The Longitudinal Social Protection Survey harmonized database contains individual information from Chile, Colombia, El Salvador, Paraguay and Uruguay. It has 320 variables, and 120 of them can be compared in all countries.

The Longitudinal Social Protection Survey harmonized database contains individual information from Chile, Colombia, El Salvador, Paraguay and Uruguay. It has 320 variables, and 120 of them can be compared in all countries.

Click here to access the data: https://mydata.iadb.org/d/ck3a-ui6v

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

The phase-3 program was implemented across 75 Female sex worker (FSW) Community-led organizations (COs) and covered more than 1,00,000 FSWs across five states of India (Andhra Pradesh, Telangana, Karnataka, Maharashtra, and Tamil Nadu). The main focus of Avahan-III is to reduce the vulnerabilities (including HIV risk) among FSWs by improving the access to financial security, social protection services, and to make the COs strong and sustainable. The longitudinal survey (2015-2017) was designed to measure different vulnerabilities and capture the key behavioral indicators (e.g. HIV risk behaviors, social protection, financial security, violence, institutional development etc.) among FSWs at the initiation of the program (2015) and after the completion of the program (2017). The eligibility criteria for inclusion in the study was, women, aged 18 or above, who engaged in consensual sex in exchange of money/payment in kind in the last one month. Along with the FSWs longitudinal study, COs level study was also done and information on COs was collected at two-time points (e.g. Baseline (2015) and Endline (2017)) among 38 FSWs COs, who have implemented the Avahn-3 program.

Abstract

Nigeria was among the first few countries in Sub-Saharan Africa to identify cases of COVID-19. Reported cases and fatalities have been increasing since it was first identified. The government implemented strict measures to contain the spread of this virus (such as travel restrictions, school closures and home-based work). While the Government is implementing these containment measures, it is important to understand how households in the country are affected and responding to the evolving crises, so that policy responses can be designed well and targeted effectively to reduce the negative impacts on household welfare.

The objective of Nigeria COVID-19 NLPS is to monitor the socio-economic effects of this evolving COVID-19 pandemic in real time. These data will contribute to filling critical gaps in information that could be used by the Nigerian government and stakeholders to help design policies to mitigate the negative impacts on its population. The Nigeria COVID-19 NLPS is designed to accommodate the evolving nature of the crises, including revision of the questionnaire on a monthly basis.

The households were drawn from the sample of households interviewed in 2018/2019 for Wave 4 of the General Household Survey—Panel (GHS-Panel). The extensive information collected in the GHS-Panel just over a year prior to the pandemic provides a rich set of background information on the Nigeria COVID-19 NLPS households which can be leveraged to assess the differential impacts of the pandemic in the country.

Each month, the households will be asked a set of core questions on the key channels through which individuals and households are expected to be affected by the COVID-19-related restrictions. Food security, employment, access to basic services, coping strategies, and non-labour sources of income are channels likely to be impacted. The core questionnaire is complemented by questions on selected topics that rotate each month. This provides data to the government and development partners in near real-time, supporting an evidence-based response to the crisis.

Geographic coverage

National

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

Wave 4 of the GHS-Panel conducted in 2018/19 served as the frame for the Nigeria COVID-19 NLPS survey. The GHS-Panel sample includes 4,976 households that were interviewed in the post-harvest visit of the fourth wave in January/February 2019. This sample of households is representative nationally as well as across the 6 geopolitical Zones that divide up the country. In every visit of the GHS-Panel, phone numbers are collected from interviewed households for up to 4 household members and 2 reference persons who are in close contact with the household in order to assist in locating and interviewing households who may have moved in subsequent waves of the survey. This comprehensive set of phone numbers as well as the already well-established relationship between NBS and the GHS-Panel households made this an ideal frame from which to conduct the COVID-19 monitoring survey in Nigeria.

Among the 4,976 households interviewed in the post-harvest visit of the GHS-Panel in 2019, 4,934 (99.2%) provided at least one phone number. Around 90 percent of these households provided a phone number for at least one household member while the remaining 10 percent only provided a phone number for a reference person. Households with only the phone number of a reference person were expected to be more difficult to reach but were nonetheless included in the frame and deemed eligible for selection for the Nigeria COVID-19 NLPS.

To obtain a nationally representative sample for the Nigeria COVID-19 NLPS, a sample size of approximately 1,800 successfully interviewed households was targeted. However, to reach that target, a larger pool of households needed to be selected from the frame due to non-contact and non-response common for telephone surveys. Drawing from prior telephone surveys in Nigeria, a final contact plus response rate of 60% was assumed, implying that the required sample households to contact in order to reach the target is 3,000.

