Mali ML: New Business Density: New Registrations per 1000 People Aged 15 to 64 data was reported at 0.259 Number in 2020. This records a decrease from the previous number of 0.395 Number for 2019. Mali ML: New Business Density: New Registrations per 1000 People Aged 15 to 64 data is updated yearly, averaging 0.191 Number from Dec 2009 (Median) to 2020, with 12 observations. The data reached an all-time high of 0.395 Number in 2019 and a record low of 0.041 Number in 2009. Mali ML: New Business Density: New Registrations per 1000 People Aged 15 to 64 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mali – Table ML.World Bank.WDI: Businesses Registered Statistics. New businesses registered are the number of new limited liability corporations (or its equivalent) registered in the calendar year.;World Bank's Entrepreneurship Database (https://www.worldbank.org/en/programs/entrepreneurship).;Unweighted average;For cross-country comparability, only limited liability corporations that operate in the formal sector are included.

Canada CA: New Business Density: New Registrations per 1000 People Aged 15 to 64 data was reported at 9.834 Number in 2022. This records a decrease from the previous number of 10.334 Number for 2021. Canada CA: New Business Density: New Registrations per 1000 People Aged 15 to 64 data is updated yearly, averaging 8.241 Number from Dec 2017 (Median) to 2022, with 6 observations. The data reached an all-time high of 10.334 Number in 2021 and a record low of 0.204 Number in 2017. Canada CA: New Business Density: New Registrations per 1000 People Aged 15 to 64 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Canada – Table CA.World Bank.WDI: Businesses Registered Statistics. New businesses registered are the number of new limited liability corporations (or its equivalent) registered in the calendar year.;World Bank's Entrepreneurship Database (https://www.worldbank.org/en/programs/entrepreneurship).;Unweighted average;For cross-country comparability, only limited liability corporations that operate in the formal sector are included.

Summary statistics of business dynamism taken from the Longitudinal Business Database (LBD), UK.

The Small Business Administration maintains the Dynamic Small Business Search (DSBS) database. As a small business registers in the System for Award Management, there is an opportunity to fill out the small business profile. The information provided populates DSBS. DSBS is another tool contracting officers use to identify potential small business contractors for upcoming contracting opportunities. Small businesses can also use DSBS to identify other small businesses for teaming and joint venturing.

The Business Structure Database (BSD) contains a small number of variables for almost all business organisations in the UK. The BSD is derived primarily from the Inter-Departmental Business Register (IDBR), which is a live register of data collected by HM Revenue and Customs via VAT and Pay As You Earn (PAYE) records. The IDBR data are complimented with data from ONS business surveys. If a business is liable for VAT (turnover exceeds the VAT threshold) and/or has at least one member of staff registered for the PAYE tax collection system, then the business will appear on the IDBR (and hence in the BSD). In 2004 it was estimated that the businesses listed on the IDBR accounted for almost 99 per cent of economic activity in the UK. Only very small businesses, such as the self-employed were not found on the IDBR.

The IDBR is frequently updated, and contains confidential information that cannot be accessed by non-civil servants without special permission. However, the ONS Virtual Micro-data Laboratory (VML) created and developed the BSD, which is a 'snapshot' in time of the IDBR, in order to provide a version of the IDBR for research use, taking full account of changes in ownership and restructuring of businesses. The 'snapshot' is taken around April, and the captured point-in-time data are supplied to the VML by the following September. The reporting period is generally the financial year. For example, the 2000 BSD file is produced in September 2000, using data captured from the IDBR in April 2000. The data will reflect the financial year of April 1999 to March 2000. However, the ONS may, during this time, update the IDBR with data on companies from its own business surveys, such as the Annual Business Survey (SN 7451).

The data are divided into 'enterprises' and 'local units'. An enterprise is the overall business organisation. A local unit is a 'plant', such as a factory, shop, branch, etc. In some cases, an enterprise will only have one local unit, and in other cases (such as a bank or supermarket), an enterprise will own many local units.

For each company, data are available on employment, turnover, foreign ownership, and industrial activity based on Standard Industrial Classification (SIC)92, SIC 2003 or SIC 2007. Year of 'birth' (company start-up date) and 'death' (termination date) are also included, as well as postcodes for both enterprises and their local units. Previously only pseudo-anonymised postcodes were available but now all postcodes are real.

The ONS is continually developing the BSD, and so researchers are strongly recommended to read all documentation pertaining to this dataset before using the data.

Linking to Other Business Studies
These data contain IDBR reference numbers. These are anonymous but unique reference numbers assigned to business organisations. Their inclusion allows researchers to combine different business survey sources together. Researchers may consider applying for other business data to assist their research.

Latest Edition Information
For the sixteenth edition (March 2024), data files and a variable catalogue document for 2023 have been added.

