Abstract

The Project for Statistics on Living standards and Development was a countrywide World Bank Living Standards Measurement Survey. It covered approximately 9000 households, drawn from a representative sample of South African households. The fieldwork was undertaken during the nine months leading up to the country's first democratic elections at the end of April 1994. The purpose of the survey was to collect statistical information about the conditions under which South Africans live in order to provide policymakers with the data necessary for planning strategies. This data would aid the implementation of goals such as those outlined in the Government of National Unity's Reconstruction and Development Programme.

Geographic coverage

National

Analysis unit

Households

Universe

All Household members. Individuals in hospitals, old age homes, hotels and hostels of educational institutions were not included in the sample. Migrant labour hostels were included. In addition to those that turned up in the selected ESDs, a sample of three hostels was chosen from a national list provided by the Human Sciences Research Council and within each of these hostels a representative sample was drawn on a similar basis as described above for the households in ESDs.

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) SAMPLING DESIGN

Sample size is 9,000 households. The sample design adopted for the study was a two-stage self-weighting design in which the first stage units were Census Enumerator Subdistricts (ESDs, or their equivalent) and the second stage were households. The advantage of using such a design is that it provides a representative sample that need not be based on accurate census population distribution in the case of South Africa, the sample will automatically include many poor people, without the need to go beyond this and oversample the poor. Proportionate sampling as in such a self-weighting sample design offers the simplest possible data files for further analysis, as weights do not have to be added. However, in the end this advantage could not be retained, and weights had to be added.

(b) SAMPLE FRAME

The sampling frame was drawn up on the basis of small, clearly demarcated area units, each with a population estimate. The nature of the self-weighting procedure adopted ensured that this population estimate was not important for determining the final sample, however. For most of the country, census ESDs were used. Where some ESDs comprised relatively large populations as for instance in some black townships such as Soweto, aerial photographs were used to divide the areas into blocks of approximately equal population size. In other instances, particularly in some of the former homelands, the area units were not ESDs but villages or village groups. In the sample design chosen, the area stage units (generally ESDs) were selected with probability proportional to size, based on the census population. Systematic sampling was used throughout that is, sampling at fixed interval in a list of ESDs, starting at a randomly selected starting point. Given that sampling was self-weighting, the impact of stratification was expected to be modest. The main objective was to ensure that the racial and geographic breakdown approximated the national population distribution. This was done by listing the area stage units (ESDs) by statistical region and then within the statistical region by urban or rural. Within these sub-statistical regions, the ESDs were then listed in order of percentage African. The sampling interval for the selection of the ESDs was obtained by dividing the 1991 census population of 38,120,853 by the 300 clusters to be selected. This yielded 105,800. Starting at a randomly selected point, every 105,800th person down the cluster list was selected. This ensured both geographic and racial diversity (ESDs were ordered by statistical sub-region and proportion of the population African). In three or four instances, the ESD chosen was judged inaccessible and replaced with a similar one. In the second sampling stage the unit of analysis was the household. In each selected ESD a listing or enumeration of households was carried out by means of a field operation. From the households listed in an ESD a sample of households was selected by systematic sampling. Even though the ultimate enumeration unit was the household, in most cases "stands" were used as enumeration units. However, when a stand was chosen as the enumeration unit all households on that stand had to be interviewed.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

All the questionnaires were checked when received. Where information was incomplete or appeared contradictory, the questionnaire was sent back to the relevant survey organization. As soon as the data was available, it was captured using local development platform ADE. This was completed in February 1994. Following this, a series of exploratory programs were written to highlight inconsistencies and outlier. For example, all person level files were linked together to ensure that the same person code reported in different sections of the questionnaire corresponded to the same person. The error reports from these programs were compared to the questionnaires and the necessary alterations made. This was a lengthy process, as several files were checked more than once, and completed at the beginning of August 1994. In some cases, questionnaires would contain missing values, or comments that the respondent did not know, or refused to answer a question.

These responses are coded in the data files with the following values: VALUE MEANING -1 : The data was not available on the questionnaire or form -2 : The field is not applicable -3 : Respondent refused to answer -4 : Respondent did not know answer to question

Data appraisal

The data collected in clusters 217 and 218 should be viewed as highly unreliable and therefore removed from the data set. The data currently available on the web site has been revised to remove the data from these clusters. Researchers who have downloaded the data in the past should revise their data sets. For information on the data in those clusters, contact SALDRU http://www.saldru.uct.ac.za/.

It is important to understand metapopulation dynamics and underlying demographic processes in heterogeneous landscapes. Traditionally demographic parameters are estimated using capture-recapture data that can be difficult to collect. Spatially explicit dynamic N-mixture models allow inference for demographic parameters, including dispersal, using count data of unmarked animals, but these models have only been shown effective under constant demographic parameters and dispersal between adjacent local populations.

In this study I aimed to compensate the weakness of spatially explicit dynamic N-mixtures and multistate capture-recapture models by jointly analyzing count and capture-recapture data. This spatially explicit integrated population model allows for spatiotemporal variation of demographic parameters in relation to environmental and density covariates and dispersal between any local populations. I conducted simulations to evaluate this model (1) for species with distinct life his...

Abstract

The Côte d'Ivoire Living Standards Survey (LSS) was the first LSMS Survey to have field tested the methodology and questionnaire developed by LSMS. It consists of three complementary surveys: the household survey, the community survey and the price survey. The household survey collected detailed information on expenditures, income, employment, assets, basic needs and other socio-economic characteristics of the households. The Community Survey collected information on economic and demographic characteristics of the rural communities to which each cluster of households belonged. This was designed to enable the linkage of community level with household level data. The price survey component of the CILSS collected data on prices at the nearest market to each cluster of households, so that regional price indices could be constructed for the household survey. The Côte d'Ivoire Living Standards Survey (LSS) was undertaken over a period of four years, 1985-88, by the Direction de la Statistique in Côte d'Ivoire, with financial and technical support from the World Bank during the first two years of the survey. It was the first year-round household survey to have been undertaken by the Ivorian Direction de la Statistique. The sample size each year was 1600 households and the sample design was a rotating panel. That is, half of the households were revisited the following year, while the other half were replaced with new households. The survey thus produced four cross-sectional data sets as well as three overlapping panels of 800 households each (1985-86, 1986-87, 1987-88).

Geographic coverage

National

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) SAMPLE DESIGN The principal objective of the sample selection process for the LSS Household Survey was to obtain a nationally representative cross-section of African households, some of which could be interviewed in successive years as panel households. A two-stage sampling procedure was used. In the first stage, 100 Primary Sampling Units (PSUs) were selected across the country from a list of all PSUs available in the sampling frame. At the second stage, a cluster of 16 households was selected within each PSU. This led to a sample size of 1600 households a year, in 100 cluster s of 16 households each. Half of the households were replaced each year while the other half (the panel households in 1986, 1987 and 1988) were interviewed a second time. It is important to note that there was a change in the sampling procedures (the sampling frame, PSU selection process and listing procedures), used to select half of the clusters/households interviewed in 1987 (the other half were panel households retained from 1986), and all of the clusters/households interviewed in 1988. Households selected on the basis of the first set of sampling procedures will henceforth be referred to as Block 1 data while households based on the second set of sampling procedures will be referred to as Block 2 data.

(b) SAMPLE FRAME 1. Sampling Procedures for Block 1 Data The Sampling Frame. The sampling frame for the 1985, 1986, and half of the 1987 samples (except for Abidjan and Bouaké) was a list of localities constructed on the basis of the 1975 Census, updated to 1983 by the demographers of the Direction de la Statistique and based on a total population estimated at 9.4 million in 1983.The Block 1 frame for Abidjan and Bouaké was based on data from a 1979-80 electoral census of these two cities. The electoral census had produced detailed maps of the two cities that divided each sector of the city into smaller sub-sectors (îlots). Sub-sectors with similar types of housing were grouped together by statisticians in the Direction de la Statistique to form PSUs. From a list of all PSUs in each city, along with each PSU's population size, the required number of PSUs were selected using a systematic sampling procedure. The step size was equal to the city's population divided by the number of PSUs required in each city. One problem identified in the selection process for Abidjan arose from the fact that one sector of the city (Yopougon) which had been relatively small in 1980 at the time of the electoral census, had since become the largest agglomeration in Côte d'Ivoire. This problem was presumably unavoidable since accurate population data for Yopougon was not available at the time of the PSU selection process.

Selection of PSUs. Geographic stratification was not explicitly needed because the systematic sampling procedure that was used to select the PSUs ensured that the sample was balanced with respect to region and by site type, within each region. The main geographical regions defined were: East Forest, West Forest, and Savannah. Site types varied as follows: large cities, towns, large and small villages, surrounding towns, village centers, and villages attached to them. The 100 PSUs were selected, with probabilities proportional to the size of their population, from a list of PSUs sorted by region and within each region, by site type. Selection of households within each PSU. A pre-survey was conducted in June-July of 1984, to establish the second-stage sampling frame, i.e. a list of households for each PSU from which 16 households could be selected. The same listing exercise was to be used for both the 1985 and 1986 surveys, in order to avoid having to conduct another costly pre-survey in the second year. Thus, the 1984 pre-survey had to provide enough households so as to be able to select two clusters of households in each PSU and to allow for replacement households in the event that some in the sample could not be contacted or refused to participate. A listing of 64 households in each PSU met this requirement. In PSUs with 64 households or fewer, every household was listed. In selecting the households, the "step" used was equal to the estimated number of households in the PSU divided by 64. For example, if the PSU had an estimated 640 households, then every tenth household was included in the listing, counted from a random starting point in the PSU. For operational reasons, the maximum step allowable was a step of 30. In practice, it appears that enumerators used doors, instead of housing structures, in counting the step. Al though enumerators were supposed to start the listing process from a random point in the PSU, in rural areas and small towns, reportedly, the lister started from the center of the PSU.

1. Sampling Procedures for Block 2 Data

The Sampling Frame. The sampling frame for Block 2 data was established from a list of places from the results of the Census of inhabited sites (RSH) performed in preparation for the 1988 Population Census. Selection of PSUs. The PSUs were selected with probability proportional to size. However, in order to save what might have been exorbitant costs of listing every household in each selected PSU in a pre-survey, the Direction de la Statistique made a decision to enumerate a smaller unit within each PSU. The area within each PSU was divided into smaller blocks called `îlots'. Households were then selected from a randomly chosen îlot within each PSU. The sample îlot was selected with equal probability within each PSU, not on the basis of probability proportional to size. (These îlots are reportedly relatively small compared with the size of PSUs selected for the Block 1 frame, but no further information is available about their geographical position within the PSUs.) Selection of households within each PSU. All households in each îlot selected for the Block 2 sample were listed. Sixteen households were then randomly chosen from the list of households for each îlot.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

The Household Questionnaire was almost entirely pre-coded, thus reducing errors involved in the coding process. Also, the decentralized data entry system allowed for immediate follow-up on inconsistencies that were detected by the data entry program. Household and personal identification codes were recorded in each section, facilitating merging data across sections

Sampling error estimates

(a) ACCURACY The general consensus is that the quality of the LSS household data is very good. An informal review of data quality conducted by Ainsworth and Mehra (1988) assessed the 1985 and 1986 LSS data in terms of their accuracy, completeness, and internal consistency. The LSS household data were found to score high marks on each of these three counts. One measure of data quality is the extent to which individuals in question respond for themselves during the interview, rather than having proxy responses provided for them by other household members. The investigation of CILSS household survey data for 1985 and 1986 showed that 93 percent of women responded for themselves to the fertility section and that 79 to 80 percent of all adult household members responded for themselves to the employment module. The percent of children responding for themselves to the employment module was far less, 43 to 45 percent. Nevertheless, these rates were found to be higher than for the Peru Living Standards Survey (29 percent).

