Assessing sectoral linkages and sustainability dynamics in Tunisia – insights from a social accounting matrix

COVID-19 and the recent war in Ukraine have significantly squeezed the economic condition of Tunisia. The trade deficit widened by 61%, and reached 11.6% of GDP, in the first two quarters of 2022 amid high commodity prices (World Bank, 2022). Political uncertainty, slow structural reforms and a large fiscal deficit have suppressed private investment. However, the government has committed to recovering to its pre-crisis levels by taking different measures for instance: openness to foreign trade and foreign investment in the “offshore” sector, priority to reduce tensions in the labor market and structural reforms to meet intermediate and exported demand in several economic sectors' production (Boughzala, El Lahga, Buassida, & Ferjani, 2020; Diwan, 2019; Labidi, 2022). Although trade openness, foreign direct investment and human capital have significantly impacted Tunisia's economic growth (Soltani, 2012), labor demand has remained weak in most Tunisian industries, even following trade liberalization (Haouas & Yagoubi, 2004). On the other hand, offshore firms have outperformed domestic firms but tend to underperform when exporting and importing (Baghdadi, Kheder, & Arouri, 2019). To this end, more efforts are required, and prudent policies are to be enacted amid the commitment to achieve a sustained economic recovery. Determining the key economic sectors is pivotal for a developing country like Tunisia to consolidate the government's efforts to drive economic recovery.

Tunisia's mixed economic system, which combines various forms of individual freedom with centralized economic planning and government regulation, is complicated, especially regarding links across industries. The nation continues to experience rising inflation, growing government budget deficits and restricted access to foreign finance, making it challenging to achieve long-term economic progress. The International Monetary Fund reports that Tunisia's economic growth is slowing down; the country's GDP growth was just 2.5% in 2022 and is forecasted to reach 2.3% in 2023. The slow growth rates underscore the urgent need for policy changes and strategies to jump-start Tunisia's economy. The COVID-19 outbreak negatively affected Tunisia by affecting the tourism sector, which represents approximately 15% of the country's GDP.

Moreover, there was a significant decline in remittances because Tunisian expatriates in Europe lost jobs and income. Conflicts between Russia and Ukraine have caused global Supply chain disruptions and rising commodity prices on international markets. These factors have affected trade deficits and caused inflation that has surpassed 9% in Tunisia. The economic recession resulting from external shocks negatively impacted government revenues; however, substantial spending on healthcare, subsidies and social support during the crisis led to increasing fiscal deficits and further elevated Tunisia's already high public debt levels.

The identification of key sectors that can propel economic growth and job creation is a significant strategy for tackling Tunisia's economic problems and fostering sustainable growth. The government can allocate resources and expenditures to regions that will have the greatest economic impact by focusing on high-potential industries. The expansion of important industries, particularly those with strong links to other industries, can benefit the rest of the economy by increasing aggregate demand, generating jobs and transferring knowledge. The government can benefit from knowledge of industries with strong direct and indirect linkages in several ways. Firstly, they benefit from sectoral cyclical fluctuations because when commodity demand rises, so does demand for the goods used to manufacture them, which quickens the production cycle (Ahmed, Socci, Severini, Yasser, & Pretaroli, 2018a).

Secondly, sectors with strong industrial linkages tend to produce higher output and employment compared to sectors with weak industrial linkages (Bhattacharya, Bhandari, & Bairagya, 2020). As a result, policymakers seeking to promote broad-based economic growth and job creation should focus on them. Thirdly, enclave sectors, which are geographically and technologically isolated and oriented towards exports, tend to create a skewed income distribution (Auty, 1980). Finally, a high level of inter-industry linkages indicates the significance of economic and financial transactions between sectors (Freytag & Fricke, 2017) and highlights the importance of technology transfer and productivity growth from one sector to another (Apergis, Economidou, & Filippidis, 2008).

To this end, the current study constructs a social accounting matrix (SAM) for Tunisia to study the economic sectors' linkages using a multi-sector and multi-factor extended input–output (IO) model. In particular, it examines how changes in income and final demand, such as increased government spending or higher exports, reverberate through the economy. The SAM is a detailed accounting of how industries and institutions are interconnected through flows of money and resources. By tracing these inter-sectoral flows, this study provides important insights into supply chain effects and other aggregate relationships that influence an economy's performance.

