Content

The "Global Country Rankings Dataset" is a comprehensive collection of metrics and indicators that ranks countries worldwide based on their socioeconomic performance. This datasets are providing valuable insights into the relative standings of nations in terms of key factors such as GDP per capita, economic growth, and various other relevant criteria.

Researchers, analysts, and policymakers can leverage this dataset to gain a deeper understanding of the global economic landscape and track the progress of countries over time. The dataset covers a wide range of metrics, including but not limited to:

Economic growth: the rate of change of real GDP- Country rankings: The average for 2021 based on 184 countries was 5.26 percent.The highest value was in the Maldives: 41.75 percent and the lowest value was in Afghanistan: -20.74 percent. The indicator is available from 1961 to 2021.

GDP per capita, Purchasing Power Parity - Country rankings: The average for 2021 based on 182 countries was 21283.21 U.S. dollars.The highest value was in Luxembourg: 115683.49 U.S. dollars and the lowest value was in Burundi: 705.03 U.S. dollars. The indicator is available from 1990 to 2021.

GDP per capita, current U.S. dollars - Country rankings: The average for 2021 based on 186 countries was 17937.03 U.S. dollars.The highest value was in Monaco: 234315.45 U.S. dollars and the lowest value was in Burundi: 221.48 U.S. dollars. The indicator is available from 1960 to 2021.

GDP per capita, constant 2010 dollars - Country rankings: The average for 2021 based on 184 countries was 15605.8 U.S. dollars.The highest value was in Monaco: 204190.16 U.S. dollars and the lowest value was in Burundi: 261.02 U.S. dollars. The indicator is available from 1960 to 2021.

source: https://www.theglobaleconomy.com/

This is the dataset of World's most visited Countries by international travellers. France has the most visitors in 2021 and dataset contains data of 50 countries. Spain is the second country for tourists.

Analysis of ‘Population by Country - 2020’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/tanuprabhu/population-by-country-2020 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

I always wanted to access a data set that was related to the world’s population (Country wise). But I could not find a properly documented data set. Rather, I just created one manually.

Content

Now I knew I wanted to create a dataset but I did not know how to do so. So, I started to search for the content (Population of countries) on the internet. Obviously, Wikipedia was my first search. But I don't know why the results were not acceptable. And also there were only I think 190 or more countries. So then I surfed the internet for quite some time until then I stumbled upon a great website. I think you probably have heard about this. The name of the website is Worldometer. This is exactly the website I was looking for. This website had more details than Wikipedia. Also, this website had more rows I mean more countries with their population.

Once I got the data, now my next hard task was to download it. Of course, I could not get the raw form of data. I did not mail them regarding the data. Now I learned a new skill which is very important for a data scientist. I read somewhere that to obtain the data from websites you need to use this technique. Any guesses, keep reading you will come to know in the next paragraph.

https://fiverr-res.cloudinary.com/images/t_main1,q_auto,f_auto/gigs/119580480/original/68088c5f588ec32a6b3a3a67ec0d1b5a8a70648d/do-web-scraping-and-data-mining-with-python.png" alt="alt text">

You are right its, Web Scraping. Now I learned this so that I could convert the data into a CSV format. Now I will give you the scraper code that I wrote and also I somehow found a way to directly convert the pandas data frame to a CSV(Comma-separated fo format) and store it on my computer. Now just go through my code and you will know what I'm talking about.

Below is the code that I used to scrape the code from the website

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3200273%2Fe814c2739b99d221de328c72a0b2571e%2FCapture.PNG?generation=1581314967227445&alt=media" alt="">

Acknowledgements

Now I couldn't have got the data without Worldometer. So special thanks to the website. It is because of them I was able to get the data.

Inspiration

As far as I know, I don't have any questions to ask. You guys can let me know by finding your ways to use the data and let me know via kernel if you find something interesting

--- Original source retains full ownership of the source dataset ---

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.

