Better Incentives or Stronger Buffer? Weather Shocks and Agricultural Decollectivization in China†
Ying Bai and James Kai-sing KUNG*
Hong Kong University of Science and Technology
This version, August 2012
†
We thank Nancy Qian, Hongbin Li, Justin Lin and Li-an Zhou for detailed com-
ments and helpful suggestions on earlier drafts of this manuscript. We alone are responsible for any remaining errors.
* 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].
Better Incentives or Stronger Buffer?
Weather Shocks and Agricultural De-collectivization in China
Abstract
We exploit variations in the pace of agricultural de-collectivization in China in 19781984 to estimate the possible tradeoffs between alternative farming institutions in
terms of stronger work incentives and greater public goods provision. By assuming
that work incentives under collective agriculture are inversely related to the severity
of China’s Great Leap Famine, we found that provinces with higher death rates
tended to de-collectivize their agricultural institutions earlier when struck by bad
weather. Conversely, the more successful a province developed its irrigation facilities
under collective agriculture the more ready it was to abandon it. Our study highlights the role played by weather adversity and its interactions with the above triggering factors in institutional change.
Keywords: Famine, Weather Shocks, Work Incentives, Public Goods Provision, Decollectivization, China.
JEL Classification Nos.: 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).
China collectivized its agriculture in the 1950s on grounds that a collectivized
agriculture could better mobilize the construction of irrigation facilities for counteracting the vagaries of weather, thereby reducing the output variability of crops. After
roughly a quarter of a century however, China de-collectivized its agriculture. The
decisive switch to family farming was allegedly triggered by a bad drought, which, if
not for the ingenious assignment of the “residual claimant” status to the farmers they
would not have had the incentives to plant wheat on arid soil (e. g., Yang and Liu,
1987). Unlike when Chinese agriculture was communalized in 1958,3 which took less
than one month for virtually all the provinces to comply, the entire process of collectivization had taken several years to complete and exhibited enormous spatial variations. To the extent that agricultural de-collectivization reflected an institutional
evaluation of collective agriculture, the varying pace provinces exhibited leads us to
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
Chinese agriculture was in fact collectivized in several stages prior to the eventual establishment of
the communes in 1958. Since our primary concern here is de-collectivization (of the communes), our
reference point is the communalization process.
1
wonder why they differed in the intensity of preferences they had for family farming.
Instead of focusing on the endogenous reasoning of why some provinces rushed to
de-collectivize their agricultural institutions ahead of others,4 however, we examine
how provincial leaders and the people alike were faced with the choice between two
specific modes of farming institution—collective versus family—when struck by bad
weather in the pertinent decision-making process.
Our proposed test is based on the hypothesized reasoning that collective and
family farming institutions have their unique strengths and weaknesses in the presence of exogenous weather shocks, so that the choice between them rests essentially
on the tradeoffs between incentives—a distinct institutional strength of individualized
farming, and public goods (in particular irrigation) provision—something which the
collective institutions are far more capable of providing.5 By employing appropriate
proxies, we examine the extent to which the varying pace of agricultural decollectivization reflects this tradeoff between work incentives on the one hand, and
public goods provision on the other.
To proxy incentives, we employ the differing severity of the Great Leap Famine—which varied enormously across China’s provinces—as the pertinent measure
of collective agriculture’s ability to respond to negative weather shocks.6 Based on
4
By appealing to a variety of factors, a number of attempts have indeed 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. 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 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, de-collectivization (See, among
others, Chung, 2000; Johnson, 1982; Kelliher, 1992; Lin, 1987, 1992; O’Leary and Watson, 1982; Siu,
1989; Unger, 1985-86; Zweig, 1983, 1985).
5
Bradley and Clark (1972) compare the work incentives between household and collective farms in the
presence of what they term “stochastic disturbances” of the weather and conclude, unambiguously, that
the powerful incentives to deal with such exogenous shocks could only be solicited on the private
farms.
