Abstract

To facilitate the use of data collected through the high-frequency phone surveys on COVID-19, the Living Standards Measurement Study (LSMS) team has created the harmonized datafiles using two household surveys: 1) the country’ latest face-to-face survey which has become the sample frame for the phone survey, and 2) the country’s high-frequency phone survey on COVID-19.

The LSMS team has extracted and harmonized variables from these surveys, based on the harmonized definitions and ensuring the same variable names. These variables include demography as well as housing, household consumption expenditure, food security, and agriculture. Inevitably, many of the original variables are collected using questions that are asked differently. The harmonized datafiles include the best available variables with harmonized definitions.

Two harmonized datafiles are prepared for each survey. The two datafiles are: 1. HH: This datafile contains household-level variables. The information include basic household characterizes, housing, water and sanitation, asset ownership, consumption expenditure, consumption quintile, food security, livestock ownership. It also contains information on agricultural activities such as crop cultivation, use of organic and inorganic fertilizer, hired labor, use of tractor and crop sales.
2. IND: This datafile contains individual-level variables. It includes basic characteristics of individuals such as age, sex, marital status, disability status, literacy, education and work.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

See “Malawi - Integrated Household Panel Survey 2010-2013-2016-2019 (Long-Term Panel, 102 EAs)” and “Malawi - High-Frequency Phone Survey on COVID-19” available in the Microdata Library for details.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Malawi Integrated Household Panel Survey (IHPS) 2019 and Malawi High-Frequency Phone Survey on COVID-19 data were harmonized following the harmonization guidelines (see “Harmonized Datafiles and Variables for High-Frequency Phone Surveys on COVID-19” for more details).

The high-frequency phone survey on COVID-19 has multiple rounds of data collection. When variables are extracted from multiple rounds of the survey, the originating round of the survey is noted with “_rX” in the variable name, where X represents the number of the round. For example, a variable with “_r3” presents that the variable was extracted from Round 3 of the high-frequency phone survey. Round 0 refers to the country’s latest face-to-face survey which has become the sample frame for the high-frequency phone surveys on COVID-19. When the variables are without “_rX”, they were extracted from Round 0.

Response rate

See “Malawi - Integrated Household Panel Survey 2010-2013-2016-2019 (Long-Term Panel, 102 EAs)” and “Malawi - High-Frequency Phone Survey on COVID-19” available in the Microdata Library for details.

Abstract

To facilitate the use of data collected through the high-frequency phone surveys on COVID-19, the Living Standards Measurement Study (LSMS) team has created the harmonized datafiles using two household surveys: 1) the country’ latest face-to-face survey which has become the sample frame for the phone survey, and 2) the country’s high-frequency phone survey on COVID-19.

The LSMS team has extracted and harmonized variables from these surveys, based on the harmonized definitions and ensuring the same variable names. These variables include demography as well as housing, household consumption expenditure, food security, and agriculture. Inevitably, many of the original variables are collected using questions that are asked differently. The harmonized datafiles include the best available variables with harmonized definitions.

Two harmonized datafiles are prepared for each survey. The two datafiles are: 1. HH: This datafile contains household-level variables. The information include basic household characterizes, housing, water and sanitation, asset ownership, consumption expenditure, consumption quintile, food security, livestock ownership. It also contains information on agricultural activities such as crop cultivation, use of organic and inorganic fertilizer, hired labor, use of tractor and crop sales. 2. IND: This datafile contains individual-level variables. It includes basic characteristics of individuals such as age, sex, marital status, disability status, literacy, education and work.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

See “Ethiopia - Socioeconomic Survey 2018-2019” and “Ethiopia - COVID-19 High Frequency Phone Survey of Households 2020” available in the Microdata Library for details.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Ethiopia Socioeconomic Survey (ESS) 2018-2019 and Ethiopia COVID-19 High Frequency Phone Survey of Households (HFPS) 2020 data were harmonized following the harmonization guidelines (see “Harmonized Datafiles and Variables for High-Frequency Phone Surveys on COVID-19” for more details).

The high-frequency phone survey on COVID-19 has multiple rounds of data collection. When variables are extracted from multiple rounds of the survey, the originating round of the survey is noted with “_rX” in the variable name, where X represents the number of the round. For example, a variable with “_r3” presents that the variable was extracted from Round 3 of the high-frequency phone survey. Round 0 refers to the country’s latest face-to-face survey which has become the sample frame for the high-frequency phone surveys on COVID-19. When the variables are without “_rX”, they were extracted from Round 0.

Response rate

See “Ethiopia - Socioeconomic Survey 2018-2019” and “Ethiopia - COVID-19 High Frequency Phone Survey of Households 2020” available in the Microdata Library for details.

Abstract

To facilitate the use of data collected through the high-frequency phone surveys on COVID-19, the Living Standards Measurement Study (LSMS) team has created the harmonized datafiles using two household surveys: 1) the country’ latest face-to-face survey which has become the sample frame for the phone survey, and 2) the country’s high-frequency phone survey on COVID-19.

The LSMS team has extracted and harmonized variables from these surveys, based on the harmonized definitions and ensuring the same variable names. These variables include demography as well as housing, household consumption expenditure, food security, and agriculture. Inevitably, many of the original variables are collected using questions that are asked differently. The harmonized datafiles include the best available variables with harmonized definitions.

