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Quantifying potential gains from efficient resource reallocation: A counterfactual analysis of economic growth in 284 Chinese cities
Tekijät: Dai, Sheng; Kuosmanen, Timo; Liao, Zhiqiang
Julkaisuvuosi: 2026
Lehti: Economic Modelling
Artikkelin numero: 107501
Vuosikerta: 157
ISSN: 0264-9993
eISSN: 1873-6122
DOI: https://doi.org/10.1016/j.econmod.2026.107501
Julkaisun avoimuus kirjaamishetkellä: Ei avoimesti saatavilla
Julkaisukanavan avoimuus : Osittain avoin julkaisukanava
Verkko-osoite: https://doi.org/10.1016/j.econmod.2026.107501
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/515782701
Rinnakkaistallenteen lisenssi: CC BY NC ND
Rinnakkaistallennetun julkaisun versio: Final draft
This study examines how efficient resource reallocation across cities affects potential aggregate growth, addressing the limited empirical evidence on output gains from reallocation under realistic constraints. Using data on 284 prefecture-level cities in China from 2003 to 2019, we simulate counterfactual scenarios in which existing aggregate resources are reallocated across cities to quantify the costs of resource misallocation. We find that a nationwide efficient allocation can raise aggregate output by more than 30 percent, with further gains arising from adjustments to administrative divisions. While local reallocation also generates output gains, its effects are relatively modest compared with nationwide reallocation. Even when incorporating geographic constraints, urban development strategies, and restrictions on factor mobility, substantial potential gains remain. These results suggest that reducing resource misallocation can unlock significant untapped growth potential. The findings carry important policy implications for improving allocation efficiency in China and offer broader insights into the links between urban structure, regional integration, and long-run economic growth.
Julkaisussa olevat rahoitustiedot:
This work was supported by the National Natural Science Foundation of China [grant no. 72501303]. Zhiqiang Liao gratefully acknowledges financial support from the BNBU Start-up Research Fund [grant no. UICR0700139-26].