Prediction of High-Tech Talents Flow Impact on Labor Income Share: Based on DEA and Fractional Hausdorff Grey Model
The purpose of this paper is to analyze the impact of high-tech talents flow on labor income share and explore the influencing mechanism. It can be proved that high-tech talents flow affects labor income share by production function, with technological progress as a mediator variable. The labor inco...
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Wiley
2021-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2021/9936968 |
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| author | Wei Cui Anwei Wan Yongbo Yang |
| author_facet | Wei Cui Anwei Wan Yongbo Yang |
| author_sort | Wei Cui |
| collection | DOAJ |
| description | The purpose of this paper is to analyze the impact of high-tech talents flow on labor income share and explore the influencing mechanism. It can be proved that high-tech talents flow affects labor income share by production function, with technological progress as a mediator variable. The labor income share is the dependent variable, and the gravity of high-tech talents as the independent variable is the index to measure the high-tech talents flow, constructing the panel data model with the Malmquist index of technological progress as a mediator variable. Furthermore, the Malmquist index of technological progress is decomposed into catching-up of technological progress index and leapfrogging of technological progress index, which, respectively, replaces the Malmquist index of technological progress as a mediator variable in the panel data model. Regression analysis shows that technological progress is a mediator variable for high-tech talents flow to reduce labor income share, and the impact mainly comes from leapfrogging of technological progress. However, although the mediating effect of catching-up technological progress index is not significant at the significance level of 10%, it is a mediator variable for high-tech labor mobility to increase income share at the significance level of 20%. Finally, this paper predicts the change in labor income share from 2018 to 2027 by the fractional Hausdorff grey model, and the results show that it is an increasing trend. However, the Gini coefficient whose change trend is opposite to the labor income share remains high in the past two years, indicating that there are other factors affecting the income gap, such as the urbanization rate and the transportation convenience. The innovation of this paper is mainly to reveal that the leapfrogging of technological progress is the major cause of the high-tech talents flow rising income inequality gap, while the catching-up of technological progress is the source of the former narrowing the latter. The fractional Hausdorff grey model predicts that the key determinants of income inequality gap are more than labor income share. |
| format | Article |
| id | doaj-art-3e8dea5b29214c48a6974a27e34dc909 |
| institution | Kabale University |
| issn | 2314-4629 2314-4785 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
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| series | Journal of Mathematics |
| spelling | doaj-art-3e8dea5b29214c48a6974a27e34dc9092025-02-03T01:25:25ZengWileyJournal of Mathematics2314-46292314-47852021-01-01202110.1155/2021/99369689936968Prediction of High-Tech Talents Flow Impact on Labor Income Share: Based on DEA and Fractional Hausdorff Grey ModelWei Cui0Anwei Wan1Yongbo Yang2Faculty of Finance and Economics, Jiangsu University, Zhenjiang, Jiangsu 212013, ChinaFaculty of Finance and Economics, Jiangsu University, Zhenjiang, Jiangsu 212013, ChinaFaculty of Finance and Economics, Jiangsu University, Zhenjiang, Jiangsu 212013, ChinaThe purpose of this paper is to analyze the impact of high-tech talents flow on labor income share and explore the influencing mechanism. It can be proved that high-tech talents flow affects labor income share by production function, with technological progress as a mediator variable. The labor income share is the dependent variable, and the gravity of high-tech talents as the independent variable is the index to measure the high-tech talents flow, constructing the panel data model with the Malmquist index of technological progress as a mediator variable. Furthermore, the Malmquist index of technological progress is decomposed into catching-up of technological progress index and leapfrogging of technological progress index, which, respectively, replaces the Malmquist index of technological progress as a mediator variable in the panel data model. Regression analysis shows that technological progress is a mediator variable for high-tech talents flow to reduce labor income share, and the impact mainly comes from leapfrogging of technological progress. However, although the mediating effect of catching-up technological progress index is not significant at the significance level of 10%, it is a mediator variable for high-tech labor mobility to increase income share at the significance level of 20%. Finally, this paper predicts the change in labor income share from 2018 to 2027 by the fractional Hausdorff grey model, and the results show that it is an increasing trend. However, the Gini coefficient whose change trend is opposite to the labor income share remains high in the past two years, indicating that there are other factors affecting the income gap, such as the urbanization rate and the transportation convenience. The innovation of this paper is mainly to reveal that the leapfrogging of technological progress is the major cause of the high-tech talents flow rising income inequality gap, while the catching-up of technological progress is the source of the former narrowing the latter. The fractional Hausdorff grey model predicts that the key determinants of income inequality gap are more than labor income share.http://dx.doi.org/10.1155/2021/9936968 |
| spellingShingle | Wei Cui Anwei Wan Yongbo Yang Prediction of High-Tech Talents Flow Impact on Labor Income Share: Based on DEA and Fractional Hausdorff Grey Model Journal of Mathematics |
| title | Prediction of High-Tech Talents Flow Impact on Labor Income Share: Based on DEA and Fractional Hausdorff Grey Model |
| title_full | Prediction of High-Tech Talents Flow Impact on Labor Income Share: Based on DEA and Fractional Hausdorff Grey Model |
| title_fullStr | Prediction of High-Tech Talents Flow Impact on Labor Income Share: Based on DEA and Fractional Hausdorff Grey Model |
| title_full_unstemmed | Prediction of High-Tech Talents Flow Impact on Labor Income Share: Based on DEA and Fractional Hausdorff Grey Model |
| title_short | Prediction of High-Tech Talents Flow Impact on Labor Income Share: Based on DEA and Fractional Hausdorff Grey Model |
| title_sort | prediction of high tech talents flow impact on labor income share based on dea and fractional hausdorff grey model |
| url | http://dx.doi.org/10.1155/2021/9936968 |
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