Two harmonized datafiles are prepared for each survey. The two datafiles are: 1. HH: This datafile contains household-level variables. The information include basic household characterizes, housing, water and sanitation, asset ownership, consumption expenditure, consumption quintile, food security, livestock ownership. It also contains information on agricultural activities such as crop cultivation, use of organic and inorganic fertilizer, hired labor, use of tractor and crop sales.
2. IND: This datafile contains individual-level variables. It includes basic characteristics of individuals such as age, sex, marital status, disability status, literacy, education and work.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

See “Nigeria - General Household Survey, Panel 2018-2019, Wave 4” and “Nigeria - COVID-19 National Longitudinal Phone Survey 2020” available in the Microdata Library for details.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Nigeria General Household Survey, Panel (GHS-Panel) 2018-2019 and Nigeria COVID-19 National Longitudinal Phone Survey (COVID-19 NLPS) 2020 data were harmonized following the harmonization guidelines (see “Harmonized Datafiles and Variables for High-Frequency Phone Surveys on COVID-19” for more details).

The high-frequency phone survey on COVID-19 has multiple rounds of data collection. When variables are extracted from multiple rounds of the survey, the originating round of the survey is noted with “_rX” in the variable name, where X represents the number of the round. For example, a variable with “_r3” presents that the variable was extracted from Round 3 of the high-frequency phone survey. Round 0 refers to the country’s latest face-to-face survey which has become the sample frame for the high-frequency phone surveys on COVID-19. When the variables are without “_rX”, they were extracted from Round 0.

Response rate

See “Nigeria - General Household Survey, Panel 2018-2019, Wave 4” and “Nigeria - COVID-19 National Longitudinal Phone Survey 2020” available in the Microdata Library for details.

Abstract

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

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

Geographic coverage

National coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

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

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

Mode of data collection

Face-to-face [f2f]

Cleaning operations

(a) DATA ENTRY

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

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

Abstract

The Living Standards Measurement Survey (LSMS) is a multi-purpose household survey conducted to measure living conditions and poverty situation, and to help policymakers in monitoring and developing social programs.

LSMS has been carried out in Albania in the context of continuing monitoring of poverty and the creation of policy evaluation system in the framework of the National Strategy for Development and Integration (previously the National Strategy for Economic and Social Development).

The first Albania LSMS was conducted in 2002, followed by 2003, 2004, 2005, 2008 and 2012 surveys. In 2012, 6,671 households participated in the survey.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey includes a sample of 6,671 households that constitute the survey units. The sample is chosen randomly by two rounds of selection. The sample frame was provided from Population and Housing Census done on October 2011. In the first round, 834 Primary Selection Units (PSUs) have been chosen randomly to represent the whole territory of the country. Then, 8 households for each PSU were chosen to be interviewed in the second round through a procedure of systematic sample. To handle cases of non response or no contact other 4 households for each PSU were chosen as substitutes that ensured the target of 6,671 completed questionnaires near the households.

The methodology of the 2012 LSMS has been kept similar with the surveys conducted in the previous years. However, the geographic domains of analysis have been expanded to include the 12 individual prefectures of Albania, by urban and rural strata, compared to four geographic regions (Central, Coastal, Mountain and Tirana) by urban and rural strata defined previously as domains for the survey. This required a considerable increase in the sample size from 3,600 to 6,671 households making possible to calculate indicators of living standard for 24 strata and even for the four main areas of the country in order to compare the regional results to those from the 2002, 2005 and 2008 surveys and study the regional trends for various indicators.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was divided in two sections, and was administered to households in two visits, one section per visit. During the second visit the interviewer would also collect additional information of use for the eventual tracking of the household in the next waves of the panel.

The Booklet for Recording Daily Household Consumption was left with the household by the interviewer during the first visit for the household to compile, and collected during the second visit. Upon collection, interviewers took care of checking the entries (also with the help of a checklist provided at the end of the booklet) and correct them as appropriate with the help of the most knowledgeable person in the household.

A specific column was provided for the household to record the reference period for any purchases of food. The checklist was compiled by the interviewer, with the help of the most knowledgeable person in the household, upon collection of the diary. Interviewers were instructed to check, for 14 main food staples, whether any consumption of the item had been recorded in the diary. Whenever an item had not been recorded the interviewer would ask the respondent to report whether the item (a) had not been used in the 14-day period, or (b) had been consumed but the household had forgotten to record its consumption, or else (c) had been consumed by the household drawing on stocks purchased or produced outside the 14-day period. If the inclusion of an item had simply been forgotten the interviewer would then fill the appropriate section of the diary by asking the household to recall the details of that consumption. If the household reported consuming an item purchased before the beginning of the 14-day period, then information on the frequency of purchase, quantity, unit of measure and value of the purchase were recorded in the columns provided to this end in the checklist.

Abstract

Starting June 1999, after the intervention of NATO in the conflict between Kosovo and Serbia (FRY), the United Nations provided interim administration for the province. The consequences of the conflict on the living standards of the population were severe, with the collapse of the industrial sector, the paralysis of agriculture, and extensive damage to private housing, education and health facilities and other infrastructure. In addition, the conflict brought massive population displacement both within Kosovo and abroad.

A year later, Kosovo was in a process of transition from emergency relief to long-term economic development. The purpose of the survey was to provide crucial information for policy and program design for use by the United Nations Interim Administration Mission in Kosovo (UNMIK), international donors, non-governmental organizations (NGOs), and the Kosovar community at large for poverty alleviation and inequality reduction.

During the same period, the Food and Agriculture Organization (FAO) was planning an agriculture and livestock survey. It was decided to join both surveys, in order to pool resources and provide better assistance to the newly re-formed Statistical Office of Kosovo (SOK) and to take into account the extensive Kosovar peasant household economy. Therefore the agriculture and food aid modules are more developed than those of a standard LSMS survey.

The International Organization for Migration (IOM) also was interested in information related to labor force and employment. They had run a socio-demographic and reproductive health survey with the United Nations Population Fund, covering approximately 10,000 households at the end of 1999. IOM provided the urban sampling frame for the present survey.

