Key information about China Labour Productivity Growth

• China Labour Productivity improved by 5.84 % YoY in Dec 2024, compared with a growth of 4.43 % in the previous year
• China Labour Productivity Growth data is updated yearly, available from Dec 1953 to Dec 2024, averaging at 7.34 %
• The data reached an all-time high of 15.12 % in Dec 1970 and a record low of -26.48 % in Dec 1961

CEIC calculates Labour Productivity Growth from annual Real GDP Index and annual Employment. The National Bureau of Statistics provides Real GDP, at 1978 prices. The Ministry of Human Resources and Social Security provides Employment. Employment excludes Foreign Nationals working within the country. Real GDP prior to 1979 is based on Real GDP PY=100.


Further information about China Labour Productivity Growth

• In the latest reports, China Population reached 1,408.28 Person mn in Dec 2024
• Its Unemployment Rate remained the same at 5.10 % in Dec 2024
• Monthly Earnings of China stood at 1,392.46 USD in Dec 2023
• The country's Labour Force Participation Rate increased to 65.83 % in Dec 2023

Russia Capital Productivity: OKVED2: Human Health & Social Work Activities data was reported at 98.680 % in 2017. Russia Capital Productivity: OKVED2: Human Health & Social Work Activities data is updated yearly, averaging 98.680 % from Dec 2017 (Median) to 2017, with 1 observations. Russia Capital Productivity: OKVED2: Human Health & Social Work Activities data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Investment – Table RU.OD029: Indices of Capital Equipment per Employee and Capital Productivity.

Workplace learning and development spending per employee has seen fluctuations over the years, with a notable decrease in 2022. Despite this recent dip, the overall trend shows a commitment to employee growth, with spending reaching ***** U.S. dollars per worker in 2023. This investment in human capital reflects the growing importance of continuous learning in today's rapidly evolving work environment. Adapting to new technologies As companies navigate the integration of artificial intelligence into their operations, learning and development strategies are evolving. In 2023, U.S. companies planned to invest in online courses as a primary method for AI training, while also valuing face-to-face training and live events. This balanced approach to learning reflects the complex nature of new technologies and the need for diverse training methods. Interestingly, by 2024, AI had become a significant tool in human resources, with ** percent of HR professionals reporting its use in recruiting, interviewing, and hiring processes. (1413448, 1500122) Measuring impact and optimizing resources Organizations are increasingly focused on measuring the impact of their learning and development initiatives. In 2023, L&D professionals identified performance reviews as the most useful method for assessing the impact on overall business performance, followed by employee productivity metrics. This emphasis on measurable outcomes aligns with the need to optimize training expenditures, especially in light of fluctuations in corporate training budgets. For instance, U.S. corporate training expenditure decreased by almost **** billion U.S. dollars in 2024 compared to the previous year, highlighting the importance of efficient and effective learning strategies. (1472187, 788521)

Russia Index of Capital Equipment per Employee: OKVED2: Human Health & Social Work Activities data was reported at 102.520 % in 2017. Russia Index of Capital Equipment per Employee: OKVED2: Human Health & Social Work Activities data is updated yearly, averaging 102.520 % from Dec 2017 (Median) to 2017, with 1 observations. Russia Index of Capital Equipment per Employee: OKVED2: Human Health & Social Work Activities data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Investment – Table RU.OD029: Indices of Capital Equipment per Employee and Capital Productivity.

This paper methodically investigates the influence of inclusive income growth on city size, examining it through the dual lenses of "income" and "distribution." The analysis leverages meticulously collected panel data encompassing 276 Chinese cities at the prefecture level and above, spanning the period from 2005 to 2019. Theoretical analysis indicates that the effect of city size expansion on per capita income adheres to a ’U’-shaped trajectory, while its influence on the urban-rural income gap manifests an ’inverted U’ pattern. Moreover, the inclusive income growth stemming from city size demonstrates notable heterogeneity across various geographic locations and city hierarchies. The findings reveal that human capital serves as the primary mechanism through which city size influences inclusive income growth. After decomposing the income inclusiveness index, it becomes evident that the expansion of city size exerts a more potent direct driving effect on the income of urban residents. On the one hand, city size expansion directly increases rural residents’ income levels by improving labor productivity. On the other hand, it facilitates leapfrog income development by inducing the rural labor force to move to cities.

