Economic analysis of the Fisheries and Agriculture sectors for the economic development of Tanzania. An Empirical Study with the VAR Approach

Economic analysis of the Fisheries and Agriculture sectors for the economic development of Tanzania. 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An Empirical Study with the VAR Approach Jackson Bulili Machibya This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6797016/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Jan, 2026 Read the published version in International Journal of Agricultural Social Economics and Rural Development (Ijaserd) → Version 1 posted You are reading this latest preprint version Abstract Fisheries and agriculture sectors play a pivotal role in the economic development of Tanzania but are faced with several challenges, including but not limited to climate change, inadequate infrastructure and overfishing. This study investigates the economic interdependencies between the fisheries and agriculture sectors in Tanzania, utilising a Vector Autoregression (VAR) approach with time series data from 1990 to 2021. Given the significance of these sectors, the study explores how fluctuations in fisheries and agricultural productivity impact GDP growth. The study aims to identify causal relationships and dynamic interactions, providing insights into how policy interventions can influence sectoral output. The findings from the VAR analysis reveal significant relationships among fishing, agriculture and GDP growth, highlighting the interconnectedness of these sectors. Notably, agriculture demonstrates a strong Granger-causal effect on both GDP and fishing, suggesting that advancements in agricultural practices and productivity can stimulate economic growth and positively influence the fishing sector widely. The study underscores the policy significance of a holistic approach to agricultural and fisheries development in achieving sustainable economic growth in Tanzania. Furthermore, it highlights the need for collaborative strategies that enhance resource management and investment in both sectors for improving the socio-economic conditions of communities reliant on these industries. Agricultural Economics & Policy Aquaculture and Mariculture Fisheries Agriculture and economic development 1.0 Introduction Tanzania's economic development has shown notable progress, with GDP growth averaging around 6 to 7 percent annually since 2000, despite a slight dip during the COVID-19 pandemic (National Bureau of Statistics, 2021 ; Mwabukojo, 2019 ; Kyara et al, 2022 ). Besides, the GDP stood at approximately $ 64 billion in 2022, with agriculture contributing about 28 percent of the economy and employing over 60 percent of the workforce (de Castro, 2023 ). The mining sector, particularly Gold and Gas, has attracted significant foreign direct investment, which reached nearly $ 1.5 billion in 2021 (Mvile and Bishoge, 2024 ). Additionally, Tanzania's tourism sector has rebounded, contributing approximately $ 2.5 billion to the economy in 2021 (Muoki, 2021 ). However, challenges such as poverty affecting around 26 percent of the population and infrastructure deficits persist (Mawejje and Odhiambo, 2020 ), highlighting the need for continued reforms and sustainable growth strategies in the country. It is obvious that, economic development is a global escalating agenda enshrined in the Sustainable Development Goals (SDGs) 2030 (see Arora and Mishra, 2019 ). Tanzania, being one of the East African countries in the Global, is endowed with diverse natural resources that form the backbone of its economy. Such natural resources include, but are not limited to, water bodies, fertile land, and minerals. The agricultural and fisheries sectors being under the land and water resources, are particularly significant, contributing not only to national income but also to food security and employment for a large portion of the population (see Mtaturu, 2020 ; Mdegela et al, 2021 ; Wineman et al, 2020 ). So, to speak, it is approximated that over 70 per cent of Tanzanians are engaged in agriculture (Leavens et al, 2019 ), while fisheries play a crucial role in both domestic consumption and export earnings (Ulega et al, 2022 ). Moreover, the agriculture sector in Tanzania is characterized by smallholder farming, with crops such as maize, coffee, and tea dominating production. This sector has historically been a primary driver of economic growth, contributing approximately 30 per cent to the GDP according to recent estimates (Mpogole et al, 2020 ). Additionally, it serves as a critical source of livelihood for rural households and significantly influences food security (Kitole et al, 2023 ). However, the sector faces numerous challenges, including climate change, inadequate infrastructure, and limited access to markets and credit (Jha et al, 2020 ). These challenges have been the major forces pulling down agricultural production and growth in Tanzania for several decades (Wineman et al, 2020 ). On the other hand, the country is home to several prominent water bodies, including Lake Victoria, Lake Tanganyika, Lake Nyasa, and the Indian Ocean, which provide abundant fish resources. Thus, as the country strives for achieving sustainable development through Blue Economy, the fisheries sub-sector is essential for nutritional needs and contributes to local economies, especially in coastal and lakeside communities (see Temesgen et al, 2019 ). Despite its potential, the fisheries sector also grapples with issues like overfishing, environmental degradation, and inefficient management practices, which hinder its potential contribution to the economic growth and development of a nation. The sector contributed about 1.3 per cent to the GDP in 2009 and slowly rose to 1.8 per cent in 2021 while providing nutritional food and livelihoods to millions of people (see Zakayo and Mbilinyi, 2023 ; Mosha and Daudi, 2020 ). This contribution shows a slow growth rate (only growing at 2.5 per cent) due to mismanagement of the water resources, causing disturbance to the ecosystem and biodiversity (Zakayo and Mbilinyi, 2023 ). Besides, fish production volume is declining in the major water bodies such as Lake Victoria, causing a dramatic effect on the economies of millions of people along the lake's shores and the nation at large. Furthermore, most of the previous studies have presented the findings for the fishing and agriculture sectors in isolation, not in the interconnectedness and specific to Tanzania (see Epaphra and Mwakalasya, 2017 ; Mhagama et al, 2023 ; Moh’d, 2020 ). Hence, this study is significantly conducted in Tanzania given the fact that a thorough understanding of the interrelationship between fisheries and agriculture sectors can help maximize their economic potential on GDP, providing insights that are essential for effective policy formulation in the country. Also, policymakers need empirical evidence to design strategies that enhance productivity and sustainability in these areas, particularly given the increasing pressures from climate change and the overexploitation of fish resources (Lwesya, 2018 ). Moreover, agriculture and fisheries are critical for ensuring food security; thus, analyzing their dynamics can help address pressing issues related to hunger and malnutrition, especially in rural communities. Additionally, this study also has the potential to attract both domestic and international investment by highlighting the economic contributions of these sectors, fostering growth and innovation. By emphasizing the need for integrated development strategies that recognise the synergies between agriculture and fisheries, the study promotes a holistic approach to economic growth. Finally, employing the VAR approach provides a robust framework for analyzing the complex relationships among these variables, thereby contributing valuable insights to the academic literature on economic development in Tanzania. The purpose of the study is to analyse the economic contributions of the fisheries and agriculture sectors to Tanzania's economic development using a VAR approach to inform policy and promote sustainable growth. The study is guided by two hypotheses; H 0 : fisheries and agriculture sectors have no significant positive effect on GDP growth indicating that improvement in these sectors cannot contribute to economic development; H 1 : fisheries and agriculture sectors have a significant positive effect on GDP growth indicating that improvement in these sectors can contribute to economic development. The rest of this paper is organized as follows: Section 2 includes a literature review, Section 3 details the data and methods, Section 4 reports the results and discussion, and Section 5 provides the conclusion and policy implications. 2.0 Literature Review 2.1 Theoretical Framework The theoretical framework for this study is grounded in the interdependence theory (see Rusbult and Arriaga,1997), which posits that different economic sectors are interconnected and can influence each other's performance through various channels. The study adopted this theory for the following reasons: first, it wanted to effectively capture the complex relationships between the agriculture and fisheries sectors in Tanzania. Second, this theory recognizes that these sectors do not operate in isolation; rather, they influence one another through various economic channels, such as resource allocation, market dynamics, and environmental factors. Third, by applying this theory, the study can better analyse how changes in agricultural output impact fisheries production and vice versa, providing a holistic view of their contributions to economic growth. Fourth, the interdependence theory supports the use of the VAR approach, which allows for the exploration of dynamic relationships over time, making it possible to identify feedback loops and causal effects. This is particularly relevant for informing policy interventions that seek to enhance the productivity and sustainability of both sectors, ultimately contributing to improved food security and economic development in Tanzania. The key assumptions of this theory are: first, Mutual influence, whereby economic sectors, such as agriculture and fisheries, are interconnected and can significantly influence each other's performance through various channels. Second, Dynamic relationships whereby the interactions between sectors are not static; they evolve throughout time, with feedback effects that can amplify or dampen outcomes. Third, resource allocation whereby resources (such as labor, capital, and land) are limited and must be allocated efficiently across sectors to optimize overall economic performance. Thus, in the context of Tanzania, the agricultural and fisheries sectors are viewed as complementary components of the economy, where changes in one sector can have significant effects on the other (Scialabba,1998). The framework draws from economic growth theories that emphasise the roles of productivity, resource allocation, and external factors, such as market access and climate conditions, in shaping sectoral dynamics (see Mwaijande and Lugendo, 2015 ; Magoti and Mtui,2020). Therefore, by employing the Vector Autoregression (VAR) approach, the study incorporates a time series (1990–2021) analysis to capture the temporal relationships and feedback mechanisms between agricultural output, fisheries production, and GDP growth. This theoretical foundation allows for a comprehensive examination of how shocks in agriculture, such as changes in crop yields or market prices, can affect fisheries and, consequently, overall economic growth. Additionally, it highlights the importance of integrated policy frameworks that recognise these interconnections to achieve sustainable development and enhance food security in Tanzania. 