West Virginia and Kansas had the lowest cost of living across all U.S. states, with composite costs being half of those found in Hawaii. This was according to a composite index that compares prices for various goods and services on a state-by-state basis. In West Virginia, the cost of living index amounted to **** — well below the national benchmark of 100. Virginia— which had an index value of ***** — was only slightly above that benchmark. Expensive places to live included Hawaii, Massachusetts, and California. Housing costs in the U.S. Housing is usually the highest expense in a household’s budget. In 2023, the average house sold for approximately ******* U.S. dollars, but house prices in the Northeast and West regions were significantly higher. Conversely, the South had some of the least expensive housing. In West Virginia, Mississippi, and Louisiana, the median price of the typical single-family home was less than ******* U.S. dollars. That makes living expenses in these states significantly lower than in states such as Hawaii and California, where housing is much pricier. What other expenses affect the cost of living? Utility costs such as electricity, natural gas, water, and internet also influence the cost of living. In Alaska, Hawaii, and Connecticut, the average monthly utility cost exceeded *** U.S. dollars. That was because of the significantly higher prices for electricity and natural gas in these states.

Of the most populous cities in the U.S., San Jose, California had the highest annual income requirement at ******* U.S. dollars annually for homeowners to have an affordable and comfortable life in 2024. This can be compared to Houston, Texas, where homeowners needed an annual income of ****** U.S. dollars in 2024.

Quality of life is a measure of comfort, health, and happiness by a person or a group of people. Quality of life is determined by both material factors, such as income and housing, and broader considerations like health, education, and freedom. Each year, US & World News releases its “Best States to Live in” report, which ranks states on the quality of life each state provides its residents. In order to determine rankings, U.S. News & World Report considers a wide range of factors, including healthcare, education, economy, infrastructure, opportunity, fiscal stability, crime and corrections, and the natural environment. More information on these categories and what is measured in each can be found below:

Healthcare includes access, quality, and affordability of healthcare, as well as health measurements, such as obesity rates and rates of smoking. Education measures how well public schools perform in terms of testing and graduation rates, as well as tuition costs associated with higher education and college debt load. Economy looks at GDP growth, migration to the state, and new business. Infrastructure includes transportation availability, road quality, communications, and internet access. Opportunity includes poverty rates, cost of living, housing costs and gender and racial equality. Fiscal Stability considers the health of the government's finances, including how well the state balances its budget. Crime and Corrections ranks a state’s public safety and measures prison systems and their populations. Natural Environment looks at the quality of air and water and exposure to pollution.

As of September 2024, Mumbai had the highest cost of living among other cities in the country, with an index value of ****. Gurgaon, a satellite city of Delhi and part of the National Capital Region (NCR) followed it with an index value of ****.  What is cost of living? The cost of living varies depending on geographical regions and factors that affect the cost of living in an area include housing, food, utilities, clothing, childcare, and fuel among others. The cost of living is calculated based on different measures such as the consumer price index (CPI), living cost indexes, and wage price index. CPI refers to the change in the value of consumer goods and services. The wage price index, on the other hand, measures the change in labor services prices due to market pressures. Lastly, the living cost indexes calculate the impact of changing costs on different households. The relationship between wages and costs determines affordability and shifts in the cost of living. Mumbai tops the list Mumbai usually tops the list of most expensive cities in India. As the financial and entertainment hub of the country, Mumbai offers wide opportunities and attracts talent from all over the country. It is the second-largest city in India and has one of the most expensive real estates in the world.

The average for 2021 based on 165 countries was 79.81 index points. The highest value was in Bermuda: 212.7 index points and the lowest value was in Syria: 33.25 index points. The indicator is available from 2017 to 2021. Below is a chart for all countries where data are available.

This table contains data on the percent of households paying more than 30% (or 50%) of monthly household income towards housing costs for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Department of Housing and Urban Development (HUD), Consolidated Planning Comprehensive Housing Affordability Strategy (CHAS) and the U.S. Census Bureau, American Community Survey (ACS). The table is part of a series of indicators in the [Healthy Communities Data and Indicators Project of the Office of Health Equity] Affordable, quality housing is central to health, conferring protection from the environment and supporting family life. Housing costs—typically the largest, single expense in a family's budget—also impact decisions that affect health. As housing consumes larger proportions of household income, families have less income for nutrition, health care, transportation, education, etc. Severe cost burdens may induce poverty—which is associated with developmental and behavioral problems in children and accelerated cognitive and physical decline in adults. Low-income families and minority communities are disproportionately affected by the lack of affordable, quality housing. More information about the data table and a data dictionary can be found in the Attachments.

