The poverty rate in Tanzania declined to **** percent in 2020, according to estimates considering the national poverty line. Previously, in 2018, the rate was measured at **** percent. According to the source, individuals are defined as poor when they are not able to meet their basic consumption needs. In 2018, the national basic needs poverty line was ****** Tanzanian shillings (**** U.S. dollars) per adult per month.

As of 2022, nearly ** million people in Tanzania lived in extreme poverty, with the poverty threshold at **** U.S. dollars a day. Roughly 100,000 people were pushed into poverty compared to 2021, possibly a remaining effect of the coronavirus (COVID-19) pandemic. The headcount was, however, forecast to decrease in the coming years. By 2025, **** million Tanzanians are projected to live on a maximum of **** U.S. dollars per day.

Actual value and historical data chart for Tanzania Poverty Headcount Ratio At National Poverty Line Percent Of Population

In 2025, nearly 11.7 percent of the world population in extreme poverty, with the poverty threshold at 2.15 U.S. dollars a day, lived in Nigeria. Moreover, the Democratic Republic of the Congo accounted for around 11.7 percent of the global population in extreme poverty. Other African nations with a large poor population were Tanzania, Mozambique, and Madagascar. Poverty levels remain high despite the forecast decline Poverty is a widespread issue across Africa. Around 429 million people on the continent were living below the extreme poverty line of 2.15 U.S. dollars a day in 2024. Since the continent had approximately 1.4 billion inhabitants, roughly a third of Africa’s population was in extreme poverty that year. Mozambique, Malawi, Central African Republic, and Niger had Africa’s highest extreme poverty rates based on the 2.15 U.S. dollars per day extreme poverty indicator (updated from 1.90 U.S. dollars in September 2022). Although the levels of poverty on the continent are forecast to decrease in the coming years, Africa will remain the poorest region compared to the rest of the world. Prevalence of poverty and malnutrition across Africa Multiple factors are linked to increased poverty. Regions with critical situations of employment, education, health, nutrition, war, and conflict usually have larger poor populations. Consequently, poverty tends to be more prevalent in least-developed and developing countries worldwide. For similar reasons, rural households also face higher poverty levels. In 2024, the extreme poverty rate in Africa stood at around 45 percent among the rural population, compared to seven percent in urban areas. Together with poverty, malnutrition is also widespread in Africa. Limited access to food leads to low health conditions, increasing the poverty risk. At the same time, poverty can determine inadequate nutrition. Almost 38.3 percent of the global undernourished population lived in Africa in 2022.

Tanzania TZ: Poverty Headcount Ratio at National Poverty Lines: Rural: % of Rural Population data was reported at 33.300 % in 2011. Tanzania TZ: Poverty Headcount Ratio at National Poverty Lines: Rural: % of Rural Population data is updated yearly, averaging 33.300 % from Dec 2011 (Median) to 2011, with 1 observations. Tanzania TZ: Poverty Headcount Ratio at National Poverty Lines: Rural: % of Rural 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: Poverty. Rural poverty headcount ratio is the percentage of the rural population living below the national poverty lines.; ; World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines.; ; This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries.

Poverty ratio at national poverty line of United Republic of Tanzania dropped by 6.38% from 28.2 % in 2011 to 26.4 % in 2017. Since the 3.37% reduction in 2007, poverty ratio at national poverty line plummeted by 23.26% in 2017. National poverty headcount ratio is the percentage of the population living below the national poverty lines. National estimates are based on population-weighted subgroup estimates from household surveys.

Tanzania TZ: Poverty Gap at National Poverty Lines: Urban: % data was reported at 3.900 % in 2011. Tanzania TZ: Poverty Gap at National Poverty Lines: Urban: % data is updated yearly, averaging 3.900 % from Dec 2011 (Median) to 2011, with 1 observations. Tanzania TZ: Poverty Gap at National Poverty Lines: Urban: % 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: Poverty. Urban poverty gap at national poverty lines is the urban population's mean shortfall from the poverty lines (counting the nonpoor as having zero shortfall) as a percentage of the poverty lines. This measure reflects the depth of poverty as well as its incidence.; ; World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines.; ; This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries.

The coronavirus (COVID-19) pandemic impacted East Africa's poverty level. Extreme poverty rate in the region increased from ** percent in 2019 to ** percent in 2021. South Sudan and Brurundi had the highest share of population living on less than **** U.S. dollars per day, ** percent and ** percent, respectively.

