Gender ratio of tertiary enrolment by select OECD countries
2013, Ratio female to male
tertiary enrolment rates (gross) female/male
|United Kingdom of Great Britain and Northern Ireland||135.569|
|United States Of America||139.209|
This is the overall Web Index score. The Web Index assesses the Web’s contribution to social, economic and political progress in countries across the world.
ACCESS AND AFFORDABILITY:
This is one of the components of the Universal Access sub-Index, and it assesses various issues around the extent of access and the cost of access to the Web.
Cost of mobile broadband per capita income:
This is the ratio of mobile-broadband monthly subscription charge (the US dollar value of the monthly subscription charge for mobile broadband Internet service) to monthly Per capita income. The cost of mobile broadband Indicates mobile-broadband prices collected from the operator with the largest market share in the country, for the following technologies: umts, hsdpa+/hsdpa, cdma2000 and ieee 802.16e. Prices are collected for the least expensive plan with a (minimum) data allowance of: 1 GB for computer-based subscriptions; 250 MB and 500 MB for handset-based subscriptions providing access to the greater internet over (a minimum of) 30 days. This indicator is expressed in US Dollars as a share of monthly per capita income.
Indicates broadband speeds (peak Mbps, average Mbps)
Secure Internet servers per million population:
Indicates the number of servers using encryption technology in transactions divided by population multiplied by 1,000,000.
Existence of IXPs:
Indicates the number of internet exchange points - the physical infrastructure through which internet service providers exchange internet traffic between their networks.
CONTENT AND USE:
The Relevant Content and Use sub-Index consists of one component only: Content and Use. It assesses various aspects of the availability on the Web of relevant and useful content to various stakeholders in each country, in the main local languages.
Government Online Services Index:
Assesses the quality, relevance and usefulness of government websites in providing online information and participatory tools and services for people.
% of individuals using the Internet:
Assesses the percentage of the population using the Internet. Internet use may be facilitated by any device enabling Internet access. This includes mobile phones, palmtop computers, games machines and digital televisions. Here, use can be via a fixed or mobile network.
EDUCATION AND AWARENESS:
This is one of the components of the Universal Access sub-Index, and it assesses various aspects of the skills needed to be able to access and benefit from the Web.
School life expectancy (years) male/female:
Indicates gender parity for school life expectancy in primary to tertiary education.
Secondary school enrolment rates:
Gross enrolment ratio, secondary. This is total enrolment in secondary education, regardless of age, expressed as a percentage of the population of official secondary education age. This can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition.
Tertiary school enrolment rates:
Indicates the ratio of female to male tertiary enrolment - the percentage of men to women enrolled at tertiary level in public and private schools.
This sub-Index aims to assess the difference that the Web is making to people, and the extent to which use of the Web by stakeholders is fostering positive change in four key areas: society, economy, politics and the environment.
FREE AND OPEN:
The Freedom and Openness sub-Index consists of one component only - Free and Open. This Sub-Index (and component) assesses the extent to which citizens enjoy rights to information, opinion, expression, safety and privacy online, and some aspects of Internet neutrality.
Freedom of the Press:
Freedom House’s Freedom of the Press index, is an annual survey of media independence in 197 countries and territories. The index assesses the degree of print, broadcast, and internet freedom in every country in the world, analyzing the events of each calendar year. It provides numerical rankings and rates each country's media as "Free," "Partly Free," or "Not Free." The research and scores are largely based on 23 methodology questions and 132 indicators divided into three broad categories: the legal environment, the political environment, and the economic environment. For each question, a lower number of points is allotted for a more free situation, while a higher number of points is allotted for a less free environment. A country’s final score (from 0 to 100) is based on the total of the scores allotted for each question: A score of 0 to 30 places the country in the Free press group; 31 to 60 in the Partly Free press group; and 61 to 100 in the Not Free press group.
Freedom House’s Freedom in the World survey provides an annual evaluation of the state of global freedom as experienced by individuals. The survey measures freedom—the opportunity to act spontaneously in a variety of fields outside the control of the government and other centers of potential domination—according to two broad categories: political rights and civil liberties. Political rights enable people to participate freely in the political process, including the right to vote freely for distinct alternatives in legitimate elections, compete for public office, join political parties and organizations, and elect representatives who have a decisive impact on public policies and are accountable to the electorate.
Press Freedom Index:
Assesses levels of press freedom in this country - covering violations affecting journalists (murder, imprisonment etc.) and news media (censorship, confiscation of newspaper issues), plus degrees of self-censorship, i.e. the ability of the media to investigate and criticise. This indicator also takes into account the legal and economic status of the media.
Freedom House’s Freedom in the World survey provides an annual evaluation of the state of global freedom as experienced by individuals. The survey measures freedom—the opportunity to act spontaneously in a variety of fields outside the control of the government and other centers of potential domination—according to two broad categories: political rights and civil liberties. Civil liberties allow for the freedoms of expression and belief, associational and organizational rights, rule of law, and personal autonomy without interference from the state.
This is one of the components of the Empowerment sub-Index, and it assesses various aspects of the political impact of Web use.
Assesses the extent of the use of online services to facilitate provision of information by governments to citizens, and the interaction with stakeholders and engagement in decision-making processes.
The Web Index is a composite measure that summarises the impact and value derived from the Web in various countries in a single, average number.
There are serious challenges when attempting to measure and quantify some of the dimensions the Index covers (e.g. the social and political), and suitable proxies are used instead.
