Denmark Number of Family: Couples: with Children data was reported at 588,412.000 Unit in 2017. This records an increase from the previous number of 587,852.000 Unit for 2016. Denmark Number of Family: Couples: with Children data is updated yearly, averaging 592,436.000 Unit from Dec 2000 (Median) to 2017, with 18 observations. The data reached an all-time high of 601,022.000 Unit in 2000 and a record low of 584,183.000 Unit in 2015. Denmark Number of Family: Couples: with Children data remains active status in CEIC and is reported by Statistics Denmark. The data is categorized under Global Database’s Denmark – Table DK.H011: Number of Family: by Family Type.

Families and children in the UK by family type including married couples, cohabiting couples and lone parents. Also shows household size and people living alone.

Denmark Number of Family: Couples: without Children data was reported at 778,911.000 Unit in 2017. This records an increase from the previous number of 771,661.000 Unit for 2016. Denmark Number of Family: Couples: without Children data is updated yearly, averaging 732,434.000 Unit from Dec 2000 (Median) to 2017, with 18 observations. The data reached an all-time high of 778,911.000 Unit in 2017 and a record low of 722,402.000 Unit in 2000. Denmark Number of Family: Couples: without Children data remains active status in CEIC and is reported by Statistics Denmark. The data is categorized under Global Database’s Denmark – Table DK.H011: Number of Family: by Family Type.

Abstract

The primary objective of the 2017-18 Jordan Population and Family Health Survey (JPFHS) is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the 2017-18 JPFHS: - Collected data at the national level that allowed calculation of key demographic indicators - Explored the direct and indirect factors that determine levels of and trends in fertility and childhood mortality - Measured levels of contraceptive knowledge and practice - Collected data on key aspects of family health, including immunisation coverage among children, the prevalence and treatment of diarrhoea and other diseases among children under age 5, and maternity care indicators such as antenatal visits and assistance at delivery among ever-married women - Obtained data on child feeding practices, including breastfeeding, and conducted anthropometric measurements to assess the nutritional status of children under age 5 and ever-married women age 15-49 - Conducted haemoglobin testing on children age 6-59 months and ever-married women age 15-49 to provide information on the prevalence of anaemia among these groups - Collected data on knowledge and attitudes of ever-married women and men about sexually transmitted infections (STIs) and HIV/AIDS - Obtained data on ever-married women’s experience of emotional, physical, and sexual violence - Obtained data on household health expenditures

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-59

Universe

The survey covered all de jure household members (usual residents), children age 0-5 years, women age 15-49 years and men age 15-59 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2017-18 JPFHS is based on Jordan's Population and Housing Census (JPHC) frame for 2015. The current survey is designed to produce results representative of the country as a whole, of urban and rural areas separately, of three regions, of 12 administrative governorates, and of three national groups: Jordanians, Syrians, and a group combined from various other nationalities.

The sample for the 2017-18 JPFHS is a stratified sample selected in two stages from the 2015 census frame. Stratification was achieved by separating each governorate into urban and rural areas. Each of the Syrian camps in the governorates of Zarqa and Mafraq formed its own sampling stratum. In total, 26 sampling strata were constructed. Samples were selected independently in each sampling stratum, through a two-stage selection process, according to the sample allocation. Before the sample selection, the sampling frame was sorted by district and sub-district within each sampling stratum. By using a probability-proportional-to-size selection for the first stage of selection, an implicit stratification and proportional allocation were achieved at each of the lower administrative levels.

In the first stage, 970 clusters were selected with probability proportional to cluster size, with the cluster size being the number of residential households enumerated in the 2015 JPHC. The sample allocation took into account the precision consideration at the governorate level and at the level of each of the three special domains. After selection of PSUs and clusters, a household listing operation was carried out in all selected clusters. The resulting household lists served as the sampling frame for selecting households in the second stage. A fixed number of 20 households per cluster were selected with an equal probability systematic selection from the newly created household listing.

