In 2024, the annual cost for a private room in an assisted living facility in the U.S. amounted to ****** U.S. dollars. However, costs varied greatly from one state to another. The most expensive states for a private room in assisted living was found in Hawaii, followed by Alaska and DC.

This statistic illustrates the most popular social networks among Millennials for finding the most relevant content on the cost of living crisis in the United States in 2023. According to a survey by We Are Social and Statista Q, 61 percent of Millennials who use TikTok find the most relevant content over there, followed by another 59 percent of the consumers who use YouTube.

Quality of Life by State 2024

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1937611%2F82267b1a15f8669ec2a072972bebccb5%2Fquality-of-life-by-us-state.png?generation=1717697280376438&alt=media" alt="">

This dataset provides insights into the quality of life across different states in the United States for the year 2024. Quality of life, encompassing aspects like comfort, health, and happiness, is evaluated through various metrics including affordability, economy, education, and safety. Dive into this dataset to understand how different states fare in terms of overall quality of life and its individual components.

Columns Description

• State: The name of the U.S. state.
• QualityOfLifeTotalScore: The total score representing the overall quality of life for the respective state. This score is calculated based on various quality of life metrics.
• QualityOfLifeQualityOfLife: The score representing the quality of life aspect for the respective state. This aspect may include subjective factors related to happiness, satisfaction, and overall well-being. Higher scores may indicate a higher level of subjective well-being, happiness, or overall satisfaction among residents. Lower scores could suggest lower levels of subjective well-being.
• QualityOfLifeAffordability: The score representing the affordability aspect of the quality of life for the respective state. This aspect evaluates factors such as cost of living, housing affordability, and income levels. Higher scores typically indicate greater affordability of housing, cost of living, and basic necessities. Lower scores may suggest that these essentials are less accessible or more expensive for residents.
• QualityOfLifeEconomy: The score representing the economic aspect of the quality of life for the respective state. This aspect assesses factors such as employment opportunities, economic growth, and income distribution. Higher scores may reflect a stronger economy with more job opportunities, higher incomes, and lower levels of poverty. Lower scores might indicate economic challenges such as unemployment or income inequality.
• QualityOfLifeEducationAndHealth: The score representing the education and health aspect of the quality of life for the respective state. This aspect considers factors such as access to quality education, healthcare facilities, and overall public health indicators. Higher scores generally signify better access to quality education, healthcare services, and overall public health. Lower scores may indicate deficiencies in these areas, such as limited access to healthcare or lower educational attainment levels.
• QualityOfLifeSafety: The score representing the safety aspect of the quality of life for the respective state. This aspect evaluates factors such as crime rates, public safety measures, and community well-being initiatives. Higher scores suggest lower crime rates, better community safety, and a higher sense of security among residents. Lower scores may indicate higher crime rates or concerns about safety.

These descriptions provide an overview of what each column represents and the specific aspects of quality of life they assess for each U.S. state.

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 cost of living is spiraling. Prices are going up, household expenses are rising, and the U.S. inflation rate reached a 40-year record high in 2023. Many consumers are looking for new ways to deal with this situation and refer to social media for support. So, which social media platforms have the most helpful content to deal with the current cost of living crisis in the U.S.? According to an exclusive survey by We Are Social and Statista Q, around 61 percent of TikTok users in the United States find helpful content there. Coming on number second is YouTube, as 56 percent of YouTube users find life hacks, tricks, money saving tips and other suitable advice to deal with inflation in 2023.

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 2025 State Employee Pay page provides a comprehensive breakdown of salary structures, cost-of-living adjustments (COLAs), pay raises, and classification changes for state employees across various departments and positions. It includes information on:

Updated salary schedules by classification and grade

Annual cost-of-living adjustments (if approved by legislature)

Bonus or incentive pay (where applicable)

Pay equity adjustments

Job title and classification updates

Agency-specific pay plans

This resource is essential for current state workers, HR professionals, policy analysts, and those considering employment in the public sector.

