Financial Literacy amongst Small Scale Farmers in Zambia

International Journal of Business Marketing and Management (IJBMM)
Volume 7 Issue 4 July-Aug 2022, P.P. 76-88
ISSN: 2456-4559
www.ijbmm.com
International Journal of Business Marketing and Management (IJBMM) Page 76
Financial Literacy amongst Small Scale Farmers in
Zambia
1
Mr Eledy Sakala, 2
Dr. Stephen C. Mpembele, AZIBFS
1
Lecturer, Researcher and Consultant,Accounting, Banking and Finance Copperbelt University,
School of Business,Jambo Drive, Riverside,P.O Box 21692,Kitwe, Zambia.
PhD student in Accounting and Finance
2
Lecturer, Researcher and Consultant Accounting, Banking and Finance
Copperbelt University,School of Business,Jambo Drive, Riverside,P.O Box 21692,
Kitwe, Zambia.Tel: 0212 225556(Home) Cell: 0955/0966 598726/0978 411004
Abstract: This study aimed to determine the levels of financial literacy of Small Scale Farmers and to establish
the link with their usage of financial services.
The OECD/INFE financial literacy measurement household telephone survey questionnaire was adapted and
administered to Small Scale Farmers. Financial literacy was measured by adding up scores in financial
knowledge, financial attitude and financial behaviour. Financial service usage was assessed by asking
respondents whether the respondents had used any of the specified services. Pearson’s Chi-square test for
independence was used to test the hypotheses as categorical variables were mostly involved.
Most respondents (90.6%) scored low to medium (0 – 14.9). Only the respondent’s educational level had a
significant and positive effect on the financial literacy score. Registration status and annual sales revenue and
had an effect on financial service usage.
However, the majority of the farmers (73.5%) had used financial services. This could be attributed to the
broader definition of financial services used for the study.
Field of Research: Finance
Keywords: Financial Literacy, Small Scale Farmers, Financial Services, Usage.
I. Introduction
Given the high levels of poverty and unemployment in Africa, economic growth is necessary to move
the majority of people out of poverty. The contribution of well-functioning financial markets to economic
growth and development is generally not disputed. However, low levels of financial inclusion have been
recorded in Africa, with many economic units financially excluded due to constraints on the supply and the
demand side of financial services.
One effective way to grow economies and create employment is to foster entrepreneurship through SMMEs
(Ministry of Commerce, Trade and Industry, MCTI, 2006). Despite the informal nature of most Small Scale
Farmers in developing countries, studies have shown that they have the ability to drive economic development
(Tang & Boon, 1996; International Monetary Fund, IMF, 2012). However, access to financial services has been
identified as a major hindrance to the growth of SMMEs and small scale farmers in particular, in Africa (African
Development Bank, ADB, 2012). On the demand side of financial services, lack of awareness of such services
on the part of SMME owners including Small Scale Farmers has been identified as a major factor (Financial
Sector Deepening Kenya, 2013).
According to Fin Scope Zambia (2009), the main barriers to financial access include poor physical access,
unaffordable and inappropriate financial products, lack of confidence in the formal financial services, and poor
understanding of available products and services. The last two barriers relate to what is referred to as financial
literacy. Financial literacy is variously described as financial capability, financial education, consumer literacy,

Financial Literacy Amongst Small Scale Farmers In Zambia
financial culture, or financial insight. Atkinson and Messy (2012:14), define financial literacy as ―a combination
of awareness, knowledge, skill, attitude and behaviour necessary to make sound financial decisions and
ultimately achieve individual financial well-being‖.
Financial literacy has attracted attention around the world because of its importance in financial decision
making. A number of economic, demographic and social developments have further highlighted its significance.
These include over-indebtedness, financial scams, financial crises, globalisation, changing demographics, and
sophisticated and complex financial products and services (Organisation for Economic Co-operation and
Development, OECD, 2005). Information asymmetry in financial markets implies that financial service
providers possess information advantage over consumers which can be detrimental to consumers. Market
conduct rules, transparency and disclosure norms enshrined in consumer protection regimes may compliment
financial literacy efforts. The Fin Scope Survey of 2009 found that 16% of adults in Zambia reported using more
than half of their income to service debt1
, while 18% said they had missed a loan repayment in the previous 12
months (Fin Scope, 2009).
