Practices of Agricultural Producer Cooperatives in Transition Economies: The Case of CIS Countries

Agricultural producer cooperatives have re-emerged as pivotal institutional arrangements in economies undergoing structural transformation, particularly where fragmented farm structures, volatile markets, and environmental pressures converge to undermine individual producers’ capacity to compete and adapt. In transition economies, cooperatives increasingly function not merely as residual organizational forms inherited from past systems, but as adaptive mechanisms through which farmers negotiate coordination failures, technological gaps, and sustainability demands [1]. Globally, smallholders cultivate more than 70% of agricultural land in developing and transition economies while accounting for less than 35% of total agricultural income, underscoring persistent coordination and productivity gaps that cooperatives are expected to mitigate [2]. Moreover, agricultural productivity growth in transition economies has lagged behind the global average by approximately 20–25% over the past decade, reinforcing the need for collective institutional solutions [3]. This paper redresses the imbalances in previous studies by shifting analytical attention away from isolated case narratives and static cross-sectional assessments toward a systematic, longitudinal examination of cooperative practices across multiple transition contexts.

Existing scholarship has convincingly demonstrated that cooperatives can strengthen vertical coordination within agri-food value chains, particularly where food safety requirements and quality standards exceed the capacity of individual farmers to comply independently [4]. However, much of this literature remains confined to micro-level case studies or single-country analyses, which limits the generalizability of findings and obscures dynamic patterns of change over time. Moreover, while cooperatives are frequently discussed as instruments of rural inclusion or social cohesion, their role as economically rational, efficiency-seeking organizations operating under technological and environmental constraints has received comparatively less empirical scrutiny in transition settings.

Recent studies stress the rising role of green technologies and environmentally responsible practices in shaping cooperative strategies. This trend is most visible in countries that seek to align agricultural growth with climate and sustainability goals [5]. Yet much of this work concentrates on policy design, training programs, or descriptive accounts of best practices. Evidence on economic and productivity effects remains limited. As a result, it is still unclear to what extent green and digital transitions produce measurable gains in income and efficiency for cooperative members, especially when assessed through rigorous econometric and efficiency-based methods.

Parallel research examines the entrepreneurial and institutional environments of cooperatives in post-socialist economies. These studies highlight the role of policy regimes, education systems, market access, and networks [6]. Such perspectives deepen theoretical insight. However, they rely largely on qualitative or exploratory approaches. This limits their capacity to test performance-related hypotheses across regions and over time. Similarly, studies examining the success factors of newly formed cooperatives underscore the importance of group characteristics, governance, and value-chain positioning, but are typically constrained by cross-sectional data and donor-driven samples [7].

Against this backdrop, this paper presents an original and reflexive study in how agricultural producer cooperatives in transition economies evolve when observed through the combined lenses of income dynamics, technological adoption, and efficiency performance. The study uses a longitudinal panel covering the period 2015–2025. It combines econometric methods with Data Envelopment Analysis. This design addresses persistent gaps in the literature related to temporal dynamics, cross-country comparison, and the measurement of sustainability-oriented practices. The inclusion of several Commonwealth of Independent States (CIS) countries alongside Vietnam serves a comparative purpose. It enables a clearer separation of context-specific institutional effects from broader cooperative development patterns. The comparative structure strengthens causal interpretation. National particularities become distinguishable from mechanisms that operate across transition economies.

The practical relevance of the research is substantial. The results offer guidance for cooperative support policies, rural development strategies, and sustainability-focused interventions. By identifying organizational and technological factors linked to stronger economic performance, the study provides actionable guidance for policymakers and development agencies. At the same time, the research contributes to global scholarship. It advances empirical understanding of how collective agricultural organizations operate under transition conditions. In doing so, it connects cooperative theory, innovation economics, and sustainability research within a single analytical framework.

1.1 Literature review

Recent research on agricultural cooperatives reflects a broadening analytical focus. Cooperatives are no longer viewed solely as social arrangements. They are increasingly analyzed as economic actors embedded in institutional, technological, and policy systems. Across diverse national contexts, scholars describe cooperatives as tools for overcoming coordination failures, reducing market power imbalances, and supporting compliance with quality and sustainability standards. These roles are especially pronounced in transition and developing economies [4, 8].

One major research stream highlights the role of cooperatives in value chain coordination and farmer bargaining power. Empirical studies show that cooperative structures can lower transaction costs and foster trust-based relationships. This effect is particularly relevant when downstream buyers demand collective compliance with food safety or sustainability requirements [4, 9]. Yet many of these studies stop at institutional outcomes. They rarely connect coordination mechanisms to measurable productivity or efficiency gains. This limits their ability to explain economic performance.

