tourism development in zanzibar

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Tanzania Economic Update: How to Transform Tourism into a More Sustainable, Resilient and Inclusive Sector

Stone Town, Zanzibar

Photo credit: Christian Morgan/World Bank.

STORY HIGHLIGHTS

• The latest Tanzania Economic Update highlights the huge untapped potential of the tourism sector to drive the country’s development agenda
• The new analysis discusses long-standing issues facing tourism in Tanzania as well as new challenges brought on by the COVID-19 pandemic
• The report says that the pandemic offers an opportunity for policy actions for the sector to recover in the near term and become a sustainable engine of private-sector-driven growth, social and economic inclusion, and climate adaptation and mitigation over the long term

DAR ES SALAAM, July 29, 2021— Tourism offers Tanzania the long-term potential to create good jobs, generate foreign exchange earnings, provide revenue to support the preservation and maintenance of natural and cultural heritage, and expand the tax base to finance development expenditures and poverty-reduction efforts.

The latest World Bank Tanzania Economic Update, Transforming Tourism: Toward a Sustainable, Resilient, and Inclusive Sector highlights tourism as central to the country’s economy, livelihoods and poverty reduction, particularly for women, who make up 72% of all workers in the tourism sector.

“Without tourism, the situation would be bad,” said Rehema Gabriel, a hotel attendant in Dar es Salaam. “I have been working in the tourism industry for eight years now, so I do not know what it would be like without it.”

The economic system around tourism had grown in value over the years and in 2019 was the largest foreign exchange earner, the second largest contributor to the gross domestic product (GDP) and the third largest contributor to employment, the report says. On the semiautonomous Zanzibar archipelago, the sector has also experienced rapid growth, accounting for almost 30% of the island’s GDP and for an estimated 15,000 direct and 50,000 indirect jobs. However, the report notes, only a small fraction of Tanzania’s natural and cultural endowments has been put to economic use through tourism development.

“Tourism offers countries like Tanzania, with abundant natural and cultural endowments, access to many foreign markets,” said Shaun Mann, World Bank Senior Private Sector Development Specialist and co-author of the Tanzania Economic Update. “But the absence of tourism revenues, as we have seen during this pandemic, compromises the integrity and viability of not only endowments, but also the economic, environmental and social ecosystems built up around those endowments.”

Amid the ongoing COVID-19 (coronavirus) pandemic, the World Bank estimates that Tanzania’s GDP growth decelerated to 2.0% in 2020. Business slowed across a wide range of sectors and firms, especially export-oriented sectors such as tourism and manufacturing. The report highlights the impact of the crisis on tourism specifically, which has had consequences beyond just the industry, given the many other sectors that support, and are supported by, tourism. The 72% drop in the sector’s revenues in 2020 (from 2019 levels) closed businesses and caused layoffs.

Zanzibar’s economy was even more severely impacted with GDP growth slowing to an estimated 1.3%, driven by a collapse of the tourism industry. As the hospitality industry shut down between March and September 2020, occupancy rates dropped to close to zero. While the Zanzibar tourism sector started slowly rebounding in the last quarter of 2020, with tourist inflows in December 2020 reaching almost 80% of those in 2019, receipts from tourism fell by 38% for the year.

As the tourism sector transitions gradually into recovery mode with the rest of the world, the report urges authorities to look toward its future resilience by addressing long running challenges that could help position Tanzania on a higher and more inclusive growth trajectory. Areas of focus include destination planning and management, product and market diversification, more inclusive local value chains, an improved business and investment climate and new business models for investment that are built on partnership and shared value creation.

Tanzania is a globally recognized destination for nature-based tourism, a competitive market segment in eastern and southern Africa. Beyond attracting tourists, the country’s landscapes and seascapes produce a wide range of ecosystem services, including carbon sequestration and biodiversity co-benefits that are not efficiently priced and often generate little or no financial return. The global climate crisis has created significant demand for investment in these forms of natural capital, and Tanzania is well positioned to take advantage of nature-positive investment opportunities. The additional revenue derived from global climate programs could be an opportunity to ease the government’s fiscal constraints while also supporting the livelihoods of local communities.