2.2 Empirical Review Various scholars have attempted to study and present empirical findings using various methodologies that support the interconnectivity and positive relationship between agriculture, fisheries, and economic growth in different countries, as follows; Mtaturu ( 2020 ) conducted a study using time series data for the period from 1971 to 2013. The study employed the Ordinary Least Squares (OLS) and Newey-West estimators to analyse the contributory effect of crop, livestock, and fishery sub-sectors on economic growth in Tanzania. The primary finding reveals that agricultural sub-sectors have a positive effect on Tanzanian economic growth. The comparative analysis provides suggestive evidence that livestock production exhibits the highest contributory effect to economic growth, followed by crop and fishery sub-sectors, respectively. The policy outlook implies that more resources should be reallocated in the livestock sub-sector to boost the overall performance of agricultural production as one of the keys out-of-poverty strategies for economic growth in Tanzania. Dey ( 2020 ) investigated the relationship between Rice Production, Fisheries Production, and Gross Domestic Product (GDP) in Bangladesh using time series data from 1971 to 2017. Different kinds of econometric techniques were applied to conduct the study, namely, augmenting the Dickey-Fuller (ADF) test, Phillips-Perron (PP) test, Johansen co-integration test, fully modified least squares (FMOLS) method and dynamic least squares (DOLS) method. To justify the model, some residual diagnostic tests were employed. The results of the study indicated that rice production has a positive and significant impact on gross domestic product (GDP) in Bangladesh. On the other hand, although fisheries production has a positive effect on the gross domestic product (GDP), this impact is not significant at a 5 or 10 per cent level of significance. Since the contribution of fisheries production is not significant to gross domestic product (GDP), the government should create more facilities and provide more subsidies, new funding schemes, and training programs to the fish farmers. Furthermore, the government should increase government expenditure in the fisheries sector and implement more modern technologies to enhance the fish production of Bangladesh. Last but not least, the government should be concerned about some climate-changing and man-made factors, which are also the causes for the reduction of fish diversity and production in Bangladesh. Ulega et al ( 2022 ) conducted the study in Tanzania, utilising primary data collected from a sample size of 1,026 respondents from eight (8) fishing villages in four coastal regions of Tanzania's Mainland. Their study used a mixed-methods research approach and triangulation of methods. The results indicated that fishing as a primary source of income in coastal communities may contribute to household food security through the consumption of households' catch. The results also suggested that local fishers have limited access to resources in the exclusive economic zones (EEZs) and deep waters due to the continued use of traditional fishing gear and vessels. It is recommended that local fishers be capacitated to exploit EEZ and deep-sea resources through support to access credit facilities and the provision of modern fishing vessels and appropriate gear. This could pave the way for the development of a fisheries sector based on the EEZ and deep-sea resources. Oad et al ( 2022 ) examined the link between marine resources exports and their impact on economic development in Pakistan by using Vector Error-Correction Models (VECM). The study utilised time series data from 1960 to 2017. The findings of this research were obtained as there is no long-run association discovered between fisheries exports and economic development. However, they discovered that there is a short-run relationship sustained between them. In addition, the results from the Ordinary Least Squares (OLS) regression also validate that there is a robust relationship between GDP and exports of fisheries products. The findings indicated that the sub-sector (Fisheries) of the agriculture sector is playing an extensive role in the economic growth of Pakistan. Mhagama et al ( 2023 ) investigated the contributions of agricultural sub-sectors to economic growth in Tanzania using quarterly time series secondary data from 2010 to 2018, collected from the National Bureau of Statistics (NBS) office. The Auto auto-distributive lag (ARDL) technique was applied to estimate the long-run dynamics and short-run dynamics of the study variables. The findings were revealed to be significant at the 5% level of significance, hence giving strong evidence on the contribution of agricultural sub-sectors to economic growth in Tanzania Mainland." Furthermore, the empirical findings of the study revealed that agricultural sub-sectors (crops, livestock, and fisheries), except forestry, had positive contributions to the economic growth of Tanzania Mainland in both the long run and short run. The study recommends that massive attention and investments be directed to the agricultural sub-sectors, especially forestry, to boost economic growth in Tanzania. Mkuna and Baiyegunhi ( 2019 ) analysed the Nile perch fishers' technical efficiency, using a stochastic production frontier (SPF) model based on the sample of 268 Nile perch fishers in the Tanzanian portion of Lake Victoria. The translog stochastic frontier model results indicated the technical efficiency of Nile perch fishers ranges between 61% and 80%, with an overall average technical efficiency of 75%. This finding implies that, based on existing fishery resources, the current quantity of Nile perch catch can be improved efficiently by 25%, a reality highlighting the mismanagement of the lake's fishery resources. The quantity of bait and petrol and the number of hooks used per trip are the most important fishing inputs, indicating a positive Nile perch fishing output–input elasticity. To address the Nile perch fisher's inefficiency, it is important to provide subsidised inputs such as outboard engines and mesh gill nets. The provision of access to affordable credits will enable fishers to purchase less destructive fishing inputs and improve the current structure of fishery organisations. Elalaoui et al ( 2021 ) studied the Agriculture and GDP causality nexus in Morocco using time series over the period 1980 to 2017 employing the Granger causality based on the vector autoregressive model (VAR) in a dynamic multivariate framework, using five macroeconomic variables: GDP per capita, agricultural GDP, investment rate, money supply, and trade openness. The empirical results from the analysis detected the presence of bidirectional Granger causality between agriculture and GDP, implying a feedback relationship, and some unidirectional causal relationships involving the other macroeconomic variables used in the VAR model. The findings have important policy implications for the government to establish effective agricultural strategies, in particular with the inauguration of the new agricultural strategy Green Generation in 2020. Xu ( 2024 ) conducted a study in China on "The Correlation between Marine Fishery Economy, Fishermen's Fishery Investment and Fishery Science and Technology Progress based on VAR Model: A Case Study of Zhoushan" using time series data from 2000–2021. The results indicated that: (1) there is a significant positive mutual promotion effect between fishermen's fishery investment and marine fishery economic growth; (2) The progress of marine fishery technology and the growth of marine fishery economy also have a significant positive mutually promoting effect; (3) The investment expenditure of marine fishermen on fisheries has a certain positive effect on the progress of marine fishery technology, but the progress of marine fishery technology has no impact on the investment expenditure of fishermen on fisheries. Therefore, to achieve high-quality development of the marine economy, attention should be paid to the guidance of fishermen's fishery investment, further improving the market protection mechanism of fishermen's investment expenditure, and strengthening technological innovation in marine fisheries to promote high-speed growth of the marine fishery economy. Overall, from the reviewed literature, it seems there is a gap in the literature discourse on the interconnectivity of the economic roles of fisheries and agriculture sectors in economic growth, specifically for Tanzania. Therefore, this study wants to fill the gap in existing literature regarding the interconnected economic roles of the fisheries and agriculture sectors in Tanzania, an area that has received limited empirical attention despite its significance for national development. Moreover, while previous research often examined these sectors in isolation, this study adopts a holistic approach by utilising a Vector Autoregression (VAR) model to analyse the dynamic relationships and feedback mechanisms between agricultural output, fisheries production, and GDP growth for Tanzania specifically. Hence, the novelty of this research lies in its focus on the interdependence of these sectors, providing a comprehensive understanding of how agricultural fluctuations can impact fisheries and vice versa. Additionally, by linking these sectoral outputs directly to economic growth, the study offers valuable insights for policymakers seeking to enhance food security and promote sustainable development. This integrated approach not only contributes to academic discourse but also has practical implications for resource management and economic policy in Tanzania. 