Chapter 1- Introduction
1.3 Research Process
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1.3 Research Process
Literature Review
Methodology and Data Information
Precautionary Savings Theory;
Life-Cycle Model and Treatment of the Indicators
Results & Discussion
Conclusions
Context & Motivation and Objectives & Variables
Figure 1 – Research Process
Hypothesis Introduction
Final Words Empirical Results
Analysis of the Turning Points:
1995-1996 and 2000-2002
Contributions & Limitations Policy Implication
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2. Literature Review
This chapter is divided into three sections. The first two sections explain the two main theories used in this thesis: The Precautionary Saving Model and the Life-Cycle Hypothesis. Each of these sections contains their main postulates, empirical applications and results. The third section explain the treatment and the reasons of the adoption of each one of the indicators.
2.1 The Precautionary Savings Theory
Although varying in their explanations, both the classical Precautionary Savings Model and Life-Cycle Model predict that households tend to increase their savings (and thus, decrease their consumption) according to the greater income uncertainty (Carroll, 1994; Engen & Gruber, 2001; Meng, 2003; Modigliani & Cao, 2004).
Leland (1968) introduced the Precautionary Saving Theory to the world explaining that the “’Precautionary’ demand for saving usually is described as the extra saving caused by future income being random rather than determinate”, and argued that the function of savings is positively related to the function of uncertainty.
Sandmo (1970), in turn, contributed to the debate insisting that the reasons which lead someone to save more might vary due to uncertainty on the returns of investment and on the predictability of income.
In complement to it, empirical tests found that precautionary motives are decisive determinant of the households saving and consumption behavior (Chou, Liu, & Hammitt, 2003; Engen & Gruber, 2001) and that this effect is stronger over the less wealthy families, since prudence8 declines in wealthier families (Chou, Liu,
8 Define as “the sensitivity of precautionary saving to risk”
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& Hammitt, 2003; Pratt, 1964). As mentioned by Kimball (1990): “[…] if absolute prudence is increasing, labor income uncertainty will lower the marginal propensity to consume out of wealth at a given level of consumption”.
Most of the recent empirical studies related to precautionary savings understands Education, Health and Housing as the main and the most important consumptions/variables within a household; and that the variation of these variables, and not just the uncertainty of future incomes, could lead the households to save more/consume less.
Studying the relationship between the health and consumption, Chou, Liu, & Hammitt (2003) found that uncertainty over future expenses in health leads people to save more and added saying that the implementation of the National Health Insurance in Taiwan (NHI) had an impact of 8.6% - 13.7% on the savings, especially over those households with fewer savings. Furthermore, they suggest that
“NHI yields a larger welfare improvement, through consumption smoothing, for households with smaller saving”.
Meng (2003), studying China, says that after the State-Owned Companies Reforms, the health-care system was no long fully provided by the state, but it became a “two-tier system” in which the state and the families started to share all the costs of health-care, and thus, it might have impacted on the household consumption.
In concordance with their results, Barnett & Brooks (2010) also found that for every additional yuan spent by the government in the healthcare system, urban Chinese families would tend to increase their consumption by 2 yuan. They explain that, with the increase of the governmental expenditures, urban households
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spending of the Chinese households, in the second half of the 2000`s – at that time the government expenditure on health-care increased significantly. In contrast, the same research, did not find any relationship between governmental expending on education and household consumption9.Baldacci, et al. (2010), in turn, find that the high level of precautionary savings may be related to inadequate policies on “health, old-age, and the elevated private cost of higher education” – differently to the findings of Barnett & Brooks (2010) by looking from the perspective of governmental expenditures on Education.
For Baldacci, et al. (2010), the government’s social spending has a non-linear impact on household savings; and governmental spending on health-care has the largest negative impact on household savings. The effects of government social expending on education, could only be observed when the “individual social spending” is considered separately. In that sense, depending on the perspective, expenditure on education might have a different relationship with the APC, which could lead to different results.
With all that said, and considering that the governmental expenditure on health and on education (understanding the restrictions previously mentioned) has a positive impact over the consumption, then the following two hypotheses can be raised:
Hypothesis 1: The more households expend on health-care, the bigger the negative impact it has on the APC.
9 Their results were statistically insignificant, but they argue that it might be because the data available counted just for the elementary and primary school, and that for the families, the most significant spending in education happens at the university level.
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Hypothesis 2: The greater the expenditure per household on education, the larger its negative impact on the Average Propensity to Consume of the Households.
Another very important variable that might have a big impact over the household consumption is the housing factor, especially after the “massive privatization of the housing stock in China”. In 1980`s, the housing sector started to be reformed, and as a result, the official rent increased and occupants of government housing could then buy their own house so that, in 1999, the great majority of the household were already expecting to buy their own house (Meng, 2003).
In their work, Chamon & Prasad (2010) suggest that using simple regression calculations, they noticed that “savings driven by the motive of home ownership could account for about 3 percentage points of the increase in the household saving rate from 1995 to 2005” – which represents a great impact if the growth of the housing price since the real estate open-process is considered. At the beginning of 1990, only 17 percent of the households owned their own houses, and this proportion had increased to 86 percent, in 2009. Therefore, Chamon & Prasad (2010) affirm that the rise of the household saving rate can be in part explained by the massive privatization of the housing sector.
