Household income is a potential predictor for a number of environmental influences, for example, application of urban pesticides. This product is a U.S. conterminous mapping of block group income derived from the 2010-2014 Census American Community Survey (ACS), adjusted by a 2013 county-level Cost-of-Living index obtained from the Council for Community and Economic Research. The resultant raster is provided at 200-m spatial resolution, in units of adjusted household income in thousands of dollars per year.

Abstract copyright UK Data Service and data collection copyright owner.

The Living Costs and Food Survey, 2013: Unrestricted Access Teaching Dataset is based on the Living Costs and Food Survey, 2013 (LCF), available from the UK Data Archive under SN 7702, and constitutes real data which are used by government, business and other organisations. The teaching dataset is a subset which has been subjected to certain simplifications and additions for the purpose of learning and teaching.

The main differences are:

• the number of variables has been reduced
• a reduced User Guide with codebook is provided
• some variables have been recoded to reduce the level of detail

Further information is available in the study documentation (see below) which includes a User Guide.

For the second edition (August 2016) a new weighting variable has been added to the data and the user guide has been updated accordingly.
Main Topics:

Topics covered include: total expenditure; household income; socio-economic status; and household characteristics.

This web mapping service contains data from the American Community Survey (ACS), which is an ongoing survey that provides data every year in order to give communities the current information they need to plan investments and services. Information from the survey generates data that help determine how more than $400 billion in federal and state funds are distributed each year. This survey contains information about the age, sex, race, family and relationships, income and benefits, health insurance, education, veteran status, disabilities and the cost of living of the communities surveyed. The Census ACS 2013 WMS web mapping service contains data as of January 1, 2013.

Taiwan Product Price Index: Paid by Farmer: Living Cost data was reported at 102.910 2006=100 in Feb 2013. This records an increase from the previous number of 102.420 2006=100 for Jan 2013. Taiwan Product Price Index: Paid by Farmer: Living Cost data is updated monthly, averaging 104.193 2006=100 from Jan 2003 (Median) to Feb 2013, with 122 observations. The data reached an all-time high of 112.380 2006=100 in Aug 2012 and a record low of 93.672 2006=100 in Mar 2003. Taiwan Product Price Index: Paid by Farmer: Living Cost data remains active status in CEIC and is reported by Council of Agriculture, Executive Yuan. The data is categorized under Global Database’s Taiwan – Table TW.I057: Product Price Index: 2006=100: Paid by Farmer: Council of Agriculture.

Average monthly disposable salary Years: 2013-2014 DEFINITION: Average Monthly Disposable Salary (After Tax). Based on 0-50 contributions for Afghanistan, Aland Islands, Andorra and 81 more countries and 50-100 contributions for Albania, Algeria, Armenia and 19 more countries and over 100 contributions for Argentina, Australia, Austria and 82 more countries. The surveys were conducted by numbeo.com from May, 2011 to February, 2014. See this sample survey for the United States, respondents were asked "Average Monthly Disposable Salary (After Tax)". Prices in current USD.

Source: https://www.nationmaster.com/country-info/stats/Cost-of-living/Average-monthly-disposable-salary/After-tax#

This data set describes the share of children living in households where more than 30 percent of the monthly income was spent on rent, mortgage payments, taxes, insurance, and/or related expenses in the State of Utah.

Housing-Burdened Low-Income Households. Percent of households in a census tract that are both low income (making less than 80% of the HUD Area Median Family Income) and severely burdened by housing costs (paying greater than 50% of their income to housing costs). (5-year estimates, 2013-2017).

The cost and availability of housing is an important determinant of well- being. Households with lower incomes may spend a larger proportion of their income on housing. The inability of households to afford necessary non-housing goods after paying for shelter is known as housing-induced poverty. California has very high housing costs relative to much of the country, making it difficult for many to afford adequate housing. Within California, the cost of living varies significantly and is largely dependent on housing cost, availability, and demand.

Areas where low-income households may be stressed by high housing costs can be identified through the Housing and Urban Development (HUD) Comprehensive Housing Affordability Strategy (CHAS) data. We measure households earning less than 80% of HUD Area Median Family Income by county and paying greater than 50% of their income to housing costs. The indicator takes into account the regional cost of living for both homeowners and renters, and factors in the cost of utilities. CHAS data are calculated from US Census Bureau's American Community Survey (ACS).

