International oil shocks and household consumption in China

International oil shocks and household consumption in China

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International oil shocks and household consumption in China Dayong Zhang a, David C. Broadstock a,b,n, Hong Cao c a b c

Team for Integrated Energy and Environmental Research Studies (TIERS), Southwestern University of Finance and Economics, China Surrey Energy Economics Centre, Department of Economics, University of Surrey, UK Beijing Institute of Technology, School of Management and Economics, China



We study the impact of oil price shocks on residential consumption in China. The most immediate effect passes through expenditure on transportation. Effects also appear for health, education and food and clothing expenditure. Existing price regulation offers no great benefit. We argue that a compelling case for removing current price regulation exists.

art ic l e i nf o

a b s t r a c t

Article history: Received 14 April 2014 Received in revised form 22 August 2014 Accepted 25 August 2014

We investigate the impacts that oil price shocks have on residential consumption in China. While it is well understood that oil prices affect consumption in a multitude of ways, the timing and directness of these effects on specific consumption categories is not clear. We demonstrate that the most immediate and direct effect passes through transportation consumption, as might be expected. But we also show that significant effects pass through consumption in other sectors—including “food and clothes”, “medical expenditure”, and other general “living expenditure”—with less immediacy. Given the results, particularly observed asymmetries with respect to rises and falls in international oil prices, we discuss some implications for future adjustments to domestic price policies, in particular the case for removal of domestic price regulation. & 2014 Elsevier Ltd. All rights reserved.

Keywords: Oil shocks Consumption Residential sector Oil price pass-through

1. Introduction “China is now at such a crucial stage that without structural transformation and upgrading, we will not be able to achieve a sustained economic growth. In readjusting the structure, the most important aspect is to expand domestic demand ….” (Li Keqiang, Summer Davos opening ceremony, September 11th, 2013)1 China has experienced more than 20 years of persistent highpaced economic growth, driven among other things by continued corporate and government investment, high levels of exports, and historically cheap labor. The Chinese government has openly set n Correspondence to: Research Institute of Economics and Management, Southwestern University of Finance and Economics, 55 Guanghuacun Street, Chengdu 610074, China. Tel.: þ 86 152 0834 0910. E-mail address: [email protected] (D.C. Broadstock). 1 The full speech is available from the Xinhua News Agency at: 〈http://news.〉.

forth policy objectives designed to continue recent economic trends. 2 There is a general consensus that to complete this process and further stimulate domestic demand, and by implication the level of household consumption, China will undergo significant structural transformation. On the surface, this appears to be a natural progression for the Chinese economy. Growth in the demand for all goods and services, however, will necessarily increase the level of energy consumed in the economy. This follows immediately from two facts: (a) energy is a critical factor of production and (b) transportation is required, to a greater or lesser degree, for the consumption of all goods and services, and transportation is an energy-intensive (and emissions-intensive) activity. In this regard, the stated objective of growing domestic demand is somewhat at odds with targets on the reduction of emissions and energy consumption that were committed to in the Chinese government's 12th five-year plan. 2 This is, for example, a stated objective in the recent 12th five-year plan of the Chinese government. 0301-4215/& 2014 Elsevier Ltd. All rights reserved.

Please cite this article as: Zhang, D., et al., International oil shocks and household consumption in China. Energy Policy (2014), http://dx.


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Growth of the Chinese economy during recent years has coincided with huge increases in the consumption of oil, underpinned by a surge in the rates of private car ownership, which have increased more than 30-fold, from 0.6 cars per 100 urban households in 2000 to 18.28 in 2011. The consumption of oil used for transportation in China doubled in the decade between 2000 and 2010.3 The growth in car ownership increases demand for and consumption of oil, posing genuine policy concerns since the domestic consumption of oil far exceeds domestic production. Although China does produce oil, for many years now the economy has been a net importer and it is already among the top three global importers of oil, with 68% of total oil consumed in 2010 being from imported sources. With an oil supply gap that continues to grow, China is unavoidably affected by international energy markets, meaning that international oil price shocks can pass through to domestic activity. That rising oil prices can impact transportation costs is quite straight-forward, however there are further mechanisms by which changing international oil prices can impact upon the wider range of prices that consumers see. This is discussed in some detail for the Chinese context by Tang et al. (2010) who, following Brown and Yucel (2002), attribute the transmission mechanisms in to six general channels of effect: supply side effects; wealth transfers; general price inflation; real balance effects (as a result of changes in the demand for money); sector adjustments/re-structuring; and effects from increased uncertainty in the oil price. For example, a rise in the price of oil generates inflation and pushes up the producer price index—the price faced by industrial consumers— which is in general then passed on to the consumer via an increase in the consumer price index—the price that consumers pay. The price changes will intuitively have some consequence upon the consumption expenditures of households in the short-run. In addition, the changing prices will further impact upon firm profits and investment, leading to lower levels of activity throughout the economy in the long-run. To shield domestic consumers against often volatile international oil prices, domestic retail oil prices in China are regulated. The pricing system currently follows a form of floating-peg regulation (see next section for further detail) against a bundle of international market prices. The trend of regulation has been to increasingly normalize domestic prices against a bundle of international prices, moving from a centrally controlled price in the 1970s toward something today that quite closely reflects a market mechanism. In light of the rapid growth of car ownership, it stands to reason that international oil shocks could play an increasingly important role in domestic household consumption decisions. Investigating the nature, strength, and timing of these price pass-through effects therefore seems of interest and relevance to policy debates, particularly regarding price regulation. A number of studies, both for China (e.g., Fan et al., 2007; Du et al., 2010) and elsewhere (e.g., Brown and Yucel, 2002; Hamilton and Herrera, 2004; Kilian, 2008), have already established the predominantly negative influence of oil shocks to the macroeconomy. However, largely due to data availability, the impact of oil shocks to household consumption in China has remained an under-researched phenomenon. This is a significant omission since it is important to understand the household sector when considering the welfare implications of exposure to international oil shocks. In this paper, we therefore aim to assess whether and to what extent oil shocks pass through to consumption by the Chinese household sector, looking at aggregate consumption as 3 Similarly, ownership of air conditioning units has increased fourfold, from 30 units per 100 households in 2000 to 121 in 2011 (China statistical yearbook, 2011), reflecting the increased desire of Chinese households to complement their growing incomes with high energy–consuming luxury items.

