It focuses on poverty-monitoring indicators and offers a set of baseline measurements for the future. Data on key poverty indicators are presented for each region. Trends over the 1990s are also assessed by comparison with the 1991/92 HBS.
The analysis provides important baseline measures for the monitoring of poverty in the future. In addition, trends in many indicators over the 1990s can be assessed by comparison of the 2000/01 HBS with the earlier 1991/92 Household Budget Survey. Information collected in the 2000/01 Household Budget Survey includes:
• 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, and
• food security.
Information on consumption/expenditure was collected in two main formats. The first was a diary that records all transactions and consumption for that household for one calendar month. This was completed on a regular basis by the interviewers. The second was recall of the purchase of non-food items over the twelve months preceding the survey.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
Individuals and hosueholds
The scope includes:
- HOUSEHOLD: Household characteristics, household listing, water and sanitation, access to amenities, security of tenure and durability of housing and household expenditure.
- MEMBERS: Sex, age, education, health, labour, and income
The survey covered all household members (usual residents).
Producers and sponsors
Nationla Bureau of Statistics
Oxford Policy Management (UK)
Sampling, data processing, analysis and report writing
United Kingdom Department for International Development (Tanzania)
Government of Sweden
Government of Canada
Netherlands (the Royal Netherlands Embassy), Denmark
Government of Japan
Government of United States
United Nations Development Programme
Sample design: The sample of households interviewed in the 2000/2001 HBS was selected in two stages. In the first stage, 1,161 small areas called Primary Sampling Units (PSUs) were selected throughout the country. In the second stage, 24 households were initially selected in each PSU. The sampled households are located in the National Master Sample (NMS) of PSUs. The NMS is a generalised set of area units that can be used as PSUs for conducting various household surveys. It is a fixed sample of rural and urban clusters, which, among other things, make possible the performance of a continuous survey programme as well as ad hoc sample surveys.The NMS has four modules,A,A+B,A+B+C and A+B+C+D, which can provide urban and rural estimates at National, Zonal, Regional and District levels respectively. The HBS 2000/01 used Module A+B+C of the NMS comprising 621 urban EAs and 540 rural villages drawn from each of the 20 regions of Mainland Tanzania. In the second stage, 24 households were selected using systematic random sampling (SRS) from stratified lists of households compiled from each of the sampled PSUs. These lists were stratified into high, middle and low socioeconomic groups based on socio-economic data collected during the listing exercise. The stratification and selection of households was conducted in the NBS head office and interviewers were supplied with a list of pre-selected households for interview.
Sample frame and sample selection: (a) Rural frame. The initial rural NMS frame was based on the 1978 Population Census and later updated with information from the 1988 Population Census. At the beginning, a ward or a group of wards was used as a Primary Sampling Unit (PSU), but later a village was used instead.The rural frame of the NMS was divided into "normal," "large town surroundings" and "low density" strata. In total, 150 strata were created and 2 to 8 PSUs (villages) were selected from each stratum to come up with the sample of villages that can provide estimates for each region of Mainland Tanzania (Module A+B+C). These villages were selected using the probability proportional to size (PPS) selection procedure.The PSUs (villages) for Module A of the rural NMS are automatically included in the regional sample. (b) Urban frame.The urban frame for the NMS was the sample used for the 1988 Population Census detailed questionnaire. For each district in a region, a list of the urban EAs was compiled and a specific number of EAs was selected from this frame using the systematic random sampling (SRS) procedure to produce the regional urban sample.
Sample size, losses and replacement: The final sample analysed for the 2000/01 HBS consisted of 22,178 households, a large sample for any household budget survey.Three PSUs were lost entirely from the sample. Households were included in the analysis if they had at least one record in both the roster and the monthly diary.The weights were calculated for this group of households. Field supervisors were supplied with a list of twelve ‘replacement’ households, drawn as a separate sample at the same time as the main household sample, to be used if a sampled household could not be interviewed for the duration of the survey.The 2000/01 HBS sample had a high level of replacement of households that were not interviewed – around 12 per cent. A total of 4,823 households were analysed for the 1991/92 sample. Losses were higher; levels of replacement were lower (Table A1.2 in main report). In both surveys, households that were part of the initial selection constitute around 85 per cent of the sample analysed.
The 2000/01 HBS interviewed 98 per cent of the (revised) intended sample size. It did so by relatively frequent use of replacement households, selected from a list provided by the head office. Almost 12 per cent of households included in the final analysis were replacements.The 1991/92 HBS suffered higher levels of losses but used a smaller proportion of replacements.The use of replacements is not usually considered good practice in sampling, since it runs the risk of estimates being biased by replacement with non-comparable households. However, it was considered necessary because of the large sample size and demanding character of the data collection process.
The weighting coefficient is HH_WT for household level estimates.
For population level estimates use IND_WT.
Dates of Data Collection
Data Collection Mode
National Bureau of Statistics
Data Collection Notes
Two households were enumerated each month of the survey in each PSU. Over the course of the survey, 24 households would normally be interviewed per PSU. Enumerators, resident in or near the PSU, conducted an initial interview with the two households at the beginning of the survey month. They then visited the households during that month on a regular basis to record household transactions, covering expenditure, consumption and income. These visits were scheduled to take place every day for households without a literate member and every two to three days for others. Enumerators were supervised by field supervisors working out of the NBS regional offices. Supervisors collected and checked questionnaires, which were then sent on to the head office for data entry.
National Bureau of Statistics
HBS 2000/01 questionnaire
- Forms I Household questionnaire
- Form II for daily record of hosuehold consumption expenditures and receipts
- Form III daily records of individual receipts and consumption expenditures
A number of data consistency checks were undertaken early in the fieldwork to assess quality and to assist in the development of the data processing system. These identified a large number of problems in the data coming in from the field, which reflected in part the ambitious size of the survey. The errors identified included consumption unit miscoding, miscoding of transactions, out of range unit prices and problems in the identifier variables. As a consequence, automatic consistency checking programmes were strengthened and a data editing team was created. Where possible, errors were corrected at the data processing centre and the field teams were notified of the problems.This resolved a large number of problems.
Additional cleaning was also carried out at the beginning of the analysis.The main area in which additional cleaning was required was in the consumption/expenditure information, particularly in the household diary which consisted of over 5.6 million records. Similar cleaning was required in the 1991/92 data.Under-reporting of household size was also identified as a problem.
On the whole, there were few other problems in the data by the time it was analysed.There was some evidence of ‘age heaping’ – a tendency for individuals to round their reported ages to certain memorable digits (10, 15, 20 etc) - as is common in most developing country surveys. There was also some overreporting of four year olds, which is likely to be due to interviewers misrecording ages to avoid completing the extra questions for respondents of five and above. Other data quality issues are discussed together with the analysis to which they are relevant.
Estimates of Sampling Error
Sampling errors: Table A1.4 (main report) shows standard errors and confidence intervals around a number of estimates, calculated in STATA. It also presents the results of statistical tests for a significant difference between the 2000/01 and 1991/92 estimates, for the total population and each of the three areas.While STATA allows the specification of sample design in the calculation of sampling errors, identifying the strata and PSUs used, it is not possible to specify fully the complexity of the design of the HBS 2000/01.The standard errors, confidence intervals and tests are therefore approximate.
Use of the dataset must be acknowledged using a citation which would include:
- the Identification of the Primary Investigator
- the title of the survey (including country, acronym and year of implementation)
- the survey reference number
- the source and date of download
National Bureau of Statistics
Disclaimer and copyrights
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.