AN EMPIRICAL ASSESSMENT OF PROBABILITY RATES FOR FINANCIAL TECHNOLOGY ADOPTION AMONG AFRICAN ECONOMIES: A MULTIPLE LOGISTIC REGRESSION APPROACH
1Postgraduate Student, Faculty of Commerce, Administration and Law, Department of Economics, University of Zululand, South Africa.
2Professor of Financial Economics, Faculty of Commerce, Administration and Law, Department of Economics, University of Zululand, South Africa.
ABSTRACT
The extent of financial exclusion in Africa drives the adoption of fintech across the continent, but the disruption it can cause hinders progress. This study therefore assesses both the probability and actual rates of fintech adoption in 32 African economies between 2002 and 2018. Based on the information spill-over and rank theories, multiple logistic regression analysis revealed that the average probability of fintech adoption for all, emerging and frontier African economies to be 50.9%, 83.1%, and 23.1%, respectively, whereas the actual rates are 27%, 40%, and 29%, respectively. The fragile economies, however, had no reasonable probability or actual rates of fintech adoption. Further, odds ratios of 1 or more- suggest a one-unit change in the predicators will exert no impact on these rates. Thus, it is concluded that emerging economies and mobile phone banking drive fintech adoption in Africa, and is largely dependent mainly on structural changes rather than economic and financial factors. The current study consequently recommends improved literacy, ICT training, and structural changes to promote fintech across the continent.
Keywords:Africa, Financial technology, Internet banking, Mobile phone banking, Multiple logistic regression.
JEL Classification: O55; G00; C24; A11.
ARTICLE HISTORY: Received:22 June 2020, Revised:20 August 2020, Accepted:9 November 2020, Published:24 November 2020
Contribution/ Originality: This study is one of very few studies that empirically investigate the probability and actual rate of fintech adoption in African economies. The findings reveal that the quality of human capital and the dissemination of information, particularly in emerging economies are the major driving force behind the adoption of fintech in Africa.
Financial technology (fintech hereafter) is an emerging field in the world of finance, combining both financial models and information technology to extend financial services to the general public faster and at a lower cost (Arner, Barberis, & Buckley, 2015). Due to this unique quality, its adoption is inevitable especially in regions with high rates of financial exclusion such as Africa; however, its disruptive impact on conventional business etiquette, especially in the banking sector, raises both prospects and problems in Africa, hindering progress (Jugurnath, Bissessur, Ramtohul, & Mootooganagen, 2018). As a result, some African economies doubt its future potential and reliability (Ernest & Young, 2017)- and prefer to pursue raditional means of delivering and accessing financial services. Nevertheless, increasing global commercialization alongside customers’ ever-growing demands on banks to meet their needs means the adoption of fintech is inevitable. This is evident in the rate of fintech adoption in the recent years globally from 16% in 2015 to 33% in 2017 (Ernest & Young, 2017) with a predicted rise to over 70% in the near future. In fact, of particular relevance to Africa, South Africa’s adoption beyond the 33% global average at 35% was probably due to the extensive financial exclusion across the continent (Mihasonirina & Kpodar, 2012). It is essential to examine the rate and determining factors of fintech adoption in Africa which has previously been neglected by researchers.
Moreover, the variation in adoption among different economies suggests that the process could be country’s or region’s specific, implying that fintech will thrive in some areas but fail in others. The success of the mobile phone money transfer service, M-Pesa, in Kenya and Tanzania but not South Africa is an example (Alexander, Shi, & Solomon, 2017). Therefore, this study aims to identify not only the aforementioned rates and factors in Africa, but also the various economic groups across the continent to be able to predict future fintech adoption rates according to the unique attributes of different areas.
As a recent innovation in finance, there are few empirical studies of fintech. Most of those are limited in terms of scope and/or measurement tools. This section presents recent studies in this area such as Khatimah and Halim (2016). They adopted the theory of planned behavior, assessed the factors influencing the adoption of e-money in Indonesia, and found social influences could positively impact users’ intentions. Their findings corroborated those of Abdulkadir, Galoji, and Razak (2013), who reported that not only social influences such as peer group pressures, but also perceived usefulness can greatly affect the adoption of mobile banking. Meanwhile, Oliveira and Martins (2011) employed the diffusion of innovation theory when assessing an organization’s adoption of information and communication (ICT). They demonstrated individual external and internal characteristics of organizational structure were important factors influencing innovativeness. This suggests that a firm’s or country’s unique attributes are major determinants in the decision on adoption.
Khalifa (2016) supported this conclusion in their assertion that a firm’s absorptive capacity, structural features, information spill-over characteristics and environmental factors were key to Tunisian firms adopting ICT. However, this comprehensive study suffers two major drawbacks; first, it is based on a single country’s survey and ICT in general, meaning it cannot be used to generalize about the adoption of fintech among African economies; second, despite examining robust determinants, the rate of was not explored through a binary discrete choice. This current study attempts to address these problems by investigating the rates of both the probability of and actual adoption of fintech in a broad representative sample of 32 African economies. Furthermore, from an international perspective, Glass and Saggi (2002) believed that the transfer/adoption of technology can be diffused through various channels; meaning that more than the usual determinants of fintech acceptance exists.
In Africa, the rate at which the use of mobile phones has spread and been exchanged for smartphones capable of financial transactions is extraordinary, bridging the digital-divide and enhancing financial inclusion in developing countries. According to Gough and Grezo (2005), the average penetration rate for mobile phones in Africa was 6.2%, with more recent studies reporting a higher rate (Ernest & Young, 2017).
According to a study using binary logistic regression Jugurnath et al. (2018) found that marital status and occupational group played a major role in the use of mobile banking in Mauritius; those from higher socioeconomic classes, married, and with no children were more likely to use mobile banking than those lower down the socioeconomic ladder (Jugurnath et al., 2018). These findings confirmed those of Kweyu and Ngare (2014) in Kenya and Fall, Ky, and Birba (2015) in Senegal, where personal income was very highly significant in the use of mobile banking. Likewise, Bhatt and Bhatt (2016) asserted that high-income earners who were married were more likely to use mobile banking. As this further suggests that economic and social attributes determine the adoption of new innovations, this current study will test the hypothesis the adoption of fintech in Africa depends on psychological, demographic and socioeconomic factors more than financial indicator variables.
The theoretical model is based on two distinct theories: information spill over and rank. The former theory states that as information about an innovation or new technology spreads (spills over) from users to non-users, the rate of adoption increases (Mansfield, 1961). Hollenstein (2004) and Battisti, Canepa, and Stoneman (2009) assert that information spillover is currently the principal driver of adoption in both developed and developing countries; the more frequently new and existing users come into contact and find out about new technology, the greater the number who will adopt it. This assertion implies that the adoption of new technology is directly correlated with the previous (lagged) level of adoption within a given social group; hence adoption is a function of previous users’ experience. In this study, information spill-over model can be presented as Equation 1, taking the lag of previous users (fintech)-the dependent variable-in a first order autoregressive (AR 1) model.
Fintech Adoptiont = f (Fintech Adoptiont-1) (1)
Where: Fintech Adoptiont represents a vector of fintech adoption in the current period.
Fintech Adoptiont-1 represents a vector of fintech adoption in the previous period.
Equation 1 reveals that the level of adoption of fintech in the current period depends on previous levels of adoption or information spillover.
