The Impact of Manufacturing Transfer from China to India on China’s GDP and Employment Article Swipe
Xiaoxu Zhang
,
Kunfu Zhu
,
Shouyang Wang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1515/cfer-2024-0023
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1515/cfer-2024-0023
With the rising labor costs and increasing resource and environmental constraints in China, coupled with geopolitical conflicts, related industries or production processes are shifting to emerging economies such as Southeast Asia, South Asia, and Mexico. Among these, India’s development potential has garnered significant attention, and the “China-to-India Industrial Transfer Model” in the global industrial chain poses a greater impact and threat to China. This paper constructs a quantitative model to measure the impact of industrial transfer on the home country. It designs three scenarios—ultra-long-term, medium-to-long-term, and short-to-medium-term—and uses counterfactual analysis to assess the impact of India’s absorption of China’s industrial transfer on China’s GDP and employment under different scenarios. The research results indicate that the transfer of industries from China to India will generate significant socio-economic shocks. In the ultra-long-term, this industrial transfer could lead to a 15.6% reduction in China’s GDP, a 16.8% decrease in the overall income of the workforce, and a reduction in the number of employed people by 110 million. The impacts are also substantial in the medium- to-long-term and short-to-medium-term scenarios. By sectors, the relocation of low and medium-low R&D intensity manufacturing sectors has a significant impact on the Chinese economy in both the short-to-medium and medium-to-long term perspectives. The relocation of high R&D intensity manufacturing sectors, represented by the computer industry, also causes considerable negative effects on the Chinese economy in the ultra-long-term perspective. This quantitative analysis helps anticipate the economic impact of future changes in industrial layout on China’s economy and facilitates the development of preemptive strategies. Based on the medium-to-long-term international economic outlook and the characteristics of domestic regional and industrial economic development, we propose three policy implications.
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- https://doi.org/10.1515/cfer-2024-0023
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- References
- 20
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The Impact of Manufacturing Transfer from China to India on China’s GDP and EmploymentWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-12-01Full publication date if available
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Xiaoxu Zhang, Kunfu Zhu, Shouyang WangList of authors in order
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https://doi.org/10.1515/cfer-2024-0023Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1515/cfer-2024-0023Direct OA link when available
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China, Economics, International economics, Development economics, Labour economics, Geography, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
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| abstract_inverted_index.greater | 58 |
| abstract_inverted_index.impacts | 166 |
| abstract_inverted_index.measure | 71 |
| abstract_inverted_index.medium- | 172 |
| abstract_inverted_index.outlook | 261 |
| abstract_inverted_index.overall | 148 |
| abstract_inverted_index.propose | 273 |
| abstract_inverted_index.related | 18 |
| abstract_inverted_index.results | 112 |
| abstract_inverted_index.sectors | 188 |
| abstract_inverted_index.shocks. | 127 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Model” | 50 |
| abstract_inverted_index.Transfer | 49 |
| abstract_inverted_index.analysis | 90, 233 |
| abstract_inverted_index.computer | 216 |
| abstract_inverted_index.country. | 80 |
| abstract_inverted_index.decrease | 145 |
| abstract_inverted_index.domestic | 266 |
| abstract_inverted_index.economic | 237, 260, 270 |
| abstract_inverted_index.emerging | 26 |
| abstract_inverted_index.employed | 160 |
| abstract_inverted_index.garnered | 42 |
| abstract_inverted_index.generate | 124 |
| abstract_inverted_index.indicate | 113 |
| abstract_inverted_index.million. | 164 |
| abstract_inverted_index.negative | 221 |
| abstract_inverted_index.regional | 267 |
| abstract_inverted_index.research | 111 |
| abstract_inverted_index.resource | 8 |
| abstract_inverted_index.sectors, | 178, 212 |
| abstract_inverted_index.shifting | 24 |
| abstract_inverted_index.transfer | 76, 101, 116, 133 |
| abstract_inverted_index.China’s | 99, 103, 141, 246 |
| abstract_inverted_index.India’s | 38, 96 |
| abstract_inverted_index.Southeast | 30 |
| abstract_inverted_index.different | 108 |
| abstract_inverted_index.economies | 27 |
| abstract_inverted_index.industry, | 217 |
| abstract_inverted_index.intensity | 186, 210 |
| abstract_inverted_index.potential | 40 |
| abstract_inverted_index.processes | 22 |
| abstract_inverted_index.reduction | 139, 155 |
| abstract_inverted_index.Industrial | 48 |
| abstract_inverted_index.absorption | 97 |
| abstract_inverted_index.anticipate | 235 |
| abstract_inverted_index.attention, | 44 |
| abstract_inverted_index.conflicts, | 17 |
| abstract_inverted_index.constructs | 66 |
| abstract_inverted_index.employment | 106 |
| abstract_inverted_index.increasing | 7 |
| abstract_inverted_index.industrial | 54, 75, 100, 132, 243, 269 |
| abstract_inverted_index.industries | 19, 118 |
| abstract_inverted_index.medium-low | 184 |
| abstract_inverted_index.preemptive | 253 |
| abstract_inverted_index.production | 21 |
| abstract_inverted_index.relocation | 180, 206 |
| abstract_inverted_index.scenarios. | 109, 176 |
| abstract_inverted_index.workforce, | 152 |
| abstract_inverted_index.constraints | 11 |
| abstract_inverted_index.development | 39, 251 |
| abstract_inverted_index.facilitates | 249 |
| abstract_inverted_index.represented | 213 |
| abstract_inverted_index.significant | 43, 125, 191 |
| abstract_inverted_index.strategies. | 254 |
| abstract_inverted_index.substantial | 169 |
| abstract_inverted_index.considerable | 220 |
| abstract_inverted_index.development, | 271 |
| abstract_inverted_index.geopolitical | 16 |
| abstract_inverted_index.perspective. | 230 |
| abstract_inverted_index.quantitative | 68, 232 |
| abstract_inverted_index.to-long-term | 173 |
| abstract_inverted_index.environmental | 10 |
| abstract_inverted_index.implications. | 276 |
| abstract_inverted_index.international | 259 |
| abstract_inverted_index.manufacturing | 187, 211 |
| abstract_inverted_index.perspectives. | 204 |
| abstract_inverted_index.counterfactual | 89 |
| abstract_inverted_index.medium-to-long | 202 |
| abstract_inverted_index.socio-economic | 126 |
| abstract_inverted_index.characteristics | 264 |
| abstract_inverted_index.short-to-medium | 200 |
| abstract_inverted_index.ultra-long-term | 229 |
| abstract_inverted_index.ultra-long-term, | 130 |
| abstract_inverted_index.“China-to-India | 47 |
| abstract_inverted_index.medium-to-long-term | 258 |
| abstract_inverted_index.medium-to-long-term, | 85 |
| abstract_inverted_index.short-to-medium-term | 175 |
| abstract_inverted_index.short-to-medium-term—and | 87 |
| abstract_inverted_index.scenarios—ultra-long-term, | 84 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.51312537 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |