This graph shows the average size of households in China from 1990 to 2023. That year, statistically about 2.8 people were living in an average Chinese household. Average household size in China A household is commonly defined as one person living alone or a group of people living together and sharing certain living accommodations. The average number of people living in one household in China dropped from 3.96 in 1990 to 2.87 in 2011. Since 2010, the figure was relatively stable and ranged between 2.87 and 3.17 people per household. The average Chinese household still counts as rather large in comparison to other industrial countries. In 2023, an average American household consisted of only 2.51 people. Comparable figures have already been reached in the bigger cities and coastal areas of China, but in the rural provinces the household size is still much larger. According to the National Bureau of Statistics of China, the household size in China was diametrically correlated to its income. Birth rates and household sizes The receding size of Chinese households may be linked to the controversial one-child policy introduced in 1979. The main aim of the policy was to control population growth. While the fertility rate in China had been very high until the 1970s, it fell considerably in the following decades and resided at only 1.7 children per woman in 2018, nearly the same as in the United States or in the United Kingdom. A partial ease in the one-child policy was introduced in 2013, due to which couples where at least one parent was an only child were allowed to have a second child. In October 2015, the law was changed into a two-child policy becoming effective in January 2016.

China Population: Average Household Size data was reported at 2.800 Person in 2023. This records an increase from the previous number of 2.760 Person for 2022. China Population: Average Household Size data is updated yearly, averaging 3.150 Person from Dec 1982 (Median) to 2023, with 31 observations. The data reached an all-time high of 4.430 Person in 1982 and a record low of 2.620 Person in 2020. China Population: Average Household Size data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: No of Person per Household.

In 2023, the number of people per household across different regions in China varied between around 2.3 in Heilongjiang province and 3.5 in Tibet. The national average was 2.8 people per household in 2023. The relation between household sizes, birth rates, and income In general, data on average household sizes as well as household income are often derived from the same national household survey. Therefore, average household sizes can be used to calculate per capita income from household income and vice versa. In many regions of China, a larger average household size correlates with lower income. At the same time, a strong positive correlation between household size and birth rate exists, which is in itself not surprising. Different household sizes across China In China, the largest average household sizes were recorded in the western part of the country, where birth rates are high. Lower medium figures prevailed in the prosperous coastal regions, while the lowest figures were recorded in the large municipalities and in Northeast China. The northeastern provinces, which are still dominated by their heavy industries, suffer from bad economic perspectives and population decline, which results in their smaller household sizes.

In 2024, the average annual per capita disposable income of households in China amounted to approximately 41,300 yuan. Annual per capita income in Chinese saw a significant rise over the last decades and is still rising at a high pace. During the last ten years, per capita disposable income roughly doubled in China. Income distribution in China As an emerging economy, China faces a large number of development challenges, one of the most pressing issues being income inequality. The income gap between rural and urban areas has been stirring social unrest in China and poses a serious threat to the dogma of a “harmonious society” proclaimed by the communist party. In contrast to the disposable income of urban households, which reached around 54,200 yuan in 2024, that of rural households only amounted to around 23,100 yuan. Coinciding with the urban-rural income gap, income disparities between coastal and western regions in China have become apparent. As of 2023, households in Shanghai and Beijing displayed the highest average annual income of around 84,800 and 81,900 yuan respectively, followed by Zhejiang province with 63,800 yuan. Gansu, a province located in the West of China, had the lowest average annual per capita household income in China with merely 25,000 yuan. Income inequality in China The Gini coefficient is the most commonly used measure of income inequality. For China, the official Gini coefficient also indicates the astonishing inequality of income distribution in the country. Although the Gini coefficient has dropped from its high in 2008 at 49.1 points, it still ranged at a score of 46.5 points in 2023. The United Nations have set an index value of 40 as a warning level for serious inequality in a society.