“While restoring the trade and financial flows associated with tourism is an urgent priority, the disruption of the sector has created an opportunity to realign tourism development with economic, social, and environmental resilience,” said Marina Bakanova, World Bank Senior Economist, and co-author of the report. “The pandemic has created an opportunity to implement long-discussed structural reforms in the sector and use tourism as a leading example of improvement of the overall business climate for private investment.”

The authors suggest five priorities for a sustainable and inclusive recovery that lay the foundation for the long-term transformation of the tourism sector:

• Creating an efficient, reliable, and transparent business environment to reduce red tape and multiple distortions and inefficiencies, hindering decisions on private investments, domestic and foreign
• Establishing an information-management system that consolidates data from tourists and firms, enabling policymakers to improve sectoral planning and identify viable investment opportunities
• Ensuring that firms across the sector, as well as those in downstream value chains, have access to affordable transitional finance
• Consistently promoting, monitoring, and reporting on adherence to health and safety protocols.
• Developing co-investment and partnership arrangements to support nature-based landscape and seascape management
• Press Release: Tanzania has an Opportunity to Ignite Inclusive Economic Growth by Transforming its Tourism Sector
• Report: 16th Tanzania Economic Update: ‘Transforming Tourism: Toward a Sustainable, Resilient, and Inclusive Sector’
• Video: Launch event: 16th Tanzania Economic Update
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Tourism is Zanzibar’s largest service industry because of its lush nature, heritage, wildlife, tradition, culture, and prominent hospitality, Zanzibar has a competitive advantage in the tourism industry. The tourism industry has been the backbone of the economy, accounting for 15% of GDP.

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Sunday 31st of March 2024

Zanzibar Tourism Statistics

Over the past six decades, tourism has expanded and diversified to become one of the world’s largest and fastest growing economic sectors. It has contributed to the socio-economic progress of many nations through export revenues, job and enterprise creation, and infrastructure development. As a result, each year an increasing number of destinations around the world invest in and encourage tourism. The global tourism industry continued to perform well, buoyed by stable consumer confidence and improving economic conditions.

It continues to play a key role as a driver of growth and job creation, growing at 4% in 2014 and providing 266 million jobs, directly and indirectly. This means that the industry now accounts for one in 11 jobs on the planet, a number that could even rise to one in 10 jobs by the year 2022, according to the World Tourism and Travel Council.

Zanzibar continues to rate as one of the world’s most desirable tourism destinations. Zanzibar has what more and more international travellers want – spectacular natural beauty, a pristine environment, safety and security, friendly and welcoming people, and world-class historical sites. Indeed, promoting cultural tourism provides a distinct competitive advantage. The growth of percentage share of tourism in GDP as well as increased visitor expenditure and numbers are evidence of Zanzibar’s appeal as a destination. It is estimated that tourism account for more than 27 percent in Zanzibar’s Growth Domestic Product and 80 percent of foreign exchange earnings.

In 2013, the overall average expenditure of visitors to Zanzibar was US$284 per person per night compared to US$ 217 recorded in 2007. The country received a total of 294,243 arrivals in 2015. This shows a decline of 6 percent (17,648) from the 2014 figure of 311,891. The decrease in overseas tourist arrivals may be attributed to the global economic and financial crisis affecting industrialized countries, especially these in the Euro zone. The peaceful environment of the country urged to attract huge tourist traffic to Zanzibar. International travel demand for Zanzibar is expected to increase steadily over the next year. The number of international visitors is forecast to increase to 360,000 and 500,000 in 2016 and 2020 respectively. The numbers is expected to be served by at least 420 accommodation units, 305 tour operators, 48 diving Centres and 98 registered boutiques shops.

The improved global economic outlook - especially the sustained economic recovery in Zanzibar’s traditional inbound source markets such as the UK and the USA – and a growing interest for long haul travel bodes well for growth in the number of international visitor arrivals. Throughout this period, Zanzibar will face an increasingly competitive environment, with more marketing by competitor destinations, changing distribution channels and broadening media options.

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Re-thinking Tourism in Zanzibar: How Artificial Intelligence is Creating Value in the Industry

February 21, 2023.

