Levels of Structural Change: An Analysis of China's Development Push 1998-2014

Heinrich, Torsten; Yang, Jangho; Dai, Shuanping

doi:10.2139/ssrn.3593018

Cited by 5 publications

(5 citation statements)

References 71 publications

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“…Chinese firm-level distributions follow the same general patterns found elsewhere (Ma et al, 2008;Yu et al, 2015;Heinrich and Dai, 2016;Heinrich et al, 2020). While firm sizes seem to follow power laws (Ma et al, 2008;Heinrich and Dai, 2016), for labor productivities and growth rates, two distributions have been suggested: Asymmetric Exponential Power distributions from the exponential distribution family (Yu et al, 2015) were found to be a much better fit than Gaussians.…”

Section: Firm-level Productivity and Growth In The Pr Chinasupporting

confidence: 64%

“…While firm sizes seem to follow power laws (Ma et al, 2008;Heinrich and Dai, 2016), for labor productivities and growth rates, two distributions have been suggested: Asymmetric Exponential Power distributions from the exponential distribution family (Yu et al, 2015) were found to be a much better fit than Gaussians. Lévy alpha-stable distributions, which have power-law tails on both sides, have been suggested as an alternative since the data seems to be heavy-tailed (Heinrich et al, 2020). The distinction has important consequences.…”

Section: Firm-level Productivity and Growth In The Pr Chinamentioning

confidence: 99%

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Growth, Development, and Structural Change at the Firm-Level: The Example of the PR China

2020

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Section: Firm-level Productivity and Growth In The Pr Chinasupporting

confidence: 64%

Section: Firm-level Productivity and Growth In The Pr Chinamentioning

confidence: 99%

Growth, Development, and Structural Change at the Firm-Level: The Example of the PR China

2020

Self Cite

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“…It would be of interest to see how this extends across all scales to the firm level. Heinrich et al ( 64 ) show that correlation structures found on the sector level can vanish when economic analysis is taken to the firm level. Also for this phenomenon the intrasector heterogeneity of firms could be part of the explanation.…”

Section: Discussionmentioning

confidence: 99%

Estimating the loss of economic predictability from aggregating firm-level production networks

Diem,

Borsos,

Reisch

et al. 2024

PNAS Nexus

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To estimate the reaction of economies to political interventions or external disturbances, input-output (IO) tables—constructed by aggregating data into industrial sectors—are extensively used. However, economic growth, robustness, and resilience crucially depend on the detailed structure of non-aggregated firm-level production networks (FPNs). Due to non-availability of data little is known about how much aggregated sector-based and detailed firm-level-based model-predictions differ. Using a nearly complete nationwide FPN, containing 243,399 Hungarian firms with 1,104,141 supplier-buyer-relations we self-consistently compare production losses on the aggregated industry-level production network (IPN) and the granular FPN. For this we model the propagation of shocks of the same size on both, the IPN and FPN, where the latter captures relevant heterogeneities within industries. In a COVID-19 inspired scenario we model the shock based on detailed firm-level data during the early pandemic. We find that using IPNs instead of FPNs leads to an underestimation of economic losses of up to 37%, demonstrating a natural limitation of industry-level IO-models in predicting economic outcomes. We ascribe the large discrepancy to the significant heterogeneity of firms within industries: we find that firms within one sector only sell 23.5% to and buy 19.3% from the same industries on average, emphasizing the strong limitations of industrial sectors for representing the firms they include. Similar error-levels are expected when estimating economic growth, CO2 emissions, and the impact of policy interventions with industry-level IO models. Granular data is key for reasonable predictions of dynamical economic systems.

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“…In the article "Levels of structural change: An analysis of China's development push 1998-2014", Heinrich et al (2021 examine the correspondence of empirical regularities at the meso and micro levels for one of the most astonishing economic transformation processes we can currently observe. They combine sectoral data from the World Input-Output Database (WIOD) with extensive company data from the Chinese Industrial Enterprise Database (CIE DB) for the years 1998 to 2013.…”

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confidence: 99%

Evolutionary perspectives on economic policy

Lehmann-Waffenschmidt

Peneder

2022

J Evol Econ

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Evolutionary processes are characterised by genuine uncertainty. Consequently, economic policy must take into account that environmental conditions and alternatives for action are often indeterminate. Scientifically sound concepts, methods and instruments are needed that take into account the partial openness of economic processes in real time. This editorial introduction provides a brief summary of the main findings of the individual articles in this special issue. Although each contribution can only highlight a specific problem, taken together they impressively demonstrate the relevance and urgency of developing a truly evolutionary perspective on economic policy.

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Levels of Structural Change: An Analysis of China's Development Push 1998-2014

Cited by 5 publications

References 71 publications

Growth, Development, and Structural Change at the Firm-Level: The Example of the PR China

Growth, Development, and Structural Change at the Firm-Level: The Example of the PR China

Estimating the loss of economic predictability from aggregating firm-level production networks

Evolutionary perspectives on economic policy

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