The subsistence minimum for children in Russia has been growing gradually year-on-year. In 2020, the indicator was measured at average 11,216 Russian rubles per month, marking an over 1.7 thousand Russian rubles increase since 2015.

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 84.8 - well below the national benchmark of 100. Nevada - which had an index value of 100.1 - 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 427,000 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 200,000 U.S. dollars. That makes living costs in these states significantly lower than in states such as Hawaii and California, where housing is much more expensive. 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 500 U.S. dollars. That was because of the significantly higher prices for electricity and natural gas in these states.

LEBEN IN EUROPA is the name of the German survey conducted within the scope of the Community statistics on income and living conditions (EU-SILC) conducted all over Europe. Issues of the survey are not only the various income elements, but also other important areas of life such as the housing situation or health. EU-SILC is the new standard data source used to measure poverty and living conditions in the European Union member states.

EU-SILC has been conducted since 2005 in all European Union member states as well as in Norway and Iceland. To ensure the comparability of results, the same variables are covered all over the European Union. Binding minimum standards apply to the survey methods. The survey has been tailored especially to calculating comparable indicators of social inclusion (so-called Laeken indicators) and, consequently, is a major basis for European social policy.

This statistic shows the minimum living guarantee for urban residents in Beijing from 2016 to 2023. In 2023, the minimum living guarantee for urban residents in China's capital city was 1,395 yuan per month, an increase from 1,320 yuan per month in the previous year.

The survey charted Finnish perceptions of social security. Working respondents were asked about the length of their contract, working hours, wages, the nature of their job, interest in job sharing and whether they had been unemployed or laid off during the past three years. Unemployed or laid off respondents were asked how long they had been out of work. Further questions pertained to unemployment benefits, respondents' economic situation, standard of living and participation in job training. State of health was examined by asking whether respondents had chronic illnesses or health problems affecting their everyday life or ability to work. Chronically ill persons were asked whether they needed regular medical care, rehabilitation or prescription drugs. Experiences of burnout, insomnia or absent-mindedness were charted. Social relationships were studied by asking about close and confidential relationships. Respondents were asked whether there had been any major changes in their life recently and did they feel they could influence their own life and society. One topic covered voting in the 1994 presidential elections, in the 1994 EU referendum and in the 1995 parliamentary elections. The survey carried a set of attitudinal statements relating to e.g. social and health services, social security, public sector financing, means-tested social benefits, universal basic income (UBI, citizen's income) and income disparity. The extent to which respondents felt able to control their life was surveyed by asking how often they felt uncertainty or indifference, felt their actions were meaningless or that their life was interesting. Confidence in their own ability to solve problems or co-operate with others was assessed. Values were charted by asking how important the following things were to the respondent: religion, religious values, political participation, health services, social services, work, nature, world peace and high standard of living. Opinions on the minimum level of income support were canvassed. Finally, respondents were asked whether they felt poor or debt-laden. Background variables included respondents' sex, year of birth, marital status, basic and vocational education, respondents' and spouses' economic activity and occupation, province of residence.

Abstract

Over the past decade, Albania has been seeking to develop the framework for a market economy and more open society. It has faced severe internal and external challenges in the interim - extremely low income levels and a lack of basic infrastructure, the rapid collapse of output and inflation rise after the shift in regime in 1991, the turmoil during the 1997 pyramid crisis, and the social and economic shocks accompanying the 1999 Kosovo crisis. In the face of these challenges, Albania has made notable progress in creating conditions conducive to growth and poverty reduction. A poverty profile based on 1996 data (the most recent available) showed that some 30 percent of the rural and some 15 percent of the urban population are poor, with many others vulnerable to poverty due to their incomes being close to the poverty threshold. Income related poverty is compounded by the severe lack of access to basic infrastructure, education and health services, clean water, etc., and the ability of the Government to address these issues is complicated by high levels of internal and external migration that are not well understood. To date, the paucity of household-level information has been a constraining factor in the design, implementation and evaluation of economic and social programs in Albania. Multi-purpose household surveys are one of the main sources of information to determine living conditions and measure the poverty situation of a country and provide an indispensable tool to assist policymakers in monitoring and targeting social programs. Two recent surveys carried out by the Albanian Institute of Statistics (INSTAT) - the 1998 Living Conditions Survey (LCS) and the 2000 Household Budget Survey (HBS) - drew attention, once again, to the need for accurately measuring household welfare according to well accepted standards, and for monitoring these trends on a regular basis. In spite of their narrow scope and limitations, these two surveys have provided the country with an invaluable training ground towards the development of a permanent household survey system to support the government strategic planning in its fight against poverty. In the process leading to its first Poverty Reduction Strategy Paper (PRSP; also known in Albania as Growth and Poverty Reduction Strategy, GPRS), the Government of Albania reinforced its commitment to strengthening its own capacity to collect and analyse on a regular basis the information it needs to inform policy-making. In its first phase (2001-2006), this monitoring system will include the following data collection instruments:

(i) Population and Housing Census (ii) Living Standards Measurement Surveys every 3 years (iii) annual panel surveys.

The Population and Housing Census (PHC) conducted in April 2001, provided the country with a much needed updated sampling frame which is one of the building blocks for the household survey structure. The focus during this first phase of the monitoring system is on a periodic LSMS (in 2002 and 2005), followed by panel surveys on a sub-sample of LSMS households (in 2003, 2004 and 2006), drawing heavily on the 2001 census information. The possibility to include a panel component in the second LSMS will be considered at a later stage, based on the experience accumulated with the first panels. The 2002 LSMS was in the field between April and early July, with some field activities (the community and price questionnaires) extending into August and September. The survey work was undertaken by the Living Standards unit of INSTAT, with the technical assistance of the World Bank. The present document provides detailed information on this survey. Section II summarizes the content of the survey instruments used. Section III focuses on the details of the sample design. Sections IV describes the pilot test and fieldwork procedures of the survey, as well as the training received by survey staff. Section V reviews data entry and data cleaning issues. Finally, section VI contains a series of annotations that all those interested in using the data should read.

