A structural estimation of the return to infrastructure investment in China

Wu, Guiying Laura; Feng, Qu; Wang, Zhifeng

doi:10.1016/j.jdeveco.2021.102672

Cited by 17 publications

(11 citation statements)

References 61 publications

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“…They find large multiplier effects in both aggregated time-series data and in panel data at the provincial level. Wu et al (2021) developed a structural model of endogenous productivity in a firm-level production function, matching Chinese firm-level production data with province-level infrastructure investment data and find robust evidences on the productivity effect of public infrastructure investment. They found that the estimated rates of return are approximately 6% averaged from 1999 to 2007, and the returns triple if national-level spillover effects are taken into account.…”

Section: Orcidmentioning

confidence: 99%

China's efficient urban bias

Rangazas

Wang

Zou

2022

Pacific Economic Review

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This paper develops a dynamic general equilibrium model with endogenous rural–urban migration to analyse the provision of rural and urban government services in China, with special emphasis on the role of the household registration (hukou) system in shaping its urbanization process. It argues that China's urban bias policy, which is enabled by the hukou system restricting rural–urban migration, did not necessarily reduce economic efficiency. Rather, it might have only raised urban welfare at the expense of rural residents. As the hukou system also ties people to particular geographical locations, this paper argues that China's continuous bias towards coastal and big cities has started to cause economic inefficiency as well as inequality. It suggests that progressive hukou reform reducing barriers to cross‐region migration would improve economic efficiency and welfare.

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Section: Orcidmentioning

confidence: 99%

China's efficient urban bias

Rangazas

Wang

Zou

2022

Pacific Economic Review

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“…First of all, "assessment", "regional economic", "restorability", "resilience", "major public emergencies" are taken as keywords to search the relevant literatures in Web of Science, Science Direct, Springer Databases, Wiley Online Regional GDP ( C1 ) [32,33] Location advantages ( C2 ) [34,35] Foreign trade dependence ( C3 ) [2,4,36,37] New infrastructure investment ( C4 ) [2,38,39] Industrial structure ( T2 ) Diversification of industrial structure ( C5 ) [5,8,40,41] Industrial chain system ( C6 ) [3,[5][6][7] Industrial clusters competitiveness ( C7 ) [8,42] R&D investment in high and new technology industries ( C8 ) [40] Transformation of digital economy ( C9 ) [9,43] Internet economy development environment ( C10 ) [44,45] Small and medium-sized enterprises develop vitality ( C11 ) [46,47] Labor forces ( T3 ) Scientific and technological innovation talent resources allocation efficiency ( C12 ) [9,10,28,29] Unemployment rate ( C13 ) [48] Introducing and training of the high level and the high-quality talents ( C14 ) [46][47][48] Financial support ( T4 )…”

Section: Assessment Indicator System Of Rer Under the Stress Of Covid-19mentioning

confidence: 99%

Assessment of regional economic restorability under the stress of COVID-19 using the new interval type-2 fuzzy ORESTE method

Zhang

Gao

Лю

2022

Complex Intell. Syst.

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The economic implications from the COVID-19 crisis are not like anything people have ever experienced. As predictions indicated, it is not until the year 2025 may the global economy recover to the ideal situation as it was in 2020. Regions lacked of developing category is among the mostly affected regions, because the category includes weakly and averagely potential power. For supporting the decision of economic system recovery scientifically and accurately under the stress of COVID-19, one feasible solution is to assess the regional economic restorability by taking into account a variety of indicators, such as development foundation, industrial structure, labor forces, financial support and government's ability. This is a typical multi-criteria decision-making (MCDM) problem with quantitative and qualitative criteria/indicator. To solve this problem, in this paper, an investigation is conducted to obtain 14 indicators affecting regional economic restorability, which form an indicator system. The interval type-2 fuzzy set (IT2FS) is an effective tool to express experts’ subjective preference values (PVs) in the process of decision-making. First, some formulas are developed to convert quantitative PVs to IT2FSs. Second, an improved interval type-2 fuzzy ORESTE (IT2F-ORESTE) method based on distance and likelihood are developed to assess the regional economic restorability. Third, a case study is given to illustrate the method. Then, robust ranking results are acquired by performing a sensitivity analysis. Finally, some comparative analyses with other methods are conducted to demonstrate that the developed IT2F-ORESTE method can supporting the decision of economic system recovery scientifically and accurately.

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“…China does not directly report public investment data. Following Jin (2016) and Wu et al (2021), we define infrastructure investment as the total investment across four industries: (1) production and supply of electricity, gas, and water; (2) transport, storage, and post; (3) information transmission, computer services, and software (or telecommunications and other information transmission services); and (4) management of water conservancy, environment, and public facilities. The infrastructure investment to GDP ratio increased dramatically from 6.5% to 9.7% during the period 2003-13 based on data from the NBSC.…”

Section: Stylized Factsmentioning

confidence: 99%

“…Rents growth in the data, however, was low throughout our sample period.13 In order to simplify our calibration, we assume that infrastructure does not enter the housing sector's productivity. This assumption is unlikely to change our model prediction because (1) the housing sector is relatively small, (2) based on the estimation inWu et al (2021) and Chinese input-output table, we find that the output elasticity of infrastructure in the housing sector is only half of the nonhousing sector.…”

mentioning

confidence: 92%

China's Housing Bubble, Infrastructure Investment, and Economic Growth

Jiang

Miao

Zhang

2021

Int Economic Review

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China's housing prices have been growing rapidly over the past few decades, despite low growth in rents. We study the impact of housing bubbles on China's economy, based on the understanding that local governments use land-sale revenue to fuel infrastructure investment. We calibrate our model to the Chinese data over the period 2003-13 and find that our calibrated model can match the declining capital return and GDP growth, the average housing price growth, and the rising infrastructure to GDP ratio in the data. We conduct two counterfactual experiments to estimate the impact of a bubble collapse and a property tax.

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A structural estimation of the return to infrastructure investment in China

Cited by 17 publications

References 61 publications

China's efficient urban bias

China's efficient urban bias

Assessment of regional economic restorability under the stress of COVID-19 using the new interval type-2 fuzzy ORESTE method

China's Housing Bubble, Infrastructure Investment, and Economic Growth

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