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

Mode of data collection

Face-to-face [f2f]

Research instrument

Four questionnaires were used for the 2017-18 JPFHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. These questionnaires, based on The DHS Program’s standard Demographic and Health Survey questionnaires, were adapted to reflect population and health issues relevant to Jordan. After all questionnaires were finalised in English, they were translated into Arabic.

Cleaning operations

All electronic data files for the 2017-18 JPFHS were transferred via IFSS to the DOS central office in Amman, where they were stored on a password-protected computer. The data processing operation included secondary editing, which required resolution of computer-identified inconsistencies and coding of open-ended questions. Data editing was accomplished using CSPro software. During the duration of fieldwork, tables were generated to check various data quality parameters, and specific feedback was given to the teams to improve performance. Secondary editing and data processing were initiated in October 2017 and completed in February 2018.

Response rate

A total of 19,384 households were selected for the sample, of which 19,136 were found to be occupied at the time of the fieldwork. Of the occupied households, 18,802 were successfully interviewed, yielding a response rate of 98%.

In the interviewed households, 14,870 women were identified as eligible for an individual interview; interviews were completed with 14,689 women, yielding a response rate of 99%. A total of 6,640 eligible men were identified in the sampled households and 6,429 were successfully interviewed, yielding a response rate of 97%. Response rates for both women and men were similar across urban and rural areas.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2017-18 Jordan Population and Family Health Survey (JPFHS) to minimise this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2017-18 JPFHS is only one of many samples that could have been selected from the same population, using the same design and sample size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

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

If the sample of respondents had been selected by simple random sampling, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2017-18 JPFHS sample was the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed using SAS programmes developed by ICF International. These programmes use the Taylor linearisation method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

The Taylor linearisation method treats any percentage or average as a ratio estimate, r = y/x, where y represents the total sample value for variable y, and x represents the total number of cases in the group or subgroup under consideration.

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

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months

See details of the data quality tables in Appendix C of the survey final report.

Burundi BI: Demand for Family Planning Satisfied by Modern Methods: % of Married Women with Demand for Family Planning data was reported at 39.400 % in 2017. This records an increase from the previous number of 32.700 % for 2010. Burundi BI: Demand for Family Planning Satisfied by Modern Methods: % of Married Women with Demand for Family Planning data is updated yearly, averaging 36.050 % from Dec 2010 (Median) to 2017, with 2 observations. The data reached an all-time high of 39.400 % in 2017 and a record low of 32.700 % in 2010. Burundi BI: Demand for Family Planning Satisfied by Modern Methods: % of Married Women with Demand for Family Planning data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Burundi – Table BI.World Bank.WDI: Social: Health Statistics. Demand for family planning satisfied by modern methods refers to the percentage of married women ages 15-49 years whose need for family planning is satisfied with modern methods.;Demographic and Health Surveys (DHS).;Weighted average;This is the Sustainable Development Goal indicator 3.7.1 [https://unstats.un.org/sdgs/metadata/].

Performance metrics for by year No. of Child and Family Support Networks Operating

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for May 2017.

