The Global Housing Watch tracks developments in housing markets across the world on a quarterly basis. It provides current data on house prices as well as metrics used to assess valuation in housing markets, such as house price‑to‑rent and house-price‑to‑income ratios.

This collection includes only a subset of indicators from the source dataset.

Abstract

From 2002 to 2010, the GSO plans to conduct the Vietnam Household Living Standards Survey every 2 years (in the years ending with even numbers) in order to monitor systematically living standards of Vietnam population's classes and at the same time, to monitor and assess the implementation of the Comprehensive Poverty Alleviation and Growth Strategy defined in the Country Strategy Paper approved by the Government Prime Minister. In addition, this survey also contributes to the evaluation of results of realization of the Millennium Development Goals (MDG) and Socio-Economic Development Goals set out by Vietnamese Government.

The VHLSS 2004 includes topics which reflect the population's living standards: demographic characteristics, education background, professional/ technical level, income, expenditures, use of health services, employment status, housing, amenity as possession, property, goods, electricity, water and sanitation conditions. In addition, this survey includes two new topics: “Agricultural, forestry and fishery land” and “Non-agricultural, forestry and fishery sectors” for more in-depth analysis. Technical assistance was provided by experts of the UN Statistics Division and the World Bank in designing questionnaires for the 2 new contents and sampling.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Community
• Consumption expenditure item/product

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

Survey sample was selected, based on the Population and Housing Census 1999. The sample size included 45,900 households representative of the whole country, urban and rural area and 64 provinces. Survey sample was divided into 2 types: 36,720 households surveyed on income and 9,180 households surveyed on income and expenditures. The survey sample was sub-divided into 2 minor samples for data collection in 2 stages: the first in May 2004 and the second in September 2004.

Mode of data collection

Face-to-face [f2f]

Research instrument

VHLSS 2004 questionnaires are developed based on the VHLSS 2002 questionnaires to ensure the comparability between two surveys. These are some changes in households and commune questionnaires. The major changes in the VHLSS 2004 household questionnaire are -- two additional new modules for long household questionnaire that are found in section 9 (additional section) and section 10 (non-farm self employment activities).

The questionnaires are structured as follows:

• Section 1: Demographic characteristics (Roster)

• Section 2: Education and vocational training

• Section 3: Health and health care

• Section 4: Income

• Section 5: Expenditure

• Section 6: Fixed assets and consumer durables

• Section 7: Housing, water and sanitation

• Section 8: Participation in the poverty alleviation and hunger eradication programme and credit

• Section 9: Agriculture, forestry and aquaculture (expanded)

• Section 10: Business other than agriculture, forestry and aquaculture (expanded)

The commune questionnaire consists of 10 sections:

• Section 0: Survey information

• Section 1: Demographic characteristics and general situation of the commune

• Section 2: General economic status and assistance programmes

• Section 3: Opportunity for non-farm employment

• Section 4: Agriculture and land

• Section 5: Infrastructure

• Section 6: Education

• Section 7: Health

• Section 8: Public security and social issues

• Section 9: Credit and saving

The World Intellectual Property Organization (WIPO) Patent Indicators are part of WIPO's comprehensive intellectual property (IP) statistics program. These indicators provide quantitative measures of patent activity across countries, industries, and time—offering insights into innovation trends, technology development, and the global IP landscape. This collection includes only a subset of indicators from the source dataset.

Charges for the use of intellectual property, payments (BoP, current US$) in World was reported at 595121073264 USD in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. World - Royalty and license fees, payments (BoP, current US$) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

Laos LA: Time Required to Register Property data was reported at 53.000 Day in 2017. This stayed constant from the previous number of 53.000 Day for 2016. Laos LA: Time Required to Register Property data is updated yearly, averaging 53.000 Day from Dec 2004 (Median) to 2017, with 14 observations. The data reached an all-time high of 53.000 Day in 2017 and a record low of 45.000 Day in 2009. Laos LA: Time Required to Register Property data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Laos – Table LA.World Bank: Company Statistics. Time required to register property is the number of calendar days needed for businesses to secure rights to property.; ; World Bank, Doing Business project (http://www.doingbusiness.org/).; Unweighted average; Data are presented for the survey year instead of publication year.

Abstract

To facilitate the use of data collected through the high-frequency phone surveys on COVID-19, the Living Standards Measurement Study (LSMS) team has created the harmonized datafiles using two household surveys: 1) the country’ latest face-to-face survey which has become the sample frame for the phone survey, and 2) the country’s high-frequency phone survey on COVID-19.

