Financial Factors, Financial Literacy and Coffee Output among Smallholder Coffee Farmers in Kenya

Financial literacy is crucial for smallholder coffee farmers as it provides them with the knowledge and skills needed to manage resources effectively, make informed decisions, and optimize financial practices, which ultimately enhances productivity. This study investigates how financial literacy moderates the relationship between financial factors and coffee productivity among smallholder farmers in Kenya, focusing on Murang’a County and grounded in the Resource-Based View Theory. Data from 232 randomly selected farmers, collected through structured questionnaires and analyzed using STATA, reveals that financial literacy significantly moderates the relationship between financial factors and coffee productivity. Specifically, it strengthens the effects of market accessibility, credit accessibility, and price volatility on coffee production, enabling farmers to make better-informed decisions and thereby increasing production.

Introduction

Agriculture remains the backbone of Kenya’s economy, employing a significant proportion of the population and contributing substantially to the country’s gross domestic product (KNBS, 2024). The sector accounts for 65% of Kenya’s total exports (Wanzala & Lwanga, 2024), making it the largest contributor to the country’s foreign exchange earnings through the export of horticulture, tea, and coffee (Wambuaet al., 2021). Within this sector, coffee stands out as one of the key cash crops that has shaped the livelihoods of millions of smallholder farmers (Kamauet al., 2017). The sector directly and indirectly supports over 10 million people (Gichuruet al., 2021), and contributes to an average of 6% to GDP (KNBS, 2024).

Despite its historical significance and the global recognition of Kenyan coffee as a premium product, coffee productivity has faced notable challenges over the years (Gichuruet al., 2021; Wambuaet al., 2021). Fluctuating yields, varying quality, and declining production levels have threatened the sustainability of this vital industry (Wairegiet al., 2018). Moreover, while the production of coffee is majorly practiced by the over 700,000 smallholder farmers who constitute 86.2% of the producers (Gichuruet al., 2021), their production is about 54% of the total coffee production with the rest being produced by estates (Kamauet al., 2017). This indicates that smallholder farmers, despite their predominance in numbers, may be constrained by limited resources or support, resulting in lower productivity levels relative to larger estates.

The financial environment in which smallholder coffee farmers operate is characterized by a myriad of challenges, ranging from limited access to credit facilities (Wambuaet al., 2021) to volatile coffee prices (Kamauet al., 2017). These financial constraints can significantly hinder farmers’ ability to purchase necessary inputs such as fertilizers, pesticides, and modern farming equipment (Anderzénet al., 2020). Moreover, as asserted by Civeraet al. (2019), the lack of adequate financial resources can impede farmers’ capacity to adopt innovative farming practices, hindering their productivity.

The role of financial institutions in providing affordable credit and the importance of financial planning in ensuring that farmers make the most of the available resources cannot be overstated (Wambuaet al., 2021). However, financial factors alone do not paint the full picture of the challenges faced by smallholder coffee farmers. The ability of businessmen to effectively manage their finances, make informed decisions, and plan for the future may equally play an equally crucial role in determining productivity levels (Maosa & Kenyanya, 2018). However, there is a paucity of research on how financial literacy moderates the relationship between these financial factors and smallholders’ coffee output in Kenya.

Financial literacy may play a crucial role in shaping coffee productivity by influencing farmers’ financial decisions and risk management. However, its generally low levels among Kenyan smallholder farmers (Wanzala & Lwanga, 2024), could limit the effectiveness of financial interventions, suggesting that targeted, skill-based strategies might be necessary to enhance productivity and resource utilization. By analyzing how financial literacy interacts with financial factors, specifically access to credit, income stability, and investment in farming inputs, we seek to provide insights into the complex dynamics that affect coffee productivity.

Research Objective

The aims to analyze the moderating effect of financial literacy on the relationship between financial factors and coffee productivity among smallholder farmers in Murang’a County, Kenya.

