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Can Consumption Growth in China Keep Up as Investment Slows?


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


  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.


  1. Anderson, T.W., and C. Hsiao. 1982. Formulation and estimation of dynamic models using panel data. Journal of Econometrics 18(1): 47–82.

    Article  Google Scholar 

  2. Arellano, M., and S. Bond. 1991. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies 58(2): 277–297.

    Article  Google Scholar 

  3. Arellano, M., and O. Bover. 1995. Another look at the instrumental variable estimation of errorcomponents models. Journal of Econometrics 68(1): 29–51.

    Article  Google Scholar 

  4. Bagliano, F.C., and C.A. Favero. 1998. Measuring monetary policy with VAR models: an evaluation. European Economic Review 42(6): 1069–1112.

    Article  Google Scholar 

  5. Baker, S.R., N. Bloom, and S.J. Davis. 2016. Measuring economic policy uncertainty. The Quarterly Journal of Economics 131(4): 1593–1636.

    Article  Google Scholar 

  6. Barnett, S.A., and R. Brooks. 2006: What's Driving Investment in China?, IMF Working Paper No. 06/265.

  7. Bernanke, B.S., and I. Mihov. 1998. The liquidity effect and long-run neutrality. Carnegie-Rochester Conference Series on Public Policy 49(1): 149–194.

    Article  Google Scholar 

  8. Blundell, R., and S. Bond. 1998. Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics 87(1): 115–143.

    Article  Google Scholar 

  9. Bruno, G.S.F. 2005. Approximating the bias of the LSDV estimator for dynamic unbalanced panel data models. Economics Letters 87(3): 361–366.

    Article  Google Scholar 

  10. Buysse, K., D. Essers, and E. Vincent. 2018. Can china avoid the middle-income trap? NBB Economic Review 2018: 63–77.

    Google Scholar 

  11. Celik, S, Aslanoglu, E and Uzun, S. 2010: Determinants of consumer confidence in emerging economies: A panel cointegration analysis. Topics in Middle Eastern and North African Economies 12.

  12. Chamon, M., K. Liu, and E. Prasad. 2013. Income uncertainty and household savings in China. Journal of Development Economics 105(C): 164–177.

    Article  Google Scholar 

  13. Chen, H., K. Chow, and P. Tillmann. 2017. The effectiveness of monetary policy in China: Evidence from a qual VAR. China Economic Review 43(C): 216–231.

    Article  Google Scholar 

  14. Chen, H., M. Funke, and A. Mehrotra. 2017. What drives urban consumption in mainland China? The role of property price dynamics. Pacific Economic Review 22(3): 383–409.

    Article  Google Scholar 

  15. Chen, W, Chen, X, Hsieh, C.-T and Zheng, M. 2019: A forensic examination of china’s national accounts. BPEA Conference Draft, Spring.

  16. Curtis, C.C., S. Lugauer, and N.C. Mark. 2015. Demographic patterns and household saving in China. American Economic Journal: Macroeconomics 7(2): 58–94.

    Google Scholar 

  17. De Vos, I., G. Everaert, and I. Ruyssen. 2015. Bootstrap-based bias correction and inference for dynamic panels with fixed effects. Stata Journal 15(4): 986–1018.

    Article  Google Scholar 

  18. Dees, S., and P. Soares Brinca. 2013. Consumer confidence as a predictor of consumption spending: Evidence for the United States and the Euro area. International Economics 134: 1–14.

    Article  Google Scholar 

  19. Dieppe, A, van Roye, B and Legrand, R. 2016: The BEAR toolbox, ECB Working Paper Series 1934.

  20. Dion, D.-P. 2006: Does consumer confidence forecast household spending?, MPRA Paper 902 .

  21. Dumitrescu, E.-I., and C. Hurlin. 2012. Testing for Granger non-causality in heterogeneous panels. Economic Modelling 29(4): 1450–1460.

    Article  Google Scholar 

  22. Estrada, N., D. Garrote, E. Valdeolivas, and J. Valls. 2015. Household debt and uncertainty: Private consumption after the great recession. Monetaria 1: 71–109.

    Google Scholar 

  23. Fan, C.S., and P. Wong. 1998. Does consumer sentiment forecast household spending? The Hong Kong case. Economics Letters 58(1): 77–84.

    Article  Google Scholar 

  24. Furlanetto, F., F. Ravazzolo, and S. Sarferaz. 2017. Identification of financial factors in economic fluctuations. The Economic Journal.

