NCRMP CORAL DEMOGRAPHIC DATA HAS NOW BEEN MERGED INTO A PACIFIC-WIDE METADATA RECORD -- PLEASE REFER TO https://www.fisheries.noaa.gov/inport/item/71550 FOR MOST UPDATED DATA. The data described here result from benthic coral demographic surveys for two life stages (juveniles, adults) across the Mariana archipelago since 2014. Juvenile colony surveys include morphology and size. Adult colony surveys record morphology, colony size, partial mortality in two categories - old dead and recent dead, cause of recent dead partial mortality, and non-lesion forming condition including bleaching and disease). In 2023 some segment observations were repeated for internal quality control; to filter for non-repeated data, be certain filter for TRANSECTNUM = 1. A two-stage stratified random sampling (StRS) design was employed to survey the coral reef ecosystems throughout the U.S. Pacific regions. The survey domain encompassed the majority of the mapped area of reef and hard bottom habitats in the 0-30 m depth range. The stratification scheme included island, reef zone, and depth in all regions, as well as habitat structure type in the Mariana Archipelago. Sampling effort was allocated based on strata area and sites were randomly located within strata. Sites were surveyed using belt transects to collect juvenile and adult coral colony metrics. These data provide information on juvenile and adult coral abundance (density, proportion occurrence, and total colony abundance), size distribution, partial mortality, prevalence and abundance of recent mortality and cause, prevalence of disease and bleaching, and diversity. The StRS design effectively reduces estimate variance through stratification using environmental covariates and by sampling more sites rather than sampling more transects at a site. Therefore, site-level estimates and site to site comparisons should be used with caution. The data from the coral demographic surveys can be accessed online via the NOAA National Centers for Environmental Information (NCEI) Ocean Archive.

Abstract

To better understand the impact of the shock induced by the COVID-19 pandemic on micro and small enterprises in Tunisia and assess the policy responses in a rapidly changing context, reliable data is imperative, and the need to resort to a dynamic data collection tool at a time when countries in the region are in a state of flux cannot be overstated. The COVID-19 MENA Monitor Survey was led by the Economic Research Forum (ERF) to provide data for researchers and policy makers on the economic and labor market impact of the global COVID-19 pandemic on enterprises.

The ERF COVID-19 MENA Monitor Survey is constructed using a series of short panel phone surveys, that are conducted approximately every two months, and it will cover business closure (temporary/permanent) due to lockdowns, ability to telework/deliver the service, disruptions to supply chains (for inputs and outputs), loss of product markets, increased cost of supplies, worker layoffs, salary adjustments, access to lines of credit and delays in transportation. Understanding the strategies of enterprises (particularly micro and small enterprises) to cope with the crisis is one of the main objectives of this survey. Specific constraints such as weak access to the internet in some areas or laws constraining goods' delivery will be analyzed. Enterprise owners will also be asked about prospects for the future, including ability to stay open, and whether they benefited from any measures to support their businesses. The ERF COVID-19 MENA Monitor Survey is a wide-ranging, nationally representative panel survey. The wave 3 of this dataset was collected from August to September 2021 and harmonized by the Economic Research Forum (ERF) and is featured as data for enterprise data.

The harmonization was designed to create comparable data that can facilitate cross-country and comparative research between other Arab countries (Morocco, Egypt, and Jordan). All the COVID-19 MENA Monitor surveys incorporate similar survey designs, with data on enterprises within Arab countries (Egypt, Jordan, Tunisia, and Morocco).

Geographic coverage

National

Analysis unit

Enterprises

Universe

The sample universe for the enterprise survey was enterprises that had 6-199 workers pre-COVID-19

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample universe for the firm survey was firms that had 6-199 workers pre-COVID-19. Stratified random samples were used to ensure adequate sample size in key strata. A target of 500 firms was set as a sample. Up to Five attempts were made to ensure response if a phone number was not picked up/answered, was disconnected or busy, or picked up but could not complete the interview at that time. After the fifth failed attempt, a firm was treated as a non-response and a random firm from the same stratum was used as an alternate.