Whether you're a classified employee, exempt worker, or part of a unionized workforce, this guide outlines how your pay may be affected throughout 2025 based on legislation, union negotiations, and state budget allocations.

This dataset provides insights into the cost of living and average monthly income across various countries and regions worldwide from 2000 to 2023. It includes critical economic indicators such as housing costs, taxes, healthcare, education, transportation expenses, and savings rates. The data is ideal for analyzing economic trends, regional comparisons, and financial planning.

Column Descriptions: Country: The name of the country where the data was recorded. Region: The geographical region to which the country belongs (e.g., Asia, Europe). Year: The year when the data was recorded. Average_Monthly_Income: The average monthly income of individuals in USD. Cost_of_Living: The average monthly cost of living in USD, including essentials like housing, food, and utilities. Housing_Cost_Percentage: The percentage of income spent on housing expenses. Tax_Rate: The average tax rate applied to individuals' income, expressed as a percentage. Savings_Percentage: The portion of income saved monthly, expressed as a percentage. Healthcare_Cost_Percentage: The percentage of income spent on healthcare services. Education_Cost_Percentage: The percentage of income allocated to educational expenses. Transportation_Cost_Percentage: The percentage of income spent on transportation costs.

In 2024, the consumer price index (CPI) was 315.61. Data represents U.S. city averages. The monthly inflation rate for the United States can be found here. United States urban Consumer Price Index (CPI) The U.S. Consumer Price Index is a measure of change in the price of consumer goods and services purchased by households. The CPI is defined by the United States Bureau of Labor Statistics as "a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services." To calculate the CPI, the Bureau of Labor Statistics considers the price of goods and services from various categories: housing, transportation, apparel, food & beverage, medical care, recreation, education and other/uncategorized. The CPI is a useful measure, as it indicates how the cost of urban living in the United States has changed over time, compared to a base period. CPI is also used to calculate inflation, or change in the purchasing power of money. According to the U.S. Bureau of Labor Statistics, the U.S. urban CPI has been rising steadily since 1992. As of 2023, the CPI was 304.7, up from 233 ten years earlier and up from 184 twenty years earlier. This indicates the extent to which, compared to a base period 1982-1984 = 100, the price of various goods and services has risen.

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

Around 64 percent of U.S. consumers spend less on non-essentials amidst the ongoing cost of living crisis in 2023. This is according to a survey conducted by We are Social and Statista Q, which shows that rising inflation rates have caused around a similar percentage of customers to pay more attention to bargains, good deals, or offers (when going shopping). Furthermore, around 39 percent of U.S. consumers do not go out for dinner/lunch anymore to deal with the situation.

The statistic above provides information about the income level in the United States at which money won't make you happier. In 2010, a household in Hawaii needs to make about 122 thousand U.S. dollars per year to reach the happiness plateau, in which more income doesn't provide better emotional well-being. The state-by-state comparison takes into account the disparity in cost of living between the states.

Approximately 81 percent of people in the Republic of Ireland thought that the state of the global economy was the main contributing factor to the rising cost of living in the country. By contrast, just 49 percent of people in Ireland believed that workers demanding pay rises was the main reason.

