Aquaculture Subsidies and Production: Mexico’s Impact

Government subsidies, broadly defined as financial aid provided by the government to various economic sectors, serve as a vital tool in promoting specific policy agendas—whether social, economic, or environmental^1,2,3. Historically, subsidies have been employed by policymakers to advance a range of objectives, such as ensuring labor security and stability, fostering economic growth, protecting the environment, and achieving food sovereignty^4,5,6,7. In essence, subsidies are designed to influence the behavior of industries and individuals, steering them toward outcomes deemed beneficial by the state⁸.

In the context of agriculture and the broader food production sector, subsidies have been a cornerstone of policy efforts to reduce production costs and boost output. The logic underpinning these subsidies is twofold: to prevent food price inflation, which could make basic foodstuffs unaffordable for large segments of the global population, particularly in developing countries⁹, and to maximize agricultural output by incentivizing productivity enhancements (e.g., subsidized fertilizer, distribution of enhanced seeds)¹⁰. Additionally, subsidies are often strategically employed to increase the competitiveness of high-income countries in international markets. By reducing production costs through financial support, these countries can compete more effectively with low-wage countries where production costs are naturally lower due to cheaper labor and inputs. This approach enables high-income countries to maintain a foothold in the global market despite their higher baseline costs, sometimes negatively impacting lower-income developing countries^11,12. A clear example of the use and impact of subsidizing food production was seen in 2008, during the Global Financial Crisis, when food prices spiked. In response, several countries invested in heavy use of subsidies to stabilize prices and prevent a further price escalation^13,14.

Despite the well-intended nature of subsidies, they can produce unintended, and often harmful, distortions across the three pillars of sustainability: society, the environment, and the economy. The sustainability dimensions in aquaculture are interdependent; negative impacts on one pillar affect the others. For instance, subsidies aimed at reducing energy costs, while beneficial in the short term, can lead to significant environmental degradation and social inequities^15,16. Similarly, subsidies designed to encourage the adoption of specific technologies can inadvertently distort the economy, leading to inefficiencies or the marginalization of certain groups³. These unintended consequences have led to the classification of some subsidies as “perverse,” indicating that while they may achieve their immediate goals, they do so at a broader societal cost^17,18. On the other hand, the interconnected nature of these pillars suggests that well-designed interventions can significantly enhance the sector’s overall sustainability performance¹⁵.

The use of agricultural subsidies, in particular, has been a topic of extensive study and debate, remaining controversial to this day^19,20,21. Subsidies in agriculture have been credited with stabilizing prices, securing jobs, and enhancing the competitiveness of the agricultural industry^21,22. However, these benefits are often counterbalanced by negative environmental impacts, such as soil degradation and increased greenhouse gas emissions^23,24, as well as social consequences, including the exacerbation of income inequality and the marginalization of small-scale farmers^18,25,26.

The distribution of agricultural subsidies has also been criticized for its unevenness, with the majority of financial aid favoring large-scale producers over smaller, independent farmers. What was initially conceived as a means to support the livelihoods of all farmers has, in some cases, become a tool for reducing industrial costs and distorting the market in favor of the more powerful and well-connected²⁷. This trend is particularly evident in developing countries, where subsidies from developed nations to their own agricultural sectors are sometimes seen as a form of market distortion. This was especially notable during the 2007-2008 period, when over 1 billion dollars per year were allocated to the US and EU agricultural sectors²⁸. Furthermore, energy subsidies in agriculture have been shown to be economically and socially inefficient, often leading to unintended negative outcomes^{16,29,30,31,32}.

The fisheries sector presents a parallel narrative, where fuel subsidies have been extensively documented as having a detrimental impact on natural fish stocks, thereby threatening the long-term sustainability of the industry. Unlike in agriculture, where subsidies can enhance productivity through improved inputs, subsidies in fisheries predominantly increase fishing capacity or lower extraction costs. This dynamic often results in economically incentivized overexploitation in poorly managed fisheries, depleting fish stocks and threatening the industry’s long-term viability³³. The economic and social repercussions of these subsidies are equally concerning, as they often undermine the viability of fishing communities and contribute to the depletion of resources that these communities depend on³⁴. Additionally, the distribution of subsidies in the fisheries sector has been uneven, with a disproportionate share going to industrial-scale operations rather than to small-scale, artisanal fisheries²⁷. This raises questions about the social equity of these subsidies and their alignment with broader sustainability goals^35,36,37.

In contrast to agriculture and fisheries, aquaculture is a relatively new industry, having only emerged as a significant contributor to the human food supply over the past 50 years^38,39. Despite its youth, aquaculture is the fastest-growing segment of the food production industry, expanding at a rate of approximately 4% annually⁴⁰. However, due to the similarities that exist in the issues faced by the aquaculture and agricultural sector, the subsidy framework developed for aquaculture closely mirrors that of agriculture, focusing primarily on reducing production costs through inputs such as seed, energy, equipment, and infrastructure. This approach raises important questions about the appropriateness and effectiveness of applying traditional agricultural subsidy models to an industry that is still in its developmental stages⁴¹. In spite of the current relevance of aquaculture, subsidies have received little attention compared to those in the fisheries industry and other food production sectors, such as agriculture.

