This link contains downloadable data for the Atlas of Rural and Small-Town America which provides statistics by broad categories of socioeconomic factors: People: Demographic data from the American Community Survey (ACS), including age, race and ethnicity, migration and immigration, education, household size, and family composition. Jobs: Economic data from the Bureau of Labor Statistics and other sources, including information on employment trends, unemployment, and industrial composition of employment from the ACS. County classifications: Categorical variables including the rural-urban continuum codes, economic dependence codes, persistent poverty, persistent child poverty, population loss, onshore oil/natural gas counties, and other ERS county typology codes. Income: Data on median household income, per capita income, and poverty (including child poverty). Veterans: Data on veterans, including service period, education, unemployment, income, and other demographic characteristics.

This report presents a comprehensive analysis of poverty and its main determinants using the most recent 2007 Living Standards Measurement Survey (LSMS) data. It provides an in-depth analysis of rural poverty, the linkages between labor markets and poverty, the importance of social transfers for poverty alleviation, and the progress made since 2003 in the health and education sectors, and also presents some findings on incomes trends based on the Household Budget Survey from 2003 to 2006. The report also simulates possible poverty impacts from the dual shocks of the August 2008 conflict and the current global financial crisis. Main messages in the document are: (1) the available data indicate that living standards in Georgia have improved in many dimensions since 2003; (2) poverty in Georgia continues to be deeply entrenched in rural areas, accounting for 60 percent of the poor; (3) the performance of the labor markets has so far not contributed much to poverty reduction; (4) social assistance became an increasingly important lifeline for Georgia's poor; (5) the double shocks of the August 2008 conflict and the global financial crisis risk undermining the poverty reduction effort; and (6) the poverty reduction strategy of the government of Georgia should focus on: extending the coverage of the Targeted Social Assistance to reach more of the poor; promoting investments in infrastructure and creating opportunities for off-farm employment in rural areas; and continuing reforms in the health and education sectors to improve human capital, which is the prerequisite for sustainable economic growth and poverty reduction. The main findings state that since 2003, Georgia has implemented an impressive array of reforms. Unfortunately, the absence of comparable household surveys over time precludes a proper analysis of the impact of these reforms on growth and poverty reduction.

Explore gender statistics data focusing on academic staff, employment, fertility rates, GDP, poverty, and more in the GCC region. Access comprehensive information on key indicators for Bahrain, China, India, Kuwait, Oman, Qatar, and Saudi Arabia.

academic staff, Access to anti-retroviral drugs, Adjusted net enrollment rate, Administration and Law programmes, Age at first marriage, Age dependency ratio, Cause of death, Children out of school, Completeness of birth registration, consumer prices, Cost of business start-up procedures, Employers, Employment in agriculture, Employment in industry, Employment in services, employment or training, Engineering and Mathematics programmes, Female headed households, Female migrants, Fertility planning status: mistimed pregnancy, Fertility planning status: planned pregnancy, Fertility rate, Firms with female participation in ownership, Fisheries and Veterinary programmes, Forestry, GDP, GDP growth, GDP per capita, gender parity index, Gini index, GNI, GNI per capita, Government expenditure on education, Government expenditure per student, Gross graduation ratio, Households with water on the premises, Inflation, Informal employment, Labor force, Labor force with advanced education, Labor force with basic education, Labor force with intermediate education, Learning poverty, Length of paid maternity leave, Life expectancy at birth, Mandatory retirement age, Manufacturing and Construction programmes, Mathematics and Statistics programmes, Number of under-five deaths, Part time employment, Population, Poverty headcount ratio at national poverty lines, PPP, Primary completion rate, Retirement age with full benefits, Retirement age with partial benefits, Rural population, Sex ratio at birth, Unemployment, Unemployment with advanced education, Urban population

Bahrain, China, India, Kuwait, Oman, Qatar, Saudi Arabia

Follow data.kapsarc.org for timely data to advance energy economics research.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.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..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Employment and unemployment estimates may vary from the official labor force data released by the Bureau of Labor Statistics because of differences in survey design and data collection. For guidance on differences in employment and unemployment estimates from different sources go to Labor Force Guidance..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

This report, which has been prepared by the World Bank in cooperation with the National Statistical Committee, provides an assessment of poverty in the Kyrgyz Republic using the most recent data available. The objective of this report is to understand to what extent economic growth has reduced poverty and led to improved living conditions for the population during 2000-2005. The report also attempts to answer three questions about the Kyrgyz Republic: what is the profile of poor How has economic growth affected the level and composition of poverty How has the labor market contributed to changes in poverty The report is divided into two volumes. The first volume begins with this chapter which provides an international comparison of social and other key indicators of the Kyrgyz Republic followed by a profile of the poor based upon 2005 household survey data. The second chapter analyzes the linkages between growth and poverty during 2000-2005. The third chapter provides our key findings of labor market outcomes and poverty and what the implications are for policy making. The final chapter synthesizes the information from the earlier chapters and provides some policy directions. The second volume provides a more thorough analysis of labor markets. It covers developments in the labor market, urban labor markets, rural labor markets and differences between men and women in the labor market.

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..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, 2017 American Community Survey 1-Year Estimates

Abstract

This study is an impact evaluation of the KALAHI-CIDSS (KC) program. The impact evaluation's key research questions can be divided into the following four themes:

1. Socio-Economic Effects: Does KC increase household consumption? Does KC increase labor force participation?
2. Governance Effects: Does KC increase government leader responsiveness to community needs? Does KC reduce corruption and increase transparency?
3. Community Empowerment Effects: Does KC increase participation in local governance? Does KC increase collective action and contribution to local public goods?
4. Social Capital Effects: Does KC build groups and networks? In what ways are these networks applied? Does KC enhance trust?