Abstract Cost management is a fundamental step in any project, regardless of size and characteristics, as it is essential to balance finances and maintain the quality of services. It is important to develop good cost management through a systematic and judicious approach, as the market has become increasingly competitive, especially with regard to the construction sector. In this way, seeking to align the client's needs with the use of project management methodology, as well as the use of its tools within the scope of renovation, this work aims to plan and control costs throughout the project's renovation process, the in order to prevent failures during the stages that could affect the defined deadlines and planned costs. Keywords: WBS; Parametric; Bottom-Up; EVA; TCPI. According to data released by “Mordor Intelligence” MI, the homebuilding sector has been the driving force in the US economic recovery from the COVID-19 crisis since the third quarter of 2020, generating double-digit growth rates and making significant contributions to the economy and the overall recovery of the construction industry. Limited mortgage rates, robust demand for larger living spaces, and a low inventory of homes on the market continue to drive the sector. Home renovation, in addition to new construction, is a significant aspect of residential construction. According to the author, by 2023, the annual value of residential building upgrades in the United States is expected to exceed $205 billion. More than 330,000 new rental units are scheduled to be delivered nationwide. Eight metros are expected to set five-year highs in new apartment deliveries, despite the obstacles of the pandemic. Considering the growth forecasts in the residential sector until the end of 2023 and the great competitiveness in the construction industry, it is important to find new ways to optimize routines and comply with plans. It is not uncommon to face problems with delays in deliveries, which eventually lead to increased expenses that can even compromise the quality of the project. To prevent this from happening, good cost management in the construction is essential. And to help in this process, it is necessary to rely on technology and process tools to ensure better cost control in projects, applying solutions that enhance the organization of activities and the company's results (hinc.com.br, April 13, 2023) According to the “Project Management Body of Knowledge [PMBOK]” (“Project Management Institute [PMI]”, 2017), project cost management includes the processes involved in planning, estimating, budgeting and cost control, so that the project can be completed within the approved budget. Thus, the final objective of this work is based on planning and controlling labor and material costs, using cost management tools as a method, in order to avoid internal and external interference that could compromise planned costs, deadlines and the 30% gross profit expected by the end of the work. In the initial phase of the project, some resistance points were observed during the implementation of the methodology, mainly regarding the team composed of subcontractors, since it was a methodology unknown to most of them. Despite the initial resistance, it was possible to notice an adherence on the part of those involved, when the work began. It was also notable that before the project, the works were sequenced in a disorganized manner, mainly regarding the control of documentation, costs and deadlines. The project brought great improvements in this sense, generating historical data that will serve as a basis for new projects. With regard to the use of cost management tools, it is considered that the objective of this case study was achieved, mainly regarding the completion performance index (IDPT), since it allowed a predictive analysis by pointing out deviations related to delays in the schedule, and consequently in the cost of some activities. This allowed for important decision-making, preventing such deviations from compromising the reserves determined at the beginning of the project. Regarding the 30% gross profit expected by the end of the project, the project achieved 28.77%. It is also worth mentioning that the results left a great impression and satisfaction on the part of the client and the sponsor after the project was completed, both in relation to the new practices applied, which provided great learning for those involved, and also the final result. It can be concluded that cost management aspects are essential for obtaining better results in projects. It is also suggested that, for future projects, improvements in the development of schedules should be made, as well as the use of more structured software to control deadlines and subcontracted services.

Manufactured Homes, Modular Homes, and Mobile Homes Sales Market Outlook

The manufactured homes, modular homes, and mobile homes sales market size is projected to witness robust growth, with a compound annual growth rate (CAGR) of 6.2% from 2024 to 2032. In 2023, the global market size was valued at approximately USD 25 billion and is anticipated to reach around USD 41.5 billion by 2032. This growth is driven by several factors including increasing demand for affordable housing solutions, advancements in construction technology, and rising awareness about the benefits of modular and manufactured homes.

One of the primary growth factors for the market is the escalating demand for affordable housing. With urbanization on the rise and more people moving to cities, the need for cost-effective housing solutions has never been higher. Manufactured homes, modular homes, and mobile homes offer a lower-cost alternative to traditional housing, making them highly attractive to first-time homebuyers, retirees, and those looking to downsize. In addition, the quicker build times associated with these types of homes make them a viable solution to housing shortages prevalent in many urban areas.

Technological advancements in construction methods have significantly contributed to the market's growth. Innovations such as improved materials, automation in manufacturing processes, and advanced design software have made it possible to produce high-quality manufactured and modular homes that meet stringent building codes and customer expectations. These advancements also contribute to reducing construction time and cost, thereby enhancing market attractiveness. Moreover, the ability to customize homes according to individual preferences and local building codes further fuels market demand.

Environmental concerns and the push for sustainable living have also played a critical role in market expansion. Manufactured and modular homes are often more energy-efficient than traditional homes due to better insulation and the use of sustainable materials. These homes can be built to meet or exceed energy efficiency standards, which not only helps in reducing the carbon footprint but also significantly lowers utility bills for homeowners. This environmental advantage appeals to a growing segment of environmentally conscious consumers.

