This survey was conducted in Namibia by the Ministry of Health and Social Services. 5,421 women between the ages of 15 - 49 were interviewed from July 1992 - November 1992. Major topics covered: Anthropometry; Arm Circumference; Causes of Death; Maternal Mortality

Data access requires registration with USAID. USAID now makes this data available directly on their website, which can be accessed here: https://dhsprogram.com/methodology/survey/survey-display-48.cfm - along with additional years of data here: https://dhsprogram.com/data/available-datasets.cfm

We advise you use this location to access the data as they have updated formats, etc. This material remains in the archive for preservation and historical purposes.

Abstract

The first full-scale Labour Force Survey (LFS) in Namibia was carried out in 1997 under the National Household Survey Programme, launched after the Government endorsed the Five Year Plan of Development of Statistics in Namibia in 1993. Since then, five Labour Force Surveys have been conducted in the country at more or less regular intervals of every four years. This survey was conducted only one year after the previous survey, in 2012, and the Namibia Statistics Agency plans to continue to conduct the LFS on an annual basis going forward. Like all its predecessors, the 2014 survey was conducted with the objective of generating "timely collection and release of key socio-economic indicators for assessment of labour market conditions in Namibia." The survey covers all aspects of people's work, including the education and training needed to equip them for work, the jobs themselves, job search of those out of work, and income and benefits from work.

As with previous LFS analysis this report covers wide-range of topics to meet the demands of local stakeholders in labour statistics, and as well standard reporting demanded by SADC, the AU, and the ILO. For example we have included SADC Minimum Indicator List page, for a quick glance of standard employment and unemployment indicators for accessing Namibia's efforts in meeting its various developmental goals in particular, those relating job creations.

Moreover, the anonymised micro-level data used for this report are available via the NSA website at http://www.nsa.org.na to enable other agencies and individuals to conduct further analysis of the data. In this way, the country will derive full benefit from the resources that were allocated to conduct the survey.

In conclusion, I would like to address my sincere thanks to the International Labour Organisation (ILO) for their technical inputs to the 2014 labour force survey in Namibia. A word of appreciation goes also to our stakeholders for their contributions towards the review of questions use for the survey.

Finally, I also thank all the staff of the NSA that worked hard for the successful and timely conclusion of the first annual labour force survey in Namibia.

Geographic coverage

The survey had national coverage. The lowest level of geographic aggregation of the data is Region (the 14 regions in the country).

Analysis unit

The units of analysis in the survey includes households and individuals

Topics Topic Vocabulary URI Population size
Population composition
Economically active population
Economically active population by sex
Employed and Unemployed - broad
working conditions

Universe

Universe The universe of the survey was all household members. Homeless people and the people who are living in institutions (institutional population) are excluded.

Sampling procedure

The target population of the LFS 2014 was members of private households in Namibia. The population living in institutions, such as hospitals, hostels, police barracks and prisons was not covered in the survey. However, private households within institutional settings were covered, such as teachers' houses on school premises. The sample design for the LFS 2014 was a stratified two-stage probability sample, where the first stage units were geographical areas designated as the Primary Sampling Units (PSUs) and the second stage units were the households. Up-to-date listings of households in the selected PSU was prepared during the field work, and 18 households were selected in each PSU using systematic sampling.

Mode of data collection

Face-to-Face

Abstract

During 2018, the National Statistical Agency (NSA) of Namibia received technical assistance from the Global Strategy to Improve Agricultural and Rural Statistics hosted by FAO on the measurement of Post-Harvest Losses (PHL). In this regard, a pilot survey was conducted by NSA in the Kavango West region to compare estimations using subjective and objective methods. The main crops analyzed are millet and maize. Subjective measurement methods included farmer recall, while the objective methods chosen were implemented through crop cutting, and samples of harvested crop analyzed in a lab. Unfortunately, the project ended before the samples were received from the lab, so these data are not available.

The pilot survey was conducted in Kavango West region only and the Primary Sampling Units (PSUs) were derived from the 2013/14 Agricultural Census frame. Staff from the Ministry of Agriculture, Water and Forestry (MAWF), (agricultural technicians as enumerators and agricultural technician as team supervisors) carried out field activities. In total, a sample of 350 farms were enumerated. The data collection took place from May 2018 to August 2018 (30 working days) and included both the subjective and objective measure of the PHL.

Geographic coverage

Regional Coverage

Analysis unit

Households

Universe

Agricultural households in the Kavango West region.