Geographic coverage

National

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) SAMPLING FRAME

The Republic of Albania is divided geographically into 12 Prefectures (Prefekturat). The latter are divided into Districts (Rrethet) which are, in turn, divided into Cities (Qyteti) and Communes (Komunat). The Communes contain all the rural villages and the very small cities. For the April 2001 General Census of Population and Housing census purposes, the cities and the villages were divided into Enumeration Areas (EAs). These formed the basis for the LSMS sampling frame. The EAs in the frame are classified by Prefecture, District, City or Commune. The frame also contains, for every EA, the number of Housing Units (HUs), the number of occupied HUs, the number of unoccupied HUs, and the number of households. Occupied dwellings rather than total number of dwellings were used since many census EAs contain a large number of empty dwellings. The Housing Unit (defined as the space occupied by one household) was taken as the sampling unit, instead of the household, because the HU is more permanent and easier to identify in the field. A detailed review of the list of censuses EAs shows that many have zero population. In order to obtain EAs with a minimum of 50 and a maximum of 120 occupied housing units, the EAs with zero population were first removed from the sampling frame. Then, the smallest EAs (with less than 50 HU) were collapsed with geographically adjacent ones and the largest EAs (with more than 120 HU) were split into two or more EAs. Subsequently, maps identifying the boundaries of every split and collapsed EA were prepared Sample Size and Implementation Since the 2002 LSMS had been conducted about a year after the April 2001 census, a listing operation to update the sample EAs was not conducted. However, given the rapid speed at which new constructions and demolitions of buildings take place in the city of Tirana and its suburbs, a quick count of the 75 sample EAs was carried out followed by a listing operation. The listing sheets prepared during the listing operation became the sampling frame for the final stage of selection. The final sample design for the 2002 LSMS included 450 Primary Sampling Units (PSUs) and 8 households in each PSU, for a total of 3600 households. Four reserve units were selected in each sample PSU to act as replacement unit in non-response cases. In a few cases in which the rate of migration was particularly high and more than four of the originally selected households could not be found for the interview, additional households for the same PSU were randomly selected. During the implementation of the survey there was a problem with the management of the questionnaires for a household that had initially refused, but later accepted, to fill in the food diary. The original household questionnaire was lost in the process and it was not possible to match the diary with a valid household questionnaire. The household had therefore to be dropped from the sample (this happened in Shkoder, PSU 16). The final sample size is therefore of 3599 households.

(b) STRATIFICATION

The sampling frame was divided in four regions (strata), Coastal Area, Central Area, and Mountain Area, and Tirana (urban and other urban). These four strata were further divided into major cities, other urban, and other rural. The EAs were selected proportionately to the number of housing units in these areas. In the city of Tirana and its suburbs, implicit stratification was used to improve the efficiency of the sample design. The implicit stratification was performed by ordering the EAs in the sampling frame in a geographic serpentine fashion within each stratum used for the independent selection of EAs.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

(a) QUALITY CHECKS Besides the checks built-in in the DE program and those performed on the preliminary versions of the dataset as it was building up, and additional round of in depth checks on the household questionnaire and the food diary was performed in late September and early October in Tirana. Wherever possible data entry errors or inconsistencies in the dataset were spotted, the original questionnaires or diary were retrieved, and the information contained therein checked. Changes were made to the August version of the dataset as needed and the dataset was finalized in October.

(b) DATA ENTRY Data Entry Operations Data entry for all the survey instruments was performed using custom made applications developed in CS-Pro. Data entry for the household questionnaire was performed in a decentralized fashion in parallel with the enumeration, so as to allow for 'real-time' checking of the data collected. This allowed a further tier of quality control checks on the data. Where errors in the data were spotted during data entry, it was possible to instruct enumerators and supervisors to correct the information, if necessary, revisiting the household, when the teams were still in the field. A further round of checks was performed by the core team in Tirana and Bank staff in Washington as the data were gathered from the field and the entire dataset started building up. All but one of the 16 teams in the districts had one DEO, the Fier team had two, and there were four DEO's for Tirana. Each DEO worked with a laptop computer, and was given office space in the regional Statistics Offices, or in INSTAT headquarters for the Tirana teams. The DEO's received Part 1 of the household questionnaire from the supervisor once the supervisor had checked the enumerator's work, within two

Abstract

The study included four separate surveys:

1. The LSMS survey of general population of Serbia in 2002

2. The survey of Family Income Support (MOP in Serbian) recipients in 2002 These two datasets are published together separately from the 2003 datasets.

3. The LSMS survey of general population of Serbia in 2003 (panel survey)

4. The survey of Roma from Roma settlements in 2003 These two datasets are published together.

Objectives

LSMS represents multi-topical study of household living standard and is based on international experience in designing and conducting this type of research. The basic survey was carried out in 2002 on a representative sample of households in Serbia (without Kosovo and Metohija). Its goal was to establish a poverty profile according to the comprehensive data on welfare of households and to identify vulnerable groups. Also its aim was to assess the targeting of safety net programs by collecting detailed information from individuals on participation in specific government social programs. This study was used as the basic document in developing Poverty Reduction Strategy (PRS) in Serbia which was adopted by the Government of the Republic of Serbia in October 2003.

The survey was repeated in 2003 on a panel sample (the households which participated in 2002 survey were re-interviewed).

Analysis of the take-up and profile of the population in 2003 was the first step towards formulating the system of monitoring in the Poverty Reduction Strategy (PRS). The survey was conducted in accordance with the same methodological principles used in 2002 survey, with necessary changes referring only to the content of certain modules and the reduction in sample size. The aim of the repeated survey was to obtain panel data to enable monitoring of the change in the living standard within a period of one year, thus indicating whether there had been a decrease or increase in poverty in Serbia in the course of 2003. [Note: Panel data are the data obtained on the sample of households which participated in the both surveys. These data made possible tracking of living standard of the same persons in the period of one year.]

Along with these two comprehensive surveys, conducted on national and regional representative samples which were to give a picture of the general population, there were also two surveys with particular emphasis on vulnerable groups. In 2002, it was the survey of living standard of Family Income Support recipients with an aim to validate this state supported program of social welfare. In 2003 the survey of Roma from Roma settlements was conducted. Since all present experiences indicated that this was one of the most vulnerable groups on the territory of Serbia and Montenegro, but with no ample research of poverty of Roma population made, the aim of the survey was to compare poverty of this group with poverty of basic population and to establish which categories of Roma population were at the greatest risk of poverty in 2003. However, it is necessary to stress that the LSMS of the Roma population comprised potentially most imperilled Roma, while the Roma integrated in the main population were not included in this study.

