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.

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.

ABOUT

The Global GDP Per Capita Dataset provides a comprehensive record of annual economic output per person across various countries and regions. It includes key economic indicators such as GDP per capita (adjusted for inflation and purchasing power parity), country codes, and yearly data points. This dataset is valuable for economists, researchers, policymakers, and analysts interested in studying economic growth, income distribution, and global development trends.

Key features of the dataset:

✅ Covers multiple countries and regions worldwide
✅ Provides annual GDP per capita data from 1990 to 2023
✅ Adjusted for inflation and purchasing power parity (PPP, constant 2021$)
✅ Sourced from the World Bank - World Development Indicators
✅ Useful for economic analysis, policy-making, and financial forecasting

This dataset serves as a crucial resource for understanding global economic trends, comparing living standards across nations, and making data-driven decisions in economic research and policy development.

FILE INFORMATION

The dataset consists of structured records related to GDP per capita, compiled from the World Bank’s World Development Indicators (WDI). Each file contains country-level economic data, including GDP per capita values in constant 2021 international dollars (PPP). This allows researchers, economists, and data analysts to study economic growth patterns and trends over time. The file type is CSV.

COLUMNS DESCRIPTION

• Entity – The country or region name.
• Code – The country code (ISO Alpha-3 format or OWID custom code) [NOTE : missing value if there is no ISO Alpha-3 code].
• Year – The year of the GDP observation (1990–2023).
• GDP per capita (PPP, 2021$) – The economic output per person, adjusted for inflation and cost of living.

This dataset provides valuable insights into economic trends over three decades, helping researchers analyze global income levels, economic development, and policy impacts.

The average for 2021 based on 11 countries was 67.5 index points. The highest value was in Uruguay: 100.24 index points and the lowest value was in Suriname: 43.15 index points. The indicator is available from 2017 to 2021. Below is a chart for all countries where data are available.

Graph and download economic data for Consumer Price Index for Ethiopia (DDOE02ETA086NWDB) from 1965 to 2017 about Ethiopia, CPI, price index, indexes, and price.

Context

There can be multiple motivations for analyzing country specific data, ranging from identifying successful approaches in healthcare policy to identifying business investment opportunities, and many more. Often, all these various goals would have to analyze a substantially overlapping set of parameters. Thus, it would be very good to have a broad set of country specific indicators at one place.

This data-set is an effort in that direction. Of-course there are still plenty more parameters out there. If anyone is interested to integrate more parameters to this dataset, you are more than welcome.

Content

This dataset contains about 95 statistical indicators of the 66 countries. It covers a broad spectrum of areas including

General Information Broader Economic Indicators Social Indicators Environmental & Infrastructure Indicators Military Spending Healthcare Indicators Trade Related Indicators e.t.c.

This data-set for the year 2017 is an amalgamation of data from SRK's Country Statistics - UNData, Numbeo and World Bank.

The entire data-set is contained in one file described below:

soci_econ_country_profiles.csv - The first column contains the country names followed by 95 columns containing the various indicator variables.

Acknowledgements

This is a data-set built on top of SRK's Country Statistics - UNData which was primarily sourced from UNData.

Additional data such as "Cost of living index", "Property price index", "Quality of life index" have been extracted from Numbeo and a number of metrics related to "trade", "healthcare", "military spending", "taxes" etc are extracted from World Bank data source. Given that this is an amalgamation of data from three different sources, only those countries(about 66) which have sufficient data across all the three sources are considered.

Please read the Numbeo terms of use and policieshere Please read the WorldBank terms of use and policies here Please read the UN terms of use and policies here

Photo Credits : Louis Maniquet on Unsplash

Graph and download economic data for Consumer Price Index for Islamic Republic of Iran (DDOE01IRA086NWDB) from 1960 to 2017 about Iran, CPI, price index, indexes, and price.

Economic, demographic, and social indicators for South Asia (IDA-eligible countries) — including GDP, population, inflation, and cost of living, based on data from the World Bank, IMF, and UN.

This portion of the GapMinder data includes one year of numerous country-level indicators of health, wealth and development for 213 countries.