3.0 Methodology 3.1 Research Design This study used a Longitudinal research design with time series data because it enables the analysis of dynamic relationships and causal interactions among multiple variables over time (Bala, 2020 ). This design helps in identifying trends and patterns, allowing researchers to establish causality rather than mere correlation ( ibid ). Also, by using time series data at multiple time points, longitudinal design control for unobserved heterogeneity and account for changes within variables, providing a clearer understanding of how they influence each other over time (Dannels, 2018 ). Thus, this study used the VAR model approach to utilize the time series data to capture interdependencies and lagged effects, enhancing the robustness and reliability of the findings. Furthermore, the Vector Auto-regression model constructed based on endogenous variables to estimate and provide robust results from the STATA analysis. Hence, the study quantitatively analyzed the endogenous variables (dependent variable: GDP and independent variables: Fisheries and Agriculture). In that case, using a VAR model in the economic analysis of fisheries and agriculture for Tanzania's economic development is particularly advantageous because it enables the examination of the intricate interdependencies between these sectors and their collective impact on economic growth (Dinh, 2020 ). Thus, by capturing the dynamic relationships over time, the VAR approach allows researchers to evaluate how fluctuations in agricultural output, fishery production, and other economic indicators influence each other and contribute to overall development (Akkaya, 2021 ). This approach can help identify lagged effects, inform policy decisions, and facilitate a deeper understanding of how these vital sectors interact within Tanzania's economy, ultimately guiding strategies for sustainable economic growth. Model of estimation The estimation model is given as follows; Y t ​​ =∝+β1 t − 1 ​+β2 ​t−2 ​ + ϵ t ​​ Where; Y t = is a vector of endogenous variables, GDP growth at time t in the VAR equation ∝ =is the vector constant β1 and β2 = are the vector coefficients estimated for endogenous variables (Fishing and Agriculture), respectively t = is time lags at 1, 2…….n th ϵ =is a vector of error terms, assumed to be white noise 3.2 Data Source and Selection of indicators This study used quantitative data obtained from the World Bank dataset. The data type is Time series from 1990–2021. The selected indicators from the World Bank development indicators dataset were categorised as dependent and independent variables in this study for easy analysis. The dependent variable in this study is GDP growth (Gross Domestic Product growth in annual percentage) as an indicator of economic development. While the independent variables (explanatory variables) were: (1) Fishing (Total fisheries production metric tons) and (2) Agriculture (Agriculture and forestry value added in percentage of GDP). 4.0 Results and Discussion 4.1 Stationarity Test The VAR model requires undertaking a unitary root test by using the Augmented Dickey-Fuller (ADF) test to check if the time series is stationary or non-stationary. Under this study, all variables were tested after being naturalised log and differenced, and all variables were found stationary at the 5% critical value (See Table 1). The results show that all variables have t-values greater than critical values at 5%; also, p-values less than the critical value 0.05 at the 5% confidence level, hence, they are all found stationary, allowing for subsequent analysis. Table 1: Stationarity test results Source: World Bank (2020-21) 4.2 Selection order of time lag The estimation of the VAR model requires the selection of the optimal lag of the model, which the study determines by minimising the information criterion (Ma et al , 2022 in Xu, 2024). The value of each information criterion of the VAR model set by the study is shown in Table 2. It shows the value of each information criterion of the VAR model constructed. The study uses the Akaikei Information Criterion (AIC), Schwarz Criterion (HQIC) and Hannankei (BIC) to select the optimal lag order, respectively. The result shows that the selected lag in this study is the optimal lag of 4 periods, which allows for further VAR model analysis. Table 2: Information criterion value and optimal lag of VAR model Source: World Bank (2020-21) 4.3 Regression Results of the VAR Model The results from the regression analysis (Table 4) indicate that, in equation two, the Fishing sub-sector is statistically significant with a positive effect on the GDP growth in the time lag 2 period meaning that, the increase in one unit production of fishing in the previous periods can boost the fishing supply in the economy by about 0.1 metric tons in current period thus stimulating the economic growth through increasing fish trade among the population. Also, this indicates a positive interaction effect, where an increase in the second lag of Fishing positively influences the relationship with economic growth. Besides, in the same equation, at period lag 1 (first lag): The coefficient is -0.8508 and the p-value is 0.000 which is statistically significant; this shows a strong negative effect of the first lag of Fishing on its current value, indicating that, a higher Fishing in the previous period significantly reduces the current value of GDP. The implication of these results tells the fact that, overfishing in the previous period will increase fish supply and trade growth but will led to declines in fish production and supply in the economy for future period thus leading to economic retardation as a result of a fall in trade and lack of enough nutrition for the workforce. Furthermore, the implications of these results apply the same to lags 2 and 4 periods as well in the same equation. Again, in the same equation, the agriculture at the fourth lag is statistically significant, having a positive coefficient. This signifies a positive effect of the fourth lag of Agriculture on its current value to the GDP growth, suggesting that an increase in Agriculture production from four periods ago is associated with a significant increase in GDP growth as well as economic development. Furthermore, the study found that, in equation three of the model results, agriculture has negative effects at the first lag and positive at the third lag but remains statistically significant at 0.05 in both period lags. The negative effects suggest that increases in Agriculture from one period ago are negatively associated with the current fall in GDP growth within the economy; this may be due to climatic changes that affect farm production over times. While a positive effect shows that, an increase in agricultural production from one period ago is positively associated with the current GDP growth and better development of the nation. Agriculture has the potential for GDP growth and livelihoods of the population in the country, as the majority depend on it for their economic welfare. If the country increases its agricultural production, then food security is ensured, employment is ensured, GDP growth is ensured, industrialization is ensured, and the general welfare of the population will be better. Overall, clear evidence from the findings indicates that, both the fisheries and agriculture sectors have positive effects on GDP growth. Therefore, we reject the null hypothesis (H 0 ) in favor of the alternative hypothesis (H 1 ). Thus, suggests that improvements in these sectors can indeed contribute to economic development in the country. Above all, improvements in the fisheries and agriculture sectors in Tanzania can significantly contribute to economic development by increasing production and job creation, enhancing food security, and fostering sustainable practices (Pawlak & Kołodziejczak, 2020). For instance, adopting modern farming and sustainable fishing techniques can boost yields and create more job opportunities in rural/coastal/offshore areas, leading to reduced unemployment and improved livelihoods. Additionally, a more reliable local food supply enhances nutrition and health outcomes for the workforce; also, investments in infrastructure, such as roads, marketplaces and storage facilities, facilitate market information access and reduce post-harvest losses. All these advancements not only stimulate economic growth but also promote environmental sustainability and social well-being, leading to a more resilient economy in the country. 4.4 The Granger Causality Wald Test Results The Granger causality Wald test is performed in this study to assess the directional influence between time series variables, helping to determine whether past values of one variable can predict future values of another (Vasile et al , 2020). This is crucial for understanding causal relationships, as it allows researchers to identify whether changes in one variable, such as economic indicators or sector outputs, precede and potentially drive changes in another ( ibid ). Thus, by clarifying these relationships, the test aids in building more accurate models for forecasting, informing policy decisions, and guiding strategic interventions in economic or sectoral development, including fisheries and agriculture sectors. Ultimately, this test enhances the understanding of dynamic interactions within complex systems. Therefore, the results in Table 3 suggest that, while agriculture has a significant effect on both GDP and fishing, the reverse relationships are weaker or borderline significant, particularly for fishing's influence on GDP. This highlights the pivotal role of the agricultural sector in driving economic growth and its interconnections with the fishing sector. Table 3: Granger Causality Wald Test Results. Source: World Bank (2020-21) NB: All variables are differenced Table 4: Regression results of VAR model Source: World Bank (2020-21) NB: All variables are differenced 4.4 Robust Check-Stability Condition of the Model After performing the diagnostic test for stability of the model, the results indicate that all the eigenvalues lie inside the unit circle, satisfying the condition for stability of the model fit. Thus, the model VAR was found to be stable and fit (see Table 5). In this study, the results remain robust throughout the analysis. A stable VAR model indicates that any shocks to the system will gradually dissipate over time, leading to consistent and accurate predictions, while Instability can result in forecasts that diverge unpredictably, undermining the model's usefulness for policy analysis and decision-making. Table 5: Eigenvalue stability condition results Source: World Bank (2020-21) 5.0 Conclusion and Policy implications This study analysed the economic contributions of the fisheries and agriculture sectors to Tanzania's economic development using a VAR approach. The findings from the VAR analysis and Granger causality Wald test reveal significant relationships among fishing, agriculture and GDP growth, highlighting the interconnectedness of these sectors. Notably, agriculture demonstrates a strong Granger-causal effect on both GDP and fishing, suggesting that advancements in agricultural practices and productivity can stimulate economic growth and positively influence the fishing sector at large. Conversely, while there is some clear evidence of fishing impacting GDP, it is less pronounced, indicating that the fishing sector may play a supportive rather than a leading role in economic development. These insights emphasise the importance of understanding sectoral dynamics for effective economic planning. Furthermore, given the significant influence of agriculture and fishing on GDP growth, policymakers and decision-makers should prioritise investments in agricultural development as a means of enhancing overall economic growth. This can include increasing funding for agricultural research, promoting sustainable practices in both sectors and providing training for farmers to improve productivity. Additionally, integrating policies that support the fishing sector, such as sustainable fishing practices and technology adoption, can further enhance its contributions to the economy. Besides, by fostering a synergistic approach between agriculture and fishing, policymakers can create a more resilient economic framework that leverages the strengths of both sectors, ultimately driving sustainable growth and improving food security for the entire population. The limitation of this study is that it did not consider external factors in the analysis, such as market conditions, climate variability, and policy changes that can impact both sectors, creating interdependencies that must be considered for future study analysis. Declarations Acknowledgement The study has no acknowledgement requirements. Declaration of Competing Interest The author declares that, there is no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding Declaration The study has no funding requirements Clinical trial declaration Not applicable in this study Data access World Bank link : https://databank.worldbank.org/reports.aspx?source=2&country=ARE References Akkaya M (2021) Vector autoregressive model and analysis. 