Eastern Africa Journal of Official Statistics
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Eastern Africa Statistical Training Centre
The Impact of Trade Policies on Business Growth Across Borders: A case of Maize Business at Tunduma Border
The study assessed the impact of cross-border trade policies. The study associated labour productivity, capital productivity and opportunity cost as among the important factors on business growth and its performance. The study explained trade policy as a moderating factor towards maize business growth. The study was conducted at the Tunduma town border, and data was collected from 311 respondents using closed-ended questionnaires. Primary data analysis revealed increases in profitability through revenue and profit. The available policies and their regular changes seemed to affect cross-border traders, maize business dealers. The findings provide solutions for policymakers and maize business chain dealers by providing areas for improvement to the available policies and issues to consider in designing the new policies related to cross-border trade. The results explained that the trade policy positively influences the maize business growth
Cross-border trade, trade policy, business growth, and maize business growth
Copyright (c) 2026 Eastern Africa Journal of Official Statistics https://creativecommons.org/licenses/by-nc-sa/4.0
Determinants of Agricultural Productivity Among Smallholder Farmers in Tanzania: Evidence from the National Panel Survey, 2021
Agricultural productivity in Tanzania remains low, limiting food security and rural livelihoods. To inform evidence-based interventions, this study analyzed determinants of smallholder staple crop harvests using Wave 5 of the National Panel Survey with 237 households having complete socio-economic, farm input, and environmental data. A generalized linear model with a Gamma distribution and log link was used to account for the right-skewed, strictly positive nature of harvest data. Stepwise models were estimated separately for socio-economic, farm input, and environmental/institutional factors. The socio-economic model showed positive effects of asset ownership (β = 0.0125, p < 0.1) and negative effects of older household heads (β = –0.0159, p < 0.05; log pseudolikelihood = –1804.43, AIC = 15.28, BIC = –967.21, deviance = 355.92). The farm input model identified plot size as strongly positive (β = 0.216, p < 0.01) and irrigation as negative (β = –1.701, p < 0.05; log pseudolikelihood = –1802.23, AIC = 15.28, BIC = –895.19, deviance = 351.53), indicating slightly better fit than the socio-economic model. The environmental/institutional model performed poorly (log pseudolikelihood = –1840.04, AIC = 15.59, BIC = –830.58, deviance = 427.15). The full combined model, integrating all variables, provided the best overall fit (log pseudolikelihood = –1775.08, AIC = 15.15, BIC = –889.35, deviance = 297.22), with positive effects of plot size, asset ownership, household size, and organic fertilizer, and negative effects of irrigation, hired labor, and older household heads. Findings indicate that socio-economic and farm input factors jointly drive productivity, emphasizing interventions to strengthen household resources and improve input efficiency.
Keywords: Agricultural productivity, Smallholder farming, Generalized Linear Model
Readiness for Smart Learning Transformation in Tanzanian Higher Learning Institutions: A Case Study of EASTC and IFM
The transition to smart learning is accelerating globally, including in Tanzania, where higher learning institutions are gradually shifting from traditional to technology-enhanced learning. This study examined the readiness of higher learning institutions in Tanzania for smart learning transformation. A cross-sectional design with a quantitative approach was adopted. Using stratified sampling, 375 students were selected, of whom 349 completed structured online questionnaires distributed via Google Forms. Data were analyzed using IBM SPSS version 20. The findings indicate that while internet connectivity is generally available, its performance remains unsatisfactory. A positive relationship (r = 0.308) was found between internet reliability and the frequency of using digital academic resources. The study also identified the presence of smart classrooms, although some are non-functional. Findings further indicate that most students demonstrated high proficiency in using smart learning tools, with a significant relationship between gender and tool usage (r = 0.163, p < 0.01). However, the integration of digital library services into smart learning platforms is still incomplete. Overall, the study concludes that higher learning institutions in Tanzania are ready for smart learning adoption, though key improvements are required. It is therefore recommended that institutions strengthen technological infrastructure, particularly by ensuring stable and high-performance internet connectivity. Additionally, institutions should increase the number of fully functional smart classrooms and prioritize the comprehensive integration of digital library services into smart learning platforms to enhance flexible and efficient access to academic resources.