That said, then a third hypothesis related to the housing sector can be raised as:
Hypothesis 3: The bigger the rise in the rate “Housing Price/Total Income”, the bigger is its negative impact on the household Average Propensity to Consume (APC).
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In summary, according to the theories and the empirical findings, this research expects to find a strong relationship between the variation of housing price, the expending on education and health and the variation of the APC.
2.2 The Life-Cycle Model
Looking from another perspective, the Theory of Consumption observed through the Life-Cycle Hypothesis (Modigliani & Brumberg, 1954) shows that the patterns of consumption may vary according to the stages of one`s life, future incomes and wealth accumulated – a person would tend to “dis-save” (spend its savings) while young and old, but would then try to accumulate during his/her middle-ages. This is because a rational person wants to “maximize utility derived from their life resources by allocating them optimally between current and future consumption”
(Modigliani & Cao, 2004), which means, the rational consumer will tend to smooth their incomes over their life-cycle.
Therefore, a “prudent consumer” whenever faced by any factor of uncertainty related to their future income, will consequently try to increase their savings and/or decrease their consumption (Carroll, 1994) in order to smooth their spending throughout their life – which is also in accordance with the Precautionary Saving Model. In other words, the consumption reacts to the variations in the market value of wealth (Ando & Modigliani, 2005), and consequently it depends indirectly on the agents responsible for these variations. So, any agent that interferes with someone’s predictability of future wealth has an (in)direct affect over his/her savings/consumption.
According to Ando & Modigliani (2005) the model assumes that:
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different points in time […]; (b) The individual neither expects to receive nor desires to leave any inheritance […] (c) The consumer at any age plans to consume his total resources evenly over the remainder of his life span […]; (d) Every household has the same (expected and actual) total life and earning spans […]; (e) The rate of return on assets is constant and is expected to remain constant.” (Ando & Modigliani, 2005)Still according to Modigliani in an empirical application of the Life-Cycle Hypothesis (LCH) over the Chinese households, he found that the reason for the Chinese households maintain its savings rate high is because of the “high growth of income and the demographic structure of the economy” (Modigliani &
Cao, The Chinese Saving Puzzle and the Life-Cycle Hypothesis, 2004).
When Modigliani & Cao (2004) refer to the demographic structure, they mean that what they call “minority” (E/M)10 decreased drastically since the implementation of the One-Child Policy in China. Since then, the ratio of people under fifteen to working population decreased more than 50 percent; and, the burden assumed by the younger generation to provide support to the older generation increased, which means by itself a stimulus to save. So, the increase of the intergenerational dependence became a negative impact on the overall levels of consumption. They found that these two effects “contributed equally more than ten basis points to the rise in the saving rate of some thirty basis points (from 3 percent to 33 percent)”. And, in addition, this effect might be related to some demographical transformations in the core of the Chinese society since the families are more nuclearized (other relatives such grandparents started to compose their own households) and the internal migration of the past decades started to develop an important role in the distributions of wealth, labor and size of the family.
10 Modigliani understands “minority” (E/M) as “Number of persons employed / Number of persons 14 years and younger”.
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In contrast to the results achieved by Modigliani & Cao (2004) and the Life-Cycle Hypothesis, Chamon & Prasad (2010) using the Urban Household Survey found that the poorer, young and older Chinese people would tend to save more in comparison with other sections of the population, and thus this part of the population would tend to increase its savings instead of “dis-save”. This is explained by the “rising private burden of expenditures on housing, education, and health care”, and the authors conclude that “[…]the risk of large health expenditures can explain high savings among households headed by older persons, and that savings are also higher for households whose composition portends large education expenditures in the future”.
Therefore, assuming that the young and elderly also belong to the group of “dependents” on the working members of their family, then based on this dilemma between both authors whether the dependence has a positive relation to the APC the Hypothesis 4 is raised:
Hypothesis 4: The more the increase in the number of dependents in a household, the more this household would have incentives to save, and thus, decrease its consumption.
And in addition to Modigliani`s findings about the relation between income-growth and consumption, the Hypothesis 5 is also raised:
Hypothesis 5: Understanding that the income growth is positive in relation to the savings, then the bigger the income growth, the bigger its negative impact on the APC.
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2.3 Treatment of the Indicators
This section introduces the indicators used in the calculations followed by the reasons for their use.
Most of the studies about consumption use the aggregate consumption expenditure as an independent variable, since they want to understand the direct impact of the independent variables over the consumption. As this research aims to understand what are the reasons that leads the Chinese Households to consume less proportionally to the income along the time, a different, more comprehensive model must be adopted. This study uses the proportion of this consumption over the income rather than the consumption expenditure. Variations of the study of consumption through other dependent variables can be seen for example in Barnett
& Brooks (2010), which used the total savings instead of consumption expenditures.
