Does Institutional Quality Shape Agricultural Credit Orientation? Evidence from D-8 Nations

Abstract

The agricultural sector, which has long been overshadowed by industrialization, has reemerged with renewed strategic significance in the face of global crises, including pandemics and armed conflicts. This study examines the causal relationship between institutional quality and agricultural credit orientation in the Developing-Eight countries from 2002 to 2023. Using the agriculture orientation index for credit as a key indicator, this study investigates how disaggregated institutional dimensions—control of corruption, government effectiveness, political stability and absence of violence, rule of law, regulatory quality, and voice and accountability—affect the allocation of commercial bank credit to agriculture. Both the standard Kónya panel causality test and its time-varying extension are employed to capture static and dynamic causal patterns. The findings demonstrate that institutional quality exerts a substantial effect on credit orientation, although the magnitude and characteristics of this influence differ across countries. Türkiye, Indonesia, Nigeria, and Egypt exhibit consistent causal relationships, whereas other countries reveal episodic or latent effects linked to specific political or legal shifts. By combining dynamic methodology with a policy-relevant indicator, this study offers novel insights into how governance shapes agricultural finance. The results underscore the need for country-specific and institution-sensitive credit strategies to increase resilience and equity in financial systems.

1. Introduction

The agricultural sector plays a pivotal role in the socioeconomic fabric of developing countries, which collectively account for approximately 83% of the global population [1,2]. Agriculture serves not only as a primary source of livelihood but also as a critical component of food security and rural development. However, persistent challenges, including food insecurity, low productivity, and inadequate access to financing, continue to threaten the sustainability of agricultural systems in these regions [3]. The importance of effective agricultural policies and financial interventions is thus increasingly recognized, particularly given the reliance of large rural populations on agrarian income [4,5]. In this context, efforts to reduce agricultural input costs, expand subsidy mechanisms, disseminate modern farming techniques, and enhance farmers’ agricultural literacy are vital for achieving agrarian sustainability [6,7,8]. Understanding the structural determinants that influence access to agricultural credit is particularly important. Accordingly, this study aims to investigate how components of institutional quality shape the orientation of commercial bank credit toward agriculture in the Developing-Eight (D-8) countries.

Agricultural credit provides farmers with essential resources such as fertilizer, irrigation equipment, and improved seeds, while also enabling the adoption of modern technologies [9,10]. In developing countries, such credit plays a pivotal role in ensuring the sustainability of production, fostering crop diversification, shielding farmers from the adverse effects of global competition, and supporting adaptation to drought and climate change [11]. Moreover, agricultural credit that expands financial access in rural areas improves household welfare, strengthens food security, and reinforces the socioeconomic resilience of the sector.

Households face multiple barriers to accessing agricultural credit. Macroeconomic factors such as inflation, interest rates, and exchange rate volatility directly constrain farmers’ borrowing capacity [12]. Institutional weaknesses, policy uncertainty, inadequate anti-corruption measures, and insufficient protection of property rights further heighten farmers’ perceptions of risk and reduce credit demand [13]. Policy reforms in these areas foster farmers’ long-term investment and production planning, thereby directly influencing sustainable agriculture [14].

The sustainability of agricultural activities is disrupted by various factors, including climate change, overexploitation of natural resources, and economic volatility [15]. Sudden economic shocks, for instance, may trigger sharp increases in food prices, thereby undermining food security [16]. Ensuring that access to agricultural credit remains both affordable and accessible is critical for mitigating such risks and enhancing food system resilience [1,17]. Broader access to credit enables farmers to adopt innovative technologies, reduces the environmental vulnerabilities of agricultural practices, helps curb rural-to-urban migration, and insulates producers from input cost fluctuations [10,12,17]. Yet, it remains unclear whether these positive effects persist consistently or manifest only during specific periods. The existing research appears insufficient in addressing this question. Therefore, this study also seeks to determine whether the influence of institutional quality components on agricultural finance is sustained over time or episodic.

Institutional quality, broadly defined, refers to the presence of transparent, accountable, and efficiently functioning governance, legal, and political systems in a country. While the efficient operation of financial systems is fundamental to overall economic performance, it is equally crucial for the sustainability of the agricultural sector. However, financial system efficiency is not solely a function of optimal resource allocation. Institutional attributes, such as political stability, anti-corruption efforts, rule of law, and freedom of expression, also play a vital role in shaping financial outcomes [18,19,20].

The positive impact of these institutional variables, chiefly their contribution to establishing a trustworthy environment, is especially evident in developing countries. Trust reduces banking sector risks, facilitates credit expansion, and improves the functioning of lending channels [20]. As a result, reforms in financial governance can enhance the inclusivity and resilience of financial systems. Broader access to credit strengthens societal financial inclusion and improves resilience to systemic crises. In particular, for vulnerable sectors such as agriculture, fostering conditions of reduced financial risk and increased stability is essential [19,21]. Therefore, the proper functioning of agricultural credit mechanisms depends not only on sound financial and economic structures but also on the effectiveness of institutional frameworks.

The question of which specific components of institutional quality influence agricultural credit orientation is particularly relevant. Despite the growing interest in agricultural finance, limited research exists on how institutional quality impacts credit allocation to agriculture in the D-8 context. Accordingly, this study seeks to analyze, for each D-8 country individually, the determinants of agricultural credit orientation. Such a country-specific approach may support the formulation of tailored policy measures for the region.

D-8 is an economic cooperation organization established in 1997 under the leadership of Türkiye. Its member states include Türkiye, Nigeria, Indonesia, Egypt, Malaysia, Bangladesh, Pakistan, and Iran [22]. The organization was founded to enhance cooperation among member countries in sectors such as agriculture, economy, industry, health, and energy, while also aiming to increase intragroup trade [23]. A common feature of these nations is that they are all developing economies, with the agricultural sector playing a significant role in employment generation. Moreover, the effectiveness of agricultural credit is instrumental in strengthening the agrarian policies of these countries [24]. In addition to financial infrastructure, institutional quality dimensions are critical in shaping the effectiveness of agricultural credit.

Therefore, in a group of countries such as the D-8—characterized by structural similarities in agriculture but notable institutional differences—it is meaningful to analyze how institutional variables affect the agricultural credit orientation index. While studies have been conducted on the factors influencing agricultural credit in developing countries, research specifically covering D-8 countries remains scarce, with a predominant focus on the micro-level. Most available studies focus on single-country analyses [1,8,10,22,25].

To date, no study has applied the Kónya panel causality test to all D-8 countries. Identifying the causal effects of institutional quality components on the agriculture orientation index in these countries would not only enable cross-country comparisons but also offer valuable insights into developing tailored policy responses. This study aims to address this gap in the literature. In doing so, it introduces a novel contribution in both methodological and empirical terms. In addition to evaluating the effectiveness of agricultural finance, this study also aims to enrich policy debates surrounding agrarian strategies in the D-8 region.

Furthermore, this study brings attention to a valuable yet underutilized indicator in the literature: the Agriculture Orientation Index (AOI) for credit. The index is calculated by dividing the share of commercial banks’ total credit allocated to agriculture by the share of agriculture in Gross Domestic Product (GDP). In this way, the AOI for credit provides a clear reflection of the relative importance commercial banks attach to financing the agricultural sector [26]. The methodology includes not only traditional causality tests but also advanced techniques to uncover hidden (time-varying) causal dynamics between institutional quality and agricultural finance. This approach offers deeper insights for stakeholders interested in the institutional underpinnings of agricultural credit systems.