The LSMS team has extracted and harmonized variables from these surveys, based on the harmonized definitions and ensuring the same variable names. These variables include demography as well as housing, household consumption expenditure, food security, and agriculture. Inevitably, many of the original variables are collected using questions that are asked differently. The harmonized datafiles include the best available variables with harmonized definitions.

Two harmonized datafiles are prepared for each survey. The two datafiles are: 1. HH: This datafile contains household-level variables. The information include basic household characterizes, housing, water and sanitation, asset ownership, consumption expenditure, consumption quintile, food security, livestock ownership. It also contains information on agricultural activities such as crop cultivation, use of organic and inorganic fertilizer, hired labor, use of tractor and crop sales.
2. IND: This datafile contains individual-level variables. It includes basic characteristics of individuals such as age, sex, marital status, disability status, literacy, education and work.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals

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

See “Nigeria - General Household Survey, Panel 2018-2019, Wave 4” and “Nigeria - COVID-19 National Longitudinal Phone Survey 2020” available in the Microdata Library for details.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Nigeria General Household Survey, Panel (GHS-Panel) 2018-2019 and Nigeria COVID-19 National Longitudinal Phone Survey (COVID-19 NLPS) 2020 data were harmonized following the harmonization guidelines (see “Harmonized Datafiles and Variables for High-Frequency Phone Surveys on COVID-19” for more details).

The high-frequency phone survey on COVID-19 has multiple rounds of data collection. When variables are extracted from multiple rounds of the survey, the originating round of the survey is noted with “_rX” in the variable name, where X represents the number of the round. For example, a variable with “_r3” presents that the variable was extracted from Round 3 of the high-frequency phone survey. Round 0 refers to the country’s latest face-to-face survey which has become the sample frame for the high-frequency phone surveys on COVID-19. When the variables are without “_rX”, they were extracted from Round 0.

Response rate

See “Nigeria - General Household Survey, Panel 2018-2019, Wave 4” and “Nigeria - COVID-19 National Longitudinal Phone Survey 2020” available in the Microdata Library for details.

Abstract

To facilitate the use of data collected through the high-frequency phone surveys on COVID-19, the Living Standards Measurement Study (LSMS) team has created the harmonized datafiles using two household surveys: 1) the country’ latest face-to-face survey which has become the sample frame for the phone survey, and 2) the country’s high-frequency phone survey on COVID-19.

The LSMS team has extracted and harmonized variables from these surveys, based on the harmonized definitions and ensuring the same variable names. These variables include demography as well as housing, household consumption expenditure, food security, and agriculture. Inevitably, many of the original variables are collected using questions that are asked differently. The harmonized datafiles include the best available variables with harmonized definitions.

Two harmonized datafiles are prepared for each survey. The two datafiles are: 1. HH: This datafile contains household-level variables. The information include basic household characterizes, housing, water and sanitation, asset ownership, consumption expenditure, consumption quintile, food security, livestock ownership. It also contains information on agricultural activities such as crop cultivation, use of organic and inorganic fertilizer, hired labor, use of tractor and crop sales. 2. IND: This datafile contains individual-level variables. It includes basic characteristics of individuals such as age, sex, marital status, disability status, literacy, education and work.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals

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

See “Ethiopia - Socioeconomic Survey 2018-2019” and “Ethiopia - COVID-19 High Frequency Phone Survey of Households 2020” available in the Microdata Library for details.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Ethiopia Socioeconomic Survey (ESS) 2018-2019 and Ethiopia COVID-19 High Frequency Phone Survey of Households (HFPS) 2020 data were harmonized following the harmonization guidelines (see “Harmonized Datafiles and Variables for High-Frequency Phone Surveys on COVID-19” for more details).

The high-frequency phone survey on COVID-19 has multiple rounds of data collection. When variables are extracted from multiple rounds of the survey, the originating round of the survey is noted with “_rX” in the variable name, where X represents the number of the round. For example, a variable with “_r3” presents that the variable was extracted from Round 3 of the high-frequency phone survey. Round 0 refers to the country’s latest face-to-face survey which has become the sample frame for the high-frequency phone surveys on COVID-19. When the variables are without “_rX”, they were extracted from Round 0.

Response rate

See “Ethiopia - Socioeconomic Survey 2018-2019” and “Ethiopia - COVID-19 High Frequency Phone Survey of Households 2020” available in the Microdata Library for details.

