Tanzania TZ: GDP: PPP data was reported at 163,886.239 Intl $ mn in 2017. This records an increase from the previous number of 150,311.178 Intl $ mn for 2016. Tanzania TZ: GDP: PPP data is updated yearly, averaging 52,856.251 Intl $ mn from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 163,886.239 Intl $ mn in 2017 and a record low of 23,560.054 Intl $ mn in 1990. Tanzania TZ: GDP: PPP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Tanzania – Table TZ.World Bank.WDI: Gross Domestic Product: Purchasing Power Parity. PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current international dollars. For most economies PPP figures are extrapolated from the 2011 International Comparison Program (ICP) benchmark estimates or imputed using a statistical model based on the 2011 ICP. For 47 high- and upper middle-income economies conversion factors are provided by Eurostat and the Organisation for Economic Co-operation and Development (OECD).; ; World Bank, International Comparison Program database.; Gap-filled total;

Tanzania Exports of machinery parts, not containing electrical features to Norway was US$131 during 2016, according to the United Nations COMTRADE database on international trade. Tanzania Exports of machinery parts, not containing electrical features to Norway - data, historical chart and statistics - was last updated on November of 2025.

Tanzania TZ: PPP Conversion Factor: GDP data was reported at 708.430 TZS/Intl $ in 2017. This records an increase from the previous number of 686.367 TZS/Intl $ for 2016. Tanzania TZ: PPP Conversion Factor: GDP data is updated yearly, averaging 246.437 TZS/Intl $ from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 708.430 TZS/Intl $ in 2017 and a record low of 35.259 TZS/Intl $ in 1990. Tanzania TZ: PPP Conversion Factor: GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Tanzania – Table TZ.World Bank: Gross Domestic Product: Purchasing Power Parity. Purchasing power parity conversion factor is the number of units of a country's currency required to buy the same amounts of goods and services in the domestic market as U.S. dollar would buy in the United States. This conversion factor is for GDP. For most economies PPP figures are extrapolated from the 2011 International Comparison Program (ICP) benchmark estimates or imputed using a statistical model based on the 2011 ICP. For 47 high- and upper middle-income economies conversion factors are provided by Eurostat and the Organisation for Economic Co-operation and Development (OECD).; ; World Bank, International Comparison Program database.; ;

Tanzania's National Strategy for Growth and Reduction of Poverty (NSGRP) sets an ambitious target of 6 to 8 percent annual economic growth to achieve rapid reduction in poverty. This report focuses on three issues that are central to the success of Tanzania's poverty reduction efforts: 0 what factors explain Tanzania's recent acceleration in economic growth; has the accelerated economic growth translated into reduced poverty; and what must be done to sustain economic growth that is pro-poor. The report presents evidence from the macroeconomic, sectoral, and firm and household levels that shed light on these questions. The report is presented in two volumes. Volume I summarizes the main findings and recommendations. Volume II contains the main report.

Tanzania TZ: SPI: Pillar 1 Data Use Score: Scale 0-100 data was reported at 90.000 NA in 2023. This stayed constant from the previous number of 90.000 NA for 2022. Tanzania TZ: SPI: Pillar 1 Data Use Score: Scale 0-100 data is updated yearly, averaging 55.000 NA from Dec 2004 (Median) to 2023, with 20 observations. The data reached an all-time high of 90.000 NA in 2023 and a record low of 20.000 NA in 2006. Tanzania TZ: SPI: Pillar 1 Data Use Score: Scale 0-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Tanzania – Table TZ.World Bank.WDI: Governance: Policy and Institutions. The data use overall score is a composite score measuring the demand side of the statistical system. The data use pillar is segmented by five types of users: (i) the legislature, (ii) the executive branch, (iii) civil society (including sub-national actors), (iv) academia and (v) international bodies. Each dimension would have associated indicators to measure performance. A mature system would score well across all dimensions whereas a less mature one would have weaker scores along certain dimensions. The gaps would give insights into prioritization among user groups and help answer questions as to why the existing services are not resulting in higher use of national statistics in a particular segment. Currently, the SPI only features indicators for one of the five dimensions of data use, which is data use by international organizations. Indicators on whether statistical systems are providing useful data to their national governments (legislature and executive branches), to civil society, and to academia are absent. Thus the dashboard does not yet assess if national statistical systems are meeting the data needs of a large swathe of users.;Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators);Weighted average;

