Does financial literacy matter for village-owned enterprises’ (VOEs) performance? Evidence from East Java Indonesia

Abstract

This study offers novelty by estimating the effect of financial literacy on village-owned enterprises’ (VOEs) performance in Indonesia. The data were collected from a survey using a structured questionnaire involving 220 VOEs. The performance was measured from the profit and return on investment (ROI). A two-stage predictor substitution (2SPS) was applied to address the endogeneity issue in estimating the impact of financial literacy on VOE performance. The first stage uses a Tobit regression, and the second stage uses ordinary least squares (OLS). The results of the first stage indicate that VOEs managers’ financial literacy is positively and significantly influenced by education, incentive, number of employees, and training and is negatively influenced by age, marital status, and distance to the capital city. The main finding evaluated in the second stage of 2SPS indicates that financial literacy significantly improves firm performance. However, further estimation based on the sector (agricultural and non-agricultural) shows different results. The impact of financial literacy is more significant for non-agriculture VOEs. The findings imply that financial literacy should be considered in promoting VOEs performance. Therefore, policymakers and organizations should prioritize designing and implementing financial literacy programs for VOEs’ managers. This can be achieved by providing financial-related training for the managers of VOEs.

Keywords:

• VOEs
• two-stage predictor substitution
• profit
• ROI
• financial literacy
• Indonesia

1. Introduction

The establishment of village-owned enterprises (VOEs) is the Indonesian government’s effort to improve the rural economy. As stipulated in Law Number 32 of 2004 concerning Regional Government, villages can form VOEs based on their needs and potential. The community and the village government run the management to boost the local economy. Unfortunately, VOEs tend to underperform, so their contribution to the improvement of the village’s economy remains insignificant (Nugroho, 2020). One of the causes is the low level of literacy and knowledge of company management, especially in finance, which results in poor decision-making (Abiodun & Amos, 2018).

Financial literacy is a set of knowledge and skills about financial systems’ concepts, principles, and technologies (Travnichek, 2008). Individuals and institutions need financial literacy to manage their finances and make sound financial decisions (Servon & Kaestner, 2008). Research has shown that individuals with high levels of financial literacy have good financial management (Grohmann, 2018). This is critical to achieving objectives and surviving in the modern era (Robb & Woodyard, 2011). The literature has also shown that financial literacy has become increasingly important in financial decision-making among governments, businesses, and households in the past decades (Jang et al., 2014).

However, in Asia, knowledge of financial concepts is limited, resulting in low financial literacy (Grohmann, 2018). This can lead to poor cash flow management (Lusardi & Mitchell, 2011), lower economic performance, or business failure. To strengthen the economy, this rate must be improved (Huston, 2010) through training for the targeted groups (Bayrakdaroğlu & Şan, 2014). Higher financial literacy can boost business performance and welfare, as well as overcome individuals’ financial problems (Huston, 2010). For example, a previous study has shown that a manager’s high financial literacy correlates positively with the company’s productivity (Hakim et al., 2018). Likewise, households with higher financial literacy earn higher incomes (Carman & Zamarro, 2016). Another study shows a positive and significant impact of financial literacy on technical efficiency in the rice farming industry in Bangladesh (Afrin et al., 2017).

The role of financial literacy on VOEs has not been extensively studied or identified in the existing literature. The importance of financial literacy in corporate performance has been well-documented, but most empirical evidence focuses on manufacturing companies and small and medium-sized enterprises (SMEs) in urban areas (Adomako et al., 2016; Li & Qian, 2020; Mabula & Ping, 2018). Consequently, the research gap is to understand the specific effects of financial literacy on VOEs, which play a crucial role in rural economies and contribute to local development and livelihoods. These enterprises typically operate in unique contexts with limited access to financial resources, limited infrastructure, and specific socioeconomic dynamics. Investigating the relationship between financial literacy and the performance of VOEs is crucial to boost economic development. VOEs have been developed in several countries in Asia, such as China (Han et al., 2021; Kung, 2022; Liu & Yang, 2019; Zhang & Wu, 2022), Thailand (Natsuda et al., 2012). On the other hand, in Africa has also embraced the concept of VOEs, such as Kenya (Gichuki et al., 2014), as and South Africa (Tada et al., 2013). Exploring this research gap can provide insights into the potential benefits of enhancing financial literacy among VOE managers. Such insights can inform policymakers, development organizations, and local communities in formulating strategies to promote economic growth and sustainable development in rural areas. Additionally, this research can contribute to the broader literature on financial literacy by expanding its application and relevance to different organizational contexts. Theoretically, the study can contribute to the literature on financial literacy and its role in developing rural areas. Finally, the finding of this study supports the sustainable development goals (SDGs), specifically Goal number 8 “Decent work and economic growth”.

