China Number of Job Postings: Active: Manufacturing data was reported at 186,781.000 Unit in 05 May 2025. This records an increase from the previous number of 184,176.000 Unit for 28 Apr 2025. China Number of Job Postings: Active: Manufacturing data is updated weekly, averaging 8,237.000 Unit from Jan 2008 (Median) to 05 May 2025, with 905 observations. The data reached an all-time high of 539,155.000 Unit in 25 Apr 2022 and a record low of 0.000 Unit in 26 Jan 2009. China Number of Job Postings: Active: Manufacturing data remains active status in CEIC and is reported by Revelio Labs, Inc.. The data is categorized under Global Database’s China – Table CN.RL.JP: Number of Job Postings: Active: by Industry.

Wages in Manufacturing in China increased to 103932 CNY/Year in 2023 from 97528 CNY/Year in 2022. This dataset provides - China Average Yearly Wages in Manufacturing - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Employment in industry (% of total employment) (modeled ILO estimate) in China was reported at 31.84 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. China - Employment in industry (% of total employment) - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

Manufacturing Production in China increased 5.70 percent in August of 2025 over the same month in the previous year. This dataset provides - China Manufacturing Production- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Industrial Production in China increased 5.20 percent in August of 2025 over the same month in the previous year. This dataset provides - China Industrial Production - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This paper examines the effect of increased import competition and export demand on local labour markets in the context of India's trading relationship with China. Using an instrumental variables approach, I find that Indian districts exposed to Chinese imports experience decreased manufacturing employment growth relative to their positive pre-existing trend with no offsetting adjustment in services employment or population. The negative employment effect of imports is greater for large establishments. On average, manufacturing employment does not grow in response to export demand shocks, but districts with sufficient development, density, or access to Chinese markets see increased manufacturing employment in response to Chinese export demand.

Economic performance is an important indicator of the efficiency and quality of a company’s production, which is closely related to the profitability of the company and is crucial for the development of the manufacturing industry.This paper aims to develop a theoretical framework for assessing economic performance within the Chinese manufacturing industry. It achieves this by incorporating inputs, outputs, and energy consumption costs into the production function. By analyzing manufacturing data from 2000 to 2021, it quantifies the impact of various factors, including labor costs and technological advancements, on economic performance. The findings highlight technological progress as the primary driver of economic growth within the Chinese manufacturing sector. Notably, there exists a U-shaped relationship between technical progress and economic performance, suggesting nuanced dynamics at play. Contrary to expectations, the rate of change in per capita wages shows no significant positive impact on economic performance. However, technical progress in the eastern and central regions exhibits a capital bias and positively influences economic performance. Similarly, a U-shaped relationship characterizes the relationship between the western region and manufacturing industry performance. These results underscore the crucial role of technological innovation in sustaining economic performance amid challenges such as rising labor and environmental costs. Emphasizing the reliance on scientific and technological progress emerges as imperative for enhancing the industry’s economic resilience and growth.

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.

Chinese investments and construction activities have generated widespread controversy. However, there has been a notable lack of systematic evidence regarding the conditions of employment in these jobs. The ‘Industrial Development, Construction and Employment in Africa (IDCEA)’ research project conducted systematic comparative surveys of firms and workers in Ethiopia and Angola on employment conditions in both manufacturing and construction. Our quantitative worker survey covered 76 firms and 1,519 detailed questionnaire-interviews with workers employed in manufacturing activities and infrastructure construction (mainly road building) in Angola and Ethiopia (682 in Angola and 837 in Ethiopia). The samples included workers in Chinese, Ethiopian, Angolan and other foreign firms, among the leading companies in the target sectors for this research. The aim was to provide comparative data at three levels: country, sector and firm. Detailed information about individual workers was also collected, in order to build profiles of construction and manufacturing workers in these countries, as well as to account for labour market segmentation.

Labour Costs in China increased to 58.40 points in August from 54.20 points in July of 2025. This dataset provides - China Labour Costs - actual values, historical data, forecast, chart, statistics, economic calendar and news.

