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Chamon: Research Department, IMF, email@example.com; Liu: Johns Hopkins University, firstname.lastname@example.org; Prasad: Cornell University, Brookings Institution and NBER, email@example.com. We are grateful to Loren Brandt, Chris Carroll, Mark Dorfman, Robert Moffitt, Damiano Sandri, participants at the NBER Summer Institute, China Economics Summer Institute, IMF Research Seminar, and the Workshop on China’s Macroeconomy at the University of Toronto for comments and suggestions. We thank Lei (Sandy) Ye for research assistance. The views expressed in this paper are those of the authors and do not necessarily reflect those of the institutions the authors are affiliated with.
Other recent studies analyzing the determinants of household savings in China include those using aggregate data (e.g., Modigliani and Cao, 2004; Kuijs, 2006), provincial-level data (e.g., Qian, 1998; Kraay, 2000; Horioka and Wan, 2007; Wei and Zhang, 2009) and micro data at the household or individual levels (e.g., Song and Yang, 2010). Banerjee, Meng and Qian (2010) use a single-year cross-sectional survey to examine the effect of fertility on household savings.
Other papers in this literature include Lillard and Weiss (1979), MaCurdy (1982), Abowd and Card (1989), Moffitt and Gottschalk (1995), and Baker and Solon (2003). Articles in a recent special issue of the Review of Economic Dynamics (2010) document the evolution of idiosyncratic income risk in different countries.
The social pension is financed by employer contributions of 17 percent of wages. The individual accounts are financed by employer and employee contributions of 3 percent and 8 percent of wages, respectively. Sin (2005) and Arora and Dunaway (2007) provide more details on various aspects of the pension reform, including the gaps in coverage under the new system. Herd, Hu and Koen (2010) document that labor mobility is impeded by limited pension portability under the current system and also note that effective replacement rates are projected to decline further under the current rules.
The survey is a collaborative effort between the Carolina Population Center at the University of North Carolina at Chapel Hill and the National Institute of Nutrition and Food Safety at the Chinese Center for Disease Control and Prevention. Details are at http://www.cpc.unc.edu/projects/china
Gottschalk and Moffitt (2009) discuss the need for trimming. The sample is unbalanced because attrition, new respondents introduced into the survey, transitions into and out of employment, and aging affect households’ and individuals’ movement into and out of the analysis sample in different years.
In our sample of urban households, labor earnings comprise 97 percent of total individual income on average, so these two measures are approximately equivalent.
State enterprise reform has involved selective privatization and hardening of budget constraints (reductions in explicit state subsidies) for the remaining enterprises. For more details on the reform process and how it has affected the operations and labor structure of these firms, see Lin, Cai and Li (1998), Bai, Lu and Tao (2006) and Li and Putterman (2008). Brandt, Hsieh and Zhu (2008) analyze the effects of the reallocation of labor from the state sector to the non-state sector.
To conserve space, we do not report these regression results in detail here. The estimates show rising returns to education. The pattern of returns to potential labor market experience is less clear. We re-estimated the income regressions using alternative sets of covariates (including adding a dummy for SOCE employment) and also tried using the detrended log of total household income. The trends in estimated transitory and permanent income uncertainty that we report below remain very similar.
The transitory shocks do not appear to be serially correlated. We estimate the following autocovariances of unexplained income growth at lags 1 to 3 (standard errors in parentheses): -0.101 (0.011), 0.007 (0.014), -0.000 (0.018). Autocovariances of order 2 and higher are not statistically significant. If we test the null hypothesis of zero autocovariances in income growth (allowing autocovariances to be different across years), we reject the null hypothesis at lag one but not for higher order lags. These results indicate that the transitory shocks are either i.i.d or follow an MA(1) process. The latter is consistent with much of the literature (Abowd and Card, 1989; Meghir and Pistaferri, 2004; Blundell, Pistaferri and Preston, 2008). Because of the gaps between years of observations in the data, it is not possible to further test the stochastic process of transitory shocks. As we discuss later, the permanent uncertainty identified by our model is consistent regardless of whether the transitory shock follows an MA(1) process or is i.i.d.
