The Impact of Monetary Policy Through Production Networks—Empirical Evidence from Sectoral Electricity Consumption in China
Abstract
1. Introduction
2. Theoretical Background and Relevant Literature
2.1. Theoretical Framework of Monetary Policy Shock Transmission and Dynamic Effects
2.2. Heterogeneous Sectoral Effects of Monetary Policy
2.3. Production Networks and Macro Shock Transmission
3. Indicator Construction and Model Specification
3.1. Selection of Monetary Policy Variables
3.2. Measurement of Sectoral Output Based on Electricity Consumption
3.3. Construction of Sectoral Linkage Network
3.4. Measuring Network Linkage Effects of Output Growth Under Monetary Policy Shocks
4. Empirical Results
4.1. Measurement of Network Effects of Monetary Policy Shocks
4.2. Heterogeneity Analysis of Upstream, Midstream, and Downstream Sectors’ Responses to Monetary Policy Shocks
4.3. Robustness Checks
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Sector | Index | 2020 | 2021 | 2022 | Person |
---|---|---|---|---|---|
Industry | value-added growth | −0.0099 | 0.1646 | −0.0027 | 0.6048 |
electricity consumption growth | −0.2257 | 0.0801 | 0.0500 | ||
Construction | value-added growth | 0.0184 | 0.0996 | 0.0158 | 0.7037 |
electricity consumption growth | 0.0346 | 0.0597 | −0.0460 | ||
Wholesale and retail trade | value-added growth | 0.0793 | 0.2132 | 0.1371 | 0.8409 |
electricity consumption growth | −0.0434 | 0.1225 | −0.0698 | ||
Transport, Warehousing and Post | value-added growth | −0.0308 | 0.1759 | 0.0517 | 0.9933 |
electricity consumption growth | −0.1112 | 0.0767 | −0.0557 | ||
Hotels, Eating and drinking places | value-added growth | −0.0872 | 0.2094 | 0.0609 | 0.9035 |
electricity consumption growth | −0.1197 | 0.1832 | −0.0929 |
Classification | Sector | Upstreamness |
---|---|---|
Upstream Sector | Metal ore mining | 27.54 |
Non-metal minerals and other mining | 14.75 | |
Coal mining and processing | 11.16 | |
Midstream Sector | Metal smelting and processing | 4.65 |
Chemical products | 4.64 | |
Communication equipment, Electronic computer and Other computer devices | 4.14 | |
Repair services for metal products, machinery, and equipment | 4.02 | |
Petroleum refining and Coking | 3.88 | |
Water production and supply | 3.77 | |
Gas production and supply | 3.75 | |
Products and Technical services for agriculture, forestry, livestock and fishing | 3.11 | |
Downstream Sector | Textile | 2.93 |
Metallic mineral products | 2.80 | |
Furniture and products of wood | 2.71 | |
Instruments | 2.64 | |
Food and Tobacco products | 2.61 | |
Printing and Cultural goods | 2.46 | |
Telecommunication, Computing services and software | 2.43 | |
Leasehold and Business services | 2.38 | |
Wholesale and retail trade | 2.13 | |
General sectoral machinery | 2.12 | |
Non-metallic mineral products | 2.12 | |
Transport, Warehousing and Post | 2.10 | |
Transport equipment | 1.99 | |
Special sectoral equipment | 1.96 | |
Knitted mills, Wearing apparel, Leather, furs, down and related products | 1.86 | |
Electric machinery and equipment | 1.86 | |
Hotels, Eating and drinking places | 1.62 | |
Real estate | 1.51 | |
Construction | 1.10 |
Monetary Policy Rules | The Quantity Rule | The Interest Rate Rule | ||||
---|---|---|---|---|---|---|
OLS | W = D | W = S | OLS | W = D | W = S | |
M2 | 0.0646 * (0.0343) | 0.0662 *** (0.0216) | 0.0202 ** (0.0095) | |||