(b) COMPLETENESS

Investigation of several variables and modules in the LSS (sex, age, parental characteristics, schooling, health, employment, migration, fertility, farming and family business), found that missing data in the household survey are rare. Rates for missing data were found to be close to 0 (0.01 to 0.05 percent) in many cases, but in any case, no higher than 0.76 percent.

Container Home Design Software Market Outlook

The global market size for Container Home Design Software is projected to grow significantly, from USD 1.2 billion in 2023 to approximately USD 3.8 billion by 2032. This impressive growth represents a compound annual growth rate (CAGR) of 13.2% over the forecast period. Key factors driving this growth include the rising trend of sustainable living, increasing urbanization, and the growing demand for customizable and affordable housing solutions.

One of the primary growth drivers for the Container Home Design Software market is the increasing inclination towards eco-friendly and sustainable living. As awareness about environmental conservation rises, more individuals and organizations are opting for container homes, which are renowned for their low carbon footprint. These homes utilize repurposed shipping containers, making them a sustainable alternative to traditional housing. The demand for specialized software that can design these unique homes is therefore on the rise, providing a significant boost to this market.

Another significant factor contributing to the market's growth is the surge in urbanization. As more people migrate to urban areas, the demand for affordable and efficient housing solutions is skyrocketing. Container homes offer a viable solution to the housing crisis, particularly in densely populated urban regions. The need for sophisticated software that can aid architects, designers, and builders in creating functional and aesthetically pleasing container homes is thus becoming increasingly critical, driving market expansion.

Moreover, the customization capabilities offered by container homes are fueling demand for design software. Homeowners and businesses alike are increasingly seeking personalized living and working spaces that reflect their unique tastes and functional requirements. Container Home Design Software enables users to visualize and customize their spaces effectively, thereby enhancing their appeal. This trend is expected to continue, further propelling the market's growth.

Regionally, North America is expected to remain a dominant player in the Container Home Design Software market, owing to the high adoption rate of innovative housing solutions and the presence of key market players. However, significant growth is also anticipated in the Asia Pacific region, driven by rapid urbanization, population growth, and increasing awareness of sustainable living practices. Europe is also expected to see considerable market growth due to the region's strong emphasis on sustainability and eco-friendly practices.

Component Analysis

The Container Home Design Software market is segmented by component into Software and Services. The software segment encompasses various types of design and visualization tools that help in creating detailed plans and 3D models of container homes. As the backbone of the market, this segment is expected to witness substantial growth, largely driven by advancements in technology that make these tools more sophisticated and user-friendly. Innovative features such as virtual reality (VR) and augmented reality (AR) integrations are making these software solutions more appealing to users, thereby driving market expansion.

Services, on the other hand, include consulting, customization, training, and support services. These services are crucial for ensuring that users can maximize the potential of their software solutions. With the increasing adoption of container homes, there is a rising demand for professional services that can provide expert advice and support during the design and construction phases. This segment is also expected to grow steadily as more users seek to leverage professional expertise to create optimized and compliant container home designs.

The integration of artificial intelligence (AI) into Container Home Design Software is another significant trend within the software segment. AI-powered tools can provide valuable insights and recommendations based on design principles, material usage, and space optimization. This not only enhances the user experience but also ensures that the designs are both functional and aesthetically pleasing. The growing focus on AI integration is expected to further drive the software segment's growth over the forecast period.

Moreover, cloud-based software solutions are gaining traction due to their flexibility and scalability. Unlike on-premises software, cloud solutions offer the advantage of remote access and collaboration, m

1. Since the initial development of the robust design, this capture‐recapture model structure has been modified to estimate temporary emigration, and expanded to include auxiliary information such as band recovery and live resight data using maximum likelihood approaches. These developments have allowed investigators to separately assess individual and group effects on true survival, site fidelity, and temporary emigration. Additionally, recent advances in the BUGS language have allowed researchers to develop increasingly complex, user‐specified models in Bayesian frameworks. 2. The robust design has rarely been implemented in the BUGS language, and previous attempts to parameterize the robust design in BUGS exhibited strong bias in estimates of temporary emigration rates. Given the limitations of current parameterizations of the robust design in Bayesian frameworks, and our research objectives, we have developed a parameterization of the robust design in the BUGS language that produces unbiased estimates of all model parameters. 3. We use this novel model structure to examine lifetime carry‐over effects of environmental conditions during early life on annual breeding probabilities of Pacific black brent (Branta bernicla nigricans) breeding on the Yukon‐Kuskokwim River Delta in western Alaska. We found that individuals that were more structurally developed as goslings bred at increased rates as adults (β = 0.14, f = 0.94), with no effect on adult survival (β = 0.01, f = 0.62). Additionally, we provide evidence for long‐term declines in apparent survival of breeding adult females at the population level (β = ‐0.01, f = 0.90). 4. This novel model structure can be easily expanded (Gibson et al., in review), and has important implications for population modelling at broad scales, where we apply it to a declining population of Pacific black brent. Given long‐term declines in gosling growth on the Yukon‐Kuskokwim Delta, we predict future declines in population trajectories as a result of lifetime carry‐over effects of environmental conditions during growth on adult fecundity, and long‐term declines in adult survival.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample size is 2,155 households

LSMS Sample Design

The LSMS design consisted of an equal-probability sample of housing units (HUs) within each of 16 explicit strata. These were selected in two stages. The first was to select - within strata - an agreed number of enumeration units (EAs) with probability proportional to number of HUs in the EA (according to 2001 Census data). The second stage was to select 8 HUs systematically from each selected EA. (Substitutes were used where necessary to ensure that 8 households were successfully interviewed in each EA, but I shall ignore that for current purposes.) Although probabilities within strata were (approximately) equal, probabilities varied greatly between the strata. Notably, the mountain region was heavily over-represented and the Central Rural region was under-represented in the sample.

Panel Survey Sample Design

The LSMS was so-designed, partly to enable separate analysis by broad strata (e.g. separate estimates for the mountain region). Regional analysis is much less important for the panel. The sample size will in any case be considerably smaller, so some regional sample sizes would inevitably be too small to permit robust estimation. The prime objective for the panel is to enable national-level estimates with the highest possible precision. To achieve this, the sample was structured in a way that minimises the overall variation in households' selection probabilities. In other words, the sample distribution over strata matched as closely as possible the population distribution.

• Statistical precision for national estimates is greatly improved, compared with the LSMS design. Design effects (under the assumption of equal stratum population variances) can be expected to be around 1.02 for the panel sample, compared with 1.28 for the LSMS sample. In other words, a panel sample of 1500 interviews would give precision equivalent to an equal-probability sample of 1172 households if it followed the LSMS distribution of households over strata, but gives precision equivalent to an equal-probability sample of 1471 households with the panel design. Precision is also further improved by retaining all 450 EAs in the sample, thus reducing the design effect due to the clustering (as mean responding sample size per cluster will reduce from 8.0 to around 3.3);
• The design was simple to implement as, within each stratum, the number of households to select was the same in each EA. (Note that sampling fractions have been expressed as a fraction of 8 for this reason);
• The sample size was set so as to make it likely that the number of achieved interviews would be between 1600 and 1700. Substitute households were not be used in the case of non-response.
  Rather, all attempts were made to maximise the response rate. This enables the use of potentially powerful non-response weighting using the LSMS data.

Panel design

The Albanian panel survey sample was selected from households interviewed on the 2002 LSMS conducted by INSTAT with support from the World Bank. The sample size for the panel took approximately half the LSMS households and has re-interviewed these households annually in each of 2003 and 2004. The LSMS data collected in 2002 therefore constitute 'Wave 1' of the panel survey and giving three waves of panel data altogether. The fieldwork for Wave 3 was carried out in the spring of 2004.

The sample selected from the LSMS for the panel was designed to provide a nationally representative sample of households and individuals within Albania (see Appendix B for full description of the sample design and selection procedure). This differs from the LSMS where the sample was designed to be representative of each strata which broadly represented the main regions in Albania so that regional level statistics could be generated (Mountain, Central, Coastal, Tirana).

The panel also has no over-sampling as in the LSMS. This design was adopted as the smaller sample size for the panel would have made it more difficult to produce regionally representative samples and increased sampling error while over-sampling can introduce additional complications for analysis in the context of a panel. The panel data can be used for analysis broken down by strata to assess any differences between areas but should not be used to produce cross-sectional estimates at the regional level. The relatively small sample size for the panel must always be considered as cell sizes which are small have higher levels of error and can produce estimates which are less reliable. Panel surveys have a number of elements of which data users need to be aware when carrying out their analysis. The main features of the panel design are as follows: - All members of Wave 1 households were designated as original sample members (OSMs) including children aged under 15 years. - New members living with an OSM become eligible for inclusion in the sample - All sample members are followed as they move address and any new members found to be living in their household included - Sample members moving out of Albania are considered to be out of scope for that year of the survey (note that they remain potentially eligible for interview and it is possible they may return to a sample household at a future wave) - From Wave 2, only household members aged 15 years and over are eligible for interview. As children turn 15, they become eligible for interview (This differs from the LSMS where the individual questionnaire collected some data on children under 15 from the mother or main carer).

The panel is essentially an individual level survey as individuals are followed over time regardless of the household they are living in at a given interview point. This is the key element of the panel design. Households change in composition over time as members move in and out, children are born and others die. New households are formed as people marry or children leave the parental home and households can disappear if all members die or all members move in different directions. The fact that households do not remain constant over time means that it is only possible to follow individuals over time, observing them in their household context at each interview point.

It should also be noted that a 'household' is not equivalent to a current address. A household may move to a new address but maintain the same composition. Similarly, an individual sample member may move between several addresses during the life of the survey. In this design, there is no substitution or recruitment of new households moving into addresses vacated by sample members.

Mode of data collection

Face-to-face [f2f]

Research instrument

Panel questionnaire content

The data for Wave 1 of the panel survey are the LSMS data so contains all the modules carried for the LSMS. To minimise respondent burden and help maintain response rates in the panel survey it was necessary to reduce the length and complexity of the LSMS questionnaire. However, it was also important to maintain comparability in question wording and response categories wherever possible as only variables which are comparable over time can be used for longitudinal analysis. The Wave 2 questionnaire is therefore a reduced version of the LSMS questionnaire with some additional elements that were required for the panel e.g. collecting details of people moving into and out of the household, and some new elements that had not been included on the LSMS. A cross-wave list of variables for Waves 1 and 2 shows which variables have been carried at both waves, which were carried at Wave 1 only and which at Wave 2 only (see ‘Variable Reconciliation LSMS_PANEL_final). The most notable changes were that the LSMS detailed consumption module was not collected at Wave 2 and the agriculture module was a reduced form compared to the LSMS.

The Wave 2 individual questionnaire contains some routing depending on whether or not the person is an original sample member interviewed on the LSMS or a new person who had joined the household since Wave 1. This is because some information only needs to be collected once e.g. place of birth and other information only needs to be updated on an annual basis. For example all qualifications were collected on the LSMS so for original members we only need to know if they have gained any new qualifications in the past year but for new members we need to ask about all qualifications. Users of the data need to be aware of this routing and in some cases may need to get information from an earlier wave if it was not collected at the current wave. Users are recommended to use the data in conjunction with the questionnaires so they are aware of the routing for different sample members.

