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 manufacturing and services sectors are the primary business sectors of interest. This corresponds to firms classified with International Standard Industrial Classification of All Economic Activities (ISIC) codes 15-37, 45, 50-52, 55, 60-64, and 72 (ISIC Rev.3.1). Formal (registered) companies with 5 or more employees are targeted for interview. Services firms include construction, retail, wholesale, hotels, restaurants, transport, storage, communications, and IT. Firms with 100% government/state ownership are not eligible to participate in an Enterprise Survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Azerbaijan was selected using stratified random sampling. Three levels of stratification were used in this country: industry, establishment size, and oblast (region).

Industry stratification was designed in the way that follows: the universe was stratified into 23 manufacturing industries, 2 services industries -retail and IT-, and one residual sector. Each sector had a target of 90 interviews.

Size stratification was defined following the standardized definition for the rollout: 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. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

Regional stratification was defined in eight regions. These regions are Praha, Stredni Cechy, Jihozapad, Severozapad, Severovychod, Jihovychod, Stredni Morava, and Moravskoslezsko.

Given the stratified design, sample frames containing a complete and updated list of establishments for the selected regions were required. Great efforts were made to obtain the best source for these listings. However, the quality of the sample frames was not optimal and, therefore, some adjustments were needed to correct for the presence of ineligible units. These adjustments are reflected in the weights computation.

For most countries covered in BEEPS IV, two sample frames were used. The first was supplied by the World Bank and consisted of enterprises interviewed in BEEPS 2005. The World Bank required that attempts should be made to re-interview establishments responding to the BEEPS 2005 survey where they were within the selected geographical regions and met eligibility criteria. That sample is referred to as the Panel. The second frame for the Czech Republic was an official database known as Albertina data [Creditinfo Czech Republic], which is obtained from the complete Business Register [RES] of the Czech Statistical Office. An extract from that frame was sent to the TNS statistical team in London to select the establishments for interview.

The quality of the frame was assessed at the onset of the project. The frame proved to be useful though it showed positive rates of non-eligibility, repetition, non-existent units, etc. These problems are typical of establishment surveys, but given the impact these inaccuracies may have on the results, adjustments were needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of contacts to complete the survey was 28% (572 out of 2041 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 the document "Description of Czech Republic Implementation 2009.pdf"

Question Paper Solutions of chapter Meaning, scope and significance of social research of Research Methodology & Business Statistics, 1st Semester , Master of Business Administration (2023-24)

In 2019, about a third of employees of Italian companies believed that gamification applied to business was a method to influence and modify people's behavior. As reported by the survey, interviewees who would define in this way gamification applied to business, accounted for 30 percent of all respondents. Additionally, 25 percent of interviewees believed that it was a way of enhancing participation and engagement.

Data on enterprise births, deaths, active enterprises and survival rates across boroughs.

Data includes:
1) the most recent annual figures for enterprise births and deaths. Births and deaths are identified by comparing active populations of enterprises for different years
2) time series of the number of births and deaths of entrprises together with a percentage of births and deaths to active enterprises in a given year
3) a time series of the number of active enterprises. Active enterprises are businesses that had either turnover or employment at any time during the reference period.
4) survival rates of enterprises for up to 5 years after birth

Using the most recent data, a chart showing the trend of the percentage of businesses that survive a year is shown below.

https://s3-eu-west-1.amazonaws.com/londondatastore-upload/busi-demog-survival-chart.png" alt="1 year survival">

Data on size of firms (micro-business, SME, large) for business and employees in London by industry can be found on the ONS website.

More Business Demographics data on the ONS website

Data collected as part of the City of Melbourne's Census of Land Use and Employment (CLUE). The data covers the period 2002-2023. It show business establishments with their business address, industry (ANZSIC4) classification, location and CLUE block and small area allocation.

A business establishment is defined as a • Commercial occupant in a building • Separate land use • Any permanent presence of economic activity in accordance with standard Industry classification (ANZSIC).

Hence, if one organisation has its presence in several buildings in the CLUE area, each time it will be counted as a separate establishment. Consequently, the count of establishments presented in CLUE represents the number of locations, rather than 'enterprises'.