Tanzania TZ: Marine Protected Areas: % of Total Surface Area data was reported at 3.015 % in 2017. This records an increase from the previous number of 2.496 % for 2016. Tanzania TZ: Marine Protected Areas: % of Total Surface Area data is updated yearly, averaging 2.755 % from Dec 2016 (Median) to 2017, with 2 observations. The data reached an all-time high of 3.015 % in 2017 and a record low of 2.496 % in 2016. Tanzania TZ: Marine Protected Areas: % of Total Surface Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Tanzania – Table TZ.World Bank.WDI: Land Use, Protected Areas and National Wealth. Marine protected areas are areas of intertidal or subtidal terrain--and overlying water and associated flora and fauna and historical and cultural features--that have been reserved by law or other effective means to protect part or all of the enclosed environment.; ; World Database on Protected Areas (WDPA) where the compilation and management is carried out by United Nations Environment World Conservation Monitoring Centre (UNEP-WCMC) in collaboration with governments, non-governmental organizations, academia and industry. The data is available online through the Protected Planet website (https://www.protectedplanet.net/).; Weighted average; Restricted use: Please contact the Protected Planet for third-party use of these data.

Tanzania has made significant social and economic progress in recent decades. The economy has grown by an average of 6.1 percent per year since 2000, elevating Tanzania from Low-Income Country (LIC) status to Lower Middle-Income Country (LMIC) status in 2020. Between 2000 and 2020, life expectancy rose from 52 years to 67 years (versus an increase from 60 years to 69 years in LMICs), and the duration of school attendance increased from 3.8 years to 6.4 years (versus growth from 4.9 years to 6.6 years in LMICs). In the last decade alone, the share of Tanzanians with access to electricity has increased from 5.6 percent to 39.9 percent (and from 2.5 percent to 22 percent in rural areas). Reducing the cost of trade should be an immediate policy priority. Addressing high trade costs requires both behind-the-border and at-the border policies, aiming to enhance operations both at border crossings and while goods are in transit. While all factors relevant to the business climate also affect trade performance, direct actions that can shape the volume and composition of trade include: (i) eliminating high tariffs that discourage exports and reduce access to imported inputs; (ii) addressing existing NTMs and curtailing the development of new ones, particularly for primary agricultural commodities; and (iii) simplifying documentation and procedures to improve customs risk management. The African Continental Free Trade Area (AfCFTA) offers an opportunity to reduce trade costs and improve market access, but comprehensive reforms are also needed.

Techsalerator's Corporate Actions Dataset in Tanzania offers a comprehensive collection of data fields related to corporate actions, providing valuable insights for investors, traders, and financial institutions. This dataset includes crucial information about the various financial instruments of all 28 companies traded on the Dar es Salaam Stock Exchange (XDAR).

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Top 5 used data fields in the Corporate Actions Dataset for Tanzania:

• Dividend Declaration Date: The date on which a company's board of directors announces the dividend payout to its shareholders. This information is crucial for investors who rely on dividends as a source of income.

• Stock Split Ratio: The ratio by which a company's shares are split to increase liquidity and affordability. This field is essential for understanding changes in share structure.

• Merger Announcement Date: The date on which a company officially announces its intention to merge with another entity. This field is crucial for investors assessing the impact of potential mergers on their investments.

• Rights Issue Record Date: The date on which shareholders must be on the company's books to be eligible for participating in a rights issue. This data helps investors plan their participation in fundraising events.

• Bonus Issue Ex-Date: The date on which a company's shares start trading without the value of the bonus issue. This information is vital for investors to adjust their portfolios accordingly.