In the United States, Hawaii was the state with the most expensive housing, with the typical value of single-family homes in the 35th to 65th percentile range exceeding ******* U.S. dollars. Unsurprisingly, Hawaii also ranked top as the state with the highest cost of living. Meanwhile, a property was the least expensive in West Virginia, where it cost under ******* U.S. dollars to buy the typical single-family home. Single-family home prices increased across most states in the United States between December 2023 and December 2024, except in Louisiana, Florida, and the District of Colombia. According to the Federal Housing Association, house appreciation in 13 states exceeded **** percent in 2023.

Consumer Price Index CPI in the United States increased to 321.47 points in May from 320.80 points in April of 2025. This dataset provides the latest reported value for - United States Consumer Price Index (CPI) - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Zillow Properties Listing dataset to access detailed real estate listings, including property prices, locations, and features. Popular use cases include market analysis, property valuation, and investment decision-making in the real estate sector.

Use our Zillow Properties Listing Information dataset to access detailed real estate listings, including property features, pricing trends, and location insights. This dataset is perfect for real estate agents, investors, market analysts, and property developers looking to analyze housing markets, identify investment opportunities, and assess property values.

Leverage this dataset to track pricing patterns, compare property features, and forecast market trends across different regions. Whether you're evaluating investment prospects or optimizing property listings, the Zillow Properties dataset offers essential information for making data-driven real estate decisions.

Dataset Features

• zpid: Unique property identifier assigned by Zillow.
• city: The name of the city where the property is located.
• state: The state in which the property is located.
• homeStatus: Indicates the current status of the property
• address: The full address of the property, including street, city, and state.
• isListingClaimedByCurrentSignedInUser: This field shows if the current Zillow user has claimed ownership of the listing.
• isCurrentSignedInAgentResponsible: This field indicates whether the currently signed-in real estate agent is responsible for the listing.
• bedrooms: Number of bedrooms in the property.
• bathrooms: Number of bathrooms in the property.
• price: Current asking price of the property.
• yearBuilt: The year the home was originally constructed.
• streetAddress: Specific street address (usually excludes city/state/zip).
• zipcode: The postal ZIP code of the property.
• isCurrentSignedInUserVerifiedOwner: This field indicates if the signed-in user has verified ownership of the property on Zillow.
• isVerifiedClaimedByCurrentSignedInUser: Indicates whether the user has claimed and verified the listing as the current owner.
• listingDataSource: The original source of the listing. Important for data lineage and trustworthiness.
• longitude: The longitudinal geographic coordinate of the property.
• latitude: The latitudinal geographic coordinate of the property.
• hasBadGeocode: This indicates whether the geolocation data is incorrect or problematic.
• streetViewMetadataUrlMediaWallLatLong: A URL or reference to the Street View media wall based on latitude and longitude.
• streetViewMetadataUrlMediaWallAddress: A similar URL reference to the Street View, but based on the property’s address.
• streetViewServiceUrl: The base URL to Google Street View or similar services. Enables interactive visuals of the property’s surroundings.
• livingArea: Total internal living area of the home, typically in square feet.
• homeType: The category/type of the home.
• lotSize: The size of the entire lot or land the home is situated on.
• lotAreaValue: The numerical value representing the lot area is usually tied to a measurement unit.
• lotAreaUnits: Units in which the lot area is measured (e.g., sqft, acres).
• livingAreaValue: The numeric value of the property's interior living space.
• livingAreaUnitsShort: Abbreviated unit for living area (e.g., sqft), useful for compact displays.
• isUndisclosedAddress: Boolean indicating if the full property address is hidden, typically used for privacy reasons.
• zestimate: Zillow’s estimated market value of the home, generated via its proprietary model.
• rentZestimate: Zillow’s estimated rental price per month, is helpful for rental market analysis.
• currency: Currency used for price, Zestimate, and rent estimate (e.g., USD).
• hideZestimate: Indicates whether the Zestimate is hidden from public view.
• dateSoldString: The date when the property was last sold, in string format (e.g., 2022-06-15).
• taxAssessedValue: The most recent assessed value of the property for tax purposes.
• taxAssessedYear: The year in which the property was last assessed.
• country: The country where the property is located.
• propertyTaxRate: The most recent tax rate.
• photocount: This column provides a photo count of the property.
• isPremierBuilder: Boolean indicating whether the builder is listed as a premier (trusted) builder on Zillow.
• isZillowOwned: Indicates whether the property is owned or managed directly by Zillow.
• ssid: A unique internal Zillow identifier for the listing (not to be confused with network SSID).
• hdpUrl: URL to the home’s detail page on Zillow (Home Details Page).
• tourViewCount: Number of times users have viewed the property tour.
• hasPublicVideo: This