Literature Review

Theoretical Review

The Resource-Based View (RBV) theory, proposed by Barney (1991), provides a framework for examining how financial factors and financial literacy impact coffee productivity among smallholder farmers. The RBV theory highlights the strategic importance of effectively managing valuable resources to achieve competitive advantage (Collins, 2022). In this study, financial factors and financial literacy are the key constructs. This study applies the Resource-Based View to examine how financial factors, financial literacy, and coffee productivity interact to influence smallholder farmers’ coffee productivity in Kenya. The theory proponents argue that a firm’s resources and capabilities are central to achieving competitive advantage. The proposition is that valuable, rare, inimitable, and non-substitutable resources (Kenyanya & Kisavi, 2020), such as financial resources and financial literacy, significantly enhance coffee productivity. The constructs of financial factors and financial literacy serve as critical resources within this framework. According to the theory, the smallholder farmers who effectively utilize these resources will achieve higher productivity levels (Pfeffer & Salancik, 1978). Practically, RBV identifies and enhances these key resources, empowering smallholder farmers to improve productivity and maintain competitiveness in the coffee industry.

Empirical Review

While there is growing empirical evidence on studies seeking to understand the root causes of challenges facing smallholder farmers in Kenya (Gichuruet al., 2021; Kamauet al., 2017; Wambuaet al., 2021), rigorous empirical to support the role of financial factors as a major player in coffee productivity among the smallholder farmers is scanty. Gathura (2013)’s study which focused on establishing factors that affect small-scale coffee production in Kenya found that market, financial, policy, and resource factors impact coffee output. Civeraet al. (2019) conducted a research which sought to explore the engagement and empowerment of low-power stakeholders, specifically smallholder coffee farmers, by Lavazza, an Italian coffee roaster operating in Brazil, India, Haiti, and in East Africa. The study developed a framework connecting empowerment areas with specific actions, highlighting how these strategies can transform vulnerable stakeholders into active business partners, enhancing value creation through cooperative partnerships.

Gichichiet al. (2019) studied how entrepreneurial finance access affects performance in Murang’a coffee agribusinesses. The research showed that access to finance has a significant positive effect on business performance. The study recommended training by stakeholders to encourage the use of external funds to enhance agribusiness performance. Elsewhere Wanzala and Lwanga (2024) investigated how agricultural financing impacts coffee productivity in Kiambu County, Kenya, amidst low credit adoption by farmers. The research found that while farmers view credit positively in terms of inputs and returns, they perceive it negatively regarding labor demand and productivity efficiency.

Wambuaet al. (2021) evaluated the socioeconomic factors’ impact and how technology adoption impacts smallholder coffee output in Kenya, revealing that access to credit positively affects productivity. The study recommends encouraging income diversification, credit financing, and the adoption of these technologies among farmers. Karyaniet al. (2024) examined farming capital sources and the factors influencing credit choices among coffee smallholder farmers in Indonesia. The study found that 54.66% of farmers rely on internal capital, and that factors influencing credit access include proximity to financial institutions, farming income, and credit payment deadlines.

While the evidence base in the impact of financial factors on coffee productivity is clearly growing, there is clearly little formal evidence on the interplay between the financial factors and the knowledge farmers possess regarding financial management, encapsulated in the concept of financial literacy, which further complicates the productivity landscape. The study explores how financial literacy moderates the relationship between financial factors and coffee productivity, hypothesizing that farmers with higher financial literacy can more effectively leverage financial resources to improve productivity. Based on this, the following null hypotheses was set:

H₀₁:Financial literacy does not significantly moderate the relationship between financial factors and coffee productivity in Kenya.