"Digital technology has transformed the way we live, work, and relate to each other. Now, it is transforming tourism. We need to continue to work together to maximize the opportunities and minimize the challenges that tourism can bring to communities and destinations and make tourism a driver of positive change for all." Zurab Pololikashvili, Secretary-General of the United Nations World Tourism Organization (UNWTO)

Participants in the AI4CI Conference at Hamad Bin Khalifa University in Doha, Qatar, in 2022

In the world of tourism, online reviews can make or break a business. Understanding customer feedback is crucial for businesses to improve their services and increase customer satisfaction. The UNDP Accelerator Lab Tanzania has been working on using Natural Language Processing (NLP) models to analyze compliments and complaints in the tourism sector by mining online text data from popular booking websites

The UNDP Accelerator Lab Tanzania was invited to the Artificial Intelligence for Collective Intelligence (AI4CI) workshop in the summer of 2022. The technical sessions brought together development actors from around the world and leading minds in the field of data science to advance UNDP's work on leveraging collective intelligence to inform sustainable development. AI4CI was co-organized by the United Nations Development Programme (UNDP) Accelerator Labs, UNDP Innovation in the Arab States, and the Qatar Computing Research Institute's Social Computing Department (QCRI). The Tanzania lab presented its work on using natural language processing model to understand compliments and complaints in the tourism sector by mining online text data from popular booking websites. A series of two experiments focused on quantitative and qualitative web-based data from private and public tourist attractions, which focused on the extraction and application of data analytics methods to clean, annotate, analyze, interpret, and visualize data from popular tourist websites to collect feedback from visitors visiting the Zanzibar islands of Unguja and Pemba. The experiments were done in collaboration with machine learning engineers from Xsense AI , a company based in Dar es Salaam, Tanzania. This blog provides a brief overview of the entire technical process.

Data Collection

The TripAdvisor and booking.com websites were used as sources for the data collection exercise. Ethical web scraping techniques were used to collect texts that represented tourist reviews on a subject (an attraction or service).

Web Scrapping Process

Python programming was first used to scrape data from websites using the Selenium and Beautiful Soup packages. However, we noted significant challenges that hindered our data collection process using Python, as it was slow even when using multi-threading techniques. Additional complexities were observed from the web pages we were scraping, which were highly dynamic, making the Selenium web drivers fail to identify web elements of interest at this time.

Then we combined the Node.js and GoLang programming languages. In web pages where reviews were few and there was less pagination, Node.js performed well, but became problematic in pages with 1000+ reviews (30 + review pages) as it is hard for it to perform asynchronous operations. In such cases, Go was our preferred data scraping language because it was designed with multi-threading at its core and can handle multiple requests at once. At the end of the scrapping exercise, we managed to collect more than 600,000 reviews on both websites.

Text Classification

Based on a preliminary analysis done over the extracted dataset, we were able to determine the type of classification approach to be used in the work. We had 600,000 rows of unlabeled data that needed to go through a two-stage text classification process, first to determine whether a review was positive or negative, and then to classify that review in which service category (location, internet, cleanliness, staff, food, comfort, value for money) it belonged.

We decided to classify the reviews using a deep learning sentiment analysis approach. A deep learning approach would enable the work to analyze the dataset in an unsupervised learning manner. NLP applications typically require large amounts of labeled data in all the classes that need to be classified to achieve high performance. Even with the 600K+ data we had, this was not achievable, especially in the service category. Hence, we used "transfer learning," a technique in deep learning common to computer vision and NLP tasks that uses pre-trained weights from language models to make predictions over your datasets after fine-tuning.

Sentiment Analysis with Transformer Models

NLP tasks have been commonly implemented with recurrent neural network (RNN) architectures such as long short-term memory (LSTM) and gated recurrent units (GRU). Regardless of their powerful capabilities in modeling sequences, they still suffer from information loss over long sentences due to their word-by-word sequential processing. Using these models for sentiment analysis in this work would be challenging and complex. We therefore chose Transformer models, which discard recurrence in RNNs and instead use self-attention mechanisms in their architecture to create powerful language models.

DistillBERT, a pre-trained transformer model from a family of BERT models pre-trained on English with a dataset consisting of 11,038 unpublished books and English Wikipedia, was used in this work. It recorded a 91.3 glue test result on the sequence classification task for predicting polarity (negative or positive).

