Unemployment Rate in China decreased to 5.10 percent in October from 5.20 percent in September of 2025. This dataset provides - China Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

China Consumer Confidence Score: Job Loss data was reported at 39.000 Score in Jan 2023. This records an increase from the previous number of 35.000 Score for Dec 2022. China Consumer Confidence Score: Job Loss data is updated monthly, averaging 34.400 Score from Mar 2010 (Median) to Jan 2023, with 155 observations. The data reached an all-time high of 56.600 Score in Jul 2019 and a record low of 5.800 Score in Mar 2010. China Consumer Confidence Score: Job Loss data remains active status in CEIC and is reported by Ipsos Group S.A.. The data is categorized under Global Database’s China – Table CN.IPSOS: Consumer Confidence Survey.

In 2022, the total number of jobs generated, directly and indirectly, by travel and tourism worldwide remained below the figures reported before the impact of the coronavirus (COVID-19) pandemic. Overall, among the countries with the highest number of travel and tourism jobs worldwide in 2022, China recorded the sharpest drop in employment, with around 19 million fewer travel and tourism jobs compared to 2019.

In 2024, the employment rate in China decreased to around 62.4 percent, from 62.8 percent in the previous year. China is the world’s most populous country and its rapid economic development over the past decades has profited greatly from its large labor market. While the overall working conditions for the Chinese people are improving, the actual size of the working-age population in China has been shrinking steadily in recent years. This is mainly due to a low birth rate in the country. Economic slowdown – impact on labor market After decades of rapid development, the world’s second largest economy now seems to have difficulties to boost its economy further. The GDP growth rate indicated a declining trend over the last decade and the number of employed people decreased for the first time since decades in 2015. Under the influence of the global economic downturn, the coronavirus pandemic, and the US-China tensions, many Chinese enterprises are having tough times, which leads to a recession in China’s labor market. Chances for better employment situation The long-lasting Sino-U.S. trade war has caused China great loss on its international trade sector, which has been driving China’s economic growth for decades. However, there is also a lot China could improve. First, the potential of domestic demands could be further developed and satisfied with high-quality products. Second, it’s a good timing to eliminate backward industries with low value added, and the high-tech and environment-friendly industries should be further promoted. In addition, China’s market could be more open to services, especially in the financial sector and IT services, to attract more foreign investors. Highly skilled talents should be better valued in the labor market. Efficient vocational education and further education could also help change the structure of China’s labor market.

China Consumer Confidence Score: Future Job Loss data was reported at 23.000 Score in Jan 2023. This records an increase from the previous number of 6.000 Score for Dec 2022. China Consumer Confidence Score: Future Job Loss data is updated monthly, averaging 27.400 Score from Mar 2010 (Median) to Jan 2023, with 155 observations. The data reached an all-time high of 41.900 Score in Feb 2015 and a record low of 1.000 Score in Nov 2022. China Consumer Confidence Score: Future Job Loss data remains active status in CEIC and is reported by Ipsos Group S.A.. The data is categorized under Global Database’s China – Table CN.IPSOS: Consumer Confidence Survey.

In September 2025, the surveyed unemployment rate in urban areas of China ranged at 5.2 percent, down from 5.3 percent in the previous month. The annual unemployment rate in China was 5.1 percent in 2024. Surveyed versus registered unemployment Figures on surveyed unemployment were published by the National Bureau of Statistics of China in 2018 for the first time. The use of surveys was initiated to get a more accurate picture of actual unemployment in urban areas of China. The surveys cover all permanent residents between the age of 16 and retirement age living in cities. In contrast, registered unemployment figures take only those people into account that have actively reported their unemployment. As most migrant workers and other groups that do not qualify for unemployment compensations in China normally do not report their unemployment status, the figures for registered unemployment are considerably lower than those for surveyed unemployment. Youth unemployment in China Youth unemployment has become a growing problem in China in recent years. Unemployment figures for young people fluctuate over the year and normally peak in July and August in China, when the largest number of graduates enter the job market. The youth unemployment rate increased from 13.9 percent in July 2019 to 16.8 percent in July 2020, 19.9 percent in July 2022, and 21.3 percent in June 2023. This is mainly due to difficult economic conditions and rising numbers of college graduates who often do not fit the demand for more practically skilled work in the job market.

China’s economy is opening up to the outside world. This worries those who fear that country’s huge pool of low-cost labor will drain jobs from U.S. shores, and less expensive goods will spark trade problems. The author points out that not only does China’s untapped market present huge opportunities for U.S. businesses that would surely outweigh any loss of jobs, but the sort of jobs that would move to China left the U.S. a long time ago. And with respect to fair trading practices, China has made much progress.