The core objective of this research is to determine the causal relationships between components of institutional quality—namely, control of corruption, government effectiveness, political stability and absence of violence, rule of law, regulatory quality, and voice and accountability—and the AOI for credit in the D-8 countries. This study aims to identify the structural vulnerabilities that contribute to fluctuations in agricultural credit orientation and determine which variables influence credit orientation in each country. The use of panel data from 2002 to 2023 enhances the relevance of the findings to the current structural challenges.

Moreover, applying both the standard Kónya panel causality test, introduced by [27], and its time-varying version [28,29,30] allows for a nuanced exploration of whether these causal relationships persist over time or are subject to temporal fluctuations. This multidimensional and robust approach strengthens the analytical framework of the study. Figure 1 presents the evolution of the AOI for credit across the D-8 countries during the period 2002–2023.

The threshold value of “1” is used as a reference point. A value of 1 represents a “neutral” position, where the share of credit equals exactly agriculture’s share in the economy. This equilibrium point indicates that agriculture receives neither preferential treatment nor discrimination in credit allocation. Values below 1 indicate under-allocation, while values above 1 reflect preferential lending. This metric also allows transparent comparisons across countries and time periods. For instance, an AOI value of 0.3 implies that agriculture receives only one-third of the credit commensurate with its share in GDP, whereas a value of 2 shows that agriculture obtains twice the level of credit relative to its economic weight.

As illustrated in Figure 1, except for Iran (Iran has been excluded from the analysis due to data limitations), all D-8 countries registered values below the threshold of 1 for the AOI for credit. This finding indicates that farmers in these countries receive proportionally less credit from commercial banks relative to their economic contributions. In other words, the financial commitment of commercial banks to the agricultural sector remains limited compared with the sector’s value-added. Additionally, the index values exhibit structural breaks in certain years, suggesting the presence of external shocks or policy shifts. Identifying the underlying drivers of these fluctuations requires a detailed analysis that incorporates relevant variables with significant explanatory power. This study is motivated by such a need.

The study excludes Iran due to missing data; however, this exclusion does not compromise the validity or reliability of the findings, since the methodology conducts country-specific rather than aggregate panel-level estimations.

In line with the objectives and motivation stated above and to deepen our understanding of the institutional determinants of agricultural credit orientation, our study is structured around the following research questions:

RQ1. 

Do the components of the institutional quality index shape the orientation of commercial banks toward agricultural credit in D-8 countries?

RQ2. 

Which specific component(s) of institutional quality most strongly influence agricultural credit orientation within D-8?

RQ3. 

Are the observed effects consistent over time, or do they occur sporadically in response to specific conditions?

To summarize, this study aims to investigate not only whether institutional quality matters, but also which aspects are most significant, and how they persistently influence commercial bank behavior toward agricultural lending across different national contexts and periods. Against this backdrop, the scope of this research is limited to national-level data across D-8 countries, providing insights into the macro-institutional factors that shape credit orientation trends. While the study does not cover micro-level borrower characteristics, it contributes to the literature by highlighting the role of broader institutional frameworks in shaping sectoral credit distribution. Furthermore, the findings have important implications for policymakers seeking to enhance agricultural financing mechanisms through governance reforms.

The remainder of this paper is organized as follows: Section 2 presents a comprehensive review of existing studies related to the topic. Section 3 outlines the data sources and the methodological approach. The empirical findings are discussed in detail in Section 4. Finally, Section 5 concludes the study by reflecting on the key results, discussing their policy implications, and offering recommendations for future research.

2. Literature Review

This section reviews the literature on the nexus between institutions and agricultural credit. It first outlines the broad effects of credit on rural development and production. It then evaluates the structural and institutional factors shaping credit markets. Subsequently, emphasis is placed on sub-dimensions of institutional quality—such as anti-corruption, political stability, and the rule of law—and their influence on credit allocation. The review concludes by identifying the limitations of prior research and clarifying the gaps this study seeks to address. Overall, the discussion converges on a central theme: the decisive role of institutional quality in shaping agricultural credit.

2.1. Agricultural Credit and Rural Development

Agricultural credit is widely recognized as a cornerstone of rural development, agricultural modernization, and food security, particularly in developing economies. Access to credit enhances farmers’ investment capacity, boosts agricultural productivity and efficiency, and mitigates both operational and financial risks. In this context, a growing body of empirical research has documented the positive effects of agricultural credit utilization on productivity, income diversification, income growth, and infrastructure development [1,10,17]. Grounded in neoclassical production theory, these findings suggest that the efficient allocation of capital input facilitates high-return agricultural activities, thereby increasing agricultural output levels and fostering the structural transformation of rural economies [31,32,33,34].

However, this growth-oriented framework has been increasingly scrutinized in recent years. Critics have argued that economic factors do not solely determine the effectiveness of credit access but are also contingent on broader structural and institutional conditions [35,36]. Reflecting this critical perspective, the agricultural credit policies of international development institutions such as the World Bank have also been called into question. For instance, ref. [37] examines these policies within the broader context of neoliberal reform strategies, highlighting how practices such as interest rate liberalization, the removal of subsidies, and the privatization of agricultural banks may generate outcomes that are at odds with long-term development goals. These discussions highlight that agricultural credit allocation is shaped not only by economic factors but also by institutional conditions.

2.2. Structural and Institutional Conditions in Agricultural Credit

The impact of such policies on credit systems is not only limited to the macro level but also manifests through interactions observed at the micro level. For instance, ref. [38] examining the case of Türkiye, identifies a bidirectional relationship between agricultural output and credit volume, emphasizing that the structural characteristics of the banking sector play a pivotal role in shaping this dynamic. Similarly, ref. [39] shows that in transition economies, direct payment schemes have proven insufficient to promote the commercialization of smallholder farmers. In the absence of robust financial infrastructure, access to credit remains a persistent and formidable barrier.

In another study, ref. [40] demonstrated that integrating farmers into Ghana’s agricultural value chain significantly enhances their access to both formal and informal credit sources. Collectively, these findings suggest that agrarian credit policies should not be confined to production-oriented incentives alone. Rather, they must be embedded within a broader strategic framework that incorporates supply-side institutional reforms, inclusive financial infrastructure development, and mechanisms that facilitate farmers’ integration into markets and participation in structural networks. This holistic approach is essential for addressing systemic constraints and fostering sustainable agricultural finance.

Within this context, institutional quality plays a pivotal role in shaping the functioning of agricultural credit systems. Elements such as the rule of law, anti-corruption measures, governance effectiveness, integrity of regulatory frameworks, operation of financial markets, and political stability significantly influence banks’ risk perceptions and their orientation toward lending in the agricultural sector [8,18,19,20]. Studies such as those by [41] underscore that institutional transformation is as essential as economic reform for ensuring the long-term sustainability of financial systems. Therefore, the sustainability of the agricultural credit system depends not only on the robustness of the financial infrastructure but also on the quality of institutional governance.

