How does the association between gender attitudes and housework share vary across countries and time? We examine the second demographic transition as it unmasks in the association between gender attitudes and housework participation. Using data of the 2002 and 2012 International Social Survey Programme (ISSP) for 24 countries, we find that the association between gender attitudes and housework share became stronger over time in most countries, signifying that the Second Demographic Transition was in place. The results also show that the association varied across the 24 countries, reaching an equilibrium in many but at different stages. Our findings suggest that equilibria in the domestic division of labour take various forms and paces in the ISSP countries.

This dataset includes data on 25 transitions of a matrix demographic model of the invasive species Vincetoxicum nigrum (L.) Moench (black swallow-wort or black dog-strangling vine) and Vincetoxicum rossicum (Kleopow) Barb. (pale swallow-wort or dog-strangling vine) (Apocynaceae, subfamily Asclepiadoideae), two invasive perennial vines in the northeastern U.S.A. and southeastern Canada. The matrix model was developed for projecting population growth rates as a result of changes to lower-level vital rates from biological control although the model is generalizable to any control tactic. Transitions occurred among the five life stages of seeds, seedlings, vegetative juveniles (defined as being in at least their second season of growth), small flowering plants (having 1–2 stems), and large flowering plants (having 3 or more stems). Transition values were calculated using deterministic equations and data from 20 lower-level vital rates collected from 2009-2012 from two open field and two forest understory populations of V. rossicum (43°51’N, 76°17’W; 42°48'N, 76°40'W) and two open field populations of V. nigrum (41°46’N, 73°44’W; 41°18’N, 73°58’W) in New York State. Sites varied in plant densities, soil depth, and light levels (forest populations). Detailed descriptions of vital rate data collection may be found in: Milbrath et al. 2017. Northeastern Naturalist 24(1):37-53. Five replicate sets of transition data obtained from five separate spatial regions of a particular infestation were produced for each of the six populations. Note: Added new excel file of vital rate data on 12/7/2018. Resources in this dataset:Resource Title: Matrix model transition data for Vincetoxicum species. File Name: Matrix_model_transition_data.csvResource Description: This data set includes data on 25 transitions of a matrix demographic model of two invasive Vincetoxicum species from six field and forest populations in New York State.Resource Title: Variable definitions. File Name: Matrix_model_metadata.csvResource Description: Definitions of variables including equations for each transition and definitions of the lower-level vital rates in the equationsResource Title: Vital Rate definitions. File Name: Vital_Rate.csvResource Description: Vital Rate definitions of lower-level vital rates used in transition equations - to be substituted into the Data Dictionary for full definition of each transition equation.Resource Title: Data Dictionary. File Name: Matrix_Model_transition_data_DD.csvResource Description: See Vital Rate resource for definitions of lower-level vital rates used in transition equations where noted.Resource Title: Matrix model vital rate data for Vincetoxicum species. File Name: Matrix_model_vital rate_data.csvResource Description: This data set includes data on 20 lower-level vital rates used in the calculation of transitions of a matrix demographic model of two invasive Vincetoxicum species in New York State as well as definitions of the vital rates. (File added on 12/7/2018)Resource Software Recommended: Microsoft Excel,url: https://office.microsoft.com/excel/

In a time of global change, having an understanding of the nature of biotic and abiotic factors that drive a species’ range may be the sharpest tool in the arsenal of conservation and management of threatened species. However, such information is lacking for most tropical and epiphytic species due to the complexity of life history, the roles of stochastic events, and the diversity of habitat across the span of a distribution. In this study, we conducted repeated censuses across the core and peripheral range of Trichocentrum undulatum, a threatened orchid that is found throughout the island of Cuba (species core range) and southern Florida (the northern peripheral range). We used demographic matrix modeling as well as stochastic simulations to investigate the impacts of herbivory, hurricanes, and logging (in Cuba) on projected population growth rates (? and ?s) among sites. Methods Field methods Censuses took place between 2013 and 2021. The longest census period was that of the Peripheral population with a total of nine years (2013–2021). All four populations in Cuba used in demographic modeling that were censused more than once: Core 1 site (2016–2019, four years), Core 2 site (2018–2019, two years), Core 3 (2016 and 2018 two years), and Core 4 (2018–2019, two years) (Appendix S1: Table S1). In November 2017, Hurricane Irma hit parts of Cuba and southern Florida, impacting the Peripheral population. The Core 5 population (censused on 2016 and 2018) was small (N=17) with low survival on the second census due to logging. Three additional populations in Cuba were visited only once, Core 6, Core 7, and Core 8 (Table 1). Sites with one census or with a small sample size (Core 5) were not included in the life history and matrix model analyses of this paper due to the lack of population transition information, but they were included in the analysis on the correlation between herbivory and fruit rate, as well as the use of mortality observations from logging for modeling. All Cuban sites were located between Western and Central Cuba, spanning four provinces: Mayabeque (Core 1), Pinar del Rio (Core 2 and Core 6), Matanzas (Core 3 and Core 5), and Sancti Spiritus (Core 4, Core 7, Core 8). At each population of T. undulatum presented in this study, individuals were studied within ~1-km strips where T. undulatum occurrence was deemed representative of the site, mostly occurring along informal forest trails. Once an individual of T. undulatum was located, all trees within a 5-m radius were searched for additional individuals. Since tagging was not permitted, we used a combination of information to track individual plants for the repeated censuses. These include the host species, height of the orchid, DBH of the host tree, and hand-drawn maps. Individual plants were also marked by GPS at the Everglades Peripheral site. If a host tree was found bearing more than one T. undulatum, then we systematically recorded the orchids in order from the lowest to highest as well as used the previous years’ observations in future censuses for individualized notes and size records. We recorded plant size and reproductive variables during each census including: the number of leaves, length of the longest leaf (cm), number of inflorescence stalks, number of flowers, and the number of mature fruits. We also noted any presence of herbivory, such as signs of being bored by M. miamensis, and whether an inflorescence was partially or completely affected by the fly, and whether there was other herbivory, such as D. boisduvalii on leaves. We used logistic regression analysis to examine the effects of year (at the Peripheral site) and sites (all sites) on the presence or absence of inflorescence herbivory at all the sites. Cross tabulation and chi-square analysis were done to examine the associations between whether a plant was able to fruit and the presence of floral herbivory by M. miamensis. The herbivory was scored as either complete or partial. During the orchid population scouting expeditions, we came across a small population in the Matanzas province (Core 5, within 10 km of the Core 3 site) and recorded the demographic information. Although the sampled population was small (N = 17), we were able to observe logging impacts at the site and recorded logging-associated mortality on the subsequent return to the site. Matrix modeling Definition of size-stage classes To assess the life stage transitions and population structures for each plant for each population’s census period we first defined the stage classes for the species. The categorization for each plant’s stage class depended on both its size and reproductive capabilities, a method deemed appropriate for plants (Lefkovitch 1965, Cochran and Ellner 1992). A size index score was calculated for each plant by taking the total number of observed leaves and adding the length of the longest leaf, an indication of accumulated biomass (Borrero et al. 2016). The smallest plant size that attempted to produce an inflorescence is considered the minimum size for an adult plant. Plants were classified by stage based on their size index and flowering capacity as the following: (1) seedlings (or new recruits), i.e., new and small plants with a size index score of less than 6, (2) juveniles, i.e., plants with a size index score of less than 15 with no observed history of flowering, (3) adults, plants with size index scores of 15 or greater. Adult plants of this size or larger are capable of flowering but may not produce an inflorescence in a given year. The orchid’s population matrix models were constructed based on these stages. In general, orchid seedlings are notoriously difficult to observe and easily overlooked in the field due to the small size of protocorms. A newly found juvenile on a subsequent site visit (not the first year) may therefore be considered having previously been a seedling in the preceding year. In this study, we use the discovered “seedlings” as indicatory of recruitment for the populations. Adult plants are able to shrink or transition into the smaller juvenile stage class, but a juvenile cannot shrink to the seedling stage. Matrix elements and population vital rates calculations Annual transition probabilities for every stage class were calculated. A total of 16 site- and year-specific matrices were constructed. When seedling or juvenile sample sizes were < 9, the transitions were estimated using the nearest year or site matrix elements as a proxy. Due to the length of the study and variety of vegetation types with a generally large population size at each site, transition substitutions were made with the average stage transition from all years at the site as priors. If the sample size of the averaged stage was still too small, the averaged transition from a different population located at the same vegetation type was used. We avoided using transition values from populations found in different vegetation types to conserve potential environmental differences. A total of 20% (27/135) of the matrix elements were estimated in this fashion, the majority being seedling stage transitions (19/27) and noted in the Appendices alongside population size (Appendix S1: Table S1). The fertility element transitions from reproductive adults to seedlings were calculated as the number of seedlings produced (and that survived to the census) per adult plant. Deterministic modeling analysis We used integral projection models (IPM) to project the long-term population growth rates for each time period and population. The finite population growth rate (?), stochastic long-term growth rate (?s), and the elasticity were projected for each matrices using R Popbio Package 2.4.4 (Stubben and Milligan 2007, Caswell 2001). The elasticity matrices were summarized by placing each element into one of three categories: fecundity (transition from reproductive adults to seedling stage), growth (all transitions to new and more advanced stage, excluding the fecundity), and stasis (plants that transitioned into the same or a less advanced stage on subsequent census) (Liu et al. 2005). Life table response experiments (LTREs) were conducted to identify the stage transitions that had the greatest effects on observed differences in population growth between select sites and years (i.e., pre-post hurricane impact and site comparisons of same vegetation type). Due to the frequent disturbances that epiphytes in general experience as well as our species’ distribution in hurricane-prone areas, we ran transient dynamic models that assume that the populations censused were not at stable stage distributions (Stott et al. 2011). We calculated three indices for short-term transient dynamics to capture the variation during a 15-year transition period: reactivity, maximum amplification, and amplified inertia. Reactivity measures a population’s growth in a single measured timestep relative to the stable-stage growth, during the simulated transition period. Maximum amplification and amplified inertia are the maximum of future population density and the maximum long-term population density, respectively, relative to a stable-stage population that began at the same initial density (Stott et al. 2011). For these analyses, we used a mean matrix for Core 1, Core 2 Core 3, and Core 4 sites and the population structure of their last census. For the Peripheral site, we averaged the last three matrices post-hurricane disturbance and used the most-recent population structure. We standardized the indices across sites with the assumption of initial population density equal to 1 (Stott et al. 2011). Analysis was done using R Popdemo version 1.3-0 (Stott et al. 2012b). Stochastic simulation We created matrices to simulate the effects of episodic recruitment, hurricane impacts, herbivory, and logging (Appendix S1: Table S2). The Peripheral population is the longest-running site with nine years of censuses (eight