Australia New Business Density: New Registrations per 1000 People Aged 15 to 64 data was reported at 17.258 Number in 2022. This records a decrease from the previous number of 17.991 Number for 2021. Australia New Business Density: New Registrations per 1000 People Aged 15 to 64 data is updated yearly, averaging 13.482 Number from Dec 2006 (Median) to 2022, with 17 observations. The data reached an all-time high of 17.991 Number in 2021 and a record low of 9.703 Number in 2006. Australia New Business Density: New Registrations per 1000 People Aged 15 to 64 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Businesses Registered Statistics. New businesses registered are the number of new limited liability corporations (or its equivalent) registered in the calendar year.;World Bank's Entrepreneurship Database (https://www.worldbank.org/en/programs/entrepreneurship).;Unweighted average;For cross-country comparability, only limited liability corporations that operate in the formal sector are included.

InfoGroup’s Historical Business Backfile consists of geo-coded records of millions of US businesses and other organizations that contain basic information on each entity, such as: contact information, industry description, annual revenues, number of employees, year established, and other data. Each annual file consists of a “snapshot” of InfoGroup’s data as of the last day of each year, creating a time series of data 1997-2019. Access is restricted to current Harvard University community members. Use of Infogroup US Historical Business Data is subject to the terms and conditions of a license agreement (effective March 16, 2016) between Harvard and Infogroup Inc. and subject to applicable laws. Most data files are available in either .csv or .sas format. All data files are compressed into an archive in .gz, or GZIP, format. Extraction software such as 7-Zip is required to unzip these archives.

Djibouti DJ: New Business Density: New Registrations per 1000 People Aged 15 to 64 data was reported at 1.628 Number in 2020. Djibouti DJ: New Business Density: New Registrations per 1000 People Aged 15 to 64 data is updated yearly, averaging 1.628 Number from Dec 2020 (Median) to 2020, with 1 observations. The data reached an all-time high of 1.628 Number in 2020 and a record low of 1.628 Number in 2020. Djibouti DJ: New Business Density: New Registrations per 1000 People Aged 15 to 64 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Djibouti – Table DJ.World Bank.WDI: Businesses Registered Statistics. New businesses registered are the number of new limited liability corporations (or its equivalent) registered in the calendar year.;World Bank's Entrepreneurship Database (https://www.worldbank.org/en/programs/entrepreneurship).;Unweighted average;For cross-country comparability, only limited liability corporations that operate in the formal sector are included.

ABSTRACT Along with entrepreneurship´s continuing emergence among the management sciences, there is an ongoing debate about what the field is - or should be. In this regard, to have a better understanding of the development of this research field, it is useful to understand the scientific structure. This study uses bibliometric techniques and cluster analyses to present an empirically grounded picture of the entrepreneurship research. This research analyzes the 1,112 full-length papers published in the FER Proceedings between 1981 and 2009, and the 378 articles published in the Journal of Business Venturing between 2000 and 2010. Both forums are considered representative in the exchange of entrepreneurial thought. The results indicate that entrepreneurship research published in these forums is characterized by varied themes that are not necessarily connected. Rather, they reflect the disciplinary training and lens of their authors; and considerable dynamism and change in key research themes over time. Hopefully, the results presented here provide abundant opportunities for identifying insightful, influential, and creative research topics in the entrepreneurship field.

Search for a business by name. You can obtain business information and then proceed to purchase a certificate of good standing or other documents. The purpose of this search is simply to determine whether a company/entity exists and to provide basic information on the company/entity.

All the datasets uploaded contain all the variables required for the analysis carried out in the paper titled: “The impact of entrepreneurship training and credit on labour market outcomes of disadvantaged youth” psm_DP_Labd This dataset contains all the variables used to match the propensity scores. 1_Promise_single_974_DP_Labd This dataset has variables regarding ownership of businesses, savings and expenditure. 2_Promise_roster_974_DP_Labd Variables covering all the demographic characteristics are all gathered in this dataset. 3_Promise_q10_occup_974_DP_Labd Variables regarding employment are all in this dataset. 4_Promise_q12_loan_974_DP_Labd All the variables pertaining to loan are filed in this dataset.

The Universal Basic Income dataset, which was indexed Scopus from 1996 to 2020. The dataset contains data authors, authors ID Scopus, title, year, source title, volume, issue, article number in Scopus, DOI, link, affiliation, index keywords, reference, correspondence address, editors, publisher, conference name, conference data, conference code, ISSN, language, document type, access type, and EID.

Comprehensive dataset of 39,588 Business centers in United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Does entrepreneurship education to teenagers have different impacts over time - some evidences based on a an Entrepreneurship Education Programme on Mozambique Youth. With geographic focus on Africa, Mozambique.

