Of Calamity and Institutional Change: The Great Leap Famine,
Weather Shocks and Agricultural De-collectivization in China
James Kai-sing KUNG* and Ying BAI
Hong Kong University of Science and Technology
This version, July 2010
* Corresponding author: James Kai-sing Kung, Division of Social Science, Hong Kong
University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong. Phone: (852)
2358-7782, Fax: (852) 2335-0014, Email: [email protected].
Of Calamity and Institutional Change: The Great Leap Famine,
Weather Shocks and Agricultural De-collectivization in China
Abstract
Compared with the incentive effect of institutions on economic performance, relatively little
is known about how institutional change actually comes about. Using province-level data
from 1978 to 1984 to examine the effects of the Great Leap Famine and adverse weather on
agricultural de-collectivization in China, we analyze, empirically, an institutional change that
affected the economic incentives of hundreds of millions of peasants. Our estimations yield
two interesting results. First, the more severe the famine was in a province, the more negative
its evaluation of collective agriculture was and the earlier de-collectivization tended to begin.
Second, the effect of the severity of the famine on de-collectivization increased with adverse
weather, because the famine experience undermined the belief that collective agriculture
could effectively cope with negative weather shocks. Our empirical results remain robust
after correcting for the endogeneity of the severity of the famine and controlling for
variations in irrigation, standard of living, and the role of governments in relation to the
varying pace of de-collectivization.
Keywords: Famine, Weather Shocks, Institutional Change, De-collectivization, China.
JEL Classification Nos.: N55, Q15, O11
1. Introduction
It has been a widely accepted premise that institutions, of which property rights are a
crucial part, determine the incentives of resource use and thus economic performance
(Acemoglu, Johnson, and Robinson, 2001, 2002; North, 1981, 1990, 2005; North and
Thomas, 1973, Sokoloff and Engerman, 1997, 2000, among many others). Much less is
known about the nature of institutional change (Menard and Shirley, 2005).1 There are, to
begin with, few empirical studies of institutional change.2 Moreover, while these few
empirical studies find significant regularities (that institutions matter), they “lack a theory
that would transform regularities into causal explanation” (Menard and Shirley, 2005: 15),
not to mention that institutions are typically endogenous (Engerman and Sokoloff, 2005).
The de-collectivization of agriculture in China is a remarkable institutional change in
terms of its extraordinary contribution to output growth—48.69% (Lin, 1992), which led to
further reforms and development in other spheres of the Chinese economy (Milgrom, Qian,
and Roberts, 1991, among others). Moreover, compared to the demise of Feudalism in
Europe, de-collectivization is also deemed important in political terms, as it concerns a “basic
constitutional issue in the organization of society”, namely “who has control over a person’s
labor and has residual claims to its product” (Yang, 1996, p. 2). Indeed, by appealing to a
variety of factors, a number of attempts have been made to account for this important
institutional change, except that almost all of the proposed explanations suffer from the
problem of an endogenous reasoning.3 To the extent that agricultural de-collectivization
1
Thus Menard and Shirley (2005) lament that we know very little about “how long institutions persist or why
and how they change” (p. 15). What we know from the analytical framework that North (1990) proposes is that
institutional change entails a rational calculus of the costs and benefits of alternative institutional arrangements,
political entrepreneurship, collective action, and the mental or cognitive processes by which humans process
information. How these “ingredients” actually interact with each other and how they vary from one case to
another are however not elucidated.
2
Alston, Eggertsson, and North (1996) remain to this day the only collection on the subject matter.
3
The attempted explanations include patterned differences in levels of economic development (which includes
output and income) and mechanization, strengths of the collective sidelines (and its corollary sources and levels
1
reflected an institutional response to, or evaluation of, collective agriculture, the fact that it
occurred with enormous temporal variations across the Chinese provinces suggests that the
incentives of the people to abandon collective agriculture had likely varied across these
provinces.4 In this light, the causal mechanisms by which this important institutional change
came about—one which affected the economic incentives and livelihood of hundreds of
millions of people—is what concern us here.
Premised upon North’s (1990) analytical framework, which acknowledges that
institutional changes are not only incremental but also path dependent in nature, we begin our
examination with history. Our starting point is the historical episode popularly known as the
Great Leap Forward,5 a period (circa 1959-1961) when tens of millions of excess deaths and
lost or postponed births were estimated to have occurred as a result of the implementation by
the Chinese leadership of a set of radical policies pertaining to development in general and
the collectivization of agricultural institutions in particular.6 By employing the differing
severity of this famine across China’s provinces as the pertinent measure of the people’s
evaluation of collective agriculture, we set out to test, first of all, the hypothesis that the
varying pace of agricultural de-collectivization in China can be explained by differences in
of income), cropping patterns, the average size of production teams, and so forth, its importance can be reflected
in the voluminous literature that attempts to explain the underlying causes of, and spatial variations in, decollectivization (See, among others, Chung, 2000; Johnson, 1982; Kelliher, 1992; Lin, 1987; O’Leary and
Watson, 1982; Siu, 1989; Unger, 1985-86; Zweig, 1983, 1985).
4
The extent to which central and provincial governments were involved in the process of China’s agricultural
de-collectivization has been a subject of heated debate. In light of the temporal variations among provinces in
adopting the new farming institution, Chung (2000) suggests that provinces were allowed the discretions to
experiment with various forms of non-collective farming in the first few years before it became universally
preferred and sanctioned by the new leadership. This interpretation is consistent with the experimentation-cumevaluation approach adopted by the Chinese government (Heilmann, 2008; Rodrik, 2008; Ravallion, 2009).
5
The Great Leap Forward was conceived to accelerate the transformation of China’s predominantly agrarian
economy to attain a rate of development unparalleled in history. A distinct hallmark of China’s Great Leap
Forward was what may be regarded as the excessiveness of collectivization, whereby already sizeable
collectives were further amalgamated into gigantic communes with no due regard paid to the importance of
individual material incentives in work organization and income distribution (Donnithorne, 1967; Dutt, 1967; Lin,
1990; Perkins and Yusuf, 1984; Riskin, 1987).
6
A number of demographers have estimated that 16.5-30 million excess deaths and about 30 million lost or
postponed births may have resulted from the Great Leap Famine (Ashton et al., 1984; Banister, 1984; Coale,
1984; Peng, 1987, among others).
2
the severity of its Great Leap Famine.7 Specifically, this hypothesis postulates that, when
presented with the opportunity to leave the collective, those who suffered more during the
famine were more likely to leave more quickly than those less affected by the famine. In
short, de-collectivization tended to occur earlier in provinces that had suffered from higher
mortality rates during the Leap.8 The facts that the severity of the famine varied enormously
across provinces and that de-collectivization exhibited large temporal variations across the
country together enable us to measure the differing effects of the severity of the famine on the
varying pace of de-collectivization that subsequently occurred.9 However, excess death rate is
not entirely exogenous.10 To address this concern, we employ differences in weather
conditions in two pertinent periods, 1956-1958 and 1959-1961 to instrument excess death
rate, based on the reasoning that it is likely correlated with excess deaths but does not bear
upon the institutional change in question.
Our second and more important contribution is that, motivated by the unique
experience of Anhui Province, we consider the effect of poor weather, an exogenous shock, in
affecting the capability of collective institutions to handle the shock. Both as the first
province to have abandoned collective agriculture and one of the most famine-stricken
provinces during the Leap, Anhui rationalized its bold decision to de-collectivize agriculture
on the grounds that family farming would provide much stronger incentives for peasants to
7
Yang (1996) argues that a major crisis such as the Great Leap Famine has the power of “exposing the
vulnerability of existing institutions and reshaping actors’ motivations and preferences”, thereby pushing “the
actors to choose from among alternative institutions” (p. 14).
8
Our work thus represents an extension of Yang’s (1996) proposition, which makes the connection between the
severity of the Great Leap Famine and the institutional choice pertaining to work organization and income
distribution. See Section 3 for further details.