End-to-end modeling process

First, the reviews are tokenized and represented as one-hot vectors called token encodings. These token encodings are converted to token embeddings, which are vectors living in a lower-dimensional space. The token embeddings are then passed through the encoder block layers to yield a hidden state for each input token. We then down streamed the language model to perform a classification task that outputs two labels (positive and negative).

Zero-Shot-Learning Process

At this stage of the analysis, the first stage of the sentiment analysis has been achieved, as each review has been labelled with either a positive or negative label. In identifying the service category (location, internet, cleanliness, staff, food, comfort, and value for money) a particular review belonged to, a second stage of sentiment analysis had to be done. Here, we performed a multi-label sentiment analysis using a zero-shot learning process, which can work with unlabeled data. In the zero-shot learning process, a model that has already been trained, in this case DistillBERT, is used to predict the review's label or sentiment without any fine-tuning.

Heat map visualization

We needed to create a graphical description of the outputs we had produced from our two-stage sentiment analysis. Since we had collected the geographical location information (longitude and latitude) of each review, we then visualized each review's sentiment using a heat map to understand the spatial distribution of tourists' reviews in each service category, which can be significant for intervention purposes in Zanzibar. The number of data points in each review led us to decide to use a heat map to display the information.

The results of the two experiments were shared with the Zanzibar Tourism Commission for further consideration. At the time of this writing, UNDP and the commission were in the process of hiring a consultant to create an AI model that will be owned by the government. The model will generate real-time feedback on online compliments and complaints from tourists visiting the islands. The results should help the government improve the experience of tourists in Zanzibar.

Lessons learned and conclusion:

The experiments conducted highlighted the importance of utilizing data analytics methods for extracting insights from online feedback. By using Natural Language Processing models to look at both quantitative and qualitative data from popular tourist websites, the lab was able to learn more about how people felt when they went to private and public tourist attractions on the islands of Unguja and Pemba, which are part of the Zanzibar archipelago.

One of the major insights gained from this work is the value of using NLP algorithms to analyze large volumes of unstructured data. By pulling out sentiment, finding key topics, and visualizing patterns in the data, businesses and governments in the tourism industry can learn a lot about how customers feel about their experiences and make decisions based on the data to improve their services.

Another important lesson learned is the significance of cleaning and annotating data to ensure accurate analysis. Cleaning and annotating the data was a very important technical step that made the NLP algorithm more accurate and decreased the chance that the analysis would be biased.

Finally, the collaboration between the UNDP Accelerator Lab, Xsense AI , and technical inputs from experts from the Two Sigma Data Clinic , Accern , and the QCRI of the Hamad Bin Khalifa University (HBKU) highlighted the importance of partnerships in achieving effective data analytics. With the help of machine learning engineers, the lab was able to come up with a more thorough and effective way to collect, analyste, interepret and visualize data using Ai methods.

Missed our previous blog? Please click here .

Written by: Peter Nyanda, Team Lead, UNDP Accelerator Lab, Tanzania

Deogtatias Mzurikwao, Director and researcher at Xsense AI in Tanzania 

Asa Kalonga, Machine Learning Engineer, Xsense Ai, Tanzania

Daniel Mlabwa, Information Management Specialist, UNDP Tanzania

Special thanks to: Ms. Hafsa Mbamba, the Executive Secretary, Zanzibar Commission for Tourism  

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Join 500 others in Zanzibar, for a three-days event of inspiring keynotes and new connections at the premier conference for Tourism Investors and Exhibitionists in Zanzibar.

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The Zanzibar Declaration on Sustainable Tourism i s an informal statement of intent and a rallying cry for the Zanzibar tourism industry to take real strides towards sustainability, in support of people, planet and prosperity. The Zanzibar Declaration recognises and contributes to global sustainable tourism actions adopted by international institutions and world leaders; this includes the One Planet Network Glasgow Declaration: Climate Action in Tourism  with which this declaration is aligned, and many of the UN Sustainable Development Goals.

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Join 2000+ others in Zanzibar, for a three-days event of inspiring keynotes and new connections at the premier conference for Tourism Investors and Exhibitionists in Zanzibar.