Addis Ababa, in Ethiopia, ranked as the most expensive city to live in Africa as of 2024, considering consumer goods prices. The Ethiopian capital obtained an index score of ****, followed by Harare, in Zimbabwe, with ****. Morocco and South Africa were the countries with the most representatives among the ** cities with the highest cost of living in Africa.

Abstract

The main objectives of the 2018/19 NLSS are: i) to provide critical information for production of a wide range of socio-economic and demographic indicators, including for benchmarking and monitoring of SDGs; ii) to monitor progress in population’s welfare; iii) to provide statistical evidence and measure the impact on households of current and anticipated government policies. In addition, the 2018/19 NLSS could be utilized to improve other non-survey statistical information, e.g. to determine and calibrate the contribution of final consumption expenditures of households to GDP; to update the weights and determine the basket for the national Consumer Price Index (CPI); to improve the methodology and dissemination of micro-economic and welfare statistics in Nigeria.

The 2018/19 NLSS collected a comprehensive and diverse set of socio-economic and demographic data pertaining to the basic needs and conditions under which households live on a day to day basis. The 2018/19 NLSS questionnaire includes wide-ranging modules, covering demographic indicators, education, health, labour, expenditures on food and non-food goods, non-farm enterprises, household assets and durables, access to safety nets, housing conditions, economic shocks, exposure to crime and farm production indicators.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Communities

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2018/19 NLSS sample is designed to provide representative estimates for the 36 states and the Federal Capital Territory (FCT), Abuja. By extension. The sample is also representative at the national and zonal levels. Although the sample is not explicitly stratified by urban and rural areas, it is possible to obtain urban and rural estimates from the NLSS data at the national level. At all stages, the relative proportion of urban and rural EAs as has been maintained.

Before designing the sample for the 2018/19 NLSS, the results from the 2009/10 HNLSS were analysed to extract the sampling properties (variance, design effect, etc.) and estimate the required sample size to reach a desired precision for poverty estimates in the 2018/19 NLSS.

EA SELECTION: The sampling frame for the 2018/19 NLSS was based on the national master sample developed by the NBS, referred to as the NISH2 (Nigeria Integrated Survey of Households 2). This master sample was based on the enumeration areas (EAs) defined for the 2006 Nigeria Census Housing and Population conducted by National Population Commission (NPopC). The NISH2 was developed by the NBS to use as a frame for surveys with state-level domains. NISH2 EAs were drawn from another master sample that NBS developed for surveys with LGA-level domains (referred to as the “LGA master sample”). The NISH2 contains 200 EAs per state composed of 20 replicates of 10 sample EAs for each state, selected systematically from the full LGA master sample. Since the 2018/19 NLSS required domains at the state-level, the NISH2 served as the sampling frame for the survey.

Since the NISH2 is composed of state-level replicates of 10 sample EAs, a total of 6 replicates were selected from the NISH2 for each state to provide a total sample of 60 EAs per state. The 6 replicates selected for the 2018/19 NLSS in each state were selected using random systematic sampling. This sampling procedure provides a similar distribution of the sample EAs within each state as if one systematic sample of 60 EAs had been selected directly from the census frame of EAs.

A fresh listing of households was conducted in the EAs selected for the 2018/19 NLSS. Throughout the course of the listing, 139 of the selected EAs (or about 6%) were not able to be listed by the field teams. The primary reason the teams were not able to conduct the listing in these EAs was due to security issues in the country. The fieldwork period of the 2018/19 NLSS saw events related to the insurgency in the north east of the country, clashes between farmers and herdsman, and roving groups of bandits. These events made it impossible for the interviewers to visit the EAs in the villages and areas affected by these conflict events. In addition to security issues, some EAs had been demolished or abandoned since the 2006 census was conducted. In order to not compromise the sample size and thus the statistical power of the estimates, it was decided to replace these 139 EAs. Additional EAs from the same state and sector were randomly selected from the remaining NISH2 EAs to replace each EA that could not be listed by the field teams. This necessary exclusion of conflict affected areas implies that the sample is representative of areas of Nigeria that were accessible during the 2018/19 NLSS fieldwork period. The sample will not reflect conditions in areas that were undergoing conflict at that time. This compromise was necessary to ensure the safety of interviewers.

