Abstract

The Estimating the Size of Populations through a Household Survey (EPSHS), sought to assess the feasibility of the network scale-up and proxy respondent methods for estimating the sizes of key populations at higher risk of HIV infection and to compare the results to other estimates of the population sizes. The study was undertaken based on the assumption that if these methods proved to be feasible with a reasonable amount of data collection for making adjustments, countries would be able to add this module to their standard household survey to produce size estimates for their key populations at higher risk of HIV infection. This would facilitate better programmatic responses for prevention and caring for people living with HIV and would improve the understanding of how HIV is being transmitted in the country.

The specific objectives of the ESPHS were: 1. To assess the feasibility of the network scale-up method for estimating the sizes of key populations at higher risk of HIV infection in a Sub-Saharan African context; 2. To assess the feasibility of the proxy respondent method for estimating the sizes of key populations at higher risk of HIV infection in a Sub-Saharan African context; 3. To estimate the population size of MSM, FSW, IDU, and clients of sex workers in Rwanda at a national level; 4. To compare the estimates of the sizes of key populations at higher risk for HIV produced by the network scale-up and proxy respondent methods with estimates produced using other methods; and 5. To collect data to be used in scientific publications comparing the use of the network scale-up method in different national and cultural environments.

Geographic coverage

National

Analysis unit

• Household
• Individual

Sampling procedure

The Estimating the Size of Populations through a Household Survey (ESPHS) used a two-stage sample design, implemented in a representative sample of 2,125 households selected nationwide in which all women and men age 15 years and above where eligible for an individual interview. The sampling frame used was the preparatory frame for the Rwanda Population and Housing Census (RPHC), which was conducted in 2012; it was provided by the National Institute of Statistics of Rwanda (NISR).

The sampling frame was a complete list of natural villages covering the whole country (14,837 villages). Two strata were defined: the city of Kigali and the rest of the country. One hundred and thirty Primary Sampling Units (PSU) were selected from the sampling frame (35 in Kigali and 95 in the other stratum). To reduce clustering effect, only 20 households were selected per cluster in Kigali and 15 in the other clusters. As a result, 33 percent of the households in the sample were located in Kigali.

The list of households in each cluster was updated upon arrival of the survey team in the cluster. Once the listing had been updated, a number was assigned to each existing household in the cluster. The supervisor then identified the households to be interviewed in the survey by using a table in which the households were randomly pre-selected. This table also provided the list of households pre-selected for each of the two different definitions of what it means "to know" someone.

For further details on sample design and implementation, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Estimating the Size of Populations through a Household Survey (ESPHS) used two types of questionnaires: a household questionnaire and an individual questionnaire. The same individual questionnaire was used to interview both women and men. In addition, two versions of the individual questionnaire were developed, using two different definitions of what it means “to know” someone. Each version of the individual questionnaire was used in half of the selected households.

Cleaning operations

The processing of the ESPHS data began shortly after the fieldwork commenced. Completed questionnaires were returned periodically from the field to the SPH office in Kigali, where they were entered and checked for consistency by data processing personnel who were specially trained for this task. Data were entered using CSPro, a programme specially developed for use in DHS surveys. All data were entered twice (100 percent verification). The concurrent processing of the data was a distinct advantage for data quality, because the School of Public Health had the opportunity to advise field teams of problems detected during data entry. The data entry and editing phase of the survey was completed in late August 2011.

Response rate

A total of 2,125 households were selected in the sample, of which 2,120 were actually occupied at the time of the interview. The number of occupied households successfully interviewed was 2,102, yielding a household response rate of 99 percent.

From the households interviewed, 2,629 women were found to be eligible and 2,567 were interviewed, giving a response rate of 98 percent. Interviews with men covered 2,102 of the eligible 2,149 men, yielding a response rate of 98 percent. The response rates do not significantly vary by type of questionnaire or residence.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) non-sampling errors, and (2) sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made to minimize this type of error during the implementation of the Rwanda ESPHS 2011, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the ESPHS 2011 is only one of many samples that could have been selected from the same population, using the same design and identical 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 all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the ESPHS 2011 sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the ESPHS 2011 is a SAS program. This program uses the Taylor linearization method for variance estimation for survey estimates that are means or proportions.

A more detailed description of estimates of sampling errors are presented in Appendix B of the survey report.

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

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) SAMPLE SIZE

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. 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.

(b) SAMPLE DESIGN

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. Census population data, however, was available only for 1991. An assumption on population growth was thus made to obtain an approximation of the population size for 1993, the year of the survey. The sampling interval at the level of the household was determined in the following way: Based on the decision to have a take of 125 individuals on average per cluster (i.e. assuming 5 members per household to give an average cluster size of 25 households), the interval of households to be selected was determined as the census population divided by 118.1, i.e. allowing for population growth since the census. It was subsequently discovered that population growth was slightly over-estimated, but this had little effect on the findings of the survey. 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.

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/.

Abstract copyright UK Data Service and data collection copyright owner.

The variables in the General Household Survey, 2000-2001: Social Capital Teaching Dataset are a subset taken from the full General Household Survey, 2000-2001 (GHS). For that year of the GHS, a social capital 'trailer' was conducted alongside the main survey, which included questions on respondents' local area, fear of crime, participation and trust. The trailer was funded by the Health Development Agency as part of a larger body of work to further understanding of social capital in terms of its meaning, measurement and links to health within the British population. The variables included here are those from the social capital file and others from the main survey, chosen to reflect different dimensions of social capital in relation to a variety of demographic variables, and some outcome variables such as, health, income and employment.

Further information can be found in the Social capital: introductory user guide.

The second edition of the study (released February 2008) replaced the previous edition (released February 2006). The second edition contains a rescaled weight with a mean of 1 (correcting the previous version) and corrects a systematic error in the data which affected the internal consistency of the social capital module variables in relation to those from the main file. Current users of the data are strongly advised to switch to the second edition of the study.

The full General Household Survey series is held at the UK Data Archive under GN 33090.

Main Topics:

Topics covered in this teaching dataset include views about respondents' local area; civic participation, social networks (including contact with friends and relatives) and social participation (involvement with groups and voluntary activities). A range of demographic variables are also included.

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 50% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE CENTRAL AGENCY FOR PUBLIC MOBILIZATION AND STATISTICS (CAPMAS)

The Household Income, Expenditure and Consumption Survey (HIECS) is of great importance among other household surveys conducted by statistical agencies in various countries around the world. This survey provides a large amount of data to rely on in measuring the living standards of households and individuals, as well as establishing databases that serve in measuring poverty, designing social assistance programs, and providing necessary weights to compile consumer price indices, considered to be an important indicator to assess inflation.

The HIECS 2008/2009 is the tenth Household Income, Expenditure and Consumption Survey that was carried out in 2008/2009, among a long series of similar surveys that started back in 1955.

The survey main objectives are: - To identify expenditure levels and patterns of population as well as socio- economic and demographic differentials. - To estimate the quantities, values of commodities and services consumed by households during the survey period to determine the levels of consumption and estimate the current demand which is important to predict future demands. - To measure mean household and per-capita expenditure for various expenditure items along with socio-economic correlates. - To define percentage distribution of expenditure for various items used in compiling consumer price indices which is considered important indicator for measuring inflation. - To define mean household and per-capita income from different sources. - To provide data necessary to measure standard of living for households and individuals. Poverty analysis and setting up a basis for social welfare assistance are highly dependant on the results of this survey. - To provide essential data to measure elasticity which reflects the percentage change in expenditure for various commodity and service groups against the percentage change in total expenditure for the purpose of predicting the levels of expenditure and consumption for different commodity and service items in urban and rural areas. - To provide data essential for comparing change in expenditure against change in income to measure income elasticity of expenditure. - To study the relationships between demographic, geographical, housing characteristics of households and their income and expenditure for commodities and services. - To provide data necessary for national accounts especially in compiling inputs and outputs tables. - To identify consumers behavior changes among socio-economic groups in urban and rural areas. - To identify per capita food consumption and its main components of calories, proteins and fats according to its sources and the levels of expenditure in both urban and rural areas. - To identify the value of expenditure for food according to sources, either from household production or not, in addition to household expenditure for non food commodities and services. - To identify distribution of households according to the possession of some appliances and equipments such as (cars, satellites, mobiles ...) in urban and rural areas. - To identify the percentage distribution of income recipients according to some background variables such as housing conditions, size of household and characteristics of head of household.

