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The Official Journal of the Pan-Pacific Association of Input-Output Studies (PAPAIOS)

Analysing the impacts of a reform on harmful fishery subsidies in Spain using a social accounting matrix


The effects of discontinuing harmful fishery subsidies in Spain were analysed using a social accounting matrix. The study found that the removal of these subsidies would have negative consequences for the economy. Specifically, there would be a significant decline in the production value of marine resource industries, and industries dependent on fish and shellfish as inputs would experience increased production costs. The exports, mainly to EU countries, would also be impacted, and certain marine resource sectors would experience significant job losses. Fishing families would suffer the greatest reduction in income once subsidies are removed. However, there is potential to mitigate most of the negative impacts by redirecting the removed subsidies, as it is shown for the case of their redirection to research and development industries. The findings of this study provide valuable insights for EU policymakers in implementing specific policies to address the potential consequences on affected sectors, families, and employers as part of the next European strategy, Horizon 2021–2027.

1 Introduction

The issue of fishery resource depletion has been an increasing concern since the 1950s (Chuenpagdee et al. 2005), and four main factors have contributed to this problem. Firstly, there is a mismatch between stocks’ regenerative capacity and productive systems’ increased capacity. Secondly, advancements in fishing technology have granted access to previously unreachable areas. Thirdly, there has been a significant rise in demand for fish, particularly in advanced economies. Finally, the large amounts of money with which countries have subsidised their fishing fleets to help them develop and become more competitive (Pauly et al. 2002; Sumaila et al. 2010a). The systematic granting of subsidies to companies and individuals has created a feeling of ‘entitlement’ and dependence on them. Political leaders should consider the negative impact on marine resources of these subsidies. However, eliminating subsidies in only one country would lead to economic losses and make their fishing sector less competitive, while the overfishing problem would persist (Lam and Pauly 2010).

It is important to acknowledge that certain subsidies can positively impact the environment (Sakai 2017). For instance, subsidies targeted towards specific regions or industries can help balance economic and environmental considerations (Cisneros-Montemayor et al. 2016; Sakai 2017). Some subsidies can even improve resource management or human capital, resulting in more sustainable fish production (Sumaila et al. 2010b; Villasante and Sumaila 2010). However, most fishing subsidies have contributed to the increase in European fishing capacity, directly or indirectly leading to the depletion of marine resources (Froese et al. 2018; Smith 2019). The aquaculture sector has also been affected by financial aid, which has led to intensive resource use, such as increasing demand for fish and other feed for fish farming. Furthermore, other funds that promote the expansion of the seafood industry with new fish processing plants have indirectly impacted marine resources, leading to an increase in fishing pressure to meet production needs (Arthur et al. 2019).

When considering the economic impact of subsidies, it is crucial to consider the potential for unfair competition and trade distortions. This is particularly relevant when multiple countries fish in the same area and share resources between different Exclusive Economic Zones.Footnote 1 Subsidising a fishing sector can give one country an advantage in exploiting the resource (Schrank 2003). Moreover, subsidies can create distortions in the international market by favouring exports from one country or limiting imports from others (Schrank 2003).

As we look towards the EU’s European Horizon 2021–2027 agenda, it is clear that the gradual but complete phasing out of harmful subsidies should be top priority. To ensure a smooth transition, it is vital to understand the potential economic and social consequences of this reform. Spain is one of the top three European fish and seafood producers. It is also one of the major European aquaculture producers, and has an important fish processing industry. While the weight on total GDP of the three sectors considered is moderate (0.36%) (INE 2020), and their impact on employment is also modest (76,737 full-time equivalent workers in 2015) (EC, 2016a), several coastal areas are highly dependent of these sectors. We will see below (Sect. 3.4) that these sectors receive an important amount of subsidies. In 2015, Spain was the principal receiver of marine industry subsidies among the all EU-27-member states, representing a share of 2.8% (€161.26 millions), which was double (€81.6 millions) than was allocated to France, the second largest beneficiary, on a total budget of €5749.33 million EC (2016).