For more information about CLUE see http://www.melbourne.vic.gov.au/clue

For more information about the ANZSIC industry classification system see http://www.abs.gov.au/ausstats/abs@.nsf/mf/1292.0

2010 census data on the number of establishments per zip code grouped by sector and payrolled employees/establishment size. To create a pointmap of this dataset you must filter for "Meaning of 2007 North American Industry Classification System (NAICS)" AND "Number of Employees".The filter has been loaded with the necessary values. Dot size = number of establishments. To see changes in dot saize that reflect selected values, the map must be saved. Otherwise it defaults to Construction/1-4 employees.

Abstract

This research was conducted in Venezuela between May 2010 and April 2011 as part of the Latin America and Caribbean (LAC) Enterprise Survey 2010, an initiative of the World Bank. Data from 320 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses.

The objective of the study 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 face-to-face 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.

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 in the way that follows: the universe was stratified into 5 manufacturing industries, 1 service industry -retail -, and 1 residual sector. Each of specified manufacturing stratum had a target of 175 interviews, with residual manufacturing having a target of 120 interviews. Retail and other services had targets each of 120 interviews.

Size stratification was defined following the standardized definition 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. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

Regional stratification was defined in three locations (city and the surrounding business area): Caracas, Maracay, and Valencia.

For Venezuela, two sample frames were used. The first was supplied by the World Bank and consists of enterprises interviewed in Venezuela 2006. The World Bank required that attempts be made to re-interview establishments responding to the Venezuela 2006 survey where they were within the selected geographical locations and met eligibility criteria. That sample is referred to as the Panel. The second sample frame was produced by StatMark, using census materials from 2006, including efforts made to update frame information of un-contacted firms from the previous round of the surveys.

The quality of the frame was assessed at the onset of the project through visits to a random subset of firms and local contractor knowledge. The sample frame was not immune from the typical problems found in establishment surveys: positive rates of non-eligibility, repetition, non-existent units, etc. In addition, the sample frame contains no telephone/fax numbers so the local contractor had to screen the contacts by visiting them. Due to response rate and ineligibility issues, additional sample had to be extracted by the World Bank in order to obtain enough eligible contacts and meet the sample targets.

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 12.17% (112 out of 920 establishments).

Mode of data collection

Face-to-face [f2f]

Research instrument

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

The number of realized interviews per contacted establishment was 0.35. The estimate is based on the total number of firms contacted including ineligible establishments. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 0.21.

Complete information regarding the sampling methodology, sample frame, weights, response rates, and implementation can be found in "Description of Venezuela ES 2010 Implementation" in external resources.

CompanyKG is a heterogeneous graph consisting of 1,169,931 nodes and 50,815,503 undirected edges, with each node representing a real-world company and each edge signifying a relationship between the connected pair of companies.

Edges: We model 15 different inter-company relations as undirected edges, each of which corresponds to a unique edge type. These edge types capture various forms of similarity between connected company pairs. Associated with each edge of a certain type, we calculate a real-numbered weight as an approximation of the similarity level of that type. It is important to note that the constructed edges do not represent an exhaustive list of all possible edges due to incomplete information. Consequently, this leads to a sparse and occasionally skewed distribution of edges for individual relation/edge types. Such characteristics pose additional challenges for downstream learning tasks. Please refer to our paper for a detailed definition of edge types and weight calculations.

Nodes: The graph includes all companies connected by edges defined previously. Each node represents a company and is associated with a descriptive text, such as "Klarna is a fintech company that provides support for direct and post-purchase payments ...". To comply with privacy and confidentiality requirements, we encoded the text into numerical embeddings using four different pre-trained text embedding models: mSBERT (multilingual Sentence BERT), ADA2, SimCSE (fine-tuned on the raw company descriptions) and PAUSE.

Evaluation Tasks. The primary goal of CompanyKG is to develop algorithms and models for quantifying the similarity between pairs of companies. In order to evaluate the effectiveness of these methods, we have carefully curated three evaluation tasks:

Similarity Prediction (SP). To assess the accuracy of pairwise company similarity, we constructed the SP evaluation set comprising 3,219 pairs of companies that are labeled either as positive (similar, denoted by "1") or negative (dissimilar, denoted by "0"). Of these pairs, 1,522 are positive and 1,697 are negative.

Competitor Retrieval (CR). Each sample contains one target company and one of its direct competitors. It contains 76 distinct target companies, each of which has 5.3 competitors annotated in average. For a given target company A with N direct competitors in this CR evaluation set, we expect a competent method to retrieve all N competitors when searching for similar companies to A.

