In this paper, the effects of informal financial literacy training on rural smallholders’ financial behavior was assessed, focusing on the intermediary effects on farmers who are engaged in informal financial markets, particularly the village savings and loans associations (VSLAs). The study is based on the financial literacy training that was offered by different agricultural projects funded by aBi Development Limited in Uganda between 2014 and 2018. It is part of a comprehensive evaluation of the VSLA approach on smallholder farmers in Uganda, which was conducted in January, 2020. The VSLA model was first introduced by CARE in Niger in 1991 as a microfinance model under which saving groups are formed at community level to reduce poverty by socially and financially empowering the poor and vulnerable people (CARE Uganda). The original goal of VSLAs was to provide members especially rural women with access to credit, to meet subsistence needs or invest in income generating activities (Greyling and Rossouw, 2019).

The main objective of promoting VSLAs and training members was to enable farmers increase their savings and access to credit so as to engage in commercial agriculture and strengthen their competitiveness in the domestic and export market. Members of the VSLAs were trained over time in different dimensions of financial literacy including; financial planning and management, financial products and services, importance of saving, record keeping and balancing books. Other areas were budgeting, setting up rules and regulations, financial rules and discipline, how to access credit and investment opportunities. This training is unique in that it did not have a specific curriculum; it was rather informal where farmers were found in their own setup during their regular weekly meetings. As to whether this training model influences farmers’ financial behaviour is the empirical question that was addressed in this study. Specifically, the effects were measured on four dimensions of financial literacy namely; planning and managing finances, financial goals, knowledge about financial products and saving and managing financial shocks. Measuring effects on financial knowledge is vital to understanding the potential impact of literacy programs on the rural population. This evaluation will guide policy and other actors engaged in rural development on financial inclusion of the rural farmers.

Financial literacy is here defined as the ability to use knowledge and skills to manage financial resources effectively for a lifetime of financial wellbeing (Nicolini and Haupt,    2019).  As   farmers  increasingly   participate  in market-oriented production, it is important that they know and understand the forms, functions and use of money and financial services (Aggarwal et al., 2014; Carpena et al., 2011). Financially illiterate households may face negative consequences of their financial decisions. They are likely to be more prone to over-indebtedness and to the use of exploitative sources of borrowing (Guiso and Jappelli, 2008; Abreu and Mendes, 2010). Financial literacy training aims at helping consumers develop the skills and confidence to become more aware of financial risks and opportunities, in order to make informed decisions and to take other effective actions to improve their financial well-being (Mian, 2014; Cole et al., 2009; OECD 2011). Financial education programs therefore, can increase financial knowledge and information seeking behavior such as improved saving and financial decision-making (Gibson et al., 2014; Lusardi 2008) of close to one hundred million adults in sub-Saharan Africa who use informal methods to save and borrow (Klapper and Singer, 2015).

The outcomes of financial literacy training depend on various factors. Cognitive constraints specifically have been reported as key barriers to improving financial knowledge (Jappelli and Padula, 2013; Carpena et al., 2011). The evidence on impact of financial literacy is, in some cases, mixed (Hastings et al., 2013) but largely, studies have shown that financial literacy programs have significant positive effects on financial behavior (Kaiser and Menkhoff, 2017). Sayinzoga et al. (2016) found that training increased financial literacy of participants, changed their savings, borrowing behavior and had positive effects on business start-ups in Rwanda. Similarly, in Tanzania, Krause (2016) found positive effects of financial literacy training on savings ability, employment confidence and personal finance of the youth. Financial literacy education has also positively impacted financial decisions in farming, for instance on rainfall insurance adoption in India (Gaurav et al., 2011) and performance of women farm enterprises in Kenya (Cherotich et al. (2019). However, Stoughton et al. (2011) argues that the impact of financial literacy training is influenced by the delivery mechanism of the training program. When financial advisors are also acting as sellers of financial products the training might be biased and farmers may not demand the financial advice.

Financial literacy is determined by various factors: Education qualifications (Mian, 2014; Lusardi, 2008), annual income and land size holding are reported to have a strong positive association with the level of financial literacy (ZHang and Xiong, 2019; Akoto et al., 2017; Aggarwal et al., 2014). Previous studies also indicate that males are more financially literate than females and that older people have a higher level of financial literacy compared with young people (ZHang and Xiong, 2019; Mian, 2014). In addition, geographic and racial/ethnicity disparities in financial literacy are commonly reported (Xu and  Zia,  2012).  In  Ghana,  for  instance,  Akoto   et   al.  (2017) report higher literacy levels among urban cocoa farmers with increased access to commercial banks. This paper contributes to the existing literature by providing insights on how financial literacy education has impacted the less educated rural farmers.

