As of 2023, the bulk of the Chinese population was aged between 25 and 59 years, amounting to around half of the population. A breakdown of the population by broad age groups reveals that around 61.3 percent of the total population was in working age between 16 and 59 years in 2023. Age cohorts below 25 years were considerably smaller, although there was a slight growth trend in recent years. Population development in China Population development in China over the past decades has been strongly influenced by political and economic factors. After a time of high fertility rates during the Maoist regime, China introduced birth-control measures in the 1970s, including the so-called one-child policy. The fertility rate dropped accordingly from around six children per woman in the 1960s to below two at the end of the 20th century. At the same time, life expectancy increased consistently. In the face of a rapidly aging society, the government gradually lifted the one-child policy after 2012, finally arriving at a three-child policy in 2021. However, like in most other developed countries nowadays, people in China are reluctant to have more than one or two children due to high costs of living and education, as well as changed social norms and private values. China’s top-heavy age pyramid The above-mentioned developments are clearly reflected in the Chinese age pyramid. The age cohorts between 30 and 39 years are the last two larger age cohorts. The cohorts between 15 and 24, which now enter childbearing age, are decisively smaller, which will have a negative effect on the number of births in the coming decade. When looking at a gender distribution of the population pyramid, a considerable gender gap among the younger age cohorts becomes visible, leaving even less room for growth in birth figures.

According to latest figures, the Chinese population decreased by 1.39 million to around 1.408 billion people in 2024. After decades of rapid growth, China arrived at the turning point of its demographic development in 2022, which was earlier than expected. The annual population decrease is estimated to remain at moderate levels until around 2030 but to accelerate thereafter. Population development in China China had for a long time been the country with the largest population worldwide, but according to UN estimates, it has been overtaken by India in 2023. As the population in India is still growing, the country is very likely to remain being home of the largest population on earth in the near future. Due to several mechanisms put into place by the Chinese government as well as changing circumstances in the working and social environment of the Chinese people, population growth has subsided over the past decades, displaying an annual population growth rate of -0.1 percent in 2024. Nevertheless, compared to the world population in total, China held a share of about 17 percent of the overall global population in 2024. China's aging population In terms of demographic developments, the birth control efforts of the Chinese government had considerable effects on the demographic pyramid in China. Upon closer examination of the age distribution, a clear trend of an aging population becomes visible. In order to curb the negative effects of an aging population, the Chinese government abolished the one-child policy in 2015, which had been in effect since 1979, and introduced a three-child policy in May 2021. However, many Chinese parents nowadays are reluctant to have a second or third child, as is the case in most of the developed countries in the world. The number of births in China varied in the years following the abolishment of the one-child policy, but did not increase considerably. Among the reasons most prominent for parents not having more children are the rising living costs and costs for child care, growing work pressure, a growing trend towards self-realization and individualism, and changing social behaviors.

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.

These estimates take into account the counts of the 2006 Census,adjusted for net census undercoverage and are based on the 2006 Standard Geographical Classification (SGC). The publication includes statistics for the demographic components that were used to produce the population estimates (births, deaths, marriages, divorces, immigration, emigration, net temporary emigration, returning emigration, internal migration and non-permanent residents) by age and sex. In addition, the publicat ion contains highlights of current demographic trends and a description of the methodology. It also provides additional data such as a chronological series of estimates by various levels of geography. With regard to provinces and territories, the estimates date back to 1971 (tables and animated age pyramid), 1996 for census divisions, census metropolitan areas and economic regions as well as census families. Note that the title of this product has changed for the 2008/09 edition, which is called Canada's Demographic Estimates.

The article analyzes whether there is an association between the different levels of the BoP and the area of residence with the variables of access to essential utilities in a region of Colombia.

A popular hypothesis in international studies states that a ‘youth bulge’ – an age pyramid dominated by large cohorts between 15 and 29 years of age – increases the risk of political violence. However, empirical evidence on this link remains inconclusive to date. In this article, we systematically assess the youth effect using new data from 183 countries between 1996 and 2015. We find that within countries, a decrease in the youth ratio is generally associated with a decrease in the number of violent deaths from terrorism or other internal conflicts, and vice versa. This is also confirmed in out-of-sample predictions. However, the association is not evident in all constellations and sensitive to modeling issues. In particular, large cohorts of young males can become a disruptive power in countries that increase enrollment in post-primary education. Although this is usually followed by fertility decline, youth bulges often remain at record levels for quite some time due to high birth rates in the past. Strong labor markets can in general suppress the detrimental consequences of youth bulges. However, the combination of growing youth cohorts and educational expansion often leads to increased political violence even in the presence of low youth unemployment.