Compared to previous surveys, the current survey experienced certain peculiarities, among which: 1- Doubling the number of area segments from 1200 in the previous survey to 2526 segments with decreasing the number of households selected from each segment to be (20) households instead of (40) in the previous survey to ensure appropriate representatives in the society. 2- Changing the survey period to 15 days instead of one month in the previous one 200412005, to lighten the respondent burden and encourage more cooperation. 3- Adding some additional questions: a- Participation or the benefits gained from pension and social security system. b- Participation in health insurance system. 4- Increasing quality control Procedures especially for fieldwork to ensure data accuracy and avoid any errors in suitable time.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing household surveys in several Arab countries.

Geographic coverage

Covering a sample of urban and rural areas in all the governorates.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 50% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE CENTRAL AGENCY FOR PUBLIC MOBILIZATION AND STATISTICS (CAPMAS)

The sample of HIECS, 2008-2009 is a two-stage stratified cluster sample, approximately self-weighted, of nearly 48000 households. The main elements of the sampling design are described in the following.

1- Sample Size
It has been deemed important to retain the same sample size of the previous two HIECS rounds. Thus, a sample of about 48000 households has been considered. The justification of maintaining the sample size at this level is to have estimates with levels of precision similar to those of the previous two rounds: therefore trend analysis with the previous two surveys will not be distorted by substantial changes in sampling errors from round to another. In addition, this relatively large national sample implies proportional samples of reasonable sizes for smaller governorates. Nonetheless, over-sampling has been introduced to raise the sample size of small governorates to about 1000 households As a result, reasonably precise estimates could be extracted for those governorates. The over-sampling has resulted in a slight increase in the national sample to 48658 households.

2- Cluster size
An important lesson learned from the previous two HIECS rounds is that the cluster size applied in both surveys is found to be too large to yield an accepted design effect estimates. The cluster size was 40 households in the 2004-2005 round, descending from 80 households in the 1999-2000 round. The estimates of the design effect (deft) for most survey measures of the latest round were extraordinary large. As a result, it has been decided to decrease the cluster size to only 19 households (20 households in urban governorates to account for anticipated non-response in those governorates: in view of past experience non-response is almost nil in rural governorates).

A more detailed description of the different sampling stages and allocation of sample across governorates is provided in the Methodology document available among the documentation materials published in both Arabic and English.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three different questionnaires have been designed as following: 1- Expenditure and consumption questionnaire. 2- Diary questionnaire for expenditure and consumption. 3- Income questionnaire.

In designing the questionnaires of expenditure, consumption and income, we were taking into our consideration the following: - Using the recent concepts and definitions of International Labor Organization approved in the International Convention of Labor Statisticians held in Geneva, 2003. - Using the recent Classification of Individual Consumption according to Purpose (COICOP). - Using more than one approach of expenditure measurement to serve many purposes of the survey.

A brief description of each questionnaire is given next:

1- Expenditure and Consumption Questionnaire

This questionnaire comprises 14 tables in addition to identification and geographic data of household on the cover page. The questionnaire is divided into two main sections.

Section one: Household schedule and other information. It includes: - Demographic characteristics and basic data for all household individuals consisting of 18 questions for every person. - Members of household who are currently working abroad. - The household ration card. - The main outlets that provide food and beverage. - Domestic and foreign tourism. - The housing conditions including 15 questions. - Means of transportation used to go to work or school. - The household possession of appliances and means of transportation. - This section includes some questions which help to define the social and economic level of households which in turn, help interviewers to check the plausibility of expenditure, consumption and income data.

Section two: Expenditure and consumption data It includes 14 tables as follows: - The quantity and value of food and beverages commodities actually consumed. - The quantity and value of the actual consumption of alcoholic beverages, tobacco and narcotics. - The quantity and value of the clothing and footwear. - The household expenditure for housing. - The household expenditure for furnishings, household equipment and routine maintenance of the house. - The household expenditure for health care services. - The household expenditure for transportation. - The household

Abstract

The ECVMA is an integrated multi-topic household survey done for the purpose of evaluating poverty and living conditions in Niger. The main objectives of the ECVMA are to: - Gauge the progress made with achievement of the Millennium Development Goals (MDGs); - Facilitate the updating of the social indicators used in formulating the policies aimed at improving the living conditions of the population; - Provide data related to several areas that are important to Niger without conducting specific surveys on individual topics ; - Provide data on several important areas for Niger that are not necessarily collected in other more specific surveys.

The ECVMA involves two visits, which means that each household is visited twice. The first visit takes place during the planting season. The second visit takes place during the harvest season. The household and agriculture/livestock, as well as, the community/price questionnaire are administered during the first visit. During the second visit, only the household and agriculture/livestock questionnaires are administered.

Geographic coverage

National Coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The ECVMA 2011 has been designed to have national coverage, including both urban and rural areas in all the regions of the country. The domains are defined as the entire country, the city of Niamey; and other urban areas, rural areas, and in the rural areas, agricultural zones, agro-pastoral zones and pastoral zones. Taking this into account, 26 explicit sampling strata were selected: Niamey, and urban, agriculture, agro-pastoral and pastoral zones of the seven regions other than Niamey. The target population is drawn from households in all 8 regions of the country with the exception of certain strata found in Arlit (Agadez Region) because of difficulties in going there, the very low population density, and collective housing. The portion of the population excluded from the sample represents less than 0.4% of the total population of Niger. Out of a total of 36,000 people not included in the sample design, about 29,000 live in Arlit and 7,000 in collective housing. The sample was chosen through a random two stage process.

In the first stage a certain number of Enumeration Areas (known as Zones de Dénombrement or ZDs) were selected with Probability Proportional to Size (PPS) using the 2001 General Census of Population and Housing as the base for the sample, and the number of households as a measure of size. In the second stage, 12 or 18 households were selected with equal probability in each urban or rural ZD respectively. The base for the sample was an exhaustive listing of households that will be done before the start of the survey. The total estimated size of the sample is 4,074 households. The fact that this is the first survey with panel households to be revisited in the future was taken into account in the design and therefore it is possible to lose households between the two surveys with minimal adverse effects on the analyses.

Mode of data collection

Face-to-face paper [f2f]

Cleaning operations

The data entry was done in the field simultaneously with the data collection. Each data collection team included a data entry operator who entered the data soon after it was collected. The data entry program was designed in CSPro, a data entry package developed by the US Census Bureau. This program allows three types of data checks: (1) range checks; (2) intra-record checks to verify inconsistencies pertinent to the particular module of the questionnaire; and (3) inter-record checks to determine inconsistencies between the different modules of the questionnaire. The data entry from the first passage was completed in September 2011 and data cleaning was completed in December. The data cleaning process took longer than expected because it was done simultaneously with preparing for the second visit. Data entry from the second visit was completed in January 2012 and the data cleaning for both rounds was completed in August 2012.

Abstract

Survey based Harmonized Indicators (SHIP) files are harmonized data files from household surveys that are conducted by countries in Africa. To ensure the quality and transparency of the data, it is critical to document the procedures of compiling consumption aggregation and other indicators so that the results can be duplicated with ease. This process enables consistency and continuity that make temporal and cross-country comparisons consistent and more reliable.

Four harmonized data files are prepared for each survey to generate a set of harmonized variables that have the same variable names. Invariably, in each survey, questions are asked in a slightly different way, which poses challenges on consistent definition of harmonized variables. The harmonized household survey data present the best available variables with harmonized definitions, but not identical variables. The four harmonized data files are

a) Individual level file (Labor force indicators in a separate file): This file has information on basic characteristics of individuals such as age and sex, literacy, education, health, anthropometry and child survival. b) Labor force file: This file has information on labor force including employment/unemployment, earnings, sectors of employment, etc. c) Household level file: This file has information on household expenditure, household head characteristics (age and sex, level of education, employment), housing amenities, assets, and access to infrastructure and services. d) Household Expenditure file: This file has consumption/expenditure aggregates by consumption groups according to Purpose (COICOP) of Household Consumption of the UN.

Geographic coverage

National

Analysis unit

• Individual level for datasets with suffix _I and _L
• Household level for datasets with suffix _H and _E

Universe

The survey covered all de jure household members (usual residents).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design The 1999/2000 Household Income, Consurnption, and Expendi.ture Survey covered both the urban and the sedentary rural parts of the country. The survey has not covered six zones in Somalia Region and two zones in Afar Region that are inhabited mainly by nomadic population. For the purpose of the survey, the country was divided into three categories . That is, the rural parts of the country and the urban areas that were divided into two broad categories taking into account sizes of their population. Category I: Rural parts of nine Regional States and two administrative regions were grouped in this category each of which were the survey dornains (reporting levels). These regions are Tigrai,Afar, Amhara, Oromia, Sornalia, Eenishangul-Gunuz, SNNP,Gambela, Flarari, Addis Ababa and Dire Dawa.