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Top 5 corporate actions in Tanzania:

Natural Resources and Mining Developments: Corporate actions related to mining, including mineral exploration, investments, and partnerships, have been prominent in Tanzania due to its rich natural resource deposits.

Infrastructure Projects: Infrastructure development, including transportation, energy, and telecommunications projects, often involves corporate actions such as partnerships, joint ventures, and project financing.

Agricultural and Agribusiness Initiatives: Given the importance of agriculture to the Tanzanian economy, corporate actions involving agribusiness projects, technology adoption, and value chain development contribute to food security and rural development.

Tourism and Hospitality Investments: Tanzania's tourism sector is a major contributor to its economy. Corporate actions related to hotel developments, tourism services, and hospitality investments play a significant role.

Financial Sector Expansion: Developments in Tanzania's financial sector, including new banking services, fintech innovations, and regulatory changes, can lead to corporate actions that reshape the financial landscape and promote financial inclusion.

Top 5 financial instruments with corporate action Data in Tanzania

Dar es Salaam Stock Exchange (DSE) Domestic Company Index: The main index that tracks the performance of domestic companies listed on the Dar es Salaam Stock Exchange. This index would provide insights into the performance of the Tanzanian stock market.

Dar es Salaam Stock Exchange (DSE) Foreign Company Index: The index that tracks the performance of foreign companies listed on the Dar es Salaam Stock Exchange, if foreign listings were present. This index would give an overview of foreign business involvement in Tanzania.

TanzaniaMart: A Tanzania-based supermarket chain with operations in multiple regions. TanzaniaMart focuses on providing essential products to local communities and contributing to the retail sector's growth.

FinLink Tanzania: A financial services provider in Tanzania with a focus on promoting financial inclusion and access to banking services, particularly among underserved communities.

AgriTech Tanzania: A company dedicated to advancing agricultural technology in Tanzania, focusing on optimizing crop yields and improving food security to support the country's agricultural sector.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Tanzania, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

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Data fields included:

Dividend Declaration Date Stock Split Ratio Merger Announcement Date Rights Issue Record Date Bonus Issue Ex-Date Stock Buyback Date Spin-Off Announcement Date Dividend Record Date Merger Effective Date Rights Issue Subscription Price ‍

Q&A:

How much does the Corporate Actions Dataset cost in Tanzania?

The cost of the Corporate Actions Dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

How complete is the Corporate Actions Dataset cov...

Tanzania TZ: Terrestrial & Marine Protected Areas: % of Total Territorial Area data was reported at 30.843 % in 2016. This records an increase from the previous number of 26.050 % for 2014. Tanzania TZ: Terrestrial & Marine Protected Areas: % of Total Territorial Area data is updated yearly, averaging 24.435 % from Dec 1990 (Median) to 2016, with 4 observations. The data reached an all-time high of 30.843 % in 2016 and a record low of 21.600 % in 1990. Tanzania TZ: Terrestrial & Marine Protected Areas: % of Total Territorial Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Tanzania – Table TZ.World Bank: Land Use, Protected Areas and National Wealth. Terrestrial protected areas are totally or partially protected areas of at least 1,000 hectares that are designated by national authorities as scientific reserves with limited public access, national parks, natural monuments, nature reserves or wildlife sanctuaries, protected landscapes, and areas managed mainly for sustainable use. Marine protected areas are areas of intertidal or subtidal terrain--and overlying water and associated flora and fauna and historical and cultural features--that have been reserved by law or other effective means to protect part or all of the enclosed environment. Sites protected under local or provincial law are excluded.; ; United Nations Environmental Program and the World Conservation Monitoring Centre, as compiled by the World Resources Institute, based on data from national authorities, national legislation and international agreements.; Weighted Average;

Dietary diversity and socio-economic data of households in Tanzania. This quantitative data consist of farmer socio-economic characteristics ( e.g age, education, access to credit and off-farm income, land size, household participation in nutrition training etc), foods consumed in 24 hours prior to the survey date by children (1-5 years old), women (15 -35 years old) and entire household. The survey was conducted from December, 2015 to January, 2016.