9
Although the entire process of collectivization had taken several years to complete and it exhibited spatial
variations, it took less than one month for virtually all the provinces to comply with communalization. Since our
primary concern here is de-collectivization (of the communes), our reference point is the communalization
process.
10
For instance, Kung and Lin (2003) show that excess death rate is correlated significantly with excessive grain
procurement. Other proposed reasons for excess deaths include the irrational consumption of food caused by
free communal dining practices (Chang and Wen, 1997; Yang, 1996), the removal of exit right from collective
agriculture upon communalization (Lin, 1990), and a development strategy that biased against the countryside
(Lin and Yang, 2000).
3
plant wheat on arid soil (e. g., Yang and Liu, 1987). Thus, based on the example of Anhui,
we conjecture that poor weather strengthened the perception that collective agriculture could
not protect farmers from natural disasters.
Using the system-GMM method, we obtain robust empirical results to substantiate the
hypotheses that the severity of the Great Leap famine speeded up the pace of institutional
change, and that adverse weather experienced during the period of de-collectivization further
increased the effects of the severity of the famine. It is interesting to note that adverse
weather in fact slowed down the pace of de-collectivization, although only in provinces that
had not suffered enormously from the Leap’s famine. Our study thus helps to resolve an
intriguing paradox regarding the role of weather in institutional change, balancing the
seemingly contradictory arguments of poor weather contributing to de-collectivization on the
grounds that stronger incentives are required to counteract negative weather shocks, on the
one hand, against the premise that a primary goal of collectivization was that collective
institutions should be better able to construct irrigation works, and which are crucial for
counteracting natural disasters. Our empirical results remain robust after we correct for the
endogeneity of excess death rates, and after controlling for provincial variations in irrigated
acreage, the standard of living, and not the least the role of governments in the decollectivization process, the omission of which may affect a province’s discretion (through its
interaction with poor weather) on the pace of de-collectivization.
The remainder of this article is organized as follows. In Section 2 we provide a
descriptive account of the institutional changes in Chinese agriculture for the entire period of
1950-1984. In Section 3, we present our hypotheses, introduce our data sources and account
for the rationale employed in constructing the pertinent variables. After laying out our
4
empirical strategy in Section 4, we discuss the empirical results in Section 5. Section 6
summarizes and concludes the paper.
2. Institutional Change in Chinese Agriculture, 1950-1984
China collectivized its agriculture in the 1950s and de-collectivized it approximately
three decades later, completing a full circle of socialist agriculture in a little more than a
quarter of a century. The chronology of the collectivization of Chinese agriculture is
illustrated in Figure 1, where the main (longest) curve, which charts the changing degree of
collectivization over time, represents the percentage share of households engaged in
collective organizations for the entire 1950-1984 period regardless of the type of collective
units to which a household belonged. It shows that the percentage of farm households
engaged in collective farming increased steadily from a little over 10% in 1950 to more than
90% in 1956, when collective farming stabilized and remained constant for nearly a quarter
of a century (circa. 1956-1979), before dropping to nearly zero in 1984. The other three
shorter lines illustrate what may be regarded as the “evolution” of the three principal forms of
cooperatives/collectives before collectivization reached its peak in 1958, which culminated in
a nationwide communalization movement. There were three main kinds of collective
organizations before 1958, namely the Mutual Aid Teams, the Elementary Agricultural
Cooperatives, and the Advanced Agricultural Cooperatives (Lin, 1990, 1994).
Mao believed that, by increasing the farm acreage served by irrigation,
collectivization would help to reduce the stochastic disturbances of weather on laborintensive agriculture, thereby raising agricultural productivity. Mao indeed saw a vastly
decentralized agriculture as being not conducive to large-scale irrigation works, as public
projects of this nature typically span large areas and thus require sizeable administrative units
for efficacious management and coordination. Secondly, Mao believed, erroneously, that
5
there were economies of scale in team production (Bradley and Clark, 1972; Nolan, 1988).
The fact that total factor productivity in Chinese agriculture had remained more or less
stagnant, even after correcting for the “excesses” of the Great Leap, for nearly two decades
has been invoked as evidence bearing upon the work incentive problem in collectivized
agriculture (e.g., Lin, 1990).
China began de-collectivizing its agriculture soon after the demise of Mao and the
downfall of the “Gang of Four”. The most famous and probably the first production team in
China to have carved up the collective holdings—Xiaogang in Anhui Province—is said to
have done so because of bad weather. A main catalyst, according to many, was that a serious
drought, caused by a ten-month lack of rain in 1978, had adversely affected over 90% of
Anhui’s cultivated land, to the extent that it became nearly impossible for the peasants to
plant wheat (see, e.g., Yang and Liu, 1987). The practice of contracting output responsibility
allegedly began when some peasants, on incentive grounds, requested that the land be carved
up and rented to individual households or work groups—a sub-unit of the (already smallest)
production team—for cultivation.
Compared with collectivization, the process of de-collectivization differed across
provinces and took altogether six years to complete whereas the communes were established
within a matter of months. The “tolerance” of the provincial authorities to the shift back to
family farming was noted to be especially profound during the initial period of 1978 through
June 1981, when the adoption rate was a mere 37.9% (Chung, 2000).11 The most telling
evidence of the central government’s early tolerance of local autonomy in terms of farming is
the observation that some provinces, dubbed the “pace-setters”, allegedly took advantage of
the government’s initial cognizance of the varied conditions in different regions—a Chinese
11
By the end of 1982 the state decided not only to sanction family farming but also to have it universally
implemented across China. The compliance rate for the longer period ending in December 1982 was 80.6%,
according to Chung (2000, p. 67).
6
principle known as yindi zhiyi. This principle permitted or even encouraged province-wide
experimentation with, and diffusion of, local innovations related to family farming.
Conversely, despite the fact that some provinces had endeavored to resist the switch to family
farming even after official policy had already made it clear that it should now (around mid1982) be established as China’s primary farming institution, these “recalcitrant” provinces
eventually succumbed to political pressure from the center, thereby drawing collective
agriculture to a close by the end of 1983, when roughly 98% of the farm households in China
switched to farming on an individualized basis (the right-hand side of the main curve, Figure
1).12
3. Hypotheses
After its initial success, Mao hastened the pace of agricultural collectivization. Within
the context of the Great Leap Forward (1958-1961), the advanced cooperatives were
amalgamated into much larger communes, which culminated in a severe catastrophe with an
estimated 30 million excess deaths (Ashton et al., 1984; Banister, 1984; Coale, 1984). In this
context, the relationship between the (varying) severity of the famine and institutional choice
should be examined. Yang’s (1996) observation that provincial variations in the severity of
the famine distinctly impacted the communes’ choices of organizational structure and income
distribution on the eve of de-collectivization (1979-81) is inspiring. He finds that provinces
with the most severe casualties during the Great Leap tended to place more of a premium
upon work incentives than did their less unfortunate counterparts. Instead of organizing work
and distributing income at the level of the larger brigade (which consisted of several
production teams)—a practice that was advocated as being closer to communist ideals and
12
Prominent examples of these “recalcitrant” provinces included the northeastern part of China (Heilongjiang,
Jilin, and Liaoning) and provinces with high agricultural productivity and output that had also developed a
number of profitable “sideline” economic activities by the local collectives, most notably Jiangsu and Zhejiang
provinces (Chung, 2000; Zweig, 1983).