Category II: All Regional capitals and five major urban centers of the country were grouped in this category. Each of the urban centers in this category was the survey domain (reporting level) for which separate survey results for rnajor survey characteristics were reported.

Category III: Urban centers in the country other than the urban centers in category II were grouped in this category and formed a single reporting level. Other than the reporting levels defined in category II and category III one additional domain, namely total urban (country level) can be constructed by eombining the basic domains defined in the two categories. All in all 35'basie rural and urban domains (reporting levels) were defined for the survey. In addition to the above urban and rural domains, survey results are to be reported at regional and eountry levels by aggregating the survey results for the conesponding urban and rural areas. Definition of the survey dornains was based on both technical and resource considerations. More specifically, sample size for the domains were determined to enable provision of major indicators with reasonable precision subject to the resources that were available for the survey.

Selection Scheme and Sample Size in Each Category CategoryI : A stratified two-stage sample design was used to select the sample in which the primary sampling units (PSUs) were EAs. Sample enumeration areas( EAs) from each domain were selected using systematic sampling that is probability proportional to the size being number of households obtained from the 1994 population and housing census.A total of 722 EAs were selected from the rural parts of the country. Within each sample EA a fresh list of households was prepared at the beginning of the survey's field work and for the administration of the survey questionnaire 12 households per sample EA for rural areas were systematically selected.

Category II: In this category also,a stratified two-stage sample design was used to select the sample. Here a strata constitutes all the "Regional State Capitals" and the five "Major Urban Centers" in the country and are grouped as a strata in this category. The primary sampling units (PSUs) are the EA's in the Regional State Capitals and the five Major Urban Centers and excludes the special EAs (non-conventional households). Sample enumeration areas( EAs) from each strata were selected using systematic sampling probability proportional to size, size being number of households obtained from the 1994 population and housing census. A total of 373 EAs were selected from this domain of study. Within each sample EAs a fresh list of households was prepared at the beginning of the survey's field work and for the administration of the questionnaire 16 household per sample EA were systematically selected-

Category III: Three-stage stratified sample design was adopted to select the sample from domains in category III. The PSUs were other urban centers selected using systematic sampling that is probability proportional to size; size being number of households obtained from the 1994 population and housing census. The secondary sampling units (SSUs) were EAs which were selected using systematic sampling that is probability proportional to size; size being number of households obtained from the 1994 population and housing census. A total of 169 sample EAs were selected from the sample of other urban centers and was determined by proportional allocation to their size of households from the 1994 census. Ultimately, 16 households within each of the sample EAs were selected systematically from a fresh list of households prepared at the beginning of the survey's fieldwork for the administration of the survey questionnaire.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Household Income, Consumption and Expenditure Survey questionnaire contains the following forms: - Form 1: Area Identification and Household Characteristics - Form 2A: Quantity and value of weekly consumption of food and drinks consumed at home and tobacco/including quantity purchased, own produced, obtained, etc for first and second week. - Form 2B: Quantity and value of weekly consumption of food and drinks consumed at home and tobacco/including quantity purchased, own produced, obtained, etc for third and fourth week . - Form 3A: All transaction (income, expenditure and consumption) for the first and second weeks except what is collected in Forms 2A and 2B - Form 3B: All transaction (income, expenditure and consumption) for the third and fourth weeks except what is collected in Forms 2A and 2B - Form 4: All transaction (expenditure and consumption) for last 6 months for Household expenditure on some selected item groups - Form 5: Cash income and receipts received by household and type of tenure. The survey questionnaire is provided as external resource.

Abstract

By the middle of the 1990s, Indonesia had enjoyed over three decades of remarkable social, economic, and demographic change and was on the cusp of joining the middle-income countries. Per capita income had risen more than fifteenfold since the early 1960s, from around US$50 to more than US$800. Increases in educational attainment and decreases in fertility and infant mortality over the same period reflected impressive investments in infrastructure.

In the late 1990s the economic outlook began to change as Indonesia was gripped by the economic crisis that affected much of Asia. In 1998 the rupiah collapsed, the economy went into a tailspin, and gross domestic product contracted by an estimated 12-15%-a decline rivaling the magnitude of the Great Depression.

The general trend of several decades of economic progress followed by a few years of economic downturn masks considerable variation across the archipelago in the degree both of economic development and of economic setbacks related to the crisis. In part this heterogeneity reflects the great cultural and ethnic diversity of Indonesia, which in turn makes it a rich laboratory for research on a number of individual- and household-level behaviors and outcomes that interest social scientists.

The Indonesia Family Life Survey is designed to provide data for studying behaviors and outcomes. The survey contains a wealth of information collected at the individual and household levels, including multiple indicators of economic and non-economic well-being: consumption, income, assets, education, migration, labor market outcomes, marriage, fertility, contraceptive use, health status, use of health care and health insurance, relationships among co-resident and non- resident family members, processes underlying household decision-making, transfers among family members and participation in community activities. In addition to individual- and household-level information, the IFLS provides detailed information from the communities in which IFLS households are located and from the facilities that serve residents of those communities. These data cover aspects of the physical and social environment, infrastructure, employment opportunities, food prices, access to health and educational facilities, and the quality and prices of services available at those facilities. By linking data from IFLS households to data from their communities, users can address many important questions regarding the impact of policies on the lives of the respondents, as well as document the effects of social, economic, and environmental change on the population.

The Indonesia Family Life Survey complements and extends the existing survey data available for Indonesia, and for developing countries in general, in a number of ways.

First, relatively few large-scale longitudinal surveys are available for developing countries. IFLS is the only large-scale longitudinal survey available for Indonesia. Because data are available for the same individuals from multiple points in time, IFLS affords an opportunity to understand the dynamics of behavior, at the individual, household and family and community levels. In IFLS1 7,224 households were interviewed, and detailed individual-level data were collected from over 22,000 individuals. In IFLS2, 94.4% of IFLS1 households were re-contacted (interviewed or died). In IFLS3 the re-contact rate was 95.3% of IFLS1 households. Indeed nearly 91% of IFLS1 households are complete panel households in that they were interviewed in all three waves, IFLS1, 2 and 3. These re-contact rates are as high as or higher than most longitudinal surveys in the United States and Europe. High re-interview rates were obtained in part because we were committed to tracking and interviewing individuals who had moved or split off from the origin IFLS1 households. High re-interview rates contribute significantly to data quality in a longitudinal survey because they lessen the risk of bias due to nonrandom attrition in studies using the data.

Second, the multipurpose nature of IFLS instruments means that the data support analyses of interrelated issues not possible with single-purpose surveys. For example, the availability of data on household consumption together with detailed individual data on labor market outcomes, health outcomes and on health program availability and quality at the community level means that one can examine the impact of income on health outcomes, but also whether health in turn affects incomes.

Third, IFLS collected both current and retrospective information on most topics. With data from multiple points of time on current status and an extensive array of retrospective information about the lives of respondents, analysts can relate dynamics to events that occurred in the past. For example, changes in labor outcomes in recent years can be explored as a function of earlier decisions about schooling and work.

Fourth, IFLS collected extensive measures of health status, including self-reported measures of general health status, morbidity experience, and physical assessments conducted by a nurse (height, weight, head circumference, blood pressure, pulse, waist and hip circumference, hemoglobin level, lung capacity, and time required to repeatedly rise from a sitting position). These data provide a much richer picture of health status than is typically available in household surveys. For example, the data can be used to explore relationships between socioeconomic status and an array of health outcomes.

Fifth, in all waves of the survey, detailed data were collected about respondents¹ communities and public and private facilities available for their health care and schooling. The facility data can be combined with household and individual data to examine the relationship between, for example, access to health services (or changes in access) and various aspects of health care use and health status.

Sixth, because the waves of IFLS span the period from several years before the economic crisis hit Indonesia, to just prior to it hitting, to one year and then three years after, extensive research can be carried out regarding the living conditions of Indonesian households during this very tumultuous period. In sum, the breadth and depth of the longitudinal information on individuals, households, communities, and facilities make IFLS data a unique resource for scholars and policymakers interested in the processes of economic development.