So, this is a reasonable way to understand what factors lead the behavior of this proportion (independently of inflation), other than just the variation of the nominal consumption.
Since the purchasing power of consumers and the value of currency varies according to the development of the economy, when household consumption in China is studied the obtained results are an average of the impact of a certain dependent variable over the independent one; but this does not consider the effect of inflation, the real evaluation of the income and its effect over the other expenses, such education and health. An exception in studies applied to China is the research lead by Modigliani & Cao (2004) in which inflation was considered an independent variable.
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In that sense, the variables education, health and housing will have their indicator divided by the Household Total Income. This is also because the share of these expenses over the Household Total Income translates not just into the ability of the family to pay such expenses, but also tracks down the real variation of these expenses in relation to the family`s income – since the analysis is made by level of income. For that, in order to find the income and expenditures (consumption, health and education) per household, the data obtained through CEIC database of income and expenses per capita was multiplied by the number of people in a household, which generated the Household Total Consumption Expenditure, Household Total Income and Consumption Exp. per Household on: (1) Recreation, Educational &
Cultural Service ; and on (2) Medicine & Medical Service.
In particular, for the housing variable, the indicator PTI was obtained from the rate between Commodity Bldg. Selling Price: Residential and Household Total Income for each income level. It is understood that the impact of the housing price on the less wealthy families might be underestimated; and for the wealthiest ones, overestimated. This, in a last analysis, might alter the coefficients obtained at the extremes of the income levels. It is also understood that the housing price might vary by region and by income level, since for each level, the families would choose their real estate according to its affordability. But the insistence of this indicator is to test if and how the burden assumed by the families of buying a house would impact on the consumption, otherwise, this analysis would not be possible due to restrictions on available data.
For the variable “Dependence”, the used indicator was obtained from the difference between the No of persons per household and the No of employees per household. The use of this indicator is to check if and how the number of
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dependents in a household affects the APC. As said, it is still not completely clear whether this variable impacts the propensity to consume of the families, specifically in China.
And finally, as defended by Modigliani and the Life-Cycle Hypothesis, the growth of the households` income has a negative relationship with consumption.
So, to measure this variable, the indicator used is obtained simply by the difference between the income of the previous year and the current one. It is known that others methods to capture the variation of income exist, such as those proposed by Friedman with the Permanent Income Hypothesis (Friedman, 1957) but in order to maintain the length of the analyzed period and minimize interventions on data, this study opted to use the simple variation of income.
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3. Methodology and Data Information
3.1 Methodology
Basically the approach used in this research, classified as quantitative, follow two steps. The main and first one consists of running the Quantile Regression, shared into quintiles, and test the effects of the independent variables over the APC. It is followed by the results of the regressions of each income level and their interpretation. The results and analysis are shown firstly for the National level and it is followed by the results for each of the income level. The second step consists of a qualitative analysis attempting to track down the possible reasons for the main downturns (drastic variations) in the National APC during the studied period.
3.1.1 Quantile Regression
The OLS Regression, also called Ordinary Least Squares, is the most used method to test and run models throughout most of the literature about theories of consumption (Chamon & Prasad, 2010; Jin, Li, & Wu, 2011; Meng, 2003;
Modigliani & Cao, 2004 etc). But, running the ARCH test (Autoregressive Conditional Heteroskedasticity), the results show the presence of heteroskedasticity, which justifies the use of Quantile Regression for the analysis of this study, instead the OLS Regression.
Following Jin, Li, & Wu (2011), in order to identify the effect of the dependent variables over the consumption behavior, we will estimate an empirical model, as follow:
APC = α + βE + γH + δD + ⱷPTI + πΔY + ε (1)
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Where APCis the Average Propensity to Consume, E the Indicator for the variable Education; H, for health; D, for Dependence; PTI, for Housing; and ΔY, for the variation on the income. The coefficients β, γ, δ, ⱷ and π are their respective coefficients. α is the intercept coefficient, ε is the coefficient for standard error – values considered as zero, for simplification.
In Eq. (1), each of the coefficients shows the impact their respective variables over the APC. Which means that the coefficient β and γ measure the impact over the APC for each variation on Consumption Expenditure per Household on Recreation, Educational & Cultural Service, and on Consumption Exp. per Household on Medicine & Medical Service over Household Total Income, respectively. For the variable D, δ expresses the relationship between APC and the dependency rate for each household. And for PTI, ⱷ represents the impact of the burden on a family of purchasing a house on the APC. The value of π, in turn, shows the impact of variation of income on the APC.
Following this method, it is expected to better understand: (1) the relationship of all these variables for each level of income, based on the findings of previous studies; and, (2) which variables really have influence over the APCof each level of income. Besides, it is also expected to understand why different authors could get different results even if studied the same variables. The answer for this puzzle might be in an analysis of the Chinese households from the perspective of each income level.
In addition, the use of quantile regression opens a new perspective on understanding the relationship between the variables according to the variations in the APC along the past 20 years in the Chinese economy by income and national
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level. In other words, how do the variables behave and what are their interactions
level. In other words, how do the variables behave and what are their interactions