Use the National Institute of Statistics (INS) and Agency for the Promotion of Industry and Innovation (APII) databases as follow: o Tunisia did not have a Yellow Pages or similar database, so administrative/statistics data sources had to be used o The sample started with the INS frame with 1,238 enterprises with 6-200 wage employees § Enterprises were stratified into: (1) Agriculture (2) Industry (3) Construction (4) Trade (5) Accommodation (6) Service § Enterprises were also stratified by size in terms of 6-49 versus 50-200 employees § A random stratified sample (order) was selected § Further restricted to enterprises with 6-199 workers in February 2020 based on an eligibility question during the phone interview § This sample frame was eventually exhausted o After the INS sample was exhausted, the APII sample was used § APII only covered enterprises with 10+ workers § APII only covered (1) services & transport, and (2) industry o Weights are based on the underlying data on all enterprises from INS, specifically: Entreprises privées selon l'activité principale et la tranche de salariés (RNE 2019). § We ultimately stratify the Tunisia weights by industry and enterprises sized: 6-9 employees (since APII only covered 10+), 10-49, and 50-199.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The enterprise questionnaire is carried out to understand the strategies of enterprises -particularly micro and small enterprises- to cope with the crisis as well as related constraints and prospects for the future. It includes questions on business closure (temporary/permanent) due to lockdowns, ability to telework/deliver the service, disruptions to supply chains (for inputs and outputs), loss of product markets, increased cost of supplies, worker layoffs, salary adjustments, access to lines of credit and delays in transportation.

Note: The questionnaire can be seen in the documentation materials tab.

Stratum boundaries from 2000 to present for the Waterfowl Breeding Population and Habitat Survey. This data view presents polygons which represent the stratum boundaries for the survey.

Descriptive statistics of the sample stratified by sex and race.

Fishing event data (e.g. year, date, time, location, catch and effort) and associated biological data from the Offshore Stratified Random Survey component of the annual Sablefish Research and Assessment Survey on the British Columbia coast.

Introduction

DFO and the Canadian Sablefish Association (CSA) collaborate to undertake an annual fishery-independent research survey under a joint agreement. The survey employs longline trap gear to obtain catch rate data, gather biological samples, capture oceanographic measurements, and collect tag release and recapture data.

Data summaries provided here are for the offshore stratified random sampling design (StRS) component of the survey, which has been conducted annually since 2003. The design of the sablefish survey has developed over time by incorporating and discontinuing components, including individual experimental studies (not available on OpenData). This StRS Survey component differs in methodology from the other two survey components:

(1) Standardized trap survey – mainland inlets (1994-present; available on OpenData using link below), and

(2) Standardized trap survey – offshore indexing and offshore tagging (1990 – 2010; not yet available on OpenData).

The Sablefish offshore stratified random trap survey (StRS) follows a depth and area stratified random sampling design. The survey area is partitioned into five spatial strata (S1 to S5) and three depth strata (RD1 to RD3) for a total of 15 strata. The five spatial strata are S1 (South West Coast Vancouver Island or SWCVI), S2 (North West Coast Vancouver Island or NWCVI), S3 (Queen Charlotte Sound or QCS), S4 (South West Coast of Haida Gwaii or SWCHG), and S5 (North West Coast of Haida Gwaii or NWCHG). The three depth strata are 100-250 fathoms (RD1), 250-450 fathoms (RD2), and 450-750 fathoms (RD3). The area within each of the 15 strata are sectioned into 2 km x 2 km grid cells or ‘fishing blocks’ from which set locations are randomly chosen each year. Survey procedures are standardized and documented in Canadian Technical Reports of Fisheries and Aquatic sciences.

Data tables provided for the offshore stratified random survey include (i) effort, (ii) catch, (iii) biological information, (iv) the sampling frame from which blocks are selected for sampling each year, and (v) the calculated coastwide Sablefish biomass index.

StRS Effort

This table contains information about the annual survey trips and fishing events (sets). Trip-level information includes the year the survey took place, a unique trip identifier, the vessel that conducted the survey and the trip start and end dates (the dates the vessel was away from the dock conducting the survey). Set-level information includes the date, time, location and depth that fishing took place, the survey spatial and depth strata for the set, reason for the set, soak time, number of traps deployed and number of traps fished. All successful fishing events are included, i.e., those sets that conformed to specified survey standards.

