Skip to main content

Advertisement

SpringerLink
Log in

Investigating the humanitarian labor efficiency of China: a factor-specific model

  • S.I. : Design and Management of Humanitarian Supply Chains
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

This study constructs an index of humanitarian labor efficiency to measure labor utilization based on a factor-specific data envelopment analysis model. Compared with the traditional labor productivity index, humanitarian labor efficiency is a more reliable and comprehensive measure; it not only excludes the contribution of non-labor input but also considers the undesirable output, such as accidental deaths. The results show that China’s humanitarian labor efficiency is low, ranging from 0.3 to 0.7, and it should be improved further. Unlike labor productivity, humanitarian labor efficiency did not increase markedly between 2007 and 2017. Further analysis revealed that pure labor efficiency declined while pure humanitarian efficiency increased for this period, which offset each other. Tobit regression shows that industrial structure has a significant influence on humanitarian labor efficiency. Baumol’s disease, caused by the growing tertiary industry, may result in the decline of labor utilization efficiency in China. This study presents suggestions on how to deal with Baumol’s disease.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Aczél, J. (1966). Lectures on functional equations and their applications. New York: Academic Press.

    Google Scholar 

  • Agarwal, K. S., Tatekar, S. S., Makwana, N. V., & Sharma, A. A. (2018). Analysis of labor efficiency and determination of the factors governing labor productivity in Mumbai Metropolitan Region. Paper presented at the 2018 International Conference on Smart City and Emerging Technology (ICSCET).

  • Banker, R. D., & Morey, R. C. (1986). Efficiency analysis for exogenously fixed inputs and outputs. Operations Research, 34(4), 513–521.

    Article  Google Scholar 

  • Baumol, W. J. (1967). Macroeconomics of unbalanced growth: The anatomy of urban crisis. The American Economic Review, 57(3), 415–426.

    Google Scholar 

  • Bear, T. (1978). Murphy’s law was born here. Edwards Air Force Base: Desert Wings.

    Google Scholar 

  • Bewley, J., Palmer, R. W., & Jackson-Smith, D. B. (2001). Modeling milk production and labor efficiency in modernized Wisconsin dairy herds. Journal of Dairy Science, 84(3), 705–716.

    Article  Google Scholar 

  • Cameron, G. (2005). The sun also rises: Productivity convergence between Japan and the USA. Journal of Economic Growth, 10(4), 387–408.

    Article  Google Scholar 

  • Ceglowski, J., & Golub, S. S. (2012). Does China still have a labor cost advantage? Global Economy Journal, 12(3), 185–270.

    Article  Google Scholar 

  • Chelius, J. R. (1974). The control of industrial accidents: Economic theory and empirical evidence. Law and Contemporary Problems, 38(4), 700–729.

    Article  Google Scholar 

  • Chen, J. D., Gao, M., Li, D., & Song, M. L. (2019a). Analysis of the rebound effects of fossil and nonfossil energy in China based on sustainable development. Sustainable Development. https://doi.org/10.1002/sd.1991.

    Article  Google Scholar 

  • Chen, J. D., Gao, M., Ma, K., & Song, M. L. (2019b). Different effects of technological progress on China’s carbon emissions based on sustainable development. Business Strategy and the Environment. https://doi.org/10.1002/bse.2381.

    Article  Google Scholar 

  • Chen, J. D., Gao, M., Mangla, S. K., Song, M. L., & Wen, J. (2020a). Effects of technological changes on China’s carbon emissions. Technological Forecasting and Social Change. https://doi.org/10.1016/j.techfore.2020.119938.

    Article  Google Scholar 

  • Chen, J. D., Xu, C., & Song, M. L. (2020b). Determinants for decoupling economic growth from carbon dioxide emissions in China. Regional Environmental Change. https://doi.org/10.1007/s10113-020-01605-w.

    Article  Google Scholar 

  • Cheng, S. L., Fan, W., Chen, J. D., Meng, F. X., Liu, G. Y., Song, M. L., et al. (2020). The impact of fiscal decentralization on CO2 emissions in China. Energy. https://doi.org/10.1016/j.energy.2019.116685.