well as consumption within specific expenditure categories, including “transportation and communication,” “food and clothing,” “medical expenditure”, “education and entertainment,” and general “living expenditure.” In this regard, we follow a series of studies by Mehra and Peterson (2005), Edelstein and Kilian (2009), Odusami (2010), and Wang (2013), which define the linkages between international oil shocks and household consumption under a permanent income hypothesis (PIH). Considering the results carefully, they indicate that household consumption expenditure is not adversely affected by rising oil prices; while falling oil prices seem to stimulate overall consumption to increase. The nuances of these effects, including timing, asymmetry and transmission routes (through alternative consumption categories) are all interesting, and each discussed in the main results. But more interesting is that, taken together, our results point towards the conclusion that lifting the domestic oil price policy is a very serious option that domestic policy makers should entertain. The paper is organized as follows. Section 2 presents a general background discussion, outlining the nature of the oil pricing system in China and offering discussion of the literature on oil shocks and the economy. Section 3 describes the methodology, establishing the context of the planned consumption model under a PIH along with the econometric formulation. Data are presented in Section 4, with the analysis results and discussion offered in Section 5. The paper concludes in Section 6.

2. Background Despite an ongoing debate as to the specific role of oil in shaping household consumption, the general mechanisms by which it contributes to the economy are reasonably straightforward. Consider a rise in the price of oil. Scholars widely agree that oil is needed to support most economic activities, underpinning the energy requirements of transportation needed to move both goods and the individuals who provide services. Bhattacharyya (2011), for example, provides a succinct review of these mechanisms, highlighting that a rise in the price of oil effectively increases the costs of all consumption, thereby reducing the quantity demanded by consumers for all goods and services. At the same time, an increase in energy prices makes energy a less desirable factor of production for firms, causing firms to substitute energy for other factor inputs, such as capital investment or more labor intensive production processes, see for example Broadstock et al. (2007) for a global review of capital-energy substitution or Su et al. (2012) for a China specific example looking at capital, labor and energy, where further discussion on such types of substitution effects is available. To establish the importance of oil shocks on consumption in the household sector of China, we first give some context to the Chinese oil pricing system. In so doing, we firmly establish the nature of price regulation and that international oil shocks do have a route through to domestic oil prices. We then provide a more general discussion of the existing literature on how oil shocks pass throughout the economy. 2.1. The oil pricing system in China: an overview China's oil pricing system has gone through three broad phases since the People's Republic of China was established. The first phase, which was in place primarily during the purely central planned political system, ended in 1981. In this phase, the price of oil was set by the central government, with no scope for international prices to pass through.

Please cite this article as: Zhang, D., et al., International oil shocks and household consumption in China. Energy Policy (2014), http://dx.

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Phase two came into force at about the same time that China initiated market reforms and introduced the famous “open-door” policy in 1978. Since the reform, all parts of the economic system in China, including the domestic oil market, have continued to shift toward a unique system mixing market-based and centrally planned elements. From 1981 to 1998, a “dual-pricing” system was used. In this mechanism, a base level of oil production was required at a fixed price set by the central government; production beyond this base level enabled domestic oil producers to choose between (a) selling any remaining oil on the international markets (exporting) and accepting the international price or (b) accepting a regulated domestic price. This phase of the system appears to have been aimed at providing protection for domestic oil producers to help them establish and grow. The final phase represented a shift away from a strong protectionist regime and toward a form of floating peg against a bundle of international prices. This phase has been in place since 1998 but has seen some revisions to the definition of the international price peg, see for example Li and Ma (2011, in Chinese). From 1998 onward, price adjustments have been announced by the National Development and Reform Commission (formerly known as the National Planning Committee). From June 1998 until 2001, the Singaporean market price was used as the benchmark peg for Chinese domestic prices. During 2001, the peg remained attached, in part, to the Singaporean price, but the basket of oil prices was expanded to also include Rotterdam and Minas (Indonesian oil) prices. Most recently, in 2008, the basket was again adjusted to track against European Brent, Dubai, and Minas oil prices. The domestic price in China therefore depends on the dynamics of international crude oil, including taxes and other fees. The rule by which price adjustments are made is as follows: when the 22-business-day moving (weighted) average of the basket of international oil prices changes by more than 4%, domestic Chinese prices are revised upward or downward accordingly. The nature of this mechanism means that shocks must, in general, be long-lived to substantially shift the 22-day moving average; thus, a certain resilience to oil shocks should exist. But if either (a) the shocks are sufficiently large or (b) multiple increases (or decreases) are sustained, they can pass through to the economy within less than a month.4 Some have argued that exposure of China to shocks from international oil markets would not necessarily be a bad thing, as it may force the industry to be more responsible in cost management and create further benefits for industrial structural reform (Lin and Mou, 2008). However, one must also recognize that, since 1993, China has been a net oil importer, and the pricing policies discussed above will have played a role in ensuring stable economic growth and protection for domestic Chinese oil producers. 2.2. Oil shocks and the Chinese economy: existing evidence Numerous studies characterize the general impact of oil shocks to the economy, with a growing number dedicated to understanding the specific Chinese context. The purpose here is not to provide a complete review, but rather to recapitulate some of the key insights of the literature. The following quote from Hamilton (2009) neatly summarizes what might be referred to as a stylized fact of oil and the economy: “… a slowdown in overall consumer spending and a big drop in consumer sentiment [is] again very much consistent with what was observed after earlier oil shocks.” (Hamilton, 2009, pp. 251) 4 In our empirical work, we use quarterly data; thus, shocks can reasonably be expected to occur with the same time period.