Patterns of educational attainment vary greatly across countries, and across population groups within countries. In some countries, virtually all children complete basic education whereas in others large groups fall short. The primary purpose of this database, and the associated research program, is to document and analyze these differences using a compilation of a variety of household-based data sets: Demographic and Health Surveys (DHS); Multiple Indicator Cluster Surveys (MICS); Living Standards Measurement Study Surveys (LSMS); as well as country-specific Integrated Household Surveys (IHS) such as Socio-Economic Surveys.As shown at the website associated with this database, there are dramatic differences in attainment by wealth. When households are ranked according to their wealth status (or more precisely, a proxy based on the assets owned by members of the household) there are striking differences in the attainment patterns of children from the richest 20 percent compared to the poorest 20 percent.In Mali in 2012 only 34 percent of 15 to 19 year olds in the poorest quintile have completed grade 1 whereas 80 percent of the richest quintile have done so. In many countries, for example Pakistan, Peru and Indonesia, almost all the children from the wealthiest households have completed at least one year of schooling. In some countries, like Mali and Pakistan, wealth gaps are evident from grade 1 on, in other countries, like Peru and Indonesia, wealth gaps emerge later in the school system.The EdAttain website allows a visual exploration of gaps in attainment and enrollment within and across countries, based on the international database which spans multiple years from over 120 countries and includes indicators disaggregated by wealth, gender and urban/rural location. The database underlying that site can be downloaded from here.

Analysis of ‘Unsupervised Learning on Country Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/rohan0301/unsupervised-learning-on-country-data on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Clustering the Countries by using Unsupervised Learning for HELP International

Objective:

To categorise the countries using socio-economic and health factors that determine the overall development of the country.

About organization:

HELP International is an international humanitarian NGO that is committed to fighting poverty and providing the people of backward countries with basic amenities and relief during the time of disasters and natural calamities.

Problem Statement:

HELP International have been able to raise around $ 10 million. Now the CEO of the NGO needs to decide how to use this money strategically and effectively. So, CEO has to make decision to choose the countries that are in the direst need of aid. Hence, your Job as a Data scientist is to categorise the countries using some socio-economic and health factors that determine the overall development of the country. Then you need to suggest the countries which the CEO needs to focus on the most.

--- Original source retains full ownership of the source dataset ---

Country Information Dataset

Overview

Welcome to the Country Information Dataset, meticulously curated by Aadarsh Vani. This dataset serves as an extensive resource for anyone interested in exploring the rich tapestry of countries around the globe, providing detailed information on various aspects of each nation.

Dataset Description

This dataset contains valuable insights into countries worldwide, featuring the following attributes:

• country_name: The name of the country.
• capital_city: The capital city of the country.
• currency: The official currency used.
• official_lang: The main languages spoken.
• population: The most recent population estimate.
• area: The total land area (in square kilometers).
• continent: The continent to which the country belongs (e.g., Asia, Europe).
• largest_city: The largest city by population.
• independence_year: The year the country gained independence.
• landmarks: Notable landmarks and attractions.
• national_animals: The officially recognized national animals.
• national_bird: The country's national bird.
• govt_type: The type of government (e.g., republic, monarchy).
• dish: A popular traditional dish.
• Major Religions: The predominant religions practiced.
• leader: The current political leader or head of state.
• driving_side: The side of the road on which vehicles drive.
• national_sport: The recognized national sport.
• major_fest: Important festivals celebrated in the country.

Purpose

The aim of this dataset is to provide a comprehensive and reliable resource for researchers, data scientists, and cultural enthusiasts. It can facilitate analysis and visualizations that reveal global patterns in demographics, cultures, and economies.

Applications

• Comparative studies of countries based on population, area, and GDP.
• Analysis of cultural diversity through languages, foods, and religions.
• Visualization of geographical data to highlight landmarks and major cities.
• Research into the historical context of independence and governance.

Data Format

• The dataset is available in CSV format, ensuring ease of access and compatibility with data analysis platforms.

Acknowledgment

Created by Aadarsh Vani, this dataset is a labor of love aimed at enriching the understanding of our world's countries. I encourage users to share their insights, visualizations, and analyses arising from this dataset. Together, we can foster a deeper appreciation of global diversity!

Thank you for exploring this dataset, and I hope it inspires your work in studying the fascinating intricacies of countries worldwide.

Note: This data set will be updated frequently to keep it updated by adding new columns and updating the updated values. Kindly use it for practice and projects only as it has missing values and may have unintentional wrong data in some cells.