Abstract

Population and Housing Census of Bhutan 2005 collected data on demographic, eduation, health, migration, household and housing characteristics. It covered the entire country irrespective of human habitation or not and counted all structures, census house, households and people whether Bhutanese or non-Bhutanese residing in the country at a specific point of time. The Census was carried out for two days, 30 and 31 May, 2005. A total of 7500 enumerators, supervisors and administrators were involved.

General Objectives: The 2005 Census seeks to create an inventory of Bhutan's population size, socio-economic information, labour and demographic characteristics.

Specific Objectives: - to obtain an up-to date count of the population size, by age and sex - to obtain geographic distribution of the population by demographic and socio-economic characteristics - to provide frames for surveys and other statistical activities - to gather information about migration and fertility

Salient features of a census: 1. The population census forms an integral part of a country’s National Statistical System. 2. The census provides valuable benchmark data on a wide range of characteristics, a frame for statistical survey and data to compile a variety of social and economic indicators. These indicators must be comparable between areas within as well as with that of other countries. 3. The census provides the demographic, housing, social and economic data not provided by population registers. 4. Most importantly a census provides data at the smallest area level like a village. Extensive and detailed cross-classification is possible. This is not possible in a sample survey. 5. The population census has a legitimate methodology, which is acceptable internationally.

Geographic coverage

National, District (Dzongkhag), Sub-district (Gewogs), Urban (or Rural) areas.

Analysis unit

Individuals, Households, Gewogs, Dzongkhags, National

Universe

The Census covered all de facto household members. It covered the entire country irrespective of human habitation or not and counted all structures, census house, households and people whether Bhutanese or non-Bhutanese residing in the country at a Census Night ( Midngiht of 30 May)

Kind of data

Census/enumeration data [cen]

Sampling procedure

Not Applicable

Mode of data collection

Face-to-face [f2f]

Research instrument

To develop the census questionnaires, consultative meetings were conducted with all ministries. This was followed by a workshop for all sector heads to finalise the contents of the census questionnaires. Necessary changes were incorporated into the census questionnaires based on the outcome of the workshops and consultative meetings. The questionnaires were pre-tested in the three regions of the country. After making all necessary changes the forms were printed in adequate numbers.

Form PHCB - 2A - Household List Update: This section collects data on village code, structure number, census house number, use of census house, serial number of household, name of household head, sex and age with geographical codes. Form PHCB - 2B - Household Members List: This section collects information on household members, relationship, sex, age, member status, members absent and duration absent. Form PHCB -2C - Individual Member Details: This section has three parts. Part A collects information on general demographic characteristics and migration. Part B collects information on education and employment and Part C collects information on fertiliy of women age 15-49 years. Form PHCB - 2D - Household Informamtion: This section has two parts. Part A collects information on housing conditions and facilities. Part B collects information on particulars of the deceased in the past twelve months.

Cleaning operations

Data editing was done in several stages. The first editing of data was done by the field supervisors and then followed by the manual editing at the dzongkhag level immediately after the field operation. The final manual editing was done at the centre by 20 Dzongkhag Statistical Officers, 1 Registration Officer and 28 graduates who were trained and deployed on temporary basis for three months.

Response rate

100% response rate.

Note: The Royal Government of Bhutan declared 30 May - 31 May, 2005, as public holidays.

Sampling error estimates

Since PHCB, 2005 involved complete enumeration of respondents, Sampling procedures were not applicable thus sampling errors were not computed.

Data appraisal

Standard tables and graphs were generated to assess the data reliablity. This includes the computation of population pyramid, grapha of male and female population by single years of age, age and sex structure, age distribution of the household population.