GapMinder collects data from a handful of sources, including the Institute for Health
Metrics and Evaluation, US Census Bureau’s International Database, United Nations Statistics Division, and the World Bank. Source: https://www.gapminder.org/

Variable Name , Description of Indicator & Sources Unique Identifier: Country

1. incomeperperson : 2010 Gross Domestic Product per capita in constant 2000 US$.The inflation but not the differences in the cost of living between countries has been taken into account. [Main Source : World Bank Work Development Indicators]

2. alcconsumption: 2008 alcohol consumption per adult (age 15+), litres Recorded and estimated average alcohol consumption, adult (15+) percapita consumption in liters pure alcohol [Main Source : WHO]

3. armedforcesrate: Armed forces personnel (% of total labor force) [Main Source : Work Development Indicators]

4. breastcancerper100TH : 2002 breast cancer new cases per 100,000 female Number of new cases of breast cancer in 100,000 female residents during the certain year. [Main Source : ARC (International Agency for Research on Cancer)]

5. co2emissions : 2006 cumulative CO2 emission (metric tons), Total amount of CO2 emission in metric tons since 1751. [*Main Source : CDIAC (Carbon Dioxide Information Analysis Center)] *

6. femaleemployrate : 2007 female employees age 15+ (% of population) Percentage of female population, age above 15, that has been employed during the given year. [ Main Source : International Labour Organization]

7. employrate : 2007 total employees age 15+ (% of population) Percentage of total population, age above 15, that has been employed during the given year. [Main Source : International Labour Organization]

8. HIVrate : 2009 estimated HIV Prevalence % - (Ages 15-49) Estimated number of people living with HIV per 100 population of age group 15-49. [Main Source : UNAIDS online database]

9. Internetuserate: 2010 Internet users (per 100 people) Internet users are people with access to the worldwide network. [Main Source : World Bank]

10. lifeexpectancy : 2011 life expectancy at birth (years) The average number of years a newborn child would live if current mortality patterns were to stay the same. [Main Source : 1) Human Mortality Database, 2) World Population Prospects: , 3) Publications and files by history prof. James C Riley , 4) Human Lifetable Database ]

11. oilperperson : 2010 oil Consumption per capita (tonnes per year and person) [Main Source : BP]

12. polityscore : 2009 Democracy score (Polity) Overall polity score from the Polity IV dataset, calculated by subtracting an autocracy score from a democracy score. The summary measure of a country's democratic and free nature. -10 is the lowest value, 10 the highest. [Main Source : Polity IV Project]

13. relectricperperson : 2008 residential electricity consumption, per person (kWh) . The amount of residential electricity consumption per person during the given year, counted in kilowatt-hours (kWh). [Main Source : International Energy Agency]

14. suicideper100TH : 2005 Suicide, age adjusted, per 100 000 Mortality due to self-inflicted injury, per 100 000 standard population, age adjusted . [Main Source : Combination of time series from WHO Violence and Injury Prevention (VIP) and data from WHO Global Burden of Disease 2002 and 2004.]

15. urbanrate : 2008 urban population (% of total) Urban population refers to people living in urban areas as defined by national statistical offices (calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects) [Main Source : World Bank]

Graph and download economic data for Inflation, consumer prices for the United States (FPCPITOTLZGUSA) from 1960 to 2024 about consumer, CPI, inflation, price index, indexes, price, and USA.

Abstract

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

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

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Communities

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

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

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

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

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

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

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

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

Sampling deviation

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

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

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

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

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

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

Cleaning operations

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

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GapMinder collects data from a handful of sources, including the Institute for Health Metrics and Evaluation, the US Census Bureau’s International Database, the United Nations Statistics Division, and the World Bank.

Variable Name & Description of Indicator:

• country: Unique Identifier
• incomeperperson: Gross Domestic Product per capita in constant 2000 US$. The inflation but not the differences in the cost of living between countries has been taken into account.
• Internetuserate: Internet users (per 100 people) Internet users are people with access to the worldwide network.
• urbanrate: Urban population (% of total) Urban population refers to people living in urban areas as defined by national statistical offices (calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects)

More information is available at www.gapminder.org

Content

This analysis focuses on income inequailty as measured by the Gini Index* and its association with economic metrics such as GDP per capita, investments as a % of GDP, and tax revenue as a % of GDP. One polical metric, EIU democracy index, is also included.

