Chart and table of population level and growth rate for the Chengdu, China metro area from 1950 to 2025.

The statistic shows the population of Chengdu in China from 1980 to 2010, with forecasts up until 2035. In 2010, the population of Chengdu had amounted to about **** million inhabitants and was forecasted to grow up to ten million by 2025.

Population: Census: Sichuan: Chengdu data was reported at 20,937.757 Person th in 12-01-2020. This records an increase from the previous number of 14,047.625 Person th for 12-01-2010. Population: Census: Sichuan: Chengdu data is updated decadal, averaging 14,047.625 Person th from Dec 2000 (Median) to 12-01-2020, with 3 observations. The data reached an all-time high of 20,937.757 Person th in 12-01-2020 and a record low of 11,108.534 Person th in 12-01-2000. Population: Census: Sichuan: Chengdu data remains active status in CEIC and is reported by Chengdu Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GE: Population: Prefecture Level City: By Census.

Population: Inflow: Sichuan: Chengdu data was reported at 348.938 Person th in 2023. This records a decrease from the previous number of 351.426 Person th for 2022. Population: Inflow: Sichuan: Chengdu data is updated yearly, averaging 272.080 Person th from Dec 2001 (Median) to 2023, with 23 observations. The data reached an all-time high of 485.044 Person th in 2018 and a record low of 158.700 Person th in 2001. Population: Inflow: Sichuan: Chengdu data remains active status in CEIC and is reported by Chengdu 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: Household Registration: Death Rate: Sichuan: Chengdu data was reported at 3.850 ‰ in 2023. This records a decrease from the previous number of 12.780 ‰ for 2022. Population: Household Registration: Death Rate: Sichuan: Chengdu data is updated yearly, averaging 6.200 ‰ from Dec 2000 (Median) to 2023, with 24 observations. The data reached an all-time high of 13.200 ‰ in 2017 and a record low of 3.850 ‰ in 2023. Population: Household Registration: Death Rate: Sichuan: Chengdu data remains active status in CEIC and is reported by Chengdu Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GE: Population: Prefecture Level City: Household Registration: Natural Growth Rate.

Population: Sichuan: Chengdu: Household Registration data was reported at 15,982.354 Person th in 2023. This records an increase from the previous number of 15,715.700 Person th for 2022. Population: Sichuan: Chengdu: Household Registration data is updated yearly, averaging 8,747.300 Person th from Dec 1949 (Median) to 2023, with 75 observations. The data reached an all-time high of 15,982.354 Person th in 2023 and a record low of 5,013.200 Person th in 1949. Population: Sichuan: Chengdu: Household Registration data remains active status in CEIC and is reported by Chengdu Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GE: Population: Prefecture Level City.

Population: Sichuan: Chengdu: Xindu data was reported at 718.600 Person th in 2014. This records an increase from the previous number of 702.300 Person th for 2013. Population: Sichuan: Chengdu: Xindu data is updated yearly, averaging 680.000 Person th from Dec 2004 (Median) to 2014, with 11 observations. The data reached an all-time high of 718.600 Person th in 2014 and a record low of 613.000 Person th in 2004. Population: Sichuan: Chengdu: Xindu 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: Sichuan: Chengdu: Usual Residence data was reported at 21,403.000 Person th in 2023. This records an increase from the previous number of 21,268.000 Person th for 2022. Population: Sichuan: Chengdu: Usual Residence data is updated yearly, averaging 14,842.000 Person th from Dec 2000 (Median) to 2023, with 24 observations. The data reached an all-time high of 21,403.000 Person th in 2023 and a record low of 11,108.500 Person th in 2000. Population: Sichuan: Chengdu: Usual Residence data remains active status in CEIC and is reported by Chengdu Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GE: Population: Prefecture Level City.

ObjectiveThis study aimed to evaluate the fairness and efficiency of health resource allocation (HRAE) in Chengdu-Chongqing Economic Circle after the new healthcare reform. This study also aimed to identify existing problems, providing empirical evidence for the government to formulate regional health plans scientifically and reasonably.MethodsThe fairness of health resource allocation was analyzed using the Gini coefficient, Theil index, and agglomeration degree from population and geographical area perspectives. The three-stage data envelopment analysis and the Malmquist productivity index were used to analyze HRAE from static and dynamic perspectives.ResultsThe Gini coefficient for population allocation in Chengdu-Chongqing Economic Circle was 0.066–0.283, and the Gini coefficient for geographical area allocation was 0.297–0.469. The contribution rate within a region was greater than that between regions, and health resources were mainly concentrated in economically developed core areas. The overall fairness of Chengdu Economic Circle was relatively better than that of Chongqing Economic Circle. Moreover, the adjusted mean technical efficiency was 0.806, indicating room for HRAE improvement in Chengdu-Chongqing Economic Circle. Stochastic Frontier Analysis found that different environmental variables have varying degrees of impact on HRAE. The adjusted mean total factor productivity change (Tfpch) was 1.027, indicating an overall upward trend in HRAE since the new healthcare reform. However, scale efficiency change (Sech) (0.997) limited the improvement of Tfpch.ConclusionThe fairness of health resources allocated by population was better than that allocated by geographical area. The unfairness of health resources mainly stemmed from intra-regional differences, with considerable health resources concentrated in core areas. Over the past 13 years, HRAE has improved but exhibited spatial heterogeneity and Sech-hindered productivity improvement. The study recommends strengthening regional cooperation and sharing to promote the integrated and high-quality development of the health and well-being in Chengdu–Chongqing Economic Circle.

