Do remittances increase tax revenues in developing countries? Evidence from the threshold regression models

ABSTRACT

This paper provides original econometric evidence on the effect of remittances on tax revenues in developing countries. In applying the system generalized methods of moments (SGMM) and the panel threshold regression method to a sample of 83 developing countries over the period 1990–2019, we find two major results. First, remittances positively and significantly affect tax revenues in developing countries. Second, as far as the nonlinear relationship is concerned, we find two extreme regimes with a smooth shift characterizing the remittances-tax revenue nexus, with respect to conditional variables; remittance effects are positive and significant under the first regime and negative and significant under the second. Furthermore, our findings show that the nonlinear relationship between remittances and tax revenues depends on the levels of informal economy, corruption, and growth volatility.

KEYWORDS:

• Remittances
• tax revenues
• nonlinear relationship
• panel threshold regression

JEL CLASSIFICATION:

• F22
• E62
• B23

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ Further details of the method can be found in Seo and Shin (2016) and Seo et al. (2019).

² Albania, Antigua and Barbuda, Argentina, Armenia, Azerbaijan, Bahamas, Barbados, Belarus, Belize, Bhutan, Bosnia and Herzegovina, Botswana, Brazil, Bulgaria, Burkina Faso, Cabo Verde, Cambodia, Cameroon, Chile, China, Democratic Republic of Congo, Congo, Costa Rica, Cote d’Ivoire, Croatia, Cuba, Czech Republic, Dominican Republic, Ecuador, El Salvador, Equatorial Guinea, Estonia, Eswatini, Ethiopia, Fiji, Georgia, Ghana, Greece, Guatemala, Honduras, Hungary, India, Indonesia, Iran, Jamaica, Jordan, Kenya, Korea Republic, Kyrgyzstan, Lao, Latvia, Lesotho, Lithuania, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Mauritania, Mauritius, Mexico, Moldova, Mongolia, Montenegro, Morocco, Myanmar, Namibia, Nicaragua, Niger, Nigeria, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Romania, Russia, Rwanda, Senegal, Serbia, Seychelles, Singapore, Slovakia, Slovenia, South Africa, Tajikistan, Thailand, Togo, Tunisia, Turkey, Uganda, Ukraine, Uruguay, Uzbekistan, Vietnam, Yemen.

³ We recall that the corruption’s indicator ranges from o to 1, where a value close to 1 reflects a high level of corruption.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.