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JAPS: J Anim Plant Sci, 32 (3) Pawlak K, Kołodziejczak M (2020) The role of agriculture in ensuring food security in developing countries: Considerations in the context of the problem of sustainable food production. Sustainability 12(13):5488 Rusbult CE, Arriaga XB (1997) Interdependence theory Scialabba N (ed) (1998) Integrated coastal area management and agriculture, forestry and fisheries. Food & Agriculture Org… Temesgen M, Getahun A, Lemma B (2019) Livelihood functions of capture fisheries in Sub-Saharan Africa: Food security, nutritional, and economic implications. Reviews Fisheries Sci Aquaculture 27(2):215–225 Ulega A, Mgaya Y, Lokina R, Mushy R (2022) The contribution of marine fisheries to socio-economic development in Tanzania mainland: Reflections on the blue economy concept from selected coastal villages. J Geographical Association Tanzan 42(2):1–22 Vasile V, Ştefan D, Bunduchi COMESCA, E., Ştefan AB (2020) GRANGER CAUSALITY. Romanian J Economic Forecast 23(4):131 Wineman A, Jayne TS, Modamba I, E., Kray H (2020) The changing face of agriculture in Tanzania: Indicators of transformation. Dev Policy Rev 38(6):685–709 Xu B (2024) The Correlation between Marine Fishery Economy, Fishermen's Fishery Investment and Fishery Science and Technology Progress based on VAR Model: A Case Study of Zhoushan Fishery. J Global Econ Bus Finance 6(7):40–48 Zakayo EZ, Mbilinyi R (2023) Assessment of the Potentials of the Blue Economy Resources for Poverty Reduction in Tanzania. J Maritime Sci Technol (JMST) 1(1):1–5 Additional Declarations The authors declare no competing interests. Supplementary Files APPENDIX.docx Cite Share Download PDF Status: Published Journal Publication published 10 Jan, 2026 Read the published version in International Journal of Agricultural Social Economics and Rural Development (Ijaserd) → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6797016","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":464841967,"identity":"b36a9eb0-db34-45e1-bf03-0ed1fb076467","order_by":0,"name":"Jackson Bulili Machibya","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYBACAwbmhgMPgCQDAxuILyEHIg88wKuFseFAAkKLhTFYSwIBLQwQBWAtFYkNIAqfFnOJxMYDCQUM7Pztx5I/fNwhkT4/7PBDoC12croN2LVYzkiEOEziTNoxyZlnJHI33k4zAGpJNjY7gMNhN6BaGG6wtzHztgG1zE4AaTmQuI2QFvkb7M2fgVrSDWenfyBOi8ENtgPSQC0J8tI5+G2x7HkI0iLBbHgmLU1yZpuE4QbpnAKgCG6/mLMnH/7w4Y9NstzxY8YfPrbVycvPTt/84UOFnRwuLVAgkYxwKlilAV7lYGAHZ8k3EFY9CkbBKBgFIwsAAJjoYsxyHam8AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-4656-148X","institution":"INSTITUTE OF RURAL DEVELOPMENT PLANNING","correspondingAuthor":true,"prefix":"","firstName":"Jackson","middleName":"Bulili","lastName":"Machibya","suffix":""}],"badges":[],"createdAt":"2025-06-01 17:30:12","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-6797016/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6797016/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.37149/ijaserd.v5i2.2498","type":"published","date":"2026-01-11T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":101704629,"identity":"948e8054-6081-43dc-bf11-6b9a7c319c4d","added_by":"auto","created_at":"2026-02-02 19:09:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":931364,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6797016/v1/63d36616-a90e-4e88-9b93-0a3af326ac6d.pdf"},{"id":83814336,"identity":"005aa8ed-f8d1-4522-b032-35c51fadbd6b","added_by":"auto","created_at":"2025-06-03 07:26:54","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":256785,"visible":true,"origin":"","legend":"","description":"","filename":"APPENDIX.docx","url":"https://assets-eu.researchsquare.com/files/rs-6797016/v1/e84e652d6a18902ba195edcb.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eEconomic analysis of the Fisheries and Agriculture sectors for the economic development of Tanzania. An Empirical Study with the VAR Approach\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eTanzania's economic development has shown notable progress, with GDP growth averaging around 6 to 7 percent annually since 2000, despite a slight dip during the COVID-19 pandemic (National Bureau of Statistics, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mwabukojo, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kyara et al, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Besides, the GDP stood at approximately \u003cspan\u003e$\u003c/span\u003e64\u0026nbsp;billion in 2022, with agriculture contributing about 28 percent of the economy and employing over 60 percent of the workforce (de Castro, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The mining sector, particularly Gold and Gas, has attracted significant foreign direct investment, which reached nearly \u003cspan\u003e$\u003c/span\u003e1.5\u0026nbsp;billion in 2021 (Mvile and Bishoge, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, Tanzania's tourism sector has rebounded, contributing approximately \u003cspan\u003e$\u003c/span\u003e2.5\u0026nbsp;billion to the economy in 2021 (Muoki, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, challenges such as poverty affecting around 26 percent of the population and infrastructure deficits persist (Mawejje and Odhiambo, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), highlighting the need for continued reforms and sustainable growth strategies in the country.\u003c/p\u003e \u003cp\u003eIt is obvious that, economic development is a global escalating agenda enshrined in the Sustainable Development Goals (SDGs) 2030 (see Arora and Mishra, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Tanzania, being one of the East African countries in the Global, is endowed with diverse natural resources that form the backbone of its economy. Such natural resources include, but are not limited to, water bodies, fertile land, and minerals. The agricultural and fisheries sectors being under the land and water resources, are particularly significant, contributing not only to national income but also to food security and employment for a large portion of the population (see Mtaturu, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mdegela et al, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wineman et al, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). So, to speak, it is approximated that over 70 per cent of Tanzanians are engaged in agriculture (Leavens et al, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), while fisheries play a crucial role in both domestic consumption and export earnings (Ulega et al, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Moreover, the agriculture sector in Tanzania is characterized by smallholder farming, with crops such as maize, coffee, and tea dominating production. This sector has historically been a primary driver of economic growth, contributing approximately 30 per cent to the GDP according to recent estimates (Mpogole et al, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, it serves as a critical source of livelihood for rural households and significantly influences food security (Kitole et al, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the sector faces numerous challenges, including climate change, inadequate infrastructure, and limited access to markets and credit (Jha et al, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These challenges have been the major forces pulling down agricultural production and growth in Tanzania for several decades (Wineman et al, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn the other hand, the country is home to several prominent water bodies, including Lake Victoria, Lake Tanganyika, Lake Nyasa, and the Indian Ocean, which provide abundant fish resources. Thus, as the country strives for achieving sustainable development through Blue Economy, the fisheries sub-sector is essential for nutritional needs and contributes to local economies, especially in coastal and lakeside communities (see Temesgen et al, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Despite its potential, the fisheries sector also grapples with issues like overfishing, environmental degradation, and inefficient management practices, which hinder its potential contribution to the economic growth and development of a nation. The sector contributed about 1.3 per cent to the GDP in 2009 and slowly rose to 1.8 per cent in 2021 while providing nutritional food and livelihoods to millions of people (see Zakayo and Mbilinyi, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mosha and Daudi, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This contribution shows a slow growth rate (only growing at 2.5 per cent) due to mismanagement of the water resources, causing disturbance to the ecosystem and biodiversity (Zakayo and Mbilinyi, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Besides, fish production volume is declining in the major water bodies such as Lake Victoria, causing a dramatic effect on the economies of millions of people along the lake's shores and the nation at large.\u003c/p\u003e \u003cp\u003eFurthermore, most of the previous studies have presented the findings for the fishing and agriculture sectors in isolation, not in the interconnectedness and specific to Tanzania (see Epaphra and Mwakalasya, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mhagama et al, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Moh\u0026rsquo;d, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Hence, this study is significantly conducted in Tanzania given the fact that a thorough understanding of the interrelationship between fisheries and agriculture sectors can help maximize their economic potential on GDP, providing insights that are essential for effective policy formulation in the country. Also, policymakers need empirical evidence to design strategies that enhance productivity and sustainability in these areas, particularly given the increasing pressures from climate change and the overexploitation of fish resources (Lwesya, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Moreover, agriculture and fisheries are critical for ensuring food security; thus, analyzing their dynamics can help address pressing issues related to hunger and malnutrition, especially in rural communities. Additionally, this study also has the potential to attract both domestic and international investment by highlighting the economic contributions of these sectors, fostering growth and innovation. By emphasizing the need for integrated development strategies that recognise the synergies between agriculture and fisheries, the study promotes a holistic approach to economic growth. Finally, employing the VAR approach provides a robust framework for analyzing the complex relationships among these variables, thereby contributing valuable insights to the academic literature on economic development in Tanzania.\u003c/p\u003e \u003cp\u003eThe purpose of the study is to analyse the economic contributions of the fisheries and agriculture sectors to Tanzania's economic development using a VAR approach to inform policy and promote sustainable growth. The study is guided by two hypotheses; H\u003csub\u003e0\u003c/sub\u003e: fisheries and agriculture sectors have no significant positive effect on GDP growth indicating that improvement in these sectors cannot contribute to economic development; H\u003csub\u003e1\u003c/sub\u003e: fisheries and agriculture sectors have a significant positive effect on GDP growth indicating that improvement in these sectors can contribute to economic development.