Luxembourg stands out as the European leader in quality of life for 2025, achieving a score of 220 on the Quality of Life Index. The Netherlands follows closely behind with 211 points, while Albania and Ukraine rank at the bottom with scores of 104 and 115 respectively. This index provides a thorough assessment of living conditions across Europe, reflecting various factors that shape the overall well-being of populations and extending beyond purely economic metrics. Understanding the quality of life index The quality of life index is a multifaceted measure that incorporates factors such as purchasing power, pollution levels, housing affordability, cost of living, safety, healthcare quality, traffic conditions, and climate, to measure the overall quality of life of a Country. Higher overall index scores indicate better living conditions. However, in subindexes such as pollution, cost of living, and traffic commute time, lower values correspond to improved quality of life. Challenges affecting life satisfaction Despite the fact that European countries register high levels of life quality by for example leading the ranking of happiest countries in the world, life satisfaction across the European Union has been on a downward trend since 2018. The EU's overall life satisfaction score dropped from 7.3 out of 10 in 2018 to 7.1 in 2022. This decline can be attributed to various factors, including the COVID-19 pandemic and economic challenges such as high inflation. Rising housing costs, in particular, have emerged as a critical concern, significantly affecting quality of life. This issue has played a central role in shaping voter priorities for the European Parliamentary Elections in 2024 and becoming one of the most pressing challenges for Europeans, profoundly influencing both daily experiences and long-term well-being.

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

Cost of Living Index (Excl. Rent) is a relative indicator of consumer goods prices, including groceries, restaurants, transportation and utilities. Cost of Living Index does not include accommodation expenses such as rent or mortgage. If a city has a Cost of Living Index of 120, it means Numbeo has estimated it is 20% more expensive than New York (excluding rent).

Please refer further to: https://www.numbeo.com/cost-of-living/cpi_explained.jsp for motivation and methodology.

All credits to https://www.numbeo.com .

This dataset would surely help socio-economic researchers to analyse and get deeper insights regarding the life of people country-wise.

Thanks to @andradaolteanu for the motivation! Upwards and onwards...

As of 2022, Israel had the highest price level index among listed countries, amounting to 138, with 100 being the average of OECD countries. Switzerland and Iceland followed on the places behind. On the other hand, Turkey and India had the lowest price levels compared to the OECD average. This price index shows differences in price levels in different countries. Another very popular index indicating the value of money is the Big Mac index, showing how much a Big Mac costs in different countries. This list was also topped by Switzerland in 2023.

Abstract

Timor-Leste experienced a fundamental social and economic upheaval after its people voted for independence from Indonesia in a referendum in August 1999. Population was displaced, and public and private infrastructure was destroyed or rendered inoperable. Soon after the violence ceased, the country began rebuilding itself with the support from UN agencies, the international donor community and NGOs. The government laid out a National Development Plan (NDP) with two central goals: to promote rapid, equitable and sustainable economic growth and to reduce poverty.

Formulating a national plan and poverty reduction strategy required data on poverty and living standards, and given the profound changes experienced, new data collection had to be undertaken to accurately assess the living conditions in the country. The Planning Commission of the Timor-Leste Transitional Authority undertook a Poverty Assessment Project along with the World Bank, the Asian Development Bank, the United Nations Development Programme and the Japanese International Cooperation Agency (JICA).

This project comprised three data collection activities on different aspects of living standards, which taken together, provide a comprehensive picture of well-being in Timor-Leste. The first component was the Suco Survey, which is a census of all 498 sucos (villages) in the country. It provides an inventory of existing social and physical infrastructure and of the economic characteristics of each suco, in addition to aldeia (hamlet) level population figures. It was carried out between February and April 2001.

A second element was the Timor-Leste Living Standards Measurement Survey (TLSS). This is a household survey with a nationally representative sample of 1,800 families from 100 sucos. It was designed to diagnose the extent, nature and causes of poverty, and to analyze policy options facing the country. It assembles comprehensive information on household demographics, housing and assets, household expenditures and some components of income, agriculture, labor market data, basic health and education, subjective perceptions of poverty and social capital.

Data collection was undertaken between end August and November 2001.

The final component was the Participatory Potential Assessment (PPA), which is a qualitative community survey in 48 aldeias in the 13 districts of the country to take stock of their assets, skills and strengths, identify the main challenges and priorities, and formulate strategies for tackling these within their communities. It was completed between November 2001 and January 2002.

Geographic coverage

National coverage. Domains: Urban/rural; Agro-ecological zones (Highlands, Lowlands, Western Region, Eastern Region, Central Region)

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLE SIZE AND ANALYTIC DOMAINS

A survey relies on identifying a subgroup of a population that is representative both for the underlying population and for specific analytical domains of interest. The main objective of the TLSS is to derive a poverty profile for the country and salient population groups. The fundamental analytic domains identified are the Major Urban Centers (Dili and Baucau), the Other Urban Centers and the Rural Areas. The survey represents certain important sub-divisions of the Rural Areas, namely two major agro-ecologic zones (Lowlands and Highlands) and three broad geographic regions (West, Center and East). In addition to these domains, we can separate landlocked sucos (Inland) from those with sea access (Coast), and generate categories merging rural and urban strata along the geographic, altitude, and sea access dimensions. However, the TLSS does not provide detailed indicators for narrow geographic areas, such as postos or even districts. [Note: Timor-Leste is divided into 13 major units called districts. These are further subdivided into 67 postos (subdistricts), 498 sucos (villages) and 2,336 aldeias (sub-villages). The administrative structure is uniform throughout the country, including rural and urban areas.]