The evidence behind this and similar claims is grounded in a stream of influential studies that have followed from the early empirical contribution of Hamilton (1983). Over the decades since, numerous studies—using different methods and concentrating on different countries from across the world—all reinforce the underlying notion that oil shocks are important (see, for example, Mork, 1989; Mork et al., 1994; Lee et al., 1995; Bernanke et al., 1997; Lee and Ni, 2002; Hamilton, 2003; Kilian, 2008; Kilian and Vigfusson, 2011). The point of departure among these studies comes by way of debate around the ways in which oil is important, particularly regarding the role of asymmetry in shocks (e.g., positive and negative shocks). Following for example Broadstock et al. (2014)—who study the relationship between oil prices and financial market performance in the Asia Pacific Region—we argue that the transmission mechanism by which international oil price shocks transmit to domestic consumption expenditure can go through both a direct channel and an indirect channel. Each of these effects intuitively revolves around the implied transportation cost resulting from an oil shock, but account for the nature of consumption across other product types also. The direct effect derives from the recognition that people need to travel (and hence use petroleum, which is made from oil) to travel and work and other places of recreation, either in a car or for example as a passenger on a bus. An increase in the price of oil, and hence oil related products, will directly increase the transportation cost and in turn alter the quantity demanded/consumption for goods that directly involve transport. That is to say, following a rise in the cost of the service, a fall in consumption expenditure would derive directly from a reduction in the quantity demanded for the service itself. The indirect channel, is however more complicated, and may manifest in alternative ways. The first indirect effect may come through inflationary concerns and/or general income effects. Bernanke et al. (1997) and Hamilton and Herrera (2004) for example study the connections between oil shocks and monetary policy. Their works have stimulated debate on how oil shocks pass through the real economy. The general idea is that rising oil prices lead to overall price inflation across all goods and services (Bernanke et al., 1997), which can motivate the monetary authority to respond with contractionary measures and in turn cause further depression in the economy e.g. a spiral effect. In such circumstances consumption expenditure would be negatively affected also. The second source of indirect effect will manifest as a substitution effect resulting from a rise in the price of oil. Increasing oil prices will cause transportation costs to raise as mentioned earlier, accordingly the quantity demanded for goods/ services that have a high transport cost will decrease and in many cases so will the level of expenditure on these items. For instance consumers may allocate a higher share of expenditure towards entertainment in the home (e.g. TV's or movies) as a result of higher transportation costs. To date, only a handful of studies concentrate on the household sector of the economy and the role that oil shocks may have on it. Recent works include, for example, Mehra and Peterson (2005), Edelstein and Kilian (2009), Odusami (2010), and Wang (2013). As one of the most important economies and largest importers of oil in the world, China inevitably attracts a large amount of research interest. However, due to limited availability of long-duration time series data in China, empirical studies on the Chinese context have been relatively limited until recently. Fan et al. (2007) provide one the earliest research studies to concentrate on China. Using a computable general equilibrium model of the economy, they show that international oil shocks have a generally negative impact on the Chinese economy, including reductions in gross domestic product, investment, consumption, and exports. A number of studies have followed, offering generally supportive evidence. For example, Huang and

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Guo (2007) find that oil shocks affect the long-term exchange rate in China, while Faria et al. (2009) suggest that the export balance in China is positively linked with international oil prices. Studies have also begun to highlight the connection to Chinese financial markets. Cong et al. (2008) and Broadstock et al. (2012), for example, highlight a dynamic relationship between international oil shocks and stock market behavior in China. Tang et al. (2010) offer a more focused discussion, aiming to discern explicitly how oil shocks transmit throughout the Chinese economy and to establish whether the six channels suggested by Brown and Yucel (2002)—the supply-side shock effect, wealth transfer effect, inflation effect, real balance effect, sector adjustment effect, and unexpected effect—hold for China. Tang et al. (2010) establish a structural vector auto-regression model to show that oil shocks reduce output and investment but increase both inflation and interest rates. They further claim that the effect of an oil shock on China's real economy persists for a long time due to the price control policies in place in China. Du et al. (2010) look more specifically at the impacts of oil shocks on economic growth. Their analysis demonstrates that shocks in international oil prices do affect economic growth as well as price inflation in China. Moreover, they provide evidence that policy reforms in the price mechanism in China have had significant impacts on this relationship, causing structural instability in their empirical model. Ou et al. (2012) offer one of the more comprehensive empirical studies taking into consideration a total of 71 macroeconomic indicators. Using a structural dynamic factor model, they reveal the most probable transmission mechanisms of oil shocks to the macroeconomy, suggesting that the inflation effect comes first, followed by supply-side effects and then real balance effects. To the best of our knowledge, however, no studies have explicitly targeted the effect of oil shocks upon consumption expenditure by the residential sector of the economy. Our research here is in effect a conceptual mix of the works of Mehra and Peterson (2005) and Edelstein and Kilian (2009). We ultimately prefer the assumptions/modeling approach of Mehra and Peterson (2005) which are consistent with the idea that households, when faced by a rise in oil prices, may reallocate their consumption patterns within the context of a planned consumption framework, allowing for important dis-equilibrium levels of expenditure to appear. One limitation of their study is the failure to reflect the existence of the “reallocation” effect which may result from a sudden price change. This was one of the major empirical contributions of Edelstein and Kilian (2009), and the core motivation for wanting to look across the consumption categories. The contributions in our study are therefore two-fold: first is the conceptual union of the two analytical approaches discussed above; second is the application to the Chinese context, which is desperately in need of empirical evidence. The latter contribution is quite important, since China is markedly different from US where: price regulations in China are much more severe and less transparent; and also because China is undergoing a gradual process of market liberalization, but at a cautious pace.

by Palumbo et al., 2006) to describe consumption by the household sector of the economy. Defining consumption, income, wealth, and the interest rate in real terms as C t , Y t ,W t , and r t , respectively, we can write the household budget constraint as follows: W t þ 1 ¼ ð1 þ r t ÞðW t þ Y t  C t Þ;


such that next-period wealth equals the discounted value of current-period wealth plus earned income minus any consumption expenditure. Assuming a constant real interest rate ðr t ¼ r t þ 1 ¼ r Þ and imposing the condition that lim W t þ i = i-1

