The Great Famine and Savings Rate in China∗†

HENG CHEN

University of Hong Kong

MAELYS DE LA RUPELLE

University of Cergy-Pontoise

FABRIZIO ZILIBOTTI

University of Zurich

January 5, 2016

The Great famine in China (1959-1961) is one of the most dramatic tragedies in

history, which may have long term consequences for economic behaviors of Chi-

nese population. In this paper, we explore how it has affected the saving choices

of rural households in particular. Employing a dataset across 122 Chinese coun-

ties, we find that the savings rate of rural households in 2002 tend to be higher

in counties where the famine was more severe. We measure famine severity by

exploring the demographical structure and instrument it by a rich set of histori-

cal variables, including natural disasters and resources diversion during the Great

leap forward at the county level, as well as political radicalism of leaders at the

province level. We show that in a county with a famine survival rate one stan-

dard deviation below the mean, households increase their savings rate by about 8

percentage points. This finding is consistent with predictions of the literature on

endogenous formation of time preference and its transmission.

Keywords. famine, saving puzzle in China, endogenous preferences

JEL Classification. D14, D91, Q54

∗Our research is funded by the Research Grants Council of Hong Kong (Project No. HKU 742211B)and has benefited from the support of the CEPREMAP. The research is part of the project Labex MME-DII (ANR11-LBX-0023-01). We thank François Libois who provided us with the satellite rainfall datafor China. We thank Chloé Duvivier, Zhan Peng and Christelle Dumas.†TRMM data were acquired as part of the Tropical Rainfall Measuring Mission (TRMM). The algo-

rithms were developed by the TRMM Science Team. The data were processed by the TRMM ScienceData and Information System (TSDIS) and the TRMM office; they are archived and distributed by theGoddard Distributed Active Archive Center. TRMM is an international project jointly sponsored bythe Japan National Space Development Agency (NASDA) and the US National Aeronautics and SpaceAdministration (NASA) Office of Earth Sciences.

1. Introduction

The China’s Great famine (1959-1961) was one of the most devastating events in the20th century. It caused at least an estimated 16 million death in rural areas and pro-duced profound impacts on the demographic structures and changes in both economicand political systems in China. While the existing studies focus mostly on the demo-graphic consequences of this tragedy, in this paper we identify one important novelmechanism, through which the Great famine may still influence the economic choicesof Chinese households more than half century later. Specifically, our hypothesis is thatthis rare disaster that took place in late 1950’s may affect the formation of the time pref-erence and may raise the patience level of households of the subsequent generations inChina, which may contribute to the observed high level of savings rate in householdsector. This paper provides evidence for this hypothesis and empirically demonstratesthat the severity of the Great famine affects the current household saving behavior.

Our hypothesis has three key elements. First, adults who experienced the severefamine and the associated lack of food consumption may change their perception ofthe likelihood of the future famines of the same magnitude and become more cau-tious in expenditure (“experience mechanism”). Second, those surviving adults mayalso influence the formation of time preference of their offsprings and instill a higherlevel of patience. After experiencing the Great famine, those adults believe that se-vere famines, once considered rare, are more likely to happen. Therefore they maychoose to indoctrinate their children to be more patient, so that the younger generationcan save more resources to cushion the future severe disasters (“preference formationmechanism”). Third, parents may prefer their children having a patience level closerto their own. In other words, the time preference can be transmitted from the oldergeneration to the younger ones (“preference transmission mechanism”). It is impliedthat not only the generation who were children during the Great famine were more pa-tient, but also the offsprings of that generation who have not experienced or observedthe Great famine may also be more patient.

The three mechanisms above have been studied in the literature of endogenouspreferences in different contexts and environments. The first mechanism that indi-vidual life experiences of macroeconomic shocks or natural disasters may shape thepreferences has been the focus of a set of empirical studies. Callen (2015) shows thatthe patience of individuals that was severely affected by the Indian Ocean Earthquaketsunami has been increased and such a change is solely due to the experience of thenatural disaster, instead of other changes.1 Malmendier and Nagel (2011) study the

1Callen (2015) demonstrates empirically how the natural disaster has shaped the time preference ofa population of of Sri Lankan wage workers, by exploiting exogenous variations in exposure to thatdisaster to estimate the effect of the natural disaster on time preferences of individuals.

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effect of the Great Depression experience on the risk preferences of individuals anddemonstrate that the Great Depression generation was less inclined to take financialrisks.2

The second and third mechanisms have been theoretically established in Doepkeand Zilibotti (2008) and their subsequent work (Doepke and Zilibotti 2014). The essenceof their contributions is that time preference can be shaped by altruistic parents’ effortof instilling in a way that best fits with their future material circumstances. Parentsinvest in offspring’s patience, which determines the weight that a child attaches, inadult age, to utility late in life relative to the present. This type of investment can beexemplified by parents’ efforts in instilling parsimony and thrift into their children.One immediate implication is that preference can be therefore transmitted across gen-erations, when parents prefer a smaller distance between their own and children’spreferences. They apply the theory to the transmission of time preference and studyits effect on human capital and wealth accumulation.

Our hypothesis is based on this theoretical contribution: the Great famine experi-ence may affect the formation of time preference and its transmission. This paper isthe first study that empirically establishes the relevance and importance of the pref-erence formation and transmission mechanisms with the quasi-experimental settingprovided by the Great famine in China. Note that the preference formation and trans-mission mechanisms are particularly different from the experience mechanism, be-cause they imply that subsequent generations that do not have direct experiences ofmacroeconomic shocks or natural disasters may also be affected through the intergen-erational interactions.

One natural implication of our patience transmission conjecture is that the Greatfamine may still have a long lasting effect on the saving decisions of Chinese house-holds in recent years. As documented by Kuijs (2006), the household saving rates was26 percent in rural areas and 24 percent in urban areas in 2004. As established in theliterature on economic decisions of saving and consumption, more patient individ-uals have higher propensity to save for the future.The mechanism proposed in thispaper may constitute one important determinant of the high saving propensity of thehousehold sector in China.

It would seem natural to test our hypothesis by employing cohort level data andexploring how the savings rate evolves across generations and over time, so as to iden-tify the effects of the Great famine. However, China has been undergoing dramatical

2They use data from the Survey of Consumer Finances from 1960 to 2007 and show that individualswho experienced low stock market returns throughout their lives are less inclined to take risks in thefinancial market, e.g., they participate less in stock markets, hold a smaller fraction of their liquid assetsin stocks and tend to be more pessimistic on the future return.

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changes in institutional constraints and market arrangements since late 1970’s, whenit transformed itself from a planned economy to a market-based economy. Thosechanges at the aggregate level may affect the consumption-saving behavior of Chi-nese households substantially. For example, the high economic growth experienced inChina in the last 30 years may also induce substantial changes in household’s savingsrate (Yang, Zhang, and Zhou 2012), which may confound the preference-transmissionmechanism that we propose in this paper. It is also not convincing to compare thesavings rate of the famine cohorts with that of the subsequent cohorts. The differencebetween older famine cohorts and younger non-famine cohorts may reflect the differ-ence in their life-cycle concerns, one of the primary reasons that motivate households’savings. Song and Yang (2010) also empirically document a dramatic change in thesaving-age profile in China since the market reform in 1990’s, which makes such acomparison even less conclusive.3

In this paper, we bypass those obstacles by employing a novel empirical design.Instead of studying the effects of the Great famine at the aggregate level, we exploitthe local county-level variations in the famine severity and study its effect on saving-consumption choices in recent years, i.e., the variations of the savings rate at the house-hold level. We measure the famine severity following Meng, Qian, and Yared (2015),by using the 1990 population census, and by dividing the average size of the cohortsborn before the Famine (in 1954-1957) by the average size of the cohorts born in 1959-1960. It is important to realize that the feasibility of our identification strategy relieson the fact that there exists sufficient geographical variations in famine severity.4

Another premise for our identification is that the disastrous effects of the Greatfamine at the aggregate level were unknown to individuals during the famine yearsand afterwards. When parents make decisions on the time preference of their childrenand instill their preferred patience, they regard the local severity as a proxy for that ofthe Great famine itself. Therefore, local variations in famine severity may help us toidentify its effect on households’ preference formation decisions and the consumption-saving decisions in recent years. First, the consequences of the Great famine wereclassified information and not accessible to the rural households during or after thefamine. There was no official or unofficial release of any information related to themortality rate as well as other damages, especially before 1980’s. Second, there is noconsensus in the total famine casualties even nowadays. The earliest estimate of thetotal death toll is 16.5 million, according to Coale (1981), and subsequent studies con-

3One consequence of economic transition experienced in China and the associated high economicgrowth in recent years is that the income of young cohorts has risen more rapidly than that of olderones, which gives rise to a flattened cohort-specific age-earnings profiles during the transition period.

4Earlier works on the Great famine have established that there are significant variations in famineseverity across counties within provinces. See the recent contribution by Meng, Qian, and Yared (2015).

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stantly revise the estimate up. For example, according to Banister (1991) and Dikötter(2010), the total death tolls were 30 million and 45 million, respectively. Third, boththe flow of information and migrants were highly controlled during the famine pe-riod and afterwords (Dikötter 2010). People in the rural areas were not allowed toleave their counties that they registered until early 1980s. Therefore, it is reasonable toconjecture that rural households do not have a concrete idea about the severity of thefamine on the aggregate level.5

The dataset from Chinese Household Income Project in 2002 offers detailed infor-mation on the expenditures and incomes of rural households in about 120 countiesin China, which allows us to measure the savings rate at the household level. Fol-lowing the literature, we compute the famine severity at the county level with 1990population census data from the National Bureau of Statistics of China. In the baselineestimations, we study the impact of county-level famine severity on the household-level savings rate, by controlling for a range of characteristics at the household, vil-lage, and county levels. We also take into account the long run climate characteristicsat the county level as well as natural disasters a few years prior to the survey at thevillage level, accurately measured by highly reliable satellite data. We find that, in allspecifications, households tend to have a higher savings rate in counties where faminewas more severe and thus implied a higher mortality rate. Roughly speaking, a onestandard deviation increase in the famine severity raises the savings rate by 7%. Wealso find that the effect is larger for the poorest households, who are most likely to bethreatened by the lack of food and whose savings are mainly motivated by securingsubsistence consumption for the future. The savings rate of relatively richer house-holds may be less responsive to the variations in famine severity because they maysave out of other motives as well, such as retirement and bequests.

Despite the fact that we use a rich set of controls at the household, village, andcounty levels, we still cannot remove completely the concern that the Great faminemay have changed some unobserved aspects of the economic environments which arenot captured in our base line estimations but affect the current household saving pat-tern. To deal with this concern, we employ a rich set of instruments for our measure-ment of famine severity. First, we collect extensive historical data regarding variousnatural disasters experienced in the 1950s at the both county and province levels. Wealso construct a measure for the abnormality of rainfalls (i.e., z-score) to proxy for theweather conditions for agricultural production in 1958.

Second, we also take advantage of variations of resources diversion during theGreat leap forward, a political movement that preceded the Great famine. During

5In fact, both the immigration and rural’s households partial knowledge about the severity of theGreat famine may bias our estimate towards zero.

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this movement, in response to the call of Mao, leaders at the county level diverteda substantial amount of agricultural inputs to build irrigation projects to signal loy-alty. Those projects often proved to be useless white elephants in the long run andcontributed to the immediate fall of agricultural output. We have hand-collected de-tailed information regarding those irrigation projects in each county in late 1950’s fromcounty gazetteers, for example, the number of irrigation projects undertaken, and in-formation on whether these projects had been a pure waste.

