Industrialization policy and economic growth nexus within political dynamics in Tanzania

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This study uses time-series data from 1990 to 2021 to investigate the relationship between industrial policy execution and economic performance in Tanzania's changing political situation. A Simultaneous Quantile Regression (SQR) model is used to identify heterogeneous effects throughout the economic growth distribution and a Multiple Linear Regression (MLR) model is utilized to evaluate and strengthen the findings. The findings show that excellent governance considerably boosts economic growth at the bottom end of the distribution (25th quantile), stressing its importance during periods of economic underperformance. At the top end (75th quantile), industrial sector expansion is identified as a major contributor to higher economic growth. Regime-specific study suggests that during President John Magufuli's fifth administration, the industrial sector provided 7% of GDP growth, while agriculture grew negatively. Interestingly, agriculture only showed a beneficial influence during the third and sixth administrations, with a major revival under President Samia Suluhu Hassan, which can be linked to greater budgetary allocations and policy priorities. Furthermore, the findings emphasize the role of political will, institutional quality, and sector-specific investment in generating long-term economic growth. The study suggests that strengthening institutional frameworks coupled with stakeholder participation is crucial to ensure that industrialization initiatives are inclusive and effective. These findings lead broader discussion of structural change and impactful for policymakers in seeking industrialization as a path for long-term prosperity in Tanzania. Development Economics industrialization policy economic growth political regime Tanzania Figures Figure 1 1.0 INTRODUCTION Economic growth and development as result of industrialization is an escalating agenda in Tanzania for so many years to date (Byaro et al, 2022 ; Mandalu et al, 2018 ). Moreover, industrial growth is an engine for economic development and growth in the country. Tanzania is among the countries whereby the contribution of both processing and manufacturing industries to the GDP cannot be neglected (Afolabi and Laseinde, 2019 ; Abbasi et al, 2021 ; Wang et al, 2020 ). The country is striving for achieving industrialization for decades, though the development of its achievement is at a meager rate (Odijie, 2022). The crucial sectors such as agriculture and energy remained at stunting growth since independence despite of various strategies and policies to reach the level of industrialization (Rahman, 2020 ). "It is an undeniable fact that, the manufacturing sector plays a key role in the growth of any economy, especially in those economies that are yet to be fully developed (Zheng and Walsh, 2019 ). It is from this sector that developing countries can catch up with the rest of the world. The manufacturing industry makes room for innovation and growth of production technology, the creation of massive employment especially in small and medium-scale manufacturing industries like textile, food, beverage, iron and steel industries (Mwang'onda, et al , 2018; Pingkuo and Xue, 2022 ). If well connected with other sectors, manufacturing industries are not only large consumers of natural resources and of primary products, but also a supplier of inputs to other medium and large-scale industries thus support their growth and development (Wang et al, 2022 ). Basically, since independence, the Tanzanian industrial sector is growing at a low pace given the existing policy and strategies diluted by political changes in the country (Mandalu et al, 2018 ). Since 1961 to date, there are six political regimes existed in the country, and in each regime, there have been different policy reforms and implementation approaches toward achieving industrialized economy (Khan, 2018 ). Of all six regimes, the 5th regime under His Excellence, Late John P. Magufuli insisted on industrialization strategies by applying the Swahili slogan of " Tanzania ya viwanda " meaning " Industrialized Tanzania ". The struggle among others was to reform policies including export and import policies. The importation of some products such as cooking oils was reduced to create internal demands for local industries' products. Nevertheless, there was a remarkable struggle to stimulate local production of Agricultural raw materials such as Palm and Sunflower crops which could increase the production of cooking oil in the country. However, with all the struggles, there are still a lot of manufacturing and processing industries still not working properly and others are even extremely dead (Bidu et al, 2021 ). These dead industries to mention few are the textiles industries including but not limited to MWATEX in Mwanza, MUTEX in Musoma, and URAFIKI in Dar es Salaam. These industries were very significant in driving the agricultural sector and the industrial growth of the country but have recently been left like debris residuals. Mwang'onda, et al , (2018) found that, the stagnant contribution share of the manufacturing sector is linked with; implementation lags on ambitious uncoordinated plans, slow transforming economic structure which is dominated by agriculture, and competition from low-priced manufactured imports from Asian economies. Besides, while singing the song of industrialization in the country, there is no real seriousness of political leaders to reflect what they say on public podiums regarding industrialization in the country (Chinsinga et al, 2022 ). Also, the budget for the agriculture sector which is the key driver of industrialization in the country has been allocated little in previous years compared to other sectors like construction and others; this justifies the opposite of the true colors of the political leaders preaching about industrial development. Notwithstanding this fact however, the current regime (6th regime) for the first time, the budget for the agriculture sector was significantly high aiming at the growth of the sector at 10% rate by 2030 1 . Furthermore, most studies have delved on the investigation for the impact of industrialization on the economic growth and perverse but not within the political dynamics specifically in Tanzania using simultaneous quantile regression model; this is the novelty of this study. Therefore, the purpose of this study was to analyze the relationship between different political regimes in Tanzania and their influence on the implementation effectiveness of industrialization policies, as well as to assess how these factors collectively affect economic growth. The key research question guided this study was “How do varying political regimes in Tanzania influence the effectiveness of industrialization policy implementation and its subsequent impact on economic growth. The rest body 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, Section 5 provides a detailed discussion of the results, and Section 6 provides the conclusion and policy implications. 2.0 LITERATURE REVIEW 2.1 Theoretical review This study adopts the theory of de facto power relations propagated by Acemoglu and Robinson ( 2006 ). The central idea described in the theory of de facto power asserts that, there are always dynamics that happen within the political regime changes. The power and development dynamics changes happen within the power transition in the sovereign states (Linderfalk, 2022 ; Sibiya, 2021 ; Teorell and Lindberg, 2019 ). The economic growth and development of a nation are linked with good state power and a sTable institutional setup (Acemoglu and Robinson, 2005). In developing countries like Tanzania, the presence of sTable institutional quality of the existing regime, the agricultural sector seems to have a significant contribution to the economic growth and development of her population which is paramount to industrial development (Muoneke et al, 2022 ; Yimenu, 2023 ; Sodhi et al, 2022 ). The reason why this study adopts this theory is that; First, by applying the theory of de facto power relations can provide a nuanced understanding of how industrialization policies in Tanzania are implemented and resisted in the economic development process. Second, it allows for an analysis of how various political regimes shape the power dynamics surrounding economic growth and development. Third, it offers a critical lens for analyzing power dynamics in society, emphasizing the importance of informal practices and real-world interactions; by focusing on how power is actually exercised, it enriches our understanding of governance, social structures, and economic policies. It is in that case obvious that, the development of a country is therefore linked directly to the performance of each political power ruling at a particular period. Ironically, industrial policies involve the distribution of state resources; distributional policies that run against the survival interest of ruling elites (for example, by emboldening oppositional groups) will face direct challenges from ruling elites. Besides, in the case of advanced economies, political power changes are not an issue for industrial development and economic growth. It is just the best allocation of resources and better policy implementation that surpass the development (Madariaga, 2020 ; Xue et al, 2020 ). The key assumptions (Pegg, 2019 ) of this theory among others are; firstly, Power is not possessed but is rather exercised in relationships between individuals, groups, or institutions. Secondly, the context in which power operates including cultural, social, and economic factors are significantly influences power dynamics and outcomes. Third, Individuals and groups are not merely passive recipients of power; they have agency and can resist, negotiate, or redefine power relations. Lastly, the way power is exercised in practice can lead to different outcomes than those predicted by formal theories or policies, highlighting the importance of understanding power dynamics. The states encourage the development of industrialization as the driver for economic growth and competitive advantage over the rest of the world (Brock et al, 2021 ). Many countries in the west world underwent industrialization so many years ago while African countries still struggling to achieve this kind of economic power. Therefore, the study merges the central idea to this theory since it wants to present the nexus between industrialization and economic growth while controlling for intermittent variables of agriculture, energy and good governance within the political power dynamics in Tanzania since 1990. The study aims to see what is the reality in the practice of industrial policy implementation for the said political power dynamics on economic growth to date; does it have a positive impact on the livelihoods of Tanzanians or may be the situation is getting worse while political leaders are singing the song that, the industrialization is possible in Tanzania. To test this theory empirically, the study employs the Simultaneous Quantile regression model. 2.2 Empirical Review Several scholars investigated and present empirical evidence that support that the areas of industrial development, agriculture, energy, and good governance contribute greatly to economic growth and perverse as follows: According to Opoku and Yan ( 2019 ), industrialization is a significant driver of economic growth and trade openness strengthens the impact of industrialization on economic growth. The study focused on time series data from 1980–2014 to explore "Industrialization as a Driver of Sustainable Economic Growth in Africa". They used the generalized method of moments. Then, Kessy ( 2022 ) investigated "The Long Waiting for Relocating Capital City in Tanzania: The Continuity of the Game Changer and the Challenges Ahead." The study found that a number of variables combined to successfully move the government from Dar es Salaam to Dodoma. The primary one, however, was the fifth government's strong political commitment and the sixth government's subsequent continuation to offer strategic leadership in carrying out the concept that Tanzania's first president had proposed more than 40 years earlier. The findings shed light on the concepts and tactics used to design Tanzania's new capital city. It also looked at some literature on the relocation of capital cities in other parts of the world. It closes by emphasizing that the strong personal motivations demonstrated by President John Magufuli and later President Samia Suluhu Hassan had shattered the long-held taboo of empty words since the early 1970s. This is because the previous government's efforts to relocate the city were hampered by intractable administrative problems and a shortage of foreign cash. This is an illustration of how political dynamics (good governance) lead to developmental advances in Tanzania. Nguyen et al. ( 2021 ) used fixed-effect regressions to examine the impacts of poverty province, income inequality, public administration quality, and governance on per capita income. The findings indicate a positive and nonlinear relationship between per capita income and public administration and governance. Improved public administration and governance also seem to enhance income distribution and lower poverty in a country. On the other hand, using panel fixed effects regressions, Mao et al. ( 2021 ) evaluated the overall impact of China's industrial policies from 2000 to 2012. They discovered that the relative development stage of an industry in relation to the global frontier determines whether or not China's industrial policies and S&T policies contribute to productivity growth in that industry. In particular, they contended that China's S&T and industrial policies boost productivity development in high-tech, globally growing industries more than in domestically mature and catching-up businesses. Besides, Azam et al. ( 2021 ) conducted a study in ten newly industrialized nations titled "Analyzing the relationship between economic growth and electricity consumption from renewable and non-renewable sources: Fresh evidence from newly industrialized countries." They used the Granger causality methods, panel unit root test, panel heterogeneous co-integration method, and panel Fully Modified Ordinary Least Square. The Granger causality test results also show that there is bidirectional causation between renewable electricity consumption and economic growth in both the short-run and long-run, supporting