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      The effect of remittance and volatility in remittances on macroeconomic performance in Africa: any lessons for COVID-19?

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          Abstract

          Premised on the World Bank’s projection of a 20% fall in global remittances due to the effect of the COVID-19 pandemic, there have been concerns that remittance-dependent countries may be excessively affected. In this study, we explore the link between remittance, remittance volatility and macroeconomic performance to make a case for the potential impact of the COVID-19 pandemic. Specifically, the study examined the impact of remittance volatility on some macroeconomic variables [real gross domestic product (RGDP), consumption, investment, export and exchange rate] in a panel of seven African countries with the highest remittance–GDP ratio. This was done within a fixed effects and random effects model, using annual secondary data from 2004 to 2018. Our results show that remittance volatility exerts a negative but insignificant impact on RGDP, consumption, investment, export and exchange rate; while remittances itself has positive significant impact on RGDP, consumption and investment. Based on these findings, while any COVID-19-induced volatility in remittances flow into Africa may yield negative macroeconomic consequences, it is not likely to significantly affect the macroeconomic fundamentals of the most remittance-dependent African countries due to strong kinsmanship and the altruistic nature of remitting African migrants.

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          Most cited references36

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          Postwar U.S. Business Cycles: An Empirical Investigation

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            Is Open Access

            Multicollinearity and misleading statistical results

            Jong Kim (2019)
            Multicollinearity represents a high degree of linear intercorrelation between explanatory variables in a multiple regression model and leads to incorrect results of regression analyses. Diagnostic tools of multicollinearity include the variance inflation factor (VIF), condition index and condition number, and variance decomposition proportion (VDP). The multicollinearity can be expressed by the coefficient of determination (Rh 2) of a multiple regression model with one explanatory variable (Xh ) as the model’s response variable and the others (Xi [i≠h] as its explanatory variables. The variance (σh 2) of the regression coefficients constituting the final regression model are proportional to the VIF ( 1 1 - R h 2 ) . Hence, an increase in Rh 2 (strong multicollinearity) increases σh 2. The larger σh 2 produces unreliable probability values and confidence intervals of the regression coefficients. The square root of the ratio of the maximum eigenvalue to each eigenvalue from the correlation matrix of standardized explanatory variables is referred to as the condition index. The condition number is the maximum condition index. Multicollinearity is present when the VIF is higher than 5 to 10 or the condition indices are higher than 10 to 30. However, they cannot indicate multicollinear explanatory variables. VDPs obtained from the eigenvectors can identify the multicollinear variables by showing the extent of the inflation of σh 2 according to each condition index. When two or more VDPs, which correspond to a common condition index higher than 10 to 30, are higher than 0.8 to 0.9, their associated explanatory variables are multicollinear. Excluding multicollinear explanatory variables leads to statistically stable multiple regression models.
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              The impact of terms of trade and real exchange rate volatility on investment and growth in sub-Saharan Africa

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                Author and article information

                Contributors
                awodesegun@gmail.com , ss.awode@niser.gov.ng
                akpaemeka@gmail.com
                okwua@babcock.edu.ng
                Journal
                SN Bus Econ
                SN Business & Economics
                Springer International Publishing (Cham )
                2662-9399
                27 September 2021
                2021
                : 1
                : 10
                : 131
                Affiliations
                [1 ]GRID grid.412320.6, ISNI 0000 0001 2291 4792, Department of Economics, , Olabisi Onabanjo University, ; P.M.B 2002, Ago-Iwoye, Ogun State Nigeria
                [2 ]Economic and Business Policy Department, Nigerian Institute of Social and Economic Research (NISER), Ibadan, Nigeria
                [3 ]GRID grid.9582.6, ISNI 0000 0004 1794 5983, Centre for Econometrics and Allied Research, , University of Ibadan, ; Ibadan, Nigeria
                [4 ]GRID grid.442581.e, ISNI 0000 0000 9641 9455, Department of Economics, , Babcock University, ; Ilishan, Nigeria
                Author information
                http://orcid.org/0000-0003-3793-1805
                http://orcid.org/0000-0002-2566-3156
                Article
                138
                10.1007/s43546-021-00138-6
                8475383
                d53203e2-3b2b-489f-93f0-42facd77dfd0
                © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 29 March 2021
                : 23 August 2021
                Categories
                Original Article
                Custom metadata
                © Springer Nature Switzerland AG 2021

                remittances,remittance volatility,macroeconomy,covid-19,f24,f62,j61
                remittances, remittance volatility, macroeconomy, covid-19, f24, f62, j61

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