This study advances previous SAM-based analyses of Tunisia in several important ways. Earlier work, such as El Mekki et al. (2015), relied on a 2005 SAM and employed a simple IO framework that did not incorporate endogenous income generation or the circular flow between production and institutional sectors. In contrast, our study constructs an updated SAM for 2017 – reflecting significant post-2011 structural changes – and integrates it into a multi-sector, multi-factor extended IO model. This approach endogenizes household income, institutional transfers and savings–investment behavior, allowing us to capture the full income cycle and its feedback effects on production. As a result, the analysis reveals new sectoral interdependencies and identifies key sectors whose roles differ from those found in earlier studies, offering fresh insights for Tunisia's current policy challenges in sustainable resource management and economic recovery. The next section presents the literature review, followed by the methodology in Section 3. Section 4 describes results and discussion and finally Section 5 concludes.

Structural transformation involves reallocating resources from low-productivity activities toward modern, higher-productivity sectors, a process central to sustained economic upgrading (El-Haddad, 2018). In the case of Tunisia, furniture; plastics; and rubber products are key industries with the potential to drive growth through enhanced export capacity. Productivity dynamics further shape this transition. Plane, Chaffai, and Triki (2011) show that total factor productivity in Tunisian manufacturing improves with greater openness and international trade, with several industries exhibiting stochastic convergence toward OECD productivity levels. Complementary empirical studies also examine how sectoral integration influences Tunisia's growth trajectory (Brini, Amara, & Jemmali, 2017; Cruz, Baghdadi, & Arouri, 2022; Kruse, Martinez-Zarzoso, & Baghdadi, 2021), underscoring the broader developmental significance of structural change.

The extant literature underscores the need to account for technological, environmental and institutional heterogeneity when examining production structures and sectoral linkages (Abid, 2025; Abid, Hechmi, & Chaabouni, 2024; Abid & Goaied, 2015; Blancas, 2006). However, the use of an IO analysis in the Tunisian economy is limited and mainly focuses on the energy sector to study the environmental impacts. For instance, Lehr, Monnig, Missaoui, Marrouki, and Salem (2016) examined the economic impacts of renewable energy and energy efficiency on machine and metal industries. Similarly, Herrera et al. (2020) investigated the socioeconomic impacts of a renewable electricity system hybrid concentrated solar power energy using IO analysis. Moreover, Belhadj, Dakhlaoui, and Gouider (2022) study assessed the macroeconomic and environmental impacts of subsidy reform in Tunisia's energy sector using IO and partial equilibrium methods. Besides energy studies, Gebs and Nabi (2021) quantified the macroeconomic impacts of digitalization through an extended IO model. They presented insights into how assumptions regarding investment and productivity gains influence overall economic performance during Tunisia's digital transformation.

It is essential to mention here that total productivity depends on the structure of industrial production, which presents the country's inter-industry connections and the linkages between the production and institutional sectors. These connections identify the key economic sectors. Thus, acquaintance with production structures helps policymakers conduct impact analysis for various policy options to boost economic growth.

Economic literature identifies the production structure according to the technological concept or the economic sectors' importance in output (Stone, 1985). The seminal works of Leontief (1953) and Rasmussen (1956) describe the production structure by presenting the interactions among different economic sectors. Leontief studied US inter-industry transactions using IO tables and identified interdependence among industries, whereas Rasmussen presented Denmark’s industrial connections using IO tables. Several other studies focused on the inter-industry linkages in single and multiple economies (Chang & Lahr, 2016; Su, Yang, & Lin, 2017; Wiebe, & Lenzen, 2016). Moreover, a few studies identified the nature of the nexus among various industries, such as (Antonioli, Di Berardino, & Onesti, 2020; Sarmidi, Khairuddin, & Zainuddin, 2021; Sun, 2022). Similarly, the production structures were examined by (Ahmed et al., 2018a; Ahmed, Socci, Severini, Yasser, & Pretaroli, 2018b; Sposi, 2019).