The data is for years 2006 - 2016

This investigation can be considered a starting point for complex questions such as:

1. Is a higher tax revenue as a % of GDP associated with less income inequality?
2. Is a higher EIU democracy index associated with less income inequality?
3. Is higher GDP per capita associated with less income inequality?
4. Is higher investments as a % of GDP associated with less income inequality?

This analysis uses the gapminder dataset from the Gapminder Foundation. The Gapminder Foundation is a non-profit venture registered in Stockholm, Sweden, that promotes sustainable global development and achievement of the United Nations Millennium Development Goals by increased use and understanding of statistics and other information about social, economic and environmental development at local, national and global levels.

*The Gini Index is a measure of statistical dispersion intended to represent the income or wealth distribution of a nation's residents, and is the most commonly used measurement of inequality. It was developed by the Italian statistician and sociologist Corrado Gini and published in his 1912 paper Variability and Mutability.

The dataset contains data from the following GapMinder datasets:

EIU Democracy Index:

"This democracy index is using the data from the Economist Inteligence Unit to express the quality of democracies as a number between 0 and 100. It's based on 60 different aspects of societies that are relevant to democracy universal suffrage for all adults, voter participation, perception of human rights protection and freedom to form organizations and parties. The democracy index is calculated from the 60 indicators, divided into five ""sub indexes"", which are:

1. Electoral pluralism index;
2. Government index;
3. Political participation indexm;
4. Political culture index;
5. Civil liberty index.

The sub-indexes are based on the sum of scores on roughly 12 indicators per sub-index, converted into a score between 0 and 100. (The Economist publishes the index with a scale from 0 to 10, but Gapminder has converted it to 0 to 100 to make it easier to communicate as a percentage.)" https://docs.google.com/spreadsheets/d/1d0noZrwAWxNBTDSfDgG06_aLGWUz4R6fgDhRaUZbDzE/edit#gid=935776888

Income: GDP per capita, constant PPP dollars

GDP per capita measures the value of everything produced in a country during a year, divided by the number of people. The unit is in international dollars, fixed 2011 prices. The data is adjusted for inflation and differences in the cost of living between countries, so-called PPP dollars. The end of the time series, between 1990 and 2016, uses the latest GDP per capita data from the World Bank, from their World Development Indicators. To go back in time before the World Bank series starts in 1990, we have used several sources, such as Angus Maddison. https://www.gapminder.org/data/documentation/gd001/

Investments (% of GDP)

Capital formation is a term used to describe the net capital accumulation during an accounting period for a particular country. The term refers to additions of capital goods, such as equipment, tools, transportation assets, and electricity. Countries need capital goods to replace the older ones that are used to produce goods and services. If a country cannot replace capital goods as they reach the end of their useful lives, production declines. Generally, the higher the capital formation of an economy, the faster an economy can grow its aggregate income.

Tax revenue (% of GDP)

refers to compulsory transfers to the central governement for public purposes. Does not include social security. https://data.worldbank.org/indicator/GC.TAX.TOTL.GD.ZS

Context

Gapminder is an independent Swedish foundation with no political, religious or economic affiliations. Gapminder is a fact tank, not a think tank. Gapminder fights devastating misconceptions about global development. Gapminder produces free teaching resources making the world understandable based on reliable statistics. Gapminder promotes a fact-based worldview everyone can understand. Gapminder collaborates with universities, UN, public agencies and non-governmental organizations. All Gapminder activities are governed by the board. We do not award grants. Gapminder Foundation is registered at Stockholm County Administration Board. Our constitution can be found here.

Acknowledgements

Thanks to gapminder.org for organizing the above datasets.

Inspiration

Below are some research questions associated with the data and some ...

Graph and download economic data for Inflation, consumer prices for the Philippines (FPCPITOTLZGPHL) from 1960 to 2024 about Philippines, consumer, CPI, inflation, price index, indexes, and price.

Context : - A country's gross domestic product (GDP) at purchasing power parity (PPP) per capita is the PPP value of all final goods and services produced within an economy in a given year, divided by the average (or mid-year) population for the same year. This is similar to nominal GDP per capita, but adjusted for the cost of living in each country.

