The average for 2021 based on 196 countries was 656013 sq. km. The highest value was in Russia: 16376870 sq. km and the lowest value was in Monaco: 2 sq. km. The indicator is available from 1961 to 2022. Below is a chart for all countries where data are available.

Even though Canada is the second largest country in the world in terms of land area, it ranks 33rd in terms of population. Almost all of Canada’s population is concentrated in a narrow band along the country’s southern edge. Nearly 80% of the total population lives within the 25 major metropolitan areas, which represent only 0.79% of the total area of the country.

Even though Canada is the second largest country in the world in terms of land area, it ranks 33rd in terms of population. Almost all of Canada’s population is concentrated in a narrow band along the country’s southern edge. Nearly 80% of the total population lives within the 25 major metropolitan areas, which represent only 0.79% of the total area of the country.

The majority of the Canadian population, about 60% is concentrated within a thin belt of land representing 2.2% of the land between Windsor, Ontario and Quebec City. Even though Canada is the second largest country in the world in terms of land area, it only ranks 33rd in terms of population. The agricultural areas in the Prairies and eastern Canada have higher population densities than the sparsely populated North, but not as high as southern Ontario or southern Quebec.

The average for 2021 based on 11 countries was 671160 sq. km. The highest value was in India: 2973190 sq. km and the lowest value was in Singapore: 718 sq. km. The indicator is available from 1961 to 2022. Below is a chart for all countries where data are available.

The majority of the Canadian population, about 60% is concentrated within a thin belt of land representing 2.2% of the land between Windsor, Ontario and Quebec City. Even though Canada is the second largest country in the world in terms of land area, it only ranks 33rd in terms of population. The agricultural areas in the Prairies and eastern Canada have higher population densities than the sparsely populated North, but not as high as southern Ontario or southern Quebec.

As of March 2025, there were a reported 5,426 data centers in the United States, the most of any country worldwide. A further 529 were located in Germany, while 523 were located in the United Kingdom. What is a data center? A data center is a network of computing and storage resources that enables the delivery of shared software applications and data. These facilities can house large amounts of critical and important data, and therefore are vital to the daily functions of companies and consumers alike. As a result, whether it is a cloud, colocation, or managed service, data center real estate will have increasing importance worldwide. Hyperscale data centers In the past, data centers were highly controlled physical infrastructures, but the cloud has since changed that model. A cloud data service is a remote version of a data center – located somewhere away from a company's physical premises. Cloud IT infrastructure spending has grown and is forecast to rise further in the coming years. The evolution of technology, along with the rapid growth in demand for data across the globe, is largely driven by the leading hyperscale data center providers.

PyPSA-Eur is an open model dataset of the European power system at the transmission network level that covers the full ENTSO-E area. It can be built using the code provided at https://github.com/PyPSA/PyPSA-eur.

It contains alternating current lines at and above 220 kV voltage level and all high voltage direct current lines, substations, an open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power.

Not all data dependencies are shipped with the code repository, since git is not suited for handling large changing files. Instead we provide separate data bundles to be downloaded and extracted as noted in the documentation.

This is the full data bundle to be used for rigorous research. It includes large bathymetry and natural protection area datasets.

While the code in PyPSA-Eur is released as free software under the MIT, different licenses and terms of use apply to the various input data, which are summarised below:

corine/*

CORINE Land Cover (CLC) database

Source: https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012/

Terms of Use: https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012?tab=metadata

natura/*

Natura 2000 natural protection areas

Source: https://www.eea.europa.eu/data-and-maps/data/natura-10

Terms of Use: https://www.eea.europa.eu/data-and-maps/data/natura-10#tab-metadata

gebco/GEBCO_2014_2D.nc

GEBCO bathymetric dataset

Source: https://www.gebco.net/data_and_products/gridded_bathymetry_data/version_20141103/

Terms of Use: https://www.gebco.net/data_and_products/gridded_bathymetry_data/documents/gebco_2014_historic.pdf

je-e-21.03.02.xls

Population and GDP data for Swiss Cantons

Source: https://www.bfs.admin.ch/bfs/en/home/news/whats-new.assetdetail.7786557.html