Smart learning, Digital tools, Tanzania, Higher learning, Smart classrooms
An Econometric Analysis of the Influence of Trade Liberalisation on Sesame Export in Tanzania
This study investigates the impact of trade liberalisation on sesame exports in Tanzania, with domestic production, real exchange rate, and sesame real world prices as controlling variables. Using a quantitative time-series approach, the analysis employs an ARDL-ECM framework to estimate both short-run and long-run relationships. The results confirm the existence of a long-run equilibrium among the variables, as indicated by a negative and statistically significant error correction term (coefficient = -0.4268, p = 0.001), suggesting that approximately 42.7% of short-run deviations adjust in each production period. Results further confirm that, in the long run, domestic sesame production quantity (lnPQ) is the only significant determinant, with a 1% increase in production leading to a 0.83% rise in exports (p = 0.004), highlighting the importance of production capacity for sustained export growth. In the short run, trade liberalisation and real exchange rate influence sesame exports. Trade liberalisation has a positive and significant effect, where a 1% increase in trade liberalisation in the previous period raises exports by 1.58% (p = 0.047). In contrast, a 1% increase in the real exchange rate temporarily reduces exports by 1.21% (p = 0.041), reflecting short-term fluctuations in competitiveness. These findings indicate that immediate export expansion is driven by liberalised trade policies, whereas long-term sustainability relies on domestic production growth. The study recommends coordinated strategies that combine effective trade liberalisation measures, improved market access, strengthened production capacity, and institutional support for exporters to enhance Tanzania’s global competitiveness and achieve sustainable export growth in the sesame sub-sector.
Decomposing Rural-Urban Inequalities in Adolescent Fertility in Tanzania: Evidence from the 2022 Tanzania Population and Housing Census
Adolescent fertility remains a major public health and demographic concern in many developing countries, including Tanzania. This study examines the determinants of adolescent fertility and quantifies the contribution of socio-economic and demographic factors to rural–urban disparities in adolescent childbearing in Tanzania. The study uses data from the 2022 Tanzania Population and Housing Census. It applies the Fairlie decomposition technique to explain the extent to which differences in characteristics between rural and urban adolescents account for the observed fertility gap. The results show that several factors significantly influence adolescent fertility, including the adolescent's age, marital status, education level, household wealth, household size, age of the household head, education level of the household head, and sex of the household head. Marital status and age of adolescents are the strongest predictors of adolescent childbearing, indicating that early marriage substantially increases the likelihood of early fertility. Higher educational attainment and improved household wealth are associated with a lower probability of adolescent childbearing. The decomposition results reveal that adolescents in rural areas are significantly more likely to have given birth compared to their urban counterparts. Differences in household wealth, marital status of adolescents, and education level of adolescents explain a large proportion of the rural–urban fertility gap. The findings highlight the importance of addressing socio-economic inequalities, expanding educational opportunities for girls, delaying early marriage, and improving access to adolescent-friendly reproductive health services. Targeted interventions focusing on rural communities are essential to reduce adolescent fertility and narrow rural–urban disparities in Tanzania.
Macroeconomic Determinants of Economic Growth in Sub-Saharan Africa: A Panel CS-ARDL Approach
This study examines the macroeconomic determinants of economic growth in 49 Sub-Saharan African countries using a Panel Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) approach over the period 1980–2024. Empirical results indicate that, in the short run, government expenditure (coefficient = 0.178, p < 0.001) and gross capital formation (coefficient = 0.009, p = 0.037) significantly promote economic growth. In contrast, other variables, such as exports, imports, Foreign Direct Investment (FDI), inflation, and exchange rates, do not exhibit statistically significant short-run effects. The lagged GDP term (coefficient = –0.661, p < 0.001) and the Error Correction Term (ECT = –1.661, p < 0.001) indicate a strong adjustment mechanism, suggesting that deviations from long-run equilibrium are corrected rapidly over the period. In the long run, only government expenditure (coefficient = 0.097, p < 0.001) and marginally gross capital formation (coefficient = 0.0065, p = 0.07) show significant positive effects on growth. At the same time, other variables remain statistically insignificant, reflecting structural heterogeneity and the limited long-run influence of trade, FDI, and inflation across the region. The study recommends prioritizing public investment in productive sectors, promoting private sector participation and high-quality foreign direct investment, supporting export diversification, and strengthening governance and macroeconomic stability to foster sustainable economic growth in the region
Cashless Economy and GDP Growth in Tanzania: Implications for Macroeconomic Performance
This study investigates the long-run and short-run determinants of GDP growth using the Autoregressive Distributed Lag modelling framework over the period 1993-2022, based on 30 observations. The analysis focuses on the roles of currency in circulation relative to GDP growth, inflation rate, foreign direct investment (FDI), and the real effective exchange rate (REER). The ARDL bounds testing approach confirms the existence of a stable long-run cointegrating relationship among the variables, as evidenced by an F-statistic that exceeds the upper critical bounds at all conventional significance levels. The estimated error correction term is negative and statistically significant, indicating a rapid speed of adjustment whereby approximately 74 percent of short-run disequilibrium is corrected within one year. Long-run results reveal that currency in circulation and inflation exert significant negative effects on GDP growth, while FDI has a positive and significant impact. On the other hand, short-run dynamics indicate that currency in circulation and REER positively influence GDP growth, whereas FDI exhibits negative short-run effects, reflecting adjustment costs and transitional dynamics.