Third, we also explore variations in the characteristics of the leaders at the provin-cial level, which contributed to the degree of their radicalism during the Great leap for-ward. We find that the lower ranked leaders who aspired for promotion have strongerincentive to take effort to prevent the death toll from skyrocketing.

One might be concerned by the exogeneity of the instruments. It can be the casethat the assignment of political leaders with certain characteristics was related to char-acteristics of the provinces and it persisted until even recent years. The climatic shockswhich aggravated the famine in late 1950’s could be a recurring problem for thesecounties even nowadays. We verify that those concerns are unlikely to be the case byadding an extensive set of controls for rainfalls shocks history and climatic conditionsas well as recent political conditions. More importantly, the high number of instrumen-tal variables allows us to test the exogeneity of the instruments using overidentifyingrestrictions, whose result strongly confirms that our set of instrumental variable isvalid. Not only the main findings from the ols estimations hold using the instrumentalstrategy, but the main effect is found to be actually much stronger. If the survival prob-ability is smaller by one standard deviation, the savings ratio of households decreasesby more than 10%.

In this paper, we make three important contributions. First, we propose a new ex-planation for the determinants of the observed high household savings rate in China,which is complimentary to the existing ones, including the income hypothesis, theincreasing income uncertainty, the age structure, etc, which we review in Section 2.Second, while the experience mechanism in the endogenous preference literature hasbeen well studied, little effort has been made to establish the preference formationand transmission mechanisms. This paper fills this gap by providing direct evidenceon these theoretical contributions and enriches the literature on endogenous prefer-ences. Third, the new famine literature that emerged in 1980’s employ the tools ofeconomic analysis to shed light on why famines happened repeatedly and what canbe done to avoid or relieve them (Ravallion 1997). Our study contributes some empir-ical knowledge to this literature and adds an alternative perspective to the studies offamines in human history.

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The remainder of this paper is organized as follows. In Section 2, we review therelated existing literature on the causes and consequences of the Great famine, theliterature on endogenous preferences and the literature on savings rate in China. InSection 3, we provide a brief background of the Great famine and explain in detailsthose data that are used in this paper. In Section 4, we present our key results. Wealso include a subsection at the end to address the robustness issues. The last sectionconcludes.

2. Literature review

This paper is mostly related to a line of studies on the consequences of the Greatfamine. In the literature, both the short term and long term effects of the Great faminehave been studied. Ashton et al. (1984) empirically establish that the demographiccrisis during Great Leap Forward was caused by the Great famine, employing dataon demographics and food availability. Chen and Zhou (2007) quantify the serioushealth and economic consequences for the survivors of the Great famine in China, es-pecially for those in early childhood during the famine. Meng and Qian (2009) showthat early childhood exposure to famine had large negative effects on adult height,weight, weight-for-height, educational attainment and labor supply. Peng (1987) fo-cuses on the demographic consequences of the Great Leap Forward by analyzing themassive fertility deficits and excess deaths that occurred during and immediately afterthe Leap. Although we also explore one aspect of the long term consequences of thisevent, our work has a distinctive focus. We study its effect on the preference forma-tion and transmission of households, as well as its lasting effects on the savings rate ofChinese rural households half century later.

A set of empirical studies have been developed to establish specific mechanismsthrough which the Great famine has caused the demographic crisis. Lin (1990) ar-gued that the collectivism is the key driver of this tragedy, because farmers had noright to withdraw from the collective agricultural production and therefore they hadno incentive to self-discipline. Kung and Lin (2003) focus on the effects of resourcesmisallocation, communal dining, and grain procurement, respectively, on differencesin provincial death rates during the famine years. Lin and Yang (2000) quantify theextent to which the urban bias and the decline in food availability contributed to thedemographic crisis during the Great Famine. Li and Yang (2005) argue that the ma-jor cause of the large scale of death toll is that the government diverted agriculturalresources to industry and imposed excessive grain procurement burden on peasants.They estimate that 61 percent of the output decline is attributable to the policies ofresource diversion and excessive procurement. In our paper, we study the long termeffects of the Great famine by exploring the regional variations across counties, instead

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of focusing on its causes.

Our work is particularly related to Meng, Qian, and Yared (2015) and Kung andChen (2011), the most recent contributions to this literature. The former shows thatthe inflexibility of the government procurement policy was the major driver for theobserved positive correlation between rural mortality rates and per capita food pro-duction and it was responsible for the documented severe total famine mortality. Inthis paper, we measure the famine severity across counties by following Meng, Qian,and Yared (2015) and approximate it by the cohort size of survivors as compare as thecohort size of individuals born before the Great Famine amongst the agricultural pop-ulation of each county. Kung and Chen (2011) show empirically that the variation inpolitical radicalism at the province level may explain a large fraction of the variationin excessive grain procurement. The provincial leaders’ motive for seeking promo-tion may explain their radical move during the Great Leap Forward. In our analysis,we instrument the variation in famine severity with both the variation in the weatherconditions at the local county level and the variation in the party rank of the leadersat the provincial level. However, our results indicate that lower ranked leaders at theprovince level may have stronger incentive to take efforts to scale down the disastrousconsequences of the famine.

Our hypothesis contributes to the literature on endogenous preferences. Early con-tributions in this literature are mostly theoretical and explore the effect of endogenoustime preferences in standard macroeconomic environments. Uzawa (1996) analyzeshow capital accumulation may be affected when the time preference is endogenousand affected by wealth level itself. Becker and Mulligan (1997) explicitly treats pa-tience as an asset that agents can invest in and construct a model where one can livefor multiple periods but make a one-time choice of a discount factor. Barro (1999) stud-ies the effect of variable time preferences, in which the rate of time preference is highin the near term but roughly constant in the distant future, in an otherwise standardneoclassical model.

In the recent development of this literature, there exists a set of new empirical stud-ies which explore how the preference is shaped by social, institutional and natural en-vironments. Broadly speaking, much effort has been made to establish the so-called“experience mechanism.” Eckel et al. (2009) find that individuals turned out to bemore risk-loving after experiencing hurricane Katarina. Voors et al. (2012) offers a sim-ilar finding that the civil war in Burundi caused individuals to be more risk-seeking, todiscount future more heavily and to behave more altruistically. Some other papers onthe same theme but in different contexts have also offered concrete evidence suggest-ing that natural disasters (e.g., tsunamis, earthquakes) and devastating events (e.g.,violent conflicts), may help shape individuals preferences, including Bchir and Will-

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inger (2013), Cameron and Shah (2013), Cassar et al. (2011), Castillo and Carter (2011),and Callen et al. (2014).

The new mechanism that we establish in this paper contributes to the literatureon the determinants of the high and rising savings rate in the household sector inChina. It has been studied intensively in the recent literature that the household sav-ings rate has been rising. Chamon and Prasad (2010) study the rising savings rateof urban households in China and document a U-shape saving-age profile. Wei andZhang (2011) argue that household may save because of the competitive motive in themarriage market: the households with male offspring intend to save more to improvethe marriage prospects of their children. They argue that a large fraction of the in-crease in household savings can be explained by the rising imbalances in the sex ratiowhich exacerbates the competition. Banerjee, Meng, and Qian (2010) study the effectsof one-child policy on household savings by using cross-sectional survey. Song andYang (2010) empirically demonstrate that the increase in household savings is due tothe structural changes in the labor market. Chamon, Liu, and Prasad (2013) empiricalwork shows that the rise in savings rate is related to the higher income uncertainty.Yang, Zhang, and Zhou (2012) attributes the rising savings rate in urban householdssector to the rapid income growth.

As the trend of the savings rate has attracted much of the attention in the literature,there are relatively fewer recent works that focus on the level of the savings rate inthe household sector. Previous works include evidence on the aggregate level (e.g.,Modigliani and Cao 2004)and provincial-level (e.g., Qian 1988; Kraay 2000; Horiokaand Wan 2007). Our paper differs from the previous contributions in that we employdata on the household level and explore how the variation of famine severity on thecounty level may affect the saving decisions of current households.

3. Historical Background, Measurement and Data

According to Ravallion (1997), famines were characterized by both high mortalityrisks and unusually severe threat to the food consumption. The twentieth centurywitnessed several severe famines with high casualties in Asia (e.g., Bengal Famine in1943-44), Africa (e.g., Sudan and Uganda famines in 1980s), the former Soviet Unionand Holland (1944-45). The Great famine (1959-1961) in China was the ultimatelyworst one with much longer duration and more severely curtailed food availability.

Prior to the famine years, the agricultural production and food distribution systemhad been collectivized over a short period of time. When Mao launched the “GreatLeap Forward” campaign in 1958, all rural households were organized in the form ofpeople’s communes. The traditional organization of agricultural production with fam-

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ily as the basic production unit had been replaced completely. Farmers worked col-lectively in production teams organized and led by the village officials, without rightsto withdraw or work separately (Lin 1990). They had no control over the agriculturaloutput and could only consume in communal kitchens managed by the village (Thax-ton 2008). Famine occurred when there was a sudden, sharp drop in grain productionin 1959-1961 and the grain redistributed to the village level was below the subsistence.In 1961, the famine ended when the government temporarily increased the amount ofgrain delivered to the rural areas and reverted some radical policies pursued (Meng,Qian, and Yared 2015 and Walker 2010).

Recent studies have provided evidence that the high mortalities during the Greatfamine were mainly caused by policy-induced decline in agricultural output and de-fects of the procurement system, e.g., the radical infrastructure programs implementedby the government to expedite the rural industrialization (Li and Yang 2005); collec-tivism programs which reduced the farmers’ working incentives (Lin 1990); and inflex-ible and progressive government procurement system (Meng, Qian, and Yared 2015).However, the official account for this disaster provided by the government, duringor after the demographical crisis, was unfavorable weather conditions. Large-scalepropaganda campaigns after the famine were intended to convince the local farmersand survivors in rural areas that the bad weather was the primary reason for the ob-served high mortality rate. This famine period (1959-61) was typically referred to as“three-year natural calamities” in most of the official historical records.6

Since the main purpose of this paper is to study whether the experience of the se-vere lack of food consumption during the crisis would change the preferences of thesurviving adults, whether such a change can affect their choices of time preference in-stallation of their children and therefore the consumption-saving choices more than 50years later, it is important to measure both the famine severity and rural households’savings rate properly.

Famine severity. Ideally, the severity of famine can be inferred from the mortalityrates and food availability. Chen and Zhou (2007) used excess mortality in 1960 at theprovince level to generate a measure of severity of the famine. Kung and Chen (2011)study the variations in the excess grain procurement at the provincial level (a proxyfor the political radicalism), which had a significant effect on the death rate during thefamine years. However, such data do not exist at the lower county or village level.The primary approach in the literature to measuring the variations in severity at thecounty-level is to extract information from the demographical structures. Both Huang

6For example, the Central Committee of Chinese Communist Party released an official documentwhich provided an explanation for the cause of the food shortage during the famine years and mainlyblamed the bad weather for the abnormal mortality rate, see Decisions on Several Historical Issues of theCommunist Party of China since the Founding of the Republic, 1981.

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et al. (2010) and Meng, Qian, and Yared (2015) used a 1% sample of China’s 1990population census to derive a measure of famine intensity at the county level based onthe size of birth cohorts.7 This approach is justified because both reduced fertility andincreased mortality during the famine may lead to a smaller size of the cohorts bornduring the famine years, relative to other cohorts. In our paper, we follow the sameapproach and use 1% sample of China’s 1990 census to compute a survival index at thecounty-level. It is the ratio of the birth-cohort size of famine cohort (1959-1961) to thatof non-famine cohort (1954-1957), among the agricultural population. That index iscorrelated with the probability of survival: the smaller is the index in a given county,the severer is the famine.