Key information about China Household Income per Capita

• China Annual Household Income per Capita reached 4,805.935 USD in Dec 2013, compared with the previous value of 4,273.467 USD in Dec 2012.
• China Annual Household Income per Capita data is updated yearly, available from Dec 1985 to Dec 2013, with an averaged value of 711.353 USD.
• The data reached an all-time high of 4,805.935 USD in Dec 2013 and a record low of 262.918 USD in Dec 1986.
• In the latest reports, Retail Sales of China grew 12.700 % YoY in May 2023.

This statistic illustrates the annual household income of international school students in China as of 2018. During the survey period, around ** percent of international school students' families in China had an annual income between ******* and one million yuan.

Key information about China Household Expenditure per Capita

• China Annual Household Expenditure per Capita reached 4,802.362 USD in Dec 2024, compared with the last update number of 4,660.367 USD in Dec 2023.
• China Annual Household Expenditure per Capita data is updated yearly, available from Dec 1957 to Dec 2024, with an averaged value of 607.205 USD.
• The data reached an all-time high of 4,802.362 USD in Dec 2024 and a record low of 89.641 USD in Dec 1964.
• In the latest reports, Retail Sales of China grew 12.700 % YoY in May 2023.

Abstract

China Living Standards Survey (CLSS) consists of one household survey and one community (village) survey, conducted in Hebei and Liaoning Provinces (northern and northeast China) in July 1995 and July 1997 respectively. Five villages from each three sample counties of each province were selected (six were selected in Liaoyang County of Liaoning Province because of administrative area change). About 880 farm households were selected from total thirty-one sample villages for the household survey. The same thirty-one villages formed the samples of community survey. This document provides information on the content of different questionnaires, the survey design and implementation, data processing activities, and the different available data sets.

Geographic coverage

The China Living Standards Survey (CLSS) was conducted only in Hebei and Liaoning Provinces (northern and northeast China).

Kind of data

Sample survey data [ssd]

Sampling procedure

The CLSS sample is not a rigorous random sample drawn from a well-defined population. Instead it is only a rough approximation of the rural population in Hebei and Liaoning provinces in Northeastern China. The reason for this is that part of the motivation for the survey was to compare the current conditions with conditions that existed in Hebei and Liaoning in the 1930’s. Because of this, three counties in Hebei and three counties in Liaoning were selected as "primary sampling units" because data had been collected from those six counties by the Japanese occupation government in the 1930’s. Within each of these six counties (xian) five villages (cun) were selected, for an overall total of 30 villages (in fact, an administrative change in one village led to 31 villages being selected). In each county a "main village" was selected that was in fact a village that had been surveyed in the 1930s. Because of the interest in these villages 50 households were selected from each of these six villages (one for each of the six counties). In addition, four other villages were selected in each county. These other villages were not drawn randomly but were selected so as to "represent" variation within the county. Within each of these villages 20 households were selected for interviews. Thus the intended sample size was 780 households, 130 from each county.

Unlike county and village selection, the selection of households within each village was done according to standard sample selection procedures. In each village, a list of all households in the village was obtained from village leaders. An "interval" was calculated as the number of the households in the village divided by the number of households desired for the sample (50 for main villages and 20 for other villages). For the list of households, a random number was drawn between 1 and the interval number. This was used as a starting point. The interval was then added to this number to get a second number, then the interval was added to this second number to get a third number, and so on. The set of numbers produced were the numbers used to select the households, in terms of their order on the list.

In fact, the number of households in the sample is 785, as opposed to 780. Most of this difference is due to a village in which 24 households were interviewed, as opposed to the goal of 20 households

Mode of data collection

Face-to-face [f2f]

Research instrument

Household Questionnaire

The household questionnaire contains sections that collect data on household demographic structure, education, housing conditions, land, agricultural management, household non-agricultural business, household expenditures, gifts, remittances and other income sources, and saving and loans. For some sections (general household information, schooling, housing, gift-exchange, remittance, other income, and credit and savings) the individual designated by the household members as the household head provided responses. For some other sections (farm land, agricultural management, family-run non-farm business, and household consumption expenditure) a member identified as the most knowledgeable provided responses. Identification codes for respondents of different sections indicate who provided the information. In sections where the information collected pertains to individuals (employment), whenever possible, each member of the household was asked to respond for himself or herself, except that parents were allowed to respond for younger children. Therefore, in the case of the employment section it is possible that the information was not provided by the relevant person; variables in this section indicate when this is true.