Methodology

Data

The study investigates how financial literacy moderates the relationship between financial factors and coffee productivity among smallholder farmers. The data on financial literacy (FL), financial factors proxied by production costs (PDN), price volatility (PVL), market access (MKT) and credit access (CRD) as well as the dependent variable, coffee productivity (CP) was obtained using structured questionnaires from 243 smallholder coffee farmers. To avoid spurious results that would have arisen from the use of ordinal data (Saunderset al., 2008), the questions in the instrument were set to meet the ratio scale. The data collection instrument was tested for reliability using Cronbach alpha and validity using Principal Component Analysis. This was to ensure instrument accuracy and consistency (Kothari, 2012).

Data Analysis

The Baron and Kenny (1986) moderated multiple linear regression methodology was used to analyse the moderating effect of financial literacy on the financial factors and coffee productivity relationship. Before analysis, the data was subjected to linearity, normality, multicollinearity and heteroscedasticity tests to check that it met assumptions of the classical linear regression (Baltagi, 2008). The analysis was based on the following general linear regression models:

(1) $$\begin{array}{rcl}CP& =& {\beta }_0+{\beta }_{11}PDN+{\beta }_{21}PVL+{\beta }_{31}MKT\\ & +& {\beta }_{41}CRD+\epsilon \end{array}$$

(2) $$\begin{array}{rcl}CP& =& {\beta }_0+{\beta }_{12}PDN+{\beta }_{22}PVL+{\beta }_{32}MKT+{\beta }_{42}CRD\\ & +& {\beta }_{52}PDN\ast FL+{\beta }_{62}PVL\ast FL+{\beta }_{72}MKT\ast EL\\ & +& {\beta }_{82}CRD\ast FL+\epsilon \end{array}$$

According to Baron and Kenny (1986), a moderation effect is present if the coefficient of determination changes significantly in model 2 compared to model 1. Moreover, Kenyanyaet al. (2017) assert that if the coefficient of a moderated term in a regression is significant, then it is moderated by the moderating variable.

Results

Correlation Results

Table I shows the pairwise correlation between the study variables. The analysis reveals that coffee production shows a moderate positive significant correlation with coffee market accessibility (MKT), (R = 0.370; p = 0.00), which suggests that coffee market accessibility increases with coffee production. This might imply that better access to markets, such as improved infrastructure or reduced barriers to entry, can lead to higher coffee production levels. Furthermore, coffee production was shown to have a strong positive correlation with access to credit (CRD), (R = 0.699; p = 0.000). This implies that as access to credit improves, coffee production also tends to increase significantly. This might highlight the crucial role of financial resources in enabling farmers to invest in better farming practices, purchase necessary inputs, or expand their production capacity, thereby boosting overall coffee output.

Table I. Pairwise Correlations

Variables	CPDN	MKT	CRD	PVL
CPDN	1.000
MKT	0.370	1.000
	(0.000)
CRD	0.699	0.214	1.000
	(0.000)	(0.001)
PVL	−0.089	0.047	−0.186	1.000
	(0.175)	(0.475)	(0.004)

On the other hand, coffee production has a very weak negative correlation with coffee price volatility (PVL), R = −0.089; p = 0.175). This suggests a slight tendency for coffee production to decrease as price volatility increases, though the relationship is not strong. Practically, this could mean that when coffee prices are highly unpredictable, farmers may be hesitant to invest heavily in production, leading to slightly lower output.

Regression Results

Table II shows the regression results. The results reveals that coffee market accessibility (MKT) positively and statistically significantly affects coffee production, (ß = 0.174; p < 0.01). This suggests that improvements in market accessibility are associated with increased coffee production. Similarly, access to credit (CRD) shows a strong positive impact on coffee production, (ß = 0.67; p < 0.01), indicating that better access to financial resources significantly boosts production levels. On the other hand, coffee price volatility (PVL) does not significantly affect coffee production, (ß = 0.015; p = 0.638), implying that fluctuations in coffee prices do not have a substantial direct impact on production within the context of this model.

