Abstract
Domestic saving in China is the highest in the world in terms of GDP, even surpassing the investment share, which is still very high by international standards. This excessive saving produces a large current account surplus. Understanding why the Chinese save so much is a central issue in the debate on global imbalances. The goal of our paper is to make an empirical analysis of the saving behavior of Chinese households, taking into account the disparities at country and provincial level, and between rural and urban households. First, we show that notwithstanding the rising contribution of government and firms to national savings, the real peculiarity lies with Chinese families themselves. We move from Modigliani and Caos (2004) attempt to explain rising personal saving in China within the life cycle hypothesis (LCH) and show that life cycle determinants are insufficient to explain the phenomenon, especially in the most recent period. Once we consider regional differences and distinguish between urban and rural households using province-level data, it becomes clear that additional explanations are needed and that precautionary motives and liquidity constraints are playing an important role. Our results suggest that in order to reduce the propensity to save of Chinese households it is necessary to improve the provision of social services and to facilitate access to credit.
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Notes
- 1.
Over the last decade China’s current account surplus increased sixfold as a percentage of GDP, peaking at 11% in 2007, or about $372 billion. In 2008 that surplus amounted to roughly 50% of the aggregated surpluses of the rest of Asia (including Japan) and the major oil exporting countries. Foreign reserves rose rapidly, reaching $2.4 trillion at the end of last year (more than half of China’s GDP).
- 2.
At purchasing power parity (PPP).
- 3.
- 4.
Zhang et al. (2010).
- 5.
- 6.
On how the hukou system works, see Wang (1997).
- 7.
Modigliani and Brumberg (1954).
- 8.
Reviewing the literature, Browning and Lusardi (1996) list nine possible motives that induce people to save (eight of which were already envisaged by Keynes in The General Theory) and focus on a “standard model” that captures the basic insights of the LCH.
- 9.
- 10.
- 11.
Zhou (2009).
- 12.
See, among others, Morishima (1982) for Japan and the Governor of the People’s Bank of China Zhou (2009) for China.
- 13.
Data availability imposed a limitation on the scope of the comparison. The “take-off phases” were selected via inspection of the data on growth. However, the results are robust with respect to small variations in the time periods selected.
- 14.
Flow-of-funds data are available for the 1992–2008 period only.
- 15.
For the corporate sector, by definition saving coincides with disposable income, therefore we can only compare it with total GDP. At global level, while the rise in corporate saving was in general offset by an equivalent decline in household saving, this was not true in emerging Asia and in China in particular (IMF 2009). Also, the available micro evidence shows that the Chinese corporate sector is not particularly thrifty. Chinese firms (either SOEs or private) do not distribute systematically lower dividends than other firms in Asia, the commonly held belief that poor corporate governance in the SOEs and windfalls in resource sectors are causing high saving rates in China is not borne out by a firm-level dataset comprising China and other Asian countries (see Bayoumi et al. 2010).
- 16.
In the last 30 years China experienced a dramatic demographic change: in 1978 the share of young people (aged 0–14) in the total population was 65%, by 2008 this share had fallen below 30%.
- 17.
In their paper M&C consider different alternatives for long-term growth. Here we chose a 14 years average growth rate from 1966 onwards, approximating it with the longest possible average for the period from 1957 to 1965. This choice allows us to reproduce almost exactly the regression results reported in Table 3 of M&C’s paper. The variable they select to take into account the changes in the population structure is a proxy of the young dependency ratio, given by the number of employees divided by the number of persons below 14 years of age.
- 18.
Several theories consider the role of income growth. At aggregate level the relationship between saving rates and income growth is positive; in standard growth models the direction of causation goes from saving rates to growth rates. The evidence, however, suggests that the causation could also run in the other direction, with the saving rate responding to income growth, both at aggregate and microeconomic level (Carroll et al. 2000). The LCH reconciles the evidence with the theory arguing that in fast-growing economies, like China, young generations in their saving phase being much richer than older generations in their dissaving phase push the average saving rate of the economy progressively upwards. Additional refinements to the theory include the habit formation hypothesis (HFH) according to which individuals care about both the level and the rate of growth of consumption, smoothing the two. As a consequence, following a positive (negative) income shock consumption adjusts slowly and the saving-to-income ratio increases (decreases).
- 19.
See Appendix A for details.
- 20.
The main factor behind the change in the results is therefore given by the new data rather than by the revision, as can be seen by comparing the equations for the period 1953–1985, affected only by the latter and almost identical in terms of the significance of the regressors.
- 21.
Godfrey’s test for serial correlation up to the fourth order shows no evidence of autocorrelation in the residuals of the estimated equation (Chi-Square(4) p-value = 0.2014).
- 22.
According to flow-of-funds data household savings reached 38% of disposable income in 2007. The discrepancies between flow-of-funds and survey data are well- known and often highlighted in the literature as puzzling. An inspection of the data reveals that survey data are more volatile and tend to underestimate both income and savings, the underestimation of the latter is more pronounced (see Appendix A).
- 23.
Unfortunately, to our knowledge there are no data that allow us to estimate internal migration flows from rural to urban areas by age. Nevertheless, it is plausible that these flows consist primarily of young working-age individuals. Assuming that those workers were formerly employed in rural areas, when they move to urban areas the working-age population falls in rural areas and so does the average saving rate there, while in urban areas, where this population increases, the average saving rate also tends to increase.
- 24.