In this paper, we test our main hypothesis with data on the consumption and sav-ing behavior of rural households in China. In principal, the preference formation andtransmission hypothesis should be also applicable to the urban households, given theurban areas were also severely affected by the lack of food availability during thefamine years. However, it is even more difficult to measure impacts of the Greatfamine in urban areas. The data on the availability of food consumption is also re-stricted for the urban areas. The measure of the famine severity in the rural areas can-not be applied either, since fewer urban citizens die of starvation during the famineyears, even though the lack of food consumption substantially undermined the heathconditions of urban residents.

Savings of rural households It is typically a challenge to properly measure the savingsfor rural households (Qian 1988). First, unlike urban households, families in rural aresusually consume food produced by themselves and therefore it is harder to measurethe non-durable consumption. Second, in some cases, durable goods purchases maybe regarded either expenditure or investment or both. To address these difficulties, weemploy the 2002 Chinese Household Income Project (CHIP) rural sample, which pro-vides very detailed information about items related to incomes and expenditures ofrural households.8 The sample size of this survey is fairly large: in total, 9200 house-holds from 961 villages and 122 counties were surveyed, which were well distributedacross the Chinese territory.

Regarding incomes, the survey reported both the self-declared income value byhouseholds and disaggregate income items from various sources. In the paper, we use

7The famine cohort in Huang et al. (2010) refers to the cohort of women born during the famineyears (1959-1961) and the non-famine cohort refers to the cohort of women born during the 3 yearsimmediately before the famine (1956-1958) and the 3 years immediately after the famine (1962-1964). InMeng, Qian, and Yared (2015), the famine cohort is defined by rural population that were born duringthe famine years (1959-1961) and the non-famine benchmark is the average county birth-cohort sizeover the period 1949-1966.

8The survey was conducted by the Chinese Academy of Social Sciences in 2003 and inquired ruralhouseholds about their situation during the preceding year.

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the self-declared income value in our baseline regressions and check the robustness ofour results with the sum of incomes from all the reported sources.

Regarding expenditures, the survey provides information on items including sta-ple and non staple food, other food, clothing, transport and communication, dailyuse goods, medical care paid by self and by government, educational expenditure,durable goods, housing repair, other expenditures. In the baseline regressions, we usethe value of expenditures that is aggregated from all the survey items. We also com-pute various alternative measures of expenditures by excluding some of the reporteditems, e.g., durable goods and/or medical care paid by government, and by imputingthe value of durable consumption differently.

Saving of rural households is defined by the difference between incomes and ex-penditures. To avoid dealing with negative savings, the main dependent variable thatwe use is the logarithm of income to expenditure ratio, namely household income di-vided by household consumption. To ensure that our results are not driven by thedefinition of incomes and expenditures, we perform robustness checks by computingthe savings rate with all the alternative measures of incomes and expenditures.

Characteristics of households, villages and counties The consumption-saving decisionsof households may be affected by a range of factors including those related to the char-acteristics of households and the economic environment, to which they are subject.Primary motivations for savings include maintaining consumption after retirement(loosing ability to work) and maintaining material living standard for the dependentsin the family. We therefore control for the age of household head, the proportionof dependent members in the households and the family size. We also include theproportion of woman in the household to control for the gender difference in savingpropensity. Another important determinant of savings rate is income or wealth levelof the household. Education attainment may be correlated with the income level of thehousehold. We therefore also control for the average education within the householdto approximate the stock of human capital possessed. Income can be also correlatedwith the form of the household’s usage of energy and water. The household may bepoorer if they access water provided by pump or a well but richer if they have access totap water. The same can be applied to the usage of coal, firewood or fuel. We includethis set of characteristics of the household in our baseline regressions.

It is also legitimate to conjecture that individuals may display different saving pat-terns in relatively developed and under-developed rural areas. We control for a largeset of variables at both the village and county levels, which capture the conditions foragricultural production and the characteristics that are correlated with the stage of de-velopment. Since the properties of terrain are critical for the agricultural production

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and economic development, we include dummies for the geographical conditions ofthe village (whether it is plain, hilly or mountainous). Similarly, terrain conditions andsoil properties at the county level are also important. The average elevation, rugged-ness of the terrain, as well as the share of plain terrain, the share of sandy soil and theshare of clayey soil in the county were included in the baseline regressions. Such datawere computed by ?) using the GIS Soil and Terrain database (SOTER) of China.9

It is also likely that the remote villages are less likely to achieve economic develop-ment. We therefore control for the distance between the village and the county seat,which approximates its remoteness. An indirect indication of the development sta-tus of the village is the salary of village cadres. And one more direct measure of thestandards of living or wealth of the village is the land available per household. Bothare included in our baseline estimations. Another related measure for developmentincluded in our baseline estimations is whether the county is in a sub-urban area.Supposedly, it is easier to benefit from the development of urban areas if the locationof the county is close to cities. Contemporary economic growth may also affect theinter-temporal consumption choices of households. To compute the growth rates, weobtain county level income per capita for the rural households from the China data on-line. We also approximate growth at the village level by including population growthand cadres salary growth between 1998 and 2002.

Considering there might be a difference in saving pattern caused by the differencein ethnicity, we also include the dummy for whether the village is populated by anethnic minority. To control for village size, we consider the number of villagers in2002, reported in the CHIP dataset.

Climate conditions In our baseline estimations, we include a set of variables on theclimate conditions at the village and county levels to address three potential impor-tant concerns. First, in general, climate conditions are important for the agriculturalconditions. Areas that are subject to unfavorable climate conditions, for example alarge variation in the precipitations in the long run, may tend to be underdeveloped.Second, the variations in savings rate of rural households may reflect their response tothe weather shocks and the fall of output immediately before the survey year. Third,it may be the case that certain very long run characteristics of the climate conditionsdrive both the mortality rate during the famine years and saving decisions of rural

9SOTER GIS information have been originally compiled by the Institute of Soil Science, ChineseAcademy of Science (ISSAS) and ISRIC-World Soil Information within the framework of the LandDegradation Assessment in Drylands (LADA, GLADA) project. The primary data were compiled usingthe SOTER methodology (ISSS, 1986). SOTER unit delineation was based on the raster format of the soilmap of China, correlated and converted to FAO’s Revised Legend (1988), combined with SOTER land-form characterization derived from Shuttle Radar Topographic Mission (SRTM) 90 m digital elevationmodel (DEM). The definition of the different types of soil has followed the standard USDA soil texturetriangle.

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households in 2002.

To address the first concern, we include average rain anomalies at the village levelin both the 1980s and 1990s. To deal with the second concern, we control for the rainanomalies at the village level in 1999, 2000, and 2001, i.e., the three years prior tothe survey year. Last, to capture the long run rainfall conditions at the county level,we compute the long term average and standard deviation of precipitations for eachcounty in our sample from 1901 to 2002.

The two datasets that allow us to extract the rainfall information and some detailsof our methodology merit further discussion. The “Tropical Rainfall Measuring Mis-sion”offers very accurate satellite data from 1997.10 The measures are provided for0.25 x 0.25 degree grid squares (around 25 km x 25 km), which allows us to constructvery precise climatic variables even at the village level. They are recognized to beone of the most accurate precipitation data, as they combine satellite measures withmonthly terrestrial rain gauge data. In the economics literature, the TRMM data havefirst been exploited by Baland, Libois, and Mookherjee (2013), Fetzer (2014) and Libois(2015) for other contexts; we are the first to take advantage of this dataset for stud-ies on China. To construct the standardized precipitation index (rainfall z-scores), wefirst assign grid points to villages based on latitude and longitude coordinates, aggre-gate monthly data to obtain yearly rainfall variables, and then compute village-levelaverages for each year.

In order to compute county-level long term averages and standard deviations, wecombine the above described satellite data for the 1998-2014 period with the monthlygridded time series provided by the Department of Geography of the University ofDelaware for the 1901-1997 period.11 The size of the grid (2.5x2.5 degree grid squares)is closer to the area of a whole county. The obtained normalized variable is the county-level rainfall value subtracting the long-run mean and being divided by the long-runstandard deviation. A positive value for year t in county j means that precipitationsin year t in county j are higher than long-term average. Conversely, a negative valuemeans that precipitations have been lower than that. Tables 13 and 14 report summarystatistics of variables that we have described.

10The TRMM is a joint project between the NASA and the Japanese Aerospace Exploration Agencywhich has been launched in 1997 to study tropical rainfalls. The satellite employ a set of five instrumentsto construct gridded rainfall rates at very high spatial and temporal resolution. Various technologicalinnovations have been used to increase the accuracy of the climatic measures, including a precipitationradar, flying for the first time on an earth orbiting satellite, and the low flying altitude of the satellite.Technical details about TRMM can be found at https://climatedataguide.ucar.edu/climate-data/trmm-tropical-rainfall-measuring-mission.

11This dataset can be downloaded from http://climate.geog.udel.edu/~climate/html_pages/download.html.

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4. Empirical Strategy and Results

We discuss two empirical designs for identifying the effects of the Great famine onrural household savings rate in 2002. First, we estimate the impact of famine severityduring the famine years (1959-1961) on the savings rate of rural households in 2002.We also discuss the relation of our endogenous preferences mechanism with other hy-potheses for the determinants of high household savings rate. Second, to further dealwith the potential problem with omitted variables, we use a large set of instrumentsfor the famine severity. Both strategies provide highly significant and consistent esti-mates.

4.1. Linking the Great Famine to current savings rate

Our baseline estimations are based on the CHIP dataset, covering more than 8000households across 122 counties. We examine the link between famine severity andsavings rate using the following specification:

log(Y/C)ikj = βSj + ηiZi + ηjZj + ηkZk + εikj (1)

where log(Y/C)ikj is the log value of income to consumption ratio for household iin village k and county j; Sj is the survival index for county j; Zi, Zk and Zj capturethree sets of controls, at the household, village and county levels, respectively, whichwe explain in details in the previous section; and εikj is the normal error term. As weexpect to observe some positive correlation within counties, we cluster the standarderrors at the county level.

We report the impact of the Great famine on the savings rate in 2002 in Table 1.The basic result is presented in Columns (1). It is implied that higher chances of sur-vival during the famine years are associated with a decline in household savings ratein 2002. In other words, households that reside in counties with a higher mortalityrate during the famine years, tend to have a higher savings rate even nowadays. Themagnitude of the impact is sizable: a standard deviation decrease in the survival indexraises the household savings rate by roughly 5% on average.

We further control for the growth at the county level and income at the householdlevel, given both income and growth are important determinants for households sav-ings rate. In Columns (2) and (3), we find that the estimates of the impact of faminesurvival are rather similar. That is due to the fact that, in the baseline regression, wehave controlled for a range of variables that are correlated with the income level ofhouseholds and that of the village and the county that they reside in. Moreover, wefind that richer households tend to have a higher savings rate. This finding is consis-

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tent with that in Yang, Zhang, and Zhou (2012) which analyzes this issue with urbanhouseholds sample.12

In column (4), we present results when we include the interaction terms betweenfamine severity and income group dummies. They suggest that the poorest ruralhouseholds in 2002 tend to be the most responsive to the cross county variations inthe survival index. A decrease in the survival index, which indicates a lower chanceof survival or a higher mortality rate during the famine years, raises the savings rateof the poorest households to the largest extent.