Tanzania TZ: Poverty Gap at National Poverty Lines: % data was reported at 6.700 % in 2011. Tanzania TZ: Poverty Gap at National Poverty Lines: % data is updated yearly, averaging 6.700 % from Dec 2011 (Median) to 2011, with 1 observations. Tanzania TZ: Poverty Gap at National Poverty Lines: % 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: Poverty. Poverty gap at national poverty lines is the mean shortfall from the poverty lines (counting the nonpoor as having zero shortfall) as a percentage of the poverty lines. This measure reflects the depth of poverty as well as its incidence.; ; World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines.; ; This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries.

Poverty gap at $3.2 a day of United Republic of Tanzania fell by 1.37% from 36.5 % in 2011 to 36.0 % in 2017. Since the 24.52% jump in 2000, poverty gap at $3.2 a day plummeted by 44.62% in 2017. Poverty gap at $3.20 a day (2011 PPP) is the mean shortfall in income or consumption from the poverty line $3.20 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence.

Tanzania TZ: Poverty Gap at National Poverty Lines: Rural: % data was reported at 7.800 % in 2011. Tanzania TZ: Poverty Gap at National Poverty Lines: Rural: % data is updated yearly, averaging 7.800 % from Dec 2011 (Median) to 2011, with 1 observations. Tanzania TZ: Poverty Gap at National Poverty Lines: Rural: % 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: Poverty. Rural poverty gap at national poverty lines is the rural population's mean shortfall from the poverty lines (counting the nonpoor as having zero shortfall) as a percentage of the poverty lines. This measure reflects the depth of poverty as well as its incidence.; ; World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines.; ; This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries.

Poverty gap at $5.5 a day of United Republic of Tanzania slipped by 1.72% from 58.0 % in 2011 to 57.0 % in 2017. Since the 12.07% surge in 2000, poverty gap at $5.5 a day sank by 27.76% in 2017. Poverty gap at $5.50 a day (2011 PPP) is the mean shortfall in income or consumption from the poverty line $5.50 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence.

In 2022, the international poverty (based on 2017 purchasing power parity (PPP)) and the lower-income poverty rate (3.65 U.S. dollars in 2017 PPP), was highest in Burundi within the East African region, with 83 percent and 96.6 percent, respectively. However, the upper middle-income poverty rate was highest in Somalia, at 98.8 percent.

Tanzania TZ: Increase in Poverty Gap at $3.10: Poverty Line Due To Out-of-Pocket Health Care Expenditure: 2011 PPP: % of Poverty Line data was reported at 1.599 % in 2012. This records a decrease from the previous number of 1.711 % for 2010. Tanzania TZ: Increase in Poverty Gap at $3.10: Poverty Line Due To Out-of-Pocket Health Care Expenditure: 2011 PPP: % of Poverty Line data is updated yearly, averaging 1.655 % from Dec 2010 (Median) to 2012, with 2 observations. The data reached an all-time high of 1.711 % in 2010 and a record low of 1.599 % in 2012. Tanzania TZ: Increase in Poverty Gap at $3.10: Poverty Line Due To Out-of-Pocket Health Care Expenditure: 2011 PPP: % of Poverty Line 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: Poverty. Increase in poverty gap at $3.10 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure, as a percentage of the $1.90 poverty line; ; Wagstaff et al. Progress on Impoverishing Health Spending: Results for 122 Countries. A Retrospective Observational Study, Lancet Global Health 2017.; Weighted Average;

Abstract

The main objective of the Tanzania NPS is to provide high-quality household-level data to the Tanzanian government and other stakeholders for monitoring poverty dynamics, tracking the progress of the Mkukuta poverty reduction strategy1, and to evaluate the impact of other major, national-level government policy initiatives. As an integrated survey covering a number of different socioeconomic factors, it compliments other more narrowly focused survey efforts, such as the Demographic and Health Survey on health, the Integrated Labour Force Survey on labour markets, the Household Budget Survey on expenditure, and the National Sample Census of Agriculture. Secondly, as a panel household survey in which the same households are revisited over time, the Tanzania NPS allows for the study of poverty and welfare transitions and the determinants of living standard changes

Geographic coverage

National

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample design for the second round of the NPS revisits all the households interviewed in the first round of the panel, as well as tracking adult split-off household members. The original sample size of 3,265 households was designed to representative at the national, urban/rural, and major agro-ecological zones. The total sample size was 3,265 households in 409 Enumeration Areas (2,063 households in rural areas and 1,202 urban areas). It is also be possible in the final analysis to produce disaggregated poverty rates for 4 different strata: Dar es Salaam, other urban areas on mainland Tanzania, rural mainland Tanzania, and Zanzibar.