Geographic coverage

The surveys were conducted on the whole territory of Serbia (without Kosovo and Metohija).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample frame for both surveys of general population (LSMS) in 2002 and 2003 consisted of all permanent residents of Serbia, without the population of Kosovo and Metohija, according to definition of permanently resident population contained in UN Recommendations for Population Censuses, which were applied in 2002 Census of Population in the Republic of Serbia. Therefore, permanent residents were all persons living in the territory Serbia longer than one year, with the exception of diplomatic and consular staff.

The sample frame for the survey of Family Income Support recipients included all current recipients of this program on the territory of Serbia based on the official list of recipients given by Ministry of Social affairs.

The definition of the Roma population from Roma settlements was faced with obstacles since precise data on the total number of Roma population in Serbia are not available. According to the last population Census from 2002 there were 108,000 Roma citizens, but the data from the Census are thought to significantly underestimate the total number of the Roma population. However, since no other more precise data were available, this number was taken as the basis for estimate on Roma population from Roma settlements. According to the 2002 Census, settlements with at least 7% of the total population who declared itself as belonging to Roma nationality were selected. A total of 83% or 90,000 self-declared Roma lived in the settlements that were defined in this way and this number was taken as the sample frame for Roma from Roma settlements.

Planned sample: In 2002 the planned size of the sample of general population included 6.500 households. The sample was both nationally and regionally representative (representative on each individual stratum). In 2003 the planned panel sample size was 3.000 households. In order to preserve the representative quality of the sample, we kept every other census block unit of the large sample realized in 2002. This way we kept the identical allocation by strata. In selected census block unit, the same households were interviewed as in the basic survey in 2002. The planned sample of Family Income Support recipients in 2002 and Roma from Roma settlements in 2003 was 500 households for each group.

Sample type: In both national surveys the implemented sample was a two-stage stratified sample. Units of the first stage were enumeration districts, and units of the second stage were the households. In the basic 2002 survey, enumeration districts were selected with probability proportional to number of households, so that the enumeration districts with bigger number of households have a higher probability of selection. In the repeated survey in 2003, first-stage units (census block units) were selected from the basic sample obtained in 2002 by including only even numbered census block units. In practice this meant that every second census block unit from the previous survey was included in the sample. In each selected enumeration district the same households interviewed in the previous round were included and interviewed. On finishing the survey in 2003 the cases were merged both on the level of households and members.

Stratification: Municipalities are stratified into the following six territorial strata: Vojvodina, Belgrade, Western Serbia, Central Serbia (Šumadija and Pomoravlje), Eastern Serbia and South-east Serbia. Primary units of selection are further stratified into enumeration districts which belong to urban type of settlements and enumeration districts which belong to rural type of settlement.

The sample of Family Income Support recipients represented the cases chosen randomly from the official list of recipients provided by Ministry of Social Affairs. The sample of Roma from Roma settlements was, as in the national survey, a two-staged stratified sample, but the units in the first stage were settlements where Roma population was represented in the percentage over 7%, and the units of the second stage were Roma households. Settlements are stratified in three territorial strata: Vojvodina, Beograd and Central Serbia.

Mode of data collection

Face-to-face [f2f]

Research instrument

In all surveys the same questionnaire with minimal changes was used. It included different modules, topically separate areas which had an aim of perceiving the living standard of households from different angles. Topic areas were the following: 1. Roster with demography. 2. Housing conditions and durables module with information on the age of durables owned by a household with a special block focused on collecting information on energy billing, payments, and usage. 3. Diary of food expenditures (weekly), including home production, gifts and transfers in kind. 4. Questionnaire of main expenditure-based recall periods sufficient to enable construction of annual consumption at the household level, including home production, gifts and transfers in kind. 5. Agricultural production for all households which cultivate 10+ acres of land or who breed cattle. 6. Participation and social transfers module with detailed breakdown by programs 7. Labour Market module in line with a simplified version of the Labour Force Survey (LFS), with special additional questions to capture various informal sector activities, and providing information on earnings 8. Health with a focus on utilization of services and expenditures (including informal payments) 9. Education module, which incorporated pre-school, compulsory primary education, secondary education and university education. 10. Special income block, focusing on sources of income not covered in other parts (with a focus on remittances).

Response rate

During field work, interviewers kept a precise diary of interviews, recording both successful and unsuccessful visits. Particular attention was paid to reasons why some households were not interviewed. Separate marks were given for households which were not interviewed due to refusal and for cases when a given household could not be found on the territory of the chosen census block.

In 2002 a total of 7,491 households were contacted. Of this number a total of 6,386 households in 621 census rounds were interviewed. Interviewers did not manage to collect the data for 1,106 or 14.8% of selected households. Out of this number 634 households

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.

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.

Abstract

The main objective of the survey (TLSS-03) was to measure the level of living of the people of Turkmenistan with respect to various social and economic indicators and produce comparable statistics to the TLSS-98. The survey results formed an important database for building a system of monitoring of the living standards in the country.

The survey will focus on income level and expenditure pattern of households along with their social opportunity and access to public services. The survey will integrate the social and economic aspects of living standards and reveal the social strata that need more attention and protection from state. The survey will analyse the different factors affecting the living standards and will produce valuable information required in development planning and policy making.

A wide range of information collected from the survey was analysed to reveal the major socio-economic factors affecting the level of living. The basic survey approach and the questionnaire was designed to ensure the comparability of statistics with TLSS-98, so that data analysis can be made in cross-statistics as well as in time series.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

Like in 1998, the survey was designed as a two-stage stratified cluster sampling. The principle of stratification into urban and rural for each 5 regions (Velayats) also remains unchanged. It created 11 independent strata (10 from 5 regions plus one stratum of Ashgabad). Primary sampling units (psu) were clusters formed of enumeration area units as described above. Households were listed in the selected clusters and sub-sampled by field staffs from the listing sheets.