    Article  Google Scholar 

  • Cheng, S. L., Wu, Y. Y., Chen, H., Chen, J. D., Song, M. L., & Hou, W. X. (2019). Determinants of changes in electricity generation intensity among different power sectors. Energy Policy, 130, 389–408.

    Article  Google Scholar 

  • Chou, J. (1995). Old and new development models: The Taiwanese experience Growth theories in light of the East Asian experience. In T. Ito & A. O. Krueger (Eds.), Growth theories in light of the East Asian experience, NBER-EASE (Vol. 4, pp. 105–127). Chicago: University of Chicago Press.

    Google Scholar 

  • Chung, Y. H., Färe, R., & Grosskopf, S. (1997). Productivity and undesirable outputs: A directional distance function approach. Journal of Environmental Management, 51(3), 229–240.

    Article  Google Scholar 

  • Corrado, C., Haskel, J., & Jona-Lasinio, C. (2017). Knowledge spillovers, ICT, and productivity growth. Oxford Bulletin of Economics and Statistics, 79(4), 592–618.

    Article  Google Scholar 

  • da Silva, A. F., Marins, F. A. S., Dias, E. X., & Ushizima, C. A. (2020). Improving manufacturing cycle efficiency through new multiple criteria data envelopment analysis models: An application in green and lean manufacturing processes. Production Planning and Control. https://doi.org/10.1080/09537287.2020.1713413.

    Article  Google Scholar 

  • Dalgaard, C.-J., & Kreiner, C. T. (2001). Is declining productivity inevitable? Journal of Economic Growth, 6(3), 187–203.

    Article  Google Scholar 

  • Das, A., Ray, S. C., & Nag, A. (2009). Labor-use efficiency in Indian banking: A branch-level analysis. Omega, 37(2), 411–425.

    Article  Google Scholar 

  • Dean, E. R., & Kunze, K. (1992). Productivity measurement in service industries. In Z. Griliches (Ed.), Output measurement in the service sectors (pp. 73–107). Chicago: University of Chicago Press.

    Google Scholar 

  • Deming, J., Kinsella, J., O’Brien, B., & Shalloo, L. (2019). An examination of the effects of labor efficiency on the profitability of grass-based, seasonal-calving dairy farms. Journal of Dairy Science, 102(9), 8431–8440.

    Article  Google Scholar 

  • Deprins, D., Simar, L., & Tulkens, H. (2006). Measuring labor-efficiency in post offices. In P. Chander, J. Drèze, C. K. Lovell, & J. Mintz (Eds.), Public goods, environmental externalities, and fiscal competition (pp. 285–309). Boston, MA: Springer.

    Chapter  Google Scholar 

  • Duan, H., Zhang, G., Wang, S., & Fan, Y. (2019). Integrated benefit-cost analysis of China’s optimal adaptation and targeted mitigation. Ecological Economics, 160, 76–86.

    Article  Google Scholar 

  • Dubey, R., Altay, N., & Blome, C. (2019). Swift trust and commitment: The missing links for humanitarian supply chain coordination? Annals of Operations Research, 283, 159–177.

    Article  Google Scholar 

  • Dubey, R., Gunasekaran, A., Bryde, D. J., Dwivedi, Y. K., & Papadopoulos, T. (2020). Blockchain technology for enhancing swift-trust, collaboration, and resilience within a humanitarian supply chain setting. International Journal of Production Research. https://doi.org/10.1080/00207543.2020.1722860.

    Article  Google Scholar 

  • Dubey, R., Luo, Z., Gunasekaran, A., Akter, S., Hazen, B., & Douglas, M. (2018). Big data and predictive analytics in humanitarian supply chains: Enabling visibility and coordination in the presence of swift trust. International Journal of Logistics Management, 29(2), 485–512.

    Google Scholar 

  • Färe, R., Grosskopf, S., & Margaritis, D. (2015). Distance functions in primal and dual spaces. In J. Zhu (Ed.), Data envelopment analysis: A handbook of models and methods (pp. 1–21). New York: Springer.