The second, rank theory states that a firm’s/country’s specific attributes or heterogeneities determine its adoption levels of technology. These attributes include psychological, demographic, and socioeconomic factors: the quality of human capital/literacy rate, population growth rate, extent of its financial openness, among others. This suggests that factors other than macroeconomic ones could be important drivers of fintech in Africa. Thus, as the quality of a country’s human capital improves through educational achievement, potential users will perceive the usefulness of modern devices more easily and be more likely to adopt them for accessing financial services. Likewise, a rapidly growing population in countries with a high level of financial openness is more likely to lead to adopting fintech than in those with a declining population and strict financial repression. This model is therefore, expressed as:
Fintech Adoption t = f (TSEt, POPGt, FOt) (2)
Where: Fintech Adoptiont represents a vector of fintech adoption in the current period.
TSEt represents tertiary school enrolment in the current period (a measure for literacy rate).
POPGt represents population growth rate in the current period.
FOt represents financial openness in the current period.
Equation 2 models the heterogeneities that affects the adoption of fintech in a particular country, which is expressed as a function of the quality of human capital/literacy rate, population growth and financial openness in the current year.
Although other theories such as the technology acceptance model (TAM: Davis, Bagozzi, and Warshaw (1989) and the unified theory of acceptance and use of technology (UTAUT: Venkatesh, Morris, Davis, and Davis (2003) are widely used, information spill over and rank models are the dominant theories for explaining the adoption of new technologies (Canepa & Stoneman, 2004). Moreover, these theories are consistent with the objective to investigate binary discrete choice based on socioeconomic drivers for adopting new innovations (Mercer, 2004). Consequently, both theories were combined to form a single unique model to investigate this relationship.
The two models expressed in Equations 1 and 2 were merged to re-express a final model on Equation 3; however, due to the data on financial openness not being available for all countries, as well as for simplicity, the FO predicator was removed. The final model is expressed as:
Fintech Adoption t = f (Fintecht-1, TSEt, POPGt) (3)
This empirical logistic model estimated in its econometric form, expressed as:
Transforming the binary model in Equation 5 means the probability of African economies adopting fintech can be calculated:
Equation 6 is used to solve for each of the fintech proxy measures- internet banking (INTB), mobile phone banking (MPB), and automated teller machines (ATM) - for all the economic groups - emerging, frontier and fragile African economies. Moreover, the odds ratios were reported to verify whether a one unit increase in the average value of the explanatory (independent) variables affected the level of fintech adoption. The assumption is that – if the confidence intervals of the odds ratio crosses 1, then the explanatory variables do not affect the level of adoption.
A multiple logistic regression (MLR) analysis was conducted to predict the logit of fintech adoption (the event outcome) from the set of predictors. The logit - the natural logarithm of the odds (probability/[1 - probability]) was then transformed into a measure of probability with which to validate that high probability is associated with a high level of adoption, and vice versa, using the actual outcome variables as specified in Equation 3. This model can be estimated as follows:
Taking the anti-log of each side of Equation 7 and the model can be transformed to calculate the probability of African economies adopting fintech.
Equations (7) and (8) are the closed-form models of Equations 5 and 6, respectively, using the predictors selected for investigation.
In the MLR analysis, the outcome variable, “(Y)”, is a binary/dichotomous variable; taking value of 1 (if fintech is adopted and 0 (zero) otherwise. The probability of cases in which Y = 1 is defined as π = P(Y=1), with Y = 0 as
1 - π = P(Y = 0), hence, based on theory and empirical reviews, the set of predictors, relate to and determine Y. In addition, a comparative probability assessment was conducted for the three economic groups used in this study (see Appendix A, Table A.1). Furthermore, time periods selected for the study were consolidated by a principal component analytical (PCA), conducted to generate fintech indices for the three proxy measures that showed the negative values before 2008 became positive afterwards for most countries, especially those with emerging economies (see Appendix B. Tables B.1 and B.2).
The three proxy measures of fintech were the automated teller machines (ATM), also used by Nina (2007) the number of mobile phone subscriptions as a proxy for mobile phone banking (MPB), previously used by Gough and Grezo (2005) as a proxy for mobile banking (Jugurnath et al. (2018); and the number of individuals using internet banking (INTB). Therefore, the current study treats the concepts of fintech in a limited sense. This study is a panel analysis of 32 African economies over the period 2002 - 20181 , disaggregated into 3 emerging, 24 frontier and 5 fragile economies based on the Financial Times Stock Exchange FTSE (2017) classification of countries, which takes into account the heterogeneity of these economic groups. Finally, the data were extracted from the online World Bank Data Base (2018) and the International Financial Statistics Database (2018).
The results of the analyses are presented in two parts in the section. The first subsection discusses the descriptive analysis that ascertained the rate at which the economic groups adopted fintech, while the second subsection explores the empirical results from the MLR analysis.
The summary of the descriptive statistics are shown in Table 1. The overall weighted average rate for all African economies adopting fintech is approximately 27%, below the global average of 33% (Ernest & Young, 2017), but that for emerging African economies is above it at 40%; however, frontier and fragile African economies show averages only 29% and 12%, respectively. These results suggest that the rate of adopting fintech in Africa is undermined by fragile economies; thus, further analysis using advanced estimation methods are needed.
These results further reveal that, with an average of 57%, mobile phone banking is the most used fintech by all economic groups, which implies that about 71% of the total African population are using this fintech. Moreover, with a 40% rate of fintech adoption, emerging African economies constitute about 49% of total African population using mobile phone banking. This corroborates the findings of Ernest and Young (2017) that around 46% of fintech adoption occurs in emerging economies. Such success could be attributed to high income levels, greater financial development and openness, high quality human capital, as well as significant funding in research and development compared with other African economies.
Economies | ATM |
Internet Banking |
Mobile Phone Banking |
Average |
Percentage of Total |
Emerging | 23.85 |
25 |
70.7 |
40 |
49 units |
Frontier | 6.56 |
12.1 |
68.1 |
29 |
36 units |
Fragile | 1.33 |
2 |
32.22 |
12 |
15 units |
All | 10.58 |
13.03 |
57.01 |
27 |
|
Percentage | 13 |
16 |
71 |
100 units |
The results shown in Table 1 are further represented on a bar chart in Figure 1, which provides more comprehensive view of the average rate of fintech adoption across the different economic groups. It is evident once more that mobile phone banking is the fastest growing therefore strongest driver for fintech in Africa. It can also be seen that emerging economies are experiencing the highest growth rate for all three proxy measures, although followed closely by frontier economies. On average, the rates of adoption in Africa measured by ATM, INTB and MPB are 10.58%, 13.03%, and 57.01%, respectively.
Figure-1. Average percentage change in fintech adoption among African economies.
It should be recalled that the dependent variable, - Fintech, in Equation 3 is a vector of three unknown proxies, ATM, INTB, and MPB, which the probability of adoption in all 32 African economies was calculated using Equation 8. This calculation involved inserting the coefficient estimates (β’s) of the significant variables and evaluating the resultant probabilities against the average values shown in Table 1. The MLR results are presented in Tables 2, 3 and 4.
The results for all 32 African economies shown in Table 2, reveal that the major drivers for adopting fintech were information spillover and the literacy rate of potential adopters. This implies that the more users interact and discuss their experiences with non-users, the higher the rate of adoption, while the quality of human capital also positively drives fintech; this is called it perception of ease-of-use in the TAM (Davis et al. (1989). This finding explains why the population growth exerts no significant impact, although there is a simultaneous increase in the quality of human capital. Thus, despite Africa’s large population, the adoption rate is still very low (Muzari, Gatsi, & Muvhunzi, 2012).