ð1 þ rÞi Þ ¼ 0, then, by repeated substitution of the budget constraint, current-period wealth is obtained as follows: 1


Ct þ i

Wt ¼ ∑

i ¼ 0 ð1 þ rÞ



Yt þi

i ¼ 0 ð1 þ rÞ




Using the result from Hall (1978) that consumption follows a martingale process gives EðC t þ 1 Þ ¼ C t ; then, taking the expectations of Eq. (2) results in the common form of the PIH:  1 E Y r r t þi Wt: þ ð3Þ Ct ¼ ∑ ð1 þ rÞ i ¼ 0 ð1 þ rÞi ð1 þ rÞ Assuming a constant growth rate of real income, g, we have EðY t þ 1 Þ ¼ ð1 þ g ÞY t þ ηt þ 1 , where ηt þ 1 is a white noise process. Then we have Ct ¼

1 r r ηt þ i Yt þ Wt þ ∑ : i r g ð1 þ rÞ i ¼ 1 ð1 þ rÞ


The derivation to this point establishes that a long-run relationship exists between consumption, income, and wealth. Mehra and Peterson (2005) refer to this as the planned level of consumption, C pt , expressing it in a simpler form by first taking expectations of the error term and adding a constant term, leading to the estimable long-run relationship C pt ¼ a0 þ a1 Y t þ a2 W t ;


where a1 ¼ ðr=r gÞ and a2 ¼ ðr=ð1 þ rÞÞ. Actual consumption, however, differs from planned consumption (Campbell and Mankiw, 1989). The short-run dynamics of consumption can therefore be written in the form of an error correction model: k  ΔC t ¼ b0 þ b1 C pt 1 C t  1 þ b2 ΔC pt 1 þ ∑ b3s ΔC t  s þμt : s¼1

Substituting Eq. (5) into (6), we have ΔC t ¼ b0 þ b1 ða0 þ a1 Y t þ a2 W t  C t  1 Þ þ b2 Δða0 þ a1 Y t  1 k

þ a2 W t  1 Þ þ ∑ b3s ΔC t  s þ μt :



Assuming that future income grows constantly relative to the current level, and that consumers have rational expectations, the expected value of accumulated and discounted future income streams is proportional to the current income. Therefore, the model can be simplified to the following equation: k  ΔC t ¼ β0 þ β1 C pt 1  C t  1 þ β2 ΔY t  1 þ β 3 ΔW t  1 þ ∑ β4s ΔC t  s þ μt : s¼1

3. Methodology and empirical framework The model used here is based on the “planned consumption” framework used by Mehra (2001) and Mehra and Peterson (2005). The empirical framework begins with a general/standard macroeconomic specification of (per capita) household consumption, where the level of consumption in an economy, C t , is affected by the existing level of wealth, W t , as well as current and discounted expected future income, Y t and EðY t þ i Þ, respectively, where i ¼ 1; :::; 1. In this regard, the approach therefore applies the commonly used PIH, which has been used recently (for example,



Eq. (8) is the baseline model used in our analysis to capture the dynamics of consumption changes. Following Mehra and Peterson (2005), oil prices are augmented into the short-run equation5:  ΔC t ¼ β0 þ β1 C pt 1  C t  1 þ β2 ΔY t  1 þ β3 ΔW t  1 k




þ ∑ β4s ΔC t  s þ ∑ β5s Δoilt  s þ μt :


5 Mehra and Peterson (2005) also include interest rates in their model. We also considered interest rates but found that they were insignificant in all specifications, so we do not discuss them further here.

Please cite this article as: Zhang, D., et al., International oil shocks and household consumption in China. Energy Policy (2014), http://dx.

D. Zhang et al. / Energy Policy ∎ (∎∎∎∎) ∎∎∎–∎∎∎ Table 1 Variables used in the analysis. Variable names


ΔC t ΔY t  1 ΔW t  1 dit oilt goilt

Real consumption growth rate Lagged real disposable income growth rate Lagged real wealth growth rate Changes in the short-term interest rate Log real Brent price Δoilt ; Growth rate of real oil price max{0, goilt }, Positive oil price growth


goilt  goilt ECTt

min{0, goilt }, Negative oil price growth The “error correction term”, that is produced as the residual from estimation of the long-run consumption equation (Eq. (5)).

Eqs. (5) and (9) establish the main equations for the empirical analysis. The variables used in the analysis are summarized in Table 1.

4. Data The data on consumption expenditure ðC t Þ, income ðY t Þ, and wealth ðW t Þ for China are collected from the DATASTREAM database,6 seasonally adjusted, and then deflated using consumer price index (base year: 2000). Wealth is defined as per-capita net worth in constant prices, while labor income captures the current period disposable income again in per-capita terms, these definitions are the same as Mehra and Peterson (2005). For consumption expenditure, in addition to the per-capita total consumption (i.e., across all goods and services combined), in urban households, we also have consumption expenditure for some specific consumption categories, including transportation and communication, food and clothes, medical expenditure, education and entertainment, and other living expenditure. The data are collected quarterly, and the sample covers the first quarter of 2000 through the third quarter of 2012. Consistent with most existing studies, for oil prices, we use the European Brent crude spot price reported in the Energy Information Administration's online database. To ensure that the effects of exchange rates are controlled for, the international oil price is converted from US dollars into Chinese renminbi. After deflating, the series is then transformed into a growth rate by taking log differences. Although the growth rate of oil prices is a commonly used measure of oil shocks (following Hamilton 2003), to reflect the potential nonlinearity (asymmetry), we also separate positive oil price growth and negative oil price growth in our analysis. The simple logic behind such decompositions is that consumers tend to react quickly to price falls, being eager to boost their consumption, however as prices rise consumers can be slower to reduce their consumption, favoring instead to maintain their current level of consumption (and the lifestyle attached to it) as long as possible due to habit formation or lock-in effects, see for example Dargay and Gately (1995). Regarding the use of European Brent crude prices, from earlier discussion we know that the actual regulated price of oil in China includes Brent prices only since 2008, and that other international prices are also used in determining the domestic price. In principle 6 DATASTREAM is a commercial database that combines a large array of national and international data collected from official data sources, such as national statistical bureaus. It is one of the most comprehensive sources of economic and financial data compiled in a consistent and easy to use manner, which has made it a widely used source of information in academic research. The DATASTREAM is provided by Thomson-Reuters and further information can be found at https://