The household questionnaire was completed in a one-time interview in the summer of 1995. The survey was designed so that more sensitive issues such as credit and savings were discussed near the end. The content of each section is briefly described below.

Section 0 SURVEY INFORMATION

This section mainly summarizes the results of the survey visits. The following information was entered into the computer: whether the survey and the data entry were completed, codes of supervisor’s brief comments on interviewer, data entry operator, and related revising suggestion (e.g., 1. good, 2. revise at office, and 3. re-interview needed). Information about the date of interview, the names of interviewer, supervisor, data enterer, and detail notes of interviewer and supervisor were not entered into the computer.

Section 1 GENERAL HOUSEHOLD INFORMATION

1A HOUSEHOLD STRUCTURE 1B INFORMATION ABOUT THE HOUSEHOLD MEMBERS’ PARENTS 1C INFORMATION ABOUT THE CHILDREN WHO ARE NOT LIVING IN HOME

Section 1A lists the personal id code, sex, relationship to the household head, ethnic group, type of resident permit (agricultural [nongye], non-agricultural [fei nongye], or no resident permit), date of birth, marital status of all people who spent the previous night in that household and for household members who are temporarily away from home. The household head is listed first and receives the personal id code 1. Household members were defined to include “all the people who normally live and eat their meals together in this dwelling.” Those who were absent more than nine of the last twelve months were excluded, except for the head of household. For individuals who are married and whose spouse resides in the household, the personal id number of the spouse is noted. By doing so, information on the spouse can be collected by appropriately merging information from the section 1A and other parts of the survey.

Section 1B collects information on the parents of all household members. For individuals whose parents reside in the household, parents’ personal id numbers are noted, and information can be obtained by appropriately merging information from other parts of the survey. For individuals whose parents do not reside in the household, information is recorded on whether each parent is alive, as well as their schooling and occupation.

Section 1C collects information for children of household members who are not living in home. Children who have died are not included. The information on the name, sex, types of resident permit, age, education level, education cost, reasons not living in home, current living place, and type of job of each such child is recorded.

Section 2 SCHOOLING

In Section 2, information about literacy and numeracy, school attendance, completion, and current enrollment for all household members of preschool age and older. The interpretation of pre-school age appears to have varied, with the result that while education information is available for some children of pre-school age, not all pre-school children were included in this section. But for ages 6 and above information is available for nearly all individuals, so in essence the data on schooling can be said to apply all persons 6 age and above. For those who were enrolled in school at the time of the survey, information was also collected on school attendance, expenses, and scholarships. If applicable, information on serving as an apprentice, technical or professional training was also collected.

Section 3 EMPLOYMENT

3A GENERAL INFORMATION 3B MAJOR NON-FARM JOB IN 1994 3C THE SECOND NON-FARM JOB IN 1994 3D OTHER EMPLOYMENT ACTIVITIES IN 1994 3E SEARCHING FOR NON-FARM JOB 3F PROCESS FOR GETTING MAJOR NON-FARM JOB 3G CORVEE LABOR

All individuals age thirteen and above were asked to respond to the employment activity questions in Section 3. Section 3A collects general information on farm and non-farm employment, such as whether or not the household member worked on household own farm in 1994, when was the last year the member worked on own farm if he/she did not work in 1994, work days and hours during busy season, occupation and sector codes of the major, second, and third non-farm jobs, work days and total income of these non-farm jobs. There is a variable which indicates whether or not the individual responded for himself or herself.

Sections 3B and 3C collect detailed information on the major and the second non-farm job. Information includes number of months worked and which month in 1994 the member worked on these jobs, average works days (or hours) per month (per day), total number of years worked for these jobs by the end of 1994, different components of income, type of employment contracts. Information on employer’s ownership type and location was also collected.

