Costa Rica CR: Population in Largest City data was reported at 1,482,460.000 Person in 2024. This records an increase from the previous number of 1,461,989.000 Person for 2023. Costa Rica CR: Population in Largest City data is updated yearly, averaging 791,543.000 Person from Dec 1960 (Median) to 2024, with 65 observations. The data reached an all-time high of 1,482,460.000 Person in 2024 and a record low of 229,792.000 Person in 1960. Costa Rica CR: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Costa Rica – Table CR.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.;United Nations, World Urbanization Prospects.;;

Costa Rica CR: Population in Largest City: as % of Urban Population data was reported at 34.747 % in 2024. This records an increase from the previous number of 34.658 % for 2023. Costa Rica CR: Population in Largest City: as % of Urban Population data is updated yearly, averaging 46.499 % from Dec 1960 (Median) to 2024, with 65 observations. The data reached an all-time high of 51.171 % in 1963 and a record low of 34.420 % in 2020. Costa Rica CR: Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Costa Rica – Table CR.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.;United Nations, World Urbanization Prospects.;Weighted average;

Abstract (en): The Research on Early Life and Aging Trends and Effects (RELATE) study compiles cross-national data that contain information that can be used to examine the effects of early life conditions on older adult health conditions, including heart disease, diabetes, obesity, functionality, mortality, and self-reported health. The complete cross sectional/longitudinal dataset (n=147,278) was compiled from major studies of older adults or households across the world that in most instances are representative of the older adult population either nationally, in major urban centers, or in provinces. It includes over 180 variables with information on demographic and geographic variables along with information about early life conditions and life course events for older adults in low, middle and high income countries. Selected variables were harmonized to facilitate cross national comparisons. In this first public release of the RELATE data, a subset of the data (n=88,273) is being released. The subset includes harmonized data of older adults from the following regions of the world: Africa (Ghana and South Africa), Asia (China, India), Latin America (Costa Rica, major cities in Latin America), and the United States (Puerto Rico, Wisconsin). This first release of the data collection is composed of 19 downloadable parts: Part 1 includes the harmonized cross-national RELATE dataset, which harmonizes data from parts 2 through 19. Specifically, parts 2 through 19 include data from Costa Rica (Part 2), Puerto Rico (Part 3), the United States (Wisconsin) (Part 4), Argentina (Part 5), Barbados (Part 6), Brazil (Part 7), Chile (Part 8), Cuba (Part 9), Mexico (Parts 10 and 15), Uruguay (Part 11), China (Parts 12, 18, and 19), Ghana (Part 13), India (Part 14), Russia (Part 16), and South Africa (Part 17). The Health and Retirement Study (HRS) was also used in the compilation of the larger RELATE data set (HRS) (N=12,527), and these data are now available for public release on the HRS data products page. To access the HRS data that are part of the RELATE data set, please see the collection notes below. The purpose of this study was to compile and harmonize cross-national data from both the developing and developed world to allow for the examination of how early life conditions are related to older adult health and well being. The selection of countries for this study was based on their diversity but also on the availability of comprehensive cross sectional/panel survey data for older adults born in the early to mid 20th century in low, middle and high income countries. These data were then utilized to create the harmonized cross-national RELATE data (Part 1). Specifically, data that are being released in this version of the RELATE study come from the following studies: CHNS (China Health and Nutrition Study) CLHLS (Chinese Longitudinal Healthy Longevity Survey) CRELES (Costa Rican Study of Longevity and Healthy Aging) PREHCO (Puerto Rican Elderly: Health Conditions) SABE (Study of Aging Survey on Health and Well Being of Elders) SAGE (WHO Study on Global Ageing and Adult Health) WLS (Wisconsin Longitudinal Study) Note that the countries selected represent a diverse range in national income levels: Barbados and the United States (including Puerto Rico) represent high income countries; Argentina, Cuba, Uruguay, Chile, Costa Rica, Brazil, Mexico, and Russia represent upper middle income countries; China and India represent lower middle income countries; and Ghana represents a low income country. Users should refer to the technical report that accompanies the RELATE data for more detailed information regarding the study design of the surveys used in the construction of the cross-national data. The Research on Early Life and Aging Trends and Effects (RELATE) data includes an array of variables, including basic demographic variables (age, gender, education), variables relating to early life conditions (height, knee height, rural/urban birthplace, childhood health, childhood socioeconomic status), adult socioeconomic status (income, wealth), adult lifestyle (smoking, drinking, exercising, diet), and health outcomes (self-reported health, chronic conditions, difficulty with functionality, obesity, mortality). Not all countries have the same variables. Please refer to the technical report that is part of the documentation for more detail regarding the variables available across countries. Sample weights are applicable to all countries exc...