StRS Catch

This table contains the catch information from successful fishing events. Catches are identified to species or to the lowest taxonomic level possible. Catches are recorded as fish counts and / or weight. The unique trip identifier and set number are included so that catches can be related to the fishing event information (including capture location).

StRS Biological Information

This table contains the biological data for sampled catches. Data may include any or all of length, weight, sex, maturity, and age. Most of the sampled catch is Sablefish; however, some biological information has been collected on Rockfish, Flatfish and other Roundfish species in some years. Age structures are collected and are archived until required for analyses; therefore, all existing structures have not been aged at this time. Tissue samples (usually a fin clip) may be collected for genetic (DNA) analysis for specific species. Genetic samples may be archived until required for analyses; for more information, please see the data contacts. The unique trip identifier and set number are included so that samples can be related to the fishing event and catch information.

Sample Frame

This table contains a list of all of the 2km x 2km grid cells or ‘fishing blocks’ that are part of the stratified random sampling frame. A subset of blocks are randomly selected for sampling each year from this list. For each grid cell, the corresponding depth and spatial strata ID is included. This sample frame can be used to calculate design-based abundance indices for the survey.

StRS Biomass Index

This table contains a coastwide relative biomass index for Sablefish based on the annual StRS survey. Stratified random sampling mean index values and 95% confidence intervals are calculated by year using the classical survey stratified random sampling estimator (Cochran 1977) and the number of possible sampling units per stratum provided by Wyeth et al. (2007). The relative biomass index has been input to the operating model and management procedure used to provide management advice for BC Sablefish since 2011 (Cox et al. 2011).

Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which

This data set shows labour force participation rate (LFPR) by urban and rural strata for all states in Malaysia. The statistics is derived from Labour Force Survey (LFS) which is conducted every month using household approach from year 1982 until 2020. LFPR is defined as the ratio of the labour force to the working age population (15-64 years), expressed as percentage. W.P. Labuan is gazzeted as a Federal Territory in 1984 while W.P. Putrajaya is gazzeted as a Federal Territory in 2001. The statistics for W.P. Putrajaya for 2001-2010 is treated as part of Selangor. Statistics for W.P. Putrajaya is available separately since 2011 onwards. LFS was not conducted during the years 1991 and 1994. No. of Views : 299

This file only contains the stratum sizes of the contexts analyzed. The information is freely accessible.

Statistical Use of the Central Business Directory: Companies by province and wage earner stratum. Annual. Provinces.

The United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS) area sampling frame is a delineation of all parcels of land for the purpose of later sampling the parcels. The area frame is constructed by visually interpreting satellite imagery to divide a state into homogenous land use areas (strata) based on percent cultivated. The strata are typically defined as low, medium or high percent cultivated, non-agricultural land, urban use, agri-urban, or water. The boundaries of the strata usually follow identifiable features such as roads, railroads and waterways. The strata boundaries do not coincide with any political boundaries, with the exception of state boundaries. This site provides links to download ESRI shape and symbology layer files, as well as low resolution JPEG or higher resolution PDF images for each state. Also included in the FAQ are how to cite the data set, time period, how geographic features are represented and described, originators and contributors, contacts to address questions about the data, how the data set was created (previous works, e.g. USGS topographic quadrangles, US Census Bureau, space imagery, etc.), data generation-, processing-, and modification methods, and similar or related data. Applicable legal restrictions on access or use of the data and disclaimers are provided. Resources in this dataset:Resource Title: Land Use Strata - Selected States. File Name: Web Page, url: https://www.nass.usda.gov/Research_and_Science/stratafront2b.php This site provides links to download ESRI shape and symbology layer files, as well as low resolution JPEG or higher resolution PDF images for each state.