A second strand of literature focuses on technical efficiency and productivity in agriculture. These studies commonly apply stochastic frontier analysis or data envelopment analysis. Across countries, crops, and methods, results point to substantial inefficiencies. Reported average efficiency levels typically range between 60% and 80% [10, 11]. Evidence from European Union member states demonstrates that cooperative-affiliated farms frequently achieve efficiency levels exceeding 75–80%, particularly where cooperative services are integrated with advisory systems and agri-environmental schemes [8, 10]. Similarly, studies from Latin America indicate that cooperatives contribute to productivity gains primarily through market access and risk-sharing mechanisms, although outcomes vary substantially depending on institutional quality and policy coherence, reinforcing the context-dependent nature of cooperative efficiency effects [9]. Yet, most efficiency analyses remain centered on individual farms rather than cooperative organizations, implicitly assuming that collective arrangements do not fundamentally alter production frontiers or input–output relationships.

Where cooperative membership is explicitly considered, findings tend to indicate a positive association with efficiency. Evidence from China shows that cooperative members achieve technical efficiency levels that exceed those of non-members by 3–7 percentage points once selection bias is controlled. This gap highlights the role of cooperatives in improving access to information, production inputs, and markets [12]. The result aligns with findings from post-socialist agricultural systems. In these contexts, cooperative membership is associated with higher performance following policy reforms, although outcomes vary widely across regions and production types [13].

Despite these shared findings, the literature reports persistent disagreement over the effects of subsidies and state support. Some studies identify positive efficiency effects from fiscal assistance. These effects arise through better access to modern inputs and improved infrastructure. The impact is strongest in large-scale farms and in sectors targeted by strategic policy priorities [14, 15]. Others, however, document neutral or even negative relationships between subsidies and efficiency, attributing such outcomes to distorted incentives, misallocation of resources, or scale mismatches [16, 17]. These contradictions suggest that the efficiency effects of public support are highly context-dependent and mediated by institutional and organizational structures.

The emergence of sustainability and green transformation as central analytical themes further complicates the literature. Recent studies emphasize the role of cooperatives in mitigating opportunistic behavior and facilitating compliance with environmental requirements, particularly under asymmetric power relations within agri-food supply chains [18]. Complementary research highlights how cooperative performance can amplify the effects of external support in promoting precision agriculture and cleaner production practices [19]. Despite their contributions, many of these studies rely on cross-sectional survey data or models of behavioral intention. As a result, questions about long-term economic effects remain unresolved.

A separate and increasingly influential stream of research examines the impact of digital platforms and agrotechnologies on cooperative performance and coordination. Empirical evidence shows that digital tools—such as precision agriculture technologies, data-sharing platforms, and digital extension services—reduce information gaps and transaction costs. They also support collective decision-making. Together, these effects improve economic efficiency and strengthen environmental compliance within cooperative settings [15, 19]. Yet most studies treat digitalization as an external technological input. They do not frame it as an organizational change process. This leaves open the question of how digital adoption interacts with cooperative governance and long-term performance.

Research on agricultural cooperatives reflects a core theoretical divide. Institutional approaches stress governance arrangements, trust, and coordination. Efficiency-oriented approaches focus on productivity, cost reduction, and frontier outcomes. Work grounded in entrepreneurial ecosystem theory highlights the role of policy frameworks, education systems, and networks in shaping cooperative viability in post-socialist contexts. However, this research remains largely qualitative or exploratory [6, 20]. At the same time, studies of internal governance, member diversity, and opportunism show that cooperative success is uncertain. Conflicting interests can erode performance, especially in complex or multi-tier organizational forms [21].

Across this diverse body of work, several gaps become evident. First, longitudinal analyses capturing cooperative evolution over time remain scarce, particularly in transition economies. Second, few studies integrate efficiency analysis with sustainability and digitalization indicators within a unified empirical framework. Third, comparative research spanning multiple institutional contexts is limited, constraining the generalizability of findings. Finally, cooperative-level analyses often rely on surveys or single-country samples, which restrict replication and policy relevance.

Against this fragmented backdrop, the present study positions itself at the intersection of cooperative economics, efficiency analysis, and sustainability research by employing a multi-country, panel-based approach that captures both economic and technological dimensions of cooperative performance over time.

1.2 Problem statement

Agricultural producer cooperatives operating in transition economies continue to occupy an ambiguous position within both policy design and academic inquiry. While they are frequently promoted as instruments for rural inclusion, market access, and sustainability, empirical evidence capable of disentangling their actual economic and efficiency effects remains fragmented, temporally static, and methodologically inconsistent. Much of the existing research relies on cross-sectional surveys, single-country case studies, or farm-level efficiency analyses that implicitly abstract from the collective and institutional nature of cooperative organizations. As a result, policymakers and practitioners lack robust, longitudinal evidence on how cooperatives perform over time when confronted simultaneously with technological change, environmental constraints, and evolving institutional frameworks.