Knowledge of population demographics is important for species management but can be challenging in low-density, wide-ranging species. Population monitoring of the endangered Sonoran pronghorn (Antilocapra americana sonoriensis) is critical for assessing the success of recovery efforts, and noninvasive DNA sampling (NDS) could be more cost-effective and less intrusive than traditional methods. We evaluated faecal pellet deposition rates and faecal DNA degradation rates to maximize sampling efficiency for DNA-based mark–recapture analyses. Deposition data were collected at five watering holes using sampling intervals of 1–7 days and averaged one pellet pile per pronghorn per day. To evaluate nuclear DNA (nDNA) degradation, 20 faecal samples were exposed to local environmental conditions and sampled at eight time points from one to 124 days. Average amplification success rates for six nDNA microsatellite loci were 81% for samples on day one, 63% by day seven, 2% by day 14 and 0% by day 60. We evaluated the efficiency of different sampling intervals (1–10 days) by estimating the number of successful samples, success rate of individual identification and laboratory costs per successful sample. Cost per successful sample increased and success and efficiency declined as the sampling interval increased. Results indicate NDS of faecal pellets is a feasible method for individual identification, population estimation and demographic monitoring of Sonoran pronghorn. We recommend collecting samples >7 days old and estimate that a sampling interval of 4–7 days in summer conditions (i.e. extreme heat and exposure to UV light) will achieve desired sample sizes for mark–recapture analysis while also maximizing efficiency. SW Supp info datasetInput file and legendFalse allele SAS codeSAS code for calculating false allelesfa.sasAllelic dropout SAS codeSAS code for calculating Allelic dropoutado.sasPCR sas codeSAS code for PCR successpcr.sas

Abstract

In 1992, Bosnia-Herzegovina, one of the six republics in former Yugoslavia, became an independent nation. A civil war started soon thereafter, lasting until 1995 and causing widespread destruction and losses of lives. Following the Dayton accord, BosniaHerzegovina (BiH) emerged as an independent state comprised of two entities, namely, the Federation of Bosnia-Herzegovina (FBiH) and the Republika Srpska (RS), and the district of Brcko. In addition to the destruction caused to the physical infrastructure, there was considerable social disruption and decline in living standards for a large section of the population. Alongside these events, a period of economic transition to a market economy was occurring. The distributive impacts of this transition, both positive and negative, are unknown. In short, while it is clear that welfare levels have changed, there is very little information on poverty and social indicators on which to base policies and programs. In the post-war process of rebuilding the economic and social base of the country, the government has faced the problems created by having little relevant data at the household level. The three statistical organizations in the country (State Agency for Statistics for BiH -BHAS, the RS Institute of Statistics-RSIS, and the FBiH Institute of Statistics-FIS) have been active in working to improve the data available to policy makers: both at the macro and the household level. One facet of their activities is to design and implement a series of household series. The first of these surveys is the Living Standards Measurement Study survey (LSMS). Later surveys will include the Household Budget Survey (an Income and Expenditure Survey) and a Labour Force Survey. A subset of the LSMS households will be re-interviewed in the two years following the LSMS to create a panel data set.

The three statistical organizations began work on the design of the Living Standards Measurement Study Survey (LSMS) in 1999. The purpose of the survey was to collect data needed for assessing the living standards of the population and for providing the key indicators needed for social and economic policy formulation. The survey was to provide data at the country and the entity level and to allow valid comparisons between entities to be made. The LSMS survey was carried out in the Fall of 2001 by the three statistical organizations with financial and technical support from the Department for International Development of the British Government (DfID), United Nations Development Program (UNDP), the Japanese Government, and the World Bank (WB). The creation of a Master Sample for the survey was supported by the Swedish Government through SIDA, the European Commission, the Department for International Development of the British Government and the World Bank. The overall management of the project was carried out by the Steering Board, comprised of the Directors of the RS and FBiH Statistical Institutes, the Management Board of the State Agency for Statistics and representatives from DfID, UNDP and the WB. The day-to-day project activities were carried out by the Survey Management Team, made up of two professionals from each of the three statistical organizations. The Living Standard Measurement Survey LSMS, in addition to collecting the information necessary to obtain a comprehensive as possible measure of the basic dimensions of household living standards, has three basic objectives, as follows: 1. To provide the public sector, government, the business community, scientific institutions, international donor organizations and social organizations with information on different indicators of the population's living conditions, as well as on available resources for satisfying basic needs. 2. To provide information for the evaluation of the results of different forms of government policy and programs developed with the aim to improve the population's living standard. The survey will enable the analysis of the relations between and among different aspects of living standards (housing, consumption, education, health, labour) at a given time, as well as within a household. 3. To provide key contributions for development of government's Poverty Reduction Strategy Paper, based on analysed data.

Geographic coverage

National coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) SAMPLE SIZE A total sample of 5,400 households was determined to be adequate for the needs of the survey: with 2,400 in the Republika Srpska and 3,000 in the Federation of BiH. The difficulty was in selecting a probability sample that would be representative of the country's population. The sample design for any survey depends upon the availability of information on the universe of households and individuals in the country. Usually this comes from a census or administrative records. In the case of BiH the most recent census was done in 1991. The data from this census were rendered obsolete due to both the simple passage of time but, more importantly, due to the massive population displacements that occurred during the war. At the initial stages of this project it was decided that a master sample should be constructed. Experts from Statistics Sweden developed the plan for the master sample and provided the procedures for its construction. From this master sample, the households for the LSMS were selected. Master Sample [This section is based on Peter Lynn's note "LSMS Sample Design and Weighting - Summary". April, 2002. Essex University, commissioned by DfID.] The master sample is based on a selection of municipalities and a full enumeration of the selected municipalities. Optimally, one would prefer smaller units (geographic or administrative) than municipalities. However, while it was considered that the population estimates of municipalities were reasonably accurate, this was not the case for smaller geographic or administrative areas. To avoid the error involved in sampling smaller areas with very uncertain population estimates, municipalities were used as the base unit for the master sample. The Statistics Sweden team proposed two options based on this same method, with the only difference being in the number of municipalities included and enumerated.

(b) SAMPLE DESIGN For reasons of funding, the smaller option proposed by the team was used, or Option B. Stratification of Municipalities The first step in creating the Master Sample was to group the 146 municipalities in the country into three strata- Urban, Rural and Mixed - within each of the two entities. Urban municipalities are those where 65 percent or more of the households are considered to be urban, and rural municipalities are those where the proportion of urban households is below 35 percent. The remaining municipalities were classified as Mixed (Urban and Rural) Municipalities. Brcko was excluded from the sampling frame. Urban, Rural and Mixed Municipalities: It is worth noting that the urban-rural definitions used in BiH are unusual with such large administrative units as municipalities classified as if they were completely homogeneous. Their classification into urban, rural, mixed comes from the 1991 Census which used the predominant type of income of households in the municipality to define the municipality. This definition is imperfect in two ways. First, the distribution of income sources may have changed dramatically from the pre-war times: populations have shifted, large industries have closed, and much agricultural land remains unusable due to the presence of land mines. Second, the definition is not comparable to other countries' where villages, towns and cities are classified by population size into rural or urban or by types of services and infrastructure available. Clearly, the types of communities within a municipality vary substantially in terms of both population and infrastructure. However, these imperfections are not detrimental to the sample design (the urban/rural definition may not be very useful for analysis purposes, but that is a separate issue).

Mode of data collection

Face-to-face [f2f]

Cleaning operations

(a) DATA ENTRY

An integrated approach to data entry and fieldwork was adopted in Bosnia and Herzegovina. Data entry proceeded side by side with data gathering to ensure verification and correction in the field. Data entry stations were located in the regional offices of the entity institutes and were equipped with computers, modem and a dedicated telephone line. The completed questionnaires were delivered to these stations each day for data entry. Twenty data entry operators (10 from Federation and 10 from RS) were trained in two training sessions held for a week each in Sarajevo and Banja Luka. The trainers were the staff of the two entity institutes who had undergone training in the CSPro software earlier and had participated in the workshops of the Pilot survey. Prior to the training, laptop computers were provided to the entity institutes, and the CSPro software was installed in them. The training for the data entry operators covered the following elements:

• Introduction to the LSMS Survey questionnaire; Introduction to the personal computers/ lap top computers; Copying data on diskette and printing of output
• The Data entry programme (CSPro). Understanding of the Round 1 data entry screens (Modules 1-10)
• Practice of Round 1 (data entry trainees enter questionnaires completed by interviewer trainees during practice interviews)
• Understanding of Round 2 Data entry screen (Modules 11-13)
• Practice of Round 2 Data entry screens (data entry trainees entered the questionnaires completed by interviewer trainees)
• Control Procedures; Copying

Abstract

Over the past decade, Albania has been undergoing a transition toward a market economy and a more open society. It has faced severe internal and external challenges, such as lack of basic infrastructure, rapid collapse of output and inflation rise after the collapse of the communist regime, turmoil during the 1997 pyramid crisis, and social and economic instability because of the 1999 Kosovo crisis. Despite these shocks, Albanian economy has recovered from a very low income level through a sustained growth during the past few years, even though it remains one of the poorest countries in Europe, with GDP per capita at around 1,300$.

Based on the Living Standard Measurement Study (LSMS) 2002 survey data (wave 1, henceforth), for the first time in Albania INSTAT has computed an absolute poverty line on a nationally representative poverty survey at household level. Based on this welfare measure, one quarter (25.4 percent) of the Albanian population, or close to 790,000 individuals, were defined as poor in 2002. The distribution of poverty is also disproportionately rural, as 68 percent of the poor are in rural areas, against 32 percent in urban areas (as compared to a total urban population well over 40 percent). These estimates are quite sensitive to the choice of the poverty line, as there are a large number of households clustered around the poverty line. Income related poverty is compounded by the severe lack of access to basic infrastructure, education and health services, clean water, etc., and the ability of the Government to address these issues is complicated by high levels of internal and external migration that are not well understood.

The availability of a nationally representative survey is crucial as the paucity of household-level information has been a constraining factor in the design, implementation and evaluation of economic and social programs in Albania. Two recent surveys carried out by the Albanian Institute of Statistics (INSTAT) –the 1998 Living Conditions Survey (LCS) and the 2000 Household Budget Survey (HBS)– drew attention, once again, to the need for accurately measuring household welfare according to well-accepted standards, and for monitoring these trends on a regular basis. This target is well-achieved by drawing information over time on a panel component of LSMS 2002 households, namely the Albanian Panel Survey (APS), conducted in 2003 and 2004.

An increasing attention to the policies aimed at achieving the Millennium Development Goals (MDGs) is paid by the National Parliament of Albania, recently witnessed by the resolution approved in July 2003, where it pushes “[...] the total commitment of both state structures and civil society to achieve the MDGs in Albania by 2015”. The path towards a sustained growth is constantly monitored through the National Reports on Progress toward Achieving the MDGs, which involves a close collaboration of the UN with the national institutions, led by the National Strategy for Social and Economic Development (NSSED) Department of the Ministry of Finance. Also, in the process leading to the Poverty Reduction Strategy Paper (PRSP; also known in Albania as Growth and Poverty Reduction Strategy, GPRS), the Government of Albania reinforced its commitment to strengthening its own capacity to collect and analyze on a regular basis information it needs to inform policy-makers.