Drivers of Bilateral Trade Flows between Tanzania and Kenya: An Augmented Gravity Model Approach
This study examines the determining factors of bilateral trade flows between Tanzania and Kenya using an augmented gravity model. The study relies on secondary time series data obtained from decent international databases, including the World Bank, United Nations Comtrade, and International Monetary Fund, to ensure evenness, reliability, and equivalence. After the estimation, the empirical findings indicate that trade openness has a positive influence on trade flows, as a 1% increase in openness leads to approximately a 16.34% increase in trade, reflecting a strong and substantial effect of liberalisation on trade expansion. Similarly, Kenya’s GDP exerts a positive influence, with a 1% increase resulting in about a 13.06% rise in trade flows, highlighting the significant role of Kenya’s economic growth in driving bilateral trade. In contrast, Tanzania’s GDP shows a negative influence, where a 1% increase leads to a 4.26% decrease in trade flows, suggesting that domestic growth may reduce reliance on bilateral trade. Distance, unexpectedly, has a positive influence, with a 1% increase associated with a 2.46% rise in trade flows, indicating that improved connectivity and trade complementarities may offset distance-related costs. Based on these findings, the study recommends that Tanzania should enhance production in sectors that complement Kenya’s demand, particularly in industrial and manufactured goods, to benefit from Kenya’s economic growth. It further recommends promoting export-oriented industries, improving product quality, and strengthening trade promotion mechanisms to ensure domestic growth translates into increased trade. Additionally, both countries should sustain trade liberalisation through reduced tariffs, streamlined customs procedures, and harmonised regulations, while continuing to invest in transport infrastructure, logistics, and regional connectivity to improve trade efficiency and maximise bilateral trade potential.
Towards Gender-Inclusive Statistical Leadership: A Case Study of the ‘Women into Leadership’ Training Programme in Africa’s National Statistical Offices
Numerous studies confirm the effectiveness of leadership training. However, there is limited evidence of the transfer occurrence especially in women in Africa. The study investigates how pre-engagement with participants informs training design, delivery and affects the impact of seven ‘Women into Leadership’ (WiL) training sessions delivered by the Office for National Statistics of the United Kingdom (ONS-UK) to female leaders in National Statistical Offices in Africa. Findings from thematic and descriptive content analyses reveal that pre-training expectations link to post training reactions as well as likelihood of training application. Key sentiments about the training include; laying foundations for stronger character, courage and assertiveness while providing knowledge and impetus for organisational change to trainees. The study also revealed country level variations whereby West Africans were more captivated by personal development and coaching, while East and Southern Africans leaned more towards leadership and management dynamics. Trainees also offered vital inputs for improvement which points to the importance of their engagement in all stages of the of training. An integrated framework for statistical leadership development and diffusion that guides further suggested trainings like the role of technology, AI and innovation in leadership, advanced strategic thinking, communication and emotional intelligence is also generated.
Leadership, Training Effectiveness, Statistical Offices, Gender-inclusiveness
Machine Learning-based Analysis and Prediction of Factors Influencing Share of Agriculture Sector in the GDP in Tanzania from 1981-2024
Share of agricultural sector on GDP has been declining overtime, suggesting a need to deploy machine learning method to predict factors influencing agricultural GDP growth in Tanzania. The used time series data from 1970-2024 with and utilized LSTM, XGBoost, and Random Forest regression models for prediction. Results showed that the l.STM model had the highest value of R² compared to XGBoost and Random Forest regression model. Furthermore, results from LSTM model showed that lending rate dynamics played a critical role in the prediction of the share of agriculture on GDP. Also, results from XGBoost showed that rural population share and exchange rates were influential factors which predicts share of agriculture in GDP. Results from Random Forest regression model showed that exchange rates and the first lag of the share of agriculture in GDP. In general findings suggest the need to monitor macroeconomic factors to prevent their ill effect on the agriculture sector. Also, findings suggest the need of modernize agricultural value chain to improve contribution of agriculture in the GDP.
Agricultural GDP, Machine learning prediction
Analysis of Technical Efficiency Levels and Productivity of Seaweed among Smallholder Farmers in Wete District, Zanzibar
This study analyzed the technical efficiency and productivity of smallholder seaweed farmers in Wete District, Pemba Island, Zanzibar. Using a three-stage cluster sampling technique, 126 farmers were selected. Data were collected through structured questionnaires covering farm inputs, production, and socio-economic characteristics. Analysis involved Pearson correlation, Cobb-Douglas production function, stochastic frontier analysis, and multiple linear regression, all conducted with STATA software. Results showed strong positive correlations between capital equipment and seed quantity (r = 0.730, p = 0.002), seed quantity and number of lines planted (r = 0.820, p < 0.001), and seed quantity and hired labor (r = 0.760, p = 0.003). The Cobb-Douglas model identified lines planted as the most significant factor, with a 1% increase leading to a 3.07–3.25% yield rise (p < 0.001). Capital equipment and seed quantity increased output by 0.64–0.90% and 0.09–0.12%, respectively, and technical efficiency averaged 60.64%. and productivity was positively influenced by farming experience (2.6% increase per 1% experience, p < 0.001), access to extension services (coefficient = 0.850, p < 0.001), and harvest frequency (0.22% increase per cycle, p = 0.007). Female farmers produced 0.36 units less than males (p = 0.002). The study recommends improving access to quality seedlings, modern equipment, inclusive extension services, promoting gender equity, supporting cooperatives, and vocational training to enhance productivity and sustainability.
Seaweed productivity, Technical efficiency levels
The influence of trade policy on the relationship between capital productivity and the growth of cross-border trade of maize business at Tunduma, Songwe region
Capital productivity was one of the factors influencing the growth of the cross-border trade of maize,this study used trade policy as a moderating factor. The study examined the influence of capital productivity on cross-border trade growth of maize businesses and the influence of the moderating role of trade policies on growth of cross-border trade of maize business at the Tunduma border. Data was gathered from 311 respondents using closed-ended questionnaires and analysed using PLS-SEM system. Questionnaires were created based on labour productivity, capital productivity and opportunity cost as independent variables with maize business growth as the dependent variable and trade policy as a moderating variable. The study discussed capital productivity as one of the important factors, connected to its significant effect on maize business growth. Tanzania has a comparative advantage over Zambia in producing maize and the primary data revealed an increase of revenue and profit resulted from production increase. The findings informed policy makers, agribusiness, and cross-border stakeholders to strategize in enhancing cross-border maize trade by improving the productivity of cross-border maize business and optimizing policy frameworks. The results contributed to the cross-border trade operations knowledge and opened room for further studies related to cross-border trade.
Capital productivity, maize business growth and cross-border trade policy
Copyright (c) 2025 Eastern Africa Journal of Official Statistics https://creativecommons.org/licenses/by-nc-sa/4.0
Contribution of Agricultural Exports to the Economic Growth in Tanzania from 1984 to 2023
This study investigates the short-term contribution of agricultural exports to Tanzania’s economic growth from 1984 to 2023 using annual time series data and a Vector Autoregressive (VAR) model. The results reveal that agricultural exports are the most significant driver of GDP growth, with a 1% increase leading to a combined GDP growth of approximately 0.71% after periods two and three, indicating a delayed but substantial positive effect. Granger causality tests further confirm a unidirectional causal relationship from agricultural exports to economic growth, highlighting their key role in driving GDP. The labour force also positively influences growth, whereas gross capital formation exhibits a negative short-term impact. These findings align with the Export-Led Growth (ELG) theory, emphasising that exports stimulate foreign exchange earnings, investment, and productivity. Based on these results, the study recommends that the government prioritise policies promoting agricultural exports, including incentives for value addition, diversification of export crops, and improved access to international markets. Strengthening infrastructure, improving market access, providing technical support to farmers and agribusinesses, and facilitating trade through streamlined customs procedures can further enhance export competitiveness. Additionally, policies should focus on improving labour productivity through education, skills development, and inclusive participation in export-related activities. Implementing these measures is crucial for achieving the government’s target of raising agricultural export value to USD 5 billion by 2030.
Government Spending Priorities and Their Impact on Economic Development in Tanzania
This study analyses the impact of Tanzania’s government spending priorities (1990–2023) on economic growth, focusing on four key sectors (public services, defence, health and education) constituting 89% of fiscal spending. Using an ARDL approach with diagnostic and cointegration tests, we examine short-run dynamics and long-run equilibrium relationships while controlling for trade openness and exchange rates. The findings reveal significant sectoral heterogeneity: health expenditures drive long-run growth, while education spending shows short-run benefits but long-run inefficiencies, likely due to skills mismatches. Defence and public services exhibit minimal growth impacts. Granger causality tests confirm unidirectional links, education fosters growth, while health and defence spending respond to GDP expansion. The study recommends reallocating budgets toward health and education, coupled with efficiency reforms in public services and defence, to achieve sustainable development. These findings offer a framework for fiscal policy optimization in resource-constrained economies
FACTORS INFLUENCING TECHNICAL EFFICIENCY OF MAIZE PRODUCTION AMONG LARGE-SCALE FARMERS IN TANZANIA
This study analyzed the factors influencing the technical efficiency of maize production among large-scale farms in Tanzania, using data from the 2019/2020 National Sample Census of Agriculture. Analytical methods included correlation analysis, multiple linear regression, and the stochastic frontier production function. Correlation results showed strong positive relationships between maize output and the costs of fertilizers (r=0.92), improved seeds (r=0.93), and modern machinery (r=0.89), while weaker correlations were observed with yield, agrochemical costs, and labor inputs. Regression analysis indicated that increased investment in improved seeds (1% increase leading to 63.1% rise in output), fertilizers (43.6%), and modern machinery (30.6%) significantly boosts maize production. Conversely, agrochemical costs and labor inputs negatively affected output, suggesting inefficiencies and possible misuse. Stochastic frontier results revealed that 48.69% of farmers operate with low technical efficiency (scores between 0.1 and 0.3), largely due to inadequate farm management, poor input access, and limited technical support. Around 43.46% fell into a moderate efficiency range (0.4–0.6), while only 7.85% were highly efficient (0.7–0.9). The study concluded that most large-scale maize farmers are not reaching optimal efficiency levels. It recommended enhancing access to fertilizers, improved seeds, and modern machinery through supportive policies and capacity building.