Roca Florido and Padilla Rosa (2023) have preliminarily estimated the impact of eliminating harmful subsidies to fishing in Spain by analysing the inter-industrial effects through an input–output model. We here take a broader approach by using a social accounting matrix (SAM) of the Spanish economy to focus on the production of sectors, intersectoral demand for products, household income, exports, and possible restructuring in employment. We have considered four different scenarios: (a) eliminating subsidies in the three sectors directly related to marine resources; (b) eliminating them only in the fishing sector; (c) eliminating them only in the aquaculture sector; and (d) eliminating them only in the seafood processing sector. Additionally, we have analysed four other scenarios (e–h) that result from the reorientation of resources towards the Research and Development (R&D) sector,Footnote 2 which is expected to promote economic, social, and environmental development in the medium and long term (Skerritt and Sumaila 2021). For this study, we consider all industries involved in R&D, classified under the standard industrial classification code 72, which can broadly be categorised into three sectors: basic research, applied research, and experimental development.Footnote 3 Our research aims to provide policy decision-makers with a complete picture of the effects that a reform aimed at eliminating subsidies would have, enabling them to make informed decisions and design the necessary policies to achieve an adequate transition towards an industry without harmful subsidies.

The related literature is reviewed in Sect. 2. Section 3 explains the methodology and data sources. Section 4 presents and briefly discusses the results. The last section provides insightful conclusions and policy recommendations based on the results.

2 Literature review

As fisheries policymakers strive to make informed decisions, they place great importance on assessing the potential impact of their choices on regions that rely on the marine resource industry. They often turn to economic impact models like input–output and SAM methods. These models help estimate reforms’ social and economic implications and provide valuable insights into the contribution to the national economy of sectors related to marine resources. Several studies have employed these models, including those by Leung and Pooley (2001), Dyck and Sumaila (2010), Lee and Yoo (2014), Garza-Gil et al. (2017), Fernández-Macho et al. (2008), Seung and Waters (2009), Seung (2017), Morrissey et al. (2019), and Kim and Seung (2020).

In some cases, an increase in subsidies for agricultural production may affect the availability and pricing of fish products. This is due to the “artificial” increase in agriculture production caused by these subsidies directly impacting the market. Similarly, aquaculture subsidies can threaten marine resources as aquaculture products and fish from fisheries compete for the same market. The increase in aquaculture production, which is often backed by subsidies, can have the same impact on prices and quantities as subsidies for agricultural products. It is also worth noting that many aquaculture products, such as salmon, are fed using fresh fish from the lower end of the food chain. This can lead to issues with the biomass and catches of larger species found in the upper links of the food chain, which can have severe consequences for the ecosystem as a whole (Schrank and Keithly 1999).

The distribution of subsidies among different fishery segments is uneven. According to a study by Schuhbauer et al. (2017), only 16% of the global subsidies in 2009 were allocated to artisanal fishing, while the rest went to large-scale fishing. This has caused inequalities among fishers, which is a significant concern for the EU’s Blue Economy strategy. To address these social inequalities, there are ongoing initiatives, as highlighted in the works of Bennett et al. (2019), Cisneros-Montemayor et al. (2019), and Pauly (2018).

It is interesting to note that when it comes to redirecting subsidies, currently developed countries tend to prioritise the sustainability of natural resources and the environment, while developing countries tend to prioritise aid for low-income fishers (Kumar et al. 2020). Additionally, the impact of subsidies on a country’s fishing fleet’s economic importance can vary (Skerritt and Sumaila 2021). When considering the removal of subsidies, it is crucial to also take into account income redistribution among affected families and industrial sectors, as highlighted by Jiang et al. (2015). However, notwithstanding the importance of distributive issues, the main factor to consider when evaluating the removal of fisheries subsidies is the state of marine resources and whether they are being overexploited or not (Flaaten 2021).

Numerous studies have examined the effects of reducing subsidies in different contexts. Specifically, there is a vast body of literature on the effects of reducing subsidies in the electricity sector (Fathurrahman et al. 2017) and fossil fuel subsidies (IEA, OECD and World Bank, 2010). Although there are fewer studies on the subject, some have analysed the impact of subsidy elimination in fishing-related sectors. Da-Rocha et al. (2017) utilised a general equilibrium model to investigate the consequences of a regulatory policy that eliminated diesel subsidies in the shrimp fishing industry in Mexico. Their findings suggest that the impact of subsidy elimination depends on its effect on fish stocks. If this action benefits fish growth, fishers will benefit more, leading to a higher return on investment, increased capitalisation of the sector, marginal productivity, consumption, and reduced inequality. However, when the effect on fish stocks is excluded, capital investment in the sector may decrease (Da-Rocha et al. 2017).

Certain subsidies, including those that offset operating costs such as diesel, are crucial for maintaining the competitiveness of a country’s fishing fleet in the face of international competition. These subsidies also serve to reduce the negative impact of fluctuations in oil prices. Then, it is not easy to eliminate them (Martini 2012). Removing these subsidies would cause an increase in the prices of catches when stocks are scarce, which could significantly affect countries that are fish exporters (Ruseski 1998; Bayramoglu et al. 2018).