Kind of data

Sample survey data [ssd]

Sampling procedure

The PHL pilot study mainly followed the National Census of Agriculture (NCA) 2013/14 methodology. The NCA 2013/14 used a stratified two stage cluster sample design. At the first stage, primary sampling units (PSUs) were selected with Probability Proportional to Size (PPS) from the sampling frame based on the Enumeration Areas of 2011 Population and Housing Census. The size measure of a PSU in the sampling frame was the number of agricultural households which was derived from the questions included in 2011 Population and Housing Census as per the FAO recommendations.

The list of agricultural households was prepared through the listing process within a selected PSU to compile the sampling frame for agricultural households which was selected systematically.

A third stage of sampling was also conducted to select plots which contained the two main crops, maize, and millet for objective measurement as described below.

A list of plots planted with maize or millet in each sampled PSU was created. Then, one plot was randomly selected from the two main crops of the holder. An area was then marked within the selected plot according to the FAO guidelines and the matured crop inside this marked area was cut and weighed when the crop was wet and dry.

Crop cutting enable estimation of the yield of a crop and the losses during harvesting, threshing/shelling, and cleaning/winnowing. This was done through processing the produce of sub-plots in selected fields. Interviewers did the crop cutting manually according to the techniques used by the farmer. After the manual harvesting was done, the second team of supervisors entered the field and collected all fallen ears/cobs, grains and weighed them after which the information was recorded. These figures are used to estimate the average yields of each of the crops.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

The dataset received by the Office of Chief Statistician (OCS) team was already cleaned by Aliou Mballo directly with NSA. During the cleaning process, all direct identifiers were removed. Furthermore, the declaration, phyiscal measurement, and storage data for the second crops, were transposed from wide to long. So instead of the farmer declaration variables of the second crop captured by the variables titled from “D6” to “D10-6” in the questionnaire being in their own columns, there is a second row in the dataset containing data from sections C, D, E and G containing data for the second crop, spread across columns “crop_code” to “D5-6”. The same logic applies to the physical measurements and storage data.

The sections CDEG dataset contains data for some crops which do not correspond to records in the Section C dataset on agricultural practices. This is due to a mistake amongst some enumerators which filled in directly Section D for some crops and skipped agricultural practices. This is especially prevelant for measurement data for maize. The data from the lab was not received in time for the project deadline. Accordingly, section “H_Storage_Lab” from the questionnaire was not available to be included in the dataset.

Abstract

The Country Opinion Survey in Namibia assists the World Bank Group (WBG) in better understanding how stakeholders in Namibia perceive the WBG. It provides the WBG with systematic feedback from national and local governments, multilateral/bilateral agencies, media, academia, the private sector, and civil society in Namibia on 1) their views regarding the general environment in Namibia; 2) their overall attitudes toward the WBG in Namibia; 3) overall impressions of the WBG’s effectiveness and results, knowledge work and activities, and communication and information sharing in Namibia; and 4) their perceptions of the WBG’s future role in Namibia.

Geographic coverage

National coverage

Analysis unit

Stakeholders of the World Bank Group in Namibia

Kind of data

Sample survey data [ssd]

Sampling procedure

From March to May 2024, a total of 819 stakeholders in Namibia were invited to provide their opinions on the WBG’s work by participating in a Country Opinion Survey. The WBG country team compiled a list of potential participants. Participants were drawn from the office of the President, Prime Minister, office of a minister or parliamentarian, government institutions, local governments, bilateral or multilateral agencies, the private sector, civil society, academia, and the media. Of these stakeholders, 105 participated in the survey.

Mode of data collection

Internet [int]

Research instrument

The survey was conducted in English language and is provided as related material.

Response rate

The response rate was 13%

This year’s survey results were compared to the FY18 Survey, which had a response rate of 41% (N=125). Comparing responses across Country Surveys reflects changes in attitudes over time and changes in respondent samples, methodology, and the survey instrument itself. To reduce the influence of the latter factor, only those questions with similar response scales/options were analyzed. This year's survey achieved greater outreach and received more responses from government principals, government institutions, and the media. However, there was a decline in participation from civil society and the private sector. These differences in stakeholder composition between the two years should be considered when interpreting the results of the past-year comparison analyses.

Abstract

The World Bank is interested in gauging the views of clients and partners who are either involved in development in Namibia or who observe activities related to social and economic development. The World Bank Country Assessment Survey is meant to give the World Bank's team that works in Namibia, greater insight into how the Bank's work is perceived. This is one tool the World Bank uses to assess the views of its critical stakeholders. With this understanding, the World Bank hopes to develop more effective strategies, outreach and programs that support development in Namibia. The World Bank commissioned an independent firm to oversee the logistics of this effort in Namibia.