Table 2 also demonstrates that an odds ratio of 1 represents a one-unit increase in that particular predictor that will not significantly change the probability of fintech adoption if the confidence interval crosses 1 or the odds ratio includes a whole number, then a one-unit increase in the explanatory variable will exert no effect on the independent variable; however, an odds ratio of 0 or less than one means that a one-unit change in the predictor will change the probability of adoption. As can be seen, except for population growth rate, the odds ratios of the predictors were greater than one and thus cannot significantly change the probability of fintech adoption.
Table-2. Multiple logistic regression result and estimated probability of fintech adoption among African Economies (N=32). |
Adoption of ATM |
Adoption of INTB |
Adoption of MPB |
||||
Predictors | Coefficient |
Odds Ratio |
Coefficient |
Odds Ratio |
Coefficient |
Odds Ratio |
Information Spillover | 5.378 |
216.480 |
3.830 |
46.058 |
0.0000013 |
1.000 |
(8.910)*** |
(8.910)*** |
(8.76)*** |
(8.760)*** |
(5.15)*** |
(5.15)*** |
|
Population Growth (POPG) | 6.414 |
610.242 |
0.771 |
2.161 |
-0.397 |
0.672 |
(1.81) |
(1.810) |
(0.300) |
(0.300) |
(0.200) |
(0.200) |
|
Literacy Rate (TSE) | 1.999 |
7.379 |
0.554 |
1.741 |
0.973 |
2.646 |
(4.820)*** |
(4.820)*** |
(1.61) |
(1.610) |
(3.90)*** |
(3.900)*** |
|
constant | -75.531 |
7.85e-35 |
-51.700 |
3.52e-23 |
-26.947 |
1.98e-12 |
(5.930)*** |
(5.930)*** |
(6.69)*** |
(6.690)*** |
(6.69)*** |
(6.690)*** |
|
Observation | 425 |
425 |
425 |
|||
No in Group | 32 |
32 |
32 |
|||
LR (Chi Squared) | 89.470*** |
87.170*** |
65.630*** |
|||
Eq. 3.7 (sig variables only) | 0.576 |
-2.102 |
1.257 |
|||
Probability of Adoption (Eq 3.8) |
0.6402 or 64.02% |
0.1090 or 10.9% |
0.779 or 77.9% |
Note: Absolute value of z-statistics in parentheses *** significant at 1%; ** significant at 5%; LR=Long-run. |
Finally, the estimated probability of fintech adoption in Africa comprises 64.02%, 10.9%, and 77.85% for ATM, INTB and MPB, respectively; meaning that mobile phone banking and ATMs are more likely to be used than internet banking. Specifically, the current population of Africa totals about 1.3billion (United Nations Organisation, 2019) 832,260,000 (0.6402*1.3billion) people will use ATMs, 141,700,000 internet banking, and 1,012,050,000 mobile phone banking.
Furthermore, with reference to Table 1, since 49% of fintech users in Africa reside in emerging, 36% in frontier, and 15% in fragile economies, of those 1,012,050,000 mobile banking users, 495,904,500 come from emerging economies, 364,338,000 frontier and 151,807,500 from fragile economies. Therefore, it can be concluded that African economies will adopt ATM and MPB but probably not internet banking in the near future, which can now be compared with the rates of fintech adoption in emerging and frontier economies, identifying in which fintech adoption in more probable.
The current study studied the hypothesis that emerging economies drive fintech adoption and promote financial integration in Africa by investigating the probability of adoption in Egypt, Morocco, and South Africa. Emerging economies are defined as having established financial system, a relatively knowledgeable population or work force, and a high inflow of foreign direct investment (Latif et al., 2018) which previous studies (Ernest & Young, 2017) have confirmed by demostrating a higher rate of adoption than the 33% global average.
The MLR results for the three emerging economies presented in Table 3 are consistent with those for all the African economies, except that the probability of fintech adoption is reasonably high for all proxy measures – 79.81%, 73.49%, and 95.89% for ATM, INTB and MPB, respectively; thus it appears that Egypt, Morocco, and South Africa are the major driving force behind fintech on the Africa economies. In particular, information spillover and the literacy level of potential adopters were once more the major drivers of fintech adoption, however, the odds ratios reveal that a one-unit increase in these predictors will again not significantly affect the probability of adoption decision. In contrast, while the population growth rate does not necessarily exert a positive impact on fintech adoption, an odds ratio less than 1 implies that a one-unit change in this predictor can improve the probability of adoption.
Furthermore, the high probability rate among the emerging economies reveals that they have a very high propensity for adoption than the other economies under consideration. The analysis further strengthens the earlier assertion that the information spill over and quality of human capital had positive impact across the three models, whereas population growth rate does not.
Table-3. Multiple logistic regression result and estimated probability of fintech adoption among emerging African economies (N=3). |
Adoption of ATM |
Adoption of INTB |
Adoption of MPB |
||||
Particulars | Coefficient |
Odds Ratio |
Coefficient |
Odds Ratio |
Coefficient |
Odds Ratio |
Information Spillover | 0.1319*** |
1.2482** |
0.087*** |
2.3428** |
0.00000019*** |
1.000** |
Population Growth (POPG) | -146.5 |
0** |
-1.1989*** |
0** |
0.601*** |
0** |
Literacy Rate (TSE) | 1.835*** |
2.0155*** |
0.011*** |
1.9310 |
1.023*** |
5.873** |
Constant | -45.718*** |
0.1930** |
2.042 |
3.4152** |
-31.386*** |
0.121** |
Observations | 37 |
37 |
37 |
|||
No in Group | 3 |
3 |
3 |
|||
LR (Chi Squared) | 25.35** |
25.35*** |
25.35** |
|||
Eq. 3.7 (sig variables only) | 1.375 |
1.020 |
3.149 |
|||
Probability of Adoption (Eq 3.8) | 0.7981 or 79.81% |
0.7349 or 73.49% |
0.9589 or 95.89% |
Note: *** significant at 1%; ** significant at 5%; LR= Long-run. |
The 24 frontier African economies, with generally less liquidity than their emerging counterparts are those with an increasing lower middle-income class, rapid economic growth leading to rising living standards, low levels of internal and foreign debts, poorly developed stock markets, and a low level of urbanization, but implementing economic reforms to promote further economic growth (Broome & Seabrooke, 2007). Although, the population growth rate again exerted no significant impact on fintech adoption as can be seen from Table 4, neutral technical changes as a result of fintech can in theory, improve the quality of human capital, the workforce. This assertion was supported by Hicks (1932) when he theorized that a technical change that arises from innovations is capable of improving labour productivity and quality. It is possible to test this hypothesis empirically.
Once more, it is more likely that frontier economies will use mobile phone banking than internet banking or ATMs, although in this case, the ATMs are least to be adopted. This inconsistent result could be attributed to certain phenomena or specification errors; since certain factors fintech adoption differently in frontier and emerging economies, the same predicators cannot be assumed for both types of economy. This could explain the reason for MPESA, a mobile phone-based money transfer service, proving more popular in Kenya, Tanzania, and other frontier economies but failing to launch in the emerging economy of South Africa (Alexander et al., 2017). These heterogeneous factors that affect the adoption of fintech in emerging and frontier economies is therefore a unique area for further research.