Technical efficiency of maize production among Large-scale farmers in Tanzania
FACTORS AFFECTING PRODUCTIVITY AND LABOR USE ON SMALL-SCALE MAIZE PRODUCTION IN TANZANIA
This study examines factors influencing maize productivity and labor use among small-scale farmers in Tanzania, highlighting the role of predictive inputs, methods, and policies that enhance resilience to climate change and support progress toward the UNSDGs. Using MANOVA and a Logit model, the study analyzes socio-economic and agricultural determinants of maize yield and labor use, based on cross-sectional data from the 2019/2020 National Agricultural Census and GIS data from NBS. The findings reveal that most farmers use local seeds rather than improved ones. Regionally, Lindi, Mtwara, Singida, Tabora and Morogoro have higher yields on local seed but lower yield on improved seed. However, several inputs and practices such as soil covering, inorganic fertilizers, extension services, and modern equipment significantly increase yield only when improved seeds are used. Off-farm income negatively correlates with maize yield, suggesting reduced on-farm commitment. Labor use is strongly influenced by the household head’s education and credit access. Farmers with only primary education are more likely to hire labor than those with higher education, and those with credit access are far more likely to use hired labor. In addition, contract farming increases farm-size. The study recommends promoting of improved seed adoption, expanding credit access to boost labor demand, and encouraging contract farming to increase scale. Extension services should be restructured to meet the diverse needs of local and improved seed users.
Maize-Yield, Labour-use, Small-scale maize production, climate resilience, farm inputs
Impact of Value Added by Agriculture Sub-Sectors on National Income of Tanzania from 1990 to 2023
Contribution of agricultural share to GDP has been declining from 42% in year 1990 to 26.5% in 2023 in Tanzania, posing a necessity to assess impact of agricultural sub-sectors’ value added on national income from 1990 to 2023 meanwhile testing the causal effect of the 2013 National Agriculture Policy. The study applied ADF unit root test to test of stationarity, Johansen test for co-integration, and Granger causality test for analysis. Results showed that in the long-run, value added by crop production, livestock production, fishing production influenced negatively GDP, while inflation influenced positively GDP. Furthermore, in short-run, value added by value added by crop sub-sector continued to influence negatively GDP while presence of the 2013 national agricultural policy influenced positively GDP. Also, results from Granger causality test showed that there was a bi-directional relationship between inflation and GDP; and uni-directional relationship between agricultural policy and GDP. The study recommended the need to intensify contribution of every agriculture sub-sector through re-designing profitable value chain activities which will promote GDP growth.
DETERMINANT OF COFFEE EXPORT PERFOMANCE IN TANZANIA FROM 1991-2024
This study examines the determinants of coffee export performance in Tanzania using secondary annual time series data collected from NBS spanning the years 1991 to 2024. Specifically, the analysis employed an Autoregressive Distributed Lag (ARDL) model to investigate the long run effects of four key variables: real producer price, relative export price to producer price, inflation, and the exchange rate on the volume of coffee exports. Furthermore, an ARIMA (1,1,3) model was utilized to forecast export trends up to the year 2030. The results indicated the existence of a long-run cointegration relationship between coffee export volume and the selected variables. The study findings shows that, the exchange rate had a positive and statistically significant impact on coffee exports, implying that depreciation of the Tanzanian shilling enhances competitiveness in international markets. Conversely, the relative export price to producer price exhibited a negative and significant effect, suggesting weak price transmission and limited benefits to farmers from export gains. Meanwhile, real producer price and inflation showed no significant long-run effects. The error correction term was correctly signed and highly significant, confirming a strong tendency toward long-run equilibrium after short-run deviations. The study concludes that exchange rate management and improving producer price incentives are critical to enhancing coffee export performance. Structural interventions to ensure better price transmission and institutional support for smallholder farmers are also essential. These findings support Tanzania’s policy ambitions under Vision 2025, FYDP III, ASDP II, and global targets such as SDGs 2, 8, and 9
Farm Income Determinants Among Agricultural Households in Tanzania: The Impact of Pre-Harvest Losses
Agriculture remains central to Africa’s development aspirations as outlined in Agenda 2063 and the Sustainable Development Goals, particularly in eradicating poverty and hunger. In Tanzania, despite efforts to enhance productivity, farm incomes remain low, with limited empirical focus on pre-harvest losses, a critical yet underexplored constraint. This study investigates the impact of pre-harvest losses on farm income among maize and paddy farming households in Tanzania, using data from the 2019/2020 National Panel Survey (NPS Wave 5). Guided by the Agricultural Household Model (AHM), the study employed a Robust Linear Mixed Effects Regression Model to account for data heterogeneity, outliers, and hierarchical structure. The response variable, farm income, was proxied by the log-transformed value of crop harvests. Key explanatory variables included pre-harvest loss, agronomic practices, and household-level characteristics, with soil type, crop type, and location (strata) modeled as random effects. Findings reveal a significant negative association between pre-harvest losses and maize income, highlighting the adverse effects of inadequate crop management during production. While the effect on paddy income was statistically insignificant, the overall trend underscores the importance of addressing losses before harvest. Mechanization, inorganic fertilizer use, and improved seeds were positively associated with higher farm income, while hired labor showed a negative association, suggesting inefficiencies in labor use. Intercropping and organic fertilizer use had mixed or marginal effects depending on the crop. Based on the study findings, the study concludes that reducing pre-harvest losses is vital for improving farm incomes and recommends integrated strategies involving improved agronomic practices, access to quality inputs, targeted extension services, and investment in efficient irrigation and labor-saving technologies. These insights provide a critical foundation for agricultural policy reform focused on enhancing productivity and rural livelihoods in Tanzania.