\u003c/p\u003e \u003cp\u003eThe rest of this paper is organized as follows: Section 2 includes a literature review, Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e3\u003c/span\u003e details the data and methods, Section 4 reports the results and discussion, and Section 5 provides the conclusion and policy implications.\u003c/p\u003e"},{"header":"2.0 Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Theoretical Framework\u003c/h2\u003e \u003cp\u003eThe theoretical framework for this study is grounded in the interdependence theory (see Rusbult and Arriaga,1997), which posits that different economic sectors are interconnected and can influence each other's performance through various channels. The study adopted this theory for the following reasons: first, it wanted to effectively capture the complex relationships between the agriculture and fisheries sectors in Tanzania. Second, this theory recognizes that these sectors do not operate in isolation; rather, they influence one another through various economic channels, such as resource allocation, market dynamics, and environmental factors. Third, by applying this theory, the study can better analyse how changes in agricultural output impact fisheries production and vice versa, providing a holistic view of their contributions to economic growth. Fourth, the interdependence theory supports the use of the VAR approach, which allows for the exploration of dynamic relationships over time, making it possible to identify feedback loops and causal effects. This is particularly relevant for informing policy interventions that seek to enhance the productivity and sustainability of both sectors, ultimately contributing to improved food security and economic development in Tanzania. The key assumptions of this theory are: first, Mutual influence, whereby economic sectors, such as agriculture and fisheries, are interconnected and can significantly influence each other's performance through various channels. Second, Dynamic relationships whereby the interactions between sectors are not static; they evolve throughout time, with feedback effects that can amplify or dampen outcomes. Third, resource allocation whereby resources (such as labor, capital, and land) are limited and must be allocated efficiently across sectors to optimize overall economic performance. Thus, in the context of Tanzania, the agricultural and fisheries sectors are viewed as complementary components of the economy, where changes in one sector can have significant effects on the other (Scialabba,1998). The framework draws from economic growth theories that emphasise the roles of productivity, resource allocation, and external factors, such as market access and climate conditions, in shaping sectoral dynamics (see Mwaijande and Lugendo, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Magoti and Mtui,2020). Therefore, by employing the Vector Autoregression (VAR) approach, the study incorporates a time series (1990\u0026ndash;2021) analysis to capture the temporal relationships and feedback mechanisms between agricultural output, fisheries production, and GDP growth. This theoretical foundation allows for a comprehensive examination of how shocks in agriculture, such as changes in crop yields or market prices, can affect fisheries and, consequently, overall economic growth. Additionally, it highlights the importance of integrated policy frameworks that recognise these interconnections to achieve sustainable development and enhance food security in Tanzania.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Empirical Review\u003c/h2\u003e \u003cp\u003eVarious scholars have attempted to study and present empirical findings using various methodologies that support the interconnectivity and positive relationship between agriculture, fisheries, and economic growth in different countries, as follows;\u003c/p\u003e \u003cp\u003eMtaturu (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) conducted a study using time series data for the period from 1971 to 2013. The study employed the Ordinary Least Squares (OLS) and Newey-West estimators to analyse the contributory effect of crop, livestock, and fishery sub-sectors on economic growth in Tanzania. The primary finding reveals that agricultural sub-sectors have a positive effect on Tanzanian economic growth. The comparative analysis provides suggestive evidence that livestock production exhibits the highest contributory effect to economic growth, followed by crop and fishery sub-sectors, respectively. The policy outlook implies that more resources should be reallocated in the livestock sub-sector to boost the overall performance of agricultural production as one of the keys out-of-poverty strategies for economic growth in Tanzania.\u003c/p\u003e \u003cp\u003eDey (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) investigated the relationship between Rice Production, Fisheries Production, and Gross Domestic Product (GDP) in Bangladesh using time series data from 1971 to 2017. Different kinds of econometric techniques were applied to conduct the study, namely, augmenting the Dickey-Fuller (ADF) test, Phillips-Perron (PP) test, Johansen co-integration test, fully modified least squares (FMOLS) method and dynamic least squares (DOLS) method. To justify the model, some residual diagnostic tests were employed. The results of the study indicated that rice production has a positive and significant impact on gross domestic product (GDP) in Bangladesh. On the other hand, although fisheries production has a positive effect on the gross domestic product (GDP), this impact is not significant at a 5 or 10 per cent level of significance. Since the contribution of fisheries production is not significant to gross domestic product (GDP), the government should create more facilities and provide more subsidies, new funding schemes, and training programs to the fish farmers. Furthermore, the government should increase government expenditure in the fisheries sector and implement more modern technologies to enhance the fish production of Bangladesh. Last but not least, the government should be concerned about some climate-changing and man-made factors, which are also the causes for the reduction of fish diversity and production in Bangladesh.\u003c/p\u003e \u003cp\u003eUlega et al (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) conducted the study in Tanzania, utilising primary data collected from a sample size of 1,026 respondents from eight (8) fishing villages in four coastal regions of Tanzania's Mainland. Their study used a mixed-methods research approach and triangulation of methods. The results indicated that fishing as a primary source of income in coastal communities may contribute to household food security through the consumption of households' catch. The results also suggested that local fishers have limited access to resources in the exclusive economic zones (EEZs) and deep waters due to the continued use of traditional fishing gear and vessels. It is recommended that local fishers be capacitated to exploit EEZ and deep-sea resources through support to access credit facilities and the provision of modern fishing vessels and appropriate gear. This could pave the way for the development of a fisheries sector based on the EEZ and deep-sea resources.\u003c/p\u003e \u003cp\u003eOad et al (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) examined the link between marine resources exports and their impact on economic development in Pakistan by using Vector Error-Correction Models (VECM). The study utilised time series data from 1960 to 2017. The findings of this research were obtained as there is no long-run association discovered between fisheries exports and economic development. However, they discovered that there is a short-run relationship sustained between them. In addition, the results from the Ordinary Least Squares (OLS) regression also validate that there is a robust relationship between GDP and exports of fisheries products. The findings indicated that the sub-sector (Fisheries) of the agriculture sector is playing an extensive role in the economic growth of Pakistan.\u003c/p\u003e \u003cp\u003eMhagama et al (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) investigated the contributions of agricultural sub-sectors to economic growth in Tanzania using quarterly time series secondary data from 2010 to 2018, collected from the National Bureau of Statistics (NBS) office. The Auto auto-distributive lag (ARDL) technique was applied to estimate the long-run dynamics and short-run dynamics of the study variables. The findings were revealed to be significant at the 5% level of significance, hence giving strong evidence on the contribution of agricultural sub-sectors to economic growth in Tanzania Mainland.\" Furthermore, the empirical findings of the study revealed that agricultural sub-sectors (crops, livestock, and fisheries), except forestry, had positive contributions to the economic growth of Tanzania Mainland in both the long run and short run. The study recommends that massive attention and investments be directed to the agricultural sub-sectors, especially forestry, to boost economic growth in Tanzania.\u003c/p\u003e \u003cp\u003eMkuna and Baiyegunhi (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) analysed the Nile perch fishers' technical efficiency, using a stochastic production frontier (SPF) model based on the sample of 268 Nile perch fishers in the Tanzanian portion of Lake Victoria. The translog stochastic frontier model results indicated the technical efficiency of Nile perch fishers ranges between 61% and 80%, with an overall average technical efficiency of 75%. This finding implies that, based on existing fishery resources, the current quantity of Nile perch catch can be improved efficiently by 25%, a reality highlighting the mismanagement of the lake's fishery resources. The quantity of bait and petrol and the number of hooks used per trip are the most important fishing inputs, indicating a positive Nile perch fishing output\u0026ndash;input elasticity. To address the Nile perch fisher's inefficiency, it is important to provide subsidised inputs such as outboard engines and mesh gill nets. The provision of access to affordable credits will enable fishers to purchase less destructive fishing inputs and improve the current structure of fishery organisations.\u003c/p\u003e \u003cp\u003eElalaoui et al (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) studied the Agriculture and GDP causality nexus in Morocco using time series over the period 1980 to 2017 employing the Granger causality based on the vector autoregressive model (VAR) in a dynamic multivariate framework, using five macroeconomic variables: GDP per capita, agricultural GDP, investment rate, money supply, and trade openness. The empirical results from the analysis detected the presence of bidirectional Granger causality between agriculture and GDP, implying a feedback relationship, and some unidirectional causal relationships involving the other macroeconomic variables used in the VAR model. The findings have important policy implications for the government to establish effective agricultural strategies, in particular with the inauguration of the new agricultural strategy Green Generation in 2020.