Table-4. A Multiple logistic regression result and a probability table for fintech adoption among frontier African economies (n=24). |
Adoption of ATM |
Adoption of INTB |
Adoption of MPB |
||||
Predictors | Coefficient |
Odds Ratio |
Coefficient |
Odds Ratio |
Coefficient |
Odds Ratio |
Information Spillover | 3.319 |
27.642 |
2.887 |
17.942 |
0.000001 |
1 |
(1.890)** |
(1.890)** |
(5.510)*** |
(5.510)*** |
(4.790)*** |
(4.790)*** |
|
Population Growth (POPG) | 6.939 |
1031.300 |
3.895 |
49.159 |
-0.219 |
0.803 |
(1.200) |
(1.20) |
(1.460) |
(1.460) |
(0.130) |
(0.130) |
|
Literacy Rate (TSE) | 1.635 |
5.131 |
0.943 |
2.569 |
1.223 |
13.398 |
(2.770)*** |
(2.77)*** |
(2.780)*** |
(2.780)*** |
(4.740)*** |
(4.740)*** |
|
Constant | -60.718 |
4.27e-27 |
-52.042 |
2.50e-23 |
-27.386 |
1.28e-12 |
(4.270)*** |
(4.270)*** |
(4.380)*** |
(4.380)*** |
(7.040)*** |
(7.040)*** |
|
Observations | 314 |
314 |
314 |
|||
No in Groups | 23 |
23 |
23 |
|||
LR(chi-squared) | 14.510*** |
33.140*** |
73.970*** |
|||
Eq. 3.7 (sig variables only) | -8.152 |
-7.175 |
-2.784 |
|||
Probability of Adoption (Eq 3.8) | 0.030 or 3% |
0.080 or 8% |
0.582 or 58.2% |
Note: Absolute value of z statistics in parentheses; *** significant at 1%;** significant at 5%; LR = Long-run. |
Finally, the non-convergence of the MLR results for the fragile African economies could be attributed to their poor infrastructural and financial development.
This study examined both the probability and actual rates of fintech adoption in 32 African economies by means of MLR and descriptive analyses, respectively. These analyses were based on the information spillover and rank theories applied to emerging, frontier and fragile African economies. The results revealed that the overall average probability of fintech adoption in Africa to be 50.9%: 64.02%, 10.9% and 77.85% for ATM, INTB, and MPB, respectively. In particular, the average probability in the emerging economies was 83.1% compared with 23.1% in the frontier economies, indicating that a higher level of fintech adoption, mainly mobile phone banking will be witnessed in the emerging African economies. In fact, the mobile phone banking is widespread across Africa facilitating economic growth. Therefore, the adoption of fintech poses no economic challenges in Africa because it can be predicted and explored for its benefits.
These results further revealed that fintech adoption in Africa is driven by mobile phone banking and the emerging economies, whereas ATMs and fragile economies inhibited it. With 27% average rate of fintech adoption in Africa, it will be about 3.5 years for saturation point to be reached and financial exclusion resolved, if the adoption rate is maintained. However, the rate is below the global average of 33% (Ernest & Young, 2017); although this could be attributed to the low rate of adoption in fragile economies at 12% that weaken the predictors’ effects on all economies, the rate of adoption in emerging African economies at 40% is above the global rate. The average adoption rates of 40% in emerging and 29% in frontier economies also implies that it will be 2.5 years emerging, 3.5 in frontier, but 9 years in fragile economies before fintech can end financial exclusion. These findings carry serious implications for not only Africa financial market development but also its macroeconomic stability.
Finally, the adoption of fintech across Africa is mainly influenced by information and the literacy rate, or quality of human capital, according to the levels of significance shown in the analyses; this is consistent with Khalifa (2016), who reported that these two predictors were the major determinants in whether firms adopted ICT. The odds ratio analysis emphasized the MLR results, because where it is below 1, Table 2, POPG, 0.6723, any change in this predictor will, over time, change the adoption rate. It is therefore inferred that with an overall average probability of 50.9%, the rate of fintech adoption in Africa will be higher in future than its current rate of 27%. Consequently, this study recommends improvements in literacy/education and ICT training should be the way forward.
Funding: This study received no specific financial support. |
Competing Interests: The authors declare that they have no competing interests. |
Acknowledgement: Both authors contributed equally to the conception and design of the study. |
Abdulkadir, N., Galoji, S. I., & Razak, R. B. A. (2013). An investigation into the adoption of mobile banking in Malaysia. American Journal of Economics, 3(3), 153-158.
Alexander, A. J., Shi, L., & Solomon, B. (2017). Retrieved online from: https://openknowledge.worldbank.org/handle/10986/30360 (Page 11) How fintech is reaching the poor in Africa and Asia. International Finance Corporation, World Bank Group.
Battisti, G., Canepa, A., & Stoneman, P. (2009). E-Business usage across and within firms in the UK: Profitability, externalities and policy. Research Policy, 38(1), 133-143.Available at: https://doi.org/10.1016/j.respol.2008.10.021.
Bhatt, A., & Bhatt, S. (2016). Factors affecting customers adoption of mobile banking services. The Journal of Internet Banking and Commerce, 21(1), 21: 161.
Broome, A., & Seabrooke, L. (2007). Seeing like the IMF: Institutional change in small open economies. Review of International Political Economy, 14(4), 576-601.Available at: https://doi.org/10.1080/09692290701475346.
Canepa, A., & Stoneman, P. (2004). Comparative international diffusion: patterns, determinants and policies. Economics of Innovation and New Technology, 13(3), 279-298.Available at: https://doi.org/10.1080/10438590410001628404.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical Models. Management Science, 35(8), 982-1003.
Ernest, & Young. (2017). EY fin tech aadoption Index. The rapid emergence of FinTech . Retrieved from: http://www.ey.com/Publication/vwLUAssets/ey-fintech-adoption-index-2017/$FILE/ey-fintech-adoption-index-2017.pdf . [Accessed April 17, 2018].
Fall, F. S., Ky, Y., & Birba, O. (2015). Analyzing the mobile-banking adoption process among low-income populations: A sequential logit model'. Economics Bulletin, 35(4), 2085-2103.
Financial Times Stock Exchange FTSE. (2017). FT guide to world currencies. 11. Retrieved from: https://markets.ft.com/data/archive.
Glass, A. J., & Saggi, K. (2002). Multinational firms and technology transfer. Scandinavian Journal of Economics, 104(4), 495-513.
Gough, N., & Grezo, C. (2005). Africa: The impact of mobile phones. Moving the Debate Forward the Vodafone Policy Paper Series, 3(2), 1-71.
Hicks, J. R. (1932). The theory of wages, reprinted in the theory of wages (2nd ed.). London: Macmillan 1963.
Hollenstein, H. (2004). Determinants of the adoption of Information and Communication Technologies (ICT): An empirical analysis based on firm-level data for the Swiss business sector. Structural Change and Economic Dynamics, 15(3), 315-342.
International Financial Statistics Database. (2018). 12. Retrieved from: https://data.imf.org/?sk=14c514d548-b516ba-549ed-518ab519-552b510c511a0179b.
Jugurnath, B., Bissessur, R., Ramtohul, P., & Mootooganagen, R. (2018). Fintech and digital banking: Perception and usage in Mauritius, a logistic regression approach. Paper presented at the The 5th IBSM International Conference on Business, Management and Accounting.
Khalifa, A. B. (2016). Determinants of information and communication technologies adoption by Tunisian firms. Journal of Innovation Economics Management, 20(2), 151-177.Available at: https://doi.org/10.3917/jie.020.0151.
