I. Introduction

Recent economic reforms and developments in China have drawn great attention from economists. One of the most important factors in both the theory of, and policy prescription for, economic development is the domestic savings rate. As the reform moves the economy in a more decentralized direction, can China maintain her savings rate at the high level of the past few years?

With the annual publication of the Statistical Yearbook of China (hereinafter referred to as the Yearbook) since 1981, a systematic study on the savings of China has become possible. Such a study serves to improve our understanding about the effects of the recent economic liberalization in China and to provide preliminary results for future studies as well.

By the domestic savings rate, we mean total domestic savings as a percent of gross domestic product (GDP). According to the sources of the savings, total domestic savings constitute government savings, business savings, and household savings. The structure of the saving is then the proportion of these three parts in the total savings. Tables 1 and 2 present data on the domestic savings rate and the structure of domestic savings in China and selected countries for the period 1976-84. Household savings rates (total household savings as a percent of household disposable income) are shown in Table 3. An examination of these tables yields the following observations.

Table 1.

China: International Comparisons of the Domestic Savings Rate, 1980-84 ^1/

(In percent of GDP)

Sources:(1) China, author’s estimates based on the Yearbooks;(2) Others, from the World Bank, World Development Report, various issues.

^1/It should be noted that the term “country” used in this paper does not in all cases refer to a territorial entity that is a state as understood by international law and practice. The term also covers some territorial entities that are not states but for which statistical data are maintained and provided internationally on a separate and independent basis.

Table 1.

China: International Comparisons of the Domestic Savings Rate, 1980-84 ^1/

(In percent of GDP)

	1980	1981	1982	1983	1984
OECD countries	22	22	20	20	19
Developing Countries	24	23	22	22	23
Korea	23	22	24	26	30
Hong Kong	24	24	25	25	29
Singapore	30	33	41	42	43
Japan	31	32	31	30	31
Yugoslavia	32	29	33	37	30
Hungary	22	29	29	29	28
China	31	30	32	32	33

Sources:(1) China, author’s estimates based on the Yearbooks;(2) Others, from the World Bank, World Development Report, various issues.

^1/It should be noted that the term “country” used in this paper does not in all cases refer to a territorial entity that is a state as understood by international law and practice. The term also covers some territorial entities that are not states but for which statistical data are maintained and provided internationally on a separate and independent basis.

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Table 1.

China: International Comparisons of the Domestic Savings Rate, 1980-84 ^1/

(In percent of GDP)

	1980	1981	1982	1983	1984
OECD countries	22	22	20	20	19
Developing Countries	24	23	22	22	23
Korea	23	22	24	26	30
Hong Kong	24	24	25	25	29
Singapore	30	33	41	42	43
Japan	31	32	31	30	31
Yugoslavia	32	29	33	37	30
Hungary	22	29	29	29	28
China	31	30	32	32	33

Sources:(1) China, author’s estimates based on the Yearbooks;(2) Others, from the World Bank, World Development Report, various issues.

^1/It should be noted that the term “country” used in this paper does not in all cases refer to a territorial entity that is a state as understood by international law and practice. The term also covers some territorial entities that are not states but for which statistical data are maintained and provided internationally on a separate and independent basis.

Table 2.

China: International Comparisons of the Structure of Domestic Savings, 1976-84

(In percent of total)

Sources:(1) China, 1982-84, author’s estimates based on the Yearbooks;(2) Others, Table 9.1 of Lim and Wood (1985).Notes: (1) For China’s data, government savings include only budgetary saving. Enterprise savings include state and collectively owned enterprise savings and local government extrabudgetary savings (mainly depreciation funds and after-tax profits handed over to local governments).

Table 2.

China: International Comparisons of the Structure of Domestic Savings, 1976-84

(In percent of total)

		Government	Enterprises	Household
United States (1976-80)		7	58	35
Japan	(1976-80)	9	37	54
Korea	(1976-80)	26	35	38
India	(1976-80)	13	22	65
China	(1978) 1/	51	34	15
	(1982) 1/	18	57	25
	(1983) 1/	19	48	33
	(1984) 1/	20	34	46

Sources:(1) China, 1982-84, author’s estimates based on the Yearbooks;(2) Others, Table 9.1 of Lim and Wood (1985).Notes: (1) For China’s data, government savings include only budgetary saving. Enterprise savings include state and collectively owned enterprise savings and local government extrabudgetary savings (mainly depreciation funds and after-tax profits handed over to local governments).

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Table 2.

China: International Comparisons of the Structure of Domestic Savings, 1976-84

(In percent of total)

		Government	Enterprises	Household
United States (1976-80)		7	58	35
Japan	(1976-80)	9	37	54
Korea	(1976-80)	26	35	38
India	(1976-80)	13	22	65
China	(1978) 1/	51	34	15
	(1982) 1/	18	57	25
	(1983) 1/	19	48	33
	(1984) 1/	20	34	46

Sources:(1) China, 1982-84, author’s estimates based on the Yearbooks;(2) Others, Table 9.1 of Lim and Wood (1985).Notes: (1) For China’s data, government savings include only budgetary saving. Enterprise savings include state and collectively owned enterprise savings and local government extrabudgetary savings (mainly depreciation funds and after-tax profits handed over to local governments).

Table 3.

China: Household Savings Rates

(In percent of household income)

Sources:(1) Data for countries other than China are from Table 1 of Horioka (1986);(2) Data on China are from urban and rural household sample surveys in the Yearbooks and calculated by the formula: $\begin{array}{c}\begin{array}{c}\text{Urbansavingsrate=1}\frac{\text{-livingexpenditure}}{\text{incomeavailable}}\hfill \\ \text{Ruralsavingsrate=1-}\frac{\text{livingexpenditure-housingexpenditure}}{\text{netincome}}\hfill \end{array}\hfill \\ \text{Averagesavingsrate=1-}\frac{\text{0.8}\times \text{ruralsavings+0.2}\times \text{urbansavings}}{0.8\times \text{ruralincome+0.2}\times \text{urbanincome}}\hfill \end{array}$Notes:(1) China’s urban savings rate is biased downward owing to the omission of consumer durables from total savings.(2) China’s rural savings rate is biased upward; see Section VI about rural data.

Table 3.

China: Household Savings Rates

(In percent of household income)

United States (1976-82)					8.1
Germany, Fed. Rep. of (1976-81)					12.8
Belgium (1976-82)					16.1
Japan (1976-82)					21.2
China
		1957	1982	1983	1984
Average		5.3	20.0	21.5	24.7
	Urban	5.7	4.8	3.8	7.9
	Rural	4.9	26.8	28.8	32.0

Sources:(1) Data for countries other than China are from Table 1 of Horioka (1986);(2) Data on China are from urban and rural household sample surveys in the Yearbooks and calculated by the formula: $\begin{array}{c}\begin{array}{c}\text{Urbansavingsrate=1}\frac{\text{-livingexpenditure}}{\text{incomeavailable}}\hfill \\ \text{Ruralsavingsrate=1-}\frac{\text{livingexpenditure-housingexpenditure}}{\text{netincome}}\hfill \end{array}\hfill \\ \text{Averagesavingsrate=1-}\frac{\text{0.8}\times \text{ruralsavings+0.2}\times \text{urbansavings}}{0.8\times \text{ruralincome+0.2}\times \text{urbanincome}}\hfill \end{array}$Notes:(1) China’s urban savings rate is biased downward owing to the omission of consumer durables from total savings.(2) China’s rural savings rate is biased upward; see Section VI about rural data.

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Table 3.