This result is consistent with the implication of our hypothesis. The poorest ru-ral households typically strive for maintaining consumption at the subsistence leveland they save mainly for the reason of building a buffer to cushion the future incomeshocks. Therefore, patience becomes the primary driving force underlying the savingbehavior for this group of households. In contrast, the richer households may have ac-cumulated a buffer stock to smooth income shocks and save out of alternative motives,for which patience plays a less important role, such as bequests or conspicuous con-sumption (e.g., a fancy wedding). Therefore, the cross-county difference in the savingsrate of richer households may be less responsive to variations in county-level famineseverity, as suggested by our estimation. Note that this results does not suggest thatricher households are more patient.

However, even with a large set of controls, it is unlikely that they exhaust the rel-evant common factors that matter for both the pre-famine characteristics and post-famine development paths of the Chinese villages. To ensure that the estimates ob-tained do not simply capture unobserved heterogeneity at the village level, we re-investigate the issue by including the village fixed effects. Given that our ols regres-sions have demonstrated that the famine had heterogenous impacts across incomegroups, we focus on the interaction between famine survival and income quartiles.

log(Y/C)ikj = βSj + Σl βlYil + ΣlαlYil × Sj + ηiZi + ηjZj + ηkZk + δk + εikj (2)

where δk captures the village fixed effects, and Yil where l = 1, 2, 3, 4 are dummiesvariables for each income quartile computed from the information on households in-come.

In Columns (5), we present results by exploring the within-village variations. Itremains to be the case that poorer households tend to have lower savings rate but thelowest income quartile is the most sensitive to the variations in the survival index.

One may argue that the famine could be less sever in places where the population

12The empirical analysis of Yang, Zhang, and Zhou (2012) establishes that the Chinese householdsavings are positively related to household incomes.

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(and their offsprings) was characterized by high patience, so that they had accumu-lated a larger buffer to cushion the fall of the agricultural output during the famineyears. On the one hand, this mechanism predicts that the savings rate should be higherin counties with less sever famine, which may bias our ols estimates towards zero. Onthe other hand, as we documented in Section 3, the food consumption in the ruralareas had been collectivized prior to the famine years and the rural households werenot allowed to store food individually. Therefore, it may be reasonable that faminemortality should be explained by various institutional causes and weather conditions,instead of the patience of the population.

4.2. Results from two stage least squares estimation (2SLS)

One likely concern with the baseline estimations is that they may suffer from the po-tential omitting variables problem. The Great famine that struck the rural areas mayproduce certain unobserved long-run effects on the population and economic struc-tures of the local areas. For example, Meng and Qian (2009) show that famine mayaffect the health conditions, education attainment and labor market performance ofthose famine survivors, whose early childhood was exposed to famine. It is plausiblethat the affected characteristics of the survivors may have an impact on the economicenvironment of the county, for example, the total stock of human capital, which in turninfluenced households’ saving decisions in 2002. To address this concern, we collectand employ a rich set of instruments for the survival index at both the county andprovince levels.

First, we consider a set of variables capturing the various disasters experiences inthe late 1950s at the county level. Conceptually, the unfavorable weather conditionsand natural disasters immediately before and during the famine years may have ag-gravated the mortality of famine.13 Given the famine occurred after a sudden fall ingrain output and availability of food consumption, it is reasonable to conjecture thatadverse weather conditions and natural disasters may be negatively affect the chancesof survival. Moreover, it is unlikely that the natural disasters that happened in late1950’s still influence the saving pattern in recent years.

We collect detailed information regarding natural disasters at the county level fromthe Natural Disasters section of the county gazetteers, which documented four types ofmajor natural disasters (hailstone, drought, pets invasion and flood), of which hap-pened often in rural areas, and other minor types for each county. Therefore, we in-clude a set of dummy variables indicating whether the county was subject to any ofthe major natural disasters from 1956 to 1958.

13Li and Yang (2005) has empirically established that the bad weather indeed contributed to the fallof agricultural production.

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We also consider rain anomaly in 1958, a county-level variable, which indicates theabnormal rainfall in 1958 and is calculated with our climate datasets. The effects ofrain anomaly on the famine severity at the county level may also depend on whethera large fraction of the province was subject to disasters. When fewer counties in thesame province were affected by natural disasters, each affected county may receivedisaster relief provided at the province level. Therefore, we also interact the rainanomaly with a province-level natural disaster index, defined by the proportion ofaffected areas in each province by disasters 1958. Given the the proportion of affectedareas in each province in both 1958 and 1959 are strong predictors for the famine sur-vival index, we also include both of them.

The second category of instrument is related to the diversion of agricultural re-sources during the Great leap forward. In 1950’s, it was widely believed that naturaldisasters were the major cause of the fall of agricultural outputs in rural China andbuilding irrigation projects, as part of the industrialization in the rural area, was con-sidered an effective response to alleviate the situation (People’s Daily, 1957, Dec 22nd).In late 1955, Mao made an official comment and encouraged all the counties to buildirrigation projects to “secure the growth of agricultural output” (Mao, 1999, vol 6, page451). In September of 1957, Mao further called for a movement of building irrigationprojects in rural areas of China and instructed that those projects would not be fundedby the government and they had to be built by the mass (Chen, 2005). A media cam-paign was waged immediately and local government officials responded by proposingand building irrigation projects of all kinds.

On the one hand, those projects were built at the expense of a substantial diversionof the man power and other resources from agricultural production. In October of1957, roughly 30 millions of famers were mobilized to participate. The number ofparticipants reached around 70 millions at the end of 1957 and 100 millions in Januaryof 1958 (Wu, 2006). Li and Yang (2005) found that the resources diversion was onemajor cause of the fall of agricultural output during the famine years. On the otherhand, those irrigation projects undertaken during the political movement were largelyshowcases and turned out to be white elephants, which were useless for economicdevelopment. Some county gazetteers even explicitly document that those projectswere “a pure waste,” and were destroyed or abandoned quickly after they were builtbecause of the quality issue. To some degree, the number or frequency of attemptingto build those showcase projects should be correlated with the effort local officials hadtaken to signal political loyalty.

We have collected the details of those large irrigation projects built during theGreat leap forward in the section of Major Events in County Gazetteers or in IrrigationGazetteers. We consider the number of the irrigation projects build in 1956, 1957 and

17

1958 in each county and whether they had been registered explicitly as a waste maybe valid instruments, given they may be correlated with the mortality rate during thefamine years but may not have an impact on the saving pattern of households in 2002through other channels. Even though some of them may actually been used in agricul-tural production, the initial value of investment may have depreciated fully in 2002.

Third, we also consider the characteristics of the provincial leaders during thefamine years, which may matter for the survival index but may not affect the savingpattern in recent years. Kung and Chen (2011) study the promotion incentives of theprovincial leaders, i.e., the First party secretory (FPS), and show that it may, to a largedegree, drive the political radicalism observed during the Great Leap Forward, whichcan be approximated by the excess procurement of grain. There were three types ofprovincial leaders, full members of the Central party committee, alternate membersand non-members. They demonstrate empirically that those alternate members thatwere not yet promoted to the full members had stronger incentives to signal commit-ment to the Great Leap Forward movement called for by Mao, by increasing the grainprocured.14

We suspect that the rank of the leaders and associated efforts taken during thefamine years may also impact the survival index we construct. On the one hand,lower ranked leaders may tend to increase the grain procurement which deprivedfurther the food availability and therefore increased the mortality rate and decreasedthe birth rate.15 One the other hand, the leaders who aspired for promotion may alsotake extra effort to respond to the disastrous consequences of the movement. Thegovernment decided to systematically respond to the demographical crisis after themiddle of 1960. Radical policies were, to some degree, reverted. For example, rurallabors who were diverted to the industrialization in urban areas during the movementwere largely returned back to the field (Li and Yang 2005). Households were alsoallowed to produce and store food for their own consumption (Thaxton 2008). Thefamine was ended when the government decided to send large amounts of grain intothe rural areas in 1961 (Meng, Qian, and Yared 2015). It is conceivable that the lowerranked provincial leaders took extra effort to mitigate death tolls and increased thesurvival chances, when that became the goal of the central government. The survivalindex we construct with the birth cohort size may increase in counties which were

14That is, the ratio of the procurement of grain during the movement to that of previous normal yearstended to be higher in provinces where the FPS was an alternate member.

15One plausible candidate for instrumental variable is the excess procurement ratio, which shouldbe correlated with both the mortality and birth rates. However, it is possible that the level of grainprocurement was determined partly by characteristics of a province, e.g., the stage of development, theimportance of agricultural sector as well as factor endowments. Those characteristics may be persis-tent and have impacts on the economic environments, which therefore affect the saving decisions ofrural households in 2002. Indeed, we experiment with this instrument and find that it cannot pass theexogeneity test.

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located in a province governed by an alternate member of the central committee, if thesecond channel dominates the former.

Therefore, we consider the characteristics of the provincial leaders during the famineyears are reasonable candidates for instrumental variables. Because those leaders werenot leading the provinces in 2002 any more, their party rank during the famine years,may not be likely to affect the consumption-saving decisions of the rural households in2002 through other mechanisms. Specifically, employing the dataset offered by Kungand Chen (2011), we consider the following instrumental variable: dummies variablesfor the FPS being alternate member, full member or non-member in 1959. We alsoinclude party age of the leader in 1959, which is a measure of the experience and ca-pability of the leader.

One may be concerned with the exogeneity of the instruments. Regarding the char-acteristics of the provincial leaders, it might be the case that some provinces have beenalways assigned higher ranked officials to govern, given they are relatively more im-portant. Or the assignment is persistent and correlated with some unchanged long runcharacteristics of the provinces, which also drive the differences in savings pattern ob-served in 2002. We consider this concern less likely, given that the vast majority of theprovinces were governed by full members in 2002 and there was little variation. Todeal with this concern more carefully, we include the rank of the FPS in 2002 and theirparty age as our control variables.

Similarly, the climatic shocks that aggravated the famine could be a recurring prob-lem for these counties. There may be certain pattern in saving and consumption forthe households that reside in the counties that are subject to volatile rain shocks. How-ever, the set of controls for rainfalls shocks history (i.e., volatility of rain-shocks in both1980’s and 1990’s as well as rainfall anomaly three years prior to 2002 and climatic con-ditions allow us to verify that it is not likely to be the case.

More importantly, in all estimations, we have checked the relevance and exogene-ity of instruments used. Since we instrument the famine survival index with multiplevariables, we perform the Hansen’s J test using overidentifying restrictions. Giventhe rain shock anomaly in 1958 is exogenous and do not affect the households savingdecision in 2002 through any other channels, we can test the exogeneity of other in-struments. We compute and report the p-value of the over-id Test in each regression,which indicate that the instruments we use are indeed exogenous. We also report thefirst stage F-statistics, which implies that our instruments are not weak.

In Table 2, we report the results from the second stage regressions.16 For compar-

16In Table 4, we report results from the first stage regressions. We find that the survival index, definedwith the birth cohort size tends to be higher in provinces governed by alternate members of the central

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ison of the magnitude, we present results from OLS regressions when we restrict theobservations to the sub-smaple used in the IV regressions in Table 3.17

In column (1) of Table 2, with the basic set of controls, the magnitude of our es-timate increases substantially, almost doubling the ols estimate. It implies that in acounty with a famine survival rate one standard deviation below the mean, house-holds raise their savings rate by about more than 10 percentage points. Consideringthe median savings rate in our sample of rural households is 24%, the impact of Greatfamine seems to be quite sizable. Our IV estimates appear to be substantially largerthan those in ols regressions, which is consistent with the concern of omitted variablesdiscussed in the beginning of this section.