Quercus chungii, a rare and endangered endemic tree species, is found exclusively in subtropical regions of China. Understanding the population structure and temporal dynamics of Q. chungii is pivotal for effective conservation and restoration of its populations and associated ecosystems. However, large knowledge gaps remain about its population structure and temporal change, and its key demographic rates across size classes. In this study, we investigated the population structures of Q. chungii in 2013 and 2023 in a nature reserve specifically established to better conserve this species and its associated ecosystems. We found that Q. chungii increased in its overall abundance, and tree size in the past decade, suggesting active regeneration and a rapid growth rate for this species and the effectiveness of past conservation efforts. The age structure in 2023 showed a pyramid shape, with a sharp decline in the numbers of individuals from germinated seeds to seedlings and from seedlings to saplings. These led to the low numbers of seedlings and saplings and high age-specific death probabilities at the early developmental stages. These results indicated potential risks of future population decline. These risks may have already manifested over the past decade, as a high mortality rate during the seedling-to-sapling transition could be one of the primary reasons contributing to the decreased proportion of saplings in 2023 compared to 2013. We propose that future studies may benefit from in-depth studies on the regeneration processes of Q. chungii by considering seed predation and germination under changing climate. This study improves the prediction of population development of Q. chungii, thereby offering theoretical guidance essential for its conservation.

This statistic depicts the age distribution of India from 2013 to 2023. In 2023, about 25.06 percent of the Indian population fell into the 0-14 year category, 68.02 percent into the 15-64 age group and 6.92 percent were over 65 years of age. Age distribution in India India is one of the largest countries in the world and its population is constantly increasing. India’s society is categorized into a hierarchically organized caste system, encompassing certain rights and values for each caste. Indians are born into a caste, and those belonging to a lower echelon often face discrimination and hardship. The median age (which means that one half of the population is younger and the other one is older) of India’s population has been increasing constantly after a slump in the 1970s, and is expected to increase further over the next few years. However, in international comparison, it is fairly low; in other countries the average inhabitant is about 20 years older. But India seems to be on the rise, not only is it a member of the BRIC states – an association of emerging economies, the other members being Brazil, Russia and China –, life expectancy of Indians has also increased significantly over the past decade, which is an indicator of access to better health care and nutrition. Gender equality is still non-existant in India, even though most Indians believe that the quality of life is about equal for men and women in their country. India is patriarchal and women still often face forced marriages, domestic violence, dowry killings or rape. As of late, India has come to be considered one of the least safe places for women worldwide. Additionally, infanticide and selective abortion of female fetuses attribute to the inequality of women in India. It is believed that this has led to the fact that the vast majority of Indian children aged 0 to 6 years are male.

In 2023, the population of the United Kingdom reached 68.3 million, compared with 67.6 million in 2022. The UK population has more than doubled since 1871 when just under 31.5 million lived in the UK and has grown by around 8.2 million since the start of the twenty-first century. For most of the twentieth century, the UK population steadily increased, with two noticeable drops in population occurring during World War One (1914-1918) and in World War Two (1939-1945). Demographic trends in postwar Britain After World War Two, Britain and many other countries in the Western world experienced a 'baby boom,' with a postwar peak of 1.02 million live births in 1947. Although the number of births fell between 1948 and 1955, they increased again between the mid-1950s and mid-1960s, with more than one million people born in 1964. Since 1964, however, the UK birth rate has fallen from 18.8 births per 1,000 people to a low of just 10.2 in 2020. As a result, the UK population has gotten significantly older, with the country's median age increasing from 37.9 years in 2001 to 40.7 years in 2022. What are the most populated areas of the UK? The vast majority of people in the UK live in England, which had a population of 57.7 million people in 2023. By comparison, Scotland, Wales, and Northern Ireland had populations of 5.44 million, 3.13 million, and 1.9 million, respectively. Within England, South East England had the largest population, at over 9.38 million, followed by the UK's vast capital city of London, at 8.8 million. London is far larger than any other UK city in terms of urban agglomeration, with just four other cities; Manchester, Birmingham, Leeds, and Glasgow, boasting populations that exceed one million people.

This statistic shows the age structure in Brazil from 2013 to 2023. In 2023 about 19.94 percent of Brazil's total population were aged 0 to 14 years. Population of Brazil Brazil is the fifth largest country in the world by area and population and the largest in both South America and the Latin American region. With a total population of more than 200 million inhabitants in 2013, Brazil also ranks fifth in terms of population numbers. Brazil is a founding member of the United Nations, the G20, CPLP, and a member of the BRIC countries. BRIC is an acronym for Brazil, Russia, India, and China, the four major emerging market countries. The largest cities in Brazil are São Paulo, Rio de Janeiro and Salvador. São Paulo alone reports over 11.1 million inhabitants. Due to a steady increase in the life expectancy in Brazil, the average age of the population has also rapidly increased. From 1950 until 2015, the average age of the population increased by an impressive 12 years; in 2015, the average age of the population in Brazil was reported to be around 31 years. As a result of the increasing average age, the percentage of people aged between 15 and 64 years has also increased: In 2013, about 68.4 percent of the population in Brazil was aged between 15 and 64 years.