China Number of Employee: Automobile Manufacturing data was reported at 4,817.000 Person th in Mar 2025. This records an increase from the previous number of 4,766.000 Person th for Feb 2025. China Number of Employee: Automobile Manufacturing data is updated monthly, averaging 4,134.000 Person th from Dec 1998 (Median) to Mar 2025, with 180 observations. The data reached an all-time high of 4,878.300 Person th in Dec 2017 and a record low of 1,415.414 Person th in Dec 2001. China Number of Employee: Automobile Manufacturing data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BIJ: Automobile.

Primary socio-economic data. Collected in 2011 (unskilled workers) and late 2012 (skilled workers). The survey questions were about occupation, training, job security and attitudes to labour standards. There were 40-50 responses. The factories were located at Hangzhau and Jinhua in Zhejiang province, which is about two hours’ from Shanghai. The data is in paper form. The questions were asked in Mandarin and the answers translated into English by the researcher. The data was collected as part of an Australian Catholic University Faculty of Business mini-grant received in 2011 to research a project called Trade and the Distributional Politics of International Labour Standards.

The data comprises qualitative data derived from semi-structured interviews in China, Ghana, Nigeria and Sudan. In addition, there were short surveys with communities affected by oil investments in Nigeria and Ghana. In China data was collected during a workshop and the deliberations were full transcribed. The interviews and workshop discussions were transcribed and translated into English where necessary. Where audio recordings were not permitted researchers took field notes which also form part of the collection. There is also one expert report from Sudan where data collection proved highly sensitive (see Notes on access). The African data is organised by country and sub-divided by stakeholder groups which appear is separate folders. These are Chinese oil companies, African oil companies, international oil companies, suppliers, MDAs, civil society organisations, and communities. The China data is more limited and comprises field notes and workshop transcripts.After decades of being regarded as 'basket cases' some African economies are experiencing growth rates that are among the fastest in the world. Much of this growth is based on the export of commodities, like oil, to China and other emerging economies. Driving this engagement are Chinese national oil companies (NOCs) that have grown up through China's reform period and, as such, carry with them many key features of the 'China model'. While we hypothesise that the Chinese do things 'differently' to other oil investors in Africa we do not know whether the different corporate strategies of the leading Chinese NOCs and the specificities of African political economies they engage with generates unique forms of development, and if so in whose interests? Crucially it is a mistake to see this as one-way traffic with Chinese firms entirely determining the agenda. Our past ESRC-funded research reveals the importance of African agency in shaping the terms of this engagement and with it the potentials for development. In terms of DFID-ESRC's priorities the project addresses Chinese FDI, resource-based growth models, and infrastructure given that many Chinese oil deals are tied to infrastructure. This project will be the first to assess whether and how such developmental benefits may be occurring. We will start by investigating the Chinese NOCs and their relationships to key state and semi-private agencies in China, before undertaking field research in Africa. Important here are the complex 'packages' of aid, trade and investment in Africa through Chinese NOCs, banks and ministries. Chinese NOCs are active across Africa but three countries - Ghana, Angola and Sudan - represent different aspects of their engagement with the continent. These countries are also unique so these contextual differences allow us to examine the role that African agency plays in shaping the nature of and benefits from this new investment in their oil sectors. We will also assess their impacts and the extent to which the growth they generate - directly, through oil-backed infrastructure, and via state revenue - trickles down to Africa's poorest. Data collection will be both quantitative and qualitative; the former being data sets on Chinese FDI and African social indicators, and the latter interviews with key Chinese and African actors. To successfully carry out this data collection in countries and an industrial sector renowned for lacking transparency we will work with partners who have track-records of research in this area, and are embedded in key policy circles which will maximise the impact of our research. Having established the drivers, dynamics and impacts of these investment flows we will address the needs of various users of knowledge. Academics across a range of disciplines will benefit from new knowledge of the character of these flows and impacts, as well as rethinking debates on the nature of international relations, resource-based development and the role of 'Southern' actors in that. National and international policy-makers will benefit from better information about the nature of these oil-related trade and investment flows, as well as benefitting from the recommendations we make for interventions that could enhance the mutual benefits from these new business relations. International and African business people will benefit through greater knowledge of the opportunities available, but also about how to do business in such complex cross-cultural settings. To achieve this, the research team will deliver bespoke training programmes on Chinese NOCs and local linkages. Finally, the general public will benefit from better-informed debate about the nature of Africa's development, and the real costs and benefits of actors like the Chinese. The Open University, where the core team is based, has wide experience of engaging the public in learning about the world and will produce a MOOC on oil and development in Africa. The data is primarily qualitative. It was derived from two main methods. The first was semi-structured interviews with key informants from the oil industry, government officials, civil society organisations, and researchers. These interviews were conducted in all three African countries. The second method was a short community survey in Nigeria and Ghana with those affected by Chinese oil projects. Security issues in Sudan prevented us visiting the oil producing areas. In addition, in China we held an expert workshop where participants were aware that the deliberations were being recorded for research purposes. In addition, in Sudan we commissioned an expert review of the oil sector which appears as a report.