Malaysia MOF Projection: CGE: Current: SS: Travel Expenses &Cost of Living data was reported at 908.286 MYR mn in 2019. This records a decrease from the previous number of 986.314 MYR mn for 2018. Malaysia MOF Projection: CGE: Current: SS: Travel Expenses &Cost of Living data is updated yearly, averaging 1,286.611 MYR mn from Dec 2010 (Median) to 2019, with 9 observations. The data reached an all-time high of 2,491.400 MYR mn in 2013 and a record low of 754.463 MYR mn in 2017. Malaysia MOF Projection: CGE: Current: SS: Travel Expenses &Cost of Living data remains active status in CEIC and is reported by Ministry of Finance. The data is categorized under Global Database’s Malaysia – Table MY.F007: Central Government Expenditure: Annual: Projection: Ministry of Finance.

This dataset tracks annual reduced-price lunch eligibility from 2013 to 2023 for Jubilee Living Way vs. Texas and Jubilee Academies School District

Graph and download economic data for Expenses for Assisted Living Facilities for The Elderly, All Establishments, Employer Firms (ALFFTEEAEEF3623312) from 2013 to 2022 about elderly, assistance, employer firms, establishments, expenditures, and USA.

The GLA undertake regular polling of Londoners views both online and by telephone. A representative sample is interviewed, with quotas set by age, gender and borough. The results from these polls appear on this page.

April 2009 - Economic outlook, and the Mayor's role

Link to PDF of toplines (PDF)

Link to PDF tables (PDF)

June 2009 - Quality of life

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November 2009 - Waste and recycling

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Link to PDF tables (PDF)

March 2010 - Culture

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May 2010 - Climate Change

Link to PDF of toplines (PDF)

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August 2010 - Energy, and Safety in Parks

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December 2010 - Mayoral Priorities

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March 2011 - Volunteering

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June 2011 - Housing, economy, sport, 2012 games

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September 2011 - Community cohesion and festivals

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November 2011 - Economy, community cohesion, young people, sports

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February 2012 - Economy and volunteering

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June 2012 - Economy and Londoners priorities

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October 2012 - Economy, Mayoral responsibilities and 2012 Games

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January 2013 - Economy, apprenticeships, aiport, housing and EU

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Link to Excel tables (XLS)

March 2013 – Economy, volunteering, ULEZ, stamp duty, cycling

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June 2013 - Economy, culture and community cohesion

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September 2013 - Economy, Mayoral responsibilities

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November 2013 - Economy, cost of living, technology and aiports

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Link to Excel tables (XLS)

February 2014 - Water Cannon

Link to Data Full Tables (XLS)

Tables – rebased (XLS)

Tables - ethnicity (XLS)

Tables - summary (XLS)

February 2014 - Economy, cost of living, priorities and culture

Link to PDF of toplines (PDF)

Link to Excel tables (XLS)

March 2014 - Health Survey

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May 2014 - Priorities for Safety

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June 2014 - Economy, cost of living, personal finance, housing and airports

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August 2014 - Health Survey

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September 2014 - Awareness, sources, carrier bags and big dance

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January 2015 – Economy, cost of living, living wage, affordable eating, cooking fats, physical activity major events

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March 2015 - Growth, recycling and reuse

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Inflation is generally defined as the continued increase in the average prices of goods and services in a given region. Following the extremely high global inflation experienced in the 1980s and 1990s, global inflation has been relatively stable since the turn of the millennium, usually hovering between three and five percent per year. There was a sharp increase in 2008 due to the global financial crisis now known as the Great Recession, but inflation was fairly stable throughout the 2010s, before the current inflation crisis began in 2021. Recent years Despite the economic impact of the coronavirus pandemic, the global inflation rate fell to 3.26 percent in the pandemic's first year, before rising to 4.66 percent in 2021. This increase came as the impact of supply chain delays began to take more of an effect on consumer prices, before the Russia-Ukraine war exacerbated this further. A series of compounding issues such as rising energy and food prices, fiscal instability in the wake of the pandemic, and consumer insecurity have created a new global recession, and global inflation in 2024 is estimated to have reached 5.76 percent. This is the highest annual increase in inflation since 1996. Venezuela Venezuela is the country with the highest individual inflation rate in the world, forecast at around 200 percent in 2022. While this is figure is over 100 times larger than the global average in most years, it actually marks a decrease in Venezuela's inflation rate, which had peaked at over 65,000 percent in 2018. Between 2016 and 2021, Venezuela experienced hyperinflation due to the government's excessive spending and printing of money in an attempt to curve its already-high inflation rate, and the wave of migrants that left the country resulted in one of the largest refugee crises in recent years. In addition to its economic problems, political instability and foreign sanctions pose further long-term problems for Venezuela. While hyperinflation may be coming to an end, it remains to be seen how much of an impact this will have on the economy, how living standards will change, and how many refugees may return in the coming years.