Similarity Ranking (SR) is designed to assess the ability of any method to rank candidate companies (numbered 0 and 1) based on their similarity to a query company. Paid human annotators, with backgrounds in engineering, science, and investment, were tasked with determining which candidate company is more similar to the query company. It resulted in an evaluation set comprising 1,856 rigorously labeled ranking questions. We retained 20% (368 samples) of this set as a validation set for model development.

Edge Prediction (EP) evaluates a model's ability to predict future or missing relationships between companies, providing forward-looking insights for investment professionals. The EP dataset, derived (and sampled) from new edges collected between April 6, 2023, and May 25, 2024, includes 40,000 samples, with edges not present in the pre-existing CompanyKG (a snapshot up until April 5, 2023).

Background and Motivation

In the investment industry, it is often essential to identify similar companies for a variety of purposes, such as market/competitor mapping and Mergers & Acquisitions (M&A). Identifying comparable companies is a critical task, as it can inform investment decisions, help identify potential synergies, and reveal areas for growth and improvement. The accurate quantification of inter-company similarity, also referred to as company similarity quantification, is the cornerstone to successfully executing such tasks. However, company similarity quantification is often a challenging and time-consuming process, given the vast amount of data available on each company, and the complex and diversified relationships among them.

While there is no universally agreed definition of company similarity, researchers and practitioners in PE industry have adopted various criteria to measure similarity, typically reflecting the companies' operations and relationships. These criteria can embody one or more dimensions such as industry sectors, employee profiles, keywords/tags, customers' review, financial performance, co-appearance in news, and so on. Investment professionals usually begin with a limited number of companies of interest (a.k.a. seed companies) and require an algorithmic approach to expand their search to a larger list of companies for potential investment.

In recent years, transformer-based Language Models (LMs) have become the preferred method for encoding textual company descriptions into vector-space embeddings. Then companies that are similar to the seed companies can be searched in the embedding space using distance metrics like cosine similarity. The rapid advancements in Large LMs (LLMs), such as GPT-3/4 and LLaMA, have significantly enhanced the performance of general-purpose conversational models. These models, such as ChatGPT, can be employed to answer questions related to similar company discovery and quantification in a Q&A format.

However, graph is still the most natural choice for representing and learning diverse company relations due to its ability to model complex relationships between a large number of entities. By representing companies as nodes and their relationships as edges, we can form a Knowledge Graph (KG). Utilizing this KG allows us to efficiently capture and analyze the network structure of the business landscape. Moreover, KG-based approaches allow us to leverage powerful tools from network science, graph theory, and graph-based machine learning, such as Graph Neural Networks (GNNs), to extract insights and patterns to facilitate similar company analysis. While there are various company datasets (mostly commercial/proprietary and non-relational) and graph datasets available (mostly for single link/node/graph-level predictions), there is a scarcity of datasets and benchmarks that combine both to create a large-scale KG dataset expressing rich pairwise company relations.

Source Code and Tutorial:https://github.com/llcresearch/CompanyKG2

Paper: to be published

Abstract

The survey covers a sample of larger establishments with five or more paid employees engaged in production, distribution, construction, transport, storage and other business activities like finance, insurance, business services etc, which come under the broad definition of "Industries". It covers both public and private sectors. The public sector coverage is limited to Boards, Corporations, Co-operatives and some Government Departments like the Department of Buildings, the Marketing Department, the Government Factory, and the Railway Workshop which are engaged in the types of activities mentioned above. The bulk of the public services i.e. Government Departments othe than those mentioned above Offices, Schools, Hospitals etc, are not covered in the survey as they do not come under the definition of "Industries".

The objective of the sample survey is to collect detailed labour statistics on a repetitive basis to indicate trends. Statistics of earnings and hours of work are collected including number employed, man days worked and paid for normal, and overtime man hours and the components of the total wage bill. These statistics are collected according to the eightfold broad occupational categories e.g. Administrative & Managerial Workers, Professional, Technical and Related Workers, Clerical and Related Workers etc.

Geographic coverage

National coverage.

Analysis unit

The survey covers establishments with 5 or more paid employees.

Universe

Industries with five or more paid employees.

Kind of data

Sample survey data [ssd]

Sampling procedure

A method of stratified cut-off random sampling is adopted to select a sample which is about 1700 establishments for the survey from some 12,000 establishments which furnish returns under the annual employment survey of the previous year. First 22 industry strata have been set up to give the estimates for the sampled data for each industry stratum, with a five percent error at 95 percent confidence level. In each industry stratum all establishments above a particular size ( defined to be the cut-off point ) were included in the survey and the rest were sampled such that the sample would be optimum in size. It was noticed that in 18 out of 22 industries, the number of employees per establishment was less than 100 and in the other four industries the employment figure per establishmentwas well over 1000. Thus establishments with less than 100 employees in the 18 industries and establishments with less than 1000 employees in other four industries were sampled. All establishments employing over 100 and 1000 respectively were automatically included in the survey.