it is possible to construct a regulated domestic price series, however in practice this is not feasible since the finer details on the regulation schemes (e.g. the weightings attached to the basket members) and price revisions made by the NDRC are not perfectly transparent. Accordingly to create any such series would involve analyst judgment and make room for incidental errors that could impact on the statistical results, which is best avoided. Panel A of Fig. 1 plots a selection of the oil prices that have constituted the regulated basket over the years, including Minas, Cinta, Brent, Dubai and for comparison the Chinese Daqing oil price. The correlations among these series are extremely high, each at 0.99, with their movements being almost identical in all periods, with the exception of some modest deviations in 2011–2012. Any weighted combination, which would require analyst assumptions, would clearly be virtually identical to any of the individual series, which require no strong analyst assumptions. Accordingly the use of Brent prices as an empirical measure of the oil prices relevant to China does not seem unreasonable. Panel B of Fig. 1 shows the regulated gasoline price for China's RON 95 gasoline7 sold in Beijing. Consistent data on regulated gasoline prices do not cover a sufficiently long time span to include into our empirical framework, being available from only 2005 onwards as far as we could obtain the data. Nonetheless looking at these data alongside the oil price series it can be seen that oil and gasoline prices share some similar trends. Between 2005 and 2008 both series are rising fairly steadily. The 2008 oil price collapse is reflected in the gasoline prices by a fall also, though admittedly the fall in the gasoline price is much less dramatic than that of oil prices.8 From 2008 onwards gasoline price revisions are evidently more frequent than before 2008, indirectly revealing the additional flexibility that resulted from the oil pricing scheme revisions that took place in 2008 also. From 2011 to the end of the sample both oil and gasoline prices fluctuate mildly around a fairly constant value, with no strong pattern of price growth or decline. Hence the variation in gasoline prices are broadly reflected by variation in international oil prices, as would be expected. Summary statistics for the untransformed variables are given in Table 2, and each of the series is plotted in Fig. 2. Table 2 shows that income has roughly tripled over the 12-year period, and wealth is five times larger, on average. Total consumption has increased around two and one-half times, with the most significant increase for consumption of transportation, which is almost five times greater at the end of the sample than at the beginning. This rapid growth in transport expenditure implies some support to our earlier claims that oil shocks are becoming increasingly more relevant.

5. Empirical results This section presents and discusses the empirical results, proceeding first by confirming the nature of cointegration among the variables and thereby justifying the error correction model for estimation. Following this, aggregate consumption (i.e., making no distinction among the consumption categories) is modeled using the short-run equation in (9), and the importance of international oil shocks is confirmed. The results are then extended to distinguish among the specific consumption categories, offering a richer image of the location and timing of oil shocks. 7

RON stands for research octane number. The reasons for this are not immediately clear, but it seems that the timing of some limited gasoline price reductions coincide with the end of the oil price collapse—it might be argued that the regulators exercised some discretion during this period of oil price collapse, and waited to see how far prices would fall before revising the domestic price. 8

Please cite this article as: Zhang, D., et al., International oil shocks and household consumption in China. Energy Policy (2014), http://dx.

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Gasoline - Chinese RMB/Liter.

Cinta Daqing Minas Brent Dubai

140 120 100 80 60 40 20


10 9


8 500 7 400

6 5

Brent oil - Chinese RMB/barrel.

RON 93 Gasoline price and the Brent crude oil price. 160


0 99




















Gasoline = Solid line; Oil = Dashed line (second y-axis) Fig. 1. (A) International oil price comparisons and (B) Domestic gasoline prices in China. (sample periods differ due to data availability). Note: panel A shows the Cinta, Daqing, Minas, Brent and Dubai international oil prices from 1999 to 2014. Panel B show the Chinese RON 95 gasoline price from 2005 to 2013 and for comparison includes the (quarterly) Brent oil price series used for estimation.

Fig. 2. Main variables used in the analysis. Note: these plots are for de-trended data in levels and not the estimation-transformed variables.

D. Zhang et al. / Energy Policy ∎ (∎∎∎∎) ∎∎∎–∎∎∎


Table 2 Descriptive statistics. Variable

Initial value

Final value

Avg. growth




Income (Y) Wealth (W)

1522.79 9749.09

4809.01 49,553.96

2.26% 3.25%

2897.54 25,615.22

1552.79 9749.09

4809.01 49,553.96

Consumption variables Total Transportation Food and clothing Medical expenditure Educ. and ent. Other living expenses

1229.07 97.78 616.86 77.12 151.33 108.05

3220.41 484.78 1521.89 204.71 384.71 288.45

1.93% 3.20% 1.81% 1.95% 1.87% 1.96%

2110.65 269.61 996.69 144.89 273.41 211.54

1229.07 97.78 616.40 77.12 151.33 101.01

3220.41 484.78 1521.89 207.33 399.84 292.65

222.23 8.82 8.82

535.99 1.44 1.44


389.56 1.90 6.41

161.42  72.85 0.00

716.71 28.45 28.45






Oil price variables oilt goilt þ

goilt  goilt

Notes: All variables are expressed in real monetary terms except for oil growth, measured in Chinese RMB. Statistics were calculated for data from the first quarter of 2000 through the third quarter of 2012.

expressed in a standard linear regression form as follows:

Table 3 Cointegration and break test results for variables used in the analysis.

yt ¼ μ1 þ μ1 DBt þ α1 xt þ α2 DBt xt þξt ;

Panel I. Cointegration test Engle and Granger residual-based test Test statistics Tau statistic Z-statistic

Cointegrating vector nnn

 5.2439  38.0149nnn

Income Wealth

0.9316 0.0451

Johansen approach No. of CI vectors None At most 1 At most 2

Max-Eigen 25.9031 8.4646 0.0355


Trace 34.4032 8.5001 0.0355

Cointegrating Vector nn

Income Wealth


1.0622 0.1322nn

Panel II. Gregory and Hansen break test Statistics Inf ADF Inf Z


 7.2553  6.5993nnn

Breaking point

Critical value

2008Q2 2007Q4

5% 1%

 5.96  6.45

nnn and nn denote significance at the 1% and 5% levels respectively. Note: CI ¼ cointegrating.