Geographic coverage

Kosovo. Domains: Urban/rural; Area of Responsibility (American, British, French, German, Italian); Serbian minority

Analysis unit

• Households
• Individuals
• Community

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLE DESIGN

The sample design used in the Kosovo LSMS 2000 had to contend with the fact that the last census, conducted in 1991, was rendered obsolete by the boycott of the Albanian population and by the massive displacements since March 1998. A Housing Damage Assessment Survey (HDAS) was conducted in February 1999 and updated in June 1999 by the International Management Group (IMG) and the United Nations High Commissioner for Refugees (UNHCR) in the rural areas. The survey covered 95 percent of the Albanian rural areas and provided the basis for the rural sampling frame, after updating. The updating and household listings in selected villages were conducted by FAO.

Since the HDAS did not cover Serbian villages, a quick counting4 of housing units was performed in these villages, following a procedure similar to the one in the urban areas. In urban areas, the original plan was to use the information from the on-going individual voters’ registration conducted by the Organization for Security and Cooperation in Europe (OSCE). Since the registration was limited to individuals above 16 years old, it was then decided to conduct a quick counting of households in the 22 urban areas. The quick counting and subsequent listing of households was performed by IOM, under the supervision of the sampling expert hired by the World Bank. . FRAMEWORK

UNMIK divided Kosovo into 5 areas of responsibility (AR), roughly equivalent to the former regions (American – Southeast, British – East including Pristina, French – North, German-South, Italian – West). The rural frame used the IMG/UNHCR Housing Damage Assessment Survey. It was updated with the collaboration of FAO and provided much better information on which to build the sample for the survey. Aerial pictures of the villages selected in the survey were used to help identifying housing units. Only one household was interviewed in each housing unit. For the Serbian villages, counting households and making listings had to be elaborated by the survey team.

In urban areas, IOM contracted the quick counting to SOK in the Albanian cities and to firms in the Serb areas. These firms updated existing lists, or performed some quick counting. Using the updated information IOM created enumeration areas of size 150-200 housing units. Based on this quick counting, a full listing took place in all the selected EAs and 12 households were randomly selected. Given safety issues and quality problems discovered at the enumeration stage, the Serb urban listings were revised after the end of the survey, by the Serb survey team, who had performed the rural listings.

The sample was preset at 2,880 households in order to allow analyses in the following breakdowns: (a) Kosovo as a whole; (b) by area of responsibility, (c) by urban/rural locations. In addition, the survey data can be used to derive separate estimates for the Serbian minority.

In the rural area, 30 Albanian villages were randomly selected in each AR and a listing of all households in the village was established.5 In each village, 12 households were then randomly selected (8 for interviewing and 4 reserve households). Similarly, 30 urban enumeration areas (between 150 and 200 households lie in each urban EA) were randomly selected in the Albanian part of each AR. Twelve households were then selected in each EA. In the rural area, 30 Serb villages were selected from the three municipalities in the northern part of Kosovo, the enclaves and the municipality of Strepce. Thirty urban EA were selected in the same region. In each village and urban area, 12 households were then randomly selected.

STRATIFICATION

In addition to the explicit stratification of the areas of responsibility and the ethnic composition in each rural and urban category, an implicit stratification of geographic ordering in a serpentine method in the villages and urban enumeration areas was followed. In order to be able to provide estimates for the separate domains described above, it was recommended that 240 households be interviewed in each domain. We had very little prior knowledge of response rates. In the rural villages, it was decided to select 12 households and identify 4 of them as “reserve households”. These reserve households were to be used only in specific cases, described at length to the logistics person/driver of the interviewing team. The final sample size was 1,200 rural and urban Albanian households and 240 rural and urban Serb households, for a total sample size of 2,880 households.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two questionnaires were used to collect the information: a household questionnaire and a community questionnaire. No anthropometric information was collected as malnutrition problems, facing Kosovar children and women, would not be detected by these procedures.

Since FAO and SOK were conducting a price survey in 7 cities of Kosovo, on a monthly basis, it was decided to not include a separate price questionnaire but use the data from the FAO-SOK price survey. The Kosovo LSMS 2000 collected information using a household questionnaire, which was based in part on the standard LSMS questionnaire developed in Grosh and Glewwe (2000).

The standard questionnaire was adapted to the specifics of the Kosovar environment and special modules about displacement, food aid and social protection were added. Individual modules were administered as much as possible to most informed respondents. Box 1 contains a summary of the content of the questionnaire.

The community questionnaire was designed to collect information on community-level infrastructure, with a special emphasis on school and health facilities as well as displaced persons issues. Box 2 contains a summary of the content of the community questionnaire. [Note: Community is defined as the Primary Sampling Unit (PSU) of the survey. In rural areas, it generally encompasses villages unless these are less than 50 households (in which case, they were grouped with a neighboring village) or more than 200 households (in which case, they were broken-up in PSUs of 50-200 households). In urban areas, community is defined as the Enumeration Area but includes the larger city when referring to secondary school and university, hospitals and factories.]

Response rate

Households from the original sample selection which could not be interviewed were replaced by reserve households to reach the final sample size. The non-response rate among households originally selected for inclusion in the sample in rural Albanian areas was 11.8 percent and 20.8 percent in urban Albanian areas. These rates in the Serbian areas were 14.2 percent among rural households and 39.2 percent among urban households.

In the rural Albanian areas, non-response came mostly from households having moved outside of the village. A few refusals were due to the fact that households were in mourning or celebrating other religious occasions (wedding, baptisms, circumcisions, etc…), or the household head was a women alone. There were only 20 actual refusals of the originally selected households, only 2 percent of the 1,200 households originally contacted.