Khatimah, H., & Halim, F. (2016). The effect of attitude and its decomposed, perceived behavioral control and its decomposed and awareness on intention to use e-money mobile in Indonesia. Journal of Scientific Research and Development, 3(1), 39-50.
Kweyu, M., & Ngare, P. (2014). Factor analysis of customers perception of mobile banking services in Kenya. Journal of Emerging Trends in Economics and Management Sciences, 5(1), 1-8.Available at: https://doi.org/10.36106/gjra/3907094.
Latif, Z., Latif, S., Ximei, L., Pathan, Z. H., Salam, S., & Jianqiu, Z. (2018). The dynamics of ICT, foreign direct investment, globalization and economic growth: Panel estimation robust to heterogeneity and cross-sectional dependence. Telematics and Informatics, 35(2), 318-328.Available at: https://doi.org/10.1016/j.tele.2017.12.006.
Mansfield, E. (1961). Technical change and the rate of imitation. Econometrica, 29(4), 741-766.Available at: https://doi.org/10.2307/1911817.
Mercer, D. E. (2004). Adoption of agroforestry innovations in the tropics: A review. Agroforestry Systems, 61(1-3), 311-328.Available at: https://doi.org/10.1023/b:agfo.0000029007.85754.70.
Mihasonirina, A., & Kpodar, K. (2012). Mobile phones, financial inclusion, and growth. Review of Economics and Institutions, 3(2), 1-30.Available at: https://doi.org/10.5202/rei.v3i2.75.
Muzari, W., Gatsi, W., & Muvhunzi, S. (2012). The impacts of technology adoption on smallholder agricultural productivity in sub-Saharan Africa: A review. Journal of Sustainable Development, 5(8), 69.Available at: https://doi.org/10.5539/jsd.v5n8p69.
Nina, M. (2007). Exploring consumer adoption of mobile payments–A qualitative study. The Journal of Strategic Information Systems, 16(4), 413-432.Available at: https://doi.org/10.1016/j.jsis.2007.08.001.
Oliveira, T., & Martins, M. F. (2011). Literature review of information technology adoption models at firm level. Electronic Journal of Information Systems Evaluation, 14(1), 110- 121.
United Nations Organisation. (2019). 12. Retrieved from: https://www.worldometers.info/world-population/africa-population/ .
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view1. MIS Quarterly, 27(3), 425-425.
World Bank Data Base. (2018). 12. Retrieved from: https://databank.worldbank.org/source/world-development-indicators#.
Appendix A: Classification of African Economies.
Emerging Economies | Frontier Economies | Fragile Economies | |||
Egypt | Algeria | Ethiopia | Mauritania | Senegal | Chad |
Morocco | Angola | Ghana | Mauritius | Seychelles | Cote d'Ivoire |
South Africa | Botswana | Kenya | Mozambique | Swaziland | Niger |
Burkina Faso | Madagascar | Namibia | Tanzania | Sudan | |
Burundi | Malawi | Nigeria | Tunisia | Togo | |
Cameroon | Mali | Rwanda | Zambia |
Source: World economic groupings under Standard & Poor (S&P) and FTSE (2017). |
Appendix B. Principal component analysis and fintech indices.
Component | Eigenvalue |
Difference |
Proportion |
Cumulative |
Comp1 | 1.9084 |
1.0686 |
0.6361 |
0.6361 |
Comp2 | 0.8398 |
0.5880 |
0.2799 |
0.9161 |
Comp3 | 0.2518 |
….. |
0.0839 |
1.0000 |
Principal components (Eigenvectors) | ||||
Variables | Component 1 |
Component 2 |
Component 3 |
Unexplained |
ATM | 0.6086 |
-0.4784 |
0.6330 |
0 |
INTB | 0.6682 |
-0.1213 |
-0.7341 |
0 |
MPB | 0.4279 |
0.8697 |
0.2458 |
0 |
Components: ATM, Internet Banking and Mobile Phone Banking | ||||
Number of observations = 544; Number of components = 3; Trace = 3 |
CtryN |
Year |
Fnth |
CtryN |
Year |
Fnth |
CtryN |
Year |
Fnth |
Algeria |
2002 |
-1.1753 |
Botswana |
2002 |
-1.098 |
Burundi |
2002 |
-1.2477 |
Algeria |
2003 |
-1.1307 |
Botswana |
2003 |
-1.0978 |
Burundi |
2003 |
-1.2438 |
Algeria |
2004 |
-0.9116 |
Botswana |
2004 |
-0.5175 |
Burundi |
2004 |
-1.2346 |
Algeria |
2005 |
-0.6913 |
Botswana |
2005 |
-0.4755 |
Burundi |
2005 |
-1.2252 |
Algeria |
2006 |
-0.4574 |
Botswana |
2006 |
-0.3087 |
Burundi |
2006 |
-1.2193 |
Algeria |
2007 |
-0.1956 |
Botswana |
2007 |
0.2451 |
Burundi |
2007 |
-1.2162 |
Algeria |
2008 |
-0.154 |
Botswana |
2008 |
0.1774 |
Burundi |
2008 |
-1.2068 |
Algeria |
2009 |
0.0388 |
Botswana |
2009 |
0.1306 |
Burundi |
2009 |
-1.1867 |
Algeria |
2010 |
0.1193 |
Botswana |
2010 |
0.1931 |
Burundi |
2010 |
-1.1594 |
Algeria |
2011 |
0.2832 |
Botswana |
2011 |
0.2419 |
Burundi |
2011 |
-1.1431 |
Algeria |
2012 |
0.4679 |
Botswana |
2012 |
0.5292 |
Burundi |
2012 |
-1.1216 |
Algeria |
2013 |
0.7058 |
Botswana |
2013 |
1.1951 |
Burundi |
2013 |
-1.0926 |
Algeria |
2014 |
1.1222 |
Botswana |
2014 |
1.5324 |