Since the NPS is a panel survey, the second round of the fieldwork revisits all households originally interviewed during round one. If a household has moved from its original location, the members were interviewed in their new location. If that location was within one hour of the original location, the field team did the interview at the time of their visit to the enumeration area. If the household had located more than an hour from the original location, details of the new location were recorded on specialized forms, and the information passed to a dedicated tracking team for follow-up.

If a member of the original household had split from their original location to form or join a new household, information was recorded on the current whereabouts of this member. All adult former household members (those over the age of 15) were tracked to their new location. Similar to the protocol for the re-located households, if the new household is within one hour of the original location, the new household was interviewed by the main field team at the time of the visit to the enumeration area. For those that have moved more than one hour away, their information was passed to the dedicated tracking team for follow-up. Once the tracking targets have been found, teams are required to interview them and any new members of the household.

The total sample size for the second round of the NPS has a total sample size of 3924 households. This represents 3168 round-one households, a re-interview rate of over 97 percent. In addition, of the 10,420 eligible adults (over age 15 in 2010), 9,338 were re-interviewed, a re-interview rate of approximately 90 percent.

Sampling deviation

The total sample size for the second round of the NPS has a total sample size of 3924 households. This represents 3168 round one household, a re-interview rate of over 97 percent. In addition, of the 10,420 eligible adults (over age 15 in 2010), 9,338 were re-interviewed, a re-interview rate of approximately 90 percent. To obtain the attrition adjustment factor the probability that a sample household was successfully re-interviewed in the second round of surveys is modelled with the linear logistic model at the level of the individual. A binary response variable is created by coding the response disposition for eligible households that do not respond in the second round as 0, and households that do respond as 1.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

CSPro-based data entry/editing system was used. A cross comparison between the entered values in the field based data entry and double entry was conducted and any differences in values between the two were flagged for manual inspection of the physical questionnaire. Corrections based on this inspection exercise were ultimately encoded in the dataset.

Additionally, an extensive review of data files was conducted, including interviewer errors such as missing values, ranges and outliers. Observations were returned for manual inspection of the physical questionnaires if continuous values fell outside five standard deviations of the mean, categorical values were not eligible responses, or there were internal inconsistencies within the dataset (for example, the age of an individual was not consistent with their educational status, there was more than one head of household listed, an individual was engaged in multiple primary activities, the quantity of crops and their by-products produced, harvested, and sold not listed, the distance from the market and an individual's plot was not listed, the number of weeks, days per week, and hours per day an individual engaged in fishery activity was not recorded, the species and quantity of fish caught, bought, sold, or traded was not listed, etc). When it was determined that these values were the result of data-entry error, the values were corrected. In addition, cases deemed to reflect obvious enumerator error were also corrected in this cleaning process. The majority of such cases involved the use of incorrect measurement units, e.g. recording grams as kilograms or vice versa.

Response rate

Approximately 95 percent

Sampling error estimates

To reduce the overall standard errors, and weight the population totals up to the known population figures, a post-stratification correction is applied. Based on the projected number of households in the urban and rural segments of each region, adjustment factors are calculated. This correction also reduces overall standard errors.

Data appraisal

The estimated logistic model is used to obtain a predicted probability of response for each household member in the 2010/2011 survey. These response probabilities were then aggregated to the household level (by calculating the mean), the using the household-level predicted response probabilities as the ranking variable, all households are ranked into 10 equal groups (deciles). An attrition adjustment factor was then defined as the reciprocal of the empirical response rate for the household-level propensity

Then a logistic response propensity model is fitted, using 2005 UNHS household and individual characteristics measured in the first wave as covariates. In a few limited cases, values of unit level variables were missing from the 2008/2009 household dataset. These values were imputed using multivariate regression and logistic regression techniques. Imputations are done using the 'impute' command in Stata at the level of the UNPS strata (urban/rural and region). Overall, less than one percent of the variables required imputation to replace missing values.

