A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä

Quantifying potential gains from efficient resource reallocation: A counterfactual analysis of economic growth in 284 Chinese cities




TekijätDai, Sheng; Kuosmanen, Timo; Liao, Zhiqiang

Julkaisuvuosi2026

Lehti: Economic Modelling

Artikkelin numero107501

Vuosikerta157

ISSN0264-9993

eISSN1873-6122

DOIhttps://doi.org/10.1016/j.econmod.2026.107501

Julkaisun avoimuus kirjaamishetkelläEi avoimesti saatavilla

Julkaisukanavan avoimuus Osittain avoin julkaisukanava

Verkko-osoitehttps://doi.org/10.1016/j.econmod.2026.107501

Rinnakkaistallenteen osoitehttps://research.utu.fi/converis/portal/detail/Publication/515782701

Rinnakkaistallenteen lisenssiCC BY NC ND

Rinnakkaistallennetun julkaisun versioFinal draft


Tiivistelmä

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].


Last updated on