7
principles—these famine-stricken provinces opted for pragmatism, with stronger emphasis
placed upon work incentives, instead.13 Extending this line of reasoning easily leads us to
hypothesize that those who suffered the most during the Great Leap Famine tended to have
the strongest desire to extricate themselves from collective agriculture when the emerging
political conditions allowed them to do so. We formally posit that:
Hypothesis 1: The higher the death rate in a province during the Great Leap Famine, the
more rapid it tended to de-collectivize its agriculture. The hypothesized effect
of the severity of the famine on the rate of de-collectivization is thus
positive.14
The historical record tells us that Anhui Province suffered badly during the Great
Leap and that it switched to family farming rapidly not because of the famine but because of
poor weather. These observations suggest that the effect of the severity of the famine on decollectivization was not constant, but that it varied according to other factors, including, most
importantly, exogenous weather shocks. The people in provinces that suffered higher death
rates during the Great Leap were unlikely to believe that collectives were better able than
family farms to deal with exogenous weather shocks, which brings us to our second
hypothesis.
13
Specifically, Yang (1996) argues that “(t)he more a province suffered during the Great Leap Famine, the more
painful the lesson was for the province as a collective and the less likely the province would favor measures of
agrarian radicalism, such as brigade accounting” (p. 134).A production brigade belonged to the middle tier of
the three-tiered commune structure. Brigade accounting is considered more radical because the brigade
leadership could conceivably equalize income among the various production teams under its auspices. See
Zweig (1985) for further details on commune accounting.
14
If provinces that suffered the most from the Leap’s famine had been able to sustain the family-based farming
practices that some had restored in 1962 throughout the remaining period of collectivized agriculture, the
efficiency loss associated with collectivized institutions between roughly 1962 and 1978 may have been the
least in these provinces, which would imply a causality that goes in the opposite direction. While a number of
provinces had indeed adopted family-based farming practices in the wake of the Leap, virtually all were
“corrected” within a couple of years.
8
Hypothesis 2: The effect of the severity of the famine increases with adverse weather;
namely, poor weather and a severe famine experience combined to accelerate
the rate of de-collectivization.
4. Estimation Strategy
4.1 Definition of Variables
Testing our proposed hypotheses requires measuring the varying pace of decollectivization across provinces during 1978-1984—our dependent variable. While a variety
of responsibility systems had been adopted during the evolution from collective to household
farming, such as contracting to the (small) group, for the sake of consistency, we employ in
our estimations only that variant known as “contracting everything to the household” (da
baogan). This variant ultimately became the standard or universal practice. Information on
the pace of de-collectivization comes primarily from two sources. Data for the initial 1978-82
period are meticulously compiled in Chung (2000), whereas those for the final two years are
based upon the Compendium on Agricultural Collectivization since the Founding of the
People’s Republic, edited by Huang, Yu, and Wang (1992). Assuming that all farm
households were in the communes in 1978, the overall pace of de-collectivization for the
country as a whole is indicated on the left-hand side of Figure 1.
To capture the independent variable of the relative severity of the famine, we employ
the provincial death rate series for the period of 1959-61. Following Chen and Zhou (2007),
the excess death rate is calculated as the difference between the annual death rate ( rit ) during
the famine period and the average death rate of 1956-1958 ( ri 1956−58 ), which can be expressed
as ritexcess = rit − ri 1956 −58 . To check the robustness of our estimations, we employ, in addition to
maximum excess death rates during the Great Leap Famine [ ri max = max(ritexcess ) ], the mean
9
excess death rate from 1959 to 1961 [ ri mean = mean(ritexcess ) ]. In Figure 2, we divide all the
provinces into two groups based on the maximum excess death rate and find that provinces
that experienced severe famine tended to de-collectivize earlier.
Figure 2 about here
Our second key explanatory variable is the weather; its indices are usefully provided
in Report on China’s Natural Disasters [Zhongguo Zaiqing Baogao] (Ministry of Civil
Affairs, 1995, see Appendix 1 for details). According to this compendium, adverse weather is
defined as “the area covered by natural disasters in each province” (shouzai mianji),
measured in terms of: 1) drought; 2) floods; 3) gusts and hailstorms; and 4) frost and cold.15
By summing the total area of natural disasters caused by (1) to (4) above, we are able to
generate a natural disaster index for 29 Chinese provinces for the period 1978-1984. With dit
defined as the percentage of total arable land in province i covered by various climatic
disasters in a given year t ,16 adverse weather can be expressed as
AWit = (dit −
1
1
dit ) /( ∑ dit ) . Since we normalize AWit by the average percentage of
∑
T t
T t
sown area affected in province i between 1978 and 1984, it can be interpreted as measuring
the shock in a given year t in province i relative to the normal weather conditions. The
pertinent variables, including definition and sources, are summarized in Table 1.
Table 1 about here
4.2 Econometric Method
We begin with a baseline model in which the effect respectively of provincial
variations in death rates during the Great Leap Famine and of (adverse) climatic variations on
15
To the extent that different types of institutions have varying capabilities of responding to natural calamities,
the area “affected” by natural disasters (chengzai mianji) is endogenous to institutional choice. But the area
“covered” by natural disasters (shouzai mianji), which typically is larger than the affected area, is not
determined by institutional choice.
16
This is the sown acreage covered by climate disasters (shouzai mianji) divided by total sown acreage.
10
the gradual variations in the degree of decollectivization from the benchmark year 1978
through 1984 are estimated, using the pertinent panel data which includes the province-and
year-specific effects:
yit = X it β + α i + λt + vit
t = 1, 2,L , T .
(1)
Where yit measures the percentage of households adopting household responsibility during
year t . X it are the key explanatory variables of weather indices and famine death rate and
their interaction, by province, α i is the time-invariant and province-specific effect for
province i , and λt is the province-invariant and time-specific effect for year t and vit is the
residual error term. To estimate equation (1), we first employ the Random Effects model,
followed by the Fixed Effects model to control for the unobserved province-specific effect.
Since the percentage of households adopting household responsibility in a given year
might be affected by the degree of de-collectivization ( Yi t −1 ) at year beginning, it is necessary
that we employ the following dynamic panel model with unobserved specific effects:
L
yi t = ∑ ρlYi t −l + X i t β + α i + λt + vi t
(2)
l =1
Where Yi t −l is the l th lagged degree of de-collectivization and ρl is the parameter associated
with it. To estimate all the pertinent parameters consistently, we need to account for the
dynamic structure of the model as well as to control for the unobserved province-specific
effects. Given that yit = Yi t − Yi t −1 , equation (2) can be rewritten as follows:
L
Yi t = (1 + ρ1 )Yi t −1 + ∑ ρlYi t −l + X i t β + α i + λt + vi t
(3)
l =2
Equation (3) can be estimated using the “difference GMM” method developed in dynamic
panel models by Holtz-Eakin, Newey, and Rosen (1990), Arellano and Bond (1991) and
Arellano and Bover (1995), in which the equation is first differenced in order to purge the
11
province-specific effect, followed by the use of lags of the explanatory variables as
instruments for their differences. The potential bias conceivably generated by this particular
difference estimator is dealt with using the “system GMM” estimator (Arellano and Bover,
1995; Blundell and Bond, 1998; see Appendix 2 for details). To present the empirical results
accurately, we will report the significance of the first-step GMM estimator, which is more
reliable but less efficient, the significance of the second-step GMM estimator, whose standard
error is under-estimated, and the p-value based on the Windmeijer-corrected (2005) standard
errors.
The validity of the GMM estimator is premised on the satisfaction of two required
conditions.17 The first is that the error term vit has to be serially uncorrelated. Specifically, a
serially uncorrelated error term means that the first-differenced error term Δvi t should have
significant first-order serial correlation but insignificant second-order serial correlation,
which can be tested by the test-statistics m1 and m2 for the GMM estimator developed by
Bond (2002). Another condition required for the assumption of the validity of GMM
estimates is that the instruments are exogenous. Under the null hypothesis of joint validity,
the vector of empirical moments
1 ˆ
Z ′E is supposed to randomly distribute around zero.
N
Both Hansen (1982) and Sargan (1958) suggest that when the error is non-spherical as in the
robust one-step GMM, the Hansen-statistic from a two-step estimate is theoretically superior.
5. Empirical Results
5.1 Static Panel Model
17
In testing the estimator’s validity we use the two specification tests suggested by Arellano and Bond (1991),
Blundell and Bond (1998), and Bond (2002).