In its first phase (2001-2006), this monitoring system will include the following data collection instruments: (i) Population and Housing Census; (ii) Living Standards Measurement Surveys every 3 years, and (iii) annual panel surveys. The focus during this first phase of the monitoring system is on a periodic LSMS (in 2002 and 2005), followed by panel surveys on a sub-sample of LSMS households (APS 2003, 2004 and 2006), drawing heavily on the 2001 census information. Here our target is to illustrate the main characteristics of the APS 2004 data with reference to the LSMS.

The survey work was undertaken by the Living Standards Unit of INSTAT, with the technical assistance of the World Bank.

Geographic coverage

National coverage. Domains: Tirana, other urban,rura

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

Panel sample, with LSMS 2002 and 2004

The APS 2004 collects information on 1,797 valid observations at household level and 7,476 at individual level. The sample of the second and third waves of the panel (APS) has been selected from the LSMS 2002 in order to be representative of Albanian households and individuals at national level. The LSMS 2002 differs from the APS 2003 and 2004 in that the former is designed to be representative at regional level (Mountain, Central, Coastal and Tirana) as well as for urban and rural domains, while the latter are for last domains only (urban and rural)

LSMS 2002 sample design

The LSMS is based on a probability sample of housing units (HUs) within the 16 strata of the sampling frame. It is divided in three regions: Coastal, Central, and Mountain Area. In addition, urban areas of Tirana are also considered as a separate region/stratum. The three regions are further stratified in major cities (the most important cities in the region), other urban (other cities in the region), and rural. The city of Tirana and its suburbs have been implicitly stratified to improve the efficiency of the sample design. Each stratum has been divided in Enumeration Area (EA), in accordance with the 2001 Census data, and each Primary Sampling Unit (PSU) selected with probabilities proportional to the number of occupied HUs in the EA. Every EA includes occupied and unoccupied HUs. Occupied rather than total units have been used because of the large amount of empty dwellings registered in the Census data.

The Housing Unit, defined as the space occupied by one household, is taken as sampling unit because is more permanent and easy to identify compared to the household. 10 EAs for each major city (75 for Tirana) and 65 EAs for each rural region -with the exception of the mountain area which is over-represented (75 EAs)- are selected. 8 households, plus 4 eventual substitutes, have been systematically selected in each EAs. As the LSMS consists of 450 EAs, total sample size is 3,600 households.

The sample is not self-weighted, hence to obtain correct estimates data need to be weighted. The weights, at household level, are included in the dataset ("weights" file). When working at individual level, household weights must be multiplied by household size.

APS 2003-2004 sample design

The panel component selected from the LSMS is designed to provide a nationally representative sample of households and individuals within Albania. It consists of roughly half of the households in the 2002 LSMS, interviewed both in 2003 and 2004. Contrarily to what done for the LSMS, no over-sampling in the Mountain Area has been performed for the panel survey.

The sample is designed to minimize the variability in households' selection probabilities. It insures national representativeness by matching the sample distribution across strata with the population distribution drawn from 2001 Census data. In Table 3 the ex-ante sampling scheme of the 2003-2004 APS is shown.

Compared to the LSMS design, statistical precision has improved. Under equal stratum population variances hypothesis, sample design effects are expected to be around 1.02, compared to the 1.28 of the LSMS sample. Moreover, further precision is obtained by keeping all 450 EAs of LSMS in the panel sample, thus reducing the eventual bias due to clustering because of new design.

Finally, the panel survey has a number of peculiar features that should be considered when using the data. The sample is designed to focus on individuals, who have been also traced when moving from the original household to a new one. This possibility represents the only way a household can enter the panel sample if it has not been already interviewed in the wave 1 (or in wave 2 for the APS 2004). If an original survey member (OSM) moves to a new household, his/her old and new household -and their members- are both included in the panel sample. Though a moved OSM will be present in the roster of both sampled households, he/she is a valid member only in the new one. In the old household he/she is taken into account as "moved away", hence not a valid member. This might generate some confusion.

Three modalities exist to classify an individual in the third wave. First, when he/she is an OSM, that is a respondent interviewed both in wave 1 and 2. Second, when he is a rejoiner from 2002, that is an OSM not interviewed in 2003 (i.e. because temporarily absent) who returns in 2004. Third, when he/she is a new member, whenever he/she is a newborn of an original household, a member joined by an OSM or a person who co-resides with an original survey household. So the APS is an indefinite life panel study, without replacement by drawing new sample units.

From wave 2, only individuals aged 15 years and over are considered valid members, hence eligible for the interview. Individuals moved out of Albania are not accounted as valid for this survey year, though they are still eligible for future waves.

Mode of data collection

Face-to-face [f2f]

Research instrument

A single questionnaire on households has been used to collect information in the APS 2004. Contrary to the LSMS 2002 survey (see Basic Information Document, 2003), both in 2003 and 2004 the Diary for Household Consumption (the “booklet”), the Community questionnaire and the Price questionnaire were not repeated. The target is to collect a similar set of information (only data comparable across time is

Abstract

The Bosnia and Herzegovina MICS4 2011–2012 was conducted using a representative sample in order to provide estimates for a large number of indicators on the situation of children, women and men as well as household living conditions at the level of BiH, the Federation of BiH (FBiH), the Republic of Srpska (RS) and for urban and rural areas. The survey is based on a representative sample of 6,838 households in BiH (4,107 in FBiH, 2,408 in RS and 323 in Brcko District (BD) of BiH) with an overall response rate of 91 per cent (in total, 5,778 households were interviewed). The results reflect data collected during the period November 2011 and March 2012.

The survey was undertaken as part of the fourth global round of the MICS programme and implemented by the Federal Ministry of Health (FMH) and the Ministry of Health and Social Welfare of the Republic of Srpska (MHSW RS) in cooperation with the Institute for Public Health of the FBiH (IPH FBiH) and the Agency for Statistics of BiH (BHAS). Financial and technical support was provided by UNICEF with additional financial support provided by UN Women for preparing the master sample frame, as well as by UNFPA and UNHCR.

The primary aim of MICS is to provide indicators for monitoring the level of progress towards the Millennium Development Goals, the Plan of Action for A World Fit for Children as well as other international and national commitments undertaken by BiH. The survey findings are presented from the equity perspective by indicating disparities in accordance with administrative units, sex, area type, the level of education of the respondent or head of the household, household wealth and other characteristics.

Geographic coverage

National

Analysis unit

• individuals
• households

Universe

The survey covered all de jure household members (usual residents), all women aged between 15-49 years, all children under 5 living in the household, and all men aged 15-49 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary objective of the sample design for the BiH Multiple Indicator Cluster Survey was to produce statistically reliable estimates of most indicators at the BiH, FBiH and RS level and for urban and rural areas. A two stage stratified sampling approach was used for the selection of the cluster sample.

The official population estimate for BiH is 3.8 million inhabitants living in about one million households. However, some sampling frame exercises conducted due to the lack of an official Census since 1991 estimate this number at approximately 3.3 million.

As stated previously, BiH is composed of three administrative units: two entities, the FBiH and RS and a third administrative unit, BD. The FBiH covers approximately 51 per cent of the territory of BiH and 62 per cent of the population. RS covers approximately 49 per cent of the territory and about 36 per cent of the population and BD covers less than 1 per cent of the territory and approximately 2 per cent of the population.

The target sample size was 6,800 households, which was determined based on lessons learned through the previous round of MICS as well as by budgetary limitations. The standard sample design used in most of the countries participating in the MICS programme needed to be adapted for BiH due to the low birth rate; therefore, it was necessary to target (oversample) households with children under 5 and members aged 5-24.

Accordingly, the sample was stratified by households with children under 5 (type 1), households with children aged 5-24 (type 2) and all other households (type 3). In addition, the size of the three strata could not jeopardise the indicator estimates for the other target populations, such as the indicators that referred to fertile women.

As the sample size was defined as 6,800 households it was necessary to calculate the size of stratum 1 and stratum 2. The size of stratum 3 was obtained as the difference between the total sample size and the sum of the size of strata 1 and 2.

The sampling procedures are more fully described in "Bosnia and Herzegovina Multiple Indicator Cluster Survey 2011 - Final Report" pp.150-153.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the Generic MICS were structured questionnaires based on the MICS4 model questionnaire with some modifications and additions. Household questionnaires were administered in each household, which collected various information on household members including sex, age and relationship. The household questionnaire includes household listing form, education, water and sanitation, household characteristics, child discipline and hand washing.

In addition to a household questionnaire, questionnaires were administered in each household for women age 15-49, children under age five and men age 15-49. For children, the questionnaire was administered to the mother or primary caretaker of the child.

The women's questionnaire includes woman's background, access to mass media and ICT, child mortality, desire for last birth, maternal and newborn health, illness symptoms, contraception, unmet need, attitudes toward domestic violence, marriage/union, sexual behavior, HIV/AIDS, tobacco and alcohol use, life satisfaction and health care.

The children's questionnaire includes child's age, early childhood development, breastfeeding, care of illness, immunisation and anthropometry.

The men's questionnaire includes man's background, access to mass media and ICT, attitudes toward domestic violence, marriage/union, sexual behavior, HIV/AIDS, tobacco and alcohol use, life satisfaction and health care.

The questionnaires were based on the MICS4 model questionnaire.19 From the MICS4 model English version, the questionnaires were translated into local languages used in BiH. The questionnaires were pre-tested in the FBiH and RS in the City of Banja Luka and in Sarajevo Canton during September 2011. The pre-test plan provided for interviews to be conducted in 48 households in the FBiH and 24 households in RS. The households, of which 50 per cent were urban and rural households respectively, were randomly selected from the Master Sample template. Based on the results of the pre-test, modifications were made to the wording and translation of the questionnaires.

Cleaning operations

Data entry and processing was conducted separately for the FBiH, RS and BD. The data was entered using CSPro software. Data was entered into a total of 11 microcomputers by 8 data entry operators in the FBiH and 6 persons in RS; the process was supervised by data entry supervisors.

Data entry commenced in the FBiH four weeks after the start of data collection (December 2011) and was concluded in April 2012. In RS data entry for the RS and BD started one week after data collection began (December 2011) and was concluded in May 2012.

The data was analysed using the SPSS (Statistical Package for Social Sciences) software programme (Version 18) and the model syntax and tabulation plans developed by UNICEF were also used for this purpose. In order to ensure quality control all of the questionnaires were double entered and internal consistency checks were performed. Procedures and standard programmes developed under the global MICS4 programme and adapted to the BiH questionnaires were used throughout.

Response rate

Of the 6,838 households in the sample 6,334 were found to be occupied; of these, 5,778 households were successfully interviewed for a household response rate of 91 percent. In the interviewed households 4,645 women aged 15-49 were identified and 4,446 successfully interviewed, yielding a response rate of 96 percent. In addition, 4,718 men aged 15-49 were listed in the household questionnaire as being household members. Questionnaires were completed for 4,353 eligible men, which corresponds to a response rate of 92 percent within the interviewed households. There were 2,332 children under age five listed in the household questionnaire. Questionnaires were completed for 2,297 children, which corresponds to a response rate of 99 percent within the interviewed households. The overall response rate for the women’s, men’s and children’s questionnaires were 87 percent, 84 percent, and 90 percent, respectively.