    Chapter  Google Scholar 

  • Färe, R., Grosskopf, S., & Pasurka, C. A. (2007). Environmental production functions and environmental directional distance functions. Energy, 32(7), 1055–1066.

    Article  Google Scholar 

  • Färe, R., Grosskopf, S., & Whittaker, G. (2013). Directional output distance functions: Endogenous directions based on exogenous normalization constraints. Journal of Productivity Analysis, 40(3), 267–269.

    Article  Google Scholar 

  • Ferreira, J. J. M., Fernandes, C. I., & Ferreira, F. A. F. (2020). Technology transfer, climate change mitigation, and environmental patent impact on sustainability and economic growth: A comparison of European countries. Technological Forecasting and Social Change, 150, 119770.

    Article  Google Scholar 

  • Fogarty, M. S., & Garofalo, G. A. (1988). Urban spatial structure and productivity growth in the manufacturing sector of cities. Journal of Urban Economics, 23(1), 60–70.

    Article  Google Scholar 

  • Fried, H. O., Lovell, C. K., & Schmidt, S. S. (2008). Efficiency and productivity. In H. O. Fried, C. K. Lovell, & S. S. Schmidt (Eds.), The measurement of productive efficiency and productivity growth (pp. 3–91). New York: Oxford University Press.

    Chapter  Google Scholar 

  • Greenwald, B. C., & Stiglitz, J. E. (1986). Externalities in economies with imperfect information and incomplete markets. The Quarterly Journal of Economics, 101(2), 229–264.

    Article  Google Scholar 

  • Hartwig, J. (2011). Testing the Baumol–Nordhaus model with EU KLEMS data. Review of Income Wealth, 57(3), 471–489.

    Article  Google Scholar 

  • Hu, B. A., & Cai, L. A. (2004). Hotel labor productivity assessment. Journal of Travel and Tourism Marketing, 16(2–3), 27–38.

    Article  Google Scholar 

  • International Statistical Information Center of National Bureau of Statistics (Producer). (2016). International comparison shows that China’s labor productivity is growing rapidly. Retrieved June 20, 2020, from http://www.stats.gov.cn/tjsj/sjjd/201609/t20160901_1395572.html#.

  • Jin, H., Qian, Y., & Weingast, B. R. (2005). Regional decentralization and fiscal incentives: Federalism, Chinese style. Journal of Public Economics, 89(9–10), 1719–1742.

    Article  Google Scholar 

  • Knight, F. H. (1965). The economic organization: With an article: Notes on utility and cost (Vol. 1214). Manhattan: Harper & Row.

    Google Scholar 

  • Kose, M. A., Prasad, E. S., & Terrones, M. E. (2009). Does openness to international financial flows raise productivity growth? Journal of International Money, 28(4), 554–580.

    Google Scholar 

  • Krugman, P. (1994). The myth of Asia’s miracle. Foreign Affairs, 73(6), 62–78.

    Article  Google Scholar 

  • Langemeier, M. R. (2018). Labor efficiency and productivity benchmarks. Journal of ASFMRA, 17–28.

  • Li, J., Ji, J., & Zhang, Y. (2019a). Non-linear effects of environmental regulations on economic outcomes. Management of Environmental Quality: An International Journal, 30(2), 368–382.

    Article  Google Scholar 

  • Li, G., Li, L., Choi, T. M., & Sethi, S. P. (2019b). Green supply chain management in Chinese firms: Innovative measures and the moderating role of quick response technology. Journal of Operations Management. https://doi.org/10.1002/joom.1061.

    Article  Google Scholar 

  • Liu, H.-W. (2018). Regulatory reform in China and the EU: A law and economics perspective. Asia Pacific Law Review, 26(2), 233–236.

    Article  Google Scholar 

  • Luenberger, D. G. (1992). Benefit functions and duality. Journal of Mathematical Economics, 21(5), 461–481.

    Article  Google Scholar 

  • Malkina, M. (2019). Spatial wage inequality and its sectoral determinants: The case of modern Russia. Oeconomia Copernicana, 10(1), 69–87.