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

This database contains tobacco consumption data from 1970-2015 collected through a systematic search coupled with consultation with country and subject-matter experts. Data quality appraisal was conducted by at least two research team members in duplicate, with greater weight given to official government sources. All data was standardized into units of cigarettes consumed and a detailed accounting of data quality and sourcing was prepared. Data was found for 82 of 214 countries for which searches for national cigarette consumption data were conducted, representing over 95% of global cigarette consumption and 85% of the world’s population. Cigarette consumption fell in most countries over the past three decades but trends in country specific consumption were highly variable. For example, China consumed 2.5 million metric tonnes (MMT) of cigarettes in 2013, more than Russia (0.36 MMT), the United States (0.28 MMT), Indonesia (0.28 MMT), Japan (0.20 MMT), and the next 35 highest consuming countries combined. The US and Japan achieved reductions of more than 0.1 MMT from a decade earlier, whereas Russian consumption plateaued, and Chinese and Indonesian consumption increased by 0.75 MMT and 0.1 MMT, respectively. These data generally concord with modelled country level data from the Institute for Health Metrics and Evaluation and have the additional advantage of not smoothing year-over-year discontinuities that are necessary for robust quasi-experimental impact evaluations. Before this study, publicly available data on cigarette consumption have been limited—either inappropriate for quasi-experimental impact evaluations (modelled data), held privately by companies (proprietary data), or widely dispersed across many national statistical agencies and research organisations (disaggregated data). This new dataset confirms that cigarette consumption has decreased in most countries over the past three decades, but that secular country specific consumption trends are highly variable. The findings underscore the need for more robust processes in data reporting, ideally built into international legal instruments or other mandated processes. To monitor the impact of the WHO Framework Convention on Tobacco Control and other tobacco control interventions, data on national tobacco production, trade, and sales should be routinely collected and openly reported. The first use of this database for a quasi-experimental impact evaluation of the WHO Framework Convention on Tobacco Control is: Hoffman SJ, Poirier MJP, Katwyk SRV, Baral P, Sritharan L. Impact of the WHO Framework Convention on Tobacco Control on global cigarette consumption: quasi-experimental evaluations using interrupted time series analysis and in-sample forecast event modelling. BMJ. 2019 Jun 19;365:l2287. doi: https://doi.org/10.1136/bmj.l2287 Another use of this database was to systematically code and classify longitudinal cigarette consumption trajectories in European countries since 1970 in: Poirier MJ, Lin G, Watson LK, Hoffman SJ. Classifying European cigarette consumption trajectories from 1970 to 2015. Tobacco Control. 2022 Jan. DOI: 10.1136/tobaccocontrol-2021-056627. Statement of Contributions: Conceived the study: GEG, SJH Identified multi-country datasets: GEG, MP Extracted data from multi-country datasets: MP Quality assessment of data: MP, GEG Selection of data for final analysis: MP, GEG Data cleaning and management: MP, GL Internet searches: MP (English, French, Spanish, Portuguese), GEG (English, French), MYS (Chinese), SKA (Persian), SFK (Arabic); AG, EG, BL, MM, YM, NN, EN, HR, KV, CW, and JW (English), GL (English) Identification of key informants: GEG, GP Project Management: LS, JM, MP, SJH, GEG Contacts with Statistical Agencies: MP, GEG, MYS, SKA, SFK, GP, BL, MM, YM, NN, HR, KV, JW, GL Contacts with key informants: GEG, MP, GP, MYS, GP Funding: GEG, SJH SJH: Hoffman, SJ; JM: Mammone J; SRVK: Rogers Van Katwyk, S; LS: Sritharan, L; MT: Tran, M; SAK: Al-Khateeb, S; AG: Grjibovski, A.; EG: Gunn, E; SKA: Kamali-Anaraki, S; BL: Li, B; MM: Mahendren, M; YM: Mansoor, Y; NN: Natt, N; EN: Nwokoro, E; HR: Randhawa, H; MYS: Yunju Song, M; KV: Vercammen, K; CW: Wang, C; JW: Woo, J; MJPP: Poirier, MJP; GEG: Guindon, EG; GP: Paraje, G; GL Gigi Lin Key informants who provided data: Corne van Walbeek (South Africa, Jamaica) Frank Chaloupka (US) Ayda Yurekli (Turkey) Dardo Curti (Uruguay) Bungon Ritthiphakdee (Thailand) Jakub Lobaszewski (Poland) Guillermo Paraje (Chile, Argentina) Key informants who provided useful insights: Carlos Manuel Guerrero López (Mexico) Muhammad Jami Husain (Bangladesh) Nigar Nargis (Bangladesh) Rijo M John (India) Evan Blecher (Nigeria, Indonesia, Philippines, South Africa) Yagya Karki (Nepal) Anne CK Quah (Malaysia) Nery Suarez Lugo (Cuba) Agencies providing assistance: Irani... Visit https://dataone.org/datasets/sha256%3Aaa1b4aae69c3399c96bfbf946da54abd8f7642332d12ccd150c42ad400e9699b for complete metadata about this dataset.