In order to isolate KC's effects, a randomized control trial evaluation design was chosen. The impact evaluation sample consists of 198 municipalities (with 33 to 69 percent poverty incidence), spread over 26 provinces and 12 regions. The 198 municipalities were paired based on similar characteristics (99 pairs) and then randomly assigned into treatment and control groups through public lotteries. The sample size is large enough to be able to detect MCC's projected eight percent change in household income as well as other smaller effects. As part of the impact evaluation, baseline quantitative data were collected in the study area from April to July 2012. The quantitative data came from 5,940 household surveys in 198 barangays (one from each municipality) and 198 barangay surveys implemented in these same barangays

Geographic coverage

National coverage: The sample consists of 5,940 households in 198 barangays in 198 municipalities in 26 provinces in 12 regions. The sample is representative of the KALAHI-CIDSS target population across the nation.

Analysis unit

Individuals, households, community

Universe

The study population consists of barangays (villages) from the Philippines' poorest provinces. Survey respondent were barangay captains (village captains) and randomly selected households (30 randomly selected per barangay) from the sample of 198 barangays (villages).

Kind of data

Sample survey data [ssd]

Sampling procedure

The impact evaluation focuses on municipalities with between 33-69% poverty incidence. A total of 198 eligible municipalities were matched on poverty incidence, population, land area, and number of barangays. The paired municipalities were then randomly assigned into treatment and control groups through public lotteries. This resulted in the final sample of 198 municipalities (when determining the number of treatment and control municipalities, we used sample size of 30 households per municipality, ensuring an 8% (positive) change in income would be detectable at 95% significance and 80% power). The large number of municipalities included in the evaluation will provide a sufficient level of precision to estimate KC's impacts nationwide in municipalities with a poverty incidence between 33-69%. One barangay within each of the 198 municipalities participating in the evaluation was randomly chosen, with a weighted probability favoring barangays with the highest poverty rates. Within each municipality, IPA divided barangays into quintiles based on poverty and dropped the quintile with the lowest poverty incidence. For each municipality, the barangay to be surveyed for the sample was then randomly selected from the remaining barangays. Within each barangay, 30 households were randomly selected from among all households to comprise the household surveyed sample.

Sampling deviation

N/A

Research instrument

The baseline study included a barangay (village) questionnaire and a household questionnaire implemented in the following four different languages: Tagalog, Bisaya, Cebuano, llongo and llocano.

1. Household questionnaire: This questionnaire was composed of modules on education, labor income sources, household assets and amenities, expenditures, social networks, and other topics.

2. Barangay questionnaire: The barangay captains (village leaders were the principal respondents. The questionnaire collected data on the barangay's development projects, budget, demographics, the relationship between the existing barangay captain and its previous leadership, and other topics.

Cleaning operations

In the field, the field supervisor and data editor checked the questionnaires before the first data entry. The survey firm then conducted the second data entry in the main office and then checked the discrepancies between the first and the second data entry. The data cleaning process implemented by the survey firm included the following: 1. Naming and labelling the data 2. Checking the unique identifiers 3. Range checks and setting variable bounds 4. Check skip patterns and misisng data 5. Check logical consistency 6. Standardize string variable coding

After receiving the clean datasets from the survey firm, IPA conducted a second stage of data cleaning needed to construct variables for the analysis. This process involved carefully creating, summarizing and cross-checking key indicators.

Response rate

100 percent

Sampling error estimates

N/A

In 2023, the number of child workers in Indonesia amounted to around **** million people. This indicated a decrease of approximately ** thousand people compared to 2021. Child labor in Indonesia is still an ongoing issue due to poverty and a lack of access to education in some parts of the country. The pandemic notably affected the problem, as the number of child workers increased in 2020. Although the numbers have decreased since then, they remain higher than the pre-pandemic level. The challenges of child labor in Indonesia The persisting issue of child labor in Indonesia stems from different factors such as economics, social norms, and education. Poverty acts as a crucial driving factor in the case of child labor practices. Many children are pushed to stop attending school and get to work to help the family’s income, as over **** percent of the Indonesian population still lives below the poverty line. The islands in the eastern part of the archipelago, such as Maluku and Papua, had the highest poverty rates of over ** percent in 2022. It was also found that Papua had the highest share of students who had to attend school and work simultaneously. Moreover, in certain areas of the archipelago, cultural beliefs are linked to entering the labor force at an early age, with some believing this to help shape children to have better life opportunities in the future. The lack of awareness about the effects of child labor and some companies not complying with the laws against child labor further exacerbate the issue. Child labor in the Indonesian agricultural sector Child labor in Indonesia is more prevalent in rural areas. As of 2022, there has been an increase in the child labor rate in Indonesia’s rural areas in the agricultural sector, which most commonly offers informal employment with minimal employment protections. Child workers in this sector face higher risks of being exposed to harmful chemicals used in pesticides and fertilizers, causing raised concerns about their safety. Despite the efforts to overcome this issue, such as child protection laws, government allocations for infrastructure, and government allocations for education to improve living conditions and educational access, the need for strategic initiatives to combat child labor in Indonesia remains.