China: Household Savings Rates

(In percent of household income)

United States (1976-82)					8.1
Germany, Fed. Rep. of (1976-81)					12.8
Belgium (1976-82)					16.1
Japan (1976-82)					21.2
China
		1957	1982	1983	1984
Average		5.3	20.0	21.5	24.7
	Urban	5.7	4.8	3.8	7.9
	Rural	4.9	26.8	28.8	32.0

Sources:(1) Data for countries other than China are from Table 1 of Horioka (1986);(2) Data on China are from urban and rural household sample surveys in the Yearbooks and calculated by the formula: $\begin{array}{c}\begin{array}{c}\text{Urbansavingsrate=1}\frac{\text{-livingexpenditure}}{\text{incomeavailable}}\hfill \\ \text{Ruralsavingsrate=1-}\frac{\text{livingexpenditure-housingexpenditure}}{\text{netincome}}\hfill \end{array}\hfill \\ \text{Averagesavingsrate=1-}\frac{\text{0.8}\times \text{ruralsavings+0.2}\times \text{urbansavings}}{0.8\times \text{ruralincome+0.2}\times \text{urbanincome}}\hfill \end{array}$Notes:(1) China’s urban savings rate is biased downward owing to the omission of consumer durables from total savings.(2) China’s rural savings rate is biased upward; see Section VI about rural data.

First, China’s domestic savings rate, at about 30 percent, is higher than the rate of either the “average” developed country or the “average” developing country. China’s savings rate is, however, less exceptional in comparison with the rates for some Eastern European countries or some Asian countries, such as Japan and Singapore.

Secondly, the structure of savings varies from country to country and over time. In most market economies, the share of household savings is substantial; in general, it exceeds one third of the total savings. The structure of domestic savings in China has changed dramatically since the start of liberalization in 1979 and has been characterized by a shift from government savings to enterprises, local government extra-budgetary, and, especially, household savings. The high figure of household savings in 1984 does not appear representative because of the unusual increase in bonus income in the last quarter of that year. However, household savings in 1983 still accounted for one third of total domestic savings. This figure is close to those of market economies such as Korea and the United States.

Thirdly, in recent years, household savings in China increased not only as a share of total saving, but relative to household income, as well. Unfortunately, data for deriving the household savings rate are incomplete. Only a rough estimate can be made from the sample survey, which shows a quite high level, even compared to some market economies. We will see later that this is mainly due to the high savings rate in the rural sector of China.

Economists are not satisfied by fragmentary observations; they want a systematic explanation--from both theoretical and empirical aspects--of the observed phenomena. In particular, they are interested in the nature of the household savings function, and further, the reasons for the recent high savings rate: Does it reflect the underlying structural shift and behavioral change, or is it merely a monetary phenomenon, a result of repressed inflation, for example? The answer to this question concerns predictions for the future performance as the Chinese economy moves toward further liberalization, as well as the macroeconomic policies the Chinese Government may choose.

The topic of household savings behavior in China is particularly interesting, not only because of the important role of household savings in total domestic savings, but also because of the nature of the decision-making process vis-a-vis household savings. In an economy that is still very centralized in many aspects, the autonomous decisions of several hundred million households on the amount of their individual savings has important macroeconomic policy implications. Furthermore, in China, household savings may directly relate to the Government’s monetary policy--if price controls remain important, savings could be an important channel to balance overall liquidity.

The paper is organized as follows: Section II is a review of previous studies of household savings in China; Section III provides a background of the Chinese economy and singles out what we believe are the most important factors influencing household savings. Section IV analyzes motives for savings in the urban and rural sectors, and Section V presents alternative savings models. Sections VI and VII present empirical results of this study for the urban sector and rural sector, respectively. Section VIII contains the study’s conclusions.

II. Review of Previous Studies on Household Savings in China

The intellectual background of studies on Chinese household savings can be traced back to the literature of studies of savings for other countries. Like many other economic subjects, modern savings/consumption theories and models originated from the studies of savings behavior in the Western, developed, market economies. Among well-known savings models in this context are the Keynesian absolute-income hypothesis; Duesenberry’s relative-income hypothesis; Friedman’s permanent-income hypothesis; the Modigliani-Brumberg life-cycle hypothesis, and the asset adjustment models associated with, among others, Houthakker and Taylor, and Leff and Sato. Later on, these models have been applied to the developing market economies, as seen in the surveys by Mikesell and Zinser (1973) and Snyder (1974).

Since the late 1970s, there have been quite a number of studies of savings behavior in centrally planned economies, mainly of Eastern European countries and the U.S.S.R. One approach is to apply the above cited models directly to those economies (e.g. Pickersgill, (1976, 1980)), ignoring potential disequilibrium concerning the intention and realization of current household consumption and saving, which is believed by many authors to persist in such economies. Another approach takes potential disequilibrium directly into account (e.g. Portes and Winter, (1980)). The latter approach is seen as theoretically more appealing, but technically more difficult.

Recently, some studies of the exceptionally high savings rate in Japan have focused on aspects not included in traditional theories. In Horioka (1985) the role of target savings for housing and education has been singled out, while Hayashi (1986) drew special attention to the role of bequests.

Empirical studies on household savings behavior in China-- a developing, centrally planned economy -- did not start until 1982. To our knowledge, four studies have been done prior to the present paper: De Wulf and Goldstein (1983, 1985), Naughton (1986), Feltenstein, Lebow, and van Wijnbergen (1986), and Armitage (1986).

The studies by De Wulf and Goldstein and Naughton shared common methods in estimating savings functions of China, namely applying time series data to the traditional linear models cited above. Naughton (1986) used aggregate nominal data while De Wulf and Goldstein (1983) applied per capita, real data, and they further separated the rural sector from the urban sector in their later paper (1985). Both studies found very low Durbin-Watson (D-W) statistics in the estimation indicating misspecification, and that the estimated marginal propensity to save (MPS) varies, depending on the time period selected. However, in neither study was the model respecified for further testing. Although the results were not fully satisfactory, the studies contributed in identifying the problems, in constructing the time series data, and in providing preliminary results.

To avoid the misspecification problems arising with time series data, Armitage instead used rural and urban household sample survey data of recent years to estimate separately the savings function in each sector. Constrained by the availability of data (for the rural sector, 1982-84 data from 28 provinces; for the urban sector, 1983 data from 29 cities), Armitage was only able to estimate a Keynesian savings model for each year from 1982 to 1984 for the rural sector and for 1983 for the urban sector and Friedman’s permanent-income model for the rural sector in 1984. She found a substantial difference in household savings behavior between the rural and urban sectors, a result that is consistent with the common observation. Her estimation favors the permanent income model over the absolute-income model for the rural sector in 1984, which is the only year for which the comparison can be made. Though the separation of the rural and urban sectors is justifiable, the results may not be reliable, especially because the urban sector data cover only one year.

Stressing the potential disequilibrium in the Chinese economy, Feltenstein, et al. approached the problem quite differently. Instead of using the official price index as a deflator, they adopted what they called the “virtual price,” defined as that price level which would induce the observed quantity of consumption and savings in the absence of rationing. The approximation of the virtual price, p, in terms of observables is p/P = (M/PR)^a where P is the official price index, M is the money supply, PR is total retail sales and a is a parameter, a constant. By using p instead of P as a deflator (where money supply is defined as M2) they found (1) only two out of five specifications give very low D-W statistics and for the permanent-income model, D-W increased from 0.8 to 1.86; and (2) by defining “real virtual interest rate” as 1 + r = (1 + i) × p/P₊₁, where i is the official nominal interest rate, they were able to estimate a significant value of the elasticity of consumption with respect to the real virtual interest rate, on the order of about -0.20. They concluded that there was no regime shift in household savings behavior, as long as potential disequilibrium is allowed.