In column (2), we include the rank of provincial leaders and party age of thoseleaders as controls. Our estimate barely changes, which indicates that the concernthat political radicalism during the GLF is correlated with unobserved provincial levelcharacteristics in 2002 is not important.

In column (3), (4), (5) and (6), we report our IV estimates when controlling growth,income, interactions between famine survival and income quartiles dummies and vil-lage level of fixed effects. The only difference from ols regressions is that the magnitudeof the famine impact found is larger.

4.3. Robustness checks

In Table 5, we redo the exercise by using an alternative expenditure definition whichincludes the medical care paid by the government. The savings of household membersworking in the public sector are underestimated, if such expenditure is substantial andimportant. Our estimate can be biased, if there are less civil servants in the area whichwere the most severely hit by the famine. The results are nonetheless the same.

In Table 6, the income variable is computed by adding various items listed in thesurvey. Measurement errors are expected to increase, which may downward biasingour coefficient. The results show that it is indeed the case and our estimates tend to besmaller in magnitude but still statistically significant.

The famine survival index has been computed taking into account all cohorts.

committee. It is consistent with the conjecture that leaders motivated by promotion to a higher rankmay take more effort to respond to the consequences of the famine, especially when it became a goalof the central government. A longer party age of the leaders up to 1959 is associated with a highersurvival index, indicating that the leaders’ experience may matter. Disaster and rain fall anomaly indexare typically negatively correlated with the survival index, which is consistent with our conjecture aswell.

17In the IV regressions, we have relevant data for counties from 24 provinces. We re-run the olsregressions with the smaller sample used in IV regressions. The estimates are still consistent with ourfindings with the full sample.

20

However, very small cohorts (with one or two members) could lead to measurementerror. In Table 7, we consider only cohorts with at least 5 members to compute thefamine survival index. When the measure is not available at the county level becauseof small cohort size, we compute the measure at the prefecture level. This will reducesvariation and might thus increase the standard errors. In table 8, we consider cohortswith at least 10 members.

In the two stage least squares estimation, we include multiple instruments for sur-vival index and we investigate how our results can be affected after we removing someof them. In column (1) and (2), we exclude the subset of variables related to disastersand rainfall shocks, namely rain anomalies (z-score) in 1958, province area strickenby natural disasters in 1958 and 1959, interaction between province area stricken bynatural disaster and county level rain anomaly (z-score) in 1958, dummies for hail-stones, droughts, pests and flood from 1956 to 1959. In column (3), we report resultsby estimating the village fixed effect model and excluding this set of instruments. Weobserve that F-stat dropped substantially, which indicates that such estimations maybe subject to weak instrument issues. But those estimates are still consistent with ourresults from Table 2.

In column (4) and (5), we exclude the subset of political variables, namely leaderrank (alternate member or full member), party age of leaders, irrigation projects from1956 to 1958 and whether such projects were explicitly registered as wastes. In col-umn (6), we report estimation results with village fixed effect by excluding this set ofinstruments.

4.4. Additional evidence: famine and saving deposits

Local bank deposits are typically regarded as an approximation for household savingdecisions (Wei and Zhang 2011). Informaiton on bank deposits is particularly usefulfor studies of rural households’ saving decisions in China, given few of them haveaccess to the financial market and possess other types of financial wealth. Therefore,it is interesting to investigate the relationship between famine severity and the bankdeposits. From China data online, we collect data on bank deposits at the county levelduring the period of 1997 to 2010, for 1624 rural counties in China, which allows us tostudy how local deposit per capita reacts to GDP per capita and famine severity in afixed effect model.18

In column (1) of Table 11, we regress log of deposit per capita on log of GDP percapita and its interaction with a dummy of the lowest quartile survival index, by con-trolling the county and year fixed effects. Not surprisingly, the income elasticity of

18We lag GDP for one period in all exercises. However, the results are much similar by using contem-porary GDP.

21

bank deposits is positive and statistically significant. That is consistent with perviousfindings that a higher income does raise savings. More interestingly, the estimate ofthe interaction term is almost as large as that of the income per capita and it is statis-tically significant. That is implies that, in response to the rising income, householdsin counties most severely affected by famine, i.e., the ones with the lowest 25% sur-vival index, raise their bank deposits roughly twice as much as the rest of households.This result is consistent with our conjecture that the famine severity in late 1950’s stillaffects the current propensity of households savings or patience level.

In column (2), we control for the size of the county (i.e., population) and rainfallanomaly, which potentially affects output or income in rural counties. Our estimatesbarely change. In column (3) and (4), we add the interaction between the log of GDPper capita and a measure of development level (i.e., a dummy of the lowest quartileincome group) and interaction between the log of GDP per capita and a measure ofurbanization (i.e., a dummy of rural population exceeding 95% in the county), respec-tively. The magnitude of the effect of survival index (or famine severity) is ratherstable. In column (5) and (6), we replace person level averages with household levelcounterparts and re-do the exercises reported in column (1) and (2). The results arestill robust.

It is a justified conjecture that the growth rate at the county level may be auto-correlated over time, which may bias the estimator when controlling for unobservedheterogeneity.19 To address the potential bias, we adopt the conditional maximumlikelihood estimator to deal with the dynamic linear panel data model, by followingclosely Verardi and Desbordes (2015). In the column (1) of Table 12, we report ourestimates when controlling for rain anomaly and county size. Both the effect of log ofGDP per capita and the effect added from its interaction with famine survival indexare close to their counterparts in Table 11. As shown in column (2) and (3), in this dy-namic model, adding development and urbanization related controls does not affectour estimates either.

5. Conclusions

In this paper, we build the link between the Great famine that struck China more than50 years ago with the observed the saving behavior of Chinese households in recentyears. We argue that the Great famine may still have a long-lasting effect now and con-stitute one important determinant of high saving propensity of Chinese households.Exploiting the variations of famine severity at the county level, we find that the sav-ings rate of rural households in 2002 is higher in counties where the famine is severer.

19In standard dynamic linear panel data models, a correlation between the error term and the ex-planatory variables may arise, when controlling for unobserved heterogeneity.

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Our results are still robust using IV and matching estimations.

Our findings suggest that the effects of famine can be found not only for the faminesurvivors, the cohorts borne during or immediately after famines, but also for their off-springs. The findings lend support to the theoretical contributions in the endogenouspreference literature: patience can be formed endogenously and transmitted acrossgenerations. Interestingly, we also find that the savings rate of the poorest rural house-holds in 2002 is the most responsive to the variations in famine severity. That is alsoconsistent with our conjecture: the poorest rural households mainly save for the sub-sistence consumption, which is driven largely by patience.

Even though this paper focuses on the famine event in only one country, the resultscontribute something of general importance to the understanding of long term conse-quences of famines in other countries. It indicates that the long run consequences offamines and associated demographical crises that took place in 20th century may notdisappear completely and famines may still have lingering effects on the economicdecisions of households nowadays.

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Wei, S.-J. and X. Zhang (2011). The competitive saving motive: Evidence from risingsex ratios and savings rates in china. Journal of Political Economy 119(3), 511–564.

Yang, D. T., J. Zhang, and S. Zhou (2012). Why are saving rates so high in china? InCapitalizing China, pp. 249–278. University of Chicago Press.

3

Table 1. Savings ratio and famine - OLS regressions using all sample - The dependent variableis the log of the savings ratio (income/consumption).

(1) (2) (3) (4) (5)

Famine survival index -0.263*** -0.255*** -0.209** -0.400***(0.08) (0.09) (0.09) (0.12)

famine X 1 inc. quart. -0.419***(0.10)

famine X 2 inc. quart. 0.320*** -0.109(0.10) (0.07)

famine X 3 inc. quart. 0.183 -0.165**(0.12) (0.07)

famine X 4 inc. quart. 0.328**(0.15)

1st income quartile -0.681***(0.07)

2nd income quartile 0.432*** 0.224*** -0.465***(0.03) (0.07) (0.05)

3rd income quartile 0.634*** 0.513*** -0.210***(0.03) (0.08) (0.05)

4th income quartile 0.945*** 0.727***(0.04) (0.10)

Household characteristics Yes Yes Yes Yes YesLong run conditions Yes Yes Yes Yes NoRain shocks history (80s, 90s) Yes Yes Yes Yes NoRecent rain shocks (1999-2001) Yes Yes Yes Yes NoGrowth No Yes Yes Yes NoLand, income and migration No No Yes Yes Yes

Observations 8085 6675 6675 6675 8495Number of villages (FE) 890Adjusted R-squared 0.0723 0.0682 0.329 0.331 0.285

Significance level: ∗ 10%, ∗∗ 5%, ∗∗∗ 1%. Standard errors (in parentheses) are clustered at thecounty level in (1)-(4), at the village level in (5). (5) includes village fixed effects. Householdcharacteristics includes the age of the household head, the proportion of dependent membersin the household, the proportion of women in the household, household size and ethnicity, av-erage education, access to water (via pump, well or tap water), and whether energy is providedby coal, firewood or fuel. Long run conditions include terrain condition (whether the villageterrain is plain, hilly or mountainous; the slope, elevation, and shape of the nearby environ-ment), agricultural suitability (the share of clayey soil, of sandy soil, of sloping land and of plainland in the county, the amount of land per capita in the village), the distance to the county seat,whether the village was a former CCP base, the mean and the standard deviation of precipita-tions in the county throughout the XXst century, whether the province is a coastal, a central or awestern one. Rain shocks history comprises of the averages of yearly rain anomalies (z-score) inthe 1980s and in the 1990s in the county. Recent rain shocks are yearly rain anomalies (z-score)in 1999, 2000 and 2001 in the village. Growth is the growth of the village population and of theaverage salary of village cadres between 1998 and 2002, and the average of county GDP growthbetween 1998 and 2002. Land is the amount of cultivated land by the household and the amountof total land (including forest, pasture, fishponds) per household in the village as of 1998. In-come includes dummies indicating the quartile of income distribution to wich the household be-longs, whether the village implemented the tax reform, and the amount of cadres salary in 2002.Migration is the number of migrants in the household. Interpretation : if the famine survivalindex decreases (famine cohorts survived less than non famine ones) by one standard deviation,the savings ratio increases by 5.1 % to 6.3 % (col (2) and (3)).

4

Table 2. Savings ratio and famine - 2SLS regressions - The dependent variable is the log of the savings ratio(income/consumption).