2. Literature review

2.1. Financial literacy and firm performance

The importance of financial literacy in economic development has been documented extensively in past studies. Sreelakshmi (2017) shows that financial literacy significantly impacts access to financial services. Similarly, Hilgert et al. (2003) showed that a high financial literacy index translates into a higher financial practice index, i.e., higher participation in financial services. Meanwhile, Filipiak and Walle (2015) found that low adoption of financial services can be caused by low trust in financial services, which stems from a low understanding of basic financial concepts (financial literacy) (Robb & Woodyard, 2011). Wachira and Kihiu (2012) also show that individuals with low financial literacy tend not to participate in financial institutions. In addition, the study noted that other factors influence access to financial services, such as income level, distance from the bank, age, marital status, gender, household size, and education. The study also noted that individuals without knowledge of financial concepts need to be encouraged to improve their understanding of financial concepts and their awareness of the importance of financial services.

2.2. The VOE managers’ financial literacy

A manager’s financial literacy can proxy for the firm’s financial literacy, as their decisions and actions can significantly impact financial health and performance (Iramani et al., 2018; Tuffour et al., 2022). Financial literacy refers to the understanding of financial concepts, such as financial statements, budgeting, investment strategies, and risk management (Kumari, 2020). A financially literate manager is better equipped to make informed decisions that align with their firm’s financial objectives and strategies (Tuffour et al., 2022), effectively analyze financial statements, identify key financial indicators, and interpret the implications of the financial data (Plakalović, 2015). This enables them to assess the firm’s financial health, identify its strength and weaknesses, and make informed decisions to improve its financial performance. Financial literacy also covers budgeting and resource allocation (Braunstein & Welch, 2002), so financially literate managers can develop realistic budgets, allocate resources efficiently, and track financial performance against set targets. Moreover, they can evaluate investment opportunities and assess their potential risks and returns (Widdowson & Hailwood, 2007; Ye & Kulathunga, 2019). They can analyze a project’s financial viability, evaluate investment options, and make decisions maximizing shareholders’ value. Understanding financial concepts and techniques, such as capital budgeting and cost of capital, can lead to sound investment decisions that align with the firm’s strategic goals. Another crucial aspect of financial literacy is risk management. Managers must understand different types of risks, such as market, credit, and operational risks, and implement appropriate mitigation strategies. Financial literacy allows them to manage cash flow effectively, optimize working capital, and implement risk management techniques to safeguard the firm’s financial stability (Dwiastanti, 2015).

In other words, a manager’s financial literacy can represent a firm’s financial literacy as it deals with informed decision-making, financial performance assessment, effective resource allocation, the evaluation of investment opportunities, and risk mitigation. Enhancing financial literacy among them can strengthen a firm’s financial management capabilities and improve performance and long-term sustainability.

3. Research methodology

3.1. Research data

This research aims to investigate the impact of financial literacy on village-owned enterprises (VOEs) in Blitar Regency, East Java, Indonesia. Blitar Regency was selected as the study area because it has many villages with VOEs. Geographically, Blitar Regency is situated in the southern part of East Java and encompasses 220 villages (refer to Figure 1). This study employs a census survey methodology and structured interviews. A comprehensive census survey was conducted to analyze the financial literacy levels in all villages with VOEs in Blitar Regency (220 VOE managers). Structured interviews were conducted with the managers using a standardized questionnaire to reveal their profiles, financial literacy levels, and business profiles, including capital, assets, production, and finance. The use of standardized questionnaires ensures the consistency and reliability of the data. The survey was conducted between November and December 2022. The time selection was determined based on the VOEs finalizing their financial reports, which can provide updated data for this study. We also involved the VOE managers in administering the structured interviews and collecting the necessary information related to financial literacy and business characteristics.

Figure 1. Research location.

3.2. Measuring financial literacy and VOE performance

Financial literacy refers to an individual’s fundamental capability in financial aspects, so the measurement often uses questions about financial concepts and adopted from Lusardi and Mitchell (2007); Lusardi and Mitchell (2011); Knoll and Houts (2012). The questions about financial literacy are presented in the Appendix 1. The managers of VOEs answer the questions. A correct answer is scored 1, and a wrong answer receives 0. The financial literacy score is calculated by summing up the points. The financial literacy variable is derived from the total score of financial literacy questions, measured on a censored scale ranging from 0 (lowest score) to 6 (highest score).

Meanwhile, to measure performance, this study follows Ma and Abdulai (2017), using two indicators: firm profit and return on investment (ROI). Profit, the surplus amount when the total revenue exceeds the total costs, is a fundamental measure of financial performance and sustainability. Therefore, this study uses profit as an indicator to evaluate the financial performance of VOEs and calculates it from the difference between revenue and total production costs. Meanwhile, ROI is a financial metric to assess the profitability of an investment. It measures the return or profit earned against the amount invested. ROI is employed as a performance indicator to evaluate the effectiveness and efficiency of the investments made by the VOEs. It allows researchers to calculate the financial returns from the invested capital and gauge insights into the profitability of these investments. ROI is calculated using the following formula:

$ROI=\frac{Totalsell-Investment}{Investment}$

3.3. Estimation strategy

Estimating the impact of financial literacy on firm performance requires a robust strategy that considers potential confounding factors, such as the two-stage predictor substitution (2SPS) approach. The first stage of this approach employs Tobit regression to estimate the likelihood of firms’ financial literacy since the financial literacy score is censored (Li & Qian, 2020). The second stage uses an ordinary least squares (OLS) regression to assess the impact of financial literacy on firm performance and control other relevant factors. The 2SPS approach is commonly used in econometric studies to address endogeneity issues, where independent variables may be correlated with error terms in the regression model. It addresses endogeneity issues by considering the observable (i.e., age, education, and marital status) and unobservable factors (i.e., skill and motivation) that affect VOE managers’ financial literacy levels. Using the predicted value of the independent variable in the second stage model, the approach eliminates the potential bias caused by endogeneity. It provides more accurate estimates of the relationship between the independent and dependent variables (Rahman et al., 2022, 2023).

The first stage Tobit regression model is as follows:

(1) $Financialliteracy={\beta }_0+a_i{x}_i+c_{i}i{v}_i+e_i\mathrm{w}\mathrm{i}\mathrm{t}\mathrm{h}\mathrm{f}\mathrm{i}\mathrm{n}\mathrm{a}\mathrm{n}\mathrm{c}\mathrm{i}\mathrm{a}\mathrm{l}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{c}\mathrm{y}min=0andMax=6$(1)

where ${\beta }_0$ is a constant for the regression model, $x_i$ is the control variable, including managers’ characteristics (age, education, incentive, and marital status) and the VOEs’ characteristics, i.e., sector (agriculture or non-agriculture), distance to the capital city, access to the digital market and local market, social activities, promotions, transportation, and the use of social media. $i{v}_i$ is an instrumental variable, namely training undertaken by the managers, measured by the number of sessions the managers attended. The selection of instrumental variable (IV) should ideally have a strong correlation with the financial literacy variable. However, it may not have a significant correlation with the performance of VOEs. To validate the IV, we follow Zhu et al. (2020), namely, to employ a person correlation test. Based on the results in Table A1 (Appendix 2) the IV has a strong correlation with financial literacy but is not statistically significant with profit and ROI. Therefore, this study confirms the validity of the IV used in this study. $a_i$ and $c_i$ are parameters to be estimated, and $e$ is an error term.

The second stage, the OLS regression model estimates the impact of financial literacy on VOEs’ performance. In this stage, we assumed that firm performance is affected by financial literacy (Tuffour et al., 2022), the confounding factors $x_i$ that have been explained earlier. Financial literacy was predicted by Equation 1(1) $Financialliteracy={\beta }_0+a_i{x}_i+c_{i}i{v}_i+e_i\mathrm{w}\mathrm{i}\mathrm{t}\mathrm{h}\mathrm{f}\mathrm{i}\mathrm{n}\mathrm{a}\mathrm{n}\mathrm{c}\mathrm{i}\mathrm{a}\mathrm{l}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{c}\mathrm{y}min=0andMax=6$(1) , and the original value of the financial literacy score is replaced, formulated as follows:

(2) $performance={\beta }_0+q_i{P}_{Financial}literac{y}_i+p_i{x}_i+u_{i}withperformance=cointinouse$(2)

where $performance$ is measured by VOEs’ profit and ROI. $P\mathrm{\_}Financialliterac{y}_i$ is the independent variable predicted in Equation 1(1) $Financialliteracy={\beta }_0+a_i{x}_i+c_{i}i{v}_i+e_i\mathrm{w}\mathrm{i}\mathrm{t}\mathrm{h}\mathrm{f}\mathrm{i}\mathrm{n}\mathrm{a}\mathrm{n}\mathrm{c}\mathrm{i}\mathrm{a}\mathrm{l}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{c}\mathrm{y}min=0andMax=6$(1) .$x_i$ is the managers’ characteristics. $q_i$ and $p_i$ are parameters to be estimated, and $u_i$ is an error term. Overall, the 2SPS approach with Tobit regression in the first stage and OLS regression in the second stage is a robust method to estimate the impact of financial literacy on firm performance, controlling all relevant factors.

4. Results and discussion

4.1. Descriptive statistics

Table 1 presents the descriptive statistics of the VOE managers in Blitar, Indonesia, showing they have a fair understanding of financial concepts and can make relatively sound financial decisions. The mean financial literacy score was 3.2715, with a standard deviation of 1.4424, suggesting a considerable variation in scores among the managers. It can be concluded that the VOE managers’ financial literacy is moderate, which means they are familiar with basic financial terminologies and can read and understand financial statements, such as bank and credit card statements. However, they still need guidance and education on more complex financial concepts and strategies. The mean return on investment (ROI) is 2.0958 or 20.958%, which can be considered low. The standard deviation is 0.1291, indicating a relatively consistent ROI among the VOEs. The mean monthly profit is USD 6732.5810, with a standard deviation of 151.8868, suggesting a stable profit but with a considerable variation in the profits generated by VOEs.