    Article  Google Scholar 

  • Mankiw, N. G., Romer, D., & Weil, D. N. (1992). A contribution to the empirics of economic growth. The Quarterly Journal of Economics, 107(2), 407–437.

    Article  Google Scholar 

  • Ministry of emergency management of the People’s Republic of China. (2019). 2018 National chemical accident analysis report. Retrieved June 20, 2020, from https://www.sohu.com/a/298275693_479771.

  • Ministry of emergency management of the People’s Republic of China. (2020a). Dangerous chemical accidents in March in history. Retrieved June 20, 2020, from https://www.mem.gov.cn/fw/jsxx/202002/t20200228_344856.shtml.

  • Ministry of emergency management of the People’s Republic of China. (2020b). The death toll of chemical accidents in 2019 has exceeded half of last year. Retrieved June 20, 2020, from https://www.sohu.com/a/310410658_100063060?sec=wd.

  • Moomaw, R. L. (1981). Productivity and city size: A critique of the evidence. The Quarterly Journal of Economics, 96(4), 675–688.

    Article  Google Scholar 

  • Nehru, V., Swanson, E., & Dubey, A. (1993). A new database on human capital stock: Sources, methodology, and results. Journal of Development Economics. Retrieved June 20, 2020, from http://documents.worldbank.org/curated/en/526571468766542648/pdf/multi-page.pdf.

  • Ozcan, Y. A., Yeh, S.-C., McCollum, D., & Begun, J. W. (1996). Trends in labor efficiency among American hospital markets. Annals of Operations Research, 67(1), 61–81.

    Article  Google Scholar 

  • Pagés-Serra, C. (2010). The age of productivity: Transforming economies from the bottom up. London: Palgrave Macmillan.

    Book  Google Scholar 

  • Radjenovic, M. (2019). Perception of worker quality in tourism in selected countries of the former Yugoslavia. Transformations in Business and Economics, 18(3), 128–136.

    Google Scholar 

  • Ray, S. C. (2004). Data envelopment analysis: Theory and techniques for economics and operations research. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Rebelo, S. (1991). Long-run policy analysis and long-run growth. Journal of Political Economy, 99(3), 500–521.

    Article  Google Scholar 

  • Reynolds, D. (1998). Productivity Analysis: In the on-site food-service segment. Cornell Hotel and Restaurant Administration Quarterly, 39(3), 22–31.

    Article  Google Scholar 

  • Robert, S. (1987). We’d better watch out. New York Times Book Review, 12(7), 36.

    Google Scholar 

  • Romer, P. M. (1990). Endogenous technological change. Journal of Political Economy, 98(5, Part 2), S71–S102.

    Article  Google Scholar 

  • Shapiro, S. A. (2000). Occupational safety and health regulation. Cheltenham: Edward Elgar Publishing Limited.

    Google Scholar 

  • Song, M., & Wang, J. (2018). Environmental efficiency evaluation of thermal power generation in China based on a slack-based endogenous directional distance function model. Energy, 161, 325–336.

    Article  Google Scholar 

  • Song, M., Wang, J., Zhao, J., Baležentis, T., & Shen, Z. (2018). Production and safety efficiency evaluation in Chinese coal mines: Accident deaths as undesirable output. Annals of Operations Research. https://doi.org/10.1007/s10479-018-2804-4.

    Article  Google Scholar 

  • Song, M., Zhu, S., Wang, J., & Zhao, J. (2020). Share green growth: Regional evaluation of green output performance in China. International Journal of Production Economics, 219, 152–163.

    Article  Google Scholar 

  • Torgersen, A. M., Førsund, F. R., & Kittelsen, S. A. (1996). Slack-adjusted efficiency measures and ranking of efficient units. Journal of Productivity Analysis, 7(4), 379–398.

    Article  Google Scholar 

  • Unel, B. (2010). Analyzing skilled and unskilled labor efficiencies in the US. Journal of Macroeconomics, 32(4), 957–967.

    Article  Google Scholar 

  • Viscusi, W. K. (1979). Employment hazards: An investigation of market performance. Cambridge: Harvard University Press.