the feedback hypothesis. Secondly, the panel Fully Modified Ordinary Least Square confirms that all studied variables have a positive long-run effect on economic growth, with a 1% increase in renewable and non-renewable electricity consumption increasing economic growth by 0.095% and 0.017%, respectively and finally the panel unit root test and panel co-integration method confirm that economic growth, renewable electricity consumption, non-renewable electricity consumption, gross capital formation, labor force, and trade openness are co-integrated. Ferrannini et al. ( 2021 ) also investigated industrial policy for sustainable human development in the post-Covid19 period by a systematic literature study. The findings show that a better grasp of industrial policy might make government mistakes even more hazardous. The study showed that in order to play a new role in our post-Covid 19 society, theoretical foundations, as well as governance and execution methods related to industrial policies, sustainability, and development, particularly during economic and financial crises, are required. In conclusion, a lack of studies has highlighted the relationship between industrialization policy implementation and economic growth within Tanzanian political dynamics using a simultaneous quantile regression model using time series data such as decoupling or entropy-weighted TOPSIS. As a result, this will provide novelty while also filling the study gap. 2.2.1 Evaluation of industrial policy across different political regimes Tanzania's industrial policy has developed alongside its political regimes. Based on Table 1 , the second regime, led by President Mwinyi, implemented structural adjustment plans that liberalized and privatized the economy, but inadequate state support stifled industrial expansion (Mandalu et al., 2018 ). Mkapa's third regime pursued reforms and implemented Vision 2025, but industrial policy remained fragmented, with little direct incentives (Byaro et al., 2022 ). Next, Kikwete's fourth regime connected industrialization and agriculture through the Industrial Development Strategy (IIDS) and Kilimo Kwanza (Kessy, 2022 ), but faced finance and coordination challenges. However, Magufuli's 5th regime adopted a more aggressive attitude with " Tanzania ya Viwanda ," spending substantially in infrastructure and implementing import restrictions, resulting in a 7% industrial GDP share (Chinsinga, et al., 2022 ). According to Kitole et al. ( 2023 ), the sixth regime in Samia’s regime prioritizes continuity by increasing agriculture budgets and fostering agro-industrial ties through public-private partnerships. The emphasis now is on sustainable, inclusive growth that is more closely aligned with the Sustainable Development Goals (SDGs). Therefore, this section provides a better understanding of how industrial policy focus have changed in response to political ideologies and agendas. Table 1 Policy instruments summary Regime Core Industrial Policy Focus Key Instruments Sources 2nd Structural Adjustment Programs (SAPs) Privatization and trade liberalization Mandalu et al. ( 2018 ) 3rd Tanzania Development Vision 2025 Institutional reforms and private sector engagement Byaro et al ( 2022 ) 4th Industrial Development Strategy (IIDS) Kilimo Kwanza initiative Sectoral strategies and integration with agriculture Kessy ( 2022 ) 5th Tanzania ya Viwanda agenda Infrastructure expansion and import restrictions Chinsinga et al. ( 2022 _ 6th Agro-industrial growth Inclusive development Increased agriculture funding and SDG alignment Kitole et al ( 2023 ) Source: Literature review 3.0 METHODOLOGY 3.1 Research approach and design This study employed purely quantitative methods approach involving econometric analysis of time series data on industrialization, GDP growth rate, Agriculture, energy and good governance as economic indicators across different political regimes in Tanzania over several decades. These data were analyzed using econometric models to identify correlations and causal relationships. Besides, the study adopted a longitudinal research design which is a research design used to study the same subjects over an extended period of time, often months, years, or even decades. Thus, this design allows to observe changes and developments in economic growth as a result of industrialization policy implementation over different periods, making it particularly useful for studying long-term effects GDP growth within different political dynamics. Unlike cross-sectional studies, which compare different individuals at one point in time, longitudinal studies track the same individuals repeatedly, providing valuable insights into cause-and-effect relationships and how variables evolve. 3.2 Data type and source This study used time series data from the World Bank dataset for 32 years (1990–2021) downloaded and analyzed in the STATA program. However, some specific data from the previous literatures have been used to substantiate the argument on the discussion of findings section. 3.3 Estimation Model In this study, a Simultaneous Quantile regression model was used to estimate the relationship between variables (dependent and independent) in different percentile levels across the distribution of the independent variables simultaneously. This study used Simultaneous Quantile regression because (Waldmann, 2018 ) it is a statistical technique that estimates the conditional quantiles of a response variable; unlike ordinary least squares (OLS) regression, which estimates the mean of the dependent variable given the independent variables, Simultaneous quantile regression provides a more comprehensive view by estimating the effects of predictors at different points (quantiles) in the distribution of the response variable ( ibid ). This is relevance model to this study given the fact that, the study is looking at the changes that happened in implementing industrial policy on economic growth as well as general development of the nation. With Simultaneous Quantile Regression, “in cases where either the requirements for mean regression, such as homoscedasticity, are violated or interest lies in the outer regions of the conditional distribution, Simultaneous quantile regression can explain dependencies more accurately than classical methods’ (Das et al, 2019 ). Table 2 Variables, symbols, unit of measures and sources of data Variables Symbols Unit of measure Sources Gross Domestic Product GDP GDP growth (annual %) World Bank (1990-21) Industry IND Industry (including construction), value added (% of GDP) World Bank (1990-21) Agriculture AGR Agriculture, forestry, and fishing, value added (% of GDP) World Bank (1990-21) Energy ERG electricity access (% of Population) World Bank (1990-21) Good governance GG Control of Corruption: Percentile Rank, Upper Bound of 90% Confidence Interval World Bank (1990-21) Source: World Bank data (2020–2021) 3.3.1 Empirical model The empirical model of this study was designed including the main and control variables enabling to test and prove the envisaged theory; basically, empirical models validate or refute theoretical frameworks through experimentation during statistical analysis. Thus, the independent variable of interest in this study is the industry because the study is interested at presenting the observation on what happened to industrial development as far as policy implementation concern during different political regimes which may be impacted the economic growth and development. However, other control variables such as agriculture, energy and good governance are considered in the model since they also have contribution to economic growth and development in the country. The linear form of the empirical model is given as follows; $$\:SqGDP\:\left(\tau\:|X\right)={\beta\:}_{0\:}\left(\tau\:\right)+{\beta\:}_{1\:}\left(\tau\:\right)\:IND+\:{\beta\:}_{2\:}\left(\tau\:\right)\:AGR+\:{\beta\:}_{3}\left(\tau\:\right)\:ERG+\:{\beta\:}_{4\:}\left(\tau\:\right)\:GG+\:\mu\:$$ Where; \(\:SqGDP\:\left(t|X\right)\) - conditional quantile of Economic growth (GDP) given Industrial growth, Agricultural growth, Energy and Good governance at 25th, 50th and 75th quantiles. \(\:{\beta\:}_{\text{1,2},\text{3,4}\:}\left(\tau\:\right)\) - coefficients that vary with the quantile levels \(\:{\beta\:}_{0\:}\left(\tau\:\right)\) - Constant term at Quantile level IND - Industrial growth (manufacturing) AGR - Agricultural growth ERG -Access to electricity of population GG - good governance 𝜇- error term The coefficients from quantile regression represent the change in the quantile of the dependent variable (GDP) for a one-unit change in the independent variables (Industry, Agriculture, Energy and Good governance), holding other things constant. 4.0 RESULTS 4.1 Descriptive Statistics and trends The summary results in Table 3 reveals that, Tanzania's GDP grew on average increase of 5.18 per cent annually up to the year 2021, this growth was a result of contribution of manufacturing output which was growing at an average of 7 per cent and so boosted the GDP up to 8.3 per cent by the year 2023 as shown in Fig. 1 ; this implies positive effects of different political regimes on the policy implementation outcome. Actually, the growth of manufacturing sector suggests several interrelated factors that fostered a conducive environment for industrial expansion. Firstly, government initiatives aimed at promoting industrialization, such as the National Development Vision and the industrial development Policy of 1996–2020, have provided strategic frameworks and incentives for local and foreign investments. Secondly, improvements in infrastructure, including transport, energy, and communication networks, have facilitated easier access to markets and reduced operational costs for manufacturers. Third, growing demand for both local and export products, driven by a rising middle class and increased urbanization, has further stimulated the sector. Fourth, the government's focus on value addition in agriculture has encouraged agro-industrial development, leading to greater diversification within the manufacturing sector. Collectively, these factors have created a robust ecosystem that supports sustained growth in Tanzania's manufacturing industry (see Mbelwa, 2023 ). Also, in Table 3 agriculture sector contribution to the GDP was at an average of 21.5 per cent; while average of population having access to electricity was meagre for 32 years this suggests for more initiatives in the energy sector to ensure more supply of electricity to the citizens and industries at low cost. Besides, the good governance in the country average score was 32.4 suggesting that, while there are some functioning elements in the governing settings, there is significant potential for enhancement in other elements such as accountability, rule of law and citizen participation to achieve more effective and fair governance. Table 3 Descriptive statistics Variable Observation Mean Standard Deviation Minimum Maximum Gross Domestic Product 32 5.177347 2.001396 .5843221 7.672155 Industry 32 7.946562 1.897883 4.32 11.07 Agriculture 32 21.53741 4.924768 13.00126 29.27897 Energy 32 1.21875 2.528136 -4.47 7.1 Good Governance 32 32.46083 21.3032 0 57.56097 Source: World Bank data (2020–2021) 4.2 Simultaneous Quantile Regression results The simultaneous quantile regression results are categorized into two Tables as follows; Table 4 indicates the general results for economic growth in different quantiles given the main independent variables of industry while accounting for other control variables (agriculture, energy, good governance); and Table 5 indicates the results for economic growth change within five different political regimes after interaction effect starting with the 2nd regime under his Excellence late Ally Hassan Mwinyi up to the 6th regime led by Her Excellence Dr Samia Suluhu Hassan. For the purpose of the analysis indicated in Table 5 , We used only two independent variables of industry (main variable) and agriculture (intermittent variable) given the fact that, Agriculture is the most potential and being key leading economic sector since independence that drives the industrial development and economic growth of the country by contributing to about 30 per cent of the GDP and employing about 80 per cent of the population (see Gupta, 2020 ; Kitole, et al, 2023 ). In that regard, there is a clear link of the two sectors (industry and agriculture) which are key contributors of the economic growth in Tanzania. The study also considered to use five regimes instead of all six regimes due to data availability as the data of selected variables for the 1st regime under President Mwalimu Julius K. Nyerere was not available. The five regimes are led by different presidents as follows; 2nd regime under his Excellence late Ally Hassan Mwinyi, 3rd regime under his excellence late Benjamin Mkapa, 4th regime under his excellence Dr. Jakaya M. Kikwete, 5th regime under his excellence late Dr. John P. Magufuli and the last 6th regime currently is her excellence Dr. Samia S. Hassan. The interest of this analysis in Table 5 was to respond to the basic research question of the study by checking through model estimation the changes happened on economic growth as a result of industrial policy implementation and agricultural development within five different political dynamics/regimes in the country since 1990–2021. The dummy variable “regime_2 up to regime 6 was introduced on the dataset in order to track the changes of the economic growth after interaction effect within each regime. The analysis in Tables 5 and 6 , the interaction term “regime multiplied by a specific independent variable”; because interaction term is useful in estimating the difference between the main effect and the effect of the interaction term when another variable changes. So, we generated on STATA another new two interaction variables of industry and agriculture multiplied by dummy variable “regime_2 up to 6 in order to see the impact of industry and agriculture on GDP during the 2nd, 3rd, 4th, 5th and 6th regime compared to the main effect of industry and agriculture on economic growth indicated on Table 4 . The results in Table 4 indicates that, there is positive relationship between industrial development and economic growth. For example, at 75th Quantile of Table 4 , the results are significant with positive coefficient indicating that, the industrial growth has direct effect on economic growth. This suggests for further strategies to boost the sector by more than 15 per cent in order to increase its contribution to the GDP in the