Tanzania TZ: Increase in Poverty Gap at $1.90: Poverty Line Due To Out-of-Pocket Health Care Expenditure: 2011 PPP: % of Poverty Line data was reported at 1.191 % in 2012. This records a decrease from the previous number of 1.197 % for 2010. Tanzania TZ: Increase in Poverty Gap at $1.90: Poverty Line Due To Out-of-Pocket Health Care Expenditure: 2011 PPP: % of Poverty Line data is updated yearly, averaging 1.194 % from Dec 2010 (Median) to 2012, with 2 observations. The data reached an all-time high of 1.197 % in 2010 and a record low of 1.191 % in 2012. Tanzania TZ: Increase in Poverty Gap at $1.90: Poverty Line Due To Out-of-Pocket Health Care Expenditure: 2011 PPP: % of Poverty Line 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: Poverty. Increase in poverty gap at $1.90 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure, as a percentage of the $1.90 poverty line; ; Wagstaff et al. Progress on Impoverishing Health Spending: Results for 122 Countries. A Retrospective Observational Study, Lancet Global Health 2017.; Weighted average;

Tanzania TZ: Poverty Gap at $1.90 a Day: 2011 PPP: % data was reported at 15.400 % in 2011. This records a decrease from the previous number of 23.300 % for 2007. Tanzania TZ: Poverty Gap at $1.90 a Day: 2011 PPP: % data is updated yearly, averaging 26.950 % from Dec 1991 (Median) to 2011, with 4 observations. The data reached an all-time high of 46.100 % in 2000 and a record low of 15.400 % in 2011. Tanzania TZ: Poverty Gap at $1.90 a Day: 2011 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: Poverty. Poverty gap at $1.90 a day (2011 PPP) is the mean shortfall in income or consumption from the poverty line $1.90 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. The aggregated numbers for low- and middle-income countries correspond to the totals of 6 regions in PovcalNet, which include low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia). See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Abstract

The objectives of the Smallholder Household Survey in Tanzania were to:

• Generate a clear picture of the smallholder sector at the national level, including household demographics, agricultural profile, and poverty status and market relationships • Segment smallholder households in Tanzania according to the most compelling variables that emerge • Characterize the demand for financial services in each segment, focusing on customer needs, attitudes and perceptions related to both agricultural and financial services • Detail how the financial needs of each segment are currently met, with both informal and formal services, and where there may be promising opportunities to add value

Geographic coverage

National

Analysis unit

Households

Universe

The universe for the survey consists of smallholder households defined as households with the following criteria: 1) Household with up to 5 hectares OR farmers who have less than 50 heads of cattle, 100 goats/sheep/pigs, or 1,000 chickens 2) Agriculture provides a meaningful contribution to the household livelihood, income, or consumption.

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) SAMPLING FRAME

The smallholder household survey in Tanzania is a nationally representative survey with a target sample size of 3,000 smallholder households. The sample was designed to provide reliable survey estimates at the national level. The sampling frame is the list of enumeration areas (EAs) containing agricultural households. These EAs were created in preparation for the 2012 population and housing census. The census questionnaire included a question on whether any household member operated any land for agricultural purposes during the 2011-2012 agricultural year. The information collected helped to identify agricultural households during the census.

(b) SAMPLE ALLOCATION AND SELECTION.

For the sample allocation, regions were combined into the following zones: • Border: Ruvuma, Iringa, Mbeya, Rukwa, and Kigoma • Coastal: Tanga, Pwani, Dar es Salaam, Lindi, and Mtwara • Inland: Dodoma, Arusha, Kilimanjaro, Morogoro, Singida, Tabora, Manyara, Njombe, and Katavi • Lake: Shinyanga, Kagera, Mwanza, Mara, Simiyu, and Geita • Zanzibar: all regions

To take nonresponse into account, the target sample size was increased to 3,158 households assuming a nonresponse rate of 5 percent observed in similar national household surveys. The total sample size was first allocated to the zones in proportion to the number of agricultural households in the sampling frame. Within each zone, the resulting sample was then distributed to urban and rural areas in proportion to number of agricultural households. Given that EAs were the primary sampling units and 15 households were selected in each EA, a total of 212 EAs were selected. The sample for the smallholder survey is a stratified multistage sample. Stratification was achieved by separating each zone into urban and rural areas. The urban/rural classification is based on the 2012 population census. Therefore, 10 strata were created, and the sample was selected independently in each stratum.

In the first stage, EAs were selected as primary sampling units with probability proportional to size, the size being the number of agricultural households in the EAs. A household listing operation was conducted in all selected EAs to identify smallholder households and to provide a frame for selecting smallholder households to be included in the sample. In the second stage, 15 smallholders were sampled in each EA with equal probability. In each sampled household, the household questionnaire was administered to the head of the household, the spouse, or any knowledgeable adult household member to collect information about household characteristics. The multiple respondent questionnaire was administered to all adult members in each sampled household to collect information on their agricultural activities, financial behaviours, and mobile money use. In addition, in each sampled household only one household member was selected using the Kish grid and was administered the single respondent questionnaire.