Each type of subsidy in aquaculture can have distinct impacts on the economy, society, and the environment. For instance, while subsidies aimed at reducing input costs might improve short-term profitability, they could also encourage practices that are environmentally unsustainable or socially inequitable¹⁶. The European Union provides a case in point: between 2000 and 2020, over 2.8 billion euros were invested in aquaculture with the goal of increasing production, improving quality and safety, and enhancing the environmental performance of the industry⁴². As a result of this policy, the EU achieved a modest increase in production volume but a significant overall increase in value. This outcome is explained by a rise in the average trophic level of production, driven by increased cultivation of high-value carnivorous species and a decline in mussel and oyster production (lower-value species⁴³). This suggests that while subsidies can drive value creation in the industry, their impact on production volume may be limited²².

Given these mixed outcomes, the impact of subsidies on aquaculture production remains an open question. This study seeks to address this gap by examining whether subsidies have a meaningful impact on production within the aquaculture sector. By exploring the relationship between financial aid and industry growth, this research aims to contribute to the ongoing debate about the role of subsidies in promoting sustainable development in aquaculture, using Mexico as a case study.

Analysis of Subsidy Allocation and Trends (2010-2021)

Between 2010 and 2020, the allocation of subsidies in the Mexican aquaculture sector underwent significant changes, both in the number of programs and the total funding available. Initially, from 2010 to 2013, the sector experienced robust support with an average of 10 subsidy programs per year and an annual budget of approximately 3,859.3 million MXN (FX rate as of 21/02/2025: 1 MXN = 0.04918 USD = 0.04702 EUR). This period demonstrated a strong governmental commitment to developing aquaculture through various support mechanisms, focusing on increasing production capacity and infrastructure. Key programs during this time, such as the Programa de Apoyo a la Inversión en Equipamiento e Infraestructura (PAIEI), aimed to enhance production capabilities by subsidizing equipment and infrastructure improvements, particularly for smaller producers.

From 2014 to 2017, the number of subsidy programs nearly doubled, averaging 24 programs per year. Despite this expansion, the average annual budget decreased significantly to around 2,203.8 million MXN, representing a reduction of about 43% compared to the previous period. This decrease in funding per program suggests that financial resources were spread more thinly across a larger number of initiatives, potentially reducing the impact of each. During this period, programs such as the Programa de Fomento a la Productividad Pesquera y Acuícola (PROPESCA) and the Programa de Desarrollo de la Acuacultura (PRODEAC) were introduced or expanded to support operational productivity, research and development, and the adoption of sustainable practices. These programs sought to promote innovation, enhance technical capacities, and support sustainable aquaculture practices looking to ensure the long-term viability of the sector.

From 2018 onwards, there was a notable decline in both the number of subsidy programs and the transparency of fund allocation. The average number of programs dropped sharply to just four per year, with an average annual budget close to 2 billion MXN. This reduction suggests a consolidation of subsidy efforts or a shift in policy focus away from broad-based support towards more targeted initiatives. During this period, the Federation Expenditure Budget (PEF) began aggregating funding for agriculture, livestock, fishing, and aquaculture under broader categories, complicating efforts to discern specific allocations to individual sectors. This shift in policy was further exemplified by the transition from PROPESCA to the Bienpesca program, which moved towards more generalized direct financial support instead of compensating for specific events like closures or adverse weather.

Overall, the evolution of Mexico’s aquaculture subsidy programs over the past decade highlights shifting policy priorities, from enhancing production capacity and supporting infrastructure development to promoting sustainability and adapting to broader economic pressures. These changes have significantly impacted the growth and sustainability of the aquaculture industry in Mexico, underscoring the need for transparent and targeted subsidy strategies to support this critical sector effectively.

Comparison of program budgets

In 2017, the combined budget for the Propesca (276 million MXN), Marine Diesel (MXN 1.47 billion MXN), and Capacity Building (70 million MXN) programs amounted to 1.81 billion MXN, for both fisheries and aquaculture. By 2020, however, the budget for the Bienpesca program, which covers both fishing and aquaculture, was reduced to 1.4 billion MXN. This reduction reflects a broader trend of decreasing financial support specifically targeted at aquaculture.

Despite the 40% reduction in the total amount of subsidies spent in 2020 compared to 2010, there was a 127% increase in total production over the same period (Fig. 1). This suggests that the relationship between subsidies and production is not as straightforward as expected. The positive values of parameters a and c from the model correspond to the increase in Total Factor Productivity (TFP) and the number of players in the aquaculture industry (Table 1). This aligns with the historical increase in production and the expansion of farms observed in Mexican aquaculture over the last decades

However, the negative value of parameter b reveals a counterintuitive finding: a negative relationship between the use of subsidies and production. This suggests that higher subsidy levels are associated with lower production outcomes, possibly indicating inefficiencies, poor targeting, or unintended behavioral responses. In particular, this result brings renewed attention to the structure and purpose of aquaculture subsidy programs in Mexico.