  25. Heim, J.J. 2010. The impact of consumer confidence on consumption and investment spending. Journal of Applied Business and Economics 11(2): 37–54.

    Google Scholar 

  26. Horioka, C.Y. 2010. Aging and saving in Asia. Pacific Economic Review 15(1): 46–55.

    Article  Google Scholar 

  27. Judson, R., and Owen, A. L. 1997: Estimating dynamic panel data models: a practical guide for macroeconomists. Finance and Economics Discussion Series 1997-3 Board of Governors of the Federal Reserve System (U.S.).

  28. Kaplan, G., and G.L. Violante. 2018. Microeconomic heterogeneity and macroeconomic shocks. Journal of Economic Perspectives 32(3): 167–194.

    Article  Google Scholar 

  29. Kaplan, G., G.L. Violante, and J. Weidner. 2014. The wealthy hand-to-mouth. Brookings Papers on Economic Activity 45(1): 77–153.

    Article  Google Scholar 

  30. Kiviet, J.F. 1995. On bias, inconsistency, and eciency of various estimators in dynamic panel data models. Journal of Econometrics 68(1): 53–78.

    Article  Google Scholar 

  31. Kiviet, J.F. 1999. Expectation of expansions for estimators in a dynamic panel data model; some results for weakly exogenous regressors. In Analysis of Panel data and limited dependent variables, ed. C. Hsiao, K. Lahiri, L.-F. Lee, and M.H. Pesaran. Cambridge: Cambridge University Press.

    Google Scholar 

  32. Koivu, T. 2012. Monetary policy, asset prices and consumption in China. Economic Systems 36(2): 307–325.

    Article  Google Scholar 

  33. Lee, I. H, Syed, M and Liu, X. 2013: China’s path to consumer-based growth: Reorienting investment and enhancing efficiency. IMF Working Paper No. 13/83.

  34. Lopez, H. B, Durre, A. 2003: The determinants of consumer confidence: the case of United States and Belgium. CORE Discussion Papers 2003053.

  35. Ludvigson, S.C. 2004. Consumer confidence and consumer spending. Journal of Economic Perspectives 18(2): 29–50.

    Article  Google Scholar 

  36. Ludvigson, S., C. Steindel, and M. Lettau. 2002. Monetary policy transmission through the consumption-wealth channel. Economic Policy Review 5: 117–133.

    Google Scholar 

  37. Ma, G., I. Roberts, and G. Kelly. 2017. Rebalancing China’s economy: Domestic and international implications. China & World Economy 25(1): 1–31.

    Article  Google Scholar 

  38. Nabar, M. S. 2011: Targets, interest rates, and household saving in urban China. IMF Working Paper No. 11/223.

  39. Nickell, S.J. 1981. Biases in dynamic models with fixed effects. Econometrica 49(6): 1417–1426.

    Article  Google Scholar 

  40. Peltonen, T.A., R.M. Sousa, and I.S. Vansteenkiste. 2012. Wealth effects in emerging market economies. International Review of Economics & Finance 24(C): 155–166.

    Article  Google Scholar 

  41. Pounder Demarco, L. 2009: Consumption response to expected future income. International Finance Discussion Papers 971 Board of Governors of the Federal Reserve System (U.S.).

  42. Roodman, D. 2009. How to do xtabond2: An introduction to difference and system GMM in Stata. Stata Journal 9(1): 86–136.

    Article  Google Scholar 

  43. Sims, C.A. 1998. Comment on Glenn Rudebusch’s “Do measures of monetary policy in a VAR make sense”. International Economic Review 39(4): 933–941.

    Article  Google Scholar 

  44. Soudan, M. (forthcoming): Quarterly National Account for China. ECB (forthcoming).

  45. Tsatsaronis, K, and Zhu, H. 2004: What drives housing price dynamics: cross-country evidence. BIS Quarterly Review.

  46. Wei, S.-J., and X. Zhang. 2011. The competitive saving motive: Evidence from rising sex ratios and savings rates in China. Journal of Political Economy 119(3): 511–564.

    Article  Google Scholar 

  47. Zhang, L. 2016. Rebalancing in China-progress and prospects. IMF Working Papers 16(183): 1.

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Corresponding author

Correspondence to Bernhard Kassner.

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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.



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

Fig. 7

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

Source: Authors’ calculations

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

Fig. 9

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

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

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

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).

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  • Reforms
  • Investment
  • Consumption
  • China

JEL Classification

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