3,000 households were selected from the frame of 4,934 households with contact details. Given the large amount of auxiliary information available in the GHS-Panel for these households, a balanced sampling approach (using the cube method) was adopted. The balanced sampling approach enables selection of a random sample that still retains the properties of the frame across selected covariates. Balancing on these variables results in a reduction of the variance of the resulting estimates, assuming that the chosen covariates are correlated with the target variable. Calibration to the balancing variables after the data collection further reduces this variance (Tille, 2006). The sample was balanced across several important dimensions: state, sector (urban/rural), household size, per capita consumption expenditure, household head sex and education, and household ownership of a mobile phone.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

BASELINE (ROUND 1): One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; knowledge regarding the spread of COVID-19; behaviour and social distancing; access to basic services; employment; income loss; food security; concerns; coping/shocks; and social safety nets.

ROUND 2: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; access to basic goods and services; employment (including non-farm enterprise and agricultural activity); other income; food security; and social safety nets.

ROUND 3: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; access to basic goods and services; housing; employment (including non-farm enterprise and agricultural activity); other income; coping/shocks; and social safety nets.

ROUND 4: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; access to basic goods and services; credit; employment (including non-farm enterprise, crop farming and livestock); food security; income changes; concerns; and social safety nets.

ROUND 5: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; education; employment (including non-farm enterprise and agricultural activity); and other income.

ROUND 6: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; education; employment (including non-farm enterprise); COVID testing and vaccination; and other income.

ROUND 7: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; access to basic services; employment (including non-farm enterprise); food security; concerns; and safety nets.

ROUND 8: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; employment (including non-farm enterprise and agriculture); and coping/shocks.

ROUND 9: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; education; early childhood development, access to basic services, employment (including non-farm enterprise and agriculture); and income changes.

ROUND 10: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; access to basic services; employment (including non-farm enterprise and agricultural activity); concerns and COVID testing and vaccination.

ROUND 11: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; credit; access to basic services; education; employment (including non-farm enterprise); safety nets; youth contact details; and phone signal.

ROUND 12: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on youth aspirations and employment; and COVID vaccination.

Cleaning operations

COMUPTER ASSISTED TELEPHONE INTERVIEW (CATI): The Nigeria COVID-19 NLPS exercise was conducted using Computer Assisted Telephone Interview (CATI) techniques. The household questionnaire was implemented using the CATI 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 two tablets, which they used to conduct the interviews. Overall, implementation of survey using Survey Solutions CATI was highly successful, as it allowed for timely availability of the data from completed interviews.

DATA COMMUNICATION SYSTEM: The data communication

The National Longitudinal Survey of Children and Youth (NLSCY) is a long-term study conducted in partnership by Human Resources Development Canada (HRDC)and Statistics Canada. The primary objective of the NLSCY is to monitor the development and well being of Canada's children as they grow from infancy to adulthood. The NLSCY is designed to follow a representative sample of Canadian children, aged newborn to 11 years, into adulthood, with data collection occurring at two-year intervals. The objectives of the NLSCY are: To determine the prevalence of various risk and protective factors for children and youth. To understand how these factors, as well as life events, influence childrens development. To make this information available for developing policies and programs that will help children and youth. Collect information on a wide variety of topics biological, social, economic. Collect information about the environment in which the child is growing up family, peers, school, community Information comes from different sources (parent, child, teacher) and from direct measures (PPVT, math/reading tests, etc.) The NLSCY survey population consists of two sample groups. They are the: longitudinal sample, cross-sectional sample.

The National Longitudinal Survey of Children and Youth (NLSCY) is a long-term survey designed to measure child development and well-being. The first cycle of the survey was conducted by Statistics Canada in 1994-1995 on behalf of Human Resources Development Canada. The survey looked at households containing children 0 to 11 years of age. It will follow these children over time, collecting information on the children and their families, education, health, development, behaviour, friends, activities, etc. The data collected has been released in two cycles of the NLSCY. The amount of information collected was so extensive a decision was made to have two releases rather than waiting for all of the data to be processed. Release 1 contains information on medical/biological childbirth information, motor and social development, parenting, child care, behaviour, etc.. Release 2 contains information on health, activities, literacy, family and custody history, parent health, neighbourhood safety, puberty, drinking and drugs. A complete list of the sections included in the first and second release can be found in the General Contents files.