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Department of Museums and Antiquities (DoMA), which is the sole public entity responsible for supervising museums and conserving heritage assets, has been established in 1925. In this year, the Peace Memorial Museum, Beit – el – Amaan located at Mnazi Mmoja in Zanzibar Stone Town was the first to be opened along the coast of East Africa. The museum was designed as an ethnographic display to exhibit the works of art and culture. In 1930, an annex museum – Natural History Museum was opened. The museum embodies the display of birds, animals, insects, reptiles, trees and plants, and sea creatures. The construction of these buildings, Beit el Amaan in particular, was attributed to the donations made several people in Zanzibar to commemorate the peace after the end of the First World War.

Both museums will soon be reopened to exhibit the themes of the art, culture and nature after intensive restoration works being accomplished. The Department of Museums and Antiquities (DoMA) is proud to support ZTITE and offering both museum as venue for this an amazing, wonderful and potential event in Zanzibar.

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The Impact of Tourism Development on Economic Development: The Case of Zanzibar

2021, Asian Journal of Education and Social Studies

Tourism is perceived as one of the world's fastest growing service sectors and a major source of economic development for many, if not all, developing countries. Zanzibar as a developing country and also is a small island which have small economy, its national income depend much on tourism contribution, Therefore this paper aim to examine the impact of tourisms development to the economic development of Zanzibar, using the data based on annual time series from the period 1989-2019 and also employing Vector Error Correlation Model (VECM) to arrive at conclusions from the data in the study area. The study results found a long-run stable relationship among tourism development and economic development of Zanzibar, there is a positive and significant impact that exists between GDP and international tourism arrivals, inflation and government expenditure respectively while only inflation results show positive but insignificant impact. In order to increase the economic development in Zanzibar through the tourism sector, there is a need for the government and other stakeholders of tourism to put much consideration on this sector so as to improve overall development of Zanzibar economy.

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Lloyd George Banda

Malawi has very few but highly attractive tourist centres and with the concerted efforts of government, tourism is becoming an essential part of the country’s economy. The World Travel and Tourism Council (WTTC) in 2017 envisaged a growth of 4.9 percent per annum for period of ten years for the Malawian tourism industry. In view of this, the study focuses on empirical investigation of the contribution of the fairly developing tourism sector to economic growth in Malawi. The popular time series data for the period spanning from 1985 to 2015 was analysed with econometric STATA package. The findings reveal a no causality and negative long-run relationship between tourism development and economic growth. Meaning that neither the tourism-led economic growth hypothesis (TLEG) nor the Economic driven tourism growth hypothesis (EDTG) is confirmed for Malawi. This study provides sound policy recommendations on increasing investment in infrastructure and tourist centers for the sake of boosting tourism activities in the country though there is insufficient data leading to econometric omission such as economic freedom index and tourism related investment which were potential variables.

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This paper, which is the first of its kind in the case of Zimbabwe, uses annual time series on international tourism demand in Zimbabwe from 1980 to 2019, to model and forecast the demand for international tourism using the Box-Jenkins ARIMA approach. This research has been guided by the following objectives: to analyze international tourism trends in Zimbabwe over the study period, to develop and estimate a reliable international tourism forecasting model for Zimbabwe based on the Box-Jenkins ARIMA technique and to project international tourism demand in Zimbabwe over the next decade (2020-2030). Based on the Akaike Information Criterion (AIC), the study presents the ARIMA (2, 1, 0) model as the optimal model. The ARIMA (2, 1, 0) model proves beyond any reasonable doubt that over the period 2020 to 2030, international tourism demand in Zimbabwe will increase and that indeed, the future of Zimbabwe's tourism industry is bright. Amongst other policy recommendations, the study advocates for the continued implementation and enforcement of COVID-19 preventive and control measures as well as unwavering support for tourism sector development through policies such as the National Tourism Recovery and Growth Strategy.

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Given the importance of the air traffic analysis both for airlines and civil aviation authorities, the main aim of this paper is to develop an econometric model to analyze, assess and forecast the air travel demand in Morocco. To select the relevant variables, the all possible regression procedure was conducted. The model containing price index consumer, the gross national product, household final consumption per capita and international tourist's arrivals is the most appropriate model to represent the demand for air travel in Morocco.