HOUSEHOLD SELECTION: Following the listing, the 10 households to be interviewed were selected from the listed households. These households were selected systemically after sorting by the order in which the households were listed. This systematic sampling helped to ensure that the selected households were well dispersed across the EA and thereby limit the potential for clustering of the selected households within an EA.

Occasionally, interviewers would encounter selected households that were not able to be interviewed (e.g. due to migration, refusal, etc.). In order to preserve the sample size and statistical power, households that could not be interviewed were replaced with an additional randomly selected household from the EA. Replacement households had to be requested by the field teams on a case-by-case basis and the replacement household was sent by the CAPI managers from NBS headquarters. Interviewers were required to submit a record for each household that was replaced, and justification given for their replacement. These replaced households are included in the disseminated data. However, replacements were relatively rare with only 2% of sampled households not able to be interviewed and replaced.

Sampling deviation

Although a sample was initially drawn for Borno state, the ongoing insurgency in the state presented severe challenges in conducting the survey there. The situation in the state made it impossible for the field teams to reach large areas of the state without compromising their safety. Given this limitation it was clear that a representative sample for Borno was not possible. However, it was decided to proceed with conducting the survey in areas that the teams could access in order to collect some information on the parts of the state that were accessible.

The limited area that field staff could safely operate in in Borno necessitated an alternative sample selection process from the other states. The EA selection occurred in several stages. Initially, an attempt was made to limit the frame to selected LGAs that were considered accessible. However, after selection of the EAs from the identified LGAs, it was reported by the NBS listing teams that a large share of the selected EAs were not safe for them to visit. Therefore, an alternative approach was adopted that would better ensure the safety of the field team but compromise further the representativeness of the sample. First, the list of 788 EAs in the LGA master sample for Borno were reviewed by NBS staff in Borno and the EAs they deemed accessible were identified. The team identified 359 EAs (46%) that were accessible. These 359 EAs served as the frame for the Borno sample and 60 EAs were randomly selected from this frame. However, throughout the course of the NLSS fieldwork, additional insurgency related events occurred which resulted in 7 of the 60 EAs being inaccessible when they were to be visited. Unlike for the main sample, these EAs were not replaced. Therefore, 53 EAs were ultimately covered from the Borno sample. The listing and household selection process that followed was the same as for the rest of the states.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Two sets of questionnaires – household and community – were used to collect information in the NLSS2018/19. The Household Questionnaire was administered to all households in the sample. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

Household Questionnaire: The Household Questionnaire provides information on demographics; education; health; labour; food and non-food expenditure; household nonfarm income-generating activities; food security and shocks; safety nets; housing conditions; assets; information and communication technology; agriculture and land tenure; and other sources of household income.

Community Questionnaire: The Community Questionnaire solicits information on access to transported and infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information.

Cleaning operations

CAPI: The 2018/19 NLSS was conducted using the Survey Solutions Computer Assisted Person Interview (CAPI) platform. The Survey Solutions software was developed and maintained by the Development Economics Data Group (DECDG) at the World Bank. Each interviewer and supervisor was given a tablet

This table contains data on the living wage and the percent of families with incomes below the living wage for California, its counties, regions and cities/towns. Living wage is the wage needed to cover basic family expenses (basic needs budget) plus all relevant taxes; it does not include publicly provided income or housing assistance. The percent of families below the living wage was calculated using data from the Living Wage Calculator and the U.S. Census Bureau, American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. The living wage is the wage or annual income that covers the cost of the bare necessities of life for a worker and his/her family. These necessities include housing, transportation, food, childcare, health care, and payment of taxes. Low income populations and non-white race/ethnic have disproportionately lower wages, poorer housing, and higher levels of food insecurity. More information about the data table and a data dictionary can be found in the About/Attachments section.