Roca Florido and Padilla Rosa (2023) examine the effects of eliminating the subsidies that promote the overexploitation of fishing resources in the case of Spain. Using an input–output model, they computed the reductions in the added value for the marine resource sectors and how it would affect the supply and demand of inputs, ultimately impacting other parts of the economy. They also computed how this would affect the final demand for the different sectors. However, that analysis was limited to inter-industry transactions and did not consider an alternative use of the public resources subtracted from fishing. Therefore, it could not fully monitor the flow of resources across the entire economy. To complement that study, this article employs a SAM to analyse the impact on industries and other components of the Spanish economy, including families, public institutions, production factors, and international trade, following the reform of harmful subsidies and considering to their reallocation to the R&D industries to illustrate how it can mitigate the negative economic and social impacts of the removal.

3 Methodology

Figure 1 shows the circular flow of income within an economy. It illustrates the different transactions that occur between economic agents and industrial sectors. As income is earned by one agent, it becomes the expenditure of another agent, and this cycle continues. For example, when industries purchase inputs for production, they generate income for workers who contribute labour to other industries. These industries, in turn, pay income taxes to public administrations. The figure highlights the interconnectedness of the various parts of the economy and how their actions can affect one another.

Fig. 1
figure 1

Circular flow of income. Source: Adapted from Breisinger et al. (2009)

All these transactions are described and included in a SAM, where income is represented in rows and expenditure in columns. The double-entry principle of accounting requires that total expenses match total income. Therefore, the sum of the rows and columns should be equal (Breisinger et al. 2009). The SAM offers a more complete view than input–output models because it enables tracking the income flow through all economic agents, not just interindustry analysis. This also enables the endogenisation of all the economic agents of interest while leaving the rest exogenous (Breisinger et al. 2009).

3.1 SAM multipliers

Multipliers are important in understanding how external shocks affect the entire economy. They are especially useful when certain sectors are left out of the equation. Demand multipliers are particularly helpful in showing how demand shocks impact the economy (Fernández-Macho et al. 2008).

The removal of harmful fishery subsidies will have an impact on various aspects of the economy, including production, production factors, family consumption, foreign trade and employment. When a demand-side shock occurs, such as a decrease in exports or an increase in foreign investment, it will have a direct effect on the sectors affected by the shock, as well as an indirect effect on other sectors and the economy as a whole (Fig. 2). Multipliers are used to simulate and measure the impact of these external shocks on the economy, and their choice depends on which sectors are endogenous or exogenous in the input–output model (Miller and Blair 2009). The total multiplicative effect will be determined by the sum of direct and indirect effects on production and consumption (Breisinger et al. 2009).

Fig. 2
figure 2

Direct and indirect effect of a subsidy reform. Source: Adapted from Breisinger et al. (2009)

When analysing the impact of production multipliers, it is essential to consider the technological level of the industries. This is reflected in the input–output section of the SAM, which determines the backward and forward multipliers. Backward multipliers refer to the quantity of inputs demanded by producers to produce an additional quantity of goods or services. Forward multipliers refer to the quantities of inputs supplied to subsequent industries. For instance, if fishing production expands, there will be more inputs for the fish processing sector. The higher the forward and backward multipliers, the greater the total multiplicative effect (Breisinger et al. 2009). In our research, these multipliers collect all the impact caused by a hypothetical reform of subsidies in the Spanish economy in 2015.

3.2 SAM-based price model

Considering that the elimination of subsidies will primarily affect production costs and eventually prices, while it will not affect productivity, an effective way to capture the effect on prices is through a SAM-based price model. In order to make a similar interpretation between the quantity and the price model, it is necessary to assume in the former that prices remain fixed while productivity levels vary. That is, it is assumed that there is an excess of productivity and unlimited availability of resources. For the price model, together with the already mentioned unlimited availability of resources, it is assumed that the technical coefficients of the sectors are fixed (Roland-Holst and Sancho 1995).

Assuming that prices only affect production costs and not productivity, based on the assumed premises indicated above, prices can be estimated (without considering production levels) in this way (Roland-Holst and Sancho 1995):

$${\mathbf{p}} = {\mathbf{pA}} + {\mathbf{v}} = {\mathbf{v}}{\left( {{\mathbf{I}} - {\mathbf{A}}} \right)^{ - {1}}} = {\mathbf{vM}}$$

Based on expression. (1)Footnote 4, we can determine that v represents the exogenous vector of costs, specifically subsidies.Footnote 5 Meanwhile, M represents the multiplier matrix.Footnote 6 With this in mind, we can conclude that Δp = Δv M. By looking at the rows of the inverse of the Leontief matrix, we can interpret this further. The row j of M shows how an external shock in sector j’s costs affects the other sectors’ prices.