United States CPI U: AW: Housing: HFO: FB: Living Room, Kitchen & Dining Furniture data was reported at 0.347 % in 2017. This records a decrease from the previous number of 0.359 % for 2016. United States CPI U: AW: Housing: HFO: FB: Living Room, Kitchen & Dining Furniture data is updated yearly, averaging 0.473 % from Dec 1997 (Median) to 2017, with 21 observations. The data reached an all-time high of 0.621 % in 1998 and a record low of 0.330 % in 2013. United States CPI U: AW: Housing: HFO: FB: Living Room, Kitchen & Dining Furniture data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.I011: Consumer Price Index: Urban: Weights (Annual).

United States CPI UW: Housing: HFO: FB: Living Room, Kitchen & Dining Furniture data was reported at 0.428 % in Jun 2018. This records a decrease from the previous number of 0.430 % for May 2018. United States CPI UW: Housing: HFO: FB: Living Room, Kitchen & Dining Furniture data is updated monthly, averaging 0.460 % from Jan 1998 (Median) to Jun 2018, with 246 observations. The data reached an all-time high of 0.629 % in Apr 1998 and a record low of 0.330 % in Dec 2013. United States CPI UW: Housing: HFO: FB: Living Room, Kitchen & Dining Furniture data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.I010: Consumer Price Index: Urban: Weights.

This dataset tracks annual reduced-price lunch eligibility from 2013 to 2023 for School Of Math Science And Healthy Living vs. New York and New York City Geographic District #20 School District

Details of solar hot water systems installed for which a rebate was paid.\r \r This dataset was last updated in 2013\r \r For current energy concession programs visit https://www.qld.gov.au/community/cost-of-living-support/energy-concessions.\r

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 50% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE CENTRAL AGENCY FOR PUBLIC MOBILIZATION AND STATISTICS (CAPMAS)

The Household Income, Expenditure and Consumption Survey (HIECS) is of great importance among other household surveys conducted by statistical agencies in various countries around the world. This survey provides a large amount of data to rely on in measuring the living standards of households and individuals, as well as establishing databases that serve in measuring poverty, designing social assistance programs, and providing necessary weights to compile consumer price indices, considered to be an important indicator to assess inflation.

The First Survey that covered all the country governorates was carried out in 1958/1959 followed by a long series of similar surveys . The current survey, HIECS 2012/2013, is the eleventh in this long series.

Starting 2008/2009, Household Income, Expenditure and Consumption Surveys were conducted each two years instead of five years. this would enable better tracking of the rapid changes in the level of the living standards of the Egyptian households.

CAPMAS started in 2010/2011 to follow a panel sample of around 40% of the total household sample size. The current survey is the second one to follow a panel sample. This procedure will provide the necessary data to extract accurate indicators on the status of the society. The CAPMAS also is pleased to disseminate the results of this survey to policy makers, researchers and scholarly to help in policy making and conducting development related researches and studies

The survey main objectives are:

• To identify expenditure levels and patterns of population as well as socio- economic and demographic differentials.

• To measure average household and per-capita expenditure for various expenditure items along with socio-economic correlates.

• To Measure the change in living standards and expenditure patterns and behavior for the individuals and households in the panel sample, previously surveyed in 2008/2009, for the first time during 12 months representing the survey period.

• To define percentage distribution of expenditure for various items used in compiling consumer price indices which is considered important indicator for measuring inflation.

• To estimate the quantities, values of commodities and services consumed by households during the survey period to determine the levels of consumption and estimate the current demand which is important to predict future demands.

• To define average household and per-capita income from different sources.

• To provide data necessary to measure standard of living for households and individuals. Poverty analysis and setting up a basis for social welfare assistance are highly dependent on the results of this survey.

• To provide essential data to measure elasticity which reflects the percentage change in expenditure for various commodity and service groups against the percentage change in total expenditure for the purpose of predicting the levels of expenditure and consumption for different commodity and service items in urban and rural areas.