2.3. Dimensions of Institutional Quality and Their Effects

Institutional quality is inherently multidimensional and cannot be adequately captured by a single metric. Accordingly, this study emphasizes the need to evaluate the sub-dimensions of institutional quality [42]. Such an approach enables a comprehensive assessment of whether governance mechanisms, economic indicators, and financial systems function effectively [43]. By incorporating variables that reflect this complexity, the study examines the key sub-components of institutional quality shaping agricultural credit orientation. The theoretical framework guiding the study’s empirical analysis is presented through a diagram below (see Scheme 1).

Effective control of corruption reduces favoritism and bribery, thereby fostering a fairer allocation of credit [44]. Political stability is equally vital, as it ensures the continuity of agricultural support policies and mitigates uncertainty. This control and stability environment, in turn, lowers banks’ risk perceptions and facilitates the provision of long-term agricultural loans [12]. Strengthening the rule of law creates a climate of trust for both farmers and financial institutions, preventing potential disputes over unpaid debts or delayed loan repayments. As a result, banks face lower credit risk, enabling the implementation of more sustainable lending policies [45].

Government effectiveness enhances the capacity to implement agricultural policies. This empowers farmers to assume long-term debt obligations with confidence, supported by price guarantees and a secure market environment, while alleviating concerns over product marketing [46]. Similarly, regulatory quality promotes transparency and stability, which encourages banks’ willingness to extend credit more securely while reducing costs and risks. Moreover, voice and accountability strengthen farmers’ sense of operating in a democratic environment, increasing their confidence that policymakers will respond to their needs and mitigate production risk. Through local cooperatives, farmers can organize collectively, ensuring equitable access to credit and preventing credit monopolization by specific groups [12,47].

In countries with strong institutional frameworks, financial institutions are more inclined to allocate resources to the agricultural sector. Conversely, political instability, legal uncertainty, and weak regulatory capacity tend to reverse this trend, discouraging credit flow to agriculture [21]. This is particularly critical in developing economies, where establishing institutional trust helps lower perceived risks in the banking sector and enables more inclusive and accessible credit channels [48,49,50]. In the absence of such trust, factors such as information asymmetries, high transaction costs, and political interference may lead to exclusionary effects in agricultural credit markets [14,35]. Each dimension of institutional quality directly influences banks’ risk perceptions and lending behavior.

2.4. Measuring Credit Orientation and Addressing Gaps

Furthermore, the effectiveness of policy instruments, such as public agricultural expenditures, subsidies, collateral systems, and agricultural insurance schemes, is heavily contingent upon the quality of the surrounding institutional environment [16,51]. For this reason, credit allocation processes must incorporate not only financial indicators but also structural variables that reflect institutional capacity [52]. In a similar vein, ref. [53] argue that agricultural finance should be assessed through a holistic lens—one that integrates governance indicators alongside fiscal structures and incentive mechanisms.

Despite the growing recognition of the role of institutional dynamics in shaping agricultural finance, empirical evidence on how such institutional interactions influence agricultural credit allocation remains limited. Most existing studies concentrate on indicators of financial development or the overall volume of credit without adequately examining whether credit distribution aligns proportionately with the economic significance of the agricultural sector. In this regard, the AOI for credit offers a meaningful and comparable metric that captures the extent to which credit allocation corresponds with the sectoral priorities implied by agriculture’s share of GDP [12,21,54].

AOI thus fills a critical gap in the literature by providing an evaluative lens through which the alignment between financial flows and sectoral needs can be assessed. However, the application of this index remains relatively rare in academic literature, and its integration with dynamic institutional analyses, particularly in cross-country contexts, has been underexplored.

In addition, the methodological approaches employed in existing empirical studies are not without criticism. Traditional panel data techniques, such as fixed effects models and the generalized method of moments, often fail to capture cross-country heterogeneity and structural changes over time. Moreover, by neglecting cross-sectional dependence, these methods may compromise the validity and robustness of the findings [10].

To address these limitations, the Kónya panel causality test offers a more appropriate framework by accommodating cross-country heterogeneity and relaxing the assumption of parameter homogeneity. Furthermore, the time-varying causality approach [28,29,30] enables the exploration of both overt and latent causal relationships by revealing the temporal dynamics of causality across different periods.

Although some studies, such as those by [19,25], have demonstrated the utility of these advanced techniques, much of the literature still treats institutional quality as an undifferentiated construct or relies on small-sample, single-region analyses, thereby limiting the generalizability of their findings.

In this regard, the D-8 countries offer a particularly suitable and insightful sample for comparative analysis, owing to their shared development objectives and institutional diversity. However, there is a notable gap in the literature concerning comprehensive investigations into the relationship between agricultural credit and institutional quality in the context of D-8 countries. Most studies either focus on single-country case analyses or examine credit usage at the micro-level, particularly among individual farmers. As a result, cross-national studies that jointly consider macro-level financial indicators and institutional variables remain scarce [1,10,22,25]. AOI provides an appropriate tool for assessing the impact of such institutional factors on the share of credit allocated to the agrarian sector.

2.5. Research Gaps and Contributions

Despite the diverse perspectives on the issue discussed in the literature, several gaps remain unaddressed. In particular, the extent to which macro-level institutional factors shape micro-level agricultural credit allocation has not been adequately examined. A comparative cross-country framework is therefore necessary to build a more systematic understanding and to develop effective policy recommendations [8,14].

While it is widely acknowledged that microeconomic barriers, such as gender inequality, a lack of property rights, digital exclusion, and weak social capital, limit access to agricultural credit [35,55,56,57], their connection to institutional reform efforts remains underexplored in the literature. To demonstrate the productivity effects of agrarian credit at the farm level, ref. [58] conducted a study on farmers in Pakistan. Their findings indicate that access to agricultural credit enables farmers to acquire more advanced technological equipment, which in turn enhances wheat productivity. Some recent contributions, including those by [52,59], suggest that such structural inequities can be addressed not only through individual-level interventions but also through institutional policies aimed at reshaping the broader enabling environment.

Another often-overlooked dimension is the temporal variability of institutional influence. In developing countries, institutional quality indicators frequently fluctuate in response to dynamic factors such as economic shocks, political instability, and regime changes. Given this volatility, there is a clear need for time-sensitive modeling approaches to accurately capture the effects of institutional shifts on credit allocation and orientation [18,48]. Nevertheless, studies employing such dynamic analytical frameworks remain scarce in the current literature.

At the same time, growing scholarly attention has been directed toward the environmental and public health externalities associated with access to credit. While credit-supported agricultural expansion may stimulate economic growth, it can also trigger serious ecological consequences, including increased pesticide use, biodiversity loss, water pollution, and resource depletion [60,61]. These concerns reinforce the need to redesign agricultural finance policies—not only to meet economic goals but also to align with environmental sustainability principles [41].

This comprehensive assessment highlights three critical gaps in the literature:

(1)

the limited application of AOI for credit in multi-country analyses that incorporate institutional frameworks;

(2)

the lack of disaggregated evaluations of institutional quality components and their respective influences on agricultural credit orientation; and

(3)

the absence of dynamic models that capture the evolution of institutional-credit interactions over time.

