Tanzania TZ: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 9.000 % in 2011. This records a decrease from the previous number of 13.200 % for 2007. Tanzania TZ: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 12.300 % from Dec 1991 (Median) to 2011, with 4 observations. The data reached an all-time high of 13.200 % in 2007 and a record low of 9.000 % in 2011. Tanzania TZ: Proportion of People Living Below 50 Percent Of Median Income: % 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: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

The national gross income per capita in Tanzania saw no significant changes in 2023 in comparison to the previous year 2022 and remained at around 1,220 U.S. dollars. Nevertheless, 2023 still represents a peak in the national gross income in Tanzania with 1,220 U.S. dollars. Gross national income (GNI) per capita is the total amount of money received by a country (regardless of whether it originates in the country or abroad) divided by the midyear population. The World Bank uses a conversion system known as the Atlas method, which uses a price adjusted, three year moving average, which smooths out exchange rate fluctuations.Find more key insights for the national gross income per capita in countries like Kenya and Mozambique.

Abstract

The Household Budget Survey (HBS) was carried out to get information from private households on economic activities, household income, expenditure, housing characteristics as well as asset ownership. HBS outlined the final poverty statistics for 2011/12. The aspect of comparison of poverty trends with previous HBS's was presented in the survey taking into account the improvements that were made to the HBS 2011/12.The basic needs approach is used to measure absolute poverty in mainland Tanzania . The data collection for the survey lasted for a period of one year and covered 10,400 randomly selected households in mainland Tanzania. The survey was carried out by the National Bureau of Statistics (NBS) in collaboration with the Poverty Eradication Division in the Ministry of Finance in Mainland Tanzania.

The objectives of the HBS included: 1. To acquire data on households levels of consumption and expenditure which can be used for poverty mapping and analyzing the changes in standard of living over time when HBS is repeated. 2. To constitute a platform in order to specify a set of basic economic and social welfare indicators to be monitored at regular time intervals. 3. To obtain macro estimates of household consumption and expenditure patterns to construct the weighting system for the Consumer Price Index (CPI) 4. To provide macroeconomic estimates, on the household consumption for the National Accounts. 5. To obtain household consumption and expenditure patterns to make market analysis. 6. Ownership of durable goods and income generating facilities 7. To obtain data on non-expenditure consumption, i.e. consumption of own production, payments in kind and barter, which can only be measured by a survey like HBS.

Geographic coverage

National Coverage

Analysis unit

• Household
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The Household Budget Survey (HBS) was administered to a representative sample of households. A total of 10,400 households were selected and 10,186 households completed the interview. The survey was based on the 2002 Population and Housing Census (PHC) frame. Sampling weights were used to make estimates representative of population. This survey provided estimates for mainland Tanzania as a whole. A stratified multi-stage sample design was used for this survey. At the first stage the primary sampling units (PSUs) selected 400 enumeration areas (EAs). At the second stage the EAs had an average of 133 households each, (155 for rural EAs and 94 for urban EAs), this was an effective size for conducting a new listing of households. In each enumeration area 26 households were covered for one year.

Mode of data collection

Face-to-face [f2f]

Research instrument

The HBS was comprised of a set of survey instruments. These were the following questionnaires: 1. Individual Demographics Questionnaire 2. Dwelling and Household Expenditure Questionnaire 3. Labor Status and Individual Questionnaire 4. Agriculture, Land and Livestock Questionnaire 5. Household Diary Questionnaire 6. Sheet for recording Purchase Questionnaire 7. Data Dictionary Questionnaire 8. Enumeration Area Questionnaire

Tanzania TZ: Households: Gross Disposable Income data was reported at 6,573,877.320 TZS mn in Dec 2015. This records a decrease from the previous number of 6,688,497.802 TZS mn for Sep 2015. Tanzania TZ: Households: Gross Disposable Income data is updated quarterly, averaging 6,731,337.322 TZS mn from Mar 2015 (Median) to Dec 2015, with 4 observations. The data reached an all-time high of 6,986,663.993 TZS mn in Mar 2015 and a record low of 6,573,877.320 TZS mn in Dec 2015. Tanzania TZ: Households: Gross Disposable Income data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Tanzania – Table TZ.IMF.FSI: Sectoral Financial Statement: Income and Expense: Quarterly.