However, it is vital to include income distribution and consumption data due to their significant effect on inter-industry linkages (Ahmed et al., 2023, 2024). The production of any economic sector is related to consumption, and changes in income distribution can lead to changes in consumption structure, thus affecting production (Ahmed & Medabesh, 2020). The core theory regarding the endogenization of income distribution and the consumption of households is based on the work of (Miyazawa, 1976), where the author considered the existence of several classes of households with different wages and incomes and multiple expenditure ways (Blancas, 2006; Aliphat & Blancas, 2025). The SAM is used as a framework to endogenize variables, such as income and expenditure of various economic actors, including firms, households and government, into traditional IO analysis to create an extended model of IO analysis (Socci et al., 2023).

Several studies have focused on SAM to gain a better perspective on linkages among industries and financial sectors, as conducted by Li (2008) and Ahmed et al. (2018a), and to highlight their effect on the agricultural sector (Ahmed et al., 2018b). Moreover, the SAM is also utilized for studying the effects of economic policies and income inequality on poverty in developing countries (Gakuru & Mathenga, 2012; Round, 2003) and to analyze the impact of trade policy on poverty in Botswana (Tsheko, 2005). For Tunisia, the study of inter-industry linkages using SAM was done by El Mekki et al. (2015). However, the SAM used by them is for 2005, and linkage analysis was carried out using a simple IO model, not the extended IO model. In other words, the role of endogenous income creation and circular flow between sectors has not been thoroughly analyzed for the Tunisian economy. Therefore, the present study aims to address this gap by constructing an updated SAM for Tunisia and utilizing it to determine key sectors and interdependencies based on circular flows. The findings will provide vital insights into the production structure and inform policymaking to leverage key sectors for endogenous income distribution and economic growth in Tunisia. The study utilizes a multi-sector and multi-factor extended IO model that focuses on SAM data to provide an extended income cycle flow, including interactions between sectors and institutions of the economy.

The study uses a SAM as a data framework for the linkage analysis. The SAM represents all economic transactions and interactions within a country or region (Ahmed, Socci, Severini, & Pretaroli, 2019; Ciaschini, Pretaroli, Severini, & Socci, 2012). It captures how various industries and institutions, such as households, governments and the rest of the world, exchange goods, services, labor and assets. As such, SAMs are useful tools for analyzing direct and indirect economic linkages, as well as macroeconomic relationships. The current study constructed a SAM for Tunisia using 2017 data from Tunisia's National Institute of Statistics and Central Bank. The year 2017 represents the most recent period for which Tunisia provides complete and internally consistent national accounts, institutional accounts and supply–use data required to construct a full Social Accounting Matrix. Although some recent studies have attempted to build experimental SAMs for later years using partial data and cross-entropy balancing, no officially validated or publicly released SAM exists for Tunisia beyond 2017. International SAM repositories (e.g. JRC, DataM) also do not report any post-2017 SAM for Tunisia. Therefore, the 2017 SAM constitutes the latest reliable structural baseline for extended IO analysis, although we acknowledge that post-2020 shocks may limit its external validity.

The SAM focuses on production sector data from national accounts reports and balance of payments data. In particular, the data on intermediate consumption, total production, taxes, imports and exports and final demand were taken from the National Institute of Statistics. In contrast, the data on saving/investment and inter-institutional transfer were taken from the Central Bank. Table 1 presents the SAM structure.

A SAM more precisely displays the economic interactions in rows and columns. Column-wise, it records the outflows (payments) made by each account to the accounts identified in the rows, while row-wise it records the inflows (receipts) that each account obtains from the accounts listed in the columns. This framework allows us to separate the diverse impacts of a policy change or shock on the macro economy and on the various institutional sectors that make up the economy (households, financial and non-financial firms and governments), allowing us to focus on the most important effects considering the policy goals and/or the nature of the shock.

The Tunisian SAM consists of 23 commodities {1…i} and 23 activities {1…j}, two factors of production – labour (L) and capital (K) – and the main institutional sectors, namely non-financial corporations, financial corporations, households {1…h}, the government (g) and the rest of the world. The primary distribution of income accounts for the allocation of the generated value added to the institutional sectors based on their factors' ownership, whereas the secondary distribution of income accounts for the transfers between institutional sectors. (Ahmed et al., 2019). Private sector transfers inside the home economy are endogenous and are determined as a percentage of private sector income; public sector transfers and those from the rest of the world are exogenous. Use of income, whether for final demand (consumption, investment, export) or capital accumulation, marks the end of the income cycle. Ahmed et al. (2018a, b). The public sector and the private sector have different patterns of investment and saving.