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..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..Source: U.S. Census Bureau, 2021 American Community Survey 1-Year Estimates.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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Employment and unemployment estimates may vary from the official labor force data released by the Bureau of Labor Statistics because of differences in survey design and data collection. For guidance on differences in employment and unemployment estimates from different sources go to Labor Force Guidance..The 2021 American Community Survey (ACS) data generally reflect the March 2020 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..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..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Goal 1End poverty in all its forms everywhereTarget 1.1: By 2030, eradicate extreme poverty for all people everywhere, currently measured as people living on less than $1.25 a dayIndicator 1.1.1: Proportion of the population living below the international poverty line by sex, age, employment status and geographic location (urban/rural)SI_POV_DAY1: Proportion of population below international poverty line (%)SI_POV_EMP1: Employed population below international poverty line, by sex and age (%)Target 1.2: By 2030, reduce at least by half the proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitionsIndicator 1.2.1: Proportion of population living below the national poverty line, by sex and ageSI_POV_NAHC: Proportion of population living below the national poverty line (%)Indicator 1.2.2: Proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitionsSD_MDP_MUHC: Proportion of population living in multidimensional poverty (%)SD_MDP_ANDI: Average proportion of deprivations for people multidimensionally poor (%)SD_MDP_MUHHC: Proportion of households living in multidimensional poverty (%)SD_MDP_CSMP: Proportion of children living in child-specific multidimensional poverty (%)Target 1.3: Implement nationally appropriate social protection systems and measures for all, including floors, and by 2030 achieve substantial coverage of the poor and the vulnerableIndicator 1.3.1: Proportion of population covered by social protection floors/systems, by sex, distinguishing children, unemployed persons, older persons, persons with disabilities, pregnant women, newborns, work-injury victims and the poor and the vulnerableSI_COV_MATNL: [ILO] Proportion of mothers with newborns receiving maternity cash benefit (%)SI_COV_POOR: [ILO] Proportion of poor population receiving social assistance cash benefit, by sex (%)SI_COV_SOCAST: [World Bank] Proportion of population covered by social assistance programs (%)SI_COV_SOCINS: [World Bank] Proportion of population covered by social insurance programs (%)SI_COV_CHLD: [ILO] Proportion of children/households receiving child/family cash benefit, by sex (%)SI_COV_UEMP: [ILO] Proportion of unemployed persons receiving unemployment cash benefit, by sex (%)SI_COV_VULN: [ILO] Proportion of vulnerable population receiving social assistance cash benefit, by sex (%)SI_COV_WKINJRY: [ILO] Proportion of employed population covered in the event of work injury, by sex (%)SI_COV_BENFTS: [ILO] Proportion of population covered by at least one social protection benefit, by sex (%)SI_COV_DISAB: [ILO] Proportion of population with severe disabilities receiving disability cash benefit, by sex (%)SI_COV_LMKT: [World Bank] Proportion of population covered by labour market programs (%)SI_COV_PENSN: [ILO] Proportion of population above statutory pensionable age receiving a pension, by sex (%)Target 1.4: By 2030, ensure that all men and women, in particular the poor and the vulnerable, have equal rights to economic resources, as well as access to basic services, ownership and control over land and other forms of property, inheritance, natural resources, appropriate new technology and financial services, including microfinanceIndicator 1.4.1: Proportion of population living in households with access to basic servicesSP_ACS_BSRVH2O: Proportion of population using basic drinking water services, by location (%)SP_ACS_BSRVSAN: Proportion of population using basic sanitation services, by location (%)Indicator 1.4.2: Proportion of total adult population with secure tenure rights to land, (a) with legally recognized documentation, and (b) who perceive their rights to land as secure, by sex and type of tenureSP_LGL_LNDDOC: Proportion of people with legally recognized documentation of their rights to land out of total adult population, by sex (%)SP_LGL_LNDSEC: Proportion of people who perceive their rights to land as secure out of total adult population, by sex (%)SP_LGL_LNDSTR: Proportion of people with secure tenure rights to land out of total adult population, by sex (%)Target 1.5: By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social and environmental shocks and disastersIndicator 1.5.1: Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 populationVC_DSR_MISS: Number of missing persons due to disaster (number)VC_DSR_AFFCT: Number of people affected by disaster (number)VC_DSR_MORT: Number of deaths due to disaster (number)VC_DSR_MTMP: Number of deaths and missing persons attributed to disasters per 100,000 population (number)VC_DSR_MMHN: Number of deaths and missing persons attributed to disasters (number)VC_DSR_DAFF: Number of directly affected persons attributed to disasters per 100,000 population (number)VC_DSR_IJILN: Number of injured or ill people attributed to disasters (number)VC_DSR_PDAN: Number of people whose damaged dwellings were attributed to disasters (number)VC_DSR_PDYN: Number of people whose destroyed dwellings were attributed to disasters (number)VC_DSR_PDLN: Number of people whose livelihoods were disrupted or destroyed, attributed to disasters (number)Indicator 1.5.2: Direct economic loss attributed to disasters in relation to global gross domestic product (GDP)VC_DSR_GDPLS: Direct economic loss attributed to disasters (current United States dollars)VC_DSR_LSGP: Direct economic loss attributed to disasters relative to GDP (%)VC_DSR_AGLH: Direct agriculture loss attributed to disasters (current United States dollars)VC_DSR_HOLH: Direct economic loss in the housing sector attributed to disasters (current United States dollars)VC_DSR_CILN: Direct economic loss resulting from damaged or destroyed critical infrastructure attributed to disasters (current United States dollars)VC_DSR_CHLN: Direct economic loss to cultural heritage damaged or destroyed attributed to disasters (millions of current United States dollars)VC_DSR_DDPA: Direct economic loss to other damaged or destroyed productive assets attributed to disasters (current United States dollars)Indicator 1.5.3: Number of countries that adopt and implement national disaster risk reduction strategies in line with the Sendai Framework for Disaster Risk Reduction 2015–2030SG_DSR_LGRGSR: Score of adoption and implementation of national DRR strategies in line with the Sendai FrameworkSG_DSR_SFDRR: Number of countries that reported having a National DRR Strategy which is aligned to the Sendai FrameworkIndicator 1.5.4: Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategiesSG_DSR_SILS: Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategies (%)SG_DSR_SILN: Number of local governments that adopt and implement local DRR strategies in line with national strategies (number)SG_GOV_LOGV: Number of local governments (number)Target 1.a: Ensure significant mobilization of resources from a variety of sources, including through enhanced development cooperation, in order to provide adequate and predictable means for developing countries, in particular least developed countries, to implement programmes and policies to end poverty in all its dimensionsIndicator 1.a.1: Total official development assistance grants from all donors that focus on poverty reduction as a share of the recipient country’s gross national incomeDC_ODA_POVLG: Official development assistance grants for poverty reduction, by recipient countries (percentage of GNI)DC_ODA_POVDLG: Official development assistance grants for poverty reduction, by donor countries (percentage of GNI)DC_ODA_POVG: Official development assistance grants for poverty reduction (percentage of GNI)Indicator 1.a.2: Proportion of total government spending on essential services (education, health and social protection)SD_XPD_ESED: Proportion of total government spending on essential services, education (%)Target 1.b: Create sound policy frameworks at the national, regional and international levels, based on pro-poor and gender-sensitive development strategies, to support accelerated investment in poverty eradication actionsIndicator 1.b.1: Pro-poor public social spending