Financial literacy benefits everyone: consumers, financial market players and institutions, government,
employers, civil society organisations and donors. Financial literacy encourages appropriate and productive use
of financial products and services by individuals, households and business Enterprises (OECD, 2013). Several
countries have now embraced financial literacy at policy level. The Zambian government has adopted the
National Strategy on Financial Education (NSFE) and the New National Education Curriculum, and also
participates in the annual International Youth Literacy week (Musona, PiprekGerda & Simbotwe, 2011; World
Bank, 2012).
1.1 The Research Problem
Small Scale farmers and other SMMEs have been identified as a channel to create more equitable
income distribution and much-needed jobs (MCTI, 2006). However, a major challenge facing SMMEs and
Small Scale Farmers in emerging countries is that they fail to survive beyond their launch, and those that survive
do not grow for a number of reasons, and most often remain informal (Ihua, 2009; Franco & Haase, 2010).Most
Small Scale farmers remain peasant farmers forever.
Despite notable growth in access to financial services in the country, challenges remain, especially amongst the
vulnerable and marginalised groups such as the youth, women, peasant farmers, rural inhabitants, and the low
income groups, the self-employed and those in the informal sector. The reasons for this include poor physical
access and usage barriers (Fin Scope, 2005, 2009, Financial Sector Deepening Zambia, FSDZ, 2016).
This study, thus, identified financial literacy and financial services utilisation as the potential causes of the
failure and lack of growth of Small Scale Farmers.
1.2 Research Objectives
The main objective was to measure the levels of financial literacy among the Small Scale Farmers and
to determine the extent to which this impacts their’ utilisation of financial services in three rural and three urban
districts of Zambia. Other objectives were to establish the determinants of financial literacy among Small Scale
Farmers in Zambia, and to determine the extent to which individual farmer’s characteristics affect Enterprise
financial service usage.
II. Literature Review
In the broader sense, literacy consists of the use and understanding and knowledge of words, symbols
and arithmetic operations. This idea of literacy is applied to particular skill set areas such as computer literacy
(Wecker, Kohnle, & Fischer, 2007).
Financial literacy can thus be seen as having two broad dimensions: understanding financial matters or having
financial knowledge, and using or applying such knowledge (Lusardi & Turfano, 2009; Huston, 2009). Ability
1
The regulatory threshold is 35% of disposable income; this is monitored by the central bank
through inspections.

and confidence is required to use financial knowledge to make decisions to improve one’s financial well-being;
this is considered an appropriate financial behavior.
Measuring financial literacy thus entails assessing how well an individual can understand and use financial
information. However, there is no common measure of financial literacy. Different measures have, thus, been
used by researchers (USAID, 2009; Oseifuah, 2010; Wise, 2013; Sucualu, 2013). Most studies have used scores
and indices as proxies based on financial knowledge, behaviours, attitudes and financial experiences (Oseifuah,
2010; Huston, 2010).
Miller, Godfrey, Levesgne, and Stark (2009) argue that while there is widespread agreement that financial
literacy is generally low worldwide, there is relatively little objective and comparative data. In addition,
consumers tend to overestimate their financial skills.
Several studies around the world reveal widespread financial illiteracy especially among specific population
groups (Bernheim, 1995; Klapper, Lusardi and Panos, 2012). Lusardi and Mitchell, (2011). Lusardi and Turfano
(2009), established a relationship between gender, age and educational level, and financial literacy.
Few studies have been conducted on financial literacy in Africa and so estimates of financial literacy levels are
either scarce or non-existent (Sayinzoga, Bulte and Lensink, 2014). But the few studies done reveal varying
levels of financial literacy, but generally low, amongst different demographic groups across Africa (Lusimbo &
Muturi, 2016).
A diagnostic review of consumer protection and financial literacy in Zambia found high levels of financial
exclusion associated with the high cost of financial products and low consumer confidence in formal financial
providers (Musona et al, 2013).
Lack of awareness and appreciation of financial services and how to access them (financial literacy) has been
identified as one of the major factors hindering Small Scale Farmers’ access to financial services and financial
inclusion (ADB, 2012).
The Consumer Buying Decision economic model is based on assumptions of rationality, near perfect knowledge
and utility maximization; the consumer will take all available information into account in an objective manner
before making a decision (Nicosia, 1966).