Taxes on products and services are also included in a SAM, with the major allocation of income being between the federal government and state and municipal governments. Income taxes are an element of the secondary distribution of income and are allocated between the segments of society that impose the taxes and those who are responsible for collecting them. This study converts the Tunisian SAM into a commodity-by-commodity symmetric framework following Miller and Blair's (2009) approach (see details: Ahmed et al., 2025; Miller & Blair, 2009)

The current study uses a multi-sector and multi-factor IO model, also referred to as an extended IO model, to include the income and expenditure of institutional sectors into the production structures for further linkage analysis. The extended IO model evaluates how shocks propagate through the various income circular flow stages (Ahmed & Medabesh, 2020). Policymakers can decide which variables are affected by a particular occurrence and evaluate the outcomes. The concept of the “key commodity” or “key industry” is crucial for analyzing intricately intertwined components in an IO framework because it describes how a given good or industry affects the growth of the entire economic system (Lahr & Dietzenbacher, 2001). This supposition becomes more relevant when the institutional sectors are included in defining the key commodity in a SAM framework. According to this perspective, output halts in crucial sectors caused by significant exogenous events, including pandemics and natural catastrophes, might result in amplified consequences that other models cannot account for (Socci et al., 2023). As a result, by using the extended IO model, it is feasible to analyze the consequences of a significant exogenous event on the economic system as well as the formation, distribution and redistribution of income (El Meligi et al., 2018).

The basic IO model derives the information from the table that presents inputs and outputs, value added generated in production and final demand by households, firms and government, along with the exogenous final demand comprising net exports (exports minus imports) (Miller & Blair, 2009). However, the extended IO model utilizes the SAM, which captures the entire circular flow of income, including the primary distribution, the secondary distribution and the use of income.

By displaying several phases, Figure 1 illustrates the full income distribution system and establishes a feedback loop between the final demand components and commodity output. Taking the production system as an example, the process starts with the generation of commodity output x, which leads to primary income v(x). The c value added components, v_c(x), are allocated across commodities according to their contribution to total value added. The cycle is subsequently expanded to incorporate primary income distribution to the s institutional sectors, denoted by v_s(x). The institutional sectors use this income for transfers and taxes, resulting in disposable income y_s(x). Finally, disposable income determines institutional sectors' final demand for commodities, f(x).

The following elementary equation of the IO model can be used to develop the multi-sector and multi-factor IO model.

where x (n x 1) represents the commodity output vector, m (n x 1) is the import vector, r (n x1) represents the intermediate commodity use and f (n x 1) is the final demand vector. We decompose final demand into exogenous and endogenous components as follows:

where $fex$ and $fend$ are exogenous and endogenous final demand, respectively. From Equation (2), we have:

In this case, exogenous final demand consists of net exports and endogenous final demand includes household consumption, public expenditure and investments. Considering that the difference between exports and imports represents the current accounts, we replace $fex−m$ with $fca$ and write Equation (3) as follows:

In an economic system with i commodities, intermediate demand is expressed as:

where A(n x n) is the commodity-by-commodity technical coefficient matrix, formed by column-normalizing the commodity use matrix, $A=Uq−1$⁠. Substituting Equation (5) into Equation (4):

Figure 1 distinguishes:

Generation of value added by commodity

Where L [n x n] is the value added shares of each commodity.

Allocation of value added by components.

where matrix $W$ (c x n) distributes commodity value added across its components.

Distribution of primary income to institutional sectors.

where P[s x c] allocates factor income to institutional sectors.

Distribution of secondary income by institutional sectors.

where T[s x s] refers to the transfer of net income among the institutional sectors. Alternatively, the last equation can be written as:

Formation of final demand (commodity level).

or;

where F [n x s] is commodity shares of institutional consumption, C [s x s] is institutional consumption propensities, K [n x s] maps institutional investment to commodities and S [s x s] represents the institutional sectors' saving propensities.

Generation of output.