This data set shows the number of unemployed persons by urban and rural strata for all states in Malaysia for year 1982 until 2020. The statistics is derived from Labour Force Survey (LFS) which is conducted every month using household approach and refers to those between the working age of 15-64 years old. The unemployed are classified into two groups that is the actively unemployed and inactively unemployed. The actively unemployed include all persons who did not work during the reference week but were available for work and were actively looking for work during the reference week. Inactively unemployed persons include the following categories: a. persons who did not look for work because they believed no work was available or that they were not qualified; b. persons who would have looked for work if they had not been temporarily ill or had it not been for bad weather; c. persons who were waiting for result of job applications; and d. persons who had looked for work prior to the reference week. W.P. Labuan is gazzeted as a Federal Territory in 1984 while W.P. Putrajaya is gazzeted as a Federal Territory in 2001. The statistics for W.P. Putrajaya for 2001-2010 is treated as part of Selangor. Statistics for W.P. Putrajaya is available separately since 2011 onwards. LFS was not conducted during the years 1991 and 1994. More info: https://www.dosm.gov.my No. of Views : 460

Survey on the Use of Information and Communication Technologies and Electronic Commerce in Companies: Standard error of data analytics variable, by different strata. National.

This data set shows the number of labour force by urban and rural strata for all states in Malaysia for year 1982 until 2021. The statistics is derived from Labour Force Survey (LFS) which is conducted every month using household approach. Labour force refers to those who during the reference week of LFS, are in the 15-64 years age group and who are either employed or unemployed. W.P. Labuan is gazzeted as a Federal Territory in 1984 while W.P. Putrajaya is gazzeted as a Federal Territory in 2001. The statistics for W.P. Putrajaya for 2001-2010 is treated as part of Selangor. Statistics for W.P. Putrajaya is available separately since 2011 onwards. LFS was not conducted during the years 1991 and 1994. Working less than 30 hours only available for Malaysia More info: https://www.dosm.gov.my No. of Views : 477

Table 2a: Optimal samples per stratum as a function of sample size. Table 2b: Optimal allocation of residential structures per stratum as a function of sample size. Table 2c: The inclusion probability πh = h[h]/Nh[h] as a function of sample size. Table 2d: The upper strata boundaries as a function of sample size.Table 2a lists the number of residential structures to be sampled in each stratum for optimal stratification of the variable “persons per residential structure.” Table 2b is the total number of residential structures per stratum, while Table 2c specifies the ratios of samples per stratum divided by the total number of residential structures per stratum. These ratios are not constant for each sample size because the optimization was constrained by Neyman allocation, rather than proportional allocation. Table 2d lists the upper boundary limits as a function of sample size.Neyman-optimized allocation as a function of sample size and stratum.

We used a stratified-random sampling approach to estimate the total abundance of Wedge-tailed Shearwater (Ardenna pacifica) nest sites across Kīlauea Point National Wildlife Refuge (KPNWR), Kauaʻi, during 1-7 July 2019. We first identified strata as unique geographic areas of the refuge to account for potential differences in nesting habitat and non-uniform nest site clustering. We then sub-divided strata where we expected high, low, minimal, or no nest site abundance. These distinctions were based on knowledge of shearwater nesting distribution gained while performing extensive ground-searching for tropicbirds across the entire refuge in April and May 2019. We delineated strata boundaries using recent satellite imagery in ArcGIS (version 10.7) and, based on direct observations in the field, refined in order to remove large contiguous areas lacking shearwater presence or nesting habitat. Planar area of each polygon was automatically calculated by ArcGIS. To calculate surface area in each stratum, we obtained a 1/3 arc-second (10-m resolution) digital elevation model from the National Elevation Dataset (USGS 2013). This elevation raster was projected to Universal Transverse Mercator projection (Zone 4 North, North American Datum 1983) and converted to a surface area raster using the raster and sp packages in R (Hijmans 2019; Pebesma & Bivand 2005). We calculated stratum-specific surface area by summing the surface area values of raster cells in each stratum using XToolsPro (version 18; Zonal Statistics tool) in ArcMap (version 10.7). Hijmans RJ. 2019. raster: Geographic Data Analysis and Modeling. R package version 3.0-7. https://CRAN.R-project.org/package=raster Pebesma EJ, Bivand RS. 2005. Classes and methods for spatial data in R. R News 5(2):9-13. U.S. Geological Survey. 2013. USGS NED n23w160 1/3 arc-second 2013 1 x 1 degree ArcGrid: U.S. Geological Survey. Accessed at https://www.sciencebase.gov/catalog/item/581d21dae4b08da350d53be2

This collection brings together five interrelated datasets from the University of Hull research program on the turbulent suspension of sediment in stratified shear flows. It includes: numerical simulations of thermally stratified and sediment-laden channel flows performed using open-source NEK5000 v19, with initial conditions and post processed data; experimental measurements from a novel Stratified Flow Facility (SFF), with raw and processed (insitu) ultrasonic and optical equipment; and a full brief of facility design. The collection also links to a GitHub repository containing a Python-based processing suite for stratified flow simulations and experiments. Together, these datasets provide raw and processed data, experimental metadata, and technical documentation to support the study of turbulence, internal waves, particle transport, and measurement methodologies in stratified fluid dynamics.

