The following data set is information obtained about counties in the United States from 2010 through 2019 through the United States Census Bureau. Information described in the data includes the age distributions, the education levels, employment statistics, ethnicity percents, houseold information, income, and other miscellneous statistics. (Values are denoted as -1, if the data is not available)

Data Dictionary

The Country-Level Population and Downscaled Projections Based on Special Report on Emissions Scenarios (SRES) A1, B1, and A2 Scenarios, 1990-2100, were adopted in 2000 from population projections realized at the International Institute for Applied Systems Analysis (IIASA) in 1996. The Intergovernmental Panel on Climate Change (IPCC) SRES A1 and B1 scenarios both used the same IIASA "rapid" fertility transition projection, which assumes low fertility and low mortality rates. The SRES A2 scenario used a corresponding IIASA "slow" fertility transition projection (high fertility and high mortality rates). Both IIASA low and high projections are performed for 13 world regions including North Africa, Sub-Saharan Africa, China and Centrally Planned Asia, Pacific Asia, Pacific OECD, Central Asia, Middle East, South Asia, Eastern Europe, European part of the former Soviet Union, Western Europe, Latin America, and North America. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN). To provide data on country-level population and downscaled projections based on the SRES A1, B1, and A2 marker scenarios.

TThe ERS International Macroeconomic Data Set provides historical and projected data for 181 countries that account for more than 99 percent of the world economy. These data and projections are assembled explicitly to serve as underlying assumptions for the annual USDA agricultural supply and demand projections, which provide a 10-year outlook on U.S. and global agriculture. The macroeconomic projections describe the long-term, 10-year scenario that is used as a benchmark for analyzing the impacts of alternative scenarios and macroeconomic shocks.

Explore the International Macroeconomic Data Set 2015 for annual growth rates, consumer price indices, real GDP per capita, exchange rates, and more. Get detailed projections and forecasts for countries worldwide.

Annual growth rates, Consumer price indices (CPI), Real GDP per capita, Real exchange rates, Population, GDP deflator, Real gross domestic product (GDP), Real GDP shares, GDP, projections, Forecast, Real Estate, Per capita, Deflator, share, Exchange Rates, CPI

Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Côte d'Ivoire, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Congo, Costa Rica, Croatia, Cuba, Cyprus, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kuwait, Kyrgyzstan, Laos, Latvia, Lebanon, Lesotho, Liberia, Libya, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Mauritania, Mauritius, Mexico, Moldova, Mongolia, Morocco, Mozambique, Myanmar, Namibia, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, Norway, Oman, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Samoa, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovakia, Slovenia, Solomon Islands, South Africa, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Thailand, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, Yemen, Zambia, Zimbabwe, WORLD Follow data.kapsarc.org for timely data to advance energy economics research. Notes:

Developed countries/1 Australia, New Zealand, Japan, Other Western Europe, European Union 27, North America

Developed countries less USA/2 Australia, New Zealand, Japan, Other Western Europe, European Union 27, Canada

Developing countries/3 Africa, Middle East, Other Oceania, Asia less Japan, Latin America;

Low-income developing countries/4 Haiti, Afghanistan, Nepal, Benin, Burkina Faso, Burundi, Central African Republic, Chad, Democratic Republic of Congo, Eritrea, Ethiopia, Gambia, Guinea, Guinea-Bissau, Liberia, Madagascar, Malawi, Mali, Mozambique, Niger, Rwanda, Senegal, Sierra Leone, Somalia, Tanzania, Togo, Uganda, Zimbabwe;

Emerging markets/5 Mexico, Brazil, Chile, Czech Republic, Hungary, Poland, Slovakia, Russia, China, India, Korea, Taiwan, Indonesia, Malaysia, Philippines, Thailand, Vietnam, Singapore

BRIICs/5 Brazil, Russia, India, Indonesia, China; Former Centrally Planned Economies

Former centrally planned economies/7 Cyprus, Malta, Recently acceded countries, Other Central Europe, Former Soviet Union

USMCA/8 Canada, Mexico, United States

Europe and Central Asia/9 Europe, Former Soviet Union

Middle East and North Africa/10 Middle East and North Africa

Other Southeast Asia outlook/11 Malaysia, Philippines, Thailand, Vietnam

Other South America outlook/12 Chile, Colombia, Peru, Bolivia, Paraguay, Uruguay

Indicator Source

Real gross domestic product (GDP) World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service all converted to a 2015 base year.

Real GDP per capita U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table and Population table.

GDP deflator World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Real GDP shares U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table.

Real exchange rates U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, CPI table, and Nominal XR and Trade Weights tables developed by the Economic Research Service.

Consumer price indices (CPI) International Financial Statistics International Monetary Fund, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Population Department of Commerce, Bureau of the Census, U.S. Department of Agriculture, Economic Research Service, International Data Base.

This dataset shows different breakdowns of London's resident population by their country of birth. Data used comes from ONS' Annual Population Survey (APS). The APS has a sample of around 320,000 people in the UK (around 28,000 in London). As such all figures must be treated with some caution. 95% confidence interval levels are provided. Numbers have been rounded to the nearest thousand and figures for smaller populations have been suppressed. Four files are available for download: Country of Birth - Borough: Shows country of birth estimates in their broad groups such as European Union, South East Asia, North Africa, etc. broken down to borough level. Detailed Country of Birth - London: Shows country of birth estimates for specific countries such as France, Bangladesh, Nigeria, etc. available for London as a whole Demography Update 09-2015: A GLA Demography report that uses APS data to analyse the trends in London for the period 2004 to 2014. A supporting data file is also provided. Country of Birth Borough 2004-2016 Analysis Tool: A tool produced by GLA Demography that allows users to explore different breakdowns of country of birth data. An accompanying Tableau visualisation tool has also been produced which maps data from 2004 to 2015. Nationality data can be found here: https://data.london.gov.uk/dataset/nationality Nationality refers to that stated by the respondent during the interview. Country of birth is the country in which they were born. It is possible that an individual’s nationality may change, but the respondent’s country of birth cannot change. This means that country of birth gives a more robust estimate of change over time. Data and Resources Country of Birth - Borough Shows estimates of the population by their country/region of birth by Borough

Workers' remittances refer to the money sent back home by individuals who are employed in foreign countries, typically to support their families or communities in their home country. For Bangladesh, remittances are a crucial component of the national economy, playing a significant role in poverty alleviation, foreign exchange reserves, and overall economic development. Bangladesh is one of the top recipients of remittances in South Asia, with millions of its citizens working overseas, especially in the Middle East, Southeast Asia, Europe, and North America.

Importance of Workers Remittance in Bangladesh:

Economic Contribution: Workers' remittances are a major source of foreign exchange in Bangladesh, helping stabilize the national currency and supporting the balance of payments. They help Bangladesh offset trade deficits and reduce reliance on foreign loans. The remittance inflows also support the growth of sectors like real estate, retail, and small businesses.

Poverty Reduction: Remittances directly support households in rural and urban areas by providing financial assistance for education, healthcare, housing, and other essential needs. This inflow helps to reduce poverty and improve the standard of living for millions of families.

Social Impact: Many families rely on remittances to meet daily expenses and maintain a decent standard of living. Remittances also contribute to social mobility by enabling families to invest in education, which in turn has long-term positive effects on the workforce and future generations.

Economic Stability: Remittances provide a buffer against economic crises, such as natural disasters or economic downturns. During times of economic instability, remittances often serve as a critical lifeline, ensuring that the affected population has access to essential resources.

Trends in Workers Remittance: The trends in remittances fluctuate depending on factors such as:

Global Economic Conditions: Economic conditions in major remittance-sending countries affect the flow of funds. For instance, during periods of economic recession or crisis in host countries, remittance flows might decrease. Migration Patterns: Changes in migration patterns due to geopolitical factors, labor demands, or government policies also affect the volume of remittances. Exchange Rates and Banking Systems: The stability of exchange rates and the efficiency of banking systems and remittance services (e.g., Western Union, MoneyGram) influence the ease and cost of sending remittances. Impact on Bangladesh’s GDP: Remittances contribute significantly to Bangladesh's Gross Domestic Product (GDP). They not only help with household income but also boost domestic consumption, which drives economic growth. As a result, the country has seen improvements in its infrastructure, poverty rates, and social services due to the continuous inflow of remittances.