TLSS-03 had a self-weighting design and samples were spread out over the wide area of the country. For this purpose, psu's were arranged in the order of geographical location across the different Etraps. Selection of PSU's was made systematically probability proportional to the number of households in clusters.

A fixed sample of 20 households was selected from each cluster using simple random sampling method. Selection of psu's by pps method at first stage and inversely proportional to the number of households at second stage resulted in a self-weighting sample, which was very important for this survey, especially because a large number of indicators are means and proportions. In a self-weighting design, sample means and sample proportions are unbiased estimators of population means and population proportions.

See detail sampling information in "Turkmenistan Living Standards Survey 2003 Technical Report" document.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey was collected using two type of questionnaires: - Household Questionnaire - Community Questionnaire

Cleaning operations

Prior to the data entry, questionnaires filled and returned from the field were checked and edited especially with regard to household identification numbers and data items. The questionnaire included, household listing form, household questionnaire and the community questionnaire. To facilitate the smooth data entry, the community questionnaires were folioed by Oblast, while the household questionnaires were folioed by the survey block. Each folio was provided with appropriate folio cover, which included the household identification and indicators to determine the status of every folio during machine processing. The total folios produced were as follows. - Community Questionnaire, 6 folios - Household Questionnare, 120 folios

The data entry programme was developed in CS Pro 2.3. The screen format for data entry was designed to make its look as similar as possible to the questionnaire. The form labels were made in both English and Russian versions. The programme also included the necessary control mechanism to ensure validity of entries. As mentioned above, there were two levels of questionnaires, so programme files were developed separately for community and household questionnaires.

Several department of TMH housed the data entry process. However, it was not felt necessary to install a network due to the relatively smaller size of the data load. An additional computer was designated for batch editing, form receipts and control and the monitoring purposes. The data entry was conducted from 4 January to 7 February 2004.

CSPro 2.3 was also used for editing. A batch edit program was developed to control the quality of data. Range checks were done on every data item. Additional consistency checks between data items were included in the edit programme. The program generated a list of errors for all questionnaires belonging to a particular household. The data items with error were manually compared with the corresponding questionnaire for verification. All necessary corrections were recorded in the error list and were later used for data correction. Since this is a sample based survey, automatic imputations were not done to preserve reliability of data.

Sampling error estimates

Estimation of the standard error was made based on the Balanced Repeated Replicates (BRR method). The method required exactly two psu’s per stratum. It takes half sample from each stratum and as many complements. The squared differences of two estimates provide an unbiased estimate of variance.

See detail estimation of the standard error and design effect information in "Turkmenistan Living Standards Survey 2003 Technical Report" document.

Data appraisal

Limitations of the survey Although, the utmost attention was paid to ensure the quality of survey results, TLSS had some limitations. Users are strongly recommended to take these limitations into considerations while using the data of this survey. The limitations of the survey are broadly described below.

The survey frame 1. The main limitation of the survey was the quality of the frame used in the survey design. The last population census in Turkmenistan was conducted in 1995. Since then, a lot of demographic changes were observed mainly due the emigration of the Russian speaking population and internal replacement caused by massive housing reconstruction. Despite of all possible attempts directed to improve the frame, it must be recognised that the baseline data still came from the last census.

1. While the last population census results are no more a valid database for any kind of plausible statistical investigations, it is unfortunate that the upcoming Population census in 2005 has now been cancelled, which will be replaced by a “Mini-census of 5%”. Such census may produce the population figures, however, it will not provide so acutely required data for household surveys. Therefore, the problem of the frame is most likely to affect adversely also the quality of other household surveys to be conducted in future.

2. The problem of the frame is related also to the lack of maps of enumeration blocks used in the survey. The size of the earlier blocks in terms of the number of households has significantly changed, so new boundaries were fixed for this survey. However, there was no map available to show the recent changes. Field staffs prepared a new map by themselves for the selected blocks based on the list of households. However, the quality of such map could affect the accuracy of the size of blocks due to the omission or duplication that could occur in the absence of good map. In the absence of the decennial census, maps throughout the country are not updated in terms of the boundaries of enumeration blocks and the number of households. Again, it could also create difficulties in conducting other surveys in future.

Training and the fieldwork 4. During the data editing and consistency checking, several mistakes of field staffs were found in filling the questionnaire. These mistakes actually were the result of insufficient training of the field staffs. The supervisor’s training in the centre was limited only to those from TMH. Field staffs recruited from the centre and from the regional offices did not get the sufficient time of interaction on the various conceptual issues of the questionnaire, so could not sufficiently address much of the expected problems of the survey.

1. The effect of the poor training could have been minimised by an intensive and close supervision of the survey staffs. However, the number of supervisors deployed in the field was often below the initially planned number due to the constraints of time and manpower. There was no coordinated supervision of the fieldwork because the core survey staffs themselves were involved in data collection.

Total survey error 6. Although, sampling error of major variables of interest were at the accepted level, non-sampling errors of the survey were relatively high due to the poor quality of the frame, lack of sufficient training of the field staffs and weak supervision of data collection. Non-sampling error was also caused by measurement and non-response problem as mentioned in the earlier chapter. Therefore, the total margin of error of major estimates was higher, often substantially, than the estimated value of sampling error.

Profile of the living standard 7. The analysis of the living standards requires a statistically viable baseline that allows the results of the survey for comparison over time and territory. In international practice, such baseline is the subsistence minimum, which serves as an objective criterion of measuring the level of living of population. In Turkmenistan, the subsistence minimum is not used for living standard analysis

Abstract

In 2001, the World Bank in co-operation with the Republika Srpska Institute for Statistics (RSIS), the Federal Office of Statistics (FOS) and the Agency for Statistics of Bosnia and Herzegovina (BHAS), carried out a Living Standards Measurement Survey (LSMS).

The Living Standard Measurement Survey LSMS, in addition to collecting the information necessary to obtain a comprehensive as possible measure of the basic dimensions of household living standards, has three basic objectives, as follows: 1. To provide the public sector, government, the business community, scientific institutions, international donor organizations and social organizations with information on different indicators of the population's living conditions, as well as on available resources for satisfying basic needs. 2. To provide information for the evaluation of the results of different forms of government policy and programs developed with the aim to improve the population's living standard. The survey will enable the analysis of the relations between and among different aspects of living standards (housing, consumption, education, health, labor) at a given time, as well as within a household. 3. To provide key contributions for development of government's Poverty Reduction Strategy Paper, based on analyzed data.