    Google Scholar 

  • Wang, J., Tian, Y., Song, M., Zhao, J., & Li, H. (2018). Coal saving in China: From Pareto–Koopmans to Kaldor–Hicks criterion. Journal of Organizational Change Management, 31(1), 103–117.

    Article  Google Scholar 

  • Wang, S., Xing, L., & Chen, H. (2020). Impact of marine industrial structure on environmental efficiency. Management of Environmental Quality: An International Journal, 31(1), 111–129.

    Article  Google Scholar 

  • Wu, Y. (2016). China’s capital stock series by region and sector. Frontiers of Economics in China, 11(1), 156–172.

    Google Scholar 

  • Xinhuanet. (2018). 219 accidents and 375 deaths occurred in coal mines nationwide in 2017. Retrieved June 20, 2020, from http://www.xinhuanet.com/fortune/2018-01/29/c_1122336631.htm.

  • Yang, X. (2001). Economics: New classical versus neoclassical framework. New York: Wiley-Blackwell.

    Google Scholar 

  • Young, A. (1995). The tyranny of numbers: Confronting the statistical realities of the East Asian growth experience. The Quarterly Journal of Economics, 110(3), 641–680.

    Article  Google Scholar 

  • Zhang, D., Li, M., Ji, X., Wu, J., & Dong, Y. (2019). Revealing potential of energy-saving behind emission reduction: A DEA-based empirical study. Management of Environmental Quality: An International Journal, 30(4), 714–730.

    Article  Google Scholar 

  • Zhang, J. (2008). Estimation of China’s provincial capital stock (1952–2004) with applications. Journal of Chinese Economic and Business Studies, 6(2), 177–196.

    Article  Google Scholar 

  • Zheng, L., Batuo, M. E., & Shepherd, D. (2017). The impact of regional and institutional factors on labor productive performance: Evidence from the township and village enterprise sector in China. World Development, 96, 591–598.

    Article  Google Scholar 

  • Zhu, J. (2000). Multi-factor performance measure model with an application to Fortune 500 companies. European Journal of Operational Research, 123(1), 105–124.

    Article  Google Scholar 

  • Zhu, J. (2014). Measure-specific DEA models. In J. Zhu (Ed.), Quantitative models for performance evaluation and benchmarking: Data envelopment analysis with spreadsheets (pp. 103–119). Cham: Springer.

    Google Scholar 

Download references

Funding

This study was funded by the National Natural Science Foundation of China (Grant Nos. 71934001, 71471001, 41771568, 71533004); the National Key Research and Development Program of China (Grant No. 2016YFA0602500); the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDA23070400); the Youth Project of Humanities and Social Sciences Research from the Ministry of Education of China (Grant No. 16YJCZH155); the Humanities and Social Science Research Project of the Education Department in Liaoning, China (Grant No. LN2017QN001); the Social Science Foundation of Liaoning Province, China (Grant Nos. L18AJY006 and L18BJY024); and the Fundamental Research Funds for the Central Universities of China (Grant No. JBK2001044).

Author information

Authors and Affiliations

  1. School of Public Administration, Southwestern University of Finance and Economics, Chengdu, 611130, Sichuan, China

    Shulei Cheng

  2. School of Public Finance and Taxation, Southwestern University of Finance and Economics, Chengdu, 611130, Sichuan, China

    Wei Fan

  3. Center for Industrial and Business Organization, Dongbei University of Finance and Economics, Dalian, 116025, Liaoning, China

    Jianlin Wang

Authors
  1. Shulei Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jianlin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Jianlin Wang. The first draft of the manuscript was written by Jianlin Wang. Shulei Cheng, Wei Fan, and Jianlin Wang commented on previous versions of the manuscript. Shulei Cheng, Wei Fan, and Jianlin Wang read and approved the final manuscript.

Corresponding author

Correspondence to Jianlin Wang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cheng, S., Fan, W. & Wang, J. Investigating the humanitarian labor efficiency of China: a factor-specific model. Ann Oper Res 319, 439–461 (2022). https://doi.org/10.1007/s10479-020-03736-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-020-03736-z

Keywords

Search

Navigation