In 2025, India overtook China as the world's most populous country and now has almost 1.46 billion people. China now has the second-largest population in the world, still with just over 1.4 billion inhabitants, however, its population went into decline in 2023. Global population As of 2025, the world's population stands at almost 8.2 billion people and is expected to reach around 10.3 billion people in the 2080s, when it will then go into decline. Due to improved healthcare, sanitation, and general living conditions, the global population continues to increase; mortality rates (particularly among infants and children) are decreasing and the median age of the world population has steadily increased for decades. As for the average life expectancy in industrial and developing countries, the gap has narrowed significantly since the mid-20th century. Asia is the most populous continent on Earth; 11 of the 20 largest countries are located there. It leads the ranking of the global population by continent by far, reporting four times as many inhabitants as Africa. The Demographic Transition The population explosion over the past two centuries is part of a phenomenon known as the demographic transition. Simply put, this transition results from a drastic reduction in mortality, which then leads to a reduction in fertility, and increase in life expectancy; this interim period where death rates are low and birth rates are high is where this population explosion occurs, and population growth can remain high as the population ages. In today's most-developed countries, the transition generally began with industrialization in the 1800s, and growth has now stabilized as birth and mortality rates have re-balanced. Across less-developed countries, the stage of this transition varies; for example, China is at a later stage than India, which accounts for the change in which country is more populous - understanding the demographic transition can help understand the reason why China's population is now going into decline. The least-developed region is Sub-Saharan Africa, where fertility rates remain close to pre-industrial levels in some countries. As these countries transition, they will undergo significant rates of population growth.

Resumo The demographic dividend has aroused interest among demographers and economists because it is seen as a window of oportunity for the economic development of countries that have experienced a demographic transition. There are reasons to question the sole virtuosity of the pure demographic dividend in economic growth. Crespo-Cuaresma et al. (2014) found that educational expansion has an important role in economic gains during the demographic dividend. To verify these results for the Brazilian case, we performed a decomposition exercise of economic support ratio (ESR), an alternative to demographic dependency ratio, to analyze the first demographic dividend. A simulation, applied for the period from 1970 to 2100 considering three scenarios of educational expansion, shows that educational expansion was and will be responsible for a big share of the economic gains of the Brazilian demographic dividend period, outperforming the change in age structure effect. In addition, an increase in a work-age population with post-secondary education appears to potentialize these results.

The world's population first reached one billion people in 1805, and reached eight billion in 2022, and will peak at almost 10.2 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two-thirds of the world's population lives in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a few years later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.

In this paper, we test the hypothesis of the Neolithic Demographic Transition in the Central Balkan Early Neolithic (6250–5300 BC) by applying the method of summed calibrated probability distributions to the set of more than 200 new radiocarbon dates from Serbia. The results suggest that there was an increase in population size after the first farmers arrived to the study area around 6250 BC. This increase lasted for approximately 250 years and was followed by a decrease of the population size proxy after 6000 BC, reaching its minimum around 5800 BC. This was followed by another episode of growth until 5600 BC when population size proxy rapidly declined, reaching its minimum around 5500 BC. The reconstructed intrinsic growth rate value indicates that the first episode of growth might have been fuelled both by high fertility and migrations, potentially related to the effects of the 8.2 ky event. The second episode of population growth after 5800 BC was probably due to the high fertility alone. It remains unclear what caused the population to decrease episodes.This article is part of the theme issue ‘Cross-disciplinary approaches to prehistoric demography'

Abstract

The transition from socialism to a market economy has transformed the lives of many people. What are people's perceptions and attitudes to transition? What are the current attitudes to market reforms and political institutions?