In a survey conducted in the beginning of 2022, travel and tourism employees in China were asked about their employment situation during COVID-19 pandemic. Approximately ** percent of respondents stated that they lost their jobs and stayed unemployed.

Coal power phasedown in China could exacerbate regional employment disparities, particularly in coal-dependent provinces. With coal power increasingly serving as a flexible backup for renewable energy integration, reducing utilization rates has been proposed to balance decarbonization and grid stability. However, the socio-economic impacts of this approach remain unclear. This study quantifies how reductions in utilization rates, coupled with workforce resettlement strategies, can mitigate employment disparities under 1.5°C and 2°C climate targets. Through unit-level analysis and Integrated Assessment Model simulations, we find that achieving climate targets could risk up to 480,000 jobs nationwide by 2060, where northern provinces facing 2.6 times job losses of southern coastal regions before 2030. Lowering utilization rates preserves up to 23.6% of jobs in key northern provinces (e.g. Shandong) by 2040, postpones the national peak unemployment period by 10–15 years, and reduces peak losses by 23.8%, creating a critical buffer for regional energy and economic transition. Redeployment among workforce resettlement strategies incurs the lowest per capita cost (140,800 RMB), which should be prioritized for workforce resettlement. Through a combination of lower utilization rate and resettlement portfolio strategies, it is possible to reduce resettlement cost by 2.27∼2.58 billion RMB. But Coal-dependent provinces like Shandong and Inner Mongolia still face high economic burdens due to larger job losses scales. The employment disparities during the coal power transition requires region-specific strategies and sustained policy support. Our findings offer practical insights for a just transition in China and other coal-reliant economies. KEY POLICY INSIGHTSThe phasedown of coal power is essential for achieving climate targets. However, for this transition to succeed, it is crucial that it be carried out in a fair and equitable way, addressing the socio-economic impacts on affected regions and workers.In order to ease transitions in high-impact regions, coal power retirement timelines should be tailored by provincial governments, considering local economic dependence and workforce risks as well as flexible operational adjustments of power plants.Integrate renewable energy development with comprehensive social protection systems in coal-intensive regions to ensure a just transition through coordinated industrial and labour market policies. The phasedown of coal power is essential for achieving climate targets. However, for this transition to succeed, it is crucial that it be carried out in a fair and equitable way, addressing the socio-economic impacts on affected regions and workers. In order to ease transitions in high-impact regions, coal power retirement timelines should be tailored by provincial governments, considering local economic dependence and workforce risks as well as flexible operational adjustments of power plants. Integrate renewable energy development with comprehensive social protection systems in coal-intensive regions to ensure a just transition through coordinated industrial and labour market policies.

This paper links the sharp drop in US manufacturing employment after 2000 to a change in US trade policy that eliminated potential tariff increases on Chinese imports. Industries more exposed to the change experience greater employment loss, increased imports from China, and higher entry by US importers and foreign-owned Chinese exporters. At the plant level, shifts toward less labor-intensive production and exposure to the policy via input-output linkages also contribute to the decline in employment. Results are robust to other potential explanations of employment loss, and there is no similar reaction in the European Union, where policy did not change.

Robotization has caused widespread concern about job losses, but few scholars have paid attention to changes in employment quality. This study provides supplementary evidences on the impact of robotization on youth employment quality and compares the effectiveness of various measures. Using data about individual employment and robot usage in China, this study finds that robotization reduces youth employment quality, especially for males and the middle-educated, aged 26 to 35, and in regions with insufficient workers. The substitution effect, skill preparation effect, and productivity effect play important roles in this process. Besides the common strategy of education, the mitigating capabilities of skill training has been demonstrated, but self-entrepreneurship has not. This study suggests that the exploration of various youth self-development measures, such as skill training, is warranted to improve employment quality.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within China town. The dataset can be utilized to gain insights into gender-based income distribution within the China town population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within China town, among individuals aged 15 years and older with income, there were 1,732 men and 1,570 women in the workforce. Among them, 1,152 men were engaged in full-time, year-round employment, while 727 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 0.78% fell within the income range of under $24,999, while 9.77% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 14.06% of men in full-time roles earned incomes exceeding $100,000, while 7.29% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for China town median household income by race. You can refer the same here

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within China. The dataset can be utilized to gain insights into gender-based income distribution within the China population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within China, among individuals aged 15 years and older with income, there were 220 men and 398 women in the workforce. Among them, 113 men were engaged in full-time, year-round employment, while 137 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 10.62% fell within the income range of under $24,999, while 1.46% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 22.12% of men in full-time roles earned incomes exceeding $100,000, while 2.19% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for China median household income by race. You can refer the same here