Abstract

The main purpose of these surveys is to provide data for the study of multiple aspects of household welfare and behavior, analysis of poverty, and understanding the effect of government policies on households.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Community

Kind of data

Sample survey data [ssd]

Sampling procedure

In order to expedite the survey process, NATSTATCOM used much of the same sample design and survey instruments as those used for the 1993 Baseline Survey. However, the Fall 1996-1998 KPMS surveys used a new sampling frame based on the Kyrgyz Household Registration System. This system was taken from the Census Posts intended for use by the first National Census of the Kyrgyz Republic. Using this system, NATSTATCOM updated the central household registration files effective January 1, 1996, and the information that was used for the sampling frame was as up to date as possible. The procedures followed in the stratification and identification of Primary Sampling Units (PSUs) were similar for all rounds of the KPMS as discussed below.

Formation of Strata

Initially the country was divided into seven (7) strata defined by oblasts (Oblasts are administrative divisions of the country which in turn are sub-divided in to Rayons) and by residence location (i.e. urban vs. rural) within oblasts. The rural portion of Bishkek oblast was combined with the rural portion of neighboring Chui oblast for stratification purposes as Bishkek has practically no rural population.

Mode of data collection

Face-to-face [f2f]

Research instrument

The KPMS surveys were carried out using a household questionnaire and a community (population point) questionnaire. The household questionnaires were used to collect demographic information on the composition of the household, housing, household consumption including home production, as well as economic activities in agricultural and non-agricultural sectors. For each household member, individual level data on health, education, migration and labor was collected using the household questionnaires. Community questionnaires were used to collect price data and the presence of social services and infrastructure in the community (population point) where the sampled household is located.

The household questionnaire was extensive and required several hours of intense interviewing to gather all that was needed from each household and its embers. The household questionnaire was split into two parts. The first part was used to collect data through a face to face interview on household roster, dwelling, education, health, migration, etc. At the end of the first part, members who shop for food for the whole household and those who know most about income, expenditure and savings of other household members were identified and designated as respondents for the next part (second round). The second round of interview was administered two weeks after the first half and collected data on crops, food and animal products produced by the household, food expenditure and home produced food consumption.

Some sections of the household questionnaire such as those that deal with dwelling and expenditure information were administered to the person most knowledgeable of the family's overall expenditures, income and other finances as well as about the family's business activities and employment. In other sections, each adult in each sample household was interviewed individually. The information gathered from each household included extensive data on education, health, employment, migration, reproduction and reproductive health (for women aged 15 to 49), land use, expenditure, revenue and other financial matters, as well as anthropometric measurements (for children 5 years and younger). Information about children under 14 years of age was collected by asking the relevant questions to the adult household member who is primarily responsible for each child's care.

The community (Population Point) questionnaires were administered to each sample cluster. They were used to collect data on prices of goods and services, distance to schools, shopping and medical facilities, types of housing, commercial and private land use and availability of infrastructure.

HOUSEHOLD QUESTIONNAIRE

The KPMS household questionnaires generally contain 15 major sections, and each of these sections covers a separate aspect of household activity. In some cases, the section has sub-sections. These household questionnaires were designed to better assess the changing environment brought about by the advent of a market economy and to enable a more in depth analysis of topics such as housing, health, and education. The various sections of the KPMS household questionnaire are described below.The household questionnaires administered in the KPMS surveys are more or less similar with minor modifications and additions in the successive rounds of the KPMS.