• In 2019, the estimated average GDP per capita (PPP) of all of the countries of the world was Int$ 18,381.[a] For rankings regarding wealth.

Method

• The gross domestic product (GDP) per capita figures on this page are derived from PPP calculations. Such calculations are prepared by various organizations, including the IMF and the World Bank. As estimates and assumptions have to be made, the results produced by different organizations for the same country are not hard facts and tend to differ, sometimes substantially, so they should be used with caution.

• Comparisons of national wealth are frequently made on the basis of nominal GDP and savings (not just income), which do not reflect differences in the cost of living in different countries (see List of countries by GDP (nominal) per capita); hence, using a PPP basis is arguably more useful when comparing generalized differences in living standards between economies because PPP takes into account the relative cost of living and the inflation rates of the countries, rather than using only exchange rates, which may distort the real differences in income.

• This is why GDP (PPP) per capita is often considered one of the indicators of a country's standard of living,[3][4] although this can be problematic because GDP per capita is not a measure of personal income. (See Standard of living and GDP.)

• GDP (PPP) and GDP (PPP) per capita are usually measured by international dollar, which is a hypothetical currency that has the same purchasing power in every economy as the U.S. dollar in the United States.

Content

All figures are in current international dollars, and rounded to the nearest whole number.

The table initially ranks each country or territory with their latest available year's estimates, and can be reranked by either of the sources

• Nearly all country links in the table connect to articles titled "Income in (country or territory)" or to "Economy of (country or territory)".

Data Columns:

• Index of Serial Number
• Name of the U N Region
• International Monetary Fund (IMF) Data.
• World Bank Data.
• Central Intelligence Agency (CIA) Data.

Acknowledgements

The Method for collecting the Data is Web Scraping Wikipedia.

Abstract

The General Household Survey-Panel (GHS-Panel) is implemented in collaboration with the World Bank Living Standards Measurement Study (LSMS) team as part of the Integrated Surveys on Agriculture (ISA) program. The objectives of the GHS-Panel include the development of an innovative model for collecting agricultural data, interinstitutional collaboration, and comprehensive analysis of welfare indicators and socio-economic characteristics. The GHS-Panel is a nationally representative survey of approximately 5,000 households, which are also representative of the six geopolitical zones. The 2023/24 GHS-Panel is the fifth round of the survey with prior rounds conducted in 2010/11, 2012/13, 2015/16 and 2018/19. The GHS-Panel households were visited twice: during post-planting period (July - September 2023) and during post-harvest period (January - March 2024).

Geographic coverage

National

Analysis unit

• Households • Individuals • Agricultural plots • Communities

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The original GHS‑Panel sample was fully integrated with the 2010 GHS sample. The GHS sample consisted of 60 Primary Sampling Units (PSUs) or Enumeration Areas (EAs), chosen from each of the 37 states in Nigeria. This resulted in a total of 2,220 EAs nationally. Each EA contributed 10 households to the GHS sample, resulting in a sample size of 22,200 households. Out of these 22,200 households, 5,000 households from 500 EAs were selected for the panel component, and 4,916 households completed their interviews in the first wave.

After nearly a decade of visiting the same households, a partial refresh of the GHS‑Panel sample was implemented in Wave 4 and maintained for Wave 5. The refresh was conducted to maintain the integrity and representativeness of the sample. The refresh EAs were selected from the same sampling frame as the original GHS‑Panel sample in 2010. A listing of households was conducted in the 360 EAs, and 10 households were randomly selected in each EA, resulting in a total refresh sample of approximately 3,600 households.

In addition to these 3,600 refresh households, a subsample of the original 5,000 GHS‑Panel households from 2010 were selected to be included in the new sample. This “long panel” sample of 1,590 households was designed to be nationally representative to enable continued longitudinal analysis for the sample going back to 2010. The long panel sample consisted of 159 EAs systematically selected across Nigeria’s six geopolitical zones.

The combined sample of refresh and long panel EAs in Wave 5 that were eligible for inclusion consisted of 518 EAs based on the EAs selected in Wave 4. The combined sample generally maintains both the national and zonal representativeness of the original GHS‑Panel sample.

