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Original Research

A REGRESSION ANALYSIS OF INFLATION AND CASHLESS TRANSACTIONS IN KENYA, TANZANIA, AND NIGERIA

DMITRY GANZHA

Vol 19, No 05 ( 2024 )   |  DOI: 10.5281/zenodo.11631125   |   Author Affiliation: Department of International Economic Relations & Management, University of Applied Sciences Burgenland, Eisenstadt, Austria.   |   Licensing: CC 4.0   |   Pg no: 618-627   |   Published on: 31-05-2024

Abstract

This study aimed to assess whether higher Inflation leads to more cashless transactions in Africa. Annual data was collected from the Central banks of Kenya, Tanzania, and Nigeria, including variables for cashless transactions and average inflation rates. The approach consisted of data cleaning and exploration, descriptive analytics, a simple regression model, and correlation testing between the variables to check the magnitude of their relationships. Using the inflation rate as the dependent variable and the cashless transactions as the independent variable, the results from the regression model for the three countries were assessed using the p-value metric, where a p-value below 0.05 was generally considered statistically significant, implying a low probability that the relationship observed is due to chance. Nigeria (p-value = 0.0238) and Tanzania (p-value =0.00102) revealed p-values all less than 0.05, the usual statistical significance level, thus indicating a statistically significant relationship. On the other hand, Kenya (p-value = 0.167) was statistically insignificant. Thus, there was enough evidence to reject the null hypothesis and accept the claim that higher inflation leads to more cashless payments in Africa. Another finding was that other factors, such as technological advancements, government laws on cashless operations, and changes in consumer preferences affect cashless transactions in Africa. Thus, it is recommended for policymakers to promote cashless transactions and put in place supportive regulations strategically. Further, the use of cashless transactions reduces the impact of inflation. The analysis was conducted using R, Power BI, and Excel.


Keywords

Inflation, Cashless Transactions, Regression Analysis, Financial Inclusion, Macroeconomic Indicators.