Kotler, Keller and Jha (2009) identify five stages in the purchase process: Problem or need recognition,
information search, evaluation of alternatives, purchase, and post-purchase behaviour. Each stage requires
awareness and knowledge to make the decision. The particular order the consumer takes will depend on the
nature of the purchase, the importance of the purchase, the uncertainty of the task, the extensiveness of choice,
and perceived buyer power (Bunn, 1993).
Given the complexity and uncertainty involved, prospective consumers of financial services also proceed within
the above framework; they need information on, about, and around the service. They need knowledge,
awareness and skills in relation to the product or service. Financial knowledge, skills and confidence result in
positive financial behaviour exhibited through appropriate usage of basic financial services, subsequently
moving on to more sophisticated services (Nunno & Andoh, 2012).
This conceptualisation is in line with the definition of financial access in Finscope surveys which define
financial access as ―the ability of an individual to obtain, and on a sustainable basis, use financial services that
are affordable and appropriate to their financial needs‖ (Finscope, 2009:2).
This study focused on Small Scale Farmers in their capacity as decision-makers who may access financial
services for the farm as individuals as there is usually no legal or practical distinction between the individual
owner and the farm/Enterprise.
USAID (2009:5) defines a financially literate SMME/farm owner/manager as ―someone who knows what the
most suitable financing and financial management options for his or her business at the various growth stages of
his/her business; knows where to obtain the most suitable products and services; and interacts with confidence
with the suppliers of these products and services. He or she is familiar with the legal and regulatory framework
and his or her rights and recourse options‖.

Applying the above definition, and drawing on Kotler et al.’s (2009) five decision stages, for a farm owner to
decide to utilise financial services, he or she needs to be aware of his or her needs.
The theoretical framework for this study was adapted from the OECD/INFE (2012) under which the
measurement of financial literacy is based on Atkinson and Messy’s (2012) definition of financial literacy. A
financially literate person is expected to have basic knowledge of key financial concepts and the ability to
display numeracy skills. He or she is also expected to exhibit positive attitudes and preferences in financial
matters. Underlying traits relating to knowledge, skills, attitudes and behaviours are thus identified to measure
financial literacy.
2.1 Hypothesis of the Study
In line with the conceptual framework the following sets of hypothesis were developed:
Set A:
Hi: the Small Scale Farmer’s financial literacy has an effect on the Enterprise’s usage of financial services.
Set B:
Hi: the gender of the Small Scale Farm owner has an influence on his/her financial literacy score.
Hi: the level of education of the Small Scale Farm owner has an influence on his/her financial literacy score.
Hi: the age of the Small Scale Farm owner has an influence on his/her financial literacy score.
Set C
Hi: the level of sales revenue of the Small Scale Farmer affects the Small Scale Farmer’s usage of financial
services.
Hi: the investment outlay of the Small Scale Farmer affects the Small Scale Farmer’s usage of financial services.
Hi: the location of the Small Scale Farmer affects the Small Scale Farmer’s usage of financial services.
Hi: the period of activity or existence of the farm affects the Small Scale Farmer’s usage of financial services.
Hi: the registration status of the Small Scale Farmer affects the Small Scale Farmer’s usage of financial services.
Hi: the nature of business formation of the Small Scale Farm affects the Small Scale Farmer’s usage of financial
services.
Hi: the number of employees the Small Scale Farmer have affects the Small Scale Farmer’s usage of financial
services.
Hi: the number of hours worked affects the Small Scale Farmers’ utilisation/usage of financial services.
Hi: the regularity of income/sales revenue affects the Small Scale Farmer’s usage of financial services.
III. Methodology
This was an exploratory, explanatory and descriptive empirical study that employed the survey research
methodology. Data was collected by means of a paper-based, self-completion, structured and pre-defined
questionnaire. The questionnaires were administered by the Alliance of Zambia Informal Economy Associations
(AZIEA).
The study adapted the OECD/INFE financial literacy measurement core questionnaire for informal Enterprise
owners with three explanatory variables: gender, level of education, and age.

The level of financial literacy was measured and scored in line with Table 1.1 below:
Table 1.1 Financial Literacy Measure
Component Measure Maximum Score
Financial
Knowledge
Eight questions on a range of basic financial topics with
correct and wrong answers. Correct response scored 1.