To analyze these issues, the EBRD and the World Bank have jointly conducted the comprehensive, region-wide "Life in Transition Survey" (LiTS), which combines traditional household survey features with questions about respondents' attitudes and is carried out through two-stage sampling with a random selection of households and respondents.

The LiTS assesses the impact of transition on people through their personal and professional experiences during the first 15 years of transition. LiTS attempts to understand how these personal experiences of transition relate to people’s attitudes toward market and political reforms, as well as their priorities for the future.

The main objective of the LiTS was to build on existing studies to provide a comprehensive assessment of relationships among life satisfaction and living standards, poverty and inequality, trust in state institutions, satisfaction with public services, attitudes to a market economy and democracy and to provide valuable insights into how transition has affected the lives of people across a region comprising 16 countries in Central and Eastern Europe (“CEE”) and 11 in the Commonwealth of Independent State (“CIS”). Turkey and Mongolia were also included in the survey.

Geographic coverage

The LITS was to be implemented in the following 29 countries: Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Former Yugoslav Republic of Macedonia (FYROM), Georgia, Hungary, Kazakhstan, Kyrgyz Republic, Latvia, Lithuania, Moldova, Mongolia, Poland, Romania, Russia, Serbia and Montenegro, Slovak Republic, Slovenia, Tajikistan, Turkey, Turkmenistan, Ukraine and Uzbekistan.

Kind of data

Sample survey data [ssd]

Sampling procedure

A total of 1,000 face-to-face household interviews per country were to be conducted, with adult (18 years and over) occupants and with no upper limit for age. The sample was to be nationally representative. The EBRD’s preferred procedure was a two stage sampling method, with census enumeration areas (CEA) as primary sampling units and households as secondary sampling units. To the extent possible, the EBRD wished the sampling procedure to apply no more than 2 stages.

The first stage of selection was to use as a sampling frame the list of CEA's generated by the most recent census. Ideally, 50 primary sampling units (PSU's) were to be selected from that sample frame, with probability proportional to size (PPS), using as a measure of size either the population, or the number of households.

The second sampling stage was to select households within each of the primary sampling units, using as a sampling frame a specially developed list of all households in each of the selected PSU's defined above. Households to be interviewed were to be selected from that list by systematic, equal probability sampling. Twenty households were to be selected in each of the 50 PSU's.

The individuals to be interviewed in each household were to be selected at random, within each of the selected households, with no substitution if possible.

ESTABLISHMENT OF THE SAMPLE FRAME OF PSU’s

In each country we established the most recent sample frame of PSU’s which would best serve the purposes of the LITS sampling methodology. Details of the PSU sample frames in each country are shown in table 1 (page 10) of the survey report.

In the cases of Armenia, Azerbaijan, Kazakhstan, Serbia and Uzbekistan, CEA’s were used. In Croatia we also used CEA’s but in this case, because the CEA’s were very small and we would not have been able to complete the targeted number of interviews within each PSU, we merged together adjoining CEA’s and constructed a sample of 1,732 Merged Enumeration Areas. The same was the case in Montenegro.

In Estonia, Hungary, Lithuania, Poland and the Slovak Republic we used Eurostat’s NUTS area classification system.

[NOTE: The NUTS (from the French "Nomenclature des territoriales statistiques" or in English ("Nomenclature of territorial units for statistics"), is a uniform and consistent system that runs on five different NUTS levels and is widely used for EU surveys including the Eurobarometer (a comparable survey to the Life in Transition). As a hierarchical system, NUTS subdivides the territory of the country into a defined number of regions on NUTS 1 level (population 3-7 million), NUTS 2 level (800,000-3 million) and NUTS 3 level (150,000-800,000). At a more detailed level NUTS 3 is subdivided into smaller units (districts and municipalities). These are called "Local Administrative Units" (LAU). The LAU is further divided into upper LAU (LAU1 - formerly NUTS 4) and LAU 2 (formerly NUTS 5).]

Albania, Bulgaria, the Czech Republic, Georgia, Moldova and Romania used the electoral register as the basis for the PSU sample frame. In the other cases, the PSU sample frame was chosen using either local geographical or administrative and territorial classification systems. The total number of PSU sample frames per country varied from 182 in the case of Mongolia to over 48,000 in the case of Turkey. To ensure the safety of our fieldworkers, we excluded from the sample frame PSU’s territories (in countries such as Georgia, Azerbaijan, Moldova, Russia, etc) in which there was conflict and political instability. We have also excluded areas which were not easily accessible due to their terrain or were sparsely populated.

In the majority of cases, the source for this information was the national statistical body for the country in question, or the relevant central electoral committee. In establishing the sample frames and to the extent possible, we tried to maintain a uniform measure of size namely, the population aged 18 years and over which was of more pertinence to the LITS methodology. Where the PSU was based on CEA’s, the measure was usually the total population, whereas the electoral register provided data on the population aged 18 years old and above, the normal voting age in all sampled countries. Although the NUTS classification provided data on the total population, we filtered, where possible, the information and used as a measure of size the population aged 18 and above. The other classification systems used usually measure the total population of a country. However, in the case of Azerbaijan, which used CEA’s, and Slovenia, where a classification system based on administrative and territorial areas was employed, the measure of size was the number of households in each PSU.

The accuracy of the PSU information was dependent, to a large extent, on how recently the data has been collected. Where the data were collected recently then the information could be considered as relatively accurate. However, in some countries we believed that more recent information was available, but because the relevant authorities were not prepared to share this with us citing secrecy reasons, we had no alternative than to use less up to date data. In some countries the age of the data available makes the figures less certain. An obvious case in point is Bosnia and Herzegovina, where the latest available figures date back to 1991, before the Balkan wars. The population figures available take no account of the casualties suffered among the civilian population, resulting displacement and subsequent migration of people.

Equally there have been cases where countries have experienced economic migration in recent years, as in the case of those countries that acceded to the European Union in May, 2004, such as Hungary, Poland and the Baltic states, or to other countries within the region e.g. Armenians to Russia, Albanians to Greece and Italy; the available figures may not accurately reflect this. And, as most economic migrants tend to be men, the actual proportion of females in a population was, in many cases, higher than the available statistics would suggest. People migration in recent years has also occurred from rural to urban areas in Albania and the majority of the Asian Republics, as well as in Mongolia on a continuous basis but in this case, because of the nomadic population of the country.

SAMPLING METHODOLOGY

Brief Overview

In broad terms the following sampling methodology was employed: · From the sample frame of PSU’s we selected 50 units · Within each selected PSU, we sampled 20 households, resulting in 1,000 interviews per country · Within each household we sampled 1 and sometimes 2 respondents The sampling procedures were designed to leave no free choice to the interviewers. Details on each of the above steps as well as country specific procedures adapted to suit the availability, depth and quality of the PSU information and local operational issues are described in the following sections.

Selection of PSU’s

The PSU’s of each country (all in electronic format) were sorted first into metropolitan, urban and rural areas (in that order), and within each of these categories by region/oblast/province in alphabetical order. This ensured a consistent sorting methodology across all countries and also that the randomness of the selection process could be supervised.