Burundi |
2014 |
-1.0774 |
Algeria |
2015 |
1.5363 |
Botswana |
2015 |
1.684 |
Burundi |
2015 |
-0.8937 |
Algeria |
2016 |
1.8317 |
Botswana |
2016 |
1.764 |
Burundi |
2016 |
-0.8738 |
Algeria |
2017 |
2.0485 |
Botswana |
2017 |
1.9182 |
Burundi |
2017 |
-0.8234 |
Algeria |
2018 |
1.9401 |
Botswana |
2018 |
1.8411 |
Burundi |
2018 |
-0.8486 |
Angola |
2002 |
-1.2394 |
Burkina-F. |
2002 |
-1.243 |
Cameroon |
2002 |
-1.2253 |
Angola |
2003 |
-1.2312 |
Burkina-F. |
2003 |
-1.2331 |
Cameroon |
2003 |
-1.2085 |
Angola |
2004 |
-1.1864 |
Burkina-F. |
2004 |
-1.1648 |
Cameroon |
2004 |
-1.1703 |
Angola |
2005 |
-1.1172 |
Burkina-F. |
2005 |
-1.157 |
Cameroon |
2005 |
-1.1276 |
Angola |
2006 |
-1.013 |
Burkina-F. |
2006 |
-1.144 |
Cameroon |
2006 |
-1.0804 |
Angola |
2007 |
-0.9161 |
Burkina-F. |
2007 |
-1.1215 |
Cameroon |
2007 |
-1.0074 |
Angola |
2008 |
-0.8022 |
Burkina-F. |
2008 |
-1.0974 |
Cameroon |
2008 |
-0.9445 |
Angola |
2009 |
-0.6771 |
Burkina-F. |
2009 |
-1.0652 |
Cameroon |
2009 |
-0.879 |
Angola |
2010 |
-0.5496 |
Burkina-F. |
2010 |
-0.9923 |
Cameroon |
2010 |
-0.8369 |
Angola |
2011 |
-0.3976 |
Burkina-F. |
2011 |
-0.896 |
Cameroon |
2011 |
-0.7509 |
Angola |
2012 |
-0.1374 |
Burkina-F. |
2012 |
-0.8194 |
Cameroon |
2012 |
-0.5836 |
Angola |
2013 |
0.0483 |
Burkina-F. |
2013 |
-0.545 |
Cameroon |
2013 |
-0.4041 |
Angola |
2014 |
0.1661 |
Burkina-F. |
2014 |
-0.4875 |
Cameroon |
2014 |
-0.0873 |
Angola |
2015 |
0.2845 |
Burkina-F. |
2015 |
-0.3546 |
Cameroon |
2015 |
0.122 |
Angola |
2016 |
0.2832 |
Burkina-F. |
2016 |
-0.2144 |
Cameroon |
2016 |
0.2479 |
Angola |
2017 |
0.3543 |
Burkina-F. |
2017 |
-0.073 |
Cameroon |
2017 |
0.2712 |
Angola |
2018 |
0.3187 |
Burkina-F. |
2018 |
-0.1437 |
Cameroon |
2018 |
0.2595 |
Chad |
2002 |
-1.2459 |
Egypt |
2002 |
-1.0528 |
Ghana |
2002 |
-1.2102 |
Chad |
2003 |
-1.2385 |
Egypt |
2003 |
-0.9711 |
Ghana |
2003 |
-1.1868 |
Chad |
2004 |
-1.2338 |
Egypt |
2004 |
-0.4875 |
Ghana |
2004 |
-0.9958 |
Chad |
2005 |
-1.2294 |
Egypt |
2005 |
-0.3167 |
Ghana |
2005 |
-0.9695 |
Chad |
2006 |
-1.2168 |
Egypt |
2006 |
-0.1558 |
Ghana |
2006 |
-0.8883 |
Chad |
2007 |
-1.1963 |
Egypt |
2007 |
0.2039 |
Ghana |
2007 |
-0.7951 |
Chad |
2008 |
-1.1614 |
Egypt |
2008 |
0.5325 |
Ghana |
2008 |
-0.7062 |
Chad |
2009 |
-1.1332 |
Egypt |
2009 |
0.9195 |
Ghana |
2009 |
-0.5892 |
Chad |
2010 |
-1.1103 |
Egypt |
2010 |
1.3016 |
Ghana |
2010 |
-0.4413 |
Chad |
2011 |
-1.0854 |
Egypt |
2011 |
1.7301 |
Ghana |
2011 |
-0.316 |
Chad |
2012 |
-1.0664 |
Egypt |
2012 |
2.048 |
Ghana |
2012 |
-0.1106 |
Chad |
2013 |
-1.032 |
Egypt |
2013 |
2.258 |
Ghana |
2013 |
0.2307 |
Chad |
2014 |
-0.9952 |
Egypt |
2014 |
2.4221 |
Ghana |
2014 |
0.74 |
Chad |
2015 |
-0.9559 |
Egypt |
2015 |
2.6353 |
Ghana |
2015 |
1.1667 |
Chad |
2016 |
-0.877 |
Egypt |
2016 |
2.9074 |
Ghana |
2016 |
1.406 |
Chad |
2017 |
-0.7917 |
Egypt |
2017 |
3.247 |
Ghana |
2017 |
1.5357 |
Chad |
2018 |
-0.8344 |
Egypt |
2018 |
3.0772 |
Ghana |
2018 |
1.4708 |
Cote d'Iv |
2002 |
-1.2134 |
Ethiopia |
2002 |
-1.2498 |
Kenya |
2002 |
-1.1791 |
Cote d'Iv |
2003 |
-1.1973 |
Ethiopia |
2003 |
-1.2483 |
Kenya |
2003 |
-1.0951 |
Cote d'Iv |
2004 |
-1.0299 |
Ethiopia |
2004 |
-1.2434 |
Kenya |
2004 |
-1.0103 |
Cote d'Iv |
2005 |
-1.0089 |
Ethiopia |
2005 |
-1.236 |
Kenya |
2005 |
-0.9711 |
Cote d'Iv |
2006 |
-0.9576 |
Ethiopia |
2006 |
-1.2234 |
Kenya |
2006 |
-0.8466 |
Cote d'Iv |
2007 |
-0.8824 |
Ethiopia |
2007 |
-1.2142 |
Kenya |
2007 |
-0.6703 |
Cote d'Iv |
2008 |
-0.8284 |
Ethiopia |
2008 |
-1.1962 |
Kenya |
2008 |
-0.4955 |
Cote d'Iv |
2009 |
-0.7672 |
Ethiopia |
2009 |
-1.1534 |
Kenya |
2009 |
-0.3505 |
Cote d'Iv |
2010 |
-0.7083 |
Ethiopia |
2010 |
-1.086 |
Kenya |
2010 |
-0.1369 |
Cote d'Iv |
2011 |
-0.6373 |
Ethiopia |
2011 |
-0.9393 |
Kenya |
2011 |
-0.0026 |
Cote d'Iv |
2012 |
-0.5367 |
Ethiopia |
2012 |
-0.7393 |
Kenya |
2012 |
0.1361 |
Cote d'Iv |
2013 |
-0.1542 |
Ethiopia |
2013 |
-0.5749 |
Kenya |
2013 |
0.27 |
Cote d'Iv |
2014 |
0.2575 |
Ethiopia |
2014 |
-0.3494 |
Kenya |
2014 |
0.4632 |
Cote d'Iv |
2015 |
1.171 |
Ethiopia |
2015 |
0.1344 |
Kenya |
2015 |
0.5432 |
Cote d'Iv |
2016 |
1.3326 |
Ethiopia |
2016 |
0.3615 |
Kenya |
2016 |
0.5447 |
Cote d'Iv |
2017 |
1.5513 |
Ethiopia |
2017 |
0.2967 |
Kenya |
2017 |
0.7161 |
Cote d'Iv |
2018 |
1.4419 |
Ethiopia |
2018 |
0.3291 |
Kenya |
2018 |
0.6304 |
Madagascar |
2002 |
-1.2359 |
Mali |
2002 |
-1.243 |
Mauritius |
2002 |
-0.7938 |
Madagascar |
2003 |
-1.23 |
Mali |
2003 |
-1.2357 |
Mauritius |
2003 |
-0.7061 |
Madagascar |
2004 |