To address these gaps, this study examines the relationships between six key institutional quality indicators—Control of Corruption, Government Effectiveness, Political Stability and Absence of Violence, Rule of Law, Regulatory Quality, and Voice and Accountability—and the AOI across D-8 countries, employing dynamic panel causality methods. This approach enables the identification of both time- and country-specific causal patterns, offering a more nuanced understanding of how agricultural finance responds to variations in the institutional context. This study extends AOI analysis to multiple countries, offering a broader comparative perspective. By disaggregating the effects of institutional quality components on AOI, the analysis reveals which institutional dimensions drive credit orientation in each country. Moreover, the study examines temporal and cross-country variations alongside potential drivers of these differences. Thus, the study directly responds to key gaps identified in the literature.

Additionally, this study significantly contributes to our understanding of how credit orientation is shaped not only by economic factors but also by institutional conditions. It aims to unpack the multidimensional, time-varying, and context-dependent dynamics of the relationship between agricultural finance and institutional quality. Ultimately, the findings are expected to offer deeper, policy-relevant insights into the political economy of agrarian finance in developing countries.

3. Data and Methodology

This study investigates the causal relationship between institutional quality components and the AOI for credit across D-8 countries. Owing to data availability, the dataset covers the years 2002–2023. The variables and data sources are listed in

Table 1. Variables and data sources.

Data	Abbreviations Used in Models	Data Source
Institutional Quality Dimensions (Control of Corruption; Government Effectiveness; Political Stability and Absence of Violence; Rule of Law; Regulatory Quality; Voice and Accountability)	CC, GE, PS, RL, RQ, VA	[62]
Agriculture Orientation Index for Credit	AOI	[54]

.

The methodological framework for AOI for credit is presented below (see Equations (1)–(3)) [63]:

$$Shareofagricultureintotalcredit={\displaystyle \frac{Agriculturalcredit}{Totalcredit}}$$

(1)

$$ShareofagricultureinGDP={\displaystyle \frac{Agriculturalvalueadded}{GDP}}\ast 100$$

(2)

$$AOI={\displaystyle \frac{Shareofagricultureintotalcredit}{ShareofagricultureinGDP}}$$

(3)

The AOI for credit differs from other conventional agrarian credit indicators by assessing agricultural credit supply in proportion to the sector’s relative weight in the economy. In this way, AOI specifically measures the sectoral fairness of commercial banks’ agricultural credit allocation. Its key advantages lie in its simplicity, comparability across countries, policy relevance, and ability to be tracked over time. For instance, a study conducted on Ukraine employed the AOI metric to assess the degree of sectoral fairness in agricultural credit allocation [64]. Another study compared AOI values across developed, developing, and selected African countries, demonstrating how the index varies in relation to agricultural credit provision. While Germany records a very high AOI (5.565), African economies report values as low as 0.1 to 0.4. These results underscore a critical imbalance—advanced economies channel proportionally greater financial resources to agriculture. In contrast, in many underdeveloped countries, the sector receives insufficient credit despite its significant role and weight in the economy [65].

The Institutional Quality Index is derived from the Worldwide Governance Indicators and captures six key dimensions of governance listed in Table 1 [66]. Each dimension is constructed through a three-stage procedure designed to ensure comparability across diverse data sources. In the first stage, survey questions and variables from multiple underlying datasets are assigned to the governance dimension they most accurately represent. For example, a firm-level survey item on bribery is specified to the “Control of Corruption” component, while an indicator of press freedom is allocated to “Voice and Accountability”. The second stage involves rescaling individual variables to a common 0–1 range, with higher values denoting better outcomes. To illustrate, if a survey item asks respondents to rate an issue on a scale from 1 (minimum) to 4 (maximum), a score of 2 would be rescaled as $(2-min.)/(max.-min.)=(2-1)/(4-1)=0.33.$ When multiple questions pertain to the same governance component, their rescaled values are averaged. Although this step places indicators on the same scale, differences in distributional properties across sources remain. To correct these discrepancies, the third stage applies an Unobserved Components Model (UCM). This statistical approach assumes that each observed indicator reflects the underlying governance dimension plus an error term. The UCM adjusts for non-comparability across data sources and generates weighted averages, giving greater weight to sources that are more strongly correlated with others. This procedure enhances both the reliability and precision of composite governance scores.

The final output is expressed in percentile ranks ranging from 0 (lowest) to 100 (highest), which indicates a country’s relative position among all nations assessed. Equation (4) formalizes the percentile rank, with $R_i$ representing country i’s position in the distribution.

$$PercentileRank={\displaystyle \frac{R_i-1}{N-1}}\ast 100$$

(4)

For example, if a country scores at the 75th percentile in the rule of law dimension, it performs better than 75 percent of the countries in the sample. Mathematically, percentile ranks are obtained by ordering the raw values ($X_c$) of each governance dimension, determining the number of countries with lower scores, and dividing by the total number of countries (N). Thus, a country’s percentile represents the proportion of states with weaker institutional outcomes in that specific dimension. Through this multi-step process, the Institutional Quality Index provides a transparent, methodologically rigorous measure of governance performance.

The empirical analysis of the study employs the standard Kónya panel causality test along with its time-varying extension, which allows for a detailed examination of causal patterns over time.

The standard Kónya panel causality test does not require the preliminary application of unit root or cointegration tests. However, since this methodology accounts for heterogeneity and cross-sectional dependence (CD), it is essential to first assess the presence of these conditions before implementing the test.

In panel data analysis, shocks originating from one series can manifest similar effects in other series included in the model. This interconnectedness is referred to as cross-sectional dependence. To detect the existence of CD among the series, several diagnostic tests have been established in the literature. Among the various methods, the Lagrange Multiplier (LM) test introduced by [67] is widely regarded as a foundational approach. It is especially suitable for cases where the cross-sectional size is smaller than the time dimension. The test operates based on the set of equations (Equations (5) and (6)) outlined by [68]:

$$y_{it}={\alpha }_i+{\beta }_i^{\prime }{x}_{it}+{\mu }_{it}i=1,\dots ,N;t=1,\dots ,T$$

(5)

$$LM=T\sum _{i=1}^{N-1}\sum _{j=i+1}^N{\widehat{\rho }}_{ij}^2,{\chi }_{N(N-1)/2}^2$$

(6)

${\widehat{\rho }}_{ij}^2$ represents the correlation coefficient between the residuals obtained from the individual ordinary least squares estimations, specified in Equation (5).