SOEs are mainly located in urban areas. In 1978, out of 95 million urban workers 75 million were employed in SOEs, enjoying generous benefits, for pensions, health insurance, schooling and housing. In 2008, some 65 million urban workers, out of a total of 302 million, were employed in SOEs, with considerably lower benefits. The new pension system has set the replacement rate at 58.5% of average earnings on retirement, down from 80% in the pre-reform period. This replacement rate, however, will be difficult to reach, as the interest rate used to revalue part of the contributions is considerably lower than the rate of growth of average wages. In 2005 the ratio of the average pension to the average wage was 49% (it was 77% in 1990) and it is expected to decline further, provoking a considerable cut in the pension wealth for the youngest cohort in the labor force (OECD 2010).
- 25.
Survey data by province are collected annually by the National Bureau of Statistics of China (NBSC). A description of the survey can be found on the NBSC website: http://www.stats.gov.cn/tjsj/ndsj/2009/indexeh.htm. China has 31 provinces; we excluded Tibet and Chongqing because data were missing for several years.
- 26.
Using data from the 2002 Chinese Income Project survey, Di Stefano (2012) shows that rural migrants (whose average age is 26) have a saving rate slightly above the urban average and send about one third of the savings to their relatives.
- 27.
The young dependency ratio variable is particularly important for its trend component. In the sample period this component is common to the young dependency ratio and the SOEs’ employment share, as they both decreased sharply. On the other hand the cross-sectional dimension of the latter has much greater explanatory power for provincial saving rates, hence when both are considered in the same regression only the SOE variable remains significant.
- 28.
With the sole exception of real disposable income, for which we consider a 10% increase evaluated at the sample mean, since it enters in the regressions in a non-linear way. For summary statistics concerning the regressors see Table A.3 in the Appendix.
- 29.
Even compared with South Korea, which has the lowest public social expenditure as a percentage of GDP within the OECD countries and a demographic structure similar to China, the Chinese government spends about 50% less as a share of GDP overall on social and education programs.
- 30.
These policies should be implemented at central level, since local governments might have incentives to not enforce labor market regulations in order to attract businesses (see Park and Cai 2011).
- 31.
An important area of intervention, not analyzed in this paper, is land property rights. A better definition of these rights, particularly in rural areas, would provide much needed collateral for households (see Marconi and Santoro 2006).
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Appendices
Appendix A. Data
1.1 A.1. Data Sources
“Modigliani and Cao” regressions over the sample period 1953–2000, reported in Table 6.4, are conducted using original data from Modigliani and Cao (2004; Table 1, p. 147). “Modigliani and Cao” regressions over the “extended” period (1953–2008), reported in Table 6.5, are run on an updated version of the original dataset. We updated the variables as follows:
Regressions reported in Tables 6.7 and 6.8 are based on household survey data by province. Household survey data at provincial level are collected annually by the National Bureau of Statistics and are available for urban and rural households. Disposable income and consumption expenditures are available only in nominal per-capita terms. A fully balanced set of data for rural and urban households spans from the period 1995–2008. Data sources and methodologies are described in Table A.2.
1.2 A.2 Discrepancies Between Flow-Of-Funds-Based and Household Survey-Based Measures of Saving in China
It is well-known that national level statistics are seldom completely coherent with aggregations derived from surveys and we do not expect this to be contradicted by data on China. Data may not only be affected by measurement errors but they also often refer to different concepts. Differences in the definition of consumption, population of reference and income sources prevent a direct comparison between aggregate measures of saving rates and measures derived from micro sources. However it is important to establish to what extent the main trends shown by national level data are also borne out by the evidence derived from aggregating micro data. A comparison between household saving rates derived from flow-of-funds statistics and those derived from urban and rural household surveys highlights wide differences in time profiles over the period 1992–2008.
China: household saving rate (%). Comparison between flow-of-funds and household survey data (Source: CEIC and authors’ calculations)
Major differences emerge from 1995 to 2000, when the flow-of-funds saving rate was more or less stable while the saving rate that emerged from household surveys was trending upward steeply; on the contrary from 2000 onwards the flow-of-funds saving rate started to increase at a much faster pace compared to that derived from the household survey. As for the levels, wide gaps emerge with the flow-of-funds saving rate lying always above that of survey (at about 12 points of disposable income in 2008, Fig. A.1). Further insight can be gained by examining the two determinants of the saving rate separately: income and saving per capita. As can be seen (Fig. A.2) in both cases survey data give a lower estimate in terms of levels and the gap between the series, almost constant considering disposable income, is widening in the case of savings, likely reflecting different definitions of consumption in the two accounting systems. In particular, household saving derived from survey data does not include household investment (Kraay 2000). In addition, underestimation might be due to low coverage or underreporting of high-income households.
Despite these large differences, however, we maintain that the main trend we are interested in, i.e. the sharp rise in savings, is not affected by the choice of data. We can therefore be confident that the internal coherence of data allow us to carry out a meaningful analysis on provincial level data that also goes some way towards explaining aggregate behavior.
Chinese households’ disposable income and savings. A comparison between flow-of-funds and households’ survey data (Source: CEIC and author’s calculations)
1.3 Summary Statistics
Appendix B. Tables and Figures
Households saving rates: actual and fitted values from system GMM estimations
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Cristadoro, R., Marconi, D. (2012). Urban and Rural Household Savings in China: Determinants and Policy Implications. In: Gomel, G., Marconi, D., Musu, I., Quintieri, B. (eds) The Chinese Economy. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28638-4_6
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