This dataset provides an indicator of dependency ratios for OECD Functional Urban Areas (FUAs) and cities.

   <h3>Data sources and methodology</h3>
   <p align="justify">
   Dependency ratios are derived from population by age and sex data collected at the level of small administrative units (e.g. municipalities) and aggregated at the FUA and city level. The correspondence table between SAUs and FUAs/cities is available at <a href=https://stats.oecd.org/wbos/fileview2.aspx?IDFile=21612592-67a6-4718-baf5-23c7f832ffed>this link</a>.<br /><br />
   When population by age and sex data is not available at such granular level, FUA and city level indicators are estimated by adjusting the regional (OECD TL3 regions) values to the FUA and city boundaries, based on the population distribution in each region. Regional values (population by age and sex) in TL3 regions are used as data inputs and combined with gridded total population data <a href=https://doi.org/10.2760/098587>(European Commission, GHSL Data Package 2023)</a>. FUA and city boundaries are intersected with TL3 borders and coefficients are computed for each region, based on the share of the regional population that lives within the FUA/city. These coefficients are then applied to the variables of interest (e.g. population by age groups) and allocated to the FUA/city. In case several regions intersect the FUA/city, the adjusted values of intersecting regions are summed. This approach assumes that the variables of interest have the same spatial distribution as the total population. Therefore, the modelled indicators should be interpreted with caution.<br /><br />
   </p>

   <h3>Defining FUAs and cities</h3>
   <p align="justify">The OECD, in cooperation with the EU, has developed a harmonised <a href="https://www.oecd.org/en/data/datasets/oecd-definition-of-cities-and-functional-urban-areas.html">definition of functional urban areas</a> (FUAs) to capture the economic and functional reach of cities based on daily commuting patterns <a href=https://doi.org/10.1787/9789264174108-en>(OECD, 2012)</a>. FUAs consist of:
   <ol>
   <li><b>A city</b> – defined by urban centres in the degree of urbanisation, adapted to the closest local administrative units to define a city.</li>
   <li><b>A commuting zone</b> – including all local areas where at least 15% of employed residents work in the city.</li>
   </ol>
   The delineation process includes:
   <ul>
   <li>Assigning municipalities surrounded by a single FUA to that FUA.</li>
   <li>Excluding non-contiguous municipalities.</li>
   </ul>
   The definition identifies 1 285 FUAs and 1 402 cities in all OECD member countries except Costa Rica and three accession countries.</p>
   <h3>Cite this dataset</h3>
   <p>OECD Regions, cities and local areas database (<a href=http://data-explorer.oecd.org/s/1ds>Dependency ratio - Cities and FUAs</a>), <a href="http://oe.cd/geostats">http://oe.cd/geostats</a></p>
   <h3>Further information</h3>
   <p align="justify">For any question or comment, please write to <a href="mailto:RegionStat@oecd.org">RegionStat@oecd.org</a><br /><br />FUA and City Statistics can be further explored with the interactive <a href="https://regions-cities-atlas.oecd.org">OECD Regions and Cities Statistical Atlas</a> web-tool.</p>

Abstract

Polluted air is a major health hazard in developing countries. Improvements in pollution monitoring and statistical techniques during the last several decades have steadily enhanced the ability to measure the health effects of air pollution. Current methods can detect significant increases in the incidence of cardiopulmonary and respiratory diseases, coughing, bronchitis, and lung cancer, as well as premature deaths from these diseases resulting from elevated concentrations of ambient Particulate Matter (Holgate 1999).

Scarce public resources have limited the monitoring of atmospheric particulate matter (PM) concentrations in developing countries, despite their large potential health effects. As a result, policymakers in many developing countries remain uncertain about the exposure of their residents to PM air pollution. The Global Model of Ambient Particulates (GMAPS) is an attempt to bridge this information gap through an econometrically estimated model for predicting PM levels in world cities (Pandey et al. forthcoming).