Estimating the effects of financial literacy training on financial knowledge and behavior of rural farmers presents the common challenge of identifying causal effects. The challenges arise from selection bias and endogeneity associated with placement of the program, omitted variables and measurement errors. Very often, development programs are not randomly offered to the participants. There are driving factors such as convenience of location, infrastructure, previous activities, political influence that determine program placement. Such factors could have influenced the outcome of the program hence leading to overestimation of the outcomes.

Estimation strategy

Previous studies have addressed endogeneity in program evaluation such as training, by using various approaches including; experimental approaches, (randomized control trials and quasi-experiments), propensity score matching (PSM), panel data, instrumental variable and switching regression models. Others employ a combination of different approaches depending on the nature of the training, its design and the data available in order to reach a conclusion of causality. Since financial literacy is not observed, empirical studies face measurement error problems and OLS gives biased estimates due to unobserved factors.

A majority of studies have used experiments while others have used a mixed approach of both surveys and experiments. Cole et al. (2009) used a field experiment and small financial incentives with unbanked households in India and Indonesia to determine the effect of a financial literacy program on demand for financial services. Gaurav et al. (2011) conducted a randomized controlled trial to assess the effect of financial literacy training on personal financial management and on formal hedging of agricultural production risks, specifically rainfall insurance adoption randomly offered to selected farmers in the state of Gujarat. Sayinzoga et al. (2016) also measured the impact of financial literacy training on financial knowledge and behavior of small holder farmers in Rwanda using a field experiment. The experiments have the advantage of minimizing both allocation and selection bias, thus providing the strongest empirical causal evidence as it takes care of both observable and unobservable characteristics of the sample.

A few studies have addressed endogeneity in estimating impact of financial literacy by using the Instrumental variables (IV) approach. However, it is difficult to find a suitable instrument. Behrman et al. (2012) used survey data in Chile and IV approach to estimate the causal effect of financial literacy on wealth accumulation and wealth components. They used 11 instruments and find a strong effect on wealth accumulation. Jappelli and Padula (2013) used pre-labour market entry literacy endowment as an instrument to isolate the causal effect of financial literacy on wealth accumulation in 11 European countries. They assume that the life of a consumer covers two-period; where they earn income in one period 0 and live in retirement in period 1. In the first period, people choose saving and financial literacy investment.

The training evaluated in this study did not apply randomization in its implementation. Since the study is based on data collected from a cross sectional survey, we opt to use propensity score matching (PSM) approach to assess the effects of  financial  literacy training on rural small holders’ financial behavior. Propensity score matching has been used by other similar studies including Krause et al. (2016) who employed propensity score matching and fixed effects estimation methods to assess the changes in knowledge skills and attitude of marginalized youth as a result of financial literacy training program. Cherotich et al. (2019) in Kenya also used PSM to examine the effect of financial knowledge on performance of women farm enterprises using levels of savings and enterprise margins. The PSM approach has also been used to measure the impact of rural residents’ financial education on financial literacy (ZHang and Xiong, 2019).

Analytical framework

In the case of this study, there is self-selection of individuals into membership of VSLAs. Such individuals may have unobservable characteristics or attributes such as entrepreneurship that non-members may not have. Yet, such characteristics may affect program outcomes thus, basic regression analysis for direct measurement of program effects on specific outcome variables produce biased estimates. Moreover, participation in training activities is also a choice that individuals make and this could be influenced by certain characteristics such as education, age etc.

In this study, we used matching techniques based on general household characteristics and other relevant exogenous variables to identify a control/comparison group from the non-beneficiaries of VSLA training; to compare with famers who are in VSLAs that were trained so that we can assess the effects of the training of farmers’ financial behaviour. Propensity score (p) is the conditional probability (P(X)) of a farmer participating in training given observable characteristics (X) that are not affected by the intervention (Rosenbaum and Rubin, 1983). The propensity of observations to be assigned into the treated group are estimated by a Logit model

P(X)=prob(d=1|X=E(d|X);

Where d is the dependent variable; d =1 for farmers participating in training and d =0 for the comparison group. It is assumed that 0 <prob(d=1|X)<1.

This method gives us an indication of what the outcome variables (planning and managing finances, financial goals, level of confidence of a financial plan and knowledge about financial products and saving and financial shocks) would have been for the beneficiaries had they not benefited from VSLAs supported training.

Further, we do a multiple regression analysis to identify the demographic and social economic factors that are associated with financial literacy. The equation is expressed as follows;

Where;  is the financial literacy score for farmer ;   is a dummy variable for participation in financial literacy training.   are parameters to be estimated.   is a vector for social economic and demographic variables believed to be associated with financial literacy.  is the error term.