The survey has a sample size of 1,800 households, or about one percent of the total number of households in Timor-Leste. The experience of Living Standards Measurement Surveys in many countries - most of them substantially larger than Timor-Leste - has shown that samples of that size are sufficient for the requirements of a poverty assessment.

The survey domains were defined as follows. The Urban Area is divided into the Major Urban Centers (the 31 sucos in Dili and the 6 sucos in Baucau) and the Other Urban Centers (the remaining 34 urban sucos outside Dili and Baucau). The rest of the country (427 sucos in total) comprises the Rural Area. The grouping of sucos into urban and rural areas is based on the Indonesian classification. In addition, we separated rural sucos both by agro-ecological zones and geographic areas. With the help of the Geographic Information System developed at the Department of Agriculture, sucos were subsequently qualified as belonging to the Highlands or the Lowlands depending on the share of their surface above and below the 500 m level curve. The three westernmost districts (Oecussi, Bobonaro and Cova Lima) constitute the Western Region, the three easternmost districts (Baucau, Lautem and Viqueque) the Eastern Region, and the remaining seven districts (Aileu, Ainaro, Dili, Ermera, Liquica, Manufahi and Manatuto) belong to the Central Region.

SAMPLING STRATA AND SAMPLE ALLOCATION

Our next step was to ensure that each analytical domain contained a sufficient number of households. Assuming a uniform sampling fraction of approximately 1/100, a non-stratified 1,800-household sample would contain around 240 Major Urban households and 170 Other Urban households -too few to sustain representative and significant analyses. We therefore stratified the sample to separate the two urban areas from the rural areas. The rural strata were large enough so that its implicit stratification along agro-ecological and geographical dimensions was sufficient to ensure that these dimensions were represented proportionally to their share of the population. The final sample design by strata was as follows: 450 households in the Major Urban Centers (378 in Dili and 72 in Baucau), 252 households in the Other Urban Centers and 1,098 households in the Rural Areas.

SAMPLING STRATEGY

The sampling of households in each stratum, with the exception of Urban Dili, followed a 3-stage procedure. In the first stage, a certain number of sucos were selected with probability proportional to size (PPS). Hence 4 sucos were selected in Urban Baucau, 14 in Other Urban Centers and 61 in the Rural Areas. In the second stage, 3 aldeias in each suco were selected, again with probability proportional to size (PPS). In the third stage, 6 households were selected in each aldeia with equal probability (EP). This implies that the sample is approximately selfweighted within the stratum: all households in the stratum had the same chance of being visited by the survey.

A simpler and more efficient 2-stage process was used for Urban Dili. In the first stage, 63 aldeias were selected with PPS and in the second stage 6 households with equal probability in each aldeia (for a total sample of 378 households). This procedure reduces sampling errors since the sample will be spread more than with the standard 3-stage process, but it can only be applied to Urban Dili as only there it was possible to sort the selected aldeias into groups of 3 aldeias located in close proximity of each other.

HOUSEHOLD LISTING

The final sampling stage requires choosing a certain number of households at random with equal probability in each of the aldeias selected by the previous sampling stages. This requires establishing the complete inventory of all households in these aldeias - a field task known as the household listing operation. The household listing operation also acquires importance as a benchmark for assessing the quality of the population data collected by the Suco Survey, which was conducted in February-March 2001. At that time, the number of households currently living in each aldeia was asked from the suco and aldeia chiefs, but there are reasons to suspect that these figures are biased. Specifically, certain suco and aldeia chiefs may have answered about households belonging, rather than currently living, in the aldeias, whereas others may have faced perverse incentives to report figures different from the actual ones. These biases are believed to be more serious in Dili than in the rest of the country.

Two operational approaches were considered for the household listing. One is the classical doorto-door (DTD) method that is generally used in most countries for this kind of operations. The second approach - which is specific of Timor-Leste - depends on the lists of families that are kept by most suco and aldeia chiefs in their offices. The prior-list-dependent (PLD) method is much faster, since it can be completed by a single enumerator in each aldeia, working most of the time in the premises of the suco or aldeia chief; however, it can be prone to biases depending on the accuracy and timeliness of the family lists.

After extensive empirical testing of the weaknesses and strengths of the two alternatives, we decided to use the DTD method in Dili and an improved version of the PLD method elsewhere. The improvements introduced to the PLD consisted in clarifying the concept of a household "currently living in the aldeia", both by intensive training and supervision of the enumerators and by making its meaning explicit in the form's wording (it means that the household members are regularly eating and sleeping in the aldeia at the time of the operation). In addition,

Cost of Living Index data was reported at 7,726.308 1913=1 in 2017. This records an increase from the previous number of 7,642.160 1913=1 for 2016. Cost of Living Index data is updated yearly, averaging 5.167 1913=1 from Dec 1861 (Median) to 2017, with 157 observations. The data reached an all-time high of 7,726.308 1913=1 in 2017 and a record low of 0.766 1913=1 in 1865. Cost of Living Index data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Italy – Table IT.I030: Cost of Living Index: 1913=1.