The National Longitudinal Study of Adolescent to Adult Health (Add Health) Parent Study Public Use collection includes data gathered as part of the Add Health longitudinal survey of adolescents. The original Add Health survey 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. In Wave 1 of the Add Health Study (1994-1995), a parent of each Add Health Sample Member (AHSM) was interviewed. The Add Health Parent Study gathered social, behavioral, and health survey data in 2015-2017 from the parents of Add Health Sample members who were originally interviewed at Wave 1 (1994-1995). Wave 1 Parents were asked about their adolescent children, their relationships with them, and their own health. The Add Health Parent Study interview is a comprehensive survey of Add Health parents' family relations, education, religious beliefs, physical and mental health, social support, and community involvement experiences. In addition, survey data contains cognitive assessments, a medications log linked to a medications database lookup table, and household financial information collection. The survey also includes permission for administrative data linkages and includes data from a Family Health History Leave-Behind questionnaire. Interviews were conducted with parents' spouse/partner when available. Research domains targeted in the survey and research questions that may be addressed using the Add Health Parent Study data include: Health Behaviors and Risks Many health conditions and behaviors run in families; for example, cardiovascular disease, obesity and substance abuse. How are health risks and behaviors transmitted across generations or clustered within families? How can we use information on the parents' health and health behavior to better understand the determinants of their (adult) children's health trajectories? Cognitive Functioning and Non-Cognitive Personality Traits What role does the intergenerational transmission of personality and locus of control play in generating intergenerational persistence in education, family status, income and health? How do the personality traits of parents and children, and how they interact, influence the extent and quality of intergenerational relationships and the prevalence of assistance across generations? Decision-Making, Expectations, and Risk Preferences Do intergenerational correlations in risk preferences represent intergenerational transmission of preferences? If so, are the transmission mechanisms a factor in biological and environmental vulnerabilities? Does the extent of genetic liability vary in response to both family-specific and generation-specific environmental pressures? Family Support, Relationship Quality and Ties of Obligation How does family complexity affect intergenerational obligations and the strength of relationship ties? As parents near retirement: What roles do they play in their children's lives and their children in their lives? What assistance are they providing to their adult children and grandchildren? What do they receive in return? And how do these ties vary with divorce, remarriage and familial estrangement? Economic Status and Capacities What are the economic capacities of the parents' generation as they reach their retirement years? How have fared through the wealth and employment shocks of the Great Recession? Are parents able to provide for their own financial need? And, do they have the time and financial resources to help support their children and grandchildren and are they prepared to do so?

Abstract

The primary objective of the 2017 Indonesia Dmographic and Health Survey (IDHS) is to provide up-to-date estimates of basic demographic and health indicators. The IDHS provides a comprehensive overview of population and maternal and child health issues in Indonesia. More specifically, the IDHS was designed to: - provide data on fertility, family planning, maternal and child health, and awareness of HIV/AIDS and sexually transmitted infections (STIs) to help program managers, policy makers, and researchers to evaluate and improve existing programs; - measure trends in fertility and contraceptive prevalence rates, and analyze factors that affect such changes, such as residence, education, breastfeeding practices, and knowledge, use, and availability of contraceptive methods; - evaluate the achievement of goals previously set by national health programs, with special focus on maternal and child health; - assess married men’s knowledge of utilization of health services for their family’s health and participation in the health care of their families; - participate in creating an international database to allow cross-country comparisons in the areas of fertility, family planning, and health.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-54

Universe

The survey covered all de jure household members (usual residents), all women age 15-49 years resident in the household, and all men age 15-54 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2017 IDHS sample covered 1,970 census blocks in urban and rural areas and was expected to obtain responses from 49,250 households. The sampled households were expected to identify about 59,100 women age 15-49 and 24,625 never-married men age 15-24 eligible for individual interview. Eight households were selected in each selected census block to yield 14,193 married men age 15-54 to be interviewed with the Married Man's Questionnaire. The sample frame of the 2017 IDHS is the Master Sample of Census Blocks from the 2010 Population Census. The frame for the household sample selection is the updated list of ordinary households in the selected census blocks. This list does not include institutional households, such as orphanages, police/military barracks, and prisons, or special households (boarding houses with a minimum of 10 people).

The sampling design of the 2017 IDHS used two-stage stratified sampling: Stage 1: Several census blocks were selected with systematic sampling proportional to size, where size is the number of households listed in the 2010 Population Census. In the implicit stratification, the census blocks were stratified by urban and rural areas and ordered by wealth index category.

Stage 2: In each selected census block, 25 ordinary households were selected with systematic sampling from the updated household listing. Eight households were selected systematically to obtain a sample of married men.