The motivation for this research arises from the need to overcome these limitations by generating empirically grounded insights based on real administrative data and advanced efficiency assessment techniques. Specifically, the study is driven by the necessity to move beyond descriptive accounts of cooperative development and to quantify how green technologies, digital tools, and organizational characteristics interact to influence income dynamics and productivity in transition contexts. By integrating panel econometric analysis with efficiency measurement, the research addresses a concrete analytical gap concerning the performance mechanisms of agricultural producer cooperatives.

The purpose of the study is to evaluate the economic, technological, and efficiency performance of agricultural producer cooperatives in selected CIS countries, using Vietnam as a comparative benchmark, over the period 2015–2025.

The objectives of the work are threefold:

(1) to examine the structural composition and evolution of agricultural producer cooperatives across countries;

(2) to assess the relationship between technological adoption, sustainability practices, and cooperative income and employment outcomes using panel econometric methods; and

(3) to measure and compare the relative efficiency of cooperatives through frontier-based analysis, thereby identifying patterns of convergence and divergence in cooperative performance under transition conditions.

Building on the literature, the study formulates the following testable hypotheses:

H1 (Green technology effect):

Higher levels of green technology adoption are associated with increased cooperative member income.

H2 (Digitalization effect):

Greater adoption of digital tools improves cooperative efficiency and income outcomes.

H3 (Institutional moderation):

Institutional context moderates the relationship between technology adoption and performance, contributing to cross-country variation in outcomes.

Figures 1 and 2 show a sustained and converging increase in green and digital adoption among agricultural producer cooperatives across the three countries over the period 2015–2025.

Table 4 summarizes the composition of the cooperative sample by country and cooperative type. The results demonstrate pronounced institutional differentiation across the three countries examined.

In Kazakhstan, production-oriented agricultural cooperatives dominate the sector. This pattern reflects the state-led consolidation strategy introduced during the post-2015 agricultural reforms. Credit and marketing cooperatives are nearly absent. As a result, functional diversification remains limited. Cooperatives continue to rely on external financial institutions rather than internal financing mechanisms.

Russia presents a more diversified cooperative landscape. Agricultural credit cooperatives form the largest group. This structure aligns with long-standing institutional support for rural credit associations. Marketing, processing, and service cooperatives also operate across rural areas. Their presence points to partial vertical integration and internal service provision within value chains. Structural diversity increases organizational scope. It also raises coordination costs.

Vietnam offers a contrasting case. All sampled entities fall under the producer/service cooperative category, reflecting the standardized cooperative model promoted under Vietnam’s cooperative law reforms. The homogeneity of cooperative types in Vietnam facilitates administrative oversight but may constrain organizational experimentation relative to CIS countries.

Table 5 reports average indicators of green and digital technology adoption for 2015 and 2025, enabling an assessment of technological trajectories over the study period. Figure 3 visualizes these indicies.

Table 4. Structure of the cooperative sample by country and type

Table 5. Green and digital technology adoption (country means)

Table 6. Income, employment, and sustainability indicators (2025)

Across all three countries, green and digital adoption indices rose sharply between 2015 and 2025. By 2025, green index values converged. This convergence suggests rapid diffusion of environmental practices once policy incentives and access to technology crossed a critical threshold. Yet adoption paths differ. Russia places greater weight on renewable energy use. Vietnam shows the highest uptake of precision agriculture tools, reflecting targeted digital extension policies.

The sharp convergence of green and digital indices toward values near 0.9–1.0 by 2025 raises the possibility that calibration procedures may mechanically compress cross-country variation. Sensitivity checks using alternative scaling and observed-only subsamples show that while convergence remains present, its magnitude is reduced. This suggests that convergence reflects both genuine diffusion dynamics and partial measurement effects introduced by calibration.

Carbon intensity declines in all countries. The largest reduction occurs in Vietnam. This pattern highlights technological leapfrogging in late-transition economies, where cooperatives adopt advanced technologies without legacy constraints.

Table 6 presents a snapshot of cooperative income, employment, and sustainability outcomes for 2025, alongside an income index normalized to 2015 levels.

Absolute income levels are not directly comparable because of currency differences. Growth indices therefore offer clearer insight. Vietnam records the highest income growth relative to 2015. Kazakhstan follows closely. Russia shows a lower growth index. Income growth within Russian cooperatives has been more gradual. This pattern likely reflects sector maturity and saturation effects.

Employment and membership data reveal further structural contrasts. Vietnamese cooperatives employ more workers and include more members. This scale aligns with a standardized organizational model. Kazakhstani cooperatives remain smaller. Yet they achieve rapid income growth. The evidence points to productivity gains rather than expansion in scale.

Correlation results for 2025 show strong positive links between average member income and green technology adoption, digitalization, and asset intensity. Carbon intensity shows a negative relationship with income and green adoption. Environmentally efficient cooperatives tend to generate stronger economic outcomes.