Section 3D collects information on average hours spent doing chores and housework at home every day during non-busy and busy season. The chores refer to cooking, laundry, cleaning, shopping, cutting woods, as well as small-scale farm yard animals raising, for example, pigs or chickens. Large-scale animal

According to a study released by Kitchen Stories during 2019 in China, it emerged that 31.11 percent of Chinese citizens cooked for their family every day. Nevertheless, it was also revealed that almost 24 percent of the respondents did it very rarely.

This dataset comprises data on Chinese residents’ digital competence and common prosperity levels for the years 2016, 2018, 2020, and 2022, covering 25 provincial-level administrative regions. It includes both individual-level and provincial-level data. The main data sources are the China Family Panel Studies (CFPS) and the China Statistical Yearbook for the corresponding years.The dataset consists of three main categories of variables: (1) digital competence and its multiple dimensions; (2) indicators of common prosperity; (3) mechanism and control variables used in regression analysis.

As of January 2022, the largest share of Chinese middle-class families had an annual income of between *** thousand and *** thousand yuan per year. According to the same survey, almost ** percent of respondents have at least one child. Many middle-class families in China face significant financial burdens because not only do living costs continuously increase but they also often have to support their parents. In that case, one family has to care for four elders and least one kid.

Objectives: Genetic testing, a gold standard for long QT syndrome (LQTS) diagnosis, is time-consuming and costly when all the 15 candidate genes are screened. Since genotype-specific ECG patterns are present in most LQT1-3 mutation carriers, we tested the utility of ECG-guided genotyping in a large cohort of Chinese LQTS patients. Methods and Results: We enrolled 230 patients (26 ± 17 years, 66% female) with a clinical diagnosis of LQTS. Genotypes were predicted as LQT1-3 based on the presence of ECG patterns typical for each genotype in 200 patients (85 LQT1, 110 LQT2 and 5 LQT3). Family-based genotype prediction was also conducted if gene-specific ECG patterns were found in other affected family members. Mutational screening identified 104 mutations (44% novel), i.e. 46 KCNQ1, 54 KCNH2 and 4 SCN5A mutations. The overall predictive accuracy of ECG-guided genotyping was 79% (157/200) and 79% (67/85), 78% (86/110) and 80% (4/5) for LQT1, LQT2 and LQT3, respectively. The predictive accuracy was 98% (42/43) when family-based ECG assessment was performed. Conclusions: From this large-scale genotyping study, we found that LQT2 is the most common genotype among the Chinese. Family-based ECG-guided genotyping is highly accurate. ECG-guided genotyping is time- and cost-effective. We therefore recommend it as an optimal approach for the genetic diagnosis of LQTS.

According to the Chinese parents-children relationship survey, about ** percent of the surveyed parents reported that they spent on average ***** to *** hours a day with their children on weekends. Approximately ** percent of the parents spent more than **** hours with their kids. However, on weekdays, only about ** percent of the parents spent more than ***** hours accompanying their kids.

The purpose of this study is to investigate the mediating role of exercise value cognition between family function (FF) and exercise behavior and the moderating role of an only-child status. A questionnaire survey was conducted on 504 Chinese college students using the FF scale, the exercise value cognition scale, and the exercise behavior scale. The analysis yielded four main findings. (1) There are significant differences between an only-child and a non-only-child for negative exercise behavior and FF. The only-child group has a higher average FF score and a lower average negative exercise score. (2) Exercise behavior and four of its dimensions—exercise autonomy, attention control, exercise planning, and situational induction—are each significantly positively correlated with FF and exercise value cognition. (3) FF is a significantly positive predictor of exercise behavior, both directly and through exercise value cognition, which plays a partial mediating role. (4) Only-child status significantly moderates the mediating effect of exercise value cognition in the link between FF and exercise behavior. The intergroup differences mainly manifest in the influence of FF on exercise behavior and the influence of exercise value cognition on exercise behavior. In the only-child subsample, exercise value cognition plays a complete mediating role. The results of the current study demonstrated the important role that FF and exercise value cognition played in promoting the exercise behavior of college students. These findings have important implications for exercise behavior in adolescents by maintaining sound communication between family members and developing a healthy lifestyle or value cognition.