In the Serbian rural areas, half of the non-responses were due to households having traveled to Serbia for visits (holidays, health care issues, indefinite travel….). Other reasons included: interviewer’s safety (houses too isolated) and households refusing to respond in the absence of the head. There were only 5 such cases, again only 2 percent of the 240 households originally contacted. In the urban areas, 10 percent of the non-responses were linked to listings problems (non-existent addresses).

Descriptive statistics including mean and standard deviation of dependent and independent variables for all villages and for villages in warm humid and warm sub-humid zones.

Abstract

In 2001, the World Bank in co-operation with the Republika Srpska Institute for Statistics (RSIS), the Federal Office of Statistics (FOS) and the Agency for Statistics of Bosnia and Herzegovina (BHAS), carried out a Living Standards Measurement Survey (LSMS).

The Living Standard Measurement Survey LSMS, in addition to collecting the information necessary to obtain a comprehensive as possible measure of the basic dimensions of household living standards, has three basic objectives, as follows: 1. To provide the public sector, government, the business community, scientific institutions, international donor organizations and social organizations with information on different indicators of the population's living conditions, as well as on available resources for satisfying basic needs. 2. To provide information for the evaluation of the results of different forms of government policy and programs developed with the aim to improve the population's living standard. The survey will enable the analysis of the relations between and among different aspects of living standards (housing, consumption, education, health, labor) at a given time, as well as within a household. 3. To provide key contributions for development of government's Poverty Reduction Strategy Paper, based on analyzed data.

The Department for International Development, UK (DFID) contributed funding to the LSMS and is also providing funding for a further two years of data collection for a panel survey, to be known as the Household Survey Panel Series (HSPS). Birks Sinclair & Associates Ltd. are responsible for the management of the HSPS with technical advice and support being provided by the Institute for Social and Economic Research (ISER), University of Essex, UK.

The aim of the panel survey is to provide longitudinal data through re-interviewing approximately half the LSMS respondents for two years following the LSMS, in the autumn of 2002 and again in 2003. The LSMS constitutes wave 1 of the panel survey so there will be three years of panel data available for analysis under current funding plans. For the purposes of this document we are using the following convention to describe the different rounds of the panel survey: Wave 1 LSMS conducted in 2001 forms the baseline survey for the panel Wave 2 Second interview of 50% of LSMS respondents in Autumn/Winter 2002 Wave 3 Third interview with sub-sample respondents in Autumn/Winter 2003

The panel data will allow the analysis of key transitions and events over this period such as labour market or geographical mobility and observe the consequent outcomes for the well-being of individuals and households in the survey.

The panel data will provide information on income and labour market dynamics within FBiH and RS. A key policy area is developing strategies for the reduction of poverty within FBiH and RS. The panel will provide information on the extent to which continuous poverty is experienced by different types of households and individuals over the three year period. And most importantly, the co-variates associated with moves into and out of poverty and the relative risks of poverty for different people can be assessed. As such, the panel aims to provide data, which will inform the policy debates within FBiH and RS at a time of social reform and rapid change.

Geographic coverage

National coverage. Domains: Urban/rural/mixed; Federation, Republic

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The panel survey sample is made up of over 3,000 households drawn from the Living Standards Measurement Survey (LSMS) conducted by the World Bank in co-operation with the SIs in 2002. Approximately half the households interviewed on the LSMS were selected and carried forward into the panel survey. These households were re-interviewed in 2003 and will be interviewed for a third time in September 2004.

Sampling Frame

The 5,400 households interviewed on LSMS formed the sampling frame for the panel survey. The aim was to achieve interviews with approximately half of these (2,700) at wave 2 (1,500 in FBiH and 1,200 in RS). A response rate of 90% was anticipated (as the sample is based on households that have already co-operated with LSMS) and therefore the selected sample consisted of 3,000 households. Unlike the LSMS, the HSPS does not have a replacement element to the sample, only the original 3,000 issued addresses. This approach was new to the Supervisors and Interviewers and special training was given on how to keep non-response to a minimum.

The LSMS Sample

The LSMS sample design process experienced some difficulties which resulted in a sample with a disproportionately high number of households being selected in urban areas. Work by Peter Lynn from ISER identified the source of this problem by establishing the selection probabilities at each stage of the LSMS sampling process. Essentially, the procedures used for selecting households within municipalities would have been appropriate had municipalities been selected with equal probabilities. But in fact municipalities had been selected with probability proportional to size, and using different overall sampling fractions in each of three strata. The details are documented in a memo by Peter Lynn dated 25-3-2002. Consequently, household selection probabilities varied considerably across municipalities.

Compensating for the LSMS sample imbalance

Having established the selection probability of every LSMS household, it became possible to derive design-based weights that should provide unbiased estimates for LSMS. However, the considerable variability in these weights means that the variance of estimates (and hence standard errors and confidence intervals) is greatly increased. For the HSPS, there was an opportunity to reduce the variability in weights by constructing the subsample in a way that minimised the variability in overall selection probabilities. The overall selection probability for each household would be the product of two probabilities - the probability of being selected for LSMS, and the probability of being selected for HSPS, conditional upon having been selected for LSMS, i.e. P(HSPS) = P(LSMS) * P(HSPS)/(LSMS)

Ideally, then, we would have set the values of P(HSPS)/(LSMS) to be inversely proportional to P(LSMS). This would have resulted in each HSPS household having the same overall selection probability, P(HSPS), so that there would no longer be an increase in the variance of estimates due to variability in selection probabilities. However, this was not possible due to the very considerable variation in P(LSMS) and the limited flexibility provided by a large overall sampling fraction for HSPS (3,000 out of 5,400).