Maximum score 8 with 6
and above considered high
score.
Financial
Attitude
Three attitude statements on money and planning for the
future requiring respondents to agree or disagree on a scale
ranging from: Completely agree = 1 to Completely
disagree = 5. Correct Attitude was given either 1 or 2
points depending on how positive the attitude is.
Maximum score was 5
with an attitude indicator
of more than 3 considered
high.
Financial
Behaviour
Nine behaviour questions with responses on a scale ranging
from Never = 1 to Always = 5. Correct behaviour was
given either 1 or 2 points depending on the strength of the
behavior.
Maximum score of 9 with
6 and above considered
high.
Financial
Literacy
Overall financial literacy score ranging from 0 to 22. Maximum score of 22 with
average combined score of
13.7 for OECD/INFE pilot
study countries.
Source: Author, April 2022
Small Scale Farmers’ characteristics were analysed in relation to utilisation of financial services, whereas owner
characteristics were analysed in relation to financial literacy scores attained.
The sample size was 3 000 but only 1000 (33.33%) correct responses were received. The survey areas were
divided equally between rural and urban districts where financial services can currently be physically accessed.
The population was 822 000 informal Enterprises split into 419 000 urban and 404 000 rural (Fin scope, 2009).
Purposive and convenience sampling was used to select 6 districts with a with a quota of 500 for each.
Individual informal Enterprises were then randomly selected.
The OECD/INFE coding guide was used to code responses to the survey questions and then inputted to SPSS
Version 20 for analysis. Secondary data on financial literacy and informal Enterprises’ access to financial
services was obtained by way of desktop research.
In line with Pauly and Satterwaite (1981) and Nunno and Andoh (2012), the basic empirical model for analysis
was as follows:
(1) (2).
= usage of financial service (0, 1)
= financial literacy level of owner of informal Enterprise
= vector of owner characteristics relevant to obtaining
= Small Scale Farmer characteristics (REV, INVES, LOC, ACT, PER, REG)
and = are random error terms
= the mechanical interpretation may relate to imposed financial service usage.
and are partial regression co-efficients.
Equation 1 expresses the relationship between usage of financial services and the financial literacy of the Small
Scale Farmer, with usage taking any of the following: transactions, investment, credit or risk management
services.
Equation 2 regresses the financial literacy of the Small Scale Farmer on Small Scale Farmer’s characteristics
namely: age (AGE), gender (GEN), and level of education (EDU).

(3). =
= random error terms.
to = relevant co-efficients
Equation 3 specifies Small Scale Farmers’ characteristics that may be determinants of financial service usage:
sales revenue (REV), investment outlay (INVES), location (LOC), period of activity (PER), and registration
status (REG) (Nunno & Andoh, 2012).
IV. Data Analysis and Results
The data was analysed and interpreted in line with the OECD/INFE pilot survey (OECD, 2012). Under
the OECD/INFE approach, financial literacy consists of financial knowledge, financial behaviour and financial
attitudes (See Table 1.1 above).
Of the 3 000 questionnaires distributed, only 1 000 responses were received giving a response rate of 33.33%.
Though the response rate was low it exactly meets the OECD/INFE threshold of a minimum of 1 000
respondents.
In terms of financial knowledge, only 8% of respondents scored 6 and above which is considered a high score,
with 92% scoring below 6. The most frequent score was 4 with 13.2% of respondents scoring 4. This is an
indication that overall, Small Scale Farmers in Zambia, have low levels of financial knowledge.
In the OECD/INFE pilot study of 14 countries around the world, most countries recorded scores below six.
South Africa, Peru and Norway recorded the lowest scores for financial knowledge although the latter used
different questions. At the other end Malaysia, Germany, Czech Republic, Ireland, Island, British Virgin Island,
and Estonia had higher average scores. Hungary had the highest score at 69 %( Atkinson & Messy, 2012).
In terms of financial attitude, out of the 1 000 respondents 11.4% scored 3 or more which is considered a high
score with 88.6% scoring below 3. The most frequent score was 2 with 26.2% of respondents achieving the
score. These are much lower scores than OECD/INFE pilot study findings.