The project will study "high performance work systems" and company performance in the plants of a large Chinese food/noodle manufacturing firm. The principal investigators are Stan Siebert and Xiangdong Wei (Lingnan University, Hong Kong), with John Heywood of Wisconsin-Milwaukee as co-investigator. The aim is to find the root of China's world-beating productivity, and in particular to assess how the company has adapted to China's relatively high levels of labour regulation. (as measured, for example, by the World Bank's current Ease of Doing Business Report). The company is experimenting with various innovative labour practices such as team-working and incentive pay schemes, and the results will be tracked. A further aspect of the research is assessing the consequences of these practices for workers, by conducting periodic job satisfaction surveys. The project addresses central concerns of personnel economics and strategic human resource researchers. The evidence on the high performance paradigm tends to be distorted by omission of the management ability factor which our quasi-experimental approach avoids. In fact, our results may well not support the paradigm, or support a "contingency" view whereby high performance practices improve outcomes when applied to some worker groups (eg full-timers), but not when applied to others. survey questionnaire of employees, employee payroll records

The paper utilizes a panel data set compiled from 27238 firm-year observations representing 2872 Chinese listed firms from 2007 to 2023. This comprehensive dataset allows for an extensive analysis of the relationship between corporate digitalization and labor investment practices.

BackgroundHuman exposure to silica dust is very common in both working and living environments. However, the potential long-term health effects have not been well established across different exposure situations. Methods and FindingsWe studied 74,040 workers who worked at 29 metal mines and pottery factories in China for 1 y or more between January 1, 1960, and December 31, 1974, with follow-up until December 31, 2003 (median follow-up of 33 y). We estimated the cumulative silica dust exposure (CDE) for each worker by linking work history to a job–exposure matrix. We calculated standardized mortality ratios for underlying causes of death based on Chinese national mortality rates. Hazard ratios (HRs) for selected causes of death associated with CDE were estimated using the Cox proportional hazards model. The population attributable risks were estimated based on the prevalence of workers with silica dust exposure and HRs. The number of deaths attributable to silica dust exposure among Chinese workers was then calculated using the population attributable risk and the national mortality rate. We observed 19,516 deaths during 2,306,428 person-years of follow-up. Mortality from all causes was higher among workers exposed to silica dust than among non-exposed workers (993 versus 551 per 100,000 person-years). We observed significant positive exposure–response relationships between CDE (measured in milligrams/cubic meter–years, i.e., the sum of silica dust concentrations multiplied by the years of silica exposure) and mortality from all causes (HR 1.026, 95% confidence interval 1.023–1.029), respiratory diseases (1.069, 1.064–1.074), respiratory tuberculosis (1.065, 1.059–1.071), and cardiovascular disease (1.031, 1.025–1.036). Significantly elevated standardized mortality ratios were observed for all causes (1.06, 95% confidence interval 1.01–1.11), ischemic heart disease (1.65, 1.35–1.99), and pneumoconiosis (11.01, 7.67–14.95) among workers exposed to respirable silica concentrations equal to or lower than 0.1 mg/m3. After adjustment for potential confounders, including smoking, silica dust exposure accounted for 15.2% of all deaths in this study. We estimated that 4.2% of deaths (231,104 cases) among Chinese workers were attributable to silica dust exposure. The limitations of this study included a lack of data on dietary patterns and leisure time physical activity, possible underestimation of silica dust exposure for individuals who worked at the mines/factories before 1950, and a small number of deaths (4.3%) where the cause of death was based on oral reports from relatives. ConclusionsLong-term silica dust exposure was associated with substantially increased mortality among Chinese workers. The increased risk was observed not only for deaths due to respiratory diseases and lung cancer, but also for deaths due to cardiovascular disease. Please see later in the article for the Editors' Summary