The best that could be done was to minimise the variability in sampling fractions by retaining all the LSMS households in the (mainly rural or mixed urban/rural) municipalities where LSMS household selection probabilities had been lowest and sub-sampling only in the municipalities where LSMS selection probabilities had been much higher. In 16 of the 25 LSMS municipalities, all households were retained for HSPS. In the other 9 municipalities, households were sub-sampled, with sampling fractions ranging from 83% in Travnik to just 25% in Banja Luka and Tuzla.

To select the required number of households within each municipality, every group of enumeration districts (GND) was retained from LSMS. The sub-sampling took place within the GNDs. Households were sub-sampled using systematic random sampling, with a random start and fixed interval. For example, in Novo Sarajevo, where the sampling fraction was 1 in 2, 6 households were selected out of the 12 LSMS households in each GND by selecting alternate households. In Prijedor, where the fraction was 1 in 3, 4 out of 12 were selected by taking every third LSMS household. And so on.

The total selected sample for the HSPS consists of 3,007 households (1681 in the FBIH and 1326 in the RS).

The overall design weight for the HSPS sample will be the product of the LSMS weight for the household and this extra design weight (which will of course tend to increase the size of the smallest LSMS weights).

Panel design

Eligibility for inclusion

The household and household membership definitions are the same standard definitions as used on the LSMS (see Supervisor Instructions, Annex A). While the sample membership status and eligibility for interview are as follows: i) All members of households interviewed at wave 1 (LSMS) have been designated as original sample members (OSMs). OSMs include children within households even if they are too young for interview. ii) Any new members joining a household containing at least one OSM, are eligible for inclusion and are designated as new sample members (NSMs). iii) At each wave, all OSMs and NSMs are eligible for inclusion, apart from those who move outof-scope (see discussion below). iv) All household members aged 15 or over are eligible for interview, including OSMs and NSMs.

Following rules and the definition of 'out-of-scope'

The panel design means that sample members who move from their previous wave address at either wave 2 or 3 must be traced and followed to their new address for interview. The LSMS sample was clustered and over the two waves of the panel some de-clustering will occur as people move. In some cases the whole household will move together but in others an individual member may move away from their previous wave household and form a new split-off household of their own.

Following rules

All sample members, OSMs and NSMs, are followed at each wave and an interview attempted. This means that a four person household at Wave 1 could generate

Abstract

Niger is part of the Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) program. This program has developed a household level survey with a view to enhancing our knowledge of agriculture in Sub-Saharan Africa, in particular, its role in poverty reduction and the techniques for promoting efficiency and innovation in this sector. To achieve this objective, an innovative model for agricultural data collection in this region will need to be developed and implemented. To this end, activities conducted in the future will be supported by four main pillars: a multisectoral framework, institutional integration, analytical capacity building, and active dissemination.

First, agricultural statistical data collection must be part of an expanded and multisectoral framework that goes beyond the rural area. This will facilitate generation of the data needed to formulate effective agricultural policies throughout Niger and in the broader framework of the rural economy.

Second, agricultural statistical data collection must be supported by a well-adapted institutional framework suited to fostering collaboration and the integration of data sources. By supporting a multi-pronged approach to data collection, this project seeks to foster intersectoral collaboration and overcome a number of the current institutional constraints.

Third, national capacity building needs to be strengthened in order to enhance the reliability of the data produced and strengthen the link between the producers and users of data. This entails having the capacity to analyze data and to produce appropriate public data sets in a timely manner. The lack of analytical expertise in developing countries perpetuates weak demand for statistical data.

Consequently, the foregoing has a negative impact on the quality and availability of policy-related analyses. Scant dissemination of statistics and available results has compounded this problem.

In all countries where the LSMS-ISA project will be executed, the process envisioned for data collection will be a national household survey, based on models of LSMS surveys to be conducted every three years for a panel of households. The sampling method to be adopted should ensure the quality of the data, taking into account the depth/complexity of the questionnaire and panel size, while ensuring that samples are representative.

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.

Geographic coverage

National Coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

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 key 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 as distributed represent the best effort to provide complete information. The data were collected and cleaned prior to the construction of the consumption aggregate. Using the same guidelines as were used in 2011, the households that are provided in the data set should have consumption data for both visits. This may not be the case. During the cleaning process, it was found that households had been misidentified which allowed more households to be included in the final consumption aggregate file (see below). The raw data that contains household/item level data that was used to calculate the consumption aggregate has been included in the distribution file.There are 3,614 households and 26,579 individuals in the data.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample size is 2,155 households

LSMS Sample Design

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

Panel Survey Sample Design

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

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

Panel design

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

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

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

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

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

Mode of data collection

Face-to-face [f2f]

Research instrument

Panel questionnaire content

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

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

Results from the second stage of the double hurdle model for processed fish consumption and forest cover around rivers at the village level across all measures of forest cover (see S6 Table for full results) Z-statistics are given in parentheses.

Abstract

This study aims to help address the issue of the appropriate use of statistical data in policy development in Serbia. Faced with enterprise restructuring, high unemployment and high levels of social exclusion, as well as the consequences of internal population displacement, the Government of Serbia (GoS) has recognized and acknowledged the need for fundamental reforms in social policy area and the collection of adequate data of social statistics. Reliable household data are scarce in Serbia, with the result that social policy making is put on a precarious basis. The exceptional circumstances of Serbia have left a legacy of immense complexity, in which social groups have become fractured and excluded. A statistically reliable basis for policy making, particularly in the social sphere, is a priority. Data on poverty and living standards are seen as a part of information system to support decision making by the GoS and its line Ministries. The public is also keenly interested in poverty data. Therefore poverty data are also crucially important for strategic planning bodies within GoS, and for donors in assessing their strategies in support of the Poverty Reduction Strategy (PRS).