In the OECD/INFE pilot study, Peru had the highest attitudes and preferences score with 71% of the
respondents scoring above the average attitude indicator of above 3 followed by Albania and Hungary, British
Virgin Islands and Germany and with Armenia having the lowest score followed by Poland, Estonia, the UK
and Ireland .
In terms of financial behaviour, out of the 1 000 respondents, 8% scored 6 or above which is considered high,
with 92% scoring below 6. The most frequent score was 4 with 13.2% of the respondents achieving this score.
In comparison, there were large variations in scores in the OECD/INFE study, with Hungary recording the
lowest saving behaviour at 27% and Malaysia the highest at 97 % (Atkinson & Messy, 2012).
Armenia had the highest negative behavior (borrowing) at 47%. British Virgin Islands had the highest positive
behaviour with 71% achieving at least a score of 6. The lowest scores were exhibited by Estonia, Hungary and
Albania (Atkinson & Messy, 2012).
The three components’ scores were summed up into a score of financial literacy ranging from 0 to 22. The
average score for the 1 000 respondents was 10.3900, with a mode score of 10 and a standard deviation of
3.43368. The minimum score was 0 and the maximum 21. For purposes of analysis, the scores were classified
into ranges: Low - 0 to 9.9, Medium - 10 to 14.9, and High - 15 to 22.
Just over half of the respondents (50.9%) scored Low, 65.1% scored Medium, and 9.4% scored High with
44.7% scoring above the average score. This is an indication of a low level of financial literacy (See Appendix
1). The OECD/INFE average score was 13.7. However, it should be noted that OECD/INFE survey targeted
households in general whereas this study targeted a specific demographic group – Small Scale Farmers.

These findings are also contrary to findings by Andoh and Nunno, (2012), who found high levels of financial
literacy amongst SMME owners in Ghana. Oseifuah, (2010), also found above average financial literacy levels
amongst youth Entrepreneurs in South Africa.
Set A:
There is a relationship between the financial literacy of the Small Scale Farmer and the Small Scale Farmer’s
usage of financial services (See Appendix 4).
Of the 1 000 respondents, 73.5% had used some or all of the four financial service categories whereas 26.5% of
respondents had not used financial services. The greater proportion of those who scored Low and Medium
actually had their Enterprises using financial services.
The most commonly used services were savings accounts (29.7%) and mobile money transfers (18.8%). Only
1.9% of the respondents had used or were using all the financial services listed. A total of 26.5% had not used
financial services (See Appendix 3).
Only 2 Small Scale Farmer characteristics were found to be significant determinants of financial services usage,
namely: the level of sales revenue and the Small Scale Farmer’s registration status (See Appendix 2).
A significant and positive relationship between the financial literacy score of the Small Scale Farmer and the
Small Scale Farmer’s usage of financial services was established though not very strong. The proportion of
owners that scored High on financial literacy using financial services was higher than those that scored Low.
However, there was also significant usage of financial services by Small Scale Farmers whose owners were
lower scorers (See Appendix 4).
The most significant socio-demographic factor linked to financial literacy was the educational level (See
Appendix 6). Out of the 1000 respondents, 23.1% of respondents had some secondary education and of these
7.1% had low scores, 15.1% had medium scores and 0.6% had high scores; 17.8% had completed primary
school and of these 5% had low scores, 8.3% had medium scores and 0.7% had high scores; 18.4% of the
respondents had some primary school and out of these 3.6% had low scores,6.4% had medium scores and 0.4%
had high scores;13.5% had no formal education and of these 2.0% had low scores, 8.7% had medium scores,
and 2.8% had high scores; 2.9% had technical or vocational training and of these 1.3% had low scores, 1.3%
had medium scores, and 0.3% had high scores; 11.5% had university education and of these 1.4% had low
scores, 7.5% had medium scores, and 2.6% had high scores; 12.8% could not tell their level of education and
out of these 0.7% had low scores, 1.1% had medium scores and none had high scores.
It can be seen from the findings that the largest proportion of the High scores were taken up by the University,
Vocational/technical training and completed secondary school categories. The Medium scores were mainly
taken up by the University down to some primary school education categories with complete secondary school
and some secondary school education taking up the largest proportions in that order. The Low scores were
mainly taken up by the complete primary school, complete secondary school and some primary school
education categories. But the highest proportion of Low scores was attributed to the no formal education
category.