POPULATION POINT QUESTIONNAIRE

The community (population point) questionnaire was used to collect information and data that are relevant to the community/population point where the household is located. The questionnaire was designed to be administered in the geographical area of each sample cluster. It was used to collect data regarding prices of goods and services in the local area and data on community infrastructure. Respondents to these questionnaires are those believed to be well informed members of the community that the interviewers identified by going to the rayon, city, oblast administration or other governmental agency located in the population point6. The questionnaire also contains sections to be administered to retail outlets in the neighborhoods that sell various products such as food, drinks, tobacco products and fuel. Other data collected using the population point questionnaire includes distance to schools, distance to shopping and to medical facilities, commercial and private land use in the community, availability of electricity, water, communication and other infrastructure. Similar population point questionnaires were used in all KPMS. The population point questionnaires were completed by the field supervisors. The population point questionnaire contains nine (9) major sections

Data appraisal

There are no significant data quality problems, but the following deserve mentioning.

i) Reproductive health/Nutrition Module (section 8): There are many missing observations in this section of the data. During the data collection stage, there was a restriction that only up to 3 (three) adult women (14 to 49 years of age) per household can be interviewed for this section, but even with this restriction, the number of observations with valid data is very low.

ii) Information on parents of household members (section 1B): The ID codes for the Father or Mother of household members in this section are mostly incorrect. The interviewers in most cases used the code for 'relationship to the head of the household' and entered the value of '5' -- i.e. they copied the values of question 3 of section 1A (Roster) instead of copying the ID codes of the Fathers/Mothers of household members from that section.

iii) Anthropometric data (section 15): The anthropometric data are also not very reliable. The height variable varies significantly because in some places it was recorded in inches and in others in Centimeters.

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Abstract

The Core Welfare Indicators Questionnaire (CWIQ) currently constitutes one of the largest socio-economic household survey databases on Tanzania. Since 2003 EDI has interviewed roughly 20,000 households in 35 different districts. For 9 districts repeat surveys have been organised to track changes over time.

Rationale: Absence of district level survey data does not rhyme with the devolution of power to districts. Tanzania is undergoing a decentralisation process whereby each of its roughly 128 districts is becoming an increasingly important policy actor. A district taking on this challenge needs accurate information to monitor and develop its own policies. Much relevant information is currently not available as national statistics are not representative at district level and many of the routine data collection mechanisms are still under development. CWIQ then provides an attractive, one-stop survey-based method to collect basic development indicators. Furthermore, the survey results can be disseminated - through Swahili briefs and posters - to a district's population; thus increasing the extent to which people are able to hold their local governments accountable. Exciting new ground is being broken on such population-wide dissemination by the Prime Minister's Office.

Methodology: The data are collected through a small 10-page questionnaire, called the Core Welfare Indicators Questionnaire (CWIQ). The questionnaire and data software constitute an off-the-shelf survey package developed by the World Bank to produce standardised monitoring indicators of welfare. The questionnaire is purposively concise and is designed to collect information on household demographics, employment, education, health and nutrition as well as utilisation and satisfaction with social services. Questionnaires are scannable, with interviewers shading bubbles and writing numbers later recognised by the scanning software. The data system is fully automated allowing the results to roll out within weeks of the fieldwork.

Funding: projects are typically funded by organisations that care about making decentralisation work in Tanzania. CWIQ is a method to promote evidence-based policy formulation and debate in the district and a tool for the population to hold their local governments accountable. With funding from the RNE (Royal Netherlands Embassy) and SNV (Stichting Nederlands Vrijwilligers), CWIQ surveys were implemented between 2003-2005 in 16 districts. In 2006/07 PMO-RALG (Prime Minister's Office - Regional Administration and Local Government) commissioned EDI to cover a further 28 districts. In 9 of these districts this constituted a repeat survey and thus a unique opportunity arises to monitor changes that occurred in the district over this time period.

Dissemination: EDI disseminated the results of CWIQ on posters and briefs to district level stakeholders (councillors, district officials, NGOs, CBOs, Advocacy Groups, MPs, 'interested citizens', etc.), with the aim at district level, to: (i) promote evidence-based policy debate, (ii) promote evidence-based policy formulation, (iii) provide tools for district level M&E and (iv) increase accountability of LGA to citizens.

Public Domain: Currently in the public domain are (i) all CWIQ reports - note that Shinyanga 2004 and Kagera 2003 reports are organised into one region-wide report (ii) Swahili and English briefs for 5 pilot dissemination districts funded by the Prime Minister's Office - and (iii) raw data for all CWIQs conducted between 2003 and 2007.

Geographic coverage

Five rural districts of Kagera: Ngara, Biharamulo, Muleba, Bukoba Rural and Karagwe.

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

Data from the 2002 Population and Housing Census was used to select 15 households in 30 Enumeration areas in each rural district of the Kagera region. This brings the total number of households to 450 per district or 2,250 at rural regional level. Selection of households did not include refugee camps. Households were further stratified into rural and peri-urban areas and given statistical weights reflecting the number of households they represent.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Due to logistical constraints the completed questionnaires could not be scanned and automatically analysed through CWIQ software. This meant that the lay-out of the questionnaire had to be redesigned slightly to allow easy manual data entry. In order to avoid any problems with coding, missing variables, outliers etc. and to keep continuous thorough checks throughout the data analysis process, all tables and figures were manually produced and assessed for consistency with the data.

The "State Fact Sheets" provide the most recently available farm and rural data compiled by the Economic Research Service, USDA. It provide 2 pages of facts for each state and also a summary fact sheet for the United States. Included are data on population, jobs, income and poverty, and farms. The data comes from a variety of sources including the Bureau of Labor Statistics, Bureau of Economic Analysis, the Bureau of the Census, and ERS.

Collection Organization: Economic Research Service.

Collection Methodology: The data come from a variety of sources including the Bureau of Labor Statistics, Bureau of Economic Analysis, the Bureau of the Census, and ERS.

Collection Frequency: Varies by data source.

Update Characteristics: Selective updates 2 times a year.

STATISTICAL INFORMATION:

The data reside in 52 ASCII text files. LANGUAGE:

English ACCESS/AVAILABILITY:

Data Center: USDA Economic Research Service Dissemination Media: Diskette, Internet gopher, Internet home page File Format: ASCII, Lotus/dBase Access Instructions: Call NASS at 1-800-999-6779 for historical series data available on diskette. For historical series data available online, connect to the Internet home page at Cornell University.