Table 1. Descriptive statistics of study’s variables

Variable	Measurement	Mean	Std.
Financial Literacy	Managers’ financial literacy score (0–6)	3.2715	1.4424
Return on Investment (ROI)	VOEs’ return on investment (%)	2.0958	0.1291
Profit	VOEs’ profit USD per-month	6732.5810	151.8868
Age	Age of the VOE manager in a year	47.0362	11.9346
Education	Education level of the VOE manager in a year	11.3575	3.7043
Marital status	Dummy, 1 if the VOE manager is married; 0 otherwise	0.9593	0.1981
Incentive	Dummy, 1 of the VOE provides an incentive for the employee; 0 otherwise	0.4842	0.5009
Employee	Numbers of VOE employees in person	27.1222	25.9366
Agriculture	Dummy 1 for agricultural VOEs; 0 otherwise	0.4887	0.5010
Distance	Distance from the VOE to the capital city of Blitar (km)	8.7172	9.5562
Digital market	Dummy, 1 if the VOE adopts the digital marketing; 0 otherwise	0.1900	0.3932
Local market	Dummy, 1 if the VOE focuses on the local market; 0 otherwise	0.6109	0.4887
Social Activity	Dummy, 1 if the VOE provides a social activity; 0 others	0.0995	0.3001
Promotion	Dummy, 1 the VOE spends financial allocation for promotion 0 otherwise	0.2398	0.4279
Transportation	Dummy, 1 if the VOE spends financial allocation for transportation, 0 otherwise	0.9774	0.1490
Social media	Dummy 1 if the VOE has social media, 0 otherwise	0.4615	0.4997
IV	Number of training sessions joined by the VOE Manager	6.5896	6.0503

The VOE managers’ profiles show that the average age is 47.0362 years, with a standard deviation of 11.9346, indicating that they are relatively mature and experienced. The mean education level is 11.3575 years, meaning they have completed 11 years of education. This is a low level of education as it means graduating from junior high school. Regarding marital status, most of the managers are married.

Meanwhile, data on the VOEs’ characteristics show that 48.42% of the VOEs provide incentives to their employees. The average number of employees is around 27 people, with a standard deviation of 25.9366, indicating considerable variation in firm sizes. The mean distance between the VOEs and the capital city is 8.7172 kilometers, with a standard deviation of 9.5562. Regarding digital marketing, only 19% of the VOEs have gone digital, while the majority (61.09%) focus on the local market. As for social activities and events involving rural residents, only 9.9% of the VOEs are active. Likewise, only a few VOEs (23.98%) promote their products to boost sales, but almost half (46.15%) have adopted social media. Regarding transportation, 97.74% of the VOEs own a vehicle for VOE-related activities. Finally, the mean number of training sessions attended by the VOE managers is 6.5896, with a standard deviation of 6.0503. This means that the managers are relatively well-trained, albeit by varying degrees.

The descriptive statistics in Table 1 provide valuable insights into the financial literacy, ROI, profit, age, education, headcount, distance to the capital city, and VOE managers’ training levels. This can help policymakers, and stakeholders make informed decisions about VOE performance and identify areas for improvement. Meanwhile, Table 2 presents the mean differences in characteristics between agricultural and non-agricultural VOEs. The results indicate agricultural VOEs have lower financial literacy, ROI, and profit. They provide incentives but conduct fewer social activities compared to non-agricultural VOEs. This finding implies that the impact of financial literacy on VOE performance between agricultural and non-agricultural VOEs may differ. Therefore, a disaggregate estimation based on VOE types, i.e., agricultural, and non-agricultural, is needed.

Table 2. Mean differences in characteristics between agricultural and non-agricultural VOEs

Variable	Agriculture		Non-Agriculture		Diff
	Mean	Std. Dev	Mean	Std. Dev
Financial Literacy	2.9722	1.2444	3.4161	1.5117	−0.4439**
ROI	2.0706	0.1144	2.1079	0.1344	−0.0373**
Profit	6699.3370	123.4258	6748.6440	161.8242	−49.3070**
Age	46.4444	11.2161	47.3222	12.2933	−0.8777
Education	10.8056	3.4708	11.6242	3.7945	−0.8186
Marital status	0.9583	0.2012	0.9597	0.1973	−0.0014
Incentive	0.3333	0.4747	0.5570	0.4984	−0.2237***
Employee	29.5417	28.1239	25.9530	24.8254	3.5887
Distance	6.2292	3.6627	9.9195	0.9154	−3.6903***
Digital market	0.5972	0.4939	0.6174	0.4876	−0.0202
Local market	0.1667	0.3753	0.2013	0.4024	−0.0347
Social Activity	0.1528	0.3623	0.0738	0.2624	0.0790*
Promotion	0.2500	0.4361	0.2349	0.4254	0.0151
Transportation	1.0000	0.0000	0.9664	0.1807	0.0336
Social media	0.4722	0.5027	0.4564	0.4998	0.0158
IV	6.0097	4.1498	6.8698	6.7758	−0.8601

Note: * = sig 10%; ** = sig 5%; *** = sig 1%

4.2. The determinants of VOE managers’ financial literacy

Table 3 presents the Tobit regression model results that examine the determinants of VOE managers’ financial literacy in different sectors (pooled, non-agriculture, and agriculture. The variables are age, education, incentive, marital status, the number of employees, sector (agriculture or non-agriculture), distance to the capital city, access to the digital market and local market, social activities, promotions, transportation, the use of social media, and training. The constant term is also included in the model. The results suggest that age, education, incentive, distance, and training are positively associated with financial literacy. Meanwhile, marital status, promotion, and social media negatively affect financial literacy. The effects of these variables vary across sectors.