This map is adapted from the outstanding work of Dr. Joseph Kerski at ESRI. A map of political, social, and economic indicators for 2010. Created at the Data Analysis and Social Inquiry Lab at Grinnell College by Megan Schlabaugh, April Chen, and Adam Lauretig.Data from Freedom House, the Center for Systemic Peace, and the World Bank.Shapefile:Weidmann, Nils B., Doreen Kuse, and Kristian Skrede Gleditsch. 2010. The Geography of the International System: The CShapes Dataset. International Interactions 36 (1).Field Descriptions:

Variable Name Variable Description Years Available Further Description Source

TotPop Total Population 2011 Population of the country/region World Bank

GDPpcap GDP per capita (current USD) 2011 A measure of the total output of a country that takes the gross domestic product (GDP) and divides it by the number of people in the country. The per capita GDP is especially useful when comparing one country to another because it shows the relative performance of the countries. World Bank

GDPpcapPPP GDP per capita based on purchasing power parity (PPP) 2011

World Bank

HDI Human Development Index (HDI) 2011 A tool developed by the United Nations to measure and rank countries' levels of social and economic development based on four criteria: Life expectancy at birth, mean years of schooling, expected years of schooling and gross national income per capita. The HDI makes it possible to track changes in development levels over time and to compare development levels in different countries. World Bank

LifeExpct Life expectancy at birth 2011 The probable number of years a person will live after a given age, as determined by mortality in a specific geographic area. World Bank

MyrSchool Mean years of schooling 2011 Years that a 25-year-old person or older has spent in schools World Bank

ExpctSch Expected years of schooling 2011 Number of years of schooling that a child of school entrance age can expect to receive if prevailing patterns of age-specific enrolment rates persist throughout the child’s life. World Bank

GNIpcap Gross National Income (GNI) per capita 2011 Gross national income (GNI) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. GNI per capita is gross national income divided by mid-year population. World Bank

GNIpcapHDI GNI per capita rank minus HDI rank 2011

World Bank

NaIncHDI Nonincome HDI
2011

World Bank

15+LitRate Adult (15+) literacy rate (%). Total 2010

UNESCO

EmplyAgr Employment in Agriculture 2009

World Bank

GDPenergy GDP per unit of energy use 2010 The PPP GDP per kilogram of oil equivalent of energy use. World Bank

GDPgrowth GDP growth (annual %) 2011

World Bank

GDP GDP (current USD) 2011

World Bank

ExptGDP Exports of Goods and Service (% GDP) 2011 The value of all goods and other market services provided to the rest of the world World Bank

ImprtGDP Imports of Goods and Service (% GDP) 2011 The value of all goods and other market services received from the rest of the world. World Bank

AgrGDP Agriculture, Value added (% GDP) 2011 Agriculture corresponds to ISIC divisions 1-5 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. World Bank

FDI Foreign Direct Investment, net (current USD) 2011 Foreign direct investment are the net inflows of investment to acquire a lasting management interest (10 percent or more of voting stock) in an enterprise operating in an economy other than that of the investor. It is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in the balance of payments. World Bank

GNIpcap GNI per capita PP 2011 GNI per capita based on purchasing power parity (PPP). PPP GNI is gross national income (GNI) converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GNI as a U.S. dollar has in the United States. GNI is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. World Bank

Inflatn Inflation, Consumer Prices (annual %) 2011 Inflation as measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly. World Bank

InfltnGDP Inflation, GDP deflator (annual %) 2011 Inflation as measured by the annual growth rate of the GDP implicit deflator shows the rate of price change in the economy as a whole. The GDP implicit deflator is the ratio of GDP in current local currency to GDP in constant local currency. World Bank

PctWomParl % women in national parliament 2010

United Nations

IntnetUser Internet Users, per 100 peple 2011 Internet users are people with access to the worldwide network. World Bank

HIVPrevlnc Estimated HIV Prevalence% - (Ages 15-49) 2009 Prevalence of HIV refers to the percentage of people ages 15-49 who are infected with HIV. UNAIDS estimates. UNAIDS

AgrLand Agricultural land (% of land area) 2009 Agricultural land refers to the share of land area that is arable, under permanent crops, and under permanent pastures. World Bank

AidRecPP Aid received per person (current US$) 2010 Net official development assistance (ODA) per capita consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients; and is calculated by dividing net ODA received by the midyear population estimate. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). World Bank

AlcohAdul Alcohol consumption per adult (15+) in litres 2008 Liters of pure alcohol, computed as the sum of alcohol production and imports, less alcohol exports, divided by the adult population (aged 15 years and older). World Health Organization

ArmyPct Military expenditure (% of central government expenditure) 2008 Military expenditures data from SIPRI are derived from the NATO definition, which includes all current and capital expenditures on the armed forces, including peacekeeping forces; defense ministries and other government agencies engaged in defense projects; paramilitary forces, if these are judged to be trained and equipped for military operations; and military space activities. Such expenditures include military and civil personnel, including retirement pensions of military personnel and social services for personnel; operation and maintenance; procurement; military research and development; and military aid (in the military expenditures of the donor country). World Development Indicators (World Bank)