Geographic coverage

National coverage

Analysis unit

• Communities
• Facilities
• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

Because it is a longitudinal survey, the IFLS3 drew its sample from IFLS1, IFLS2, IFLS2+. The IFLS1 sampling scheme stratified on provinces and urban/rural location, then randomly sampled within these strata (see Frankenberg and Karoly, 1995, for a detailed description). Provinces were selected to maximize representation of the population, capture the cultural and socioeconomic diversity of Indonesia, and be cost-effective to survey given the size and terrain of the country. For mainly costeffectiveness reasons, 14 of the then existing 27 provinces were excluded. The resulting sample included 13 of Indonesia's 27 provinces containing 83% of the population: four provinces on Sumatra (North Sumatra, West Sumatra, South Sumatra, and Lampung), all five of the Javanese provinces (DKI Jakarta, West Java, Central Java, DI Yogyakarta, and East Java), and four provinces covering the remaining major island groups (Bali, West Nusa Tenggara, South Kalimantan, and South Sulawesi).

Household Survey:

Within each of the 13 provinces, enumeration areas (EAs) were randomly chosen from a nationally representative sample frame used in the 1993 SUSENAS, a socioeconomic survey of about 60,000 households. The IFLS randomly selected 321 enumeration areas in the 13 provinces, over-sampling urban EAs and EAs in smaller provinces to facilitate urban-rural and Javanese-non-Javanese comparisons.

Within a selected EA, households were randomly selected based upon 1993 SUSENAS listings obtained from regional BPS office. A household was defined as a group of people whose members reside in the same dwelling and share food from the same cooking pot (the standard BPS definition). Twenty households were selected from each urban EA, and 30 households were selected from each rural EA.This strategy minimized expensive travel between rural EAs while balancing the costs of correlations among households. For IFLS1 a total of 7,730 households were sampled to obtain a final sample size goal of 7,000 completed households. This strategy was based on BPS experience of about 90% completion rates. In fact, IFLS1 exceeded that target and interviews were conducted with 7,224 households in late 1993 and early 1994.

IFLS3 Re-Contact Protocols The sampling approach in IFLS3 was to re-contact all original IFLS1 households having living members the last time they had been contacted, plus split-off households from both IFLS2 and IFLS2+, so-called target households (8,347 households-as shown in Table 2.1*) Main field work for IFLS3 went on from June through November, 2000. A total of 10,574 households were contacted in 2000; meaning that they were interviewed, had all members died since the last time they were contacted, or had joined another IFLS household which had been previously interviewed (Table 2.1*). Of these, 7,928 were IFLS3 target households and 2,646 were new split-off households. A 95.0% re-contact rate was thus achieved of all IFLS3 "target" households. The re-contacted households included 6,800 original 1993 households, or 95.3% of those. Of IFLS1 households, somewhat lower re-contact rates were achieved in Jakarta, 84.5%, and North Sumatra,

Abstract

The purpose of the LFS is to provide information on the economically active population. The main objective of collecting data on the economically active population is among others to provide basic information on the size and structure of Botswana’s workforce.

Unlike the first and second Labour Force Surveys, the 2005/06 collected information from persons aged 7 years old and above while the previous surveys collected information from persons aged 12 years and above. The inclusion of the 7 years and above came about as a request from other stakeholders as to measure the extent of child labour in this country. The coding of the occupations was based on the 1988 International Standard Classification of Occupation (ISCO-88), whilst the definition of the informal sector is in accordance with the 1993 System of National Accounts (SNA -1993). Few questions were asked about the informal activities and migration status of the labour force as this survey was mainly designed to capture information on the labour force characteristics.

Survey Objectives The broad objective of the survey was to obtain comprehensive data on the status of the labour market prevailing in Botswana. More detailed objectives were; • To provide measures of both current and usual economic activity. • To obtain a measure of the size of employment in both formal and informal sector. • To provide measures of unemployment and underemployment. • To estimate the extent of child labour, obtain child employment activities and reasons for working. • To estimate total population for the period.

The survey data would provide, among others, baseline information on indicators of employment and unemployment levels, and information necessary that can be used to develop, manage, evaluate and report on labour market policies.

Geographic coverage

National

Analysis unit

• Households
• Individuals age 7 years and above

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING FRAME In general the 2001 Population and Housing Census, undertaken in August, is the Sampling Frame on which sample selection for the Survey Programmes are based. The census result gives information on population, number of household at Locality, Enumeration Area (EA), village and district/town levels. Also given for each EA is information on ecological zones in rural areas.

The Sampling frame was defined and constituted by all Enumeration Areas (EAs) found in three geographical regions viz. (i) Cities & Towns (ii) Urban Villages, and (iii) Rural Districts as defined by the 2001 Population and Housing Census.

Being a two-stage design, two frames were required one for each stage.

The sampling frame for the first stage based on the 2001 Population and Housing Census. This comprised the list of all Enumeration Area (EA) together with number of households. In the census the EAs were frames of manageable size (in terms of dwellings/households).

The sampling frame for the second stage was produced only in the selected EAs. Before the beginning of the survey interviews, the field teams listed all private habitable dwellings/households in their EAs. Thus the number of occupied households in the selected EA served as sampling frame for that EA.

The frame for the Botswana Labour Force Survey 2005/6 consisted of 4,143 EAs being the total number of Enumeration Areas (EAs) delineated during the 2001 Population and Housing Census.

STRATIFICATION When national level estimates are the main focus a type of stratification that is simple to implement and highly efficient is implicit stratification. It is a form of geographic stratification, which when used together with systematic pps sampling automatically distributes the sample proportionately into each of the nation's administrative subdivisions, as well as the urban and rural sectors.

Creation of strata is dictated by two principal criteria. These include a need to: i. provide estimates for each major region of the country. ii. increase precision

Thus, stratification variables included cities/towns and administrative districts. Apart from national and rural estimates, the Government, which is the main user of CSO data, requires accurate estimates for all regions for planning and monitoring of development projects. Stratification was therefore undertaken such that all districts and major urban centres become their own strata. With regard to increase precision consideration was also given to group EAs according to ecological zones in rural districts and according to income categories in cities/towns.

Geographical stratification along ecological zones and income categories was expected to improve the accuracy of survey data in view that homogeneity of the variables was relatively high (implicit stratification).

There are five major rural ecological zones, namely: -Village, -Lands -Cattle Post -Freehold Farms -Mixture of Land and Cattle Post

During the delineation of the maps, each EA was associated with unique ecological zone and thus, grouping the EAs into respective zones was not a problem. To facilitate the selection according to the stratification variables and EAs were listed in some order, for example starting cattle post, then farms etc. in case of rural areas.

Note: See detail sampling procedure description in final report

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires are the primary recording documents of the survey. In the development of the questionnaires, a reference group was formed to work into the questionnaire. The final version of the questionnaires were finalized on the basis of the experiences gained from the Pilot Survey conducted using the drafted questionnaires for the survey.

The 2005/6 BLFS consisted of two questionnaires, namely i. The Household Questionnaire, and ii. The Individual Questionnaire

Household Questionnaire: This questionnaire is a standardized questionnaire of the CSO's Household Survey Programme except with a little modification as per the need of the designated survey. This questionnaire also set the criteria for eligibility of being an BLFS individual questionnaire respondent.

The Household questionnaire was divided into four major sets of questions, namely i. Socio-Demographic Characteristics ii. Parental Survivor and Fostering iii. Education and Training

Eligibility Criteria was not a question asked by the respondent. It was meant for the interviewer to identify persons who were eligible for an individual questionnaire. UCriteriaU: “Those respondents who were aged 7 years and more and also usual members of this household were eligible for an individual respondent.”

Individual Questionnaire: All the eligible individuals from the household questionnaire were asked questions on the individual questionnaire.

The process of individual questionnaire development was not a simple task. The challenge was to develop the types of questions that led to achieving the survey objectives. Standardised questionnaire were developed so as to provide the basis for current (where feasible) and future comparability. More specifically, questions and the design structure of the questionnaire took into full account a set of objectives spelt out above with a view to address them.

The individual questionnaire has the questions mainly on the following topics: Section 1: For all persons aged 7 years and above A: Usual Activity. B: Current Activity Section 2: For all who did not work in the last 7 days and who were available for work (12 years and above) Section 3: For all employed in the 7 days. A: Main Economic Activity (for 7 years and above) B: Secondary Activity (for 12 years and above) C: Usual Hours Worked (for 7 years and above) D: Actual Hours Worked (for 7 years and above) E: Additional Work F: Different Work G: On the Job Training H: Income from Employment Business Section 4: Migration (For all persons aged 12 years and above) Section 5: Housework and Work at School Section 6: Health and Safety

PRE-TEST The Botswana Labour Force Survey instruments (household and individual) were pretested in areas in and around Gaborone on the 14-16 April 2005. Households were selected at random from EAs belonging to different strata according to the stratification in the sample design.