Abstract (en): The National Longitudinal Study of Adolescent Health (Add Health) is a longitudinal study of a nationally representative sample of adolescents in grades 7-12 in the United States during the 1994-1995 school year. The Add Health cohort has been followed into young adulthood with four in-home interviews, the most recent in 2008, when the sample was aged 24-32. Add Health combines longitudinal survey data on respondents' social, economic, psychological, and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships, providing unique opportunities to study how social environments and behaviors in adolescence are linked to health and achievement outcomes in young adulthood. The fourth wave of interviews expanded the collection of biological data in Add Health to understand the social, behavioral, and biological linkages in health trajectories as the Add Health cohort ages through adulthood. This component of the Add Health restricted data is the Biomarker Data. The Glucose/HbA1c data file contains two measures of glucose homeostasis based on assays of the Wave IV dried blood spots: Glucose (mg/dl) and Hemoglobin A1c (HbA1c, %). Six additional constructed measures -- fasting duration, classification of fasting glucose, classification of non-fasting glucose, classification of HbA1c, diabetes medication, and a joint classification of glucose, HbA1c, self-reported history of diabetes, and anti-diabetic medication use -- are also included. The Lipids data file contains measures of triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), non-high-density lipoprotein cholesterol, and total cholesterol to high-density lipoprotein cholesterol ratio. Additional variables include, measurement method for triglycerides (TG), total cholesterol (TC), high-density lipoprotein choleserol (HDL-C), Antihyperlipidemic medication use, joint classification of self-reported history of Hyperlipidemia and Antihyperlipidemic medication use, and fasting duration. For more information, please see the study website. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Standardized missing values.; Checked for undocumented or out-of-range codes.. Adolescents in grades 7-12 and their families. Wave I, Stage 1 School sample: stratified, random sample of all high schools in the United States. A school was eligible for the sample if it included an 11th grade and had a minimum enrollment of 30 students. A feeder school, a school that sent graduates to the high school and that included a 7th grade, was also recruited from the community. Wave I, Stage 2: An in-home sample of 27,000 adolescents was drawn consisting of a core sample from each community plus selected special over samples. Eligibility for over samples was determined by an adolescent's responses on the In-School Questionnaire. Adolescents could qualify for more than one sample. In addition, parents were asked to complete a questionnaire about family and relationships. The Wave II in-home interview sample is the same as the Wave I in-home interview sample, with a few exceptions. Information about neighborhoods/communities was gathered from a variety of previously published databases. Wave III: The in-home Wave III sample consists of Wave I respondents who could be located and re-interviewed six years later. Wave III also collected High School Transcript Release Forms as well as samples of urine and saliva. 2013-11-14 Public release of documentation guides and codebooks.2013-11-07 Part 4 was added and it includes new Biomarker Lipid Data.2013-03-08 Part 2 was updated following a resupply of the data by the Principal Investigators. Specifically, additional variables added to the data file, and CRP and EBV values have been recalculated, resulting in minimal changes to the data. The associated documentation and codebook files were also updated. Finally, a user guide describing measures of inflammation and immune function for Part 2 was also added.2012-11-07 The codebook associat...

Abstract

In the West Africa Economic Monetary Union (WAEMU) countries, COVID-19 is expected to affect households in many ways. First, governments might reduce social transfers to households due to the decline in revenue arising from the potential COVID-19 economic recession. Second households deriving income from vulnerable sectors such as tourism and related activities will likely face risk of unemployment or loss of income. Third an increase in prices of imported goods can also negatively impact household welfare, as a direct consequence of the increase of these imported items or as indirect increase of prices of local good manufactured using imported inputs. In this context, there is a need to produce high frequency data to help policy makers in monitoring the channels by which the pandemic affects households and assessing its distributional impact. To do so, the sample of the longitudinal survey is a sub-sample of the Enquête Harmonisée sur les Conditions de Vie des Ménages (EHCVM), a harmonized household survey conducted in 2018/19 household survey in the WAEMU countries.

For Burkina Faso, the survey, which is implemented by the Institut National de la Statistique et la Demographie (INSD), is conducted using cell phone numbers of household members collected during the 2018/19 EHCVM survey. The extensive information collected in the EHCVM provides a rich set of background information for the COVID-19 High Frequency Phone Survey of households. This background information can be leveraged to assess the differential impacts of the pandemic in the country. Every month, the sampled households will be asked a set of core questions on the key channels through which individuals and households are expected to be affected by the COVID-19-related restrictions. Employment, access to basic services, non-labor sources of income are channels likely to be impacted. The core questionnaire is complemented by questions on selected topics that rotate each month. This provides data to the government and development partners in near real-time, supporting an evidence-based response to the crisis.