12
The results of our baseline model are presented in Table 2. The number of
observations is 126 after dropping the missing data. Altogether we perform four regressions.
Whereas the year of maximum excess death rate ( ri max ) during the Great Leap Famine is
employed to measure the severity of famine in estimations (1) and (2), the average yearly
excess death rate ( ri mean ) is employed in estimations (3) and (4).18 Controlling for the
interaction between weather adversity and famine severity in the regressions, we may
interpret the coefficient of weather adversity as representing the sole effect of weather in the
extreme instance where excess death rate equals zero, i.e., no famine severity. As mentioned
in the section of estimation strategy, the Random Effects model is employed in estimations (1)
and (3) whereas the Fixed Effects model is employed in estimations (2) and (4).
Table 2 about here
With the exception of excess death rate, all the pertinent coefficients are highly
significant in the benchmark estimations. Hypothesis 1 (H1) is thus not supported by these
baseline results. As expected, weather adversity correlates negatively with the percentage of
households adopting the Household Responsibility System. For instance, if the area covered
by natural disasters (comprehensively defined) is twice the average (i.e., AWit equals 1), the
percentage of farm households adopting the Household Responsibility System is
approximately 18.497 percentage lower (column 1) if there were no famine during 1959-61,
namely ri max = 0 . An alternative measure of famine severity yields similar results; where the
affected acreage is twice the mean, the percentage of households adopting the Household
Responsibility System is approximately 16.184 percentage lower (column 3) if the average
yearly excess death rate during 1959-61 equals to zero, namely ri mean = 0 . After controlling
18
Since famine severity is time-invariant, it is dropped in the Fixed Effects model, but its interaction with
weather adversity is retained in columns (2) and (4).
13
for unobserved province-specific effect, the results—both in terms of direction and
significance–remain robust (columns 2 and 4).
Also consistent with our expectation is the positive correlation found between the
interaction term (between weather adversity and famine severity) and the degree of decollectivization. As expected, the effect of adverse weather turns positive either where the
maximum excess death rate is higher than 19.47 thousandths (or 1.947%) (column 1:
18.497/0.950), or where the mean death rate is higher than 10.08 thousandths (or 1.008%)
(column 3: 16.184/1.605). These evidences lend support to our second hypothesis (H2). The
corresponding results for columns (2) and (4) are, respectively, 20.36 thousandths (or 2.036%)
in the case of maximum excess death rate (column 2: 23.696/1.164), or 10.48 thousandths (or
1.048%) when evaluated using average yearly excess death rate (column 4: 20.258/1.933).
5.2 Dynamic Panel Model
Estimation results of the dynamic panel models are presented in Table 3, in which the
severity of the Great Leap famine is now included in the regressions. Since all the m1 and
m2 statistics indicate that the first-differenced error-term has significant first-order serial
correlation but no significant second-order serial correlation, the null hypothesis of no serial
correlations for the level error terms cannot be rejected. The p-value of the Hansen-statistics
indicates that the over-identifying restrictions are not rejected.
As for the estimation results, the coefficient of the lagged share of collective
production teams is significant, indicating that gradual changes in the degree of decollectivization does exhibit strong time-dependent properties. Consistent with the results in
Table 2, the effect of weather adversity is negative and significant in both the first-step and
second-step estimations. Same as before, the interaction term between famine severity and
14
adverse weather is positive and significant. For instance, the effect of adverse weather turns
positive where the maximum excess death rate is higher than 22.38 thousandths (column 1:
29.447/1.315), or where the average yearly excess death rate is higher than 9.885 thousandths
(column 3: 27.074/2.739), respectively. These results lend further support to H2. Excess
death rate, in particular ri max , which is not significant in the static panel model, is now
significant, thereby lending support to H1 (columns 1 and 2).
Table 3 about here
As a robustness check of whether weather might have a lagged effect on the rate of
de-collectivization (since it was often during the slack winter season that de-collectivization
occurred), we lag the weather variable and its interaction with the two measures of mortality
rate in a separate estimation. Reported in columns (2) and (4), the results show that the lagged
variables are not significant. Moreover, the magnitude and significance of our pertinent
explanatory variables change trivially after controlling for the lagged weather adversity.
5.3 Robustness Check – Role of Standard of Living
To ensure that the relationship between excess deaths of the Great Leap Famine and
agricultural de-collectivization is not spurious, we control for the omitted variable of the
provincial differences in the standard of living, as it might be correlated with famine severity.
Intuitively, we may think of the institutional choice in question as depending on how close to
minimum subsistence households were, with the assumption that the farther away they were
from it the more likely collective institutions were to persist in the event of an adverse
weather shock at the time of de-collectivization. To proxy for the standard of living (or “the
distance to minimum subsistence”), we employ per capita food availability, which is
15
estimated using data on provincial grain output and procurement.19 Denoted as ( fi 78 − f 78 ) ,
fi 78 stands for per capita grain availability of province i in 1978, whereas f 78 stands for the
mean per capita grain availability of all provinces in the same year. We
use ( fi 78 − f 78 ) instead of fi 78 because it is more meaningful to examine the coefficient of
weather adversity at average levels of per capita grain availability than when f i 78 equals to
zero.
Reported in Table 4, the results of these estimations show that differences in “distance
to minimum subsistence” do have the expected effect of sustaining the collective fabric of
China’s agricultural institutions in the presence of an exogenous weather shock. In the GMM
but not the fixed effect regressions, the interaction term between weather adversity and
famine severity is highly significant. This result implies that provinces with higher per capita
grain availability in 1978 tended not to dismantle their communes when struck by bad
weather—at least not immediately. More importantly, our key explanatory variables remain
significant after this additional control.
Table 4 about here
5.4 Robustness Check – Role of Irrigation
That it is necessary to control for differences in irrigated conditions across provinces
is because collectivization is likely correlated with both the construction of irrigation projects
and famine severity. Hence, to control for this possible effect, we add an interaction term
between adverse weather and the percentage of irrigated acreage in 1978 in our estimation.20
Denoted as ( I i78 − I 78 ) , I i78 stands for the percentage of irrigated acreage of province i in
19
We obtain this variable from Materials of Agricultural Economy [Nongye Jingji Ziliao] (Ministry of
Agriculture, Animal Husbandry And Fisheries, 1983).
20
We obtain this variable from Comprehensive statistical data and materials on 50 years of new China.
16
1978, whereas f 78 stands for the mean percentage of irrigated acreage of all provinces in the
same year. We use ( I i78 − I 78 ) instead of I i78 because it is more meaningful to examine the
coefficient of weather adversity at average levels of per capita grain availability than when
I i78 equals to zero.
Reported in Table 5, the results of these estimations show that differences in irrigated
conditions do have the expected positive effect of dismantling the collective fabric of China’s
agricultural institutions in the presence of an exogenous weather shock. In both the static and
dynamic panel models, the interaction term between weather adversity and percentage of
irrigated acreage is highly significant. This result implies that provinces with better irrigation
works in 1978 tended to dismantle their communes when struck by bad weather. Similar to
the previous result, our key explanatory variables remain significant after controlling for this
variable.
Table 5 about here
5.5 Robustness Check – Role of Government
As mentioned earlier, political scientists have debated heatedly on the role played by
the government—both central and provincial—in China’s de-collectivization process. The
experimental (gradual) nature of family farming, and the varying pace by which provinces
de-collectivized their agricultural institutions, together suggest that the two uppermost levels
of government had likely played a decisive role in this profound institutional change.21 To
ensure that our empirical results are not biased by the omission of the possible role of the
central and provincial governments in controlling which provinces were to be de21
A distinct feature of China’s economic reforms after Mao is the willingness of the new leadership to embrace
a gradual, experimental approach, whereby new policies were tried out in certain localities first before they were
scaled up upon proven successful (see Heilmann, 2008; Rodrik, 2008; and Ravallion, 2009). The fact that
certain pioneering provinces were approved whereas others prohibited during the initial phase of the experiment
suggests that the provincial governments did play a role in affecting the pace of agricultural de-collectivization
(see Zweig, 1997, especially p. 157).