The Gross Domestic Product per capita in Tanzania was last recorded at 1092.86 US dollars in 2023. The GDP per Capita in Tanzania is equivalent to 9 percent of the world's average. This dataset provides the latest reported value for - Tanzania GDP per capita - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

In 2023, the national gross income per capita in Kenya decreased by 60 U.S. dollars (-2.76 percent) compared to 2022. Nevertheless, the last two years recorded a significantly higher national gross income than the preceding years.Gross national income (GNI) per capita is the total value of money received by a country, from both domestic or foreign sources, divided by the midyear population. The World Bank uses a conversion system known as the Atlas method, which implements a price adjusted, three year moving average, smoothing out fluctuations in exchange rates.Find more key insights for the national gross income per capita in countries like Tanzania and Mozambique.

The national gross income per capita in Mozambique increased by 40 U.S. dollars (+8 percent) in 2023 in comparison to the previous year. In total, the national gross income amounted to 540 U.S. dollars in 2023. Gross national income (GNI) per capita is the total amount of money received by a country (regardless of whether it originates in the country or abroad) divided by the midyear population. The World Bank uses a conversion system known as the Atlas method, which uses a price adjusted, three year moving average, which smooths out exchange rate fluctuations.Find more key insights for the national gross income per capita in countries like Tanzania and Kenya.

Poverty Headcount Ratio at Societal Poverty Lines: % of Population data was reported at 49.600 % in 2018. This records a decrease from the previous number of 49.700 % for 2011. Poverty Headcount Ratio at Societal Poverty Lines: % of Population data is updated yearly, averaging 56.200 % from Dec 1991 (Median) to 2018, with 5 observations. The data reached an all-time high of 84.000 % in 2000 and a record low of 49.600 % in 2018. Poverty Headcount Ratio at Societal Poverty Lines: % of Population 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: Social: Poverty and Inequality. The poverty headcount ratio at societal poverty line is the percentage of a population living in poverty according to the World Bank's Societal Poverty Line. The Societal Poverty Line is expressed in purchasing power adjusted 2017 U.S. dollars and defined as max($2.15, $1.15 + 0.5*Median). This means that when the national median is sufficiently low, the Societal Poverty line is equivalent to the extreme poverty line, $2.15. For countries with a sufficiently high national median, the Societal Poverty Line grows as countries’ median income grows.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Soil and water conservation technologies are critical in reducing drought and soil erosion risks and increasing crop yields and incomes. Yet, there is limited empirical evidence on the extent and impacts of adopting soil and water conservation technologies in Tanzania. The study’s objective is to evaluate the adoption (as well as the duration of adoption) and the impacts of soil and water conservation technologies on income and food security in Tanzania. The study employs a control function approach and the instrumental variable quantile treatment effects model to survey data from 575 households to estimate the average and distributional impacts of adoption. The results show that the adoption and duration of adopting soil and water conservation technologies had significant and positive effects on the total value of crop production and household income. Moreover, we find that the adoption and its duration had a significant and positive impact on the food security indicator—household dietary diversity. The results from the instrumental variable quantile treatment effects model also show that the impacts of adopting soil and water conservation technologies on the outcome variables are positive and significant, although they vary significantly across the income and food security distributions. The results indicate that even though adoption benefits households in both the lower and upper quantiles of the income and food security distributions, the marginal impacts of adoption are generally more significant for the households in the upper quantiles. The paper concludes by discussing the policy options for increasing and sustaining the adoption and impacts of soil and water conservation technologies in Tanzania.