The Department for International Development, UK (DFID) contributed funding to the LSMS and is also providing funding for a further two years of data collection for a panel survey, to be known as the Household Survey Panel Series (HSPS). Birks Sinclair & Associates Ltd. are responsible for the management of the HSPS with technical advice and support being provided by the Institute for Social and Economic Research (ISER), University of Essex, UK.

The aim of the panel survey is to provide longitudinal data through re-interviewing approximately half the LSMS respondents for two years following the LSMS, in the autumn of 2002 and again in 2003. The LSMS constitutes wave 1 of the panel survey so there will be three years of panel data available for analysis under current funding plans. For the purposes of this document we are using the following convention to describe the different rounds of the panel survey: Wave 1 LSMS conducted in 2001 forms the baseline survey for the panel Wave 2 Second interview of 50% of LSMS respondents in Autumn/Winter 2002 Wave 3 Third interview with sub-sample respondents in Autumn/Winter 2003

The panel data will allow the analysis of key transitions and events over this period such as labour market or geographical mobility and observe the consequent outcomes for the well-being of individuals and households in the survey.

The panel data will provide information on income and labour market dynamics within FBiH and RS. A key policy area is developing strategies for the reduction of poverty within FBiH and RS. The panel will provide information on the extent to which continuous poverty is experienced by different types of households and individuals over the three year period. And most importantly, the co-variates associated with moves into and out of poverty and the relative risks of poverty for different people can be assessed. As such, the panel aims to provide data, which will inform the policy debates within FBiH and RS at a time of social reform and rapid change.

Geographic coverage

National coverage. Domains: Urban/rural/mixed; Federation, Republic

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The panel survey sample is made up of over 3,000 households drawn from the Living Standards Measurement Survey (LSMS) conducted by the World Bank in co-operation with the SIs in 2002. Approximately half the households interviewed on the LSMS were selected and carried forward into the panel survey. These households were re-interviewed in 2003 and will be interviewed for a third time in September 2004.

Sampling Frame

The 5,400 households interviewed on LSMS formed the sampling frame for the panel survey. The aim was to achieve interviews with approximately half of these (2,700) at wave 2 (1,500 in FBiH and 1,200 in RS). A response rate of 90% was anticipated (as the sample is based on households that have already co-operated with LSMS) and therefore the selected sample consisted of 3,000 households. Unlike the LSMS, the HSPS does not have a replacement element to the sample, only the original 3,000 issued addresses. This approach was new to the Supervisors and Interviewers and special training was given on how to keep non-response to a minimum.

The LSMS Sample

The LSMS sample design process experienced some difficulties which resulted in a sample with a disproportionately high number of households being selected in urban areas. Work by Peter Lynn from ISER identified the source of this problem by establishing the selection probabilities at each stage of the LSMS sampling process. Essentially, the procedures used for selecting households within municipalities would have been appropriate had municipalities been selected with equal probabilities. But in fact municipalities had been selected with probability proportional to size, and using different overall sampling fractions in each of three strata. The details are documented in a memo by Peter Lynn dated 25-3-2002. Consequently, household selection probabilities varied considerably across municipalities.

Compensating for the LSMS sample imbalance

Having established the selection probability of every LSMS household, it became possible to derive design-based weights that should provide unbiased estimates for LSMS. However, the considerable variability in these weights means that the variance of estimates (and hence standard errors and confidence intervals) is greatly increased. For the HSPS, there was an opportunity to reduce the variability in weights by constructing the subsample in a way that minimised the variability in overall selection probabilities. The overall selection probability for each household would be the product of two probabilities - the probability of being selected for LSMS, and the probability of being selected for HSPS, conditional upon having been selected for LSMS, i.e. P(HSPS) = P(LSMS) * P(HSPS)/(LSMS)

Ideally, then, we would have set the values of P(HSPS)/(LSMS) to be inversely proportional to P(LSMS). This would have resulted in each HSPS household having the same overall selection probability, P(HSPS), so that there would no longer be an increase in the variance of estimates due to variability in selection probabilities. However, this was not possible due to the very considerable variation in P(LSMS) and the limited flexibility provided by a large overall sampling fraction for HSPS (3,000 out of 5,400).

The best that could be done was to minimise the variability in sampling fractions by retaining all the LSMS households in the (mainly rural or mixed urban/rural) municipalities where LSMS household selection probabilities had been lowest and sub-sampling only in the municipalities where LSMS selection probabilities had been much higher. In 16 of the 25 LSMS municipalities, all households were retained for HSPS. In the other 9 municipalities, households were sub-sampled, with sampling fractions ranging from 83% in Travnik to just 25% in Banja Luka and Tuzla.

To select the required number of households within each municipality, every group of enumeration districts (GND) was retained from LSMS. The sub-sampling took place within the GNDs. Households were sub-sampled using systematic random sampling, with a random start and fixed interval. For example, in Novo Sarajevo, where the sampling fraction was 1 in 2, 6 households were selected out of the 12 LSMS households in each GND by selecting alternate households. In Prijedor, where the fraction was 1 in 3, 4 out of 12 were selected by taking every third LSMS household. And so on.

The total selected sample for the HSPS consists of 3,007 households (1681 in the FBIH and 1326 in the RS).

The overall design weight for the HSPS sample will be the product of the LSMS weight for the household and this extra design weight (which will of course tend to increase the size of the smallest LSMS weights).

Panel design

Eligibility for inclusion

The household and household membership definitions are the same standard definitions as used on the LSMS (see Supervisor Instructions, Annex A). While the sample membership status and eligibility for interview are as follows: i) All members of households interviewed at wave 1 (LSMS) have been designated as original sample members (OSMs). OSMs include children within households even if they are too young for interview. ii) Any new members joining a household containing at least one OSM, are eligible for inclusion and are designated as new sample members (NSMs). iii) At each wave, all OSMs and NSMs are eligible for inclusion, apart from those who move outof-scope (see discussion below). iv) All household members aged 15 or over are eligible for interview, including OSMs and NSMs.

