Do natural disasters influence long-term saving?:
Assessing the impact of the 2008 Sichuan earthquakeon household saving rates using synthetic control

Kevin Luo
Tomoko Kinugasa

February 2018
Discussion Paper No.1804

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANDo natural disasters influence long-termsaving?: Assessing the impact of the 2008
Sichuan earthquake on household saving
rates using synthetic control**
KEVIN LUO
Graduate School of Economics, Kobe University
2-1 Rokkodai, Nada-Ku, Kobe 657-8501, Japan
luokaiwen1991@gmail.com
TOMOKO KINUGASA**
Faculty of Economics, Kobe University
2-1 Rokkodai, Nada-Ku, Kobe 657-8501, Japankinugasa@econ.kobe-u.ac.jp

Abstract: In this study, employing the synthetic control method (Abadie et al., 2003), we examine
the short- and long-term effects of the 2008 Sichuan earthquake on saving behaviors. The results
indicate that, in the short-run, the earthquake has caused drastic declines in household saving
rates—from 24% to 7% and from 23% to 21% for rural and urban populations, respectively. However, household saving rates recovered to the baseline shortly after the shock, and they exactly
match their counterfactual counterparts in the period ahead. The estimates imply that, at the aggregate level, the earthquake has no discernible long-run impact on the saving propensity of the affected population.
Keywords: Natural disaster, Sichuan earthquake, Saving, Synthetic control method, long-term impact
JEL classification: Q54, D81, E21*

We thank Prof. Mitoshi Yamaguchi and Prof. Kazufumi Yugami for valuable comments. This
work was supported by JSPS KAKENHI Grant numbers JP26292118 and JP17K18564.
**
Corresponding author.
11. Introduction
The economic impact of natural disasters has been attracting considerable attention in thepast few decades. The ongoing debate mainly centers on how and whether natural catastrophes
affect economic growth, with a large body of literature providing divergent insights. Economic
models have predicted that the growth effects of natural events can be in distinct directions (positive,
negative, or neutral) and magnitudes, both in the short- and long-run1. On the other hand, existing
empirical literature has highlighted diverse findings as well. Incipient literature, such as AlbalaBertrand (1993), Tol and Leek (1999), and Skidmore and Toya (2002), reveals that economic
growth tends to be positively correlated with the frequency and magnitude of moderate disasters,
whereas more recent studies (Vigdor, 2008; Noy, 2009; Coffman and Noy, 2012; Strobl, 2012)
discover that natural catastrophes lead to negative economic consequences, particularly for lessdeveloped countries in the short term. Moreover, there is emerging evidence implying that natural
disasters have a mixed impact on the aggregate growth. The overall effect depends on the featuresof disasters (type, intensity, and spatial extent, etc.), geographical conditions, and national characteristics (Toya and Skidmore, 2007; Loayza et al., 2009; Noy and Vu, 2010; Cavallo et al., 2013).
Literature on disaster impact has long been preoccupied with the growth effect, whereas
the characterization of aggregate growth barely provides complete answers, thus the conundrum is
ultimately an empirical one. Instead of investigating the economy as a whole and focusing merely

1

For instance, according to traditional neoclassical models, provided that natural disasters
only cause capital losses, technological progress should remain intact; thus, the shock spurts shortterm growth but has a neutral effect in the long-run (in the steady state). On the other hand, endogenous growth models generally contend that destruction will lead to a permanent slowdown ingrowth, resulting in deviation from the equilibrium growth path, which can translate into a sluggish
growth or even a poverty trap. Further, the “creative destruction” and “build back better” theories
suggest that destruction can boost replacement, construction, and the use of new technologies,
which might foster greater growth and probably a better economic potential in the event aftermath(Aghion and Howitt, 1990; Caballero and Hammour, 1991).
2

on economic growth, it seems desirable to take a further look into the mechanisms and channels
through which the growth effect is formed. In this study, we make explicit attempts to identify thedisaster impact on household saving—a critical and decisive determinant of economic growth. This
perspective applies very well to China’s case, as its economic development is largely driven by thecapital-extensive investment pattern fueled by its extraordinarily high household saving rate.
Saving behavior is a central topic in economics. The common belief on the subject is thatpeople tend to be conservative and save more in the wake of natural disasters, or they might simply
become self-indulgent and consume more instead of saving. In this study, we employ the synthetic
control method developed by Abadie et al. (2003, 2010, 2015) to estimate the short- and long-term
effects of the 2008 Sichuan earthquake on household saving, to extend the empirical knowledge ofsaving behaviors. According to the results, the earthquake has caused substantial declines in Sichuan household saving rates—from 24% to 7% for the rural population, and from 23% to 21% for
the urban population—as a manifestation of the direct economic losses from the disaster. However,
we notice that both the rural and urban saving rates have rebounded to the “counterfactual” levels
shortly after their cliff-fall drops. Accordingly, the earthquake has resulted in a short-term negative
effect, while having no perceivable long-term effect on the saving propensity. This finding is confirmed by a “placebo test,” and the main conclusion is robust across a series of checks employing
alternative predictors.
The structure of the paper is as follows: In the next section, we discuss the existing literature relevant to the disaster impact on saving behaviors, and we concisely summarize the mechanisms of the effects. In Section 3, we give a brief overview of the 2008 Sichuan earthquake; what
follows covers the empirical strategy (the synthetic control method), the data source, and the definition of variables. Section 4 presents the estimates, placebo test, and robustness checks. Section 5
closes the paper with brief concluding remarks.