Abstract

The principal objective of this survey is to collect basic data reflecting the actual living conditions of the population in Tajikistan. These data will then be used for evaluating socio-economic development and formulating policies to improve living conditions.

The first assessment of living standards in Tajikistan was conducted in 1999. This assessment is bringing about data in order to update the 1999 assessment.

The survey collects information on education, health, employment and other productive activities, demographic characteristics, migration, housing conditions, expenditures and assets.

The information gathered is intended to improve economic and social policy in Tajikistan. It should enable decision-makers to 1) identify target groups for government assistance, 2) inform programs of socio-economic development, and 3) analyse the impact of decisions already made and the current economic conditions on households.

Geographic coverage

National coverage. The 2003 data are representative at the regional level (4 regions) and urban/rural.

Analysis unit

• Households
• Individuals
• Communites

Kind of data

Sample survey data [ssd]

Sampling procedure

The Tajikistan Living Standards Survey (TLSS) for 2003 was based on a stratified random probability sample, with the sample stratified according to oblast and urban/rural settlements and with the share of each strata in the overall sample being in proportion to its share in the total number of households as recorded in the 2000 Census. The same approach was used in the TLSS 1999 although there were some differences in the sampling. First the share of each strata in the overall sample in 1999 was determined according to ‘best estimates’, as it was conducted prior to the 2000 Census. Second the TLSS 2003 over-sampled by 40 percent in Dushanbe, 300 percent in rural Gorno-Badakhshan Administrative Oblast (GBAO) and 600 percent in urban GBAO. Third the sample size was increased in 2003 in comparison with 1999 in order to reduce sampling error. In 2003, the overall sample size was 4,156 households compared with 2,000 households in 1999. [Note: Taken from “Republic of Tajikistan: Poverty Assessment Update”, Report No. 30853, Human Development Sector Unit, Central Asia Country Unit, Europe and Central Asia Region, World Bank, January 2005.]

In addition to the capital city of Dushanbe, the country has several oblasts (regions): (i) Khatlon (comprising Kurban-Tube and Khulyab), which is an agricultural area with most of the country’s cotton growing districts; (ii) the Rayons of Republican Subordination (RRS) with the massive aluminum smelter in the west and agricultural valleys in the east growing crops other than cotton; (iii) Sugd which is the most industrialized oblast; and (iv) Gorno-Badakhshan Administrative Oblast which is mountainous and remote with a small population.

The 2003 data are representative at the regional level (4 regions) and urban/rural.

Mode of data collection

Face-to-face [f2f]

A table comparing the cost of living in various European Union countries, including expenses for rent, utilities, food, and transportation in major cities

According to the survey, as of February 2023, four out of the six countries in the Gulf Cooperation Council ranked amongst the top ** in the world for expatriate quality of life. Qatar and the United Arab Emirates topped the list for quality of life, whereas Saudi Arabia and Kuwait came last in the region. Quality of life; an amalgamation of many metrics Since quality of life is dependent on many indicators, it can give us a good insight into many aspects of state welfare policies and services. Saudi Arabia, where the number of foreign workers in the private sector topped *** million, also ranked as having one of the region's lowest quality of life for expatriates. Qatar, which had the second-highest quality of life for expatriates living in the GCC, was ranked as one of the most challenging countries in the region for ease of settling in. The UAE and Qatar, both of which ranked the highest in the survey, also have the highest average salaries and living standards in the region. Foreign workers are a key pillar of the GCC economy Countries in the GCC all have sizable expatriate populations for which their economies are heavily reliant. Roughly ********** of the workforce in the GCC is foreign. Although the share of foreign workers in the GCC has slightly decreased in recent years, they still considerably outweigh the local workforce. Most of these workers comprise the unskilled portion of the occupational category in the GCC. However, with diversifying investments and programs such as Vision 2030, countries have seen a rise in the number of skilled foreign workers.

Abstract

The General Director, the General Statistics Office (GSO), issued Decision Number 308/QD-TCTK dated 5nd April 2006 on Conducting the Vietnam Household Living Standards Survey in 2006 (VHLSS 2006) in order to collect necessary information for monitoring, supervising and evaluating the implementation of the “Comprehensive Strategy for Growth and Poverty Alleviation” approved by the Government Prime Minister. The survey was conducted nation-wide, involving a sample scale of 45,945 households (36,756 households for income survey, 9,189 households for income and expenditure survey) in 3,063 communes/wards, representative for whole country, 8 regions, urban/ rural area and provinces. Organizationally, the survey was conducted to collect information in 2 rounds, 2006 and by direct interviews with headed households and key commune officials.

Geographic coverage

The VHLSS 2006 was conducted nation-wide, involving a sample scale of 45,945 households (36,756 households for income survey, 9,189 households for income and expenditure survey) in 3,063 communes/wards, representative for whole country, 8 regions, urban/ rural area and provinces.

Analysis unit

• Households
• Individuals
• Communes

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

Survey sample was selected, based on the Population and Housing Census 1999. The sample size included 45,900 households representative of the whole country, urban and rural area and 64 provinces. Survey sample was divided into 2 types: 36,720 households would be surveyed on income and 9,180 households would be surveyed on income and expenditures. The survey sample was sub-divided into 2 minor samples for data collection in 2 stages: the first in May 2006 and the second in September 2006.