Putting $G=FC+KS$ and using Equation (12), endogenous final demand becomes:

Let $E=G[(I+T)PWL]$⁠, we have:

Substituting into Equation (6):

Finally, the extended model is presented by:

Alternatively:

From Equation (19), we have a structural matrix R, represented as:

The structural matrix R, derived from the multi-sector and multi-factor model, has a dimension (c,c) and it is used to analyze inter-commodity linkages. For comparison, we also compute linkages using the simple Leontief model:

where $(I−A)−1$ is called Leontief inverse and is denoted by L.

Matrix L is used for linkage analysis to present connections between commodities with exogenous income and final demand. Linkage analysis provides two classical indicators-power of dispersion and sensitivity of dispersion-which summarize each commodity's role within the production system (Ahmed et al., 2018a). Rasmussen proposed these measures to quantify backward and forward linkages (Rasmussen, 1956).

Linkage analysis provides two classical indicators-power of dispersion and sensitivity of dispersion-which summarize each commodity's role within the production system (Ahmed et al., 2018a). Rasmussen proposed these measures to quantify backward and forward linkages (Rasmussen, 1956). Backward linkages characterize a commodity's dependence on upstream inputs. A commodity with strong backward connections requires significant intermediate inputs from other commodities. In a SAM-based framework. backward linkages capture how shocks to final demand propagate through the commodity supply chain. Forward linkages describe the extent to which a commodity is used as an input by other commodities.

The sensitivity of the dispersion index measures forward linkages and is calculated from the supply-side structure of the multiplier matrix. It represents the output volume of other sectors necessary to affect the primary sector's output. The indicator is computed from the supply side. The power of dispersion index, computed from the demand side of the multiplier matrix, measures backward linkages. It represents the necessary output required across all commodities to satisfy a unit increase in final demand for a given commodity.

The mathematical form of power of dispersion index, $π.j$ is as follows:

where r._j is the backward linkage of commodity j, $∑j=1Cr.j$ is the sum of all backward linkages and C is the total number of commodities.

On the other hand, the index of sensitivity dispersion, $τi.$⁠, is presented mathematically as:

where r_i. is the i^th forward linkage of commodity i, $∑i=1Cri.$ is the sum of all forward linkages and C is the total number of commodities.

The commodities are classified based on the average index value, which is one. Therefore, commodities with an index value of one or more present strong linkage. On the other hand, the commodities with index values below one display weak linkages. A commodity may have strong forward (backward) linkage but weak forward (backward) linkage. However, a key commodity is that which exhibits strong forward and backward connections.

Figures 2 and 3 present the results of the forward and backward linkage analysis, with Leontief and extended models. In the Leontief model, the income distribution and final demand are exogenous, whereas they are endogenous in the extended model.

The commodities with strong forward linkages in the Leontief model are: “Miscellaneous, index 1.54,” “Oil refining, index 1.02,” “Chemical, index 2.35,” “Mechanical and electrical, index 2.92,” “Oil and natural gas extraction, index 1.81,” “Transport, index 1.20,” and “Other merchant services, index 1.17.” The remaining commodities have weak or poor forward linkages with index values below 1. On the other hand, commodities with strong backward linkages having index value 1 or above are: “Food and beverage, index 1.25,” “Tobacco, index 1.39,” “Textile, clothing and leather, index1.40,” “Miscellaneous, index 1.44,” “Oil refining, index 1.08” “Chemical, index 1.57” “(a8) Building materials, ceramics and glass, index 1.08” “Mechanical and electrical, index 1.50” “Mines, index 1.01,” “Electricity and gas, index 1.11,” “Building and civil engineering, index 1.29,” and “Maintenance and repair, index 1.42.”

Figure 3 depicts the indexes with an extended IO model and demonstrates that after endogenizing the income distribution and final demand, the production structures evidence a significant change. For instance, “Agriculture and fisheries,” “Food and beverage,” “Textile, clothing, and leather,” and “Non-market services, including public administration,” have weak or poor forward linkages in the exogenous setup, while they become strongly connected with index values of 1 or above in the endogenous arrangement. Whereas sectors “Oil refining” and “Transport” have strong forward linkages in an exogenous configuration, while, in an endogenous design using an extended model, their links are weak. Similarly, backward linkage indexes in the endogenous setup also exhibit variations in some sectors as compared to those in the exogenous design. However, an important consideration is identifying key commodities with strong forward and backward linkages. Table 2 presents the linkage analysis results for Tunisia's economy using both the standard Leontief model and the extended model. The table classifies commodities according to their forward and backward linkage strengths, offering a clearer and more comprehensive view of inter-relationships.

