Financial Development and Household Saving in China: Implications for Rebalancing

1. Financial Development and Household Saving in China: Implications for Rebalancing the Global Economy Peter C.Y. Chow The City College and Graduate Center City University of New York

2. Part I. • Introduction

3. China’s gross national savings ratio was as high as 30% of its total GDP in the 1980’s and exceeded 50% after 2005 with a marginal propensity to save of 54% reached in 1982-2008 (Ma and Wang, 2010). Such a high saving ratio is a mirror image of China’s persistent current account surplus, which accounted for the largest percentage share in the U.S. trade deficit and allegedly contributed to the imbalance of the world economy.

4. China’s financial system is still backward relative to those of OECD countries. (Estrada, Park, and Ramayandi, 2010) Paradoxically, if financial development will mobilize national savings for capital investment, as predicted by conventional models, then why did the backward financial sector foster such a high savings-investment ratio in China which is far above those in most OECD countries? One argument is that China’s high savings ratio was due to its precautionary savings The other is the corporate structure in China

5. Given that China has a high savings ratio and as well as trade surpluses which contribute to the thesis of a global saving glut (Bernanke, 2005), it is interesting to ask: a) What are the determinants of saving behavior in China and the extent to which its high saving ratio was affected by development of financial sector? b) Is China’s high savings ratio a structural or cyclical phenomenon?

6. Part II. • China’s High Savings Ratio and Its Components

7. To investigate the saving behavior in China, the first step is to decompose the gross national savings into three components: household, corporate and government. The percentage of each of these three components in total GDP is reported in Figure 1.

8. Part III. • Financial Development, • Household Savings, • and Global Imbalances

9. Edwards (1996) argues that financial deepening would generate more saving due to higher rate of return and lower risk involved in an advanced and sophisticated financial system. McKinnon (1973) argued that financial liberalization which leads to higher real rate of interests would further enhance household saving as well. The Edwards-McKinnon thesis is in stark contrast with the conceptualized view that a more developed financial system would make credit more readily available and reduced the precautionary demand for saving in China.

10. Another argument focuses on the mechanism by which financial development leads to economic growth. While the McKinnon-Shaw (1973) thesis argues that financial development will increase the domestic savings and enhanced capital accumulation for economic growth, Goldsmith (1969) argues that financial development will channel financial capitals to the most productive investments project and enhanced the marginal efficiency of capital.

11. The asymmetry in financial development between countries with trade surpluses and those with deficits seems to be an important clue toward resolving the global imbalance. Most cross country studies on saving-investment behaviors, with the exception of Modigliani and Cao (2004), are based on panel data and focus only the Keynesian effect of saving without clearly specifying China’s unique socio-cultural and demographic factors.

12. Modiganliani and Cao (2004) analyze the puzzle of China’s high saving from the approach of life-cycle hypothesis. • Kraay (1998) adopted the 10 core determinants of household saving in China identified by Schmidt-Hebbel, and Serven (1998), and analyzes the household saving behavior from the approach of forward-looking by using the “expected future income” as one of the explanatory variables. • Chamon and Prasad (2010) focus on urban household saving behavior based on survey data.

13. Ferrucci and Miralles(2007) group the determinants of household saving in the following four groups of variables: • Demographic factors such as the ageing population and the youth dependency ratio • Fiscal policies such as government spending on social welfare and retirement benefits • Macroeconomic environments such as inflation, per capita and its growth rate • Institutional factors such as borrowing constraints, and the availability of consumer and mortgage loans

14. Ferrucci and Miralles (2007) require several caveats: • use the private saving in total GDP as the dependent variable; • includes inflation rate as one of the explanatory variables • includes the terms of trade to evaluate the Harberger-Larusen-Metzler effect • uses total government consumption and budget separately in the regression model without specifying what kinds of government spending would affect private saving in China

15. Part IV. • Regression Model • and Data Specification

16. Equation (1) St= household saving as percentage of total GDP or disposable income in t Xt1 = controlled variables under the Keynesian effect such as GDP per capita or the growth of GDP and the real interest rate Ztn = index of financial development as well as socio-economic variables such as the youth dependency ratio, the aged dependency ratio, social welfare expenditure as percentage of GDP, and government budget gap

17. Equation (1) is a dynamic structure for the consumption – saving process where the dependent variable follows an Autoregressive Distributed Lags (ARDL). Because of the annual frequency of the data, an ARDL (1, 1, 1, 1, and 1) was chosen.

18. Equation (2) Equation (2) is the error-correcting model (ECM). This form of the model could be seen as comprising the short-run transitory effect and the long run relationship, and could describe how the long run solution is achieved via error correction feedback.

19. Data prior to 1992 was derived from Kraay (2000), who constructed two series for household saving as percentage of GNP in China, both from 1978 to 1995. The first one is based on a survey approach, and the second one is based on an asset approach. The process for merging both series of data on household saving has several steps.