32. Natural disasters and saving behaviors
Disaster impact on saving behaviors has seldom been explicitly examined so far. As a notable exception, Japan—as a disaster-prone country—has offered numerous valuable studies andfairly rich data sources on the topic of household decisions against natural events. In a literature
survey, Horioka (1990) mentions that the high frequency and enormous damages of natural eventsoccurring in Japan have contributed to its high saving persistence in the postwar periods. To thebest of our knowledge, Skidmore (2001) is the first empirical attempt in English academic circles
to look into the correlation between disasters and saving accumulation. Employing a cross-country
estimation based on a sample of 14 developed nations, the analyst finds that disaster loss is positively correlated with household saving rate, in support of the precautionary saving hypothesis. In
a subsequent study (Skidmore and Toya, 2002), the authors reveal a growth impact of climatic
disasters on national capital accumulation. In a similar vein, Leiter et al. (2009) estimate that firmsthat were flood victims in Europe tend to pursue a higher growth in asset accumulation.
On the other hand, more recent empirical literature has presented different insights. Dercon(2004) discovers that rainfall shocks have significantly stimulated the consumption of rural residents in Ethiopia. Sawada and Shimizutani (2008) investigate how local residents insured themselves against the 1995 Kobe earthquake. Their analysis indicates that homeowners have dis-saved
and borrowed extensively after the shock, implying that the formal and informal insurance systems
were essentially ineffective and that the complete risk-sharing hypothesis can be rejected. Gignoux
and Menéndez (2016) find that individuals who have experienced short-term economic losses fromthe Indonesian earthquakes, recovered in the medium-run, and even exhibited welfare gains (consumption growth) in the long term. Berlemann et al. (2015) study the effect of the 2002 European
flood on saving behaviors. They conclude that mostly owning to the generous government compensation, the flood turns out to have depressed the victims’ motive to save. Their finding lends

4support to the much-debated hypotheses referred as “Samaritan’s Dilemma” and “Charity hazard”.
In a study similar to the present one, Filipski et al. (2015) investigate the impact of the 2008 Sichuan
earthquake on consumer behaviors. They find that households who lived closer to the epicenter,tend to save less, spend more on alcohol and cigarettes, and pursue more leisure activities after theshock.
The existing literature has provided diverse evidence with respect to the disaster impact on
saving behaviors, yet the sign and magnitude of the overall effect remain ambiguous. To be clear,
the disaster impact is a multi-dimensional issue that is rather complicated to quantify, since thedestructive nature of disasters not only generates economic costs, but also triggers changes in consumer behaviors and influences people’s attitude towards the future2. Matters are aggravated whenit comes to empirical assessments because the statistical inference will be disturbed to a certain
extent by the existing transfer mechanisms (e.g., disaster relief and donations). Here we briefly
summarize the mechanisms and channels through which saving behavior is potentially driven.
First, natural disasters cause direct physical destruction. Households exposed to natural
events confront the increases in expenditures (e.g., home, contents, and medical services). That is,
those severely affected might have required available resources to cope with the adverse consequences, and they might encounter a higher risk of unemployment and negative income shocks(Vigdor, 2007; Deryugina et al., 2014). In this sense, individuals usually earn less and expend more

2

Saving propensity generally increases with risk aversion (attitude towards risk) and decreases with time preference (the strategy to “discount” future outcomes). In behavioral economics,
there is ample evidence suggesting that individuals respond to natural disasters by becoming morerisk averse towards uncertainty (e.g., Van Den Berg et al., 2009; Cameron and Shah, 2015; Cassar
et al., 2017). However, some support the contrary, that disaster victims tend to be risk tolerant andreckless, gamble more, and spend more on consumer goods (e.g., Eckel et al., 2009; Li et al., 2011;
Page et al., 2014; Hanaoka et al., 2015; Filipski et al., 2015). In the context of time preference,
Callen (2015) argues that consumers become less impatient after catastrophic events, whereas Cassar et al. (2017) advocate the opposite. Beyond economics, the impact known as post-traumaticstress disorder (PTSD) has been exclusively studied in medical fields with a consensus that extreme
shocks usually depress saving motives.
5after the event. In the short-run, the “volume effect” of natural disasters (e.g., destruction, income
loss, losses of domestic and foreign investments) can lead to higher consumption and lower saving
both in household and aggregate levels.
Second, through rational expectation, natural events influence the preference and behavioral responses of victims, which in turn induce a reallocation of time resources (Eckel et al., 2009;
Cameron and Shah, 2015; Cassar et al., 2017). To simplify, there are roughly two kinds of uncertainties that exogenous shocks might bring about. One concerns uncertainty on future earnings, theother one concerns uncertainty on life expectancy. (1) After the 2008 earthquake, there was widespread awareness that Sichuan is located in the vicinity of dense faults, making it particularly vulnerable and susceptible to seismic activity. Saving is regarded as a tool to smooth the life-time
consumption. To fend off the potential property damage, unemployment, and income loss, individuals living in disaster-prone regions react to the corresponding uncertainty by saving and othermethods of self-insurance. (2) On the other hand, in the context of extreme natural catastrophes
that can cause large number of casualties and human toll, rational agents might anticipate an increase in mortality and thus a decrease in their life expectancy. They will update their evaluationof the future by adjusting their time preferences, all of which in turn shape the patterns of consumption and saving3. In this case, extreme events are likely to cause a concentration on consumption
other than on saving accumulation in the long-run. In summary, effects of risk perception offset
each other. One promotes savings in preparation for latent economic losses, and the other one reduces savings in response to the perceived lower life expectancy. It is unclear how these effects
would balance out on net.