17
collectivized first, we control for the role of the government (or rather “provincial
spontaneity”) by generating a dummy variable, ( Si ), which includes those provinces with
more than 50 per cent of the households having adopted the Household Responsibility
System by April 1982, a time when the new farming institution had proven superior in
productivity terms and on which basis the central government decided to push for its
implementation nationwide (Chung, 2000).22 According to this reasoning all provinces are
divided into two groups: those already adopted the new farming institution prior to the
sanctions of the central government ( Si = 1 ), and those instructed to implement it only after
universal laws and regulations were drafted ( Si = 0 ). As with the other two robustness checks,
we also include an interaction term between “provincial spontaneity” and weather adversity
( Si * AWit ). Reported in Table 6, the results of these estimations show that sanctions by the
government to experiment with the alternative of household farming does have the expected
effect of dismantling the collective fabric of China’s agricultural institutions. What has not
changed is that our key explanatory variables remain significant and robust even after
controlling for this effect.
Table 6 about here
5.6 Instrumental Evidence
To the extent that excess death was not the sole result of natural disasters but caused
also by, for instance, excessive grain procurement (e.g., Kung and Lin, 2003; Lin and Yang,
2000), the variable of key analytical interest is potentially endogenous. In the previous subsections we have controlled for three additional variables, namely per capita food availability,
the percentage of irrigated acreage, and the role of governments, all of which might be
correlated with the severity of famine. However, it is not possible for us to include all the
22
Specifically, data limitations require us to use yearend 1981 as the cut-off point.
18
omitted variables. To resolve the potential endogeneity of excess death rate (denoted as
AWi1959−61 = (di1959−61 − di1956−58 ) / di1956−58 ) more generally, we resort to differences in the mean
area covered by adverse weather shocks between the famine period (1959-1961) and the
period that immediately preceding it (i.e., 1956-1958) as instrument. The choice of this
instrumental variable is based on the reasoning that the difference in question is basically
random, and, while it would most likely bear upon the severity of a famine, it should have no
direct relationship with the varying pace of de-collectivization.
The results of the instrumented evidence are presented in Table 7, with m1 and
m2 statistics indicate that the dynamic model here is just as fit and the results are also broadly
similar. For instance, while by itself AWit is strictly negative, it turns positive when the
maximum death rate is 20.33 thousandths or almost 2.033% higher than the sample mean
(column 1: 29.092/1.431), or when the mean death rate is 8.63 thousandths (column 2:
37.031/4.289). More importantly, both the maximum and mean excess death rate, ri max and
ri mean are highly significant and exhibit the expected positive sign in the dynamic estimations.
Table 7 about here
As our instrument variable, AWi1959−61 = (di1959−61 − di1956−58 ) / di1956−58 is generated based
on the statistics compiled by the Chinese government, it is possible that the resulting data
published may have been adjusted in order to suggest that the Great Leap catastrophe was
largely a result of “three consecutive years of bad weather”—the official explanation. To
ensure that our estimations do not suffer from that possible pitfall, we employ an alternative
source, namely the Graphical Compendium of Droughts and Floods for Five Hundred Years
(Hanlao Wubainian Tuji) to check robustness. Organized by the State Meteorological Society
from 1920 to 1979, information on climate—specifically wetness and aridity based on a five-
19
point scale (with 1= extreme wetness and 5= extreme aridity)—was collected from 110
weather stations in 29 provinces (State Meteorological Society, 1981). To obtain a weather
index similar to the one we have for the de-collectivization period, we first generate the mean
or “average” aridity as reported in each of these stations j for 1920-1979 using the
formula wij =
1979
∑w
t =1920
ijt
, where wijt represents weather conditions in year
t recorded at station
j in province i . After obtaining this “station index”, we then generate a “disaster index” for
each of these weather stations, dijt =
(w
ijt
− wij ) , by computing the degree of deviations
2
from the mean for each station j in province i at time t. Finally, based on the disaster index
dijt , we compute an overall provincial index and express it in terms of dit =
1
Ji
∑d
ijt
, where
j
J i is the number of stations in province i . Similar to our first instrument, weather adversity
( AWi1959−61 ) of the 1958-61 period can be computed using the same equation
( AWi1959−61 = (di1959−61 − di1956−58 ) / di1956−58 ) based on dit . The results of these alternative
estimates (columns 3 and 4, Table 7) are broadly similar. For instance, while by itself AWit is
strictly positive, it turns negative when interacts with famine severity.
6. Summary and Conclusion
Compared with the incentive effect of institutions on economic performance,
relatively little is known about how institutional change actually comes about. This is
especially the case in economies undergoing radical transformations such as China in the late
1970s/early 1980s, when rural reforms began in earnest. Chinese agriculture experienced two
monumental institutional changes since 1949. Whereas the first institutional change,
agricultural collectivization or specifically communalization, resulted in the largest famine in
20
history, the second, agricultural de-collectivization, extraordinarily affected the economic
incentives and livelihood of nearly a billion people. Given the importance of these two
events and the theoretical underpinning that stresses the importance of firmly grounding the
analysis of institutional change in relevant historical circumstances, our specific task is to
explain the subsequent institutional change in terms of the first one. Specifically, given that
the Great Leap Famine occurred within the extreme context of collectivization, we employ
variations in mortality across space to predict the varying degrees of eagerness that provinces
exhibited in overhauling the collectivized agricultural system.
We do find, empirically, that there is indeed a connection between what may be
regarded as “collective memory” and institutional choice, in that the desire to dispense with
collective agriculture is positively correlated with the extent to which a surviving population
has collectively experienced a trauma or specifically past excess mortality. But a more
interesting finding is the role played by poor weather in the process of institutional change
that we are trying to examine. In villages severely afflicted by excess mortality, poor weather
experienced at the time of de-collectivization is found to have effectively increased the
intensity of past trauma and accordingly the desire for institutional change. Empirically, what
we have thus observed is that adverse weather has an accelerating effect on agricultural decollectivization. This helps to explain why the first province to drop the collective system,
Anhui, also happened to have been among the provinces with the highest number of
casualties from the Great Leap. By controlling for a number of possible omitted variables
that are likely correlated with excess death rate—most notably differences in the standard of
living and irrigation facilities—both of which could affect the pace of agricultural decollectivization, we show that the link between the Great Leap Famine and decollectivization is more than just a spurious relationship. Likewise, our main results remain
21
unaffected after controlling for the role of governments or “provincial spontaneity” in the
process of institutional change.
An equally exciting finding is that poor weather, by itself, had a sustaining effect on
Chinese communes; a result that helps to resolve an intriguing paradox, namely, that a
primary goal of collectivization—at least in the Chinese context—was that it helped to
facilitate the construction of more irrigation systems to counteract negative weather shocks.
Given the importance of de-collectivization and, not surprisingly, the voluminous literature
this process has spawned to make sense of the initially liberal but varied provincial discretion,
our study contributes to the small but growing empirical literature on institutional change in
the context of modern Chinese economic history.
22
Share (%)
Figure 1: Share of Households in Various Collective Institutions, 1958-1984
120
100
80
60
40
20
0
19
50
19
51
19
52
19
53
19
54
19
55
19
56
19
57
1
19 958
59
-1
97
7
19
78
19
79
19
80
19
81
19
82
19
83
19
84
-20
Level of collectivization
Mutual-aid teams
Elementary cooperatives
Year
Advanced cooperat
Source. – 1950 – 1977: Huang, Yu, and Wang, 1992. Compendium on Agricultural Collectivization since the
Founding of the People’s Republic (Jianguo Yilai Nongye Hezuohua Shiliao Huibian); 1978 – 1982: Jae Ho
Chung, 2000. Central Control and Local Discretion in China: Leadership and implementation During Post-Mao
Decollectivization; 1983 – 1984: Huang, Yu, and Wang, 1992. Compendium on Agricultural Collectivization
since the Founding of the People’s Republic (Jianguo Yilai Nongye Hezuohua Shiliao Huibian).