20. Step 1: Calculate the household saving as percentage of GDP series from Kraays’s data (1978-1995) by multiplying his series by the ratio of GNP/GDP . Step 2: Calculate the growth rates for both series (1979-1995). Step 3: Iterate backwards from a base year in our data, using the growth rates from the previous step. (1978-199x). Step 4: For selecting a base year: Analyze the co-movement among the two series during the years when they overlap. Step 5: They co-move similarly between 1992 and 1994. Take an average of household saving data from NBS in these years, and use it as a base year in 1994. Step 6: Merge the iterated series (1980-1994) with the previously collected series (1992-2010), for both approaches.

21. Part V. • Results from • Regression Analysis

22. Table 1: Unit Root Tests on the Levels

23. Table 2: Unit Root Tests on the Differences

24. V.1 Regression Model 1: • Survey Approach on Household Saving (HS1/GDP)

25. Table 3: Regression results on long term relations

26. Table 4: Error Correction Model 1

27. Table 5: Error Correction Model 2

28. V.2 Regression Model 2: • Asset Approach on Household Saving (HS2/GDP)

29. Table 6: Regression results on HS2/GDP

30. Table 7: Error Correction Model 1

31. Table 8: Error Correction Model 2

32. V.3 Regression Model 3: • With the dependent variable being • household savings as percentage of disposable income

33. The saving ratio data was calculated in the following way; the household was broken down by urban and rural households. Household saving was obtained by subtracting consumption from disposable income. The overall household saving ratio was the weighted average by multiplying the proportions of urban and rural households in total population. The data is shown in Figure 2.

34. Figure 2: Household savings as proportion of disposable income

35. Table 9: Regression result on household saving as percentage of disposable income

36. Table 10: Error Correction Model 1

37. Table 11: Error Correction Model 2

38. Part VI. • Summary and • Policy Implications for Rebalancing the Global Imbalance

39. In general, the regression results were mixed and depend on how household saving is measured. Other than the growth rate of the GDP which has a consistent significant effect on household saving in all regression models, the effects of financial development variables on household saving are not robust.

40. Regression Model 1: • The ratio of private credit in total GDP has significant positive effect on saving only in error correction model 2 at the 5% level of significance. • The degree of financial deepening measured as the ratio of M2/ GDP has significant negative effect at the 1% level under both long term and short term under the survey approach (Tables 3 and 5). • (cont.)

41. Regression Model 1 (cont.): • Table 3 shows that the real interest rate has positive effect but financial deepening as measured by the ratio of M2/GDP has negative effect on household in the long term. • Maintaining positive real interest rate has positive effect on household saving whereas expanding money supply would do just the opposite in the long term. • The effect of real interest rate on household saving remains positive in the short term under error model 1 from Table 5. • Once those insignificant variables were dropped from the model, the significance of real interest rate on household saving disappears on Table 6. • (cont.)

42. Regression Model 1 (cont.): • It is interesting to point out that expanding domestic credit, albeit the quality of data which mixed up loan to state-owned enterprises will have negative effect whereas financial deepening has positive effect on household saving in the short term.

43. Regression Models 2 and 3: • Financial deepening index has only long term positive effect on household saving (Tables 6 and 9) when household was measured by assets approach or by disposable income. • The effect of financial development on household saving depends on whether saving is based on survey approach or asset approach. • Implies that financial deepening may have positive effect on household saving when it was measured by asset or disposable income. • (cont.)

44. Regression Models 2 and 3 (cont): • Government expenditure on social welfare has significant negative effect on household saving in both long and short terms models when household saving was measured by the asset approach of model 2. But when household saving was measured by the proportion of disposable income in model 3, government spending on social welfare has significant positive effect on household saving in both long and short terms (Tables 9-11). • (cont.)

45. Regression Models 2 and 3 (cont): • This implies that when one adopts the assets approach to measure the household saving, government spending on social welfare will have a substitute effect on household saving in China. • In the absence of comprehensive social safety net, health insurance and educational loans, limited pension coverage and low unemployment compensation funds, Chinese households would have to cumulate precautionary savings. • As government social welfare expenditure increases, then much of the precautionary savings could be mitigated. • (cont.)

46. On social demographic determinants, China’s youth dependency has negative effect on household saving. But China’s population policy probably won’t change just because of concern of its high saving ratio. From the regression analyses in this study, one can conclude that, if increase its government expenditure on social welfare so as to reduce its household saving.

47. Finally, a big chunk of China’s high saving was generated by its corporate saving as addressed in section II. Some concrete policy measures to restructure China’s corporate governance and ownership structure so as to reduce its corporate saving is necessary to reduce its excessive saving and mitigate the global imbalance. This is another research topic beyond the scope of this paper and to be pursued in another study.