Sampling error estimates

The sample of respondents selected for the BiH MICS was only one of the samples that could have been selected from the same population, using the same design and size. Each of these samples would have yielded results that differed somewhat from the results of the actual selected sample. Sampling errors are a measure of the variability between the estimates from all possible samples. The extent of variability is not known exactly but can be estimated statistically from the survey data.

The sampling error measures below are presented in this appendix for each of the selected indicators. - Standard error (se): Sampling errors are usually measured in terms of standard errors for particular indicators (means, proportions etc). Standard error is the square root of the variance of the estimate. The Taylor Linearization method was used for the estimation of standard errors. - Coefficient of variation (se/r): is the ratio of the standard error to the value of the indicator and is a measure of the relative sampling error. - Design effect (deff): is the ratio of the actual variance of an indicator, under the sampling method used in the survey, to the variance calculated under the assumption of simple random sampling. The square root of the design effect (deft) is used to show the efficiency of

Living Room Wall Unit Market Outlook

The global living room wall unit market size is projected to reach $XX billion by 2032, expanding from $XX billion in 2023, registering a compound annual growth rate (CAGR) of X.X%. The growth of this market is primarily driven by the increasing demand for multifunctional furniture solutions, the rising trend of home decoration and interior design, and the growing urban population that necessitates optimized living spaces.

One of the chief growth factors for the living room wall unit market is the increasing preference for modular and multifunctional furniture. With urbanization on the rise, living spaces are becoming smaller and more compact, prompting consumers to seek furniture that maximizes functionality while minimizing the use of space. Modular wall units, which can be customized and adjusted according to the available space and user requirements, have therefore seen a significant uptick in demand. Additionally, the growing trend towards minimalism in interior design has further bolstered the popularity of sleek, multifunctional wall units that blend aesthetics with practicality.

Another key driver for market growth is the rising disposable income and improving living standards across the globe. As consumers have more disposable income, there's a greater propensity to invest in home decor and furnishings to enhance their living environments. This trend is particularly notable in developing regions where economic growth is rapid, and middle-class populations are expanding. High-quality, aesthetically pleasing living room wall units, which serve both functional and decorative purposes, are becoming highly desirable among these consumers.

The rapid expansion of e-commerce platforms has also significantly contributed to the growth of the living room wall unit market. Online retailing offers consumers a wide variety of options and the convenience of purchasing from the comfort of their homes. Moreover, the ability to compare prices, read customer reviews, and access detailed product descriptions online helps consumers make informed purchasing decisions. Many furniture manufacturers and retailers are also leveraging digital platforms to expand their reach and cater to a broader audience, further propelling market growth.

Regionally, North America and Europe are expected to hold significant market shares due to high disposable incomes, a preference for premium and designer furniture, and the presence of major market players. However, the Asia-Pacific region is anticipated to witness the fastest growth during the forecast period. The growing middle-class population, rising urbanization, and increasing disposable incomes in countries like China and India are driving demand for modern living room furniture, including wall units. Furthermore, the rapid expansion of the real estate and housing sector in these regions is also a crucial factor contributing to market growth.

Product Type Analysis

The living room wall unit market can be segmented by product type into modular wall units, entertainment centers, floating wall units, built-in wall units, and others. Modular wall units are expected to dominate the market owing to their versatility and ability to adapt to different spaces and user requirements. These units can be configured in various ways, providing ample storage while maintaining a sleek and modern look. The growing trend of personalized and modular furniture is likely to fuel the demand for modular wall units significantly.

Entertainment centers are another crucial segment in the living room wall unit market. These units are designed to house televisions, gaming consoles, and other entertainment systems, making them integral to modern living rooms. The increasing consumer inclination towards home entertainment systems and the rising popularity of large-screen TVs and sound systems have spurred the demand for entertainment centers. Moreover, as more people opt for at-home entertainment over going out, the need for well-designed entertainment centers is expected to rise.

Floating wall units, which are mounted on the wall without touching the floor, offer a modern and minimalist look that appeals to many consumers. These units provide a sense of space and openness, which is particularly beneficial in small living areas. The trend towards contemporary and minimalist interior design has significantly boosted the demand for floating wall units. Their ability to provide adequate storage while maintaining a light and airy feel makes them a popular choice among urban dwellers.

The developing world continues to face substantial obstacles to achieving affordable and dependable electricity access. This issue is especially pertinent for Nigeria, where diesel generators are widely relied upon in urban and rural regions because of an underdeveloped and unreliable national grid. The lack of grid reliability is worsened in Northeastern Nigeria, an area plagued by conflict, extreme poverty, and grid infrastructure deterioration. This study investigates the feasibility of implementing community-scale microgrids in rural areas without grid connection access. It focuses on assessing the technical, economic, and environmental aspects of utilizing these microgrids to deliver inexpensive and dependable electricity to underserved populations to increase energy access. A case study was conducted in Kabuiri, a village with an estimated population of 2,300 residents and an estimated load demand of 610 kWh per day. A hybrid microgrid system was designed and optimized to meet the community’s load demand using HOMER software, sized to produce 610 kWh/day of electricity with a renewable penetration of 99%. The optimal solar PV/battery/generator system had a levelized cost of electricity (LCOE) of $ 0.093 per kWh, a net present cost (NPC) of $266,709, and an annual operating cost of $9,110. The system contributed 1,624 kg CO2 eq/year of global warming potential and 56.81 kg O3 eq/year of smog formation during operation. Sensitivity analysis showed that the system could effectively react to or adapt to substantial increases in diesel prices, requiring only marginal increases in photovoltaic capacity and reduced generator usage to maintain the most cost-efficient operation. Additionally, the system model can be adapted based on the population of the remote community without substantially impacting the LCOE, however, the NPC increases with increase in population size. This research will aid in increasing energy access in remote locations by providing insights to stakeholders and energy access project developers.

Abstract

Living Standards Measurement Study surveys have been developed by the World Bank to collect the information necessary to measure living standards and evaluate government interventions in the areas of poverty alleviation and social services. The Azerbaijan Survey of Living Conditions (ASLC) applies many of the features of LSMS surveys to provide data for the World Bank Poverty Assessment.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Community

Kind of data

Sample survey data [ssd]

Sampling procedure

Design

The methodology that was chosen reflects the purpose of the survey. To balance a desire for a large, representative sample with the expense of a detailed survey instrument, a sample size of 2,016 households was selected. Three separate populations were covered: households in Baku, households outside of Baku and households of Displaced Persons. Within each of those populations, the sample was chosen in such a manner that each household had an equal probability of being selected. At the same time, the logistics of locating the households and conducting the interviews within a specific time frame required that the households be grouped into "work loads" of 12 households each. The size of the workload was determined by the number of interviews that could be carried out in one day by one team of three interviewers and a supervisor.

The Azerbaijan Survey of Living Conditions sample design included 408 households in the eleven raions that make up the city of Baku, 1200 households in the population outside of Baku, and 408 households among the registered Internally Displaced Persons residing throughout the country. This results in an oversampling of the Internally Displaced Persons population and an undersampling of the urban population of Baku. In order to use all data to provide nationally representative estimates, weighting factors must be applied to the data to account for the difference between the population and sample distributions.

Outside of Baku

The most recent data on population came from the 1989 census, the most recent data on number of households was reported in 1994 by the National Statistical Committee. The country is divided into towns, villages of the town type, and villages. Every household is located in one of those three types of population points. A list prepared by the National Statistical Committee contains just over 4,250 of these population points. To choose the sample outside of Baku, Baku was excluded from this list as were all the population points located in raions of the country currently occupied (Agdam, Xankendi, Xodjali, Xodjvendi, Susha, Kubatli, Zangelan, Kelbadjar, Lachin, Fizuli and Djebrali). The remainder of the country included 3453 population points. Information on the number of households was not available for all population points, specifically, "villages of the town type" and cities did not have this information. Average household size was calculated for those points that had both population and the number of households and this number was used to impute the number of households for those population points where it was missing. Average household size was 4.25 which is smaller than expected but reflects the fact that numerator is a 1989 statistic and the denominator is from 1994. First stage of sampling: Using the list of actual and estimated number of households for each population point, 100 workloads were spread across the population points in the following manner: 1. the sampling interval, i, was calculated to be the total number of households outside of Baku divided by 100, 2. the random start, s, was calculated by taking the integer portion of [random number * i + 1], 3. the population point containing the sth household, the (s+i)th household, the (s+2i)th household, etc. were then selected. 4. in the event that more than one interval landed on the same population point, multiple workloads of 12 households were surveyed in that population point. In this manner 100 workloads were distributed in 91 population points. Second stage of sampling: In order to select the households within the selected population points, household lists maintained by the administrative office of each Selsoviet were used. Selsoviets are administrative units that cover from one to ten population points. In the population points covered by a single group of 12 households, 16 dwellings were selected--12 to be interviewed and 4 to be used as replacements if necessary. The sampling interval used was the total number of households on the list divided by 16. Each population point had been assigned a randomly generated number with which to calculate a starting point. In population points with more that one group of 12 households, 16 households were selected for each workload and the sampling interval was number of households divided by 16 multiplied by the number of workloads. It is possible that a second household with separate finances could occupy a dwelling that was only listed once in the Selsoviet’s list. If an interviewer discovered more than one family living in a single dwelling, separate questionnaires were to be filled out for both, and a household randomly selected from among the households not yet interviewed on the list for that population point was taken off the list. This replacement of households, opposed to adding households, was adopted because the schedule did not allow time for more than 12 interviews per workload.

Baku

In February of 1995, SORGU was commissioned to do a random sampling survey in Baku. At that time a list was compiled of 2000 households in Baku. The 2000 households were distributed across the 11 raions of Baku according to each raion’s proportion of the total population. In each raion, the passport office lists were consulted to select the required number of addresses. In each office, the depth of each drawer full of cards was measured, the total length was divided by the number of households to be selected from that raion and cards were then pulled out at those intervals. From each card a specific address in Baku was noted. There is one passport for each dwelling in that raion regardless of the number of separate household/family units occupied the dwelling. The passport lists are, in principle, continuously updated with information from the housing maintenance offices. However, dwellings that are used for business, unoccupied, abandoned or rented to foreigners may remain listed. Furthermore, it is not clear how new privately built housing units would be listed.The 408 households and 92 replacements for this survey were selected by choosing a random number between 1 and 4, starting with that number and then selecting every fifth address from the existing list.

Internally Displaced Population

The National Statistical Committee prepared a listing of population and number of households of internally displaced persons by raion in July 1995. From that list, 34 workloads of 12 households each were selected from 26 raions and 11 Baku Administrative Regions using with a sampling interval and a random start similar to the method used outside of Baku. Ten workloads were selected in Baku and 24 were selected in 17 raions. As before, some raions received more than one workload. In each raion, the administrative offices for the Ministry of Refugees was consulted to locate the internally displaced persons. Each office should have a list of internally displaced persons by households. An additional level of sampling took place to choose three places and four interviews will be conducted in each place. These places were buildings, towns, or tent camps depending on how the households were listed.

Sampling as Implemented

In the course of the field work, it was discovered that population lists are not maintained in major urban areas. In Kuba, Xachmas, Devichi, Qaxi, Sheki, Ali Bairamli, Gojai and Agdash, supervisors had to improvise. In some cases passport registration lists were used, as was done in Baku. In other cases electric users lists, gas office books and butter/meat coupon distribution lists were used in order to capture a sample that was as representative as possible. During field work, one population point, Xandar, was not accessible due to security concerns and its proximity to the occupied region. A second population point, Sofukent, was not accessible because of the weather. In both cases, it was not practicable to replace the population points with two other population points randomly selected from the national list. Instead, field teams were instructed to visit the nearest population point of approximately the same size to the chosen population point. The only major disruption to fieldwork occurred in Naxicevan where interviewers were shot at by terrorists, fortunately none was hurt.