Abstract

The Global Data Regulation Diagnostic provides a comprehensive assessment of the quality of the data governance environment. Diagnostic results show that countries have put in greater effort in adopting enabler regulatory practices than in safeguard regulatory practices. However, for public intent data, enablers for private intent data, safeguards for personal and nonpersonal data, cybersecurity and cybercrime, as well as cross-border data flows. Across all these dimensions, no income group demonstrates advanced regulatory frameworks across all dimensions, indicating significant room for the regulatory development of both enablers and safeguards remains at an intermediate stage: 47 percent of enabler good practices and 41 percent of good safeguard practices are adopted across countries. Under the enabler and safeguard pillars, the diagnostic covers dimensions of e-commerce/e-transactions, enablers further improvement on data governance environment.

The Global Data Regulation Diagnostic is the first comprehensive assessment of laws and regulations on data governance. It covers enabler and safeguard regulatory practices in 80 countries providing indicators to assess and compare their performance. This Global Data Regulation Diagnostic develops objective and standardized indicators to measure the regulatory environment for the data economy across countries. The indicators aim to serve as a diagnostic tool so countries can assess and compare their performance vis-á-vis other countries. Understanding the gap with global regulatory good practices is a necessary first step for governments when identifying and prioritizing reforms.

Geographic coverage

80 countries

Analysis unit

Country

Kind of data

Observation data/ratings [obs]

Sampling procedure

The diagnostic is based on a detailed assessment of domestic laws, regulations, and administrative requirements in 80 countries selected to ensure a balanced coverage across income groups, regions, and different levels of digital technology development. Data are further verified through a detailed desk research of legal texts, reflecting the regulatory status of each country as of June 1, 2020.

Mode of data collection

Mail Questionnaire [mail]

Research instrument

The questionnaire comprises 37 questions designed to determine if a country has adopted good regulatory practice on data governance. The responses are then scored and assigned a normative interpretation. Related questions fall into seven clusters so that when the scores are averaged, each cluster provides an overall sense of how it performs in its corresponding regulatory and legal dimensions. These seven dimensions are: (1) E-commerce/e-transaction; (2) Enablers for public intent data; (3) Enablers for private intent data; (4) Safeguards for personal data; (5) Safeguards for nonpersonal data; (6) Cybersecurity and cybercrime; (7) Cross-border data transfers.

Response rate

100%

🌍 Countries and Dependencies by Population (2025)

This dataset provides a comprehensive list of countries and dependent territories worldwide, along with their most recent population estimates.The data is sourced from the Wikipedia page List of countries and dependencies by population, which compiles figures from national statistical offices and the United Nations Population Division

📄 Dataset Overview

• Country/Territory Name Includes sovereign states, dependent territories, and regions with limited recognition.
• Population Latest available estimates, primarily from national censuses or UN projection.
• Percentage of World Population Each country's population as a percentage of the global total.
• Date of Estimate The reference date for the population figure.
• Notes Additional information, such as inclusion or exclusion of certain region.

🧠 Potential Use Cases

• Analyzing global population distribution and trends.- Creating visualizations like choropleth maps.- Normalizing other datasets by population for per capita analysis.- Educational purposes in demographics and geography.

📌 Notes

• The dataset includes territories and regions with limited recognition to provide a complete global perspective.
• Population figures are based on the most recent estimates available as of 225.
• Data may be subject to revisions as new census information becomes available.

This article introduces the most comprehensive dataset on de jure central bank independence (CBI), including yearly data from 182 countries between 1970 and 2012. The dataset identifies statutory reforms affecting CBI, their direction, and the attributes necessary to build the Cukierman, Webb and Neyapty index. Previous datasets focused on developed countries, and included non-representative samples of developing countries. This dataset’s substantially broader coverage has important implications. First, it challenges the conventional wisdom about central bank reforms in the world, revealing CBI increases and restrictions in decades and regions previously considered barely affected by reforms. Second, the inclusion of almost 100 countries usually overlooked in previous studies suggests that the sample selection may have substantially affected results. Simple analyses show that the associations between CBI and inflation, unemployment or growth are very sensitive to sample selection. Finally, the dataset identifies numerous CBI decreases (restrictions), whereas previous datasets mostly look at CBI increases. These data’s coverage not only allows researchers to test competing explanations of the determinants and effects of CBI in a global sample, but it also provides a useful instrument for cross-national studies in diverse fields, such as liberalization, diffusion, political institutions, democratization, or responses to financial crises.

