Back Matter
  • 1 https://isni.org/isni/0000000404811396, International Monetary Fund

Annex 1. Data Issues

Annex Table 1.1.

Summary Statistics

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Note: Robust clustered standard errors in parentheses. Estimates based on equation (6). VIX = Chicago Board Options Exchange Volatility Index.*p < .01; **p < .05; ***p < .01.
Annex Table 1.2.

Data Description and Sources

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Note: Robust clustered standard errors in parentheses. Estimates based on equation (6). VIX = Chicago Board Options Exchange Volatility Index.*p < .01; **p < .05; ***p < .01.
Annex Table 1.3.

Manufacturing Exporters

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References

  • Aghion, P., and I. Marinescu. 2008., “Cyclical Budgetary Policy and Economic Growth: What Do We Learn from OECD Panel Data?NBER Macroeconomics Annual, Volume 22.

    • Search Google Scholar
    • Export Citation
  • Ahuja, A., and M. Nabar. 2012., “China’s Economic Growth: International Spillovers.” IMF Working Paper 12/267, International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Arora, V. and, A. Vamvakidis. 2010. “China’s Economic Growth: International Spillovers.” IMF Working Paper 10/65, International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Blagrave, P., and E. Vesperoni 2016. “Spillover Implications of China’s Slowdown for International Trade.” Spillover Note 4, International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Cashin, P., K. Mohaddes, H. Wang, and J. Dong. 2016. “China’s Slowdown and Global Financial Market Volatility: Is World Growth Losing Out?IMF Working Paper 16/63, International Monetary Fund, Washington, DC.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doyle, B. M., and J. Faust. 2005. “Breaks in the Variability and Comovements of G-7 Economic Growth.” Review of Economics and Statistics 87 (4): 72140.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Duval, R., K. Cheng, K. H. Oh, R. Saraf, and D. Senevitrane. 2014. “Trade Integration and Business Cycle Synchronization: A Reappraisal with Focus on Asia.” IMF Working Paper 14/52, International Monetary Fund, Washington, DC.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Duval, R., K. Cheng, K. H. Oh, R. Saraf, and D. Senevitrane. 2016a. Regional Economic Outlook, Chapter 2. Asia and Pacific Department. Washington, DC: International Monetary Fund, April.

    • Search Google Scholar
    • Export Citation
  • Duval, R., K. Cheng, K. H. Oh, R. Saraf, and D. Senevitrane. 2016b. World Economic Outlook, Chapter 1. Washington, DC: International Monetary Fund.

    • Search Google Scholar
    • Export Citation
  • Jordà, O. 2005. “Estimation and Inference of Impulse Responses by Local Projections.” American Economic Review 95 (1): 16182.

  • Kalemli-Ozcan, S., E. Papaioannou, and J. L. Pedro. 2013. “Financial Regulation, Financial Globalization and the Synchronization of Economic Activity.” Journal of Finance 68 (3): 1179228.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kolerus, C., P. N’Diaye, and C. Saborowski. 2016. “China’s Footprint in Global Commodity Markets.” Spillover Note 6, International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Mwase, N., P. N’Diaye, H. Oura, F. Ricka, K. Svirydzenka, and Y. Zhang. 2016. “Spillovers from China: Financial Channel.” Spillover Note 5, International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
1

Arora and Vamvakidis (2010) find, based on VAR estimates, that a 1 percent shock to economic activity in China is associated with a short-term output effect in other countries of about 0.08 percent. Ahuja and Nabar (2012) find that a 1 percentage point slowdown in investment in China is associated with a reduction of global growth of 0.1 percentage point. Duval and others (2014) estimate the growth spillover effect of China of about 0.15 percentage point in non-Asian economies and about 0.3 percentage point in Asian economies. Cashin and others (2016), based on a global vector autoregression model for 26 countries, find that median spillover for the median economy is about 0.1 percent.

2

Blagrave and Esperoni (2016) estimate the effect of China demand shocks on export growth in advanced and emerging market economies. Using a panel vector autoregression framework, they find that a 1 percentage point shock to China’s final demand reduce export growth in other countries by 0.1–02 percentage point, on average, with the effect being larger for emerging Asia economies.

3

Kolerus and others (2016) find that China shocks have a significant effect on commodity prices. In particular, they find that a 1 percent increase in China’s industrial production leads to an increase in metal prices by about 5-7 percent and fuel prices by 7 percent.

4

China’s share of global demand for metals—such as iron ore, copper, and nickel—has increased from about 3 percent in the mid-1990s to about 40 in 2015. Similarly, China’s share of global demand for oil has increased from about 1 to 11 percent in the same period. Estimates presented in Kolerus and others (2016) suggest that the effect of China’s shock on oil and metal prices has increased over time, especially since the early 2000s.

5

Mwase and others (2016) find that recent economic and financial development in China had a significant impact on global financial markets. They find that the degree of comovements between asset prices in China and elsewhere has increased since mid-2015, and is larger in countries with stronger trade linkages with China.

6

Theoretically, the linkages depend on the nature of the shocks. While real shocks are mostly transmitted through trade linkages, financial ones are mostly transmitted through financial linkages (see, for example, IMF 2016b). Regarding the role of linkages, economic theory has ambiguous predictions about the impact of changing financial and trade linkages on output comovements. For example, an increase in financial linkages can lead to lower output comovements in the case of real shocks, but higher output comovements in the case of financial shocks. For a more detailed discussion, see Kalemli-Ozcan and others (2013) and the references therein.

7

Because of limited time series data of bilateral financial flows of each country with China, this framework does not allow to test for the role of financial linkages in transmitting shocks and how the transmission through financial linkages has changed over time. While the direct transmission of spillovers through financial channels is likely to be limited—given, for instance, the remaining restrictions on cross-border financial transitions, investment, and banking activities in China—recent empirical evidence points to an increase in the comovement between asset prices in China and elsewhere since mid-2015 (Mwase and others 2016).

8

The results—available upon request—are robust to different lag-parametrizations.

9

The results—available upon request—also suggest that the effect becomes statistically nonsignificant by the 10th year. This result, however, has to be treated with caution given the large uncertainty surrounding the estimates over the long term.

10

The effects are statistically significantly different from zero. The only exception is the short-term effect for advanced economies.

11

The effect is negative but not statistically significant in the Middle East, North Africa, Afghanistan, and Pakistan and the Commonwealth of Independent States.

12

The results—available upon request—suggests that medium-term effect of China’s idiosyncratic shocks on output in other countries has also increased, even though the increase has been more modest than for the short-term effects.

13

Due to the presence of outliers, the average of the effect for each country group tends to differ from the median effect. For example, the average effect for each country group suggests that spillovers have increased more for advanced and emerging market economies than for low-income countries.

14

The sensitivity is assessed estimating a bivariate regression of GDP growth to changes in commodity prices for each country. The results are available upon request.

15

The magnitude of this result should be treated with caution given that is not possible to separate between unobservable country-specific factors and the sensitivity of GDP growth in each country to changes in commodity prices.

16

Computed as the average spillover effect (about 0.06) plus the dummy coefficient (0.04). Also, in this case, the magnitude of this result should be treated with caution given that it is not possible to separate the effect of this variable from unobserved country-specific characteristics.

China Spillovers: New Evidence From Time-Varying Estimates
Author: Davide Furceri, João Tovar Jalles, and Ms. Aleksandra Zdzienicka