To select the 50 PSU’s from the sample frame of PSU’s, we employed implicit stratification and sampling was done with PPS. Implicit stratification ensured that the sample of PSU’s was spread across the primary categories of explicit variables and a better representation of the population, without actually stratifying the PSU’s thus, avoiding difficulties in calculating the sampling errors at a later stage. In brief, the PPS involved the

In 2023, there were five countries, where the average woman of childbearing age can expect to have over six children throughout their lifetime. In fact, of the 20 countries in the world with the highest fertility rates, Afghanistan and Yemen are the only countries not found in Sub-Saharan Africa. High fertility rates in Africa With a fertility rate of 6.13 and 6.12 children per woman, Somalia and Chad were the countries with the highest fertility rate in the world. Population growth in Chad is among the highest in the world. Lack of healthcare access, as well as food instability, political instability, and climate change, are all exacerbating conditions that keep Chad's infant mortality rates high, which is generally the driver behind high fertility rates. This situation is common across much of the continent, and, although there has been considerable progress in recent decades, development in Sub-Saharan Africa is not moving as quickly as it did in other regions. Demographic transition While these countries have the highest fertility rates in the world, their rates are all on a generally downward trajectory due to a phenomenon known as the demographic transition. The third stage (of five) of this transition sees birth rates drop in response to decreased infant and child mortality, as families no longer feel the need to compensate for lost children. Eventually, fertility rates fall below replacement level (approximately 2.1 children per woman), which eventually leads to natural population decline once life expectancy plateaus. In some of the most developed countries today, low fertility rates are creating severe econoic and societal challenges as workforces are shrinking while aging populations are placin a greater burden on both public and personal resources.

Abstract

The 2022 Philippines National Demographic and Health Survey (NDHS) was implemented by the Philippine Statistics Authority (PSA). Data collection took place from May 2 to June 22, 2022.

The primary objective of the 2022 NDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the NDHS collected information on fertility, fertility preferences, family planning practices, childhood mortality, maternal and child health, nutrition, knowledge and attitudes regarding HIV/AIDS, violence against women, child discipline, early childhood development, and other health issues.

The information collected through the NDHS is intended to assist policymakers and program managers in designing and evaluating programs and strategies for improving the health of the country’s population. The 2022 NDHS also provides indicators anchored to the attainment of the Sustainable Development Goals (SDGs) and the new Philippine Development Plan for 2023 to 2028.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, and all children aged 0-4 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling scheme provides data representative of the country as a whole, for urban and rural areas separately, and for each of the country’s administrative regions. The sample selection methodology for the 2022 NDHS was based on a two-stage stratified sample design using the Master Sample Frame (MSF) designed and compiled by the PSA. The MSF was constructed based on the listing of households from the 2010 Census of Population and Housing and updated based on the listing of households from the 2015 Census of Population. The first stage involved a systematic selection of 1,247 primary sampling units (PSUs) distributed by province or HUC. A PSU can be a barangay, a portion of a large barangay, or two or more adjacent small barangays.

In the second stage, an equal take of either 22 or 29 sample housing units were selected from each sampled PSU using systematic random sampling. In situations where a housing unit contained one to three households, all households were interviewed. In the rare situation where a housing unit contained more than three households, no more than three households were interviewed. The survey interviewers were instructed to interview only the preselected housing units. No replacements and no changes of the preselected housing units were allowed in the implementing stage in order to prevent bias. Survey weights were calculated, added to the data file, and applied so that weighted results are representative estimates of indicators at the regional and national levels.

All women age 15–49 who were either usual residents of the selected households or visitors who stayed in the households the night before the survey were eligible to be interviewed. Among women eligible for an individual interview, one woman per household was selected for a module on women’s safety.

For further details on sample design, see APPENDIX A of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Two questionnaires were used for the 2022 NDHS: the Household Questionnaire and the Woman’s Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to the Philippines. Input was solicited from various stakeholders representing government agencies, academe, and international agencies. The survey protocol was reviewed by the ICF Institutional Review Board.

After all questionnaires were finalized in English, they were translated into six major languages: Tagalog, Cebuano, Ilocano, Bikol, Hiligaynon, and Waray. The Household and Woman’s Questionnaires were programmed into tablet computers to allow for computer-assisted personal interviewing (CAPI) for data collection purposes, with the capability to choose any of the languages for each questionnaire.

Cleaning operations

Processing the 2022 NDHS data began almost as soon as fieldwork started, and data security procedures were in place in accordance with confidentiality of information as provided by Philippine laws. As data collection was completed in each PSU or cluster, all electronic data files were transferred securely via SyncCloud to a server maintained by the PSA Central Office in Quezon City. These data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams were alerted to any inconsistencies and errors while still in the area of assignment. Timely generation of field check tables allowed for effective monitoring of fieldwork, including tracking questionnaire completion rates. Only the field teams, project managers, and NDHS supervisors in the provincial, regional, and central offices were given access to the CAPI system and the SyncCloud server.

A team of secondary editors in the PSA Central Office carried out secondary editing, which involved resolving inconsistencies and recoding “other” responses; the former was conducted during data collection, and the latter was conducted following the completion of the fieldwork. Data editing was performed using the CSPro software package. The secondary editing of the data was completed in August 2022. The final cleaning of the data set was carried out by data processing specialists from The DHS Program in September 2022.

Response rate

A total of 35,470 households were selected for the 2022 NDHS sample, of which 30,621 were found to be occupied. Of the occupied households, 30,372 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 28,379 women age 15–49 were identified as eligible for individual interviews. Interviews were completed with 27,821 women, yielding a response rate of 98%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and in data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2022 Philippines National Demographic and Health Survey (2022 NDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2022 NDHS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2022 NDHS sample was the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS using programs developed by ICF. These programs use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables

• Household age distribution
• Age distribution of eligible and interviewed women
• Age displacement at age 14/15
• Age displacement at age 49/50
• Pregnancy outcomes by years preceding the survey
• Completeness of reporting
• Observation of handwashing facility
• School attendance by single year of age
• Vaccination cards photographed
• Population pyramid
• Five-year mortality rates

See details of the data quality tables in Appendix C of the final report.

Eurostat’s annual data collections on demographic and migration statistics are structured as follows:

• NOWCAST: Annual data collection on provisional monthly data on live births and deaths covering at least six months of the reference year (Article 4.3 of the Commission implementing regulation (EU) No 205/2014).
• DEMOBAL (Demographic balance): Annual data collection on provisional data on population, total live births and total deaths at national level (Article 4.1 of the Commission implementing regulation (EU) No 205/2014).
• POPSTAT (Population statistics): The most in-depth annual national and regional demographic and migration data collection. The data relate to populations, births, deaths, immigrants, emigrants, marriages and divorces, and is broken down into several categories (Article 3 of Regulation (EU) No 1260/2013 and Article 3 of Regulation (EC) No 862/2007).

The aim is to collect annual mandatory and voluntary demographic data from the national statistical institutes. Mandatory data are those defined by the legislation listed under ‘6.1. Institutional mandate - legal acts and other agreements’.

The completeness of the demographic data collected on a voluntary basis depends on the availability and completeness of information provided by the national statistical institutes. For more information on mandatory/voluntary data collection, see 6.1. Institutional mandate - legal acts and other agreements’.