This is in line with findings of the OECD/INFE pilot survey of fourteen (14) countries. The OECD/INFE pilot
study found the relationship between increased levels of education and the financial literacy scores in all
countries surveyed to be significant and notably strong in Germany, Malaysia and Poland.
These findings are also supported by the findings by Bernheim 1995, Lusardi & Mitchell (2007a, 2007b, 2008,
2011b), Lusardi & Turfano, (2009), Lusardi and Mitchell (2011), and Calvet, Campbell and Sodini (2007,
2009).
There was no significant relationship established between financial literacy and the age and gender of the Small
Scale Farm owner. The majority of the respondents were male and most were aged between 30 and 49 (See
Appendix 5).
However, these findings are contrary to findings in the OECD/INFE pilot study which found that age has an
effect on financial literacy levels with the middle-aged scoring higher and the youngest and oldest scoring

lowest. However the relationship between age and financial literacy was less clear in Armenia, Poland and
South Africa.
These findings also contradict findings by Agarwal et al (2009), Lusardi and Mitchell (2011), and Lusardi et al
(2010), who found financial literacy low amongst the young.
Another factor considered in the analysis was the distribution of total financial literacy scores by location of the
respondent. There was no significant difference between the total financial literacy scores between the rural and
urban-based Small Scale Farm owners. Of the 177 rural-based respondents, 24.9% scored low, 65.0% scored
medium and 10.2% scored high. For the 823 urban-based respondents, 25.5% scored low, 65.1% scored medium
and 9.2% scored high.
This may be explained by the fact that the survey was conducted amongst a group of Small Scale Farmers with
similar characteristics; the majority of the respondents had minimal or no education irrespective of their
location. Because of low education levels, the majority may have failed to be absorbed in the formal
employment sector and ended up in the Small Scale Farmers Sector not so much by design but by the force of
circumstances.
The only Enterprise characteristics with a significant effect on financial usage were registration Small Scale
Farmers Association of Zambia and/or other authorities, notably the local authorities that issue operating
licenses and chiefs, and the level of sales revenue especially at the lower level (See Appendix 4).
V. Discussion of Findings, Conclusions and Recommendations
The study determined levels of financial literacy of Small Scale Farmers and related this to usage of
financial services by their Enterprises, and established that a positive and significant relationship exists between
the two variables. However, there was also significant usage of financial services even by Small Scale Farm
whose owners were lower scorers.
The study also established that the level of education is a key determinant of the level of financial literacy.
These findings may be explained by the fact most of the respondents were based in peri urban areas where there
was a much higher response rate and where there is greater financial inclusion and exposure to financial services
than in rural areas with urban inclusion standing at 70.3% in 2015 up from 42% in 2009 compared with rural
inclusion at 50.1% in 2015 up from 34.4% in 2009 (Financial Sector Deepening Zambia, 2016). General literacy
levels are also higher compared to rural areas due to better access to schools and other education facilities,
though financial literacy score levels were evenly distributed between rural and urban areas.
Financial usage for the research was broadly defined to include mobile-phone based financial services such as
money transfers which are now widely accessible in rural areas. This broad definition captured more financial
service users than may have been the case with a narrower definition.
The study also established that Small Scale Farmers characteristics such as registration and licensing with Small
Scale Farmers Association of Zambia and/or other authorities, notably the local authorities and chiefs, and level
of sales revenue at lower levels have significant effect on Small Scale Farmers usage of financial services.
Most of the respondents were involved in agriculture. With minimal investment outlays compounded by limited
access to external financing, most of their transactions would be on cash basis limiting usage of financial
services.
The Zambian Government and other stakeholders in the country’s financial system should design appropriate
and well-targeted strategies to improve the financial literacy of Small Scale Farmers. This will enhance the
overall prospects of Small Scale Farmers by improving their access to and usage of financial services, especially
credit which the literature notes is a major obstacle to the growth of this sector. The sector has the greatest
potential to address unemployment and poverty challenges in the country.
.

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Appendices.