Following rules and the definition of 'out-of-scope'

The panel design means that sample members who move from their previous wave address at either wave 2 or 3 must be traced and followed to their new address for interview. The LSMS sample was clustered and over the two waves of the panel some de-clustering will occur as people move. In some cases the whole household will move together but in others an individual member may move away from their previous wave household and form a new split-off household of their own.

Following rules

All sample members, OSMs and NSMs, are followed at each wave and an interview attempted. This means that a four person household at Wave 1 could generate

Abstract

Survey based Harmonized Indicators (SHIP) files are harmonized data files from household surveys that are conducted by countries in Africa. To ensure the quality and transparency of the data, it is critical to document the procedures of compiling consumption aggregation and other indicators so that the results can be duplicated with ease. This process enables consistency and continuity that make temporal and cross-country comparisons consistent and more reliable.

Four harmonized data files are prepared for each survey to generate a set of harmonized variables that have the same variable names. Invariably, in each survey, questions are asked in a slightly different way, which poses challenges on consistent definition of harmonized variables. The harmonized household survey data present the best available variables with harmonized definitions, but not identical variables. The four harmonized data files are

a) Individual level file (Labor force indicators in a separate file): This file has information on basic characteristics of individuals such as age and sex, literacy, education, health, anthropometry and child survival. b) Labor force file: This file has information on labor force including employment/unemployment, earnings, sectors of employment, etc. c) Household level file: This file has information on household expenditure, household head characteristics (age and sex, level of education, employment), housing amenities, assets, and access to infrastructure and services. d) Household Expenditure file: This file has consumption/expenditure aggregates by consumption groups according to Purpose (COICOP) of Household Consumption of the UN.

Geographic coverage

National

Analysis unit

• Individual level for datasets with suffix _I and _L
• Household level for datasets with suffix _H and _E

Universe

The survey covered all de jure household members (usual residents).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame and Units As in all probability sample surveys, it is important that each sampling unit in the surveyed population has a known, non-zero probability of selection. To achieve this, there has to be an appropriate list, or sampling frame of the primary sampling units (PSUs).The universe defined for the GLSS 5 is the population living within private households in Ghana. The institutional population (such as schools, hospitals etc), which represents a very small percentage in the 2000 Population and Housing Census (PHC), is excluded from the frame for the GLSS 5.

The Ghana Statistical Service (GSS) maintains a complete list of census EAs, together with their respective population and number of households as well as maps, with well defined boundaries, of the EAs. . This information was used as the sampling frame for the GLSS 5. Specifically, the EAs were defined as the primary sampling units (PSUs), while the households within each EA constituted the secondary sampling units (SSUs).

Stratification In order to take advantage of possible gains in precision and reliability of the survey estimates from stratification, the EAs were first stratified into the ten administrative regions. Within each region, the EAs were further sub-divided according to their rural and urban areas of location. The EAs were also classified according to ecological zones and inclusion of Accra (GAMA) so that the survey results could be presented according to the three ecological zones, namely 1) Coastal, 2) Forest, and 3) Northern Savannah, and for Accra.

Sample size and allocation The number and allocation of sample EAs for the GLSS 5 depend on the type of estimates to be obtained from the survey and the corresponding precision required. It was decided to select a total sample of around 8000 households nationwide.

To ensure adequate numbers of complete interviews that will allow for reliable estimates at the various domains of interest, the GLSS 5 sample was designed to ensure that at least 400 households were selected from each region.

A two-stage stratified random sampling design was adopted. Initially, a total sample of 550 EAs was considered at the first stage of sampling, followed by a fixed take of 15 households per EA. The distribution of the selected EAs into the ten regions or strata was based on proportionate allocation using the population.

For example, the number of selected EAs allocated to the Western Region was obtained as: 1924577/18912079*550 = 56

Under this sampling scheme, it was observed that the 400 households minimum requirement per region could be achieved in all the regions but not the Upper West Region. The proportionate allocation formula assigned only 17 EAs out of the 550 EAs nationwide and selecting 15 households per EA would have yielded only 255 households for the region. In order to surmount this problem, two options were considered: retaining the 17 EAs in the Upper West Region and increasing the number of selected households per EA from 15 to about 25, or increasing the number of selected EAs in the region from 17 to 27 and retaining the second stage sample of 15 households per EA.

The second option was adopted in view of the fact that it was more likely to provide smaller sampling errors for the separate domains of analysis. Based on this, the number of EAs in Upper East and the Upper West were adjusted from 27 and 17 to 40 and 34 respectively, bringing the total number of EAs to 580 and the number of households to 8,700.

A complete household listing exercise was carried out between May and June 2005 in all the selected EAs to provide the sampling frame for the second stage selection of households. At the second stage of sampling, a fixed number of 15 households per EA was selected in all the regions. In addition, five households per EA were selected as replacement samples.The overall sample size therefore came to 8,700 households nationwide.

Mode of data collection

Face-to-face [f2f]

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The population for LSMS consists of Republic of Serbia residents, excluding Kosovo and Metohija . The sampling frame for the LSMS was based on the Enumeration District (ED) delineated for the 2002 Serbia Census, excluding those with less than 20 households. It is estimated that the households in the excluded EDs only represent about 1 percent of the population of Serbia.

The sampling frame also excludes the population living in group quarters, institutions and temporary housing units, as well as the homeless population: these groups also represent less than 1 percent of the population, so the sampling frame should cover at least 98 percent of the Serbian population.

Stratification was done in the same way as for the previous LSMSs. Enumeration District werestratified according to: - Region in 6 strata (Vojvodina, Belgrade, West Serbia, Sumadija and Pomoravlj e, East Serbia and South East Serbia). - Type of settlement (urban and other).

The allocation of EDs according to region and type of settlement was propoI1ionai to the number of occupied dwellings, adjusted to provide sufficient precision of estimates at the regional level. To provide optimal sample sizes in each region we decided that the minimum number of allocated EDs to each stratum should be 60. The result of this procedure was a slight deviation from strictly proportional allocation.