Mode of data collection

Other [oth]

Cleaning operations

Certain edit checks were introduced in the Computer Programs and on the basis of that some quality checks were exercised.

Listing of all active businesses currently registered with the Office of Finance. An "active" business is defined as a registered business whose owner has not notified the Office of Finance of a cease of business operations. Update Interval: Monthly.

NAICS Codes are from 2007 NAICS: https://www.census.gov/cgi-bin/sssd/naics/naicsrch?chart=2007

The turnover of the total business economy, repair of computers, personal and household goods, except financial and insurance activities in Spain decreased by 251.6 billion euros (-11.75 percent) in 2020 in comparison to the previous year. Nevertheless, the last two years in this industry recorded a significant higher turnover than the preceding years.Turnover is defined by Eurostat as the total of all sales (excluding VAT) of goods and services carried out by the enterprises of a given sector during the reference period.Find more statistics on the total business economy, repair of computers, personal and household goods, except financial and insurance activities in Spain with key insights such as number of enterprises, production value, and personnel costs.

Companies, employees and full-time equivalent (FTE) employees, according to the economic sector, the public or private sector and the size class of the company, in Ticino and by municipality, from 2011 to 2021 Source: Structural business statistics (STATENT), Federal Statistical Office, Neuchâtel Processing: Statistical Office (Ustat), Giubiasco Data version: 25.08.2023 Last modified: 25.08.2023 Variables in the data cube: Year: the common statistical year (at 31.12): Ticino municipalities (status as at 18.04.2021, 108 municipalities) economic sector: economic sector (1 Primary, 2 Secondary, 3 Tertiary) private_public: private or public sector classe_dim_add: Size class of the company Description of statistics: Companies: holdings within the meaning of STATENT Attachés: Employees within the meaning of the STATENT FTE: Full-time equivalent (FTE) employees according to STATENT The ‘info’ column shall contain one of the following information: OK: there are estimates with the characteristics described in row X: not published because it is less than 4 and is the result of estimation using a model (FSO provisions). This restriction is therefore only applied to full-time equivalent (FTE) employees. Signs, symbols, abbreviations, abbreviations and statistical concepts used in Ustat products Glossary: Companies (establishments) Attachés Full-time equivalents (FTEs)

Abstract

The survey of unregistered businesses was conducted in Somalia, between October and December 2019, simultaneously with the Somolia Enterprise Survey 2019. The survey covers two cities: Bosaso and Mogadishu. The fieldwork was implemented by Altai Consulting in collaboration with Tusmo Research and Consulting.

The primary objectives of the survey was to: i) to understand the business demographics of the sector in the two cities, and ii) to describe the environment within which these businesses operate.

A secondary objective of the survey is to provide an estimate of the number of informal businesses operating in these cities.

Geographic coverage

National

Analysis unit

Unit of analysis is informal business.

For the survey in Bosaso and Mogadishu, a business that does not have any of the following two items is considered as informal: i) Registration with the Ministry of Commerce; and ii) Registration with the respective Municipality.

Universe

The universe includes informal businesses, where informality is defined based on whether or not a business is formally registered with the government. The definition of formal registration can vary by country. For the survey in Bosaso and Mogadishu, a business that does not have any of the following two items is considered as informal: i) Registration with the Ministry of Commerce; and ii) Registration with the respective Municipality.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2019 Somalia ISBS uses an innovative technique to survey informal businesses. The survey follows an area-based sampling methodology with geographic area rather than an establishment or a business unit as a primary sampling unit. To account for potential clustering of informal business, the survey uses an area-based sampling called (stratified) Adaptive Cluster Sampling (ACS), whereby one selects a sample of starting squares and adaptively samples surrounding squares based on the number of informal firms discovered in the enumerated squares. All informal business in selected squares are enumerated using a 2 to 3-minutes questionnaire, referred to in this document as the short-form questionnaire. The short form questionnaire is a listing questionnaire where basic information about the business is collected. A randomly selected subset of the enumerated businesses is given a 20-minutes questionnaire, referred to in this document as the long-form questionnaire. This is the main questionnaire of the survey and the basis of the database posted on the ES portal.