URL: 'http://usda.mannlib.cornell.edu/usda'

Access to the data or reports may be achieved through the ERS-NASS information system:

WWW: 'http://usda.mannlib.cornell.edu/usda' Gopher client: 'gopher://gopher.mannlib.cornell.edu:70/'

For subscription direct to an e-mail address, send an e-mail message to:

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Type the word "lists" (without quotes) in the body of the message.

Abstract

The Core Welfare Indicators Questionnaire (CWIQ) currently constitutes one of the largest socio-economic household survey databases on Tanzania. Since 2003 EDI has interviewed roughly 20,000 households in 35 different districts. For 9 districts repeat surveys have been organised to track changes over time.

Rationale: Absence of district level survey data does not rhyme with the devolution of power to districts. Tanzania is undergoing a decentralisation process whereby each of its roughly 128 districts is becoming an increasingly important policy actor. A district taking on this challenge needs accurate information to monitor and develop its own policies. Much relevant information is currently not available as national statistics are not representative at district level and many of the routine data collection mechanisms are still under development. CWIQ then provides an attractive, one-stop survey-based method to collect basic development indicators. Furthermore, the survey results can be disseminated - through Swahili briefs and posters - to a district's population; thus increasing the extent to which people are able to hold their local governments accountable. Exciting new ground is being broken on such population-wide dissemination by the Prime Minister's Office.

Methodology: The data are collected through a small 10-page questionnaire, called the Core Welfare Indicators Questionnaire (CWIQ). The questionnaire and data software constitute an off-the-shelf survey package developed by the World Bank to produce standardised monitoring indicators of welfare. The questionnaire is purposively concise and is designed to collect information on household demographics, employment, education, health and nutrition as well as utilisation and satisfaction with social services. Questionnaires are scannable, with interviewers shading bubbles and writing numbers later recognised by the scanning software. The data system is fully automated allowing the results to roll out within weeks of the fieldwork.

Funding: projects are typically funded by organisations that care about making decentralisation work in Tanzania. CWIQ is a method to promote evidence-based policy formulation and debate in the district and a tool for the population to hold their local governments accountable. With funding from the RNE (Royal Netherlands Embassy) and SNV (Stichting Nederlands Vrijwilligers), CWIQ surveys were implemented between 2003-2005 in 16 districts. In 2006/07 PMO-RALG (Prime Minister's Office - Regional Administration and Local Government) commissioned EDI to cover a further 28 districts. In 9 of these districts this constituted a repeat survey and thus a unique opportunity arises to monitor changes that occurred in the district over this time period.

Dissemination: EDI disseminated the results of CWIQ on posters and briefs to district level stakeholders (councillors, district officials, NGOs, CBOs, Advocacy Groups, MPs, ‘interested citizens', etc.), with the aim at district level, to: (i) promote evidence-based policy debate, (ii) promote evidence-based policy formulation, (iii) provide tools for district level M&E and (iv) increase accountability of LGA to citizens.

Public Domain: Currently in the public domain are (i) all CWIQ reports - note that Shinyanga 2004 and Kagera 2003 reports are organised into one region-wide report (ii) Swahili and English briefs for 5 pilot dissemination districts funded by the Prime Minister's Office - and (iii) raw data for all CWIQs conducted between 2003 and 2007.

Geographic coverage

Rural districts in the Shinyanga region: Kishapu, Shinyanga Rural, Maswa, Meatu, Bukombe, Bariadi and Kahama

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The Rural Shinyanga CWIQ was sampled to be representative at district level in all seven rural districts of Shinyanga region: Kahama, Bukombe, Bariadi, Meatu, Maswa, Shinyanga Rural, and Kishapu. Data from the 2002 Census was used to select 15 households in 30 Enumeration Areas in each rural district of the Shinyanga region. This brings the total number of households to 450 per district or 3,150 at rural regional level. Households were stratified into rural and peri-urban areas and given statistical weights reflecting the number of households they represent.

Mode of data collection

Face-to-face [f2f]

Research instrument

The data are collected through a small 10-page questionnaire (downloadable below), called the Core Welfare Indicators Questionnaire (CWIQ). The questionnaire and data software constitute an off-the-shelf survey package developed by the World Bank to produce standardised monitoring indicators of welfare.Questionnaires are scannable, with interviewers shading bubbles and writing numbers later recognised by the scanning software. The data system is fully automated allowing the results to roll out within weeks of the fieldwork.

Cleaning operations

Due to logistical constraints the completed questionnaires could not be scanned and automatically analysed through CWIQ software. This meant that the lay-out of the questionnaire had to be slightly redesigned to allow easy manual data entry. In order to avoid any problems with coding, missing variables, outliers etc. and to keep continuous thorough checks throughout the data analysis process, all tables and figures were manually produced and their consistency with the data assessed.CWIQ does not collect information on consumption and thus cannot directly calculate poverty rates. Therefore the 2000/01 Household Budget Survey (HBS) was used to determine predictors of poverty that are included in CWIQ, or could be easily added without delaying the field work. Through regression analysis weights for each poverty predictor were determined. By way of this weighted sum of poverty predictors each household can be predicted to either lie above or below the poverty line. This allows Rural Shinyanga CWIQ to analyse all data by (predicted) poverty status.

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..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..Source: U.S. Census Bureau, 2018 American Community Survey 1-Year Estimates.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 .ACS Technical Documentation..). The effect of nonsampling error is not represented in these tables..Employment and unemployment estimates may vary from the official labor force data released by the Bureau of Labor Statistics because of differences in survey design and data collection. For guidance on differences in employment and unemployment estimates from different sources go to .Labor Force Guidance....While the 2018 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..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..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, or the margin of error associated with a median was larger than the median itself..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....