Table II. Linear Regression

CPDN	Coefficient	Standard error	t-value	p-value	[95% Confidenceinterval]		Sig
MKT	0.174	0.035	4.97	0	0.105	0.243	***
CRD	0.67	0.048	13.93	0	0.575	0.765	***
PVL	0.015	0.031	0.47	0.638	−0.047	0.076
Constant	0.315	0.25	1.26	0.208	−0.177	0.807
Mean dependent variable		3.734	SD dependent var		0.735
R-squared		0.540	Number of obs		232
F-test		89.192	Prob > F		0.000
Akaike crit. (AIC)		342.481	Bayesian crit. (BIC)		356.268

Overall, the model has an R-squared value of 0.540, meaning that 54% of the variation in coffee production is explained by model-included variables. The overall model is highly significant (F = 89.192, p < 0.01), further confirming the robustness of the relationships identified.

Moderated Regression Results

To estimate the moderating role of financial literacy on financial factors and coffee productivity, a linear regression was performed with the independent variables of coffee market accessibility (MKT), access to credit (CRD), and coffee price volatility (PVL). The interaction terms of these variables with financial literacy (MKT_FL, CRD_FL, and PVL_FL) were also factored in the model, as shown in Table III.

Table III. Moderation Output

CPDN	Coefficient	Standard error	t-value	p-value	[95% Confidence interval]		Sig
MKT	0.235	0.065	−3.59	0.000	0.106	0.364	***
CRD	0.272	0.047	−5.76	0.021	0.179	0.369	***
PVL	0.380	0.008	4.75	0.122	0.22	0.54	***
MKT_FL	0.200	0.005	4.00	0.000	0.100	0.300	***
CRD_FL	0.080	0.017	4.80	0.032	0.047	0.113	***
PVL_FL	0.133	0.014	9.19	0.001	0.105	0.162	***
Constant	2.074	0.079	26.13	0.041	1.918	2.231	***
Mean dependent variable		3.734	SD dependent variable			0.735
R-squared		0.969	Number of obs			232
F-test		1162.280	Prob > F			0.000
Akaike crit. (AIC)		−275.408	Bayesian crit. (BIC)			−251.281

Results from Table III generally demonstrate that the independent variables account for 96.9% of the variation in coffee production, up from 54% in Model II, suggesting a highly effective model. The moderation results specifically show that smallholder farmers with higher financial literacy are more likely to capitalize on market opportunities, as evidenced by the positive coefficient of 0.20 for the interaction between market accessibility and financial literacy (MKT_FL). This suggests that financial literacy significantly strengthens the positive impact of market accessibility on coffee production, enabling farmers to make better-informed decisions that lead to increased production.

Similarly, the interaction between access to credit and financial literacy (CRD_FL) shows a positive significant coefficient of 0.08. This indicates that financially literate farmers are more adept at managing credit, which enhances the positive effect of credit access on coffee production. These farmers likely invest the accessed credit more effectively, resulting in higher productivity. Finally, the strong positive coefficient of 0.133 for the interaction between price volatility and financial literacy (PVL_FL) (ß = 0.133, p = 0.001) demonstrates that financial literacy empowers farmers to respond more effectively to price fluctuations. By navigating these fluctuations skillfully, financially literate farmers can boost production even in volatile market conditions.

Conclusions

Results show that financial literacy significantly moderates financial factors and coffee output among Kenya’s smallholder farmers. It is recommended that policymakers prioritize programs that enhance financial literacy among farmers, as this will empower them to make better decisions regarding market opportunities. Moreover, by strengthening financial literacy, farmers can more effectively leverage market access to increase coffee production, driving economic growth in the agricultural sector. It is also recommended that financial institutions and development agencies integrate financial literacy training into credit access programs to equip farmers with the skills to manage loans and investments more effectively. Additionally, to help farmers navigate the challenges of price volatility, policymakers should focus on enhancing financial literacy that will help improve financial decision-making, to better respond to market fluctuations, ensuring sustained and even increased coffee production during volatile periods.