Population: Sichuan: Chengdu: Dayi data was reported at 510.000 Person th in 2014. This records a decrease from the previous number of 512.700 Person th for 2013. Population: Sichuan: Chengdu: Dayi data is updated yearly, averaging 514.000 Person th from Dec 2004 (Median) to 2014, with 11 observations. The data reached an all-time high of 519.000 Person th in 2011 and a record low of 499.000 Person th in 2004. Population: Sichuan: Chengdu: Dayi 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 2023, the number of permanent residents in urban areas of China's Sichuan province amounted to around ***** million, surpassing the number of rural residents of around **** millions. Sichuan is a Chinese province located in Southwest China. The capital city of Sichuan is Chengdu.

In 2023, the gender ratio in different regions in China varied greatly, from around 113.2 men per 100 women in Hainan province to only 97.1 men per 100 women in Liaoning. In most provinces in China, there are living more men than women, leading to a national gender ratio of around 104.2 men to 100 women in 2023.

Population: Sichuan: Chengdu: Shuangliu data was reported at 1,009.700 Person th in 2014. This records an increase from the previous number of 978.100 Person th for 2013. Population: Sichuan: Chengdu: Shuangliu data is updated yearly, averaging 942.000 Person th from Dec 2004 (Median) to 2014, with 11 observations. The data reached an all-time high of 1,009.700 Person th in 2014 and a record low of 906.000 Person th in 2004. Population: Sichuan: Chengdu: Shuangliu 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: Sichuan: Chengdu: Pi data was reported at 542.500 Person th in 2014. This records an increase from the previous number of 526.200 Person th for 2013. Population: Sichuan: Chengdu: Pi data is updated yearly, averaging 519.000 Person th from Dec 2004 (Median) to 2014, with 11 observations. The data reached an all-time high of 546.000 Person th in 2010 and a record low of 488.000 Person th in 2004. Population: Sichuan: Chengdu: Pi 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.

The Asian swamp eel (Monopterus albus) is one of the most widely distributed freshwater fish in China. In this study, we identified the single nucleotide polymorphisms (SNPs) of M. albus from 19 wild populations in China using restriction-site associated DNA sequencing (RAD-seq), and used SNP markers to investigate the swamp eel the genetic diversity and population genetic structure. A total of 8941794 SNPs were identified. Phylogenetic and principal component analysis suggested that the 19 populations were clustered into four groups: The Jiaoling County (JL) and Poyang Lake (PYH)populations in Group Ⅰ; the Chengdu City (CD), Dali City (YN), Eli Village (EL), Dongting Lake (DTH), Huoqiu County (HQ), and Chaohu Lake (CH) populations in Group Ⅱ; the Puyang City (PY), Chongming Island (CM), Tai Lake (TH), Gaoyou Lake (GYH), Weishan Lake (WSH), Haimen City (HM), Hongze Lake (HZH), Baiyangdian Lake (BYD), Dagushan (DGS), and Pinghu City (PH) populations in group Ⅲ; and the Lingshui County (LS) populations in Group Ⅳ. All 19 populations may have evolved from four ancestors. The genetic diversity was relatively high in CM, GYH, and HM; and low in LS, EL, and JL. The LS, and CM populations had the highest and lowest differentiation from the other populations, respectively. These findings provide new insights for germplasm resources protection and artificial breeding of M. albus.

Population: Sichuan: Chengdu: Pengzhou data was reported at 808.500 Person th in 2014. This records an increase from the previous number of 806.200 Person th for 2013. Population: Sichuan: Chengdu: Pengzhou data is updated yearly, averaging 800.000 Person th from Dec 2004 (Median) to 2014, with 11 observations. The data reached an all-time high of 808.500 Person th in 2014 and a record low of 777.000 Person th in 2004. Population: Sichuan: Chengdu: Pengzhou 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.