Abstract

The Integrated Living Conditions Survey (ILCS), conducted annually by the NSS National Statistical Service of the Republic of Armenia, formed the basis for monitoring living conditions in Armenia. The ILCS is a universally recognized best-practice survey for collecting data to inform about the living standards of households. The ILCS comprises comprehensive and valuable data on the welfare of households and separate individuals which gives the NSS an opportunity to provide the public with up to date information on the population’s income, expenditures, the level of poverty and the other changes in living standards on an annual basis.

Geographic coverage

Urban and rural communities

Analysis unit

• Households;
• Individuals.

Kind of data

Sample survey data [ssd]

Sampling procedure

During the 2001-2003 surveys two-stage random sample was used; the first stage covered the selection of settlements - cities and villages, while the second stage was focused on the selection of households in these settlements. The surveys were conducted on the principle of monthly rotation of households by clusters (sample units). In 2002 and 2003 the number of households was 387 with the sample covering 14 cities and 30 villages in 2002 and 17 cities and 20 villages in 2003.

During the 2004-2006 surveys the sampling frame for the ILCS was built using the database of addresses for the 2001 Population Census; the database was developed with the World Bank technical assistance. The database of addresses of all households in Armenia was divided into 48 strata including 12 communities of Yerevan city. The households from other regions (marzes) were grouped according to the following three categories: big towns with 15,000 and more population; villages, and other towns. Big towns formed 16 strata (the only exception was the Vayots Dzor marz where there are no big towns). The villages and other towns formed 10 strata each. According to this division, a random, two-step sample stratified at marz level was developed. All marzes, as well as all urban and rural settlements were included in the sample population according to the share of population residing in those settlements as percent to the total population in the country. In the first step, the settlements, i.e. primary sample units, were selected: 43 towns out of 48 or 90 percent of all towns in Armenia were surveyed during the year; also 216 villages out of 951 or 23 percent of all villages in the country were covered by the survey. In the second step, the respondent households were selected: 6,816 households (5,088 from urban and 1,728 from rural settlements). As a result, for the first time since 1996 survey data were representative at the marz level.

During the 2007-2012 surveys the sampling frame for ILCS was designed according to the database of addresses for the 2001 Population Census, which was developed with the World Bank technical assistance. The sample consisted of two parts: core sample and oversample.

1) For the creation of core sample, the sample frame (database of addresses of all households in Armenia) was divided into 48 strata including 12 communities of Yerevan city. The households from other regions (marzes) were grouped according to three categories: large towns (with population of 15000 and higher), villages and other towns. Large towns formed by 16 groups (strata), while the villages and towns formed by 10 strata each. According to that division, a random, two-step sample stratified at the marz level was developed. All marzes, as well as all urban and rural settlements were included in the sample population according to the share of households residing in those settlements as percent to the total households in the country. In the first step, using the PPS method the enumeration units (i.e., primary sample units to be surveyed during the year) were selected. 2007 sample includes 48 urban and 18 rural enumeration areas per month. 2) The oversample was drawn from the list of villages included in MCA-Armenia Rural Roads Rehabilitation Project. The enumeration areas of villages that were already in the core sample were excluded from that list. From the remaining enumeration areas 18 enumeration areas were selected per month. Thus, the rural sample size was doubled. 3) After merging the core sample and oversample, the survey households were selected in the second step. 656 households were surveyed per month, from which 368 from urban and 288 from rural settlements. Each month 82 interviewers had conducted field work, and their workload included 8 households per month. In 2007 number of surveyed households was 7,872 (4,416 from urban and 3,456 from rural areas).

For the survey 2013 the sample frame for ILCS was designed in accordance with the database of addresses of all private households in the country developed on basis of the 2001 Population Census results, with the technical assistance of the World Bank. The method of systematic representative probability sampling was used to frame the sample. For the purpose of drawing the sample, the sample frame was divided into 32 strata including 12 communities of Yerevan City (currently, the administrative districts). According to this division, a two-tier sample was drawn stratified by regions and by Yerevan. All regions and Yerevan, as well as all urban and rural communities were included in the sample in accordance to the shares of their resident households within the total number of households in the country. In the first round, enumeration areas - that is primary sample units to be surveyed during the year - were selected. The ILCS 2013 sample included 32 enumeration areas in urban and 16 enumeration areas in rural communities per month. The households to be surveyed were selected in the second round. A total of 432 households were surveyed per month, of which 279 and 153 households from urban and rural communities, respectively. Every month 48 interviewers went on field work with a workload of 9 households per month.

The sample frame for 2014-2016 was designed in accordance with the database of addresses of all private households in the country developed on basis of the 2011 Population Census results, with the technical assistance of the World Bank. The method of systematic representative probability sampling was used to frame the sample.
For drawing the sample, the sample frame was divided into 32 strata including 12 communities of Yerevan City (currently, the administrative districts). According to this division, a two-tier sample was drawn stratified by regions and by Yerevan. All regions and Yerevan, as well as all urban and rural communities were included in the sample in accordance to the shares of their resident households within the total number of households in the country. In the first round, enumeration areas - that is primary sample units to be surveyed during the year - were selected. The ILCS 2014 sample included 30 enumeration areas in urban and 18 enumeration areas in rural communities per month. The method of representative probability sampling was used to frame the sample. At regional level, all communities were grouped into two categories - towns and villages. According to this division, a two-tier sample was drawn stratified by regions and by Yerevan. All regions and Yerevan, as well as all rural and urban communities were included in the sample in accordance to the shares of their resident households within the total number of households in the country. In the first round, enumeration districts - that is primary sample units to be surveyed during the year - were selected. The ILCS 2015 sample included 30 enumeration districts in urban and 18 enumeration districts in rural communities per month.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Questionnaire is filled in by the interviewer during the least five visits to households per month. During face-to-face interviews with the household head or another knowledgeable adult member, the interviewer collects information on the composition and housing conditions of the household, the employment status, educational level and health condition of the members, availability and use of land, livestock, and agricultural machinery, monetary and commodity flows between households, and other information.