The survey is adaptive in the sense that if the number of informal units in a square exceeds a predefined threshold, all the squares surrounding the starting square are surveyed, following the same approach of enumeration and randomly conducting the main interview. If one of the surrounding squares exceed the threshold, then the squares surrounding that square in turn are also surveyed. This process continues until either the network is exhausted, or an arbitrary cut-off point is defined.

The first step in the sampling approach is the construction of a spatial grid as the Primary Sampling Units (PSU) frame, as shown in Appendix A - 1 for Bosaso, and 2 for Mogadishu respectively. The grid covered the total of municipal areas and each cell had a size of 150 by 150 meters. This produced a total of about 3100 squares between the two cities, excluding squares that are considered inaccessible. The second step was to stratify each grid, with in each city, based on land use type. The grids were categorized into five strata: residential, commercial/industrial, mixed (commercial and residential), Market centres and open area. The stratification was based on local knowledge of the survey implementing contractor with approval from the WBG task team leader. The third step in the sampling process was to select a pre-defined number of starting squares from each stratum for enumeration and main data collection (see Appendix B for the number of starting squares selected for each city).

It is important to note that for Mogadishu, because of security challenges data collection was conducted only in areas considered as safe (as of November 2019) for field team to conduct in person face-to-face interviews. Consequently, data for Mogadishu is representative only for these safe areas (with Bakara market among areas excluded), highlighted in light green in the map in Appendix A-2.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey data was collected using a standardized questionnaire, i.e., the long-form questionnaire. The questionnaire was developed building on previous modules used by the Enterprise Analysis Unit of the World Bank to survey informal businesses.

Abstract

This survey was conducted in Bangladesh between April 2013 and September 2013 as part of the Enterprise Survey project, 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.

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 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.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Bangladesh ES 2013 was selected using stratified random sampling. Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed in the way that follows: the universe was stratified into 7 manufacturing industries (food, apparel, leather, chemicals, transport, furniture, and other manufacturing), and 2 service industries (retail and other services).

Size stratification was defined following the standardized definition for the rollout: 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. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not common practice, apart from the construction and agriculture sectors which are not included in the survey.

Regional stratification was defined in four regions: Dhaka, Chittagong, Khulna-Jessore, and Rajshahi-Bogra.

Bangladesh Bureau of Statistics, Business Register 2009 database was used as the frame for the selection of a sample with the aim of achieving 1,320 interviews with establishments of five or more employees. This target was for fresh firms, i.e. firms that were not interviewed in the previous Enterprise Survey.

The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 1.72% (70 out of 4,072 establishments).

Mode of data collection

Face-to-face [f2f]

Research instrument

The structure of the data base reflects the fact that two different versions of the questionnaire were used for 3 categories of businesses (manufacturing, retail, and other services/non-retail). The Manufacturing Questionnaire includes all common questions asked to all establishments and some specific questions relevant to manufacturing firms. The Services Questionnaire, administered to retail and other services/non-retail establishments, includes all common questions asked to all establishments and some specific questions relevant retail and other services firms. Each variation of the questionnaire is identified by the index variable, a0.

All variables are named using, first, the letter of each section and, second, the number of the variable within the section, i.e. a1 denotes section A, question 1. Variable names proceeded by a prefix "SAR" or "BG" indicate questions specific to the South Asia region or Bangladesh only, therefore, they may not be found in the implementation of the rollout in other countries. All other suffixed variables are global and are present in all country surveys over the world. All variables are numeric with the exception of those variables with an "x" at the end of their names. The suffix "x" denotes that the variable is alpha-numeric.

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

The number of contacted establishments per realized interview was 0.56. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 0.56.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond as a different option from don’t know. b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

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Abstract

This survey was conducted in Latvia between January 2013 and December 2013 as part of the fifth round of the Business Environment and Enterprise Performance Survey (BEEPS V), a joint initiative of the World Bank Group and the European Bank for Reconstruction and Development. 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.

Stratified random sampling was used to select the surveyed businesses.

The survey topics include firm characteristics, information about sales and suppliers, competition, infrastructure services, judiciary and law enforcement collaboration, security, government policies, laws and regulations, financing, overall business environment, bribery, capacity utilization, performance and investment activities, and workforce composition.

In 2011, the innovation module was added to the standard set of Enterprise Surveys questionnaires to examine in detail how introduction of new products and practices influence firms' performance and management.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is an 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. Only formal (registered) businesses are surveyed for ES.