To identify the link between the various accounts included in the SAM (producers, families, institutions, etc.), notice that producers must pay for the factors of production and raw materials needed to carry out their activities. This allows the labour force, which is the factor of production in this case, to receive remuneration for their work and consume the production. All actors also bear the costs of imports necessary to meet the internal demand not covered by national production and pay taxes to the government, which represents an income that allows the government to consume part of the production. This multiplicative process between the different accounts is captured in the three multipliers that make up M.

The employment multipliers, which indicate the number of jobs that will change, are calculated by multiplying the output multipliers by the coefficient obtained by dividing the total employment in each sector by the total value of its production. This estimation is based on the formula \({l}_{i}={L}_{i}/{y}_{i}\), where the numerator (Li) represents total employment, and the denominator (yi) represents the value of production (Fernández-Macho et al. 2008). The employment impacts are then computed by multiplying li by the output multiplier in M.

3.3 Accounts of the Spanish SAM

No strict guideline specifies which sectors are categorised as endogenous or exogenous in a SAM. However, in most SAMs constructed, it is customary to observe production, factors of production, and institutional accounts. Only the household and government sectors are usually considered exogenous and excluded from the model as they are where policies are implemented and the origin of shocks that affect the endogenous accounts (Fathurrahman et al. 2017).

Regarding the construction of our model, we followed the framework developed by Fernández-Macho et al. (2008) to create a SAM that includes a total of 100 sectors (Table 1). Our SAM comprises five accounts (Table 2) that differentiate between seafood-related sectors and the rest of industries, industries’ intermediate consumption (raw materials),Footnote 7 non-marine and marine households, factors, and foreign trade (exports) of seafood, and those related to other activities in the Spanish economy.

Table 1 Endogenised sector summary and numbers
Table 2 Spanish SAM structure

The SAM includes 44 productive sectors, 2 of which are fishing and aquaculture. To distinguish between sectors linked to seafood and those linked to the broader Spanish economy, we relied on data from fishing and aquaculture statistical surveys conducted by MAPA (2016a, b). The fishing sector is further broken down into three categories: inshore, deep-sea, and high-sea fishing. The length of boats determines the differentiation in production; 0–12 m for inshore fishing, 12–24 m for deep sea fishing, and 24 or more meters for high-sea fishing (MAPA 2016a). The aquaculture sector is similarly broken down into marine and continental subsectors, as noted in the industry’s economic survey (MAPA 2016b). However, there is no further disaggregation between subsectors for the processed seafood sector (MAPA 2016c).

We have categorised raw materials into three groups, specifically for the three sectors we analysed (MAPA 2016a, b). These groups include fish, molluscs, and crustaceans, which make up a significant portion of production for fishing, aquaculture,Footnote 8 and processing sectors. We did not make any distinctions for other industries and assumed that each industry solely produced one particular commodity (Fernández-Macho et al. 2008).

To accurately record household income, we devised a household matrix that considers income from multiple sources, including government benefits and financial assistance from other families worldwide (Mainar-Causapé et al. 2018). We distinguished households that rely solely on fishing-related industries (such as extractive fishing, aquaculture, and marine resource processing) from those that receive wages from other industries by comparing the number of individuals employed in each activity to the total number of employed individuals in the country (Fernández-Macho et al. 2008).

The rest of the world (RoW) presents a comprehensive account of international transactions, with rows outlining the value of imported goods and services, payments made for the use of production factors from foreign sources, and transfers from national institutions to foreign entities. Conversely, columns record all export revenues, payments to national factors of production utilised abroad, and transfers received from foreign sources. The balance of payments is determined by the net difference between monetary inflows and outflows, which may result in a deficit or surplus with the rest of the world (Mainar-Causapé et al. 2018). It is important to note that our analysis only considers exports based on their destination, whether within or outside the EU (INE 2020).

3.4 Subsidy amounts and simulated scenarios to estimate impacts

The harmful subsidy estimates for Spain are presented in Table 3. These estimates were obtained from Roca Florido and Padilla Rosa’s (2023) research, which collected information from official documents of the European Commission (EC, 2016b) on the subsidies allocated by the national government and the European Maritime and Fisheries Fund (EMFF) (the fund for the EU’s maritime and fisheries policies for 2014–2020) to each sector. They also calculated the national subsidies provided by the Spanish government and identified the amounts of harmful subsidies (Sumaila et al. 2019).Footnote 9

Table 3 Estimates of harmful subsidies for Spain by sector in 2015

Generally, the SAM includes six different groups of accounts that describe all the information of an economy (Sectors and Products; Factors of production; Institutions (Private), Families and Companies; Government; Capital Accounts; RoW), represented through the rows and columns of the matrix. Meanwhile, the columns in the SAM matrix represent the payments made by each account to the accounts represented in rows. When analysing subsidies, for example, the submatrix Government-Goods and Services contains pertinent information on net taxes on production (i.e., taxes less subsidies). This can be further broken down into numerous taxes and subsidies as the availability of information allows, thereby facilitating the analysis of different fiscal instruments (Mainar-Causapé et al. 2018).