The total fertility rate in China increased by 0.02 children per woman (+1.72 percent) in 2022. In total, the fertility rate amounted to 1.18 children per woman in 2022. This increase was preceded by a declining fertility rate.The total fertility rate is the average number of children that a woman of childbearing age (generally considered 15 to 44 years) can hypothetically expect to have throughout her reproductive years. As fertility rates are estimates (similar to life expectancy), they refer to a hypothetical woman or cohort, and estimates assume that current age-specific fertility trends would remain constant throughout this person's reproductive years.Find more statistics on other topics about China with key insights such as death rate, number of tuberculosis infections , and crude birth rate.

Autosomal dominant non-syndromic hearing loss (ADNSHL) is highly heterogeneous, among them, KCNQ4 is one of the most frequent disease-causing genes. More than twenty KCNQ4 mutations have been reported, but none of them were detected in Chinese mainland families. In this study, we identified a novel KCNQ4 mutation in a five generation Chinese family with 84 members and a known KCNQ4 mutation in a six generation Chinese family with 66 members. Mutation screening of 30 genes for ADNSHL was performed in the probands from thirty large Chinese families with ADNSHL by targeted region capture and high-throughput sequencing. The candidate variants and the co-segregation of the phenotype were verified by polymerase chain reaction (PCR) amplification and Sanger sequencing in all ascertained family members. Then we identified a novel KCNQ4 mutation p.W275R in exon 5 and a known KCNQ4 mutation p.G285S in exon 6 in two large Chinese ADNSHL families segregating with post-lingual high frequency-involved and progressive sensorineural hearing loss. This is the first report of KCNQ4 mutation in Chinese mainland families. KCNQ4, a member of voltage-gated potassium channel family, is likely to be a common gene in Chinese patients with ADNSHL. The results also support that the combination of targeted enrichment and high-throughput sequencing is a valuable molecular diagnostic tool for autosomal dominant hereditary deafness.

In 2023, on average, one in two urban households and one in three rural households in China owned a car. Altogether, there were **** cars for every 100 Chinese households. This figure has increased more than ********* in the last decade.

A result of China’s economic miracle The substantial increase in car ownership in China is directly correlated with the country's rapid economic development since the 1980s. Until the late 1990s, there were few private cars in China. Cars were generally owned by public organizations, corporations, or transportation companies. The opening of joint ventures in China by foreign automotive companies such as Volkswagen and Toyota led to the introduction of more affordable models in the Chinese market. Combined with rising income levels across the country, the number of private cars in China has grown rapidly since**********, to the point where traffic-related pollution and congestion have gradually become a major problem in China's major cities.

The rise of electric vehicles In recent years, electric vehicles developed by a number of Chinese automotive companies, including BYD and XPeng, have been gaining ground thanks to the Chinese government's generous incentive policies. As a result, China's EV market has become one of the most competitive in the world. Automotive companies such as BYD, Chery and Geely are also making strong gains in the international market.

Demographic characteristics by intervention condition at baseline.

Student Performance Dataset 2024 Overview This dataset comprises detailed information about high school students in China, collected from various universities and schools. It is designed to analyze the factors influencing student performance, well-being, and engagement. The data includes a wide range of features such as demographic details, academic performance, health status, parental support, and more. The participating institutions include prominent universities such as Tsinghua University, Peking University, Fudan University, Shanghai Jiao Tong University, and Zhejiang University.