3

As a typical example, Kinugasa and Mason (2007) theoretically and empirically study theeffect of changing mortality on national saving rates.
6

Third, the follow-on saving behaviors rely crucially on the objective factors. For instance,
disaster consequences are often less severe in nations with higher insurance penetration, better institutions, greater openness, and better economic status (Kunreuther, 1996; Toya and Skidmore,2007). In fact, private insurance usually does a poor job in the face of severe natural events, evenin advanced economies with high take-up rates of insurance4. On the other side, in the wake ofnatural disasters, external elements—such as government compensation, disaster relief, adaptation
policies, external aid, private donations, and transfers—have proven profound influences on consumer behaviors (Raschky and Weck-Hannemann, 2007; Cavallo et al., 2013; Berlemann et al.,2015; Gignoux and Menéndez, 2016). These factors have no doubt helped to relieve the direct
disaster consequences, but will more or less blunt the statistical inference derived from empiricalassessments.
Theoretical models and previous empirical evidence will only take us so far. To sum up,
the overall impact of natural disasters on saving behaviors is ex-ante ambiguous. It seems a formidable enterprise to conceptually and practically reach a clear-cut conclusion on the subject, thus it
is almost impossible to distinguish between various mechanisms of what might have driven the ups
and downs in economic indicators. With respect to that, we would like to stress that the current
study is not aimed at testing any of the hypotheses on saving behaviors. Instead, we pay carefulattention to the validity of the statistics, the economic meaning, and the feasibility of empiricalapproaches to arrive at more accurate estimates of the disaster impact. All in all, there is an urgent
need to move beyond the “short-term effect” and “statistical correlation” to extend the empiricalknowledge on saving behaviors.

4

See Skidmore and Toya (2001) for a comprehensive survey of the insufficiency of private
insurance against extreme catastrophic events. See Sawada and Shimizutani (2008) and Horwich(2000) for the performance of Japanese self-insurance mechanisms in coping with the 1995 Kobe
earthquake.
7

3. Background, empirical strategy, and data
3.1 2008 Sichuan earthquake
On May 12, 2008, an earthquake measuring 8.0 on the Richter scale hit the Sichuan province of China. The earthquake is recorded as one of the strongest natural disasters in human history,
resulting in 69,268 deaths, 17,923 persons listed as missing, 374,643 injured, 5 million homeless,and 46 million people affected5. The direct economic loss is estimated at over US$ 120 billion, of
which 27.4% was for housing destructions, 20.4% for non-residential destructions, and 21.9% forinfrastructure damages. Sichuan province constituted 99% of the overall human toll and 91.3% of
the total economic losses from the catastrophe (Gansu province represented 5.8%, and the Shaanxi
province 2.8%)6. The earthquake was felt even by people living 2,000 km away from the epicenter.
Between May 12 and June 23, the main shock was followed by hundreds of aftershocks, five of
which reached the magnitude of MS 6.0. In April 2013, another severe earthquake of 7.0 on the
Richter scale—also believed one of the aftershocks of the 2008 main shock—took place in Ya'an
city, western Sichuan.

3.2 Empirical strategy
In the following parts, utilizing the earthquake as a natural experiment, we pursue a comparative event study to assess the disaster impact on household saving. Existing empirical literatureon the subject tends to be limited in several aspects: First, most studies are oriented to assessing

5

Borrowing the definition from EM-DAT (Emergency Events Database), “affected” denotes
the people in need of immediate assistance during the post-event emergency period.
6
The indicators in subsection 3.1 are directly taken from official figures available in the
publications of the National Bureau of Statistics and the National disaster reduction and management council of China.
8

only the short-run effect. As the foregoing discussion underscores, direct economic costs from natural disasters usually lead to higher household expenditures, unemployment, and income fluctuations. In this sense, the effect is most likely to be identified as negative—an unfruitful inference—as the cross-section and short-panel estimations and micro-based analyses have done so far, yet themedium- and long-term impact remains vague and inconclusive. Second, conventional regression
approaches pivot critically on the model-dependent extrapolation; these methods provide a weaksafeguard against erroneous statistical inference, and thus might jeopardize the transparency andhonesty of the estimates (Cavallo et al., 2013; Abadie et al., 2015). Third, most literature does not
fully address the endogeneity concerns. On one side, it is apparent that economic indicators—suchas disaster losses and fatalities, which are correlated with economic characteristics (the outcome
variables), cannot serve as proper proxy for disaster occurrence. Further, there is a growing literature on disaster vulnerability considering disaster impacts as dependent variables, rather than explanatory variables. On the other side, as Kousky (2014) has suggested, incorporating disaster occurrence or frequency would not satisfy the endogeneity considerations as well, due to the inclusion
criteria of databases, by which disaster occurrences in advanced economies are often under-reported. Accordingly, to utilize a natural disaster as a controlled experiment, the most harmlesschoice seems to be employing particular instrument variables (e.g., a time dummy variable andphysical indexes). Fourth, in comparative case studies based on panel data, it is vital to control fortime trends and for the heterogeneity problem7 . Inferential techniques like the traditional DID

7

In Abadie et al. (2010, 2015), the authors prove that when the length of pre-intervention
periods is sufficient, the synthetic control method controls for the un-observed effects and mitigates
the heterogeneity problem. The rationale is simple: only units that resemble in both the observed
and unobserved characteristics over sizable periods are able to precisely produce the “counterfactual” estimates.
9

framework (difference-in-difference) might generate bias since the unobserved factors are generally not properly accounted for, and this is exactly the challenge in studying the dynamics of Chinese household behaviors8.
We contribute to the literature by employing the methodological innovation formalized in
Abadie et al. (2003, 2010, 2015), which is immune to the above limitations. The core concept ofthe methodology is to construct a comparable counterfactual scenario that demonstrates what theoutcome trajectory the affected group would have experienced in the absence of the specific eventof interest. The synthetic control method does not rely on statistical extrapolation; instead, it produces a reasonable “donor pool” in a data-driven manner by incorporating control groups comprised of unaffected units. The comparability of the treated group to the control group—supportedby the visible similarities between the groups prior to the shock—facilitates us in displaying clearer
pictures and more accurate estimates of the disaster impact. Accordingly, the causal effect can bederived from the comparison between the actual trajectory we observed and its counterfactual counterpart.
Following the enlightening works by Abadie et al. (2003, 2010, 2015), the part below
briefly illustrates the theoretical underpinnings of the synthetic control method, combined with thecase to be investigated in the study. Assume that there is a number of J+1 districts (or provinces,administrative units, etc.), and the first district (j=1) is exposed to a specific event, whereas others
are not. The first district (j=1) is the “treated unit,” and the remaining J districts serve as the potential “donor pool.” Let,

be the recorded value of the outcome variable, or we can simply name it

as the saving rate. The earthquake occurred in 2008; thus, the pre-intervention period covers theinitial year to 2007—k years in total.
8