We focus on the year effect and therefore the age and cohort effects cannot be separated. Given our sample size, we cannot allow variances to also vary by age (or cohort).
However, without additional assumptions, it is not possible to identify the MA(1) process given the data availability in our sample.
See Bound and Krueger (1994). In the case of non-classical measurement errors, Pischke (1995) finds that the transitory variance is less contaminated due to the negative correlation of measurement errors with transitory earnings.
The results are similar if we define the SOCE subsample based on SOCE employment throughout the survey (i.e., excluding workers who start in the SOCE sector but later move to the non-SOCE sector).
Transfers include both private and public transfers. Subsidies constitute firm-level nonwage compensation to the worker, and include subsidies on gas, food, education and housing as well as allowances for children. In the early stages of reform, SOCEs offered workers higher levels of subsidies to compensate for noncompetitive wages and then reduced them as their budget constraints were tightened (reduced transfers from the state to SOCE firms). The ratio of subsidies to total compensation was as high as 35 percent in the 1991 wave, but steadily declines to about 5 percent in the 2006 wave.
In the survey, other income refers to income from leased land (only for 1989), rent from non-land assets, rent from lodgers or boarders, and other unspecified sources of income.
In the CHNS, the question concerning employment status is: “Are you working at present?” which is then followed up by a question regarding the reasons for not working. The question is consistent across survey waves although it is only indicative of the employment status for a worker in a particular year.
See also Fuchs-Schündeln (2008) and Kaplan and Violante (2010). Our calibration exercise sets only a lower bound on the degree of precautionary saving attributable to earnings uncertainty. We consider the variance of different shocks to earnings only for workers who report positive earnings in each period. For workers who in reality face unemployment and the prospect of zero earnings, the precautionary savings motive could be even stronger.
The sample covers the following provinces: Anhui, Beijing, Chongqin, Ganshu, Guangdong, Hubei, Jiangsu, Liaoning, Shanxi and Sichuan. Only three of these overlap with the CHNS sample.
The real interest rate is based on the nominal interest rate on one-year bank deposits deflated by the annual CPI inflation rate.
For simplicity we assume a Poisson death process, calibrated to match life expectancy in China in 2009 (73.5 years). This results in a constant survival probability of 0.925 between t and t+1 after retirement.
The replacement rate should decline over time, given the nature of the pension formula. Sin (2005) projects the replacement rate for a male retiring at age 60 to decline to about 60, 55 and 50 percent of the average wage by 2010, 2020 and 2030 respectively. Thus, our assumption for the decline in the replacement rate is a fairly conservative one, particularly for the younger workers.
This assumes that there is no cohort effect on the growth rate of earnings. That is, while younger cohorts are much richer than older ones, the age profile of income growth is the same for both. One could argue that younger cohorts should expect slower growth as China’s growth rate may eventually moderate.
Housing motives for saving are not included in the calibration. If included, they would raise the saving rates of the younger individuals, accentuating the U-shaped age-saving profile and bringing it more in conformity with the pattern observed in savings data for Chinese urban households. Lumpy and uncertain health expenditures can still contribute to savings among the elderly (particularly among those that have already retired). But while the inclusion of both effects would further contribute to savings, the combined effect should be smaller than when each is considered in isolation (e.g., a higher buffer-stock accumulated in the aftermath of the pension reform can help older households better cope with health shocks).
The saving rates used for these seven age groups (from young to old) were: 23.0, 22.7, 21.1, 23.2, 25.9, 24.4 and 22.3 percent, respectively. This age-saving profile captures the estimated saving behavior for a household with a head aged 25 years in 1997 as he or she ages (not the 1997 cross-section of savings with respect to age). Note that this age-saving profile is not U-shaped as it is based on 1990-1997 data; the U-shaped profile does not appear in the data until the 2000s.
The income growth path includes the effects of trend income growth as well as age effects on income. Controlling for trend growth, income eventually declines with age, which explains this low income growth despite the strong trend income growth.