(1) (2) (3) (4) (5) (6)

Famine survival index -0.542*** -0.592*** -0.544*** -0.420*** -0.683**(0.16) (0.19) (0.15) (0.13) (0.27)

famine X 1 inc. quart. -0.645***(0.25)

famine X 2 inc. quart. 0.527** -0.193(0.22) (0.22)

famine X 3 inc. quart. 0.304 0.028(0.32) (0.24)

famine X 4 inc. quart. 0.302(0.36)

Household characteristics Yes Yes Yes Yes Yes YesLong run conditions Yes Yes Yes Yes Yes NoRain shocks history (80s, 90s) Yes Yes Yes Yes Yes NoRecent rain shocks (1999-2001) Yes Yes Yes Yes Yes NoProvince leader 2002 No Yes No No No NoGrowth No No Yes Yes Yes NoLand, income and migration No No No Yes Yes Yes

Observations 4775 4775 4775 4775 4775 4775Number of villages (FE) 479Adjusted R-squared 0.0209 0.00906 0.0132 0.0304 0.0160 0.215KP F-stat 25.43 15.96 27.14 20.47 14.44 10.15Hansen J 25.66 31.24 28.15 31.96 43.92 17.94p-value 0.591 0.306 0.457 0.276 0.560 0.459

Significance level : ∗ 10%, ∗∗ 5%, ∗∗∗ 1%. Standard errors (in parentheses) are clustered at the county levelin (1)-(5), at the village level in (6). (6) includes village fixed effects. Household characteristics includesthe age of the household head, the proportion of dependent members in the household, the proportion ofwomen in the household, household size and ethnicity, average education, access to water (via pump, wellor tap water), and whether energy is provided by coal, firewood or fuel. Long run conditions include ter-rain condition (whether the village terrain is plain, hilly or mountainous; the slope, elevation, and shape ofthe nearby environment), agricultural suitability (the share of clayey soil, of sandy soil, of sloping land andof plain land in the county), the distance to the county seat, whether the village was a former CCP base, themean and the standard deviation of precipitations in the county throughout the XXst century, whether theprovince is a coastal, a central or a western one. Rain shocks history comprises of the averages of yearlyrain anomalies (z-score) in the 1980s and in the 1990s in the county. Recent rain shocks are yearly rainanomalies (z-score) in 1999, 2000 and 2001 in the village. Province leader 2002 are rank and party experi-ence of province leader in 2002. Growth is the growth of the village population and of the average salary ofvillage cadres between 1998 and 2002, and the average of county GDP growth between 1998 and 2002. Landis the amount of cultivated land by the household and the amount of total land (including forest, pasture,fishponds) per household in the village as of 1998. Income includes dummies indicating the quartile of in-come distribution to wich the household belongs, whether the village implemented the tax reform, and theamount of cadres salary in 2002. Migration is the number of migrants in the household.Excluded instruments in col (5) and (6) : excluded instruments for the interaction between famine indexand income quartiles are the interactions between income quartiles and : the party rank of 1959 leader, (fullmember or alternate member), rainfall shock in 1958, provincial area stricken by disasters in 1958 and 1959,and rainfall shock in 1958 interacted with provincial area stricken by disaster in 1958. Excluded instrumentsused in the first 4 columns are included in the 5th specification but not in the 6th one with village FE.Interpretation: if the famine survival index decreases (famine cohorts survived less than non famine ones)by one standard deviation, the savings ratio increases by 10.6 % to 14 % (col (3) and (4)).

5

Table 3. Savings ratio and famine - OLS regressions - The dependent variable is the log ofthe savings ratio (income/consumption).

(1) (2) (3) (4) (5)

Famine survival index -0.308** -0.317** -0.280** -0.456***(0.12) (0.12) (0.11) (0.16)

famine X 1 inc. quart. -0.342**(0.15)

famine X 2 inc. quart. 0.306** -0.061(0.12) (0.12)

famine X 3 inc. quart. 0.110 -0.070(0.16) (0.13)

famine X 4 inc. quart. 0.334*(0.19)

Household characteristics Yes Yes Yes Yes YesLong run conditions Yes Yes Yes Yes NoRain shocks history (80s, 90s) Yes Yes Yes Yes NoRecent rain shocks (1999-2001) Yes Yes Yes Yes NoGrowth No Yes Yes Yes YesLand, income and migration No No Yes Yes Yes

Observations 4775 4775 4775 4775 4775Number of villages (FE) 479Adjusted R-squared 0.0817 0.0831 0.336 0.338 0.300

Significance level : ∗ 10%, ∗∗ 5%, ∗∗∗ 1%. Standard errors (in parentheses) are clustered at thecounty level in (1)-(4), at the village level in (5). (5) includes village fixed effects.Interpretation : if the famine survival index decreases (famine cohorts survived less than nonfamine ones) by one standard deviation, the savings ratio increases by 6.9 % to 7.9 % (col (3)and (4)).

6

Table 4. Savings ratio and famine - first stage regressions - The dependentvariable is the famine survival index.

(1) (2) (3) (4)

County rain shock in 1958 (z-score) -0.036 -0.052 -0.020 -0.022(0.02) (0.03) (0.02) (0.02)

1958 county shock X prov. area hit by disasters 0.002 0.005 0.001 0.000(0.00) (0.01) (0.00) (0.00)

Province area hit by natural disasters in 1958 0.003 -0.004 -0.004 -0.000(0.01) (0.01) (0.01) (0.01)

Province area hit by natural disasters in 1959 -0.004** -0.003 -0.003** -0.003*(0.00) (0.00) (0.00) (0.00)

Hailstone in 1956 0.071 0.071 0.046 0.042(0.05) (0.06) (0.05) (0.05)

Hailstone in 1957 -0.065 -0.063 -0.062 -0.046(0.04) (0.04) (0.04) (0.04)

Hailstone in 1958 -0.057 -0.029 -0.055 -0.073(0.07) (0.09) (0.07) (0.07)

Hailstone in 1959 -0.085 -0.142 -0.121** -0.137**(0.06) (0.10) (0.06) (0.06)

Drought in 1956 -0.215*** -0.221*** -0.251*** -0.238***(0.06) (0.07) (0.06) (0.06)

Drought in 1957 0.107* 0.091 0.132** 0.140***(0.06) (0.07) (0.05) (0.05)

Drought in 1958 0.024 0.028 0.015 0.024(0.05) (0.06) (0.05) (0.05)

Drought in 1959 -0.012 -0.005 -0.009 -0.014(0.05) (0.06) (0.05) (0.05)

Pests invasion in 1956 -0.055 -0.039 -0.082 -0.079(0.06) (0.06) (0.06) (0.06)

Pests invasion in 1957 -0.036 -0.012 -0.057 -0.081(0.07) (0.07) (0.07) (0.07)

Pests invasion in 1958 0.114 0.139 0.186** 0.216**(0.08) (0.09) (0.09) (0.09)

Pests invasion in 1959 0.058 0.093 0.091** 0.079**(0.04) (0.06) (0.04) (0.04)

Flood in 1956 -0.002 0.022 0.053 0.052(0.06) (0.07) (0.06) (0.06)

Flood in 1957 -0.003 -0.026 -0.009 -0.004(0.04) (0.05) (0.04) (0.03)

Flood in 1958 -0.047 -0.061 -0.092 -0.108*(0.07) (0.08) (0.06) (0.06)

Flood in 1959 -0.084 -0.090* -0.070 -0.098**(0.05) (0.05) (0.05) (0.05)

1959 leader was alternate member 0.052 -0.160 -0.057 -0.096(0.11) (0.31) (0.13) (0.11)

1959 leader was full member -0.354** -0.569 -0.517*** -0.502***(0.17) (0.34) (0.19) (0.17)

Leader’s experience in the party in 1959 0.033** 0.027** 0.038*** 0.032**(0.01) (0.01) (0.01) (0.01)

Irrigation project in 1956 0.063** 0.074*** 0.060** 0.084***(0.03) (0.03) (0.02) (0.02)

Irrigation project in 1957 0.057** 0.066** 0.055** 0.065**(0.02) (0.03) (0.02) (0.02)

Irrigation project in 1958 -0.008 -0.006 -0.015 -0.024(0.02) (0.02) (0.02) (0.02)

A 1956 project was a waste -0.120* -0.178* -0.115* -0.113*(0.07) (0.10) (0.06) (0.06)

A 1957 project was a waste -0.006 0.037 -0.122 -0.166(0.13) (0.14) (0.13) (0.13)

A 1958 project was a waste -0.010 0.002 0.036 0.043(0.05) (0.07) (0.06) (0.06)

Full member leader in 2002 0.219(0.28)

2002 leader’s experience in the party -0.003(0.01)

Controls Yes Yes Yes YesGrowth No No Yes YesLand, income and migration No No No Yes

Observations 4775 4775 4775 4775Adjusted R-squared 0.787 0.790 0.809 0.819

7

Table 5. Savings ratio and famine - 2SLS regressions - The dependent variable is the log of the savings ratio(income/consumption). Now the consumption variable includes the medical care paid by the government.

(1) (2) (3) (4) (5) (6)

Famine survival index -0.544*** -0.593*** -0.547*** -0.422*** -0.683**(0.16) (0.19) (0.15) (0.13) (0.27)

famine X 1 inc. quart. -0.635**(0.25)

famine X 2 inc. quart. 0.525** -0.185(0.22) (0.22)

famine X 3 inc. quart. 0.305 0.039(0.32) (0.24)

famine X 4 inc. quart. 0.293(0.36)

Household characteristics Yes Yes Yes Yes Yes YesLong run conditions Yes Yes Yes Yes Yes NoRain shocks history (80s, 90s) Yes Yes Yes Yes Yes NoRecent rain shocks (1999-2001) Yes Yes Yes Yes Yes NoProvince leader 2002 No Yes No No No NoGrowth No No Yes Yes Yes NoLand, income and migration No No No Yes Yes Yes

Observations 4775 4775 4775 4775 4775 4775Number of villages (FE) 479Adjusted R-squared 0.0209 0.00921 0.0133 0.0305 0.0160 0.214KP F-stat 25.43 15.96 27.14 20.47 14.44 10.15Hansen J 25.63 31.21 28.20 31.84 43.92 17.86p-value 0.593 0.308 0.454 0.281 0.560 0.465

Significance level : ∗ 10%, ∗∗ 5%, ∗∗∗ 1%. Standard errors (in parentheses) are clustered at the county levelin (1)-(5), at the village level in (6). (6) includes village fixed effects. Excluded instruments in col (5) and (6): excluded instruments for the interaction between famine index and income quartiles are the interactionsbetween income quartiles and the party rank of 1959 leader, (full member or alternate member), rainfallshock in 1958, provincial area stricken by disasters in 1958 and 1959, and rainfall shock in 1958 interactedwith provincial area stricken by disaster in 1958. Excluded instruments used in the first 4 columns are in-cluded in the 5th specification but not in the 6th one (as there are village FE). Interpretation: if the faminesurvival index decreases (famine cohorts survived less than non famine ones) by one standard deviation,the savings ratio increases by 10.7 % to 14 % (col (3) and (4)).

8

Table 6. Savings ratio and famine - 2SLS regressions - The dependent variable is the log of the savingsratio (income/consumption). The income variable is computed from various items reported in the survey.