Rather than stimulating output, certain subsidies might distort incentives or reinforce unproductive practices. This result brings renewed attention to the structure and effectiveness of aquaculture subsidy programs in Mexico—particularly the shift from the environmentally harmful Marine Diesel program to the Bienpesca transfer scheme. While the elimination of fuel-based subsidies represents progress³³, the Bienpesca program (which corresponds to an annual direct transfer of MXN 7500 per beneficiary) remains largely unmonitored. Its funds, though unconditional, could still be used in ways that perpetuate environmentally or economically inefficient behaviors. The lack of oversight and clear usage guidelines increases the risk that such programs, instead of enhancing sustainable production, may inadvertently undermine it, aligning with the negative effect observed in the model.

Total factor productivity

The analysis of TFP from 2010 to 2020 reveals a general decline across the aquaculture sector, despite consistent increases in both the Paasche and Laspeyres price indices (Table 2). TFP represents the efficiency with which inputs—such as labor, capital, and intermediate inputs (materials and services consumed during the production process)—are transformed into output. A decline in TFP indicates a reduction in productivity efficiency, meaning that more inputs are required to produce the same or lesser amounts of output.

Between 2010 and 2020, TFP showed significant volatility, dropping from 126,238 in 2010 to 189,527 in 2020, with notable declines in 2013 and 2016. In contrast, the Paasche Price Index rose from 1.00 in 2010 to 3.06 by 2020, and the Laspeyres Price Index increased from 1.00 to 1.11 in the same period. The Fisher Quantity Index, which reflects changes in input quantities, also increased steadily, reaching 1.84 in 2020, suggesting that while more inputs were being used, productivity gains were not keeping pace. This pattern suggests that aquaculture operations increasingly relied on higher input usage without achieving proportional efficiency improvements, a trend commonly associated with diminishing returns or suboptimal resource allocation.

A sharp TFP decline in 2013 (87,578) coincided with a Laspeyres Price Index increase to 1.35, which suggests rising input costs contributed to reduced efficiency. However, this period also aligns with a policy initiative aimed at introducing sustainable aquaculture practices under Mexico’s blue economy strategy, which may have had temporary effects on industry productivity. A similar decline in TFP between 2016 and 2018 corresponds with shifts in government policies and sectoral regulations, further reinforcing the role of external economic and policy factors in shaping production efficiency.

From 2018 onward, a shift is observed, where TFP begins to recover despite a decline in Aquaculture Production Units (APU) (Fig. 2). This suggests that efficiency improvements, rather than production unit expansion, began to play a larger role in output growth. The decoupling of TFP and APU after 2018 indicates a potential transition from extensive to more intensive production models, where technological advancements or better management practices contributed to increased productivity per unit.

Model parameters and statistical analysis

The estimation of the Cobb-Douglas production function provided parameter estimates for TFP and input elasticities, as shown in Table 1. TFP was estimated at 0.5971, with the elasticities of the inputs S_t−1 and APU estimated at -0.4226 and 1.0149, respectively. The standard errors for these estimates were relatively small—0.1489 for TFP, 0.0972 for S_t−1, and 0.2313for APU—indicating a reasonable level of precision in the parameter estimates. Additionally, the statistical significance of the parameters, as presented in Table 1, suggests that TFP and APU are significant predictors of output, whereas the elasticity of S_t−1 is marginally insignificant.

The negative elasticity of S_t−1 is notable, as it suggests that increases in subsidies may be associated with a decrease in output. While this result appears counterintuitive, it could be influenced by potential misspecification of this variable, possibly due to limitations in the accuracy and transparency of reported subsidy amounts. Another possible explanation is that subsidies may be poorly designed, disproportionately supporting less productive segments of the sector rather than those driving growth⁴⁴. Alternatively, this result may reflect diminishing returns to subsidies, where additional financial support no longer translates into increased output. In contrast, the elasticity of APU exceeding unity suggests that this input may exhibit increasing returns to scale, indicating a relatively strong association with output growth.

These findings suggest that TFP and APU could have a significant influence on output, while the negative elasticity of S_t−1 warrants further investigation to determine whether it reflects misspecification, diminishing returns, or other structural factors.

Diagnostic tests on the model’s residuals indicate that the residuals do not show significant deviations from normality. The Shapiro-Wilk test (D = 0.9375, p-value = 0.4921) suggests that the residuals follow a normal distribution, with no strong evidence against this assumption. The Durbin-Watson statistic (2.1209) indicates that there is no strong presence of autocorrelation in the residuals, which supports the model’s validity in terms of independence assumptions.