Data file for:Joshua Hall and Russell Sobel, “Institutions, Entrepreneurship, and Regional Differences in Economic Growth,” Southern Journal of Entrepreneurship 1(1) 2008: 69-96.

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We cover all regions and cities: ( example) Kyiv Київ Kyiv Kharkiv Харків Kharkiv Oblast Odesa Одеса Odesa Oblast Dnipro Дніпро Dnipropetrovsk Oblast Donetsk Донецьк Donetsk Oblast Zaporizhzhia Запоріжжя Zaporizhzhia Oblast Lviv Львів Lviv Oblast Kryvyi Rih Кривий Ріг Dnipropetrovsk Oblast Mykolaiv Миколаїв Mykolaiv Oblast Sevastopol Севастополь Sevastopol Mariupol Маріуполь Donetsk Oblast Luhansk Луганськ Luhansk Oblast Vinnytsia Вінниця Vinnytsia Oblast Makiivka Макіївка Donetsk Oblast Simferopol Сімферополь Crimea Chernihiv Чернігів Chernihiv Oblast Kherson Херсон Kherson Oblast Poltava Полтава Poltava Oblast Khmelnytskyi Хмельницький Khmelnytskyi Oblast Cherkasy Черкаси Cherkasy Oblast Chernivtsi Чернівці Chernivtsi Oblast Zhytomyr Житомир Zhytomyr Oblast Sumy Суми Sumy Oblast Rivne Рівне Rivne Oblast Horlivka Горлівка Donetsk Oblast Ivano-Frankivsk Івано-Франківськ Ivano-Frankivsk Oblast Kamianske Кам'янське Dnipropetrovsk Oblast Ternopil Тернопіль Ternopil Oblast Kropyvnytskyi Кропивницький Kirovohrad Oblast Kremenchuk Кременчук Poltava Oblast Lutsk Луцьк Volyn Oblast Bila Tserkva Біла Церква Kyiv Oblast Kerch Керч Crimea Melitopol Мелітополь Zaporizhzhia Oblast Kramatorsk Краматорськ Donetsk Oblast Uzhhorod Ужгород Zakarpattia Oblast Brovary Бровари Kyiv Oblast Yevpatoria Євпаторія Crimea Berdiansk Бердянськ Zaporizhzhia Oblast Nikopol Нікополь Dnipropetrovsk Oblast Sloviansk Слов'янськ Donetsk Oblast Alchevsk Алчевськ Luhansk Oblast Pavlohrad Павлоград Dnipropetrovsk Oblast Sieverodonetsk Сєверодонецьк Luhansk Oblast Kamianets-Podilskyi Кам'янець-Подільський Khmelnytskyi Oblast Lysychansk Лисичанськ Luhansk Oblast Mukachevo Мукачево Zakarpattia Oblast Konotop Конотоп Sumy Oblast Uman Умань Cherkasy Oblast Khrustalnyi Хрустальний Luhansk Oblast Yalta Ялта Crimea Oleksandriia Олександрія Kirovohrad Oblast Yenakiieve Єнакієве Donetsk Oblast Drohobych Дрогобич Lviv Oblast Berdychiv Бердичів Zhytomyr Oblast Kadiyivka Кадіївка Luhansk Oblast Shostka Шостка Sumy Oblast Bakhmut Бахмут Donetsk Oblast Izmail Ізмаїл Odesa Oblast Novomoskovsk Новомосковськ Dnipropetrovsk Oblast Kostiantynivka Костянтинівка Donetsk Oblast Kovel Ковель Volyn Oblast Feodosiya Феодосія Crimea Nizhyn Ніжин Chernihiv Oblast Smila Сміла Cherkasy Oblast Kalush Калуш Ivano-Frankivsk Oblast Chervonohrad Червоноград Lviv Oblast Boryspil Бориспіль Kyiv Oblast Pervomaisk Первомайськ Mykolaiv Oblast Dovzhansk Довжанськ Luhansk Oblast Irpin Ірпінь Kyiv Oblast Korosten Коростень Zhytomyr Oblast Pokrovsk Покровськ Donetsk Oblast Kolomyia Коломия Ivano-Frankivsk Oblast Stryi Стрий Lviv Oblast Chornomorsk Чорноморськ Odesa Oblast Khartsyzk Харцизьк Donetsk Oblast Rubizhne Рубіжне Luhansk Oblast Novohrad-Volynskyi Новоград-Волинський Zhytomyr Oblast Druzhkivka Дружківка Donetsk Oblast Lozova Лозова Kharkiv Oblast Chystiakove Чистякове Donetsk Oblast Enerhodar Енергодар Zaporizhzhia Oblast Pryluky Прилуки Chernihiv Oblast Antratsyt Антрацит Luhansk Oblast Novovolynsk Нововолинськ Volyn Oblast Horishni Plavni Горішні Плавні Poltava Oblast Shakhtarsk Шахтарськ Donetsk Oblast Bilhorod-Dnistrovskyi Білгород-Дністровський Odesa Oblast Okhtyrka Охтирка Sumy Oblast Myrnohrad Мирноград Donetsk Oblast Snizhne Сніжне Donetsk Oblast Izium Ізюм Kharkiv Oblast Marhanets Марганець Dnipropetrovsk Oblast Rovenky Ровеньки Luhansk Oblast Nova Kakhovka Нова Каховка Kherson Oblast Brianka Брянка Luhansk Oblast Fastiv Фастів Kyiv Oblast Lubny Лубни Poltava Oblast Svitlovodsk Світловодськ Kirovohrad Oblast Zhovti Vody Жовті Води Dnipropetrovsk Oblast Sorokyne Сорокине Luhansk Oblast Vyshneve Вишневе Kyiv Oblast Varash Вараш Rivne Oblast Shepetivka Шепетівка Khmelnytskyi Oblast Podilsk Подільськ Odesa Oblast Yuzhnoukrainsk Южноукраїнськ Mykolaiv Oblast Myrhorod Миргород Poltava Oblast Romny Ромни Sumy Oblast Pokrov Покров Dnipropetrovsk Oblast Volodymyr-Volynskyi Володимир-Волинський Volyn Oblast Dzhankoy Джанкой Crimea Vasylkiv Васильків Kyiv Oblast Dubno Дубно Rivne Oblast Bucha Буча Kyiv Oblast Netishyn Нетішин Khmelnytskyi Oblast Pervomaisk Первомайськ Luhansk Oblast Kakhovka Каховка Kherson Oblast Boiarka Боярка Kyiv Oblast Slavuta Славута Khmelnytskyi Oblast Sambir Самбір Lviv Oblast Yasynuvata Ясинувата Donetsk Oblast Starokostiantyniv Старокостянтинів Khmelnytskyi Oblast Zhmerynka ...