Table 7. Two-way fixed effects regression results (dependent variable: log income)

Table 8. Model diagnostics

Panel regression results reinforce these patterns. The two-way fixed-effects model shows in Tables 7 and 8 that green and digital indices have statistically significant positive effects on member income. The model controls for assets, land, and regional cooperative density. Asset intensity emerges as the strongest predictor. Land endowment shows no statistically significant effect once cooperative fixed effects are included. This result suggests a shift away from land-based expansion. Productivity gains now depend more on technology and organization.

Regional cooperative density also has a positive coefficient. This finding points to agglomeration effects. Cooperatives in dense regions benefit from shared infrastructure, knowledge spillovers, and institutional support.

The regression results confirm that green technology adoption, digitalization, and asset intensity are positively and statistically significantly associated with cooperative member income. Variance inflation factors (VIF) remain below conventional thresholds, indicating no serious multicollinearity concerns. The Hausman test strongly favors the fixed-effects specification over random effects. Tests indicate the presence of heteroskedasticity and serial correlation; accordingly, cluster-robust standard errors are employed. Overall model fit is high, with a within R² of 0.71 and adjusted R² of 0.69, suggesting that the model explains a substantial share of within-cooperative income variation over time.

DEA results (Figure 4) add a further layer of evidence. Mean efficiency scores rise over time in all countries, but at different speeds. Vietnam consistently shows the highest average efficiency. Cooperatives there use labor and capital effectively relative to output and green performance. Kazakhstan records moderate efficiency gains. Russia displays wide dispersion. Some cooperatives operate near the efficiency frontier, while others fall far behind. The distribution suggests a clear message. Technology alone does not ensure efficiency. Managerial capacity and institutional context remain decisive. The presence of inefficient cooperatives in all countries suggests substantial scope for productivity gains without additional resource inputs.

Taken together, the findings show clear transformation in agricultural producer cooperatives across CIS countries and Vietnam between 2015 and 2025. Technological modernization stands out as a key force. Green and digital technologies drive income growth and efficiency. Development paths, however, diverge. Cooperatives in CIS countries display strong structural diversity and fragmented institutional settings. Vietnamese cooperatives operate within a more uniform organizational model. They benefit from coordinated and consistent policy support. The comparative approach highlights the article’s core contribution. Institutional capacity shapes how cooperatives adapt to technological and environmental demands.

The interpretations presented above should be understood as indicative rather than causal. While the empirical results establish robust statistical associations, the study design does not allow direct identification of underlying policy mechanisms. Explanations related to institutional design, reform strategies, or governance structures therefore remain hypotheses requiring further validation through policy analysis or case-based evidence.

This study set out to empirically assess the practices and performance of agricultural producer cooperatives operating in transition economies, with particular emphasis on income dynamics, technological adoption, and efficiency outcomes. The results demonstrate that cooperative development trajectories between 2015 and 2025 are neither uniform nor predominantly input-driven. Instead, they are increasingly shaped by the capacity of cooperatives to internalize green and digital technologies within their organizational structures. Empirically, cooperatives achieving higher levels of technological adoption exhibit superior economic outcomes, as reflected in income growth indices exceeding 200 in two of the three countries examined and mean DEA efficiency scores approaching 0,73 in the most efficient context.

From a scientific standpoint, this study advances the literature by redirecting attention away from scale- and land-based explanations of agricultural performance. It places technology-mediated collective efficiency at the center of analysis. The result that land endowment loses statistical significance after the inclusion of cooperative fixed effects challenges assumptions drawn from farm-level efficiency research. It also highlights the distinct economic logic that governs cooperative organizations.

The practical importance of the findings lies in their policy relevance. The evidence indicates that policies focused only on expanding resource access or subsidizing inputs are unlikely to deliver lasting efficiency gains. Such measures require complementary investments. Digital infrastructure, environmental technologies, and cooperative governance capacity are essential for sustained improvement. Policymakers may therefore utilize the findings to recalibrate cooperative support programs toward technology diffusion, advisory services, and inter-cooperative networking.

Beyond policy application, the results are relevant for cooperative managers and development agencies seeking to enhance income stability and environmental performance under conditions of market volatility and climate pressure. At the same time, several limitations warrant acknowledgment. Partial reliance on calibrated financial indicators constrains the precision of absolute performance measures, and national differences in cooperative classification limit full comparability.

Future research should extend the analysis by incorporating stochastic frontier methods with richer micro-level financial disclosures, examining governance quality as an explicit efficiency determinant, and expanding the geographical scope to additional CIS and non-CIS transition economies. Such extensions would further clarify the evolving role of agricultural cooperatives in global sustainable development.

The data that support the findings of this study are available from the corresponding author upon reasonable request.