Table 3. The determinants of financial literacy of VOEs managers: Tobit regression

Financial Literacy	Pooled	Non-agriculture	Agriculture
	Coef. (Std. Err.)	Coef. (Std. Err.)	Coef. (Std. Err.)
Age	−.0321 (.0050)***	−0.0250 (0.0062)***	−0.0393 (0.0084)***
Education	.1233 (.0170)***	0.1046 (0.0214)***	0.1443 (0.0254)***
Incentive	1.0369 (.1322)***	1.4044 (0.1724)***	0.6010 (0.1957)***
Marital	−.5889 (.2863)**	−0.4464 (0.3486)	−0.7348 (0.4333)*
Employee	.0091 (.0026)***	0.0101 (0.0027)***	0.0088 (0.0062)
Agriculture	.1417 (.1352)
Distance	−.0242 (.0070)***	−0.0162 (0.0098)	−0.0263 (0.0102)**
Digital market	.2286 (.1499)	0.0309 (0.1856)	0.4781 (0.2450)*
Local market	.0983 (.1269)	−0.1664 (0.1618)	0.3873 (0.1860)**
Social Activity	−.1087 (.1926)	0.0768 (0.2322)	−0.1627 (0.3111)
Promotion	−.2558 (.1402)**	−0.2852 (0.1660)*	−0.1730 (0.2407)
Transportation	.1355 (.3790)	−1.3881 (0.7616)*	0.5378 (0.4368)
Social media	.0037 (.1282)	0.1477 (0.1631)	−0.0777 (0.2051)
Training	.0616 (.0112)***	0.0519 (0.0118)***	0.0811 (0.0296)***
Constant	2.7670 (.5783)***	3.9111 (0.8864)***	2.6909 (0.8366)***
var(e.fl)|	.6579 (.0626)	0.5445 (0.0724)	0.6524 (0.0888)
Log-likelihood	−267.3226	−125.9975	−130.1841
LR chi2(15)	253.4300	151.0200	124.5300
Prob > chi2	.0000	0.0000	0.0000
Pseudo R2	.3216	0.3747	0.3235

Note: * = sig 10%; ** = sig 5%; *** = sig 1%

Age has a negative and statistically significant effect on VOE managers’ financial literacy. Younger managers tend to be more financially literate than older managers, which may be due to differences in educational opportunities or exposure to financial training. This finding highlights the importance of continuous training and professional development for VOE managers, particularly older managers with limited financial literacy skills. This is supported by a previous study by Li and Qian (2020), revealing a u-shape effect of age on financial literacy. At some point, the effect of age on financial literacy is significantly negative (Kadoya & Khan, 2020). Therefore, educational support is needed.

The Tobit regression model also shows that education positively and significantly affects managers’ financial literacy in all sectors. This result aligns with the previous studies by Tuffour et al. (2022), stating a positive and significant effect of education on the financial literacy of micro and small enterprises’ managers. The more educated the managers, the more financially literate they are, ceteris paribus. Education is indeed a key to higher financial literacy (Hung et al., 2009) as it can equip individuals with an understanding of financial concepts and the ability to make informed financial decisions.

The incentive variable has a positive and significant effect on financial literacy in all sectors—the more incentives given to VOE managers, the higher the financial literacy levels. The incentives include financial rewards or performance-based bonuses. A previous study by Wine (2017) shows that providing incentives increases employee performance, i.e., knowledge and skills. Similarly, the number of employees positively and significantly affects the financial literacy in the pooled sample and the non-agriculture sector—the more employees, the higher the VOE manager’s financial literacy. A larger workforce may give managers more opportunities to be exposed to financial matters and make financial decisions. For example, managing a larger team may involve handling larger budgets, analyzing financial reports, or implementing financial strategies. Through these experiences, managers may enhance their financial literacy over time. Nevertheless, the coefficient estimate is not statistically significant in the agriculture sector, indicating that the positive relationship applies only in non-agricultural sectors, where the nature of the business entails complex financial management.

The distance variable in the pooled model is negative and statistically significant at a 1% level. This means the distance between a VOE and the capital city significantly impacts the VOE manager’s financial literacy. A similar finding was highlighted by Raza et al. (2023). By contrast, the coefficient for the digital marketing variable is insignificant for the pooled model. However, the significance for the non-agriculture sector is only at a 10% level, indicating that the relationship may be weaker in certain sectors. Meanwhile, the local market variable is only positive and significant for the agricultural sector. This suggests that agriculture VOEs will benefit more from local market orientation. Their technology adoption tends to be low, so distributing products long distance may not be efficient.