Graph and download economic data for Import Price Index by Origin (NAICS): Plastics Product Manufacturing for China (COCHNZ3261) from Jun 2012 to Aug 2025 about plastics, China, imports, production, manufacturing, price index, indexes, and price.

As of 2022, the median annual salary of a data analyst in the Chinese data and artificial intelligence industry reached ** thousand yuan. According to the source, junior-level employees in the technology industry gained the most from changing their jobs. In contrast, from the middle-level upwards, the salary increases are much lower after taking a position at a new employer.

Analysis unit

Persons, households, and dwellings

UNITS IDENTIFIED: - Dwellings: yes - Vacant Units: no - Households: yes - Individuals: yes - Group quarters: yes

UNIT DESCRIPTIONS: - Dwellings: Not applicable - Households: Domestic household: A group of individuals who are either relatives or non-relatives, live in one residential unit and are registerered as one household. Collective household: A group of individuals who live in the same dormitory in a college (or school) or in the same living quarters for staff and workers in a factory or other organization. - Group quarters: Collective dorms of work units (including their branch units) such as organs, groups, schools, factories, mines, construction sites, farms, companies, shops, hospitals, nurseries, old people's homes, monasteries, churches, prisons, places for reform through labor and reeducation.

Universe

All individuals who have Chinese nationality and reside in China, personnel of embassies, consulates and other missions in foreign countries and staff, experts, students and trainees abroad.

Kind of data

Population and Housing Census [hh/popcen]

Sampling procedure

MICRODATA SOURCE: National Bureau of Statistics

SAMPLE SIZE (person records): 10039191.

SAMPLE DESIGN: Systematic sample

Mode of data collection

Face-to-face [f2f]

Research instrument

A single questionnaire for regular and collective households.

Analysis unit

Persons, households, and dwellings

UNITS IDENTIFIED: - Dwellings: yes - Vacant Units: no - Households: yes - Individuals: yes - Group quarters: yes

UNIT DESCRIPTIONS: - Dwellings: Not applicable - Households: Households can be classified into two types: domestic and collective. Individuals who live in the same place mostly due to family relationships are counted as a domestic household. Singles who live alone are counted as a domestic household. Individuals who live in the same domestic household should be registered as one household only, regardless of the type of working places and the type of household registrations (agricultural or non-agricultural), and whether they have the formal household registrations. - Group quarters: Collective dorms of work units (including their branch units) such as organs, groups, schools, factories, mines, construction sites, farms, companies, shops, hospitals, nurseries, old people's homes, monasteries, churches, prisons, places for reform through labor and reeducation.

Universe

All individuals who have Chinese nationality and reside in China

Kind of data

Population and Housing Census [hh/popcen]

Sampling procedure

MICRODATA SOURCE: National Bureau of Statistics

SAMPLE SIZE (person records): 11835947.

SAMPLE DESIGN: Stratified cluster sample

Mode of data collection

Face-to-face [f2f]

Research instrument

A single questionnaire for regular and collective households.