5.1. Testing for the PIH and structural break Taking consumption, wealth, and income as defined above, we begin by considering whether their long-run relationships are stable over the estimation sample. To do so, we first apply standard cointegration checks, including Johansen's approach and the Engle and Granger residual-based test for comparison. The results, reported in panel I of Table 3, indicate one cointegrating vector, suggesting that a long-run relationship exists. While the results suggest evidence of cointegration and the potential to proceed to the estimation of the short-run Eq. (9), it is nonetheless important to recognize that, among other things, our sample period incorporates the global financial crisis, which has been the source of fundamental shifts in economic activity worldwide. Notwithstanding the existence of a cointegrating relationship, a structural shift due to the global financial crisis is a distinct possibility. To test the possibility of some significant structural change in this long-run relationship, we adopt the structural break test proposed by Gregory and Hansen (1996). They propose a simple model with regime shift that can be


where the date break indicator variable, DBt , is the unknown break point to be estimated and takes the value 1 on and after the chosen break date and 0 for all other periods. Gregory and Hansen (1996) then suggest a sequential search procedure to identify the most likely breaking point using ADFn and/or Z n statistics, where    ADFn ¼ Inf ADFðτÞ ; τ A kmin ; kmax ; and  Z n ¼ Inf Z ðτÞ ;

  τ A kmin ; kmax ;

and where kmin and kmax are the trimming points from the start and end of the full sample, reflecting the fact that a break cannot happen in either the beginning or ending period of the sample.9 Gregory and Hansen (1996), Table 1 provide critical values for both test statistics. While the above test procedure is not dissimilar from alternative procedures, such as Andrews–Ploberger types of tests, the refinements by Gregory and Hansen (1996) ensure that the test retains power in the context of cointegrating variables/ relationships, making it preferable in our context. The structural break test results are presented in panel II of Table 3, and plotted in Fig. 3. According to the ADFn statistic, a structural shift occurred during the second quarter of 2008. This break date roughly coincides with two events, the global financial crisis and the latest revision to the domestic pricing policy. In light of the severity of the global financial crisis, we consider this to be the dominant of the two forces.10 These results point toward a fundamental shift in consumers' treatment of wealth before and after the structural break, with the sign of the coefficient switching. It appears that the role of the financial crisis was to fundamentally alter the long-run expectations of consumers, but short-run adjustment behaviors remained unchanged. The long-run equation is therefore modified to reflect this: C pt ¼ α0 þ α1 Y t þα3 W t þ DBt ðα0b þα1b Y t þα3b W t Þ:


9 More precisely, this means that enough observations must exist on both subsamples, at the start and at the end, to allow the test procedure to produce accurate results. 10 In the following discussion, we refer to this as a break due to the crisis, but we concede that it may, in part, also reflect structural change due to policy adjustment.

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-4.5 -5.0 -5.5 -6.0 -6.5 -7.0 2002





( Solid line = ADF statistic; Dashed line = Z statistic; Dotted line = 1% critical value ) Fig. 3. Gregory and Hansen (1996) test statistics. Notes: solid line ¼ ADF statistic; dashed line ¼Z statistic; dotted line ¼ 1% critical value. Table 4 Regression results for aggregate consumption (these tables report only the optimal model specifications obtained using Akaike Information Criterion, AIC).

Constant ECTt  1 ΔY t  1 ΔW t  1

Baseline M0

Augmented M1

Augmented M2

Augmented M3

Augmented M4

2.5555nnn (0.6070)  1.3866nnn (0.2074)  0.3033 (0.2313) 0.0304 (0.0575)

2.6221nnn (0.6473)  1.3219nnn (0.2077)  0.3518 (0.2525) 0.0542 (0.0623)  0.0216nn (0.0104)

2.4385nnn (0.5973)  1.3343nnn (0.2012)  0.3483 (0.2404) 0.0469 (0.0587)

2.5884nnn (0.6305)  1.3790nnn (0.2170)  0.3081 (0.2369) 0.0336 (0.0299)

2.3196nnn (0.5802 )  1.3546nnn (0.2054)  0.3364 (0.2341 ) 0.0379 (0.0678 )

oilt þ

goilt  1 

0.0168 (0.0306 )  0.0375nn (0.0156 )

0.4441 4.0420 3.9117

0.4818 4.0852 3.8824

 0.0345nn (0.0136)

goilt  1 Additional model information R2 AIC for general model AIC for optimal model

 0.0051 (0.0299)

0.4439 N/A 3.8713

0.4671 3.9974 3.8694

0.4794 3.9744 3.8461

Notes: nnn, nn and n denote significance at the 1%, 5% and 10% levels respectively. Heteroscedasticity and autocorrelation corrected standards errors are used since the sample size is relatively small and heteroscedasticity needs to be controlled. Standard errors are reported in parentheses. ECTt  1 is the error correction term (the residual from the first stage regression in Eq. (5)), lagged by one period. R2 is the R-squared measure of goodness of fit. A value close to zero shows a poor fit, while a value close to one shows a strong fit e.g. that the model describes the data very well.