The promotion variable negatively and significantly affects financial literacy for the pooled and non-agricultural sectors but not agriculture. Managers focusing more on promotion may have less time and resources to improve their financial literacy. They may prioritize other aspects of the business over financial management. Alternatively, managers with lower financial literacy may rely more on product promotion to generate revenue, as they may lack the skills or knowledge to manage other aspects of the business effectively. Shen et al. (2020) also pointed out that financial literacy is significantly related to promotion in China. Lastly, the training variable positively and significantly affects financial literacy in all models. A previous study has also shown that training can enhance financial literacy through the improvement of financial knowledge and skills, particularly in the context of small and medium enterprises (SMEs) and microfinance institutions (MFIs) (Buchdadi et al., 2020; Kisaka & Mwewa, 2014)

4.3. The impact of financial literacy on VOE performance

Table 4 shows the results of a regression analysis to estimate the impact of financial literacy on VOE performance, measured by profit and ROI. Both indicators’ coefficients are positive and significant, indicating that literacy can boost profit and ROI. We also calculate the disaggregate estimation between agriculture and non-agriculture VOEs to enrich the study findings. The regression estimation reveals that financial literacy significantly improves the non-agriculture VOEs’ profit and ROI. However, financial literacy significantly improves the agricultural VOEs’ ROI only, not profit.

Table 4. The impact of financial literacy and control variables on VOEs performance: a pooled model

Variables	Profit	Return on Investment
	Coef. (Std. Err)	Coef. (Std. Err)
Financial literacy	.0090 (.0027)***	0.0332 (0.0080)***
Age	−.0004 (.0001)***	−0.0006 (0.0003)*
Education	.0012 (.0004)***	0.0012 (0.0012)
Incentive	.0057 (.0039)	0.0091 (0.0117)
Marital	−.0039 (.0046)	0.0234 (0.0139)*
Employee	.0000 (.0000)	−0.0001 (0.0001)
Agriculture	−.0021 (.0021)	−0.0038 (0.0062)
Distance	.0000 (.0001)	0.0003 (0.0003)
Digital market	.0042 (.0024)*	−0.0119 (0.0071)*
Local market	.0018 (.0020)	0.0030 (0.0060)
Social Activity	.0002 (.0029)	−0.0028 (0.0088)
Promotion	−.0022 (.0021)	0.0037 (0.0063)
Transportation	−.0014 (.0059)	−0.0038 (0.0176)
Social media	−.0009 (.0020)	−0.0021 (0.0059)
Constant	8.7922 (.0117)***	0.6220 (0.0350)***
F-Value	35.1100	25.6000
Prob > F	.0000	0.0000
R-squared	.7047	0.6350
Adj R-squared	.6846	0.6102
Root MSE	.0125	0.0375

Note: * = sig 10%; ** = sig 5%; *** = sig 1%

The dependent variables are log-transformed

This study confirms that financial literacy can increase VOE performance by boosting profits and ROI. With a sound understanding of financial concepts and effective use of financial information, VOE managers can make more informed decisions to boost profitability and ROI. For instance, with improved financial literacy, VOE managers can manage their cash flow better, reduce unnecessary expenses, and improve margins. They can also make better investment decisions, e.g., investing in projects with higher ROI. In negotiations, they can secure better terms with lenders or investors, hence, reducing financing costs and increasing profit margins. Furthermore, financial literacy can also help VOE managers communicate more effectively with stakeholders, including community members, investors, and government agencies. By communicating financial information more clearly and accurately, managers can build trust and confidence in their enterprises, which can attract more investments and support.

Financial literacy significantly impacts the VOEs’ profitability and ROI. Therefore, VOE managers need to improve their financial literacy continuously. The finding in this study also confirms previous studies, showing the positive impact of financial literacy on firm performance (Agyapong & Attram, 2019; Hossain, 2020; Yakob et al., 2021). While past studies focus on small and medium enterprises (SMEs), this study offers valuable insights into the effect of financial literacy on VOEs. The finding in this study could be applicable in Indonesia and other countries with VOEs, such as China, i.e., the Chinese township-village enterprises (Kung, 2022; Kung & Lin, 2007; Liang; Liu & Yang, 2019; Putterman, 1997).

This study also highlights that the impacts of financial literacy on firm performance may vary depending on the sector, i.e., agriculture and non-agriculture VOEs. The findings show that the impact is bigger on non-agricultural VOEs than agricultural VOEs. This impact difference can be attributed to the complexity of the financial structure in non-agricultural VOEs that force managers and employees to achieve better financial literacy. Furthermore, non-agricultural VOEs may face tighter competition, which demands more expertise in financial management. In contrast, the agricultural VOEs’ finance structure tends to be simpler, and their operations are often more straightforward, which may explain the lower impact of financial literacy on performance. This suggests that policymakers must consider the type of VOE when designing financial literacy programs.