The full description of the sample design can be found in the user guide for this data set.

Sampling deviation

The smallholder survey in Tanzania is the third survey in the series, following the surveys in Mozambique and Uganda. Fieldwork in those two countries experienced a lot of failed call backs where identified eligible households and household members could not be interviewed during the time allocated to fieldwork in each country. As a result, the final sample size fell slightly short of the target. For this reason, in Tanzania the number of households selected in each EA was increased from 15 to 17 following the household listing operation in all sampled EAs.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

The data files were checked for completeness, inconsistencies and errors by InterMedia and corrections were made as necessary and where possible. Following the finalization of questionnaires, a script was developed using Dooblo to support data collection on smart phones. The script was thoroughly tested and validated before its use in the field.

Response rate

The study achieved a household response rate of 99.1 percent, 84.8 percent for the Multiple Respondent questionnaire and 93.4 percent for the Single Respondent questionnaire.

Sampling error estimates

The sample design for the smallholder household survey was a complex sample design featuring clustering, stratification and unequal probabilities of selection. For key survey estimates, sampling errors considering the design features were produced using either the SPSS Complex Sample module or STATA based on the Taylor series approximation method.

Abstract

The main objective of the TZNPS is to provide high-quality household-level data to the Tanzanian government and other stakeholders for monitoring poverty dynamics, tracking the progress of the Mkukuta poverty reduction strategy1, and to evaluate the impact of other major, national-level government policy initiatives. As an integrated survey covering a number of different socioeconomic factors, it compliments other more narrowly focused survey efforts, such as the Demographic and Health Survey on health, the Integrated Labour Force Survey on labour markets, the Household Budget Survey on expenditure, and the National Sample Census of Agriculture. Secondly, as a panel household survey in which the same households are revisited over time, the TZNPS allows for the study of poverty and welfare transitions and the determinants of living standard changes

Geographic coverage

National Coverage: Dar es Salaam, other urban areas in Mainland, rural areas in Mainland, and Zanzibar

Analysis unit

A living standards survey with community-level questionnaire with the following units of analysis: individuals, household, and communities.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample design for the second round of the NPS revisits all the households interviewed in the first round of the panel, as well as tracking adult split-off household members. The original sample size of 3,265 households was designed to representative at the national, urban/rural, and major agro-ecological zones. The total sample size was 3,265 households in 409 Enumeration Areas (2,063 households in rural areas and 1,202 urban areas). It is also be possible in the final analysis to produce disaggregated poverty rates for 4 different strata: Dar es Salaam, other urban areas on mainland Tanzania, rural mainland Tanzania, and Zanzibar.

Since the TZNPS is a panel survey, the second round of the fieldwork revisits all households originally interviewed during round one. If a household has moved from its original location, the members were interviewed in their new location. If that location was within one hour of the original location, the field team did the interview at the time of their visit to the enumeration area. If the household had located more than an hour from the original location, details of the new location were recorded on specialized forms, and the information passed to a dedicated tracking team for follow-up.

If a member of the original household had split from their original location to form or join a new household, information was recorded on the current whereabouts of this member. All adult former household members (those over the age of 15) were tracked to their new location. Similar to the protocol for the re-located households, if the new household is within one hour of the original location, the new household was interviewed by the main field team at the time of the visit to the enumeration area. For those that have moved more than one hour away, their information was passed to the dedicated tracking team for follow-up. Once the tracking targets have been found, teams are required to interview them and any new members of the household.

The total sample size for the second round of the NPS has a total sample size of 3924 households. This represents 3168 round-one households, a re-interview rate of over 97 percent. In addition, of the 10,420 eligible adults (over age 15 in 2010), 9,338 were re-interviewed, a reinterview rate of approximately 90 percent.

Sampling deviation

To obtain the attrition adjustment factor the probability that a sample household was successfully reinterviewed in the second round of surveys is modeled with the linear logistic model at the level of the individual. A binary response variable is created by coding the response disposition for eligible households that do not respond in the second round as 0, and households that do respond as 1. Then a logistic response propensity model is fitted, using 2005 UNHS household and individual characteristics measured in the first wave as covariates.

In a few limited cases, values of unit level variables were missing from the 2008/2009 household dataset. These values were imputed using multivariate regression and logistic regression techniques. Imputations are done using the ‘impute’ command in Stata at the level of the UNPS strata (urban/rural and region). Overall, less than one percent of the variables required imputation to replace missing values.