The County Health Rankings, a collaboration between the Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute, measure the health of nearly all counties in the nation and rank them within states. This feature layer contains 2022 County Health Rankings data for nation, state, and county levels. The Rankings are compiled using county-level measures from a variety of national and state data sources. Some example measures are:adult smokingphysical inactivityflu vaccinationschild povertydriving alone to workTo see a full list of variables, as well as their definitions and descriptions, explore the Fields information by clicking the Data tab here in the Item Details. These measures are standardized and combined using scientifically-informed weights."By ranking the health of nearly every county in the nation, County Health Rankings & Roadmaps (CHR&R) illustrates how where we live affects how well and how long we live. CHR&R also shows what each of us can do to create healthier places to live, learn, work, and play – for everyone."Counties are ranked within their state on both health outcomes and health factors. Counties with a lower (better) health outcomes ranking than health factors ranking may see the health of their county decline in the future, as factors today can result in outcomes later. Conversely, counties with a lower (better) factors ranking than outcomes ranking may see the health of their county improve in the future.Some new variables in the 2022 Rankings data compared to previous versions:COVID-19 age-adjusted mortalitySchool segregationSchool funding adequacyGender pay gapChildcare cost burdenChildcare centersLiving wage (while the Living wage measure was introduced to the CHRR dataset in 2022 from the Living Wage Calculator, it is not available in the Living Atlas dataset and user’s interested in the most up to date living wage data can look that up on the Living Wage Calculator website).Data Processing Notes:Data downloaded April 2022Slight modifications made to the source data are as follows:The string " raw value" was removed from field labels/aliases so that auto-generated legends and pop-ups would only have the measure's name, not "(measure's name) raw value" and strings such as "(%)", "rate", or "per 100,000" were added depending on the type of measure.Percentage and Prevalence fields were multiplied by 100 to make them easier to work with in the map.Ratios were set to null if negative to make them easier to work with in the map.For demographic variables, the word "numerator" was removed and the word "population" was added where appropriate.Fields dropped from analytic data file: yearall fields ending in "_cihigh" and "_cilow"and any variables that are not listed in the sources and years documentation.Analytic data file was then merged with state-specific ranking files so that all county rankings and subrankings are included in this layer.2010 US boundaries were used as the data contain 2010 US census geographies, for a total of 3,142 counties.

In 2023, the real median household income in the state of Alabama was 60,660 U.S. dollars. The state with the highest median household income was Massachusetts, which was 106,500 U.S. dollars in 2023. The average median household income in the United States was at 80,610 U.S. dollars.

In 2024, the CPI in U.S. cities averaged at 313.7. However, the CPI for the New York-Newark-Jersey City metropolitan area amounted to about 334.21. Prices in New York City were significantly higher than the U.S. average. Nonetheless, the San Diego-Carlsbad area ranked first with a CPI of 373.32.The monthly inflation rate for the United States can be found here.

According to a recent study, Colombia had the lowest monthly cost of living in Latin America with 546 U.S. dollars needed for basic living. In contrast, four countries had a cost of living above one thousand dollars, Costa Rica, Chile, Panama and Uruguay. In 2022, the highest minimum wage in the region was recorded by Ecuador with 425 dollars per month.

Can Latin Americans survive on a minimum wage? Even if most countries in Latin America have instated laws to guarantee citizens a basic income, these minimum standards are often not enough to meet household needs. For instance, it was estimated that almost 22 million people in Mexico lacked basic housing services. Salary levels also vary greatly among Latin American economies. In 2022, the average net monthly salary in Brazil was lower than Ecuador's minimum wage.

What can a minimum wage afford in Latin America? Latin American real wages have generally risen in the past decade. However, consumers in this region still struggle to afford non-basic goods, such as tech products. Recent estimates reveal that, in order to buy an iPhone, Brazilian residents would have to work more than two months to be able to pay for it. A gaming console, on the other hand, could easily cost a Latin American worker several minimum wages.