Geographic coverage

National

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The population for LSMS consists of Republic of Serbia residents, excluding Kosovo and Metohija . The sampling frame for the LSMS was based on the Enumeration District (ED) delineated for the 2002 Serbia Census, excluding those with less than 20 households. It is estimated that the households in the excluded EDs only represent about 1 percent of the population of Serbia. The sampling frame also excludes the population living in group quarters, institutions and temporary housing units, as well as the homeless population: these groups also represent less than 1 percent of the population, so the sampling frame should cover at least 98 percent of the Serbian population. Stratification was done in the same way as for the previous LSMSs. Enumeration District were stratified according to: (1) Region in 6 strata (Vojvodina, Belgrade, West Serbia, Sumadija and Pomoravlj e, East Serbia and South East Serbia) (2) Type of settlement (urban and other)

The allocation of EDs according to region and type of settlement was propoI1ionai to the number of occupied dwellings, adjusted to provide sufficient precision of estimates at the regional level. To provide optimal sample sizes in each region we decided that the minimum number of allocated EDs to each stratum should be 60. The result of this procedure was a slight deviation from strictly proportional allocation. The sample size for LSMS 2007 was 71 40 households from 510 selected EDs. Within each ED 14 occupied dwellings were selected. From each selected occupied dwelling one household was selected (using a Kish Grid). The sample size was determined according with the aim of achieving 5,000 household interviews with an expected non-response rate of around 30%. The final response rate was 78%, producing a sample size of 5,557 households.

Sampling deviation

The overall estimated total number of households from the 2007 LSMS based on the final weights is about 10 percent lower than the corresponding figure from the 2002 Census frame. The difference is larger for the rural strata (12.1 percent) than the urban strata (8.7 percent). These differences probably include an actual decline in the number of households in some strata and may also reflect the quality of the updating of the listing of occupied dwelling units in sample EDs.

Mode of data collection

Face-to-face [f2f]

Response rate

Response rate was 78 percent

Abstract

The General Household Survey-Panel (GHS-Panel) is implemented in collaboration with the World Bank Living Standards Measurement Study (LSMS) team as part of the Integrated Surveys on Agriculture (ISA) program. The objectives of the GHS-Panel include the development of an innovative model for collecting agricultural data, interinstitutional collaboration, and comprehensive analysis of welfare indicators and socio-economic characteristics. The GHS-Panel is a nationally representative survey of approximately 5,000 households, which are also representative of the six geopolitical zones. The 2023/24 GHS-Panel is the fifth round of the survey with prior rounds conducted in 2010/11, 2012/13, 2015/16 and 2018/19. The GHS-Panel households were visited twice: during post-planting period (July - September 2023) and during post-harvest period (January - March 2024).

Geographic coverage

National

Analysis unit

• Households • Individuals • Agricultural plots • Communities

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The original GHS‑Panel sample was fully integrated with the 2010 GHS sample. The GHS sample consisted of 60 Primary Sampling Units (PSUs) or Enumeration Areas (EAs), chosen from each of the 37 states in Nigeria. This resulted in a total of 2,220 EAs nationally. Each EA contributed 10 households to the GHS sample, resulting in a sample size of 22,200 households. Out of these 22,200 households, 5,000 households from 500 EAs were selected for the panel component, and 4,916 households completed their interviews in the first wave.

After nearly a decade of visiting the same households, a partial refresh of the GHS‑Panel sample was implemented in Wave 4 and maintained for Wave 5. The refresh was conducted to maintain the integrity and representativeness of the sample. The refresh EAs were selected from the same sampling frame as the original GHS‑Panel sample in 2010. A listing of households was conducted in the 360 EAs, and 10 households were randomly selected in each EA, resulting in a total refresh sample of approximately 3,600 households.

In addition to these 3,600 refresh households, a subsample of the original 5,000 GHS‑Panel households from 2010 were selected to be included in the new sample. This “long panel” sample of 1,590 households was designed to be nationally representative to enable continued longitudinal analysis for the sample going back to 2010. The long panel sample consisted of 159 EAs systematically selected across Nigeria’s six geopolitical zones.

The combined sample of refresh and long panel EAs in Wave 5 that were eligible for inclusion consisted of 518 EAs based on the EAs selected in Wave 4. The combined sample generally maintains both the national and zonal representativeness of the original GHS‑Panel sample.

Sampling deviation

Although 518 EAs were identified for the post-planting visit, conflict events prevented interviewers from visiting eight EAs in the North West zone of the country. The EAs were located in the states of Zamfara, Katsina, Kebbi and Sokoto. Therefore, the final number of EAs visited both post-planting and post-harvest comprised 157 long panel EAs and 354 refresh EAs. The combined sample is also roughly equally distributed across the six geopolitical zones.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The GHS-Panel Wave 5 consisted of three questionnaires for each of the two visits. The Household Questionnaire was administered to all households in the sample. The Agriculture Questionnaire was administered to all households engaged in agricultural activities such as crop farming, livestock rearing, and other agricultural and related activities. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

GHS-Panel Household Questionnaire: The Household Questionnaire provided information on demographics; education; health; labour; childcare; early child development; food and non-food expenditure; household nonfarm enterprises; food security and shocks; safety nets; housing conditions; assets; information and communication technology; economic shocks; and other sources of household income. Household location was geo-referenced in order to be able to later link the GHS-Panel data to other available geographic data sets (forthcoming).

GHS-Panel Agriculture Questionnaire: The Agriculture Questionnaire solicited information on land ownership and use; farm labour; inputs use; GPS land area measurement and coordinates of household plots; agricultural capital; irrigation; crop harvest and utilization; animal holdings and costs; household fishing activities; and digital farming information. Some information is collected at the crop level to allow for detailed analysis for individual crops.