Mode of data collection

Face-to-face [f2f]

Research instrument

DEVELOPMENT OF QUESTIONNAIRES

A questionnaire based on the Living Standards Measurement Study surveys was adapted for use in Azerbaijan. Significant reductions in the number of questions reflected the need to conduct the survey in a short period of time and the more limited scope of a poverty assessment as compared to a full-blown government policy analysis. Questionnaire development was done using the Russian language version. The finalized versions were translated into Azeri by SORGU personnel. A special version of the questionnaire with both Russian and English was prepared for use by data analysts.

DESCRIPTION OF QUESTIONNAIRES

The survey includes questionnaires at both the household and population point (community) levels. Population point is an administrative designation that can be a village, a "village of the town type" or a

Abstract

The Republic of Macedonia Multiple Indicator Cluster Survey (MICS) 2011 was conducted as part of the fourth global round of MICS surveys (MICS4). The survey was conducted in cooperation between UNICEF and the Institute of Public Health of the Republic of Macedonia with the data collection being carried out by private research company IPSOS Strategic Puls. Financial and technical support was provided by UNICEF, with additional financial support from the United Nations Population Fund (UNFPA). The Macedonia MICS 2011 was conducted using two separate samples. One sample developed specifically for the Roma population living in Roma settlements. The sample for the Roma settlements Macedonia MICS was designed to provide estimates for a large number of indicators on the situation of Roma children and women at the national level.

The 2011 Macedonia Multiple Indicator Cluster Survey primary objectives are: - To provide up-to-date information for assessing the situation of children and women in Macedonia; - To furnish data needed for monitoring progress toward goals established in the Millennium Declaration and other internationally agreed upon goals, as a basis for future action; - To contribute to the improvement of data and monitoring systems in Macedonia and to strengthen technical expertise in the design, implementation, and analysis of such systems; - To generate data on the situation of children and women, including the identification of vulnerable groups and of disparities, to inform policies and interventions.

Geographic coverage

National

Analysis unit

• Individuals
• Households

Universe

The survey covered all de jure household members, all women aged between 15-49 years, all children under 5 living in the household, and all children aged 2-9 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary objective of the sample design for the Roma settlements in the Macedonia Multiple Indicator Cluster Survey was to produce statistically reliable estimates of most indicators, for the Roma population living in Roma settlements at the national level.

A multi-stage, stratified cluster sampling approach was used for the selection of the survey sample.

The target national sample size for the Roma settlements in the Macedonia MICS was 1079 households.

For the calculation of the sample size, the key indicator used was the incidence of stunting among children aged 0-4 years. The resulting number of households from this exercise was 4,972 households which is the sample size needed to provide a sufficient number of children under 5 for drawing reliable conclusions. This sample size was reduced to 1,079 based on the original plan to stratify the listing in Roma sample PSUs by households with and without children under 5 for the second stage of selection. In this case a higher sampling rate would have been used for the households with children, similar to the sampling strategy for the national MICS. However, later it was decided that given the higher average number of children under 5 for the Roma households, the sampling procedure was simplified to select all households with equal probability in each Roma sample PSU at the second stage. The average number of households selected per cluster for the Macedonia Roma MICS was determined as 15 households, based on a number of considerations, including the design effect, the budget available, and the time that would be needed per team to complete one cluster.

In total, 70 clusters were allocated to the regions with the number of clusters proportional to the population of the individual regions.

The 2002 census frame was used for the selection of clusters. Census enumeration areas were defined as primary sampling units (PSUs), and were selected from each of the sampling strata by using systematic pps (probability proportional to size) sampling procedures, based on the estimated sizes of the enumeration areas from the 2002 Population Census. The first stage of sampling was thus completed by selecting the required number of enumeration areas at the regional level.

Since the sampling frame (the 2002 Population Census) was not up-to-date, a new listing of households was conducted in all the sample numeration areas prior to the selection of households. For this purpose, listing teams were formed, who visited each enumeration area, and listed the occupied households. Listing activities were conducted by the same company that was responsible for the data collection. The same teams that were selected for the data collection process were used for listing. The listing took place in February 2012. All teams were given the descriptions and maps of the selected clusters. The teams visited all households in the sample clusters asking for the number of members, number of women aged 15-49 and for number of children under age 5.

Lists of households with household members were prepared by the listing teams for each enumeration area. The number of selected households per enumeration area was different, depending on the total number inhabitants in the enumeration area.

The sampling procedures are more fully described in "Macedonia Multiple Indicator Cluster Survey (MICS) 2011 - Final Report" pp.151-152.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the Generic MICS were structured questionnaires based on the MICS4 model questionnaire with some modifications and additions. Household questionnaires were administered in each household, which collected various information on household members including sex, age and relationship. The household questionnaire includes household listing form, education, water and sanitation, household characteristics, child labour and child discipline.

In addition to a household questionnaire, questionnaires were administered in each household for women age 15-49, children under age five and children aged 2-9 years. For children, the questionnaire was administered to the mother or primary caretaker of the child.

The women's questionnaire includes woman's background, child mortality, desire for last birth, maternal and newborn health, illness symptoms, contraception, unmet need, attitudes toward domestic violence, marriage/union, tobacco and alcohol use and life satisfaction.

The children's questionnaire includes child's age, birth registration, early childhood development, breastfeeding, care of illness, immunization and anthropometry.

The questionnaire form for child disability contained the ten question module for identifying children with an increased risk of disability.

The questionnaire form for vaccinations at health facility was used to check the consistency in recording the immunizations between the documents kept in the health facilities and the immunization cards in the households.

The questionnaires were based on the MICS4 model questionnaire. From the MICS4 model English version, the questionnaires were customized, translated into Macedonian and Albanian, back translated into English, and pre-tested in Skopje in March 2011. Based on the results of the pre-test, modifications were made to the wording and translation of the questionnaires.

In addition to the administration of the questionnaires, fieldwork teams measured the weights and heights of children under 5 years of age. Details and findings of these measurements are provided in the respective sections of the report.

Cleaning operations

Data were entered using the CSPro software. The data were entered on 12 microcomputers and carried out by 20 data entry operators and 10 data entry supervisors. In order to ensure quality control, all questionnaires were double entered and internal consistency checks were performed. Procedures and standard programs developed under the global MICS4 programme and adapted to the Macedonia questionnaire were used throughout. Data processing began almost simultaneously with data collection in May 2011 and was completed in August 2011. Data were analysed using the Statistical Package for Social Sciences (SPSS) software program, Version 18, and the model syntax and tabulation plans developed by UNICEF were used for this purpose.

Response rate

Of the 1079 households selected for the sample, 997 were found to be occupied. Of these, 953 were successfully interviewed for a household response rate of 96 percent. In the interviewed households, 1134 women (aged 15-49 years) were identified. Of these, 1091 were successfully interviewed, yielding a response rate of 96 percent within interviewed households. There were 483 children under age 5 listed in the household questionnaire. Questionnaires were completed for 476 of these children, which correspond to a response rate of 99 percent within interviewed households. Overall, response rates of 92 and 94 percentages are calculated for the interviews with women and children under age 5.

Sampling error estimates

The sample of respondents selected in the Macedonia Multiple Indicator Cluster Survey is only one of the samples that could have been selected from the same population, using the same design and size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between the estimates from all possible samples. The extent of variability is not known exactly, but can be estimated statistically from the survey data.

The following sampling error measures are presented for each of the selected indicators:

• Standard error (se): Sampling errors are usually measured in terms of standard

Increased environmental stochasticity due to climate change will intensify temporal variance in the life-history traits, and especially breeding probabilities, of long-lived iteroparous species. These changes may decrease individual fitness and population viability and is therefore important to monitor. In wild animal populations with imperfect individual detection, breeding probabilities are best estimated using capture-recapture methods. However, in many vertebrate species (e.g., amphibians, turtles, seabirds), non-breeders are unobservable because they are not tied to a territory or breeding location. Although unobservable states can be used to model temporary emigration of non-breeders, there are disadvantages to having unobservable states in capture-recapture models. The best solution to deal with unobservable life-history states is therefore to eliminate them altogether. Here, we achieve this objective by fitting novel multievent-robust design models which utilize information obta...

Comparison of risk factors levels between the simulation, NHANES, and the NHANES sub-population with hypertension.

Abstract

The main objective of the survey (TLSS-03) was to measure the level of living of the people of Turkmenistan with respect to various social and economic indicators and produce comparable statistics to the TLSS-98. The survey results formed an important database for building a system of monitoring of the living standards in the country.

The survey will focus on income level and expenditure pattern of households along with their social opportunity and access to public services. The survey will integrate the social and economic aspects of living standards and reveal the social strata that need more attention and protection from state. The survey will analyse the different factors affecting the living standards and will produce valuable information required in development planning and policy making.

A wide range of information collected from the survey was analysed to reveal the major socio-economic factors affecting the level of living. The basic survey approach and the questionnaire was designed to ensure the comparability of statistics with TLSS-98, so that data analysis can be made in cross-statistics as well as in time series.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

Like in 1998, the survey was designed as a two-stage stratified cluster sampling. The principle of stratification into urban and rural for each 5 regions (Velayats) also remains unchanged. It created 11 independent strata (10 from 5 regions plus one stratum of Ashgabad). Primary sampling units (psu) were clusters formed of enumeration area units as described above. Households were listed in the selected clusters and sub-sampled by field staffs from the listing sheets.

TLSS-03 had a self-weighting design and samples were spread out over the wide area of the country. For this purpose, psu's were arranged in the order of geographical location across the different Etraps. Selection of PSU's was made systematically probability proportional to the number of households in clusters.

A fixed sample of 20 households was selected from each cluster using simple random sampling method. Selection of psu's by pps method at first stage and inversely proportional to the number of households at second stage resulted in a self-weighting sample, which was very important for this survey, especially because a large number of indicators are means and proportions. In a self-weighting design, sample means and sample proportions are unbiased estimators of population means and population proportions.

See detail sampling information in "Turkmenistan Living Standards Survey 2003 Technical Report" document.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey was collected using two type of questionnaires: - Household Questionnaire - Community Questionnaire

Cleaning operations

Prior to the data entry, questionnaires filled and returned from the field were checked and edited especially with regard to household identification numbers and data items. The questionnaire included, household listing form, household questionnaire and the community questionnaire. To facilitate the smooth data entry, the community questionnaires were folioed by Oblast, while the household questionnaires were folioed by the survey block. Each folio was provided with appropriate folio cover, which included the household identification and indicators to determine the status of every folio during machine processing. The total folios produced were as follows. - Community Questionnaire, 6 folios - Household Questionnare, 120 folios

The data entry programme was developed in CS Pro 2.3. The screen format for data entry was designed to make its look as similar as possible to the questionnaire. The form labels were made in both English and Russian versions. The programme also included the necessary control mechanism to ensure validity of entries. As mentioned above, there were two levels of questionnaires, so programme files were developed separately for community and household questionnaires.

Several department of TMH housed the data entry process. However, it was not felt necessary to install a network due to the relatively smaller size of the data load. An additional computer was designated for batch editing, form receipts and control and the monitoring purposes. The data entry was conducted from 4 January to 7 February 2004.