(1) (2) (3) (4) (5) (6)

Famine survival index -0.373** -0.548*** -0.391** -0.317*** -0.645***(0.16) (0.19) (0.15) (0.12) (0.25)

famine X 1 inc. quart. -0.398*(0.24)

famine X 2 inc. quart. 0.537*** -0.044(0.21) (0.22)

famine X 3 inc. quart. 0.433 0.059(0.32) (0.21)

famine X 4 inc. quart. 0.501(0.36)

Household characteristics Yes Yes Yes Yes Yes YesLong run conditions Yes Yes Yes Yes Yes NoRain shocks history (80s, 90s) Yes Yes Yes Yes Yes NoRecent rain shocks (1999-2001) Yes Yes Yes Yes Yes NoProvince leader 2002 No Yes No No No NoGrowth No No Yes Yes Yes NoLand, income and migration No No No Yes Yes Yes

Observations 4776 4776 4776 4776 4776 4776Number of villages (FE) 479Adjusted R-squared 0.0138 -0.00969 0.00126 0.0239 0.0101 0.138KP F-stat 25.43 15.98 27.21 20.74 17.77 8.934Hansen J 29.37 31.93 29.83 27.77 53.84 10.41p-value 0.394 0.277 0.371 0.477 0.199 0.918

Significance level : ∗ 10%, ∗∗ 5%, ∗∗∗ 1%. Standard errors (in parentheses) are clustered at the county levelin (1)-(5), at the village level in (6). (6) includes village fixed effects. Household characteristics includesthe age of the household head, the proportion of dependent members in the household, the proportionof women in the household, household size and ethnicity, average education, access to water (via pump,well or tap water), and whether energy is provided by coal, firewood or fuel. Long run conditions in-clude terrain condition (whether the village terrain is plain, hilly or mountainous; the slope, elevation,and shape of the nearby environment), agricultural suitability (the share of clayey soil, of sandy soil, ofsloping land and of plain land in the county), the distance to the county seat, whether the village wasa former CCP base, the mean and the standard deviation of precipitations in the county throughout theXXst century, whether the province is a coastal, a central or a western one. Rain shocks history comprisesof the averages of yearly rain anomalies (z-score) in the 1980s and in the 1990s in the county. Recent rainshocks are yearly rain anomalies (z-score) in 1999, 2000 and 2001 in the village. Province leader 2002 arerank and party experience of province leader in 2002. Growth is the growth of the village population andof the average salary of village cadres between 1998 and 2002, and the average of county GDP growthbetween 1998 and 2002. Land is the amount of cultivated land by the household and the amount of to-tal land (including forest, pasture, fishponds) per household in the village as of 1998. Income includesdummies indicating the quartile of income distribution to wich the household belongs, whether the vil-lage implemented the tax reform, and the amount of cadres salary in 2002. Migration is the number ofmigrants in the household.Excluded instruments in col (5) and (6) : excluded instruments for the interaction between famine indexand income quartiles are the interactions between income quartiles and : the party rank of 1959 leader,(full member or alternate member), rainfall shock in 1958, provincial area stricken by disasters in 1958and 1959, and rainfall shock in 1958 interacted with provincial area stricken by disaster in 1958. Excludedinstruments used in the first 4 columns are included in the 5th specification but not in the 6th one withvillage FE.Interpretation: if the famine survival index decreases (famine cohorts survived less than non famineones) by one standard deviation, the savings ratio increases by 7.9% to 9.8% (col (3) and (4)).

9

Table 7. Savings ratio and famine - 2SLS regressions - The dependent variable is the log of the savings ratio(income/consumption). The famine survival index is computed using cohorts with at least 5 members.

(1) (2) (3) (4) (5) (6)

Famine survival index -0.535*** -0.553*** -0.547*** -0.418*** -0.672**(0.15) (0.19) (0.15) (0.12) (0.27)

famine X 1 inc. quart. -0.675***(0.26)

famine X 2 inc. quart. 0.559** -0.178(0.23) (0.23)

famine X 3 inc. quart. 0.261 0.047(0.34) (0.24)

famine X 4 inc. quart. 0.273(0.36)

Household characteristics Yes Yes Yes Yes Yes YesLong run conditions Yes Yes Yes Yes Yes NoRain shocks history (80s, 90s) Yes Yes Yes Yes Yes NoRecent rain shocks (1999-2001) Yes Yes Yes Yes Yes NoProvince leader 2002 No Yes No No No NoGrowth No No Yes Yes Yes NoLand, income and migration No No No Yes Yes Yes

Observations 4775 4775 4775 4775 4775 4775Number of villages (FE) 479Adjusted R-squared 0.0182 0.00752 0.0105 0.0279 0.0138 0.216KP F-stat 28.46 28.40 36.02 23.91 20.34 10.38Hansen J 26.13 31.82 28.79 32.93 44.66 18.87p-value 0.566 0.282 0.423 0.238 0.528 0.400

Significance level : ∗ 10%, ∗∗ 5%, ∗∗∗ 1%. Standard errors (in parentheses) are clustered at the county levelin (1)-(5), at the village level in (6). (6) includes village fixed effects. Household characteristics includesthe age of the household head, the proportion of dependent members in the household, the proportion ofwomen in the household, household size and ethnicity, average education, access to water (via pump, wellor tap water), and whether energy is provided by coal, firewood or fuel. Long run conditions include ter-rain condition (whether the village terrain is plain, hilly or mountainous; the slope, elevation, and shape ofthe nearby environment), agricultural suitability (the share of clayey soil, of sandy soil, of sloping land andof plain land in the county), the distance to the county seat, whether the village was a former CCP base, themean and the standard deviation of precipitations in the county throughout the XXst century, whether theprovince is a coastal, a central or a western one. Rain shocks history comprises of the averages of yearlyrain anomalies (z-score) in the 1980s and in the 1990s in the county. Recent rain shocks are yearly rainanomalies (z-score) in 1999, 2000 and 2001 in the village. Province leader 2002 are rank and party experi-ence of province leader in 2002. Growth is the growth of the village population and of the average salary ofvillage cadres between 1998 and 2002, and the average of county GDP growth between 1998 and 2002. Landis the amount of cultivated land by the household and the amount of total land (including forest, pasture,fishponds) per household in the village as of 1998. Income includes dummies indicating the quartile of in-come distribution to which the household belongs, whether the village implemented the tax reform, and theamount of cadres salary in 2002. Migration is the number of migrants in the household.Excluded instruments in col (5) and (6) : excluded instruments for the interaction between famine indexand income quartiles are the interactions between income quartiles and : the party rank of 1959 leader, (fullmember or alternate member), rainfall shock in 1958, provincial area stricken by disasters in 1958 and 1959,and rainfall shock in 1958 interacted with provincial area stricken by disaster in 1958. Excluded instrumentsused in the first 4 columns are included in the 5th specification but not in the 6th one (as there are village FE).Interpretation: if the famine survival index decreases (famine cohorts survived less than non famine ones)by one standard deviation, the savings ratio increases by 10.5 % to 14 % (col (3) and (4)).

10

Table 8. Savings ratio and famine - 2SLS regressions - The dependent variable is the log of the savings ratio(income/consumption).The famine survival index is computed using cohorts with at least 10 members.

(1) (2) (3) (4) (5) (6)

Famine survival index -0.538*** -0.535*** -0.553*** -0.418*** -0.654**(0.15) (0.20) (0.15) (0.13) (0.27)

famine X 1 inc. quart. -0.664***(0.26)

famine X 2 inc. quart. 0.537** -0.176(0.23) (0.23)

famine X 3 inc. quart. 0.231 0.039(0.33) (0.24)

famine X 4 inc. quart. 0.221(0.36)

Household characteristics Yes Yes Yes Yes Yes YesLong run conditions Yes Yes Yes Yes Yes NoRain shocks history (80s, 90s) Yes Yes Yes Yes Yes NoRecent rain shocks (1999-2001) Yes Yes Yes Yes Yes NoProvince leader 2002 No Yes No No No NoGrowth No No Yes Yes Yes NoLand, income and migration No No No Yes Yes Yes

Observations 4775 4775 4775 4775 4775 4775Number of villages (FE) 479Adjusted R-squared 0.0207 0.0111 0.0130 0.0286 0.0145 0.216KP F-stat 27.45 28.99 36.66 27.15 15.34 10.35Hansen J 25.72 31.72 28.12 33.72 44.53 18.90p-value 0.589 0.286 0.458 0.210 0.534 0.398

Significance level : ∗ 10%, ∗∗ 5%, ∗∗∗ 1%. Standard errors (in parentheses) are clustered at the county levelin (1)-(5), at the village level in (6). (6) includes village fixed effects. Household characteristics includesthe age of the household head, the proportion of dependent members in the household, the proportion ofwomen in the household, household size and ethnicity, average education, access to water (via pump, wellor tap water), and whether energy is provided by coal, firewood or fuel. Long run conditions include ter-rain condition (whether the village terrain is plain, hilly or mountainous; the slope, elevation, and shape ofthe nearby environment), agricultural suitability (the share of clayey soil, of sandy soil, of sloping land andof plain land in the county), the distance to the county seat, whether the village was a former CCP base, themean and the standard deviation of precipitations in the county throughout the XXst century, whether theprovince is a coastal, a central or a western one. Rain shocks history comprises of the averages of yearlyrain anomalies (z-score) in the 1980s and in the 1990s in the county. Recent rain shocks are yearly rainanomalies (z-score) in 1999, 2000 and 2001 in the village. Province leader 2002 are rank and party experi-ence of province leader in 2002. Growth is the growth of the village population and of the average salary ofvillage cadres between 1998 and 2002, and the average of county GDP growth between 1998 and 2002. Landis the amount of cultivated land by the household and the amount of total land (including forest, pasture,fishponds) per household in the village as of 1998. Income includes dummies indicating the quartile of in-come distribution to wich the household belongs, whether the village implemented the tax reform, and theamount of cadres salary in 2002. Migration is the number of migrants in the household.Excluded instruments in col (5) and (6) : excluded instruments for the interaction between famine indexand income quartiles are the interactions between income quartiles and : the party rank of 1959 leader, (fullmember or alternate member), rainfall shock in 1958, provincial area stricken by disasters in 1958 and 1959,and rainfall shock in 1958 interacted with provincial area stricken by disaster in 1958. Excluded instrumentsused in the first 4 columns are included in the 5th specification but not in the 6th one (as there are village FE).Interpretation : if the famine survival index decreases (famine cohorts survived less than non famine ones)by one standard deviation, the savings ratio increases by 10.6 % to 14.2 % (col (3) and (4)).

11

Table 9. Savings ratio and famine - 2SLS regressions with subsets of ex-cluded instruments - The dependent variable is the log of the savings ratio (in-come/consumption).

Excluded instruments:Climatic and disasters variables Political variables

(1) (2) (3) (4) (5) (6)

Famine survival index -0.523*** -0.542*** -0.579*** -0.456***(0.18) (0.16) (0.19) (0.17)

famine X 1 inc. quart. -0.656* -0.549(0.37) (0.34)

famine X 2 inc. quart. -0.227 -0.079(0.31) (0.36)

famine X 3 inc. quart. -0.032 -0.167(0.24) (0.32)

Household characteristics Yes Yes Yes Yes Yes YesLong run conditions Yes Yes No Yes Yes NoRain shocks history (80s, 90s) Yes Yes No Yes Yes NoRecent rain shocks (1999-2001) Yes Yes No Yes Yes NoGrowth No Yes No No Yes NoLand, income and migration No Yes Yes No Yes YesVillage fixed effects No No Yes No No Yes

Observations 4773 4775 4773 4773 4773 4773Adjusted R-squared 0.0213 0.0314 0.0976 0.0192 0.0334 0.0980KP F-stat 6.408 6.518 6.891 14.26 12.48 12.73Hansen J 16.81 23.43 26.88 8.284 10.16 24.24p-value 0.603 0.219 0.630 0.406 0.254 0.448

Standard errors (in parentheses) are clustered at the county level. Significance level: ∗ 10%, ∗∗ 5%, ∗∗∗ 1%. Column (3) and (6) include village fixed effects. Excludedinstruments in column (1) and (2) : the subset of variables related to disastersand rainfall shocks, namely rain anomalies (z-score) in 1958, province area strickenby natural disasters in 1958 and 1959, interaction between province area strickenby natural disaster and county level rain anomaly (z-score) in 1958, hailstones,droughts, pests and flood from 1956 to 1959. Excluded instruments in column (3)are the interactions between income quartiles and rain anomalies (z-score) in 1958,province area stricken by natural disasters in 1958 and 1959, interaction betweenprovince area stricken by natural disaster and county level rain anomaly (z-score)in 1958, hailstones from 1956 to 1959, and droughts from 1957 to 1959. Excludedinstruments in column (4) and (5): the subset of political variables, namely leaderrank (alternate member or full member), leader experience in the party, irrigationprojects from 1956 to 1958 and whether such projects were wastes. Excluded in-struments in column (6) are the interactions between income quartiles and leaderrank (alternate member or full member), leader experience in the party, irrigationprojects from 1956 to 1958 and whether such projects were wastes.