Study area and sampling

This study was carried out in six districts where aBi had provided funding to development partners whose projects involved financial literacy  training. The   districts   are   Mbarara,   Bushenyi,  Isingiro, Kiboga, Iganga and Mayuge. Most farmers are engaged primarily in small scale agricultural production while a few households are engaged in small businesses. The most common small business activities include trade in produce and small retail shops. Economically, Mbarara and Bushenyi districts can be described as high economic status; Isingiro district is categorized as mid economic status while Iganga, Kiboga and Mayuge are low income economic status.

We used multisampling techniques combining purposive and random sampling procedures to get a representative sample.  From a list of projects funded by aBi, 6 projects which implemented the VSLA approach in various districts were purposively selected. The project implementing partners (IP) in the respective districts provided a list of VSLAs under their management and 6 VSLAs (3 trained and 3 non beneficiaries) from each project were randomly selected. Using a list of members in each VSLA, a random sample of 6 farmers per VSLA was selected, thus, a total of 218 farmers; -110 beneficiaries and 108 non beneficiaries (control group) were interviewed. To ensure that the treated members were as similar as possible to the control group, respondents were selected from similar villages in terms of social economic and geographic characteristics.

Table 1 presents the socio-economic characteristics of sampled farmers. The farmers are of middle age with average age of 43.5 – 46 years. A majority (90.3%) of respondents is married and is from male headed households (80%). The average household size is 8 persons. The respondents’ average education is primary level and their main occupation is agriculture with less than 35% engaged in non-farming business. Over 98% belong to farmers groups and are members of a saving and credit group. Their average land size of 4 acres shows that they are smallholders.

Data collection and type

Primary data were collected using a semi-structured questionnaire which was administered  to  VSLA  training  beneficiaries  and  non-beneficiaries. Interview guides were used for Focus Group Discussions (FGDs) with a few farmers and for interviews with key informants (loans officers, relevant local authorities, chairpersons of farmer groups, Savings and Credit Cooperatives (SACCO) managers and other key actors in financial institutions). To measure financial literacy, we used the OECD/INFE toolkit (2018). However, the original questionnaire was modified to adapt to the Ugandan conditions and to the education level of the rural farmers in the sample. Hence, only questions about basic financial literacy were asked. The questions asked were related to planning and managing finances, level of confidence of a financial plan, knowledge about financial products, saving and financial shocks and financial goals. Other financial literacy indicators such as financial attitude, access to financial services (savings and credit) and supporting institutions, investments and expenditures were assessed. In addition to financial literacy, the survey also captured quantitative data on demographics and other household characteristics that may be important determinants of financial literacy. These include age, income, family size, education, size of land holding etc for both training beneficiaries and the control group.

Village Saving and Loan Associations (VSLAs) were formed to serve smallholders who have limited or no access to formal financial products and services. How does financial literacy training affect their financial decisions and behaviour? In what follows, we assess the impact of training VSLA members on key financial literacy indicators including planning and managing finances, financial goals, level of confidence of a financial plan, knowledge about financial products, saving and financial shocks.  But  first,  we  assess  the  factors  that influence participation in VSLA training.

Determinants of participation in financial literacy training

Prior to estimation of training effects, we use a logit model to predict the probability of smallholder participation in VSLA training activities. The results of the logit formulation of the propensity score are presented in Table 2.  Results show a positive relationship between household size and participation suggesting that famers with a big number of household members are more likely to participate in VSLA training. We observe a significant negative relationship between experience in VSLA (number of months one has been a member) and participation, implying that farmers who have been members for a longer period are less likely to participate in VSLA trainings. This could mean that farmers who have been members for a longer period will have been trained in their first years of membership. In contrast to the findings of Zhang and Xiong (2019) that education level has a significant impact on rural residents participation in financial education, in this study, we find no significant relationship between education and participation in financial training.

Effects of financial literacy training on financial behaviour

Following Nicolini and Haupt (2019) and OECD (2018), we measured financial literacy using the indicators of knowledge, skills, attitude and behaviour of farmers. A descriptive analysis of financial literacy levels shows that famers who participated in VSLA training appear to have higher scores across all indicators compared to those in the control group. The overall score for the beneficiaries is 3% slightly higher than that for the control group. Financial  goals   registered  higher   scores   (70.6%   for beneficiaries against 68.5% for the control group) and the least scores were in planning and managing finances where all farmers registered below 50% across the categories. Table 3 presents a summary of farmers’ scores in the various financial literacy indicators. The details of indicators used are presented in Appendix Table A1.