Cleaning operations

Before data entry was carried out, questionnaires were edited to check if all the relevant questions have been responded to and coded according to the codes designed for the study. Editing and coding started in August 2005 by 19 Coders and finished in August 2006. Data entry was carried out under the supervision of one programmer/supervisor. Consistency checks on the data set as per the Computer edit Specifications were performed.

United States US: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data was reported at 1.310 % in 2016. United States US: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data is updated yearly, averaging 1.310 % from Dec 2016 (Median) to 2016, with 1 observations. United States US: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Poverty. The growth rate in the welfare aggregate of the bottom 40% is computed as the annualized average growth rate in per capita real consumption or income of the bottom 40% of the population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the PovcalNet (http://iresearch.worldbank.org/PovcalNet). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The final year refers to the most recent survey available between 2011 and 2015. Growth rates for Iraq are based on survey means of 2005 PPP$. The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See PovcalNet for detailed explanations.; ; World Bank, Global Database of Shared Prosperity (GDSP) circa 2010-2015 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

Abstract

The STEP (Skills Toward Employment and Productivity) Measurement program is the first ever initiative to generate internationally comparable data on skills available in developing countries. The program implements standardized surveys to gather information on the supply and distribution of skills and the demand for skills in labor market of low-income countries.

The uniquely-designed Household Survey includes modules that measure the cognitive skills (reading, writing and numeracy), socio-emotional skills (personality, behavior and preferences) and job-specific skills (subset of transversal skills with direct job relevance) of a representative sample of adults aged 15 to 64 living in urban areas, whether they work or not. The cognitive skills module also incorporates a direct assessment of reading literacy based on the Survey of Adults Skills instruments. Modules also gather information about family, health and language.

Geographic coverage

Areas are classified as urban based on each country's official definition.Some STEP surveys had narrower urban sampling. In Yunnan Province the sample covered the urban areas of Kunming. - Detailed information is provided in the weighting documentation.

Analysis unit

The units of analysis are the individual respondents and households. A household roster is undertaken at the start of the survey and the individual respondent is randomly selected among all household members aged 15 to 64 included. The random selection process was designed by the STEP team and compliance with the procedure is carefully monitored during fieldwork.

Universe

The STEP target population is the urban population aged 15 to 64 included, living in urban areas, as defined by each country's statistical office. The target population for the China-Yunnan STEP survey comprised all non-institutionalized persons 15 to 64 years of age (inclusive) living in private dwellings in urban areas of Kunming at the time of data collection.

The following are excluded from the sample: - Residents of institutions (prisons, hospitals, etc) - Residents of senior homes and hospices - Residents of other group dwellings such as college dormitories, halfway homes, workers' quarters, etc - Persons living outside the country at the time of data collection In some countries, extremely remote villages or conflict-ridden regions could not be surveyed. These cases are listed in the weighting documentation.

Kind of data

Sample survey data [ssd]

Sampling procedure

The China-Yunnan survey firm implemented a partial literacy assessment design. The partial assessment required each selected person to attempt to complete a General Booklet comprising Reading Components and a set of Core Literacy Items. The partial assessment sampling objective was to have a minimum of about 2000 selected persons attempt the General Booklet. The target population for the China-Yunnan STEP survey comprised all non-institutionalized persons 15 to 64 years of age (inclusive) living in private dwellings in urban areas of Kunming at the time of data collection. The sample frame for the selection of first stage sample units was the Excel file 'sampling frame for STEP _CHINA' that was provided by the China-Yunnan survey firm. The frame is a complete list of first stage sampling units in the urban areas of Kunming. The source of this sample frame is the National Population Census, November, 2010. The sample frame includes 5564 PSUs in 299 Census Enumeration Areas. According to the sample frame, there are 1,067,256 households in the 5564 PSUs.

The China-Yunnan sample design was a 3 stage cluster sample design.

First Stage Sample The primary sample unit (PSU) is a Census Enumeration Area (CEA) Block. The sampling objective was to conduct interviews in 135 CEA Blocks. At the first stage of sample selection, 27 additional PSUs were also selected as reserve PSUs to be used in the event that it was impossible to obtain any interviews in one or more of the initial PSUs. A total of 162 PSUs were selected with probability proportional to size, where the measure of size was the number of households in a PSU. Subsequently, from the file of 162 sampled PSUs, a PPS sample of 135 PSUs was selected to be the 'Initial' PSU sample. Note that none of the 27 reserve PSUs was activated during data collection.

Second Stage Sample The second stage sample unit (SSU) is a household. The sampling objective was to obtain interviews at 15 households within each selected PSU. At the second stage of sample selection, 30 households were selected in each PSU using a systematic random method. The 30 households were randomly divided into 15 'Initial' households, and 15 'Reserve' households that were ranked according to the random sample selection order.

Third Stage Sample The third stage sample unit was an individual aged 15-64 (inclusive). The sampling objective was to select one individual with equal probability from each selected household.

Mode of data collection

Face-to-face [f2f]

Research instrument

The STEP survey instruments include: - The background Questionnaire developed by the WB STEP team - Reading Literacy Assessment developed by Educational Testing Services (ETS).

All countries adapted and translated both instruments following the STEP Technical Standards: 2 independent translators adapted and translated the Background Questionnaire and Reading Literacy Assessment, while reconciliation was carried out by a third translator.

The WB STEP team and ETS collaborated closely with the Chinese survey firm during the process and reviewed the adaptation and translation to Mandarin using a back translation.

The survey instruments were both piloted as part of the survey pretest.

The adapted Background Questionnaires are provided in English as external resources. The Reading Literacy Assessment is protected by copyright and will not be published.

Cleaning operations

STEP Data Management Process: 1) Raw data is sent by the survey firm 2) The WB STEP team runs data checks on the Background Questionnaire data. - ETS runs data checks on the Reading Literacy Assessment data. - Comments and questions are sent back to the survey firm. 3) The survey firm reviews comments and questions. When a data entry error is identified, the survey firm corrects the data. 4) The WB STEP team and ETS check the data files are clean. This might require additional iterations with the survey firm. 5) Once the data has been checked and cleaned, the WB STEP team computes the weights. Weights are computed by the STEP team to ensure consistency across sampling methodologies. 6) ETS scales the Reading Literacy Assessment data. 7) The WB STEP team merges the Background Questionnaire data with the Reading Literacy Assessment data and computes derived variables.

Detailed information data processing in STEP surveys is provided in the 'Guidelines for STEP Data Entry Programs' document provided as an external resource. The template do-file used by the STEP team to check the raw background questionnaire data is provided as an external resource.

Response rate

The response rate for Yunnan Province (urban) was 98% (See STEP Methodology Note Table 4)

Sampling error estimates

A weighting documentation was prepared for each participating country and provides some information on sampling errors. All country weighting documentations are provided as an external resource.

Abstract

The key objectives of the household sample survey is to get reliable information which would allow to estimate living standards of the general population; to provide the government agencies of the Republic of Belarus and interested organizations with timely and reliable information reflecting the impact of economic reforms on various aspects of life; to establish the system of timely collection and processing of information with the use of computer facilities and advanced technologies.

The main components of the survey is one household questionnaire used to collect information in this survey: Consumption and Income, Turkey. A household is defined as a group of people sharing the same resources; they do not necessarily have to be related through blood or marriage. This household survey also contained questions with the purpose of gathering individual data in the household characteristics, employment, employment and income, education and health modules.

Geographic coverage

All settlement areas within the territory of Turkey were included into the scope.

Analysis unit

The Household Budget Survey have the following units of analysis: household income and expenditure, household consumption.

Universe

All members of the households living within the borders of the Republic of Turkey were included within the scope. However institutionalized population and the immigrant population (because of practical reasons) were excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

Stratified two-stage cluster sampling method is used.Household live at the address is defined as the final sampling unit. Indicators of consumption expenditure with respect to the Turkey were obtained by means of the application of 2017 Household Budget Survey on 1 296 sample households changing every month and 15 552 sample households for a year between 1st January - 31st December 2017.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

The survey has been implemented by using CAPI system at the first and the last interviews, diary keeping and data entry methods have been used at the other interviews. SAS has been used in analyzing & controlling of data.

Response rate

The non-response forms were filled in case the selected households according to the sampling techniques couldn't been surveyed and the population weights were calculated considering non-responses. For 2017 Household Budget Survey, the non-response rate was 21.8% for overall Turkey.