Universe

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.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was selected using stratified random sampling. Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed in the way that follows: the universe was stratified into one manufacturing industry, and two service industries (retail, and other services).

Size stratification was defined following the standardized definition for the rollout: 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. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not common practice, apart from the construction and agriculture sectors which are not included in the survey.

Regional stratification was defined in 6 regions (city and the surrounding business area) throughout Latvia.

The database from the National Statistical Bureau of Latvia was used as the frame for the sample selection.

In the dataset, the variables a2 (sampling region), a6a (sampling establishment's size), and a4a (sampling sector) contain the establishment's classification into the strata chosen for each country using information from the sample frame. Variable a4a is coded using ISIC Rev 3.1 codes for the chosen industries for stratification. These codes include most manufacturing industries (15 to 37), retail (52), and (45, 50, 51, 55, 60-64, 72) for other services.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three different versions of the questionnaire were used. The basic questionnaire, the Core Module, includes all common questions asked to all establishments from all sectors. The second expanded variation, the Manufacturing Questionnaire, is built upon the Core Module and adds some specific questions relevant to manufacturing sectors. The third expanded variation, the Retail Questionnaire, is also built upon the Core Module and adds to the core specific questions.

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

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether, while the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond as a different option from don’t know. b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

The number of contacted establishments per realized interview was 0.22. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 0.18.

Abstract

The survey was conducted in Uganda between January 2013 and August 2013 as part of the Africa Enterprise Survey 2013 roll-out, an initiative of the World Bank. Data from 640 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses.

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 and 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 percent 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 an 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 private 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. Companies with 100% government ownership are not eligible to participate in the Enterprise Surveys.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Uganda was selected using stratified random sampling. Three levels of stratification were used in this country: firm sector, firm size, and geographic region.

Industry stratification was designed in the way that follows: the universe was stratified into three manufacturing industry (food, textiles and garments, other manufacturing) and two service sectors (retail and other services).

Size stratification was defined following the standardized definition 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. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

Regional stratification for the Uganda ES was defined in six regions (city and the surrounding business area): Jinja, Kampala, Lira, Mbale, Mbarara, and Wakiso.

Uganda Bureau of Statistics database was used as a sampling frame with the aim of obtaining interviews with 600 establishments.

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 2% (36 out of 1,567 establishments).

Mode of data collection

Face-to-face [f2f]

Research instrument

The structure of the data base reflects the fact that 3 different versions of the survey instrument were used for all registered establishments. Questionnaires have common questions (Core module) and respectfully additional manufacturing and retail specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the Core module, plus manufacturing specific questions). Retail firms have been interviewed using the Retail questionnaire (includes the Core module plus retail specific questions) and the residual eligible services have been covered using the Core module only. Each variation of the questionnaire is identified by the index variable, a0.

All variables are named using, first, the letter of each section and, second, the number of the variable within the section, i.e. a1 denotes section A, question 1 (some exceptions apply due to comparability reasons). Variable names proceeded by a prefix "KEN" and "A2F" indicate questions specific to some countries in Africa, therefore, they may not be found in the implementation of the rollout in other countries. All other suffixed variables are global and are present in all country surveys over the world. All variables are numeric with the exception of those variables with an "x" at the end of their names. The suffix "x" denotes that the variable is alpha-numeric. In the implementation of the Africa roll out 2011 an experiment was carried in some of the countries to better estimate the effects of the use of show cards in data collection. In some of the sections (i.e. innovation) the enumerators were trained to alternatively implement the section using either show cards or asking only the questions without showing any cards, please see the variable "cards".

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

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

The number of contacted establishments per realized interview was 0.41. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 0.20.