Tests for homoscedasticity suggest that heteroscedasticity is not a major concern. The Breusch-Pagan test (3.3985, p-value = 0.3342) and White test (10.9979, p-value = 0.2759) both return non-significant results, implying that the variance of the residuals remains relatively constant across different values of the predictors. This indicates that the standard errors reported in the model are likely reliable.

The analysis of multicollinearity through Variance Inflation Factor (VIF) values suggests that collinearity among predictors is low. The highest VIF value is 2.0152, well below the common threshold of 5, indicating that multicollinearity is not a serious issue in this model.

Overall, the diagnostic tests suggest that the model satisfies key statistical assumptions, with no major violations of normality, autocorrelation, homoscedasticity, or multicollinearity.

The Standard Error of the Regression (SER = 22,255) provides an estimate of the average deviation of observed output values from the model’s predicted values. Given that the Y values range from 111,499.75 to 348,601.76, the SER suggests that, on average, the model’s predictions deviate from actual output by approximately 9% to 20% of observed values. While this level of error is moderate, it indicates that the model captures key trends in the data but still leaves room for unexplained variability, possibly due to external factors not included in the estimation. An adjusted R² value of 0.8657 was obtained, suggesting a strong model fit, although this result warrants cautious interpretation given the small sample size (n = 10).

Subsidies are often viewed as essential tools for enhancing production and competitiveness, stabilizing income, and promoting sustainable practices in both agriculture and aquaculture. However, the effectiveness of these subsidies, particularly in aquaculture, is increasingly being questioned. While subsidies aim to lower production costs and support income stability, their actual impact on production and sustainability remains contentious^16,42. Our study highlights the limited effect of subsidies on aquaculture production in Mexico, aligning with the broader literature that suggests a nuanced and sometimes counterproductive role of financial aid in this sector^22,41.

The expectation that subsidies will significantly boost production is a common rationale behind their implementation. In Mexican aquaculture, energy subsidies have been a primary mechanism to reduce operational costs, particularly for electricity and fuel, with the intent of maintaining or increasing production levels. However, the findings from this study suggest that subsidies do not have a significant impact on production. Despite the presence of subsidies, the growth in aquaculture production has been modest, and in some cases, production has remained stagnant or even declined. This trend mirrors global patterns, where subsidies have not consistently translated into substantial production increases in aquaculture production^42,45.

The limited studies on aquaculture subsidies so far suggest that they have not significantly increased production, especially in regions where structural or environmental constraints hinder the effectiveness of financial aid^14,46. This parallel suggests that the issue may not be exclusive to aquaculture but rather indicative of a broader trend where subsidies do not always translate into increased production, particularly when they are not coupled with strategic investments to overcome underlying challenges.

Our findings indicate that the primary driver of output growth in Mexico is largely explained by the expansion of production units. The strong elasticity of production units suggests that rather than achieving gains through productivity improvements, the sector has grown by increasing the number of farms or production capacity. This is consistent with the framework presented by Asche et al. (2022), where early-stage growth in aquaculture industries is typically driven by expansion rather than efficiency gains⁴⁷.

In contrast, TFP’s contribution to output growth is more limited, suggesting that efficiency improvements have played a secondary role compared to farm expansion. This stands in contrast with findings from more mature aquaculture industries, where technological advancements and productivity gains tend to play a larger role in sustaining production growth^15,48).

The downward trend in TFP, despite rising price indices, suggests that the aquaculture sector may be over-relying on increasing inputs (e.g., energy, feed, equipment) to maintain production levels rather than focusing on improving efficiency or adopting innovative technologies. The consistent rise in both the Paasche and Laspeyres indices indicates increasing operational costs, which may be tied to global input price trends, yet these costs have not translated into proportional gains in output.

This analysis highlights the importance of technological advancements and efficient resource management to reverse the declining productivity trend. Without strategic interventions, the sector may face further productivity challenges, where the cost of production continues to rise without corresponding increases in output.

Overall, these findings suggest that Mexican aquaculture has historically relied on input expansion to drive growth, but recent trends indicate a possible shift toward efficiency-driven production. The stabilization of price indices, combined with rising Fisher Quantity and improving TFP post-2018, suggests that TFP improvements may be playing an increasing role in sustaining sectoral growth. However, given the sector’s past volatility, further analysis is needed to determine the sustainability of these trends⁴⁹.

A plausible explanation for the decline in subsidy effectiveness is the emergence of dependency among aquaculture producers. Subsidies, particularly those directed to reduce production costs like the diesel subsidy and direct financial incentives, lowered operational costs but also unintentionally could influenced producer behavior in ways that did not necessarily enhance efficiency or innovate^50,51. For instance, the diesel subsidy, which remained in place until 2019, lowered fuel costs and could have facilitated energy-intensive practices such as excessive water pumping. While this may have helped maintain short-term production levels, it is possible that these practices encouraged inefficiencies that could be difficult to sustain without continued financial support. This dynamic is broadly consistent with the concept of moral hazard, where external financial assistance might reduce incentives for efficiency improvements⁵². However, determining the extent to which such mechanisms influenced long-term productivity would require further research.