Out of all 50 states, New York had the highest per-capita real gross domestic product (GDP) in 2023, at 90,730 U.S. dollars, followed closely by Massachusetts. Mississippi had the lowest per-capita real GDP, at 39,102 U.S. dollars. While not a state, the District of Columbia had a per capita GDP of more than 214,000 U.S. dollars. What is real GDP? A country’s real GDP is a measure that shows the value of the goods and services produced by an economy and is adjusted for inflation. The real GDP of a country helps economists to see the health of a country’s economy and its standard of living. Downturns in GDP growth can indicate financial difficulties, such as the financial crisis of 2008 and 2009, when the U.S. GDP decreased by 2.5 percent. The COVID-19 pandemic had a significant impact on U.S. GDP, shrinking the economy 2.8 percent. The U.S. economy rebounded in 2021, however, growing by nearly six percent. Why real GDP per capita matters Real GDP per capita takes the GDP of a country, state, or metropolitan area and divides it by the number of people in that area. Some argue that per-capita GDP is more important than the GDP of a country, as it is a good indicator of whether or not the country’s population is getting wealthier, thus increasing the standard of living in that area. The best measure of standard of living when comparing across countries is thought to be GDP per capita at purchasing power parity (PPP) which uses the prices of specific goods to compare the absolute purchasing power of a countries currency.

Abstract

The principal objective of the VNLSS is to collect basic data reflescting the actual living standard of the population. These data then be used for evaluating socio-economic development and formulationg policies to improve living standard. Followings are the main goals by the year of 2000. - Reduce the population growth rate less than 2 % peryear - Reduce the infant mortaility (under 5 years old) 0,81% (1990) to 0,55%; and from 0,46% (1990) to 0,3% (under one year old) - Reduce the mortality rate of women concerning the pregnancy and maternity - Reduce the malnutrition of children under 5years old from 51,5% at present to 40% in 1995 and under 30% by the year of 2000. Heavy malnutrition should not be existed by the year of 2000. - Population can access to safe water resources from 43% (1990) to 82% of which 40% to 80% in rural areas. Population use sanitary latrine from 22% (1990) to 65% of which in rural areas from 15% to 60% - 90 percent of children complete the endeavor universal first level education before the age of 15, and the rest should complete the third grade. By the year of 2000 no children at the age of 15 will be illiterate - Improve the cultural, spiritual life of the children, to ensure that 30% of communes (by the year of 1995) and 50% of communes (by the year of 2000) have entertaining place for children

The main information collected by the survey includes: - Household income and expenditures - Health and education - Employment and other productive and activities - Demographic characteristics and migration - Housing conditions

In addition, the information gatherd is intended to improve planning of economic and social policies in Vietnam and to assist in evaluating the impact of the policies. It should enable decision makers to: - indentify target groups for government assistance - Construct models of socio-economic development policies, both overall and on individuals groups - Analyze the impact of decisions available and of the current economic situation on living condition of household

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Community

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design The sample covers 4800 households from all areas of Viet Nam. The sample design was self-weighted, which means that each household in Viet Nam had the same probability of being selected. The overall sampling frame was stratified into two groups urban and rural, with sampling was carried out separately in each group (strata). About 20% of Vietnamese households live in urban areas, so the sample stratification ensures that 20% of selected households also come from urban areas. Within urban and rural areas, two lists of all communes was drawn up (one of urban communes and another of rural ones), province by province, in "serpentine" order. 2 The selection of communes within each list was done to ensure that they were spread out evenly among all provinces in Viet Nam.

The VNLSS sample design is the following. Within each province in Viet Nam, rural areas can be broken down into districts, and districts in turn are divided into communes (Xa). Urban areas in all provinces consist of centers/towns, which are divided into quarters (Quai), and then divided further into communes (Phuong). The number of communes in all of Viet Nam, both urban and rural, is about 10,000, and the average population in each is about 6,500. As explained in Section 4, each survey team covers 32 households in 4 weeks, 16 households in one area, and 16 in another area. For convenience all 32 households (i.e. both sets of 16 household) were selected from the same commune. This implied that 150 communes needed to be randomly selected (32x150=4800), 30 in urban areas and 120 in urban areas. Within urban areas communes can be further divided into clusters (Cum), two of which were selected from which to draw two "workloads" of 16 households (16 from each of the two clusters). The same was done in rural areas, where each commune is divided into several villages (Thon). The average size of urban clusters and rural villages is somewhat less than 1000 households.

The VNLSS sample was drawn in three stages. Because the General Statistical Office in Hanoi knows the current population of each commune in Viet Nam (but not of each cluster or village within each commune), 150 communes were selected out of the 10,000 in all of Viet Nam with the probability of selection proportional to their population size. At the second stage, information was gathered from the 150 selected communes on the population of each cluster (in urban areas) or villages (in rural areas), and two clusters or villages were randomly drawn with probability proportional to their population size. Finally, the third stage involved random selection of 20 households (16 for the sample plus four "extras" to serve as replacements if some of the 16 "originals" could not be interviewed) within each cluster or village from a list of all households within each cluster or village. Note that the first stage of the sample is based on information from the 1989 Census, but the second and third stages use updated information available from the communes. The first and second stage samples were drawn in Hanoi, while the third stage was drawn in the field (see Section 4.3 below for more details).