The sample size for LSMS 2007 was 71 40 households from 510 selected EDs. Within each ED 14 occupied dwellings were selected. From each selected occupied dwellings one household was selected (using a Kish Grid). The sample size was determined according with the aim of achieving 5,000 household interviews with an expected non-response rate of around 30%. The final response rate was 78%, producing a sample size of 5,557 households.

Mode of data collection

Face-to-face [f2f]

Response rate

Response rate was 78%.

The aim of the survey was to get a picture of Finns' financial well-being. Respondents were asked about their housing situation and their satisfaction with their current standard of living. Respondents assessed their own family's financial situation. Respondents were asked about the necessity or non-necessity of various goods and services for today's Finnish adults. Respondents were also asked whether they considered certain goods and services necessary for themselves. Among other things, these included a mobile phone, a car, clothing, eating out, home furnishings, getting a haircut, home insurance, and health services. Respondents were also asked to tell about their own health. Respondents reported on how financial matters are handled in their family and whether there are income differences between spouses. Attitudes towards social security benefits and poverty were explored, for example by asking about income support clients or those in need of income support, the minimum income and whether the minimum amount of income support is sufficient as it is. Finally, respondents indicated how much they have in terms of expenses. Background variables in the data include year of birth, gender, marital status, type of housing, size of household and number of children, region of residence at age 14 and at present, highest level of education, main activity of self and spouse, socio-economic status and monthly income of the whole family.

Pursuant to the Continuing Care Regulation under the Continuing Care Act, adherence with Accommodation Standards – Supportive Living Accommodation is a mandated requirement for supportive living accommodation operators in Alberta. The Accommodation Standards - Supportive Living Accommodation provide the standards for voluntary, public, and private organizations operating supportive living accommodations in Alberta. The accommodation standards establish the minimum standards of accommodation services, such as meals, building maintenance, security, and housekeeping, social or leisure activities, and resident and family involvement to promote safety, security, and quality of life for clients and residents.

In 2025, the minimum hourly wage in Beijing was the highest in China at 26.4 yuan per hour. In the past decade, China has been shifting from a cheap labor driven economy to more matured, service-oriented markets and industries. While the economy continues to grow, prices and wages keep on increasing as well. How do wages differ across the country? China’s provinces and municipalities are divided into districts of different levels. Most provinces set different minimum wages for different districts depending on the cost of living and level of development. Usually, provincial capitals and major cities enjoy higher hourly wages than smaller towns and rural areas of the same province. In 2025, the highest minimum hourly wages in China were to be found in Beijing and Tianjin municipalities with 26.4 and 24.4 yuan respectively, whereas employees in Hainan province who received a minimum wage were paid the least – between 16.3 and 17.9 yuan per hour. Minimum monthly wages that year were the highest in Shanghai and the lowest in Qinghai province. The average annual salary in urban China was around 120,700 yuan in 2023. What are the prospects? Regional governments in China are required to update their minimum wages at least every few years. Hebei, Fujian, and Guangdong – provinces that have not adjusted minimum wages in the past two years – are likely to do so in 2025. Along with economic development, increasing living standards, increasing prices and a shrinking labor force, overall minimum wages will likely continue growing in China.

Abstract

Household Budget Survey has been conducted annually by Statistics Lithuania since 1996. The HBS methodology was improved in 2003. Some changes were introduced into the design of the sample and recording of households' income and expenditures.

The main objectives of the survey are:
-to obtain weights for Consumer Price Index, -to estimate household expenditure for National Accounts, -to study the general structure of household incomes and expenditures, -to study income and expenditure patterns of disadvantaged groups, including pensioner households, single parent households, etc., -to study income and expenditure disparities among socio-economic groups, -to study consumer behavior among socio-economic groups, -to study effects of policy changes, especially tax changes on income and expenditure.

The target population of Household Budget Survey is based on private households in Lithuania. Households are selected using the random sampling method from the Populations Register. Participation of the selected households in the survey takes one month. After one month other households replace them.

Data is collected through face-to-face interviews and expenditure diaries.

Geographic coverage

National

Analysis unit

• Households,
• Individuals.

Universe

The universe of the survey is private households in Lithuania. Persons living in the institutional households (elderly people nursing homes, imprisonment institutions, army, etc.) have been excluded from the survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

New sample design 2003 have not increased the number of respondents. However, the sample has been redistributed by county. The new sample allows estimation of the HBS indicators by county. Since the sample is not large, just essential indicators whose errors are the least can be estimated. Also, calculation methodology of estimates was changed. Calculation of errors has been adjusted to the new sample design and new calculation method of estimates.

Target population is all private households of Lithuania. For the 2003 survey, 10,692 households were selected, of which 7,826 participated in the survey.

The population register was used as a sampling frame. Stratified sample design with simple random sample and two-stage cluster sample was used in strata. All Lithuanian territory was divided in 31 not overlapping groups - strata. The biggest towns of Lithuania counties, medium, small towns and rural areas of counties are divided in separate strata. Sample of households was selected from each stratum. Different sample design was used in each stratum.

A simple random sample of persons 16 and older is drawn from the Population Register in major county towns. Only the households abiding according to the address of the selected person are under the scope of the survey. Accordingly, 4,620 households were selected in the biggest towns of counties. Sample size in Vilnius was 1,320 households, in Kaunas - 1,188 households, in Klaipeda - 528 households, in Šiauliai and Panevežys - 396 households in each, in Taurage, Utena, Mažeikiai, Marijampole, Šilale, Jurbarkas - 132 households in each.

Two-stage cluster sample design was used in the medium and small towns of counties. The Pareto sample with probability a proportional to the cluster size was used in the first stage. Each town is a cluster. A simple random sampling of persons 16 and older withdrawn from the Population Register was used in already selected clusters in the second stage. Households, which live at the selected person's address, were surveyed. 2,376 households were selected in small and medium towns of Lithuania counties, 264 households in each strata.

A similar sample design like in the medium and small towns was used in rural areas of counties. Cluster is a territorial unit "seniunija" (local government) in this case. 3,564 households were selected in rural area. In the counties of Vilnius, Kaunas and Šiauliai 432 households were selected in each. 324 households were selected in each other county.

Mode of data collection

Face-to-face [f2f]

Research instrument

Since 2003 two types of documents have been used in Lithuania Household Budget Survey.