The United Nations Energy Statistics Database (UNSTAT) is a comprehensive collection of international energy and demographic statistics prepared by the United Nations Statistics Division. The 2004 version represents the latest in the series of annual compilations which commenced under the title World Energy Supplies in Selected Years, 1929-1950. Supplementary series of monthly and quarterly data on production of energy may be found in the Monthly Bulletin of Statistics. The database contains comprehensive energy statistics for more than 215 countries or areas for production, trade and intermediate and final consumption (end-use) for primary and secondary conventional, non-conventional and new and renewable sources of energy. Mid-year population estimates are included to enable the computation of per capita data. Annual questionnaires sent to national statistical offices serve as the primary source of information. Supplementary data are also compiled from national, regional and international statistical publications. The Statistics Division prepares estimates where official data are incomplete or inconsistent. The database is updated on a continuous basis as new information and revisions are received. This metadata file represents the population statistics during the expressed time. For more information about the country site codes, click this link to the United Nations "Standard country or area codes for statistical use": https://unstats.un.org/unsd/methodology/m49/overview/

World Population Datasets Overview:

Explore a comprehensive collection of datasets offering profound insights into global demographics and country-specific characteristics. These datasets, sourced from reputable platforms including worldometers.info and Wikipedia, cover a wide array of key indicators, providing a rich resource for in-depth analysis and exploration.

Dataset 1: World Country Stats:

Delve into detailed statistics for countries worldwide, encompassing essential factors such as regions, land area, fertility rates, and median ages. This dataset, provides a holistic view of demographic and geographical attributes.

Dataset 2: World Population Details (2023):

Gain nuanced insights into the demographic landscape of countries for the year 2023. This dataset, covers a multitude of population-related details, including yearly changes, density, net migrants, urban populations, and more.

Dataset 3: World Population by Year (1950-2023):

Uncover the evolution of world populations from 1950 to 2023, with yearly granularity for each country. This dataset allows you to analyze and understand population trends over more than seven decades.

These datasets collectively form a robust foundation for researchers, analysts, and enthusiasts seeking to explore and understand the intricate dynamics of global populations and country-specific characteristics. Whether studying historical trends or focusing on the latest demographic profiles, these datasets offer a wealth of information for diverse analytical perspectives.

Note This Dataset is created from worldometers and wikipedia.org. If you want to learn more, you can visit the Websites.

Upvote this dataset if found helpful. :blush:

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.

This dataset is about countries. It has 118 rows and is filtered where the proportion of seats held by women in national parliaments is greater than 20. It features 66 columns including ISO 3 country code, ISO 2 country code, country full name, and currency.

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

The global number of internet users in was forecast to continuously increase between 2024 and 2029 by in total 1.3 billion users (+23.66 percent). After the fifteenth consecutive increasing year, the number of users is estimated to reach 7 billion users and therefore a new peak in 2029. Notably, the number of internet users of was continuously increasing over the past years.Depicted is the estimated number of individuals in the country or region at hand, that use the internet. As the datasource clarifies, connection quality and usage frequency are distinct aspects, not taken into account here.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of internet users in countries like the Americas and Asia.