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 50% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE CENTRAL AGENCY FOR PUBLIC MOBILIZATION AND STATISTICS (CAPMAS)

The Household Income, Expenditure and Consumption Survey (HIECS) is of great importance among other household surveys conducted by statistical agencies in various countries around the world. This survey provides a large amount of data to rely on in measuring the living standards of households and individuals, as well as establishing databases that serve in measuring poverty, designing social assistance programs, and providing necessary weights to compile consumer price indices, considered to be an important indicator to assess inflation.

The First Survey that covered all the country governorates was carried out in 1958/1959 followed by a long series of similar surveys. The current survey, HIECS 2015, is the twelfth in this long series. Starting 2008/2009, Household Income, Expenditure and Consumption Surveys were conducted each two years instead of five years. this would enable better tracking of the rapid changes in the level of the living standards of the Egyptian households.

CAPMAS started in 2010/2011 to follow a panel sample of around 40% of the total household sample size. The current survey is the second one to follow a panel sample. This procedure will provide the necessary data to extract accurate indicators on the status of the society. The CAPMAS also is pleased to disseminate the results of this survey to policy makers, researchers and scholarly to help in policy making and conducting development related researches and studies

The survey main objectives are:

• To identify expenditure levels and patterns of population as well as socio- economic and demographic differentials.

• To measure average household and per-capita expenditure for various expenditure items along with socio-economic correlates.

• To Measure the change in living standards and expenditure patterns and behavior for the individuals and households in the panel sample, previously surveyed in 2008/2009, for the first time during 12 months representing the survey period.

• To define percentage distribution of expenditure for various items used in compiling consumer price indices which is considered important indicator for measuring inflation.

• To estimate the quantities, values of commodities and services consumed by households during the survey period to determine the levels of consumption and estimate the current demand which is important to predict future demands.

• To define average household and per-capita income from different sources.

• To provide data necessary to measure standard of living for households and individuals. Poverty analysis and setting up a basis for social welfare assistance are highly dependent on the results of this survey.

• To provide essential data to measure elasticity which reflects the percentage change in expenditure for various commodity and service groups against the percentage change in total expenditure for the purpose of predicting the levels of expenditure and consumption for different commodity and service items in urban and rural areas.

• To provide data essential for comparing change in expenditure against change in income to measure income elasticity of expenditure.

• To study the relationships between demographic, geographical, housing characteristics of households and their income.

• To provide data necessary for national accounts especially in compiling inputs and outputs tables.

• To identify consumers behavior changes among socio-economic groups in urban and rural areas.

• To identify per capita food consumption and its main components of calories, proteins and fats according to its nutrition components and the levels of expenditure in both urban and rural areas.

• To identify the value of expenditure for food according to its sources, either from household production or not, in addition to household expenditure for non-food commodities and services.

• To identify distribution of households according to the possession of some appliances and equipments such as (cars, satellites, mobiles ,…etc) in urban and rural areas that enables measuring household wealth index.

• To identify the percentage distribution of income earners according to some background variables such as housing conditions, size of household and characteristics of head of household.

• To provide a time series of the most important data related to dominant standard of living from economic and social perspective. This will enable conducting comparisons based on the results of these time series. In addition to, the possibility of performing geographical comparisons.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing household surveys in several Arab countries.

Geographic coverage

Covering a sample of urban and rural areas in all the governorates.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 50% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE CENTRAL AGENCY FOR PUBLIC MOBILIZATION AND STATISTICS (CAPMAS)

The sample of HIECS 2015 is a self-weighted two-stage stratified cluster sample. The main elements of the sampling design are described in the following.

1- Sample Size The sample size is around 25 thousand households. It was distributed between urban and rural with the percentages of 45% and 55%, respectively.

2- Cluster size The cluster size is 10 households in most governorates. It reached 20 households in Port-Said, Suez, Ismailiya, Damietta, Aswan and Frontier governorates, since the sample size in those governorates is smaller compared to others.

3- Sample allocation in different governorates 45% of the survey sample was allocated to urban areas (11260 households) and the other 55% was allocated to rural areas (13740 households). The sample was distributed on urban/rural areas in different governorates proportionally with the household size A sample size of a minimum of 1000 households was allocated to each governorate to ensure accuracy of poverty indicators. Therefore, the sample size was increased in Port-Said, Suez, Ismailiya, kafr el-Sheikh, Damietta, Bani Suef, Fayoum, Qena, Luxor and Aswan, by compensation from other governorates where the sample size exceeds a 1000 households. All Frontier governorates were considered as one governorate.

4- Core Sample The core sample is the master sample of any household sample required to be pulled for the purpose of studying the properties of individuals and families. It is a large sample and distributed on urban and rural areas of all governorates. It is a representative sample for the individual characteristics of the Egyptian society. This sample was implemented in January 2010 and its size reached more than 1 million household selected from 5024 enumeration areas distributed on all governorates (urban/rural) proportionally with the sample size (the enumeration area size is around 200 households). The core sample is the sampling frame from which the samples for the surveys conducted by CAPMAS are pulled, such as the Labor Force Surveys, Income, Expenditure And Consumption Survey, Household Urban Migration Survey, ...etc, in addition to other samples that may be required for outsources.

A more detailed description of the different sampling stages and allocation of sample across governorates is provided in the Methodology document available among external resources in Arabic.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three different questionnaires have been designed as following:

1- Expenditure and Consumption Questionnaire. 2- Assisting questionnaire. 3- Income Questionnaire.

In designing the questionnaires of expenditure, consumption and income, we were taking into our consideration the following: - Using the recent concepts and definitions of International Labor Organization approved in the International Convention of Labor Statisticians held in Geneva, 2003. - Using the recent Classification of Individual Consumption According to Purpose (COICOP). - Using more than one approach of expenditure measurement to serve many purposes of the survey.

A brief description of each questionnaire is given next:

----> 1- Expenditure and Consumption Questionnaire This questionnaire comprises 14 tables in addition to identification and geographic data of household on the cover page. The questionnaire is divided into two main sections.

Section one: Household schedule and other information, it includes: - Demographic characteristics and basic data for all household individuals consisting of 25 questions for every person. - Members of household who are currently working abroad. - The household ration card. - The main outlets that provide food and beverage. - Domestic and foreign tourism. - The housing conditions including 16 questions. - Household ownership of means of transportation, communication and domestic appliances. - Date of purchase, status at purchase, purchase value and

Household surveys are essential for the production of data for policy design and interventions in developing countries. Little attention is paid by commissioners, producers and consumers of data from household surveys to the issue of what the household unit used in the survey is, how it is defined, and what this definition might means for analysis and interpretation.

The household, as defined and used in household surveys, refers to a basic social unit. If a survey-defined concept of a household differs systematically from locally understood and lived basic socio-economic units, then research based on this minimal social unit definition becomes much less useful for subsequent analysis either at household level or for aggregates of households. If standard definitions of the household do not adequately capture local realities of the social unit, then this raises significant issues in terms of survey validity.

This project will use qualitative methods to systematically identify the extent of difference between the 'household' units used in household surveys and locally meaningful terms for social units. Differences will be subjected to a series of scenario models for a range of development indicators to provide substantive evidence of the impact of household definition on survey measurement and validity.

Abstract

The 2008 MICS aims essentially to do the following: - Provide up-to-date information for assessing the situation of children and women in Mozambique. - Contribute to assessing the Government Five-Year Programme 2005–2009 and the Action Plan for the Reduction of Absolute Poverty 2007–2009 (PARPA II), thus allowing an analysis of progress relative to a series of targets established in the PARPA II monitoring matrix. - Provide the data necessary to monitor progress towards the Millennium Development Goals (MDGs) and the goals of A World Fit for Children, as well as progress towards other internationally agreed targets. - Serve as a fundamental source of information for the Government on the country’s stage of development as it draws up its next five-year programme. - Contribute to the improvement of data and monitoring systems in Mozambique and strengthen specialist technical expertise in the design, implementation and analysis of these systems.

Kind of data

Sample survey data [ssd]

Sampling procedure

The universe defined for this survey included all households living in individual homes in Mozambican territory. It excluded households living in collective homes (barracks, hotels, student residences, etc.), the homeless, and diplomats living in embassies/representations.

The MICS 2008 sample was obtained from the preliminary data and the cartography of the 2007 Census. Selection of the MICS 2008 sample followed a two-stage plan: 1) select the Primary Sampling Units (PSU) or Enumeration Areas (EAs); 2) select households within the sample EAs and, within these, exhaustively select units of analysis (that is, women aged 15–49 and children under five).