Tanzania TZ: PPP Conversion Factor: Private Consumption data was reported at 809.320 TZS/Intl $ in 2016. This records an increase from the previous number of 779.208 TZS/Intl $ for 2015. Tanzania TZ: PPP Conversion Factor: Private Consumption data is updated yearly, averaging 383.087 TZS/Intl $ from Dec 1990 (Median) to 2016, with 27 observations. The data reached an all-time high of 809.320 TZS/Intl $ in 2016 and a record low of 76.113 TZS/Intl $ in 1990. Tanzania TZ: PPP Conversion Factor: Private Consumption data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Tanzania – Table TZ.World Bank.WDI: Gross Domestic Product: Purchasing Power Parity. Purchasing power parity conversion factor is the number of units of a country's currency required to buy the same amounts of goods and services in the domestic market as U.S. dollar would buy in the United States. This conversion factor is for private consumption (i.e., household final consumption expenditure). For most economies PPP figures are extrapolated from the 2011 International Comparison Program (ICP) benchmark estimates or imputed using a statistical model based on the 2011 ICP. For 47 high- and upper middle-income economies conversion factors are provided by Eurostat and the Organisation for Economic Co-operation and Development (OECD).; ; World Bank, International Comparison Program database.; ;

Tanzania Exports of machinery parts, not containing electrical features to Australia was US$4.18 Thousand during 2012, according to the United Nations COMTRADE database on international trade. Tanzania Exports of machinery parts, not containing electrical features to Australia - data, historical chart and statistics - was last updated on November of 2025.

The model includes only the demographic variables, socio-economic status and malaria interventions as predictors.The model includes only the environmental and climatic proxies as predictors.**The model includes the demographic variables, socio-economic status, malaria interventions and environmental/climatic factors as predictors.

The Nexus Project is a collaboration between IFPRI and its partners, including national statistical agencies and research institutions. Our aim is to improve the quality of social accounting matrices (SAMs) used for computable general equilibrium (CGE) modeling. The Nexus Project develops toolkits and establishes common data standards, procedures, and classification systems for constructing and updating national SAMs. The 2018 Tanzania SAM follows the Standard Nexus Structure. The open access version of the Tanzania SAM separates domestic production into 42 activities. Factors are disaggregated into labor, agricultural land, and capital. Labor is further disaggregated across three education categories. Representative households are disaggregated by rural and urban areas and by per capita expenditure quintile. The remaining accounts include enterprises, government, taxes, savings-and-investment, and the rest of the word.

Abstract

Tanzania Enterprise Skills Survey was conducted between April and August 2015 by the Enterprise Analysis Unit (DECEA) and the Education Global Practice (GEDDR) of the World Bank Group.

The objective of the survey is to develop and test a methodological approach for a diagnostic of the composition and demand for skills and the relationship between skills (and/or skills constraints) and firm performance of selected economic sectors in Tanzania. A detailed skills module was developed as part of a larger firm-level Enterprise Survey, collecting information, among others, on the characteristics of firms and their owners, innovation and export activities, and firm performance.

The sample for the survey was selected using stratified random sampling, following a broadly similar methodology used in the World Bank's Enterprise Surveys.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is an establishment. The establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

Firms in eight selected economic activities: food processing (ISIC15), textile and garments (ISIC 17 & 18), fabricated metal products (ISIC 28), furniture (ISIC 36), construction (ISIC 45), hotel and restaurant (ISIC 55), transport (ISIC 60 & 61) and Information technology (ISIC 72)).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was selected using stratified random sampling.

Three levels of stratification were used for this survey: economic activity, establishment size, and region:

1) Eight economic activities - food processing (ISIC15), textile and garments (ISIC 17 & 18), fabricated metal products (ISIC 28), furniture (ISIC 36), construction (ISIC 45), hotel and restaurant (ISIC 55), transport (ISIC 60 & 61) and information technology (ISIC 72);

2) Three sizes - small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees);

3) Five regions (city and the surrounding business area): Arusha, Dar es Salaam, Mbeya, Mwanza, and Zanzibar.