Beyond potential concerns related to producer dependency, subsidies could have interacted with environmental and market dynamics in complex ways. Some studies have noted that energy-related subsidies can contribute to increased CO₂ emissions and broader environmental degradation, which, in turn, may have unintended consequences for aquaculture productivity¹⁶. Additionally, market distortions arising from subsidies may have influenced resource allocation, potentially allowing producers to operate under cost structures that differ from true market conditions. While some forms of subsidies, such as those supporting infrastructure development or genetic improvement, are designed to yield long-term benefits, the timeframe in which such effects materialize remains a key consideration⁵³. In this study, alternative model specifications, including those with lagged effects, were tested, though only short-term impacts (t-1) reached statistical significance. The available data did not allow for a detailed analysis of how subsidies were distributed among producers of different scales, though previous research suggests that larger, well-capitalized firms often benefit disproportionately, which may have broader implications for sectoral equity^50,54.

These findings suggest that careful consideration should be given to the design and implementation of subsidy policies in aquaculture. Past research indicates that reevaluating environmentally significant subsidies may be beneficial in ensuring that sectoral growth aligns with broader sustainability goals⁵⁰. However, given the limitations of this study—including the short duration of available data and constraints in granularity—future research could help clarify the long-term implications of subsidies and identify the conditions under which they contribute most effectively to aquaculture development^50,53,54,55.

The indices analyzed in this study suggest that subsidies may have historically contributed to resource intensification without proportional efficiency gains. Between 2010 and 2017, the Paasche and Laspeyres price indices rose significantly, while TFP declined, indicating that higher input use did not lead to improved productivity. This pattern raises concerns about the effectiveness of input-based subsidies, as they may have encouraged greater resource consumption without driving meaningful production increases.

However, from 2018 to 2020, a shift is observed, where TFP begins to rise despite a stabilization in APU, suggesting that efficiency improvements may have started to play a greater role in production growth. One possible factor influencing this trend is the gradual reduction and eventual elimination of the marine diesel subsidy between 2019 and 2020. Before its removal, subsidized fuel likely encouraged higher energy consumption without strong incentives for efficiency improvements. The reduction in subsidies may have pushed farms to adapt, either by reducing fuel use, investing in alternative energy sources, or more likely optimizing management practices to offset rising energy costs. This aligns with the observed increase in TFP after 2018, suggesting that some producers may have begun adopting more efficient production strategies as a response to changing economic conditions.

While this shift toward higher efficiency is encouraging, it remains unclear whether it represents a long-term structural transformation or a temporary adaptation to cost pressures. Further research is needed to determine whether these efficiency gains can be sustained without subsidies and whether additional policy measures are required to support ongoing productivity improvements.

Subsidies have long been a tool for governments to support industries like agriculture, fisheries, and aquaculture, aiming to reduce production costs, stabilize incomes, and promote sectoral growth. However, as highlighted by recent analyses, the impact of subsidies on production has been difficult to estimate^41,54. In many cases, subsidies have not translated into significant productivity gains and, in some cases, may contribute to market distortions and environmental inefficiencies. This suggests that reconsidering how financial support is structured within the aquaculture sector could be beneficial.

One of the possible advantages of shifting from broad and indiscriminately applied subsidies to financial services is the potential to increase efficiency and reduce the fiscal burden on the state. Financial services, including access to credit, insurance, and investment in technological innovations, may provide more targeted and flexible support to aquaculture producers. Unlike broad-based subsidies, which often fail to incentivize efficiency improvements, financial services can be designed to better align with the specific needs of producers, ensuring that resources are allocated where they are most effective⁵⁶. The government may play an important role in promoting and facilitating the creation of these mechanisms, particularly in ensuring that small and medium-scale producers have access to currently limited financial opportunities. Addressing financial constraints could help reduce inequalities in the sector, where smaller producers often lack the same financial tools as larger, vertically integrated firms^57,58.

Providing aquaculture producers with access to credit could support investments in technologies and practices that enhance productivity and sustainability. Credit facilities might be developed to facilitate the purchase of advanced management systems, automation equipment, energy-efficient aerators and pumps, or sustainable feed options. By making these investments possible, credit services could contribute to reducing dependency on subsidies and promoting long-term growth⁵¹. However, access to credit remains a challenge for small and medium-sized producers, who often struggle to meet traditional lending requirements. This is particularly relevant for shrimp farming, where harvest cycles (one to two per year) create cash flow constraints that make conventional debt repayment schedules difficult to manage.

Insurance products tailored to the aquaculture sector may help mitigate the risks associated with adopting new technologies or expanding operations. Insurance mechanisms can protect producers against losses due to disease outbreaks, adverse weather conditions, or market fluctuations, all of which represent significant risks in aquaculture. By providing a financial safety net, insurance could encourage access to lending facilities hence fostering investment and innovation. This, in turn, could reduce the reliance on state intervention through subsidies, allowing governments to reallocate resources toward long-term industry development³⁸.