The following statistics on live births are collected from the National Statistical Institutes:

• Live births by month of occurrence;
• Live births by mother's age, year of birth and by:
  • region (NUTS 2) of residence
  • region (NUTS 3) of residence
  • mother's country of birth
  • mother's country of citizenship
  • live-birth order
  • sex of the new-born
  • mother's legal marital status
  • employment status of the mother
  • mother's educational attainment (ISCED 2011);
• Live births by birth weight and duration of gestation;
• Legally induced abortions by mother's age and parity;
• Late fœtal deaths by mother's age.

Statistics on fertility: based on the different breakdowns of data on live births and on legally induced abortions received, Eurostat produces the following:

• Statistics available in the online table Population change - Demographic balance and crude rates at national level (demo_gind):
  • natural change of the population, crude birth rate;
• Statistics available in the online table Fertility indicators (demo_find):
  • the proportion of live births outside marriage
  • total fertility rate
  • the mean age of women at childbirth
  • the mean age of women at the birth of first / second / third / fourth and higher child
  • the median age of women at childbirth
  • the percentage of first / second / third / fourth and higher live births Fertility rates by age (demo_frate);
• Fertility rates by age and NUTS 2 region (demo_r_frate2);
• Total fertility rate by NUTS 3 region (demo_r_frate3);
• Statistics available in the online table Abortion indicators (demo_fabortind):
  • abortion rate
  • abortion ratio

Abstract

The Côte d'Ivoire Living Standards Survey (CILSS) was the first LSMS Survey to have field tested the methodology and questionnaire developed by LSMS. It consists of three complementary surveys: the household survey, the community survey and the price survey. The household survey collected detailed information on expenditures, income, employment, assets, basic needs and other socio-economic characteristics of the households. The Community Survey collected information on economic and demographic characteristics of the rural communities to which each cluster of households belonged. This was designed to enable the linkage of community level with household level data. The price survey component of the CILSS collected data on prices at the nearest market to each cluster of households, so that regional price indices could be constructed for the household survey.

The Côte d'Ivoire Living Standards Survey (CILSS) was undertaken over a period of four years, 1985-88, by the Direction de la Statistique in Côte d'Ivoire, with financial and technical support from the World Bank during the first two years of the survey. It was the first year-round household survey to have been undertaken by the Ivorian Direction de la Statistique.

The sample size each year was 1600 households and the sample design was a rotating panel. That is, half of the households were revisited the following year, while the other half were replaced with new households. The survey thus produced four cross-sectional data sets as well as three overlapping panels of 800 households each (1985-86, 1986-87, 1987-88).

Geographic coverage

National coverage. Domains: Urban/rural; Regions (East Forest, West Forest, East Savannah, West Savannah)

Analysis unit

• Households
• Individuals
• Community

Kind of data

Sample survey data [ssd]

Sampling procedure

The principal objective of the sample selection process for the CILSS Household Survey was to obtain a nationally representative cross-section of African households, some of which could be interviewed in successive years as panel households.

A two-stage sampling procedure was used. In the first stage, 100 Primary Sampling Units (PSUs) were selected across the country from a list of all PSUs available in the sampling frame. At the second stage, a cluster of 16 households was selected within each PSU. This led to a sample size of 1600 households a year, in 100 cluster s of 16 households each. Half of the households were replaced each year while the other half (the panel households in 1986, 1987 and 1988) were interviewed a second time.

It is important to note that there was a change in the sampling procedures (the sampling frame, PSU selection process and listing procedures), used to select half of the clusters/households interviewed in 1987 (the other half were panel households retained from 1986), and all of the clusters/households interviewed in 1988. Households selected on the basis of the first set of sampling procedures will henceforth be referred to as Block 1 data while households based on the second set of sampling procedures will be referred to as Block 2 data.

Sampling Procedures for Block 1 Data

The Sampling Frame. The sampling frame for the 1985, 1986, and half of the 1987 samples (except for Abidjan and Bouaké) was a list of localities constructed on the basis of the 1975 Census, updated to 1983 by the demographers of the Direction de la Statistique and based on a total population estimated at 9.4 million in 1983.

The Block 1 frame for Abidjan and Bouaké was based on data from a 1979-80 electoral census of these two cities. The electoral census had produced detailed maps of the two cities that divided each sector of the city into smaller sub-sectors (îlots). Sub-sectors with similar types of housing were grouped together by statisticians in the Direction de la Statistique to form PSUs. From a list of all PSUs in each city, along with each PSU's population size, the required number of PSUs were selected using a systematic sampling procedure. The step size was equal to the city's population divided by the number of PSUs required in each city. One problem identified in the selection process for Abidjan arose from the fact that one sector of the city (Yopougon) which had been relatively small in 1980 at the time of the electoral census, had since become the largest agglomeration in Côte d'Ivoire. This problem was presumably unavoidable since accurate population data for Yopougon was not available at the time of the PSU selection process.

Selection of PSUs. Geographic stratification was not explicitly needed because the systematic sampling procedure that was used to select the PSUs ensured that the sample was balanced with respect to region and by site type, within each region. The main geographical regions defined were: East Forest, West Forest, and Savannah. Site types varied as follows: large cities, towns, large and small villages, surrounding towns, village centers, and villages attached to them. The 100 PSUs were selected, with probabilities proportional to the size of their population, from a list of PSUs sorted by region and within each region, by site type.

Selection of households within each PSU. A pre-survey was conducted in June-July of 1984, to establish the second-stage sampling frame, i.e. a list of households for each PSU from which 16 households could be selected. The same listing exercise was to be used for both the 1985 and 1986 surveys, in order to avoid having to conduct another costly pre-survey in the second year. Thus, the 1984 pre-survey had to provide enough households so as to be able to select two clusters of households in each PSU and to allow for replacement households in the event that some in the sample could not be contacted or refused to participate. A listing of 64 households in each PSU met this requirement. In PSUs with 64 households or fewer, every household was listed. In selecting the households, the "step" used was equal to the estimated number of households in the PSU divided by 64. For example, if the PSU had an estimated 640 households, then every tenth household was included in the listing, counted from a random starting point in the PSU. For operational reasons, the maximum step allowable was a step of 30. In practice, it appears that enumerators used doors, instead of housing structures, in counting the step. Al though enumerators were supposed to start the listing process from a random point in the PSU, in rural areas and small towns, reportedly, the lister started from the center of the PSU.

Sampling Procedures for Block 2 Data

The Sampling Frame. The sampling frame for Block 2 data was established from a list of places from the results of the Census of inhabited sites (RSH) performed in preparation for the 1988 Population Census.

Selection of PSUs. The PSUs were selected with probability proportional to size. However, in order to save what might have been exorbitant costs of listing every household in each selected PSU in a pre-survey, the Direction de la Statistique made a decision to enumerate a smaller unit within each PSU. The area within each PSU was divided into smaller blocks called `îlots'. Households were then selected from a randomly chosen îlot within each PSU. The sample îlot was selected with equal probability within each PSU, not on the basis of probability proportional to size. (These îlots are reportedly relatively small compared with the size of PSUs selected for the Block 1 frame, but no further information is available about their geographical position within the PSUs.)