The Impact of Petrol Price Changes on Economic Welfare in Tanzania: A Time Series Analysis (2000–2024)
The study investigates the impact of petrol price changes on economic welfare in Tanzania, focusing on two key indicators: the Consumer Price Index (CPI) and the poverty headcount ratio (HCR). The study employed a quantitative time-series approach using monthly data spanning from 2000 to 2024. Data were obtained from EWURA for petrol prices and National Bureau of Statistics (NBS) for CPI and poverty data. To model petrol price volatility, the study applied ARCH and GARCH models. Additionally, regression models, including log-log linear specifications, were used to assess the relationship between petrol prices and macroeconomic indicators. In this study STATA 16.0 was used to analyse the data. The findings reveal significant volatility clustering in petrol prices, which is best captured by the ARCH (1), GARCH (1) model. A key result indicates that a 1% increase in petrol prices leads to a 0.96% increase in CPI, highlighting a strong inflationary effect. Also, the findings reveal that both current and lagged petrol prices significantly influence the Poverty Headcount Ratio explained by 32.9% and 24.45% respectively. The study concludes that petrol price fluctuations have considerable implications for inflation and poverty trends in Tanzania. The study recommends strengthening price stabilization mechanisms, enhancing macroeconomic resilience, and expanding targeted social protection programs. These findings contribute to the growing body of literature on energy economics and offer practical insights for Tanzanian policymakers in formulating sustainable fuel and poverty alleviation policies.
Petrol prices; Volatility; CPI; Poverty; GARCH; Tanzania.
Bayesian and Frequentist Approach for the Mixture Cure Models with Generalized Log-logistic Baseline: An Application to Cancer Data
Most event-data studies assume that everybody involved in the study will eventually encounter an instance of interest; nevertheless, it is anticipated that some of these participants will be exposed to the event. Cure models are frequently employed in time-to-event analysis to handle this data type. In mixed cure models, the target population is considered a mix of susceptible and non-susceptible individuals. The statistical analysis of these models provides the probability of cure (incidence model) and time-to-event in the vulnerable sub- population (delay model). This research presented a maximum likelihood estimate (MLE) and a Bayesian analysis for the six-parameter Generalized Log-Logistic (GLL) mixture cure model with cured, censored, and covariate variables. A mixture cure model with a GLL baseline is proposed to account for the rate of cured participants in the analysis. To apply various parametric hazard-based regression models, we recommend the GLL baseline distribution, which is based on a reasonable baseline hazard. The recommended model performs well on real-world data sets, highlighting the need for adaptive parametric regression formulations with time-dependent and time-independent covariates to assess hazard function and rate across time. Bayesian survival analysis with a non-informative prior outperformed the MLE method in terms of simulation parameter bias. The Generalized Log-Logistic model fits real cancer data better than the other survival models.
Bayesian approach; censored; frequentist approach; mixture cure model; susceptible individuals.
The Impact of Agricultural Production on Economic Growth in Somalia
This study examines the impact of agricultural production dominated by crop and livestock output and economic growth in Somalia using time-series data from to 1990-2022. Despite agriculture’s major role in Somalia’s economy, empirical evidence on its growth effects remains limited, especially during the post-conflict period. Using an Autoregressive Distributed Lag (ARDL) framework, this study examines the long-run and short-run dynamics of GDP, agricultural production, gross fixed capital formation, arable land, and agricultural employment, supplemented by Granger causality analysis. Funding suggests a stable long-run equilibrium relationship between the variables. Agricultural production and capital formation have a positive and statistically significant impact on economic growth, whereas arable land and agricultural employment have poor and inconsistent contributions, indicating structural inefficiencies in land use and labor productivity. Evidence of bidirectional causality between agricultural production and GDP implies mutual reinforcement rather than a one-way growth process. Following the shocks, short-run equilibrium occurred rapidly. The findings highlight the necessity of productivity-enhancing agricultural investment for long-term economic growth in unstable and conflict-affected economies, such as Somalia.