The survey was designed to achieve the following objectives: - Assist the World Bank in gaining a better understanding of how stakeholders in Namibia perceive the Bank; - Obtain systematic feedback from stakeholders in Namibia regarding: · Their views regarding the general environment in Namibia; · Their overall attitudes toward the World Bank in Namibia; · Overall impressions of the World Bank's effectiveness and results, knowledge work and activities, and communication and information sharing in Namibia; and · Perceptions of the World Bank's future role in Namibia. - Use data to help inform Namibia country team's strategy.

Geographic coverage

National

Analysis unit

Stakeholder

Universe

Stakeholders of the World Bank in Namibia

Kind of data

Sample survey data [ssd]

Sampling procedure

In April and May 2013, 314 stakeholders of the World Bank in Namibia were invited to provide their opinions on the Bank's assistance to the country by participating in a country survey. Participants in the survey were drawn from among the office of the President or Prime Minister; the office of a Minister; the office of a Parliamentarian; employees of a ministry, ministerial department, or implementation agency; consultants/contractors working on World Bank-supported projects/programs; project management units (PMUs) overseeing implementation of a project; local government officials or staff; bilateral agencies; multilateral agencies; private sector organizations/firms; private foundations; the financial sector/private banks; NGOs; community-based organizations (CBOs); the media; independent government institutions; trade unions; faith-based groups; academia/research institutes/think tanks; and the judiciary branch.

Mode of data collection

Mail Questionnaire [mail]

Research instrument

The Questionnaire consists of 8 Sections:

A. General Issues Facing Namibia: Respondents were asked to indicate whether Namibia is headed in the right direction, what they thought were the top three most important development priorities, and which areas would contribute most to reducing poverty and generating economic growth in Namibia.

B. Overall Attitudes toward the World Bank: Respondents were asked to rate their familiarity with the World Bank, the Bank's effectiveness in Namibia, Bank staff preparedness to help Namibia solve its development challenges, the extent to which the Bank should seek to influence the global development agenda, their agreement with various statements regarding the Bank's work, and the extent to which the Bank is an effective development partner. Respondents were also asked to indicate the sectoral areas on which it would be most productive for the Bank to focus its resources, the Bank's greatest values and greatest weaknesses in its work, the most effective instruments in helping to reduce poverty in Namibia, with which stakeholder groups the Bank should collaborate more, and to what reasons respondents attributed failed or slow reform efforts.

C. World Bank Effectiveness and Results: Respondents were asked to rate the extent to which the Bank's work helps achieve development results in Namibia, the extent to which the Bank meets Namibia's needs for knowledge services and financial instruments, and the Bank's level of effectiveness across thirty-two development areas, such as economic growth.

D. The World Bank's Knowledge Work and Activities: Respondents were asked to indicate how frequently they consult Bank knowledge work and activities, the areas on which the Bank should focus its knowledge work and activities, and to rate the effectiveness and quality of the Bank's knowledge work and activities, including how significant of a contribution it makes to development results and its technical quality.

E. Working with the World Bank: Respondents were asked to rate their level of agreement with a series of statements regarding working with the Bank, such as working with the World Bank increasing Namibia's institutional capacity.

F. The Future Role of the World Bank in Namibia: Respondents were asked to rate how significant a role the Bank should play in Namibia's development in the near future and to indicate what the Bank should do to make itself of greater value in Namibia. Respondents were also given a list of priorities from the National Development Plan IV and asked to indicate which would benefit from the World Bank playing a leading role, which should receive little support from the Bank, and which should be left for the Government to manage.

G. Communication and Information Sharing: Respondents were asked to indicate how they get information about economic and social development issues, how they prefer to receive information from the Bank, their Internet access, and their usage and evaluation of the Bank's websites and Development Information Centre. Respondents were asked about their awareness of the Bank's Access to Information policy, past information requests from the Bank, and their level of agreement that they use more data from the World Bank as a result of the Bank's Open Data policy. Respondents were also asked about their level of agreement that they know how to find information from the Bank and that the Bank is responsive to information requests.

H. Background Information: Respondents were asked to indicate their current position, specialization, whether they professionally collaborate with the World Bank, their exposure to the Bank in Namibia, and their geographic location.

Response rate

A total of 90 stakeholders participated in the country survey (29% response rate).

Abstract

The UNHCR Livelihoods Monitoring Framework takes a program-based approach to monitoring, with the aim of tracking both outputs and the impact of UNHCR dollars spent on programming (either via partners or through direct implementation).