The purpose of the SNF study was to improve our understanding of the relationship between remotely sensed observations and important biophysical parameters in the boreal forest. A key element of the experiment was the development of methodologies to measure forest stand characteristics to determine values of importance to both remote sensing and ecology. Parameters studied were biomass, leaf area index, above ground net primary productivity, bark area index and ground coverage by vegetation. Thirty two quaking aspen and thirty one black spruce sites were studied. Use of multiple plots within each site allowed estimation of the importance of spatial variation in stand parameters. Within each plot, all woody stems greater than two meters in height were recorded by species and relevant dimensions were measured. Diameter breast height (dbh) was measured directly. Height of the tree and height of the first live branch were determined by triangulation. The difference between these two heights was used as the depth of crown. Similar measurements were made for shrubs between one and two meters tall in the aspen sites. The Forest Canopy Composition (SNF) data set provides the counts of canopy (over two meters tall) tree species and subcanopy (between one and two meters tall) tree species. Also related, for the aspen sites, in each plot a visual estimation of the percent coverage of the canopy, subcanopy and understory vegetation was made. The site averages of these coverage estimates are presented in the Aspen Forest Cover by Stratum/Plot (SNF) data set.

Financial overview and grant giving statistics of Strata Policy

Abstract

The main purpose of a Household Income and Expenditure Survey (HIES) survey was to present high quality and representative national household data on income and expenditure in order to update Consumer Price Index (CPI), improve statistics on National Accounts and measure poverty within the country. These statistics are a requirement for evidence based policy-making in reducing poverty within the country and monitor progress in the national strategic plan in place.

Geographic coverage

Urban (Funafuti) and rural areas (outer islands).

Analysis unit

Household and Individual.

Universe

Private households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling design of the Tuvalu 2022 HIES consists in the random selection of the appropriate numbers of households (within each strata urban and rural) in order to be able to disaggregate HIES results at the strata level (in addition to National level). The urban strata of Tuvalu is made of the island of Funafuti (as a whole) and the rest of the country (all outer islands) compose the rural strata. The statistical unit used to run this sampling analysis is the household. The sample procedure is based on the following steps: - Assessment of the accuracy of the previous 2015 HIES in terms of per capita total expenditure (variable of interest) and check whether the sample size at that time were appropriate and correctly distributed among both stratas, - Update this assessment process by using the most recent population count to get the new sample size and distribution, - Proceed to the random selection of households using this most recent population count. The sampling frame (most recent household listing and population count) used to update and select is the 2021 Tuvalu Household Listing conducted by the Central Statistics Division of Tuvalu. At the National level, the 2015 Tuvalu HIES reported a good accuracy of the per capita total expenditure (less than 5%) but the disaggregation results by strata showed a lower quality of the result in Tuvalu urban. The Tuvalu 2021 household listing provides the most recent distribution of the households across all the islands of Tuvalu. This step consists in updating the accuracy of the previous 2015 HIES by using this recent household count and get the appropriate RSE by changing the sample size. For budget constraint, the total sample size cannot get increased, as the funding situation does not allow higher sample size. It means that the only parameter that can be modified is the distribution of the sample across the strata. Sample size by stratum: -Urban: 350 (out of 1,010 urban households as per the 2021 listing) -Rural: 310 (out of 835 rural households as per the 2021 listing) -National: 660 (out of 1,845 total households as per the 2021 listing)

2015 per capita mean total expenditure (AUD): -Urban: 3,190 -Rural: 2,780 -National: 3,000

Relative Standard Error (RSE): -Urban: 5.1% -Rural: 4.1% -National: 3.3%

It results from this new sample design a new distribution that shows an increase in Funafuti urban, mainly due to: - The low quality of the survey results from the 2015 HIES, - The number of households that have increased by more than 15% between 2015 and 2020 in Tuvalu urban area.