CSPro 2.3 was also used for editing. A batch edit program was developed to control the quality of data. Range checks were done on every data item. Additional consistency checks between data items were included in the edit programme. The program generated a list of errors for all questionnaires belonging to a particular household. The data items with error were manually compared with the corresponding questionnaire for verification. All necessary corrections were recorded in the error list and were later used for data correction. Since this is a sample based survey, automatic imputations were not done to preserve reliability of data.

Sampling error estimates

Estimation of the standard error was made based on the Balanced Repeated Replicates (BRR method). The method required exactly two psu’s per stratum. It takes half sample from each stratum and as many complements. The squared differences of two estimates provide an unbiased estimate of variance.

See detail estimation of the standard error and design effect information in "Turkmenistan Living Standards Survey 2003 Technical Report" document.

Data appraisal

Limitations of the survey Although, the utmost attention was paid to ensure the quality of survey results, TLSS had some limitations. Users are strongly recommended to take these limitations into considerations while using the data of this survey. The limitations of the survey are broadly described below.

The survey frame 1. The main limitation of the survey was the quality of the frame used in the survey design. The last population census in Turkmenistan was conducted in 1995. Since then, a lot of demographic changes were observed mainly due the emigration of the Russian speaking population and internal replacement caused by massive housing reconstruction. Despite of all possible attempts directed to improve the frame, it must be recognised that the baseline data still came from the last census.

1. While the last population census results are no more a valid database for any kind of plausible statistical investigations, it is unfortunate that the upcoming Population census in 2005 has now been cancelled, which will be replaced by a “Mini-census of 5%”. Such census may produce the population figures, however, it will not provide so acutely required data for household surveys. Therefore, the problem of the frame is most likely to affect adversely also the quality of other household surveys to be conducted in future.

2. The problem of the frame is related also to the lack of maps of enumeration blocks used in the survey. The size of the earlier blocks in terms of the number of households has significantly changed, so new boundaries were fixed for this survey. However, there was no map available to show the recent changes. Field staffs prepared a new map by themselves for the selected blocks based on the list of households. However, the quality of such map could affect the accuracy of the size of blocks due to the omission or duplication that could occur in the absence of good map. In the absence of the decennial census, maps throughout the country are not updated in terms of the boundaries of enumeration blocks and the number of households. Again, it could also create difficulties in conducting other surveys in future.

Training and the fieldwork 4. During the data editing and consistency checking, several mistakes of field staffs were found in filling the questionnaire. These mistakes actually were the result of insufficient training of the field staffs. The supervisor’s training in the centre was limited only to those from TMH. Field staffs recruited from the centre and from the regional offices did not get the sufficient time of interaction on the various conceptual issues of the questionnaire, so could not sufficiently address much of the expected problems of the survey.

1. The effect of the poor training could have been minimised by an intensive and close supervision of the survey staffs. However, the number of supervisors deployed in the field was often below the initially planned number due to the constraints of time and manpower. There was no coordinated supervision of the fieldwork because the core survey staffs themselves were involved in data collection.

Total survey error 6. Although, sampling error of major variables of interest were at the accepted level, non-sampling errors of the survey were relatively high due to the poor quality of the frame, lack of sufficient training of the field staffs and weak supervision of data collection. Non-sampling error was also caused by measurement and non-response problem as mentioned in the earlier chapter. Therefore, the total margin of error of major estimates was higher, often substantially, than the estimated value of sampling error.

Profile of the living standard 7. The analysis of the living standards requires a statistically viable baseline that allows the results of the survey for comparison over time and territory. In international practice, such baseline is the subsistence minimum, which serves as an objective criterion of measuring the level of living of population. In Turkmenistan, the subsistence minimum is not used for living standard analysis

Abstract

The principal objective of the VNLSS is to collect basic data reflescting the actual living standard of the population. These data then be used for evaluating socio-economic development and formulationg policies to improve living standard. Followings are the main goals by the year of 2000. - Reduce the population growth rate less than 2 % peryear - Reduce the infant mortaility (under 5 years old) 0,81% (1990) to 0,55%; and from 0,46% (1990) to 0,3% (under one year old) - Reduce the mortality rate of women concerning the pregnancy and maternity - Reduce the malnutrition of children under 5years old from 51,5% at present to 40% in 1995 and under 30% by the year of 2000. Heavy malnutrition should not be existed by the year of 2000. - Population can access to safe water resources from 43% (1990) to 82% of which 40% to 80% in rural areas. Population use sanitary latrine from 22% (1990) to 65% of which in rural areas from 15% to 60% - 90 percent of children complete the endeavor universal first level education before the age of 15, and the rest should complete the third grade. By the year of 2000 no children at the age of 15 will be illiterate - Improve the cultural, spiritual life of the children, to ensure that 30% of communes (by the year of 1995) and 50% of communes (by the year of 2000) have entertaining place for children

The main information collected by the survey includes: - Household income and expenditures - Health and education - Employment and other productive and activities - Demographic characteristics and migration - Housing conditions

In addition, the information gatherd is intended to improve planning of economic and social policies in Vietnam and to assist in evaluating the impact of the policies. It should enable decision makers to: - indentify target groups for government assistance - Construct models of socio-economic development policies, both overall and on individuals groups - Analyze the impact of decisions available and of the current economic situation on living condition of household

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Community

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design The sample covers 4800 households from all areas of Viet Nam. The sample design was self-weighted, which means that each household in Viet Nam had the same probability of being selected. The overall sampling frame was stratified into two groups urban and rural, with sampling was carried out separately in each group (strata). About 20% of Vietnamese households live in urban areas, so the sample stratification ensures that 20% of selected households also come from urban areas. Within urban and rural areas, two lists of all communes was drawn up (one of urban communes and another of rural ones), province by province, in "serpentine" order. 2 The selection of communes within each list was done to ensure that they were spread out evenly among all provinces in Viet Nam.

The VNLSS sample design is the following. Within each province in Viet Nam, rural areas can be broken down into districts, and districts in turn are divided into communes (Xa). Urban areas in all provinces consist of centers/towns, which are divided into quarters (Quai), and then divided further into communes (Phuong). The number of communes in all of Viet Nam, both urban and rural, is about 10,000, and the average population in each is about 6,500. As explained in Section 4, each survey team covers 32 households in 4 weeks, 16 households in one area, and 16 in another area. For convenience all 32 households (i.e. both sets of 16 household) were selected from the same commune. This implied that 150 communes needed to be randomly selected (32x150=4800), 30 in urban areas and 120 in urban areas. Within urban areas communes can be further divided into clusters (Cum), two of which were selected from which to draw two "workloads" of 16 households (16 from each of the two clusters). The same was done in rural areas, where each commune is divided into several villages (Thon). The average size of urban clusters and rural villages is somewhat less than 1000 households.

The VNLSS sample was drawn in three stages. Because the General Statistical Office in Hanoi knows the current population of each commune in Viet Nam (but not of each cluster or village within each commune), 150 communes were selected out of the 10,000 in all of Viet Nam with the probability of selection proportional to their population size. At the second stage, information was gathered from the 150 selected communes on the population of each cluster (in urban areas) or villages (in rural areas), and two clusters or villages were randomly drawn with probability proportional to their population size. Finally, the third stage involved random selection of 20 households (16 for the sample plus four "extras" to serve as replacements if some of the 16 "originals" could not be interviewed) within each cluster or village from a list of all households within each cluster or village. Note that the first stage of the sample is based on information from the 1989 Census, but the second and third stages use updated information available from the communes. The first and second stage samples were drawn in Hanoi, while the third stage was drawn in the field (see Section 4.3 below for more details).

Implementation

The attached map shows the commune number and approximate location of the 150 communes selected in Viet Nam. Of the 150 communes chosen, one was in a very remote and inaccessible area near the Chinese border and was replaced by another not quite as inaccessible. The actual interview schedule went smoothly. In one instance (commune 68) one of the selected villages was replaced because when the survey team arrived in the village it discovered that most of the adults were away from the village and thus could not be interviewed. In each cluster or village interviews were completed for 16 households, thus the 4800 household target sample was fully achieved. About 3% of the households (155) were replaced; the main reason for replacement was that their occupants were not at home. Only four households refused to participate. Community questionnaires were completed for all 120 rural communes. Price questionnaires were completed for 118 of 120 communes (the exceptions were communes 62 and 63), and comparable price data were collected from existing sources for all 30 urban areas.

Mode of data collection

Face-to-face [f2f]

Research instrument

HOUSEHOLD QUESTIONNAIRE

The household questionnaire contains modules (sections) to collect data on household demographic structure, education, health, employment, migration, housing conditions, fertility, agricultural activities, household non-agricultural businesses, food expenditures, non-food expenditures, remittances and other income sources, savings and loans, and anthropometric (height and weight) measures.

For some sections (survey information, housing, and respondents for second round) the individual designated by the household members as the household head provided responses. For some others (agro-pastoral activities, non-farm self employment, food expenditures, non-food expenditures) a member identified as most knowledgeable provided responses. Identification codes for respondents of different sections indicate who provided the information. In sections where the information collected pertains to individuals (education, health, employment, migration, and fertility) each member of the household was asked to respond for himself or herself, except that parents were allowed to respond for younger children. In the case of the employment and fertility sections it is possible that the information was not provided by the relevant person; variables in these sections indicate when this is the case. The household questionnaire was completed in two interviews two weeks apart: Sections 0-8, were conducted in the first interview, sections 9-14 were conducted in the second interview, and section 15 was administered in both interviews. The survey was designed so that more sensitive issues such as credit and savings were discussed near the end. The content of each module is briefly described below.

I. FIRST INTERVIEW

Section 0 SURVEY INFORMATION 0A HOUSEHOLD HEAD AND RESPONDENT INFORMATION 0B SUMMARY OF SURVEY RESULTS 0C OBSERVATIONS AND COMMENTS

The date of the interview, the religion, ethnic group of the household head, the language used by the respondent and other technical information related to the interview are noted. Section 0B summarizes the results of the survey visits, i.e. whether a section was completed on the first visit or the second visit. Section 0C, not entered into the computer, contains remarks of the interviewer and the supervisor. Since the data in Section 0C are retained only on the questionnaires, researchers cannot gain access to them without checking the original questionnaires at the General Statistical Office in Hanoi.

Section 1 HOUSEHOLD MEMBERSHIP 1A HOUSEHOLD ROSTER 1B INFORMATION ON PARENTS OF HOUSEHOLD MEMBERS 1C CHILDREN RESIDING ELSEWHERE

The roster in Section 1A lists the age, sex, marital status and relation to household head of all people who spent the previous night in that household and for household members who are temporarily away from home. The household head is listed first and receives the personal id code 1. Household members were defined to include "all the people who normally live and eat their meals together in this dwelling. Those who were absent more than nine of the last twelve months were excluded, except for the head of the household and infants less than three months old. A lunar calendar is provided in the

Abstract

The Living Conditions Monitoring Survey conducted in 2002/2003 was a nation-wide survey. The sample design and sample size used in the survey allow for reliable estimates at province, location (Rural/Urban) and national levels.