TFR Total Fertility Rate 2011 The average number of children that would be born per woman if all women lived to the end of their childbearing years and bore children according to a given fertility rate at each age. This indicator shows the potential for population change in a country. World Bank

CO2perUSD CO2 kg per USD 2008 Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring. World Bank

ExpdtrPrim Expenditure per student, primary (% of GDP per capita) 2008 Public expenditure per pupil as a % of GDP per capita. Primary is the total public expenditure per student in primary education as a percentage of GDP per capita. Public expenditure (current and capital) includes government spending on educational institutions (both public and private), education administration as well as subsidies for private entities (students/households and other privates entities). World Bank

ExpdtrSecd Expenditure per student, secondary (% of GDP per capita) 2008 Public expenditure per pupil as a % of GDP per capita. Secondary is the total public expenditure per student in secondary education as a percentage of GDP per capita. World Bank

ExpdtrTert Expenditure per student, tertiary (% of GDP per capita) 2008 Public expenditure per pupil as a % of GDP per capita. Tertiary is the total public expenditure per student in tertiary education as a percentage of GDP per capita. World Bank

FDIoutf Foreign direct investment, net outflows (% of GDP) 2010 Foreign direct investment are the net inflows of investment to acquire a lasting management interest (10 percent or more of voting stock) in an enterprise operating in an economy other than that of the investor. It is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in the balance of payments. This series shows net outflows of investment from the

CPIA property rights and rule-based governance rating (1=low to 6=high) in Haiti was reported at 2 in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Haiti - CPIA property rights and rule-based governance rating (1=low to 6=high) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

Abstract

To facilitate the use of data collected through the high-frequency phone surveys on COVID-19, the Living Standards Measurement Study (LSMS) team has created the harmonized datafiles using two household surveys: 1) the country’ latest face-to-face survey which has become the sample frame for the phone survey, and 2) the country’s high-frequency phone survey on COVID-19.

The LSMS team has extracted and harmonized variables from these surveys, based on the harmonized definitions and ensuring the same variable names. These variables include demography as well as housing, household consumption expenditure, food security, and agriculture. Inevitably, many of the original variables are collected using questions that are asked differently. The harmonized datafiles include the best available variables with harmonized definitions.

Two harmonized datafiles are prepared for each survey. The two datafiles are: 1. HH: This datafile contains household-level variables. The information include basic household characterizes, housing, water and sanitation, asset ownership, consumption expenditure, consumption quintile, food security, livestock ownership. It also contains information on agricultural activities such as crop cultivation, use of organic and inorganic fertilizer, hired labor, use of tractor and crop sales.
2. IND: This datafile contains individual-level variables. It includes basic characteristics of individuals such as age, sex, marital status, disability status, literacy, education and work.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals

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

See “Malawi - Integrated Household Panel Survey 2010-2013-2016-2019 (Long-Term Panel, 102 EAs)” and “Malawi - High-Frequency Phone Survey on COVID-19” available in the Microdata Library for details.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Malawi Integrated Household Panel Survey (IHPS) 2019 and Malawi High-Frequency Phone Survey on COVID-19 data were harmonized following the harmonization guidelines (see “Harmonized Datafiles and Variables for High-Frequency Phone Surveys on COVID-19” for more details).

The high-frequency phone survey on COVID-19 has multiple rounds of data collection. When variables are extracted from multiple rounds of the survey, the originating round of the survey is noted with “_rX” in the variable name, where X represents the number of the round. For example, a variable with “_r3” presents that the variable was extracted from Round 3 of the high-frequency phone survey. Round 0 refers to the country’s latest face-to-face survey which has become the sample frame for the high-frequency phone surveys on COVID-19. When the variables are without “_rX”, they were extracted from Round 0.

Response rate

See “Malawi - Integrated Household Panel Survey 2010-2013-2016-2019 (Long-Term Panel, 102 EAs)” and “Malawi - High-Frequency Phone Survey on COVID-19” available in the Microdata Library for details.