Challenges in Managing Remittances: Despite the positive impacts, there are several challenges associated with workers' remittances:

High Transaction Costs: Remittance senders often face high transaction fees, especially when using informal channels. The government and financial institutions are working to reduce these costs by promoting digital banking and remittance services. Dependence on Remittances: Over-reliance on remittances may create economic vulnerabilities, as families might become dependent on external sources of income rather than local economic development and job creation. Policy and Regulation: Governments need to establish policies that facilitate the easy transfer of remittances while ensuring that the system is transparent and secure. File Format: CSV

Description: The "Workers Remittance" dataset provides a detailed record of the monthly remittance inflows to Bangladesh from January 2017 to March 2024. It is crucial for understanding the economic role that remittances play in the country, particularly for millions of Bangladeshi families who rely on money sent by relatives working abroad. The dataset is structured with the following columns:

Year: The year of remittance data. Month: The month of remittance data. Unit (Tk. in Million): The total remittance amount received, expressed in millions of Bangladeshi Taka (Tk.). This data is particularly valuable for economists, policymakers, and financial analysts who are interested in studying the trends in remittance inflows, their impact on the national economy, and their contribution to the livelihoods of the Bangladeshi population. Workers' remittances are a significant source of foreign exchange and play a critical role in the socio-economic development of Bangladesh.

Source: http://data.gov.bd/dataset/table-9-workers-remittance-jan-2017-mar-2024

**More Dataset:: ** https://www.kaggle.com/shuvokumarbasak4004/datasets

…………………………………..Note for Researchers Using the dataset………………………………………………………………...

Global patterns of current and future road infrastructure - Supplementary spatial data

Authors: Johan Meijer, Mark Huijbregts, Kees Schotten, Aafke Schipper

Research paper summary: Georeferenced information on road infrastructure is essential for spatial planning, socio-economic assessments and environmental impact analyses. Yet current global road maps are typically outdated or characterized by spatial bias in coverage. In the Global Roads Inventory Project we gathered, harmonized and integrated nearly 60 geospatial datasets on road infrastructure into a global roads dataset. The resulting dataset covers 222 countries and includes over 21 million km of roads, which is two to three times the total length in the currently best available country-based global roads datasets. We then related total road length per country to country area, population density, GDP and OECD membership, resulting in a regression model with adjusted R2 of 0.90, and found that that the highest road densities are associated with densely populated and wealthier countries. Applying our regression model to future population densities and GDP estimates from the Shared Socioeconomic Pathway (SSP) scenarios, we obtained a tentative estimate of 3.0–4.7 million km additional road length for the year 2050. Large increases in road length were projected for developing nations in some of the world's last remaining wilderness areas, such as the Amazon, the Congo basin and New Guinea. This highlights the need for accurate spatial road datasets to underpin strategic spatial planning in order to reduce the impacts of roads in remaining pristine ecosystems.

Contents: The GRIP dataset consists of global and regional vector datasets in ESRI filegeodatabase and shapefile format, and global raster datasets of road density at a 5 arcminutes resolution (~8x8km). The GRIP dataset is mainly aimed at providing a roads dataset that is easily usable for scientific global environmental and biodiversity modelling projects. The dataset is not suitable for navigation. GRIP4 is based on many different sources (including OpenStreetMap) and to the best of our ability we have verified their public availability, as a criteria in our research. The UNSDI-Transportation datamodel was applied for harmonization of the individual source datasets. GRIP4 is provided under a Creative Commons License (CC-0) and is free to use. The GRIP database and future global road infrastructure scenario projections following the Shared Socioeconomic Pathways (SSPs) are described in the paper by Meijer et al (2018). Due to shapefile file size limitations the global file is only available in ESRI filegeodatabase format.