On the other hand, this study also controls the managers’ characteristics in the regression model. In general, the findings indicate that education and digital marketing positively and significantly affect VOE performance (profit and ROI). However, age shows the opposite. The interesting finding of this model is that digital marketing improves firm performance four-fold. First, it increases visibility. Digital marketing allows VOEs to reach a wider audience through online channels such as social media, email marketing, and search engine optimization (Rahayu et al., 2021; Sanje & Senol, 2012; Yulianti et al., 2023). The increased visibility can attract more customers and increase sales. Second, digital marketing can help VOEs target their marketing efforts toward specific demographics or geographic areas (Sanje & Senol, 2012), so marketing campaigns become more effective, and ROI will be higher. Third, digital marketing is more cost effective than traditional marketing methods, such as print ads or billboards (Hanekom & Scriven, 2002). VOEs can save money on advertising costs and still reach a large audience through digital channels. Fourth, digital marketing allows for better engagement as customers can interact in real-time through social media, email, and other channels (Nadaraja & Yazdanifard, 2013). This can increase customer satisfaction and loyalty. Interestingly, the disaggregated estimation presented in Table 5 reveals an intriguing pattern regarding the impact of digital marketing on different sectors. The coefficient associated with the agriculture sectors is positive, indicating that digital marketing has a beneficial effect on the performance of agriculture firms. However, the coefficient for non-agriculture sectors is negative, suggesting that digital marketing does not yield the same positive outcomes for non-agriculture firms. These findings imply that while digital marketing strategies can enhance the performance of agriculture firms, they may not have a similar effect on non-agriculture firms, highlighting the sector-specific nature of the relationship between digital marketing and firm performance.

Table 5. The impact of financial literacy and control variables on VOEs performance: a disaggregate estimation

profit	Non-agriculture		Agriculture
	Profit	ROI	Profit	ROI
	Coef. (Std. Err.)	Coef. (Std. Err.)	Coef. (Std. Err.)	Coef. (Std. Err.)
Financial literacy	0.0064 (0.0030)**	0.0305 (0.0083)***	0.0092 (0.0065)	0.0332 (0.0209)***
Age	−0.0003 (0.0001)**	−0.0002 (0.0004)	−0.0005 (0.0002)*	−0.0010 (0.0008)
Education	0.0013 (0.0005)***	0.0007 (0.0013)	0.0014 (0.0009)	0.0019 (0.0027)
Incentive	0.0143 (0.0048)***	0.0240 (0.0136)*	−0.0020 (0.0079)	−0.0091 (0.0252)
Marital	−0.0020 (0.0056)	0.0246 (0.0159)	−0.0075 (0.0080)	0.0209 (0.0257)
Employee	0.0000 (0.0001)	−0.0001 (0.0001)	0.0000 (0.0001)	0.0000 (0.0004)
Distance	0.0001 (0.0001)	0.0007 (0.0004)*	−0.0001 (0.0002)	0.0001 (0.0005)
Digital market	−0.0011 (0.0030)	−0.0277 (0.0084)***	0.0128 (0.0042)***	0.0104 (0.0134)
Local market	0.0008 (0.0026)	−0.0020 (0.0072)	0.0038 (0.0030)	0.0096 (0.0097)
Social activity	0.0052 (0.0036)	0.0015 (0.0102)	−0.0017 (0.0048)	0.0032 (0.0153)
Promotion	−0.0011 (0.0025)	0.0011 (0.0070)	−0.0045 (0.0040)	0.0060 (0.0129)
Transportation	−0.0168 (0.0120)	−0.0547 (0.0338)	0.0017 (0.0069)	0.0072 (0.0221)
Social media	0.0010 (0.0026)	0.0017 (0.0072)	−0.0017 (0.0032)	−0.0044 (0.0103)
Constant	8.8053 (0.0159)***	0.6635 (0.0447)	8.7935 (0.0248)***	0.6219 (0.0792)
F-value	22.7600	20.4100	19.3100	11.5800
Prob > F	0.0000	0.0000	0.0000	0.0000
R-squared	0.7493	0.7282	0.7275	0.6156
Adj. R-squared	0.7164	0.6926	0.6899	0.5625
Root MSE	0.0116	0.0327	0.0127	0.0407

Note: * = sig 10%; ** = sig 5%; *** = sig 1%

The dependent variables are log-transformed

This study provides novel empirical evidence on the link between financial literacy on VOE performance, namely a positive and significant effect of financial literacy on VOE performance in the context of a developing country, which may also be applicable in developed countries. Secondly, VOEs or similar institutions exist not only in Indonesia but also in other Asian countries, such as China (Han et al., 2021; Kung, 2022; Liu & Yang, 2019; Zhang & Wu, 2022), Thailand (Natsuda et al., 2012). Other studies have also highlighted the existence of VOEs in Africa, such as in Kenya (Gichuki et al., 2014) and South Africa (Tada et al., 2013).