Public and private investment in research and development (R&D) could also contribute to innovation in aquaculture, fostering more efficient and sustainable production systems. If resources were shifted from input subsidies to R&D, it could help support the adoption of advanced technologies, such as automated feeding systems and precision aquaculture tools. Public-private partnerships between governments, financial institutions, and technology providers might further facilitate technology adoption by reducing costs and spreading risks⁵⁷.

Fostering technological innovation as a public policy provides a strong alternative to state-funded direct transfers to producers or energy subsidies, improving both production efficiency and sustainability in aquaculture. Innovations such as the use of probiotics in shrimp farming have demonstrated the potential to significantly reduce environmental impacts, such as the need for water exchange and the emission of pollutants, while simultaneously boosting production efficiency⁴⁰. Despite these advantages, the adoption of such technologies has been slow, primarily due to high initial costs and the absence of robust policy frameworks that encourage innovation⁵⁶.

Technological innovation not only offers a way to increase productivity but also promotes sustainable practices that align with global efforts to reduce greenhouse gas emissions and environmental footprints⁵⁹. This challenge is not unique to aquaculture; it is also observed in agriculture, where subsidies have not always aligned effectively with the promotion of sustainable practices and technological advancement. The limited impact of subsidies on production underscores the need for a strategic shift in focus. Rather than merely reducing operational costs through subsidies, financial support should be restructured to incentivize the adoption of innovative, sustainable practices that can drive meaningful growth in production while minimizing environmental harm⁶⁰.

This analysis of subsidies in the aquaculture sector presents several significant challenges that must be acknowledged to fully understand the limitations of the current findings.

Model limitations

while the model provides significant insights into the productivity and efficiency of the inputs, the negative elasticity of S_t−1 and the detected heteroscedasticity highlight areas for potential improvement in the model specification. The significant positive impact of APU and the reliability of TFP as a predictor affirm the importance of these factors in driving output, but further refinement and exploration are needed to fully understand the dynamics at play.

Limited data set size and geographical constraints

This study faces significant challenges due to the limited size of the data set, which includes only 10 years and lacks comprehensive reports for many of those years. The small sample size reduces the statistical power of the analysis, making it difficult to identify the subtle effects of subsidies on production. Another limitation is the lack of data granularity, making it difficult to assess the impact of programs by farm type and species. Our analysis provides a national-level approximation of the subsidies’ effect on production, but more detailed data—preferably at the firm level—would improve the evaluation of public program effectiveness and enable more specific recommendations

Additionally, the data is specific to aquaculture operations for all of Mexico, it may not fully capture the diversity of aquaculture practices or the variability in subsidy impacts throughout its different States. The unique environmental, economic, and regulatory conditions in different regions of Mexico mean that the findings may not apply to each State, but more at a federal level. Thus, the results are not necessarily generalizable to other countries, where the effects of subsidies could vary significantly due to different local conditions, policy environments and the nature of the subsidized programs.

Lack of detailed information on subsidy allocation

A critical challenge is the lack of specificity in the subsidy data. In Mexico, subsidies allocated to the aquaculture sector are not detailed in the Federal Budget. Only rough aggregate numbers are published, with no program-specific breakdowns available. This lack of transparency makes it difficult to assess the precise impact of different types of subsidies on production outcomes. For example, subsidies can be targeted at various aspects of aquaculture operations, such as working capital, energy costs, inputs, marketing, or price support. Each of these subsidy targets could have distinct impacts on production, efficiency, and sustainability. However, due to the aggregated nature of the available data, it is impossible to disaggregate these effects and determine how each type of subsidy influences production. This ambiguity complicates the interpretation of the results and limits the ability to make precise policy recommendations based on the findings⁶¹.

Lack of global data on subsidies

Compounding these issues is the broader challenge of the lack of comprehensive data on aquaculture subsidies at the global level. Unlike agriculture, where subsidies are more extensively documented and studied, or fisheries where significant efforts have been made to compound this information^2,35, global data on aquaculture subsidies is sparse and often incomplete. This lack of data makes it difficult to compare the impacts of subsidies across different countries and regions, limiting the ability to identify global trends and best practices. The absence of detailed and transparent global data on aquaculture subsidies also hampers efforts to assess the effectiveness of these subsidies in promoting sustainable development and environmental stewardship⁵⁶.

Given the challenges identified, several areas for future research emerge as critical for deepening our understanding of the role of subsidies in aquaculture:

Expanded data collection

Future studies should aim to collect more extensive data sets that encompass a broader range of regions and operations within the aquaculture sector. By including a more diverse set of locations and expanding the sample size, researchers can improve the robustness of the findings and enhance the generalizability of the results. This would help address the current limitations related to data scarcity and regional specificity⁶².