Examples of indicators at different levels of the E–D Pyramid.

Abstract

The main objective of the survey is to assess the living conditions of the population of St Lucia and to develop a national basket of goods and services for updating of the consumer price index. The survey contains information on housing conditions, cost of accomodation, cost of rountine household maintenance and repairs, annual cost of purching furniture and furishings for the household, cost of vehicle operations, where items are purchased, migration, anthopometric data, demographics, health, education, labour force, crime, clothing expenses, health expenses and income.

Geographic coverage

National coverage, all Administrative Districts

Analysis unit

Individuals, households, spenders (defined as persons age 18 and over and employed)

Universe

The survey covered all de jure household members (usual residents), the fertility section of the person questionnaire covers all women aged 15-49 years resident in the household, the anthropometic section covers all children aged 0-4 years (under age 5) resident in the household and the expenditure data covers all spenders 18 year of age and over and employed.

Kind of data

Sample survey data [ssd]

Sampling procedure

1302 households were selected for the sample. Of these, 1222 were occupied households and were successfully interviewed for a response rate of 94%. Within these households, 4319 persons were successfully interviewed (response rate 93.9%).

The stratification is done by district and is based on the percentage of agricultural workers for rural EDs (Enumeration Districts) and percentage of professional workers for urban EDs. There are two stages of selection, firstly the selection of EDs in all Districts then the selection of households using a random start and systematic selection proceedure. Households which refused or could not be contacted were replaced.

The sample frame used was based on the May 2001 Census and the sample size was 2.78% of the frame. Stratification was done on the district (District) and then by ED (Enumeration District) and finally by household (hhno).

Sampling deviation

There was no deviation from the sample design.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four Questionnaires were administered to each household; each household was visited at least three times. On the first visit the Household and the Individual Questionnaires were administered. At the start of the first week one Daily Diary of Expenditure Questionnaire for the household and a Memory Jogger notebook for each spender in the household was left with household respondents to record all purchases over the next one-week period. The 2nd visit to the household occurred at the end of the first week at which point the Daily Diary completed by the household for the first week and all memory jogger notebooks were collected and the second week’s Diary and memory joggers were left. The 3rd visit to the household occurred at the end of the second week at which point the Daily Diary completed by the household and memory joggers from each spender for the second week was collected.

THE HOUSEHOLD EXPENDITURE SURVEY QUESTIONNAIRES

There are four (4) questionnaires to be administered in the survey. 1. S.L.C.H.B Household Questionnaire 2. S.L.C.H.B Individual/Person Questionnaire 3. S.L.C.H.B Memory Jogger 4. S.L.C.H.B Household Daily Diary of Expenditure

THE STRUCTURE OF THE QUESTIONNAIRES Household Questionnaire Front Page: Identification and control Section 1: Housing conditions and household assets Section 2 Part 1,2,3: Expenditure on accommodation, owned and rented Section 2 Part 4: Expenditure on accommodation - Repair and maintenance of dwelling Section 3 : Major types of household expenses Section 4 Part 1: Furniture, furnishings and household equipment Section 4 Part 2: Repairs and servicing of household articles Section 5 : Agriculture products produced and consumed at home Section 6: Transportation Section 7: Regularity of purchase and main type of outlet Section 8: For Heads of households only (Status of previous household head) Section 9: To be completed for all former household members living away from the household in the past five years Section 10: For children under the age of five years

Person Questionnaire
Control: Identification and control Section 1: Characteristics – For all persons Section 2: Migration – For all persons Section 3: Health – For all persons Section 4: Education – For all persons Section 5: Employment – For person 15 years and over Section 6: Marital, union status and fertility for persons – For persons over the age of 15 years Section 7: Crime Section 8: Clothing and footwear consumed in the last 3 months Section 9: Other expenses Section 10: Other Disbursements Section 11: Income

Memory Jogger
Front Page: Identification and control Daily Record: Pages 1 to 7 Back Page: Notes on the method of completing the daily diary