Although the theoretical derivation of the model itself is correct, the selection and interpretation of the data in the time period remain a problem. First, their results are sensitive to the monetary aggregate selected: when they used M1 (defined as total cash in circulation) instead of M2 (total cash plus total savings deposits), the D-W was reduced from 1.86 to 1.27. The reasons for this are obvious: by including p = P (M/PR)^a into the regression, we expect to have a higher D-W, because M/PR is correlated to the rapid increase in savings and the correlation is stronger if M includes savings itself as we take M2. Secondly, their significant estimation of interest elasticity depends crucially on their assumption about expectations: consumers face rationing in a given year and expect rationing to be eliminated in the next year (remember 1 + r = (1 + i) × p/P₊₁), We doubt, however, that this kind of expectation could survive every year for more than 30 years. If consumers expect rationing to continue in the coming year, then 1 + r = (1 + i) × p/p₊₁ (1 + i), and the estimated results may not hold.

In their model, Feltenstein et al. assume no regime shift affecting household savings behavior, but allow a possible change of degree of disequilibrium, which can be summarized, in their view, by p. By choosing the monetary aggregate properly (in this case, M2), most variances can be explained by the virtual price model. The implication of their model is: the higher saving of recent years is the reflection of the excessive monetary expansion and the increased degree of repressed inflation in the economy, and hence represents forced saving.

In what follows, we will go to the other extreme: assume no change in the degree of disequilibrium (or no disequilibrium at all), but allow a possible change of regime affecting household savings behavior. It is not our belief that it is a more realistic assumption than the other extreme. Instead, it is one important factor that can be used to explain the recent increase in the savings rate in China; the simple forced savings theory is not complete. It is worthwhile to see how successfully the data would fit the model under this alternative assumption. Generally speaking, our approach and Feltenstein et al.’s approach are complementary. However, it should be noted that, since the two hypotheses, “no regime shift” made by Feltenstein et al., and “no disequilibrium” made by us, are not nested in either model, even the perfect fitting of one model cannot reject the hypothesis made by the other model.

III. Factors Shaping the Chinese Economy and Influencing Household Behavior

We consider that the following factors are important in shaping the contemporary Chinese economy and in influencing household behavior-- in the present case, household savings behavior.

(1) China is a low-income developing country, with the majority of its population living in rural areas. At the persistently low-income level, as well as low-asset level, for many years, households were essentially struggling to meet basic needs. Saving was then considered a kind of “luxury good,” and not a realistic option when income was below a certain level. Borrowing in general was constrained either by the lack of a credit market, or by traditional conservative attitudes toward borrowing. Under these circumstances, one cannot imagine that a consumer with a yearly disposable income of $100 would have the same marginal propensity to save as one with $1,000 a year. This basic and simple notion leads us to suspect the application of a linear savings function to China in the 30-year period (1955-85), during much of which most households were concerned with meeting their basic needs.

(2) China is a centrally planned economy, with most enterprises owned and controlled by the Government. Prior to 1979, China followed a development path similar to that of other centrally planned economies--that is, one of high accumulation, stable and low wages, full employment, and a high standard of social welfare (at least in the urban areas). On the one hand, with economic uncertainty reduced and the income flow smoothed, for a given level of income a household’s motives to save were considerably reduced: housing, education, pension, nursery, and medical services were all provided by the Government. On the other hand, the persistent shortage of consumer goods may have forced such households to save the amount that they otherwise may wish to spend in the absence of shortages. During this period, neither the Government nor most economists viewed household savings as being a part of accumulation; rather, they thought currency and savings deposits held by households were unrealized purchasing power and were potential destabilizing influences for the consumer goods market. Other things being equal, excess monetary expansion relative to the real growth of consumer goods output would lead to either inflation or forced saving if the price level is controlled. This view is very similar to the quantity theory of money, but in a quite different context. The counterpart of the monetarist’s statement “inflation is a monetary phenomenon” is the one “the excess savings is a monetary phenomenon.” Prior to 1979, the authorities were generally able, with some exceptions, to control monetary expansion in order to avoid either inflation or the rapid accumulation of household savings. Hence, the low and stable savings rate of the household sector prior to 1979, other things being equal, is the consequence of the institutional arrangement.

(3) Since 1979, economic reform has been under way in China. The main feature of the reform has been the decentralization of decision making to enterprises (in the case of urban reform), and to households (in the case of rural reform). One of the results of economic reform has been the sharp increase in both household disposable income and savings, with the measured marginal propensity to save (MPS) higher than ever before, especially in the rural sector, where per capita income nearly tripled during the past eight years. Many other results of reform are less visible. The emergence of household farms in rural areas and free and parallel markets in urban areas has increased the demand for money, as the economy has become more monetized. In the rural sector, as the household has been transformed into a production unit and investment opportunities have arisen, farmers have been encouraged to save money for investment. In the urban sector, income--now consisting of wages and bonuses, and possible extra money from various sources--is more volatile than before and most consumers feel less certain about future income. In addition, reform itself increases uncertainty. For example, the impact on uncertainty of prospective price reforms is not insignificant.

(4) China’s economy is also shaped by a number of unique factors, among which are its huge size, immense population, and its history. Traditional ideas regarding saving and the virtues of thrift, and the past 30 years’ experience of low income may now contribute to a strong motive to save, as a precautionary measure.

The above four factors are important in explaining recent high household savings behavior in China. To be specific, we postulate the following identity, vM/PQ = s, which can be viewed as a generalized form of money equation where M is the money supply, P is the price level, Q is real output, and v is the equilibium velocity of money. In the traditional theory of money, s is always set equal to 1, so that v is the observed velocity of money. As interpreted here, s is an index of the degree of disequilibrium in the economy, in particular, when s > 1, it is the index of shortage or repressed inflation. In the traditional theory with s =1, v and Q fixed, the increase in M will lead to an increase in P, that is, inflation. Now, as the possibility of disequilibrium is explicitly introduced, the increase in M, other things being equal, will result either in an increase in P, or, most often in a centrally planned economy, in an increase in s, keeping P constant.

What happened in China during the past few years was the simultaneous increase in M, P, Q, and M/PQ. This leads to one possible hypothesis of the increase in s, assuming that v is constant. This is exactly the assumption made by Feltenstein, et al., where, by choosing M2 as the monetary aggregate, they claimed to be able to explain the increased savings, maintaining the consistency of household saving behavior, especially, the MPS, over time. However, there is also another possibility. Assuming s constant over time, the money equation is still valid if v decreases as M/PQ increases. This situation is quite possible given the recent monetization process in China. Hence, another hypothesis is emerging: with s fixed, the increase in M/PQ is balanced by the decrease of v, which is considered as a signal of a regime shift and it synchronizes the shift of household savings behavior, in particular, the measured MPS. This paper shows, among other things, that increased savings in recent years can be equally well explained under the latter hypothesis.