Ref. [69] highlighted the limitations of the traditional LM test in scenarios where both the time dimension (T) and the cross-sectional dimension (N) are large, emphasizing that the test loses its validity under such conditions. To address this issue, ref. [69] proposed the cross-sectional dependence LM (CD_LM) test, which remains valid and robust even under conditions of high dimensionality in both time and cross-section. The test is grounded in the following equation (Equation (7)):

$$C{D}_{LM}={\left({\displaystyle \frac{1}{N(N-1}}\right)}^{{\displaystyle \frac{1}{2}}}\sum _{i=1}^{N-1}\sum _{j=i+1}^N(T{\widehat{\rho }}_{ij}^2-1),N(\mathrm{0,1})$$

(7)

The LM_adj test, developed by [70] as a bias-adjusted refinement of earlier methodologies, is expressed in the following equation (Equation (8)):

$$L{M}_{adj}=\sqrt{{\displaystyle \frac{2}{N(N-1)}}}\sum _{i=1}^{N-1}\sum _{j=i+1}^N{\displaystyle \frac{(T-k){\widehat{\rho }}_{ij}^2-{\mu }_{Tij}}{{\upsilon }_{Tij}^2}},N(\mathrm{0,1})$$

(8)

To determine whether the series exhibits a heterogeneous structure, the adjusted delta test (${\widetilde{∆}}_{adj}$), introduced by [71], is employed. This test relies on the following equation (Equation (9)):

$${\tilde{\mathsf{\Delta }}}_{adj}=\sqrt{N}\left({\displaystyle \frac{N^{-1}\tilde{S}-E\left({\tilde{z}}_{iT}\right)}{\sqrt{\mathit{var}({\tilde{z}}_{iT})}}}\right)$$

(9)

In implementing the standard Kónya panel causality test, the following equation, originally introduced by [72], is estimated via the Seemingly Unrelated Regression (SUR) method. This equation specifies a Vector Autoregression (VAR) system embedded within the SUR framework (Equation (10)):

$$\begin{gathered} y_{1,t}={\alpha }_{\mathrm{1,1}}+\sum _{l=1}^{l_{y1}}{\beta }_{\mathrm{1,1},l}{y}_{1,t-1}+\sum _{l=1}^{l_{x1}}{\delta }_{\mathrm{1,1},l}{x}_{1,t-1}+{\epsilon }_{\mathrm{1,1},t} \\ {y}_{2,t}={\alpha }_{\mathrm{1,2}}+\sum _{l=1}^{l_{y1}}{\beta }_{\mathrm{1,2},l}{y}_{2,t-1}+\sum _{l=1}^{l_{x1}}{\delta }_{\mathrm{1,2},l}{x}_{2,t-1}+{\epsilon }_{\mathrm{1,2},t} \\ ⋮ \\ {y}_{N,t}={\alpha }_{1,N}+\sum _{l=1}^{l_{y1}}{\beta }_{1,N,l}{y}_{N,t-1}+\sum _{l=1}^{l_{x1}}{\delta }_{1,N,l}{x}_{N,t-1}+{\epsilon }_{1,N,t} \end{gathered}$$

(10)

In the standard Kónya panel causality test, instead of relying on asymptotic critical values, individual bootstrap critical values are generated separately for each cross-sectional unit. The Wald test statistic calculated for each series is then compared with its corresponding critical value, allowing for the identification of causal relationships across units [27]. In this framework, causality from variable X to variable Y in the first cross-section of the panel is examined by testing the statistical significance of the coefficient ${\delta }_{\mathrm{1,1},l}$ via the Wald criterion. The appropriate critical values for inference are obtained through bootstrap resampling.

Despite its advantages, the standard Kónya panel causality test does not account for whether causal relationships remain stable over time. However, in practice, the causal relationship between two variables may emerge during certain periods and disappear in others. Indeed, the direction and stability of these linkages are not fixed; they can evolve in response to shifts in the global economic climate or major political developments [73]. Therefore, researchers have applied a time-varying version of the test to evaluate the temporal stability of causal relationships [28,29,30].

The application of this extended approach begins with the determination of the subsample size using the formula provided below (Equation (11)). The standard Kónya panel causality test is subsequently applied to each observation within the subsample, followed by the computation of bootstrap p-values. A p-value below the conventional 10% significance threshold signals the existence of causality from X to Y in that specific period. In the formula, T denotes the total number of observations.

$$Subsamplesize=[T(0.01+{\displaystyle \frac{1.8}{\sqrt{T}}})]$$

(11)

4. Findings

The results of the cross-sectional dependence and homogeneity tests are summarized in

Table 2. Cross-sectional dependence and homogeneity test results.

Cross-Sectional Dependence	Test Statistic Values
LM	32.590 ***
LMadj	2.628 *
LM CD	2.036 **
Homogeneity	Test Statistic Values
$\widetilde{∆}$	2.043 **
$\widetilde{∆}$adj	2.562 **

Note: *, ** and *** denote statistical significance at the 1%, 5% and 10% levels, respectively.

. The findings indicate the presence of cross-sectional dependence across the series, accompanied by a heterogeneous structure. Consequently, the necessary conditions for applying both the standard and time-varying Kónya causality tests are satisfied.

Table 3. Empirical results of the standard Kónya causality test.

Dimensions	Countries	Wald Test Results	Bootstrap Critical Values
			%1	%5	%10
Control of Corruption	Türkiye	7.269 ***	14.036	7.750	5.277
	Indonesia	1.408	9.643	5.179	3.562
	Malaysia	2.079	12.851	6.473	4.505
	Pakistan	1.995	26.481	12.415	8.465
	Egypt	6.969	20.111	10.423	7.196
	Bangladesh	0.002	14.523	7.116	4.544
	Nigeria	0.354	60.215	37.888	30.764
Government Effectiveness	Türkiye	8.347 **	10.973	5.914	3.966
	Indonesia	0.041	7.182	3.964	2.693
	Malaysia	1.684	13.323	6.750	4.570
	Pakistan	2.374	15.913	8.687	5.991
	Egypt	0.025	21.793	11.787	7.948
	Bangladesh	0.002	14.410	7.005	4.526
	Nigeria	5.765 **	8.990	4.499	3.062
Political Stability and Absence of Violence	Türkiye	3.358	17.560	9.515	6.416
	Indonesia	0.090	8.462	4.369	3.061
	Malaysia	1.388	12.913	6.977	4.494
	Pakistan	0.797	13.909	7.107	4.912
	Egypt	1.068	18.776	10.326	6.930
	Bangladesh	1.602	19.590	8.708	5.836
	Nigeria	2.809	21.281	13.345	10.365
Rule of Law	Türkiye	12.847 **	17.738	9.277	6.292
	Indonesia	11.395 *	6.875	3.710	2.592
	Malaysia	0.640	17.950	10.359	7.299
	Pakistan	0.079	14.499	7.411	5.095
	Egypt	0.099	18.805	10.079	6.710
	Bangladesh	0.480	12.599	6.857	4.730
	Nigeria	2.347	30.636	19.636	15.560
Regulatory Quality	Türkiye	7.742	24.582	14.464	10.682
	Indonesia	0.427	19.431	9.908	6.730
	Malaysia	5.181	26.799	15.430	11.069
	Pakistan	2.907	8.859	4.305	2.965
	Egypt	29.090 *	20.602	11.951	9.012
	Bangladesh	3.861	8.767	5.926	4.854
	Nigeria	3.550	20.451	12.438	9.409
Voice and Accountability	Türkiye	3.885	13.511	6.968	4.858
	Indonesia	3.927 ***	7.944	4.410	3.047
	Malaysia	0.077	10.501	5.440	3.601
	Pakistan	1.700	16.302	8.207	5.529
	Egypt	2.864	15.165	7.720	5.125
	Bangladesh	3.601	26.687	15.675	11.300
	Nigeria	12.048 **	16.386	9.308	6.676

Notes: ① *, ** and *** denote statistical significance at the 1%, 5% and 10% levels, respectively; ② Due to the structure of the Kónya panel causality test, the program codes for both the standard and time-varying versions compute only Wald statistics and bootstrap critical values (1%, 5%, 10%) and do not calculate standard errors.

summarizes the results of the standard Kónya causality test, which illustrates significant causal links between specific components of institutional quality and the AOI for credit across several countries. In Türkiye, causality is rooted in the control of corruption, government effectiveness, and the rule of law. Meanwhile, in Indonesia, causal relationships emerge from the rule of law as well as voice and accountability. In Nigeria, government effectiveness, voice, and accountability have causal influences. Finally, in Egypt, regulatory quality is causally linked to the orientation of agricultural credit.