The main objectives of the survey are to: • Identify type of households directly or indirectly affected by the pandemic; • Identify the main channels by which the pandemic affects households; • Provide relevant data on income and socioeconomic indicators to assess the welfare impact of the pandemic.

Phase 1 was conducted on a monthly basis during the period of June 2020 and July 2021 for11 Rounds. Phase 2 (starting from Round 12) was conducted on a bi-monthly basis starting in April 2022. Phase 3 (starting from Round 18) will be conducted on a bi-monthly basis, starting in July 2023.

Geographic coverage

National coverage, including Ouagadougou, rural and other urban

Analysis unit

• Households
• Individuals

Universe

The survey covered a sub-sample of the households of the 2018/19 - Enquête Harmonisée sur le Conditions de Vie des Ménages (EHCVM) survey which excluded populations in prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample of the HFS is a subsample of the 2018/19 Harmonized Living Conditions Household Survey (EHCVM). The EHCVM 2018/19 is built on a nationally and regionally representative sample of households in Burkina Faso. EHCVM 2018/19 interviewed 7,010 households in urban and rural areas. In the EHCVM interview, households were asked to provide phone numbers of the household head, or a household member, or a non-household member (e.g. friends or neighbors) so that they can be contacted for follow-up questions. At least one valid phone number was obtained for 6877 households. These households established the sampling frame for the HFS. To obtain representative strata at the national, capital (Ouagadougou), urban, and rural level, the target sample size for the HFS is 1,800 household (assuming a 50% non-response rate the minimum required sample is 1479). To account for non-response and attrition, 2500 households were called in baseline round of the HFS. 1,968 households were fully interviewed during the first round of interviews. Those 1,968 households constitute the final successful sample and will be contacted in subsequent rounds of the survey.

In addition to the 1,968 households successfully interviewed in Round 1, in Round 2, 242 additional households were sampled from the rural strata, in order to increase the representativeness in this domain. In Round 12, 231 additional households were selected from the rural stratum from the 2018/19 EHCVM sample. In Round 18, 858 additional households were selected from panel component of the 2021/22 EHCVM sample.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

BASELINE (Round 1): The Household Questionnaire provides information on demographics; knowledge regarding the spread of COVID-19; behavior and social distancing; access to basic services; employment.

Round 2: Household Roster; Access to Basic Services; Employment (with a focus on non-farm enterprises); Food Security; Shocks; Fragility, conflict, and violence.

Round 3: Household Roster; Knowledge regarding the spread of COVID-19; Behavior and social distancing; Access to Basic Services; Employment (with a focus on farm household activities); Food Security; Other revenues; Social protection.

Round 4: The following modules were administered in Round 4: Household Roster; Access to Basic Services; Credit; Employment and revenue (with a focus on livestock activities); Food Security; Other revenues; Shocks; Fragility, Conflict and Violence.

Round 5: Household Roster; Knowledge regarding the spread of COVID-19; Behavior and social distancing; Access to Basic Services; Education at individual level; Employment; Food Security; Other revenues; Social protection.

Round 6: Household Roster; Access to Basic Services; Education; Employment and revenues (with a focus on harvest activities and revenues from crop selling); Food Security; Other revenues; Shocks; Fragility, conflict and violence.

Round 7: Household Roster; Access to Basic Services; Education; Employment and revenues (with a focus on harvest activities and revenues from crop selling); Food Security; Other revenues; Shocks; Fragility, conflict and violence.

Round 8: Household Roster; Early Child Development; Access to Basic Services; Employment and revenues; Food Security; Other revenues; Shocks; Fragility, conflict and violence.

Round 9: Household Roster; Access to Basic Services; Employment and revenues; Food Security and Other revenues.

Round 10: Household Roster; Mental health; Knowledge regarding the spread of COVID-19; Behavior and social distancing; Covid-19 Testing and Vaccination; Access to Basic Services; Credit; ; Employment and revenue (with a focus on livestock activities); Food Security; Other revenues; Shocks; Concerns regarding the impact of COVID-19 on personal health and financial wealth of the household; Fragility, Conflict and Violence

Round 11: Household basic information; Access to Basic Services; Employment and revenue (with a focus on agricultural activities); Food Security; Other revenues; Concerns regarding the current situation; Social Safety Nets.

Round 12: Household Roster; Covid-19 Vaccination; Access to Health Care; and Employment and Income.

Round 13: Household Roster; Access to Health Care; Credit; Employment and Income; Food Security; Other Revenues; and Economic Sentiments.

Round 14: Household Roster; Access to Health Care; Vaccination; Concerns; Economic Sentiments.