Terms of Use:

https://www.bfs.admin.ch/bfs/en/home/fso/swiss-federal-statistical-office/terms-of-use.html

https://www.bfs.admin.ch/bfs/de/home/bfs/oeffentliche-statistik/copyright.html

nama_10r_3popgdp.tsv.gz

Population by NUTS3 region

Source: http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_10r_3popgdp&lang=en

Terms of Use:

https://ec.europa.eu/eurostat/about/policies/copyright

GDP_per_capita_PPP_1990_2015_v2.nc

Gross Domestic Product per capita (PPP) from years 1999 to 2015

Rectangular cutout for European countries in PyPSA-Eur, including a 10 km buffer

Kummu et al. "Data from: Gridded global datasets for Gross Domestic Product and Human Development Index over 1990-2015"

Source: https://doi.org/10.1038/sdata.2018.4 and associated dataset https://doi.org/10.1038/sdata.2018.4

ppp_2019_1km_Aggregated.tif

The spatial distribution of population in 2020: Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.

Rectangular cutout for non-NUTS3 countries in PyPSA-Eur, i.e. MD and UA, including a 10 km buffer

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00647

Source: https://data.humdata.org/dataset/worldpop-population-counts-for-world and https://hub.worldpop.org/geodata/summary?id=24777

License: Creative Commons Attribution 4.0 International Licens

data/bundle/era5-HDD-per-country.csv

• Link: https://gist.github.com/fneum/d99e24e19da423038fd55fe3a4ddf875- License: CC-BY 4.0- Contains country-level heating degree days in Europe for 1941-2023. Used for rescaling heat demand in weather years not covered by energy balance statistics.

data/bundle/era5-runoff-per-country.csv

• Link: https://gist.github.com/fneum/d99e24e19da423038fd55fe3a4ddf875- License: CC-BY 4.0- Contains country-level daily sum of runoff in Europe for 1941-2023. Used for rescaling hydro-electricity availability in weather years not covered by EIA hydro-generation statistics.

shipdensity_global.zip

Global Shipping Traffic Density

Creative Commons Attribution 4.0

https://datacatalog.worldbank.org/search/dataset/0037580/Global-Shipping-Traffic-Density

seawater_temperature.nc

Global Ocean Physics Reanalysis

Seawater temperature at 5m depth

Link: https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEAR_PHY_001_030/services

License: https://marine.copernicus.eu/user-corner/service-commitments-and-licence

The role of Pre- and Protohistoric anthropogenic land cover changes needs to be quantified i) to establish a baseline for comparison with current human impact on the environment and ii) to separate it from naturally occurring changes in our environment. Results are presented from the simple, adaptation-driven, spatially explicit Global Land Use and technological Evolution Simulator (GLUES) for pre-Bronze age demographic, technological and economic change. Using scaling parameters from the History Database of the Global Environment as well as GLUES-simulated population density and subsistence style, the land requirement for growing crops is estimated. The intrusion of cropland into potentially forested areas is translated into carbon loss due to deforestation with the dynamic global vegetation model VECODE. The land demand in important Prehistoric growth areas - converted from mostly forested areas - led to large-scale regional (country size) deforestation of up to 11% of the potential forest. In total, 29 Gt carbon were lost from global forests between 10 000 BC and 2000 BC and were replaced by crops; this value is consistent with other estimates of Prehistoric deforestation. The generation of realistic (agri-)cultural development trajectories at a regional resolution is a major strength of GLUES. Most of the pre-Bronze age deforestation is simulated in a broad farming belt from Central Europe via India to China. Regional carbon loss is, e.g., 5 Gt in Europe and the Mediterranean, 6 Gt on the Indian subcontinent, 18 Gt in East and Southeast Asia, or 2.3 Gt in subsaharan Africa. Global subsistence style and technological progress for the period 9500 BC to 2000 BC were hindcasted with the Global Land Use and technological Evolution Simulator (GLUES) for 685 land regions of the world. The intensification of subsistence is visible in the transition from hunting-gathering to agropastoral life style in many world regions. This transition is based on an increase of domesticated plant and animal resources and technological progress, and can sustain much higher population densities than the foraging life style.The advent of agriculture creates an areal demand for growing crops; where the crop area is occupied by forest in potential vegetation estimated with a dynamical global vegetation model (VECODE), the aboveground and belowground carbon pools are reallocated; the net release of carbon to the atmosphere is calculated.Initial values for each prognostic variable are identical at simulation start (9500 BC), but the background vegetation varies. Vegetation productivity in terms of net primary production (NPP) was derived from Climber-2 climate anomalies on the IIASA database for mean monthly precipitation and temperature and subsequent application of the Miami model.Data are presented as 50-year averages with time indicating the central year of each 50-year period (i.e. -2425 denotes the period 2450 BC - 2401 BC), and geographically on a half degree grid with latitude and longitude values denoting the central value within each grid cell.Model data are from sub-project GLUES (Global Land Use and Technological Evolution Simulations on New Paleoclimate data: Quantified impact of Holocene climate change on land use, regional agrarianisation and anthropogenic deforestation with feedback, see: hdl:10013/epic.35233.d001).