The 'financialisation' of Chinese housing, land and infrastructure - the use of financial instruments to convert the built environment into investment opportunities - generates momentum and vitality in the Chinese economy and has led to wealth accumulation. This study explores how the Chinese housing boom has been financed in the absence of a more developed financial system, and to what extent the financial sector has contributed to the overall appreciation of housing and land assets. A large questionnaire survey was conducted in six case cities including Shanghai, Shenzhen, Chengdu, Xi'an, Nanjing and Tianjin.The Chinese financial system has fostered rapid economic growth in recent decades through so-called 'land-based financing' (tudi chaizhen) in housing, land and infrastructure development. The 'financialisation' of Chinese housing, land and infrastructure - the use of financial instruments to convert the built environment into investment opportunities - generates momentum and vitality in the Chinese economy and has led to wealth accumulation. Real estate financing instruments such as the real estate investment trust (REITS), mortgage securitisation, reverse mortgages and public-private partnerships (PPP) in infrastructure have been recently invented. On the other hand, traditional real estate financial products such as household mortgages and real estate loans benefit from new internet-based finance. Chinese real estate finance has now entered a phase of 'financial explosion'. However, the concrete channels, complex arrangements and new instruments are not entirely known. This research project aims to investigate how housing, land and infrastructure are actually financed, what are the new financial instruments, to what extent there is a trend of 'financialisation', and what are the risks associated with this transformation. We examine the recent trend of financialisation in terms of the forms and extent of the involvement of both the formal and the unofficial ('shadow banking') sectors in real estate development. Recent developments in REITS and PPP will be examined to show the inflow of financial capital in housing, land, and infrastructure projects. We explore how the Chinese housing boom has been financed in the absence of a more developed financial system, and to what extent the financial sector has contributed to the overall appreciation of housing and land assets. We will also try to understand the potential impacts of financialisation on households, enterprises and local government finances (i.e. the issue of 'local debt') and what are the main factors affecting financial stability. The project investigates three levels of financing mechanisms: projects and enterprises, local governments, and individual households. We choose six case cities: in the coastal region, Shanghai and Shenzhen; the central region: Zhengzhou and Changsha; the western region: Chongqing and Chengdu. At the local government (city) level, we will examine the institutional environment and policies regarding built environment finance, including the involvement of housing provident funds. This research project will assess the recent trend of financialisation in Chinese housing, land and infrastructure sectors and provide a nuanced understanding of the changing financial mode, its dynamics and the new institutional environment. The project will examine emerging financial products and new channels in these sectors and their operational mechanisms. The project will focus on household financial behaviour to understand the new trend of financialisation of real estate and its impact on housing consumption, investment behaviour, and job preference. The project will further assess macroeconomic implications such as the impact on the Chinese financial system, financial product innovation, fiscal policies and company investment. Finally, these findings will lead to an assessment of the potential risks associated with financialisation and recommendations for risk management. The sample was collected through random face to face interview at the site of China Housing Provident Fund Centres in six cities (Shanghai, Shenzhen, Tianjin, Nanjing, Chengdu, Xi’an). Verbal consent was made before interview by the Centre in the same way as other NSFC projects. The rejection rate was 9.6%. The sample reflects the population of housing provident fund applicants rather than the total urban resident population. But because housing provident fund is a mainstream compulsory scheme, the sample reflects the population who qualifies housing provident funds and has the intention to apply for the mortgage.

Population: Sichuan: Chengdu: Dujiangyan data was reported at 619.300 Person th in 2014. This records an increase from the previous number of 615.700 Person th for 2013. Population: Sichuan: Chengdu: Dujiangyan data is updated yearly, averaging 610.000 Person th from Dec 2004 (Median) to 2014, with 11 observations. The data reached an all-time high of 619.300 Person th in 2014 and a record low of 598.000 Person th in 2004. Population: Sichuan: Chengdu: Dujiangyan 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.

Tibetans residing in the high-altitude inhospitable environment have undergone significant natural selection of their genetic architecture. Recently, highly mutational autosomal short tandem repeats were widely used not only in the anthropology and population genetics to investigate the genetic structure and relationships, but also in the medical genetics to explore the pathogenesis of multiple genetic diseases and in the forensic science to identify individual and parentage relatedness. However, genetic variants and forensic efficiency of DNATyperTM 19 amplification system and genetic background of Kham Tibetan remain uncharacterized. Thus, we genotyped 19 forensic genetic markers in 11,402 Kham Tibetans to gain insight into the genetic diversity of Chinese high-altitude adaptive population. Highly discriminating and polymorphic forensic measures were observed, which indicated that this new-developed DNATyper 19 PCR amplification is suitable for routine forensic identification purposes and Chinese national DNA database establishment. Pairwise genetic distances among the comprehensive population comparisons suggested that this high-altitude adaptive Kham Tibetan has genetically closer relationships with lowlanders of Tibeto-Burman-speaking populations (Chengdu Tibetan, Liangshan Tibetan, and Liangshan Yi). Genetic substructure analyses via phylogenetic reconstruction, principal component analysis, and multidimensional scaling analysis in both nationwide and worldwide contexts suggested that the genetic proximity exists along the linguistic, ethnic, and continental geographical boundary. Further studies with whole-genome sequencing of modern or archaic Kham Tibetans would be useful in reconstructing the Tibetan population history.