In order to obtain a deeper understanding of the data I conducted out an analytical study of the FinAccess 2009 dataset on informal groups (Malkamäki 2011) and in this as well as in the two following subsection the findings are reported.20 For the analysis, cross-tabulations are to study the percentage of the population using a particular service. Regression techniques were also used to identify which socioeconomic characteristics are most associated with access to services.21 Informal financial groups consist of ROSCAs, ASCAs and Welfare groups, but the discussion is focused on the two former types as they intermediate funds. That is, because welfare groups do not do so and instead, provide financial support for group members and their next of kin in the case of illness, death and other emergencies, they are beyond the scope of the thesis.
20 Similar analysis from the 2013 dataset is not yet available
21 Regressions in my paper were undertaken for the Johnson and Arnold (2012) paper by Arnold so there were two outputs from the same analysis.
89 As table 4.2 shows, 21.4 % of adult Kenyans were members of ROSCAs and 5.8% participated in ASCAs in 2013. It is notable that the share of ROSCA users has been substantially reduced since 2009, when 31.7% of the population used them. Women have traditionally been the dominant users of informal groups and in 2006 36.0% were members of one or several ROSCAs, which dropped to around 28% in 2013. By contrast, only 14.8% of men belonged to these groups in 2009, which remained approximately the same up until 2013. Historically, ROSCAs used to be a rural phenomenon, but nowadays they are popular both in rural and urban areas, thus indicating that they not only thrive on the intimacy of village identity but also the urban setting.
Table 4.2. Informal group and formal financial service use in Kenya Financial service
% of adult pop (weighted)
FinAccess 2006 (n=4418)
FinAccess 2009 (n=6343)
FinAccess 2013 (n=5849)
ROSCA 29.3 31.7 21.4
Women 36.0 39.6 27.9 Men 22.2 14.8 14.5 Rural 30.4 31.4 21.0 Urban 26.2 32.7 22.1
ASCA 5.7 7.8 5.8
Women 5.9 8.2 7.2 Men 4.9 3.9 4.4 Rural 6.1 8 5.5 Urban 3.2 7.7 6.6 Source: FinAccess surveys 2006, 2009 and 2013.
Table 4.2 also reports that in 2006 ASCAs were twice as popular in rural areas than in urban areas, but over the years their usage in urban areas has increased and in fact, in 2013 they were somewhat more popular in urban areas. Moreover, women also use ASCAs more frequently than men. Figures 4.2 and 4.3 below illustrate maps from a report that used 2009 data to produce a regional perspective on the density of ROSCA and ASCA usage, showing that high density areas are in Central , Eastern and Nyanza (Johnson and Fouillet 2011).
90 Figure 4.2 Estimated ROSCA use in 2009
Source: Johnson and Fouillet (2011)
91 Figure 4.3. Estimated ASCA use in 2009
Source: Johnson and Fouillet (2011)
Table 4.3 reports the percentage in each sub-groups that are using ROSCA or ASCA service.
Hence, instead of analysing what proportion of ROSCA users are female or male (e.g. 70% and 30% respectively) the table looks at the proportion of women that are ROSCA users (39.6%) compared to men (14.8%). This approach enables us to see the overall access in relation to each sub-category. The table highlights that the highest proportions of people using ROSCAs are in Eastern, Central , Nyanza, Nairobi and Western provinces, with roughly one third of the adult population in these five provinces being members. The highest use of ASCAs is in the Western region, followed by Nyanza, Central and Nairobi. In addition, Nyanza is one of the regions where both ROSCAs and ASCAs are popular and provide financial inclusion in place of the banks (Malkamäki 2011). It is commonly assumed that because ROSCAs and ASCAs are informal services they are more likely to be used by less educated people, but the evidence suggests otherwise. In table 4.3, the proportion of the population with no formal education using ROSCAs was 18.6% , whereas that which had completed either primary or secondary education was 34.9% and 32.4%, respectively.