The difficulty involving the interpretation of “disequilibrium” or “forced saving,” is more intricate than it appears, as illustrated in the following simple example. There are two kinds of television sets in the consumer market. One is a well-known brand of good quality, called A, and the other a lesser-known brand of inferior quality, called B. At the given price differential, A is still in short supply, and B is overstocked. In the given time period, some consumers bought A (by rationing), some bought B, and some saved the money (maybe waiting for A in the future). This appears to be a typical situation in China. The controversy arises in the interpretation and modeling of consumer behavior. Person 1 would argue that, because some consumers are waiting for A, which is in short supply, the saving is forced. Person 2 would argue that, since B is a close substitute for A, and always available, it constitutes a parallel market, and the consumers’ saving is therefore totally voluntary. Person 1 may reply that, for the Chinese, A and B are not close substitutes--the service of a television set can be sub-stitutable, but the brand name, “the ownership premium,” cannot. Yet Person 3 would say that, since a shortage in A causes forced substitute for B, there is a shortage (for A) and a surplus (for B) in individual markets, but there could be no overall involuntary savings. Person 4 notices that a television set is a consumer durable and argues that whether it is in short supply or not, the total savings (as opposed to financial savings) should be the same, only the composition of the total savings would be different. With the exception of person 4’s argument, with which we believe most people will agree, the arguments involve philosophical questions of the meaning of “demand,” and “voluntary or forced choice” or “preference,” which cannot easily be dealt with in applied economics. Hence, one virtue of the present approach is in avoiding the difficulty by maintaining the conventional neoclassical interpretation of “preference.”

IV. Motives for Saving: Urban vs. Rural Sector

To examine the savings behavior in China, it is essential to separate the rural and urban sectors, because as it will be seen below, different institutions and motives would apply in each case. According to the definition prior to 1984, the rural population is about 80 percent of the total population, with the remaining 20 percent urban residents. Unlike other developing countries, population movement from the countryside to cities has been strictly controlled in China, so that per capita income of urban residents was kept two times higher than that of rural residents for nearly 30 years. Farmers in rural areas and wage earners in urban areas are subject to different institutional environments. It is also evident that the two sectors experienced quite different economic development paths during the recent years of reform.

Urban wage earners have stable incomes and are extensively covered by the state or enterprise welfare plan. Saving for retirement is not necessary, as a retired worker will receive a pension of between 60-80 percent (in some cases, e.g., a model worker, up to 100 percent) of his or her final wages. Precautionary motives are strong, owing to the traditional doctrine. The argument of “no particular use at present” may or may not reflect the shortage of consumer goods. Nobody in urban areas is saving for housing as houses are provided by the “employing unit” at very low rent. But there is a strong motive to save for consumer durables (such as television sets, washing machines, and refrigerators). In addition, saving for children’s marriages is not uncommon.

In the rural sector, in contrast, incomes (especially since the reforms) are less stable, depending on the weather, the market, and management of production. Housing as well as pension funds are provided by the farmers themselves. As mentioned earlier, farmers are facing investment opportunities too, which are not available for urban residents. Many of the motives common to savings behavior in market economies have become relevant for Chinese farmers in recent years.

Consumer durables in urban areas and housing in rural areas have accounted for the bulk of the increase in Chinese household savings in recent years. These two savings can be attributed to (1) very low initial stocks, (2) the absence of rental markets, and probably most important (3) the ownership premium. One policy implication from this analysis is the effectiveness of introducing housing as a potential savings instrument in the urban areas for maintaining the household savings rate.

V. Alternative Savings Models

This section describes in general terms the four savings models that are used to obtain estimation results in Sections VI and VII. In what follows, S is real per capita household savings and Y is real per capita disposable income.

VI. Empirical Results of Urban Household Savings

1. Data

Ideally, total disposable income (including income in kind) and total savings (including acquisition of consumer durables) should be used in the empirical analysis. Owing to limited data, money income (excluding income in kind) and financial savings (excluding consumer durables) are used instead. The construction of time series data is based on the following identity (household borrowing is negligible): ^2/

$\begin{array}{cc}\text{householdmoneyincome=}& \text{householdexpenditure}\hfill \\ & \text{+householdfinancialsavings}\hfill \end{array}$

where

$\begin{array}{cc}\text{householdexpenditure=}& \text{householdexpenditureoncommodities}\hfill \\ & \text{+}\text{householdexpenditureonsecvices}\hfill \end{array}$

$\begin{array}{c}\text{householdfinancialsavings}\hfill \\ \begin{array}{c}=\text{changeofhouseholdcashholdings}\hfill \\ \begin{array}{l}+\text{changeofhouseholdsavingsdeposits}\\ +\text{changeofhouseholdbondholdings}\end{array}\end{array}\hfill \end{array}$

Chart 1 plots the time series of the urban household per capita real money income, financial savings, and consumption expenditure.

China: Urban Per Capita Household Income, Consumption, and Savings, 1953–85

(In yuan, 1950 prices)

Citation: IMF Working Papers 1988, 025; 10.5089/9781451920703.001.A001

Source: Statistical Yearbook of China, various issues.

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China: Urban Per Capita Household Income, Consumption, and Savings, 1953–85

(In yuan, 1950 prices)

Citation: IMF Working Papers 1988, 025; 10.5089/9781451920703.001.A001

Source: Statistical Yearbook of China, various issues.

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China: Urban Per Capita Household Income, Consumption, and Savings, 1953–85

(In yuan, 1950 prices)

Citation: IMF Working Papers 1988, 025; 10.5089/9781451920703.001.A001

Source: Statistical Yearbook of China, various issues.

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Urban income and savings are then expressed in real per capita terms using data on the general retail price index and urban population. Data up to 1983 on urban population are cited directly from the Yearbook; estimates for 1984 and 1985 are derived by extrapolation using trend growth rates (the rapid increase in urban population on the basis of the official series reflects a redefinition of urban areas).

Two final remarks are in order about the data for 1984 and 1985. Savings in 1984 were exceptionally high, reflecting an unusual increase in wage bonuses in the last quarter of the year, and it would be inappropriate to select 1984 as the last year of observation. Data for 1985 were estimated by the author on the basis of partial information.

2. Empirical results

It is worthwhile to plot savings/consumption against income before making any estimations. In Chart 2, figures 1 and 3 show real financial savings and real consumption in relation to real income. By taking permanent income as the past three years’ average income and transitory income as the difference between current and permanent income, Figures 2 and 4 show the relation between savings and the permanent and transitory income, respectively. Figures 1 and 2 show clearly the nonlinearity of the plotted functions, suggesting that any attempt to fit a linear equation to these data will not yield satisfactory results. On the other hand, Figures 3 and 4 show possible linear relationships in underlying variables.

China: Urban Household Per Capita Savings/Consumption vs. Income

(In yuan at 1950 prices)

Citation: IMF Working Papers 1988, 025; 10.5089/9781451920703.001.A001

Source: Statistical Yearbook of China, various issues.

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China: Urban Household Per Capita Savings/Consumption vs. Income

(In yuan at 1950 prices)

Citation: IMF Working Papers 1988, 025; 10.5089/9781451920703.001.A001

Source: Statistical Yearbook of China, various issues.

• Download Figure
• Download figure as PowerPoint slide

China: Urban Household Per Capita Savings/Consumption vs. Income

(In yuan at 1950 prices)

Citation: IMF Working Papers 1988, 025; 10.5089/9781451920703.001.A001

Source: Statistical Yearbook of China, various issues.

• Download Figure
• Download figure as PowerPoint slide

To capture the idea of regime shifts discussed in Section III, switching regression models are employed where two distinct regimes are explicitly recognized, and the switching date between the two regimes will be estimated, not predetermined. The switching regression model, which is a special case of a more general time varying parameter model, allows a simple discrete switch on the values of parameters, with the switching date determined according to the criterion of maximum likelihood. Our results of least squares estimation of the switching regression models as well as the ordinary linear regression models are presented in Tables 4-7. Interest rates were first included in the regressions, but were later dropped, as they were insignificant in all cases.

Table 4.

China: Absolute-income Model (Urban)

⁽¹⁾S = c + d D + a₁ Y (1 - D) a₂ YD

where D = 1 when t < 1979

0 when t ≥ 1979

⁽²⁾S = c + aY

Table 4.