Thus, the MICS sample covered 715 PSUs (or EAs) selected systematically, with probability proportional to the size of each urban or rural stratum within each province. In each of the 715 PSUs 20 households were selected, which resulted in a total national sample of 14,300 households. Of the 14,300 households, 6,160 were urban and 8,140 were rural.

The division of the sample by urban and rural stratum within each province is proportional, and the unit of measurement is the number of households in each stratum within each province. The minimum number of households expected in each province was 1,200; exceptions were made for Nampula and Zambézia provinces, with 1,600 households each due to their demographic weight, and Maputo City, with 1,500 households due to the greater variability of its socio-demographic characteristics.

Mode of data collection

Face-to-face [f2f]

Research instrument

To collect data, the methodology of household interviews was adopted, and three types of questionnaire were used: 1) a questionnaire to gather information on all members of the household and the house; 2) one for women aged 15–49; and 3) one for children under five, administered to mothers or caregivers of all children under five living in the household.

The household questionnaire included the following modules: - Sheet to list household members - Education - Water and sanitation - Characteristics of the household - Security of tenure of the house - Mosquito nets and spraying - Child labour - Disability - Orphaned and vulnerable children - Income - Iodized salt.

The questionnaire for women was administered to all women aged 15–49. The questionnaire had the following modules: - Characteristics of the woman interviewed - Matrimonial situation and sexual activity - Child mortality - Maternal and newborn health - Tetanus toxoid - Contraception - Attitudes towards domestic violence - HIV and AIDS.

For the questionnaire for children under five, the mothers or caregivers in each household were identified and interviewed. The questionnaire had the following modules: - Birth registration and early learning - Child development - Vitamin A - Breastfeeding - Care of illness - Malaria - Immunization - Anthropometry.

The three survey questionnaires were based on the MICS3 model questionnaires. Starting with the English version of the MICS3 model, the questionnaires were translated into Portuguese and put into the Mozambican context. Specific themes were added to meet the country’s needs. The pilot survey was held in April 2008 in Maputo City and in Boane district, Maputo province. Based on the results of the pilot survey, modifications were made to the drafting and translation of the questionnaires.

In addition to administering the questionnaires, the fieldwork teams tested the level of iodine in the kitchen salt in use in households and measured the height and weight of all children under five. Details and conclusions from these measurements are presented in the respective sections of the report.

Cleaning operations

Data processing Data processing began in October 2008 and ended in April 2009. Survey processing involved both manual and automatic procedures: receiving and verifying questionnaires, criticism (revision and codification), inputting, editing and analysis of inconsistencies. Data were captured using the interactive software CSPro (Census and Survey Processing System) on 20 microcomputers. Forty data entry operators took part, distributed in two shifts, and a supervisor. To ensure quality control, all the questionnaires were input twice. Throughout the work, procedures and standard programmes developed under the global MICS3 project were used and adapted to the local questionnaire. For cleanness and consistency of data input, the software Stata was used.

Sampling error estimates

Estimate of sampling errors

Since MICS 2008 was a survey by sampling, the results presented in this report are subject to two types of error: sampling errors and non-sampling errors. Non-sampling errors are produced during data collection and processing; sampling errors result from the fact that only a part of the population was interviewed rather than the entire population.

Non-sampling errors include such problems as: failure to question all the women and children selected, errors in formulating the questions and registering the replies, confusion or incapacity of the women in giving information about themselves or their children, and codification or processing errors. Attempts were made to keep this type of error to a minimum by following a series of procedures used in well designed and implemented samples, such as, for example, careful interview design, numerous tests of the questionnaire, intensive training of the interviewers, permanent supervision of the field work, and office review of the questionnaires by the criticism staff. Furthermore, to reduce this type of error, a coverage team was trained to assess the magnitude of such errors, including the coverage of MICS 2008. This team visited all the EAs selected for MICS in all the provinces but the contents or themes were covered by samples.

Appropriate supervision at the stage of data codification and processing, careful cleaning of the archives, feedback to the supervisors, and criticism of the interviewers based on quality control tables also helped minimize errors. The assessment elements available indicate that this type of error was kept within reasonable margins in MICS 2008.

See Appendix C of the Final Report for more detailed information and tables on Estimate of sampling errors.

Abstract

The study includes a merged core data file from the 7 country RuralStruc surveys conducted in 2007-2008.

Geographic coverage

Areas covered in the data are selected rural areas in the following regions:

• in Kenya: Bungoma, Nakuru North, Nyando

• in Madagascar: Alaotra, Antsirabe, Itasy, Morondava

• in Mali: Diema, Koutiala, Macina, Tominian

• in Mexico: Tequisquiapan (Queretaro), Sotavento (Veracruz)

• in Morocco: Chaouia, Saiss, Souss

• in Nicaragua: El Cua, El Viejo, La Libertad, Muy Muy, Terrabona

• in Senegal: Casamance, Mekhe, Nioro, Senegal River Delta.

For more detailed information on geographic coverage, data users can refer to the RuralStruc National Reports.

Analysis unit

The basic unit of observation and analysis that the study describes is the rural household, with the exception of Mali.The preference for rural and not only farm households was justified by the objective of identifying more precisely agriculture's role with respect to other rural activities and sources of income. This option was not neutral, as it refers to analytical categories whose definition are more complicated than one may believe a priori, like the definition of what “rural” is, its characterization varying between countries. The Program National teams considered the following definitions for rural housholds:

-Kenya: "The household was defined as a family living together, eating together, and making farming and other household decisions as a unit"'

-Madagascar :" Le ménage est un ensemble de personnes avec ou sans lien de parenté, vivant sous le même toit ou dans la même concession, prenant leur repas ensemble ou par petits groupes, mettant une partie ou la totalité de leurs revenus en commun pour la bonne marche du groupe, et dépendant du point de vue des dépenses d'une même autorité appelée chef de ménage », le chef de ménage étant la personne reconnue comme tel par l’ensemble des membres du ménage".

-Mali : "La Loi d’Orientation Agricole (LOA), dans ses articles 10 à 28, définit ce que sont les exploitations agricoles au Mali. « L’exploitation agricole est une unité de production dans laquelle l’exploitant et/ou ses associés mettent en oeuvre un système de production agricole. Elles sont classées en deux catégories : l’exploitation agricole familiale et l’entreprise agricole. L’exploitation agricole familiale est constituée d’un ou de plusieurs membres unis librement par des liens de parenté ou des us et coutumes et exploitant en commun les facteurs de production en vue de générer des ressources sous la direction d’un des membres, désigné chef d’exploitation, qu’il soit de sexe masculin ou féminin. Le chef d’exploitation assure la maîtrise d’oeuvre et veille à l’exploitation optimale des facteurs de production. Il exerce cette activité à titre principal et représente l’exploitation dans tous les actes de la vie civile. Sont reconnus comme exerçant un métier Agricole, notamment, les agriculteurs, éleveurs, pêcheurs, exploitants forestiers".

-Maroc : "L’unité ménage renvoie au groupe domestique qui est défini comme une unité de résidence, de production et de consommation. Le plus souvent, le groupe domestique a pour noyau une famille, à laquelle peuvent s’ajouter des parents éloignés ou des « étrangers ». Il peut aussi se composer de plusieurs familles nucléaires comme il peut rassembler des personnes sans aucun lien de parenté".

-Mexico : "El Instituto Nacional de Estadística Geografía e Informática (INEGI) usa el concepto de localidad que define como “todo lugar ocupado por una vivienda o conjunto de viviendas, de las cuales al menos una está habitada. El lugar es reconocido comúnmente por un nombre dado por la ley o la costumbre”, y por otro considera que una localidad es rural cuando tiene menos de 2 500 habitantes. El INEGI define también en concepto de hogar como una “unidad doméstica [que] hace referencia a una organización estructurada a partir de lazos o redes sociales establecidas entre personas unidas o no por relaciones de parentesco, que comparten una misma vivienda y organizan en común la reproducción de la vida cotidiana a partir de un presupuesto común para la alimentación, independientemente de que se dividan otros gastos”.

-Nicaragua : "Se define hogar como el número de personas comparten una olla común. Un hogar puede estar compuesto de una o más familias. La definición oficial en Nicaragua de rural es aquel territorio que “comprenden los poblados de menos de 1000 habitantes que no reúnen las condiciones urbanísticas mínimas indicadas y la población dispersa.” INEC, 2007".