Planning and managing finances

Farmers were asked various questions that point to how they plan and manage their finances. Results show a higher mean score (48%) for the training beneficiaries compared to the control group with 43%. Majority (77% of beneficiaries and 74% of the control group) farmers do not just spend their money but have a plan to manage their income. We also observe that at least 50.9% of the beneficiaries against 43.9% separate money for day-to-day spending from that for big expenses. Farmers scored very low in record keeping of both current and upcoming expenditures, as only 39.5 and 32.2% of beneficiaries against 33 and 27% of the control group, respectively, keep such records. Similarly, a majority of farmers do not keep track of their expenses for example by using messages from mobile money. This may pose a big challenge for market oriented farmers.

Financial goals

Farmers registered the highest scores in financial goals with 70.6 and 68.5% for the beneficiaries and the control group, respectively. Over 95% of the farmers claim to have financial goals such as buying property. About 98% had saved or invested money in the past 12 months prior to the survey. This would be expected since they are members of VSLAs. Relatedly, a majority (84% beneficiaries and 83% control group) of the respondents had accessed credit in the same period implying that they were fully utilizing the VSLAs to get the basic benefits (saving and credit). Apart from farming, we observed that a reasonably high proportion of farmers (82.8% beneficiaries and 74.2% of control group) also look for other sources of income. While about 66 and 65% indicate that they have planned for that period when they are not working, their level of confidence is relatively low at 72 and 65% for beneficiaries and control group, respectively.

Knowledge about financial products

Farmers were asked whether they knew the various financial products listed in Appendix Table A1. A loan secured on property, mobile money account, insurance and a savings account were the most known financial products in that order. Possibly, these are the common products accessed by majority of farmers in rural areas. About 95.3 and 92.5% of beneficiaries and control group respectively understand the loan secured on property. Mobile money account is also familiar to majority (92.5% beneficiaries and 87.9%) of the respondents. Surprisingly, about 90% of beneficiaries and 87.9% of the control group know about insurance as a product which is not commonly utilized by many people. Most (over 70%) of the farmers seem to know more about microfinance loan than any other type of loan. The least known product is a credit card loan with only 27% of beneficiaries and 18.5% of the control group acknowledging to know the product. This is followed by a bond with about 40% of the respondents who know about it. These findings are not unique to Uganda as similar findings have been reported in other African countries such as Mozambique, Malawi and Nigeria where a large population lack awareness of basic financial products and concepts such as savings accounts, interest on savings, insurance and loans (Xu and Zia, 2012).

Savings and financial shocks

The mean score for the farmers in savings and managing financial shocks is relatively low (59.1%) for both beneficiaries and the control group. However, a majority (97-98%) of farmers had at least saved part of their income in the 12 months prior to the survey. Farmers also save by buying property such as livestock (by 24.3 and 24.0%) and land (0.9% of beneficiaries) as reported by 77.5 and 80.3% of beneficiaries and control group, respectively (Figure 1). At least 54.6 and 52.7% of the beneficiaries and control group respectively save their money on a savings or deposit account. Farmers’ hold savings accounts mainly in VSLAs and SACCOs and just a few in formal banks (Figure 2). Interestingly a relatively higher percentage (12.8%) of beneficiaries compared to 5.5% of the control group reported saving  in  commercial banks; and more beneficiaries seem to prefer banks compared to SACCOs (8.2%). This might be explained by increased financial literacy which is evident from higher scores by the beneficiaries. Our interaction with respondents reveals that as a result of training a majority of the rural poor appreciate that it is possible to save even when their incomes are low. This corroborates the findings by Chowa et al. (2012) that, financial education and financial incentives improve saving performance. When institutional barriers to saving are removed, poor people can and do save. Figure 3 shows the trend of how members have been progressively saving in VSLAs the most common form/means of saving in the study area. The beneficiaries of training appear to save relatively higher weekly deposits compared to the control group. Again, this could be attributed to financial literacy as a result of trainings and perhaps increased income from other investments.

Changes in financial attitude and behaviour

We examined attitudes and behaviour towards use of financial services by asking farmers to assess themselves with regard to specific statements on financial behaviour. The results of those who agree and strongly agree are summarised in Table 4. Using a chi-square test, we do not find a significant difference between the proportion of beneficiaries and the control group who agree/strongly agree to specific statements. A majority (68-70%) of respondents appreciate the importance of saving for the long term. Over 80% of respondents strongly agree that when they borrow money, they have a responsibility to pay it back. This is a good indicator which might also be associated with the training in VSLAs as beneficiaries  recorded  a  higher  percentage  (85.1%) compared to the control group (79.6%). Another good indicator is that majority of farmers keep a close personal watch over their financial affairs. Interesting to note is that a reasonable proportion of farmers (24.3% beneficiaries and 28.2% control group) do not believe that their money is safe in a bank. This might explain partly why many farmers do not save in commercial banks. The result corroborates well with their claim that banks deduct high charges as one reason for not saving in banks.