This article presents an analysis of eight potential scenarios for the elimination of harmful fishery subsidies. The scenarios are divided into two categories: (a–d) which consider the elimination of the subsidies without redirecting the extracted resources, and (e–h), which take into account the redirection of the subsidies towards R&D sectors. The R&D sector seems a good candidate to receive surplus resources since it may effectively contribute to the sustainable development of society (Skerritt and Sumaila 2021). We use this appealing policy option to illustrate the possibilities and potential impacts of reallocating harmful subsidies. Nevertheless, there are other options that policymakers may consider (such as redistributing income to families or subsidising other industries), and we do not claim that the policy analysed is the best among all the possible alternatives or combinations of them. In order to evaluate the effectiveness of government policies in achieving social well-being through sustainable development, we will be following the criteria established by Clements et al. (2007). Our evaluation will be based on economic and social performances. The former will be measured by variations in industrial sector production and the latter by the effects on family consumption and employment. We will use a SAM to estimate both the economic and social impacts. Based on the results obtained from these indicators, we will conduct an evaluation of the various scenarios proposed.

3.5 Study limitations

It is worth highlighting the usefulness of SAM multipliers in comprehending the impact of external shocks on an economy. Nevertheless, it is important to consider certain limitations while calculating and interpreting these multipliers. The model assumes that positive shocks arise when there is an abundance of productive capacity and total availability of production factors. Moreover, prices are considered fixed, which does not allow a substitution effect to occur, which also makes the economy respond easily to shocks. If prices were flexible (not fixed), they would allow the quantities supplied to be adapted to those demanded, thereby reducing the total effect caused by the fixed price model. Additionally, by endogenising and exogenising the accounts, any effect caused from the latter to the former would again affect them. However, estimates of these effects are outside the scope of the linear multiplicative model. Therefore, results should be interpreted with caution. However, despite the limitations, SAM multipliers still provide a valuable estimate of the consequences of external shocks or political reforms (Round 2003).

4 Results and discussion

This section presents the effects of removing harmful fishing subsidies and the potential effects of redirecting them towards the R&D sector. Our study focuses on the marine resource industries, the remaining sectors (RoS), and the wider economy, taking into account the impact on families within the marine resource sector, other families (RoF), and intra and extra-EU exports.Footnote 11

4.1 Production impacts

Table 4 shows the impacts on production costs of the removal of harmful subsidies in the different scenarios. Scenario (a), which is the sum of scenarios (b)–(d) (removing the subsidies from the three marine resource sectors), is obviously where the highest reduction in value occurs in the whole economy (€154.41 million) and in the marine resource sectors (€135.13 million), with the high-sea fishing sector experiencing the greatest drop (€43.49 million), followed by the marine aquaculture sector (€ 28.51 million) and the deep-sea fishing sector (€ 12.29 million). Regarding the rest of scenarios, the greatest losses occur in Scenario (b), when subsidies are removed from the fishing sector, with a total drop of €69.86 million in the value of production in the whole economy and €62.39 million in the marine resource sectors. In contrast, Scenario (c) has the lowest impact.

Table 4 Impacts on industry production costs 

Table 5 presents impacts on production costs in various scenarios where subsidies are withdrawn and reintroduced into the economy in the R&D sector. Scenario (e) has a negative impact of €134.91 million on marine resource industries and a positive impact of €115.24 million on RoS. It is essential to understand that eliminating all subsidies related to marine resources would have a total impact of €-154.41 million on the economy. Therefore, the total negative impact on the economy would only be €19.67 million, much below the €154.41 million of the scenario without the reorientation of the subsidies to the R&D sector. The redistribution of subsidies positively affects RoS in all simulations, primarily due to the positive impact on the R&D sector (Table F, Appendix).

Table 5 Impacts on industry production costs after reallocating subsidies to the R&D sector

Interestingly, after Scenario (e), Scenario (h) has the most significant negative impact on the whole economy, resulting in a loss of €10.60 million. This can be attributed to the relatively limited effect on RoS when redirecting the subsidies, which only amounts to €30.22 million. Scenario (g) has the smallest negative impact on the economy (€1.16 million) and the marine resource sectors (€31.80 million).

