The statistic shows the population of Hangzhou in China from 1980 to 2010, with forecasts up until 2035. In 2010, the population of Hangzhou had amounted to about **** million inhabitants and was forecasted to grow up to almost *** million by 2035.

Population: Census: Zhejiang: Hangzhou data was reported at 11,936.010 Person th in 12-01-2020. This records an increase from the previous number of 8,700.400 Person th for 12-01-2010. Population: Census: Zhejiang: Hangzhou data is updated decadal, averaging 8,700.400 Person th from Dec 2000 (Median) to 12-01-2020, with 3 observations. The data reached an all-time high of 11,936.010 Person th in 12-01-2020 and a record low of 6,878.722 Person th in 12-01-2000. Population: Census: Zhejiang: Hangzhou data remains active status in CEIC and is reported by Hangzhou Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GE: Population: Prefecture Level City: By Census.

Historical dataset of population level and growth rate for the Hangzhou, China metro area from 1950 to 2025.

Population: Zhejiang: Hangzhou: Household Registration data was reported at 8,757.000 Person th in 2024. This records an increase from the previous number of 8,606.036 Person th for 2023. Population: Zhejiang: Hangzhou: Household Registration data is updated yearly, averaging 6,891.200 Person th from Dec 1996 (Median) to 2024, with 29 observations. The data reached an all-time high of 8,757.000 Person th in 2024 and a record low of 6,032.200 Person th in 1996. Population: Zhejiang: Hangzhou: Household Registration data remains active status in CEIC and is reported by Hangzhou Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GE: Population: Prefecture Level City.

Population: Usual Residence: Natural Growth Rate: Zhejiang: Hangzhou data was reported at 2.300 ‰ in 2024. This records an increase from the previous number of 1.300 ‰ for 2023. Population: Usual Residence: Natural Growth Rate: Zhejiang: Hangzhou data is updated yearly, averaging 4.120 ‰ from Dec 2010 (Median) to 2024, with 15 observations. The data reached an all-time high of 7.400 ‰ in 2017 and a record low of 1.300 ‰ in 2023. Population: Usual Residence: Natural Growth Rate: Zhejiang: Hangzhou data remains active status in CEIC and is reported by Hangzhou Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GE: Population: Prefecture Level City: Usual Residence: Natural Growth Rate.

Population: Zhejiang: Hangzhou: Usual Residence data was reported at 12,624.000 Person th in 2024. This records an increase from the previous number of 12,522.000 Person th for 2023. Population: Zhejiang: Hangzhou: Usual Residence data is updated yearly, averaging 9,969.500 Person th from Dec 1999 (Median) to 2024, with 22 observations. The data reached an all-time high of 12,624.000 Person th in 2024 and a record low of 6,157.500 Person th in 1999. Population: Zhejiang: Hangzhou: Usual Residence data remains active status in CEIC and is reported by Hangzhou Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GE: Population: Prefecture Level City.

Population: Usual Residence: Urbanization Rate: Zhejiang: Hangzhou data was reported at 84.800 % in 2024. This records an increase from the previous number of 84.200 % for 2023. Population: Usual Residence: Urbanization Rate: Zhejiang: Hangzhou data is updated yearly, averaging 79.300 % from Dec 2010 (Median) to 2024, with 15 observations. The data reached an all-time high of 84.800 % in 2024 and a record low of 73.300 % in 2010. Population: Usual Residence: Urbanization Rate: Zhejiang: Hangzhou data remains active status in CEIC and is reported by Hangzhou Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GE: Population: Prefecture Level City: Urbanization Rate.

As of January 2024, Hangzhou in China had the highest annual metropolitan population growth rate among megacities in the Asia-Pacific region, at about **** percent. In contrast, all three Japanese megacities Tokyo, Nagoya, and Osaka had the lowest annual population growth rates across APAC, with Osaka's population shrinking by **** percent as of January 2024.

Population: Non-natural Change: Zhejiang: Hangzhou data was reported at 129.867 Person th in 2023. This records an increase from the previous number of 101.340 Person th for 2022. Population: Non-natural Change: Zhejiang: Hangzhou data is updated yearly, averaging 57.511 Person th from Dec 2000 (Median) to 2023, with 24 observations. The data reached an all-time high of 183.686 Person th in 2021 and a record low of 29.036 Person th in 2013. Population: Non-natural Change: Zhejiang: Hangzhou data remains active status in CEIC and is reported by Hangzhou Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GE: Population: Prefecture Level City: Non-natural Change.