Dataset Description Features Student ID: Unique identifier for each student. Gender: Gender of the student (Male/Female). Age: Age of the student. Grade Level: The grade level of the student (e.g., 9, 10, 11, 12). Attendance Rate: The percentage of days the student attended school. Study Hours: Average number of hours the student spends studying daily. Parental Education Level: The highest level of education attained by the student's parents. Parental Involvement: The level of parental involvement in the student's education (High, Medium, Low). Extracurricular Activities: Whether the student participates in extracurricular activities (Yes/No). Socioeconomic Status: Socioeconomic status of the student's family (High, Medium, Low). Previous Academic Performance: Previous academic performance level (High, Medium, Low). Class Participation: The level of participation in class (High, Medium, Low). Health Status: General health status of the student (Good, Average, Poor). Access to Learning Resources: Whether the student has access to necessary learning resources (Yes/No). Internet Access: Whether the student has access to the internet (Yes/No). Learning Style: Preferred learning style of the student (Visual, Auditory, Kinesthetic). Teacher-Student Relationship: Quality of the relationship between the student and teachers (Positive, Neutral, Negative). Peer Influence: Influence of peers on the student's behavior and performance (Positive, Neutral, Negative). Motivation Level: Student's level of motivation (High, Medium, Low). Hours of Sleep: Average number of hours the student sleeps per night. Diet Quality: Quality of the student's diet (Good, Average, Poor). Transportation Mode: Mode of transportation used by the student to commute to school (Bus, Car, Walk, Bike). School Type: Type of school attended by the student (Public, Private). School Location: Location of the school (Urban, Rural). Homework Completion Rate: The rate at which the student completes homework assignments. Reading Proficiency: Proficiency level in reading. Math Proficiency: Proficiency level in mathematics. Science Proficiency: Proficiency level in science. Language Proficiency: Proficiency level in language. Physical Activity Level: The level of physical activity (High, Medium, Low). Screen Time: Average daily screen time in hours. Bullying Incidents: Number of bullying incidents the student has experienced. Special Education Services: Whether the student receives special education services (Yes/No). Counseling Services: Whether the student receives counseling services (Yes/No). Learning Disabilities: Whether the student has any learning disabilities (Yes/No). Behavioral Issues: Whether the student has any behavioral issues (Yes/No). Attendance of Tutoring Sessions: Whether the student attends tutoring sessions (Yes/No). School Climate: Overall perception of the school's environment (Positive, Neutral, Negative). Parental Employment Status: Employment status of the student's parents (Employed, Unemployed). Household Size: Number of people living in the student's household. Performance Score: Overall performance score of the student (Low, Medium, High).

BackgroundCaregivers of pediatric patients with tic disorders (TD) are at high risk for anxiety and depression, but the situation of this disorder was rarely reported based on the Chinese population. The purpose of this study was to investigate the prevalence and potential contributing factors of anxiety and depression among caregivers of Chinese pediatric patients with TD.MethodsA cross-sectional study was carried out on caregivers of pediatric patients with TD at a women’s and children’s hospital in western China from January to June 2020. A structured questionnaire was designed to collect data, including socio-demographic information, disease and medication status, family situation and social relationship, cognition and attitude towards TD and treatment. Anxiety and depression were assessed using the self-rating anxiety scale (SAS) and self-rating depression scale (SDS), respectively. The univariate analysis and multivariate logistic regression were used to analyze the cross-sectional data.ResultsA total of 318 participants were included in this study, with a response rate of 89.58% (318/355). The average age of pediatric patients with TD was 8.38 ± 2.54 years, and 78.30% (249/318) of caregivers were aged between 30–50 years old. Overall, 14.78% (47/318) of caregivers presented the symptom of anxiety, with a mean SAS score of 54.81±5.26, and 19.81% (63/318) of caregivers presented the symptom of depression, with a mean SDS score of 59.64±5.83. Logistic regression analysis revealed that the common family relationship (OR = 2.512, p = 0.024), and pediatric patients with unharmonious social relationships (OR = 5.759, p = 0.043) and with introverted personality (OR = 2.402, p = 0.023) were significantly associated with anxiety in caregivers of pediatric patients with TD, as well as the single-parent family (OR = 4.805, p = 0.011), mistaken cognition of TD (OR = 0.357, p = 0.031), and pediatric patients with fewer friends (OR = 3.377, p = 0.006) were significantly associated with depression.ConclusionsAnxiety and depression are prevalent among caregivers of TD pediatric patients, which brings up the importance of psychiatric support for this group. Longitudinal studies need to be conducted to further confirm the causality before interventions to improve mental health are developed.