Two sample frames were used:

1) The first frame was the 2011/2012 Central Registry of Establishment (CRE) of the National Bureau of Statistics (NBS);

2) The second frame was 2012 Central Registry of Establishment (CRE) of the Office of Chief Government Statistician (OCGS). The sample frame was used for the establishments in Zanzibar.

The enumerated establishments with 5 employees or more were then used as the sample frame for the 2015 Tanzania Skills Survey with the aim of obtaining interviews of 390 establishments.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The data was collected using a standardized questionnaire administered to all firms. The questionnaire has eight sections: six main sections and two sections on control information.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions.

Item non-response was addressed by re-contacting firms. That is, establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response. The response rates are particularly low for questions about the names and locations of the main universities and schools attended by the establishment's recent hires (questions l13, l14, l15 and l25 in the questionnaire). Despite repeated callbacks, respondents note that they just do not know the names and locations of schools attended by their employees.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

The number of realized interviews per contact contacted establishments was 0.28. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 0.07.

Abstract

The 2006 Integrated Labour Force Survey (ILFS) was the fourth such survey to be conducted by the Tanzanian government in collaboration with development partners and other stakeholders. This survey was similar in many respects to the 2000/01 ILFS. However, in addition to topics covered in that survey, the 2006 ILFS for the first time included time use module. The 2006 ILFS was, among others, intended to meet the data needs for monitoring of the National Strategy for Growth and Reduction of Poverty (NSGRP) or MKUKUTA in respect of economic growth and reduction of income poverty.

Geographic coverage

Tanzania Mainland

Analysis unit

Individual and Households

Universe

Individuals aged five years and above, living in private households

Kind of data

Sample survey data [ssd]

Sampling procedure

A three-stage sampling technique was agreed upon during the planning stage, which started in November 2005 and ended in December 2005 before the execution of the fieldwork for the ILFS. The sampling was based on the National Master Sample (NMS) that covers Tanzania Mainland and Zanzibar. A similar approach was adopted at the planning stage of the 2000/01 ILFS. Sampling was done by the NBS in collaboration with an expert from the University of Dar es Salaam. This report analyses the data collected in Tanzania Mainland. A simple random sampling technique was adopted at the first stage of sample design to determine representative samples of villages (140) and EAs (244) in rural and urban areas respectively. The villages, and enumeration areas (EAs) were demarcated during the 2002 Population and Housing Census. The second stage involved random selection of 80 households in each selected village and 30 households in each selected urban EA. This was then followed by the third stage of sampling which involved random selection of households to form representative samples of 20 and 30 households in each selected village and urban EA respectively that have to be interviewed in each quarter of the year. Five questionnaires focusing on different aspects were then administered in each of those households. The time use questionnaire was, however, administered only in every fifth household in the sample

Mode of data collection

Face-to-face [f2f]

Research instrument

Households- Household characteristics,usual residents,Education and Training,Economics activities, Households amenities and Community services, Labour activities, School attendance, Health and Safety, Child Labour

Cleaning operations

Data processing was carried out as soon as questionnaires were received from the field. The first stage included questionnaire reception and manual checking of the number of clusters (EAs) in a region and the number of households in each cluster. This was followed by manual editing and coding of questionnaires before data entry.

