# Mobile Money in Developing Countries: Common Factors. Khyati Malik

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4 Fig 2: Bangladesh: To what degree do you trust: (a) state owned banks, (b) mobile money services? This paper uses a logistic regression model wherein the dependent variable is the nominal variable whether a person has ever used mobile money and the explanatory variables are various economic and social variables, such as ppi score, literacy, numeracy, rural/urban, does a person have a mobile phone and has a person ever used bank services. Let P be the probability that a person has never used mobile money. The regression equation is given by, [ ] (1) Where α 0 is the intercept, X i s are different explanatory variables and α i s are corresponding regression coefficients. Table 2 provides the values of regression coefficients of the explanatory variables and the intercept. The table also provides the standard error and the p-value in brackets below the values of the coefficients. The regression analysis of Nigeria was not included as many regression coefficients turned out to be insignificant. The regression coefficient values associated with literacy are negative and significant for all countries (except for India, for which the coefficient is insignificant), implying that a literate person is more likely to adopt mobile money. India has a very small fraction of population which has adopted mobile money (only 0.21%), so we will not consider India s regression analysis results. The coefficient associated with numeracy is also negative and significant for all countries except for Kenya. For Kenya, the coefficient associated with numeracy is insignificant, implying that numerical literacy has little influence in adoption of mobile money in Kenya. The coefficient associated with urban people is also negative implying that mobile money is adopted more by the urban population. As expected, the people who own mobile phones show larger inclination to adopt mobile money. The coefficient associated with people who have never used banking services is positive and significant for all countries. This implies that the people who are already availing banking services are more likely to adopt mobile money. There can be various

5 reasons for it people who avail banking services have better awareness of different financial instruments; they are more inclined to using financial instruments for saving and transferring money etc. A person with a higher ppi score is more likely to use mobile money, as he/she will have more money to save and to withdraw/transfer. The intercept of the regression includes all other unaccounted factors, such as historical, geopolitical and regulatory reasons. The values of the intercepts are significant and large for all countries except Kenya. Table 2 Parameter Country Tanzania India Bangladesh Pakistan Kenya Uganda Intercept 1.71 (0.21,<0.0001) 7.5 (0.35, <0.0001) 1.31 (0.167, <0.0001) 3.29 (0.29, <0.0001) 0.12 (0.23, 0.6) 2.49 (0.21, <0.0001) LITERACY. Literacy [Basic Literacy] (-)0.44 (0.087, <0.0001) (-)0.14 (0.15, 0.34) (-)0.11 (0.04, 0.006) (-)0.44 (0.14, ) (-)0.15 (0.073, 0.036) (-)0.17 (0.049, ) NUMERACY. Numeracy [Basic Numeracy] (-)0.18 (0.067, ) 0.26 (0.15, 0.085) (-)0.25 (0.058,<0.0001) (-)0.31 (0.137, ) (-)0.021 (0.084, 0.80) (-)0.28 (0.069, <0.0001) Do you personally own a mobile phone? [YES] (-)1.31 (0.078, <.0001) (-)0.38 (0.13, 0.004) (-)0.62 (0.04, <0.0001) (-)0.66 (0.074, <0.0001) (-)1.26 (0.06, <0.0001) (-)0.84 (0.052, <0.0001) PPI Score (-)0.012 (0.0031, <.0001) (-)0.025 (0.007,0.0004) (-) (0.0027,0.045) (-)0.012 (0.0033, ) (-)0.027 (0.004, <0.0001) (-)0.041 (0.0036, <0.0001) Adults have ever used bank [No] 0.50 (0.073, <.0001) 0.65 (0.14, <0.0001) 0.25 (0.038,<0.0001) 0.54 (0.065, <0.0001) 0.50 (0.087, <0.0001) 0.29 (0.068, <0.0001) Urban/Rural [Urban] (-)0.56 (0.047, <.0001) (-) (0.11, 0.96) (-)0.035 (0.035,0.31) (0.06, 0.41) (-)0.13 (0.07, 0.053) (-)0.3 (0.063, <0.0001) Since all the explanatory variables in the regression are nominal variables except for the ppi score, we can generate all possible combinations of these variables for a given ppi score. In Fig. 3, we plot the value of the predicted dependent variable for each combination of explanatory variables for different countries against the predicted value of the dependent variable of Tanzania with ppi score set to 50 for all countries. Tanzania was chosen arbitrarily on the x-axis. Any other country on the x-axis would yield similar conclusions. Fig. 3 shows that the trend of predicted dependent variable of different countries versus that of Tanzania forms a linear distribution with a positive slope. The gap between the distributions in the Y-direction is attributed to the different values of the intercept for different countries. The linear positive trends in Fig. 3 imply that the studied explanatory variables show a universal behavior in influencing the adoption of mobile money. The analysis shows that mobile money is being adopted more by people with more wealth, access to banks and education, and is not percolating as strongly among the poor sections of the populations. Factors not included in the analysis, many of which are country-specific, also play a significant role in governing the extent of adoption of mobile money.