Mode of data collection

Face-to-face [f2f]

Research instrument

1. Income and Expenditure questionnaire
2. Income questionnaire
3. Commune questionnaire

Cleaning operations

Data editing took place at a number of stages throughout the processing, including: a) Office editing and coding b) During data entry c) Structure checking and completeness d) Secondary editing e) Structural checking of Stata data files Detailed documentation of the editing of data can be found in the "Data processing guidelines" document provided as an external resource.

Gross domestic product (GDP) per capita is a crucial economic indicator that represents the average economic output per person in a given country or region. It is calculated by dividing the total GDP by the population size. This metric is often used to compare the economic performance of different countries and assess the relative prosperity of their citizens. Two commonly used versions of this indicator are GDP per capita at current prices and GDP per capita adjusted for purchasing power parity (PPP). GDP per capita at current prices reflects the total economic output of a country divided by its population, using the market prices of goods and services at the time of measurement. This metric provides a snapshot of the economic activity within a country without adjusting for inflation or differences in the cost of living across regions. Global GDP per capita at current prices (PPP) provides a measure of the average economic output per person, adjusted for the differences in the cost of living between countries. This adjustment allows for a more accurate comparison of living standards and economic productivity across different nations.

Abstract

In 1992, Bosnia-Herzegovina, one of the six republics in former Yugoslavia, became an independent nation. A civil war started soon thereafter, lasting until 1995 and causing widespread destruction and losses of lives. Following the Dayton accord, BosniaHerzegovina (BiH) emerged as an independent state comprised of two entities, namely, the Federation of Bosnia-Herzegovina (FBiH) and the Republika Srpska (RS), and the district of Brcko. In addition to the destruction caused to the physical infrastructure, there was considerable social disruption and decline in living standards for a large section of the population. Alongside these events, a period of economic transition to a market economy was occurring. The distributive impacts of this transition, both positive and negative, are unknown. In short, while it is clear that welfare levels have changed, there is very little information on poverty and social indicators on which to base policies and programs. In the post-war process of rebuilding the economic and social base of the country, the government has faced the problems created by having little relevant data at the household level. The three statistical organizations in the country (State Agency for Statistics for BiH -BHAS, the RS Institute of Statistics-RSIS, and the FBiH Institute of Statistics-FIS) have been active in working to improve the data available to policy makers: both at the macro and the household level. One facet of their activities is to design and implement a series of household series. The first of these surveys is the Living Standards Measurement Study survey (LSMS). Later surveys will include the Household Budget Survey (an Income and Expenditure Survey) and a Labour Force Survey. A subset of the LSMS households will be re-interviewed in the two years following the LSMS to create a panel data set.

The three statistical organizations began work on the design of the Living Standards Measurement Study Survey (LSMS) in 1999. The purpose of the survey was to collect data needed for assessing the living standards of the population and for providing the key indicators needed for social and economic policy formulation. The survey was to provide data at the country and the entity level and to allow valid comparisons between entities to be made. The LSMS survey was carried out in the Fall of 2001 by the three statistical organizations with financial and technical support from the Department for International Development of the British Government (DfID), United Nations Development Program (UNDP), the Japanese Government, and the World Bank (WB). The creation of a Master Sample for the survey was supported by the Swedish Government through SIDA, the European Commission, the Department for International Development of the British Government and the World Bank. The overall management of the project was carried out by the Steering Board, comprised of the Directors of the RS and FBiH Statistical Institutes, the Management Board of the State Agency for Statistics and representatives from DfID, UNDP and the WB. The day-to-day project activities were carried out by the Survey Management Team, made up of two professionals from each of the three statistical organizations. 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, labour) 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 analysed data.

Geographic coverage

National coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) SAMPLE SIZE A total sample of 5,400 households was determined to be adequate for the needs of the survey: with 2,400 in the Republika Srpska and 3,000 in the Federation of BiH. The difficulty was in selecting a probability sample that would be representative of the country's population. The sample design for any survey depends upon the availability of information on the universe of households and individuals in the country. Usually this comes from a census or administrative records. In the case of BiH the most recent census was done in 1991. The data from this census were rendered obsolete due to both the simple passage of time but, more importantly, due to the massive population displacements that occurred during the war. At the initial stages of this project it was decided that a master sample should be constructed. Experts from Statistics Sweden developed the plan for the master sample and provided the procedures for its construction. From this master sample, the households for the LSMS were selected. Master Sample [This section is based on Peter Lynn's note "LSMS Sample Design and Weighting - Summary". April, 2002. Essex University, commissioned by DfID.] The master sample is based on a selection of municipalities and a full enumeration of the selected municipalities. Optimally, one would prefer smaller units (geographic or administrative) than municipalities. However, while it was considered that the population estimates of municipalities were reasonably accurate, this was not the case for smaller geographic or administrative areas. To avoid the error involved in sampling smaller areas with very uncertain population estimates, municipalities were used as the base unit for the master sample. The Statistics Sweden team proposed two options based on this same method, with the only difference being in the number of municipalities included and enumerated.