Selection of households within each PSU. All households in each îlot selected for the Block 2 sample were listed. Sixteen households were then randomly chosen from the list of households for each îlot.

Bias in the Selection of Households within PSUs, Block 1 Data

Analysis of the four years of the CILSS data revealed that household size (unweighted), dropped by 24 percent between 1985 and 1988. Three possible explanations were considered: (1) area l demographic change; (2) non-sampling measurement errors were involved; or (3) some sort of sampling bias. Investigation ruled out the first two possibilities. The third possibility clearly was an issue because the sampling frame and listing procedures had indeed changed in midstream and this was likely to have had an effect. In fact, the investigation found that the substantial part of the drop in household size over the years occurred between the first and second panel data sets in 1987, i.e. the tail end of Block 1 data and the start of Block 2 data. From this, it is reasonable to assume that differences in the sampling frame and sampling procedures between the two blocks were indeed responsible.

The listing procedures for Block 1 data indicate d that the selection of households within PSUs was likely to have been biased toward the selection of larger dwellings. Based on a discussion with Christopher Scott, statistical consultant, Demery and Grootaert explain as follows: "In the selected primary sampling units, where the listing of households was to occur, enumerators were instructed to start the listing process at a random location in the primary sampling unit and from this point to select every nth household, that is, with a given fixed "step" until sixty-four households were listed. There are two sources of potential bias in this listing procedure. First, the selection of the starting point might not have been random, but subject to motivated bias on the part of the enumerator (such as the selection of a point where there are numerous dwellings or that is easily accessible). Second, in practice, enumerators counted doors to achieve the "step", rather than counting actual households. This method leads to

There are approximately 8.16 billion people living in the world today, a figure that shows a dramatic increase since the beginning of the Common Era. Since the 1970s, the global population has also more than doubled in size. It is estimated that the world's population will reach and surpass 10 billion people by 2060 and plateau at around 10.3 billion in the 2080s, before it then begins to fall. Asia When it comes to number of inhabitants per continent, Asia is the most populous continent in the world by a significant margin, with roughly 60 percent of the world's population living there. Similar to other global regions, a quarter of inhabitants in Asia are under 15 years of age. The most populous nations in the world are India and China respectively; each inhabit more than three times the amount of people than the third-ranked United States. 10 of the 20 most populous countries in the world are found in Asia. Africa Interestingly, the top 20 countries with highest population growth rate are mainly countries in Africa. This is due to the present stage of Sub-Saharan Africa's demographic transition, where mortality rates are falling significantly, although fertility rates are yet to drop and match this. As much of Asia is nearing the end of its demographic transition, population growth is predicted to be much slower in this century than in the previous; in contrast, Africa's population is expected to reach almost four billion by the year 2100. Unlike demographic transitions in other continents, Africa's population development is being influenced by climate change on a scale unseen by most other global regions. Rising temperatures are exacerbating challenges such as poor sanitation, lack of infrastructure, and political instability, which have historically hindered societal progress. It remains to be seen how Africa and the world at large adapts to this crisis as it continues to cause drought, desertification, natural disasters, and climate migration across the region.

The region of present-day China has historically been the most populous region in the world; however, its population development has fluctuated throughout history. In 2022, China was overtaken as the most populous country in the world, and current projections suggest its population is heading for a rapid decline in the coming decades. Transitions of power lead to mortality The source suggests that conflict, and the diseases brought with it, were the major obstacles to population growth throughout most of the Common Era, particularly during transitions of power between various dynasties and rulers. It estimates that the total population fell by approximately 30 million people during the 14th century due to the impact of Mongol invasions, which inflicted heavy losses on the northern population through conflict, enslavement, food instability, and the introduction of bubonic plague. Between 1850 and 1870, the total population fell once more, by more than 50 million people, through further conflict, famine and disease; the most notable of these was the Taiping Rebellion, although the Miao an Panthay Rebellions, and the Dungan Revolt, also had large death tolls. The third plague pandemic also originated in Yunnan in 1855, which killed approximately two million people in China. 20th and 21st centuries There were additional conflicts at the turn of the 20th century, which had significant geopolitical consequences for China, but did not result in the same high levels of mortality seen previously. It was not until the overlapping Chinese Civil War (1927-1949) and Second World War (1937-1945) where the death tolls reached approximately 10 and 20 million respectively. Additionally, as China attempted to industrialize during the Great Leap Forward (1958-1962), economic and agricultural mismanagement resulted in the deaths of tens of millions (possibly as many as 55 million) in less than four years, during the Great Chinese Famine. This mortality is not observable on the given dataset, due to the rapidity of China's demographic transition over the entire period; this saw improvements in healthcare, sanitation, and infrastructure result in sweeping changes across the population. The early 2020s marked some significant milestones in China's demographics, where it was overtaken by India as the world's most populous country, and its population also went into decline. Current projections suggest that China is heading for a "demographic disaster", as its rapidly aging population is placing significant burdens on China's economy, government, and society. In stark contrast to the restrictive "one-child policy" of the past, the government has introduced a series of pro-fertility incentives for couples to have larger families, although the impact of these policies are yet to materialize. If these current projections come true, then China's population may be around half its current size by the end of the century.

Contained within the 3rd Edition (1957) of the Atlas of Canada is a plate that shows two condensed maps, and three sets of graphs to show population change for the period 1851 to 1951. The top map shows the percent changes in population in eastern Canada for the period 1851 to 1901 (Newfoundland data is for 1857 to 1901). The bottom map shows the percent changes in population for Canada for the period 1901 to 1951 (Northwest Territories data is for 1911 to 1951). The first set of graphs show birth, death and natural increase rates per 1000 population for the period 1931 to 1951 for Canada and the provinces. The second set shows the changes in density of population for the period 1851 to 1951 for Prince Edward Island, Nova Scotia, New Brunswick, Ontario, Quebec, the Western Provinces and Canada. The third graph shows the percent increase in Canada's total population by decade for the period 1851 to 1951.

Tuberculosis (TB) incidence has been in steady decline in China over the last few decades. However, ongoing demographic transition, fueled by aging, and massive internal migration could have important implications for TB control in the future. We collated data on TB notification, demography, and drug resistance between 2004 and 2017 across seven cities in Shandong, the second most populous province in China. Using these data, and age-period-cohort models, we (i) quantified heterogeneities in TB incidence across cities, by age, sex, resident status, and occupation and (ii) projected future trends in TB incidence, including drug-resistant TB (DR-TB). Between 2006 and 2017, we observed (i) substantial variability in the rates of annual change in TB incidence across cities, from -4.84 to 1.52%; (ii) heterogeneities in the increments in the proportion of patients over 60 among reported TB cases differs from 2 to 13%, and from 0 to 17% for women; (iii) huge differences across cities in the annual growths in TB notification rates among migrant population between 2007 and 2017, from 2.81 cases per 100K migrants per year in Jinan to 22.11 cases per 100K migrants per year in Liaocheng, with drastically increasing burden of TB cases from farmers; and (iv) moderate and stable increase in the notification rates of DR-TB in the province. All of these trends were projected to continue over the next decade, increasing heterogeneities in TB incidence across cities and between populations. To sustain declines in TB incidence and to prevent an increase in Multiple DR-TB (MDR-TB) in the future in China, future TB control strategies may (i) need to be tailored to local demography, (ii) prioritize key populations, such as elderly and internal migrants, and (iii) enhance DR-TB surveillance.