The estimation model is based on the latest available monitored PM pollution data from the World Health Organization, supplemented by data from other reliable sources. The current model can be used to estimate PM levels in urban residential areas and non-residential pollution hotspots. The results of the model are used to project annual average ambient PM concentrations for residential and non-residential areas in 3,226 world cities with populations larger than 100,000, as well as national capitals.

The study finds wide, systematic variations in ambient PM concentrations, both across world cities and over time. PM concentrations have risen at a slower rate than total emissions. Overall emission levels have been rising, especially for poorer countries, at nearly 6 percent per year. PM concentrations have not increased by as much, due to improvements in technology and structural shifts in the world economy. Additionally, within-country variations in PM levels can diverge greatly (by a factor of 5 in some cases), because of the direct and indirect effects of geo-climatic factors.

The primary determinants of PM concentrations are the scale and composition of economic activity, population, the energy mix, the strength of local pollution regulation, and geographic and atmospheric conditions that affect pollutant dispersion in the atmosphere.

Geographic coverage

The database covers the following countries: Afghanistan Albania Algeria Andorra Angola
Antigua and Barbuda Argentina
Armenia Australia
Austria Azerbaijan
Bahamas, The
Bahrain Bangladesh
Barbados
Belarus Belgium Belize
Benin
Bhutan
Bolivia Bosnia and Herzegovina
Brazil
Brunei
Bulgaria
Burkina Faso
Burundi Cambodia
Cameroon
Canada
Cayman Islands
Central African Republic
Chad
Chile
China
Colombia
Comoros Congo, Dem. Rep.
Congo, Rep. Costa Rica
Cote d'Ivoire
Croatia Cuba
Cyprus
Czech Republic
Denmark Dominica
Dominican Republic
Ecuador Egypt, Arab Rep.
El Salvador Eritrea Estonia Ethiopia
Faeroe Islands
Fiji
Finland France
Gabon
Gambia, The Georgia Germany Ghana
Greece
Grenada Guatemala
Guinea
Guinea-Bissau
Guyana
Haiti
Honduras
Hong Kong, China
Hungary Iceland India
Indonesia
Iran, Islamic Rep.
Iraq
Ireland Israel
Italy
Jamaica Japan
Jordan
Kazakhstan
Kenya
Korea, Dem. Rep.
Korea, Rep. Kuwait
Kyrgyz Republic Lao PDR Latvia
Lebanon Lesotho Liberia Liechtenstein
Lithuania
Luxembourg
Macao, China
Macedonia, FYR
Madagascar
Malawi
Malaysia
Maldives
Mali
Mauritania
Mexico
Moldova Mongolia
Morocco Mozambique
Myanmar Namibia Nepal
Netherlands Netherlands Antilles
New Caledonia
New Zealand Nicaragua
Niger
Nigeria Norway
Oman
Pakistan
Panama
Papua New Guinea
Paraguay
Peru
Philippines Poland
Portugal
Puerto Rico Qatar
Romania Russian Federation
Rwanda
Sao Tome and Principe
Saudi Arabia
Senegal Sierra Leone
Singapore
Slovak Republic Slovenia
Solomon Islands Somalia South Africa
Spain
Sri Lanka
St. Kitts and Nevis St. Lucia
St. Vincent and the Grenadines
Sudan
Suriname
Swaziland
Sweden
Switzerland Syrian Arab Republic
Tajikistan
Tanzania
Thailand
Togo
Trinidad and Tobago Tunisia Turkey
Turkmenistan
Uganda
Ukraine United Arab Emirates
United Kingdom
United States
Uruguay Uzbekistan
Vanuatu Venezuela, RB
Vietnam Virgin Islands (U.S.)
Yemen, Rep. Yugoslavia, FR (Serbia/Montenegro)
Zambia
Zimbabwe

Kind of data

Observation data/ratings [obs]

Mode of data collection

Other [oth]

This dataset contains the exams that were applied in the Central American and Caribbean Chemistry Olympiad 2021 held in the city of San Jose, Costa Rica, with the participation of four Central American countries (Honduras, Guatemala, El Salvador and Costa Rica). These exams can be used as research instruments in studies related to chemistry education. In addition, students' answers are not reported because most of the countries involved consider these data to be sensitive.