CPIA property rights and rule-based governance rating (1=low to 6=high) in Afghanistan was reported at 1.5 in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Afghanistan - CPIA property rights and rule-based governance rating (1=low to 6=high) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

Abstract

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

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

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

Geographic coverage

National

Analysis unit

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

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

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

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

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

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

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

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

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

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

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

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

Mode of data collection

Face-to-face [f2f]

United States US: Procedures to Register Property data was reported at 4.400 Number in 2017. This stayed constant from the previous number of 4.400 Number for 2016. United States US: Procedures to Register Property data is updated yearly, averaging 4.400 Number from Dec 2013 (Median) to 2017, with 5 observations. The data reached an all-time high of 4.400 Number in 2017 and a record low of 4.400 Number in 2017. United States US: Procedures to Register Property data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Company Statistics. Number of procedures to register property is the number of procedures required for a businesses to secure rights to property.; ; World Bank, Doing Business project (http://www.doingbusiness.org/).; Unweighted average; Data are presented for the survey year instead of publication year.

Laos LA: Procedures to Register Property data was reported at 4.000 Number in 2017. This stayed constant from the previous number of 4.000 Number for 2016. Laos LA: Procedures to Register Property data is updated yearly, averaging 4.000 Number from Dec 2004 (Median) to 2017, with 14 observations. The data reached an all-time high of 8.000 Number in 2009 and a record low of 4.000 Number in 2017. Laos LA: Procedures to Register Property data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Laos – Table LA.World Bank: Company Statistics. Number of procedures to register property is the number of procedures required for a businesses to secure rights to property.; ; World Bank, Doing Business project (http://www.doingbusiness.org/).; Unweighted average; Data are presented for the survey year instead of publication year.

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

Costa Rica CR: Time Required to Register Property data was reported at 11.000 Day in 2019. This stayed constant from the previous number of 11.000 Day for 2018. Costa Rica CR: Time Required to Register Property data is updated yearly, averaging 19.000 Day from Dec 2004 (Median) to 2019, with 16 observations. The data reached an all-time high of 21.000 Day in 2009 and a record low of 11.000 Day in 2019. Costa Rica CR: Time Required to Register Property data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Costa Rica – Table CR.World Bank.WDI: Company Statistics. Time required to register property is the number of calendar days needed for businesses to secure rights to property.;World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme;Unweighted average;Data are presented for the survey year instead of publication year.

United States US: Time Required to Register Property data was reported at 15.200 Day in 2017. This stayed constant from the previous number of 15.200 Day for 2016. United States US: Time Required to Register Property data is updated yearly, averaging 15.200 Day from Dec 2013 (Median) to 2017, with 5 observations. The data reached an all-time high of 15.200 Day in 2017 and a record low of 15.200 Day in 2017. United States US: Time Required to Register Property data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Company Statistics. Time required to register property is the number of calendar days needed for businesses to secure rights to property.; ; World Bank, Doing Business project (http://www.doingbusiness.org/).; Unweighted average; Data are presented for the survey year instead of publication year.

Chad TD: Procedures to Register Property data was reported at 6.000 Number in 2019. This stayed constant from the previous number of 6.000 Number for 2018. Chad TD: Procedures to Register Property data is updated yearly, averaging 6.000 Number from Dec 2004 (Median) to 2019, with 16 observations. The data reached an all-time high of 6.000 Number in 2019 and a record low of 6.000 Number in 2019. Chad TD: Procedures to Register Property data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Chad – Table TD.World Bank.WDI: Company Statistics. Number of procedures to register property is the number of procedures required for a businesses to secure rights to property.;World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme;Unweighted average;Data are presented for the survey year instead of publication year.

Togo TG: Time Required to Register Property data was reported at 283.000 Day in 2017. This stayed constant from the previous number of 283.000 Day for 2016. Togo TG: Time Required to Register Property data is updated yearly, averaging 295.000 Day from Dec 2004 (Median) to 2017, with 14 observations. The data reached an all-time high of 295.000 Day in 2014 and a record low of 283.000 Day in 2017. Togo TG: Time Required to Register Property data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Togo – Table TG.World Bank: Company Statistics. Time required to register property is the number of calendar days needed for businesses to secure rights to property.; ; World Bank, Doing Business project (http://www.doingbusiness.org/).; Unweighted average; Data are presented for the survey year instead of publication year.

Antigua and Barbuda AG: Time Required to Register Property data was reported at 32.000 Day in 2019. This stayed constant from the previous number of 32.000 Day for 2018. Antigua and Barbuda AG: Time Required to Register Property data is updated yearly, averaging 25.000 Day from Dec 2005 (Median) to 2019, with 15 observations. The data reached an all-time high of 108.000 Day in 2016 and a record low of 25.000 Day in 2014. Antigua and Barbuda AG: Time Required to Register Property data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Antigua and Barbuda – Table AG.World Bank.WDI: Company Statistics. Time required to register property is the number of calendar days needed for businesses to secure rights to property.;World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme;Unweighted average;Data are presented for the survey year instead of publication year.