Detailed analysis of subsidy types

There is a clear need for more detailed data on the specific types of subsidies allocated within the aquaculture sector. Future research should strive to obtain or generate more granular data that distinguishes between different subsidy targets, such as working capital, energy, inputs, marketing, and price supports. This disaggregation would allow for a more nuanced analysis of how each type of subsidy affects production, efficiency, and sustainability⁴⁶.

Global data on subsidies

A concerted effort is needed to compile and analyze data on aquaculture subsidies at the global level. This would involve international collaboration and the development of standardized reporting mechanisms to ensure that subsidy data is transparent, comprehensive, and comparable across different regions and countries. Such global data would enable more accurate assessments of the impact of subsidies and help identify best practices for promoting sustainable aquaculture development⁴⁵.

Longitudinal studies

Longitudinal studies that track the impact of subsidies over time would provide valuable insights into the long-term effects of financial support on the aquaculture sector. Such studies could help identify trends and changes in production patterns and assess whether the impacts of subsidies evolve as conditions in the sector change⁶¹.

Comparative Studies

Comparative research that examines the impact of subsidies across different countries and regions could help identify best practices and common pitfalls in subsidy allocation. By comparing the effects of subsidies in various contexts, researchers can develop a more comprehensive understanding of what works and what doesn’t in promoting sustainable aquaculture production⁶³.

Impact of technological innovations

Future research should also focus on the role of technological innovations in enhancing the effectiveness of subsidies. Studies could explore how subsidies targeted at supporting the adoption of new technologies, such as probiotics in shrimp farming or energy-efficient systems, influence production outcomes and environmental sustainability⁶⁴.

Given the findings of this study, it may be worthwhile exploring ways to transition from broad-based subsidies toward more targeted financial interventions. One possible strategy could involve fostering better integration of financial services with technological innovation. Policymakers might consider facilitating access to credit and insurance specifically tailored to the aquaculture industry, separating it from broader fisheries and agriculture financing structures. Such an approach could enable producers to invest in sustainable practices without the need for continuous state support. Additionally, public investment in technological R&D could play an important role in fostering the development and dissemination of more efficient technologies, while ensuring that capacity building and knowledge-sharing initiatives are in place to support their adoption.

One way the government could support this transition is by subsidizing a reduction in risk perception in the aquaculture sector, particularly by facilitating the adoption of technologies that enhance data collection and monitoring. Improving data availability and traceability can strengthen the creditworthiness of producers, helping lenders and insurers develop better risk assessment models. By supporting investments in digital tools such as automated monitoring systems and real-time production tracking, the government can reduce information asymmetry, making aquaculture a less risky sector for financial institutions³⁸. This, in turn, could encourage the development of tailored lending and insurance products, improving access to financial services while gradually shifting away from direct subsidies.

A phased approach to subsidy reform could be an effective strategy, gradually reducing indiscriminate subsidies aimed at short-term needs while expanding access to financial services and supporting risk-reducing technologies. This would provide producers with the necessary time to adjust and adapt to new financial mechanisms. This transition could help reduce the fiscal burden on the state while fostering a more resilient and self-sustaining aquaculture sector. In the context of agriculture, there has been increasing recognition of the need to cap subsidies for the largest farms and redirect funds toward smaller, more vulnerable producers and environmentally sustainable practices⁶⁰. A similar approach could be explored in aquaculture to ensure that financial resources are directed toward those who need them most, while also promoting technological innovations that contribute to long-term sectoral sustainability.

To assess the impact of government subsidies on the aquaculture industry in Mexico from 2010 to 2020, a data set was constructed and analyzed using a modified Cobb-Douglas production function. The creation of the data set and the subsequent analysis were carried out in several key steps:

1. 1.

   Identification of Relevant Programs: The first step involved identifying the subsidy programs specifically designed for the aquaculture sector. This was accomplished by searching for programs operating under official government rules, primarily through the Comisión Nacional de Pesca y Acuicultura (CONAPESCA). The search focused on programs published in the Federal Official Gazette (Diario Oficial de la Federación; DOF).

2. 2.

   Program Description and Objectives: Once identified, each subsidy program was documented in detail. This included a description of the program’s objectives, target audience, and specific details, such as eligibility criteria and funding mechanisms, when available. This step ensured a comprehensive understanding of each program’s intent and design.

3. 3.

   Quantification of Subsidy Allocation: The amount of funding allocated to each identified program was extracted from the official Federation Expenses Reports, as published in the DOF, specifically within the Federal Expenditure Budget (Presupuesto de Egresos de la Federación). This provided a clear record of the financial resources directed towards aquaculture through various government programs during the study period. When subsidies for fisheries and aquaculture were not itemized separately, the allocation of funds was based on the proportional output of each sector. This method ensured that the distribution of financial support reflected the relative contributions of each sector to overall production.

4. 4.

   Collection of Aquaculture Production Data: Data on aquaculture production for the evaluated period was obtained from the FAO online query database to align with international standards on data analysis and sources⁶⁵.