Household saving rates of China vary over time and across space. Official statistics indicatethat the average saving rate of rural households by prefecture has been declining from 45% to 18%,
whereas its urban counterpart has been rising from 18% to 30% in the past 20 years, demonstrated
by the descriptive statistics (Table 1). To address the problem, the synthetic control method allows
the unobserved effects to vary with time, rather than holding them constant in time as fixed effects.
10

Let

,

,

,

be the causal effect of the earthquake on the saving rate, whereis the observed saving rate in district 1 (Sichuan province) at time t,,,

is the predicted counter-factual saving rate in district 1 at time t. As Abadie et al. (2015) put it, “a combination of comparison units often does a better job of reproducing the characteristics of the treated unit than any
single comparison unit alone or an equally weighted combination of several control units.” Thestrategy is to make explicit the contribution of each comparison unit to the counterfactual outcome,with proper weights that sum to unity9. This enables us to focus more on the similarity and divergence between the treated and control groups, which helps to cautiously select comparison units inorder to reduce bias and discretion in observational case studies.
The method considers a

1 vector W of weights, and each particular vector W repre-

sents a potential synthetic control, that is, a specific weighted average of control units. In general,
the optimal vector W is chosen to minimize X1  XotherW
whereis thev

 (X1  XotherW )V (X1  XotherW ) 10 ,1 vector of the observed pre-intervention characteristics (in the study, thesecan be the saving rate and its determinants) of treated unit over k years.

is a () matrixof the same variables in the comparison units ( units in total). The method gauges whether thecharacteristics of the treated unit are efficiently matched by those of the synthetic controls. In otherwords, the synthetic control method is intended to employ a systematic algorithm—in common
with the conventional least-squares estimation—to derive the optimal weights by which the comparison units best resemble the treated one.

That is,⋯1, with 0
1, where j 2, 3 … j 1.
The process is to minimize the Euclidean distance between the pre-event values of thetreated unit and those of its synthetic version. The equation can also be written as∑, where , is the recorded value of the k-th variable for the treated
,
,
unit;
, is the recorded values of the k-th variable for the comparison units. In this regard,is the predicted value of k-th variable for the treated unit as the weighted average of the,variables in the control units.
9

10

11

Notice that there are two matrices to be solved: The algorithm-derived weights W, and thepositive semidefinite matrix V—meant to sketch the relative importance of each predictor11.
The choice of V influences the mean-square error of the estimators, but the estimated results are
generally robust to different approaches for choosing V12. For instance, it can be subjectively assessed under scrutiny, or it can be derived from cross-validation (Abadie et al., 2015), by “nested”
optimization (Kaul et al., 2015), or by regression approaches (Bohn et al., 2014). A common practice is to select the optimal matrix V among all positive and diagonal matrices that minimizes themean-square error of the synthetic control estimators over the pre-intervention period. Once thematrix V is obtained, the vector W becomes derivable; then, the causal effect can be assessed as,,∑, , for the treated unit at time t in the post-intervention period.

Although the synthetic control method has never put the long debate on “the best overall
approach” to rest, there does appear to exist a consensus among academicians that this method
offers superiorities over other approaches in many ways (Saunders et al., 2015; Gobillon and Magnac, 2016; Athey and Imbens, 2017; Gardeazabal and Vega-Bavo, 2017). The methodology isdeemed to be a bridge—which is theoretically sound as well as empirically feasible—between
quantitative and qualitative approaches in comparative case studies, and its application is becoming
increasingly popular in diverse research topics13.

3.3 Data and variables
In the current study, the main objective is to capture the short- and long-term effects of the2008 Sichuan earthquake on household saving. We will focus primarily on the rural household

11

Variables with a larger explanatory power in predicting the outcome variable can be assigned larger weights.
12
For a detailed discussion on different procedures for computing “V,” see Abadie et al.
(2003, 2010, 2015), Kaul et al. (2015), and Klößner et al. (2017).
13
The interested reader can consult Kaul et al. (2015) for a comprehensive survey of theempirical applications of the synthetic control method.
12

saving rate for several considerations: First, the epicenter (Wenchuan County) is located in rural
districts, surrounded by dozens of other villages, which subsequently became the worst devastatedregions. According to the official figures and anecdotal evidence, mountainous and less wealthy
areas in Sichuan province suffered the most, both from the direct economic losses and from thecasualties during the shock. Second, largely due to the inferior rural welfare benefits as well as thegreater fluctuation in agricultural income, rural residents rely more on household saving as a selfinsurance mechanism against risk, and they have a greater need for precautionary saving. On theother hand, urban residents have highly subsidized social securities and relatively diverse financial
approaches, making it difficult to capture the disaster impact only by specifying the effect on theirhousehold savings. The rural saving rate suits our objectives better in assessing the precautionary
motive as well as the effect of the direct disaster losses, which can translate into more pronounced
and precise estimates.
The outcome variable of interest—rural household saving rate—is calculated in a traditional manner as the average ratio of household saving to household disposable income by prefecture. We also include several standard determinants of saving behaviors as predictors (i.e., the contents in matrix X), such as the share of housing and education expenditures, sex ratio (the numberof males to that of females), transfer income ratio (share of intra-family and government transfers
in total disposable income), level (logged) and growth rate of disposable income, total bank lending(proportion of total bank lending in prefectural nominal GDP), and the lag terms of the outcome
variable14. We have also conducted experiments with an array of alternative predictors of saving
behaviors, such as the dependency ratio, urbanization ratio, life expectancy, inflation rate, FDI and