5. Conclusion

This study investigates the impact of financial literacy on village-owned enterprises’ (VOEs) performance in Indonesia. The study employs a structured questionnaire to survey 220 VOEs, measuring their performance through profit and ROI. A two-stage predictor substitution (2SPS) approach addresses potential endogeneity issues. The findings reveal that VOE managers’ financial literacy is positively influenced by education, incentives, number of employees, and training but negatively influenced by age, marital status, and distance to the capital city. The main finding indicates a significant positive impact of financial literacy on VOE performance. However, further estimation based on the sectors (agricultural and non-agricultural) shows different results. In other words, the findings suggest that promoting financial literacy could enhance VOE performance but contextual factors, such as the VOEs sector, need to be considered when designing financial literacy programs. Finally, the methodology (the 2SPS approach) provides a framework for future studies to address endogeneity issues in estimating the impact of financial literacy on firm performance.

The study has several practical implications for promoting VOE performance in Indonesia. First, the findings suggest that policymakers and organizations should prioritize designing and implementing financial literacy programs for VOE managers. Literacy will equip managers with the knowledge and skills to make informed financial decisions and manage financial resources more efficiently. Secondly, the study emphasizes the importance of tailoring financial literacy programs to the specific needs and contexts of VOEs (i.e., agricultural or non-agricultural). Finally, the study highlights the need to provide incentives, training, and education for VOEs managers to promote a culture of continuous learning and development, which eventually improve VOE performance. It should be noted that this study is limited in terms of performance indicators, namely, only focusing on profit and ROI. Other indicators, such as technical efficiency, cost efficiency, marketing, and supply chain performance, could be considered by future research to investigate the impact of financial literacy on VOE performance.

Acknowledgments

The authors acknowledge to Faculty of Agriculture, Brawijaya University

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from Moh Shadiqur Rahman upon request.

Additional information

Funding

This research was funded by the Faculty of Agriculture, Brawijaya University. No. 4108.35/UN10.F04/PN/2022

Notes on contributors

Tri Wahyu Nugroho

Tri Wahyu Nugroho holds the position of a senior lecturer within the Agriculture Socio-economics Department at Brawijaya University’s Faculty of Agriculture. His primary research emphasis is directed towards rural development and poverty-related studies.

Moh Shadiqur Rahman

Moh Shadiqur Rahman is a lecturer from the Agriculture Socio-economics Department, Faculty of Agriculture, Brawijaya University, Indonesia. His main areas of research revolve around agricultural economics, rural development, impact evaluation, climate change, well-being, and food security.

Hery Toiba

Hery Toiba is an associate professor affiliated with the Agriculture Socio-economics Department at Brawijaya University’s Faculty of Agriculture, Indonesia. He has earned a PhD in Agricultural Economics from the University of Adelaide, Australia. His current research is centered on subjects such as dietary transitions in developing nations, food policy, food security, and nutrition.

Novil Dedy Andriatmoko

Novil Dedy Andriatmoko is an educator belonging to the Agriculture Socio-economics Department within Brawijaya University’s Faculty of Agriculture in Indonesia. His primary research interests lie in the domains of production economics and food safety.

Rachman Hartono

Rachman Hartono serves as a senior lecturer within the Agriculture Socio-economics Department at Brawijaya University’s Faculty of Agriculture. His research pursuits encompass agribusiness, non-cognitive skills, and entrepreneurship.

Mohammad Ilyas Shaleh

Mohammad Ilyas Shaleh is a master student of agribusiness program at the Faculty of Agriculture, Brawijaya University His research endeavors focus on sustainable agricultural practices and the adoption of agricultural technology.

Appendix 1

Q1. “Suppose you had $100 in a savings account and the interest rate was 2% per year. After 5 years, how much do you think you would have in the account if you left the money to grow?”

1. More than $102

2. Exactly $102

3. Less than $102

4. Do not know (DK)

5. Refuse.

Q2. “Suppose you had $100 in a savings account and the interest rate is 20% per year and you never withdraw money or interest payments. After 5 years, how much would you have on this account in total?”

1. More than $200

2. Exactly $200

3. Less than $200

4. DK

5. Refuse

Q3. “Imagine that the interest rate on your savings account was 1% per year and inflation was 2% per year. After 1 year, how much would you be able to buy with the money in this account?”

1. More than today

2. Exactly the same

3. Less than today

4. DK

5. Refuse

Q4. “Assume a friend inherits $10,000 today and his sibling inherits $10,000 3 years from now. Who is richer because of the inheritance?”

1. My friend

2. His siblings

3. They are equally rich

4. DK

5. Refuse

Q5. “Suppose that in the year 2010, your income has doubled and prices of all goods have doubled too. In 2010, how much will you be able to buy with your income?”

1. More than today

2. The same

3. Less than today

4. DK

5. Refuse

Q6. “Suppose you want to buy a TV for IDR $60. If you buy from Store A, you get a discount of $10, but if you buy from Store B, you will get a 10% discount. Which shop will you buy from?

1. Store A

2. Both stores offer the same discount

3. Store B

4. DK

5. Refuse

Appendix 2

Table A1. Pearson correlation test for validity of instrumental variable

Variables	Instrumental Variable
Financial literacy	0.319
	0.000***
Profit	−0.021
	0.762
ROI	0.107
	0.112

Note: *** = sig 1%