With the data set compiled, a series of modified Cobb-Douglas production functions were applied to evaluate the effect of subsidies on aquaculture production (see Annex I for the model selection criterion). The used model was as follows:

Where Y_t represents the total aquaculture output yield in year t, and S denotes the amount of subsidy expenditure. The effect of subsidies on production was analyzed with a one-year lag (t-1). This time gap between subsidy allocation and production enhanced the model fit and proved statistically significant after testing the subsidy value at time t and subsequent lags.

Given that labor data specific to aquaculture was not available, the labor component traditionally used in Cobb-Douglas models was substituted with the number of Aquaculture Production Units (APUs) as reported by CONAPESCA in their yearbooks from 2010 to 2021 (https://www.gob.mx/conapesca/documentos/anuario-estadistico-de-acuacultura-y-pesca).

To further explore the impact of subsidies, an index-based model on Total Factor Productivity (TFP) was developed following methodologies previously applied in agricultural studies^66,67. TFP measures output efficiency relative to inputs (e.g., labor, capital, and resources), reflecting gains from technology, innovation, or better resource use.

When estimating TFP, three commonly used indices—the Laspeyres Index, the Paasche Index, and the Fisher Quantity Index—play a crucial role in understanding changes in output and input quantities over time. Each index provides a unique perspective on economic changes, allowing researchers to measure and analyze productivity more accurately⁶⁸. Value and volume as reported by the FAO were used to estimate the indexes. To obtain average unit prices, total value was divided by total volume. The year 2010 was used as base year to calculate the indexes.

The TFP was calculated as:

Where F_t is equal to the Fisher quantity Index⁶⁹, which can also be estimated as

Where L is the Laspeyres Index, and A is the Paasche Index at the time t⁶⁶.

The Laspeyres Price Index reflects price changes based on a fixed basket of goods from the base period, capturing inflationary effects and cost stability over time. In the context of estimating TFP, this index helps to assess how much the output has changed without the influence of changing input quantities (volume and price), focusing instead on the efficiency and productivity of the resources used. By holding the quantities constant at the base period, the Laspeyres Index provides a clear view of how production has improved or declined relative to the base period, which is essential for understanding TFP.

The Paasche Price Index, on the other hand, considers a current-period basket of goods, making it more responsive to recent price fluctuations and shifts in consumption patterns. This index reflects the actual changes in quantities of inputs and outputs, accounting for shifts in production or consumption that occur due to advancements in technology or changes in economic behavior. By focusing on the current period, the Paasche Index captures the immediate effects of technological improvements and innovations, showing how these changes have directly impacted production efficiency and output.

The Fisher Quantity Index, as the geometric mean of the Laspeyres and Paasche indices, provides a balanced and unbiased measure of TFP by combining both perspectives. It captures the overall impact of improved technology and productivity by considering adjusting for changes in both price levels and consumption structure. This approach allows for a more comprehensive understanding of TFP, reflecting not only the pure efficiency gains from better use of inputs but also the broader technological advancements that enhance overall production capabilities.

These indexes are closely linked to TFP, as they help distinguish between productivity-driven growth and price-driven changes. While TFP captures efficiency improvements by isolating output growth that is not solely due to increased inputs, the Fisher Quantity Index reflects changes in real output, and the Paasche and Laspeyres indexes help contextualize whether these changes are driven by inflation, cost adjustments, or shifts in economic structure. By combining these measures, researchers can better assess whether productivity gains stem from true efficiency improvements or external price effects.

In the parameterized Cobb-Douglas production function parameters a, b, and c are curve-fit parameters that represent elasticities, where values greater than 0 indicate a positive relationship between the input and output. A parameter between 0 and 1 suggests diminishing returns, meaning that as more of the input is used, its additional contribution to output becomes smaller and less effective over time. In contrast, a parameter greater than 1 indicates increasing returns, where the input has an amplifying effect on output.

For parameter a, the interpretation differs slightly from standard Cobb-Douglas models since TFP is raised to the power of a rather than acting as a simple multiplicative efficiency term. In this case, a represents the elasticity of productivity growth rather than a direct input-output elasticity. A value greater than 1 suggests that TFP has an amplifying effect on output, meaning that improvements in productivity disproportionately boost production. A value between 0 and 1 implies diminishing productivity returns, where increases in TFP lead to smaller relative gains in output.

Parameter b represents the output elasticity of subsidies, measuring their contribution to aquaculture production, while parameter c captures the output elasticity of aquaculture production units, used as a proxy for labor.

The model parameters were estimated using the non-linear least squares method, implemented through the SciPy library in the Python programming language. The Standard Errors of the Regression (SER) was used to assess the goodness of fit. Model validation was also conducted using the same library using the tests of Shapiro Wilk, Durbin Watson, Beursch-Pagan, and Variance Inflation Factor as well as other graphic validation methods (details available in the supplementary information).