-Senegal : "Le rural se définit par opposition à l’urbain, constitué par les villes et les communes, même à dominance rurale. Au Sénégal, les populations d’une commune sont de facto considérées comme des urbains ; or, plusieurs communes sont composées à plus de la moitié par des agriculteurs. Le ménage rural se définit comme un groupe familial résidant en milieu rural au sein duquel s’organisent la production agricole et/ou non agricole, la préparation et la consommation des repas. Traditionnellement, le ménage rural se confond avec le ménage agricole ; toutefois, on note de plus en plus que la nourriture du ménage rural provient de moins en moins de la production ou des revenus tirés de l’agriculture au sens large : production agricole, élevage, pêche et foresterie. L’unité familiale de production et de consommation16 ne coïncide pas forcément avec l’unité de résidence, ker en wolof ou galle en pulaar".

For detailed information on the rationale corresponding to the definition of rural households, the data users can refer to the National Reports, available as External Resources.

Universe

The universe covered by the study includes rural households and all household members that were selected in the study areas.

Kind of data

Sample survey data [ssd]

Sampling procedure

With the objective of 300 to 400 surveyed households per region (i.e. between 900 and 1,200 surveys per country),the Program National teams engaged in the sampling process in two steps. The first step was the selection of the localities to be surveyed, with consideration of regions' characteristics and national team expertise. The second step was the sampling itself, which was based on existing census lists or intentionally prepared locality household lists. Then, households were selected at random, targeting a sufficient number of households per locality allowing representativeness at local level.

In the seven countries, 8,061 rural households' surveys were selected for the sample in 26 regions and 167 localities (depending on the settlement structure), and 7,269 were successfully interviewed and kept for the analysis. In Mali, the 634 household surveys (at the family farm level) were completed by 643 interviews with dependent households and 749 interviews with women.

Mode of data collection

Face-to-face [f2f]

Research instrument

The merged dataset was constructed from variables extracted from national datasets.

For details on questions relating to these variables, see the attached questionnaires for each country survey. Each country questionnaire was derived and adapted from a questionnaire template which was designed collectively by the RuralStruc Program Coordination team and the national teams.

The original page and question numbers for each variable is included in the variable descriptions.

Cleaning operations

Secondary editing of the data in this core dataset included:

(i) Data in local currency units (for example, incomes, prices, sales of agricultural products) were converted to international dollars ($ PPP), for comparability across national surveys. Purchasing Power Parity conversion rates were calculated using the World Bank Development Data Platform. They refer to the period January 2007 to April 2008. The conversion rates between $1 PPP and local currency units are the following: - Kenya: 34 Kenyan Shilling - Madagascar: 758.7 Ariary - Mali: 239.6 CFA Franc - Mexico: 7.3 Mexican Peso - Morocco: 4.8 Dirham - Nicaragua: 6.7 Cordoba - Senegal: 258.6 CFA Franc

(ii) Data in local currency units were converted into kilo-calories, for comparability across national surveys. In all the studied zones, diets rely primarily on cereals - at least in terms of energy. Thus, the basic cereal of each zone (or basket of cereals in the case of Mali) was used as a reference. The conversion rates between Kg of cereals and Kcal are those provided by the FAO's Food Balance Sheets (FAO 2001). The prices of cereals are those used by the RuralStruc national teams to estimate the value of self-consumption. These prices correspond with the average producer sale prices (or the median in the case of Madagascar) for the surveyed year. One will note that, in general, the farm income for the poorest households largely consists of self-consumption of cereals, which are valued, therefore, at the producer sale price. The average cereal prices and kilocalorie ratios permitted calculation of a price for units of 1000 Kcal in $PPP and then to convert the estimated monetary incomes in incomes in kilocalories equivalent. For detailed information, data users can refer to the methodological annex of the synthesis report.

(iii) Recoding of the geographical component of the household identifier

For more details on data editing, the data user should refer to the variable descriptions.

Chad Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data was reported at 0.530 % in 2022. Chad Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data is updated yearly, averaging 0.530 % from Dec 2022 (Median) to 2022, with 1 observations. The data reached an all-time high of 0.530 % in 2022 and a record low of 0.530 % in 2022. Chad Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Chad – Table TD.World Bank.WDI: Social: Poverty and Inequality. The growth rate in the welfare aggregate of the total population is computed as the annualized average growth rate in per capita real consumption or income of the total population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The coverage and quality of the 2017 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2017 exercise of the International Comparison Program. See the Poverty and Inequality Platform for detailed explanations.;World Bank, Global Database of Shared Prosperity (GDSP) (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).;;The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

This file contains data on Ugandan households from six nationwide surveys conducted between 1992 and 2013. Data is available on aggregate household consumption, earnings activities, location of the household, and characteristics of the household head. The file also contains cohort level data where household have been aggregated into cohorts for pseudo-panel analysis.

National household surveys have become the standard source of data for analysis of poverty in developing countries. A major limitation of these surveys for Africa, in terms of the potential to analyse poverty dynamics, is that they are not a panel - different households are surveyed in each wave so they constitute repeated cross sections. It is therefore not possible to track the same households over time to investigate the drivers of poverty reduction. This creates challenges for analysis with endogenous variables, such as interactions between household size and poverty or migration, remittances and household income. The absence of a panel also limits analysis of determinants of household welfare over long periods. The strategy we propose to address this data restriction is to identify representative household types to construct pseudo panels making use of the repeated cross section household surveys (see the Case for Support). Analysis of the pseudo panel allows one to track similar households and complements household-level analysis for each survey. The project will develop methods for constructing pseudo-panels that can be applied, with suitable modifications for specific features of the surveys, in any country with three or more national household surveys. In principle, the methods are also applicable to census and Demographic and Health Survey data. Although the project focuses on Uganda (1992-2012 using eight existing surveys), the methods for constructing and analysing pseudo-panels can be applied to other African countries. Utilising established links with local research partners, hence largely 'off-budget', the pseudo-panel method will be applied to Ghana (1991-2013 using 6 surveys) and Tanzania (1991-2012 using 4 surveys).These three countries all have managed to roughly halve headcount poverty since the early 1990s. We use the repeated cross-section survey data to form a pseudo panel of 'representative' households by grouping individual households (the observational units) into cohorts on the basis of time invariant characteristics (location, gender and birth cohort of household head). The cohorts are then traced over time as they appear in successive surveys, forming a pseudo panel with 'lagged values'. As the cohort fixed effect is correlated with cohort (household) characteristics that are unobserved and not constant over time due to the changing membership of the cohorts in each survey, an errors-in-variables estimator is used to correct the cohort means as estimates of the unobservable population means. The lagged dependent variable is constructed from an auxiliary regression with an augmented instrumental variables estimator using time-invariant instruments. The pseudo panel therefore permits a long (20 years or more) analysis of determinants of household welfare and poverty reduction, with the potential to generate internal instruments for endogenous variables and to identify effects of policy changes (such as Universal Primary Education in Uganda).

The Survey of Carers in Households, 2009-2010 was commissioned by the Department of Health as part of the Government's Carers' Strategy programme. Funded by the Department of Health and the Department for Work and Pensions, the Health and Social Care Information Centre (NHS IC) undertook responsibility for the survey. GfK NOP was commissioned to carry out face-to-face interviews over 11 months of fieldwork in a representative sample of homes in England.

Carers were identified via a short screening 'prevalence' questionnaire at addresses which were randomly selected from the Postcode Address File (PAF). Carers were defined as those people who identified themselves as having extra responsibilities of looking after someone who has a long-term physical or mental ill health or disability, or problems related to old age. People providing care in a professional capacity were excluded.

The main questionnaire then identified carers who also fitted the General Household Survey (GHS) definition of 'Carers', which excludes those caring as volunteers for a charity or organisation, those caring for someone in an institution, those providing financial support only and those caring for someone with a temporary illness or disability, and asked a further range of questions.

Further information may be found on the NHS Digital Survey of Carers in Households - 2009/10 England webpage.

SI.SPR.PCAP.ZG. The growth rate in the welfare aggregate of the total population is computed as the annualized average growth rate in per capita real consumption or income of the total population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered.

The coverage and quality of the 2017 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2017 exercise of the International Comparison Program. See the Poverty and Inequality Platform for detailed explanations. The World Bank’s ESG Data Draft dataset provides information on 17 key sustainability themes spanning environmental, social, and governance categories.

The growth rate in the welfare aggregate of the total population is computed as the annualized average growth rate in per capita real consumption or income of the total population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The coverage and quality of the 2017 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2017 exercise of the International Comparison Program. See the Poverty and Inequality Platform for detailed explanations.