International (TI). We include the two kinds of corruption indices to ensure robustness of results. Kaufmann et al. (2013) corruption index ranges from -2.5 (totally corrupt) to 2.5 (not corrupt). The value of Corruption Perceptions Index (CPI) of TI ranges from 0 (totally corrupt) to 10 (not corrupt). The scales of the two indices are reversed for estimation and maintain consistency between the two indices. We reverse the scale for Kaufmann corruption index with 5 for totally corrupt and 0 for not corrupt country. The TI is rescaled with 0 stands for not corrupt and ten totally corrupt.

Moreover, we include shadow economy as one of the controlled variables in our study. The reason for the inclusion of shadow economy is that corrupt countries are having large shadow economies. The shadow economies affect the level of economic growth and public debt. For shadow economies, we rely on the data of Schneider et al. (2010). Schneider et al. (2010) argue that shadow economies become large due to several elements. The elements consist of increasing taxation and higher regulation with lower institutional quality. They use the Multiple Indicator Multiple Cause (MIMIC) approach to estimate the shadow economy because the shadow economy cannot be measured directly. The data run from 1999-2007; therefore, the missing data is interpolated till 2017.

 

We include foreign direct investment (FDI) as one of our control variables. A plethora of literature shows FDI and economic growth relationship. Several macro-based articles on both developed and developing countries indicate a positive effect of FDI inflows (Olofsdotter1998; Reisen and Soto 2001). However, other studies report an adverse effect of FDI on economic growth (Mencinger2003; Carkovic& Levine 2005; Johnson 2006; Türkcan, Duman, and Yetkiner2008; Herzer 2012) or an inconclusive effect (De Mello 1999).

Moreover, infrastructure also plays an essential role in economic performance. We include global infrastructure index in this study as our control variable. The construction of the global infrastructure index is explained in Donaubauer et al. (2015). The index is based on transport, energy, ICT (internet and communication technologies), and financial indicators. The data is available from 1990–2010, the rest of it is interpolated. Furthermore, secondary school enrolment and inflation are used as a proxy for human capital and macroeconomic stability. Details of all variables along with their description statistics are given in Table 1.

Table 1. Data source and summary statistics

Variable	Notation	Obs.	Mean	SD	Min	Max	Comment
GDP growth (annual %)	grow	1,652	4.41	5.12	-37.14	54.15	WDI (2018)
Public Debt% of GDP	GD	955	5.3	0.4	2.3	6.5	WDI (2018)
Corruption Index Kaufmann et al. (ranges
from approximately 0 (no corruption) to 5	KCI	1,680	2.57	1.11	0.98	5	Kaufmann et al. (2013)
(high corruption)
Corruption Perceptions Index TI. (ranges	TI	1,680	2.98	1.43	0.00	8.8	Transparency International,
from 0 (not corrupt) to 10 (totally corrupt)							2013
Shadow economy% of GDP	shadow	1,510	3.4	0.5	0.7	4.2	Schneider et al. (2010)
Global infrastructure index	GINFRA	1,652	0.8	0.4	-2.3	1.7	Donaubaueret al. (2015)
Total debt service (% of GNI)	DS	1,064	1.5	0.8	2.3	3.6	WDI (2018)
Foreign direct investment, net inflows (%	FDI	1,680	3.7	0.2	2.3	4.5	WDI (2018)
of GDP)
Inflation	INF	1,680	7.8	0.3	2.3	9.8	WDI (2018)
GINI index	GINI	1,056	3.56	0.21	2.79	4.17	WDI(2018)
GDP per capita (constant 2010 US$)	GDPPC	1,652	8.51	1.30	5.1	11.19	WDI (2018)
Secondary school enrolment (gross %)	HC	1,653	4.89	0.28	-2.30	5.37	WDI (2018)
Gross capital formation (% of GDP)	GCF	1,652	4.92	0.21	-2.30	5.44	WDI (2018)
General government final consumption	GE	1,652	3.35	0.28	-2.30	4.49	WDI (2018)
expenditure (% of GDP)
Urban population (% of total)	URBAN	1655	1.64	0.20	1.07	1.95	WDI (2018)
Exports plus imports divided by GDP	OPEN	1,644	1.77	0.51	-0.77	2.33	WDI (2018
School enrollment, secondary (% gross)	HC	1,653	4.9	0.3	2.3	5.4	WDI (2018)

Note: All variables have been converted into a logarithmic form for the empirical estimation except the corruption indices.

Before proceeding further, we need to identify the order of integration of variables in order to avoid a possible problem of spurious regression. Table 3(see Appendix) reports the results of the unit root. For robustness check, we report four different kinds of tests’ results. All the results show that our variables are stationary at level.