14

According to Kaul et al. (2015), in applying the synthetic control method, the introductionof all outcome lags as separate predictors renders other covariates irrelevant, which might generatebias and lead to totally different implications. To be conservative, we restrict the number of incorporated lags to a value less than 3—a reasonable setting adopted in Abadie et al. (2003, 2010).
13

CPI indexes; their inclusion did not change the results substantively, and we dropped these observations in the interest of space. In the robustness tests, we exploit the level and growth of cash
deposits, and total retail sales of consumer goods as alternative outcome indicators, to be revealedin the next section. For completeness, descriptive statistics are displayed in Table 1.
The analysis is based on China’s provincial data comprised of 30 units (i.e., provinces,autonomous regions, or municipalities) covering the period 1995-2015, which is to our knowledge
the most complete official dataset recording Chinese household surveys assembled to date. The
data source provides a pre-event period of at least 13 years (from 1995 to 2007), and an ex-post
period of 8 years (from 2008 to 2015). In the robustness tests, we extend the time span to 1990 byemploying alternative outcome indicators in the place of household saving rates. All variables aretaken and constructed directly from statistics available in the Chinese statistics yearbook, population and employment statistics yearbook, and finance and banking yearbook, published by the National Bureau of Statistics of China.
One limitation of the dataset is that, a handful of variables are not available for certain
administrative units over particular periods. For instance, Chongqing city became independent
from Sichuan province in 1997; thus, some indicators were not recorded until 1997. Statistics on
Tibet are often unavailable and we have to drop this observation when computing certain estimates.
Another issue is that, the synthetic control group has to be constructed as a weighted average of the unaffected units. As a result, Chongqing city and Gansu province, which have also beensignificantly affected by the earthquake, are not likely to serve as suitable comparison units. Thus,
we need to discard them from the donor pool or simply make sure that their weights in the synthetic
control group are sufficiently small.
As a major drawback, China’s historical data appears highly inconsistent across the overall
period due to the systematic changes in statistical approaches and in definitions—the revisions ofChina’s statistical systems in 1992, 2002, and 2012. To address this problem, one might be tempted14

to smooth the data trends instead of trying to retain all the time-series information. However, thisinconsistency in statistical standards does not disturb the estimated results, since the revisions arein essence “exogenous shocks” occurring nationwide simultaneously. That is to say, as long as therevisions only lead to indiscriminate overstatements or understatements on the economic indicators,
in applying the synthetic control method, these structural processes will be eliminated through the
DID procedure (confirmed in the next section).

4. Results
In this section, to estimate the impact of the 2008 Sichuan earthquake on household saving,
we construct the “synthetic Sichuan”—a weighted combination of the control units in the donor
pool—which most closely reproduces the characteristics of the treated unit preceding the disaster.
We begin by studying the evolution of Sichuan’s rural household saving rate as well as its synthetic
counterpart. As noted, we believe that the rural saving rate suits our objective best in terms ofassessing the precautionary motive and the direct economic costs of the event. The 2008 Sichuan
earthquake is one of the most economically significant disasters in human history; it is arguably
the least predictable type of natural event, and it has caused grave consequences in a relatively shorttime. In an economic point of view, the earthquake provides a feasible natural experiment to study.
The severity of the shock—relative to the frequently occurring events (other natural disasters, political changes, etc.) and other determinants of saving behaviors—helps us to capture the otherwise
insignificant impact on the aggregate economy.
Before proceeding, it is important to scrutinize the similarity and divergence among theunits. Table 2 demonstrates the pre-intervention characteristics for Sichuan, for the national average(Sichuan excluded), and for the synthetic control group. The comparison between Sichuan and thenationwide average indicates that an equally weighted combination of all the provinces—with a15

considerably higher share of housing expenditure, higher growth and level of income, and higher
proportion of transfer income—cannot serve as a proper control group for the treated unit. In contrast, the synthetic Sichuan accurately replicates the prior-to-event characteristics of Sichuan (the“inner optimization”), which verifies its overall consistency and comparability. The estimatedweights of the control units (i.e., the vector W) show that the pre-intervention trajectory of Sichuan’s household saving rate is best reproduced by Inner Mongolia (58.1%), Tibet (22.9%), Fujian(11.8%), and Ningxia (6.7%). These control units resemble Sichuan in geographic features, economic characteristics, and in demographic structures; more importantly, they were not directly exposed to the 2008 Sichuan earthquake, nor were they reported to have been through significant
shocks over the post-intervention period.
Figure 2 plots Sichuan’s rural household saving rate along with the respective synthetic
version, for the period 1995-2015. The synthetic counterpart closely tracks the actual trajectory
over the entire pre-event period; in particular, it matches the recorded saving rate in 200815, implying a sensible approximation to the counterfactual representing what would have become in theabsence of the event (the “outer optimization”). Immediately after the earthquake, the reported
saving rate dropped noticeably from 24% to 7%—a decline of 71% in the saving propensity—
within one year, whereas the synthetic trajectory appears to maintain the same level after the shock.
The discrepancy implies a pronounced negative impact on saving motives, as a manifestation of
the direct economic losses from the disaster. The result also confirms the validity of the preferredoutcome variable (rural household saving rate), as the adverse effect is proven non-trivial using the