country. While that hold, results in Table 5 after adding interaction effect of political regime indicates that, at 25th, 50th and 75th quantiles industry and agriculture sectors have shown to be statistically significant during the 5th regime under the President Late Dr John P. Magufuli. The industry seems to have positive impact on GDP growth by 7 per cent while agriculture shown negative growth and impact on the GDP, this suggests that, the reasons was due to little budget allocation and low commitment of political leaders on improving agriculture during that regime; rather more emphasis of high budget allocation was put on the infrastructures development such as roads, Airlines, bridges, Standard Gauge Railway (SGR) and Hydroelectric power projects during this regime. The study also reveals that, there is statistically significant and positive relationship between economic growth and good governance at 25th quantile in Table 4 . This implies that, the presence of good governance such as rule of law, less corruption can result to induced economic growth and development in the country due to better utilization of resources. All results are consistence thus, remain robust across quantiles. Table 4 General Simultaneous Quantile Regression results for economic growth 25th Quantile Coefficient Standard Error T-statistic P-value Industry .4220541 .4707558 0.90 0.378 Agriculture − .0993876 .282679 -0.35 0.728 Energy − .218456 .2396758 -0.91 0.370 Good Governance .0586577 .0343312 1.71 0.099* Constant 1.229423 3.194014 0.38 0.703 50th Quantile Industry .369086 .3848276 0.96 0.346 Agriculture − .0135558 .1942061 -0.07 0.945 Energy − .2208022 .2513934 -0.88 0.388 Good Governance .034911 .0280578 1.24 0.224 Constant 1.952508 2.760089 0.71 0.485 75th Quantile Industry .5736774 .1461396 3.93 0.001** Agriculture − .0306679 .0762191 -0.40 0.691 Energy − .1689888 .1593819 -1.06 0.298 Good Governance .0191523 .0220148 0.87 0.392 Constant 1.83308 2.083023 0.88 0.387 Source: World Bank (2020-21) Note: ***p < 0.01; **p < 0.05; *p < 0.1 Table 5 Simultaneous Quantile Regression results for economic growth after adding interaction effect of political regime from 2nd to 6th regime 25th Quantile Coefficient Standard Error T-statistic P-value industryregime2 1.323432 10.82907 0.12 0.904 industryregime3 − .6122854 .8536315 -0.72 0.480 industryregime4 .010422 3.090753 0.00 0.997 industryregime5 6.332585 2.319413 2.73 0.012** Industryregime6 .758611 .6737066 1.13 0.272 agricregime2 − .2341447 3.683214 -0.06 0.950 agricregime3 .6101737 .6949598 0.88 0.389 Agricregime4 .320222 1.034684 0.31 0.760 Agricregime5 -1.642411 .61158 -2.69 0.013** constant -1.762677 5.403127 -0.33 0.747 50th Quantile industryregime2 .7402329 13.06186 0.06 0.955 industryregime3 − .7344921 .5784597 -1.27 0.217 industryregime4 .380202 1.823518 0.21 0.837 industryregime5 7.121044 2.543571 2.80 0.010** Industryregime6 .5763324 .4211037 1.37 0.184 agricregime2 − .11615 4.459361 -0.03 0.979 agricregime3 .6281556 .4442024 1.41 0.171 Agricregime4 .1418301 .6295561 0.23 0.824 Agricregime5 -1.914188 .7111059 -2.69 0.013** constant − .3008018 3.377252 -0.09 0.930 75th Quantile industryregime2 11.83904 13.3995 0.88 0.386 industryregime3 − .1739402 .6925591 -0.25 0.804 industryregime4 .5052748 1.064733 0.47 0.640 industryregime5 7.488387 2.609458 2.87 0.009** Industryregime6 .4768443 .4299096 1.11 0.279 agricregime2 -3.875984 4.494175 -0.86 0.397 agricregime3 .3727152 .4874098 0.76 0.452 Agricregime4 .0691 .3194281 0.22 0.831 Agricregime5 -2.038714 .7301326 -2.79 0.010** constant .4970931 3.447875 0.14 0.887 Note: agricregime6 omitted because of collinearity (fitting base model) , ***p < 0.01; **p < 0.05; *p < 0.1 Source: World Bank (2020-21) 4.3 Ordinary Least Square regression results after adding interaction effect of political regime The study used Ordinary Least Square (OLS) estimation model (Multiple Linear Regression) in order to triangulate and strengthen on the Simultaneous Quantile Regression results and see the comparability between the two models while ensuring robust check. Triangulation of different methods in research enhances the credibility and robustness of results by cross-verifying information from different angles, thereby reducing bias and increasing the reliability of conclusions; by corroborating evidence from diverse sources, triangulation helps ensure that findings are more comprehensive and well-supported, leading to a deeper understanding of the research issue (Qassimi, 2023 ). The study used OLS in order to quantify the effect of Industry and Agriculture (predictors) on the outcome variable (GDP) for each political regime (after adding interaction effect of political regime). The Multiple Linear Regression equation is given in reduced form; $$\:regGDP={\beta\:}_{0}+{\sum\:}_{i=1}^{n}{\beta\:}_{i}({X}_{i}+{\mathbb{Z}}_{i})+\mu\:$$ Where; \(\:regGDP\) - regressing Gross Domestic Product (dependent/outcome variable) \(\:{\beta\:}_{0}\) -Constant term \(\:{\beta\:}_{i}\) - coefficients of variables with interaction term for dummy variable “regime” \(\:{X}_{i}\) – represent industry variable after adding interaction effect of political regime (from 2nd to 6th regime) \(\:{\mathbb{Z}}_{i}\) - represent agriculture variable after adding interaction effect of political regime (from 2nd to 6th regime) \(\:\mu\:\) -error term The results in Table 6 indicates that, an R-squared value of 0.7425 implies that, approximately 74.25% of the variance in the dependent variable (GDP) have been explained by the independent variables ( Industry and Agriculture in all of the selected regimes after interaction ) included in the multiple linear regression model. Only 25.75% of the variability remains unexplained. This indicates a relatively strong relationship between the predictors and the outcome, suggesting that, the model is effective in capturing the underlying patterns in the data used in this study. Besides, there is a positive relationship effect of industrial growth and economic growth, the industry sector seems to grow and contribute to the GDP by about 7 per cent during the 5th regime under President John Magufuli; but has negative growth during 3rd regime under President Benjamin Mkapa; agriculture has positive effect on GDP in a 3rd and 6th regimes and remain with negative effect in the 5th regime as noted in the quantile regression results. Interestingly, the positive growth of agriculture sector during the 6th regime depicts the push that has been put by the President Samia on agricultural sector development by allocating huge budget that never happened since independent. For example, according to the Tanzania National Budget reports, for the financial year 2022/23 the budget allocation increased from Sh.751.12 billion up to Sh.970.78 billion in the year 2023/24 which is equal to 29.24 per cent increase as compared to the year 2021/22 during 5th regime which was Sh. 294.16 billion only. Moreover, the current budget allocation for agriculture sector for financial year 2024/25 is sh.1.249 Tirion. This remark good progress in the sustainability of the sector and the output are contributing to the industrial development and ultimately economic growth in the country as indicated in Fig. 1 . Table 6 Ordinary Least Square regression results after adding interaction effect of political regime Economic Growth (GDP) Coefficient Standard Error T-statistic P-value Industryregime2 4.889141 2.691613 1.82 0.083 Industryregime3 − .592743 .3036895 -1.95 0.064* Industryregime4 .0730715 .8320932 0.09 0.931 Industryregime5 6.653629 1.802162 3.69 0.001** Agricregime2 -1.459534 .974615 -1.50 0.148 Agricregime3 .5958222 .1798483 3.31 0.003** Agricregime4 .2682343 .3028807 0.89 0.385 Agricregime5 -1.75248 .5280805 -3.32 0.003** Agricregime6 .1782717 .0806622 2.21 0.038* constant − .8982293 2.030762 -0.44 0.663 R-squared = 0.7425 Robust check: Breusch-Pagan test for heteroskedasticity: chi2(1) = 3.57; Prob > chi2 = 0.0590 (constant variance) Note : ***p < 0.01; **p < 0.05; *p < 0.1 Source: World Bank (2020-21) 5.0 DISCUSSION OF THE RESULTS This study reveals that, economic growth as result of industrialization policy implementation in various political power dynamics in Tanzania is affected by the major economic sectors, specifically industry (manufacturing value added) as shown at 25th, 50th and 75th quantiles in Tables 4 and 5 ; another sector is agriculture. These two sectors have contributed with different magnitudes on economic growth (GDP growth) in all political regimes for almost 32 years since 1990–2021. Based on the findings, the industrial development as a result of good policy implementation has positive impact on economic growth and has been growing and contributing to about 7 per cent in the GDP in the country (see Tables 4 , 5 and 6 ). According to Culot et al, ( 2020 ) industry refers to a group of productive enterprises or organizations that produce or supply goods, services, or sources of income. It encompasses various sectors, including manufacturing, agriculture, and services, and is classified into primary, secondary, tertiary, and quaternary industries. The tertiary industry provides services rather than tangible products, and the quaternary industry is concerned with knowledge-based services, such as information technology and research. The findings of this study assert that, the industrial sector plays a crucial role in driving economic growth in Tanzania by contributing to job creation, enhancing productivity, and fostering innovation (Klinger et al, 2023 ). As the government prioritizes industrialization through initiatives like the implementation of Tanzania industrialization Policy of 1996–2020, the sector has seen increased investments in manufacturing, agro-processing, and construction, which have diversified the economy beyond traditional agriculture. This growth not only facilitates the development of local supply chains but also boosts exports, leading to an improved balance of trade (Mwinuka and Mwangoka, 2023 ). Moreover, the industrial sector contributes to technological advancements and skill development, which are vital for sustaining long-term economic growth (Mazungunye, 2020). Thus, by transforming raw materials into value-added products, the industry helps stimulate consumer demand and improve living standards, thus reinforcing its significance in Tanzania's overall economic development. The study further reveals that, in all political regimes from the 2nd to the 6th regime, industry has significant impact on the economic growth. Interesting is that, of all the selected six regimes, only the 5th regime seems to perform better than other regimes in the industrial policy implementation which resulted into positive growth and contribution to the GDP (see Tables 5 and 6 ). According to Wineman et al ( 2020 ), agriculture is defined as a vital sector of the economy that encompasses the cultivation of crops, livestock rearing, and agro-based activities, significantly contributing to food security, employment, and foreign exchange earnings. The sector accounting for approximately 30 percent of the country's GDP and employing over 70 per cent of the population (Mtingele, 2020 ). The agricultural landscape in Tanzania includes a diverse range of food crops such as maize, rice, and beans, as well as cash crops like coffee, tea, and cashew nuts, which are essential for both domestic consumption, industrial development and export. Interestingly, agriculture seems to progress well in the 6th regime having positive growth as indicated in Table 6 , this suggests for further strategies such as ensuring early delivery of agro-inputs to farmers, improving extension services and mechanization which could spark the growth by at least 10 per cent and lead the industrialization movement in the country. The study further reveals the necessity for good governance as an ingredient to better and sustainable economic growth in the country (see Table 4 ). Besides, despite the energy variable being not statistically significant, but the sector is still vital for inducing the industrial development that will ultimately catalyze the economic growth and development of the country. “Natural science suggests that, energy is crucial to economic production, and ecological economists and some economic historians argue that increasing energy supply has been a principal driver of growth” (Stern, 2019 ). 6.0 CONCLUSION AND POLICY IMPLICATIONS This study investigates the nexus between industrial policy implementation and economic growth within different political dynamics by using time series data (1990–2021) while accounting for agriculture, energy and good governance as control variables in the regression model. To estimate the results, the study used Simultaneous Quantile Regression model and Multiple linear regression model for triangulation of results. The results indicate that, there is positive relationship between industrial development and economic growth in the country; the industrial sector has been growing and contributing to the GDP by about 7 per cent up to the year 2023 since 1990. The results further indicate that, out of six regimes, the political regimes that seem to have significant impact in implementing the industrial policy with good results is the 5th regime under his excellence Dr. John Pombe Magufuli. However, agriculture seems to have negative growth and contribution to the economic growth in the 3rd and 5th regimes, while positive in the 6th regime under President Dr Samia Suluhu Hassan. Energy sector especially accessibility to electricity at low cost is still vital for catalyzing industrial and economic development in the country. Overall, the finding of this study highlights the significant policy implications of industrialization strategies and policy implementation for economic growth and development. Ultimately, the study advocates for a holistic approach that integrates industrialization efforts with broader economic and political strategies to sustain long-term economic growth in Tanzania. For example, Tanzania should create an Independent Industrial Policy Council to improve the consistency and influence of industrial policy across regimes. Beyond political cycles, this council would assess, plan, and supervise industrial development policies, guaranteeing steady advancement despite changes in leadership. Stakeholder involvement, public reporting, and yearly reviews should all be part of its mandate. In nations like Rwanda, where institutional stability has facilitated persistent industrialization initiatives, such a strategy has shown promise (Nguyen et al., 2021 ; Chinsinga et al., 2022 ). Besides, given the close relationship between agriculture and industry, particularly under the sixth regime, the government should expand Special Agro-Industrial Processing Zones (SAPZs) to promote rural industrialization. These zones should combine value chains from farm production to agro-processing with targeted infrastructure, financing facilities, and tax breaks. Countries such as Ethiopia have found success with industrial parks that are linked to agriculture (Opoku & Yan, 2019 ). Finally, Tanzania should create stronger governance frameworks for implementing public investments in industrial and agricultural projects, as the study indicated that excellent governance was significant at the 25th quantile. This entails requiring community scrutiny, performance audits, and transparent procurement. As demonstrated by the reform experiences in Rwanda and Vietnam, improved governance guarantees resource efficiency and fosters trust (Nguyen et al., 2021 ; Ferrannini et al., 2021 ). The limitation of this study is that, industry, agriculture, energy and good governance cannot be the sufficient components/variables to study the economic growth changes within various political dynamics. Instead, other variables such as capital stock accumulation, labor, technology, foreign direct investment and mining should be considered for further study. Also, there was no recorded data for first regime on the independent variables under study, this led to omitting the 1st regime in the analysis. Declarations Declaration of Competing Interest The authors declare that, there is no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data access World Bank link: https://databank.worldbank.org/reports.aspx?source=2&country=ARE Funding Declaration No funding was received for conducting this study Acknowledgement The study has no acknowledgement requirements. 