• To provide data essential for comparing change in expenditure against change in income to measure income elasticity of expenditure.

• To study the relationships between demographic, geographical, housing characteristics of households and their income.

• To provide data necessary for national accounts especially in compiling inputs and outputs tables.

• To identify consumers behavior changes among socio-economic groups in urban and rural areas.

• To identify per capita food consumption and its main components of calories, proteins and fats according to its nutrition components and the levels of expenditure in both urban and rural areas.

• To identify the value of expenditure for food according to its sources, either from household production or not, in addition to household expenditure for non-food commodities and services.

• To identify distribution of households according to the possession of some appliances and equipments such as (cars, satellites, mobiles ,…etc) in urban and rural areas that enables measuring household wealth index.

• To identify the percentage distribution of income earners according to some background variables such as housing conditions, size of household and characteristics of head of household.

• To provide a time series of the most important data related to dominant standard of living from economic and social perspective. This will enable conducting comparisons based on the results of these time series. In addition to, the possibility of performing geographical comparisons.

Compared to previous surveys, the current survey experienced certain peculiarities, among which :

1- The total sample of the current survey (24.9 thousand households) is divided into two sections:

a- A new sample of 16.1 thousand households. This sample was used to study the geographic differences between urban governorates, urban and rural areas, and frontier governorates as well as other discrepancies related to households characteristics and household size, head of the household's education status, ....... etc.

b- A panel sample of 2008/2009 survey data of around 8.8 thousand households was selected to accurately study the changes that may have occurred in the households' living standards over the period between the two surveys and over time in the future since CAPMAS will continue to collect panel data for HIECS in the coming years.

2- Some additional questions that showed to be important based on previous surveys results, were added to the survey questionnaire, such as:

a- The extent of health services provided to monitor the level of services available in the Egyptian society. By collecting information on the in-kind transfers, the household received during the year; in order to monitor the assistance the household received from different sources government, association,..etc.

b- Identifying the main outlet of fabrics, clothes and footwear to determine the level of living standards of the household.

3- Quality control procedures especially for fieldwork are increased, to ensure data accuracy and avoid any errors in suitable time, as well as taking all the necessary measures to guarantee that mistakes are not repeated, with the application of the principle of reward and punishment.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing household surveys in several Arab countries.

Geographic coverage

Covering a sample of urban and rural areas in all the governorates.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 50% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE CENTRAL AGENCY FOR PUBLIC MOBILIZATION AND STATISTICS (CAPMAS)

The sample of HIECS 2012/2013 is a self-weighted two-stage stratified cluster sample, of around 24.9 households. The main elements of the sampling design are described in the following.

1- Sample Size The sample has been proportionally distributed on the governorate level between urban and rural areas, in order to make the sample representative even for small governorates. Thus, a sample of about 24863 households has been considered, and was distributed between urban and rural with the percentages of 45.4 % and 54.6, respectively. This sample is divided into two parts: a- A new sample of 16094 households selected from main enumeration areas. b- A panel sample of 8769 households (selected from HIECS 2010/2011 and the preceding survey in 2008/2009).

2- Cluster size The cluster size in the previous survey has been decreased compared to older surveys since large cluster sizes previously used were found to be too large to yield accepted design effect estimates (DEFT). As a result, it has been decided to use a cluster size of only 8 households (In HIECS 2011/2012 a cluster size of 16 households was used). While the cluster size for the panel sample was 4 households.

3- Core Sample The core sample is the master sample of any household sample required to be pulled for the purpose of studying the properties of individuals and families. It is a large sample and distributed on urban and rural areas of all governorates. It is a representative sample for the individual characteristics of the Egyptian society. This sample was implemented in January 2012 and its size reached more than 1 million household (1004800 household) selected from 5024 enumeration areas distributed on all governorates (urban/rural) proportionally with the sample size (the enumeration area size is around 200 households). The core sample is the sampling frame from which the samples for the surveys conducted by CAPMAS are pulled, such as the Labor Force Surveys, Income, Expenditure And Consumption Survey, Household Urban Migration Survey, ...etc, in addition to other samples that may be required for outsources.