Sampling deviation

Although 518 EAs were identified for the post-planting visit, conflict events prevented interviewers from visiting eight EAs in the North West zone of the country. The EAs were located in the states of Zamfara, Katsina, Kebbi and Sokoto. Therefore, the final number of EAs visited both post-planting and post-harvest comprised 157 long panel EAs and 354 refresh EAs. The combined sample is also roughly equally distributed across the six geopolitical zones.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The GHS-Panel Wave 5 consisted of three questionnaires for each of the two visits. The Household Questionnaire was administered to all households in the sample. The Agriculture Questionnaire was administered to all households engaged in agricultural activities such as crop farming, livestock rearing, and other agricultural and related activities. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

GHS-Panel Household Questionnaire: The Household Questionnaire provided information on demographics; education; health; labour; childcare; early child development; food and non-food expenditure; household nonfarm enterprises; food security and shocks; safety nets; housing conditions; assets; information and communication technology; economic shocks; and other sources of household income. Household location was geo-referenced in order to be able to later link the GHS-Panel data to other available geographic data sets (forthcoming).

GHS-Panel Agriculture Questionnaire: The Agriculture Questionnaire solicited information on land ownership and use; farm labour; inputs use; GPS land area measurement and coordinates of household plots; agricultural capital; irrigation; crop harvest and utilization; animal holdings and costs; household fishing activities; and digital farming information. Some information is collected at the crop level to allow for detailed analysis for individual crops.

GHS-Panel Community Questionnaire: The Community Questionnaire solicited information on access to infrastructure and transportation; community organizations; resource management; changes in the community; key events; community needs, actions, and achievements; social norms; and local retail price information.

The Household Questionnaire was slightly different for the two visits. Some information was collected only in the post-planting visit, some only in the post-harvest visit, and some in both visits.

The Agriculture Questionnaire collected different information during each visit, but for the same plots and crops.

The Community Questionnaire collected prices during both visits, and different community level information during the two visits.

Cleaning operations

CAPI: Wave five exercise was conducted using Computer Assisted Person Interview (CAPI) techniques. All the questionnaires (household, agriculture, and community questionnaires) were implemented in both the post-planting and post-harvest visits of Wave 5 using the CAPI software, Survey Solutions. The Survey Solutions software was developed and maintained by the Living Standards Measurement Unit within the Development Economics Data Group (DECDG) at the World Bank. Each enumerator was given a tablet which they used to conduct the interviews. Overall, implementation of survey using Survey Solutions CAPI was highly successful, as it allowed for timely availability of the data from completed interviews.

DATA COMMUNICATION SYSTEM: The data communication system used in Wave 5 was highly automated. Each field team was given a mobile modem which allowed for internet connectivity and daily synchronization of their tablets. This ensured that head office in Abuja had access to the data in real-time. Once the interview was completed and uploaded to the server, the data was first reviewed by the Data Editors. The data was also downloaded from the server, and Stata dofile was run on the downloaded data to check for additional errors that were not captured by the Survey Solutions application. An excel error file was generated following the running of the Stata dofile on the raw dataset. Information contained in the excel error files were then communicated back to respective field interviewers for their action. This monitoring activity was done on a daily basis throughout the duration of the survey, both in the post-planting and post-harvest.

DATA CLEANING: The data cleaning process was done in three main stages. The first stage was to ensure proper quality control during the fieldwork. This was achieved in part by incorporating validation and consistency checks into the Survey Solutions application used for the data collection and designed to highlight many of the errors that occurred during the fieldwork.

The second stage cleaning involved the use of Data Editors and Data Assistants (Headquarters in Survey Solutions). As indicated above, once the interview is completed and uploaded to the server, the Data Editors review completed interview for inconsistencies and extreme values. Depending on the outcome, they can either approve or reject the case. If rejected, the case goes back to the respective interviewer’s tablet upon synchronization. Special care was taken to see that the households included in the data matched with the selected sample and where there were differences, these were properly assessed and documented. The agriculture data were also checked to ensure that the plots identified in the main sections merged with the plot information identified in the other sections. Additional errors observed were compiled into error reports that were regularly sent to the teams. These errors were then corrected based on re-visits to the household on the instruction of the supervisor. The data that had gone through this first stage of cleaning was then approved by the Data Editor. After the Data Editor’s approval of the interview on Survey Solutions server, the Headquarters also reviews and depending on the outcome, can either reject or approve.