It is obvious from Table 2 that the key economic sectors from the Leontief model are: “Miscellaneous,” “Oil refining,” “Chemical,” and “Mechanical and electrical,” whereas the key commodities obtained from the extended model do not include “Oil refining” and “Mechanical and electrical.” Instead, the other two, “Textile, clothing and leather,” and “Oil and natural gas extraction,” have been included in the list of key commodities along with the previous two, “Miscellaneous,” and “Chemical”. Table 2 also presents changes in others' intermediate and weak forward and backward linkages before and after incorporating the income and consumption patterns into the production structures.

The multi-sector extended model captures additional inter-commodity relationships and, hence, confirms the authors' proposition. Moreover, the findings also conform to that of (Miyazawa, 1976; Pyatt, 2001; Ahmed & Medabesh, 2020). The current findings reveal a more nuanced picture of how the Tunisian economy's commodities interact and influence each other. Comparing the results from the two models highlights how augmenting the analysis can provide deeper insights into the economy's structure and linkages. The textiles, clothing and leather; miscellaneous; chemicals; and oil/natural gas have high backward and forward linkages, indicating their inputs and outputs strongly interconnect with others. As major exporters, these industries producing these commodities are key employers and sources of foreign exchange. The miscellaneous category incorporates paper, packaging, wood/furniture, cork processing and plastics. The current findings confirm the findings of El-Haddad (2018), who used product space methodology and identified, along with furniture, chemicals, plastic and seating (from textiles), as top commodities.

Tunisia's textile and clothing sector considerably influences growth and development. It accounts for many exports and jobs, with extensive backward and forward linkages reflecting its reliance on upstream inputs and its role in providing inputs to downstream products. The size and connections confirm the strong effects on the overall economy. Shocks or policies impacting textiles/clothing likely have major macroeconomic effects that could be anticipated using linkage analysis. On the other hand, Tunisia's miscellaneous products include packaging production, a crucial input for paper production. Packaging protects goods during transport and storage, safeguarding product quality and safety. Packaging materials like cardboard and paperboard generate employment and revenue.

Tunisia's rapidly growing chemical industry reflects the government's efforts to attract foreign investment. The policy has spurred the establishment of several chemical plants. The industry is diverse, spanning petrochemicals, fertilizers and plastics. Tunisia's oil industry provides direct and indirect employment, reducing unemployment. Thousands are directly employed, while transport, construction and logistics benefit indirectly. For developing nations, unemployment poses challenges that the oil industry may help to address. It is critical for energy security by reducing reliance on imported natural gas. The Leontief model overlooks Tunisia's agriculture due to weak backward and forward linkages. Limited processing and value addition reflect weak forward connections, while limited input access denotes weak backward links. The resultant low productivity adversely impacts farmer income. While agriculture exhibits limited backward linkages from input suppliers and weak forward linkages to processors in the basic Leontief model, the multi-sector and multi-factor model incorporating more commodities shows stronger forward linkages. These strong forward linkages reflect robust agro-processing, which can increase the prices of agricultural outputs and farmer income. The extensive processing of olive oil, dates and other agricultural products shows that Tunisia's agro-food has strong forward connections. This processing of products promotes growth and development in the broader agricultural industry, despite the limitations suggested by the standard Leontief model. Overall, the analyses highlight how extensions and applications of IO models can provide richer insights into relationships and dynamics.

The utilization of endogeneity in agriculture has yielded noteworthy enhancements in interconnections, elevating Tunisian households' socioeconomic conditions. This phenomenon highlights policymakers' need to formulate appropriate urbanization and industrialization strategies targeting the rural environment. Urbanization policies should prioritize the development of agro-processing to add value to agricultural products, which can generate employment opportunities in rural areas and diminish the need for rural-urban migration. Additionally, agro-processing can provide a market for farm products, incentivizing farmers to increase production.