Thus, the structural break is incorporated into the long-run model, and the error-correction term that is then passed through to the short-run equation incorporates the structural instability explicitly. By itself, this introduces no new statistical problems because the procedure helps to ensure stationarity of the error correction term, as required. The remaining part of this section presents the results from the estimated short-run equations.

were also considered but were not significant in any lag orders so are not reported. The results in Table 4 are not without merit, though they do arguably lack sufficient clarity over different types of consumption to accurately depict the true role of oil prices. We therefore proceed to the disaggregated consumption category results. 5.3. Regression results in categories

5.2. Short-run relationships between consumption, income, and wealth, and the role of international oil shocks We turn next to estimation of the short-run total consumption equations. First, we estimate the baseline model shown in Eq. (8) without oil shocks included and then with oil-shocks and other factors i.e. Eq. (9). The residuals from these models, as per the long-run equations above, are tested for structural stability, but no additional breaks are found in these short-run equations. From the estimation results in Table 4, we can clearly observe a role for oil shocks in shaping household consumption in China. In contrast to popular opinion, increasing oil prices do not have a significant impact, whereas falling prices increase household consumption. We also find a clear tendency for error correction, though after the correction to the long-run equilibrium, short-run dynamics in income and wealth have no significant impact on changes in consumption decisions. Changes in short-term interest

We have found that aggregate consumption in Chinese households is negatively affected by international oil shocks. One natural extension would be to try to understand how the shocks are transmitted—in other words, whether some consumption categories are affected more than others. When households meet a new budget constraint, such as would happen with a change in the cost of energy, they may have to rebalance the bundle of goods they consume. Clearly, rising oil prices may reduce household consumption in energy-related categories. For example, one would drive less when the price of fuel oil is higher and may therefore cut transportation and communication spending. However, it might not be plausible to reduce consumption of a necessity like food. It is therefore interesting to investigate the impact of international oil price shocks on consumptions of different categories. Here we investigate the impact of oil shocks on the following categories of consumption expenditure: transportation and communication, food and clothing, medical expenditure, education

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Table 5 Regression results for consumption categories (these tables report only the optimal model specifications obtained using Akaike Information Criterion, AIC).

Constant ECTt  1 ΔY t  1 ΔW t  1

Food and clothing

Medical expenditure

Educ. and ent.


M1 2.1964nnn (0.4547)  0.6189nnn (0.1975)  0.0717 (0.1578)  0.0017 (0.0857)

M1 3.1717nnn (1.0385)  0.3150 (0.7721)  0.1540 (0.2608)  0.2158 (0.2201)

M1 1.8782 (1.3803)  0.0415 (1.1624) 0.3289 (0.4517)  0.0178 (0.2226)  0.3642nn (0.1523)

M1 4.7546n (2.6069)  0.7578 (1.1600) 0.0378 (1.0941) 0.3846 (0.2727)  0.6134nnn (0.1058)  0.1834n (0.0952)  0.1425nnn

M2 3.2879nnn (0.7328)  0.5112nn (0.2354)  0.0669 (0.1375)  0.0101 (0.0752)

ΔC t  1 ΔC t  2 d

goilt  1 d

goilt  2 d

goilt  3 d

goilt  4



(0.0164)  0.0175

(0.0443)  0.0429n

(0.0171) 0.0082

(0.0240)  0.0527n

(0.0160)  0.0361nn


Other living expenses M2 5.9188nn (2.4509)  0.6220 (1.1803)  0.0431 (1.1163) 0.4529 (0.3039)  0.6082nnn (0.0992)  0.1834n (0.0839)

M1 1.7656 (1.4798)  2.8868n (1.6464) 0.5450 (0.6019)  0.0743 (0.2427)  0.4260nn (0.1582)  0.2311nn (0.0922)

M2 -1.4865 (2.8461)  2.8754n (1.5963) 0.7012 (0.5252)  0.0477 (0.3100)  0.4780nnn (0.1602)  0.1695n (0.0986)


(0.0172) þ






(0.0341)  0.0182

(0.1474)  0.1326


(0.0280)  0.0272

(0.2164) 0.0926


(0.0434)  0.0955nnn

(0.0916) 0.2261n

goilt  1 goilt  2 goilt  3 goilt  4 


(0.1315)  0.0834n (0.0422)

goilt  1 Additional model information 0.2094 R2 AIC for general model 4.0331 AIC for optimal model 3.9625

0.2428 4.0331 3.9193

0.1058 5.5786 5.5645

0.1181 6.8669 6.7091

0.3614 6.6230 6.5385

0.3794 6.6036 6.5516

0.3270 6.9137 6.7762

0.4054 6.9768 6.8487

Notes: nnn, nn and n denote significance at the 1%, 5% and 10% levels respectively. Heteroscedasticity and autocorrelation corrected standard errors are in parentheses. ECTt  1 is the error correction term (the residual from the first stage regression in Eq. (5)), lagged by one period.

Table 6 The reactions to oil shocks across consumption categories. Periods after shock

Price rise

þ1 þ2 þ3 þ4

Transportation expenditure Medical expenditure Medical expenditure Living expenditure Food and clothing expenditure

Quarter Quarter Quarter Quarter

and entertainment, and other living expenditure. We use the same general short-run equation in (9), applied to each of the consumption categories. Table 5 reports the regression results for each of the categories, using M1 to denote model specifications in which oil shocks are assumed to be symmetric and M2 for specifications in which asymmetry is allowed.11 To control for possible autocorrelation, we include four lags on both the dependent variable and oil shocks. From the most general specification, an optimal model specification is obtained using Akaike information criteria (AIC). AIC works by selecting the model specification that best balances the amount of information captured by a model, i.e. its ability to explain the data that it is intended to explain, against the size of the model used to generate that information (model

11 In these specifications, the error correction term is taken from the total consumption model, reflecting an overall disequilibrium adjustment effect.