BackgroundIllness-related costs for patients with tuberculosis (TB) ≥20% of pre-illness annual household income predict adverse treatment outcomes and have been termed “catastrophic.” Social protection initiatives, including cash transfers, are endorsed to help prevent catastrophic costs. With this aim, cash transfers may either be provided to defray TB-related costs of households with a confirmed TB diagnosis (termed a “TB-specific” approach); or to increase income of households with high TB risk to strengthen their economic resilience (termed a “TB-sensitive” approach). The impact of cash transfers provided with each of these approaches might vary. We undertook an economic modelling study from the patient perspective to compare the potential of these 2 cash transfer approaches to prevent catastrophic costs.Methods and findingsModel inputs for 7 low- and middle-income countries (Brazil, Colombia, Ecuador, Ghana, Mexico, Tanzania, and Yemen) were retrieved by literature review and included countries' mean patient TB-related costs, mean household income, mean cash transfers, and estimated TB-specific and TB-sensitive target populations. Analyses were completed for drug-susceptible (DS) TB-related costs in all 7 out of 7 countries, and additionally for drug-resistant (DR) TB-related costs in 1 of the 7 countries with available data. All cost data were reported in 2013 international dollars ($). The target population for TB-specific cash transfers was poor households with a confirmed TB diagnosis, and for TB-sensitive cash transfers was poor households already targeted by countries’ established poverty-reduction cash transfer programme. Cash transfers offered in countries, unrelated to TB, ranged from $217 to $1,091/year/household. Before cash transfers, DS TB-related costs were catastrophic in 6 out of 7 countries. If cash transfers were provided with a TB-specific approach, alone they would be insufficient to prevent DS TB catastrophic costs in 4 out of 6 countries, and when increased enough to prevent DS TB catastrophic costs would require a budget between $3.8 million (95% CI: $3.8 million–$3.8 million) and $75 million (95% CI: $50 million–$100 million) per country. If instead cash transfers were provided with a TB-sensitive approach, alone they would be insufficient to prevent DS TB-related catastrophic costs in any of the 6 countries, and when increased enough to prevent DS TB catastrophic costs would require a budget between $298 million (95% CI: $219 million–$378 million) and $165,367 million (95% CI: $134,085 million–$196,425 million) per country. DR TB-related costs were catastrophic before and after TB-specific or TB-sensitive cash transfers in 1 out of 1 countries. Sensitivity analyses showed our findings to be robust to imputation of missing TB-related cost components, and use of 10% or 30% instead of 20% as the threshold for measuring catastrophic costs. Key limitations were using national average data and not considering other health and social benefits of cash transfers.ConclusionsA TB-sensitive cash transfer approach to increase all poor households’ income may have broad benefits by reducing poverty, but is unlikely to be as effective or affordable for preventing TB catastrophic costs as a TB-specific cash transfer approach to defray TB-related costs only in poor households with a confirmed TB diagnosis. Preventing DR TB-related catastrophic costs will require considerable additional investment whether a TB-sensitive or a TB-specific cash transfer approach is used.

ObjectiveTo estimate individual and household economic impact of cardiovascular disease (CVD) in selected low- and middle-income countries (LMIC). BackgroundEmpirical evidence on the microeconomic consequences of CVD in LMIC is scarce. Methods and FindingsWe surveyed 1,657 recently hospitalized CVD patients (66% male; mean age 55.8 years) from Argentina, China, India, and Tanzania to evaluate the microeconomic and functional/productivity impact of CVD hospitalization. Respondents were stratified into three income groups. Median out-of-pocket expenditures for CVD treatment over 15 month follow-up ranged from 354 international dollars (2007 INT$, Tanzania, low-income) to INT$2,917 (India, high-income). Catastrophic health spending (CHS) was present in >50% of respondents in China, India, and Tanzania. Distress financing (DF) and lost income were more common in low-income respondents. After adjustment, lack of health insurance was associated with CHS in Argentina (OR 4.73 [2.56, 8.76], India (OR 3.93 [2.23, 6.90], and Tanzania (OR 3.68 [1.86, 7.26] with a marginal association in China (OR 2.05 [0.82, 5.11]). These economic effects were accompanied by substantial decreases in individual functional health and productivity. ConclusionsIndividuals in selected LMIC bear significant financial burdens following CVD hospitalization, yet with substantial variation across and within countries. Lack of insurance may drive much of the financial stress of CVD in LMIC patients and their families.