For further details on sample design, see Appendix B of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2017 IDHS used four questionnaires: the Household Questionnaire, Woman’s Questionnaire, Married Man’s Questionnaire, and Never Married Man’s Questionnaire. Because of the change in survey coverage from ever-married women age 15-49 in the 2007 IDHS to all women age 15-49, the Woman’s Questionnaire had questions added for never married women age 15-24. These questions were part of the 2007 Indonesia Young Adult Reproductive Survey Questionnaire. The Household Questionnaire and the Woman’s Questionnaire are largely based on standard DHS phase 7 questionnaires (2015 version). The model questionnaires were adapted for use in Indonesia. Not all questions in the DHS model were included in the IDHS. Response categories were modified to reflect the local situation.

Cleaning operations

All completed questionnaires, along with the control forms, were returned to the BPS central office in Jakarta for data processing. The questionnaires were logged and edited, and all open-ended questions were coded. Responses were entered in the computer twice for verification, and they were corrected for computer-identified errors. Data processing activities were carried out by a team of 34 editors, 112 data entry operators, 33 compare officers, 19 secondary data editors, and 2 data entry supervisors. The questionnaires were entered twice and the entries were compared to detect and correct keying errors. A computer package program called Census and Survey Processing System (CSPro), which was specifically designed to process DHS-type survey data, was used in the processing of the 2017 IDHS.

Response rate

Of the 49,261 eligible households, 48,216 households were found by the interviewer teams. Among these households, 47,963 households were successfully interviewed, a response rate of almost 100%.

In the interviewed households, 50,730 women were identified as eligible for individual interview and, from these, completed interviews were conducted with 49,627 women, yielding a response rate of 98%. From the selected household sample of married men, 10,440 married men were identified as eligible for interview, of which 10,009 were successfully interviewed, yielding a response rate of 96%. The lower response rate for men was due to the more frequent and longer absence of men from the household. In general, response rates in rural areas were higher than those in urban areas.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors and (2) sampling errors. Nonsampling errors result from mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2017 Indonesia Demographic and Health Survey (2017 IDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2017 IDHS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling error is a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

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

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2017 IDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. The computer software used to calculate sampling errors for the 2017 IDHS is a STATA program. This program used the Taylor linearization method for variance estimation for survey estimates that are means or proportions. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

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

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar year - Reporting of age at death in days - Reporting of age at death in months

See details of the data quality tables in Appendix D of the survey final report.

Structure of households by number of children in household and statistical regions, 2006-2017

The 2017-2018 School Neighborhood Poverty Estimates are based on school locations from the 2017-2018 Common Core of Data (CCD) school file and income data from families with children ages 5 to 17 in the U.S. Census Bureau’s 2014-2018 American Community Survey (ACS) 5-year collection. The ACS is a continuous household survey that collects social, demographic, economic, and housing information from the population in the United States each month. The Census Bureau calculates the income-to-poverty ratio (IPR) based on money income reported for families relative to the poverty thresholds, which are determined based on the family size and structure. Noncash benefits (such as food stamps and housing subsidies) are excluded, as are capital gains and losses. The IPR is the percentage of family income that is above or below the federal poverty level. The IPR indicator ranges from 0 to a top-coded value of 999. A family with income at the poverty threshold has an IPR value of 100. The estimates in this file reflect the IPR for the neighborhoods around schools which may be different from the neighborhood conditions of students enrolled in schools.All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section...Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau''s Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Explanation of Symbols:An ''**'' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An ''-'' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution..An ''-'' following a median estimate means the median falls in the lowest interval of an open-ended distribution..An ''+'' following a median estimate means the median falls in the upper interval of an open-ended distribution..An ''***'' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An ''*****'' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An ''N'' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An ''(X)'' means that the estimate is not applicable or not available..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2017 American Community Survey (ACS) data generally reflect the July 2015 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas, in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..One person in each household is designated as the householder. In most cases, this is the person or one of the people in whose name the home is owned, being bought, or rented and who is listed on line one of the survey questionnaire. If there is no such person in the household, any adult household member 15 years old and over could be designated as the householder...Households are classified by type according to the presence of relatives. Two types of householders are distinguished: a family householder and a nonfamily householder. A family householder is a householder living with one or more individuals related to him or her by birth, marriage, or adoption. The householder and all people in the household related to him or her are family members. A nonfamily householder is a householder living alone or with non-relatives only...To determine poverty status of a householder in family households, one compares the total income in the past 12 months of all family members with the poverty threshold appropriate for that family size and composition. If the total family income is less than the threshold, then the householder together with every member of his or her family are considered as having income below the poverty level...In determining poverty status of a nonfamily householder, only the householder's own personal income is compared with the appropriate threshold for a single person. The poverty status of a nonfamily householder does not affect the poverty status of the other unrelated individuals living in the household and the incomes of people living in the household who are not related to the householder are not considered when determining the poverty status of a householder. The income of each unrelated individual is compared to the appropriate threshold for a single person..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the...