GHS-Panel Community Questionnaire: The Community Questionnaire solicited information on access to infrastructure and transportation; community organizations; resource management; changes in the community; key events; community needs, actions, and achievements; social norms; and local retail price information.

The Household Questionnaire was slightly different for the two visits. Some information was collected only in the post-planting visit, some only in the post-harvest visit, and some in both visits.

The Agriculture Questionnaire collected different information during each visit, but for the same plots and crops.

The Community Questionnaire collected prices during both visits, and different community level information during the two visits.

Cleaning operations

CAPI: Wave five exercise was conducted using Computer Assisted Person Interview (CAPI) techniques. All the questionnaires (household, agriculture, and community questionnaires) were implemented in both the post-planting and post-harvest visits of Wave 5 using the CAPI software, Survey Solutions. The Survey Solutions software was developed and maintained by the Living Standards Measurement Unit within the Development Economics Data Group (DECDG) at the World Bank. Each enumerator was given a tablet which they used to conduct the interviews. Overall, implementation of survey using Survey Solutions CAPI was highly successful, as it allowed for timely availability of the data from completed interviews.

DATA COMMUNICATION SYSTEM: The data communication system used in Wave 5 was highly automated. Each field team was given a mobile modem which allowed for internet connectivity and daily synchronization of their tablets. This ensured that head office in Abuja had access to the data in real-time. Once the interview was completed and uploaded to the server, the data was first reviewed by the Data Editors. The data was also downloaded from the server, and Stata dofile was run on the downloaded data to check for additional errors that were not captured by the Survey Solutions application. An excel error file was generated following the running of the Stata dofile on the raw dataset. Information contained in the excel error files were then communicated back to respective field interviewers for their action. This monitoring activity was done on a daily basis throughout the duration of the survey, both in the post-planting and post-harvest.

DATA CLEANING: The data cleaning process was done in three main stages. The first stage was to ensure proper quality control during the fieldwork. This was achieved in part by incorporating validation and consistency checks into the Survey Solutions application used for the data collection and designed to highlight many of the errors that occurred during the fieldwork.

The second stage cleaning involved the use of Data Editors and Data Assistants (Headquarters in Survey Solutions). As indicated above, once the interview is completed and uploaded to the server, the Data Editors review completed interview for inconsistencies and extreme values. Depending on the outcome, they can either approve or reject the case. If rejected, the case goes back to the respective interviewer’s tablet upon synchronization. Special care was taken to see that the households included in the data matched with the selected sample and where there were differences, these were properly assessed and documented. The agriculture data were also checked to ensure that the plots identified in the main sections merged with the plot information identified in the other sections. Additional errors observed were compiled into error reports that were regularly sent to the teams. These errors were then corrected based on re-visits to the household on the instruction of the supervisor. The data that had gone through this first stage of cleaning was then approved by the Data Editor. After the Data Editor’s approval of the interview on Survey Solutions server, the Headquarters also reviews and depending on the outcome, can either reject or approve.

The third stage of cleaning involved a comprehensive review of the final raw data following the first and second stage cleaning. Every variable was examined individually for (1) consistency with other sections and variables, (2) out of range responses, and (3) outliers. However, special care was taken to avoid making strong assumptions when resolving potential errors. Some minor errors remain in the data where the diagnosis and/or solution were unclear to the data cleaning team.

Response

Abstract

The Living Standards Measurement Study (LSMS) is a household survey program focused on generating high-quality data, improving survey methods, and building capacity. The goal of the LSMS is to facilitate the use of household survey data for evidence-based policymaking. The aim of this survey was to select objective, representative and as far as possible, total information, which would enable users to draw up a picture of the actual status of living standards of the population of the Republic of Kazakhstan. This information should be the basis for the assessment of efficiency of Government Economic and Social refprms, and should assist in the application of specific levels of social protection.

Geographic coverage

The whole country.

Analysis unit

• Households
• Individuals
• Municipal

Universe

The main resources of information concerning social and economic indicators of living standard of population of the Republic of Kazakhstan are 6000 households which represent a republican network. The survey LSMS carried was a cultipurpose probability sample which covered 2000 households of the Republic of Kazakhstan.

Kind of data

Sample survey data [ssd]

Sampling procedure

A Sample designed for LSMS had to assist to:

• Reflect the realistic picture of social and territorial distribution of the population in the Republic of kazakhstan;
• Compare the outcome of LSMS in Kazakhstan with the results of LSMS in other countries, which were performed basedon possible sample principals;

Data was presented in total for all Kazakhstan (linear distribution of answers) and in different samples according to the purposes of the survey. Grouping of data within Oblasts ensures maximu interest. We did not however carry out an analysis in Oblast sample, since sub-groups which had been defined as a result did not obtain enough volume for representative conclusions. Alternatively we administered a territory (regional) sample through merging several Oblasts within regions. data has been analyses in seven samples in accordance with the purposes of the survey and the wishes of our partners:

• Sampling according to types of settlement;
• Territory sample;
• Sex sample;
• Age sample;
• Sample on household size
• Sample according to household type;
• Sample according to average per capita monthly income%.

Sampling deviation

To create a basis to design , a probability sample GOSCOMSTAT and its oblast branches in May 1996 have delivered the most actual numerical material concerning population (01-01-1996). it contains the following information:

• A list of all 2'534 agricultural regions without exeption with the measurement of their quantity (number of households and number of inhabitants). This data has been received from rural administrations where total registration is carried out;
• A list of all 263 villages, small and middle-size cities without exeption with the measurement of their quantity (number of households and number of inhabitants). This data has been receinved from village councils and from house administrations bodies for the cities where the total registration is carried out.
• A list of all 24 large citiess without exeption with the measurement of their size (number of households and number of inhabitants). This data has been receinved from house administrations bodies (eg: technical inventory Bureau, house administrations, Cooperatives, Street Committees, Brach Agencies and Dormitories).