23
%
Figure 2: Maximum Excess Death Rate and Pace of De-collectivization
100
90
80
70
60
50
40
30
20
10
0
1978
1979
1980
1981
1982
1983
1984
Year
Higher excess dath rate
Lower excess death rate
Source: Same as Table 1.
24
Great Leap
Famine’s
severity
Weather
adversity
Initial Irrigation
Initial distance to
minimum
subsistence
(3)
(3)
(4)
(5)
830.42
32.74
(485.17)
(15.79)
(0.46)
(6.37)
5.82
0.00
(13.65)
(44.37)
12.53
46.95
Table 1: Definition of Variables and Data Sources
Variables
Mean
S.D.
(1) Degree of decollectivization
Food availability per
capita in 1978
Proxies
Share of production
teams with
implementing the
Household
Responsibility System
between 1978 and
1984.
Maximum excess death
rate for the period
1959-1961.
Average excess death
rate for the period
1959-1961.
Based on drought,
floods, gusts and
hailstorms, and frost
and cold.
Percentage of irrigated
acreage in total arable
acreage in 1978
25
Department of Comprehensive Statistics of National Bureau
of Statistics. 1999. Comprehensive statistical data and
materials on 50 years of new China. (Xin Zhongguo wu shi
nian tong ji zi liao hui bian). Beijing: China Statistics Press.
Department of Planning, Ministry of Agriculture, Animal
Husbandry and Fisheries, 1983. Materials of Agricultural
Economy [Nongye Jingji Ziliao].
Ministry of Civil Affairs (1995), People’s Republic of China.
Report on China’s Natural Disasters [Zhongguo Zaiqing
Baogao].
Data sources
1983 – 1984: Huang, Yu, and Wang, 1992. Compendium on
Agricultural Collectivization since the Founding of the
People’s Republic (Jianguo Yilai Nongye Hezuohua Shiliao
Huibian).
1978 – 1982: Jae Ho Chung, 2000. Central Control and Local
Discretion in China: Leadership and Implementation During
Post-Mao De-collectivization.
Kung, James Kai-sing and Justin Yifu Lin, 2003. The Causes
of China’s Great Leap Famine, 1959 – 1961. Chen, Yuyu and
Lian Zhou. 2007. “The long term health and economic
consequences of 1959-1961 famine in China.”
Table 2: Temporal Variations of De-collectivization across Provinces (Static Panel Model)
AWit
ri max
(1)
Random
Effects
(2)
Fixed Effects
(3)
Random
Effects
(4)
Fixed Effects
-18.497***
(5.021)
-23.696***
(6.232)
-16.184***
(4.961)
-20.258***
(6.116)
0.015
(0.087)
0.022
(0.199)
ri mean
AWit * ri max
0.950***
(0.302)
1.164***
(0.352)
1.605***
(0.622)
AWit * ri mean
Observations
Number of crosssectional units
R-squared
126
27
0.56
126
27
0.53
126
27
0.53
1.933***
(0.718)
126
27
0.53
Note. – The dependent variable is the share of production teams adopting household farming each year during
1978-1984; Year dummies and constant term are included in the regressions but not reported. Standard errors in
parentheses. * p<.1. ** p<.05. *** p<.01.
26
Table 3: Temporal Variations of De-collectivization across Provinces (Dynamic Panel Model)
System GMM
1
it
AW
(1)
-29.447***
(7.298)+++
AWit1−1
ri max
0.319***
(0.117)+++
(2)
-30.543***
(7.059)+++
-5.924
(6.021)
0.307***
(0.108)+++
ri mean
AWit1 * ri max
1.315***
(0.342)+++
AWit1−1 * ri max
(3)
-27.074***
(9.283)+++
(4)
-24.787**
(11.459)+++
-0.939
(5.605)
0.574*
(0.334)++
0.463
(0.408)++
2.739***
(0.868)+++
1.489***
(0.416)+++
0.484
(0.410)
0.553***
0.510***
0.591***
2.386*
(1.271)+++
0.106
(1.086)
0.579***
(0.131)+++
-0.171*
(0.150)+++
-0.104
(0.126)+++
-0.154*
(0.136)+++
-0.136
(0.098)++
86
23
(0.105)+
86
23
(0.093)++
86
23
(0.096)++
86
23
-2.12
0.56
0.161
-2.29
0.52
0.156
-2.20
0.62
0.260
-2.33
0.68
0.274
AWit1 * ri mean
AWit1−1 * ri mean
First lag of the
dependent variable
Second lag of the
dependent variable
Observations
Number of crosssectional units
m1
m2
Hansen test (Overidentifying
Restriction)
Note. – The dependent variable is the degree of collectivization, measured by the share of production teams not
adopting household farming during 1978 to 1984; Year dummies and constant term are included in the
regressions but not reported; P-value in parentheses (based on two-step corrected standard errors). * indicates
degree of significance based upon one-step robust standard errors; * significant at 10%, ** at 5%, and ***
significant at 1%, respectively; and + indicates degree of significance based upon two-step standard errors; +
significant at 10%, ++ at 5%, and +++ at 1%, respectively; m1 and m2 are test-statistics for the first-order and
second-order serial correlation of the first-differenced residuals, asymptotically distributed N ( 0,1) ; Hansen test
(P-value), which tests the validity that the instruments are exogenous, is reported.
27
126
27
0.54
126
27
0.52
126
27
0.52
-8.473
(19.606)
1.096***
(0.387)
126
27
0.51
-19.866
(18.775)
1.733**
(0.742)
Fixed Effects
-23.535***
-20.729***
(6.271)
(6.128)
-2.17
0.46
0.288
-2.20
0.41
0.290
System-GMM
-29.200***
-30.673***
(5.773)+++
(5.716) +++
0.301**
(0.126)+++
0.510
(0.344) +
1.051***
(0.346)+++
1.993***
(0.639) +++
0.595
6.672
(27.857)
(22.662)
-50.928
-73.823**
(36.134)++
(36.185)+++
0.556***
0.575***
(0.154)+++
(0.171)+++
-0.127
-0.053
(0.176)
(0.196)
86
86
23
23
28
correlation of the first-differenced residuals, asymptotically distributed N ( 0,1) ; Hansen test (P-value), which tests the validity that the instruments are exogenous, is reported.
Note. – The dependent variable is the degree of collectivization, measured by the share of production teams not adopting household farming during 1978 to 1984; Year
dummies and constant term are included in the regressions but not reported; P-value in parentheses (based on two-step corrected standard errors). * indicates degree of
significance based upon one-step robust standard errors; * significant at 10%, ** at 5%, and *** significant at 1%, respectively; and + indicates degree of significance based
upon two-step standard errors; + significant at 10%, ++ at 5%, and +++ at 1%, respectively; m1 and m2 are test-statistics for the first-order and second-order serial
Observations
Number of cross-sectional units
R-square
m1
m2
Hansen test (Over-identifying Restriction)
Second lag of the dependent variable
Table 4: Robustness Check – Role of Food Availability
Random Effects
-19.061***
-15.972***
AWit
(5.243)
(5.041)
max
0.058
ri
(0.093)
0.086
ri mean
(0.205)
max
1.020***
AWit * ri
(0.347)
mean
1.555**
AWit * ri
(0.668)
78
5.179
5.195
fi
(4.075)
(3.996)
1
78
78
4.176
-5.643
AWit * ( fi − f )
(15.404)
(14.579)
First lag of the dependent variable
120
26
0.56
120
26
0.54
Random Effects
-16.462***
-13.855***
(5.066)
(5.063)
0.014
(0.087)
0.006
(0.199)
1.248***
(0.311)
2.134***
(0.644)
0.277
0.194
(0.135)
(0.138)
1.420***
1.331***
(.394)
(.401)
120
26
0.56
1.523***
(.511)
1.305***
(0.356)
120
26
0.54
1.486***
(.523)
2.194***
(0.730)
Fixed Effects
-16.749**
-13.166*
(6.800)
(6.747)
-2.19
0.60
0.267
-2.29
0.59
0.356
System-GMM
-21.022**
-17.488
(8.205)+++
(11.726)+++
0.344***
(0.121) +++
0.485
(0.331)++
1.300***
(0.320)+++
2.162**
(0.989)+++
0.209
0.182
(0.160)
(0.167)
1.323**
1.248*
(.666)++
(.693)++
0.513***
0.612***
(0.172)+++
(0.192)+++
-0.156
-0.176
(0.124)++
(0.109)++
81
81
22
22
29
correlation of the first-differenced residuals, asymptotically distributed N ( 0,1) ; Hansen test (P-value), which tests the validity that the instruments are exogenous, is reported.