Population: Zhejiang: Hangzhou: Fuyang data was reported at 701.432 Person th in 2023. This records an increase from the previous number of 697.888 Person th for 2022. Population: Zhejiang: Hangzhou: Fuyang data is updated yearly, averaging 662.300 Person th from Dec 2004 (Median) to 2023, with 20 observations. The data reached an all-time high of 701.432 Person th in 2023 and a record low of 628.000 Person th in 2004. Population: Zhejiang: Hangzhou: Fuyang 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.GJ: Population: County Level Region.

Population: Rural: Zhejiang: Hangzhou: Jiande data was reported at 397.000 Person th in 2012. This records a decrease from the previous number of 400.000 Person th for 2011. Population: Rural: Zhejiang: Hangzhou: Jiande data is updated yearly, averaging 400.000 Person th from Dec 2004 (Median) to 2012, with 9 observations. The data reached an all-time high of 411.000 Person th in 2004 and a record low of 397.000 Person th in 2012. Population: Rural: Zhejiang: Hangzhou: Jiande 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.GJ: Population: Rural: County Level Region.

The data files in this study have been created by merging individual per-county census files available as Microsoft Excel spreadsheets. Thus the study contains 111 data files, 1 file per census category. Each of these files contains merged census data for the following counties: HANGZHOU SHANGCHENG 杭州上城区 (330102), HANGZHOU XIACHENG 杭州下城区 (330103), JIANGGAN 江干区 (330104), GONGSHU 拱墅区 (330105), XIHU 西湖区 (330106), BINJIANG 滨江区 (330108), TONGLU 桐庐县 (330122), CHUNAN 淳安县 (330127), XIAOSHAN 萧山市 (330181), JIANDE 建德市 (330182), FUYANG 富阳市 (330183), YUHANG 余杭市 (330184), LINAN 临安市 (330185), HAISHU 海曙区 (330203), JIANGDONG 江东区 (330204), JIANGBEI 江北区 (330205), BEILUN 北仑区 (330206), ZHENHAI 镇海区 (330211), XIANGSHAN 象山县 (330225), NINGHAI 宁海县 (330226), YINXIAN 鄞县 (330227), YUYAO 余姚市 (330281), CIXI 慈溪市 (330282), FENGHUA 奉化市 (330283), LUCHENG 鹿城区 (330302), LONGWAN 龙湾区 (330303), OUHAI 瓯海区 (330304), DONGTOU 洞头县 (330322), YONGJIA 永嘉县 (330324), PINGYANG 平阳县 (330326), CANGNAN 苍南县 (330327), WENCHENG 文成县 (330328), TAISHUN 泰顺县 (330329), RUIAN 瑞安市 (330381), LEQING 乐清市 (330382), XIUCHENG 秀城区 (330402), XIUZHOU 秀洲区 (330411), JIASHAN 嘉善县 (330421), HAIYAN 海盐县 (330424), HAINING 海宁市 (330481), PINGHU 平湖市 (330482), TONGXIANG 桐乡市 (330483), HUZHOU 湖州市 (330501), DEQING 德清县 (330521), CHANGXING 长兴县 (330522), ANJI 安吉县 (330523), YUECHENG 越城区 (330602), SHAOXING 绍兴县 (330621), XINCHANG 新昌县 (330624), ZHUJI 诸暨市 (330681), SHANGYU 上虞市 (330682), SHENGZHOU 嵊州市 (330683), WUCHENG 婺城区 (330702), JINHUA 金华县 (330721), WUYI 武义县 (330723), PUJIANG 浦江县 (330726), PANAN 磐安县 (330727), LANXI 兰溪市 (330781), YIWU 义乌市 (330782), DONGYANG 东阳市 (330783), YONGKANG 永康市 (330784), KECHENG 柯城区 (330802), QUXIAN 衢县 (330821), CHANGSHAN 常山县 (330822), KAIHUA 开化县 (330824), LONGYOU 龙游县 (330825), JIANGSHAN 江山市 (330881), DINGHAI 定海区 (330902), PUTUO 普陀区 (330903), DAISHAN 岱山县 (330921), SHENGSI 嵊泗县 (330922), JIAOJIANG 椒江区 (331002), HUANGYAN 黄岩区 (331003), LUQIAO 路桥区 (331004), YUHUAN 玉环县 (331021), SANMEN 三门县 (331022), TIANTAI 天台县 (331023), XIANJU 仙居县 (331024), WENLING 温岭市 (331081), LINHAI 临海市 (331082), LIANDU 莲都区 (331102), QINGTIAN 青田县 (331121), JINYUN 缙云县 (331122), SUICHANG 遂昌县 (331123), SONGYANG 松阳县 (331124), YUNHE 云和县 (331125), QINGYUAN 庆元县 (331126), JINGNINGSHEZUZIZHIXIAN 景宁畲族自治县 (331127), LONGQUAN 龙泉市 (331181).