In addition, as the Web Index covers a large number of countries, some of which have serious data deficiencies or were not covered by the data providers, we needed to impute the missing data. We have worked with eminent experts in the relevant fields to overcome these challenges and produce a robust and rigorous Index.
Two types of data were used in the construction of the Index: existing data from other data providers (“secondary data”), and new data gathered via a multi-country questionnaire (“primary data”) that was specifically designed by the World Wide Web Foundation and its advisers. This primary data will begin to fill in some of the gaps in measurement of the utility and impact of the Web in various countries. Indeed, the data gaps in this field are significant, and we aim to continue to address this in future editions of the Index, both in terms of the questions/indicators gathered and the number of countries covered by the Index.
There has been no change from the 2013 Index in the statistical approach used for the computation of the latest Web Index. There are several steps in the process of constructing a composite Index. Some of those involve deciding which statistical method to use in the normalisation and aggregation processes. In arriving at that decision, we took into account several factors, including the purpose of the Index, the number of dimensions we were aggregating, and the ease of disseminating and communicating it in a clear, replicable and transparent way.
The following 10 steps summarise the computation process of the Index:
1. Take the data for each indicator from the data source for the 86 countries covered by the Index for the 2007-2013 time period (or until mid-2014 in the case of the Web Index expert assessment survey).
2. Impute missing data for every secondary indicator for the sample of 86 countries over the period 2007-2013. Some indicators were not imputed as it was not logical to do so (this is noted in the datasets where applicable). None of the primary data indicators were imputed. Hence the 2014 Index is very different from the 2007-2013 Indexes that were computed using secondary data only. Broadly, the imputation of missing data was done using two methods, in addition to extrapolation: country-mean substitution if the missing number is in the middle year (e.g. have 2008 and 2010 but not 2009), or taking arithmetic growth rates on a year-by-year basis. For the indicators that did not cover a particular country in any of the years, no imputation was done for that country/indicator.
3. Normalise the full (imputed) dataset using z-scores, making sure that for all indicators, a high value is “good” and a low value is “bad”.
4. Cluster some of the variables (as per the scheme in the tree diagram), taking the average of the clustered indicators post-normalisation. For the clustered indicators, this clustered value is the one to be used in the computation of the Index components.
5. Compute the eight component scores using arithmetic means, using the clustered values where relevant.
6. Compute the min-max values for each z-score value of the components, as this is what will be shown in the visualisation tool and other publications containing the component values (generally, it is easier to understand a min-max number in the range of 0 – 100 rather than a standard deviation-based number). The formula for this is : [(x – min)/(max – min)]*100.
7. Compute sub-index scores by calculating the weighted averages the z-scores of the relevant components for each sub-Index.
8. Compute the min-max values for each z-score value of the sub-Indexes, as this is what will be shown in the visualization tool and other publications containing the sub-index values.
9. Compute overall composite scores by calculating the weighted average of the sub-indexes.
10. Compute the min-max values (on a scale of 0-100) for each z-score value of the overall composite scores, as this is what will be shown in the visualisation tool and other publications containing the composite scores.
Indicator Inclusion Criteria:
We searched a large number of international databases to find indicators that measure or proxy the dimensions under study.
Before an indicator is included in the Index, it needs to fulfill five basic criteria:
1. Data providers have to be credible and reliable organisations, that are likely to continue to produce data for the same indicators (e.g. theirs is not a once-off dataset being published).
2. Data releases should be regular, with new data released at least every three years.
3. There should be at least two data years for each indicator for the majority of countries, so that basic statistical inference could be made.
4. The latest data year should be no older than three years prior to publication year. For example, if the first Index is published in 2012, data must be available, at a minimum, for 2009 and before.
5. The data source should cover at least two-thirds of the sample of countries, so that possible bias – introduced by having a large number of indicators from one source that systematically does not cover one-third or more of the countries – is reduced.
Changes to data collection/processing
Given feedback from the 2012 pilot Index (when we applied differentiated weights across the sub-Indexes), and the change to the structure and design of the 2013 and 2014 Web Indexes, we chose to apply equal weights across both the 2013 and 2014-15 Indexes. This decision reflects an approach that considers access to the web and online rights to be essential to the web’s potential to empower individuals.
Data provided by
World Wide Web Foundation Web Index 2014
How to find the data
http://thewebindex.org/ > Data > Download the data
Import & extraction details
File as imported: World Wide Web Foundation Web Index 2014
From the dataset World Wide Web Foundation Web Index 2014, this data was extracted:
- Sheet: WB C
- Provided: 546 data points
This data forms the table Web Index - Tertiary enrolment rates female/male in various countries 2007–2013.
Dataset originally released on:
December 31, 2014
About this dataset
Designed and produced by the World Wide Web Foundation, the Web Index is the world’s first measure of the World Wide Web’s contribution to social, economic and political progress in countries across the world. http://thewebindex.org/about/
Scores are given in the areas of universal access; freedom and openness; relevant content; and empowerment.
First released in 2012, the 2014-15 Index has been expanded and refined to include a total of 86 countries and features an enhanced data set, particularly in the areas of gender, Open Data, privacy rights and censorship. The Index combines existing secondary data with new primary data derived from an evidence-based expert assessment survey.
The Web Index provides an objective and robust evidence base to inform public dialogue on the steps needed for societies to leverage greater value from the Web. It is published annually and resources permitting, it will continue to be expanded to cover more countries in the coming years. It will eventually allow for comparisons of trends over time and the benchmarking of performance across countries, continuously improving our understanding of the Web’s value for humanity.