The third stage of cleaning involved a comprehensive review of the final raw data following the first and second stage cleaning. Every variable was examined individually for (1) consistency with other sections and variables, (2) out of range responses, and (3) outliers. However, special care was taken to avoid making strong assumptions when resolving potential errors. Some minor errors remain in the data where the diagnosis and/or solution were unclear to the data cleaning team.

Response

Abstract

High Frequency Phone Survey for Displaced Population in Somalia helps to fill the important data and knowledge gaps on displaced populations and host communities to support timely and evidence-informed decisions that can improve the lives of one of the most vulnerable groups in Somalia. Displaced population including IDPs, refugees and returnees are recognized as among the most vulnerable groups in the Somalia National Development Plan, but the paucity of data makes it difficult to adequately prescribe policy recommendations that will improve their lives. Humanitarian partners, including UNHCR and the International Organization for Migration, benefit from the information generated to better target their responses in times of crisis. It will also be used by the World Bank to support country dialogue, inform operations, and expand the knowledge base on displacement in Somalia. The time-series nature of the survey will enable the tracking of the impact of shocks on specific socio-economic indicators to allow for better timing of interventions.

Two survey rounds conducted from November 2021 to August 2022 yield samples for five population groups: host communities for IDPs, IDPs in and out of settlements, refugees and asylum seekers and refugee returnees. Implemented by the World Bank in collaboration with the United Nations High Commissioner for Refugees (UNHCR) and the National Bureau of Statistics (NBS) in Somalia, this cost-effective phone-based survey aimed to follow the same respondents over a period of time.

Geographic coverage

National

Analysis unit

• Household
• Individual

Universe

Households with access to phones.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample consists of five strata: (i) host communities; (ii) IDPs living in settlements; (iii) IDPs living outside settlements; (iv) refugees; and (v) refugee returnees. Each stratum consisted of about 500 households, making up the total sample of around 2,500 respondents.

Samples for the host communities and IDPs living outside settlements were selected from the previous national phone survey (Somalia high frequency phone survey - SHFPS) conducted by the World Bank in Somalia from June 2020 until October 2021. The sample for host communities was selected on the basis of frequency of interaction with IDP populations, with households that reported that they had had interacted with the IDPs at least once a month collected for the sample. For IDPs living in the settlements, phone numbers were collected by UNHCR from the settlements in Bay and Banadir, while those for refugees and refugee returnees were provided from the UNHCR database.

Except for IDPs in settlements, the majority of the displacement-affected households surveyed live in urban areas. The majority of the refugees in Somalia are either from Ethiopia (54 percent) and Yemen (41 percent). Therefore, this survey focused on these two refugee groups. The refugee households mostly live in Somaliland (53 percent) with a considerable number in Puntland (28 percent) and Banadir (15 percent). In the case of refugee returnees, about 11,606 households were registered in the UNHCR database at the time of sample selection, mostly coming from Kenya (97 percent) and Yemen (2 percent). Both these groups were included in the sample proportionally to their population share. The majority of the sampled refugee returnees live in Jubaland (78 percent). As for settlement-based IDPs, two main regions—Banadir and Bay—which host almost 50 percent of the settlement-based IDPs in Somalia were focused.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

At the end of data collection, the raw dataset was cleaned by the Research team. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes.

Only households that consented to being interviewed were kept in the dataset, and all personal information and internal survey variables were dropped from the clean dataset.

Abstract

Over the past decade, Albania has been seeking to develop the framework for a market economy and more open society. It has faced severe internal and external challenges in the interim – extremely low income levels and a lack of basic infrastructure, the rapid collapse of output and inflation rise after the shift in regime in 1991, the turmoil during the 1997 pyramid crisis, and the social and economic shocks accompanying the 1999 Kosovo crisis. In the face of these challenges, Albania has made notable progress in creating conditions conducive to growth and poverty reduction.

In the process leading to its first Poverty Reduction Strategy (that is the National Strategy for Socioeconomic Development, now renamed the National Strategy for Development and Integration), the Government of Albania reinforced its commitment to strengthening its own capacity to collect and analyze on a regular basis the information it needs to inform policy-making.