Implementation

The attached map shows the commune number and approximate location of the 150 communes selected in Viet Nam. Of the 150 communes chosen, one was in a very remote and inaccessible area near the Chinese border and was replaced by another not quite as inaccessible. The actual interview schedule went smoothly. In one instance (commune 68) one of the selected villages was replaced because when the survey team arrived in the village it discovered that most of the adults were away from the village and thus could not be interviewed. In each cluster or village interviews were completed for 16 households, thus the 4800 household target sample was fully achieved. About 3% of the households (155) were replaced; the main reason for replacement was that their occupants were not at home. Only four households refused to participate. Community questionnaires were completed for all 120 rural communes. Price questionnaires were completed for 118 of 120 communes (the exceptions were communes 62 and 63), and comparable price data were collected from existing sources for all 30 urban areas.

Mode of data collection

Face-to-face [f2f]

Research instrument

HOUSEHOLD QUESTIONNAIRE

The household questionnaire contains modules (sections) to collect data on household demographic structure, education, health, employment, migration, housing conditions, fertility, agricultural activities, household non-agricultural businesses, food expenditures, non-food expenditures, remittances and other income sources, savings and loans, and anthropometric (height and weight) measures.

For some sections (survey information, housing, and respondents for second round) the individual designated by the household members as the household head provided responses. For some others (agro-pastoral activities, non-farm self employment, food expenditures, non-food expenditures) a member identified as most knowledgeable provided responses. Identification codes for respondents of different sections indicate who provided the information. In sections where the information collected pertains to individuals (education, health, employment, migration, and fertility) each member of the household was asked to respond for himself or herself, except that parents were allowed to respond for younger children. In the case of the employment and fertility sections it is possible that the information was not provided by the relevant person; variables in these sections indicate when this is the case. The household questionnaire was completed in two interviews two weeks apart: Sections 0-8, were conducted in the first interview, sections 9-14 were conducted in the second interview, and section 15 was administered in both interviews. The survey was designed so that more sensitive issues such as credit and savings were discussed near the end. The content of each module is briefly described below.

I. FIRST INTERVIEW

Section 0 SURVEY INFORMATION 0A HOUSEHOLD HEAD AND RESPONDENT INFORMATION 0B SUMMARY OF SURVEY RESULTS 0C OBSERVATIONS AND COMMENTS

The date of the interview, the religion, ethnic group of the household head, the language used by the respondent and other technical information related to the interview are noted. Section 0B summarizes the results of the survey visits, i.e. whether a section was completed on the first visit or the second visit. Section 0C, not entered into the computer, contains remarks of the interviewer and the supervisor. Since the data in Section 0C are retained only on the questionnaires, researchers cannot gain access to them without checking the original questionnaires at the General Statistical Office in Hanoi.

Section 1 HOUSEHOLD MEMBERSHIP 1A HOUSEHOLD ROSTER 1B INFORMATION ON PARENTS OF HOUSEHOLD MEMBERS 1C CHILDREN RESIDING ELSEWHERE

The roster in Section 1A lists the age, sex, marital status and relation to household head of all people who spent the previous night in that household and for household members who are temporarily away from home. The household head is listed first and receives the personal id code 1. Household members were defined to include "all the people who normally live and eat their meals together in this dwelling. Those who were absent more than nine of the last twelve months were excluded, except for the head of the household and infants less than three months old. A lunar calendar is provided in the

Monthly indexes and percentage changes for all components and special aggregates of the Consumer Price Index (CPI), not seasonally adjusted, for Canada, provinces, Whitehorse, Yellowknife and Iqaluit. Data are presented for the corresponding month of the previous year, the previous month and the current month. The base year for the index is 2002=100.