Regional coding of the other vector datasets in shapefile and ESRI fgdb format:

• Region 1: North America
• Region 2: Central and South America
• Region 3: Africa
• Region 4: Europe
• Region 5: Middle East and Central Asia
• Region 6: South and East Asia
• Region 7: Oceania

Road density raster data:

• Total density, all types combined
• Type 1 density (highways)
• Type 2 density (primary roads)
• Type 3 density (secondary roads)
• Type 4 density (tertiary roads)
• Type 5 density (local roads)

Keyword: global, data, roads, infrastructure, network, global roads inventory project (GRIP), SSP scenarios

About Dataset: WHO COVID-19 Global Data

This dataset provides comprehensive information on the global COVID-19 pandemic as reported to the World Health Organization (WHO). The dataset is available in comma-separated values (CSV) format and includes the following fields:

Daily cases and deaths by date reported to WHO: WHO-COVID-19-global-data.csv

• Date_reported (Date): The date of reporting to WHO.
• Country_code (String): The ISO Alpha-2 country code.
• Country (String): The name of the country, territory, or area.
• WHO_region (String): The WHO regional office to which the country belongs. WHO Member States are grouped into six WHO regions, including AFRO (Regional Office for Africa), AMRO (Regional Office for the Americas), SEARO (Regional Office for South-East Asia), EURO (Regional Office for Europe), EMRO (Regional Office for the Eastern Mediterranean), and WPRO (Regional Office for the Western Pacific).
• New_cases (Integer): The number of new confirmed cases reported on a given day. This is calculated by subtracting the previous cumulative case count from the current cumulative case count.
• Cumulative_cases (Integer): The total cumulative confirmed cases reported to WHO up to the specified date.
• New_deaths (Integer): The number of new confirmed deaths reported on a given day. Similar to new cases, this is calculated by subtracting the previous cumulative death count from the current cumulative death count.- Cumulative_deaths (Integer): The total cumulative confirmed deaths reported to WHO up to the specified date.

In addition to the COVID-19 case and death data, this dataset also includes valuable information related to COVID-19 vaccinations. The vaccination data consists of the following fields:

Vaccination Data Fields: vaccination-data.csv

• COUNTRY (String): Country, territory, or area.
• ISO3 (String): ISO Alpha-3 country code.
• WHO_REGION (String): The WHO regional office to which the country belongs.
• DATA_SOURCE (String): Indicates the data source, which can be either "REPORTING" (Data reported by Member States or sourced from official reports) or "OWID" (Data sourced from Our World in Data COVID-19 Vaccinations).
• DATE_UPDATED (Date): Date of the last update.
• TOTAL_VACCINATIONS (Integer): Cumulative total vaccine doses administered.
• PERSONS_VACCINATED_1PLUS_DOSE (Decimal): Cumulative number of persons vaccinated with at least one dose.
• TOTAL_VACCINATIONS_PER100 (Integer): Cumulative total vaccine doses administered per 100 population.
• PERSONS_VACCINATED_1PLUS_DOSE_PER100 (Decimal): Cumulative persons vaccinated with at least one dose per 100 population.
• PERSONS_LAST_DOSE (Integer): Cumulative number of persons vaccinated with a complete primary series.
• PERSONS_LAST_DOSE_PER100 (Decimal): Cumulative number of persons vaccinated with a complete primary series per 100 population.
• VACCINES_USED (String): Combined short name of the vaccine in the format "Company - Product name."
• FIRST_VACCINE_DATE (Date): Date of the first vaccinations, equivalent to the start/launch date of the first vaccine administered in a country.
• NUMBER_VACCINES_TYPES_USED (Integer): Number of vaccine types used per country, territory, or area.
• PERSONS_BOOSTER_ADD_DOSE (Integer): Cumulative number of persons vaccinated with at least one booster or additional dose.
• PERSONS_BOOSTER_ADD_DOSE_PER100 (Decimal): Cumulative number of persons vaccinated with at least one booster or additional dose per 100 population.