Round 15: Household Roster; Displacement; Education; Access to basic foodstuffs; Employment and Income; Food Security; Other Revenues; Economic Sentiments; Items Price.

Round 16: Household Roster; Access to Health Care; Vaccination; Agriculture; Livestock; Shocks; Climate Change; Economic Sentiments; Items Price.

Round 17: Household Roster; Access to Basic Foodstuffs; Access to HealthCare – individual level; Credit; Employment and Income; Food Security; and Other Revenues.

Round 18: Household Roster; Access to Basic Goods and Services; Access to Health Care – individual level; Price of items; Employment and Income; Food Security; Food Consumption Score; Economic Sentiments; and Subjective Welfare.

Round 19: Household Roster; Access to Basic Goods and Services; Access to Health Care – individual level; Price of items; Employment and Income; Food Security; Shocks; Food Consumption Score; Economic Sentiments; and Subjective Welfare.

Round 20: Households Roster; Access to basic goods and services; Access to Health Care - Individual level; Price ofItems; Employment and Income; Food Security; Food Consumption Score; Economic Sentiments; SubjectiveWelfar.

Round 21: Household Roster; Access to Basic Goods and Services; Education; Price of items; Employment and Income; Agriculture; Livestock; Food Security; Food Consumption Score; Economic Sentiments; Subjective Welfare.

Round 22: Household Roster; Household Mobility; Access to Basic Goods and Services; Price of items; Access to Health Care - individual level; Employment and Income; Food Security; Food Consumption Score; Shocks; Economic Sentiments; and Subjective Welfare.

Round 23: Household Roster; Access to Basic Goods and Services; Price of items; Employment and Income; Food Security; Food Consumption Score; Economic Sentiments; and Subjective Welfare.

All the interview materials were translated in French for the INSD. The questionnaire was administered in local languages with about varying length (about 25 minutes).

Cleaning operations

At the end of data

The JLMPS 2010 offers significant advantages over the regular Employment and Unemployment (EUS) survey conducted quarterly by the Department of Statistics (DOS). Although it is only the first wave of what is to be a longitudinal survey, it contains a number of retrospective questions that allow us to reconstruct entire employment trajectories rather than simply get a snapshot of a single point in time. The main advantage of this approach is that it allows for the examination of flows into various segments of the labor market and not simply stocks over time. The JLMPS 2010 data also offers significant advantages over the EUS in its ability to identify informal employment in its various guises, including wage and salary employment without contracts or social insurance and self-employment and unpaid family employment. It also offers a more detailed view of employment conditions including paid and unpaid leaves, the presence of health insurance, hours of work, and the type and size of economic unit in which the worker is employed. Since JLMPS 2010 has the same sampling strategy as the EUS it focuses exclusively on the population residing in regular households rather than in collective residential units. This makes it equally likely as the EUS to under-sample the foreign worker population in Jordan. The data may be accessed through the ERF Data Portal: http://www.erfdataportal.com/index.php/catalog/138

This presentation explores the differences and similarities between Statistics Canada's various Labour Family products.

The National Longitudinal Study of Adolescent to Adult Health (Add Health) is a longitudinal study of a nationally representative sample of adolescents in grades 7-12 in the United States during the 1994-95 school year. The Add Health cohort has been followed into young adulthood with four in-home interviews, the most recent in 2008, when the sample was aged 24-32*. Add Health combines longitudinal survey data on respondents’ social, economic, psychological and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships, providing unique opportunities to study how social environments and behaviors in adolescence are linked to health and achievement outcomes in young adulthood. The fourth wave of interviews expanded the collection of biological data in Add Health to understand the social, behavioral, and biological linkages in health trajectories as the Add Health cohort ages through adulthood.

Wave IV
Wave IV was designed to study the developmental and health trajectories across the life course of adolescence into young adulthood. Taking place in 2008, approximately 92.5% of the original Wave I respondents were located and 80.3% of eligible cases were interviewed. The Wave IV public use file contains data on 5,114 respondents, aged 24 to 32*. In Wave IV, biological data was also gathered in an attempt to acquire a greater understanding of predisease pathway s, with a specific focus on obesity, stress, and health risk behavior.

The Wave IV public use dataset includes the following data files:

• Wave IV In-home Interview File: variables from the in-home interview, including anthropometric measures
• Relationship Data
• Pregnancy Table File
• Live Births File
• Children and Parenting File
• Wave IV Weights
• Wave IV Public Use Biomarkers, Glucose Data
• Wave IV Public Use Biomarkers, Measures of EBV and hsCRP
• Wave IV Public Use Biomarkers, Lipids Data

Abstract

Welfare Monitoring Survey (WMS) is a biennial longitudinal household survey, which covers the entire Georgia that are under the control of the Government of Georgia. It investigates the multi-dimensional wellbeing of the population and households with a particular focus on children (e.g. consumption poverty, material deprivations, and school attendance). The survey also makes a reference to social transfers and their impacts to poverty.