GDP, agricultural production, gross fixed capital formation, arable land, agricultural employment, ARDL, Granger causality, and ARIMA
Factors Affecting the Performance of Small and Medium Enterprises in Tanzania: The Case of Urban West Region, Zanzibar
Small and medium enterprises (SMEs) are critical for socio-economic development such that no country can fully achieve sustainable growth and development without the active contribution of its SMEs. Accordingly, this study examined the influence managerial expertise, marketing, and technology use on the performance of SMEs in Urban West Region, Zanzibar. The study employed a cross-sectional survey design and data were collected from 378 SME owners and managers and analyzed data via Ordinal Logistic Regression (OLR). The findings reveal that managerial expertise has a significant and positive impact on SME performance with management training and evaluation systems emerging as key contributing factors. In terms of marketing, the study found that strategic allocation of revenue to marketing activities strongly and significantly enhances performance outcomes. Regarding technology use, only investment in ICT infrastructure demonstrated a marginally significant effect on SME performance. The study contributes to the Resource-Based View (RBV) by demonstrating that internal organizational capabilities, particularly managerial expertise, marketing, and technology use, are important strategic resources influencing SME performance. The findings support the RBV assumption that effective utilization of internal resources enhances firm performance and extend the applicability of RBV in the context of SMEs in Zanzibar. The study recommends that SME owners and managers should strengthen managerial competencies through training and effective evaluation systems, allocate sufficient resources to marketing activities, and enhance investment in appropriate technological infrastructure to improve business performance.
Small and Medium Enterprises, SME Performance, Managerial expertise, Marketing, Technology use and Resource-Based View (RBV).
Evaluating Risk Factors for Perinatal Deaths in Dar Es Salaam
This study assessed the risk factors contributing to perinatal deaths in Dar es Salaam, Tanzania, where the perinatal mortality rate stands at 36 per 1,000 live births higher than the sub-Saharan Africa regional average. Despite various interventions, understanding of the underlying causes remains limited. The study aimed to identify key determinants to guide more effective strategies for improving maternal and neonatal health outcomes. Secondary data were obtained from eight health facilities in Dar es Salaam for the period 2015 to 2021. Statistical analyses were conducted using Stata Version 15. Chi-square and Cramér’s V tests were used to examine associations, and binary logistic regression was employed to assess the relationship between selected risk factors and perinatal deaths, including both neonatal deaths and stillbirths.Out of 12,070 recorded cases, 3,520 (29%) were neonatal deaths, while 8,550 (71%) were stillbirths. The most significant causes of death included intrapartum asphyxia (27%) and intrauterine death (17%). Strong associations were identified between perinatal deaths and maternal or obstetric conditions, particularly spontaneous preterm labor, antepartum hemorrhage, hypertensive disorders, and intrauterine death. Maternal hypertension and obstetric hemorrhage were found to be major contributing factors.The study further revealed that a notable proportion of perinatal deaths occurred among mothers with no documented obstetric complications. These findings underscore the need for further research into risk factors affecting apparently healthy mothers during childbirth, to enhance preventive strategies and reduce perinatal mortality in similar urban settings.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-sa/4.0
Factors Influencing Choice of Clean and Non-Clean Energy Sources for Lighting by Households in Geita, Tanzania
Utilization of clean-energy sources for lighting among households vary among regions of Tanzania such that at least half of all households in Geita region do not use clean energy sources for lighting. Following this situation, the study was undertaken to analyze factors influencing choice of clean and non-clean energy sources for lighting by households in Geita, Tanzania so as to ultimately formulate strategies for promoting sustainable use of clean and modern energy use by households. The study used secondary data of Tanzania Households Budget Survey 2017/2018 from the National Bureau of Statistics. Results showed that location of households, gender, and education level, household’s expenditure and occupation had significant effect on choice of energy sources utilized. The study recommends to the government the need to formulate strategies for improving accessibility and affordability of clean energy sources for lighting in remote and under-privileged areas in the country, and as well as formulate mechanisms for monitoring and evaluating sustainable utilization of clean energy sources by households
Contribution of Agriculture Sub-Sectors to Gross Domestic Product in Tanzania Mainland 1993 – 2022
The primary objective of this study was to assess the contribution of agricultural subsectors on Tanzania mainland GDP, highlighting their crucial role in fostering economic growth and development. This analysis utilized annual time series data from the National Bureau of Statistics, covering the period from 1993 to 2022.Augmented Dicker-Fuller and Zivot-Andrews approach to detect structural breaks and test for stationarity. The long- and short-run relationships between GDP and the agriculture subsectors were examined in the study using the Vector Error Correction Model. The coefficient of ECT indicates that the rate of adjustment of GDP to its long-run equilibrium is 6.8% annually. Short-run parameters of lagged variables were not very informative. The effects of agriculture sub-sectors on gross domestic product (GDP) reveals that crop and fishery positively impacting GDP, while livestock and forestry had a negative insignificant impact. The study concluded there’s significant long-run relationships between GDP and the agricultural sub-sectors. Forecasts indicate positive growth trends for both GDP and the individual agricultural sub-sectors. The study recommends a vertically integrated policy approach to balance out the disparities among the sub-sectors.