In addition to the vaccination data, a separate dataset containing vaccination metadata is available, including information about vaccine names, product names, company names, authorization dates, start and end dates of vaccine rollout, and more.

Vaccination metadata Fields: vaccination-metadata.csv

• ISO3 (String): ISO Alpha-3 country code
• VACCINE_NAME (String): Combined short name of vaccine: "Company - Product name" (see below)
• PRODUCT_NAME (String): Name or label of vaccine product, or type of vaccine (if unnamed).
• COMPANY_NAME (String): Marketing authorization holder of vaccine product.
• FIRST_VACCINE_DATE (Date): Date of first vaccinations. Equivalent to start/launch date of the first vaccine administered in a country.
• AUTHORIZATION_DATE (Date): Date vaccine product was authorized for use in the country, territory, area.
• START_DATE (Date): Start/launch date of vaccination with vaccine type (excludes vaccinations during clinical trials).
• END_DATE (Date): End date of vaccine rollout
• COMMENT (String): Comments related to vaccine rollout
• DATA_SOURCE (String): Indicates data source - REPORTING: Data reported by Member States, or sourced from official re...

Abstract

The Liberia Demographic and Health Survey (LDHS) was conducted as part of the worldwide Demographic and Health Surveys (DHS) program, in which surveys are being carried out in countries in Africa, Asia, Latin America, and the Middle East. Liberia was the second country to conduct a DHS and the first country in Africa to do so. THe LDHS was a national-level survey conducted from February to July 1986, covering a sample of 5,239 women aged 15 to 49.

The major objective of the LDHS was to provide data on fertility, family planning and maternal and child health to planners and policymakers in Liberia for use in designing and evaluating programs. Although a fair amount of demographic data was available from censuses and surveys, almost no information existed concerning family planning, health, or the determinants of fertility, and the data that did exist were drawn from small-scale, sub-national studies. Thus, there was a need for data to make informed policy choices for family planning and health projects.

A more specific objective was to provide baseline data for the Southeast Region Primary Health Care Project. In order to effectively plan strategies and to eventually evaluate the progress of the project in meeting its goals, there was need for data to indicate the health situation in the two target counties prior to the implementation of the project. Many of the desired topics, such as immunizations, family planning use, and prenatal care, were already incorporated into the model DHS questionnaire; nevertheless, the LDHS was able to better accommodate the needs of this project by adding several questions and by oversampling women living in Sinoe and Grand Gedeh Counties.

Another important goal of the LDHS was to enhance tile skills of those participating in the project for conducting high-quality surveys in the future. Finally, the contribution of Liberian data to an expanding international dataset was also an objective of the LDHS.

Geographic coverage

National

Analysis unit

• Households
• Children age 0-5
• Women age 15 to 49
• Men

Kind of data

Sample survey data

Sampling procedure

The sample for the Liberia Demographic and Health Survey was based on the sampling frame of about 4,500 censal enumeration areas (EAs) that were created for the 1984 Population Census. It was decided to eliminate very remote EAs prior to selecting the sample. The definition of remoteness used was "any EA in which the largest village was estimated to be more than 3-4 hours' walk from a road." According to the 1984 census, the excluded areas represent less than 3 percent of the total number of households in the country. Since the major analytic objective of the LDHS was to adequately estimate basic demographic and health indicators including fertility, mortality, and contraceptive prevalence for the whole country and the two sub-universes (Since and Grand Gedeh Counties), it was decided to oversample these two counties. Consequently, three explicit sub-universes of EAs were created: (1) Since County, (2) Grand Gedeh County, and (3) the rest of the country.

The design provided a self-weighted sample within each sub-universe, but, because of the oversampling in Sinoe and Grand Gedeh Counties, the sample is not self-weighting at the national level. Eligible respondents for the survey were women aged 15-49 years who were present the night before the interview in any of the households included in the sample selected for the LDHS.