Before the Second World War, the Soviet Union was the largest individual world power in terms of territory, at over 21 million square kilometers. When the territories of the United Kingdom it's colonies and dominions are combined, then the expanse of the British Empire totaled at almost 35 million square kilometers, making it larger than the USSR.

The Axis Powers, led by Germany, Italy, and Japan, controlled a much smaller share of the globe than the Allied Powers in 1938 - however, by 1941, the majority of Europe was under (German-led) Axis control, while Japan had taken much of the Western territories in Asia and had pushed further into China. Italy had also sought to consolidate its power in North and East Africa at this time, but was largely contained by British Commonwealth Forces. Although 1941 and 1942 marked the largest territorial gains for the Axis Powers in Europe and Asia, Italy had lost virtually all of its African colonies by the end of 1942, which totaled at just under 3.5 million square kilometers of land. Late 1942 also marked the turning point in the war in the other theaters, where the Soviet counteroffensive started pushing the German lines back in Europe, while American and Commonwealth Forces began pushing the Japanese north through the Asia-Pacific at the end of the year. The war in Europe ended in May, 1945, and in the Pacific in September, 1945.

India is one of the major players in the agriculture sector worldwide and it is the primary source of livelihood for ~55% of India’s population. India has the world's largest cattle herd (buffaloes), largest area planted to wheat, rice, and cotton, and is the largest producer of milk, pulses, and spices in the world. It is the second-largest producer of fruit, vegetables, tea, farmed fish, cotton, sugarcane, wheat, rice, cotton, and sugar. Agriculture sector in India holds the record for second-largest agricultural land in the world generating employment for about half of the country’s population. Thus, farmers become an integral part of the sector to provide us with means of sustenance.

Consumer spending in India will return to growth in 2021 post the pandemic-led contraction, expanding by as much as 6.6%. The Indian food industry is poised for huge growth, increasing its contribution to world food trade every year due to its immense potential for value addition, particularly within the food processing industry. The Indian food processing industry accounts for 32% of the country’s total food market, one of the largest industries in India and is ranked fifth in terms of production, consumption, export and expected growth.

This data contains the production and area grown for each crop at ditrict level from 1997 to 2015.

This dataset provides values for GOLD RESERVES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Abstract

The study was conducted in Cameroon from June 1 to Oct. 15, 2009, as part of the Enterprise Survey, an initiative of the World Bank.

The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance. The mode of data collection is face-to-face interviews.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for registered establishments in Cameroon was selected using stratified random sampling. Three levels of stratification were used in the Cameroon sample: firm sector, firm size, and geographic region.

Industry stratification was designed as follows: the universe was stratified into one manufacturing industry, one services industry (retail) and one services residual sector. The initial sample design had a target of 120 interviews in manufacturing, 120 interviews in retail and 120 interviews in the services residual categories.

Size stratification was defined following the standardized definition used for the Enterprise Surveys: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent fulltime workers.

Regional stratification was defined in terms of the geographic regions with the largest commercial presence in the country: Littoral (Douala), Centre (Yaoundé) and West (Bafoussam) were the three metropolitan areas selected in Cameroon.

