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Can Consumption Growth in China Keep Up as Investment Slows?

Abstract

China’s exceptionally large share of investment in GDP has been widely noted. Rebalancing away from investment to consumption has been on China’s agenda to realize more sustainable growth. As investment growth slows, the question is whether consumption could replace investment as a growth engine. This paper empirically explores the drivers of Chinese household consumption and specifically tests whether investment has affected household consumption beyond the standard income channel. Our empirical results using both national- and provincial-level data suggest that investment has had a significant impact on household consumption beyond the household income channel. The effects are particularly strong in the post-Global Financial Crisis period, suggesting that the Chinese government’s stimulus measures, which aimed mostly at investment spending, have significantly affected households’ decision to consume both through households’ current and expected future income channel.

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Fig. 1

Source: CEIC

Fig. 2

Source: BIS, CEIC

Fig. 3

Source: Authors’ calculations. (Color figure online)

Fig. 4

Source: Authors’ calculations

Fig. 5

Source: Authors’ calculations based on CEIC

Fig. 6

Notes

  1. 1.

    When using fixed asset investment (FAI), these levels reach around 80 percent.

  2. 2.

    Since it is only available on an annual basis, quarterly gross fixed capital formation (GFCF) estimates were derived from Soudan (forthcoming).

  3. 3.

    In the robustness section, we will instead use the average lending rate for loans by the three biggest banks and show that the results remain qualitatively the same. It has to be noted that due to the large “shadow” banking sector in China, the official interest rates might not necessarily reflect the true interest rate. However, the true interest rates are difficult to infer and, therefore, we have to remain with this approximation. Since we focus on households, this choice seems to be justified as the “shadow” banking sector plays only a minor role for households.

  4. 4.

    Since interest rates are only available at the national level, the real interest rates on the provincial level only differ with respect to the provincial inflation rate.

  5. 5.

    More formally: \(\mathrm{migrant} = \frac{\frac{\mathrm{resident}_p}{\mathrm{population}_p} - \mathrm{median}(\frac{\mathrm{resident}}{\mathrm{population}})}{\mathrm{median}(\frac{\mathrm{resident}}{\mathrm{population}})}\) for each province p.

  6. 6.

    Since the sample is relatively short and the estimated VAR contains six variables, a higher number of lags would add too much noise to the estimation.

  7. 7.

    These can be information delays, physical constraints (such as the process from making a decision on investment in a firm until realization), institutional knowledge, market structure, or parameter estimates from previous studies.

  8. 8.

    We furthermore checked the FAI growth rates against the GFCF growth rates and omit extreme values only observed in the FAI data.

  9. 9.

    Judson and Owen (1997) compare three groups of estimators for small N and finite T (small to moderate): (i) the Anderson and Hsiao (1982) estimator based on IV procedures; (ii) the one- and two-step GMM by Arellano and Bond (1991); and (iii) the bias-corrected LSDV estimator by Kiviet (1995). They find that in general the one-step GMM outperforms the two-step GMM, but the LSDVC and Anderson–Hsiao estimators consistently outperform all other estimators. They find that the Anderson–Hsiao has a lower bias, but the LSDVC is more effective. Hence, there is a certain bias effectiveness trade-off. They conclude that for small T, the LSDVC estimator seems more appropriate while the Anderson–Hsiao estimator is more appropriate for larger T. A drawback of the LSDVC estimator as proposed by Kiviet (1995) is that it cannot be applied to unbalanced panels. Bruno (2005) extends the version of Kiviet's LSDVC to unbalanced panel data. De Vos et al. (2015) proposed a bootstrap-based bias-corrected FE (BCFE) estimator which, however, imposes more restrictions.

  10. 10.

    Since hours worked are not available, we have to remain with this approximation of the labor part of income.

  11. 11.

    Here, we include time fixed effects since we are only interested in the correlation of investment/income growth with consumption growth.

  12. 12.

    Since the detailed data are only available after 2004 and until 2016, the sample gets smaller.

  13. 13.

    As in the panel regressions, we exclude Liaoning and Tibet from the estimations.

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Correspondence to Bernhard Kassner.

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Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The authors would like to thank participants in the ‘5th workshop of the China Expert Network’ at the ECB, in the Macro Research Seminar at the LMU Munich, and in the ‘Conference on China’s Economic Reforms: Where Do We Stand’ at the City University of Hong Kong for the lively discussion and valuable comments.

The paper was written when the author was seconded to the ECB. The opinions expressed in this paper are those of the authors and do not necessarily reflect the views of the ECB or the IMF.

Appendix

Appendix

See Figs. 7, 8, 9, 10, 11, 12 and Tables 5, 6.

Fig. 7
figure7

Source: Authors’ calculations

Baseline classification of provinces according to geography. Darker areas refer to western provinces and lighter areas to eastern provinces in the classification.

Fig. 8
figure8

Source: Authors’ calculations

Classification of provinces according to median income per capita in 2017. Darker shaded areas are below the median.

Fig. 9
figure9

Source: Authors’ calculations

Classification of provinces according to average annual fixed asset growth from 2008 to 2017. Darker shaded areas are provinces with above median average fixed asset growth.

Fig. 10
figure10

Source: Authors’ calculations

Classification of provinces according to sign of the coefficient in a standard OLS regression of household consumption growth on investment growth and various control variables of the form \(y_{i,t} = \gamma y_{i,t-1} + x'_{i,t}\beta + \epsilon _{i,t}\) for each province. Darker shaded areas are provinces with a positive correlation between investment growth and household income growth.

Fig. 11
figure11

Source: Authors’ calculations

Full set of impulse response functions to a one-standard-deviation shock. Dotted lines represent one-standard-deviation credibility bands.

Fig. 12
figure12

Source: Authors’ calculations

Robustness tests of the BVAR on the national level. Impulse response functions of consumption to various one-standard-deviation shocks. Dotted lines represent one-standard-deviation credibility bands. Baseline specification: independent normal-Wishart prior, two lags, deposit rate.

Table 5 Robustness tests for the full sample
Table 6 Robustness tests for the regional sample

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Chivakul, M., Kassner, B. Can Consumption Growth in China Keep Up as Investment Slows?. Comp Econ Stud 61, 381–412 (2019). https://doi.org/10.1057/s41294-019-00097-w

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Keywords

  • Reforms
  • Investment
  • Consumption
  • China

JEL Classification

  • E21
  • E22
  • E27
  • E44
  • E47
  • E52
  • C12
  • C32
  • C33
  • O53