The focus of this study is the implications of structural transformation for gender equality, specifically equal pay, in Sub-Saharan Africa. While structural transformation affects key development outcomes, including growth, poverty, and access to decent work, its effect on the gender pay gap is not clear ex-ante. Evidence on the gender pay gap in sub-Saharan Africa is limited, and often excludes rural areas and informal (self-)employment. This paper provides evidence on the extent and drivers of the gender pay gap in non-farm wage- and self-employment activities across three countries at different stages of structural transformation (Malawi, Tanzania and Nigeria). The analysis leverages nationally-representative survey data and decomposition methods, and is conducted separately among individuals residing in rural versus urban areas in each country. The results show that women earn 40 to 46 percent less than men in urban areas, which is substantially less than in high-income countries. The gender pay gap in rural areas ranges from (a statistically insignificant) 12 percent in Tanzania to 77 percent in Nigeria. In all rural areas, a major share of the gender pay gap (81 percent in Malawi, 83 percent in Tanzania and 70 percent in Nigeria) is explained by differences in workers’ characteristics, including education, occupation and sector. This suggests that if rural men and women had similar characteristics, most of the gender pay gap would disappear. Country-differences are larger across urban areas, where differences in characteristics account for only 32 percent of the pay gap in Tanzania, 50 percent in Malawi and 81 percent in Nigeria. Our detailed decomposition results suggest that structural transformation does not consistently help bridge the gender pay gap. Gender-sensitive policies are required to ensure equal pay for men and women.

Tanzania TZ: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: 2017 PPP per day data was reported at 1.430 Intl $/Day in 2018. This records a decrease from the previous number of 1.440 Intl $/Day for 2011. Tanzania TZ: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: 2017 PPP per day data is updated yearly, averaging 1.435 Intl $/Day from Dec 2011 (Median) to 2018, with 2 observations. The data reached an all-time high of 1.440 Intl $/Day in 2011 and a record low of 1.430 Intl $/Day in 2018. Tanzania TZ: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: 2017 PPP per day 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: Social: Poverty and Inequality. Mean consumption or income per capita (2017 PPP $ per day) of the bottom 40%, used in calculating the growth rate in the welfare aggregate of the bottom 40% of the population in the income distribution in a country.;World Bank, Global Database of Shared Prosperity (GDSP) (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).;;The choice of consumption or income for a country is made according to which welfare aggregate is used to estimate extreme poverty in the Poverty and Inequality Platform (PIP). The practice adopted by the World Bank for estimating global and regional poverty is, in principle, to use per capita consumption expenditure as the welfare measure wherever available; and to use income as the welfare measure for countries for which consumption is unavailable. However, in some cases data on consumption may be available but are outdated or not shared with the World Bank for recent survey years. In these cases, if data on income are available, income is used. Whether data are for consumption or income per capita is noted in the footnotes. Because household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution.

Tanzania TZ: Adequacy: Social Protection & Labour Programs: % of Total Welfare of Beneficiary Households data was reported at 13.349 % in 2014. This records a decrease from the previous number of 17.649 % for 2012. Tanzania TZ: Adequacy: Social Protection & Labour Programs: % of Total Welfare of Beneficiary Households data is updated yearly, averaging 16.009 % from Dec 2008 (Median) to 2014, with 4 observations. The data reached an all-time high of 21.429 % in 2008 and a record low of 13.349 % in 2014. Tanzania TZ: Adequacy: Social Protection & Labour Programs: % of Total Welfare of Beneficiary Households 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: Social Protection. Adequacy of social protection and labor programs (SPL) is measured by the total transfer amount received by the population participating in social insurance, social safety net, and unemployment benefits and active labor market programs as a share of their total welfare. Welfare is defined as the total income or total expenditure of beneficiary households. Estimates include both direct and indirect beneficiaries.; ; ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/); Simple average;

（停止更新）生活在收入中位数的50％以下的人口比例：百分比在12-01-2011达9.000%，相较于12-01-2007的13.200%有所下降。（停止更新）生活在收入中位数的50％以下的人口比例：百分比数据按年更新，12-01-1991至12-01-2011期间平均值为12.300%，共4份观测结果。该数据的历史最高值出现于12-01-2007，达13.200%，而历史最低值则出现于12-01-2011，为9.000%。CEIC提供的（停止更新）生活在收入中位数的50％以下的人口比例：百分比数据处于定期更新的状态，数据来源于World Bank，数据归类于全球数据库的坦桑尼亚 – Table TZ.World Bank.WDI: Social: Poverty and Inequality。