15

In the estimate, we set 2008 as the end of the pre-intervention period because we observeno significant change in the saving rate in 2008, but in 2009. To be candid, chances are high that
there is a time asymmetry as a result of the interruption of household surveys, which leaves thestatistical offices no choice but to rely on pre-intervention statistics to complete their work. In thissense, our estimate based on the survey data is inclined to understate the adverse impact on household saving. However, our main conclusion remains intact, since even the underestimated effectsuffices to verify its statistical significance, to be explored below.
16

proxy. The inference is broadly in line with historical experience and with the empirical findings
over the short-term disaster effects on saving behaviors (such as Sawada and Shimizutani, 2008;
Berlemann et al., 2015; Filipski et al., 2015).
As Figure 2 suggests, however, in the second year of the catastrophe, the saving rate has
rapidly recovered to its synthetic state value, and it almost exactly matches its synthetic counterpart
in the period ahead. This finding is somewhat in support of the “target saving” hypothesis16, whichindicates that a typical agent will retain a certain amount of savings as a buffer stock against risk
(Chamon and Prasad, 2010; Nabar, 2011). It also lends support to the traditional macroeconomic
theories, which predict that exogenous shock is neutral to the steady-state growth as the temporary
loss would be recovered in the future (Charvériat, 2000). In summary, our finding implies that theearthquake did not significantly influence the long-term saving propensity of the affected population.
Is this inference empirically relevant? Noted that the saving accumulation of household has
long been the main force behind China’s economic success, referring to the official figures, one
can observe that the growth rate of Sichuan’s nominal GDP has been increasing steadily from 8.5%
to 14.5% during the period 2000-2007, dropped to 11.0% in 2008, then quickly rebounded to 14.5%
in 2009, and maintained its value at around 15% until 2012. The aggregate growth of Sichuan
moves in a direction consistent with its saving cycle, sharing an acute “V-shaped pattern” over the
turbulent period.
Looking at Figure 2, one might be curious about the drastic declines in the recorded saving
rates and wonder whether this problem impairs the reliability of the analysis. As stated in Section
3, this inconsistency (triggered by the revisions of statistical systems) does not disturb the statisticalinference, since the structural processes have driven the economic indicators indiscriminately

16

Also refer to the conventional saving hypotheses known as “habit persistence,” “habit formation,” and “stickiness of consumption,” proposed by Modigliani, Duesenberry, and other numerous researchers.
17across spaces, and it is evident that the synthetic trajectory perfectly reflects the abnormal fluctuations over the transition periods (the revisions in 1992, 2002 and 2012).

4.1 Placebo test
To derive the statistical significance of the estimate, we conduct an “in-space placebo” test
frequently used in comparative case studies (e.g., Abadie et al., 2003, 2010, 2015; Bertrand et al.,2004). The strategy is to find out whether other regions (the control units) have experienced evengreater shocks relative to the treated unit, probably as a result of the “spillover effect.”17 We conduct the placebo experiment by applying the synthetic control method to every unit in the donor
pool. That is, in each exercise the intervention is artificially re-assigned to one of the control units,with the treated unit (Sichuan) shifted to the donor pool. Figure 3 displays the results, where thegray line represents the divergence between the recorded value of the saving rate and that of its
synthetic counterpart for each region18; the black line is the estimate for Sichuan, the one obtainedin Figure 2. The gap over the pre-intervention period can be interpreted as the “prediction error” inthe context of comparative case studies. If the deviation is negligible, we consider that the synthetic
control precisely reproduces the prior-to-event outcome for the treated unit. In this case, the gap
over the post-intervention period closely approximates the impact of the specific event. As Figure
3 makes apparent, the prediction error in the estimate for Sichuan is minor, verifying the validityof the methodology. The cliff-fall drop in Sichuan’s rural household saving rate in 2009 is by far
the largest one-year shock among all the estimates, and the resilience in 2010 is the most rapid

17

It is possible that the earthquake has caused non-negligible short-term effects on the saving
rates of other provinces, because some of them were directly exposed, although modestly, to theevent (Shaanxi and Gansu, etc.). Further, the adverse impact might not be restricted to Sichuan andits surrounding areas, since most of the administrative units have been putting their efforts in therelief work, which has cost enormous amounts of resources and human capital. In this regard, theestimated result might be contaminated and ill-advised if the affected regions are identified as thesynthetic control units.
18
In each estimate, we use the same modeling strategy as the estimate in Figure 2.
18

recovery in the overall observations, let alone the fact that deviations (prediction errors) in someestimates are already large prior to the event.
To obtain a comprehensive statistical significance of the result, we follow the works of
Abadie et al. (2003, 2010, 2015) and Rosenbaum (2005). Given that the process has generated 29
placebo runs for control units, under the random permutation, the probability of receiving an adverse effect or obtaining a recovery—of the magnitude greater than those for Sichuan in 2009 andin 2010—is considered to be less than 3.3% (1/30) for each instance, a sufficient significance inthe statistical sense. The placebo test validates the transitory negative effect on rural householdsaving. To be precise, the 2008 Sichuan earthquake has led to a sharp decline in Sichuan’s rural
household saving rate by 17% within a year, but it has no long-term impact, since the saving rate
rebounded to the baseline shortly after the shock.

4.2 Robustness test
As Hayashi (1986) notes it, in studying saving issues, the relevant concept to be considered
is not only the household saving rate, but also other prominent features, reflections, and manifestations of “savings.” To test the robustness, we make attempts to incorporate alternative outcome
variables to replace the rural household saving rate, evaluating to what extent the main conclusion
is limited to particular outcome specifications. The estimated results presented below confirm thatthe main inference derived from the previous estimates is fairly robust to alternative outcome indicators. In each sensitivity test, we have also conducted the placebo tests but these are not reported
here in the interest of space, and because they will only strengthen our conclusion that the earthquake has no long-term impact on saving accumulation. To recapitulate, as we change the observation angle from “the most affected” to “the less affected” and from “the micro” to “the macro,”the adverse disaster impact diminishes and the estimates do not exhibit much of a long-run impact
on the patterns of saving and consumption.