Multi-purpose household surveys are one of the main sources of information to determine living conditions and measure the poverty situation of a country. They provide an indispensable tool to assist policy-makers in monitoring and targeting social programs. In its first phase (2001-2006), this monitoring system included the following data collection instruments: (i) Population and Housing Census; (ii) Living Standards Measurement Surveys every 3 years, and (iii) annual panel surveys.

The Population and Housing Census (PHC) conducted in April 2001, provided the country with a much needed updated sampling frame which is one of the building blocks for the household survey structure. The focus during this first phase of the monitoring system is on a periodic LSMS (in 2002 and 2005), followed by panel surveys on a subsample of LSMS households (in 2003, and 2004), drawing heavily on the 2001 census information.

A poverty profile based on 2002 data showed that some 25 percent of the population are poor, with many others vulnerable to poverty due to their incomes being close to the poverty threshold. Income related poverty is compounded by poor access to basic infrastructure (regular supply of electricity, clean water), education and health services, housing, etc.

The 2005 LSMS was in the field between May and early July, with an additional visit to agricultural households in October, 2005. The survey work was undertaken by the Living Standards unit of INSTAT, with the technical assistance of the World Bank.

Geographic coverage

National coverage. Domains: Tirana, other urban, rural; Agro-ecological areas (coastal, central, mountain)

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The Republic of Albania is divided geographically into 12 Prefectures (Prefekturat). The latter are divided into Districts (Rrethet) which are, in turn, divided into Cities (Qyteti) and Communes (Komunat). The Communes contain all the rural villages and the very small cities. For census purposes, the cities and the villages have been divided into enumeration areas (EAs).

1. Sampling frame

The Enumeration Areas (EA) that make up the sampling frame come from the April 2001 General Census of Population and Housing. The EAs in the frame are classified by Prefecture, District, City or Commune. The frame also contains, for every EA, the number of Housing Units (HU), the number of occupied HUs, the number of unoccupied HUs, the number of households, and the population. We are using occupied dwellings and not total number of dwellings since many EAs contain a large number of empty dwellings.

A detailed study of the list of census EAs shows that many have zero population. In order to obtain EAs with the minimum of 50 and the maximum of 120 occupied housing units, the EAs with zero population have been taken off the sampling frame. Since the sizes of the EAs varied from 0 to 395 HUs, the smaller EAs (with less than 50 HU) have been collapsed with geographically adjacent ones and the largest EAs (with more than 120 HU) have been split into two or more EAs. Subsequently, maps identifying the boundaries of every split and collapsed EA were prepared. Given that the 2002 LSMS has been conducted less than a year after the April 2001 census, a listing operation to update the sample EAs was not conducted in the field. However, since the level of construction is very high in the city of Tirana and its suburbs, a quick count of the 75 sample EAs selected in Tirana was carried out followed by a listing operation. The check of the listing based on the Census data revealed two types of discrepancies: - HUs had become invalid, i.e. vacant, nonresidential, demolished, seasonally occupied, etc. - Instead of one small building (with one or two HU), a new one with 15 HUs was identified.

During of the listing update process, HUs identified as invalid were taken off the frame. In the case of a new building, these new HUs were entered with a new sequential code. The listing sheets prepared during the listing operation in Tirana, become the sampling frame for the final stage of selection of 12 HU which has to be interviewed. The unit of analysis and the unit of observation is the household. The universe under study consists of all the households in the Republic of Albania. We have used the Housing Unit (defined as the space occupied by one household) as the sampling unit, instead of the household, because the HU is more permanent and easier to identify in the field.

1. Sample Size

In the LSMS the sample size is 450 EA and in each EA 8 households were selected. So the total sample size of the LSMS is 3600 households. In addition, since a certain level of nonresponse is expected, 4 reserve units were selected in each sample EA.

1. Stratification

The sampling frame has been divided in three regions (strata) 1. Coastal Area 2. Central Area 3. Mountain Area and Tirana (urban and other urban) is consider as a separate strata.

The first three strata were divided into major cities (the most important cities in the region), other urban (the rest of cities in the region), and rural. In each more importance was given to the major cities and rural areas. We have selected 10 EA for each major city and 65 EAs (75 EAs for Mountain Area) for each region. In the city of Tirana and its suburbs, implicit stratification was used to improve the efficiency of the sample design.