\u003c/p\u003e \u003cp\u003eXu (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) conducted a study in China on \"The Correlation between Marine Fishery Economy, Fishermen's Fishery Investment and Fishery Science and Technology Progress based on VAR Model: A Case Study of Zhoushan\" using time series data from 2000\u0026ndash;2021. The results indicated that: (1) there is a significant positive mutual promotion effect between fishermen's fishery investment and marine fishery economic growth; (2) The progress of marine fishery technology and the growth of marine fishery economy also have a significant positive mutually promoting effect; (3) The investment expenditure of marine fishermen on fisheries has a certain positive effect on the progress of marine fishery technology, but the progress of marine fishery technology has no impact on the investment expenditure of fishermen on fisheries. Therefore, to achieve high-quality development of the marine economy, attention should be paid to the guidance of fishermen's fishery investment, further improving the market protection mechanism of fishermen's investment expenditure, and strengthening technological innovation in marine fisheries to promote high-speed growth of the marine fishery economy.\u003c/p\u003e \u003cp\u003eOverall, from the reviewed literature, it seems there is a gap in the literature discourse on the interconnectivity of the economic roles of fisheries and agriculture sectors in economic growth, specifically for Tanzania. Therefore, this study wants to fill the gap in existing literature regarding the interconnected economic roles of the fisheries and agriculture sectors in Tanzania, an area that has received limited empirical attention despite its significance for national development. Moreover, while previous research often examined these sectors in isolation, this study adopts a holistic approach by utilising a Vector Autoregression (VAR) model to analyse the dynamic relationships and feedback mechanisms between agricultural output, fisheries production, and GDP growth for Tanzania specifically. Hence, the novelty of this research lies in its focus on the interdependence of these sectors, providing a comprehensive understanding of how agricultural fluctuations can impact fisheries and vice versa. Additionally, by linking these sectoral outputs directly to economic growth, the study offers valuable insights for policymakers seeking to enhance food security and promote sustainable development. This integrated approach not only contributes to academic discourse but also has practical implications for resource management and economic policy in Tanzania.\u003c/p\u003e \u003c/div\u003e"},{"header":"3.0 Methodology","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Research Design\u003c/h2\u003e \u003cp\u003eThis study used a Longitudinal research design with time series data because it enables the analysis of dynamic relationships and causal interactions among multiple variables over time (Bala, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This design helps in identifying trends and patterns, allowing researchers to establish causality rather than mere correlation (\u003cem\u003eibid\u003c/em\u003e). Also, by using time series data at multiple time points, longitudinal design control for unobserved heterogeneity and account for changes within variables, providing a clearer understanding of how they influence each other over time (Dannels, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Thus, this study used the VAR model approach to utilize the time series data to capture interdependencies and lagged effects, enhancing the robustness and reliability of the findings. Furthermore, the Vector Auto-regression model constructed based on endogenous variables to estimate and provide robust results from the STATA analysis. Hence, the study quantitatively analyzed the endogenous variables (dependent variable: GDP and independent variables: Fisheries and Agriculture). In that case, using a VAR model in the economic analysis of fisheries and agriculture for Tanzania's economic development is particularly advantageous because it enables the examination of the intricate interdependencies between these sectors and their collective impact on economic growth (Dinh, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thus, by capturing the dynamic relationships over time, the VAR approach allows researchers to evaluate how fluctuations in agricultural output, fishery production, and other economic indicators influence each other and contribute to overall development (Akkaya, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This approach can help identify lagged effects, inform policy decisions, and facilitate a deeper understanding of how these vital sectors interact within Tanzania's economy, ultimately guiding strategies for sustainable economic growth.\u003c/p\u003e \u003cp\u003e \u003cb\u003eModel of estimation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe estimation model is given as follows;\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003c/span\u003eY\u003csub\u003et\u003c/sub\u003e​​ =\u0026prop;+β1\u003csub\u003et\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/sub\u003e​+β2\u003csub\u003e​t\u0026minus;2\u003c/sub\u003e​ + ϵ\u003csub\u003et\u003c/sub\u003e​​\u003c/p\u003e \u003cp\u003eWhere;\u003c/p\u003e \u003cp\u003eY\u003csub\u003et\u003c/sub\u003e = is a vector of endogenous variables, GDP growth at time t in the VAR equation\u003c/p\u003e \u003cp\u003e\u0026prop; =is the vector constant\u003c/p\u003e \u003cp\u003eβ1 and β2\u0026thinsp;=\u0026thinsp;are the vector coefficients estimated for endogenous variables (Fishing and Agriculture), respectively\u003c/p\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;is time lags at 1, 2\u0026hellip;\u0026hellip;.n\u003csup\u003eth\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eϵ =is a vector of error terms, assumed to be white noise \u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Data Source and Selection of indicators\u003c/h2\u003e \u003cp\u003eThis study used quantitative data obtained from the World Bank dataset. The data type is Time series from 1990\u0026ndash;2021. The selected indicators from the World Bank development indicators dataset were categorised as dependent and independent variables in this study for easy analysis. The dependent variable in this study is GDP growth (Gross Domestic Product growth in annual percentage) as an indicator of economic development. While the independent variables (explanatory variables) were: (1) Fishing (Total fisheries production metric tons) and (2) Agriculture (Agriculture and forestry value added in percentage of GDP).\u003c/p\u003e \u003c/div\u003e"},{"header":"4.0 Results and Discussion","content":"\u003cp\u003e\u003cstrong\u003e4.1 Stationarity Test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe VAR model requires undertaking a unitary root test by using the Augmented Dickey-Fuller (ADF) test to check if the time series is stationary or non-stationary. Under this study, all variables were tested after being naturalised log and differenced, and all variables were found stationary at the 5% critical value (See Table 1). The results show that all variables have t-values greater than critical values at 5%; also, p-values less than the critical value 0.05 at the 5% confidence level, hence, they are all found stationary, allowing for subsequent analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Stationarity test results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"707\" height=\"451\" 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\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003eSource: World Bank (2020-21)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Selection order of time lag\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe estimation of the VAR model requires the selection of the optimal lag of the model, which the study determines by minimising the information criterion (Ma \u003cem\u003eet al\u003c/em\u003e, 2022 in Xu, 2024). The value of each information criterion of the VAR model set by the study is shown in Table 2. It shows the value of each information criterion of the VAR model constructed. The study uses the Akaikei Information Criterion (AIC), Schwarz Criterion (HQIC) and Hannankei (BIC) to select the optimal lag order, respectively. \u0026nbsp;The result shows that the selected lag in this study is the optimal lag of 4 periods, which allows for further VAR model analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Information criterion value and optimal lag of VAR model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cimg width=\"675\" height=\"153\" 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\" alt=\"image\"\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSource: World Bank (2020-21)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Regression Results of the VAR Model\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results from the regression analysis (Table 4) indicate that, in equation two, the Fishing sub-sector is statistically significant with a positive effect on the GDP growth in the time lag 2 period meaning that, the increase in one unit production of fishing in the previous periods can boost the fishing supply in the economy by about 0.1 metric tons in current period thus stimulating the economic growth through increasing fish trade among the population. Also, this indicates a positive interaction effect, where an increase in the second lag of Fishing positively influences the relationship with economic growth. Besides, in the same equation, at period lag 1 (first lag): The coefficient is -0.8508 and the p-value is 0.000 which is statistically significant; this shows a strong negative effect of the first lag of Fishing on its current value, indicating that, a higher Fishing in the previous period significantly reduces the current value of GDP. The implication of these results tells the fact that, overfishing in the previous period will increase fish supply and trade growth but will led to declines in fish production and supply in the economy for future period thus leading to economic retardation as a result of a fall in trade and lack of enough nutrition for the workforce. Furthermore, the implications of these results apply the same to lags 2 and 4 periods as well in the same equation. Again, in the same equation, the agriculture at the fourth lag is statistically significant, having a positive coefficient. This signifies a positive effect of the fourth lag of Agriculture on its current value to the GDP growth, suggesting that an increase in Agriculture production from four periods ago is associated with a significant increase in GDP growth as well as economic development.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, the study found that, in equation three of the model results, agriculture has negative effects at the first lag and positive at the third lag but remains statistically significant at 0.05 in both period lags. The negative effects suggest that increases in Agriculture from one period ago are negatively associated with the current fall in GDP growth within the economy; this may be due to climatic changes that affect farm production over times. While a positive effect shows that, an increase in agricultural production from one period ago is positively associated with the current GDP growth and better development of the nation. Agriculture has the potential for GDP growth and livelihoods of the population in the country, as the majority depend on it for their economic welfare. If the country increases its agricultural production, then food security is ensured, employment is ensured, GDP growth is ensured, industrialization is ensured, and the general welfare of the population will be better.