Effects of training on financial literacy indicators

To assess the impact of training on financial literacy, we estimate the average treatment effect (ATT) using propensity  score  matching  approach and the results are presented in Table 5. We impose the common support condition in the estimation of propensity scores by matching in the region  of  common  support. This  allows farmers with the same values of confounding factors to have a positive probability of being among the trained group and the control group (Heckman et al., 1997).

Predicted propensity scores for trained farmers and the control group range from 0.18 to 0.84 and 0.13 to 0.76 respectively. The distribution of propensity scores are shown in Appendix Figure A1. The pseudo R2 after matching are very low and not statistically significant. The matching results are presented in Appendix Table A2. The absolute standardized difference of the means of the linear index of the propensity score in the treated and matched control group (B) and the ratio of treated to matched control group variances of the propensity score index (R) conform to Rubins’ recommendation (Rubin, 2001); B is less than 25% and R within the range 0.5 - 2. These results show that all covariates are well balanced.

Contrary to descriptive statistics, our data does not show evidence of a significant difference in financial literacy among training beneficiaries and the control group. The results are consistent with both kernel and radius matching though with a slight difference in the level of effects. Nonetheless, the scores for beneficiaries are  higher   than   for   the   control   group   by  0.4%  for knowledge about financial products and 2.1% for planning and managing finances. Similar results are reported by Jamison et al. (2014) who find no significant effects on financial planning by the youth in Uganda.

The non-significant effects in this study do not mean that financial literacy training had no impact but rather reflects the knowledge diffusion effect. Considering the fact that this study was conducted around 12 months post training, we believe that a lot of knowledge had been shared among the farmers’ networks with those that were not trained. This is confirmed by reports from the survey that many other VSLAs have emerged in the study area as other farmers were learning from trained groups. Farmers intimated that they always share information among friends and relatives. These findings suggest that financial training programs have potential to impact a much wider community than the trained participants. This is highly expected in a progressing society with increasing networks of farmers associations and interactions both physical  and  through  media  such as telephones, radios and televisions (Klarl, 2009; Ivkovi? and Weisbenner, 2007; Singh, 2005).

Determinants of financial literacy

We find that financial literacy has a strong positive association with education level of the farmer and that of the spouse, the number of persons in the household, land size owned and the period the individual has been a member of credit and saving association. Table 6 displays the regression results. These results are consistent with most previous studies (ZHang and Xiong, 2019; Akoto et al., 2017; Aggarwal et al., 2014). Farmers with relatively higher education level are likely to appreciate financial knowledge and make use of it. Farmers with large size of land are likely to be engaged in market-oriented production and that might motivate them to seek for financial knowledge and services.

This study assessed the effects of informal financial literacy training on smallholder farmers in rural Uganda using descriptive analysis and propensity score matching approach to identify causal effects of financial literacy training. It focuses on four dimensions of financial behavior and knowledge; planning and managing finances, knowledge about financial products, saving and managing financial shocks and financial goals. Our sample is drawn from members of VSLAs that received training and those that did not receive training as a comparison group. The findings indicate relatively higher scores in financial goals followed by knowledge about financial products and the lowest scores are observed in planning and managing finances.

Whereas descriptive statistics  show  higher  scores  for the trained farmers, overall, data does not show significant effects of financial literacy training on the trained farmers. We conjecture that the findings might be attributed to spillover effects and knowledge diffusion effects through networks given the period between training and evaluation. According to farmers trained, they shared information with others and they believe the training was beneficial; they were motivated to increase savings and consequently increased access to credit; they gained knowledge and skills in budgeting, planning and managing finances. Moreover, financial literacy has slightly increased financial inclusion as more rural farmers participate in both formal and informal financial institutions. However, more training and sensitization is needed for both beneficiaries and the control group on financial products and services to motivate rural households to participate in formal financial institutions where they can benefit from the variety of products offered. It is recommended that more training be provided in planning and managing finances as well as managing financial shocks. The major limitation to this study is using a small sample which was due to constrained logistics. Further research on long term impact of the knowledge acquired involving a bigger sample might be useful in informing policy on financial education. Prior planning for more robust evaluation of such training programs, for example using randomized control trials might be more informative for policy strategies.

The authors have not declared any conflict of interests.