4.2 Demand impacts

Table 6 displays variations in intermediate input prices for the different products in the three marine resource sectors.Footnote 12 The economic impacts on the demand of other sectors in the economy are also shown. In scenario (a), the impact on the demand of the marine resource sectors amounts to €100.32 million, being the most affected products fishes (fisheries), molluscs (aquaculture) and fishes (aquaculture). It is worth mentioning that scenario (d) shows greater joint demand losses for the rest of the sectors of the economy (€ -9.27 million) compared to those in the marine resource sectors (€− 4.70 million).

Table 6 Impacts on demand for intermediate inputs by product category and sector

The impacts of subsidies being extracted and redistributed back to the R&D sector can be observed in scenarios (e–h) in Table 7, with the categories of fish and shellfish products exhibiting the most significant effects on intermediate input prices. The total impact in scenario (e) is a reduction in demand of €100.10 million in the marine resource sectors and an increase of €27.62 million for RoS, leading to a total reduction of demand in the economy of €72.48 million. The aggregated impact on fish and molluscs categories would be (€45.80 million and €38.64 million, respectively. Among the other scenarios, scenario (f) shows the greatest joint losses for both product categories (from fisheries, aquaculture and seafood processing), namely € − 35.93 million for fish and € − 30.15 million for molluscs. Likewise, this scenario also shows the largest negative impact on the demand of the whole economy (€63.54 million) and the largest positive impact on the RoS (€14.18 million) among these scenarios. Conversely, Scenario (h) presents the minimum joint losses. This scenario also has the smallest negative impact on the marine resource industries (€ − 4.64 million) and on the economy (€0.50 million).

Table 7 Impacts on demand for intermediate inputs by product category and sector after reallocating subsidies to the R&D sector

4.3 Family income’s impacts

According to Table 8, the families that rely on marine aquaculture are the most affected by the subsidy changes and would experience a significant cost of €0.74 million via their income. This can be attributed to the fact that the marine aquaculture subsector receives more than 95% of the subsidies within aquaculture. Similarly, families dependent on deep-sea fishing would experience a comparable decrease in their income (€0.73 million). Notably, Scenario (d), despite withdrawing a lower amount of subsidies than Scenario b (€38.95 million- vs. €59.74 million), results in a more significant household income reduction, both in the group of families dependent on marine resources (€2.13 million) and the rest of the families in the economy (€0.31 million).

Table 8 Impacts on household income

As shown in Table 9, redistributing subsidies to the R&D sector would significantly transmit price variations through household incomes. Families in the marine resource industries would bear the brunt of this change, experiencing the greatest negative impact. RoF incomes would also be affected, albeit to a lesser degree. The analysis shows that none of the scenarios examined, which involve reintroducing funds to R&D, could fully offset the initial losses resulting from the reform. Note that families that depend on the deep-sea subsector and marine aquaculture sector would still be adversely affected, regardless of the simulated scenario.

Table 9 Impacts on household income after reallocating subsidies to the R&D sector

4.4 Impacts on exports

Table 10 shows that the effects of export cost via prices are more pronounced for those destined for the EU than for those intended for other regions. Scenario (a) implies a reduction of €0.83 million in total exports. When subsidies are only removed from one sector, scenario (d) has the most significant impact on total exports, with a net loss of €0.46 million. Conversely, scenario (c) produces the least significant impact, with a net loss of €0.07 million.

Table 10 Impacts on exports

The reintroduction of subsidies is expected to positively impact export costs, as indicated in Table 11. Scenario (f) shows a reduction of €0.18 million; in scenario (g) the reduction is minimal. This shows the balancing effect of the reorientation of subsidies in scenario (g), which can effectively counteract the decline in exports, particularly those targeted towards EU countries.

Table 11 Impacts on exports after reallocating subsidies to the R&D sector

Reallocating funds towards the R&D industries is less effective in scenario (h). This is because eliminating subsidies by sector has a greater impact on exports within and outside the EU. Consequently, the negative effect on the overall economy (0.37 million €) is amplified in this scenario. In scenarios (f) and (g), redirecting subsidies has a more significant balancing effect on the economy. This results in a total impact of 0.18 million € and almost zero, respectively.

4.5 Employment impacts

The information presented in Tables 12 and 13 reveals the potential impact of removing subsidies on the number of full-time workers. Based on the data, it can be concluded that job losses would primarily affect the six marine resource subsectors. Scenario (a), involving the subtraction of all subsidies, would obviously result in the most significant job reduction, with approximately 1757 jobs lost in these subsectors and a total of around 2008 jobs lost in the broader economy. The high sea, seafood processing, and marine aquaculture subsectors are the three most negatively affected (566, 516, and 371 jobs lost).