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The aim of this paper is to provide a comprehensive literature review on the temporal relationship between tourism and economic growth. Specifically, the role of a such economic activity, as a promoter of short and long run economic growth, is investigated by assessing the so-called Tourism Led Growth Hypothesis (TLGH). To this aim, various methodological approaches have been used, such

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Purpose: The purpose of this study was to analyze tourism-led growth hypothesis in Kenya’s economy. Materials and Methods: The descriptive research design was adopted. This study targeted international tourism receipts, employment, economies of scale and capital investments in tourism related economic activities that included hotels and food service activities, wholesale and retail trade, transport and information communication and travel agencies, entertainment and recreation in the period 1980 to 2019.The study used purposive sampling. a sample size of data for 40 years from 1980 to 2019 was used. The data were collected from KNBS, the World Bank and WTTC using a secondary data collection sheet. Using real GDP per capita as the dependent variable and international tourism receipts, tourism related employment, economies of scale and capital investments as the independent variables, the study used regression and vector error correction (VEC) to carry out the analysis. The analysis ...

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It has been a major source of contention among economics scholars, that tourism is a major driver of socioeconomic development in the world and serves as a major source of revenue across countries. Hence, this paper examines the effects of exchange rate fluctuation on tourism sector output in Nigeria for the period of 1995 to 2015, using the Vector Error Correction Model (VECM), granger causality test and co-integration approach to ascertain this relationship. Results revealed that exchange rate fluctuation indeed has a significantly negative effect on tourism sector output in Nigeria, and that an increased fluctuation reduces the contribution of the tourism sector to GDP. The granger Causality test result showed that there is a unidirectional causality and long run relationship between contribution of tourism sector to GDP and the contribution of the Tourism sector to employment, real effective exchange rate, and the international number of tourist arrival. Among various recommendations in the study is for the government of Nigeria to review existing economic policies that affect the exchange rate fluctuation, as these some of this policies may be responsible for the consistent increase in exchange rate fluctuation , as these could substantially reduce the number of tourist arrival and tourism sector output.

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GDP growth in Tanzania Q4 2021, by economic activity

GDP growth rate in Tanzania in the fourth quarter 2021, by economic activity

Agriculture's value added to the GDP in Tanzania 2015-2021

Value added of the agriculture sector to the Gross Domestic Product (GDP) in Tanzania from 2015 to 2021 (in billion Tanzanian shillings)

Manufacturing's value added to the GDP in Tanzania 2015-2021

Value added of the manufacturing sector to the Gross Domestic Product (GDP) in Tanzania from 2015 to 2021 (in billion Tanzanian shillings)

GDP of mining and quarrying in Tanzania 2015-2020

Value added by mining to the Gross Domestic Product (GDP) in Tanzania from 2015 to 2020 (in billion Tanzanian shillings)

Inflation and prices

• Basic Statistic Inflation rate in Tanzania 2028
• Basic Statistic Monthly inflation rate in Tanzania 2020-2021
• Basic Statistic Monthly consumer price index (CPI) in Tanzania 2020-2021
• Basic Statistic Quarterly inflation rate in Tanzania 2020-2021, by geographical zone

Inflation rate in Tanzania 2028

Tanzania: Inflation rate from 1988 to 2028 (compared to the previous year)

Monthly inflation rate in Tanzania 2020-2021

Monthly inflation rate in Tanzania from January 2020 to October 2021

Monthly consumer price index (CPI) in Tanzania 2020-2021

Monthly consumer price index (CPI) in Tanzania from January 2020 to March 2021

Quarterly inflation rate in Tanzania 2020-2021, by geographical zone

Inflation rate in Tanzania in 1st quarter 2020 and 1st quarter 2021, by geographical zone

• Premium Statistic Labor force participation rate in Tanzania 2022
• Basic Statistic Number of people employed in Tanzania 2015-2021
• Basic Statistic Unemployment rate in Tanzania 2022
• Basic Statistic Youth unemployment rate in Tanzania in 2022
• Basic Statistic Employment by economic sector in Tanzania 2011 to 2021