(b) SAMPLE DESIGN For reasons of funding, the smaller option proposed by the team was used, or Option B. Stratification of Municipalities The first step in creating the Master Sample was to group the 146 municipalities in the country into three strata- Urban, Rural and Mixed - within each of the two entities. Urban municipalities are those where 65 percent or more of the households are considered to be urban, and rural municipalities are those where the proportion of urban households is below 35 percent. The remaining municipalities were classified as Mixed (Urban and Rural) Municipalities. Brcko was excluded from the sampling frame. Urban, Rural and Mixed Municipalities: It is worth noting that the urban-rural definitions used in BiH are unusual with such large administrative units as municipalities classified as if they were completely homogeneous. Their classification into urban, rural, mixed comes from the 1991 Census which used the predominant type of income of households in the municipality to define the municipality. This definition is imperfect in two ways. First, the distribution of income sources may have changed dramatically from the pre-war times: populations have shifted, large industries have closed, and much agricultural land remains unusable due to the presence of land mines. Second, the definition is not comparable to other countries' where villages, towns and cities are classified by population size into rural or urban or by types of services and infrastructure available. Clearly, the types of communities within a municipality vary substantially in terms of both population and infrastructure. However, these imperfections are not detrimental to the sample design (the urban/rural definition may not be very useful for analysis purposes, but that is a separate issue).

Mode of data collection

Face-to-face [f2f]

Cleaning operations

(a) DATA ENTRY

An integrated approach to data entry and fieldwork was adopted in Bosnia and Herzegovina. Data entry proceeded side by side with data gathering to ensure verification and correction in the field. Data entry stations were located in the regional offices of the entity institutes and were equipped with computers, modem and a dedicated telephone line. The completed questionnaires were delivered to these stations each day for data entry. Twenty data entry operators (10 from Federation and 10 from RS) were trained in two training sessions held for a week each in Sarajevo and Banja Luka. The trainers were the staff of the two entity institutes who had undergone training in the CSPro software earlier and had participated in the workshops of the Pilot survey. Prior to the training, laptop computers were provided to the entity institutes, and the CSPro software was installed in them. The training for the data entry operators covered the following elements:

• Introduction to the LSMS Survey questionnaire; Introduction to the personal computers/ lap top computers; Copying data on diskette and printing of output
• The Data entry programme (CSPro). Understanding of the Round 1 data entry screens (Modules 1-10)
• Practice of Round 1 (data entry trainees enter questionnaires completed by interviewer trainees during practice interviews)
• Understanding of Round 2 Data entry screen (Modules 11-13)
• Practice of Round 2 Data entry screens (data entry trainees entered the questionnaires completed by interviewer trainees)
• Control Procedures; Copying

The Cost of living rating evaluates how much ordinary living expenses cost in different countries, including food, housing, necessary goods, services, medical insurance and other aspects.

Abstract

The purpose of the Tajikistan LSS surveys has been to provide quantitative data at the individual, household and community level that will facilitate purposeful policy design on issues of welfare and living standards of the population of the Republic of Tajikistan. Since 2007, the studies have been done in collaboration with World Bank and UNICEF and implementation by Tajik National Committee for Statistics. The 2007 LSS survey is based on the 2003 LSS and 2005 MICS survey with additional questions and modules

Geographic coverage

National

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

A detailed description of the sampling methodology is available in appendix to the document "Basic Information Document".

The Tajikistan LSS sample was designed to allow reliable estimation of poverty and most variables for a variety of other living standard indicators at the various domains of interest based on a representative probability sample on the level of: • Tajikistan as a whole
• Total urban and total rural areas • The five main administrative regions (oblasts) of the country: Dushanbe, Rayons of Republican Subordination (RRS), Sogd, Khatlon, and Gorno-Badakhshan Autonomous Oblast (GBAO)

The last census was conducted in 2000 and covered all five main administrative regions (oblasts) of the country (Dushanbe, RRS, Sogd, Khatlon, and GBAO). Each oblast was further subdivided into smaller areas called census section, instructor's sector and enumeration sector (ES). Each ES is either totally urban or rural. The list of ESs has census information on the population of each ES, and the ES lists were grouped by oblast.

In 2005, UNICEF implemented a Multiple Indicator Cluster Survey (MIC-05) in Tajikistan during which an electronic database of the ES information was created. Information in this database included: oblast, rayon, jamoat, settlement type, city/village, ES code, and population. Information from this database was used in the sample design of the TLSS07.

The total number of clusters for the Tajikistan LSS 2007 was established as 270 and total number of households per cluster was established as 18, resulting in a sample size of 4,860. The sample size was determined by considering: • The reliability of the survey estimates on both regional and national level • Quality of the data collected for the survey • Cost in time for the data collection • An oversample in 7 rayons in Khatlon

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data Entry and Cleaning

The data entry program was designed using CSPro, a data entry package developed by the US Census Bureau. This software allows programs to be developed to perform three types of data checks: (a) range checks; (b) intra-record checks to verify inconsistencies pertinent to the particular module of the questionnaire; and (c) inter-record checks to determine inconsistencies between the different modules of the questionnaire.