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Sustain Energy Technol Assess 44:100991 Bidu JM, Van der Bruggen B, Rwiza MJ, Njau KN (2021) Current status of textile wastewater management practices and effluent characteristics in Tanzania. Water Sci Technol 83(10):2363–2376 Brock A, Sovacool BK, Hook A (2021) Volatile photovoltaics: green industrialization, sacrifice zones, and the political ecology of solar energy in Germany. Annals Am Association Geographers 111(6):1756–1778 Byaro M, Mafwolo G, Mayaya H (2022) Keeping an eye on environmental quality in Tanzania as trade, industrialization, income, and urbanization continue to grow. Environ Sci Pollut Res 29(39):59002–59012 Chinsinga B, Weldeghebrael EH, Kelsall T, Schulz N, Williams TP (2022) Using political settlements analysis to explain poverty trends in Ethiopia, Malawi, Rwanda and Tanzania. World Dev 153:105827 Culot G, Nassimbeni G, Orzes G, Sartor M (2020) Behind the definition of Industry 4.0: Analysis and open questions. 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Res Policy 50(7):104287 Mbelwa G (2023) Assessing the Effects of Manufacturing Sector on Economic Growth in Tanzania (Doctoral dissertation, Institute of Accountancy Arusha (IAA)) Mtingele AM (2020) Examining the Linkages between Local Food Economies and Household Nutrition in Rural Tanzania (Doctoral dissertation) Muoneke OB, Okere KI, Nwaeze CN (2022) Agriculture, globalization, and ecological footprint: the role of agriculture beyond the tipping point in the Philippines. Environ Sci Pollut Res 29(36):54652–54676 Mwang’onda ES, Mwaseba SL, Juma MS (2018) Industrialisation in Tanzania: the fate of manufacturing sector lies upon policies implementations. Int J Bus Econ Res 7(3):71–78 Mwinuka L, Mwangoka VC (2023) Manufacturing sector’s growth in Tanzania: Empirical lessons from macroeconomic factors, 1970–2021. Cogent Econ Finance 11(1):2223419 Nguyen CV, Giang LT, Tran AN, Do HT (2021) Do good governance and public administration improve economic growth and poverty reduction? 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J Environ Manage 253:109742 Sibiya NR (2021) Diplomatic Relations on de Facto States: The Cases of Somaliland and Western Sahara (Doctoral dissertation, University of Johannesburg (South Africa)) Sodhi AS, Sharma N, Bhatia S, Verma A, Soni S, Batra N (2022) Insights on sustainable approaches for production and applications of value-added products. Chemosphere 286:131623 Stern DI (2019) Energy and economic growth. Routledge handbook of Energy economics. Routledge, pp 28–46 Teorell J, Lindberg SI (2019) Beyond democracy-dictatorship measures: a new framework capturing executive bases of power, 1789–2016. Perspect Politics, 17 (1) Waldmann E (2018) Quantile regression: a short story on how and why. Stat Modelling 18(3–4):203–218 Wang L, Vo XV, Shahbaz M, Ak A (2020) Globalization and carbon emissions: is there any role of agriculture value-added, financial development, and natural resource rent in the aftermath of COP21? J Environ Manage 268:110712 Wang S, Wang X, Lu B (2022) Is resource abundance a curse for green economic growth? Evidence from developing countries. Resour Policy 75:102533 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 Woods A, Hart A, Spandler H (2022) The recovery narrative: politics and possibilities of a genre. Cult Med Psychiatry 46(2):221–247 Xue S, Song J, Wang X, Shang Z, Sheng C, Li C, Liu J (2020) A systematic comparison of biogas development and related policies between China and Europe and corresponding insights. Renew Sustain Energy Rev 117:109474 Yimenu B (2023) Measuring and explaining de facto regional policy autonomy variation in a constitutionally symmetrical federation: The case of Ethiopia, 1995–2020. Publius: J Federalism 53(2):251–277 Zheng W, Walsh PP (2019) Economic growth, urbanization and energy consumption—A provincial level analysis of China. Energy Econ 80:153–162 Footnotes Tanzania Targets 10% Agricultural Growth in a Decade with Agenda 10/30 – Kilimo Kwanza Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Published Journal Publication published 07 Nov, 2025 Read the published version in Tanzania Journal of Community Development → 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-6857027","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":468796908,"identity":"bbdd4b9b-0f5d-4d7a-8174-8234f767000a","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":""},{"id":468797254,"identity":"2616f9fe-4df8-433e-90ff-2e8814971cef","order_by":1,"name":"Francis Callystus Nyoni","email":"","orcid":"","institution":"St. Augustine University of Tanzania","correspondingAuthor":false,"prefix":"","firstName":"Francis","middleName":"Callystus","lastName":"Nyoni","suffix":""},{"id":468797255,"identity":"b50a18af-712c-40f7-b608-0d9cf8f679a3","order_by":2,"name":"Vikniswari Vija Kumaran","email":"","orcid":"","institution":"Universiti Sains Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Vikniswari","middleName":"Vija","lastName":"Kumaran","suffix":""}],"badges":[],"createdAt":"2025-06-09 19:11:44","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6857027/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6857027/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.4314/tajocode.v4i2.3","type":"published","date":"2025-11-08T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84387399,"identity":"bf1c3865-9582-4c74-8f52-423641e42fd8","added_by":"auto","created_at":"2025-06-11 10:26:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":37691,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePercentage of Manufacturing output within GDP growth trends from 1990-2023\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSource: \u003ca href=\"http://www.macrotrends.net/global-metrics\"\u003ewww.macrotrends.net/global-metrics\u003c/a\u003e Macrotrends LLC, 2025\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6857027/v1/fa14f3f13ec626c5ea3e4ce8.png"},{"id":101443630,"identity":"9bd93ba0-a1ed-4de5-b872-88e309890a8b","added_by":"auto","created_at":"2026-01-29 17:45:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1265095,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6857027/v1/cbd62c64-2a7a-4371-99b6-41ed55182aa2.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eIndustrialization policy and economic growth nexus within political dynamics in Tanzania\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1.0 INTRODUCTION","content":"\u003cp\u003eEconomic growth and development as result of industrialization is an escalating agenda in Tanzania for so many years to date (Byaro et al, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mandalu et al, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Moreover, industrial growth is an engine for economic development and growth in the country. Tanzania is among the countries whereby the contribution of both processing and manufacturing industries to the GDP cannot be neglected (Afolabi and Laseinde, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Abbasi et al, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The country is striving for achieving industrialization for decades, though the development of its achievement is at a meager rate (Odijie, 2022). The crucial sectors such as agriculture and energy remained at stunting growth since independence despite of various strategies and policies to reach the level of industrialization (Rahman, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). \"It is an undeniable fact that, the manufacturing sector plays a key role in the growth of any economy, especially in those economies that are yet to be fully developed (Zheng and Walsh, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It is from this sector that developing countries can catch up with the rest of the world. The manufacturing industry makes room for innovation and growth of production technology, the creation of massive employment especially in small and medium-scale manufacturing industries like textile, food, beverage, iron and steel industries (Mwang'onda, \u003cem\u003eet al\u003c/em\u003e, 2018; Pingkuo and Xue, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). If well connected with other sectors, manufacturing industries are not only large consumers of natural resources and of primary products, but also a supplier of inputs to other medium and large-scale industries thus support their growth and development (Wang et al, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBasically, since independence, the Tanzanian industrial sector is growing at a low pace given the existing policy and strategies diluted by political changes in the country (Mandalu et al, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Since 1961 to date, there are six political regimes existed in the country, and in each regime, there have been different policy reforms and implementation approaches toward achieving industrialized economy (Khan, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Of all six regimes, the 5th regime under His Excellence, Late John P. Magufuli insisted on industrialization strategies by applying the Swahili slogan of \"\u003cem\u003eTanzania ya viwanda\u003c/em\u003e\" meaning \"\u003cem\u003eIndustrialized Tanzania\u003c/em\u003e\". The struggle among others was to reform policies including export and import policies. The importation of some products such as cooking oils was reduced to create internal demands for local industries' products. Nevertheless, there was a remarkable struggle to stimulate local production of Agricultural raw materials such as Palm and Sunflower crops which could increase the production of cooking oil in the country.\u003c/p\u003e \u003cp\u003eHowever, with all the struggles, there are still a lot of manufacturing and processing industries still not working properly and others are even extremely dead (Bidu et al, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These dead industries to mention few are the textiles industries including but not limited to MWATEX in Mwanza, MUTEX in Musoma, and URAFIKI in Dar es Salaam. These industries were very significant in driving the agricultural sector and the industrial growth of the country but have recently been left like debris residuals. Mwang'onda, \u003cem\u003eet al\u003c/em\u003e, (2018) found that, the stagnant contribution share of the manufacturing sector is linked with; implementation lags on ambitious uncoordinated plans, slow transforming economic structure which is dominated by agriculture, and competition from low-priced manufactured imports from Asian economies. Besides, while singing the song of industrialization in the country, there is no real seriousness of political leaders to reflect what they say on public podiums regarding industrialization in the country (Chinsinga et al, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Also, the budget for the agriculture sector which is the key driver of industrialization in the country has been allocated little in previous years compared to other sectors like construction and others; this justifies the opposite of the true colors of the political leaders preaching about industrial development. Notwithstanding this fact however, the current regime (6th regime) for the first time, the budget for the agriculture sector was significantly high aiming at the growth of the sector at 10% rate by 2030\u003csup\u003e1\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, most studies have delved on the investigation for the impact of industrialization on the economic growth and perverse but not within the political dynamics specifically in Tanzania using simultaneous quantile regression model; this is the novelty of this study. Therefore, the purpose of this study was to analyze the relationship between different political regimes in Tanzania and their influence on the implementation effectiveness of industrialization policies, as well as to assess how these factors collectively affect economic growth. The key research question guided this study was \u0026ldquo;How do varying political regimes in Tanzania influence the effectiveness of industrialization policy implementation and its subsequent impact on economic growth. The rest body 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, Section \u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003e5\u003c/span\u003e provides a detailed discussion of the results, and Section 6 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 review\u003c/h2\u003e \u003cp\u003eThis study adopts the theory of de facto power relations propagated by Acemoglu and Robinson (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The central idea described in the theory of de facto power asserts that, there are always dynamics that happen within the political regime changes. The power and development dynamics changes happen within the power transition in the sovereign states (Linderfalk, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sibiya, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Teorell and Lindberg, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The economic growth and development of a nation are linked with good state power and a sTable institutional setup (Acemoglu and Robinson, 2005). In developing countries like Tanzania, the presence of sTable institutional quality of the existing regime, the agricultural sector seems to have a significant contribution to the economic growth and development of her population which is paramount to industrial development (Muoneke et al, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yimenu, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sodhi et al, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The reason why this study adopts this theory is that; First, by applying the theory of de facto power relations can provide a nuanced understanding of how industrialization policies in Tanzania are implemented and resisted in the economic development process. Second, it allows for an analysis of how various political regimes shape the power dynamics surrounding economic growth and development. Third, it offers a critical lens for analyzing power dynamics in society, emphasizing the importance of informal practices and real-world interactions; by focusing on how power is actually exercised, it enriches our understanding of governance, social structures, and economic policies.