China: Absolute-income Model (Urban)

Time Period	c	d	a	a1	a2	DW	R2	RSS	LLF
1955-83
(1)	-74.2	68.2		0.29	0.04
	(-3.7)	(3.4)		(4.8)	(3.6)	1.125	0.931	103.8	-59.64
(2)	-22.1		0.12			0.489	0.716	461.6	-81.28
	(-6.5)		(8.5)
1955-85
(1)	-61.7	55.7		0.256	0.04
	(-10.12)	(8.2)		(15.3)	(2.9)	1.67	0.967	176.2	-70.92
(2)	-33.4		0.17			0.398	0.833	961.0	-97.21
	(-9.3)		(12.3)

⁽¹⁾S = c + d D + a₁ Y (1 - D) a₂ YD

where D = 1 when t < 1979

0 when t ≥ 1979

⁽²⁾S = c + aY

View Table

Table 4.

China: Absolute-income Model (Urban)

Time Period	c	d	a	a1	a2	DW	R2	RSS	LLF
1955-83
(1)	-74.2	68.2		0.29	0.04
	(-3.7)	(3.4)		(4.8)	(3.6)	1.125	0.931	103.8	-59.64
(2)	-22.1		0.12			0.489	0.716	461.6	-81.28
	(-6.5)		(8.5)
1955-85
(1)	-61.7	55.7		0.256	0.04
	(-10.12)	(8.2)		(15.3)	(2.9)	1.67	0.967	176.2	-70.92
(2)	-33.4		0.17			0.398	0.833	961.0	-97.21
	(-9.3)		(12.3)

⁽¹⁾S = c + d D + a₁ Y (1 - D) a₂ YD

where D = 1 when t < 1979

0 when t ≥ 1979

⁽²⁾S = c + aY

Table 5.

China: Permanent-Income Model (Urban)

⁽¹⁾S = c + d D + a ${\text{Y}}_1^{\text{p}}$ (1-D) + a ${\text{Y}}_2^{\text{p}}$ D + b Y

⁽²⁾S = c + a Y^p + b Y^t

⁽³⁾S = c + d D + a₁Y^P (1 - D) + a₂Y^P D + b₁Y_t (1 - D) + b₂Y^t D

⁽⁴⁾S = c + d D + a Y^P + b₁ Y^t (1 - D) + b₂ Y^t D

when D = 1 when t < 19790 when t ≥ 1979

Table 5.

China: Permanent-Income Model (Urban)

Time period	c	d	a	a1	a2	b	b1	b2	DW	R2	RSS	LLF
1955-83
(1)	-63.7	62.6		0.26	0.011	0.25			1.64	0.94	87.1	-57.09
	(-3.4)	(3.3)		(4.3)	(0.75)	(4.34)
(2)	-20.2		0.11			0.21			0.48	0.71	451.9	-80.97
	(-4.7)		(5.0)			(1.7)
(3)	-70.0	68.0		0.27	0.02		0.36	0.20	1.47	0.94	80.8	-56.01
	(-3.7)	(3.5)		(4.5)	(1.1)		(3.5)	(3.0)
(4)	7.0	-11.6	0.03				0.28	0.16	1.12	0.90	14.7	-64.15
	(1.1)	(-3.3)	(1.7)				(2.1)	(1.9)
1955-85
(1)	-64.8	63.6		0.27	0.01	0.24			2.00	0.97	157.5	-69.18
	(-4.4)	(4.5)		(5.4)	(0.67)	(4.28)
(2)	-19.5		0.10			0.44			0.76	0.88	654.6	-91.26
	(-4.0)		(4.2)			(5.8)
(3)	-59.1	57.1		0.25	0.02		0.27	0.20	1.93	0.97	1.554	-68.97
	(-3.3)	(3.2)		(4.1)	(0.9)		(3.6)	(2.3)
(4)	1.97	-7.9	0.04				0.49	0.14	1.30	0.95	237.3	-75.54
	(0.3)	(-2.7)	(1.7)				(10.1)	(1.4)

⁽¹⁾S = c + d D + a ${\text{Y}}_1^{\text{p}}$ (1-D) + a ${\text{Y}}_2^{\text{p}}$ D + b Y

⁽²⁾S = c + a Y^p + b Y^t

⁽³⁾S = c + d D + a₁Y^P (1 - D) + a₂Y^P D + b₁Y_t (1 - D) + b₂Y^t D

⁽⁴⁾S = c + d D + a Y^P + b₁ Y^t (1 - D) + b₂ Y^t D

when D = 1 when t < 19790 when t ≥ 1979

View Table

Table 5.

China: Permanent-Income Model (Urban)

Time period	c	d	a	a1	a2	b	b1	b2	DW	R2	RSS	LLF
1955-83
(1)	-63.7	62.6		0.26	0.011	0.25			1.64	0.94	87.1	-57.09
	(-3.4)	(3.3)		(4.3)	(0.75)	(4.34)
(2)	-20.2		0.11			0.21			0.48	0.71	451.9	-80.97
	(-4.7)		(5.0)			(1.7)
(3)	-70.0	68.0		0.27	0.02		0.36	0.20	1.47	0.94	80.8	-56.01
	(-3.7)	(3.5)		(4.5)	(1.1)		(3.5)	(3.0)
(4)	7.0	-11.6	0.03				0.28	0.16	1.12	0.90	14.7	-64.15
	(1.1)	(-3.3)	(1.7)				(2.1)	(1.9)
1955-85
(1)	-64.8	63.6		0.27	0.01	0.24			2.00	0.97	157.5	-69.18
	(-4.4)	(4.5)		(5.4)	(0.67)	(4.28)
(2)	-19.5		0.10			0.44			0.76	0.88	654.6	-91.26
	(-4.0)		(4.2)			(5.8)
(3)	-59.1	57.1		0.25	0.02		0.27	0.20	1.93	0.97	1.554	-68.97
	(-3.3)	(3.2)		(4.1)	(0.9)		(3.6)	(2.3)
(4)	1.97	-7.9	0.04				0.49	0.14	1.30	0.95	237.3	-75.54
	(0.3)	(-2.7)	(1.7)				(10.1)	(1.4)

⁽¹⁾S = c + d D + a ${\text{Y}}_1^{\text{p}}$ (1-D) + a ${\text{Y}}_2^{\text{p}}$ D + b Y

⁽²⁾S = c + a Y^p + b Y^t

⁽³⁾S = c + d D + a₁Y^P (1 - D) + a₂Y^P D + b₁Y_t (1 - D) + b₂Y^t D

⁽⁴⁾S = c + d D + a Y^P + b₁ Y^t (1 - D) + b₂ Y^t D

when D = 1 when t < 19790 when t ≥ 1979

Table 6.

China: Assets Adjustment Model (Urban)

⁽¹⁾S = a₁ S_-1 (1 - D) + a₂ S_-1 D + b₁Δ Y (1 - D) + b₂ Δ Y D

⁽²⁾S = a₁ S_-1 (1 - D) + a₂ S_-1 D + b Δ Y

⁽³⁾S = a S_-1 + b_-1 Δ Y (1 - D) + b₂ Δ Y D

⁽⁴⁾S = a S_-1 + b Δ Y

when D = 1 when t < T0 when t ≥ T

Table 6.