The data from the First Round were key entered at the Goskomstat headquarters in Dushanbe starting 4 October 2007 through 25 November 2007. The Second Round and Sughd data were key entered from 26 November 2007 through 12 December 2007. All of the data were double entered with both the First Round, Second Round and Sughd re-collection double entry being completed by 22 January 2008.

Data appraisal

The data cleaning process began in February 2008 and was completed at the end of May 2008.

How to Use the Data:

There are three separate data bases with the data from the TLSS07. The data from each data collection is maintained separately. The data sets have similar names in each of the three separate data collections. First Round data sets have names in the form of "r1mnp" where "n" is the number of the module, and "p" is the part of the module (if any). Data from the Subjective Poverty module would be stored as "r1m9" and data from the Migration module, Part C Family Members Living Away from the Household would be stored as "r1m2c". Second Round data set names have a similar form "r2mnp". Data sets from the Sughd collection replace the "m" of the First Round with "sm", such as sm12a1.

The variable names have a similar format. Each variable name includes the module in which the variable is found and the question number. For example, question 10 in Module 4 Health, Part B Utilization of Outpatient Health Care is "m4b_q10". The variable names in all three of the data collections have the same format.

In addition to the individual roster files for each data base, there is also one roster file for all three data bases, rosterall. This roster file contains the information on all of the households and household members who are included in the data. There is a variable (source) indicating if the household/member is: (a) in Round 1 only; (b) in Round 2 only; (c) in Round 1 and Round 2; or (d) in the Sughd data. It is important to pay attention to this variable as the recall periods for the Subjective Poverty and Food Security Module (9A) is the last 4 weeks in the First Round, but changed to the last 2 weeks in the Second Round and the Sughd collection. In addition, the order of the question in the Expenditure On Food In The Last 7 Days, Module 10, changed

Abstract

The Integrated Survey of Living Standards (ISLS), renamed in 2004 to Integrated Survey of Living Conditions Survey (ILCS) is conducted annually by the NSS National Statistical Service of the Republic of Armenia, formed the basis for monitoring living conditions in Armenia. The ILCS is a universally recognized best-practice survey for collecting data to inform about the living standards of households. The ILCS comprises comprehensive and valuable data on the welfare of households and separate individuals which gives the NSS an opportunity to provide the public with up to date information on the population’s income, expenditures, the level of poverty and the other changes in living standards on an annual basis.

Geographic coverage

Urban and rural communities

Analysis unit

• Households;
• Individuals.

Kind of data

Sample survey data [ssd]

Sampling procedure

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

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

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

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

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

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

Mode of data collection

Face-to-face [f2f]

Research instrument

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

The 2001 survey questionnaire had the following sections: (1) "List of Household Members", (2) "Housing Facilities", (3) "Migration", (4) "Education", (5) "Agriculture", (6) "Monetary and Commodity Flows between Households", (7) "Health (General) and Healthcare", (8) "Savings and Debts", (9) "Social Assistance".

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

The Survey Diary has the following sections: (1) food purchased during the day, (2) food consumed at home

In 2025, Luxembourg was the country with the highest gross domestic product per capita in the world. Of the 20 listed countries, 13 are in Europe and five are in Asia, alongside the U.S. and Australia. There are no African or Latin American countries among the top 20. Correlation with high living standards While GDP is a useful indicator for measuring the size or strength of an economy, GDP per capita is much more reflective of living standards. For example, when compared to life expectancy or indices such as the Human Development Index or the World Happiness Report, there is a strong overlap - 14 of the 20 countries on this list are also ranked among the 20 happiest countries in 2024, and all 20 have "very high" HDIs. Misleading metrics? GDP per capita figures, however, can be misleading, and to paint a fuller picture of a country's living standards then one must look at multiple metrics. GDP per capita figures can be skewed by inequalities in wealth distribution, and in countries such as those in the Middle East, a relatively large share of the population lives in poverty while a smaller number live affluent lifestyles.

The average for 2021 based on 20 countries was 97.17 index points. The highest value was in Bermuda: 212.7 index points and the lowest value was in Nicaragua: 49.42 index points. The indicator is available from 2017 to 2021. Below is a chart for all countries where data are available.

Abstract

The first Vietnam Living Standards Survey (VLSS) was conducted in 1992-93 by the State Planning Committee (SPC) (now Ministry of Planning and Investment) along with the General Statistical Office (GSO). The second VLSS was conducted by the GSO in 1997-98. Both VLSS surveys were funded by UNDP and Swedish International Development Authority (SIDA). The survey was part of the Living Standards Measurement Study (LSMS) household surveys conducted in a number of developing countries with technical assistance from the World Bank.

The second VLSS was designed to provide an up-to-date source of data on households to be used in policy design, monitoring of living standards and evaluation of policies and programs. The timing of the second VLSS approximately five years after the first allows analysis of medium term trends in living standards as a large part of the questionnaire is the same in both surveys.

In addition to the purpose of obtaining a comprehensive and comparable data set to the 1992-93 VLSS for policy analysis, the survey also served as a medium for training and improving survey methods and analysis within the General Statistical Office of Vietnam (GSO), the agency in charge of designing and implementing the second round of the VLSS as well as other government agencies involved in social statistics.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Community
• Schools
• Health Centers

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey sample was selected to be representative for the whole country, taking into account available funding, geographical conditions, organizational capacity and staff competence. The sample size was set at 6000 households selected from provinces and cities throughout the country, but excluding islands due to logistical difficulties in traveling and conducting the survey in those locations.