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show population in poverty by Neighborhood Statistical Areas E02 and E06 in the Atlanta region.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

SumLevel

Summary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)

GEOID

Census tract Federal Information Processing Series (FIPS) code

NAME

Name of geographic unit

Planning_Region

Planning region designation for ARC purposes

Acres

Total area within the tract (in acres)

SqMi

Total area within the tract (in square miles)

County

County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

CountyName

County Name

PopPovDet_e

# Population for whom poverty status is determined, 2017

PopPovDet_m

# Population for whom poverty status is determined, 2017 (MOE)

PopPov_e

# Population below poverty, 2017

PopPov_m

# Population below poverty, 2017 (MOE)

pPopPov_e

% Population below poverty, 2017

pPopPov_m

% Population below poverty, 2017 (MOE)

PopPovU18Det_e

# Population under 18 years for whom poverty status is determined, 2017

PopPovU18Det_m

# Population under 18 years for whom poverty status is determined, 2017 (MOE)

PopPovU18_e

# Population under 18 years below poverty, 2017

PopPovU18_m

# Population under 18 years below poverty, 2017 (MOE)

pPopPovU18_e

% Population under 18 years below poverty, 2017

pPopPovU18_m

% Population under 18 years below poverty, 2017 (MOE)

PopPov18_64Det_e

# Population 18 to 64 years for whom poverty status is determined, 2017

PopPov18_64Det_m

# Population 18 to 64 years for whom poverty status is determined, 2017 (MOE)

PopPov18_64_e

# Population 18 to 64 years below poverty, 2017

PopPov18_64_m

# Population 18 to 64 years below poverty, 2017 (MOE)

pPopPov18_64_e

% Population 18 to 64 years below poverty, 2017

pPopPov18_64_m

% Population 18 to 64 years below poverty, 2017 (MOE)

PopPov65PDet_e

# Population 65 years and over for whom poverty status is determined, 2017

PopPov65PDet_m

# Population 65 years and over for whom poverty status is determined, 2017 (MOE)

PopPov65P_e

# Population 65 years and over below poverty, 2017

PopPov65P_m

# Population 65 years and over below poverty, 2017 (MOE)

pPopPov65P_e

% Population 65 years and over below poverty, 2017

pPopPov65P_m

% Population 65 years and over below poverty, 2017 (MOE)

FamWChildPovStat_e

# Families with related children, 2017

FamWChildPovStat_m

# Families with related children, 2017 (MOE)

FamWChild150Pov_e

# Families with related children below 150 percent of the poverty line, 2017

FamWChild150Pov_m

# Families with related children below 150 percent of the poverty line, 2017 (MOE)

pFamWChild150Pov_e

% Families with related children below 150 percent of the poverty line, 2017

pFamWChild150Pov_m

% Families with related children below 150 percent of the poverty line, 2017 (MOE)

ChildPovStatRatio_e

# Children for whom poverty status is determined, 2017

ChildPovStatRatio_m

# Children for whom poverty status is determined, 2017 (MOE)

ChildInFam200Pov_e

# Children in families below 200 percent of the poverty line, 2017

ChildInFam200Pov_m

# Children in families below 200 percent of the poverty line, 2017 (MOE)

pChildInFam200Pov_e

% Children in families below 200 percent of the poverty line, 2017

pChildInFam200Pov_m

% Children in families below 200 percent of the poverty line, 2017 (MOE)

last_edited_date

Last date the feature was edited by ARC

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2013-2017

For additional information, please visit the Census ACS website.