In China, it is critical to help older adults cope with depression due to the emerging impacts of factors such as increased life expectancy and the “one-child” family planning policy. Meanwhile, differences in retirement age have different effects on health in older adults of different gender. The relationship of gender differences in social capital and depression across the elderly population was unclear. Focusing on this demographic, this study conducted a telephone survey to explore the relationship between social capital and depression. Referring to electronic medical records, we randomly selected 1,042 elderly respondents (426 men, 616 women) from four areas in Hangzhou. We used social capital measurements and the Geriatric Depression Scale (GDS-15) to assess social capital and depression, respectively, then employed a multivariate logistic regression and structural equation modeling to examine the associations between factors, along with a consideration of gender. This study was discovered that differences in both income and morbidity contributed to differences in social capital and depression. In our sample of elderly respondents, we also found gender-based differences in cognitive and structural social capital. Compared to men, women were more likely to attain higher social capital and less likely to develop depression. At the same time, social networking and social engagement had negative impacts on depression in women, which was not the case for men. We found that lower reciprocity (men and women), social work (men), and trust (women) indicated higher risks of depression. Reciprocity and social networks were significantly and negatively correlated with depression among male respondents; in the male model, factors of trust, reciprocity, and social participation had positive effects on reducing the risk of depression, while social networks had a negative effect. For elderly persons, these findings suggest that mental health is affected by differences in social capital caused by policy differences and cultural differences caused by gender differences.

Population: Zhejiang: Hangzhou: Tonglu data was reported at 418.326 Person th in 2023. This records a decrease from the previous number of 419.276 Person th for 2022. Population: Zhejiang: Hangzhou: Tonglu data is updated yearly, averaging 406.950 Person th from Dec 2004 (Median) to 2023, with 20 observations. The data reached an all-time high of 419.276 Person th in 2022 and a record low of 394.000 Person th in 2004. Population: Zhejiang: Hangzhou: Tonglu 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.GJ: Population: County Level Region.

In China, the share of the population that earned at least the equivalent of the highest ** percent of global income earners as of 2022 in purchasing power parity (PPP) terms was **** percent. Hangzhou topped the list with the highest share of middle-class and above category of consumers.

The Gym, Health and Fitness Clubs industry has been developing rapidly in recent years, largely due to increasing disposable incomes and health awareness among Chinese consumers. As the pandemic continues for nearly three years from 2020 and uncertainty increases, residents are highly uncertain about risks and future expectations, and they become cautious about food, clothing, housing, transportation and consumption. The pandemic prevention and control in first-tier cities, including Shanghai, put a lot of pressure on gyms. In 2023, with the government's liberalization of prevention and control, gym members began to retaliatory consumption and began to choose offline exercise and standardized products and services. Industry revenue is expected to rise by 2.9% in 2025 to $7.3 billion. Revenue is expected to rise by an annualized 1.1% over the five years through 2025. Profit margins have decreased to 8.4% of industry revenue in 2025. Commercial fitness clubs began to appear in China in the early 1980s. Since then, the national economy has shown soaring development and living standards have greatly improved, which has helped drive industry growth. Moreover, as requirements for physical activity at work have fallen in China, more modern and occupational diseases have begun to affect the health of professionals. Interest in pursuing physical exercise has therefore increased among China's professional class. The introduction of well-established operating models for fitness centers and advanced fitness equipment from foreign enterprises also has been promoting the industry's development and attracting more domestic consumers over the past five years. Industry revenue is forecasts that industry revenue will increase by an annualized 3.6% over the five years through 2030, to total $8.7 billion, as the Chinese population becomes increasingly health conscious. Fitness clubs will introduce more courses, equipment types to meet consumers' increasingly varied fitness demands. Clubs are also projected to increase service quality to attract more consumers as competition in the industry intensifies.