The total sample size was expected to be about 6,000 women aged 15-49 with a target by sub-universe of 1,000 each in Sinoe and Grand Gedeh Counties and 4,000 in the rest of the country. It was decided that a sample of approximately 5,500 households selected through a two-stage procedure would be appropriate to reach those objectives. Sampling was carried out independently in each sub-universe. In the rest of the country sub-universe, counties were arranged for selection in serpentine order from the northwest (Cape Mount County) to the southeast (Maryland County). In the first stage EAs were selected systematically with probability proportional to size (size = number of households in 1984). Twenty-four EAs were selected in each of Sinoe and Grand Gedeh Counties and 108 EAs in the rest of the country.

See full sample procedure in the survey final report.

Mode of data collection

Face-to-face

Research instrument

The Liberia Demographic and Health Survey (LDHS) utilized two questionnaires: One to list members of the selected households (Household Questionnaire) and the other to record information from all women aged 15-49 who were present in the selected households the night before the interview (Individual Questionnaire).

Both questionnaires were produced in Liberian English and were pretested in September 1985. The Individual Questionnaire was an early version of the DHS model questionnaire. It covered three main topics: (1) fertility, including a birth history and questions concerning desires for future childbearing, (2) family planning knowledge and use, and (3) family health, including prevalence of childhood diseases, immunizations for children under age five, and breasffeeding and weaning practices.

Cleaning operations

Data from the questionnaires were entered onto microcomputers at the Bureau of Statistics office in Monrovia. The data were then subjected to extensive checks for consistency and accuracy.

Errors detected during this operation were resolved either by referring to the original questionnaire, or, in some cases, by logical inference from other information given in the record. Finally, dates were imputed for the small number of cases where complete dates of important events were not given.

Response rate

Out of the total of 6,1306 households selected, 14.5 percent were found not to be valid households in the field, either because the dwelling had been vacated or destroyed, or the household could not be located or did not exist. Of the 5,609 households that were found to exist, 90 percent were successfully interviewed. In the households that were interviewed, a total of 5,340 women were identified as being eligible for individual interview (that is, they were aged 15-49 and had spent the night before the interview in the selected household). This represents an average of slightly over one eligible woman per household.

The response rate for eligible women was 98 percent. The main reason for nonresponse was the absence of the woman. Similar data are presented by sample subuniverse.

Sampling error estimates

The results from sample surveys are affected by two types of errors: (1) nonsampling error and (2) sampling error. Nonsampling error is due to mistakes made in carrying out field activities, such as failure to locate and interview the correct household, errors in the way questions are asked, misunderstanding of the questions on the part of either the interviewer or the respondent, data entry errors, etc. Although efforts were made during the design and implementation of the Liberia Demographic and Health Survey to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

The sample of women selected in the LDHS is only one of many samples of the same size that could have been selected from the same population, using the same design. Each one would have yielded results that differed somewhat from the actual sample selected. The variability observed between all possible samples constitutes sampling error, which, although it is not known exactly, can be estimated from the survey results. Sampling error is usually measured in terms of the "standard error" of a particular statistic (mean, percentage, etc.), which is the square root of the variance of the statistic across all possible samples of equal size and design.

The standard error can be used to calculate confidence intervals within which one can be reasonably assured the true value of the variable for the whole population falls. For example, for any given statistic calculated from a sample survey, the value of that same statistic as measured in 95 percent of all possible samples of identical size and design will fall within a range of plus or minus two times the standard error of that statistic.

If the sample of women had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the LDHS sample design depended on stratification, stages, and clusters and consequently, it was necessary to utilize more complex formulas. The computer package CLUSTERS was used to assist in computing the sampling errors with the proper statistical methodology.

Data appraisal

Information on the completeness of date reporting is of interest in assessing data quality. With regard to dates of birth of individual women, 42 percent of respondents reported both a month and year of birth, 21 percent gave a year of birth in addition to current age, and 37 percent gave only their ages. With regard to children's dates of birth in the birth history, 85 percent of births had both month and year reported, 12 percent had year and age reported, 1 percent had only age reported, and 2 percent had no date information.