The interdependence of urbanization and agriculture is essential to the economic progress of developing nations. Urban areas provide marketplaces for agricultural products while serving as a source of food and raw materials for agriculture. Agriculture is growing due to the rapid demand for food and other agricultural goods brought on by increased urbanization. An appropriate urbanization strategy can also encourage the growth of agro-products links, where agricultural products are processed and turned into products with added value. This could create new industries and employment opportunities, boosting the nation's overall development and economic growth.

Tunisian agriculture has emerged as a substantial contributor to the country's exports, encompassing various agricultural products such as olive oil, dates, citrus fruits and vegetables. The African Development Bank reports that agriculture constitutes 14.3% of Tunisia's total exports (Kolster & Matondo, 2012). By generating foreign exchange earnings, agriculture has played a pivotal role in the country's economic growth. Furthermore, the agricultural industry has emerged as a crucial source of employment in Tunisia, providing employment opportunities for rural inhabitants, particularly women and youth. Additionally, the industry has contributed to poverty reduction and diminished inequality in rural areas by creating employment opportunities.

The linkage results also have direct sustainability implications. Commodities identified as having strong backward and forward linkages – such as agriculture, food industries and energy-related – are precisely those that dominate Tunisia's water–energy–food nexus and environmental footprint. Their centrality in the production system implies that efficiency improvements, resource-saving technologies or policy interventions would generate economy-wide sustainability benefits. Likewise, SAM's institutional accounts highlight how changes in production structure affect household income distribution and social welfare, linking economic structure to social sustainability outcomes.

Policy implications require a thorough understanding of inter-commodity links since policymakers prioritize commodities with robust interlinkages over those with weak ones. The backward and forward links provide indexes that set the standards for identifying key economic commodities. This study presents a multi-sectoral analysis of Tunisia's economy, using a commodity-by-commodity SAM, highlighting the importance of inter-commodity linkages for economic growth and development. The textile, clothing and leather; miscellaneous; chemicals; and oil/natural gas sectors have strong backward and forward linkages, contributing significantly to the country's economy, generating employment opportunities and serving as major sources of exports and foreign exchange. Subsidizing these products or giving some tax exemptions or reductions could be better policies to boost their production, increasing employment and the country's GDP.

The Leontief model overlooks agriculture's significance due to weak backward and forward linkages, but the multi-sector and multi-factor model shows stronger forward linkages due to robust agro-processing. Policymakers should prioritize the development of agro-processing to add value to agricultural products, generating employment opportunities in rural areas and reducing the need for rural-urban migration. Agriculture has emerged as a substantial contributor to Tunisia's exports and a crucial source of employment, contributing to poverty reduction and diminishing inequality in rural areas. Therefore, the government may adopt policies that include adopting research and innovation technologies, easy access to credit, sufficient fertilizer procurement and distribution, adequate storage facilities and easy market access. Such policies can contribute to the country's economic growth and development, poverty reduction and diminishing inequality in rural areas. This study constructed a SAM for Tunisia and identified the key economic commodities. Future research will focus on impact analysis stimulating different policy options and assessing their economic impacts.

The identification of key sectors through the extended linkage analysis provides a foundation for more targeted and operational policy interventions. Commodities with strong backward linkages – such as agriculture, food processing and construction-related materials – should be prioritized for targeted subsidies, technology-upgrading programs and improved access to credit, as interventions in these commodities stimulate broad supply-chain effects. Commodities with strong forward linkages, including energy, transport and trade services, can benefit from industrial clustering, infrastructure modernization and renewable-energy integration, given their capacity to transmit productivity gains across the economy. For agro-processing and water–energy–food-related commodities, policies promoting value-addition, cold-chain logistics and export-oriented processing can enhance resilience and sustainability. By aligning policy tools with the structural roles revealed by this study, Tunisia can more effectively leverage its production system to support inclusive growth, resource efficiency and long-term sustainable development.

The results rely on a single SAM and a specific model specification. Alternative sectoral aggregations, institutional classifications or modest variations in technical coefficients could influence the magnitude of linkage indicators. While conducting a full sensitivity analysis is beyond the scope of this study, acknowledging these potential sources of variation is important for interpreting the findings. Future research could extend the analysis by testing the robustness of linkage results under alternative SAM structures or benchmark years.