Population: Zhejiang: Hangzhou: Linan data was reported at 542.990 Person th in 2022. This records an increase from the previous number of 542.175 Person th for 2021. Population: Zhejiang: Hangzhou: Linan data is updated yearly, averaging 527.000 Person th from Dec 2004 (Median) to 2022, with 19 observations. The data reached an all-time high of 542.990 Person th in 2022 and a record low of 519.000 Person th in 2004. Population: Zhejiang: Hangzhou: Linan 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.GJ: Population: County Level Region.

List of factors influencing the land economic density.

Population: Zhejiang: Hangzhou: Jiande data was reported at 502.521 Person th in 2023. This records a decrease from the previous number of 505.478 Person th for 2022. Population: Zhejiang: Hangzhou: Jiande data is updated yearly, averaging 510.000 Person th from Dec 2004 (Median) to 2023, with 20 observations. The data reached an all-time high of 513.400 Person th in 2009 and a record low of 502.521 Person th in 2023. Population: Zhejiang: Hangzhou: Jiande 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.GJ: Population: County Level Region.

Abstract

This research was carried out in China between December 2011 and February 2013. Data was collected from 2,700 privately-owned and 148 state-owned firms.

The objective of Enterprise Surveys is to obtain feedback from businesses on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

Usually Enterprise Surveys focus only on private companies, but in China, a special sample of fully state-owned establishments was included as this is an important part of the economy. Data on 148 state-owned enterprises is provided separately from the data of 2,700 private sector firms. To maintain comparability of the China Enterprise Surveys to surveys conducted in other countries, only the dataset of privately sector firms should be used.

Geographic coverage

Twenty-five metro areas: Beijing (municipalities), Chengdu City, Dalian City, Dongguan City, Foshan City, Guangzhou City, Hangzhou City, Hefei City, Jinan City, Luoyang City, Nanjing City, Nantong City, Ningbo City, Qingdao City, Shanghai (municipalities), Shenyang City, Shenzhen City, Shijiazhuang City, Suzhou City, Tangshan City, Wenzhou City, Wuhan City, Wuxi City, Yantai City, Zhengzhou City.

Analysis unit

The primary sampling unit of the study is an establishment.The establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or universe of the study, is the non-agricultural economy of firms with at least 5 employees and positive amounts of private ownership. The non-agricultural economy comprises: all manufacturing sectors according to the group classification of ISIC Revision 3.1: (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for China ES was selected using stratified random sampling. Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed in the following way: the universe was stratified into 11 manufacturing industries and 7 services industries as defined in the sampling manual. Each manufacturing industry had a target of 150 interviews. Sample sizes were inflated by about 20% to account for potential non-response cases when requesting sensitive financial data and also because of likely attrition in future surveys that would affect the construction of a panel. Note that 100% government owned firms are categorized independently of their industrial classification. The 148 surveyed state-owned enterprises were categorized as a separate sector group to preserve the representativeness of other sector groupings for the private economy.

Size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

Regional stratification was defined in twenty-five metro areas: Beijing (municipalities), Chengdu City, Dalian City, Dongguan City, Foshan City, Guangzhou City, Hangzhou City, Hefei City, Jinan City, Luoyang City, Nanjing City, Nantong City, Ningbo City, Qingdao City, Shanghai (municipalities), Shenyang City, Shenzhen City, Shijiazhuang City, Suzhou City, Tangshan City, Wenzhou City, Wuhan City, Wuxi City, Yantai City, Zhengzhou City.

The sample frame was obtained by SunFaith from SinoTrust.

The enumerated establishments were then used as the frame for the selection of a sample with the aim of obtaining interviews at 3,000 establishments with five or more employees. The quality of the frame was assessed at the onset of the project through calls to a random subset of firms and local contractor knowledge. The sample frame was not immune from the typical problems found in establishment surveys: positive rates of non-eligibility, repetition, non-existent units, etc.

Given the impact that non-eligible units included in the sample universe may have on the results, adjustments are needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 31% (6,485 out of 20,616 establishments).

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments are available: - Services Questionnaire, - Manufacturing Questionnaire, - Screener Questionnaire.

The Services Questionnaire is administered to the establishments in the services sector. The Manufacturing Questionnaire is built upon the Services Questionnaire and adds specific questions relevant to manufacturing.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

The number of contacted establishments per realized interview was 7.24. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 0.55.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond as a different option from don’t know. b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.