Until the 1800s, population growth was incredibly slow on a global level. The global population was estimated to have been around 188 million people in the year 1CE, and did not reach one billion until around 1803. However, since the 1800s, a phenomenon known as the demographic transition has seen population growth skyrocket, reaching eight billion people in 2023, and this is expected to peak at over 10 billion in the 2080s.

Abstract

The major objective of this survey was to provide up-to-date and accurate information on fertility, contraception, child mortality, child nutrition and health status of children.

This sample survey is further intended to serve as a source of demographic data for comparison with earlier surveys such as Sri Lanka Demographic and Health Survey 1987 (DHS87) and Sri Lanka Contraceptive Prevalence Survey 1982 (CPS82). Such comparisons help to understand the demographic changes over a period of time.

Two types of questionnaires were used in the survey. ie (1) Household and (2) Individual.

Source : Report on Sri Lanka Demographic and Health Survey 1993 published in 1995

Geographic coverage

The country has been stratified into nine zones on the basis of socio economic and ecological criteria for DHS87. The same zones were used without major changes. Although there are nine zones the survey was confined to seven excluding Northern and Eastern provinces; the few areas covered in Amparai district in the Eastern Province during DHS87 had to be excluded due to security reasons of the country.

Analysis unit

(1) Household (2) Eligible women (3) Children

Universe

The survey interviews were designed to obtain responses from all usual residents and any visitors who slept in the household the night before the interview. An eligible respondent was defined as an ever married woman aged 15 - 49 years who slept in the household the night before the interview.

Source : Report on Sri Lanka Demographic and Health Survey 1993 published in 1995

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample size - 9230 households 7078 eligible women in 9007 housing units.

Selection process : The sample is a multi-stage stratified probability sample representative of the entire country excluding Northern and Eastern Provinces. The country has been stratified into nine zones on the basis of socio-economic and ecological criteria for DHS87. The same zones were used without major changes. Although there are nine zones the survey was confined to seven, excluding Northern and Eastern Provinces. The seven zones are:

Zone 1 - Colombo Metro consisting some urban areas in Colombo and Gampaha District Zone 2 - Colombo feeder areas Zone 3 - South Western coastal low lands Zone 4 - Lower South Central hill country excluding Districts with a concentration of estates Zone 5 - South Central hill country with a concentration of estates Zone 6 - Irrigated dry zone with major or minor irrigation schemes Zone 7 - Rain-fed Dry zone

Each zone was further stratified into three strata - urban, rural and estate sectors. The number of stages of the design and the Primary Sampling Units (PSU) vary according to the sector.

In urban areas PSU is the ward and generally two census blocks have been selected per ward as the second stage unit. The selections were carried out with probability proportional to size(PPS). The number of housing units was taken as the measure of size.

The PSU's were mostly selected from a specially organized frame consisting of wards and Grama Niladhari divisions organized by zone, sector and within sector geographically. The organization provided a better basis for stratification as it is arranged on a geographical basis.

The census blocks were selected from the only frame available from 1981 Census of Population and Housing. The ever married women aged 15-49 found in the selected housing units were interviewed.

In rural areas, Grama Niladhari (GN) division was taken as PSU and generally a single village has been selected per sample GN division with PPS. As such in rural areas villages form effective PSU's. However special steps were taken to merge and divide the villages to deal with areas which are too small or too large.

Unlike the GN divisions and wards, the selection in the estate sector has to take into account the fact that many estates are very small in size to form proper units for first stage of selection. To avoid the need to group estates in the whole frame special procedure was applied to select estates depending on the relative size of the estate compared to the nearby estates.

Sampling deviation

The target sample size was 6500 ever married women in the age group 15-49. This includes an over-sampling of around 500 women in five less developed areas in zones 6 and 7. The latter addition to the sample is needed to provide Policy relevant information and permit comparative analysis of these areas. In order to get that target sample, a total of 9007 housing units were selected for the survey.

Mode of data collection

Face-to-face [f2f]

Research instrument

Household Questionnaire - listed all usual residents any visitors who slept in the household the night before the interview and some basic information was collected on the characteristics of each person listed such as age, sex, marital status, relationship to head of household. The household questionnaire was used to identify women who were eligible for the individual questionnaire.

Individual questionnaire - Administered to each eligible woman who was defined as one who is an ever married female aged between 15 - 49 who slept in the household the night before the interview. This questionnaire had eight sections such as Respondent's background, Reproduction, Contraception, Health of children, Marriage, Fertility, Husband's background, length and weight of infants.

Source : Report on Sri Lanka Demographic and Health Survey 1993 published in 1995

Cleaning operations

Manual editing covered basic investigations such as checking of identification details, completeness of the questionnaire, coding, age and birth history, checking of certain internal consistencies, checking the information recorded in filter questions and coding of few items.

Response rate

Sample size - 9230 households 7078 eligible women in 9007 housing units. Completed - 8918 households 6983 eligible women

Household response rate - 98.9% Eligible women response rate - 98.7% Overall response rate - 97.6%

Household interviews

Completed 96.6% other(vacant, incompetent responder, refused etc) 3.4% Un-weighted number 9230

Eligible women interviews

Completed 98.7% Other(not in, refused, partly complete etc) 1.3% Un-weighted number 7078

Sampling error estimates

The sample of women had been selected as a simple sample, it would have been possible to use straightforward formulas for calculating sampling errors. However the sample design for this survey depended on stratification, stages and clusters. The computer package CLUSTERS developed by the International Statistical Institute for the World Fertility Survey was used to assist in computing the sampling errors with the proper statistical methodology.

In general, the sampling errors are small, which implies that the results are reliable.

Data appraisal

Pl refer to the Source : Report on Sri Lanka Demographic and Health Survey 1993 published in 1995