The primary objectives of the survey are to provide an in-depth understanding of how the crisis impacts on Georgian children and their families and to inform policy decision-making process by identifying key priority challenges that require immediate policy responses. For this purpose the survey explores the dynamics of core welfare indicators of households. It also explores the strategies that the households resort to in order to mitigate the risks posed by the negative global developments.

This is the fourth round of the Welfare Monitoring Survey (WMS). WMS is a biennial longitudinal household survey covering all the government-controlled regions of Georgia.

Geographic coverage

The survey covers the whole country of Georgia excluding territories outside the Georgian Government's control.

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Research instrument

Two types of survey tools, were used: a) a structured questionnaire for a face-to-face interview and b) a diary questionnaire to be completed by households in the week following the face-to-face interviews.

The questionnaires explore different dimensions of well-being of the Georgian population, incorporating questions about household assets, income and consumption, employment and livelihoods, food security, access to health, education and social services and household coping strategies.

Data set of extensive information on the changing circumstances of aged and disabled beneficiaries - Living, noninstitutionalized population of the continental United States from the Social Security Administration''''s Master Benefit Record who were new recipients of Social Security benefits (first payment in mid-1980 through mid-1981) or who had established entitlement to Medicare and were eligible for, but had not received, Social Security benefits as of July 1982. Based initially on a national cross-sectional survey of new beneficiaries in 1982, the original data base was expanded with information from administrative records and a second round of interviews in 1991. Variables measured in the original New Beneficiary Survey (NBS) include demographic characteristics; employment, marital, and childbearing histories; household composition; health; income and assets; program knowledge; and information about the spouses of married respondents. The 1991 New Beneficiary Follow-up (NBF) updated marital status, household composition, and the economic profile and contains additional sections on family contacts, postretirement employment, effects of widowhood and divorce, major reasons for changes in economic status, a more extensive section on health, and information on household moves and reasons for moving. Disabled-worker beneficiaries were also asked about their efforts to return to work, experiences with rehabilitation services, and knowledge of SSA work incentive provisions. The NBDS also links to administrative files of yearly covered earnings from 1951 to 1992, Medicare expenditures from 1984 to 1999, whether an SSI application has ever been made and payment status at five points in time, and dates of death as of spring 2001. For studies of health, the Medicare expenditure variables include inpatient hospital costs, outpatient hospital costs, home health care costs, and physicians'''' charges. The survey data cover functional capacity including ADLs and IADLs. For studies of work in retirement, the survey includes yearly information on extent of work, characteristics of the current or last job, and reasons for working or not working. No other data set has such detailed baseline survey data of a population immediately after retirement or disability, enhanced with subsequent measures over an extended period of time. The data are publicly available through NACDA and the Social Security Administration Website. * Dates of Study: 1982-1991 * Study Features: Longitudinal * Sample Size: ** 18,136 (NBS 1981) ** 12,677 (NBF 1991) Links: * 1982 (ICPSR): http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08510 * 1991 (ICPSR): http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06118

Abstract

In the WAEMU countries, COVID-19 is expected to affect households in many ways. First, governments might reduce social transfers to households due to the decline in revenue arising from the potential COVID-19 economic recession. Second households deriving income from vulnerable sectors such as tourism and related activities will likely face risk of unemployment or loss of income. Third an increase in prices of imported goods can also negatively impact household welfare, as a direct consequence of the increase of these imported items or as indirect increase of prices of local good manufactured using imported inputs. In this context, there is a need to produce high frequency data to help policy makers in monitoring the channels by which the pandemic affects households and assessing its distributional impact. To do so, the sample of the longitudinal survey will be a sub-sample of the 2018/19 household survey in each country.

For Mali, the survey which is implemented by the National Statistical Office (INSTAT), is conducted using cell phone numbers of household members collected during the 2018/19 survey. This has the advantage of conducting cost effectively welfare analysis without collecting new consumption data. The 35 minutes questionnaires covered 10 modules (knowledge, behavior, access to services, food security, employment, safety nets, shocks, etc…). Data collection is planned for six months (six rounds) and the questionnaire is designed with core modules and rotating modules. Survey data collection started on May 11th, 2020 and households are expected to be called back every three to four weeks.