1. Main Household Questionnaire. The aim of this questionnaire is to collect social and economic information on household members, their living conditions and income. The interviewer shall complete the Main Household Questionnaire by asking questions and recording the answers received.

2. Diary of Household Expenditure. This diary aims at the collection of household expenditure on food and non-food products and services. Two pages of the diary are meant for everyday records in tables for food, clothing and other non-food goods. For the rest days of the month only expenditure on non- food products and services is registered. Half of the surveyed households record expenditure on food in the first half of the month, while the other - in the second.

Each household that participated in the survey shall be remunerated in the amount of 15 percent of the Minimum Standard of Living.

Brazil Project Cost: CP.1-2Q40: Popular House: Minimal: Mato Grosso do Sul data was reported at 765.100 BRL/sq m in Jun 2019. This records a decrease from the previous number of 766.530 BRL/sq m for May 2019. Brazil Project Cost: CP.1-2Q40: Popular House: Minimal: Mato Grosso do Sul data is updated monthly, averaging 444.130 BRL/sq m from Jan 1999 (Median) to Jun 2019, with 246 observations. The data reached an all-time high of 774.440 BRL/sq m in Oct 2018 and a record low of 173.780 BRL/sq m in Jan 1999. Brazil Project Cost: CP.1-2Q40: Popular House: Minimal: Mato Grosso do Sul data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Construction and Properties Sector – Table BR.EB006: Project Cost per Square Meter: Popular House (CP.1-2Q40): by Standard of Finishing: Minimal: by State. CP.1-2Q40: Popular house (CP), 1-story, living room, 2 bedrooms, traffic flow, bathroom and kitchen.

There is more to housing affordability than the rent or mortgage you pay. Transportation costs are the second-biggest budget item for most families, but it can be difficult for people to fully factor transportation costs into decisions about where to live and work. The Location Affordability Index (LAI) is a user-friendly source of standardized data at the neighborhood (census tract) level on combined housing and transportation costs to help consumers, policymakers, and developers make more informed decisions about where to live, work, and invest. Compare eight household profiles (see table below) —which vary by household income, size, and number of commuters—and see the impact of the built environment on affordability in a given location while holding household demographics constant.*$11,880 for a single person household in 2016 according to US Dept. of Health and Human Services: https://aspe.hhs.gov/computations-2016-poverty-guidelinesThis layer is symbolized by the percentage of housing and transportation costs as a percentage of income for the Median-Income Family profile, but the costs as a percentage of income for all household profiles are listed in the pop-up:Also available is a gallery of 8 web maps (one for each household profile) all symbolized the same way for easy comparison: Median-Income Family, Very Low-Income Individual, Working Individual, Single Professional, Retired Couple, Single-Parent Family, Moderate-Income Family, and Dual-Professional Family.An accompanying story map provides side-by-side comparisons and additional context.--Variables used in HUD's calculations include 24 measures such as people per household, average number of rooms per housing unit, monthly housing costs (mortgage/rent as well as utility and maintenance expenses), average number of cars per household, median commute distance, vehicle miles traveled per year, percent of trips taken on transit, street connectivity and walkability (measured by block density), and many more.To learn more about the Location Affordability Index (v.3) visit: https://www.hudexchange.info/programs/location-affordability-index/. There you will find some background and an FAQ page, which includes the question:"Manhattan, San Francisco, and downtown Boston are some of the most expensive places to live in the country, yet the LAI shows them as affordable for the typical regional household. Why?" These areas have some of the lowest transportation costs in the country, which helps offset the high cost of housing. The area median income (AMI) in these regions is also high, so when costs are shown as a percent of income for the typical regional household these neighborhoods appear affordable; however, they are generally unaffordable to households earning less than the AMI.Date of Coverage: 2012-2016 Date Released: March 2019Date Downloaded from HUD Open Data: 4/18/19Further Documentation:LAI Version 3 Data and MethodologyLAI Version 3 Technical Documentation_**The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updates**

Title: Location Affordability Index - NMCDC Copy

Summary: This layer contains the Location Affordability Index from U.S. Dept. of Housing and Urban Development (HUD) - standardized household, housing, and transportation cost estimates by census tract for 8 household profiles.

Notes: This map is copied from source map: https://nmcdc.maps.arcgis.com/home/item.html?id=de341c1338c5447da400c4e8c51ae1f6, created by dianaclavery_uo, and identified in Living Atlas.

Prepared by: dianaclavery_uo, copied by EMcRae_NMCDC

Source: This map is copied from source map: https://nmcdc.maps.arcgis.com/home/item.html?id=de341c1338c5447da400c4e8c51ae1f6, created by dianaclavery_uo, and identified in Living Atlas. Check the source documentation or other details above for more information about data sources.

Feature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=447a461f048845979f30a2478b9e65bb

UID: 73

Data Requested: Family income spent on basic need

Method of Acquisition: Search for Location Affordability Index in the Living Atlas. Make a copy of most recent map available. To update this map, copy the most recent map available. In a new tab, open the AGOL Assistant Portal tool and use the functions in the portal to copy the new maps JSON, and paste it over the old map (this map with item id

Date Acquired: Map copied on May 10, 2022

Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 6

Tags: PENDING

Brazil Project Cost: CP.1-2Q46: Popular House: Minimal: Distrito Federal data was reported at 771.380 BRL/sq m in Jun 2019. This records an increase from the previous number of 769.550 BRL/sq m for May 2019. Brazil Project Cost: CP.1-2Q46: Popular House: Minimal: Distrito Federal data is updated monthly, averaging 434.890 BRL/sq m from Jan 1999 (Median) to Jun 2019, with 246 observations. The data reached an all-time high of 771.380 BRL/sq m in Jun 2019 and a record low of 179.170 BRL/sq m in Jan 1999. Brazil Project Cost: CP.1-2Q46: Popular House: Minimal: Distrito Federal data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Construction and Properties Sector – Table BR.EB003: Project Cost per Square Meter: Popular House (CP.1-2Q46): by Standard of Finishing: Minimal: by State. CP.1-2Q46: Popular house (CP), 1-story, balcony, living room, 2 bedrooms, traffic flow, bathroom and kitchen.