19

(1) Urban household saving rate
To test the robustness, a basic solution is to employ the saving rate of urban household aswell. However, one should be cautious that this intuition might not provide accurate answers, sinceurban regions in Sichuan are remarkably different from others in many aspects. According to theofficial figures, urban residents in Sichuan have the lowest household saving rate and the highest
share of leisure expenditure among all the prefectures (except Tibet). Moreover, Sichuan’s urban
population has long been known for its high proclivity to consumption and an epicurean life-style.
Recall that the synthetic control method is designed to replicate certain outcome variables for thetreated unit through exploiting the available information in the control units with respect to theoutcome of interest, it is crucial to make sure that the economic indicators of the treated unit are in
the middle of the convex hull derived from the control units. However, saving predictors on Sichuan’s urban residents are either in the low-end or in the high-end among the overall observations.
This makes it rather difficult to precisely track down the saving rate, since there is no combinationof control units that could accurately reproduce an outlier, except by itself or by allowing for negative weights (see footnote 9).
Using the same modeling strategy, we estimate the disaster effect on Sichuan’s urbanhousehold saving rate. As predicted, Table 3 implies that the synthetic control does not well approximate the treated unit. For certain variables, the discrepancy between the actual value and thesynthetic scenario value turns out even larger than that between the actual value and national average. Of necessity, Figure 4 suggests that the prediction error of the estimate is non-negligible. In
light of this deficiency, we will be conservative in interpreting this part and limit its discussion.
However, the estimate clearly points out that the urban saving rate has also been declining in theinitial period of the shock, and it ascended to the synthetic state before long. This evolution has a
lot in common with the finding in the rural part, in which the earthquake has a transitory but significant adverse effect on household saving.

20

(2) Cash deposit and consumer goods
In the preceding parts, we employed household saving rates as the outcome variables. In
general, subjective measures derived from household surveys are often plagued by the problems of
non-randomization, reporting bias, and sample attrition. In comparison, aggregate/macro accounts
have a widespread availability, and they can be instructive in mirroring the objective reality regarding saving behaviors. Based on this idea, considering the provincial economy as a whole, we interpret the gross domestic product as “income,” and the total cash deposits as “saving.” We incorporate two kinds of deposit ratio: in “stock” and in “flow.”19 In a similar vein, the consumption ratio
is proxied by the proportion of total retail sales of consumer goods in nominal GDP, meant tocapture the tendency for consumption at the provincial level.
Tables 4-6 suggest that these outcome variables have been well reproduced by the synthetic
control units. According to Figures 5 and 6, the earthquake appears to have slightly promoted thelevel and flow of deposits accumulation, inferring that the total dis-saving to finance rebuilding andother follow-on costs is more than compensated by the growth effects. The net growth in deposits
is more likely a reflection of the reconstruction expenses and disaster reliefs, which need to be
operated through the banking systems. Nevertheless, these effects appear statistically insignificant
in consideration of the highly volatile nature of both indicators. In Figure 7, again, despite a seemly
large inconsistency in the predictor (due to the 1992 and 2002 revisions of statistical systems), thesynthetic control closely tracks the actual trajectory and it perfectly fits the post-intervention trend.
This implies that, at the aggregate level, Sichuan’s propensity to consume has not been influenced
by the earthquake.

19“Stock” denotes the proportion of total cash deposit to annual GDP by prefecture; “flow”
denotes the proportion of growth in total cash deposit to annual GDP by prefecture. The “flow” ismore consistent with the conventional notion of household saving, which is a residual concept representing the income flow, rather than stock.
21

5. Conclusion and reservation
In the current study, exploiting the synthetic control method formalized in Abadie et al.
(2003, 2010, 2015), we estimate the impact of the 2008 Sichuan earthquake on the saving behaviorsof the local residents. The findings suggest that the earthquake has a significant short-term adverse
impact on household saving rates, as a reflection of the direct economic losses from the event.
However, contrary to the popular beliefs that people will become more conservative and save more
after extreme shocks, or that they might simply abandon themselves with irrational consumption,
we reveal that the earthquake has not influenced the patterns of saving and consumption of theaffected population in the long-run. The main conclusion is confirmed by a “placebo test,” and theinference is proven robust across a series of experiments employing alternative outcome predictors
and explanatory variables. This study explores a brand new angle to investigate the growth impactof natural disasters; it presents new evidence on the short- and long-run disaster effects on saving
behaviors. Some of our findings might be helpful for reconciling the diverse conclusions of recent
studies.
On the other hand, we would like to caution that these findings do not necessarily imply aneutral disaster impact on saving propensity or that consumers are perfectly resilient to extreme
shocks. Rather, it might well be that some victims (households, corporations, and sectors) suffereddevastating losses whereas others moved ahead, with the overall effects canceling out on net. More
importantly, there are several aforementioned factors that might have contaminated the statisticalinferences, and the quick recovery in saving rates might be an indication that the victims havereceived generous disaster relief as a combination of government compensation, external aids, andother transfers. Perhaps the most important concern in this regard is the unprecedented scale of22formal and informal donations, as well as the immediate emergency responses by Chinese governments at all costs, irrespective of ideology and affiliation. The warm-hearted and cool-headed assistance has unquestionably succeeded in lessening the direct economic consequences of the disaster. In our estimates, we have taken into account some of the channels through which the results
might be disturbed, ruling out the confounding effects such as income fluctuations, enlarging housing expenses, and the receipt of transfers. Yet, due to the data limitation, drawing a definitive conclusion on the subject is beyond the scope of this paper20. We are looking forward to further exploration on the topic.

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26

Table 1: Descriptive statistics
Variation decomposition
Variable

N

Mean

SD

Min

Max
Cross provinces

Over time

Saving

630

0.309

0.149

-0.162

0.656

39%

61%

Education

630

0.072

0.023

0.005

0.160

47%

53%

Housing

630

0.121

0.041

0.018

0.255

38%

62%

Sex-ratio

630

1.046

0.036

0.923

1.204

47%

53%

Income

630

3.576

0.304

2.945

4.366

49%

51%

Pension

630

0.068

0.050

0.010

0.292

30%

70%

Income-G

630

0.115

0.074

-0.117

0.507

20%

80%

(Part 1: rural household saving behaviors)
Saving

609

0.248

0.059

0.129

0.402

35%

65%

Education

609

0.090

0.017

0.047

0.145

37%

63%

Housing

609

0.079

0.028

0.031

0.238

21%

79%

Sex-ratio

609

1.046

0.036

0.923

1.204

50%

50%

Income

609

4.024

0.287

3.457

4.724

27%

73%

Pension

609

0.236

0.057

0.091

0.375

45%

55%

Income-G

609

0.109

0.047

-0.023

0.313

15%

85%

(Part 2: urban household saving behaviors)