New Households Sample 1000 sample areas were selected across all governorates (urban/rural) using a proportional technique with the sample size. The number

The UK inflation rate was 3.4 percent in May 2025, down from 3.5 percent in the previous month, and the fastest rate of inflation since February 2024. Between September 2022 and March 2023, the UK experienced seven months of double-digit inflation, which peaked at 11.1 percent in October 2022. Due to this long period of high inflation, UK consumer prices have increased by over 20 percent in the last three years. As of the most recent month, prices were rising fastest in the communications sector, at 6.1 percent, but were falling in both the furniture and transport sectors, at -0.3 percent and -0.6 percent respectively.
The Cost of Living Crisis High inflation is one of the main factors behind the ongoing Cost of Living Crisis in the UK, which, despite subsiding somewhat in 2024, is still impacting households going into 2025. In December 2024, for example, 56 percent of UK households reported their cost of living was increasing compared with the previous month, up from 45 percent in July, but far lower than at the height of the crisis in 2022. After global energy prices spiraled that year, the UK's energy price cap increased substantially. The cap, which limits what suppliers can charge consumers, reached 3,549 British pounds per year in October 2022, compared with 1,277 pounds a year earlier. Along with soaring food costs, high-energy bills have hit UK households hard, especially lower income ones that spend more of their earnings on housing costs. As a result of these factors, UK households experienced their biggest fall in living standards in decades in 2022/23. Global inflation crisis causes rapid surge in prices The UK's high inflation, and cost of living crisis in 2022 had its origins in the COVID-19 pandemic. Following the initial waves of the virus, global supply chains struggled to meet the renewed demand for goods and services. Food and energy prices, which were already high, increased further in 2022. Russia's invasion of Ukraine in February 2022 brought an end to the era of cheap gas flowing to European markets from Russia. The war also disrupted global food markets, as both Russia and Ukraine are major exporters of cereal crops. As a result of these factors, inflation surged across Europe and in other parts of the world, but typically declined in 2023, and approached more usual levels by 2024.

Abstract

The Cambodia Socio-Economic Survey (CSES) asks questions to a country wide sample of households and household members about housing conditions, education, economic activities, household production and income, household level and structure of consumption, health, victimization, etc. There are also questions related to people in the labour force, e.g. labour force participation.

Poverty reduction is a major commitment by the Royal Government of Cambodia. Accurate statistical information about the living standards of the population and the extent of poverty is an essential instrument to assist the Government in diagnosing the problems, in designing effective policies for reducing poverty and in monitoring and evaluating the progress of poverty reduction. The Millennium Development Goals (MDG) has been adopted by the Royal Government of Cambodia and a National Strategic Development Plan (NSDP) has been developed. The MDGs are also incorporated into the “Rectangular Strategy of Cambodia”.

Cambodia is still a predominantly rural and agricultural society. The vast majority of the population get their subsistence in households as self-employed in agriculture. The level of living is determined by the household's command over labour and resources for own-production in terms of land and livestock for agricultural activities, equipments and tools for fishing, forestry and construction activities and income-earning activities in the informal and formal sector. The CSES aims to estimate household income and consumption/expenditure as well as a number of other household and individual characteristics.

The main objective of the survey is to collect statistical information about living conditions of the Cambodian population and the extent of poverty. The survey can be used for identifying problems and making decisions based on statistical data.

The main user is the Royal Government of Cambodia (RGC) as the survey supports monitoring the National Strategic Development Plan (NSDP) by different socio-economic indicators. Other users are university researchers, analysts, international organizations e.g. the World Bank and NGO’s. The World Bank has published a report on poverty profile and social indicators using CSES 2007 data . In this regard, the CSES continues to serve all stakeholders involved as essential instruments in order to assist in diagnosing the problems and designing their most effective policies. The CSES micro data at NIS is available for research and analysis by external researchers after approval by Senior Minister of Planning. The interesting research questions that could be put to the data are many; NIS welcomes new research based on CSES data.

General Objectives: CSES 2013 will continue the work started through CSES 2004 and the annual CSES 2007 to 2014 and would primarily aim at producing information needed for planning and policy making for reduction of poverty in Cambodia. Reduction of poverty has been given high priority in Cambodia's National Strategic Development Plan (NSDP 2009-2013). In addition to this, the survey data help in various other ways in developmental planning and policy making in the country. They would also prove useful for the production of National Accounts in Cambodia.

A long-term objective of the entire project is to build national capability in NIS for conducting socio-economic surveys and for utilizing survey data for planning for national development and social welfare.