The existence of a causal relationship from the control of corruption to AOI for credit in Türkiye suggests that how public resources and subsidies are allocated can directly influence credit distribution within the agrarian sector. Strengthening anti-corruption mechanisms may reduce resource misallocation, thereby increasing the banking sector’s willingness to extend credit to agriculture. Moreover, when agricultural enterprises operate within a formal and transparent framework, their perceived creditworthiness increases in the eyes of commercial banks, which can, in turn, facilitate greater access to agricultural credit.

The identification of a causal effect from government effectiveness to AOI in Türkiye further indicates that the state’s capacity to implement agricultural policies effectively is reflected in the allocation of financial resources. A more efficient public administration promotes the consistent enforcement of support mechanisms and regulatory frameworks, thereby fostering greater confidence in financial markets and potentially expanding the supply of agricultural credit.

The causal relationship between the rule of law and the AOI for credit in Türkiye reveals that the protection of property rights in the agricultural sector, trust in contractual enforcement, and the robustness of the legal infrastructure are critical determinants in credit allocation processes. A well-functioning legal system can play a pivotal role in facilitating the flow of credit to agriculture by reducing the perceived risk for both farmers and financial institutions. By enhancing legal certainty and contractual reliability, such a framework fosters a more secure investment environment, thereby encouraging greater economic engagement in the agricultural sector.

The observed influence of government effectiveness on the AOI for credit in Nigeria suggests that improvements in public administration can have a direct and measurable impact on financing the agricultural sector. In particular, the historically weak institutional structures prevalent in many African nations have often been identified as key constraints on the effective use and distribution of agricultural credit. However, well-implemented governance reforms have the potential to alleviate these institutional bottlenecks and enhance access to credit for agricultural purposes.

Furthermore, the correlation between accountability levels and agricultural credit allocation in Nigeria indicates the role that democratic consolidation can play in directing more financial resources toward the farm sector. Given that agriculture predominantly represents the interests of rural populations, who form a substantial electoral base, strengthened democratic processes and heightened demands for transparency may create political incentives for both public and private actors to increase their financial commitments to agriculture. In this context, democratic accountability not only serves governance objectives but also acts as a catalyst for improved rural finance and agricultural development.

The influence of the rule of law on the AOI for credit in Indonesia underscores the critical role of a stable and trustworthy institutional environment in the effective functioning of credit mechanisms within the agricultural sector. In this context, the protection of agrarian property rights and the predictability of legal processes are widely recognized as fundamental prerequisites for long-term investment and the utilization of credit in the sector. Without such legal safeguards, both lenders and borrowers are likely to perceive elevated risks, thereby constraining the flow of financial resources to agriculture.

Moreover, the existence of a causal relationship from accountability levels to AOI for credit in Indonesia highlights the pivotal role of transparent and responsible governance in shaping credit allocation decisions. Given that the agricultural sector employs a substantial portion of the rural population, it has emerged as a policy priority in response to democratic pressures and evolving societal demands. In such a governance landscape, heightened transparency can incentivize public authorities to support agricultural production more proactively and channel resources toward the sector with greater efficiency.

Thus, enhancing credit flows to agriculture has become both a governance and development imperative. As accountability mechanisms strengthen, financial channels can be expanded to facilitate improved access to credit for rural stakeholders. Additionally, rising accountability levels in Indonesia may help resolve the long-standing issue of informality in the agricultural sector. By fostering a more formalized and transparent operating environment, commercial banks’ perceived risk may be reduced, thereby encouraging greater engagement of private financial institutions in agricultural lending.

The strong causal relationship identified between regulatory quality and the AOI for credit in Egypt reflects the critical importance of legal and institutional frameworks in shaping the investment climate within the agricultural sector. In this regard, regulatory policies that are both predictable and geared toward supporting producers have the potential to incentivize financial institutions to increase their provision of agricultural credit.

Moreover, the reduction in bureaucratic obstacles emerges as a particularly influential factor in strengthening credit flows to agriculture. Streamlined administrative processes not only lower transaction costs but also increase lenders’ confidence, thereby facilitating greater financial engagement with the sector. In sum, improving regulatory quality through transparency, consistency, and producer-oriented reforms can lead to more robust agricultural credit allocation in Egypt, ultimately contributing to sectoral development and rural financial inclusion.

Before applying the time-varying Kónya causality test, it was necessary to determine an appropriate subsample size to ensure the reliability of the analysis. For this purpose, the formula in Equation (11) was employed. Given that the total number of observations (T) equals 22 in this study, the suitable subsample size was calculated as 9. Consequently, following the methodological requirements of the test, the first interpretable result regarding the presence of causality pertains to the 9th observation period, corresponding to the year 2010.

To provide a clearer illustration of the results, bootstrap p-values and their corresponding significance levels, which were calculated individually for each country, were visualized through a series of graphical representations (Figure 2, Figure 3, Figure 4, Figure 5, Figure 6 and Figure 7). These bootstrap p-values were derived from 10,000 simulation replications, ensuring robust and reliable inferences. While these visualizations support the findings presented in Table 3, they also reveal several instances of previously undetected (latent) causal relationships. In other words, the graphical outcomes exhibit notable divergences from the tabular results, highlighting additional dynamics not captured in Table 3. These discrepancies are further elaborated in the respective captions beneath each graph.

The causality analysis revealed a hidden relationship from the control of the corruption component to the AOI for credit in Bangladesh and Indonesia (Figure 2). In Bangladesh, this link emerged in 2017–2018, coinciding with high-profile governance scandals. The 2016–2017 Padma Bridge project became embroiled in a bribery controversy involving senior officials, prompting the World Bank to withdraw funding and intensifying concerns about transparency and accountability. In 2018, the Barapukuria coal scandal further exposed systemic irregularities in the management of public resources.

In Indonesia, this causal connection became evident in 2021 and in 2022, following a 2019 legislative reform that significantly reduced the independence of the Corruption Eradication Commission. The reform, widely perceived as weakening institutional oversight, provoked sustained public protests from 2020 to 2022.

Collectively, these episodes may have shaped governance conditions in both countries in ways that allowed corruption control to influence agricultural credit allocation. Weakening oversight mechanisms, combined with heightened public awareness of corruption, likely altered the distribution of financial resources within the agrarian sector, reinforcing the causal relationship identified in this study’s results.

Figure 3 illustrates the existence of latent causality from the government effectiveness component to the AOI for credit in Bangladesh and Pakistan. In Bangladesh, this relationship emerged in 2018, following substantial reforms to the 2013 National Agricultural Policy. The revised policy sought to increase productivity, promote mechanization, encourage commercialization, and diversify production. Simultaneously, amendments to the Seed Act restructured regulations on seed imports, exports, certification, and distribution, collectively strengthening the institutional and financial credibility of the agricultural sector.

Regarding Pakistan, similar patterns of policy-driven influence are evident, with key initiatives corresponding to the observed causal link in 2015, 2018, and 2019.