The sample for the 1997-1998 VLSS was primarily selected from the households selected in the original 150 communes/wards of the 1992-1993 VLSS. The sample was increased by 1200 households with these additional households obtained from the sample of the Multi-purpose Household survey (MPHS) which was based on a similar sampling methodology. Replacement households were selected randomly from within the clusters of the survey and used where necessary.

The selection of the original sample of 4800 households from VLSS 1992-1993 followed a method of stratified random cluster sampling. The basic sample frame was obtained from the 1989 Population Census. The sampling procedures took into account that communes or wards are the basic local level administrative unit, and each commune/ward has a number of villages or urban residential blocks. The number of households selected in a given cluster was determined primarily based on the requirements for organization of interview teams and time needed for each household interview on location.

Based on the sampling frame including two lists, list of communes and list of wards (or equivalent administrative units) throughout the country with the number of households in each commune/ward obtained from the 1989 Population Census, the sample of the 1992-1993 VLSS was selected in three steps, independently for urban and rural areas:

Step 1: Random selection of 120 communes and 30 wards throughout the country based on the method of probability proportional to the number of households in those villages or wards. The selection of primary sampling units (communes) was stratified by urban and rural areas based on the results of the 1989 Census that 80% of the population was living in rural areas and 20% in urban areas.

Step 2: Within each selected commune, two villages or urban residential blocks were selected randomly by the method of probability proportional to the number of households as in the first stage of sampling. Thus, 240 villages and 60 residential blocks were selected.

Step 3: Within each selected village or residential block, 20 households were randomly selected by systematic method with equal probability, including 16 official and 4 alternate households. To eliminate the effect of the seasonal differences, the rotation method of sample was adopted: the 6000 surveyed households were divided into 10 sub-samples and each sub-sample was surveyed for one month.

Sampling procedure is explained in details in the document called "Vietnam Living Standards Survey (VLSS), 1997-98", available in this documentation.

Mode of data collection

Face-to-face [f2f]

Research instrument

The second round of the VLSS used 5 questionnaires: household, commune, price, school and clinic. - Household Questionnaire: The household questionnaire contains 15 sections each of which covered a separate aspect of household activity.

• Commune/Ward Questionnaire: A completely new commune questionnaire was developed for the 1997-98 VLSS survey with a greatly expanded content. A few questions in the 1992-93 questionnaire were dropped or moved to other questionnaires (see below). The commune questionnaire was administered by the team supervisor and completed with the help of village chiefs, teachers, government officials and health care workers. The questionnaire was administered only in rural and minor urban areas, i.e. communes 37 to 194, corresponding to villages 73 to 388. Some sections of the questionnaire contain village/block level information, while most of the commune questionnaire refers to the commune. The commune questionnaire contains 10 sections.

• Price Questionnaire: Price data were collected in all clusters, both urban and rural by the anthropometrist for 36 food items, 33 non-food items, 6 services, 10 pharmaceutical products, and 7 agricultural inputs. Three separate observations were made and these did not necessarily involve actual purchase. However, it is possible that as the anthropometrist is not a local person, the prices quoted are not the true prices of the locality. This information was utilized in checking unit prices in the consumption modules, and for calculating poverty lines. Price indices utilized for adjusting monetary figures to real values were obtained from the GSO CPI unit. Details on how and where prices were to be collected can be found in the anthropometry manual. The actual locations of price collection were recorded in the questionnaires, but unfortunately not entered in the computer files.

• School Questionnaire: The school questionnaires were implemented by the team supervisor to all schools within the two villages selected within a commune. There are between 1 and 7 school questionnaires filled in per commune.

• Commune Health Station Questionnaire: The commune health station questionnaire was implemented by the team supervisor. The respondent could be the director, doctor or physician’s assistant of the health station.

Response rate

Response rates are shown in details in the document called "Vietnam Living Standards Survey (VLSS), 1997-98 Basic Information", available in this documentation.