China: Assets Adjustment Model (Urban)

Time Period	a	a1	a2	b	b1	b2	T	DW/H	R2	RSS	LLF
1955-83
(1)		0.86	0.51		0.31	0.14	1978	2.00	0.91	134.9	-63.4
		(10.1)	(2.7)		(5.2)	(2.3)
(2)		0.95	0.34	0.22			1977	2.02	0.90	155.0	-65.45
		(12.2)	(1.9)	(4.8)
(3)	0.81				0.34	0.08	1978	1.97	0.90	151.2	-65.1
	(9.8)				(5.4)	(1.5)
(4)	0.92			0.17				1.53/1.25	0.86	231.8	-71.3
	(9.9)			(3.2)
1955-85
(1)		0.79	0.53		0.26	0.14	1978	1.81	0.93	352.0	-81.65
		(6.1)	(1.8)		(3.8)	(1.4)
(2)		0.86	0.35	0.22			1977	1.97	0.94	363.5	-82.2
		(7.7)	(1.4)	(3.8)
(3)	0.75				0.28	0.09	1978	1.77	0.94	360.9	-82.0
	(6.2)				(4.3)	(1.2)
(4)	0.86			0.21				1.83/0.33	0.93	627.4	-84.7
	(7.2)			(3.4)

⁽¹⁾S = a₁ S_-1 (1 - D) + a₂ S_-1 D + b₁Δ Y (1 - D) + b₂ Δ Y D

⁽²⁾S = a₁ S_-1 (1 - D) + a₂ S_-1 D + b Δ Y

⁽³⁾S = a S_-1 + b_-1 Δ Y (1 - D) + b₂ Δ Y D

⁽⁴⁾S = a S_-1 + b Δ Y

when D = 1 when t < T0 when t ≥ T

View Table

Table 6.

China: Assets Adjustment Model (Urban)

Time Period	a	a1	a2	b	b1	b2	T	DW/H	R2	RSS	LLF
1955-83
(1)		0.86	0.51		0.31	0.14	1978	2.00	0.91	134.9	-63.4
		(10.1)	(2.7)		(5.2)	(2.3)
(2)		0.95	0.34	0.22			1977	2.02	0.90	155.0	-65.45
		(12.2)	(1.9)	(4.8)
(3)	0.81				0.34	0.08	1978	1.97	0.90	151.2	-65.1
	(9.8)				(5.4)	(1.5)
(4)	0.92			0.17				1.53/1.25	0.86	231.8	-71.3
	(9.9)			(3.2)
1955-85
(1)		0.79	0.53		0.26	0.14	1978	1.81	0.93	352.0	-81.65
		(6.1)	(1.8)		(3.8)	(1.4)
(2)		0.86	0.35	0.22			1977	1.97	0.94	363.5	-82.2
		(7.7)	(1.4)	(3.8)
(3)	0.75				0.28	0.09	1978	1.77	0.94	360.9	-82.0
	(6.2)				(4.3)	(1.2)
(4)	0.86			0.21				1.83/0.33	0.93	627.4	-84.7
	(7.2)			(3.4)

⁽¹⁾S = a₁ S_-1 (1 - D) + a₂ S_-1 D + b₁Δ Y (1 - D) + b₂ Δ Y D

⁽²⁾S = a₁ S_-1 (1 - D) + a₂ S_-1 D + b Δ Y

⁽³⁾S = a S_-1 + b_-1 Δ Y (1 - D) + b₂ Δ Y D

⁽⁴⁾S = a S_-1 + b Δ Y

when D = 1 when t < T0 when t ≥ T

Table 7.

China: Consumption Model (Urban)

Table 7.

China: Consumption Model (Urban)

Time period			DW	R2	RSS
1955-83	(1)	${\text{C}}_{\text{t}}\text{=}\underset{\left(\text{-}0.07\right)}{\text{-0.73}}+\underset{\left(22.98\right)}{\text{1.03}}{\text{C}}_{\text{t-1}}$	1.95	0.95	3,265
1957-83	(2)	$\begin{array}{c}{\text{C}}_{\text{t}}\text{=}\underset{\left(\text{-}0.38\right)}{\text{-4.6}}+\underset{\left(5.4\right)}{\text{1.09}}{\text{C}}_{\text{t-1}}\underset{\left(\text{-0.82}\right)}{\text{-0.24}}{\text{C}}_{\text{t-2}}\end{array}$
		$\begin{array}{c}+\underset{\left(1.17\right)}{\text{0.35}}{\text{C}}_{\text{t-3}}\underset{\left(\text{-0.68}\right)}{\text{-0.15}}{\text{C}}_{\text{t-4}}\end{array}$	1.83	0.95	2,751
1955-83	(3)	$\begin{array}{c}{\text{C}}_{\text{t}}\text{=}\underset{\left(0.3\right)}{\text{4.7}}+\underset{\left(1.21\right)}{0\text{.71}}{\text{C}}_{\text{t-1}}+\underset{\left(\text{0.54}\right)}{\text{0.29}}{\text{Y}}_{\text{t-1}}\end{array}$	1.90	0.95	3,229
1957-83	(4)	$\begin{array}{c}{\text{C}}_{\text{t}}\text{=}\underset{\left(0.005\right)}{0\text{.08}}+\underset{\left(1.38\right)}{0\text{.84}}{\text{C}}_{\text{t-1}}+\underset{\left(\text{0.57}\right)}{\text{0.32}}{\text{Y}}_{\text{t-1}}\end{array}$
		$\underset{(-1.19)}{\text{-0.34}}{\text{Y}}_{\text{t-2}}\text{+}\underset{\left(1.09\right)}{0.31}{\text{Y}}_{\text{t-3}}\underset{(-0.56)}{-0.12}{\text{Y}}_{\text{t-4}}$	1.98	0.95	2,706
1955-83	(5)	${\text{C}}_{\text{t}}\underset{\left(0.46\right)}{\text{=5.1}}\text{+}\underset{\left(1.96\right)}{0.75}{\text{C}}_{\text{t-1}}\text{+}\underset{\left(4.7\right)}{\text{0.24}}{\text{Y}}_{\text{t}}$	1.92	0.97	1,754
		(instruments: Ct-1, St-1)
1955-85	(1)	${\text{C}}_{\text{t}}\underset{\left(\text{-}2.19\right)}{\text{=-23.5}}\text{+}\underset{\left(24.0\right)}{1.14}{\text{C}}_{\text{t-1}}$	1.36	0.95	5,709
1957-85	(2)	${\text{C}}_{\text{t}}\underset{\left(1.9\right)}{\text{=25.6}}\text{+}\underset{\left(6.2\right)}{1.42}{\text{C}}_{\text{t-1}}-\underset{(-1.48)}{0.53}{\text{C}}_{\text{t-2}}$
		$+\underset{\left(0.62\right)}{0.23}{\text{C}}_{\text{t-3}}\text{+}\underset{\left(0.08\right)}{0.02}{\text{C}}_{\text{t-4}}$	1.67	0.95	4,742
1955-85	(3)	${\text{C}}_{\text{t}}\text{=}\underset{\left(1.05\right)}{15\text{.1}}-\underset{(-0.7)}{0.29}{\text{C}}_{\text{t-1}}+\underset{\left(3.6\right)}{1.2}{\text{Y}}_{\text{t-1}}$	1.54	0.96	3,905
1957-85	(4)	${\text{C}}_{\text{t}}=\underset{\left(0.5\right)}{8.3}-\underset{(-0.07)}{0.03}{\text{C}}_{\text{t-1}}\text{+}\underset{\left(3.3\right)}{1.2}{\text{Y}}_{\text{t-1}}$
		$-\underset{(-1.5)}{0.45}{\text{Y}}_{\text{t-2}}+\underset{\left(0.91\right)}{0.27}{\text{Y}}_{\text{t-3}}\text{-}\underset{(-0.21)}{0.05}{\text{Y}}_{\text{t-4}}$	1.82	0.96	3,280
1955-85	(5)	${\text{C}}_{\text{t}}\text{=}\underset{\left(1.7\right)}{10\text{.1}}+\underset{\left(4.8\right)}{0.42}{\text{C}}_{\text{t-1}}+\underset{\left(8.3\right)}{0.53}{\text{Y}}_{\text{t}}$	1.6	0.99	742
		(instruments: Ct-1, St-1)

View Table

Table 7.