Two frames were used for Cameroon. The first was obtained from the Chamber of commerce of Cameroon (2008). The sample frame collected information for 8000 companies in various sector of activities spread in all the regions of Cameroon. The second frame (the panel sample) consisted of enterprises interviewed for the Enterprise Survey in 2006, which were to be re-interviewed where they were in the selected geographical regions and met eligibility criteria. Both database contained the following information: -Name of the firm -Contact details -ISIC code -Number of employees.

According to the local contractor, the list was not accurate; it was found that many firms listed in the sample frame had wrong names, wrong addresses and phone numbers. There were also few cases of wrong classifications in terms of sector of activity: many firms classified in the sample frame as manufacturing were in reality services. Furthermore, the local implementing agency commented that some companies had wrong information in terms of number of workers.

The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 58.5% (971 out of 1,659 establishments for the ES and micro samples, including panel establishments).

Breaking down by industry, the following numbers of establishments were surveyed: Manufacturing -116, Sector 52 -132, Other Services - 114.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Core Questionnaire + Manufacturing Module [ISIC Rev.3.1: 15-37] - Core Questionnaire + Retail Module [ISIC Rev.3.1: 52] - Core Questionnaire [ISIC Rev.3.1: 45, 50, 51, 55, 60-64, 72] - Screener Questionnaire.

The “Core Questionnaire” is the heart of the Enterprise Survey and contains the survey questions asked of all firms across the world. There are also two other survey instruments - the “Core Questionnaire + Manufacturing Module” and the “Core Questionnaire + Retail Module.” The survey is fielded via three instruments in order to not ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Complete information regarding the sampling methodology, sample frame, weights, response rates, and implementation can be found in "Description of Cameroon Implementation 2009" in "Technical Documents" folder.

This dataset provides values for GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset provides values for GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Abstract

The documented dataset covers Enterprise Survey (ES) panel data collected in Sierra Leone in 2009 and 2017, as part of the Enterprise Survey initiative of the World Bank. An Indicator Survey is similar to an Enterprise Survey; it is implemented for smaller economies where the sampling strategies inherent in an Enterprise Survey are often not applicable due to the limited universe of firms.

The objective of the 2009-2017 survey is to obtain feedback from enterprises in client countries on the state of the private sector as well as to build a panel of enterprise data that will make it possible to track changes in the business environment over time and allow, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the Indicator Survey data provides information on the constraints to private sector growth and is used to create statistically significant business environment indicators that are comparable across countries. As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms' experiences and enterprises' perception of the environment in which they operate.

Questionnaire topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, land and permits, taxation, business-government relations, performance measures, AIDS and sickness. The mode of data collection is face-to-face interviews.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for registered establishments in Sierra Leone was selected using stratified random sampling, following the methodology explained in the Sampling Note.

Stratified random sampling was preferred over simple random sampling for several reasons: a. To obtain unbiased estimates for different subdivisions of the population with some known level of precision. b. To obtain unbiased estimates for the whole population. The whole population, or universe of the study, is the non-agricultural economy. It comprises: all manufacturing sectors according to the group classification of ISIC Revision 3.1: (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors. c. To make sure that the final total sample includes establishments from all different sectors and that it is not concentrated in one or two of industries/sizes/regions. d. To exploit the benefits of stratified sampling where population estimates, in most cases, will be more precise than using a simple random sampling method (i.e., lower standard errors, other things being equal.) e. Stratification may produce a smaller bound on the error of estimation than would be produced by a simple random sample of the same size. This result is particularly true if measurements within strata are homogeneous. f. The cost per observation in the survey may be reduced by stratification of the population elements into convenient groupings.

Three levels of stratification were used in the Sierra Leone sample: firm sector, firm size, and geographic region.

Industry stratification was designed as follows: the universe was stratified into one manufacturing industry and one services industry (retail).

Size stratification was defined following the standardized definition used for the Indicator Surveys: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers.

Regional stratification was defined in terms of the geographic regions with the largest commercial presence in the country: Kenema and W/A Urban. In 2017, regional stratification was done across four regions: Bo, Western Urban, Kenema, and Bombali.

Given the stratified design, sample frames containing a complete and updated list of establishments as well as information on all stratification variables (number of employees, industry, and region) are required to draw the sample. Great efforts were made to obtain the best source for these listings.