Appendix 1 Total_Financial_Literacy_Score
N
Valid 1000
Missing 0
Mean 10.3900
Median 10.0000
Mode 10.00
Std. Deviation 3.43368
Skewness .182
Std. Error of Skewness .077
Kurtosis .058
Std. Error of Kurtosis .155
Minimum .00
Maximum 21.00
Frequency Percent Valid Percent Cumulative
Percent
Valid
Low 255 25.5 25.5 25.5
Medium 651 65.1 65.1 90.6
High 94 9.4 9.4 100.0
Total 1000 100.0 100.0
Appendix 2 Logistic Regression Model Statistics
Characteristic
Variable
Co-efficient
B
Wald Degrees of
freedom df
P value
Sig.
B Exponent
Exp(B)
Total Financial Literacy
score
0.115 18.513 1 0.000 1.122
Sales Revenue 14.162 4 0.000
Up to K150 000 -0.718 4.287 1 0.038 0.488
K151 000 to K300 000 -0.038 0.012 1 0.915 0.963
K301 000 to K800 000 -0.078 0.045 1 0.832 0.925
Registration -1.500 28.058 1 0.000 0.223
Licencing/Registration 34.698 5 0.000
Local Authority licensing -0.955 1.975 1 0.16 0.385
Chiefs registration 0.126 0.034 1 0.853 1.135
WARMA licensing -0.254 0.129 1 0.719 0.776
ZASSF membership -0.640 0.579 1 0.447 0.527

Appendix 3 Type of Financial Service utilised by Informal Enterprises
Type of Financial Service Number of Respondents Percentage Cumulative Percentage
Pension 72 7.2% 7.2%
Fixed Deposit 39 3.9% 11.1%
Mortgage 10 1.0% 12.1%
Secured Bank Loan 26 2.6% 14.7%
Overdraft 18 1.8% 16.5%
Insurance 20 2.0% 18.5%
Shares 33 3.3% 21.8%
Bond 6 0.6% 22.4%
Savings Account 297 29.7% 52.1%
Mobile Money Transfer 188 18.8% 70.9%
Leasing 8 0.8% 71.7%
All of them 18 1.8% 73.5%
Don’t Know Any 40 4.0% 77.5%
Cannot Answer 225 22.5% 100%
Totals 1000 100% 100%
Source: Survey Results (2022)
Appendix 4 Regression Model Statistics
Relationship Value Degrees of freedom df P value
Sig.
Asymp. Sig.
(2-sided)
Gender and Total Financial
Literacy score
5.147 2 0.076 0.076
Educational level and Total
Financial Literacy score
99.073 14 0.000 0.000
Age and Total Financial Literacy
score
23.034 14 0.060 0.076
Appendix 5 Financial literacy score by Gender
Gender Low score
0 to 9.9
Medium score
10 to 14.9
High score
15.0 to 22
Total
Male 12.2% 36.3% 5.4% 46.1%
Female 13.3% 28.8% 4.0% 53.9%
Total 25.5% 65.1% 9.4% 100%

Appendix 6 Financial Literacy score by level of education attained
Education level Low score
0 to 9.9
Medium score
10 to 14.9
High score
15.0 to 22
Total
University 2.0% 8.7% 2.8% 13.5%
Technical/vocational 1.4% 7.5% 2.6% 11.5%
Completed secondary
school
4.4% 16.7% 2.0% 23.1%
Some secondary
schooling
7.1% 15.1% 0.6% 22.8%
Completed primary
school
5.0% 8.3% 0.7% 14.0%
Some primary
schooling
3.6% 6.4% 0.4% 10.4%
No formal education 1.3% 1.3% 0.3% 2.9%
Cannot answer 0.7% 1.1% 0.0% 1.8%
Total 25.5% 65.1% 9.4% 100%
Appendix 7 Financial Literacy score by Age
Age
Band/Answer
Low score
0 to 9.9
Medium score
10 to 14.9
High score
15 to 22
Total
Cannot Answer 0.6% 0.7% 0.0% 1.3%
18 – 19 1.1% 4.5% 0.4% 6.0%
20 – 29 4.8% 11.5% 2.2% 18.5%
30 – 39 7.5% 16.9% 3.1% 27.5%
40 – 49 5.5% 15.2% 3.1% 22.2%
50 – 59 4.6% 14.8% 1.7% 21.1%
60 – 69 0.8% 1.1% 0.2% 2.1%
70 – 79 0.6% 0.4% 0.3% 1.3%

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