The 2008 National Demographic and Health Survey (2008 NDHS) is a nationally representative survey of 13,594 women age 15-49 from 12,469 households successfully interviewed, covering 794 enumeration areas (clusters) throughout the Philippines. This survey is the ninth in a series of demographic and health surveys conducted to assess the demographic and health situation in the country. The survey obtained detailed information on fertility levels, marriage, fertility preferences, awareness and use of family planning methods, breastfeeding practices, nutritional status of women and young children, childhood mortality, maternal and child health, and knowledge and attitudes regarding HIV/AIDS and tuberculosis. Also, for the first time, the Philippines NDHS gathered information on violence against women. The 2008 NDHS was conducted by the Philippine National Statistics Office (NSO). Technical assistance was provided by ICF Macro through the MEASURE DHS program. Funding for the survey was mainly provided by the Government of the Philippines. Financial support for some preparatory and processing phases of the survey was provided by the U.S. Agency for International Development (USAID). Like previous Demographic and Health Surveys (DHS) conducted in the Philippines, the 2008 National Demographic and Health Survey (NDHS) was primarily designed to provide information on population, family planning, and health to be used in evaluating and designing policies, programs, and strategies for improving health and family planning services in the country. The 2008 NDHS also included questions on domestic violence. Specifically, the 2008 NDHS had the following objectives: Collect data at the national level that will allow the estimation of demographic rates, particularly, fertility rates by urban-rural residence and region, and under-five mortality rates at the national level. Analyze the direct and indirect factors which determine the levels and patterns of fertility. Measure the level of contraceptive knowledge and practice by method, urban-rural residence, and region. Collect data on family health: immunizations, prenatal and postnatal checkups, assistance at delivery, breastfeeding, and prevalence and treatment of diarrhea, fever, and acute respiratory infections among children under five years. Collect data on environmental health, utilization of health facilities, prevalence of common noncommunicable and infectious diseases, and membership in health insurance plans. Collect data on awareness of tuberculosis. Determine women's knowledge about HIV/AIDS and access to HIV testing. Determine the extent of violence against women. MAIN RESULTS FERTILITY Fertility Levels and Trends. There has been a steady decline in fertility in the Philippines in the past 36 years. From 6.0 children per woman in 1970, the total fertility rate (TFR) in the Philippines declined to 3.3 children per woman in 2006. The current fertility level in the country is relatively high compared with other countries in Southeast Asia, such as Thailand, Singapore and Indonesia, where the TFR is below 2 children per woman. Fertility Differentials. Fertility varies substantially across subgroups of women. Urban women have, on average, 2.8 children compared with 3.8 children per woman in rural areas. The level of fertility has a negative relationship with education; the fertility rate of women who have attended college (2.3 children per woman) is about half that of women who have been to elementary school (4.5 children per woman). Fertility also decreases with household wealth: women in wealthier households have fewer children than those in poorer households. FAMILY PLANNING Knowledge of Contraception. Knowledge of family planning is universal in the Philippines- almost all women know at least one method of fam-ily planning. At least 90 percent of currently married women have heard of the pill, male condoms, injectables, and female sterilization, while 87 percent know about the IUD and 68 percent know about male sterilization. On average, currently married women know eight methods of family planning. Unmet Need for Family Planning. Unmet need for family planning is defined as the percentage of currently married women who either do not want any more children or want to wait before having their next birth, but are not using any method of family planning. The 2008 NDHS data show that the total unmet need for family planning in the Philippines is 22 percent, of which 13 percent is limiting and 9 percent is for spacing. The level of unmet need has increased from 17 percent in 2003. Overall, the total demand for family planning in the Philippines is 73 percent, of which 69 percent has been satisfied. If all of need were satisfied, a contraceptive prevalence rate of about 73 percent could, theoretically, be expected. Comparison with the 2003 NDHS indicates that the percentage of demand satisfied has declined from 75 percent. MATERNAL