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire has been designed in the following stages:

• Family Questionnaire contains the questions which relate to all households in general and consist of following sections: Information about a family card, living standard, agriculture and cattée-breeding, expenditures/consumption, incomes;
• Individuals Questionnaire contains questions which relate to each member of a family and consists of the followingsections: General information/migration, education, care of children, employment status, work, medical services, health assessment, for women, time budget, anthropometrical measurements.
• Community Questionnaire consist of the following sections: Demographical information, economy and infrastructure, agriculture, education and health care Price Questionnaire that was designed as a separate section to Community Questionnaire. the information concerning 21 items of the most important nutrition products has been selected in it. Price were to be surveyed if possible in three different sale units (shops, markets, kiosks) of this populated area. however, interviewers were able to find in many villages, only working shop.

Response rate

Regarding the Individual Questionnaire, out of 7'223 interviewed persons, 6'955 permanently live in their households (96.3%) and 130 (1.8%) are living inside the most part of their time and 65 (0.9%) oftener living in other places.

Abstract

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

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

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

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

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

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

Geographic coverage

National coverage. Domains: Tirana, other urban,rura

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

Panel sample, with LSMS 2002 and 2004

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

LSMS 2002 sample design

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

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

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

APS 2003-2004 sample design

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

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

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

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

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

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

Mode of data collection

Face-to-face [f2f]

Research instrument

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

We included rivers that were within 1km, 5km, 10km, and 20km of each village. Forest density was measured within buffers of 100m, 500m, 1km, and 2km around rivers.

Z-statistics are given in parentheses.

The World Bank's Living Standard Measurement Study (LSMS) was adapted for use in Guyana and administered in early 1993 as part of the Guyana Bureau of Statistics' year-long Household Income and Expenditure Survey (HIES). Because the LSMS survey was to take place at about the same time as the Household Income and Expenditure Survey (HIES), it was decided to link the two surveys. The HIES questionnaire substituted for the normal LSMS modules on income, expenditures, labor activities, household businesses, housing, durable goods, and savings. The LSMS questionnaire focused only on health, education, migration, fertility, and anthropometrics.

Abstract

Cambodia Living Standards Measurement Study – Plus (LSMS+) Survey 2019- 2020 was implemented by the National Institute of Statistics, with support from the World Bank LSMS+ program (www.worldbank.org/lsmsplus). The survey attempted to conduct private interviews with all the adult household members (aged 18 and older) in each sampled household as part of a nationally-representative survey sample. The individual disaggregated data collection had a focus on (i) ownership of and rights to physical and financial assets, (ii) work and employment, and (iii) non-farm enterprises, and was anchored in the latest international recommendations for survey data collection on these topics.

Geographic coverage

National

Analysis unit

• Household
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The Primary Sampling Units (PSUs) of this survey were the subsamples of the selected PSUs of the Cambodia Socio-Economic Survey (CSES) 2019/20. The PSU in this case can either be a village (if the village is small) or an Enumeration Area (EA) from the mapping operation of 2019 General Population Census of Cambodia (if the village is large, exceeding 120 households). The Cambodia LSMS+ sample covered all the CSES’ s sample villages in three months (those selected for interviews during the October - December period of fieldwork) out of its twelve-month sample.

The Secondary Sampling Units (SSUs) in this survey constitute sample households. In this stage, 6 households were selected in each selected PSU. The selections of these households were carried out in the field by the field enumerators. The selection was done under the Circular Systematic Random Sampling (CSRS) scheme using the PSU frame of household from the household listing conducted by the CSES field enumerator in the selected PSU. More details can be found in the Basic Information Document.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The Cambodia LSMS+ covered the following topics:

Household Questionnaire: - Household Roster - Children Living Elsewhere - Housing - Food Consumption - Non-food Consumption - Household Enterprises - Land Roster - Livestock Roster - Durables Roster

Individual-level Questionnaire: - Education - Health - Internal and International Migration - Labor - Time Use - Land Ownership and Rights - Livestock Ownership - Durables Ownership - Mobile Phone Ownership - Financial Accounts

Cleaning operations

Data Entry Platform

The Cambodia LSMS+ was conducted using Computer Assisted Personal Interview (CAPI) techniques. The questionnaire was implemented using the CAPI software, Survey Solutions. The Survey Solutions software was developed and maintained by the Data Analytics and Tools Unit within the Development Economics Data Group (DECDG) at the World Bank. Each interviewer was given one tablet, which they used to conduct the interviews. Overall, implementation of survey using Survey Solutions CAPI was highly successful, as it allowed for timely availability of the data from completed interviews.

Data Management

The data communication system used in the Cambodia LSMS+ was highly automated. Field teams were provided with routers to carry with them in the field so they could connect to internet as frequently as possible to sync their questionnaires and this ensured access to the data in real-time.

Data Cleaning The data cleaning process was done in two main stages. The first stage was to ensure proper quality control during the fieldwork. This was achieved in part by incorporating validation and consistency checks into the Survey Solutions application used for the data collection and designed to highlight many of the errors that occurred during the fieldwork.

The second stage of cleaning involved a comprehensive review of the final raw data following the first stage of cleaning. Every variable was examined individually for (1) consistency with other sections and variables, (2) out of range responses, and (3) formatting.

CPU time and memory use for molecular surface generation by EDTSurf, LSMS [20] and MSMS [10].