\u003c/p\u003e\n\u003cp\u003eOverall, clear evidence from the findings indicates that, both the fisheries and agriculture sectors have positive effects on GDP growth. Therefore, we reject the null hypothesis (H\u003csub\u003e0\u003c/sub\u003e) in favor of the alternative hypothesis (H\u003csub\u003e1\u003c/sub\u003e). Thus, suggests that improvements in these sectors can indeed contribute to economic development in the country. Above all, improvements in the fisheries and agriculture sectors in Tanzania can significantly contribute to economic development by increasing production and job creation, enhancing food security, and fostering sustainable practices (Pawlak \u0026amp; Kołodziejczak, 2020). For instance, adopting modern farming and sustainable fishing techniques can boost yields and create more job opportunities in rural/coastal/offshore areas, leading to reduced unemployment and improved livelihoods. Additionally, a more reliable local food supply enhances nutrition and health outcomes for the workforce; also, investments in infrastructure, such as roads, marketplaces and storage facilities, facilitate market information access and reduce post-harvest losses. All these advancements not only stimulate economic growth but also promote environmental sustainability and social well-being, leading to a more resilient economy in the country.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 The Granger Causality Wald Test Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Granger causality Wald test is performed in this study to assess the directional influence between time series variables, helping to determine whether past values of one variable can predict future values of another (Vasile \u003cem\u003eet al\u003c/em\u003e, 2020). This is crucial for understanding causal relationships, as it allows researchers to identify whether changes in one variable, such as economic indicators or sector outputs, precede and potentially drive changes in another (\u003cem\u003eibid\u003c/em\u003e). Thus, by clarifying these relationships, the test aids in building more accurate models for forecasting, informing policy decisions, and guiding strategic interventions in economic or sectoral development, including fisheries and agriculture sectors. Ultimately, this test enhances the understanding of dynamic interactions within complex systems. Therefore, the results in Table 3 suggest that, while agriculture has a significant effect on both GDP and fishing, the reverse relationships are weaker or borderline significant, particularly for fishing\u0026apos;s influence on GDP. This highlights the pivotal role of the agricultural sector in driving economic growth and its interconnections with the fishing sector.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Granger Causality Wald Test Results.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"686\" height=\"241\" 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uAIgrhuLlkgVvjSSkUpL/2SWqIiYthFXW1ZdcY1CNSbiut79wW3XHV89LUTYrWNKe7v57PUQFaXehFF8S4agmEj+qsRVv0FoIWGuMPme/8q8ptZRuTAay/XFUXir5/V+MrIzb7kuoSPTo/HHcp1dE1e7Puc5xqzf22309ygcdqWNEFcAxHVjKXtN3yTCZour/N9d/H6muFrJDYi2rn9/cMvaUXQ3oq6FonfzmV3WFt05P0MuTFy1tAbBkzbXUyZM5md9PGscpAybqRPXJuW6QuEaS8aHdkFc24h01TCBKnGV+x8q71XX2ghxBUBcERWHstf79rHaDBWAzRDXTGAKGbwk2jUlrlkEtovimoqIOm57LW1eUcu+Jgo4b7musu5DBR7v+h+WDRNoem9daSPEFQBx3VjmfTWcTWQ5tl/pinxUTc4KlXpZNTkLOtgfzOtq0x9mPLBNmxZf/6cy6h6zfKiAenqeSNVFiKsZq2kmZ5WNcc1WzPg1+f1D9/dGeOrGxkp92hO32pbr+pJfqxZXWzxL2y/ZXtZ+BnfMapt7S9roL11pI8QVAHHdaNzJWbI6QJN89zIWMZ1Mky15lT2QWy2HlU3Qgc71h9Om/aGwFFbWnukyWiKT6kzktbDM1HB4aIYKFJfRmvSJtg//CxHXkmspE9Qq8RkG3r/1dTjLMKXLRMmrZPVme/Rg5J533nKd+sIzWVLqN/dLybJJ6vPrsvq0xPWXsvaritDmQwHyfuG/M8teuW3UlS8WiCsA4goAFyU5C4i4k4AAAHEFQFwB4GJkNY3IvdURK9U/abtqAOIKgLgCIK4AsCkPasQVAHEFQFwvK+6SVe5sceroUvaH0672B8QVAHEFQFwBYF0e1IgrAOIKgLgCAOIKgLgCIK6IKwAgrgCIKwDiCgCIKwDiCgCI6waz43vPZYHtJik+JQGB5ObWk7hU/+Sm33smiQZaJSDIFrmn7teb4uQtdWZnHzKL9pv2dlMBF7aNxh+vUlzjeHQtDHpH+m/AyZL0OI7fT7eleelNhq2ZdSN9Xnk/mnL2cQuJGfo7/3LvQWedM+dM6rXpOZfa9jX3twqiUD3SdVqSgKBpudt7e+859/efvN2z5APVbbT/34grAOIKS6RJbnr5sJb9zHqc+sN7y/uuKuWrCG5Vyld7X1hvdFuG/heSanNKbrJsWaXik2w7j3hedMS1LL3nUKnjMIpvacGNtu80zfAlaURN37fTkGZpk/MUs7Kf/XeQClHwxJyzq9hpUiUzVVXa2mUQh9t37c+ZpilY68rV3Z/dRl2K+COuAIjrRuOmfG0iruYBXCswTmTN7O+K66xjwXp96Ykfx++LiO4dHFzZJHGd+ptJ+nBTcTXik/b96LCsnC215mf5G+r6EAhzf3Kddfe3DOx2q0rh2rZc3f2JyHaxjRBXAMR1Y9EfxCp8aSQjldj+ySxxFTFtI662rLriGoVqTMR1Q/pS1q47vnpqIlibJK750IYWr6HTvzH1vfLUm62trR/sYRQGHblzzhkP/HtBEBylQwnUmT948NcuSmyT+1uVuPqe/6pJO9WVq7s/idR2sY0QVwDEdWMpe03fJOJqi6v8312Mfqa4WskNiLZuXl9yo+ibFnGNwu2/NY24xlF4ywypMa+hp6RVXjdboq/P4XtfylhbM5Qg3t293mTs+bIpu7+uRFxFQJuMua0r9/coHFbdX9ZGb7vWRogrAOKKuP5zOvLkCqd9rDZDBWAzCJX32s2olUetHHEt9AdHXOcZOrJscW26T5kU2a+hRVpl7GxZlFKkyB46ING9Lr6ZmPf1fJeupa5c3bZRRRutOuqKuAIgrhvLvEMFsoklxyYSIcjr4arJWaFSL6smZ8GG9CMnAmkPF6iT03UQ13QFjclEKj05S4Wv7XOaYQTJtT+0fx/5/iNTzh4jqSXIOqY7xjV9M+H/KNchf0NynFXP2C/D3F8XxrjaY07d+jTby9qoUM6ZgFV3f6aNZKWBLrUR4gqAuG407uQsWR2gSf55eZj3lXeSL3mVvQJttRyWv/OcNtgAaTVLYWXtmb5CTZcHCv/s/8Ne8soeIuIuhzXv8JGLEtcsamxdz2RZLP2aOF326bey5bCqxLVquajiEmL5fRfKSgSv82NcO7Yc1jDwvq4ah5yJ6y9uPdeVc5fDcu+vi22EuAIgrgDQ5Q8kEhAAIK4AiCsAIK4AiCsA4oq4AgDiCoC4AiCucF7cJavc2eLUESCuAIgrAOIKAIC4AiCuAIgrACCuAIgrAOKKuAIA4gqAuAIgrgAAiCsA4gqAuAIA4gqAuAIgrogrACCuAIgrAOIKAIC4AiCuAMsXVwAAALgwEFcAIq4AQMQVgIgrAOJKpQIA4gqAuAIgrgCAuAIgrgCIK+IKAIgrAOIKgLgCACCuAIgrAOIKAIgrAOIKgLgirgCAuAIgrgCIKwAA4gqAuAIgrgCAuAIgrgCIK+IKAIgrAOIKgLgCAOJKXQAgrgCIKwAgrgCIKwDiirjWs+N7zz3Pf7c7Hn9Qt984Dq8pzzuTurRRgwefFvfpn8w6Vptztz0mrBeR733ZpP+lfWF0NVDqG90PVf/kpt975kePP3oc+R9lYpn1S3Xmj8Yf6zLj3Q98z3tb6LP+ztO2EnpR4qqvR3k/6mtJ7mF3f/8PjesqVI+Sez/1tsLv9vb3f191f56/89y+1rJypmyovNf63lpeyyqIQ/VJ0lfervo6q+qzTbnbe3vvOX3iP6Ydbu/v/7GqjextiCsA4nppudnznzUTh0Qik4e+/bM/iO8t49xwufvfwcHeFdlvFI+vmp/lgS7iKj8/GKhPzf9lmwhuGI+vlfVbe99li2vkq0NzXSLc8sWvyXHjgX/Pvh/zdydiE/TCI3OM9F6jQ/NzWbl8WyKC5vxSZ7u78fWu9hFdV8l96b6yQnGNw+27VfU5bznpEzfuf9bX97nrf2j3ibI26kLkH3EFQFyX++Ebbd8x0VN5oLcVV1cEhDR6KhGf6eiZRMr6yjsxUSalgm/NPnLu4VAd5pEySyiqjiniIfsGgTouK6cfcvr+kt8PBp/7nvdOolC0faf632mb/mckr2q7K6P2/m5/nXWsRYurkRRzXU2OG/aCIyNsrqwWBUl9YtdDXTn7WvS+QfCki1FXfd0qeCHXZt/PKmjaDm3KmXaQn90vHl1tI8QVAHFd3kNAPhhV+HLv4ODKRCKavYovDBeoEMEyCdnx1dPtKL6TS6zXf232Cbe87+RYcj3m2pod03tuZMQu595fFKqxvJqj7de7/4mYthFXW1ZdQZQ+saqIa3r/6nvlqTdbW1s/2FLSRnx8z39V9praFbu6cjqaF0Zjua84Cm/1vN5PTa9nmYi8ydAPuc6uiWtVO7QpV9cn7Db6exQOu9JGiCsA4ro0yl6TXkTEte5YkwjodHTU3l9/oPduPmsqw/mrN6ucG02rOiasV/+zxTWNuGdjWpMvJfJQnymuWYTXHZe9bHEVQTTDHdoMtSkTH1fg3GjdrHJmuIUZPxko9W3XxLVwjWYMb3/nq1W9Lm/SDm3LiZC6fcLcn9z/UHmvutZGiCsA4rpW4lq3z6xjxfGN6xJhMxG3hYtrRRQX1qv/lb3et4/VZqjAnJ8hFyKui3jVbN+zW7dtzif7tplstGpx7IK4XkT7rWMbIa4AiOvSOPdQgZbiKh/EMjRgElGQmeHB8aLElaECa9L/zOtq0/9myEg64Sp4cfv+Z3+y+0DV5KyhUsfu5KzziOfFjXH1H5l7KIuQmhUSEul+6P5e7k9+VzWpy5741aacaYemE40us7jK5Km6+pTtpe1nl3MmYJk+UTbG1Wmjv3SljRBXAMR1qbiTs/Q40xly5y6HNb0MlrtUljqTh2g2seI4n5xlDRVwJ1/p68jGz9YdM3tVXFrOPKzz+xsODxkq0Mn+d9qm/5kvPflSUnriXfqavXQ5rKyPTS0X5SwVtWxxnbUcVpW4CsPA+7e+jpJlmOoidVXl0uWl5BW0erM9ejDq9BeetB1/toeHrOpakvr8uqodMnH9paL9Ssu5y2HZfcJto658sUBcARBXWNQD7wJeEwOQgAAAcQVAXGExsppGZ96ZqJYZpgCAuAIgrgCIKwAgrgCIKwDiirguh6qUrmZMGXUES+h/p13tf4grAOIKgLgCwLo8qBFXAMQVAHEFAMQVAHEFQFwRVwBAXAEQVwDEFQAQVwDEFQAQ1yWSJgFIF/Kv269q8tZ0IoJmWbianrvtMWG9iHzvyxb979QkGFh2vvZlJSCYRbogvf+2mPfeSrBQkmRBMomFQe8ovQd1Zmcfq9vWNdx7X1mfDdUj3RdLEhA0LXd7b+89q/1+LiTQ6O98Vd9++/+NuAIgrpeaJrnic3mwFvOXn032okWfG+h/gk7tGvpf2F+Y1klc7bSsdSlYy9D7J39/ur4qctu7KUNT6QmehFF8yz1e3baukd57dGjf+2rkefuunUq4aQrWqnJt2q9LEX/EFQBxXe6Hr5Pyta24lmWjctO3FsuNruYpX1X/RKngW7OPnHs4VIduqs66Y6YpX9VZEKjjsnKTlK+SFnTwuU5GkKWDhc70v9O2/c/0l/hx/L6k8d07OLjitPlpWZuHynstvx+MBvfSftg+YnuR4mrObf6Omhw3S538QqQt7AVHVfImUUn3b0F+LjtH3bYu0fTel4F9/ro0u/OWM+1ntkkK2S62EeIKgLgu7yEgD0sVvjQP/VQimr2KLwwXqBDBMgnZ8dXT7Si+k0us139t9tF56pNjyfWYa2t2TO+5kWe7nHt/UajGrEO7Qf0va3PpU3YEq6zN7Ye9/hKk1BuJXEkfbJtN7cKGCuhrVd8rT73Z2tr6oalAi/AagamTN3dbPPDvBUFwNJH6B38191G3rUvoex+NP55176sQV9/zXzUZLtC03FT7hdt3u9hGiCsA4ro09KtWKyJTJYZ14lAWca071iQCOh0dtffXH+i9m8+aynAuLlY58/rVjtaUHRPWr//ZZe12btLmdn+Z8zPkQsQ1jsJbRpqbDrXRr4t11NgaB+mMY538fU5eM6fSl44hNmNX493d66au67Z1hdJ7d8aArlpcm4h0k3I17fe2a22EuAIgrmslrnX7zDpWHN+4LhE2Ex1buLhWRHFh/fpfJjCFjFoStWrS5l0R13lfNVcdw/2C6NatiI89jlYieGafum1dZZOHCpQNCxhVtNGqo66IKwDiujQu6lVtU3GVD2kZGjCJMo2uBio4XpS4MlRgTfpf9po0738zZKRsPKjpA+4xy4cKqKfnWYng4sa4+o9M9KwswibyKedKZOVh1fmq5M2e+FWoN8//UfaX6KWcPxeomm2Iazm2XJZNrpPtZe1XKJf8v2xSnj3+2W2/2/v7f+xSGyGuAIjrUnEnZ+lxpjPkzl0Oa3oZLHepLHWmpSKdWHGcT86yhgq4k6/0dWSvQeuOmU7OKi9nHv75/Q2HhwwV6GT/O23a/wpLYWVtnL5CTZcHkj4xmZxVbPPiMlrVr9mXJa6zlsOqE9fC0leOmNdP+Nm+WzVGsm5bp77wTJaNmrr3ZTMMvK91fyhZDisT11/K2q+u3Lzth7gCIK6wYTSJEgNtvixxBdiIBzTiCoC4wgWKSxqdeWeiWm1nkMPatvnbRbU54gqAuAIgrgCwLg9qxBUAcQVAXLtCVUpXM6aMOoIl9L/TrvY/xBUAcQVAXAFgXR7UiCsA4gqAuAIA4gqAuAIgrogrACCuAIgrAOIKAIgrAOIKAIjrEkmTAKQL+dftVzV5azoRQbMsXE3P3faYsJkUJ2+pMzvDkFm03/RJO23p1LbR+ONVimscj66FQe9I9/uS5AN2Yoam55OlwLJ0uL+WJTXQ5w3VJ5Lz3t6W/s7UTbFOO9X2k6QNlfe3TKJQPdLtVJJIoEG7v5UsWE23ue1XtR1xBUBcLxVNc8W7C7vLz/4gvreMcwMIkjXND/0v7C9MudxUZEgz284jnhcdcXVTl0o6z0AFL0w6WEn/aQt4rRQlUmNSiMpxdnfj61NCnPzd6r81R1ybnmOlomilQq1Kl7osJItVnmo6+/xrcy11KWurtul79qNDt/0QVwDE9dLgpnxtK65lmYnc9K3FcqOrecpX1T9RKvjW7CPnHg7VoZsOtu6YacpXdRYE6ris3CTlq6RIHHyukxFkqUJh/b9kxY/j90VE9w4OrmyKuE7JivThhoLm5rgPg+CJOXaWcvmF/Oye04hr14dAmPuT60w/e6LDVV2zXYd1aVovSlzr2g9xBUBcLwX6g1+FL81DP5XYZq/iC8MFKkSwTIJ3fPV0O4rv5BLr9V+bfXSe+uRYcj3m2pod03tu5Nku595fFKox69BuUN/N2lz6lIl8bZq4StS0jaToiGsYjeX64ii81fN6PxmRNZFb2VYmrvZQgXmHUSznM0t9rzz1Zmtr64dVDmlwxdX3/FdNhwvMI666/ZJ2KWs/xBUAcb0U6FetzuvBi4i41h1rEgGdjo7a+1fJR5UM56/srHImUtVEaGB9+26bdl4ncdXSGgRPbCmfhS5jjXENlPpW5K7wezMuPfmSWHYfZqhCGwlbFiLjJoXvPK/nFy2ubWSyjbhKmwyV96rQfv07XyX//G7VbYK4AiCuayWudfvMOlYc37guEV4TEV24uFZEcWH9yCSskFHLiJbbd+x+4Iqr20e6Iq6pqKjjNtJadewyAZ0VsetSRK9OFtu+nu/StcwzxrWL7YO4AiCuS+PcQwVaiqt8uMvQgEnEZHQ1UMHxosSVoQIb3G+dmfZlfaBMTtdBXM1YRjM5q2yMq1khIfn9w6prkb/nqgmTU9Kl1DeD0YMP0yEGyeeAGh53MeIa+f4jqZcujHGViVJm6EVpGyXb69oIcQVAXBHXOXAnZ+lxpjPkzl0Oa3oZLHepLHUmUpE9kI/zyVnWUAF38pW+jux1Zt0x08lZ5eXyh765v+HwkKECGyCtZimsrI0j3/sylcmkT/zZ/0fhdarVR93lsMr67zLFderVfbYsVnG5r/waHzYR13ysqlJvtkcPRlP1p19pe29NlNqU1a/drWWmjDR3rv07thzWMPC+1tdSshxWJq6/uG009co/aXeztFXdNqv9fk7PefO7Lggj4gqAuMIipWdGlBhgWeIKsCF/D4grAOIKFyaraYTinVl+ywxTAEBcARBXAMQVABBXAMQVAHFFXJdDVUpXMy6OOgLEFXEFQFwBEFcAQFwBEFcAxBVxBQDEFQBxBUBcAQBxBUBcAQBxBQDEFQBxBUBcAQAQVwDEFQBxBQDEFQBxBUBcEVcAQFwBEFeAzRdXAAAAuDAQVwAirgBAxBWAiCsA4kqlAgDiCoC4AiCuAIC4AiCuAIgr4goAiCsA4gqAuAIAIK4AiCsA4goAiCsA4gqAuCKuAIC4AiCuAIgrAADiCoC4AiCuAIC4AiCuAIgr4goAiCsA4gqAuAIA4kpdACCuAIgrACCuAIgrAOKKuHaLHd977nn+u93x+IO6/cZxeE153pm0neepszAeX6P+No/I975s0h/SPjG6Gij1je4Xqn9y0+8986PHHz2O/I8ysfzN9Bd/NP5YlxnvfuB73tvJNu835e88bSuhFyWucTy6Fga9I33P+/t/KNvnwUB9mmx/W7W9tB5D9Sipl1NvK/xub3//91PnDdUnZcc8ONi7kl6P3J86u33/sz91ta9U3cPS++yMum5S7vbe3nuztmV99+dC3+7f+Sr5Z+VfoBBXAMT1UnGz5z9rIirmIe6H/hdq8OBT6u7y9gcRLNlvFI+v5sKlvNcirnk/yf4v20RwzZcd/SUokdVCn8r2Xba4GsJecFQmYCLhcq26ThoKWjzw79n36g/ie02OmUpr8CSM4ltd7yPpPUSHbeplMfK8fdet6yb9oq5c1TYR16AXHpn90n4cHXYh8o+4AiCuG00cbd8x0dP8AdpQXGXf+HH8vt+7+Wzv4OBK4UGmj6nO/MHgc9/z3nn+znMtBVvedzpKG4V3+8o7IWLbyf5w2qY/6Pau+fLiyqi9vyuus461KnHVoqKCF/L7KrGddawp2ak5ptSD1FnXh0DMWy+LoK6u5y3X9JgSce5KeyGuAIjrxqKlQYUvjXSm0tI/afZqeCIcO756WoigWceMQjWWV2x2WT0kQak