Mobile Money in Developing Countries: Common Factors. Khyati Malik

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1 Mobile Money in Developing Countries: Common Factors Khyati Malik Abstract In this paper, we address many questions related to the adoption of mobile money in developing countries. Is mobile money being more widely adopted by segments of the population which do not have adequate banking services, or is it replacing conventional banking services as a better, cheaper and safer option? Prior literature on mobile money often highlights that each country has its characteristics because of which a country-specific policy formulation is needed to enhance the adoption of mobile money. We ask if there exist variables which show a similar trend for the adoption of mobile money irrespective of the country. Introduction Mobile money has revolutionized the provision of financial services in the developing world. A large proportion of population in these countries lack access to basic financial services provided by the banks. This results in limited market exchange, risk enhancement and inadequate opportunities to save. Further, the households rely on informal channels of financial services at extremely high transaction costs. In such a scenario, several initiatives to utilize mobile phones for the provision of financial services to people with no access to formal banking has gone a long way. These services include long distance remittances, micropayments, provision of an alternative currency wherein prepaid mobile airtime can be exchanged, transferred and bartered. This revolutionary concept has tremendous implications for financial development and inclusion an important policy goal for the developing economies. Mobile money has become a win-win story of development of financial services with a technological base in many ways. First of all, it provides access to cash and means of payment to the unbanked population at a reduced cost. It also offers them a way of admission to a network of individuals, merchants and organizations which can provide these financial services. Financial institutions view this form of branchless banking as a way to extend profitable services to the low income customers. It is also a fertile ground of opportunity for the providers of health insurance, savings and lending products. Besides this, mobile phone operators are also viewing this concept as a means to increase the loyalty of their customers towards them by offering it as a potential service. However, there are considerable trade-offs involved as well. The opportunity cost of increased accessibility at lower costs can be financial stability of the economy. Technological innovation is hard to match with regulatory sophistication. Mobile money can present certain vulnerabilities like suspicious airtime dealings, fraudulent movement of funds, mismatch of balances, network hitches, hacking etc. in countries lacking adequate supervisory capacity. These problems can pose serious impediments in the smooth functioning of an economy. Successful operation of mobile money also calls for the creation of a conducive environment for private sector operators for attracting investment and encouraging competition. It is quite possible that the market is initially dominated by certain operators, thereby resulting in consumer protection concerns. Utilizing airtime as an alternative currency may affect the velocity of circulation, thereby affecting the

2 overall money supply, nominal output and income. This concept is also susceptible to transmission errors, accounting errors and inaccurate records. Although, innovations like mobile money are a unique way of providing financial services to a large proportion of households, it cannot be easily adopted in all developing economies. There is a need to create an environment supporting regulatory oversight which supports the concurrent achievements of financial access and stability. Considering the volatile nature of some developing economies, it is not possible to achieve these goals simultaneously. This explains why mobile money is not so much of a success in all the developing economies, which have introduced it. High levels of perceived risk can also be another major barrier in adoption of such type of innovation. For instance, in South Africa, people using mobile phones for financial services are better educated and wealthier than the average South African individual with a bank account. Hence, it would be even harder for an unbanked individual from South Africa to adopt this innovation (Ivatury & Pickens, 2006; Porteous, 2007). Mistrust and unawareness can be other primary reasons for not adopting such innovations. In this paper, we study extent of adoption of mobile money in different Asian and African countries. We attempt to determine non country-specific factors which influence the adoption of mobile money. Results and Discussion Table 1 shows some basic statistics of the seven countries we have studied: India, Pakistan, Bangladesh, Nigeria, Kenya, Tanzania and Uganda. India and Nigeria sit at the lowest percentage of population using mobile money at 0.21% and 0.3% respectively. Kenya, Tanzania and Uganda have a significant population using mobile money at 79.67%, 46.15% and 43.5% respectively. Bangladesh and Pakistan have modest popularity of mobile money at 21.12% and 7.13% respectively. On the other hand, of all the countries studied, India and Nigeria lead in the percentage of population which have access to banking services (46.64% and 46.12% respectively), followed by Kenya with 30.7% of population with access to bank services. Overall, Kenya has over 80% of its population which has either access to mobile money or banking services. Tanzania, Uganda, India and Nigeria have over 45% of its population which have used either of the two financial instruments, whereas, Pakistan has a significant fraction of population who have never accessed bank services or mobile money. Another interesting statistic that emerges from Table 1 is the fraction of the population that uses both the financial instruments. All African countries considered in this paper show that the majority of the population using banking services has also used mobile money. Hence, in these countries, people have either transitioned from banking services to mobile money or have started using both the instruments. Therefore, it is apparent that the advent of mobile money has not increased the reach of financial instruments to the sections which did not have banking facilities. In Pakistan, however, only 2% of the respondents use both the instruments. Hence, Pakistan is an exception wherein mobile money has reached the households which did not have access to banking services.

3 Table 1 Parameter Adults can access mobile phone Adults ever used an MM account Adults ever used a bank service Bank account holders Adults have ever used both bank and mobile money Adults have ever used bank or mobile money Numeracy Literacy Adults have awareness of a mobile money provider Bank account holders and have used bank in 90 days Registered MM account holder who have used MM in 90 days Country (percent yes) India Pakistan Bangladesh Nigeria Kenya Tanzania Uganda Apart from the above deductions, the data in Table 1 is quite befuddling no simple trend of adoption of mobile money has emerged among the countries. For instance, in case of India and Nigeria, low proportion of mobile money users is due to lack of awareness. However, this argument fails to explain why Pakistan and Bangladesh exhibit low rate of mobile money adoption even though their awareness levels stand at 64% and 88% respectively. Furthermore, a significant fraction of the survey respondents in these countries exhibited trust in both state-owned banks and mobile money services (Fig. 1 and Fig. 2). From such statistics, it has often been concluded that significant country-specific factors influence the adoption of mobile money. This paper attempts to identify common factors which influence the adoption of mobile money, which are not country-specific. This problem is of significance because it will give insights on how to extend the influence of mobile money in developing countries irrespective of their country-specific characteristics. Fig 1: Pakistan: To what degree do you trust: (a) state owned banks, (b) mobile money services?

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.

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