Deposit

754

0.657

0.208

0.144

1.781

51%

49%

Propensity

609

0.098

0.059

-0.268

0.645

31%

69%

Consumption

754

0.368

0.071

0.124

0.727

45%

55%

Sex-ratio

754

1.043

0.054

0.506

1.204

37%

63%

Income

754

3.455

0.383

2.633

4.366

31%

69%

Bank lending

754

1.101

0.570

0.091

4.893

60%

40%

Income-G

754

0.123

0.088

-0.173

0.635

6%

94%

Dependency

754

0.421

0.094

0.193

0.680

46%

54%

(Part 3: robustness tests)

Source: All variables are taken and constructed directly from statistics available in the Chinese
statistics yearbook, population and employment statistics yearbook, and finance and banking yearbook, published by the National Bureau of Statistics of China.
27

Figure 1: the 2008 Sichuan earthquake

Source: From the National disaster reduction and management council of China.

.1

provincial rural household saving rate
.2
.3
.4
.5

Figure 2: Rural household saving rate

1995

2000

2005
year

treated unit

2010

2015

synthetic control unit

Table 2. Predictor means (Rural)
Sichuan province
Variables
Education
Housing
Sex-ratio
IncomeTransfer
Income-growth

Observed Synthetic
0.075
0.091
1.049
3.315
0.046
0.109

0.075
0.091
1.038
3.323
0.046
0.109

Average of control groups
0.072
0.121
1.046
3.576
0.068
0.115

Note: All variables are averaged over the period 1995-2008 by prefecture. The last column presents the national average (excluding Sichuan) over the 1995-2008 period.
28

-.2

-.1

0

.1

.2

.3

Figure 3: Gaps between recorded household saving rate and its synthetic counterpart

1995

2000

2005
year

29

2010

2015

provincial urban household saving rate
.15
.2
.25
.3

Figure 4: Urban household saving rate

1995

2000

2005
year

treated unit

2010

2015

synthetic control unit

Table 3. Predictor means (Urban)
Variables
Education
Housing
Sex-ratio
IncomeTransfer
Income-growthS(1995-2005)
S(2007)

Sichuan province
Observed Synthetic
0.104
0.075
1.049
3.804
0.226
0.099
0.167
0.217

0.109
0.084
0.999
3.825
0.233
0.106
0.169
0.218

Average of control groups
0.090
0.079
1.046
4.024
0.236
0.109
0.210
0.274

Note: All variables are averaged over the period 1995-2008 by prefecture. S (1995-2005) denotes
the provincial urban household saving rate averaged over the period 1995-2005; similarly, S (2007)
denotes the rate in 2007. The last column presents the national average (excluding Sichuan) overthe 1995-2008 period.

30

.2

Ratio of cash deposit
.4
.6
.8

1

Figure 5: Ratio of total cash deposits

1990

1995

2000

year

treated unit

2005

2010

2015

synthetic control unit

Table 4. Predictor means (Deposits)
Variables
Sex-ratio
IncomeBank lending
Income-growth
Deposit(1995)
Deposit(2000)
Deposit(2006)

Sichuan province
Observed Synthetic
1.054
3.279
0.106
0.928
0.469
0.672
0.794

1.065
3.319
0.104
0.904
0.476
0.669
0.789

Average of control groups
1.043
3.455
1.101
0.123
0.539
0.686
0.731

Note: All variables are averaged over the period 1990-2007 by prefecture. Deposit denotes theprovincial deposits proportions in 1995, 2000, and 2006, respectively. The proportion is calculated
as the proportion of total cash deposit in the annual GDP by prefecture. The last column presents
the national average (excluding Sichuan) over the 1990-2007 period.

31

.05

Ratio of growth in cash deposit
.1
.15

.2

Figure 6: Proportion of growth in cash deposit

1990

1995

2000

year

treated unit

2005

2010

2015

synthetic control unit

Table 5. Predictor means (Propensity)
Variables
Sex-ratio
IncomeBank lending
Income-growth
Propensity(1995)
Propensity(2000)
Propensity(2006)

Sichuan province
Observed Synthetic
1.054
3.279
0.106
0.928
0.138
0.077
0.101

1.055
3.290
0.104
0.922
0.139
0.076
0.100

Average of control groups
1.048
3.364
1.068
0.098
0.145
0.063
0.093

Note: All variables are averaged over the period 1995-2005 by prefecture. Propensity denotes theprovincial deposit growth ratios in 1995, 2000, and 2006, respectively. The proportion is calculated
as the proportion of the growth in total cash deposits in the annual GDP by prefecture. The lastcolumn presents the national average (excluding Sichuan) over the 1995-2005 period.

32

.35

Ratio of consumption good
.4
.45

.5

Figure 7: Proportion of consumer goods

1990

1995

2000

year

treated unit

2005

2010

2015

synthetic control unit

Table 6. Predictor means (Consumption)
Variables
Sex-ratio
IncomeBank lending
Income-growth
Dependency
Consumption(1995)
Consumption(2000)
Consumption(2006)

Sichuan province
Observed Synthetic
1.054
3.279
0.928
0.106
0.444
0.374
0.380
0.396

1.056
3.345
0.933
0.104
0.438
0.362
0.381
0.395

Average of control groups
1.048
3.364
1.068
0.098
0.442
0.355
0.368
0.328

Note: All variables are averaged over the period 1995-2005 by prefecture. Consumption denotes
the provincial consumption proportions in 1995, 2000, and 2006, respectively. The proportion is
calculated as the proportion of total retail sales of consumer goods in nominal GDP by prefecture.
The last column presents the national average (excluding Sichuan) over the 1995-2005 period.

33