Daily Diary of Expenditure
Front Page: Identification and control Pages 2 – 3: Notes on the method of completing the daily diary Example: Example of method of completion (Pages 4, 5, 6) Day One: Daily expenditures (Pages 7, 8, 9, 10) Day Two: Daily expenditures (Pages 11, 12, 13, 14) Day Three: Daily expenditures (Pages 15, 16, 17, 18) Day Four: Daily expenditures (Pages 19, 20, 21, 22) Day Five: Daily expenditures (Pages 23, 24, 25, 26) Day Six: Daily expenditures (Pages 27, 28, 29, 30) Day Seven: Daily expenditures (Pages 31, 32, 33, 34)

Cleaning operations

Data editing took place at a number of stages throughout the processing, including: a) Office editing and coding b) During data capture which involved the scanning and verification of the data c) Structure checking and completeness was done in SQL 2000 Enterprise Server d) Secondary editing was done in SPSS e) Structural checking of SPSS data files Detailed documentation of the editing of data can be found in the "Data Editing and coding guidelines" document provided as an external resource.

Response rate

Response rates by Administrative District follow: Castries Urban: 98.5% Castries Rural: 94.8% Anse-La-raye/Canaries: 98.4% Soufriere: 86.7% Choiseul: 100.0% Laborie: 94.5% Vieuxfort: 89.1% Micoud: 88.1% Dennery: 97.0% GrosIslet: 92.4%

Sampling error estimates

Estimates from a sample survey are affected by two types of errors: 1) non-sampling errors and 2) sampling errors. Non-sampling errors are the results of mistakes made in the implementation of data collection and data processing. Numerous efforts were made during implementation of the 2005-2006 MICS to minimize this type of error, however, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

If the sample of respondents had been a simple random sample, it would have been possible to use straightforward formulae for calculating sampling errors. However, the SLC/HBS 2005-2006 sample is the result of a multi-stage stratified design, and consequently needs to use more complex formulae. The CENVAR module of the IMPS 4.1 has been used to calculate sampling errors for the SLC/HBS 2005-2006. This module uses the Taylor linearization method of variance estimation for survey estimates that are means or proportions.

Sampling errors have been calculated for a select set of statistics (all of which are proportions due to the limitations of the Taylor linearization method) for the national sample, urban and rural areas, and for each of the five regions. For each statistic, the estimate, its standard error, the coefficient of variation (or relative error -- the ratio between the standard error and the estimate), the design effect, and the square root design effect (DEFT -- the ratio between the standard error using the given sample design and the standard error that would result if a simple random sample had been used), as well as the 95 percent confidence intervals (+/-2 standard errors).

Details of the sampling errors are presented in the sampling errors appendix to the report and in the sampling errors table presented in the external resources.

Data appraisal

A series of data quality tables and graphs are available to review the quality of the data and include the following: - Age distribution of the household population - Age distribution of eligible children and children for whom the mother or caretaker was interviewed - Age distribution of children under age 5 by 3 month groups - Presence of mother in the household and person interviewed for the under 5 questionnaire - School attendance by single year age - Sex ratio at birth among children ever born, surviving and dead by age of respondent - Distribution of women by time since last birth - Scatter plot of weight by height, weight by age and height by age - Graph of male and female population by single years of age - Population pyramid

The results of each of these data quality tables are shown in the appendix of the final report and are also given in the external resources section.

The general rule for presentation of missing data in the final report tabulations is that a column is presented for missing data if the percentage of cases with missing data is 1% or more. Cases with missing data on the background characteristics (e.g. education) are included in the tables, but the missing data rows are suppressed and noted at the bottom of the tables in the report (not in the SPSS output, however).

In 2022, the majority of Indian adults had a wealth of 10,000 U.S. dollars or less. On the other hand, about *** percent were worth more than *********** dollars that year. India The Republic of India is one of the world’s largest and most economically powerful states. India gained independence from Great Britain on August 15, 1947, after having been under their power for 200 years. With a population of about *** billion people, it was the second most populous country in the world. Of that *** billion, about **** million lived in New Delhi, the capital. Wealth inequality India suffers from extreme income inequality. It is estimated that the top 10 percent of the population holds ** percent of the national wealth. Billionaire fortune has increase sporadically in the last years whereas minimum wages have remain stunted.