3Ek2SV8wmUgfr2R9ETNuIq913zP/NA1/6SxcjrpGvDo2YnEdcfc9/ZV5h1x1TIrVBEBylXyLkC+CDv3ZRYvU9jMYft62XZYirXdfzlmt6zFXfO+IKgLheCspeyzaNuNpl7SiZGzHTH/a9m8+K4joRXVj//mCLazoGNBv3l3xhEaGZKa5ZhFdHeecYdrJocTVDHwrjGf2d5/NE80R85OdZxzTji8241nh393rTNyHLovQe+jtfrUqwq+r6POWaHLNLwwQQVwDEFVGp+rDXr/wnk2r0ZIiDgytT4ppF8RDXze0PZa/37WO1GSow52fIUsa4Nt1eJ0VVr5rdY4q4Sh3ZYy3bRqJXKY5dENdlDRV4vOt/2KVhHYgrAOK6scz7arhMNoyMNhsqgLh2uj9kr0Lz/jBDRtIJV8ELe9a7tHHV5KyhUsfu5KzzPPS7IK5m9YRENh/a12KPVTXC3kRc00i0/6OJzka+/6grr6K7Kq62QJbVtWwvbSO7XPJ/u1zdtsmXDHV44/5n/c48oBFXAMR1k3EnZ+lIqiOaU3JjlsLKJlyly2hNlsaaTM5KjjkcHpqhAsVltCavR2mHzvWH06b9YdIvRld95f2YL3mVzZ4vXQ4rk9ip5bAavn5flLhOvfZ2lsUqXG82DGKWuArDwPu3Pm7JEk11x5Qoa9fHuFr38HPZPSybpK6/rqrrTFx/qWijynJ129pEdhFXAMQV1oCLeBUMsAxxBdiQvwfEFQBxhVaymkZg3ukIjOqfsGoAIK4AiCsA4goAfCAhrgCIKwDienkpHYtqrR5AHV3K/nDa1f6AuAIgrgCIKwCsy4MacQVAXAEQVwBAXAEQVwDEFXEFAMQVAHEFQFwBAHEFQFwBAHHdYNJEAv67Rpmz8slbadIB6q9z7TM1mWoqFa9kZmqQ3leQFKSzzt32mF0T1zgeXQuD3pGbeEB4HMfvp9uSc6n+iZ0lbBaS+Sovm/y9SNmp5AtOAgbZnidEULOzl62aOFSfJPX2dtXXGYXqke77JckCmpa7vbf3XpNtug2V95+8T+zv/xFxBUBcYck0yU1v0Kk8Q/8LN089rL593MQP8rPJZrWMvrGO4mooS12q09RG8S0tadH2naZZvlJpDZ6Ysrb02BmX0vaKDs3PIoImvagcY3c3vt7VPqkz5SXXrvvHCsVVMo3ZqYSlvzdpo7pyddvsVK9V6WARVwDEFS76w95J+dpGTmTf+HH8vqR03Ts4uFJ4kOljSqrKwec6GUGW2lWnEJUobRTe7SvvhIhto/Y5bds+RlzLMpel0dMspalzrELqVtU/USr41uwj5x4O1aGJHJrUrdYxf3WjlfLlRvYNAnWclxuNP3b6yqnbV9oIwDLEdapNkjptck65P5Prvl6e1CfF+pxIkb6mRH67GHXVAq6CF3Jts+pt0djnb5OKta5c3TbTRvKz+8UDcQVAXGERDx35sFXhSyOdqST1T9pG9HZ89dSOStjHjEI1dtf61K+8lXojUSgRJTJrLah9zHCB7EtD2RcP91jSlttRfMdIbN/rvzb76C8dybHkesy1NTum99z0FbtcSV95NE+u+2WJq0hoVc76SiEd+PeCIDiayPmDv5Zdq3tOHXENo7HsG0fhrZ7X+8kW2a4g8iZfROQ6uyauvue/atJOdeXqtqX9V32vPPVma2vrh660D+IKgLhuLPpVvxXlqRKPWWV11CwbLmD/P/+w79185soRUdbFtk9dxLXuWJMIaDp+2Y6O2vuXtWudDOdfbKxyVX2lq+Kay1q4/bemEVczNtiMiY13d69PR7mno3V6iIE1xjVQ6tuuiWvhGs043f7OV6uKOpZJZhORritXt+3vUTg0X7rbDE1AXAEQV1iBGKWv/IsZlCRyNiUjJZE5xHV54lq3z6xjxfGN6xLhNRHRhYtr1le6Lq5N9zHiao99lAis26ZmOMGs87WZbLRqceyCuC56qMC850NcARBXmJN5X0WXSZGRk2ZDBRDXVu2TCUvePg3kwLRR3YPUnUwjD18ZGmCigzJUIFDBcUFc7ehTSXS0bIJOmbjm4wKt+zN9pWvimtXDizxqKpOzVPjaPqcZRpBI6kP79/lKC8nxJEIZ+f4j9+9LXrfX/T3I+boSzeuyuMoEKTOe2HwpKrRRsr2sjQrlnElWddukLaVPMMYVAHGFJeJOztKR1Jr884WlsLKxk+kyTZOlsSaTs5JjDoeHJsJWXEZrsgQQ7TCzfU6bto9Vz6fVy2C5S2Wl7WYm2qST5rKhAlkk0J3Qlb0i1u1Xd8x0ctZk0pZdzghffn9ZX1mVuE699rYmmulXwdakNXc5rCpx1W0Ybt+1x7gW2qoiUpcuLyXDBNSb7dGDUae/YKVLe/1s3rysUt6Ggfd11TjkTFx/KWujunJV21gOCwBxhQ2kyStrgPP0FRIQACCuAIgrzC8gaQTmnYlOsWoAzOgrb6simYgrAOIKgLgCwCY9qBFXAMQVAHG9rJSORbVWD6COOtE+p7QP4gqAuAIgrgCAuAIgrgCIK+IKAIgrAOIKgLgCAOIKgLgirgCIKwAgrgCIKwDiirh2hDSRQLrAfN1+xclb6ULz1N/lpThZTJ3duP9Z32wzC/ObyWN2atOpbaPxx6sU10IyBCfrmCzblScoUM2ylwl5IgGnbgrLgFnJOPKMTXH8fhj0jvLF7edYJmwpbZ8uwv9j23pZFFGoHuk2LEkk0LTc7b2999pvuzm1DXEFQFxhCczKX28jWZH80P/CzswEl5eq/mDSvFaKzxzZsi5aXCUdq53WVdKw2pIt92bSfcq+u7vx9abiah/Hvm87Y5abMnSo1HEYxbf0MSTFrCW1XULqyci4mxJ12UiGsjy1sGQ6a5gmt67cvNsQVwDEFRb1Ye+kfG0jrrJv/Dh+X8TD5LPXD7A85aukuBx8rpMRZCk+dcpSidJG4d00tSgR20360lPWH9ZBXF3cXPciaHY/DYPgSZPoohHXWddXJbj536gTAe6auMq1ufK9bMJecJSn6K1Ipdu23LzbEFcAxBUWgH7QqPClkYxUYvsnTcTVTs+546unduTBPmYUqrG7tqgekqDUG4kojePRVTJrbUhfKukP6yiuElG1pcSIpQqjsZwnjsJbPa/3kz0kok5I7aECVcMh3PMZedZlW772Xv5niPpeeerN1tbWD03qZFni6nv+qyb1Vleu7bYuDBdAXAEQ141Fv9p1ojxNI652WROdcv9fJS2u2MBm9aUmfaCr4qqlNQieuP1T/94a4xoo9W1bSTPDEVyZmhWpjMLtv3U14ioSb754rvp1eZlINomK15WbdxviCoC4QsfENX3lX8zYJFHWKWnJIrCI62aTSV2xP2SC5oqr3UdccXX7zzLFVcRSjy1t0DdFWuaJglZFVquGCdSV60S7d+h1OUMFEFcAxHXDmXeogP1a2JXRZkMFENeN60dORLAwfKRGTrsirlo8rMlZ7hhXG/k7kciiK59yHUmZh/mEK31M9c1g9ODDdIhB8velhseu8LrjZ2X4jH0tenKWCl93c4yr/0iuswtjXGVymBlPXNZ+st1to6lyzgSzebchrgCIKywId3KWjqTW5LsvLIWVTbhKl9GaLI01mZyVHHM4PDTRtuIyWpMlgGiHNZdWsxRW1paR732ZymTSH/7s/8Ne8sogD3l3OSx729LFtbCkV34dDyczyLOxqkq92Y7iO1PSVCKu5rj2clHuslZVkbpZ5TrT/h1bDmsYeF9XjQvOxPUXt41mlWu2jeWwABBX2KhoHHUBC3xQk4AAAHEFQFxhTllNF1d/pyNXqn/CqgGAuAIgrgCIKwDwgYS4AiCuAIjr5aV0LKo1W5w6AsQVAHEFQFwBABBXAMQVAHEFAMQVAHEFQFwRVwBAXAEQVwDEFQAAcQVAXAEQVwBAXAEQVwDEFXEFAMQVAHEFQFwBABBXAMQVYPniCgAAABcG4gqwKHEFAAAAAEBcAQAAAABxBQAAAABAXAEAAAAAEFcAAAAAQFwBAAAAABBXAAAAAADEFQAAAAAQVwAAAACA1fD/AcTctRtG85pHAAAAAElFTkSuQmCC\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003eSource: World Bank (2020-21) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u003cem\u003eNB: All variables are differenced\u0026nbsp;\u003c/em\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Regression results of VAR model\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cimg width=\"693\" height=\"788\" 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\" alt=\"image\"\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSource: World Bank (2020-21) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u003cem\u003eNB: All variables are differenced\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 Robust Check-Stability Condition of the Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter performing the diagnostic test for stability of the model, the results indicate that all the eigenvalues lie inside the unit circle, satisfying the condition for stability of the model fit. Thus, the model VAR was found to be stable and fit (see Table 5). In this study, the results remain robust throughout the analysis. A stable VAR model indicates that any shocks to the system will gradually dissipate over time, leading to consistent and accurate predictions, while Instability can result in forecasts that diverge unpredictably, undermining the model\u0026apos;s usefulness for policy analysis and decision-making.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5: Eigenvalue stability condition results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"688\" height=\"221\" 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alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003eSource: World Bank (2020-21)\u0026nbsp;\u003c/p\u003e"},{"header":"5.0 Conclusion and Policy implications","content":"\u003cp\u003eThis study analysed the economic contributions of the fisheries and agriculture sectors to Tanzania's economic development using a VAR approach. The findings from the VAR analysis and Granger causality Wald test reveal significant relationships among fishing, agriculture and GDP growth, highlighting the interconnectedness of these sectors. Notably, agriculture demonstrates a strong Granger-causal effect on both GDP and fishing, suggesting that advancements in agricultural practices and productivity can stimulate economic growth and positively influence the fishing sector at large. Conversely, while there is some clear evidence of fishing impacting GDP, it is less pronounced, indicating that the fishing sector may play a supportive rather than a leading role in economic development. These insights emphasise the importance of understanding sectoral dynamics for effective economic planning.\u003c/p\u003e \u003cp\u003eFurthermore, given the significant influence of agriculture and fishing on GDP growth, policymakers and decision-makers should prioritise investments in agricultural development as a means of enhancing overall economic growth. This can include increasing funding for agricultural research, promoting sustainable practices in both sectors and providing training for farmers to improve productivity. Additionally, integrating policies that support the fishing sector, such as sustainable fishing practices and technology adoption, can further enhance its contributions to the economy. Besides, by fostering a synergistic approach between agriculture and fishing, policymakers can create a more resilient economic framework that leverages the strengths of both sectors, ultimately driving sustainable growth and improving food security for the entire population.\u003c/p\u003e \u003cp\u003eThe limitation of this study is that it did not consider external factors in the analysis, such as market conditions, climate variability, and policy changes that can impact both sectors, creating interdependencies that must be considered for future study analysis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study has no acknowledgement requirements.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares that, there is no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study has no funding requirements\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable in this study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData access\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWorld Bank link\u003cstrong\u003e: https://databank.worldbank.org/reports.aspx?source=2\u0026amp;country=ARE\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAkkaya M (2021) Vector autoregressive model and analysis. \u003cem\u003eHandbook of research on emerging theories, models, and applications of financial econometrics\u003c/em\u003e, 197\u0026ndash;214\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArora NK, Mishra I (2019) United Nations Sustainable Development Goals 2030 and environmental sustainability: race against time. 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Dev Policy Rev 38(6):685\u0026ndash;709\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu B (2024) The Correlation between Marine Fishery Economy, Fishermen's Fishery Investment and Fishery Science and Technology Progress based on VAR Model: A Case Study of Zhoushan Fishery. J Global Econ Bus Finance 6(7):40\u0026ndash;48\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZakayo EZ, Mbilinyi R (2023) Assessment of the Potentials of the Blue Economy Resources for Poverty Reduction in Tanzania. J Maritime Sci Technol (JMST) 1(1):1\u0026ndash;5\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"N/A","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Fisheries, Agriculture and economic development","lastPublishedDoi":"10.21203/rs.3.rs-6797016/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6797016/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFisheries and agriculture sectors play a pivotal role in the economic development of Tanzania but are faced with several challenges, including but not limited to climate change, inadequate infrastructure and overfishing. This study investigates the economic interdependencies between the fisheries and agriculture sectors in Tanzania, utilising a Vector Autoregression (VAR) approach with time series data from 1990 to 2021. Given the significance of these sectors, the study explores how fluctuations in fisheries and agricultural productivity impact GDP growth. The study aims to identify causal relationships and dynamic interactions, providing insights into how policy interventions can influence sectoral output. The findings from the VAR analysis reveal significant relationships among fishing, agriculture and GDP growth, highlighting the interconnectedness of these sectors. Notably, agriculture demonstrates a strong Granger-causal effect on both GDP and fishing, suggesting that advancements in agricultural practices and productivity can stimulate economic growth and positively influence the fishing sector widely. The study underscores the policy significance of a holistic approach to agricultural and fisheries development in achieving sustainable economic growth in Tanzania. Furthermore, it highlights the need for collaborative strategies that enhance resource management and investment in both sectors for improving the socio-economic conditions of communities reliant on these industries.\u003c/p\u003e","manuscriptTitle":"Economic analysis of the Fisheries and Agriculture sectors for the economic development of Tanzania. 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