The 2015 “Kissan Package” introduced direct cash transfers, low-interest credit facilities, and input subsidies for farmers. This was followed in 2019 by the “Emergency Agriculture Program”, which aimed to increase productivity, modernize irrigation infrastructure, improve rural incomes, and secure the national food supply, backed by significant fiscal allocation. Despite facing a wheat and flour crisis in 2020, government intervention underscored the strategic importance of the sector.

These policy measures in both countries imply that targeted institutional reforms and sustained governmental commitment played focal roles in shaping the allocation dynamics of agricultural credit during the identified periods.

While Table 3 shows no direct evidence of causality from the political stability and absence of violence dimension to the AOI for credit, Figure 4 reveals latent causality in Egypt, Indonesia, Nigeria, Pakistan, and Türkiye. These relationships appear to be episodic, emerging during periods marked by significant political or security events.

In the case of Egypt, the re-election of the president in 2018—following his initial rise to power through a military coup—consolidated an increasingly authoritarian governance style. That same year, the government launched “Comprehensive Operation—Sinai 2018”, a large-scale counterterrorism campaign in the Sinai Peninsula. On the other hand, in Indonesia, President Joko Widodo’s administration, which has been in office since 2014, initiated programs from 2015 onward aimed at rural development and strengthening agricultural support.

A comparable pattern is evident in Nigeria, where the 2015 general election brought Muhammadu Buhari to power, followed by intensified military action against Boko Haram. By 2017, escalating herder–farmer conflicts prompted increased state intervention, whereas in 2023, President Bola Tinubu’s administration introduced sweeping economic reforms, including the accelerated restructuring of agricultural financing. In Pakistan, the continuation of Zarb-e-Azb counterterrorism operations in 2015 reduced terrorist incidents and fostered greater rural security.

In contrast, Türkiye experienced heightened political uncertainty due to the 2010 Constitutional Amendment Referendum, two general elections in 2015, the 2013 Gezi Park protests, and the failed 2016 coup attempt, all of which undermined perceptions of national security.

Taken together, the commencement of these policies and security actions shows that the observed latent causal linkages were shaped by country-specific political dynamics and episodic stability shocks, which, in turn, influenced agricultural credit allocation patterns.

The analysis in Figure 5 reveals latent causality from the rule of law component to the AOI for credit in Egypt, Malaysia, and Nigeria, with notable linkages to periods of significant legal reforms. In Egypt, the adoption of a new constitution in 2015 and the 2018 presidential re-election were accompanied by reforms aimed at strengthening the rule of law, consolidating public administration, and enhancing the predictability of the law.

Building on this comparative perspective, Malaysia’s experience illustrates how broader economic programs can integrate such legal reforms. In Malaysia, the Economic Transformation Program, launched in 2011, prioritized property rights and rural legal infrastructure, whereas the 1MDB corruption scandal in 2017 prompted additional legislative measures to restore institutional transparency and reinforce the legal system.

Extending this pattern to another national context, Nigeria’s trajectory highlights the role of both sector-specific and governance-driven initiatives. In Nigeria, reforms under the 2014 Agricultural Transformation Agenda strengthened property rights and agricultural contracts, with further momentum provided by President Buhari’s post-2015 anti-corruption drive and subsequent judicial reforms during the Tinubu administration in 2022–2023. Viewed in their entirety, these reforms fostered more stable and predictable legal environments, thereby creating conditions that likely incentivized financial institutions to expand their lending to the agricultural sector.

The analysis identifies latent causality from the regulatory quality component to the AOI for credit in Bangladesh, Indonesia, Malaysia, Nigeria, Pakistan, and Türkiye. As shown in Figure 6, these causal linkages align with periods of regulatory reform that have direct implications for both the agricultural sector and the financial system. In Bangladesh, measures introduced between 2011 and 2016 sought to improve access to agricultural credit.

Similarly, Indonesia’s 2014 reforms to streamline investment and reduce bureaucratic barriers were reinforced by the 2021 Omnibus Law, which deepened regulatory restructuring. In relation to this, Malaysia undertook reforms in 2016–2017 aimed at enhancing transparency and improving agricultural financing. Reflecting a more long-term approach, Nigeria’s Agricultural Transformation Agenda (2011–2022) was accompanied by legislative measures that supported sectoral development.

Focusing on sectoral support, government-led initiatives implemented in Pakistan between 2011 and 2014 addressed food security and introduced agricultural credit support packages. Likewise, Türkiye’s 2015–2016 reforms targeted rural development and expanded agrarian support policies. In summary, these reforms appear to have strengthened institutional trust, reduced uncertainty, and fostered a more favorable lending environment, thereby encouraging financial institutions to allocate agricultural credit more effectively.

As illustrated in Figure 7, latent causality is also observed from the accountability variable to the AOI for credit in Bangladesh, Malaysia, Pakistan, and Türkiye. These relationships correspond to periods characterized by democratic reforms, growing societal demands, and policy agendas driven by accountability.

In Bangladesh, political tensions during the 2013 electoral process, followed by civil society mobilization in 2017–2018, heightened demands for transparency. In a similar reform-inducing context, the aftermath of the 1MDB scandal in Malaysia spurred accountability-focused governance changes, whereas the 2022 political transition reinforced participatory practices. Echoing accountability pressures, Pakistan experienced strong advocacy from farmer associations in 2015, with leadership changes in 2018 further advancing inclusive and transparent agricultural policies. Within the Türkiye context, the 2010 Constitutional Referendum initiated an era of intensified discourse on democratization, transparency, and citizen participation. Such changes suggest that stronger political accountability, whether catalyzed by civil society, institutional reforms, or leadership change, can enhance the legitimacy and responsiveness of public institutions, thereby improving conditions for agricultural lending.

5. Discussion and Conclusions

This study examines the causal relationship between institutional quality and agricultural credit orientation across D-8 countries by employing the AOI for credit as a central measure. For this aim, it addresses a significant gap in the literature: the limited understanding of how disaggregated institutional quality components influence credit allocation, particularly in a dynamic, multi-country context. Unlike earlier research that predominantly relied on static models or single-country analyses, this study applies both the standard and time-varying Kónya causality tests, thereby capturing both consistent and episodic causal patterns.

This study contributes to the existing literature by introducing a novel analytical framework that integrates the AOI for credit with dynamic institutional indicators, revealing how governance structures shape financial alignment with agricultural priorities. Although the AOI has been recognized in previous research [12,54], its application within a dynamic and multi-country institutional context has remained largely unexplored. Notably, previous studies have demonstrated that institutional trust and legal stability are essential prerequisites for effective credit allocation [18,19]. Similarly, single-country evidence from Türkiye [38] and East Africa [25] have highlighted the institutional drivers of agricultural finance. Our results extend this line of research by providing a comparative, multi-country perspective rather than assuming a uniform effect of institutional quality. We demonstrate that its influence is contingent on country-level structural conditions. This finding bridges the gap identified by [35,37], who stressed the importance of considering institutional heterogeneity in development finance.

Moreover, by employing time-varying panel causality tests, this study identifies not only overt but also latent institutional effects on credit orientation—effects that would remain undetected using traditional panel data techniques. This methodological advancement enables a deeper understanding of the evolving nature of agricultural finance systems under varying governance conditions.