With rapid population aging around the world, delaying retirement is considered as a practical way to deal with this problem by many countries, but rapid technological development may affect the employment of the old. Existing literature estimates the age-heterogeneous effect of technological development on employment but draw mixed conclusions. In this paper, we utilize China Family Panel Studies (CFPS) data spanning from 2010 to 2018 and examine the effect of technological development on the employment of workers of different ages. Using a Bartik-style instrument variable (IV) to deal with the endogeneity, we find that the effect of technological development on employment decreases with age, especially for the low socio-economic status group. Since old workers are more likely to lose their job due to technological development and the effect is heterogeneous, we further explore the effect of technological development on inequality and its effect on the mental welfare.

We cannot share any CFPS data publicly due to the confidentiality requirements of CFPS. But CFPS data is publicly available and easy to request, CFPS data from 2010 to 2018 can be requested at https://www.isss.pku.edu.cn/cfps/. We share all other city-level and county-level variables used in our paper. We also share all codes used in the empirical part. We also add a readme file which briefly introduce our data and code shared here.

In a survey conducted in the beginning of 2022, travel and tourism employees in China were asked about their employment situation during COVID-19 pandemic. Around 68 percent of respondents stated that they lost their jobs, however, only ** percent said that they had an unemployment insurance.

The statistic shows the distribution of the workforce across economic sectors in China from 2014 to 2024. In 2024, around 22.2 percent of the workforce were employed in the agricultural sector, 29 percent in the industrial sector and 48.8 percent in the service sector. In 2022, the share of agriculture had increased for the first time in more than two decades, which highlights the difficult situation of the labor market due to the pandemic and economic downturn at the end of the year. Distribution of the workforce in China In 2012, China became the largest exporting country worldwide with an export value of about two trillion U.S. dollars. China’s economic system is largely based on growth and export, with the manufacturing sector being a crucial contributor to the country’s export competitiveness. Economic development was accompanied by a steady rise of labor costs, as well as a significant slowdown in labor force growth. These changes present a serious threat to the era of China as the world’s factory. The share of workforce in agriculture also steadily decreased in China until 2021, while the agricultural gross production value displayed continuous growth, amounting to approximately 7.8 trillion yuan in 2021. Development of the service sector Since 2011, the largest share of China’s labor force has been employed in the service sector. However, compared with developed countries, such as Japan or the United States, where 73 and 79 percent of the work force were active in services in 2023 respectively, the proportion of people working in the tertiary sector in China has been relatively low. The Chinese government aims to continue economic reform by moving from an emphasis on investment to consumption, among other measures. This might lead to a stronger service economy. Meanwhile, the size of the urban middle class in China is growing steadily. A growing number of affluent middle class consumers could promote consumption and help China move towards a balanced economy.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within China township. The dataset can be utilized to gain insights into gender-based income distribution within the China township population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within China township, among individuals aged 15 years and older with income, there were 1,431 men and 1,120 women in the workforce. Among them, 808 men were engaged in full-time, year-round employment, while 354 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 0.87% fell within the income range of under $24,999, while 5.93% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 40.97% of men in full-time roles earned incomes exceeding $100,000, while 11.86% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for China township median household income by race. You can refer the same here

（停止更新）消费者信心指数：未来失业在01-01-2023达23.000分，相较于12-01-2022的6.000分有所增长。（停止更新）消费者信心指数：未来失业数据按月更新，03-01-2010至01-01-2023期间平均值为27.400分，共155份观测结果。该数据的历史最高值出现于02-01-2015，达41.900分，而历史最低值则出现于11-01-2022，为1.000分。CEIC提供的（停止更新）消费者信心指数：未来失业数据处于定期更新的状态，数据来源于Ipsos Group S.A.，数据归类于全球数据库的中国 – Table CN.IPSOS: Consumer Confidence Survey。

（停止更新）消费者信心指数：失业：负面回应在01-01-2023达68.300分，相较于12-01-2022的75.129分有所下降。（停止更新）消费者信心指数：失业：负面回应数据按月更新，03-01-2010至01-01-2023期间平均值为66.983分，共155份观测结果。该数据的历史最高值出现于07-01-2019，达86.213分，而历史最低值则出现于04-01-2022，为51.830分。CEIC提供的（停止更新）消费者信心指数：失业：负面回应数据处于定期更新的状态，数据来源于Ipsos Group S.A.，数据归类于全球数据库的中国 – Table CN.IPSOS: Consumer Confidence Survey。

This dataset provides values for UNEMPLOYMENT RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.