Note. – The dependent variable is the degree of collectivization, measured by the share of production teams not adopting household farming during 1978 to 1984; Year
dummies and constant term are included in the regressions but not reported; P-value in parentheses (based on two-step corrected standard errors). * indicates degree of
significance based upon one-step robust standard errors; * significant at 10%, ** at 5%, and *** significant at 1%, respectively; and + indicates degree of significance based
upon two-step standard errors; + significant at 10%, ++ at 5%, and +++ at 1%, respectively; m1 and m2 are test-statistics for the first-order and second-order serial
Observations
Number of cross-sectional units
R-square
m1
m2
Hansen test (Over-identifying Restriction)
Second lag of the dependent variable
First lag of the dependent variable
AWit1 * ( I i78 − I 78 )
I i78
AWit * ri mean
AWit * ri max
ri mean
ri max
AWit
Table 5: Robustness Check – Role of Irrigation
126
27
0.54
126
27
0.53
126
27
0.53
11.638
(11.372)
0.986**
(0.392)
126
27
0.53
18.351*
(10.631)
1.609**
(0.735)
Fixed Effects
-23.970***
-22.357***
(6.236)
(6.172)
-2.08
0.61
0.198
-2.17
0.78
0.206
System-GMM
-30.673***
-28.270***
(8.144)+++
(8.803)+++
0.179*
(0.099)+
0.405**
(0.197)++
1.581***
(0.535)+++
2.558***
(0.883)+++
11.260***
12.402***
(3.591)+++
(3.107)+++
-2.351
16.575
(17.246)
(10.589)+
0.493***
0.507***
(0.116)+++
(0.110)+++
-0.179*
-0.187*
(0.105)+++
(0.101)++
86
86
23
23
30
correlation of the first-differenced residuals, asymptotically distributed N ( 0,1) ; Hansen test (P-value), which tests the validity that the instruments are exogenous, is reported.
Note. – The dependent variable is the degree of collectivization, measured by the share of production teams not adopting household farming during 1978 to 1984; Year
dummies and constant term are included in the regressions but not reported; P-value in parentheses (based on two-step corrected standard errors). * indicates degree of
significance based upon one-step robust standard errors; * significant at 10%, ** at 5%, and *** significant at 1%, respectively; and + indicates degree of significance based
upon two-step standard errors; + significant at 10%, ++ at 5%, and +++ at 1%, respectively; m1 and m2 are test-statistics for the first-order and second-order serial
Observations
Number of cross-sectional units
R-square
m1
m2
Hansen test (Over-identifying Restriction)
Second lag of the dependent variable
First lag of the dependent variable
AWit * Si
Si
AWit * ri mean
AWit * ri max
ri mean
ri max
AWit
Random Effects
-18.773***
-17.755***
(5.036)
(5.009)
-0.014
(0.102)
-0.011
(0.211)
0.778**
(0.342)
1.282**
(0.646)
1.988
1.540
(3.612)
(3.324)
11.137
16.732*
(10.381)
(9.632)
Table 6: Robustness Check – Role of Governments
Table 7 Temporal Variations of De-collectivization across Provinces (System GMM)
AWit1
ri max
ri mean
AWit1 * ri max
AWit1 * ri mean
First lag of the
dependent variable
Second lag of the
dependent variable
Observations
Number of crosssectional units
m1
m2
Hansen test (Overidentifying Restriction)
Excess death rate instrumented by
weather adversity during 1959-61
IV1
IV2
(1)
(2)
(3)
(4)
-29.092***
-37.031***
-27.480***
-36.028***
(9.771)+++
(12.823)+++
(9.927)+++
(12.930)+++
0.340***
0.329***
(0.131)+++
(0.127)+++
0.760***
0.650**
(0.280)+++
(0.326)++
1.431**
1.309**
(0.610)++
(0.538)+++
4.289**
3.992***
(1.965)+++
(1.506)+++
0.575***
0.617***
0.608***
0.660***
(0.114)+++
-0.181**
(0.117)+++
-0.179
(0.116)+++
-0.201**
(0.127)+++
-0.178*
(0.082)++
82
21
(0.110)++
82
21
(0.087)++
86
23
(0.104)++
86
23
-2.12
0.52
0.393
-2.16
0.47
0.380
-2.15
0.55
0.258
-2.20
0.50
0.324
Note. – The dependent variable is the degree of collectivization, measured by the share of production teams not
adopting household farming during 1978 to 1984; Year dummies and constant term are included in the
regressions but not reported; P-value in parentheses (based on two-step corrected standard errors). * indicates
degree of significance based upon one-step robust standard errors; * significant at 10%, ** at 5%, and ***
significant at 1%, respectively; and + indicates degree of significance based upon two-step standard errors; +
significant at 10%, ++ at 5%, and +++ at 1%, respectively; m1 and m2 are test-statistics for the first-order and
second-order serial correlation of the first-differenced residuals, asymptotically distributed N ( 0,1) ; Hansen test
(P-value), which tests the validity that the instruments are exogenous, is reported.
31
References
Acemoglu, D., Johnson, S. and Robinson, J, A. (2001). ‘The Colonial Origins of Comparative
Development: An Empirical Investigation’, American Economic Review vol. 91 (5), pp.
1369-1401.
Acemoglu, D., Johnson, S. and Robinson, J, A. (2002). ‘Reversal of Fortune: Geography and
Institutions in the Making of the Modern World Income Distribution’, Quarterly Journal
of Economics vol. 64, pp. 1231-1294.
Alston, L., Eggertsson, T., and North, D.C. (1996). Empirical Studies in Institutional Change.
New York: Cambridge University Press.
Arellano, M. and Bond, S. (1991). ‘Some tests of specification for panel data: Monte Carlo
evidence and an application to employment equations’, Review of Economic Studies, vol.
58, pp. 277–97.
Arellano, M. and Bover, O. (1995). ‘Another look at the instrumental variables estimation of
error components models’, Journal of Econometrics, vol. 68, pp. 29–51.
Ashton, B., Kenneth, H., Piazza, A. and Zeitz, R. (1984). ‘Famine in China, 1958–61’,
Population and Development Review, vol. 10(4), pp. 613–45.
Banister, J. (1984). ‘Analysis of recent data on the population of China’, Population and
Development, Vol.10 (2), pp. 241-271.
Blundell, R., and Bond, S. (1998). ‘Initial conditions and moment restrictions in dynamic
panel data models’, Journal of Econometrics, vol. 87, pp.11–143.
Bond, S. (2002). ‘Dynamic panel data models: a guide to micro data methods and practice’,
Institute for Fiscal Studies Working Paper 09/02, Institute for Fiscal Studies, London.
Bradley, M.and Clark, M. (1972). ‘Supervision and efficiency in socialized agriculture’,
Soviet Studies vol. 23, pp. 465-73.