In the implementation of the Party and State policy “Doi moi”, the General Statistical Office (GSO) has conducted many household living standards survey to collect information on the living standards of all social societies to serve policy-making and socio-economic development planning. From 2002 to 2010, VHLSS are to be conducted (in every two- year) to monitor systematically the living standard of Vietnam's societies and at the same time, to exercise the monitoring and assessment of the implementation of the Comprehensive Poverty Alleviation and Growth Strategy defined in Country Strategy Paper approved by the Government Prime Minister. In addition, these surveys also serve the evaluation of realization of the Millennium Development Goals (MDGs) and the Socio-economic Development Goals set out by Vietnamese Government. The 2002 VHLSS included all the keynote contents reflecting the living standards of the population and the basic socio-economic condition of communes/wards that might affect the living standards of the local people. As regards households, it collected data in relation to demographic characteristics of the household members, the education background, professional/ technical level of each member, income, expenditures, use of medical facilities of all kinds, employment, housing and amenity as possession, personal effects, utilities (power and water supply), sanitation and participation in the poverty alleviation programme. As regards communes/wards, it collected a wide rage of information related to demography, nationality, infrastructure, farming, production promotion conditions, non-farming activity and law and order. Household questionnaires and communes/wards questionnaires of the 2002 VHLSS were designed more scientifically to ensure feasibility. They had been, in fact, piloted in Bac Ninh, Binh Dinh and Dong Nai provinces prior to the actual survey. Survey sample were selected, based on the Population and Housing Census 1999. The sample size included 75.000 households representative of the whole country, urban and rural area and 61 provinces. Survey samples were sub-divided into 4 minor samples for the quarterly surveys in 2002 for more thorough data collection in anticipation of the harvests that might somehow get in the way. To provide information on assessment of the living standards in 2001-2002, GSO developed and released the detailed results of the 2002 VHLSS, including relevant statistics and initial analysis. Expenditure related data were synthesized from samples of 30.000 households; others, from samples of 45.000 households. To bring out the changes in the living standards, the 2002 VHLSS results were compared with the results obtained from other living standards surveys, e.g. the 1992-1993 living standards survey (1993 VLSS), the 1993 rich-poor status survey (1993 RPSS), the 1997-1998 living standards surveys (1998 VLSS), the Multipurpose household surveys throughout 1994 to 1997 and 1999 (MHPS) The data on demography, labor, and employment ect… were collected from the 2002 VHLSS, not replace all the data already released from the surveys conducted in this area, but to shed more light on and make more on insightful analyses of the factors affecting the living standards.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Communites

Kind of data

Sample survey data [ssd]

Sampling procedure

A detailed description of the sampling methodology is available in appendix to the document "Basic Information Document".

The TLSS sample was designed to allow reliable estimation of poverty and most variables for a variety of other living standard indicators at the various domains of interest based on a representative probability sample on the level of: • Tajikistan as a whole
• Total urban and total rural areas • The five main administrative regions (oblasts) of the country: Dushanbe, Rayons of Republican Subordination (RRS), Sogd, Khatlon, and Gorno-Badakhshan Autonomous Oblast (GBAO)

The last census was conducted in 2000 and covered all five main administrative regions (oblasts) of the country (Dushanbe, RRS, Sogd, Khatlon, and GBAO). Each oblast was further subdivided into smaller areas called census section, instructor's sector and enumeration sector (ES). Each ES is either totally urban or rural. The list of ESs has census information on the population of each ES, and the ES lists were grouped by oblast.

In 2005, UNICEF implemented a Multiple Indicator Cluster Survey (MICS05) in Tajikistan during which an electronic database of the ES information was created. Information in this database included: oblast, rayon, jamoat, settlement type, city/village, ES code, and population. Information from this database was used in the sample design of the TLSS07.

The total number of clusters for the TLSS07 was established as 270 and total number of households per cluster was established as 18, resulting in a sample size of 4,860. The sample size was determined by taking into account: • The reliability of the survey estimates on both regional and national level • Quality of the data collected for the survey • Cost in time for the data collection • An oversample in 7 rayons in Khatlon

The final cluster allocation is as follows:

Region: Urban / Rural / Total Dushanbe 50 / 0 / 50 RRP 9 / 45 / 54 Sogd 18 / 38 / 56 Khatlon 12 / 59 / 71 GBAO 6 / 33 / 39 Total 95 / 175 / 270

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used to collect information for the TLSS07: a household questionnaire, a female questionnaire for recording information about women of child bearing age, and a community questionnaire. These questionnaires were based on the TLSS questionnaires used in 2003, but had some changes. Questions were added to existing modules and new modules were added to collect information to be used for MICS analyses. These included HIV/AIDS awareness, and Immunizations and Anthropometric Measurements for children 0 to 5 years old. Other new modules on Migration, Financial Services, Subjective Poverty and Food Security, and Subjective Beliefs were also added. The Labor Market Module was changed substantially from 2003 to better look at the informal labor market. The food expenditures module included additional food products. The HIV/AIDS questions were removed from the female questionnaire and were applied to all household members 12 to 49 years old.

The Second Round Household Questionnaire was shorter and was used primarily to collect additional information that was not possible to collect in the First Round. Because the First Round questionnaire was very long, it was decided to collect some information in a second round of visits to the households. The Household Questionnaire was the main instrument used during the Second Round. The female questionnaire was only used if females were added to the household after the First Round and the community questionnaire was not repeated. In the Second Round Household Questionnaire, the time reference period for the Food Security module was reduced from 4 weeks to 2 weeks. This was done because in the households visited at the beginning of the Second Round, a 4 week period would have included the last portion of the Ramadan period.

Cleaning operations

Data Entry and Cleaning

The data entry program was designed using CSPro, a data entry package developed by the US Census Bureau. This software allows programs to be developed to perform three types of data checks: (a) range checks; (b) intra-record checks to verify inconsistencies pertinent to the particular module of the questionnaire; and (c) inter-record checks to determine inconsistencies between the different modules of the questionnaire.

The data from the First Round were key entered at the Goskomstat headquarters in Dushanbe starting 4 October 2007 through 25 November 2007. The Second Round and Sughd data were key entered from 26 November 2007 through 12 December 2007. All of the data were double entered with both the First Round, Second Round and Sughd re-collection double entry being completed by 22 January 2008.

The data cleaning process began in February 2008 and was completed at the end of May 2008.