The Contribution of Conservation Agriculture on Food Security in Mainland Tanzania
Introduction:Conservation agriculture (CA) is increasingly recognized as one of the most practical and promising strategies for achieving sustainable agricultural practices. This study aimed to explore the impact of CA on food security in Mainland Tanzania, using time series data from 1988 to 2023. The primary focus was to evaluate both the short-term and long-term effects of key CA practices such as minimal soil tillage, water management, crop diversification, and agroforestry on food security, while also investigating causal relationships and forecasting future trends. Methodology: To estimate the effects of CA on food security, the study utilized the Vector Error Correction Model (VECM), which allows for the analysis of both short-term and long-term relationships between variables. The Granger Causality test was applied to determine the direction of causality between CA adoption and food security. Furthermore, food security was forecasted for the years 2024 to 2028 using the ARIMA (2, 1, 1) model, providing predictions based on historical trends. Results: The study’s findings revealed that CA has a positive long-term impact on food security, though its effects in the short term were negative. A unidirectional causality was identified, with food security driving the adoption of CA practices. The ARIMA model projections indicated steady increases in food security from 2024 to 2028, with forecasted values of 3532.187, 3682.171, 3837.568, 3996.199, and 4158.478 metric tons. Conclusion: The study concludes that CA practices are vital for improving food security in Tanzania. It recommends prioritizing practices such as minimal soil tillage, water management, crop diversification, and agroforestry to enhance the impact of CA. By focusing on these practices, the adoption of CA can be promoted as a key strategy for improving food security in the country.
Factors Affecting Sorghum Production in Tanzania
Sorghum, a staple crop critical to food security in Tanzania’s semi-arid regions, faces significant productivity challenges. This study examines the factors influencing sorghum production in Tanzania, using cross-sectional data from the National Sample Census of Agriculture (NSCA) 2019–2020. The research analyzes demographic characteristics, such as age, gender, and education of household heads, as well as agricultural inputs like seeds, fertilizers, and agrochemicals to determine their impact on sorghum production. Employing multiple regression analysis, the results reveal that education positively influences productivity, while fertilizers show a negative correlation, likely due to improper application. Seed quantity, herbicides, and insecticides significantly enhance production. Conversely, larger plot sizes are associated with lower productivity, suggesting inefficiencies in resource management. The study emphasizes the need for targeted policy interventions to improve access to high-quality seeds, provide farmer education on input management, and promote better resource allocation for smallholder farmers. These findings offer valuable insights for policymakers aiming to boost sorghum productivity, strengthen food security, and support rural livelihoods in Tanzania.
Youth Unemployment and Income Inequality: Exploring the Linkages in Tanzania
This study investigates the intricate relationship between youth unemployment and income inequality in Tanzania, aiming to understand the underlying linkages and evaluate the efficacy of various policy responses. Using a time series analysis, the study models the youth unemployment rate as the dependent variable, with GDP growth rate, inflation rate, income inequality, youth literacy rate, government expenditure on education, and population growth rate as independent variables. Data spanning from 1990 to 2022 are collected from reputable sources, including the World Bank and the Tanzania National Bureau of Statistics. Preliminary findings suggest significant correlations between youth unemployment and key economic indicators, particularly GDP growth and government expenditure on education. The result revealed that income inequality significantly affect youth an employment. This might occur due to the implementation of government policy such as redistributive policy which aims to promote social equity, reduce poverty and ensure fir allocation of resources within the society.
Factors Influencing Maize Production in the Coastal Zone of Mainland Tanzania during Long Rainy Season
This study sought to assess the factors affecting maize production in the Coastal zone of Mainland Tanzania, using data from the 2019/2020 National Sample Census Agriculture. Correlation and multiple linear regression were employed for analysis. The findings from correlation analysis indicated that maize output was significantly correlated with land area cultivated (r=0.94), seed quantity (r=0.97), and fertilizer quantity (r=0.57) at the 5% significance level. Furthermore, results from the multiple linear regression revealed that land area cultivated (p=0.00), use of household labour compared to non-household (p=0.00), seed quantity (p=0.00), and fertilizer quantity (p=0.00) positively influenced maize production, while pesticide use (p=0.00) had a negative effect on quantity of maize harvested. The results also showed that total production costs increased with output (p=0.00) and that household with educated fared better compared to non-educated households (p=0.01). The study concluded that strategies for improving maize yield in a cost-effective way should be developed by incorporating optimization techniques. Furthermore, the study recommended to the government the need to formulate strategies for commercializing small-scale maize production to improve welfare of farmers.