Labor force participation rate in Tanzania 2022

Tanzania: Labor force participation rate from 2012 to 2022

Number of people employed in Tanzania 2015-2021

Estimated total employment in Tanzania from 2015 to 2021 (in 1,000s)

Unemployment rate in Tanzania 2022

Tanzania: Unemployment rate from 2003 to 2022

Youth unemployment rate in Tanzania in 2022

Tanzania: Youth unemployment rate from 2003 to 2022

Employment by economic sector in Tanzania 2011 to 2021

Tanzania: Distribution of employment by economic sector from 2011 to 2021

International trade

• Basic Statistic Export of goods and services in Tanzania 2010-2019
• Basic Statistic Import of goods and services in Tanzania 2010-2019
• Basic Statistic Main exported goods from Tanzania 2020
• Basic Statistic Main exported services from Tanzania 2019

Export of goods and services in Tanzania 2010-2019

Export value of goods and services in Tanzania from 2010 to 2019 (in million U.S. dollars)

Import of goods and services in Tanzania 2010-2019

Import value of goods and services in Tanzania from 2010 to 2019 (in million U.S. dollars)

Main exported goods from Tanzania 2020

Main exported goods from Tanzania as of 2020 (in million U.S. dollars)

Main exported services from Tanzania 2019

Main exported services from Tanzania as of 2019 (in million U.S. dollars)

Government finances

• Premium Statistic Government revenue in Tanzania 2020/2021, by source
• Premium Statistic Government expenditure in Tanzania 2020/2021, by segment
• Premium Statistic Government debt as a share of GDP in East African countries 2021-2023
• Premium Statistic External debt stock in Tanzania 2021, by borrower
• Premium Statistic Domestic and external public debt in Tanzania 2015-2020
• Premium Statistic Public debt sustainability in Tanzania 2019-2020

Government revenue in Tanzania 2020/2021, by source

Government revenue in Tanzania as of 2020/2021, by source (in billion Tanzanian shillings)

Government expenditure in Tanzania 2020/2021, by segment

Government expenditure in Tanzania as of 2020/2021, by segment (in billion Tanzanian shillings)

Government debt as a share of GDP in East African countries 2021-2023

Government debt as a share of the Gross Domestic Product (GDP) in East Africa from 2021 to 2023, by country

External debt stock in Tanzania 2021, by borrower

External debt stock in Tanzania as of August 2021, by borrower (in million U.S. dollars)

Domestic and external public debt in Tanzania 2015-2020

Domestic and external public debt in Tanzania from 2015/2016 to 2019/2020 (in million U.S. dollars)

Public debt sustainability in Tanzania 2019-2020

Selected public debt sustainability indicators in Tanzania as of 2019/2020

Special focus: Zanzibar

• Premium Statistic GDP at market prices in Zanzibar 2016-2020
• Basic Statistic GDP growth rate in Zanzibar 2016-2021
• Premium Statistic Share of GDP by economic activity in Zanzibar 2020
• Premium Statistic Monthly inflation rate in Zanzibar 2018-2021
• Premium Statistic Main goods exports from Zanzibar 2019-2022
• Premium Statistic Main goods imported into Zanzibar 2019-2020
• Premium Statistic International visitors in Zanzibar 2015-2021

Gross Domestic Product at market prices in Zanzibar from 2016 to 2020 (in billion Tanzanian shillings)

GDP growth rate in Zanzibar 2016-2021

Gross Domestic Product (GDP) growth rate in Zanzibar from 2016 to 2021

Share of Gross Domestic Product in Zanzibar as of 2020, by economic activity

Monthly inflation rate in Zanzibar 2018-2021

Monthly inflation rate in Zanzibar from December 2018 to November 2021 (compared to the same month of the previous year)

Main goods exports from Zanzibar 2019-2022

Export value of goods from Zanzibar from 2019 to 2022 (in 1,000 U.S. dollars)

Main goods imported into Zanzibar 2019-2020

Import value of goods into Zanzibar in 2019 and 2020 (in million U.S. dollars)

International visitors in Zanzibar 2015-2021

Annual number of international visitors in Zanzibar, Tanzania, from 2015 to 2021

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