Refer to the current geographies boundaries table for a list of all current geographies and recent updates. This dataset is the definitive version of the annually released statistical area 2 (SA2) boundaries as at 1 January 2025 as defined by Stats NZ, clipped to the coastline. This clipped version has been created for cartographic purposes and so does not fully represent the official full extent boundaries. This clipped version contains 2,311 SA2 areas. SA2 is an output geography that provides higher aggregations of population data than can be provided at the statistical area 1 (SA1) level. The SA2 geography aims to reflect communities that interact together socially and economically. In populated areas, SA2s generally contain similar sized populations. The SA2 should: form a contiguous cluster of one or more SA1s, excluding exceptions below, allow the release of multivariate statistics with minimal data suppression, capture a similar type of area, such as a high-density urban area, farmland, wilderness area, and water area, be socially homogeneous and capture a community of interest. It may have, for example: a shared road network, shared community facilities, shared historical or social links, or socio-economic similarity, form a nested hierarchy with statistical output geographies and administrative boundaries. It must: be built from SA1s, either define or aggregate to define SA3s, urban areas, territorial authorities, and regional councils. SA2s in city council areas generally have a population of 2,000–4,000 residents while SA2s in district council areas generally have a population of 1,000–3,000 residents. In major urban areas, an SA2 or a group of SA2s often approximates a single suburb. In rural areas, rural settlements are included in their respective SA2 with the surrounding rural area. SA2s in urban areas where there is significant business and industrial activity, for example ports, airports, industrial, commercial, and retail areas, often have fewer than 1,000 residents. These SA2s are useful for analysing business demographics, labour markets, and commuting patterns. In rural areas, some SA2s have fewer than 1,000 residents because they are in conservation areas or contain sparse populations that cover a large area. To minimise suppression of population data, small islands with zero or low populations close to the mainland, and marinas are generally included in their adjacent land-based SA2. Zero or nominal population SA2s To ensure that the SA2 geography covers all of New Zealand and aligns with New Zealand’s topography and local government boundaries, some SA2s have zero or nominal populations. These include: SA2s where territorial authority boundaries straddle regional council boundaries. These SA2s each have fewer than 200 residents and are: Arahiwi, Tiroa, Rangataiki, Kaimanawa, Taharua, Te More, Ngamatea, Whangamomona, and Mara. SA2s created for single islands or groups of islands that are some distance from the mainland or to separate large unpopulated islands from urban areas SA2s that represent inland water, inlets or oceanic areas including: inland lakes larger than 50 square kilometres, harbours larger than 40 square kilometres, major ports, other non-contiguous inlets and harbours defined by territorial authority, and contiguous oceanic areas defined by regional council. SA2s for non-digitised oceanic areas, offshore oil rigs, islands, and the Ross Dependency. Each SA2 is represented by a single meshblock. The following 16 SA2s are held in non-digitised form (SA2 code; SA2 name): 400001; New Zealand Economic Zone, 400002; Oceanic Kermadec Islands, 400003; Kermadec Islands, 400004; Oceanic Oil Rig Taranaki, 400005; Oceanic Campbell Island, 400006; Campbell Island, 400007; Oceanic Oil Rig Southland, 400008; Oceanic Auckland Islands, 400009; Auckland Islands, 400010 ; Oceanic Bounty Islands, 400011; Bounty Islands, 400012; Oceanic Snares Islands, 400013; Snares Islands, 400014; Oceanic Antipodes Islands, 400015; Antipodes Islands, 400016; Ross Dependency. SA2 numbering and naming Each SA2 is a single geographic entity with a name and a numeric code. The name refers to a geographic feature or a recognised place name or suburb. In some instances where place names are the same or very similar, the SA2s are differentiated by their territorial authority name, for example, Gladstone (Carterton District) and Gladstone (Invercargill City). SA2 codes have six digits. North Island SA2 codes start with a 1 or 2, South Island SA2 codes start with a 3 and non-digitised SA2 codes start with a 4. They are numbered approximately north to south within their respective territorial authorities. To ensure the north–south code pattern is maintained, the SA2 codes were given 00 for the last two digits when the geography was created in 2018. When SA2 names or boundaries change only the last two digits of the code will change. ​ Clipped Version This clipped version has been created for cartographic purposes and so does not fully represent the official full extent boundaries. ​ High-definition version This high definition (HD) version is the most detailed geometry, suitable for use in GIS for geometric analysis operations and for the computation of areas, centroids and other metrics. The HD version is aligned to the LINZ cadastre. ​ Macrons Names are provided with and without tohutō/macrons. The column name for those without macrons is suffixed ‘ascii’. ​ Digital data Digital boundary data became freely available on 1 July 2007. Further information To download geographic classifications in table formats such as CSV please use Ariā For more information please refer to the Statistical standard for geographic areas 2023. Contact: geography@stats.govt.nz