Specific Objectives Among specific objectives, the following deserve special mention: 1) Obtain data on infrastructural facilities in villages, especially facilities for schooling and health care and associated problems. 2) Obtain data on retail prices of selected food, non-food and medicine items prevailing in the villages. 3) Collect data on utilization of education, housing and land ownership 4) Collect data on household assets and outstanding loans. 5) Collect data on household's construction activities. 6) Collect information on maternal health, child health/care. 7) Collect information on health care seeking and expenditure of the household members related to illness, injury and disability. 8) Collect information on economic activities including the economic activities for children aged between 5 and 17 years. 9) Collect information on victimization by the household 10) Collect information on the presence of the household members.

Geographic coverage

National Phnom Penh / Other Urban / Other Rural

Analysis unit

• Households
• Individuals

Universe

All resident households in Cambodia

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling design in the CSES 2013 survey is a three-stage design. In stage one a sample of villages is selected, in stage two an Enumeration Area (EA) is selected from each village selected in stage one, and in stage three a sample of households is selected from each EA selected in stage two.

Stage 1: A random sample of PSUs was selected from each stratum. The sampling method was systematic PPS (PPS=sampling with probability proportional to size). The size measure used was the number of households in the PSU according to the sampling frame.

Stage 2: One EA was selected by Simple Random Sampling (SRS), in each village selected in stage 1.

Stage 3: In each selected EA a sample of 10 households was selected. The selection of households was done in the field by the supervisors/interviewers. All households in selected EAs were listed by the enumerator. The sample of households was then selected from the list by systematic sampling with a random start (the start value controlled by NIS).

Mode of data collection

Face-to-face [f2f]

Research instrument

Three different questionnaires or forms were used in the survey:

Form 1: Household listing sheets to be used in the sampling procedure in the enumeration areas.

Form 2: Village questionnaire answered by the village leader about economy and infrastructure, crop production, health, education, retail prices and sales prices of agriculture, employment and wages, and recruitment of children for work outside the village.

Form 3: Household questionnaire with questions for each household member, including modules on migration, education and literacy, housing conditions, crop production, household liabilities, durable goods, construction activities, nutrition, fertility and child care, child feeding and vaccination, health of children, mortality, current economic activity, health and illness, smoking, HIV/AIDS awareness, and victimization.

The interviewer is responsible for filling up Form 1 and Form 3 to respondents. . For Form 2, the supervisors will be asked to canvass this form. In case that the supervisors are absent for any reason, the interviewers may be also asked to help fill up this form (Form 2).

Cleaning operations

The NIS team commenced their work of checking and coding and coding in begining of February after the first month of fieldwork was completed. Supervisors from the field delivered questionaires to NIS. Sida project expert and NIS Survey Manager helped in solving relevant matters that become apparent when reviewing questionires on delivery.

Response rate

The CSES 2013 enjoyed almost a 100 percent response rate. The high response rate together with close and systematic fieldwork supervision by the core group members were a major contribution for achieving high quality survey results.

Sampling error estimates

In order to provide a basis for assessing the reliability or precision of CSES estimates, the estimation of the magnitude of sampling error in the survey data were computed. Since most of the estimates from the survey are in the form of weighted ratios, thus variances for ratio estimates are computed.

The Coefficients of Variation (CV) on national level estimates are generally below 4 percent. The exception is the CV for total value of assets where there are rather high CVs especially in the urban areas, which should be expected.

The CVs are somewhat higher in the urban and rural domains but still generally below 7 percent. For the five zones, the average CVs are in the range 5 to 13 percent with a few exceptions where the CVs are above 20 percent. For provinces the CVs for food consumption are 9 percent on average.

The sample take within Primary Sampling Units (PSU) was set to 10 households per PSU in the CSES 1999. When data on variances became available, it was possible to make crude calculations of the optimal sample take within PSU. Calculations on some of the central estimates in the CSES 1999 show that the design effects in most cases are in the range 1 to 5.

Intra-cluster correlation coefficients have been calculated based on the design effects. These correlation coefficients are somewhat high. The reason is that the characteristics that are measured tend to be concentrated (clustered) within the PSUs. The optimal sample size within PSUs under different assumptions on cost ratios and intra-cluster correlation coefficients was then calculated. The cost ratio is the average cost for adding a village to the sample divided by the average cost of including an extra household in the sample. In the CSES, it was chosen to adopt a fairly low cost ratio due to the fact that the interview time per household is long. Under this assumption the optimal sample size is probably around 10 households per village for many of the CSES indicators.