Table 12 Impacts on employment
Table 13 Impacts on employment after reallocating subsidies to the R&D sector

In scenario (c), the loss is minimal, with only 414 workers affected in the six sectors analysed and 436 at the national level. However, 82.5% of job losses in this scenario occur in the marine aquaculture sector, with 360 workers affected. Scenario (d) has a similar effect but with more job loss (531 workers), with 77.3% concentrated in the seafood processing sector (512 workers).

On the other hand, scenarios (e–h) show that redistributing resources towards R&D significantly compensates for the loss of jobs, with a significant increase in RoS. The impacts on the Spanish economy as a whole in scenario (e) would result in around 256 job losses [much below the 2,008 jobs lost without subsidies reorientation in scenario (a)]. It is worth noting that scenario (h), with one of the lowest impacts on the analysed sectors except for the processing sector, has a higher incidence at the national level, with 137 workers affected, than scenario (f), which involves a greater subsidy subtraction. This is because introducing subsidies from the seafood processing sector to R&D has a relatively small effect on job creation in the RoS, with only 393 jobs generated.

5 Conclusions

We use a SAM to analyse the potential impact of removing harmful subsidies from Spain’s fishing-related industries, aligning with the EU’s European Horizon 2021–2027 agenda. Understanding the potential consequences of these changes can help policymakers to achieve this objective, minimising its potential negative social and economic impacts.

The findings indicate that while eliminating these subsidies would have negative impacts on the economy, especially in marine resource sectors, the adverse effects on the economy can be significantly mitigated by redirecting subsidies towards R&D. Without this redistribution, the economy would suffer a loss of €154.41 million, most of which affecting the marine resource sectors (€135.12 million). However, reassigning these subsidies to the R&D sector significantly reduces the total negative impact on the economy (to €19.67 million), demonstrating that investing in R&D is a viable option to address potential socioeconomic disparities that may arise after the subsidy removal (Cisneros-Montemayor et al. 2016; Skerritt and Sumaila 2021). The resource reassignment mainly stimulates production in other sectors of the economy but has little impact on the marine resource sectors. However, investment in R&D may also have a long-term impact on technology and production (and, if properly oriented, on the environment), which is not accounted for by our static analysis, which will likely lead to a positive net impact on the economy over time. It should be added that disparities may likely arose between urban and rural areas due to the reallocation to R&D, since technological improvement can affect both areas differently (OECD 2019, 2020). This was outside the scope of this research, but it raises an interesting line for future research.

The potential increase in costs associated with utilising fish and shellfish in various industries may also significantly impact demand. A decrease in the demand for fishery products could result in €100.42 million in losses in the scenario in which all harmful subsidies are eliminated. It is worth noting, however, that with subsidy redistribution to the R&D sector, the positive effect of €27.62 million generated in RoS goods’’ demand limits the losses in demand to €72.48 million. It is crucial to understand that redistributing subsidies to the economy will not fully compensate for the effects on input demands between sectors, unlike outputs.

It is also important to consider the consequences of income reductions for families. Scenario (e) (total subtraction of subsidies and redistribution to R&D) presents significant effects on families who rely on fishing industries. Political actions must be taken to address social imbalances that may arise from the reform to ensure the affected families receive support. The European Blue Economy strategy emphasises the need to resolve these social imbalances as a primary objective (Bennett et al. 2019; Cisneros-Montemayor et al. 2019; Pauly 2018). Furthermore, future research could explore the identification of the families that are at risk of social exclusion, which would be a valuable topic to address.

As regards the potential job losses associated to the reform, the extractive fishing industry, is among the most affected sectors. To mitigate the effects of such losses, it is crucial to implement policies that provide reorientation and training for affected employees. This will enable them to promptly transition into other labour-intensive sectors, thereby facilitating faster industrial restructuring.

The research findings indicate that foreign trade, particularly within the European Union, could benefit significantly from a strategic reallocation of subsidies. In the case of scenario (g), the results show that removing the subsidies from the aquaculture sector and reorientating them to the R&D sector would not have a negative impact on total exports. The example underscores the crucial role of redirecting subsidies towards other industries in optimising resource utilisation (Sumaila et al. 2010b; Villasante and Sumaila 2010).