\u003c/p\u003e \u003cp\u003eIt is in that case obvious that, the development of a country is therefore linked directly to the performance of each political power ruling at a particular period. Ironically, industrial policies involve the distribution of state resources; distributional policies that run against the survival interest of ruling elites (for example, by emboldening oppositional groups) will face direct challenges from ruling elites. Besides, in the case of advanced economies, political power changes are not an issue for industrial development and economic growth. It is just the best allocation of resources and better policy implementation that surpass the development (Madariaga, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Xue et al, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The key assumptions (Pegg, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) of this theory among others are; firstly, Power is not possessed but is rather exercised in relationships between individuals, groups, or institutions. Secondly, the context in which power operates including cultural, social, and economic factors are significantly influences power dynamics and outcomes. Third, Individuals and groups are not merely passive recipients of power; they have agency and can resist, negotiate, or redefine power relations. Lastly, the way power is exercised in practice can lead to different outcomes than those predicted by formal theories or policies, highlighting the importance of understanding power dynamics. The states encourage the development of industrialization as the driver for economic growth and competitive advantage over the rest of the world (Brock et al, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Many countries in the west world underwent industrialization so many years ago while African countries still struggling to achieve this kind of economic power.\u003c/p\u003e \u003cp\u003eTherefore, the study merges the central idea to this theory since it wants to present the nexus between industrialization and economic growth while controlling for intermittent variables of agriculture, energy and good governance within the political power dynamics in Tanzania since 1990. The study aims to see what is the reality in the practice of industrial policy implementation for the said political power dynamics on economic growth to date; does it have a positive impact on the livelihoods of Tanzanians or may be the situation is getting worse while political leaders are singing the song that, the industrialization is possible in Tanzania. To test this theory empirically, the study employs the Simultaneous Quantile regression model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Empirical Review\u003c/h2\u003e \u003cp\u003eSeveral scholars investigated and present empirical evidence that support that the areas of industrial development, agriculture, energy, and good governance contribute greatly to economic growth and perverse as follows:\u003c/p\u003e \u003cp\u003eAccording to Opoku and Yan (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), industrialization is a significant driver of economic growth and trade openness strengthens the impact of industrialization on economic growth. The study focused on time series data from 1980\u0026ndash;2014 to explore \"Industrialization as a Driver of Sustainable Economic Growth in Africa\". They used the generalized method of moments. Then, Kessy (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) investigated \"The Long Waiting for Relocating Capital City in Tanzania: The Continuity of the Game Changer and the Challenges Ahead.\" The study found that a number of variables combined to successfully move the government from Dar es Salaam to Dodoma. The primary one, however, was the fifth government's strong political commitment and the sixth government's subsequent continuation to offer strategic leadership in carrying out the concept that Tanzania's first president had proposed more than 40 years earlier. The findings shed light on the concepts and tactics used to design Tanzania's new capital city. It also looked at some literature on the relocation of capital cities in other parts of the world. It closes by emphasizing that the strong personal motivations demonstrated by President John Magufuli and later President Samia Suluhu Hassan had shattered the long-held taboo of empty words since the early 1970s. This is because the previous government's efforts to relocate the city were hampered by intractable administrative problems and a shortage of foreign cash. This is an illustration of how political dynamics (good governance) lead to developmental advances in Tanzania.\u003c/p\u003e \u003cp\u003eNguyen et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) used fixed-effect regressions to examine the impacts of poverty province, income inequality, public administration quality, and governance on per capita income. The findings indicate a positive and nonlinear relationship between per capita income and public administration and governance. Improved public administration and governance also seem to enhance income distribution and lower poverty in a country. On the other hand, using panel fixed effects regressions, Mao et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) evaluated the overall impact of China's industrial policies from 2000 to 2012. They discovered that the relative development stage of an industry in relation to the global frontier determines whether or not China's industrial policies and S\u0026amp;T policies contribute to productivity growth in that industry. In particular, they contended that China's S\u0026amp;T and industrial policies boost productivity development in high-tech, globally growing industries more than in domestically mature and catching-up businesses.\u003c/p\u003e \u003cp\u003eBesides, Azam et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) conducted a study in ten newly industrialized nations titled \"Analyzing the relationship between economic growth and electricity consumption from renewable and non-renewable sources: Fresh evidence from newly industrialized countries.\" They used the Granger causality methods, panel unit root test, panel heterogeneous co-integration method, and panel Fully Modified Ordinary Least Square. The Granger causality test results also show that there is bidirectional causation between renewable electricity consumption and economic growth in both the short-run and long-run, supporting the feedback hypothesis. Secondly, the panel Fully Modified Ordinary Least Square confirms that all studied variables have a positive long-run effect on economic growth, with a 1% increase in renewable and non-renewable electricity consumption increasing economic growth by 0.095% and 0.017%, respectively and finally the panel unit root test and panel co-integration method confirm that economic growth, renewable electricity consumption, non-renewable electricity consumption, gross capital formation, labor force, and trade openness are co-integrated.\u003c/p\u003e \u003cp\u003eFerrannini et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) also investigated industrial policy for sustainable human development in the post-Covid19 period by a systematic literature study. The findings show that a better grasp of industrial policy might make government mistakes even more hazardous. The study showed that in order to play a new role in our post-Covid 19 society, theoretical foundations, as well as governance and execution methods related to industrial policies, sustainability, and development, particularly during economic and financial crises, are required. In conclusion, a lack of studies has highlighted the relationship between industrialization policy implementation and economic growth within Tanzanian political dynamics using a simultaneous quantile regression model using time series data such as decoupling or entropy-weighted TOPSIS. As a result, this will provide novelty while also filling the study gap.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Evaluation of industrial policy across different political regimes\u003c/h2\u003e \u003cp\u003eTanzania's industrial policy has developed alongside its political regimes. Based on Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the second regime, led by President Mwinyi, implemented structural adjustment plans that liberalized and privatized the economy, but inadequate state support stifled industrial expansion (Mandalu et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Mkapa's third regime pursued reforms and implemented Vision 2025, but industrial policy remained fragmented, with little direct incentives (Byaro et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Next, Kikwete's fourth regime connected industrialization and agriculture through the Industrial Development Strategy (IIDS) and Kilimo Kwanza (Kessy, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), but faced finance and coordination challenges.\u003c/p\u003e \u003cp\u003eHowever, Magufuli's 5th regime adopted a more aggressive attitude with \"\u003cem\u003eTanzania ya Viwanda\u003c/em\u003e,\" spending substantially in infrastructure and implementing import restrictions, resulting in a 7% industrial GDP share (Chinsinga, et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). According to Kitole et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the sixth regime in Samia\u0026rsquo;s regime prioritizes continuity by increasing agriculture budgets and fostering agro-industrial ties through public-private partnerships. The emphasis now is on sustainable, inclusive growth that is more closely aligned with the Sustainable Development Goals (SDGs). Therefore, this section provides a better understanding of how industrial policy focus have changed in response to political ideologies and agendas.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePolicy instruments summary\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegime\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCore Industrial Policy Focus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKey Instruments\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSources\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStructural Adjustment Programs (SAPs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrivatization and trade liberalization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMandalu et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTanzania Development Vision 2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInstitutional reforms and private sector engagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eByaro et al (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndustrial Development Strategy (IIDS)\u003c/p\u003e \u003cp\u003eKilimo Kwanza initiative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSectoral strategies and integration with agriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKessy (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTanzania ya Viwanda agenda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInfrastructure expansion and import restrictions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChinsinga et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e_\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgro-industrial growth\u003c/p\u003e \u003cp\u003eInclusive development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIncreased agriculture funding and SDG alignment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKitole et al (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eSource: Literature review\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3.0 METHODOLOGY","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Research approach and design\u003c/h2\u003e \u003cp\u003eThis study employed purely quantitative methods approach involving econometric analysis of time series data on industrialization, GDP growth rate, Agriculture, energy and good governance as economic indicators across different political regimes in Tanzania over several decades. These data were analyzed using econometric models to identify correlations and causal relationships. Besides, the study adopted a longitudinal research design which is a research design used to study the same subjects over an extended period of time, often months, years, or even decades. Thus, this design allows to observe changes and developments in economic growth as a result of industrialization policy implementation over different periods, making it particularly useful for studying long-term effects GDP growth within different political dynamics. Unlike cross-sectional studies, which compare different individuals at one point in time, longitudinal studies track the same individuals repeatedly, providing valuable insights into cause-and-effect relationships and how variables evolve.