The sample frame consisted of listings of firms from two sources: For panel firms the list of 150 firms from the Sierra Leone 2009 ES was used and for fresh firms (i.e., firms not covered in 2009) firm data from 2016 Business Establishment Census and Dun & Bradstreet Global database (June 2017), was used.

Necessary measures were taken to ensure the quality of the frame; however, the sample frame was not immune to the typical problems found in establishment surveys: positive rates of non-eligibility, repetition, non-existent units, etc.

Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 8.9% (18 out of 202 establishments).

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Services and Manufacturing Questionnaire - Screener Questionnaire.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country's business environment. The remaining questions assess the survey respondents' opinions on what are the obstacles to firm growth and performance.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

There was a high response rate especially as a result of positive attitude towards the international community in collaboration with the government in their reconstruction efforts after a period of civil strife. It is period in which a lot of statistics is being collected by the Sierra Leone Statistics for reconstruction thus most respondents were enlightened on research benefits.

The average for 2021 based on 53 countries was 117869 sq. km. The highest value was in Democratic Republic of the Congo: 1250538.6 sq. km and the lowest value was in Djibouti: 58.7 sq. km. The indicator is available from 1990 to 2022. Below is a chart for all countries where data are available.

This dataset provides values for EMPLOYMENT RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The United States recorded a trade deficit of 60.18 USD Billion in June of 2025. This dataset provides the latest reported value for - United States Balance of Trade - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Abstract

The objective of the survey is to obtain feedback from enterprises in client countries on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance. The mode of data collection is face-to-face interviews.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The study was conducted using stratified random sampling. Three levels of stratification were used in the sample: firm sector, firm size, and geographic region.

Industry stratification was designed as follows: the universe was stratified into eight manufacturing industries (food, textiles, garments, shoes & leather, chemicals, machinery & equipment, auto parts, furniture), two services industries (retail and IT) and two residual sectors. The sample design had a target of 1320 interviews in manufacturing and 240 interviews each in the services and residual categories.

Size stratification was defined following the standardized definition used for the Enterprise Surveys: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers.

Regional stratification was defined in terms of the geographic regions with the largest commercial presence in the country: Amazonas, Bahia, Ceara, Distrito Federal, Goias, Maranhao, Mato Grosso, Minas Gerais, Paraiba, Parana, Pernambuco, Rio de Janeiro, Rio Grande do Sul, Santa Catarina, and Sao Paulo.

The Enterprise Survey for Brazil targeted 1800 registered establishments, including 817 establishments with 5 to 19 employees 657 with 20 to 99 employees, and 326 with 100 or more employees.

Given the stratified design, sample frames containing a complete and updated list of establishments as well as information on all stratification variables (number of employees, industry, and region) are required to draw the sample for the Enterprise Surveys.

Two frames were used for Brazil. The first was an extract from the database of all formal establishments obtained from the Brazilian Institute of Geography and Statistics (Instituto Brasileiro de Geografia e Estatística - IBGE]. This database is yearly updated and the 2007 extract was used. The second frame (the panel sample) consisted of enterprises interviewed for the Enterprise Survey in 2003, which were to be re-interviewed where they were in the selected geographical regions and met eligibility criteria. Both database contained the following information: -Name of the firm -Contact details -ISIC code -Number of employees.

Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 30.1% (3,255 out of 10,824 establishments).

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Core Questionnaire + Manufacturing Module [ISIC Rev.3.1: 15-37] - Core Questionnaire + Retail Module [ISIC Rev.3.1: 52] - Core Questionnaire [ISIC Rev.3.1: 45, 50, 51, 55, 60-64, 72] - Screener Questionnaire.

The “Core Questionnaire” is the heart of the Enterprise Survey and contains the survey questions asked of all firms across the world. There are also two other survey instruments - the “Core Questionnaire + Manufacturing Module” and the “Core Questionnaire + Retail Module.” The survey is fielded via three instruments in order to not ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Complete information regarding the sampling methodology, sample frame, weights, response rates, and implementation can be found in "Description of Brazil Implementation" in "Technical Documents" folder.