This paper methodically investigates the influence of inclusive income growth on city size, examining it through the dual lenses of "income" and "distribution." The analysis leverages meticulously collected panel data encompassing 276 Chinese cities at the prefecture level and above, spanning the period from 2005 to 2019. Theoretical analysis indicates that the effect of city size expansion on per capita income adheres to a ’U’-shaped trajectory, while its influence on the urban-rural income gap manifests an ’inverted U’ pattern. Moreover, the inclusive income growth stemming from city size demonstrates notable heterogeneity across various geographic locations and city hierarchies. The findings reveal that human capital serves as the primary mechanism through which city size influences inclusive income growth. After decomposing the income inclusiveness index, it becomes evident that the expansion of city size exerts a more potent direct driving effect on the income of urban residents. On the one hand, city size expansion directly increases rural residents’ income levels by improving labor productivity. On the other hand, it facilitates leapfrog income development by inducing the rural labor force to move to cities.

This paper methodically investigates the influence of inclusive income growth on city size, examining it through the dual lenses of "income" and "distribution." The analysis leverages meticulously collected panel data encompassing 276 Chinese cities at the prefecture level and above, spanning the period from 2005 to 2019. Theoretical analysis indicates that the effect of city size expansion on per capita income adheres to a ’U’-shaped trajectory, while its influence on the urban-rural income gap manifests an ’inverted U’ pattern. Moreover, the inclusive income growth stemming from city size demonstrates notable heterogeneity across various geographic locations and city hierarchies. The findings reveal that human capital serves as the primary mechanism through which city size influences inclusive income growth. After decomposing the income inclusiveness index, it becomes evident that the expansion of city size exerts a more potent direct driving effect on the income of urban residents. On the one hand, city size expansion directly increases rural residents’ income levels by improving labor productivity. On the other hand, it facilitates leapfrog income development by inducing the rural labor force to move to cities.

This paper is the first to propose an aggregate S-trend factor production function to estimate total factor productivity (TFP) and investment efficiency in an economy. This function implements Charles R. Hulten's organizing principle: to what extent the growth of the economy is due to an increase in "productivity" (progress in technology and organization of production) and to what extent to "capital formation" (increased investment in human capital, knowledge and fixed capital). Estimation of future members of the series is usually done by a forecast model. It is a model that approximates a trend. The Verhulst's S-curve is used as the approximation function. By aggregate S-trend production function we mean a two factor production function It represents the growth of the economy, which is by raw data and takes into account all influencing factors, and is certainly broader than the concept of " capital formation ",is a total factor productivity TFP. The error of approximation is quantitatively measured by the MAPE criterion.

Belarus BY: Human Capital Index (HCI): Scale 0-1 data was reported at 0.700 NA in 2020. Belarus BY: Human Capital Index (HCI): Scale 0-1 data is updated yearly, averaging 0.700 NA from Dec 2020 (Median) to 2020, with 1 observations. Belarus BY: Human Capital Index (HCI): Scale 0-1 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Belarus – Table BY.World Bank.WDI: Human Capital Index. The HCI calculates the contributions of health and education to worker productivity. The final index score ranges from zero to one and measures the productivity as a future worker of child born today relative to the benchmark of full health and complete education.; ; World Bank staff calculations based on the methodology described in World Bank (2018). https://openknowledge.worldbank.org/handle/10986/30498.; ;

Nigeria NG: Human Capital Index (HCI): Scale 0-1 data was reported at 0.342 NA in 2017. Nigeria NG: Human Capital Index (HCI): Scale 0-1 data is updated yearly, averaging 0.342 NA from Dec 2017 (Median) to 2017, with 1 observations. Nigeria NG: Human Capital Index (HCI): Scale 0-1 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nigeria – Table NG.World Bank: Human Capital Index. The HCI calculates the contributions of health and education to worker productivity. The final index score ranges from zero to one and measures the productivity as a future worker of child born today relative to the benchmark of full health and complete education.; ; World Bank staff calculations based on the methodology described in World Bank (2018). https://openknowledge.worldbank.org/handle/10986/30498; ;