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Data type and source\u003c/h2\u003e \u003cp\u003eThis study used time series data from the World Bank dataset for 32 years (1990\u0026ndash;2021) downloaded and analyzed in the STATA program. However, some specific data from the previous literatures have been used to substantiate the argument on the discussion of findings section.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Estimation Model\u003c/h2\u003e \u003cp\u003eIn this study, a Simultaneous Quantile regression model was used to estimate the relationship between variables (dependent and independent) in different percentile levels across the distribution of the independent variables simultaneously. This study used Simultaneous Quantile regression because (Waldmann, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) it is a statistical technique that estimates the conditional quantiles of a response variable; unlike ordinary least squares (OLS) regression, which estimates the mean of the dependent variable given the independent variables, Simultaneous quantile regression provides a more comprehensive view by estimating the effects of predictors at different points (quantiles) in the distribution of the response variable (\u003cem\u003eibid\u003c/em\u003e). This is relevance model to this study given the fact that, the study is looking at the changes that happened in implementing industrial policy on economic growth as well as general development of the nation. With Simultaneous Quantile Regression, \u0026ldquo;in cases where either the requirements for mean regression, such as homoscedasticity, are violated or interest lies in the outer regions of the conditional distribution, Simultaneous quantile regression can explain dependencies more accurately than classical methods\u0026rsquo; (Das et al, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariables, symbols, unit of measures and sources of data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSymbols\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnit of measure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSources\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGross Domestic Product\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGDP growth (annual %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Bank (1990-21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndustry (including construction), value added (% of GDP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Bank (1990-21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAgriculture, forestry, and fishing, value added (% of GDP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Bank (1990-21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eERG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eelectricity access (% of Population)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Bank (1990-21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood governance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl of Corruption: Percentile Rank, Upper Bound of 90% Confidence Interval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Bank (1990-21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eSource: World Bank data (2020\u0026ndash;2021)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Empirical model\u003c/h2\u003e \u003cp\u003eThe empirical model of this study was designed including the main and control variables enabling to test and prove the envisaged theory; basically, empirical models validate or refute theoretical frameworks through experimentation during statistical analysis. Thus, the independent variable of interest in this study is the industry because the study is interested at presenting the observation on what happened to industrial development as far as policy implementation concern during different political regimes which may be impacted the economic growth and development. However, other control variables such as agriculture, energy and good governance are considered in the model since they also have contribution to economic growth and development in the country. The linear form of the empirical model is given as follows;\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:SqGDP\\:\\left(\\tau\\:|X\\right)={\\beta\\:}_{0\\:}\\left(\\tau\\:\\right)+{\\beta\\:}_{1\\:}\\left(\\tau\\:\\right)\\:IND+\\:{\\beta\\:}_{2\\:}\\left(\\tau\\:\\right)\\:AGR+\\:{\\beta\\:}_{3}\\left(\\tau\\:\\right)\\:ERG+\\:{\\beta\\:}_{4\\:}\\left(\\tau\\:\\right)\\:GG+\\:\\mu\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere;\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:SqGDP\\:\\left(t|X\\right)\\)\u003c/span\u003e \u003c/span\u003e- conditional quantile of Economic growth (GDP) given Industrial growth, Agricultural growth, Energy and Good governance at 25th, 50th and 75th quantiles.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{\\text{1,2},\\text{3,4}\\:}\\left(\\tau\\:\\right)\\)\u003c/span\u003e \u003c/span\u003e- coefficients that vary with the quantile levels\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{0\\:}\\left(\\tau\\:\\right)\\)\u003c/span\u003e \u003c/span\u003e- Constant term at Quantile level\u003c/p\u003e \u003cp\u003eIND - Industrial growth (manufacturing)\u003c/p\u003e \u003cp\u003eAGR - Agricultural growth\u003c/p\u003e \u003cp\u003eERG -Access to electricity of population\u003c/p\u003e \u003cp\u003eGG - good governance\u003c/p\u003e \u003cp\u003e\u0026#120583;- error term\u003c/p\u003e \u003cp\u003eThe coefficients from quantile regression represent the change in the quantile of the dependent variable (GDP) for a one-unit change in the independent variables (Industry, Agriculture, Energy and Good governance), holding other things constant.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4.0 RESULTS","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Descriptive Statistics and trends\u003c/h2\u003e \u003cp\u003eThe summary results in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reveals that, Tanzania's GDP grew on average increase of 5.18 per cent annually up to the year 2021, this growth was a result of contribution of manufacturing output which was growing at an average of 7 per cent and so boosted the GDP up to 8.3 per cent by the year 2023 as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; this implies positive effects of different political regimes on the policy implementation outcome. Actually, the growth of manufacturing sector suggests several interrelated factors that fostered a conducive environment for industrial expansion. Firstly, government initiatives aimed at promoting industrialization, such as the National Development Vision and the industrial development Policy of 1996\u0026ndash;2020, have provided strategic frameworks and incentives for local and foreign investments. Secondly, improvements in infrastructure, including transport, energy, and communication networks, have facilitated easier access to markets and reduced operational costs for manufacturers. Third, growing demand for both local and export products, driven by a rising middle class and increased urbanization, has further stimulated the sector. Fourth, the government's focus on value addition in agriculture has encouraged agro-industrial development, leading to greater diversification within the manufacturing sector. Collectively, these factors have created a robust ecosystem that supports sustained growth in Tanzania's manufacturing industry (see Mbelwa, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Also, in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e agriculture sector contribution to the GDP was at an average of 21.5 per cent; while average of population having access to electricity was meagre for 32 years this suggests for more initiatives in the energy sector to ensure more supply of electricity to the citizens and industries at low cost. Besides, the good governance in the country average score was 32.4 suggesting that, while there are some functioning elements in the governing settings, there is significant potential for enhancement in other elements such as accountability, rule of law and citizen participation to achieve more effective and fair governance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGross Domestic Product\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.177347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.001396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.5843221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.672155\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.946562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.897883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.53741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.924768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.00126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.27897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.21875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.528136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood Governance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.46083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.3032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e57.56097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eSource: World Bank data (2020\u0026ndash;2021)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Simultaneous Quantile Regression results\u003c/h2\u003e \u003cp\u003eThe simultaneous quantile regression results are categorized into two Tables as follows; Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e indicates the general results for economic growth in different quantiles given the main independent variables of industry while accounting for other control variables (agriculture, energy, good governance); and Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e indicates the results for economic growth change within five different political regimes after interaction effect starting with the 2nd regime under his Excellence late Ally Hassan Mwinyi up to the 6th regime led by Her Excellence Dr Samia Suluhu Hassan. For the purpose of the analysis indicated in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, We used only two independent variables of industry (main variable) and agriculture (intermittent variable) given the fact that, Agriculture is the most potential and being key leading economic sector since independence that drives the industrial development and economic growth of the country by contributing to about 30 per cent of the GDP and employing about 80 per cent of the population (see Gupta, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kitole, et al, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In that regard, there is a clear link of the two sectors (industry and agriculture) which are key contributors of the economic growth in Tanzania. The study also considered to use five regimes instead of all six regimes due to data availability as the data of selected variables for the 1st regime under President Mwalimu Julius K. Nyerere was not available. The five regimes are led by different presidents as follows; 2nd regime under his Excellence late Ally Hassan Mwinyi, 3rd regime under his excellence late Benjamin Mkapa, 4th regime under his excellence Dr. Jakaya M. Kikwete, 5th regime under his excellence late Dr. John P. Magufuli and the last 6th regime currently is her excellence Dr. Samia S. Hassan. The interest of this analysis in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e was to respond to the basic research question of the study by checking through model estimation the changes happened on economic growth as a result of industrial policy implementation and agricultural development within five different political dynamics/regimes in the country since 1990\u0026ndash;2021. The dummy variable \u0026ldquo;regime_2 up to regime 6 was introduced on the dataset in order to track the changes of the economic growth after interaction effect within each regime. The analysis in Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the interaction term \u0026ldquo;regime multiplied by a specific independent variable\u0026rdquo;; because interaction term is useful in estimating the difference between the main effect and the effect of the interaction term when another variable changes. So, we generated on STATA another new two interaction variables of industry and agriculture multiplied by dummy variable \u0026ldquo;regime_2 up to 6 in order to see the impact of industry and agriculture on GDP during the 2nd, 3rd, 4th, 5th and 6th regime compared to the main effect of industry and agriculture on economic growth indicated on Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e indicates that, there is positive relationship between industrial development and economic growth. For example, at 75th Quantile of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the results are significant with positive coefficient indicating that, the industrial growth has direct effect on economic growth. This suggests for further strategies to boost the sector by more than 15 per cent in order to increase its contribution to the GDP in the country. While that hold, results in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e after adding interaction effect of political regime indicates that, at 25th, 50th and 75th quantiles industry and agriculture sectors have shown to be statistically significant during the 5th regime under the President Late Dr John P. Magufuli. The industry seems to have positive impact on GDP growth by 7 per cent while agriculture shown negative growth and impact on the GDP, this suggests that, the reasons was due to little budget allocation and low commitment of political leaders on improving agriculture during that regime; rather more emphasis of high budget allocation was put on the infrastructures development such as roads, Airlines, bridges, Standard Gauge Railway (SGR) and Hydroelectric power projects during this regime. The study also reveals that, there is statistically significant and positive relationship between economic growth and good governance at 25th quantile in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. This implies that, the presence of good governance such as rule of law, less corruption can result to induced economic growth and development in the country due to better utilization of resources. All results are consistence thus, remain robust across quantiles.