China: Consumption Model (Urban)

Time period			DW	R2	RSS
1955-83	(1)	${\text{C}}_{\text{t}}\text{=}\underset{\left(\text{-}0.07\right)}{\text{-0.73}}+\underset{\left(22.98\right)}{\text{1.03}}{\text{C}}_{\text{t-1}}$	1.95	0.95	3,265
1957-83	(2)	$\begin{array}{c}{\text{C}}_{\text{t}}\text{=}\underset{\left(\text{-}0.38\right)}{\text{-4.6}}+\underset{\left(5.4\right)}{\text{1.09}}{\text{C}}_{\text{t-1}}\underset{\left(\text{-0.82}\right)}{\text{-0.24}}{\text{C}}_{\text{t-2}}\end{array}$
		$\begin{array}{c}+\underset{\left(1.17\right)}{\text{0.35}}{\text{C}}_{\text{t-3}}\underset{\left(\text{-0.68}\right)}{\text{-0.15}}{\text{C}}_{\text{t-4}}\end{array}$	1.83	0.95	2,751
1955-83	(3)	$\begin{array}{c}{\text{C}}_{\text{t}}\text{=}\underset{\left(0.3\right)}{\text{4.7}}+\underset{\left(1.21\right)}{0\text{.71}}{\text{C}}_{\text{t-1}}+\underset{\left(\text{0.54}\right)}{\text{0.29}}{\text{Y}}_{\text{t-1}}\end{array}$	1.90	0.95	3,229
1957-83	(4)	$\begin{array}{c}{\text{C}}_{\text{t}}\text{=}\underset{\left(0.005\right)}{0\text{.08}}+\underset{\left(1.38\right)}{0\text{.84}}{\text{C}}_{\text{t-1}}+\underset{\left(\text{0.57}\right)}{\text{0.32}}{\text{Y}}_{\text{t-1}}\end{array}$
		$\underset{(-1.19)}{\text{-0.34}}{\text{Y}}_{\text{t-2}}\text{+}\underset{\left(1.09\right)}{0.31}{\text{Y}}_{\text{t-3}}\underset{(-0.56)}{-0.12}{\text{Y}}_{\text{t-4}}$	1.98	0.95	2,706
1955-83	(5)	${\text{C}}_{\text{t}}\underset{\left(0.46\right)}{\text{=5.1}}\text{+}\underset{\left(1.96\right)}{0.75}{\text{C}}_{\text{t-1}}\text{+}\underset{\left(4.7\right)}{\text{0.24}}{\text{Y}}_{\text{t}}$	1.92	0.97	1,754
		(instruments: Ct-1, St-1)
1955-85	(1)	${\text{C}}_{\text{t}}\underset{\left(\text{-}2.19\right)}{\text{=-23.5}}\text{+}\underset{\left(24.0\right)}{1.14}{\text{C}}_{\text{t-1}}$	1.36	0.95	5,709
1957-85	(2)	${\text{C}}_{\text{t}}\underset{\left(1.9\right)}{\text{=25.6}}\text{+}\underset{\left(6.2\right)}{1.42}{\text{C}}_{\text{t-1}}-\underset{(-1.48)}{0.53}{\text{C}}_{\text{t-2}}$
		$+\underset{\left(0.62\right)}{0.23}{\text{C}}_{\text{t-3}}\text{+}\underset{\left(0.08\right)}{0.02}{\text{C}}_{\text{t-4}}$	1.67	0.95	4,742
1955-85	(3)	${\text{C}}_{\text{t}}\text{=}\underset{\left(1.05\right)}{15\text{.1}}-\underset{(-0.7)}{0.29}{\text{C}}_{\text{t-1}}+\underset{\left(3.6\right)}{1.2}{\text{Y}}_{\text{t-1}}$	1.54	0.96	3,905
1957-85	(4)	${\text{C}}_{\text{t}}=\underset{\left(0.5\right)}{8.3}-\underset{(-0.07)}{0.03}{\text{C}}_{\text{t-1}}\text{+}\underset{\left(3.3\right)}{1.2}{\text{Y}}_{\text{t-1}}$
		$-\underset{(-1.5)}{0.45}{\text{Y}}_{\text{t-2}}+\underset{\left(0.91\right)}{0.27}{\text{Y}}_{\text{t-3}}\text{-}\underset{(-0.21)}{0.05}{\text{Y}}_{\text{t-4}}$	1.82	0.96	3,280
1955-85	(5)	${\text{C}}_{\text{t}}\text{=}\underset{\left(1.7\right)}{10\text{.1}}+\underset{\left(4.8\right)}{0.42}{\text{C}}_{\text{t-1}}+\underset{\left(8.3\right)}{0.53}{\text{Y}}_{\text{t}}$	1.6	0.99	742
		(instruments: Ct-1, St-1)

For the absolute-income model, the likelihood ratio (LR) test decisively rejects the hypothesis of no regime shift, where LR = 52.58 for 1955-85 (LR = 43.28 for 1955-83), when the 0.99 value of is 6.63. The estimated year in which the shift occurs is 1979, coinciding with the beginning of reforms. When the shift is included, the Durbin-Watson statistic is increased from 0.398 to 1.67, indicating no serious autocorrelation (from 0.489 to 1.125 though for 1955-83). R is also improved significantly. All of the indicators show strong support for the switching specification.

With regard to the parameters estimated, MPS now depends on regimes: (from 1955-85 equation)

$\begin{array}{c}\begin{array}{cc}{\text{S}}_{\text{t}}\text{=-6.0+0.04}{\text{Y}}_{\text{t}}\hfill & \hfill \text{whent<1979}\end{array}\\ \begin{array}{cc}\begin{array}{c}\begin{array}{c}{\text{S}}_{\text{t}}\text{=-61.7+0.26}{\text{Y}}_{\text{t}}\hfill \end{array}\end{array}& \hfill \text{whent}\ge 1979\end{array}\end{array}$

However, the estimate from the simple linear regression (from 1955-85 equation) is: S = -33.4 + 0.17 Y_t. Hence, the estimated MPS before (after) 1979 is lower (higher) than the estimate from the simple linear regression.

In estimating the permanent-income model, we first test the hypothesis of no shift in MPS out of permanent income, under two alternative assumptions: (A) there is no shift in MPS of transitory income; and (B) there is a shift in MPS of transitory income. Log-likelihood ratios are presented below:

	Assumption A	Assumption B
1955-83	47.76	16.28
1955-85	44.16	13.14

View Table

	Assumption A	Assumption B
1955-83	47.76	16.28
1955-85	44.16	13.14

The original hypothesis is decisively rejected under either assumption as the 0.99 value of ${\text{X}}_1^2$ is 6.63. We conclude that there is a shift in MPS of the permanent income. Next, we want to test the hypothesis that there is no shift in MPS out of transitory income, given the shift in MPS out of permanent income. The log-likelihood ratio is 0.42 (or 2.16 for 1955-83). The hypothesis cannot be rejected since the 0.90 value of ${\text{X}}_1^2$ is 2.71.