HEALTH Antenatal Care. Nine in ten Filipino mothers received some antenatal care (ANC) from a medical professional, either a nurse or midwife (52 percent) or a doctor (39 percent). Most women have at least four antenatal care visits. More than half (54 percent) of women had an antenatal care visit during the first trimester of pregnancy, as recommended. While more than 90 percent of women who received antenatal care had their blood pressure monitored and weight measured, only 54 percent had their urine sample taken and 47 percent had their blood sample taken. About seven in ten women were informed of pregnancy complications. Three in four births in the Philippines are protected against neonatal tetanus. Delivery and Postnatal Care. Only 44 percent of births in the Philippines occur in health facilities-27 percent in a public facility and 18 percent in a private facility. More than half (56 percent) of births are still delivered at home. Sixty-two percent of births are assisted by a health professional-35 percent by a doctor and 27 percent by a midwife or nurse. Thirty-six percent are assisted by a traditional birth attendant or hilot. About 10 percent of births are delivered by C-section. The Department of Health (DOH) recommends that mothers receive a postpartum check within 48 hours of delivery. A majority of women (77 percent) had a postnatal checkup within two days of delivery; 14 percent had a postnatal checkup 3 to 41 days after delivery. CHILD HEALTH Childhood Mortality. Childhood mortality continues to decline in the Philippines. Currently, about one in every 30 children in the Philippines dies before his or her fifth birthday. The infant mortality rate for the five years before the survey (roughly 2004-2008) is 25 deaths per 1,000 live births and the under-five mortality rate is 34 deaths per 1,000 live births. This is lower than the rates of 29 and 40 reported in 2003, respectively. The neonatal mortality rate, representing death in the first month of life, is 16 deaths per 1,000 live births. Under-five mortality decreases as household wealth increases; children from the poorest families are three times more likely to die before the age of five as those from the wealthiest families. There is a strong association between under-five mortality and mother's education. It ranges from 47 deaths per 1,000 live births among children of women with elementary education to 18 deaths per 1,000 live births among children of women who attended college. As in the 2003 NDHS, the highest level of under-five mortality is observed in ARMM (94 deaths per 1,000 live births), while the lowest is observed in NCR (24 deaths per 1,000 live births). NUTRITION Breastfeeding Practices. Eighty-eight percent of children born in the Philippines are breastfed. There has been no change in this practice since 1993. In addition, the median durations of any breastfeeding and of exclusive breastfeeding have remained at 14 months and less than one month, respectively. Although it is recommended that infants should not be given anything other than breast milk until six months of age, only one-third of Filipino children under six months are exclusively breastfed. Complementary foods should be introduced when a child is six months old to reduce the risk of malnutrition. More than half of children ages 6-9 months are eating complementary foods in addition to being breastfed. The Infant and Young Child Feeding (IYCF) guidelines contain specific recommendations for the number of times that young children in various age groups should be fed each day as well as the number of food groups from which they should be fed. NDHS data indicate that just over half of children age 6-23 months (55 percent) were fed according to the IYCF guidelines. HIV/AIDS Awareness of HIV/AIDS. While over 94 percent of women have heard of AIDS, only 53 percent know the two major methods for preventing transmission of HIV (using condoms and limiting sex to one uninfected partner). Only 45 percent of young women age 15-49 know these two methods for preventing HIV transmission. Knowledge of prevention methods is higher in urban areas than in rural areas and increases dramatically with education and wealth. For example, only 16 percent of women with no education know that using condoms limits the risk of HIV infection compared with 69 percent of those who have attended college. TUBERCULOSIS Knowledge of TB. While awareness of tuberculosis (TB) is high, knowledge of its causes and symptoms is less common. Only 1 in 4 women know that TB is caused by microbes, germs or bacteria. Instead, respondents tend to say that TB is caused by smoking or drinking alcohol, or that it is inherited. Symptoms associated with TB are better recognized. Over half of the respondents cited coughing, while 39 percent mentioned weight loss, 35 percent mentioned blood in sputum, and 30 percent cited coughing with sputum. WOMEN'S STATUS Women's Status and Employment.