1. Procedure for the Selection of Housing Units

A fixed number of valid dwelling units (12) was selected systematically and with equal probability from the Listing Form pertaining to Tirana and from the Census forms for the other areas. Once the 12 HUs were selected, 4 of them were chosen at random and kept as reserve units. The selected HUs were numbered within the EA and identified with a circle around the number in the listing form, as well as a circle on the maps. The reserve sample (units 9 to 12) were identified from R1 to R4 during data collection to emphasize the fact that they were reserve units.

Two copies of the sample listing sheets and two copies of maps for each EA were printed. The first copy of the listing sheet and the map were given to the supervisor and included the 12 HU, the second copy was given to the enumerator. The enumerator only received the 8 dwelling units, not the reserve ones. Each time the enumerator needed a reserve HU, he/she had to ask the supervisor and explain the reason why a reserve unit was needed. This process helped determine the reason why reserve units were used and provided more control on their use.

In the field the enumerator registered the occupancy status of every unit: - occupied as principal residence - vacant - under construction (not occupied) - demolished or abandoned (not occupied) - seasonally occupied

In the case that one HU was found to be invalid, the enumerator used the first reserve unit (identified with the code R1). In the case that in one EA more than 4 DU selected were invalid, other units from that EA chosen at random by headquarter (in Tirana) were selected as replacement units to keep the enumerator load constant and maintain a uniform sample size in each EA. Before identifying the invalid HUs, the interviewer had to note the interview status of each visit for all the units for which an interview was attempted, whether these are original units or reserve units. This was done to determine the interview status: interview completed, nonresponse, refusal, etc. In other words, this will allow identifying: the completed interviews (responses obtained), the incomplete but usable ones (responses obtained), the incomplete ones but not usable (nonresponse), the refusals (nonresponse) and the "not at home" (nonresponse). Subsequently, the invalid units identified were substituted with the available reserves, always maintaining the sample of 8 HUs.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four survey instruments were used to collect information for the 2005 Albania LSMS: a household questionnaire, a diary for recording household food consumption, a community questionnaire, and a price questionnaire.

The household questionnaire included all the core LSMS modules as defined in Grosh and Glewwe (2000)1, plus additional modules on migration, fertility, subjective poverty, agriculture, non-farm enterprises, and social capital. Geographical referencing data on the longitude and latitude of each household were also recorded using portable GPS devices. Geo-referencing will enable a more efficient spatial link among the different surveys of the system, as well as between the survey households and other geo-referenced information.

The choice of the modules was aimed at matching as much as

Abstract

The Vulnerability Assessment Framework (VAF) is a key tool used by humanitarian and development organizations in Jordan. It contributes to coherent vulnerability identification and programme delivery across sectors. It was designed in 2014 with a focus on Syrian refugees residing outside of camps.

For the fifth bi-annual VAF population study in 2022, 6,427 refugee households residing in host communities were randomly sampled across all governorates to explore thematic and sectoral vulnerabilities for refugee populations of all nationalities within Jordan. This data was collected in person between July 2021 and October 2021.

Geographic coverage

Whole country host communities (excluding camps).

Analysis unit

Household, Case (family), Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The stratified sampling strategy was developed jointly with the World Bank and designed to generate the most precise statistics possible and at the lowest possible cost and to allow for representativeness at a margin of error below 5%. Stratification was planned along two variables: nationality (Syrian, Iraqi and Other) and location. Syrians were represented across the twelve governorates, while non-Syrians were represented across the regions of Jordan; Amman, Central/outside Amman (consisting of Balqa, Madaba and Zarqa), North (consisting of Ajloun, Irbid, Jerash, Mafraq) and South (consisting of Aqaba, Karak, Tafilah, Ma'an). The sample was randomly drawn from cases registered in the ProGres registration database administered by UNHCR Jordan. The sample includes refugees residing in urban, peri-urban and rural settings and excludes those living in refugee camps.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaire contained the following sections: Household Demographics, Shelter, WASH, Consumption and Expenditure6, COVID-19 KAP7, Financial Situation, Health, Education, Livelihoods, and Child Labour.

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.