The main objectives of the LCMSIII Survey are to: - Monitor the impact of Government policies, programs and donor support on the well being of the Zambian population - Monitor and evaluate the implementation of some of the programs envisaged in the Poverty Reduction Strategy Paper (PRSP) - Monitor poverty and its distribution in Zambia - Provide various users with a set of reliable indicators against which to monitor development - Provide province specific poverty profiles using different poverty lines - Identify vulnerable groups in society and enhance targeting in policy formulation and implementation - Provide data required for developing new national and province specific weights for the Consumer Price Index (CPI) - Provide data required for estimating Gross Domestic Products? (GDP) household final consumption

The Living Conditions Monitoring Survey 2002/2003 collected data on the living conditions of households and persons in the areas of education, health, economic activities and employment, child nutrition, death in the households, income sources, income levels, food production, household consumption expenditure, access to clean and safe water and sanitation, housing and access to various socio-economic facilities and infrastructure such as schools, health facilities, transport, banks, credit facilities, markets, etc.

Geographic coverage

The survey has a nationwide coverage on a sample basis. It covers both rural and urban areas in all the nine provinces. Hence it draws a very big sample size of about 19,600 households.

Analysis unit

• Households
• Individuals

Universe

The eligible household population consisted of all households.Excluded from the sample were institutional populations in hospitals, boarding schools, colleges, universities, prisons, hotels, refugee camps, orphanages, military camps and bases and diplomats accredited to Zambia in embassies and high commissions. Private households living around these institutions and cooking separately were included such as teachers whose houses are within the premises of a school, doctors and other workers living on or around hospital premises, police living in police camps in separate houses, etc. Persons who were in hospitals, boarding schools, etc. but were usual members of households were included in their respective households. Ordinary workers other than diplomats working in embassies and high commissions were included in the survey also. Others with diplomatic status working in the UN, World Bank etc. were included. Also included were persons or households who live in institutionalized places such as hostels, lodges, etc. but cook separately. The major distinguishing factor between eligible and non eligible households in the survey is the cooking and eating separately versus food provided by an institution in a common/communal dining hall or eating place. The former cases were included while the latter were excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Living Conditions Monitoring Survey III (LCMSIII) was designed to cover 520 Standard Enumeration Areas (SEAs) or approximately 10,000 non-institutionalized private households residing in both the rural and urban areas of all the nine provinces in Zambia. The survey was carried out for a period of 12 months using a rolling sample. For the purposes of this survey, a survey reference month had 36 days instead of 30 or 31 days, as is the case with calendar months. This implies that the 360 days in a year were divided into 10 cycles of 36 days each. As a result 52 SEAs, which is one-tenth of the 520 SEAs, were covered every cycle countrywide.

Sample Stratification and Allocation The sampling frame used for LCMSIII survey was developed from the 2000 census of population and housing. The frame is administratively demarcated into 9 provinces, which are further divided into 72 districts. The districts are further subdivided into 155 constituencies, which are also divided into wards. Wards consist of Census Supervisory Areas (CSA), which in turn embrace Standard Enumeration areas (SEAs). For the purposes of this survey, SEAs constituted the ultimate Primary Sampling Units (PSUs).

In order to have equal precision in the estimates in all the provinces and at the same time take into account variation in the sizes of the provinces, the survey adopted the Square Root sample allocation method, (Lesli Kish, 1987). This approach offers a better compromise between equal and proportional allocation methods in terms of reliability of both combined and separate estimates. The allocation of the sample points (PSUs) to rural and urban strata was almost proportional. The allocated provincial samples were multiples of 10 so as to facilitate the rolling of equal samples during the 10 cycles of data collection.

Sample Selection The LCMSIII survey employed a two-stage stratified cluster sample design whereby during the first stage, 520 SEAs were selected with Probability Proportional to Estimated Size (PPES). The size measure was taken from the frame developed from the 2000 census of population and housing. During the second stage, households were systematically selected from an enumeration area listing. The survey was designed to provide reliable estimates at provincial, residential and national levels.

Selection of Standard Enumeration Areas (SEAs) Please see section 2.5.3 of the Survey Report in External Resources

Selection of Households The LCMSIII survey commenced by listing all the households in the selected SEAs. In the case of rural SEAs, households were stratified and listed according to their agricultural activity status. Therefore, there were four explicit strata created in each rural SEA namely, the Small Scale Stratum (SSS), the Medium Scale Stratum (MSS), the Large Scale Stratum (LSS) and the Non-agricultural Stratum (NAS). For the purposes of the LCMSIII survey, about 7, 5 and 3 households were supposed to be selected from the SSS, MSS and NAS, respectively. The large scale households were selected on a 100 percent basis. The urban SEAs were implicitly stratified into low cost, medium cost and high cost areas according to CSO's and local authority classification of residential areas.

About 15 and 25 households were sampled from rural and urban SEAs, respectively. However, the number of rural households selected in some cases exceeded the desired sample size of 15 households depending on the availability of large scale farming households.

The selection of households from various strata was preceded by assigning fully responding households sampling serial numbers. The circular systematic sampling method was used to select households. The method assumes that households are arranged in a circle (G. Kalton, 1983) and the following relationship applies:

Let N = nk, Where: N = Total number of households assigned sampling serial numbers in a stratum n = Total desired sample size to be drawn from a stratum in an SEA k = The sampling interval in a given SEA calculated as k=N/n.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two types of questionnaires will be used in the survey. These are:- 1. The Listing Booklet - to be used for listing all the households residing in the selected Standard Enumeration Areas (SEAs) 2. The Main questionnaire - to be used for collecting detailed information on all household members.

The Main Household questionnaire was divided into two parts, namely:- 1. Main Questionnaire Part I - used for collecting information on the various aspects of the living conditions of the households. 2. Main Questionnaire Part II - all the information collected using the household expenditure diary was later on transcribed to this questionnaire in aggregates so as to make computer data capturing easy. This part of the questionnaire was also used to collect information on household Income, Non-Farm enterprises and deaths in the households.

Cleaning operations

Data Processing and Analysis: The data from the LCMSIII survey was processed and analyzed using the CSPRO and the Statistical Analysis System (SAS) software respectively. Data entry was done from all the provincial offices with 100 percent verification, whilst data cleaning and analysis was undertaken at CSO's headquarters.

Abstract

The Household Living Conditions Survey, also known as Enquête Intégrale sur les Conditions de Vie des Ménages (EICV) in French, was conducted by the Statistics Department of the Ministry of Finance and Economic Planning. The survey was primarily intended to provide policy planners and decision-makers with basic data on household living standards in Rwanda.

In addition, the survey was to be used to: - calculate weights for the Consumer Price Index and estimate final household consumption, - measure the effect of macro-economic policies and projects on the conditions and living standards of the population, - produce key indicators of household welfare in order to assist policy-makers and development partners to improve the design of their development strategy, - identify policy target groups with a view to ensuring that state interventions are better targeted. - provide information on the socio-economic characteristics of households with a view to setting up a socio-economic data base. - carry out in-depth studies, for example on poverty, nutrition, housing conditions, etc, - improve the national capability to conduct statistical surveys, however complex they may be.

Geographic coverage

National coverage with all 11 former provinces (now 5 major provinces) and the City of Kigali.

Analysis unit

-Household -Individual -Commodity (for GDP computation)

Universe

Household members (institutional and itinerant populations excluded)

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling plan was drawn up with the technical support of the late Christopher Scott, Survey Consultant, during his mission in July 1997.

Constraints

The two main factors considered in designing the sampling plan were: - the objectives of the survey, - the fieldwork methodology given the available logistical resources. For the survey one objective was determinant: the Government wanted statistically reliable results at the level of each province, Kigali city and the "other urban sector". Thus, the objective called for 13 domain of analysis. Experience of conducting this type of survey shows that a minimum sample of 500 households per domain of study is required for sound analyses.

Sample size

The sample size was therefore 6,450 households, with 1,170 households for urban areas and 5,280 households for rural areas. Two stage sampling A two stage stratified sample was used: sampling at area level and at household level.

Sampling base

*At the area level, the chosen sampling base ( or at the enumeration district) was the "cellule"in the rural areas and the zone in urban areas, since they are usually fairly homogeneous in size and are well demarcated.

Knowledge of the size of each cellule enabled the use of the classical method of sampling with probability proportional to size at the first stage. A list of all cellules including estimates of the number of households in each was compiled from information provided by the local authorities.

*For sampling at the household level, an up-dated list of households was prepared for each of the selected first stage cellule by carrying out a listing in each sampled cellule simultaneously but with a lag in data collection before or while collecting the data. Part of this operation was carried out in collaboration with the National Population Office (ONAPO) and the Food Security Research Project (FSRP) of MINAGRI.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires are published in French.

Three types of questionnaire were used in the field for data collection: - the household questionnaire comprising of 12 modules divided in two parts, A and B. - the community questionnaire for collecting data on economic and social infrastructures in the sample units in rural areas and - a conversion form for non-standard units used by households.

Household questionnaires

Part A collects data on each member of the household. It covered the following areas: - demographic and migration characteristics, - education and health, - employment and housing.

Part B deals with the economic activity of the household. It comprises of the following five modules: - agro-pastoral activities and own-produce consumption, - household expenditure, - non-agricultural economic activities, - transfers, - durable goods, access to credit and savings.

Cleaning operations

Data Editing (see external resource entilted: Final Data Processing Report)

Questionnaires were reviewd by the controller in the field before they were dispatched for data entry. A control sheet was provided to the contollers to assist in the process of manually editing the questionnaires. Questionnaire structures were verified when the questionnaires were checked in prior to data entry. Three contracted persons reviewed the questionnaire and filled in a form that served as a primary data control sheet. Automated data editing was largely done during the data entry phase (see "Other Data Processing" for details). Some batch edit programs were used to identify inconsistent data.

Data Imputation

Data iimputation was largely done during the analysis phase by analysts. However, a "structural" imputation on the microdata was required for the own consumption data. This was done to adjust for erroneous pricing when the unit for measuring own consumption was buckets. For more information, please refer to the SPSS su=yntax files orthe data processing report.

Primary Data Issues

Coding of products was based on sequential codes for each section.

Response rate

In the course of the survey, some households did not respond, for one reason or the other. Of 6,450 households 6,431 responded, giving a response rate of 99.7%. In the course of processing the data, an additional 11 questionnaires were rejected because they did not contain useable information, in particular in respect to expenditure and consumption. Hence, the analysis was based on 6,420 households, giving a coverage rate of 99.5% of the sample households.

Sampling error estimates

Given that the survey estimates are subject to sampling variability, it is important to calculate the sampling errors for the most important estimates from each survey. The sampling error is measured by the standard error, or square root of the variance of the estimate. The CENVAR software, a component of the Integrated Microcomputer Processing System (IMPS) developed by the U.S. Census Bureau, was used for tabulating the standard errors and other measures of precision, taking into account the stratification and clustering in the sample design. The CENVAR output tables show the value of the estimates, standard errors, coefficients of variation, 95 percent confidence intervals, design effects and number of observations. Given that the confidence intervals provide a user-friendly interpretation of the sampling variability, an annex was produced with tables showing the 95 percent confidence intervals for the most important estimates from the EICV1 and EICV2 data appearing in the preliminary report. These tables provide a quick conservative test to determine whether any difference between the EICV1 and EICV2 estimates is statistically significant.

The INSR was also provided with tables showing the full CENVAR results. The design effect is defined as the variance of an estimate based on the actual sample design divided by the corresponding variance based on a simple random sample of the same size; it is a measure of the relative efficiency of the sample design. In comparing the CENVAR results from EICV1 and EICV2, it was found that the design effects are generally lower for EICV2, indicating that the stratification used for this survey was very effective. Given that the EICV1 was based on an older sampling frame from the 1991 Rwanda Census, this also contributed to the higher design effects for the EICV1 estimates.