Demographic processes that ensure the recovery and resilience of marine populations are critical as climate change sends an increasing proportion on a trajectory of decline. Yet for some populations, recovery potential remains high. We conducted annual monitoring over 9-years (2012–2020) to assess the recovery of coral populations belonging to genus Pocillopora. These populations experienced a catastrophic collapse following a severe typhoon in 2009. From the start of the monitoring period, high initial recruitment led to the establishment of a juvenile population that rapidly transitioned to sexually mature adults, which dominated the population within six years after the disturbance. As a result, coral cover increased from 1.1% to 20.2% during this time. To identify key demographic drivers of recovery and population growth rates (λ), we applied kernel resampled Integral Projection Models (IPMs), constructing eight successive models to examine annual change. IPMs were able to capture reproductive traits as key demographic drivers over the initial 3 years, whilst individual growth was a continuous key demographic driver throughout the entire monitoring period. IPMs further detected a pulse of reproductive output subsequent to two further Category 5 typhoon events during the monitoring period, exemplifying key mechanisms of resilience for coral populations impacted by disturbance. Despite rapid recovery, (i.e., increased coral cover, individual colony growth, low mortality), IPMs estimated predominantly negative values of λ, indicating a declining population. Indeed, whilst λ translates to a change in the number of individuals, the recovery of coral populations can also be driven by an increase in the size of coral surviving colonies. Our results illustrate that accumulating long-term data of historical dynamics and applying IPMs to extract demographic drivers are crucial for future predictions that are based on comprehensive and robust understandings of ecological change. Methods Data collection Orchid Island (22°03′N, 121°32′E) is a 45 km2 volcanic, tropical island 64 km off the coast of Taiwan, encircled by a narrow fringing reef (5–10 m depth), leading to a dramatic drop-off. Such reef topography is sensitive to typhoons that are both frequent and intense in the region (Ribas-Deulofeu et al., 2021). In 2009, the island was severely affected by Typhoon Morakot (Hall et al., 2013), the deadliest typhoon to hit Taiwan in recorded history, which caused a ~66% decline in mean live coral cover (~60% to ~20%) along reefs in southern Taiwan (Kuo et al., 2011). Three years after this major disturbance in 2012, three parallel 20 m transects were established at ~8 m depth spaced ~2.5 m apart at a site to the southwest of the island (named Green Grassland; 22°00'N 121°34'E). Usually, this reef site is relatively sheltered from both the waves generated by the winter north-easterly monsoon and summer south-westerly winds. However, on this occasion was proven susceptible to the typhoon in 2009, impacted by a west to south-westerly swell. To the side of each transect, 50 cm × 50 cm permanent quadrats (n = 11) were haphazardly positioned to assess demographic changes over time (Appendix S1: Figure S1 and Figure S2). A total of 33 quadrats were made permanent by placing markers (iron pegs) at each corner with tags attached indicating the designated identification number, making the quadrat easier to detect for future monitoring. With this method, only four quadrats were lost in subsequent surveys, whereas the position of the other quadrats (n = 29) remained. However, the precise location may slightly vary due to the ever-changing dynamics of the reef. For this reason, when quadrats were placed at each marked position, a wide-scale community picture was taken ca. 2 m above the substrate, in order to correct any error in positioning during the analysis, if necessary (Appendix S1: Figure S1a). The quadrat was then photographed at a higher-resolution ca. 1 m above the substrate in order to capture the overall benthic composition (Appendix S1: Figure S1b). The quadrat was then divided into four sections, which were individually captured in order to attain high-resolution images of individual colonies (Appendix S1: Figure S1c). In each of these sections, smaller sized individuals (ca. < 5 cm in length) were photographed with close-up images and scale. This protocol was repeated annually between 2012 and 2020. From photographs of the 29 permanent quadrats, every colony (n = 336) observed was first measured for its two-dimensional (2D) colony size (projected area) using Image J software (Schneider, Rasband and Eliceiri, 2012). The 2D colony size was used for the analysis of annual change in coral cover. Coral cover (%) was determined by measuring the sum of 2D projected areas of coral colonies relative to the area of all quadrats combined. All quadrats were originally occupied by Pocillopora, but from 2016 to the end of the monitoring period, 1 quadrat was empty of Pocillopora colonies with the cover calculation still taking this area into consideration. In the rare case of a slight overlap of colonies (n = 4), the 2D projected area could be easily deduced for the unseen part of colonies. Measurement of demographic vital rates In this study, we focused on locally dominant genus Pocillopora spp. The relative contribution of species to the Pocillopora complex was genetically examined by randomly sampling colonies at the site and barcoding mtORF region after extraction of genomic DNA (Johnston, Forsman and Toonen, 2018). Out of 31 sampled Pocillopora colonies, 17 were P. verrucosa and 14 were P. meandrina (Appendix S1: Table S1 and S2). Besides the two dominant Pocillopora species, there were at least two other Pocillopora species present; Pocillopora eydouxi and Pocillopora sp. These species are broadcast-spawners, with the exception of Pocillopora sp., which is a brooder (Mulla et al. 2021). Due to the difficulty in identifying species morphologically in the field, especially at the early life stages, we treated species as a Pocillopora complex (Pocillopora populations). As corals are 3D structures, colony size (surface area) was used for Pocillopora colonies in the IPMs, which allowed us to build higher-resolution models. 3D surface area (cm2) was allometric and estimated from 2D projections using a pre-established relationship. Detailed information on the 2D to 3D conversion can be found in Appendix Figure S3. We extracted information on colony growth, survival and recruitment of Pocillopora populations over the 9-year period using size-thresholds in 3D to distinguish visible recruits (0.4–10 cm2; n = 154), juveniles (10.1–100 cm2; n = 369) and adults (> 100.1 cm2; n = 532). These threshold for visible recruits was determined from the size range of newly appearing individuals from each year from the second year of monitoring. The threshold for juveniles was determined by the maximum size of visible recruits and the minimum size of sexually mature individuals (described in more detail below). These thresholds differentiate sexually immature (visible recruits/juveniles) to mature (adults) individuals, used for ecological interpretation. To identify size-specific relationships of demographic traits associated with reproduction, two nubbins (~5 cm in branch length) were collected from 40 colonies of varying size of P. verrucosa (probably including P. meandrina: 68.2–685.8 cm2 in 3D size) during the reproductive season (April, 2017) at neighbouring Green Island (Lin and Nozawa, 2017). In addition, a further 20 nubbins (of the same size) were collected (68.2–364.7 cm2 in 3D size) to determine the minimum size of sexual maturity at the same time. Nubbins were fixed in a 10% formalin-seawater solution and examined using standard histological methods. Tissue of nubbins were decalcified and dehydrated in an alcohol series using a tissue processor (Thermo Scientific, Excelsior ES, USA) and embedded in paraffin wax (Thermo Scientific Histoplast PE, USA). Samples were then cut with a microtome (Thermo Scientific, Finesse 325) at 6 µm thick intervals. Xylene was used to deparaffin samples and tissue sections were mounted on glass slides, stained with hematoxylin and eosin using a staining machine (Shandon Varistain, Thermo Scientific, USA) and then preserved with Organol/Limonene mounting medium and a glass cover. Sections were examined under a BX51 light microscope (Olympus, Japan). For each nubbin, 2 polyps were haphazardly chosen and the number of oocytes per polyp was determined by observing the entire section of each polyp (a total of 4 polyps per colony). The probability of a colony being reproductively active was determined by the presence or absence of oocytes over colony size.

Contained within the 3rd Edition (1957) of the Atlas of Canada is a plate that shows two condensed maps, and three sets of graphs to show population change for the period 1851 to 1951. The top map shows the percent changes in population in eastern Canada for the period 1851 to 1901 (Newfoundland data is for 1857 to 1901). The bottom map shows the percent changes in population for Canada for the period 1901 to 1951 (Northwest Territories data is for 1911 to 1951). The first set of graphs show birth, death and natural increase rates per 1000 population for the period 1931 to 1951 for Canada and the provinces. The second set shows the changes in density of population for the period 1851 to 1951 for Prince Edward Island, Nova Scotia, New Brunswick, Ontario, Quebec, the Western Provinces and Canada. The third graph shows the percent increase in Canada's total population by decade for the period 1851 to 1951.