This longitudinal survey was designed to add significantly to the amount of detailed information available on the economic situation of households and persons in the United States. These data examine the level of economic well-being of the population and also provide information on how economic situations relate to the demographic and social characteristics of individuals. There are three basic elements contained in the survey. The first is a control card that records basic social and demographic characteristics for each person in a household, as well as changes in such characteristics over the course of the interviewing period. The second element is the core portion of the questionnaire, with questions repeated at each interview on labor force activity, types and amounts of income, participation in various cash and noncash benefit programs, attendance in postsecondary schools, private health insurance coverage, public or subsidized rental housing, low-income energy assistance, and school breakfast and lunch participation. The third element consists of topical modules which are series of supplemental questions asked during selected household visits. No topical modules were created for the first or second waves. The Wave III Rectangular Core and Topical Module File offers both the core data and additional data on (1) education and work history and (2) health and disability. In the areas of education and work history, data are supplied on the highest level of schooling attained, courses or programs studied in high school and after high school, whether the respondent received job training, and if so, for how long and under what program (e.g., CETA or WIN). Other items pertain to the respondent's general job history and include a description of selected previous jobs, duration of jobs, and reasons for periods spent not working. Health and disability variables present information on the general condition of the respondent's health, functional limitations, work disability, and the need for personal assistance. Data are also provided on hospital stays or periods of illness, health facilities used, and whether health insurance plans (private or Medicare) were available. Respondents whose children had physical, mental, or emotional problems were questioned about the causes of the problems and whether the children attended regular schools. The Wave IV Rectangular Core and Topical Module file contains both the core data and sets of questions exploring the subjects of (1) assets and liabilities, (2) retirement and pension coverage, and (3) housing costs, conditions, and energy usage. Some of the major assets for which data are provided are savings accounts, stocks, mutual funds, bonds, Keogh and IRA accounts, home equity, life insurance, rental property, and motor vehicles. Data on unsecured liabilities such as loans, credit cards, and medical bills also are included. Retirement and pension information covers such items as when respondents expect to stop working, whether they will receive retirement benefits, whether their employers have retirement plans, if so whether they are eligible, and how much they expect to receive per year from these plans. In the category of housing costs, conditions, and energy usage, variables pertain to mortgage payments, real estate taxes, fire insurance, principal owed, when the mortgage was obtained, interest rates, rent, type of fuel used, heating facilities, appliances, and vehicles. The Wave V topical modules explore the subject areas of (1) child care, (2) welfare history and child support, (3) reasons for not working/reservation wage, and (4) support for nonhousehold members/work-related expenses. Data on child care include items on child care arrangements such as who provides the care, the number of hours of care per week, where the care is provided, and the cost. Questions in the areas of welfare history and child support focus on receipt of aid from specific welfare programs and child support agreements and their fulfillment. The reasons for not working/reservation wage module presents data on why persons are not in the labor force and the conditions under which they might join the labor force. Additional variables cover job search activities, pay rate required, and reason for refusal of a job offer. The set of questions dealing with nonhousehold members/work-related expenses contains items on regular support payments for nonhousehold members and expenses associated with a job such as union dues, licenses, permits, special tools, uniforms, or travel expenses. Information is supplied in the Wave VII Topical Module file on (1) assets and liabilities, (2) pension plan coverage, and (3) real estate property and vehicles. Variables pertaining to assets and liabilities are similar to those contained in the topical module for Wave IV. Pension plan coverage items include whether the respondent will receive retirement benefits, whether the employer offers a retirement plan and if the respondent is included in the plan, and contributions by the employer and the employee to the plan. Real estate property and vehicles data include information on mortgages held, amount of principal still owed and current interest rate on mortgages, rental and vacation properties owned, and various items pertaining to vehicles belonging to the household. Wave VIII Topical Module includes questions on support for nonhousehold members, work-related expenses, marital history, migration history, fertility history, and household relationships. Support for nonhousehold members includes data for children and adults not in the household. Weekly and annual work-related expenses are documented. Widowhood, divorce, separation, and marriage dates are part of the marital history. Birth expectations as well as dates of birth for all the householder's children, in the household or elsewhere, are recorded in the fertility history. Migration history data supplies information on birth history of the householder's parents, number of times moved, and moving expenses. Household relationships lists the exact relationships among persons living in the household. Part 49, Wave IX Rectangular Core and Topical Module Research File, includes data on annual income, retirement accounts, taxes, school enrollment, and financing. This topical module research file has not been edited nor imputed, but has been topcoded or bottomcoded and recoded if necessary by the Census Bureau to avoid disclosure of individual respondents' identities. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR08317.v2. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.