New Businesses Registered data was reported at 7.000 Number in 2022. This records a decrease from the previous number of 12.000 Number for 2021. New Businesses Registered data is updated yearly, averaging 6.000 Number from Dec 2006 (Median) to 2022, with 17 observations. The data reached an all-time high of 12.000 Number in 2021 and a record low of 2.000 Number in 2009. New Businesses Registered data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Tuvalu – Table TV.World Bank.WDI: Businesses Registered Statistics. New businesses registered are the number of new limited liability corporations (or its equivalent) registered in the calendar year.;World Bank's Entrepreneurship Database (https://www.worldbank.org/en/programs/entrepreneurship).;;For cross-country comparability, only limited liability corporations that operate in the formal sector are included.

The global big data and business analytics (BDA) market was valued at 168.8 billion U.S. dollars in 2018 and is forecast to grow to 215.7 billion U.S. dollars by 2021. In 2021, more than half of BDA spending will go towards services. IT services is projected to make up around 85 billion U.S. dollars, and business services will account for the remainder. Big data High volume, high velocity and high variety: one or more of these characteristics is used to define big data, the kind of data sets that are too large or too complex for traditional data processing applications. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets. For example, connected IoT devices are projected to generate 79.4 ZBs of data in 2025. Business analytics Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate business insights. The size of the business intelligence and analytics software application market is forecast to reach around 16.5 billion U.S. dollars in 2022. Growth in this market is driven by a focus on digital transformation, a demand for data visualization dashboards, and an increased adoption of cloud.

These are the Small Business Express projects in the Department of Economic and Community Development - Business Assistance Portfolio dataset. This data is updated in accordance with with the schedule of that dataset.

Abstract Due to the necessity for innovation and value creation for organizations, entrepreneurship education has become part of contemporary educational institutions. It is necessary to develop tools that provide a favorable learning environment. The Business Plan is an useful tool in teaching entrepreneurship, contributing to the development of entrepreneurial teachers and students from different areas. This paper aims to analyze the Circular Business Plan as an active methodology for teaching entrepreneurship during the workshop Building a Business Plan. The version of Circular Business Plan presented in this work was demonstrated through applied research, using qualitative method with exploratory goal during field of study. We analyzed 10 workshops Building a Business Plan in the same educational institution, with 160 participants develop 48 business ideas developed by groups with 3 people on average. Learning self-assessment answered by the participants, as well as the evaluation of teachers on student responses at Circular Business Plan built during the workshops Building a Business Plan, demonstrated that the active methodology contributes to the entrepreneurial development, from skills, different knowledge, attitudes and values. Future research will improve this active methodology and the teaching of entrepreneurship, as well as contribute to the development of the entrepreneurial profile.