Price fall ↓ ↓ ↓ ↑ ↓

Transportation expenditure Medical expenditure Medical expenditure

↑ ↑ ↑

parsimony). A smaller model that offers the same amount of information is favored, and reflected by smaller values of AIC, hence the optimal model is that with the smaller AIC. The transportation and communication category picks up the highest and most rapid impact from oil shocks as it is the only category to feel shocks after just one lag. M1 uses total oil returns only, whereas M2 decomposes oil price rises and oil price falls. Taken together, the results from these two specifications provide reasonably clear evidence of asymmetry insofar as increasing oil prices change consumption by a greater amount than do falling prices. The impacts of oil shocks on the other consumption categories, such as education and entertainment, is generally much smaller, and their influence, when significant, takes at least two quarters to manifest. The consequences of an oil shock to the key consumption categories are summarized in Table 6. Column one defines the time period, running from the first period after the shock occurs

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through the fourth subsequent period (þfour quarters) when all effects are realized. The second and third columns, respectively, show which consumption items are affected and in which time periods given (a) a price rise or (b) a price fall. In the period immediately following a shock, the only consumption category affected is transportation, irrespective of whether the shock is positive of negative. In the second quarter, medical expenditure decreases.12 In the third period, additional reductions in medical expenditure imply there may have been some further health benefits, greater even than in the previous period. It is only in the fourth period that other consumption transfer patterns are revealed, with expenditures on food decreasing and expenditures on other living expenses increasing at the same time. But these final effects manifest only when prices rise, not when prices fall. In sum, a positive oil shock reduces transportation expenditure, reduces medical expenditure, and causes a change in the balance of food expenditure and consumption related to other living expenses. In the literature review section it was generally found that oil shocks negatively affect economic behavior, but the results here suggest a largely positive reaction to oil shocks. To reconcile this it should be recalled that previous literature looks at economy-wide effects in most cases, the lens offered here is more clearly focused on the relatively under-studied aspect of consumer expenditure. Since it looks at a different part of the economy it should not be expected that the same conclusions are to appear. The results here do not so much offer a different conclusion for the same area, rather they offer a conclusion for a different area, and moreover the results are both plausible and intuitive. 5.4. Further discussion and policy implications We have, to this point, illustrated that oil shocks are relevant in household consumption choices, but in a generally asymmetric manner. Here we consider further the implications of our results for related policy—in particular, possible revisions to existing domestic price regulation. Some have argued that exposure to shocks from international oil markets would not necessarily be a bad thing, as it may force industry to be more responsible in cost management and create further benefits for industrial structural reform (Lin and Mou, 2008). However, since 1993, China has been a net importer of oil, and the pricing policies discussed above have played an important role in ensuring stable economic growth and protection for domestic Chinese oil producers. The results indicate that household consumption is not adversely affected by rising oil prices; rather, our results (consistent with economic theory) imply an adjustment of household consumption patterns among alternative goods and services in light of the new household budget constraint. Conversely, falling oil prices seem to stimulate overall consumption, which appears to be a positive effect for the economy. This suggests that allowing international oil price shocks to pass through to households (e.g., through international price liberalization), is not necessarily something to be afraid of. Considering the history of price regulation discussed earlier in the paper, it is clear that domestic prices have become increasingly market oriented, particularly from 1998 onward. For example, the bundle of prices pegged against was revised in 2001 and again in 2008 to reflect the true cost of importing from international oil markets. Moreover, the window size over which oil prices are pegged has been shortened over the years, allowing international 12 A speculative explanation for this could be that fewer cars are traveling on the roads (the first-period effect), resulting in reduced pollutants and fewer instances of respiratory disease and/or other ailments. However, our data provide no way to confirm this.

oil shocks to pass through to domestic activity more quickly. Arguably, the changes in domestic regulation have been cautiously applied, taking steps toward oil price liberalization very carefully. Ultimately, we consider that the path of price regulation has been in the right direction, and that it should be maintained. Taking a slightly more general perspective on the economy, Lin and Mou (2008) argue that the industrial sector of the economy in particular may benefit from oil shocks; here, we additionally argue that household consumption may also benefit, or at least not suffer. Since these effects imply reduced oil consumption, transportation-related emissions should also fall. We do, however, acknowledge the full complexity of any economic system, and that our analysis and discussion are ultimately based on a partial assessment of oil shocks and consumption.

6. Conclusions In this paper, we have attempted to answer a simple question: Do international oil price shocks pass through to domestic consumption by the household sector in China? In this regard we demonstrate not only that oil price shocks influence consumption, but that the nature of these impacts differ across various different types of consumption e.g. transport versus medical expenditure. This is in many ways intuitive, since different consumption categories have differing levels of connectivity to oil prices. Nonetheless empirical quantification of such effects at an aggregate level has to date been somewhat limited within the known literature. Ultimately, our results point towards the conclusion that lifting the domestic oil price policy that is currently in place in China, is a very serious option that domestic policy makers should at the very least entertain. To be more specific, given our results there is a compelling argument (notwithstanding wider general equilibrium effects that we do not model) that price regulations should be removed since the regulation appears to offer no great benefits, but does (i) create a costly regulation process, (ii) hinder progress towards integration with international oil markets and (iii) diminish the co-benefits of health and environmental protection that would result from a higher (and/or more flexible) price of oil. Our results have their boundaries. While on the one hand there is an argument for removing price regulation, on the other hand there exists a counter argument which potentially over-emphasizes the importance of the partial nature of our analysis, which concentrates on a reasonably narrow area of the economy. While acknowledged, this is not considered a limiting feature of the current work: though it must be conceded that other areas of the economy, for example the producers/firms nor the role of the government, are explicitly characterized, the apparent gap in the extant literature motivates the need for any empirical insights on the residential sector, partial or otherwise. However, this line of objective criticism does give rise to some directions for future study: who exactly are the players impacted by oil shocks (consumers, firms, governments, oil refiners and producers etc.); what is the balance of benefits and costs that impact each of these players; from an economists perspective, what are the welfare implications—who benefits more the consumer or the producer, and is this as intended? Better understanding the welfare implications of oil shocks would seems like a particularly worthy direction for future study. Doing this properly would, in our opinion, benefit most from complementary research to this study, that takes a more careful look into the micro-level foundations of the topics discussed here. Explorations in this direction could usefully include analysis from micro-level firm data as well as consumer data, seeking among other things to add deeper clarity to the substitution mechanisms across alternative consumption categories in response to a sudden energy price shift.

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