The findings underscore that institutional quality is indeed a decisive factor in shaping agricultural credit allocation, but the nature of this influence varies substantially across countries. Türkiye, Indonesia, Nigeria, and Egypt exhibit consistent causal relationships, where institutional factors—such as corruption control, rule of law, regulatory quality, and accountability—systematically influence agricultural credit flows. By contrast, in countries such as Malaysia, Pakistan, and Bangladesh, institutional effects are episodic or latent, surfacing primarily during periods of political instability, regulatory reform, or governance crises.

This heterogeneity underscores the importance of institutional depth. Countries with consistent causality—such as Türkiye, Indonesia, Nigeria, and Egypt—benefit from sustained reforms that anchor governance credibility and legal stability. By contrast, Malaysia and Pakistan reveal how volatility and policy-driven cycles yield only temporary impacts. In Pakistan, programs like the Kissan Package and the Emergency Agriculture Program generated episodic credit responses but lacked continuity amid political instability. Malaysia’s institutional effects similarly surfaced during reform episodes, notably the Economic Transformation Program and the post-1MDB governance reforms, yet failed to endure. These contrasts highlight that institutional quality shapes agricultural credit most effectively when reforms are continuous, credible, and embedded in resilient governance structures.

Accordingly, the answers to the research questions posed in this study can be summarized as follows:

RQ1—A1. 

General Influence—The components of institutional quality significantly influence the orientation of commercial banks toward agricultural lending in the D-8 countries. Statistically significant causal links from institutional indicators to the AOI for credit—particularly in Türkiye, Indonesia, Nigeria, and Egypt—affirm that institutional frameworks play a determining role in directing financial flows toward the agricultural sector.

RQ2—A2. 

Variation by Country and Component—The nature and intensity of this influence vary across countries and components. In Türkiye, for example, control of corruption, government effectiveness, and rule of law were found to have significant causal impacts. In Indonesia, the rule of law and accountability/freedom of expression emerged as key drivers, whereas in Nigeria, government effectiveness and accountability/freedom of expression were influential. In Egypt, regulatory quality stands out as the most critical factor.

RQ3—A3. 

Temporal Variability—In countries with direct causality (Türkiye, Indonesia, Nigeria, Egypt), institutional effects on credit orientation are more consistent. In contrast, countries with only hidden causality show episodic influence, reflecting sensitivity to external shocks or institutional volatility. This reinforces the need for time-sensitive and adaptive policy mechanisms.

These results prove the importance of institutional dynamics in shaping agricultural credit orientation, confirming the argument that successful agricultural finance systems depend on more than just economic variables—they also require supportive governance structures [41,48]. These findings also align with those of [19,21], who empirically confirm the critical role of institutional quality in shaping financial systems and sectoral outcomes.

The distinction between consistent and episodic causality offers clear policy lessons. Consistent effects arise where governance reforms are sustained and institutions are resilient, while episodic patterns reflect vulnerability to political cycles, shocks, and short-term interventions. Such volatility undermines stable rural finance. The implication is direct: financial support must be coupled with long-term institutional strengthening—anchored in the rule of law, transparency, and insulation from political shocks. Strategies that ignore these institutional realities cannot deliver sustainable outcomes. Effective agricultural credit policy requires robust institutional frameworks, tailored to country-specific conditions, to ensure both resilience and long-term impact.

The findings reveal that institutional factors influence agricultural credit orientation differently across countries. In this regard, formulating country-specific policy recommendations is essential, particularly for identifying the relationship between the AOI for credit and the institutional quality index, and for enabling policymakers to design and implement effective strategies.

For Türkiye, control of corruption, government effectiveness, and rule of law are decisive. In Indonesia, the rule of law and accountability are crucial, whereas in Nigeria, government effectiveness and accountability are central. And in Egypt, the quality of regulation stands out as the key determinant. These results underscore the need for country-specific policy design. Aligning agricultural credit policies with the most relevant institutional dimensions can strengthen both credit orientation and overall sectoral performance. Moreover, recognizing the importance of these institutional variables can help other countries adopt corrective measures, thereby promoting more sustainable agricultural development.

This study draws on official data to conduct the analysis. One of the most debated issues in the agricultural sector is the loans facilitated through clan networks and informal institutions. Clan networks refer to non-formal structures rooted in kinship, blood ties, or social relations, and are typically understood as forms of non-institutional activity. Such networks often serve as mechanisms of solidarity, particularly in developing countries [74,75,76].

Informal institutions, in turn, emerge from interpersonal connections embedded within the broader social fabric. The literature highlights both the positive and negative aspects of these structures. While they can facilitate risk-sharing and community support, their informal character and exclusionary tendencies pose significant drawbacks [77]. Consequently, the presence of clan networks and informal institutions constitutes a critical factor that cannot be overlooked in the agricultural sectors of developing economies.

This study has several limitations. The exclusion of Iran due to data limitations slightly constrains the comprehensiveness of the D-8 sample. Moreover, while the six governance indicators used in the analysis offer valuable insights, they may not fully capture informal institutional dynamics or other socio-political nuances that influence credit allocation behavior.

Iran is excluded from the analysis due to data limitations. This exclusion does not compromise methodological reliability, as the Kónya panel causality tests operate on a country-specific basis rather than producing a single aggregate result. Unlike methods that yield a single aggregated result at the panel level, these tests identify causal relationships separately for each country and derive broader inferences from the collective evidence. The omission, therefore, leaves the validity or robustness of the country-level findings intact. However, Iran remains one of the Middle East’s significant agricultural producers, and its agrarian credit system and interaction with institutional quality may differ from those observed in other D-8 members. For this reason, the results should be interpreted not as a comprehensive representation of all D-8 countries, but rather as an assessment based on the subset of members for which data were available.

This study examines the causal relationship between institutional quality components and agricultural credit orientation, employing only institutional indicators as independent variables. The Kónya panel causality test and its time-varying extension are preferred, as they are designed to capture directional linkages between variables. The time-varying approach further allows for the detection of structural breaks—such as the global food price fluctuations observed during 2014–2016—thereby offering greater flexibility than conventional fixed-coefficient panel tests.

Cross-sectional dependence and homogeneity tests confirm the presence of heterogeneity, justifying a methodological rationale for adopting the Kónya panel causality test. This approach explicitly allows for heterogeneous causal linkages across countries. Consistent with this premise, our findings are reported on a country-by-country basis, highlighting distinct causal patterns for each case. Nevertheless, a detailed exploration of the structural, economic, or political drivers underlying these heterogeneous outcomes falls beyond the scope of the present study.

Building on this foundation, future research should explore additional country-specific variables—such as digital infrastructure, land tenure systems, and farmer cooperatives—and assess how these interact with institutional quality to affect agricultural performance. Expanding the sample to include other developing regions or conducting deeper micro-level analyses could also yield valuable insights into how credit access is shaped by both structural constraints and enabling institutional factors.

In sum, this study advances a nuanced, context-sensitive, and time-aware understanding of agricultural credit orientation. This confirms that agricultural finance is shaped not only by economic variables, but also by institutional and political determinants that operate through both visible and latent channels. Recognizing and addressing these institutional dynamics is crucial for crafting more equitable, efficient, and sustainable agricultural finance systems in the developing world.