As in the absolute-income model, the estimated shifting year is 1979. Parameters estimated are:

$\begin{array}{cc}{\text{S}}_{\text{t}}\text{=}-1.2\text{+0.01}{\text{Y}}_{\text{t}}^{\text{p}}\text{+0.24}{\text{Y}}_{\text{t}}^{\text{t}}& \text{whent<1979}\end{array}$

and

$\begin{array}{cc}{\text{S}}_{\text{t}}\text{=}-64.8\text{+0.27}{\text{Y}}_{\text{t}}^{\text{p}}\text{+0.24}{\text{Y}}_{\text{t}}^{\text{t}}& \text{whent}\ge \text{1979}\end{array}$

The corresponding D-W statistic is 1.996 (or 1.64 for 1955-83); R² is 0.970 (or 0.940 for 1955-83).

We have one interesting finding from the estimation. Prior to 1979, saving propensities vis-a-vis permanent income and transitory income differed greatly, with almost all savings being in response to the increase in transitory income. Since 1979, saving propensities from permanent income and from transitory income were nearly identical. This contradicts Friedman’s permanent-income hypothesis, and at the same time, supports the Keynesian absolute-income model estimated previously, where MPS out of current income is 0.256, which is about the average of MPS out of permanent income and of transitory income here. We may interpret this result in the following way: before 1979, households’ income was so low that they saved almost nothing out of permanent income, hence only transitory increments of income were saved. Since reforms began, as discussed in Section III, urban households have been saving such a significant amount from permanent income that they behave like Keynesian consumers.

Turning to the asset adjustment model, four versions of the model, depending on the assumptions of shift in different parameters, were estimated:

$\begin{array}{cc}\left(\text{A}\right)& {\text{S}}_{\text{t}}\text{=}{\text{a}}_1{\text{S}}_{\text{t-1}}(1-\text{D})\text{+}{\text{a}}_2{\text{S}}_{\text{t-1}}\text{D+}{\text{b}}_1\mathrm{\Delta }{\text{Y}}_{\text{t}}(1-\text{D})\text{+}{\text{b}}_2\mathrm{\Delta }{\text{Y}}_{\text{t}}\text{D}\end{array}$

$\begin{array}{cc}\left(\text{B}\right)& {\text{S}}_{\text{t}}\text{=}{\text{a}}_1{\text{S}}_{\text{t-1}}(1-\text{D})\text{+}{\text{a}}_2{\text{S}}_{\text{t-1}}\text{D+}\text{b}\mathrm{\Delta }{\text{Y}}_{\text{t}}\end{array}$

$\begin{array}{cc}\left(\text{C}\right)& {\text{S}}_{\text{t}}\text{=}\text{a}{\text{S}}_{\text{t-1}}\text{+}{\text{b}}_1\mathrm{\Delta }{\text{Y}}_{\text{t}}(1-\text{D})\text{+}{\text{b}}_2\mathrm{\Delta }{\text{Y}}_{\text{t}}\text{D}\end{array}$

$\begin{array}{cc}\left(\text{D}\right)& {\text{S}}_{\text{t}}\text{=}\text{a}{\text{S}}_{\text{t-1}}\text{+}\text{b}\mathrm{\Delta }{\text{Y}}_{\text{t}}\end{array}$

where D = 1 when t < T

0 when t ≥ T

the log-likelihood ratios are given below for possible nested hypothesis:

	(D) vs. (B)	(D) vs. (C)	(B) vs. (A)	(C) vs. (A)
1955-83	11.68	12.40	4.02	3.30
1955-85	5.0	5.24	1.0	0.76

View Table

	(D) vs. (B)	(D) vs. (C)	(B) vs. (A)	(C) vs. (A)
1955-83	11.68	12.40	4.02	3.30
1955-85	5.0	5.24	1.0	0.76

Since the 0.99 value of ${\text{X}}_1^2$ is 6.63 and the 0.95 value of ${\text{X}}_1^2$ is 3.84, we may want to reject specifications of (D) and (B), and choose in favor of (C), that is, there is a shift in the saving response to the change in income, without a shift in the current savings response to the lagged savings. The estimated time of shifting is 1978 in this model:

$\begin{array}{cc}{\text{S}}_{\text{t}}\text{=}0.75{\text{S}}_{\text{t-1}}\text{+0.09}\mathrm{\Delta }{\text{Y}}_{\text{t}}& \text{whent<1978}\\ {\text{S}}_{\text{t}}\text{=}0.75{\text{S}}_{\text{t-1}}\text{+0.28}\mathrm{\Delta }{\text{Y}}_{\text{t}}& \text{whent}\ge \text{1978}\end{array}$

Using the formula presented in Section V, we are able to calculate structural parameters, in particular, the long-run savings-income ratio S/Y, under alternative interpretations. After 1978, according to the methodology of Houthakker and Taylor, the MPS out of assets is

$\text{B=2}\times \left(0.75\text{-1}\right)/(0.75\text{+1})\text{=-0.29}$

and MPS out of income is

$\text{C=2}\times 0.28/(0.75\text{+1})\text{=0.32}$

The Long-run savings rate consistent with the constant growth rate of income of 4 percent is S/Y = 0.32 × 0.04/(0.04 + 0.29) = 3.88 percent. Under the interpretation of Leff and Sato, the adjustment parameter relating actual to desired savings, k = 1 - 0.75 = 0.25 and the desired asset-income ratio, r = 0.28/0.25 = 1.12. With the constant income growth at 4 percent, the long-run savings rate under this interpretation is S/Y = 0.28 × 0.04/(0.04 + 0.25) = 3.86 percent, which is very close to the figure according to Houthakker and Taylor.

We next try to estimate the structural equations of consumption. We estimated equations C_t = a + b C_t-1 + d Y_t using C_t-1 and S_t-1 as instruments. This is equivalent to applying the method of two-stage least squares to simultaneous equations:

$\begin{array}{c}\begin{array}{c}{\text{C}}_{\text{t}}\text{=a+b}{\text{C}}_{\text{t-1}}\text{+d}{\text{Y}}_{\text{t}}\hfill \\ {\text{S}}_{\text{t}}\text{=e}{\text{S}}_{\text{t-1}}\text{+}\text{f}\left({\text{Y}}_{\text{t}}\text{-}{\text{Y}}_{\text{t-1}}\right)\hfill \end{array}\hfill \\ {\text{Y}}_{\text{t}}\text{=}{\text{C}}_{\text{t}}\text{+}{\text{S}}_{\text{t}}\hfill \end{array}$

In contrast to Chow (1985), our estimates of the coefficient of current income (d’s) are all significant, but this does not contradict the principal stochastic implications of the life cycle-permanent income hypothesis of Hall mentioned in Section V (i.e., the lagged income should not have predictive power for the current consumption).

Hall’s random-walk hypothesis is formally tested by estimating a conditional expectation, E (C_tC_t-1, X_t-1) and showing it is not a function of X_t-1. The weak version of the hypothesis takes X_t-1 as lagged consumption beyond C_t-1 while the strong version would include lagged income or any other lagged variables. It should be noted that what is estimated is the conditional expectation, not the true structural relation between consumption and its determinants.

In Table 7, where equation (2) includes C_t-2, C_t-3 and C_t-4 in addition to C_t-1 as in equation (1), the F-statistic for the hypothesis that the coefficients of C_t-2, C_t-3, and C_t-4 are all zero is 1.37 for 1955-83 (or 1.63 for 1955-85), well under the critical value of 2.96 at the 5 percent level. This implies that by adding C_t-2, C_t-3, and C_t-4 into the regression, we add no additional information. Hence the weak version of the hypothesis is supported.

We then test the predictive power of the lagged levels of disposable income. Equation (3) incorporated a single lagged value of income and equation (4) tried a four-year long lag. For data up to 1983, F-statistics are 0.29 and 1.08, respectively. Therefore, the strong version of the hypothesis can not be rejected at the 5 percent level. But for data extended to 1985, the values are 12.9 and 4.26, respectively, and the hypothesis can be rejected, though not decisively.