CSV representation of the metrics No. of Children Referred to Family Support Services by HSE Officer 2017 for 2017

Abstract

The Lao Social Indicator Survey (LSIS) II provides a set of single national figure on social indicators. It combines the Multiple Indicator Cluster Survey (MICS) and the Demographic and Health Survey modules to maximize government resources for a nationally representative sample survey. LSIS II follows the first LSIS I survey which was carried out in 2011-12 jointly by the Ministry of Health (MoH) and the Lao Statistics Bureau (LSB) of the Ministry of Planning and Investment in collaboration with other line ministries. The LSIS I provided baseline data for the 7th National Socio-Economic Development Plan (NSEDP) and the Millennium Development Goals.

The LSISII 2017 of Lao PDR has as its primary objectives:

• To provide up-to-date information that will assist with the selection of data on key social development indicators to support the monitoring of the Sustainable Development Goals (SDGs);

• To establish a baseline for national development plans and priorities including the 8th National Socio- Economic Development Plan (NSEDP), provincial core social development indicators data, as well as supporting the data for Least Developed Country Graduation;

• To produce a range of population and social indicators that are statistically sound and based on internationally comparable methodology and best practices; and

• To continue reinforcing coordination mechanisms on supporting and strengthening social statistics in Lao PDR and making use of its findings to formulate and advocate for policies, programme formulation and monitoring.

Geographic coverage

The sample for the Lao Social Indicator Survey 2017 was designed to provide estimates at the national level, for urban and rural areas, including rural with roads and rural without roads, for three regions including: North, Central and South and 18 provinces including: Vientiane Capital, Phongsaly, Luangnamtha, Oudomxay, Bokeo, Luangprabang, Huaphanh, Xayabury, Xiengkhuang, Vientinae, Borikhamxay, Khammuane, Savannakhet, Saravane, Sekong, Champasack, Attapeu and Xaysomboun.

Analysis unit

• Individuals

• Households

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The major features of the sample design are described in this appendix. Sample design features include defining the sampling frame, target sample size, sample allocation, listing in sample clusters, choice of domains, sampling stages, stratification, and the calculation of sample weights.

The primary objective of the sample design for the 2017 Lao Social Indicator Survey (LSIS 2017) was to produce statistically reliable estimates of most indicators, at the national level, for urban and rural areas, and for the 18 provinces of the country.

A multi-stage, stratified cluster sampling approach was used for the selection of the survey sample. The primary sampling units (PSUs) selected at the first stage were villages (PSU and Village are used interchangeably in this Chapter). A listing of households was conducted in each sample village, and a sample of households was selected at the second stage.

SAMPLING FRAME AND STRATIFICATION

The sampling frame for this survey consisted of a list of all villages in the country, arranged by province, with appropriate size estimates (number of households) and other relevant information about each village. The village register is maintained by Lao Statistics Bureau (LSB). It is updated in December each year. The version used as sampling frame was the village register of December 2015.

The 18 provinces were defined as the sampling strata. Within provinces a further, implicit, stratification - on village category - was achieved by systematic sampling from a list of villages ordered by village category.

SAMPLE SIZE AND SAMPLE ALLOCATION

The overall sample size for the 2017 Lao Social Indicator Survey was calculated as 23,400 households. For the calculation of the sample size, the key indicator used was the underweight prevalence among children age 0-4 years. Since the survey results are tabulated at the provincial level, it was necessary to determine the minimum sample size for each province.

The number of households selected per cluster for the survey was determined as 20 households, based on a number of considerations, including the design effect, the budget available, and the time that would be needed per team to complete one cluster. Dividing the total number of households by the number of sample households per cluster, it was calculated that 1,170 sample clusters would need to be selected for the survey.