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Table 10. Savings ratio and famine - First stage regressions with a smaller subsetof excluded instruments (correspond to col (1), (2) and (4) and (5) of the tabledisplaying the second stage)

(1) (2) (3) (4)County rain shock in 1958 (z-score) -0.0125 -0.00567

(0.0295) (0.0275)Province area hit by natural disasters in 1958 0.00770* 0.00887**

(0.00423) (0.00431)Province area hit by natural disasters in 1959 0.0000691 -0.0000656

(0.00184) (0.00165)1958 county shock X prov. area hit by disasters -0.00443 -0.00566

(0.00439) (0.00411)Hailstone in 1956 0.00750 -0.0324

(0.0511) (0.0490)Hailstone in 1957 -0.0721 -0.0769

(0.0547) (0.0515)Hailstone in 1958 -0.137* -0.118*

(0.0761) (0.0708)Hailstone in 1959 0.0804 0.0655

(0.0551) (0.0578)Drought in 1956 -0.204** -0.238***

(0.0802) (0.0756)Drought in 1957 0.115* 0.127**

(0.0589) (0.0545)Drought in 1958 0.0158 0.0188

(0.0509) (0.0450)Drought in 1959 -0.0378 -0.0258

(0.0600) (0.0581)Pests invasion in 1956 0.0123 -0.0130

(0.0733) (0.0688)Pests invasion in 1957 -0.0712 -0.0884

(0.0809) (0.0799)Pests invasion in 1958 -0.0768 -0.0238

(0.0710) (0.0692)Pests invasion in 1959 -0.00947 -0.0210

(0.0593) (0.0569)Flood in 1956 -0.0745 -0.0413

(0.0472) (0.0394)Flood in 1957 0.0196 0.0389

(0.0554) (0.0522)Flood in 1958 0.0823 0.0668

(0.0615) (0.0549)Flood in 1959 -0.0798* -0.0852**

(0.0420) (0.0388)1959 leader was alternate member -0.00393 -0.0321

(0.0815) (0.0872)Leader’s experience in the party in 1959 0.0328*** 0.0306***

(0.00542) (0.00515)1959 leader was full member -0.355*** -0.355***

(0.0903) (0.0889)Irrigation project in 1956 0.0408 0.0610*

(0.0288) (0.0329)Irrigation project in 1957 0.0205 0.0211

(0.0234) (0.0226)Irrigation project in 1958 -0.0112 -0.0235

(0.0201) (0.0199)A 1956 project was a waste -0.0257 -0.0158

(0.0629) (0.0563)A 1957 project was a waste 0.218*** 0.179**

(0.0783) (0.0775)A 1958 project was a waste -0.0223 -0.0209

(0.0530) (0.0494)Controls Yes Yes Yes YesGrowth No Yes No YesLand, income and migration No Yes No YesIncome quartiles No Yes No Yes

Observations 4776 4776 4776 4776Adjusted R-squared 0.675 0.716 0.631 0.650

Standard errors in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01

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Table 11. Famine and Saving deposits.

log(deposit per capita) log(deposit per hh)

(1) (2) (3) (4) (5) (6)

Log of GDP per capita 0.134*** 0.134*** 0.115*** 0.134***(0.02) (0.02) (0.02) (0.02)

Log GDP pc X 1st quartile survival 0.121*** 0.120*** 0.121*** 0.120***(0.02) (0.02) (0.02) (0.02)

Log population 0.031 -0.030 0.031(0.09) (0.09) (0.09)

Log of GDP per hh 0.120*** 0.118***(0.02) (0.02)

Log GDP p hh X 1st quartile of survival 0.107*** 0.105***(0.02) (0.02)

Log of number of hh -0.019(0.06)

Log GDP pc X 1st quartile of GDP 0.066***(0.01)

Log GDP pc X agr pop > 95% -0.002(0.01)

Rainfall anomaly -0.00002*** -0.00002*** -0.00002*** -0.00002**(0.00) (0.00) (0.00) (0.00)

Year fixed effects Yes Yes Yes Yes Yes YesCounty fixed effects Yes Yes Yes Yes Yes Yes

Observations 21905 21646 21581 21646 20176 19930Within R-squared 0.862 0.862 0.862 0.862 0.822 0.821

Standard errors (in parentheses) are clustered at the county level. County fixed Effect models. Dependent variable is logof savings per capita ((1) to (4)) and per household ((5) and (6)). GDP are lagged for one period.

Table 12. Dynamic panel estimated by conditional maximum likelihood.

(1) (2) (3)

Log of GDP per capita 0.165*** 0.161*** 0.166***(0.02) (0.02) (0.02)

Log GDP pc X 1st quartile survival 0.125*** 0.125*** 0.125***(0.02) (0.02) (0.02)

Rainfall anomaly -0.00002*** -0.00002** -0.00002***(0.00) (0.00) (0.00)

Log population 0.131 0.088 0.132(0.15) (0.14) (0.15)

Log GDP pc X 1st quartile of GDP 0.063***(0.01)

Log GDP pc X agr pop > 95% -0.003(0.01)

Average value of independent variables Yes Yes YesPast value of dependent variable Yes Yes YesDummy for lowest quartile Yes Yes YesYear fixed effects Yes Yes Yes

Observations 21854 21753 21854Within R-squared 0.855 0.856 0.855

Standard errors (in parentheses) are clustered at the county level. Estimator proposed byVerardi and Desbordes (2015). It is a modified random effects estimator including a pastvalue of the dependent variable and the mean of the independent variables. Significancelevel : ∗ 10%, ∗∗ 5%, ∗∗∗ 1%.

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Table 13. Descriptive statistics: household level variables

All sample OLS sample IV sampleMean Median Mean Median Mean Median

log (income / consumption) .46 .47 .45 .46 .45 .46(.58) (.58) (.59)

Famine survival index .7 .7 .65 .65 .69 .7(.24) (.2) (.19)

Household characteristicsHousehold size 4.15 4 4.15 4 4.17 4

(1.29) (1.27) (1.27)Prop. of dependents in hh 1.18 1 1.18 1 1.2 1

(1.02) (1) (1.02)Age of household head 46.3 46 46.2 46 46.18 46

(10.35) (10.23) (10.2)Ethnic minority .14 0 .12 0 .13 0

(.35) (.33) (.33)Prop. of women in hh .53 .5 .53 .5 .53 .5

(.15) (.15) (.15)Average education 6.97 7 6.93 7 7.04 7

(2.25) (2.23) (2.19)Water from tap (ref cat. : well) .34 0 .32 0 .3 0

(.47) (.47) (.46)Water from pump (ref cat. : well) .23 0 .24 0 .26 0

(.42) (.43) (.44)Energy : coal (ref : fuel) .32 0 .32 0 .39 0

(.46) (.47) (.49)Energy : firewood (ref : fuel) .6 1 .61 1 .54 1

(.49) (.49) (.5)Long run conditionsShare of plain land in the county .6 .75 .6 .77 .59 .75

(.38) (.39) (.38)Share of sloping land in the county .3 .17 .31 .17 .32 .2

(.32) (.33) (.33)Share of sandy soil in the county .05 0 .04 0 .04 0

(.1) (.08) (.08)Share of clayey soil in the county .08 0 .09 0 .08 0

(.16) (.16) (.16)

Observations 8495 6395 4775Descriptive statistics for the samples used in the paper.

Standard deviation in parentheses

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Table 14. Descriptive statistics: village and county level variables (continued)

All sample OLS sample IV sampleMean Median Mean Median Mean Median

Rain shocksAverage rain anomalies in the 1980s -.08 -.09 -.07 -.08 -.11 -.15

(.29) (.29) (.31)Average rain anomalies in the 1990s .25 .27 .26 .27 .26 .29

(.22) (.23) (.24)Rain anomaly in village in 1999 .14 .01 .18 .08 -.01 -.01

(1.15) (1.14) (1.02)Rain anomaly in village in 2000 .25 .27 .26 .25 .2 .22

(.99) (1) (1)Rain anomaly in village in 2001 -.16 -.46 -.16 -.46 -.19 -.47

(1.2) (1.17) (1.16)GrowthAverage growth 1998-2002 .07 .07 .07 .07 .07 .07

(.05) (.05) (.05)Village cadres salary growth 98-2002 -.06 0 -.06 0 -.06 0

(.39) (.39) (.21)Village population growth .02 .01 .02 .01 .02 .01

(.1) (.11) (.07)Land, income and migrationHousehold cultivated land 6.26 4.3 6.27 4.36 6.19 4.4

(8.03) (8.01) (7.84)2nd income quartile .25 0 .25 0 .25 0

(.44) (.43) (.44)3rd income quartile .25 0 .25 0 .26 0

(.43) (.43) (.44)4th income quartile .24 0 .24 0 .24 0

(.43) (.43) (.43)Village implemented tax reform .73 1 .77 1 .74 1

(.44) (.42) (.44)Cadres salary in village in 2002 10.93 9 10.84 9 10.7 9

(7.91) (7.51) (7.42)Village land per hh in 1998 9.39 6.12 8.39 5.95 8.42 6.11

(11.53) (8.28) (7.72)Number of migrants .54 0 .57 0 .55 0

(.75) (.77) (.76)

Observations 8495 6395 4775Descriptive statistics for the samples used in the paper. Standard deviation in parentheses.

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Table 15. Summary statistics - Set of instrumental variables

Mean Std. Dev. N

County rain shock in 1958 (z-score) -0.212 1.066 47751958 county shock X prov. area hit by disasters -2.89 8.098 4775Province area hit by natural disasters in 1958 6.833 5.536 4775Province area hit by natural disasters in 1959 18.502 14.142 4775Hailstone in 1956 0.201 0.401 4775Hailstone in 1957 0.218 0.413 4775Hailstone in 1958 0.084 0.277 4775Hailstone in 1959 0.178 0.382 4775Drought in 1956 0.22 0.414 4775Drought in 1957 0.232 0.422 4775Drought in 1958 0.222 0.415 4775Drought in 1959 0.383 0.486 4775Pests invasion in 1956 0.109 0.312 4775Pests invasion in 1957 0.092 0.289 4775Pests invasion in 1958 0.172 0.377 4775Pests invasion in 1959 0.151 0.358 4775Flood in 1956 0.506 0.5 4775Flood in 1957 0.36 0.48 4775Flood in 1958 0.213 0.41 4775Flood in 1959 0.437 0.496 47751959 leader was alternate member 0.552 0.497 4775Leader age in 1959 28.266 4.001 4775Alternate member X leader age 14.571 13.365 47751959 leader was full member 0.337 0.473 4775Irrigation project in 1956 0.416 0.611 4775Irrigation project in 1957 0.617 0.778 4775Irrigation project in 1958 1.15 1.048 4775A 1956 project was a waste 0.067 0.25 4775A 1957 project was a waste 0.012 0.11 4775A 1958 project was a waste 0.065 0.246 4775

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