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral Simultaneous Quantile Regression results for economic growth\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25th Quantile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT-statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.4220541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.4707558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.0993876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.282679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.218456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.2396758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.370\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood Governance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.0586577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0343312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.099*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.229423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.194014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e50th Quantile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.369086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.3848276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.346\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.0135558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.1942061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.2208022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.2513934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood Governance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.034911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0280578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.952508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.760089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e75th Quantile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.5736774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.1461396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.0306679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0762191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.1689888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.1593819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood Governance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.0191523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0220148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.83308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.083023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSource: World Bank (2020-21) Note: ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eSimultaneous Quantile Regression results for economic growth after adding interaction effect of political regime from 2nd to 6th regime\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25th Quantile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT-statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eindustryregime2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.323432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.82907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eindustryregime3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.6122854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.8536315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.480\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eindustryregime4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.010422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.090753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eindustryregime5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.332585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.319413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustryregime6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.758611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.6737066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eagricregime2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.2341447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.683214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eagricregime3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.6101737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.6949598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricregime4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.320222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.034684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricregime5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.642411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.61158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.762677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.403127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e50th Quantile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eindustryregime2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.7402329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.06186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eindustryregime3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.7344921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.5784597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eindustryregime4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.380202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.823518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eindustryregime5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.121044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.543571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.010**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustryregime6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.5763324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.4211037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eagricregime2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.11615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.459361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eagricregime3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.6281556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.4442024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricregime4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.1418301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.6295561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricregime5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.914188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.7111059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.3008018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.377252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e75th Quantile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eindustryregime2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.83904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.3995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eindustryregime3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.1739402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.6925591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eindustryregime4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.5052748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.064733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.640\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eindustryregime5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.488387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.609458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustryregime6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.4768443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.4299096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eagricregime2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.875984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.494175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eagricregime3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.3727152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.4874098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricregime4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.0691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.3194281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricregime5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.038714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.7301326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.010**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.4970931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.447875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote: agricregime6 omitted because of collinearity (fitting base model)\u003c/em\u003e, \u003cem\u003e***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSource: World Bank (2020-21)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Ordinary Least Square regression results after adding interaction effect of political regime\u003c/h2\u003e \u003cp\u003eThe study used Ordinary Least Square (OLS) estimation model (Multiple Linear Regression) in order to triangulate and strengthen on the Simultaneous Quantile Regression results and see the comparability between the two models while ensuring robust check. Triangulation of different methods in research enhances the credibility and robustness of results by cross-verifying information from different angles, thereby reducing bias and increasing the reliability of conclusions; by corroborating evidence from diverse sources, triangulation helps ensure that findings are more comprehensive and well-supported, leading to a deeper understanding of the research issue (Qassimi, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The study used OLS in order to quantify the effect of Industry and Agriculture (predictors) on the outcome variable (GDP) for each political regime (after adding interaction effect of political regime). The Multiple Linear Regression equation is given in reduced form;\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:regGDP={\\beta\\:}_{0}+{\\sum\\:}_{i=1}^{n}{\\beta\\:}_{i}({X}_{i}+{\\mathbb{Z}}_{i})+\\mu\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere;\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:regGDP\\)\u003c/span\u003e \u003c/span\u003e- regressing Gross Domestic Product (dependent/outcome variable)\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{0}\\)\u003c/span\u003e \u003c/span\u003e-Constant term \u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{i}\\)\u003c/span\u003e \u003c/span\u003e- coefficients of variables with interaction term for dummy variable \u0026ldquo;regime\u0026rdquo;\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i}\\)\u003c/span\u003e \u003c/span\u003e\u0026ndash; represent industry variable after adding interaction effect of political regime (from 2nd to 6th regime)\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\mathbb{Z}}_{i}\\)\u003c/span\u003e \u003c/span\u003e- represent agriculture variable after adding interaction effect of political regime (from 2nd to 6th regime)\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\mu\\:\\)\u003c/span\u003e \u003c/span\u003e-error term\u003c/p\u003e \u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e indicates that, an R-squared value of 0.7425 implies that, approximately 74.25% of the variance in the dependent variable (GDP) have been explained by the independent variables (\u003cem\u003eIndustry and Agriculture in all of the selected regimes after interaction\u003c/em\u003e) included in the multiple linear regression model. Only 25.75% of the variability remains unexplained. This indicates a relatively strong relationship between the predictors and the outcome, suggesting that, the model is effective in capturing the underlying patterns in the data used in this study. Besides, there is a positive relationship effect of industrial growth and economic growth, the industry sector seems to grow and contribute to the GDP by about 7 per cent during the 5th regime under President John Magufuli; but has negative growth during 3rd regime under President Benjamin Mkapa; agriculture has positive effect on GDP in a 3rd and 6th regimes and remain with negative effect in the 5th regime as noted in the quantile regression results. Interestingly, the positive growth of agriculture sector during the 6th regime depicts the push that has been put by the President Samia on agricultural sector development by allocating huge budget that never happened since independent. For example, according to the Tanzania National Budget reports, for the financial year 2022/23 the budget allocation increased from Sh.751.12\u0026nbsp;billion up to Sh.970.78\u0026nbsp;billion in the year 2023/24 which is equal to 29.24 per cent increase as compared to the year 2021/22 during 5th regime which was Sh. 294.16\u0026nbsp;billion only. Moreover, the current budget allocation for agriculture sector for financial year 2024/25 is sh.1.249 Tirion. This remark good progress in the sustainability of the sector and the output are contributing to the industrial development and ultimately economic growth in the country as indicated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eOrdinary Least Square regression results after adding interaction effect of political regime\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic Growth (GDP)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT-statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustryregime2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.889141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.691613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustryregime3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.592743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.3036895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.064*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustryregime4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.0730715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.8320932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustryregime5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.653629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.802162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricregime2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.459534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.974615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricregime3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.5958222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.1798483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricregime4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.2682343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.3028807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricregime5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.75248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.5280805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricregime6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.1782717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0806622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.038*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.8982293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.030762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eR-squared = 0.7425\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eRobust check: Breusch-Pagan test for heteroskedasticity: chi2(1)\u0026thinsp;=\u0026thinsp;3.57; Prob\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u0026thinsp;=\u0026thinsp;0.0590 (constant variance)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote\u003c/b\u003e: \u003cem\u003e***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSource: World Bank (2020-21)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5.0 DISCUSSION OF THE RESULTS","content":"\u003cp\u003eThis study reveals that, economic growth as result of industrialization policy implementation in various political power dynamics in Tanzania is affected by the major economic sectors, specifically industry (manufacturing value added) as shown at 25th, 50th and 75th quantiles in Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; another sector is agriculture. These two sectors have contributed with different magnitudes on economic growth (GDP growth) in all political regimes for almost 32 years since 1990\u0026ndash;2021. Based on the findings, the industrial development as a result of good policy implementation has positive impact on economic growth and has been growing and contributing to about 7 per cent in the GDP in the country (see Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). According to Culot et al, (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) industry refers to a group of productive enterprises or organizations that produce or supply goods, services, or sources of income. It encompasses various sectors, including manufacturing, agriculture, and services, and is classified into primary, secondary, tertiary, and quaternary industries. The tertiary industry provides services rather than tangible products, and the quaternary industry is concerned with knowledge-based services, such as information technology and research. The findings of this study assert that, the industrial sector plays a crucial role in driving economic growth in Tanzania by contributing to job creation, enhancing productivity, and fostering innovation (Klinger et al, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As the government prioritizes industrialization through initiatives like the implementation of Tanzania industrialization Policy of 1996\u0026ndash;2020, the sector has seen increased investments in manufacturing, agro-processing, and construction, which have diversified the economy beyond traditional agriculture. This growth not only facilitates the development of local supply chains but also boosts exports, leading to an improved balance of trade (Mwinuka and Mwangoka, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Moreover, the industrial sector contributes to technological advancements and skill development, which are vital for sustaining long-term economic growth (Mazungunye, 2020). Thus, by transforming raw materials into value-added products, the industry helps stimulate consumer demand and improve living standards, thus reinforcing its significance in Tanzania's overall economic development. The study further reveals that, in all political regimes from the 2nd to the 6th regime, industry has significant impact on the economic growth. Interesting is that, of all the selected six regimes, only the 5th regime seems to perform better than other regimes in the industrial policy implementation which resulted into positive growth and contribution to the GDP (see Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to Wineman et al (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), agriculture is defined as a vital sector of the economy that encompasses the cultivation of crops, livestock rearing, and agro-based activities, significantly contributing to food security, employment, and foreign exchange earnings. The sector accounting for approximately 30 percent of the country's GDP and employing over 70 per cent of the population (Mtingele, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The agricultural landscape in Tanzania includes a diverse range of food crops such as maize, rice, and beans, as well as cash crops like coffee, tea, and cashew nuts, which are essential for both domestic consumption, industrial development and export. Interestingly, agriculture seems to progress well in the 6th regime having positive growth as indicated in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, this suggests for further strategies such as ensuring early delivery of agro-inputs to farmers, improving extension services and mechanization which could spark the growth by at least 10 per cent and lead the industrialization movement in the country. The study further reveals the necessity for good governance as an ingredient to better and sustainable economic growth in the country (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Besides, despite the energy variable being not statistically significant, but the sector is still vital for inducing the industrial development that will ultimately catalyze the economic growth and development of the country. \u0026ldquo;Natural science suggests that, energy is crucial to economic production, and ecological economists and some economic historians argue that increasing energy supply has been a principal driver of growth\u0026rdquo; (Stern, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e"},{"header":"6.0 CONCLUSION AND POLICY IMPLICATIONS","content":"\u003cp\u003eThis study investigates the nexus between industrial policy implementation and economic growth within different political dynamics by using time series data (1990\u0026ndash;2021) while accounting for agriculture, energy and good governance as control variables in the regression model. To estimate the results, the study used Simultaneous Quantile Regression model and Multiple linear regression model for triangulation of results. The results indicate that, there is positive relationship between industrial development and economic growth in the country; the industrial sector has been growing and contributing to the GDP by about 7 per cent up to the year 2023 since 1990. The results further indicate that, out of six regimes, the political regimes that seem to have significant impact in implementing the industrial policy with good results is the 5th regime under his excellence Dr. John Pombe Magufuli. However, agriculture seems to have negative growth and contribution to the economic growth in the 3rd and 5th regimes, while positive in the 6th regime under President Dr Samia Suluhu Hassan. Energy sector especially accessibility to electricity at low cost is still vital for catalyzing industrial and economic development in the country.\u003c/p\u003e \u003cp\u003eOverall, the finding of this study highlights the significant policy implications of industrialization strategies and policy implementation for economic growth and development. Ultimately, the study advocates for a holistic approach that integrates industrialization efforts with broader economic and political strategies to sustain long-term economic growth in Tanzania. For example, Tanzania should create an Independent Industrial Policy Council to improve the consistency and influence of industrial policy across regimes. Beyond political cycles, this council would assess, plan, and supervise industrial development policies, guaranteeing steady advancement despite changes in leadership. Stakeholder involvement, public reporting, and yearly reviews should all be part of its mandate. In nations like Rwanda, where institutional stability has facilitated persistent industrialization initiatives, such a strategy has shown promise (Nguyen et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chinsinga et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Besides, given the close relationship between agriculture and industry, particularly under the sixth regime, the government should expand Special Agro-Industrial Processing Zones (SAPZs) to promote rural industrialization. These zones should combine value chains from farm production to agro-processing with targeted infrastructure, financing facilities, and tax breaks. Countries such as Ethiopia have found success with industrial parks that are linked to agriculture (Opoku \u0026amp; Yan, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Finally, Tanzania should create stronger governance frameworks for implementing public investments in industrial and agricultural projects, as the study indicated that excellent governance was significant at the 25th quantile. This entails requiring community scrutiny, performance audits, and transparent procurement. As demonstrated by the reform experiences in Rwanda and Vietnam, improved governance guarantees resource efficiency and fosters trust (Nguyen et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ferrannini et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe limitation of this study is that, industry, agriculture, energy and good governance cannot be the sufficient components/variables to study the economic growth changes within various political dynamics. Instead, other variables such as capital stock accumulation, labor, technology, foreign direct investment and mining should be considered for further study. Also, there was no recorded data for first regime on the independent variables under study, this led to omitting the 1st regime in the analysis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of Competing Interest\u003c/h2\u003e \u003cp\u003eThe authors declare 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 \u003c/p\u003e\u003cp\u003e \u003ch2\u003eData access\u003c/h2\u003e \u003cp\u003eWorld Bank link: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://databank.worldbank.org/reports.aspx?source=2\u0026amp;country=ARE\u003c/span\u003e\u003cspan address=\"https://databank.worldbank.org/reports.aspx?source=2\u0026amp;country=ARE\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eDeclaration\u003c/p\u003e \u003cp\u003eNo funding was received for conducting this study\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e \u003cp\u003eThe study has no acknowledgement requirements.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbbasi KR, Shahbaz M, Jiao Z, Tufail M (2021) How energy consumption, industrial growth, urbanization, and CO2 emissions affect economic growth in Pakistan? 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Energy Econ 80:153\u0026ndash;162\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Tanzania Targets 10% Agricultural Growth in a Decade with Agenda 10/30 \u0026ndash; Kilimo Kwanza\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":"No funding","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":"industrialization policy, economic growth, political regime Tanzania","lastPublishedDoi":"10.21203/rs.3.rs-6857027/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6857027/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTanzania's desire to move from an agrarian to an industrialized economy needs a careful examination especially from the perspective of industrialization strategies and political dynamics and economic growth. This study uses time-series data from 1990 to 2021 to investigate the relationship between industrial policy execution and economic performance in Tanzania's changing political situation. A Simultaneous Quantile Regression (SQR) model is used to identify heterogeneous effects throughout the economic growth distribution and a Multiple Linear Regression (MLR) model is utilized to evaluate and strengthen the findings. The findings show that excellent governance considerably boosts economic growth at the bottom end of the distribution (25th quantile), stressing its importance during periods of economic underperformance. At the top end (75th quantile), industrial sector expansion is identified as a major contributor to higher economic growth. Regime-specific study suggests that during President John Magufuli's fifth administration, the industrial sector provided 7% of GDP growth, while agriculture grew negatively. Interestingly, agriculture only showed a beneficial influence during the third and sixth administrations, with a major revival under President Samia Suluhu Hassan, which can be linked to greater budgetary allocations and policy priorities. Furthermore, the findings emphasize the role of political will, institutional quality, and sector-specific investment in generating long-term economic growth. The study suggests that strengthening institutional frameworks coupled with stakeholder participation is crucial to ensure that industrialization initiatives are inclusive and effective. These findings lead broader discussion of structural change and impactful for policymakers in seeking industrialization as a path for long-term prosperity in Tanzania.\u003c/p\u003e","manuscriptTitle":"Industrialization policy and economic growth nexus within political dynamics in Tanzania","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-11 10:26:12","doi":"10.21203/rs.3.rs-6857027/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"42d5ced6-2688-43c7-9542-4bc8d4539e36","owner":[],"postedDate":"June 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":49772840,"name":"Development Economics"}],"tags":[],"updatedAt":"2026-01-29T17:45:22+00:00","versionOfRecord":{"articleIdentity":"rs-6857027","link":"https://doi.org/10.4314/tajocode.v4i2.3","journal":{"identity":"tanzania-journal-of-community-development","isVorOnly":true,"title":"Tanzania Journal of Community Development"},"publishedOn":"2025-11-08 00:00:00","publishedOnDateReadable":"November 8th, 2025"},"versionCreatedAt":"2025-06-11 10:26:12","video":"","vorDoi":"10.4314/tajocode.v4i2.3","vorDoiUrl":"https://doi.org/10.4314/tajocode.v4i2.3","workflowStages":[]},"version":"v1","identity":"rs-6857027","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6857027","identity":"rs-6857027","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}