The main objectives of the survey are to: • Identify type of households directly or indirectly affected by the pandemic; • Identify the main channels by which the pandemic affects households; • Provide relevant data on income and socioeconomic indicators to assess the welfare impact of the pandemic.

Geographic coverage

National coverage including rural and urban

Analysis unit

• Households
• Individuals

Universe

The survey covered only households of the 2018/19 survey which excluded populations in prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Mali COVID-19 impact monitoring survey is a high frequency Computer Assisted Telephone Interview (CATI). The survey’s sample was drawn from the population of the 2018/19 - Enquête Harmonisée des Conditions de Vie des Ménages (EHCVM) -, which was conducted between October 2018 and July 2019. EHCVM is itself a sample survey representative at national, regional and by urban/rural. For the 7,000 HHs in EHCVM, phone numbers were collected for about 90 percent of them. Each HH has between 1-4 phone numbers. The sampling, which was similar across WAEMU, aimed at having representative estimates by three zones: the capital city of Bamako, other urban areas and the rural area. The minimum sample size was 1,908 for which 1,766 were successfully interviewed, that is about 98 % of the expected minimal sample size at the national level. Given that Mali is conducting a phone survey for the first time, a total of 2,270 were drawn (25% increase) to take into account unknown non-response rates or presence of invalid numbers in the database.

The total number of completed interviews in round one is 1,766. The total number of completed interviews in round two is 1,935. The total number of completed interviews in round three is 1,901. The total number of completed interviews in round four is 1,797. The total number of completed interviews in round five is 1,766.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

All the interview materials were translated in french for the NSO. The questionnaire was administered in local languages with about varying length (30-35 minutes) and covered the following topics: 1- Household Roster 2- Knowledge of COVID-19 3- Behaviour and Social Distancing 4- Access to Basic Services 5- Employment and Income 6- Prices and Food Security 7- Other Impacts of COVID-19 8- Income Loss 9- Coping/Shocks 10- Social Safety Nets 11- Fragility 12- Governance and socio-political crisis

Cleaning operations

At the end of data collection, the raw dateset was cleaned by the NSO. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes.

Response rate

The minimum sample expected is 1,809 households (with 603 households per domain). This sample was therefore 99% covered for Bamako, about 100% for other urban areas and 91% for rural areas. Overall, the minimum sample is 98% covered. This level of coverage provides reliable data at national level and for each domain.

Round one response rate was 77.8%. Round two response rate was 85.2%. Round three response rate was 83.7%. Round four response rate was 79.2%. Round five response rate was 79.7%.

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

Africa has been known to experience series of problems among which are poverty, food insecurity, lack of access to energy, lack of infrastructure among others. These problems were exacerbated by the COVID-19 pandemic, which has had a severe impact on the socioeconomic status of households in Africa. This paper examines the relationship between socioeconomic shocks, social protection, and household food security during the pandemic in Nigeria, the Africa’s largest economy. Using the World Bank’s COVID-19 national longitudinal baseline phone survey (2020) for the analysis and applied the multinomial logit regression, the study finds that socioeconomic shocks resulting from the pandemic have led to an increased level of food insecurity. Social protection programmes have played a crucial role in mitigating the impact of these shocks on households. However, the study also highlights the need for more targeted and effective social protection policies to ensure that vulnerable households are adequately protected from the adverse effects of the pandemic. The findings of this study have important implications for policymakers and stakeholders in Africa’s largest economy, as they seek to address the challenges posed by the pandemic and promote household food security for the actualisation the United Nations (UN) Sustainable Development Goal (SDG) of food and nutrition security (SDG2). The study, therefore, recommends that efforts be made to preserve food supply chains by mitigating the pandemic’s effect on food systems, increasing food production, and looking forward beyond the pandemic by building resilient food systems with the use of social protection interventions.

Model summary for analysing COVID-19 risk by socio-demographic features including occupational group for adults who work.

Abstract

In Chad, COVID-19 is expected to affect households in many ways. First, governments might reduce social transfers to households due to the decline in revenue arising from the potential COVID-19 economic recession. Second households deriving income from vulnerable sectors such as tourism and related activities will likely face risk of unemployment or loss of income. Third an increase in prices of imported goods can also negatively impact household welfare, as a direct consequence of the increase of these imported items or as indirect increase of prices of local good manufactured using imported inputs. In this context, there is a need to produce high frequency data to help policy makers in monitoring the channels by which the pandemic affects households and assessing its distributional impact. To do so, the sample of the longitudinal survey will be a sub-sample of the 2018/19 Enquête sur la Consommation des Ménages et le Secteur Informel au Tchad (Ecosit 4) in Chad.