Dataset for content analysis published in "Hornikx, J., Meurs, F. van, Janssen, A., & Heuvel, J. van den (2020). How brands highlight country of origin in magazine advertising: A content analysis. Journal of Global Marketing, 33 (1), 34-45."*Abstract (taken from publication)Aichner (2014) proposes a classification of ways in which brands communicate their country of origin (COO). The current, exploratory study is the first to empirically investigate the frequency with which brands employ such COO markers in magazine advertisements. An analysis of about 750 ads from the British, Dutch, and Spanish editions of Cosmopolitan showed that the prototypical ‘made in’ marker was rarely used, and that ‘COO embedded in company name’ and ‘use of COO language’ were most frequently employed. In all, 36% of the total number of ads contained at least one COO marker, underlining the importance of the COO construct.*Methodology (taken from publication)SampleThe use of COO markers in advertising was examined in print advertisements from three different countries to increase the robustness of the findings. Given the exploratory nature of this study, two practical selection criteria guided our country choice: the three countries included both smaller and larger countries in Europe, and they represented languages that the team was familiar with in order to reliably code the advertisements on the relevant variables. The three European countries selected were the Netherlands, Spain, and the United Kingdom. The dataset for the UK was discarded for testing H1 about the use of English as a foreign language, as will be explained in more detail in the coding procedure.The magazine Cosmopolitan was chosen as the source of advertisements. The choice for one specific magazine title reduces the generalizability of the findings (i.e., limited to the corresponding products and target consumers), but this magazine was chosen intentionally because an informal analysis suggested that it carried advertising for a large number of product categories that are considered ethnic products, such as cosmetics, watches, and shoes (Usunier & Cestre, 2007). This suggestion was corroborated in the main analysis: the majority of the ads in the corpus referred to a product that Usunier and Cestre (2007) classify as ethnic products. Table 2 provides a description of the product categories and brands referred to in the advertisements. Ethnic products have a prototypical COO in the minds of consumers (e.g., cosmetics – France), which makes it likely that the COOs are highlighted through the use of COO markers.Cosmopolitan is an international magazine that has different local editions in the three countries. The magazine, which is targeted at younger women (18–35 years old), reaches more than three million young women per month through its online, social and print platforms in the Netherlands (Hearst Netherlands, 2016), has about 517,000 readers per month in Spain (PrNoticias, 2016) and about 1.18 million readers per month in the UK (Hearst Magazine U.K., 2016).The sample consisted of all advertisements from all monthly issues that appeared in 2016 in the three countries. This whole-year cluster was selected so as to prevent potential seasonal influences (Neuendorf, 2002). In total, the corpus consisted of 745 advertisements, of which 111 were from the Dutch, 367 from the British and 267 from the Spanish Cosmopolitan. Two categories of ads were excluded in the selection process: (1) advertisements for subscription to Cosmopolitan itself, and (2) advertisements that were identical to ads that had appeared in another issue in one of the three countries. As a result, each advertisement was unique.Coding procedureFor all advertisements, four variables were coded: product type, presence of types of COO markers, COO referred to, and the use of English as a COO marker. In the first place, product type was assessed by the two coders. Coders classified each product to one of the 32 product types. In order to assess the reliability of the codings, ten per cent of the ads were independently coded by a second coder. The interrater reliability of the variable product category was good (κ = .97, p < .000, 97.33% agreement between both coders). Table 2 lists the most frequent product types; the label ‘other’ covers 17 types of product, including charity, education, and furniture.In the second place, it was recorded whether one or more of the COO markers occurred in a given ad. In the third place, if a marker was identified, it was assessed to which COO the markers referred. Table 1 lists the nine possible COO markers defined by Aichner (2014) and the COOs referred to, with examples taken from the current content analysis. The interrater reliability for the type of COO marker was very good (κ = .80, p < .000, 96.30% agreement between the coders), and the interrater reliability for COO referred to was excellent (κ = 1.00, p < .000).After the independent assessments of the two coders, the coders decided on the best coding for all cases for which they made a different initial choice. On the basis of these resulting codings, the fourth and final variable was assessed: the English language as a COO marker. Only if an ad contained the English language and at least one other type of COO marker referring to an English-speaking country, was the English language coded as a true COO marker. An example is a Dutch ad using the English language and featuring a British model. If, as in most cases, an ad contained the English language but no other marker was found that referred to an English-speaking country, the English language was not considered to be a COO marker but a marker of globalness (e.g., ‘Because sometimes, a girl’s gotta walk’ in an ad for Skechers in the Spanish corpus). This procedure to disentangle the English language as a true COO marker and a marker of globalness was only followed in the Dutch and Spanish sample. In the UK sample, the English language was not considered to be either a COO marker or a marker of globalness since English is the first language of the UK. Similarly, neither the Dutch language in the Dutch sample nor the Spanish language in the Spanish sample were considered COO markers since these languages are both countries’ first language.Statistical treatmentFor all research questions and the hypothesis, descriptive statistics were generated presenting frequencies and percentages of the categories that were compared. The first analysis (RQ1) concerned the frequency with which the different types of COO marker were used in the sample from the three different countries. For each COO marker, it was determined whether or not it occurred in each of the ads in the sample. In order to statistically test whether some types of COO marker occur more frequently than others (RQ2a), a within-subject ANOVA was conducted with Type of COO marker as independent variable, with nine levels representing the nine different COO markers classified by Aichner (2014). For RQ2b, RQ2c, and H1, frequencies were compared for the occurrence of the different categories within one variable under investigation. For RQ2c, for instance, the variable was the number of COO markers referred to in an ad; the different categories were ‘no marker’, ‘two markers’, ‘three markers’, and ‘four markers’. Non-parametric 2 tests were conducted for the research questions and the hypothesis to test for potentially significant differences between the occurrence of the categories.