The process for developing the indicators began in 2015 with a review of existing tools and approaches. Consultations were held with governments, the private sector, field-based staff and civil society partners to devise a set of common, standardized measures rooted in global good practices.

Since 2017, a data collection (survey) has been rolled out globally, and the participating operations conducted household surveys to a sample of beneficiaries of each livelihoods project implemented by UNHCR and its partner. The dataset consists of baseline and endline data from the same sample beneficiaries, in order to compare before and after the project implementation and thus to measure the impact.

More info is available on the official website: https://lis.unhcr.org

Geographic coverage

National coverage

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling was conducted by each participating operations based on general sampling guidance provided as the following: - At least 100 randomly selected beneficiaries for each project - Representativeness of sub-groups (gender, camp, etc.) should be kept as much as possible - Baseline and endline beneficiaries should be the same

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaire contains the following sections: - partner information - general information on beneficiary - agriculture - self-employment - wage-employment

CSV data files containing records of mineral dust plume events (dust point source locations lat/long, start and end time, duration, plume movement direction, and sensor used for detection), extrapolated monthly, seasonal and annual dust plume event and dust days (i.e. count of days in which a dust plume was observed) and dust optical depth (DOD) data, and associated records of meteorological and hydrological variables for dust plume events (i.e. 10 m wind speed, lake area extents, catchment precipitation totals and specific source point surface wetting frequencies by precipitation and ephemeral flooding, and El Niño Southern Oscillation [ENSO 3.4] and South Indian Ocean Dipole [SIOD] index values) for Etosha Pan, Namibia for the analysis period from July 1999 to January 2023.

All datasets are readable using CSV file viewer software.

Dust plume event data were analysed manually by the author Natasha S. Wallum. Data used for detection were sourced from Terra and Aqua satellites MODIS level 1b and Aerosol data acquired from the Atmospheric Archive and Distribution System (LAADS) Distributed Active Archive Center (DAAC), located in the Goddard Space Flight Center in Greenbelt, Maryland (https://ladsweb.nascom.nasa.gov/) and SEVIRI data procured from the EUMETSAT Data Store (https://data.eumetsat.int/search?query=). Analysis of SEVIRI imagery utilised the Clear Sky Differencing (CSD) algorithm developed by Jon Murray and colleagues (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2016JD025221).

Lake area extent data were derived by density thresholding of near-infrared (NIR) reflectance data from the MODIS Terra satellite obtained from NASA’s LAADS DAAC data portal (https://ladsweb.modaps.eosdis.nasa.gov/) and verified using Level-2 (8-day) images collected from Landsat 5 TM (1984–2012) and Landsat 8 OLI (2013 – present day) sensors were acquired through the USGS Earth Explorer data portal (www.earthexplorer.com).

The contributing catchment (Cuvelai-Etosha Basin) was derived from the HydroBASINS (Lehner and Grill, 2013) catchment database (https://hydrosheds.org/products/hydrobasins), and this area was used to derive daily precipitation inputs for 2000–2022 (July – June hydrological year) from The Integrated Multi-Satellite Retrievals for GPM (GPM-IMERG) and The Tropical Rainfall Measuring Mission (TRMM) gridded time-series of precipitation available from the Goddard Earth Science Data and Information Services Center (http://disc.gsfc.nasa.gov/).

These data were augmented by limited monthly precipitation records (2000–2022) from 10 local weather stations (Mahenene, Ondjiva, Namacunde, Oshaambelo, Ogongo, Ondangwa, Okashana, Okapya, Okaukuejo, and Mannheim) provided by the Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL; https://sasscal.org/) and continuous rain gauge measurements recorded at Windpoort located in close proximity to Etosha Pan within the Cuvelai-Etosha Basin.

Near-surface (10 m) wind speeds (m/s) and cubed wind speed anomaly data were derived from ERA5-Land reanalysis model data product available from the Copernicus Climate Change Service Data Store (https://cds.climate.copernicus.eu/).

Surface wetting frequencies and time since wetting for dust event source points were calculated by the author Natasha S. Wallum using ArcGIS Pro (education licence on behalf of the University of Oxford).

Global climate indices of SST anomalies data (ENSO 3.4 and SIOD) were obtained from the Climate Diagnostics Centre (CDC) online archives (http://psl.noaa.gov/data/climateindices) and the Indian Ocean dipole (IOD) site maintained by the Frontier Research System for Global Change (FRSGC)/Japan Agency for Marine-Earth Science and Technology (JAMSTEC) Climate Variations Research Program (http://www.jamstec.go.jp).