This has the advantage of conducting cost effectively welfare analysis without collecting new consumption data. The 30 minutes questionnaires covered many modules, including knowledge, behavior, access to services, food security, employment, safety nets, shocks, coping, etc. Data collection is planned for four months (four rounds) and the questionnaire is designed with core modules and rotating modules.

The main objectives of the survey are to: • Identify type of households directly or indirectly affected by the pandemic; • Identify the main channels by which the pandemic affects households; • Provide relevant data on income and socioeconomic indicators to assess the welfare impact of the pandemic.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The survey covered only households of the 2018/19 Enquête sur la Consommation des Ménages et le Secteur Informel au Tchad (ECOSIT 4) which excluded populations in prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Chad COVID-19 impact monitoring survey is a high frequency Computer Assisted Telephone Interview (CATI). The survey’s sample was drawn from the Enquête sur la Consommation des Ménages et le Secteur Informel au Tchad (Ecosit 4) which was conducted in 2018-2019. ECOSIT 4 is a survey with a sample size of 7,493 household’s representative at national, regional and by urban/rural. During the survey, each household was asked to provide a phone number of at least one member or a non-household member (e.g. friends or neighbors) so that they can be contacted for follow-up questions. The sampling of the high frequency survey aimed at having representative estimates by national and area of residence: Ndjamena (capital city), other urban and rural area. The minimum sample size was 2,000 for which 1,748 households (87.5%) were successfully interviewed at the national level. To account for non-response and attrition and given that this survey was the first experience of INSEED, 2,833households were initially selected, among them 1,832 households have been reached. The 1,748 households represent the final sample and will be contacted for the next three rounds of the survey.

Sampling deviation

None

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire is in French and has been administrated in French and local languages. The length of an interview varies between 20 and 30 minutes. The questionnaires consisted of the following sections: 1- Household Roster 2- Knowledge of COVID-19 3- Behavior and Social Distancing 4- Access to Basic Services 5- Employment and Income 6- Prices and Food Security 7- Other Impacts of COVID-19 8- Income Loss 9- Coping/Shocks 10- Social Safety Nets 11- Fragility 12- Vaccine

Cleaning operations

At the end of data collection, the raw dataset was cleaned by the INSEED with the support of the WB team. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes.

Response rate

The minimum sample expected is 2,000 households covering Ndjamena, other urban and rural areas. Overall, the survey has been completed for 1,748 households that is about 87.5 % of the expected minimal sample size at the national level. This provide reliable estimates at national and area of residence level.

Objective: In general, published studies analyze healthcare utilization, rather than foregone care, among different population groups. The assessment of forgone care as an aspect of healthcare system performance is important because it indicates the gap between perceived need and actual utilization of healthcare services. This study focused on a specific vulnerable group, middle-aged and elderly people with chronic diseases, and evaluated the prevalence of foregone care and associated factors among this population in China. Methods: Data were obtained from a nationally representative household survey of middle-aged and elderly individuals (≥45 years), the China Health and Retirement Longitudinal Study (CHARLS), which was conducted by the National School of Development of Peking University in 2013. Descriptive statistics were used to analyze sample characteristics and the prevalence of foregone care. Andersen’s healthcare utilization and binary logistic models were used to evaluate the determinants of foregone care among middle-aged and elderly individuals with chronic diseases. Results: The prevalence of foregone outpatient and inpatient care among middle-aged and elderly people were 10.21% and 6.84%, respectively, whereas the prevalence of foregone care for physical examinations was relatively high (57.88%). Predisposing factors, including age, marital status, employment, education, and family size, significantly affected foregone care in this population. Regarding enabling factors, individuals in the highest income group reported less foregone inpatient care or physical examinations compared with those in the lowest income group. Social healthcare insurance could significantly reduce foregone care in outpatient and inpatient situations; however, these schemes (except for Urban Employee Medical Insurance) did not appear to have a significant impact on foregone care involving physical examinations. Conclusion: In China, policymakers may need to further adjust healthcare policies, such as health insurance schemes, and improve the hierarchical medical system, to promote reduction in foregone care and effective utilization of health services.

Longitudinal household panel survey. Annual interviews collect information about economic and subjective well-being, labour market dynamics and family dynamics

The HILDA Survey is conducted by the Melbourne Institute of Applied Economic and Social Research at the University of Melbourne on behalf of the Department of Social Services, with data collection conducted by Roy Morgan Research.

For further information visit this page on the Department of Social Services website