Response rate

The realised response for the standard labour force questions was 16,445 households giving a response rate of 88.8 percent and covering 72,442 individuals

Sampling error estimates

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Country’s economic growth depends among other factors on the extent to which labour particularly female labour force participates on economic growth enhancing activities. Being the largest contributor in economic activities particularly agriculture in developing countries (over 50%), their participation enables economies to grow in response to higher labour inputs injected. As an outcome, as countries develop; women’s capabilities typically improve as well, whereas social constraints weaken, which enables females to participate on work outside the home. However, the existing literature on this topic is scant and has mixed results. This study sought to analyse female fertility rate and its influence on provision of labour in Tanzania using females aged 15–49 years from the Tanzania Demographic and Health Survey 2015–2016. The study used instrumental variable-probit and a two-stage residual inclusion as methods of analysis. Results showed that, an increase in female’s fertility rate reduces participation of females in provision of market labour by about 1.1–13%. Similarly, household size, education, contraceptive use, self-employment of their husbands and residing in rural areas was associated with increased participation while female’s age exhibited an inverted U-shaped relationship with female participation. The results imply that, to foster a more sustainable female participation in labour force, family planning, educating females as well as fostering self-employment and improving rural infrastructures is inevitable.

Abstract

This report presents the findings of the 2007 Tanzania Household Budget Survey (HBS), which covered Mainland Tanzania. The analysis focuses on poverty-relevant indicators, including those defined in the Government’s five year programme for economic and social development; the National Strategy for Growth and Reduction of Poverty (NSGRP), commonly referred to by its Kiswahili acronym, MKUKUTA.

The HBS collected information on a range of individual and household characteristics. These included • Household members’ education, economic activities, and health status • Household expenditure, consumption and income • Ownership of consumer goods and assets • Housing structure and materials • Distance to services and facilities • Food security.

Geographic coverage

TANZANIA MAINLAND

Analysis unit

Individual and Household

Universe

The survey covered all de jure household members

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was based on a revised national master sample that has been developed out of the 2002 Census information. For the 2007 HBS, the national master sample provided the primary sampling units (PSUs) for the national urban and rural sample. It was supplemented with additional PSUs to provide a regional sample for Dar es Salaam, so that the survey provides estimates for Dar es Salaam region, other urban areas and rural areas. Primary sampling units were selected using probability proportional to size, with the number of household recorded in the Census preparatory estimates being the measures of size. A comprehensive household listing was undertaken in each of the sampled clusters. Information on a number of durable assets was collected for each household during the listing exercise. This information was used to stratify households within each cluster into high, middle and low income households. Separate proportional samples were then drawn from each of these categories. The sample selection was done in the head office and each regional supervisor was supplied with their respective list of pre-selected households.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires contain information related to;Household Particulars, Household Facilities, Household Assets, Household Income, Distance to socio-Economic Facilities, Purchase of Durable items and other Services,Food security;

Cleaning operations

Data consistency checks were developed to identify any inconsistencies in the entered data and errors were corrected by referring to the original questionnaire. Data cleaning continued until July 2008 and the analysis was completed by mid-November 2008.

Response rate

In total, the analysis includes 10,466 households and 447 of the intended 448 clusters. This is over 97 percent of the original intended sample size of 10,752 households. However, of the households included in the analysis, 13 percent were interviewed as reserve (replacement) households after the originally selected ones could not be found, a similar proportion to 2000/01. Replacements were particularly high in Dar es Salaam, where they constituted almost 19 percent of the sample analysed. Replacement is not usually considered a good practice because of the risk of introducing bias into the sample. This was minimised in the survey because households used as replacements had similar characteristics to those being replaced, although its frequency in Dar es Salaam raises concerns

Sampling error estimates

In order to ease readability of the tables in this report, the sample size on which the estimates are based is not stated. However, estimates are based on more than 150 observations, unless indicated; usually they are based on many more. Sampling errors and confidence intervals are presented for some key variables in Appendix A1.

Feed the Future is a global hunger and food security initiative which aims to refocus attention on addressing the root causes of global food insecurity, including agricultural development and nutrition. Led by the United States Agency for International Development (USAID), this initiative reflects a coordinated focus on building productive, resilient agricultural systems throughout 19 countries in need, including Tanzania. Emphasis is placed on smallholder farmers and women in particular who are making progress towards the development of sustainable agriculture sectors. As food security plays a critical role in poverty reduction, Feed the Future has been made a primary development assistance tool in the reduction of poverty.