6 Fig. 3: Comparison of predicted values of dependent variable: Have you ever used mobile money? [NO] for different countries In another regression analysis, the following factors were included: Do you have a job which earns you income?, how much do you trust state-owned banks? and how much do you trust mobile money services? (Table 3). The regression analysis was done for Bangladesh and Pakistan. The intercept values from this regression analysis did not change much as compared to the regression analysis in Table 2. This implies that the added factors do not contribute towards the intercept, reinforcing the postulation that the intercept includes country-specific geopolitical and regulatory factors. The factor Do you have a job which earns you income? has negative and significant regression coefficient thereby supporting the result that people with means are more likely to adopt mobile money. The regression coefficients associated with Do you trust mobile money services [Fully Trust] and [Rather Trust] are negative and significant. This matches the expectation that people who have trust in mobile money services would be more willing to adopt it. The regression coefficients associated with Do you trust state owned bank [Fully Trust] and [Rather Trust] are insignificant. Other explanatory variables were also checked, such as, education level of individuals, age group, reasons to open a bank account etc., but these variables were found to be insignificant.

7 Table 3 Parameter Intercept LITERACY. Literacy [Basic Literacy] NUMERACY. Numeracy [Basic Numeracy] Do you personally own a mobile phone? [YES] Bangladesh 1.65 (0.29, <0.0001) (-)0.11 (0.04, 0.008) (-)0.15 (0.06,<0.01) (-)0.53 (0.04, <0.0001) Country Pakistan 3.65 (0.32, <0.0001) (-)0.40 (0.14, ) (-)0.23 (0.14, 0.10) (-)0.66 (0.074, <0.0001) PPI Score Adults have ever used bank [No] Urban/Rural [Urban] Have a job which earns you income [YES] State-owned bank [Fully Trust] State-owned bank [Rather Trust] State-owned bank [Rather not Trust] State-owned bank [Do not Trust] State-owned bank [Neither Trust Nor Distrust] Mobile money services [Fully Trust] Mobile money services [Rather Trust] Mobile money services [Rather not Trust] Mobile money services [Do not Trust] Mobile money services [Neither Trust Nor Distrust] (-)0.002 (0.003,0.048) 0.23 (0.04,<0.0001) (-)0.088 (0.036,0.16) (-)0.29 (0.035, <0.0001) 0.14 (0.23, 0.55) (-)0.01 (0.25, 0.97) (-)0.48 (0.44, 0.28) 0.24 (0.52, 0.64) 0.23 (0.93, 0.81) (-)0.91 (0.07, <0.0001) (-)0.29 (0.08, ) (0.12, 0.98) 0.21 (0.14, 0.14) 0.31 (0.23, 0.17) (-)0.013 (0.0035, ) 0.38 (0.07, <0.0001) 0.04 (0.06, 0.53) (-)0.46 (0.07, <0.0001) (-)0.061 (0.112, 0.59) (-)0.1 (0.12, 0.38) 0.29 (0.16, 0.07) 0.39 (0.22, 0.07) (-)0.053 (0.19, 0.78) (-)1.23 (0.13, <0.0001) (-)0.30 (0.11, 0.005) 0.91 (0.13, 0.37) 0.27 ( ) 0.49 (0.18, 0.008) Conclusions Regression analysis on different countries reveals that factors such as literacy, numeracy, economic status, availability / willingness to avail banking services are non-country specific factors which influence the adoption of mobile money. While considering the implementation of mobile money in the above mentioned countries, strong emphasis should be laid on these factors, besides the country-specific factors.

8 References Donner,J. and C.Tellez (2008).Mobile banking and economic development: Linking adoption, impact, and use. Asian Journal of Communication. 18(4) Donner,J.(2007).M-Banking and M-Payments Services in the Developing World: Complements or Substitutes for Trust and Social Capital? Pre conference #4 of the Annual Meeting of the International Communication Association San Francisco, California. Ivatury, G., & Pickens, M. (2006). Mobile phone banking and low-income customers: Evidence from South Africa. Washington, DC: Consultative group to assist the poor (CGAP) and the United Nations Foundation. Klein, M. and C. Mayer (2011). Mobile Banking and Financial Inclusion The Regulatory Lesson. World Bank s Policy Research Working Paper Porteous, D. (2007). Just how transformational is m-banking? Retrieved 10 January, 2008, from

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