The Single-family Detached Home Business is an emerging trend within the broader housing market, offering unique opportunities for entrepreneurs and investors. This segment focuses on standalone homes that are not attached to any other dwelling, providing privacy and space that many homeowners desire. As urban areas continue to expand, the demand for single-family detached homes is expected to rise, driven by families seeking more personal space and a connection to nature. This business model allows for customization and personalization, catering to the specific needs and preferences of individual buyers. Additionally, the flexibility in design and construction methods makes it possible to incorporate sustainable practices, appealing to environmentally conscious consumers. The growth of this sector is further supported by favorable government policies and incentives aimed at promoting homeownership and sustainable development.

From a regional perspective, North America holds a significant share of the market, primarily due to the high rate of adoption of these housing types in the United States and Canada. The Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, driven by rapid urbanization, government initiatives promoting affordable housing, and increasing disposable incomes. Europe, Latin America, and the Middle East & Africa also present substantial growth opportunities, although the market dynamics vary significantly across these regions due to differences in economic conditions, regulatory frameworks, and consumer preferences.

Product Type Analysis

The product type segment of the manufactured homes, modular homes, and mobile homes sales market is categorized into single-wide homes, double-wide homes, and triple-wide homes. Single-wide homes are a popular choice among budget-conscious buyers due to their affordability and compact size. These homes are typically narrower and can be transported in one piece, making them an ideal option for individuals and small families looking for economical living solutions without compromising on essential amenities.

"The U.S. Department of Agriculture's (USDA) Farm Service Agency (FSA) provides emergency loans to help farmers and ranchers who own or operate a farm/ranch located in a county declared by the President or designated by the Secretary of Agriculture as a primary disaster area or quarantine area.

Emergency loan funds may be used to: Restore or replace essential property Pay all or part of production costs associated with the disaster year Pay essential family living expenses Reorganize the farming operation Refinance certain debts, excluding real estate

Loan applicants may borrow up to 100 percent of their total actual production and/or physical losses. The maximum loan amount is $500,000.

Loans for crops, livestock, and non-real estate losses have a repayment term usually between 1 to 7 years depending upon the loan purpose, collateral, and repayment ability. Loans for physical losses to real estate normally have a 30-year repayment term, not to exceed 40 years."

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

Homeowners associations have demonstrated resilience and adaptability, maintaining a crucial role in managing residential communities. They primarily thrive on membership assessment payments, which are essential for daily operational expenses and funding improvements in community infrastructure. Despite challenges during the pandemic, associations attract residents by enhancing amenities and fostering a sense of community, contributing to steady growth. Over the past five years, industry revenue has grown at a CAGR of 0.0% to $37.8 billion, including a 0.9% growth in 2024 alone. Homeowners associations have navigated financial volatility, influenced by their cost structures and growing reliance on professional management. The sector has seen 60% to 70% of associations employing professionals to manage complex operational requirements, leading to sizeable wage expenses within their budgets. Functioning mainly as nonprofit entities, these organizations prioritize covering service costs over profit generation. As the primary revenue stream, membership fees range from minor monthly dues to more substantial assessments. Although essential, these fees often leave little room for profit because of unexpected expenditures like repairs or legal matters. Associations are poised to benefit from continued membership expansion, supported by rising per capita disposable income and increasing housing starts. The growing interest in community living, driven by efficient management and property value preservation, remains a strong draw for potential members. Moreover, senior living communities present new opportunities as baby boomers seek lifestyle amenities and vibrant retirement settings. By expanding amenities and integrating digital tools, associations can foster stronger community ties and streamline operations. Industry revenue will rise at a CAGR of 1.9% to $41.5 billion.

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.

YouTube and TikTok are the most popular social networks among Generation X for finding helpful content on the cost of living crisis in the United States in 2023. While 56 percent of YouTube users state they find helpful content there, it's 47 percent among TikTok users respectively.