92 Table 4.3. ROSCA and ASCA membership by socioeconomic characteristics 2223
ROSCA 09
22 This table and the following table 4.4 are sourced from Malkamäki 2011.
23 Table reports proportion respondents within category
93 In order to understand the poverty status of respondents, a self-reported indicator, namely, food security, was used. That is, they were asked how often they did not have enough food to eat. In table 4.3 the proportion of people in ROSCAs who ”often” do not have enough food to eat is 22.9%. The proportion of those who ”never”, ”rarely” or ”sometimes” do not have enough food to eat is 33.5%, 35.7% and 30.5%, respectively, thus indicating that the people who belong to a ROSCA are less likely to experience hunger.24
24 Increase in the ‘often’ and ‘sometimes’ categories for ASCAs might at least partly be related to the fact that several organisations are training ASCA groups in remote rural areas with Village Savings and Loan Association i.e. with SG methods.
94 Table 4.4. Regression analysis – ROSCA and ASCA 200925
Variable ROSCA ASCA
Pension/transfer from family or friend -0.123 *** -0.031 ***
Employed on people’s farm full time/seasonal -0.084 *** 0.031 **
Employed on domestic chores -0.055 -0.012
Government -0.068 * 0.004
Private sector 0.003 0.015
Running own business 0.066 *** 0.031 ***
Sub-letting / earning from investment etc. -0.113 ** 0.007 Dwelling general condition association of socio-economic, demographic and geographic factors with the likelihood of being a member in ROSCA and/or ASCA groups. The regression results were converted into marginal effects, which show the probability of a person with a certain characteristic (e.g. female) being a
25 Base categories for categorical variables to which the other categories are compared in table 4.4 are the following: Urban, Male, No education, Province – Nairobi, Income – sale of own produce from framing and fishing, Housing – temporary and Do not use mobile phone at all.
95 member in a group compared to the missing category (in this case male). Hence, for example the marginal effect coefficient of 0.225 for the variable female in the ROSCA regression indicates that women are 22.5% more likely to be in ROSCAs than men.
In line with table 4.3, also table 4.4. shows that having completed either primary and secondary education significantly increases the probability that the person belongs to a ROSCA. Similarly, having had an education are significantly more likely to use ASCAs than those go without.
Table 4.4. also shows that the expenditure of respondents was also significantly associated with increased probability of ASCA use (Malkamäki 2011). The evidence on food security (in table 4.3.) and on expenditure demonstrates that most people participating in ROSCAs and ASCAs are not those whose sources of income are most uncertain and vulnerable.
Another factor that is significantly associated with the use of ROSCA and ASCA services is users’
reported main source of income for most elements. Table 4.4 indicates how those depending on pensions and transfers, those employed on people’s farms full time or seasonally and those subletting land or rooms are significantly less likely (at the 1% level) to use ROSCAs than those whose main income was from farming. On the other hand, those running their own businesses are significantly more likely to use this service than those who reported that their main income source was farming. With respect to ASCAs, those depending on pensions and transfers are significantly less likely to participate, whilst those employed on people’s farms and those with their own businesses are significantly more likely to use them (Malkamäki 2011). Thus, it transpires that particular income sources decrease or increase the likelihood of being in both ROSCAs and ASCAs: being a pensioner or a recipient of transfers leads to lower engagement, whereas running your own business leads to higher participation. However, interestingly, being employed on someone’s farm full time or seasonally decreases significantly the likelihood to be in a ROSCA, whereas it significantly increases that of being in an ASCA. The findings on the main income sources seem to confirm that people whose sources of income are more insecure and vulnerable, such as those dependant on money transfers (pensioners, students and very poor people), are less likely to use these groups, whereas business people who need to manage their income are more likely to participate.
Compared to the base case of temporary dwelling, living in a permanent, semi-permanent or traditional house is significantly associated with a reduced probability of ROSCA use. On the other hand, living in a permanent or semi-permanent dwelling significantly increases the
96 probability of ASCA use (Malkamäki 2011). This higher probability in these types of dwellings again indicates that it is not the poorest that use ASCAs. The ownership of radios and bicycles is significantly associated with increased probability of ROSCA use, whereas having a car is significantly related to a reduced probability of ROSCA use. The ownership of a car also slightly lessens the probability of ASCA use. This again indicates that whilst the majority of group users are not very poor, they do not come from the top of the distribution since they tend not to own a car. The influence of mobile phone ownership or access to someone else’s is significantly associated with an increased probability of membership of both types of groups. Those who do not own a mobile phone and do not have access to one are significantly less likely to belong to either type of savings scheme.