-1.2138 |
Mali |
2004 |
-1.1139 |
Mauritius |
2004 |
0.578 |
Madagascar |
2005 |
-1.1967 |
Mali |
2005 |
-1.1039 |
Mauritius |
2005 |
0.7608 |
Madagascar |
2006 |
-1.1789 |
Mali |
2006 |
-1.0813 |
Mauritius |
2006 |
0.8729 |
Madagascar |
2007 |
-1.146 |
Mali |
2007 |
-1.0582 |
Mauritius |
2007 |
1.19 |
Madagascar |
2008 |
-1.0459 |
Mali |
2008 |
-1.0108 |
Mauritius |
2008 |
1.2269 |
Madagascar |
2009 |
-1.0166 |
Mali |
2009 |
-0.9772 |
Mauritius |
2009 |
1.3157 |
Madagascar |
2010 |
-0.9853 |
Mali |
2010 |
-0.9251 |
Mauritius |
2010 |
1.6291 |
Madagascar |
2011 |
-0.9526 |
Mali |
2011 |
-0.8351 |
Mauritius |
2011 |
2.0241 |
Madagascar |
2012 |
-0.9272 |
Mali |
2012 |
-0.735 |
Mauritius |
2012 |
2.0836 |
Madagascar |
2013 |
-0.8985 |
Mali |
2013 |
-0.5875 |
Mauritius |
2013 |
2.314 |
Madagascar |
2014 |
-0.8353 |
Mali |
2014 |
-0.3536 |
Mauritius |
2014 |
2.53 |
Madagascar |
2015 |
-0.7875 |
Mali |
2015 |
-0.2144 |
Mauritius |
2015 |
2.79 |
Madagascar |
2016 |
-0.8014 |
Mali |
2016 |
-0.215 |
Mauritius |
2016 |
2.839 |
Madagascar |
2017 |
-0.5535 |
Mali |
2017 |
-0.1159 |
Mauritius |
2017 |
2.949 |
Madagascar |
2018 |
-0.6775 |
Mali |
2018 |
-0.1654 |
Mauritius |
2018 |
2.894 |
Malawi |
2002 |
-1.2428 |
Mauritania |
2002 |
-1.2333 |
Morocco |
2002 |
-1.0376 |
Malawi |
2003 |
-1.2391 |
Mauritania |
2003 |
-1.2288 |
Morocco |
2003 |
-0.9734 |
Malawi |
2004 |
-1.2053 |
Mauritania |
2004 |
-1.0559 |
Morocco |
2004 |
-0.2452 |
Malawi |
2005 |
-1.1721 |
Mauritania |
2005 |
-1.0437 |
Morocco |
2005 |
0.2191 |
Malawi |
2006 |
-1.169 |
Mauritania |
2006 |
-1.0241 |
Morocco |
2006 |
0.4127 |
Malawi |
2007 |
-1.1343 |
Mauritania |
2007 |
-0.9982 |
Morocco |
2007 |
0.6171 |
Malawi |
2008 |
-1.1283 |
Mauritania |
2008 |
-0.9657 |
Morocco |
2008 |
1.259 |
Malawi |
2009 |
-1.0607 |
Mauritania |
2009 |
-0.9481 |
Morocco |
2009 |
1.7439 |
Malawi |
2010 |
-0.9832 |
Mauritania |
2010 |
-0.8569 |
Morocco |
2010 |
2.3928 |
Malawi |
2011 |
-0.8882 |
Mauritania |
2011 |
-0.8338 |
Morocco |
2011 |
2.28 |
Malawi |
2012 |
-0.8119 |
Mauritania |
2012 |
-0.7816 |
Morocco |
2012 |
2.7962 |
Malawi |
2013 |
-0.7571 |
Mauritania |
2013 |
-0.661 |
Morocco |
2013 |
2.9362 |
Malawi |
2014 |
-0.7068 |
Mauritania |
2014 |
-0.4123 |
Morocco |
2014 |
3.041 |
Malawi |
2015 |
-0.5308 |
Mauritania |
2015 |
-0.1586 |
Morocco |
2015 |
3.0652 |
Malawi |
2016 |
-0.4266 |
Mauritania |
2016 |
-0.0107 |
Morocco |
2016 |
3.1199 |
Malawi |
2017 |
-0.3122 |
Mauritania |
2017 |
0.1093 |
Morocco |
2017 |
3.3286 |
Malawi |
2018 |
-0.3694 |
Mauritania |
2018 |
0.0493 |
Morocco |
2018 |
3.2243 |
Mozambique |
2002 |
-1.2378 |
Niger |
2002 |
-1.2472 |
Rwanda |
2002 |
-1.2394 |
Mozambique |
2003 |
-1.2275 |
Niger |
2003 |
-1.2455 |
Rwanda |
2003 |
-1.2357 |
Mozambique |
2004 |
-1.1368 |
Niger |
2004 |
-1.2151 |
Rwanda |
2004 |
-1.2308 |
Mozambique |
2005 |
-1.1031 |
Niger |
2005 |
-1.2108 |
Rwanda |
2005 |
-1.2162 |
Mozambique |
2006 |
-1.0479 |
Niger |
2006 |
-1.2051 |
Rwanda |
2006 |
-1.1532 |
Mozambique |
2007 |
-1.0175 |
Niger |
2007 |
-1.1926 |
Rwanda |
2007 |
-1.1374 |
Mozambique |
2008 |
-0.9426 |
Niger |
2008 |
-1.1626 |
Rwanda |
2008 |
-1.0157 |
Mozambique |
2009 |
-0.8471 |
Niger |
2009 |
-1.1441 |
Rwanda |
2009 |
-0.8376 |
Mozambique |
2010 |
-0.7204 |
Niger |
2010 |
-1.1284 |
Rwanda |
2010 |
-0.7807 |
Mozambique |
2011 |
-0.6749 |
Niger |
2011 |
-1.0929 |
Rwanda |
2011 |
-0.7571 |
Mozambique |
2012 |
-0.5626 |
Niger |
2012 |
-1.0741 |
Rwanda |
2012 |
-0.6144 |
Mozambique |
2013 |
-0.4141 |
Niger |
2013 |
-1.028 |
Rwanda |
2013 |
-0.5331 |
Mozambique |
2014 |
-0.1645 |
Niger |
2014 |
-1.0009 |
Rwanda |
2014 |
-0.4287 |
Mozambique |
2015 |
0.2606 |
Niger |
2015 |
-0.926 |
Rwanda |
2015 |
-0.0829 |
Mozambique |
2016 |
0.2075 |
Niger |
2016 |
-0.8681 |
Rwanda |
2016 |
0.0132 |
Mozambique |
2017 |
0.2299 |
Niger |
2017 |
-0.5849 |
Rwanda |
2017 |
0.0863 |
Mozambique |
2018 |
0.2187 |
Niger |
2018 |
-0.7265 |
Rwanda |
2018 |
0.0497 |
Namibia |
2002 |
-1.1346 |
Nigeria |
2002 |
-1.2115 |
Senegal |
2002 |
-1.1994 |
Namibia |
2003 |
-1.1011 |
Nigeria |
2003 |
-1.1726 |
Senegal |
2003 |
-1.1468 |
Namibia |
2004 |
-0.7093 |
Nigeria |
2004 |
-1.0329 |
Senegal |
2004 |
-0.8777 |
Namibia |
2005 |
-0.6933 |
Nigeria |
2005 |
-0.7366 |
Senegal |
2005 |
-0.8487 |
Namibia |
2006 |
-0.6811 |
Nigeria |
2006 |
-0.3587 |
Senegal |
2006 |
-0.7905 |
Namibia |
2007 |
-0.6426 |
Nigeria |
2007 |
-0.0537 |
Senegal |
2007 |
-0.7207 |
Namibia |
2008 |
0.1599 |
Nigeria |
2008 |
0.5697 |
Senegal |
2008 |
-0.6822 |
Namibia |
2009 |
0.6951 |
Nigeria |