In Tanzania, Feed the Future efforts are focused on improving agricultural productivity and market access, increasing trade, and improving the nutritional status of children through promotion of fortified foods and behavior change. For maximum impact, the Feed the Future initiative has targeted its investments in six regions in the country considered to be the Zone of Influence (ZOI): Dodoma, Manyara, Morogoro, Mbeya, Iringa, and all three areas of Unguja in Zanzibar.

The Feed the Future Interim Supplemental Survey (FTFISS) was developed to measure and elaborate on consumption habits in Tanzania, and to provide a more comprehensive view of the food security situation in the country. Additionally, this project provides a valuable opportunity to expand upon food security information gathered in the Tanzania National Panel Survey (NPS), as questionnaire themes in the FTFISS were modeled to reflect those topics considered central to the comprehension of food security. To further enhance value of this expansion, only NPS households residing in the ZOI regions targeted by the Feed the Future initiative were chosen to participate in the FTFISS project. NPS households in these six regions were tracked and re-interviewed following conclusion of the 2014/2015 NPS.

The 2014/2015 NPS was the fourth round in a series of nationally representative household panel surveys that collect information on a wide range of topics including agricultural production, non-farm income generating activities, consumption expenditures, and a wealth of other socio-economic characteristics. All four rounds of the NPS were implemented by the Tanzania National Bureau of Statistics (NBS) with assistance provided by the World Bank through the Living Standards Measurement Study - Integrated Surveys on Agriculture [LSMS-ISA ] program.

This submission derives from Task 5.9 "Innovation validation across the network" of the H2020 project FoodLAND "Food and Local, Agricultural and Nutritional Diversity" (2020-2025). T5.9 had two objectives: (1) assessing the impact of different dissemination strategies (information provision or financial incentivisation) on the adoption of selected FoodLAND innovations by smallholder farmers; (2) assessing the impact of these innovations, if adopted and used, on various indicators of farm performance and household wellbeing. To achieve these closely related goals, T5.9 implemented Randomised Controlled Trials (RCTs) in three so-called Food Hubs: Meknès (Morocco), where a phone application for precision irrigation and protection was considered; Mvomero (Tanzania), where the focus was on two new bean varieties characterised by higher zinc and iron content; and Kajjansi-Masaka (Uganda), where fish farmers were introduced to the integration between aquaculture and vegetable farming. The RCTs were conducted between May 2023 (initial survey) and early January 2025 (post-implementation/final survey), depending on the country and specific local conditions. A detailed description of the methodological approach and the data collection tools is provided in deliverable D5.20 "Protocol to implement the randomised controlled trials of the innovations." This submission consists of three datasets, one per Food Hub, plus the report D5.21 "Dataset of RCTs of innovation results, linked to socioeconomic and demographic factors." The datasets are provided in Excel Workbook format. Each dataset consists of eight (Morocco and Tanzania) or 13 (Uganda) worksheets, plus the metadata and the sheet descriptions. The sheets are the following: baseline survey (base_survey), baseline crop production (base_crop_prod), baseline crop inputs (base_crop_input), planned crop production (plan_crop), baseline fish production – Uganda only (base_fish_prod), baseline fish inputs – Uganda only (base_fish_input), planned fish production – Uganda only (plan_fish), post-training survey (post_survey), final survey (final_survey), final crop production (final_crop_prod), final crop inputs (final_crop_input), final fish production – Uganda only (final_fish_prod), final fish inputs – Uganda only (final_fish_input). Each variable is assigned a short name, e.g., “treatment,” and a long name that conveys information about the country and the worksheet, e.g., “u_post_surv_treatment.” The unique ID of the farmer, “unique_id,” appears with the same name in all the worksheets to facilitate merging during the analysis; all the variable names include 32 characters or less.

Update 2025-05-23: The Ugandan dataset was reviewed based on new observations, some errors were corrected in the Moroccan dataset, and the report D5.21 was updated accordingly.