Our results show that reallocating the released funds towards R&D could significantly mitigate the adverse effects on the economy. There are other interesting alternatives that policymakers can explore (like income compensation programs for unemployed marine families or allocating the released funds to other strategic industries). Our work has illustrated the effects of reorienting these subsidies, though we do not claim that reallocating the funds only to the R&D sector is the best of all possible options (or combinations of them). Investing in R&D is an appealing alternative as, according to the literature, it can significantly contribute to productivity and job creation, attenuating the economic and social impacts of the reform. It may push the technological change that, properly oriented and accompanied by environmental policies, could enhance environmental quality and social welfare in the future. It is imperative that policymakers consider the potential environmental consequences of such reform to gain a more comprehensive understanding. Eliminating harmful subsidies is undoubtedly necessary, but it is only truly effective when it furthers sustainable development and improves social welfare and marine resources (Clements et al. 2007; Da-Rocha et al. 2017).

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. The dataset supporting the conclusions of this article is available in the INE repository, The dataset supporting the conclusions of this article is available in the MAPA repository, The dataset supporting the conclusions of this article is available in the Banco de España repository,


  1. Exclusive sovereignty of a country over the resources from its coastal zone to an area of 200 nautical miles.

  2. The NACE code classification system identifies the R&D sector with M72, where M stands for industry group (M—Professional, scientific and technical activities). The M72 code is further divided into two main groups: M72.1, which refers to Research and Experimental Development (R&D) on Natural Sciences and Engineering, and M72.2, which refers to R&D on Social Sciences and Humanities.

  3. See details in Eurostat (2008), p. 268.

  4. In this equation, p denotes the vector of output prices of different industries, whereas v represents the vector of primary input prices. I is the identity matrix, and A is the matrix of technical coefficients with n x n dimension. (IA)−1 is the inverse Leontief matrix, which includes the total requirements of direct and indirect inputs produced by industry i for each unit of final output produced by industry j.

  5. The SAM price model aims to capture the effects of cost transmission via prices that result from external shocks in an economy. Therefore, our findings are not expressed in terms of prices per quantities, but rather in prices through cost linkages.

  6. By applying the partition property of matrices to divide SAM accounts into blocks and utilising the path decomposition method, we can quantify the impact of price effects linkages among SAM sector on the overall multiplier effects (M), which can provide a significant understanding of the cost transmission mechanisms (Roland-Holst and Sancho, 1995)

  7. Our SAM differentiates between sectors and (produced) products, allowing each of the analysed fishing industries to manufacture more than one commodity (Fernández-Macho et al. 2008). We also assume that industries that do not belong to the marine resource industries only produce one output (Fernández-Macho et al. 2008).

  8. According to MAPA (2016b), the remaining production is carried out by invertebrates and aquatic plants categories.

  9. According to Roca Florido and Padilla Rosa (2023) estimates, this figureFootnote 10 represents 82%, which includes the 1% that are ambiguous subsidies.

  10. The percentage that represents the amount intended for the three sectors over the total EMFF for 2014–2020, 71.69%, has been applied to estimate the amount that is designed for European funds for 2015

  11. The complete list of impacts by sector is shown in Appendix tables.

  12. ‘F’ refers to Fishery; ‘Aq’ to aquaculture; and ‘P’ to seafood processing sector.


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Our sincere thanks to Professor Raquel Langarita for providing us with constructive and valuable suggestions. Likewise, the present investigation would not have been possible without the aid and valuable recommendations from Ferran Sancho on the methodological and technical aspects of this research. We are grateful to two anonymous reviewers for their helpful comments and suggestions. This work was supported by the Spanish Ministry of Science, Innovation and Universities (Grant number: PID2021-126295OB-I00).


This work was supported by the Spanish Ministry of Science, Innovation and Universities (Grant number: PID2021-126295OB-I00).

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Alberto Roca Florido: conceptualization, methodology, software, formal analysis, investigation, writing- original draft preparation. Emilio Padilla Rosa: supervision and writing—reviewing and editing.

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Correspondence to Alberto Roca Florido.

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The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: A. R. F. reports that financial support was provided by the Spanish Ministry of Science, Innovation and Universities and ERDF.

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See Tables

Table 14 Impacts on Production (without redistribution of subsidies)


Table 15 Impacts on Production (after redistribution of subsidies)


Table 16 Impacts on demand (without redistribution of subsidies)


Table 17 Impacts on demand (after redistribution of subsidies)


Table 18 Impacts on employment (before and after redistribution of subsidies)


Table 19 Total impact on production excluding R&D


Table 20 Total impact on demand excluding R&D


Table 21 Total impacts on employment excluding R&D


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Roca Florido, A., Padilla Rosa, E. Analysing the impacts of a reform on harmful fishery subsidies in Spain using a social accounting matrix. Economic Structures 13, 9 (2024).

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