2009 |
0.945 |
Senegal |
2009 |
-0.6318 |
Namibia |
2010 |
1.2284 |
Nigeria |
2010 |
1.2623 |
Senegal |
2010 |
-0.5967 |
Namibia |
2011 |
1.302 |
Nigeria |
2011 |
1.5334 |
Senegal |
2011 |
-0.4732 |
Namibia |
2012 |
1.2657 |
Nigeria |
2012 |
1.9334 |
Senegal |
2012 |
-0.3796 |
Namibia |
2013 |
1.4578 |
Nigeria |
2013 |
2.3972 |
Senegal |
2013 |
-0.2497 |
Namibia |
2014 |
1.6 |
Nigeria |
2014 |
2.8045 |
Senegal |
2014 |
-0.018 |
Namibia |
2015 |
2.1222 |
Nigeria |
2015 |
3.1732 |
Senegal |
2015 |
0.1844 |
Namibia |
2016 |
2.7005 |
Nigeria |
2016 |
3.3088 |
Senegal |
2016 |
0.3712 |
Namibia |
2017 |
2.7879 |
Nigeria |
2017 |
3.2127 |
Senegal |
2017 |
0.5541 |
Namibia |
2018 |
2.7442 |
Nigeria |
2018 |
3.2607 |
Senegal |
2018 |
0.4627 |
Seychelles |
2002 |
-0.6199 |
Sudan |
2002 |
-1.231 |
Tanzania |
2002 |
-1.2331 |
Seychelles |
2003 |
-0.6071 |
Sudan |
2003 |
-1.2206 |
Tanzania |
2003 |
-1.2006 |
Seychelles |
2004 |
1.1664 |
Sudan |
2004 |
-1.2 |
Tanzania |
2004 |
-1.0757 |
Seychelles |
2005 |
1.2 |
Sudan |
2005 |
-1.1639 |
Tanzania |
2005 |
-1.0454 |
Seychelles |
2006 |
1.7152 |
Sudan |
2006 |
-1.0049 |
Tanzania |
2006 |
-0.9933 |
Seychelles |
2007 |
1.9142 |
Sudan |
2007 |
-0.6801 |
Tanzania |
2007 |
-0.9241 |
Seychelles |
2008 |
2.2013 |
Sudan |
2008 |
-0.4683 |
Tanzania |
2008 |
-0.8426 |
Seychelles |
2009 |
2.2982 |
Sudan |
2009 |
-0.2815 |
Tanzania |
2009 |
-0.7064 |
Seychelles |
2010 |
2.3216 |
Sudan |
2010 |
-0.0535 |
Tanzania |
2010 |
-0.5941 |
Seychelles |
2011 |
2.4558 |
Sudan |
2011 |
0.114 |
Tanzania |
2011 |
-0.4707 |
Seychelles |
2012 |
2.8337 |
Sudan |
2012 |
0.3331 |
Tanzania |
2012 |
-0.3915 |
Seychelles |
2013 |
3.4997 |
Sudan |
2013 |
0.4141 |
Tanzania |
2013 |
-0.3502 |
Seychelles |
2014 |
3.6053 |
Sudan |
2014 |
0.5092 |
Tanzania |
2014 |
-0.151 |
Seychelles |
2015 |
3.7324 |
Sudan |
2015 |
0.6119 |
Tanzania |
2015 |
0.1361 |
Seychelles |
2016 |
4.0739 |
Sudan |
2016 |
0.6853 |
Tanzania |
2016 |
0.2685 |
Seychelles |
2017 |
4.4635 |
Sudan |
2017 |
0.8397 |
Tanzania |
2017 |
0.4033 |
Seychelles |
2018 |
4.2687 |
Sudan |
2018 |
0.7625 |
Tanzania |
2018 |
0.3359 |
South Afr. |
2002 |
-0.7111 |
Swaziland |
2002 |
-1.1722 |
Togo |
2002 |
-1.2066 |
South Afr. |
2003 |
-0.6412 |
Swaziland |
2003 |
-1.1445 |
Togo |
2003 |
-1.1964 |
South Afr. |
2004 |
0.6311 |
Swaziland |
2004 |
-0.8334 |
Togo |
2004 |
-1.0677 |
South Afr. |
2005 |
0.6467 |
Swaziland |
2005 |
-0.6257 |
Togo |
2005 |
-1.0524 |
South Afr. |
2006 |
0.8142 |
Swaziland |
2006 |
-0.5273 |
Togo |
2006 |
-1.039 |
South Afr. |
2007 |
1.0359 |
Swaziland |
2007 |
-0.4743 |
Togo |
2007 |
-1.0207 |
South Afr. |
2008 |
1.6089 |
Swaziland |
2008 |
-0.2173 |
Togo |
2008 |
-1.0071 |
South Afr. |
2009 |
2.0285 |
Swaziland |
2009 |
-0.1196 |
Togo |
2009 |
-0.9834 |
South Afr. |
2010 |
2.8954 |
Swaziland |
2010 |
0.0796 |
Togo |
2010 |
-0.965 |
South Afr. |
2011 |
3.6571 |
Swaziland |
2011 |
0.4157 |
Togo |
2011 |
-0.9277 |
South Afr. |
2012 |
4.04 |
Swaziland |
2012 |
0.6667 |
Togo |
2012 |
-0.8511 |
South Afr. |
2013 |
4.4731 |
Swaziland |
2013 |
0.8828 |
Togo |
2013 |
-0.7932 |
South Afr. |
2014 |
4.9207 |
Swaziland |
2014 |
1.1639 |
Togo |
2014 |
-0.7625 |
South Afr. |
2015 |
5.3347 |
Swaziland |
2015 |
1.1269 |
Togo |
2015 |
-0.7329 |
South Afr. |
2016 |
5.3342 |
Swaziland |
2016 |
1.254 |
Togo |
2016 |
-0.5082 |
South Afr. |
2017 |
5.491 |
Swaziland |
2017 |
1.1904 |
Togo |
2017 |
-0.4434 |
South Afr. |
2018 |
5.4126 |
Swaziland |
2018 |
1.2222 |
Togo |
2018 |
-0.4758 |
Tunisia |
2002 |
-1.011 |
Zambia |
2002 |
-1.2302 |
|||
Tunisia |
2003 |
-0.9322 |
Zambia |
2003 |
-1.2061 |
|||
Tunisia |
2004 |
-0.4801 |
Zambia |
2004 |
-1.1223 |
|||
Tunisia |
2005 |
-0.3448 |
Zambia |
2005 |
-1.0589 |
|||
Tunisia |
2006 |
-0.1203 |
Zambia |
2006 |
-0.9602 |
|||
Tunisia |
2007 |
0.1928 |
Zambia |
2007 |
-0.8825 |
|||
Tunisia |
2008 |
0.7242 |
Zambia |
2008 |
-0.7775 |
|||
Tunisia |
2009 |
1.1276 |
Zambia |
2009 |
-0.6584 |
|||
Tunisia |
2010 |
1.379 |
Zambia |
2010 |
-0.4527 |
|||
Tunisia |
2011 |
1.5456 |
Zambia |
2011 |
-0.3297 |
|||
Tunisia |
2012 |
1.6854 |
Zambia |
2012 |
-0.1487 |
|||
Tunisia |
2013 |
1.823 |
Zambia |
2013 |
-0.0281 |
|||
Tunisia |
2014 |
2.0078 |
Zambia |
2014 |
0.1687 |
|||
Tunisia |
2015 |
2.1029 |
Zambia |
2015 |
0.3131 |
|||
Tunisia |
2016 |
2.2769 |
Zambia |
2016 |
0.5293 |
|||
Tunisia |
2017 |
2.5178 |
Zambia |
2017 |
0.6641 |
|||
Tunisia |
2018 |
2.3973 |
Zambia |
2018 |
0.5967 |
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1. See’ classification in Aappendix A, Table A 1.