The household selection process is based on a simple random procedure within each stratum: - The 350 households in Funafuti are selected using the same probability of selection across all villages of the islands - The 310 household in rural Tuvalu are distributed proportionally to the size of each rural island of Tuvalu. This proportional allocation of the sample across rural Tuvalu islands generates the best accuracy at the strata level.

Distribution of sample accross strata: Urban: Funafuti 350 Rural: Nanumea 42
Nanumaga 37 Niutao 46
Nui 39
Vaitupu 75
Nukufetau 45
Nukulaelae 23
Niukalita 4

Non-response is a problem in surveys, and it is crucial that the field teams interview the selected households (the location on the map and the name of the household head are used to help to determine the selected households). During the first visit, interviewers must do their best to convince the household head to participate in the survey (and get his/her approval to proceed to interview). It may happen in the field that the first visit results in: I. A refusal: the household head does not show any interest in the survey and is reluctant to participate, II. The house is empty (household members away at the time of the visit).

(I) Refusal: if the interviewer cannot convince the household head to participate, he has to liaise with the survey management, and the supervisor will help in the discussion to convince the household head to respond. In this case, it is important to mention that all responses are kept confidential and insist on the importance of it for the benefit of Tuvalu population. (II) Empty house: the interviewer must investigate (checking with neighbours) whether or not the house is still inhabited by the family: o If it is not the case, the dwelling is then vacant, and the replacement procedure must be activated. o If the dwelling is still occupied, interviewer must come back later the same day or the day after at different time

Only in extreme cases of persistent refusal or empty house (household members away during the time of the collection) the replacement procedure must be activated. The replacement procedure consists in changing the selected household to the closest neighbour who is available.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The 2022 Tuvalu Household Income and Expenditure Survey (HIES) questionnaire was developed in English language and it follows the Pacific Standard HIES questionnaire structure. It is administered on CAPI using Survey Solution, and the diary is no longer part of the form. All transactions (food, non food, home production and gifts) are collected through different recall sections during the same visit. The traditional 14 days diary is no longer recommended in the region. This new method of implementing the HIES present some interesting and valuable advantages such as: cost saving, data quality, time reduction for data processing and reporting. The 2022 HIES of Tuvalu was directly integrated to a census through a Long Form Census (LFC). The LFC was an experiment led by the World Bank and the Pacific Community to try and group a census and a HIES collection. All households were normally enumerated during the 2022 Census and households selected to participate to the HIES were then asked the HIES questions.

Below is a list of all modules in this questionnaire: -Household ID -Demographic characteristics -Education -Health -Functional difficulties -Communication -Alcohol -Other individual expenses -Labour force -Fisheries -Handicraft and home-processed food -Dwelling characteristics -Assets -Home maintenance -Vehicles -International trips -Domestic trips -Household services -Financial support -Other household expenditure -Ceremonies -Remittances -Food insecurity -Financial inclusion -Livestock & aquaculture -Agriculture parcel -Agriculture vegetables -Agriculture rootcrops -Agriculture fruits

The survey questionnaire can be found in this documentation.

Cleaning operations

Data was edited, cleaned and imputed using the software Stata.

Response rate

There was a total of 662 households from the original selection of the sample. 592 of them were contacted 528 accepted the interviews. The number of valid households is 464, or 70% of households before replacement. After replacement, 54 households were considered valid making the final completion rate at 78% (73% in urban and 85% in rural area).

This data set shows the number of employed persons by urban and rural strata for all states in Malaysia from year 1982 until 2020. The statistics is derived from Labour Force Survey (LFS) which is conducted every month using household approach. Employed persons are those between the working age of 15-64 years old who at any time during the reference week of LFS had worked at least one hour for pay, profit or family gain (as an employer, employee, own-account worker or unpaid family worker). W.P. Labuan is gazzeted as a Federal Territory in 1984 while W.P. Putrajaya is gazzeted as a Federal Territory in 2001. The statistics for W.P. Putrajaya for 2001-2010 is treated as part of Selangor. Statistics for W.P. Putrajaya is available separately since 2011 onwards. LFS was not conducted during the years 1991 and 1994. More info: https://www.dosm.gov.my No. of Views : 185