Refer to the current geographies boundaries table for a list of all current geographies and recent updates. This dataset is the definitive version of the annually released statistical area 2 (SA2) boundaries as at 1 January 2025 as defined by Stats NZ. This version contains 2,395 SA2s (2,379 digitised and 16 with empty or null geometries (non-digitised)). SA2 is an output geography that provides higher aggregations of population data than can be provided at the statistical area 1 (SA1) level. The SA2 geography aims to reflect communities that interact together socially and economically. In populated areas, SA2s generally contain similar sized populations. The SA2 should: form a contiguous cluster of one or more SA1s, excluding exceptions below, allow the release of multivariate statistics with minimal data suppression, capture a similar type of area, such as a high-density urban area, farmland, wilderness area, and water area, be socially homogeneous and capture a community of interest. It may have, for example: a shared road network, shared community facilities, shared historical or social links, or socio-economic similarity, ​ form a nested hierarchy with statistical output geographies and administrative boundaries. It must: be built from SA1s, either define or aggregate to define SA3s, urban areas, territorial authorities, and regional councils. SA2s in city council areas generally have a population of 2,000–4,000 residents while SA2s in district council areas generally have a population of 1,000–3,000 residents. In major urban areas, an SA2 or a group of SA2s often approximates a single suburb. In rural areas, rural settlements are included in their respective SA2 with the surrounding rural area. SA2s in urban areas where there is significant business and industrial activity, for example ports, airports, industrial, commercial, and retail areas, often have fewer than 1,000 residents. These SA2s are useful for analysing business demographics, labour markets, and commuting patterns. In rural areas, some SA2s have fewer than 1,000 residents because they are in conservation areas or contain sparse populations that cover a large area. To minimise suppression of population data, small islands with zero or low populations close to the mainland, and marinas are generally included in their adjacent land-based SA2. Zero or nominal population SA2s To ensure that the SA2 geography covers all of New Zealand and aligns with New Zealand’s topography and local government boundaries, some SA2s have zero or nominal populations. These include: SA2s where territorial authority boundaries straddle regional council boundaries. These SA2s each have fewer than 200 residents and are: Arahiwi, Tiroa, Rangataiki, Kaimanawa, Taharua, Te More, Ngamatea, Whangamomona, and Mara. SA2s created for single islands or groups of islands that are some distance from the mainland or to separate large unpopulated islands from urban areas SA2s that represent inland water, inlets or oceanic areas including: inland lakes larger than 50 square kilometres, harbours larger than 40 square kilometres, major ports, other non-contiguous inlets and harbours defined by territorial authority, and contiguous oceanic areas defined by regional council. SA2s for non-digitised oceanic areas, offshore oil rigs, islands, and the Ross Dependency. Each SA2 is represented by a single meshblock. The following 16 SA2s are held in non-digitised form (SA2 code; SA2 name): 400001; New Zealand Economic Zone, 400002; Oceanic Kermadec Islands, 400003; Kermadec Islands, 400004; Oceanic Oil Rig Taranaki, 400005; Oceanic Campbell Island, 400006; Campbell Island, 400007; Oceanic Oil Rig Southland, 400008; Oceanic Auckland Islands, 400009; Auckland Islands, 400010 ; Oceanic Bounty Islands, 400011; Bounty Islands, 400012; Oceanic Snares Islands, 400013; Snares Islands, 400014; Oceanic Antipodes Islands, 400015; Antipodes Islands, 400016; Ross Dependency. SA2 numbering and naming Each SA2 is a single geographic entity with a name and a numeric code. The name refers to a geographic feature or a recognised place name or suburb. In some instances where place names are the same or very similar, the SA2s are differentiated by their territorial authority name, for example, Gladstone (Carterton District) and Gladstone (Invercargill City). SA2 codes have six digits. North Island SA2 codes start with a 1 or 2, South Island SA2 codes start with a 3 and non-digitised SA2 codes start with a 4. They are numbered approximately north to south within their respective territorial authorities. To ensure the north–south code pattern is maintained, the SA2 codes were given 00 for the last two digits when the geography was created in 2018. When SA2 names or boundaries change only the last two digits of the code will change. ​ High-definition version This high definition (HD) version is the most detailed geometry, suitable for use in GIS for geometric analysis operations and for the computation of areas, centroids and other metrics. The HD version is aligned to the LINZ cadastre. ​ Macrons Names are provided with and without tohutō/macrons. The column name for those without macrons is suffixed ‘ascii’. ​ Digital data Digital boundary data became freely available on 1 July 2007. ​ Further information To download geographic classifications in table formats such as CSV please use Ariā For more information please refer to the Statistical standard for geographic areas 2023. Contact: geography@stats.govt.nz