Shibor | −0.0700 *** (0.0190) | −0.0723 (0.0741) | −0.0131 (0.0326) | |||
IV | 1.3660 ** (0.6452) | 1.4352 (0.8909) | 0.1595 (0.3889) | 0.078 (1.4635) | 0.1112 (0.7656) | −0.2715 (0.3361) |
FAI | −0.3710 (0.3374) | −0.3946 (0.4796) | −0.0061 (0.2106) | 0.3355 (0.7706) | 0.3291 (0.4090) | 0.2266 (0.1804) |
NX | 0.0869 (0.0619) | 0.0885 (0.0955) | 0.0183 (0.0421) | 0.0748 (0.0482) | 0.0760 (0.0957) | 0.0132 (0.0422) |
rho | −0.0422 (0.0772) | 0.9138 *** (0.0110) | −0.0333 (0.0768) | 0.9144 *** (0.0109) | ||
standard error | Clustered on id | Clustered on id | Clustered on id | Clustered on id | Clustered on id | Clustered on id |
LR_Direct | LR_Indirect | LR_Total | |
---|---|---|---|
W = S | 0.0454 ** (0.0215) | 0.1975 ** (0.0992) | 0.2429 ** (0.1201) |
Sector Classification | Total Effect | Direct Effect (Share) | Network Effect (Share) |
---|---|---|---|
Upstream | 0.1074 | 0.0592 (55.20%) | 0.0482 (44.80%) |
Midstream | 0.1014 | 0.0421 (41.00%) | 0.0593 (59.00%) |
Downstream | 0.1123 | 0.0319 (28.74%) | 0.0803 (71.26%) |
Network Effect Intensity Rank | Sector Name | Group | Network Effect Intensity |
---|---|---|---|
TOP3 | Construction | Downstream | 81.31% |
Wholesale and retail trade | Downstream | 79.84% | |
General sectoral machinery | Downstream | 78.74% | |
BOTTOM3 | Non-metal minerals and other mining | Upstream | 45.55% |
Metal ore mining | Upstream | 39.97% | |
Communication equipment, Electronic computer and Other computer devices | Midstream | 30.37% |
Pearson Correlation Coefficient | Row-to-Row Correlation Analysis | Column-to-Column Correlation Analysis | QAP Correlation Analysis |
---|---|---|---|
r | 0.9997 | 0.9980 | 0.9993 |
Monetary Policy Rules | The Quantity Rule | The Interest Rate Rule | ||
---|---|---|---|---|
W = D | W = S | W = D | W = S | |
M2 | 0.0630 *** (0.0212) | 0.0360 * (0.0192) | ||
Shibor | −0.0682 (0.0723) | −0.0545 (0.0645) | ||
IV | 1.3409 (0.8643) | 1.1401 (0.7732) | 0.0891 (0.7452) | 0.4881 (0.6672) |
FAI | −0.3657 (0.4676) | −0.3647 (0.4185) | 0.3170 (0.4004) | −0.0047 (0.3594) |
NX | 0.0844 (0.0935) | 0.0413 (0.0838) | 0.0717 (0.0937) | 0.0354 (0.0837) |
rho | 0.0230 (0.0424) | 0.3157 *** (0.0411) | 0.0345 (0.0420) | 0.3280 *** (0.0401) |
standard error | Clustered on id | Clustered on id | Clustered on id | Clustered on id |
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Lan, Z.; Guo, Z.; Wu, G.; Guo, Y. The Impact of Monetary Policy Through Production Networks—Empirical Evidence from Sectoral Electricity Consumption in China. Sustainability 2025, 17, 8919. https://doi.org/10.3390/su17198919
Lan Z, Guo Z, Wu G, Guo Y. The Impact of Monetary Policy Through Production Networks—Empirical Evidence from Sectoral Electricity Consumption in China. Sustainability. 2025; 17(19):8919. https://doi.org/10.3390/su17198919
Chicago/Turabian StyleLan, Zhiqiang, Zhaoyu Guo, Guoyao Wu, and Ye Guo. 2025. "The Impact of Monetary Policy Through Production Networks—Empirical Evidence from Sectoral Electricity Consumption in China" Sustainability 17, no. 19: 8919. https://doi.org/10.3390/su17198919
APA StyleLan, Z., Guo, Z., Wu, G., & Guo, Y. (2025). The Impact of Monetary Policy Through Production Networks—Empirical Evidence from Sectoral Electricity Consumption in China. Sustainability, 17(19), 8919. https://doi.org/10.3390/su17198919