The sample allocation over provinces was determined by a procedure where the sample at first was allocated proportionally to the square root of the number of households in each province. This allocation was further adjusted so that provinces getting less than 1,100 households in the preliminary allocation were given additional households up to 1,100. These additional households were taken from the three provinces that had the largest samples according to the preliminary allocation. The sample sizes for provinces vary between 1,100 and 1,680 households. The justification for using different sample sizes is that the standard errors for national estimates will be lower than the standard errors that would have been achieved with equal sample sizes over the provinces.

Within province the sample was allocated over implicit strata defined by village category. This was achieved by systematic sampling from a list of villages ordered by village category. This way of sampling resulted in approximately proportional allocation of the province sample over the implicit strata urban villages, rural villages with road and rural villages without road.

SELECTION OF VILLAGES (CLUSTERS)

Villages were selected from each of the sampling strata (provinces) by using systematic probability proportional to size (PPS) sampling procedures. The measure of size was the number of households in the village; the number was obtained from the LBS village register. Altogether 32 villages were so large in size so they had the probability equal to one to be selected to the sample. These large villages were thus selected to the sample with certainty.

LISTING ACTIVITIES

A new listing of households was conducted in all the sample villages prior to the selection of households. For this purpose, listing teams were trained to visit all the sampled villages and list all households in the village. The listing operation took place from December 2016 to February 2017 with 70 listing team members. In each Province, there were two teams each consisting of a lister and a mapper, except in Champasack, where three teams were assigned.

Listing could not be done in four villages. In two of the villages the area had been completely cleared of dwellings due to preparations for dam construction. One village was not accessible by car or motorcycle due to poor roads and one village could not be properly identified due to village mergers.

Large villages, where the number of households exceeded 300 households, were divided into two or more segments, and one segment was picked randomly before listing. Segmentation was done in 216 villages.

SELECTION OF HOUSEHOLDS

Lists of households were prepared by the listing teams in the field for each village. The households were then sequentially numbered from 1 to Mhi (the total number of households in each village or segment) at the Lao Bureau of Statistics, where the selection of 20 households in each village was carried out using random systematic selection procedures. The MICS6 spreadsheet template for systematic random selection of households was adapted for this purpose.

The survey also included a questionnaire for individual men that was to be administered in half of the sample of households. The MICS household selection template includes an option to specify the proportion of households to be selected for administering the individual questionnaire for men, and the spreadsheet automatically selected the corresponding subsample of households. All men age 15 to 49 years in the selected households were eligible for interview.

LSIS 2017 also included water quality testing for a subsample of households within each sample cluster. A subsample of 3 of the 20 selected households was selected in each sample cluster using random systematic sampling for conducting water quality testing, for both water in the household and at the source. The MICS household selection template includes an option to specify the number of households to be selected for the water quality testing, and the spreadsheet automatically selected the corresponding subsample of households.

Mode of data collection

Face-to-face [f2f]

Research instrument

Six questionnaires were used in the survey: 1) a household questionnaire which was used to collect basic demographic information, the household, and the dwelling; 2) a water quality testing questionnaire administered in three households in each cluster of the sample; 3) a questionnaire for individual women; 4) a questionnaire for individual men; 5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and 6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household.

Questionnaires to capture anthropometry measurements among children under 5 years and to record anaemia test results for children under 5 years and women age 15-19

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the .Technical Documentation.. section......Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the .Methodology.. section..Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Explanation of Symbols:..An "**" entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An "-" entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution..An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution..An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution..An "***" entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An "*****" entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An "N" entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An "(X)" means that the estimate is not applicable or not available...Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2013-2017 American Community Survey (ACS) data generally reflect the February 2013 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see .Accuracy of the Data..). The effect of nonsampling error is not represented in these tables..Source: U.S. Census Bureau, 2013-2017 American Community Survey 5-Year Estimates