The 2015 survey questionnaire had the following sections: (1) "List of Household Members", (2) "Migration", (3) "Housing and Dwelling Conditions", (4) "Employment", (5) "Education", (6) "Agriculture", (7) "Food Production", (8) "Monetary and Commodity Flows between Households", (9) "Health (General) and Healthcare", (10) "Debts", (11) "Subjective Assessment of Living Conditions", (12) "Provision of Services", (13) "Social Assistance", (14) "Households as Employers for Service Personnel", and (15) "Household Monthly Consumption of Energy Resources".

The Diary is completed directly by the household for one month. Every day the household would record all its expenditures on food, non-food products and services, also giving a detailed description of such purchases; e.g. for food products the name, quantity, cost, and place of purchase of the product is recorded. Besides, the household records its consumption of food products received and used from its own land and livestock, as well as from other sources (e.g. gifts, humanitarian aid). Non-food products and services purchased or received for free are also recorded in the diary. Then, the household records its income received during the month. At the end of the month, information on rarely used food products, durable goods and ceremonies is recorded, as well. The records in the diary are verified by the interviewer in the course of 5

Health Insurance Coverage Status includes the following subjects: - Number Uninsured and Percent Uninsured - Uninsured by Age - Uninsured by Sex - Uninsured by Race - Uninsured by Citizenship Status - Uninsured by Education Attainment - Uninsured by Employment Status - Uninsured by Work Experience - Uninsured by Household Income - Uninsured by Poverty Level 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. Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Data and 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. Source: U.S. Census Bureau, 2008-2012 American Community Survey If no data appears in a cell the estimate is not applicable or not available 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. The Census Bureau introduced a new set of disability questions in the 2008 ACS questionnaire. Accordingly, comparisons of disability data from 2008 or later with data from prior years are not recommended. For more information on these questions and their evaluation in the 2006 ACS Content Test, see the Evaluation Report Covering Disability. While the 2008-2012 American Community Survey (ACS) data generally reflect the December 2009 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.Estimates of urban and rural population, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2000 data. Boundaries for urban areas have not been updated since Census 2000. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization.

The American Community Survey (ACS) replaced the United States (US) Census Long Form Survey after 2000 as the only systematic source of community-level information on the American Indian and Alaska Native (AIAN) population living in rural Alaska communities. We define rural Alaska as the region corresponding to Census Public Use Microdata Area (PUMA) 400, known also as the Subsistence Alaska PUMA. The ACS is an annual survey. However, due to small sample sizes, the ACS publishes estimates for places and census areas/boroughs only as five-year moving averages. Even with the averaging over five years, estimates of economic conditions vary substantially over time in many communities, and high margins of error make it difficult to distinguish communities from each other. Using individual Census and ACS records accessed through the Census Research Data Center program, we estimated censored and logistic regression equations with random community effects that explained individual survey responses for year-round employment, individual and household earnings, individual and household income, and poverty status for individuals indicating AIAN identity, either alone or in combination with other races. Explanatory variables in the equations included Alaska Department of Labor place-level employment and earnings, Alaska Permanent Fund Dividend payments, and characteristics of individuals and households such as age, gender, presence and number of children in the household, etc., that can be benchmarked with precision to the 2000 and 2010 census counts. Annual predicted values of the equations averaged over individuals in each community were combined with survey means adjusted for random interannual survey age and gender variation to produce empirical Bayes synthetic annual estimates with margins of error comparable or smaller than the published five-year averages. The synthetic estimates include the regression-based predicted values for years 2001 through 2004 when no survey data were collected. Data are restricted by the US Census Bureau to avoid disclosure of individual information until approved for public release. The data are still under disclosure review, and will be provided if released.

The focus of this study is the implications of structural transformation for gender equality, specifically equal pay, in Sub-Saharan Africa. While structural transformation affects key development outcomes, including growth, poverty, and access to decent work, its effect on the gender pay gap is not clear ex-ante. Evidence on the gender pay gap in sub-Saharan Africa is limited, and often excludes rural areas and informal (self-)employment. This paper provides evidence on the extent and drivers of the gender pay gap in non-farm wage- and self-employment activities across three countries at different stages of structural transformation (Malawi, Tanzania and Nigeria). The analysis leverages nationally-representative survey data and decomposition methods, and is conducted separately among individuals residing in rural versus urban areas in each country. The results show that women earn 40 to 46 percent less than men in urban areas, which is substantially less than in high-income countries. The gender pay gap in rural areas ranges from (a statistically insignificant) 12 percent in Tanzania to 77 percent in Nigeria. In all rural areas, a major share of the gender pay gap (81 percent in Malawi, 83 percent in Tanzania and 70 percent in Nigeria) is explained by differences in workers’ characteristics, including education, occupation and sector. This suggests that if rural men and women had similar characteristics, most of the gender pay gap would disappear. Country-differences are larger across urban areas, where differences in characteristics account for only 32 percent of the pay gap in Tanzania, 50 percent in Malawi and 81 percent in Nigeria. Our detailed decomposition results suggest that structural transformation does not consistently help bridge the gender pay gap. Gender-sensitive policies are required to ensure equal pay for men and women.