Chang, G. H., and Wen, G. J. (1997). ‘Communal dining and the Chinese famine of 19581961’, Economic Development and Cultural Change, vol. 46(1), pp. 1-34.
Chen, Y. and Zhou, L. (2007). ‘The long term health and economic consequences of 19591961 famine in China’, Journal of Health Economics, vol. 26(4), pp. 659–81.
Chung, J. H. (2000). Central Control and Local Discretion in china: Leadership and
Implementation during Post-Mao Decollectivization. New York: Oxford University Press.
Coale, A. J. (1984). Rapid Population Change in China, 1952-1982. Report no. 27,
Committee on Population and Demography, Washington D.C.: National Academy Press.
32
Department of Comprehensive Statistics of National Bureau of Statistics (1999).
Comprehensive statistical data and materials on 50 years of new China. (Xin Zhongguo
wu shi nian tong ji zi liao hui bian). Beijing: China Statistics Press.
Department of Planning, Ministry of Agriculture, Animal Husbandry and Fisheries (1983).
Materials of Agricultural Economy (Nongye Jingji Ziliao).
Donnithorne, A. (1967). China's Economic System. London: Allen & Unwin.
Dutt, G. (1967). Rural Communes of China: Organizational Problems. London: Asian
Publishing House.
Engerman, S.L. and Sokoloff, K. L. (2005). ‘Institutional and Non-Institutional Explanations
of Economic Differences’, In Handbook of New Institutional Economics. Boston: Kluwer
Academic Press.
Hansen, L. (1982). ‘Large sample properties of generalized method of moments estimators’,
Econometrica, vol. 50 (3), pp. 1029–54.
Heilmann, Sebastian. (2008). ‘Policy Experimentation in China’s Economic Rise’, Studies in
Comparative International Development, vol. 43 (1), pp. 1-26.
Holtz-Eakin, D., Newey, W. and Rosen, H.S. (1988). ‘Estimating vector autoregressions with
panel data’, Econometrica, vol. 56, pp. 1371–95.
Huang, D., Yu, Z. and Wang, X. (1992). Compendium on Agricultural Collectivization since
the Founding of the People’s Republic. (Jianguo Yilai Nongye Hezuohua Shiliao Huibian).
Beijing: Chinese Communist Party History Press (Zhonggong Dangshi Chubanshe).
Johnson, G. E. (1982). ‘The production responsibility system in Chinese agriculture: Some
examples from Guangdong’, Pacific Affairs, vol. 55 (3), pp. 430-51.
Kelliher, D. (1992). Peasant Power in China: The Era of Rural Reform, 1979-1989. New
Haven: Yale University Press.
Kung, J. K. and Lin, J. Y. (2003). ‘The causes of China's Great Leap famine, 1959-1961’,
Economic Development and Cultural Change, vol. 52 (1), pp. 51-73.
Li, W. and Yang, D. T. (2005). ‘The Great Leap forward: anatomy of a central planning
disaster’, Journal of Political Economy, vol. 113 (4), pp. 840-77.
Lin, J. Y. (1987). ‘The household responsibility reform in China: a peasant’s institutional
choice’, American Journal of Agricultural Economics, vol. 69 (2), pp. 410-15.
Lin, J. Y. (1990). ‘Collectivization and China’s agricultural crisis in 1959-1961’, Journal of
Political Economy, vol. 98, pp. 1-10.
Lin, J. Y. (1992). ‘Rural Reforms and Agricultural Growth in China’, American Economic
Review, vol. 82 (1), pp. 34-51.
33
Lin, J. Y. (1994). Chinese agriculture: institutional changes and performance. In T. N.
Srinivasan (ed.), Agriculture and Trade in China and India. San Francisco: International
Center for Economic Growth.
Lin, J. Y. and Yang, D. T. (2000). ‘Food availability, entitlements and the Chinese famine of
1959–61’, Economic Journal, vol. 110 (January), pp. 136–58.
Ménard, C. and Shirley, M. (2005). ‘Introduction, In Handbook of New Institutional
Economics. Boston: Kluwer Academic Press.
Milgrom, Paul R, Qian, Yingyi and Roberts, John (1991). ‘Complementarities, Momentum,
and the Evolution of Modern Manufacturing’, American Economic Review, vol. 81 (2),
pages 84-88.
Ministry of Civil Affairs of People’s Republic of China. (1995). Report on China’s Natural
Disasters [Zhongguo Zaiqing Baogao]. Beijing: China Statistics Press.
Nolan, P. (1988). The Polititcal Economy of Collective Farms. Westview Press.
North, D. C. (1981). Structure and Change in Economic History. New York: Norton and
Norton.
North, D. C. (1990) Institutions, Institutional Change, and Economic Performance,
Cambridge: Cambridge University Press.
North,D.C.（2005). “Institutions and the Performance of Economies Over Time.” In
Handbook of New Institutional Economics. Boston: Kluwer Academic Press.
O’Leary, G. and Watson, A. (1982). ‘The Production responsibility system and the future of
collective farming’, Australian Journal of Chinese Affairs, vol. 8 (July), pp. 1-34.
Peng, X. (1987). ‘Demographic consequences of the Great Leap forward in China's
provinces’, Population and Development Review, vol. 13, pp. 639-70.
Perkins, D. H. (1966). Market Control and Planning in Communist China. Cambridge, Mass.:
Harvard University Press.
Perkins, D. H. and Yusuf, S. (1984). Rural Development in China. Baltimore and London:
The John’s Hopkins University Press.
Riskin, C. (1987). China’s Political Economy: The Quest for Development Since 1949.
Oxford: Oxford University Press.
Rodrik, Dani. (2009). ‘The New Development Economics: We Shall Experiment, But How
Shall We Learn?’ in J. Cohen and W. Easterly, eds., What Works in Development?
Thinking Big and Thinking Small, Brookings Institution Press, Washington, DC.
34
Rural Survey Organization, National Bureau of Statistics (2000). Compendium on
Agricultural Statistics of the First Fifty Years of New China (Xin Zhongguo Wushi Nian
Nongye Tongji Ziliao). Beijing: China Statistics Press.
Sargan, J. (1958). ‘The estimation of economic relationships using instrumental variables’,
Econometrica, vol. 26(3), pp. 393–415.
Siu, H. F. (1989). Agents and Victims in South China: Accomplices in Rural Revolution. New
Haven and London:Yale University Press.
Sokoloff, K. L. and Engerman, S. L. (1997). Factor Endowments, Institutions and Differential
Paths of Growth among the new World Ecomoies. In Stephen Haber, ed., How Latin
America Fell Behind. Stanford University Press, Stanford, CA.
Sokoloff, K. L. and Engerman, S. L. (2000). ‘History Lessons: Institutions, Factors
Endowments, and Paths of Development in the New World’, Journal of Economic
Perspectives 14 (3), 217-232.
Unger, J. (1985-6). ‘The Decollectivization of the Chinese countryside: a survey of twentyeight villages’, Pacific Affairs, vol. 58(4), pp. 585-606.
Windmeijer, F. (2005). ‘A finite sample correction for the variance of linear efficient twostep GMM estimators’, Journal of Econometrics, vol. 126, pp. 25–51.
Yang, D. (1996). Calamity and Reform in China: State, Rural Society, and Institutional
Change since the Great Leap Famine. Stanford, CA: Stanford University Press.
Yang, X. and Liu, J. (1987). Zhongguo nongcun gaige de daolu: zongti shuping yu quyi
shizheng. Beijing: Beijing daxue chubanshe.
Zweig, David. 1997. ‘Rural People, the Politicians, and Power’, The China Journal, No. 38,
pp. 153-168.
Zweig, D. (1983). ‘Opposition to change in rural China: the system of responsibility and
people’s communes’, Asian Survey, vol. 23(7), pp. 879-900.
Zweig, D. (1985). ‘Strategies of policy implementation: policy ‘winds’ and brigade
accounting in rural China’, World Politics, vol. 37(2), pp. 267-93.
35