The estimated logistic model is used to obtain a predicted probability of response for each household member in the 2010/2011 survey. These response probabilities were then aggregated to the household level (by calculating the mean), the using the household-level predicted response probabilities as the ranking variable, all households are ranked into 10 equal groups (deciles). An attrition adjustment factor was then defined as the reciprocal of the empirical response rate for the household-level propensity score decile.

To reduce the overall standard errors, and weight the population totals up to the known population figures, a post-stratification correction is applied. Based on the projected number of households in the urban and rural segments of each region, adjustment factors are calculated. This correction also reduces overall standard errors (see Little et al, 1997).

Mode of data collection

Face-to-face [f2f]

Research instrument

The Household Questionnaire is comprised of thematic sections.This comprehensive questionnaire allows for the construction of a full consumption-based welfare measure, permitting distributional and incidence analysis. This project also recognizes the imperative to look beyond the household as a unit of analysis in order to improve the quality, relevance and sustainability of agricultural data systems. Although data collection is structured around a household panel survey, the data on labor, education, and health status were collected at the individual level. Moreover, in some household activities (like non-farm enterprise), the questionnaire records which specific members are engaged in the activity. A detailed description of the contents of the questionnaire can be found in the Basic Information Document report (Table 1).

The Agricultural Questionnaire collects information relative to a household’s agricultural activities. Information is collected at both the plot and crop level on inputs, production and sales. The Basic Information Document report (Table 2) provides a detailed description of the contents of the questionnaire. This questionnaire was administered to any household that engaged in any farming or livestock holding.

The Fisheries Questionnaire was developed in partnership with the World Fish Program to collect data on household fishery activities, fish processing, and fish trading. This includes data on the inputs, outputs, labour, and sales. All this data is divided into two reference periods, the high and low season. This data is collected at the household level. The Basic Information Document report (Table 3) provides a more comprehensive list of the sections found within the Fishery Questionnaire.

The Community Questionnaire collects information on physical and economic infrastructure and events in surveyed communities. In each selected survey community, key informants are interviewed by the field team supervisors. Information about the respondents for the community questionnaire is collected individually in section CI of community questionnaire.

The questionnaires were developed in collaboration with line ministries and donor partners, including the Technical Committee, over a period of several months. The NBS solicited feedback from various stakeholders in regards to survey content and design. The round two questionnaires were piloted in the Morogoro region in June 2010, in conjunction with supervisor training. After piloting, the questionnaires were further revised and finalized by August 2010. Questionnaire manuals were developed with detailed instructions for field staff during training and as the main survey reference guide over the course of the field work.

Cleaning operations

CSPro-based data entry/editing system was used.

A cross comparison between the entered values in the field based data entry and double entry was conducted and any differences in values between the two were flagged for manual inspection of the physical questionnaire. Corrections based on this inspection exercise were ultimately encoded in the dataset.

Additionally, an extensive review of data files was conducted, including interviewer errors such as missing values, ranges and outliers. Observations were returned for manual inspection of the physical questionnaires if continuous values fell outside five standard deviations of the mean, categorical values were not eligible responses, or there were internal inconsistencies within the dataset (for example, the age of an individual was not consistent with their educational status, there was more than one head of household listed, an individual was engaged in multiple primary activities, the quantity of crops and their byproducts produced, harvested, and sold not listed, the distance from the market and an individual’s plot was not listed, the number of weeks, days per week, and hours per day an individual engaged in fishery activity was not recorded, the species and quantity of fish caught, bought, sold, or traded was not listed, etc). When it was determined that these values were the result of data-entry error, the values were corrected. In addition, cases deemed to reflect obvious enumerator error were also

Tanzania TZ: Poverty Gap at $3.20 a Day: 2011 PPP: % data was reported at 36.300 % in 2011. This records a decrease from the previous number of 43.800 % for 2007. Tanzania TZ: Poverty Gap at $3.20 a Day: 2011 PPP: % data is updated yearly, averaging 47.900 % from Dec 1991 (Median) to 2011, with 4 observations. The data reached an all-time high of 64.900 % in 2000 and a record low of 36.300 % in 2011. Tanzania TZ: Poverty Gap at $3.20 a Day: 2011 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: Poverty. Poverty gap at $3.20 a day (2011 PPP) is the mean shortfall in income or consumption from the poverty line $3.20 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. The aggregated numbers for low- and middle-income countries correspond to the totals of 6 regions in PovcalNet, which include low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia). See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.