6
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). It is estimated that
2
the example of Anhui—the first province to have abandoned collective agriculture as
well as being one of the most famine-stricken provinces during the Great Leap, our
underlying hypothesis is that the severity of the Great Leap Famine represents an
inverse measure of collective agriculture’s ability to respond to negative weather
shocks; the more severe the famine the faster the people preferred to exit from collective farming at a time when they had the chance to make the switchover to family
farming.7 In short, we conjecture that poor weather has likely strengthened the perception that collective agriculture could not protect farmers from natural disasters.
By the same token, given the tradeoffs between work incentives and public
goods provision, we test how variations in the latter—specifically irrigation facilities
accumulated in the course of collective agriculture—may bear upon the proclivity to
de-collectivize. Similar to the “incentive hypothesis”, we conjecture that the proclivity to exit a collectivized agriculture would be stronger in provinces where better irrigation facilities had already been constructed during the collective era, for the simple reason that these public goods would better help a de-collectivized agriculture to
deal with the adverse weather, thereby reducing the potential costs of family farming—an institution weak (at least in principle) in providing the necessary public
goods.
Using both OLS and dynamic panel model (the latter estimated using the system-GMM method), we obtain robust empirical results to substantiate the hypotheses that the severity of the famine does increase the effect of weather adversity experienced during de-collectivization, thereby leading to an earlier adoption of family
farming. Similarly, better irrigations attained under collective agriculture also reverse the effect of negative weather shocks during de-collectivization; it too is expected to result in an earlier adoption of family farming. Our empirical results re-
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).
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).
3
main robust after we control for the preferences of the provincial governments in regard to their choice of farming institutions,8 and factors correlated with both the
Great Leap Famine and other aspects of public goods provision, and exclude the outlying instances where the pertinent famine was extremely severe.
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 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 culmi-
8
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-cum-evaluation approach adopted by the Chinese government
(Heilmann, 2008; Rodrik, 2008; Ravallion, 2009).
4
nated 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).
Figure 1 about here
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 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.
5
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).9 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 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 mid-1982) 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).10
3. Hypotheses
Collective and household farming represent two very different institutions in
terms of supplying work incentives and indivisible or “lumpy” public goods such as
irrigation, the choice of which depends, among other considerations, on their capabilities to handle negative weather shocks. Based on the evidence that a severe
drought had triggered the dismantling of collective farms in Anhui Province in 1978,
it becomes clear that a distinct advantage of household farming lies in its provision
9
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).
10
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).
6
of powerful work incentives to the farmers. However, a household-based farming
system is ill-suited to providing indivisible public goods such as irrigation projects,
which are crucial for dealing with negative weather shocks.11 There is indeed evidence to show that, compared with the huge increase experienced during the collective era, the effectively irrigated area (EIA) had actually declined between 1979 and
1986 for as many as seven Chinese provinces (Stone, 1988). Consistent with this observation, Vermeer (1998) also finds micro-level evidence that agricultural decollectivization, which rendered the commune defunct, had resulted in the ill maintenance of water conservancy facilities.12
Thus, a village faces the tradeoff between stronger work incentives and better
public goods provision when choosing an optimal farming institution. But the decision need not be complicated. When the benefits (of stronger work incentives) resulting from adopting household farming outweigh the losses (of lesser public goods
provision), a village would employ household farming, and vice versa.
Owing to the free rider problem (inherent in “team production”) in collective
institutions and accordingly their failure to provide sufficiently strong work incentives, which was an important contributing factor to the Great Leap Famine (Lin,
1990), provinces with a less severe famine are, by implication, less likely to face severe incentive problems. It follows that provinces with a more severe famine would
more likely face severe incentive problems, and thus had a greater proclivity to adopt
household farming earlier. This would especially be the case when, in the process of
making the important decision on institutional choice they were struck by negative
weather shocks. Based on this reasoning, we formally hypothesize that:
11
The use of corv’ee labor in collective agriculture is the singular most important reason why largescale irrigation projects can be more effectively provided. Indeed, an important economic rationale
behind agricultural collectivization in China in the 1950s was to overcome the institutional deficiency
of a vastly decentralized small peasantry. Chairman Mao believed that, compared with an individualized peasantry, the collectives could mobilize more effectively the construction of large-scale irrigation
projects crucial for reducing the variability of farm output caused by weather adversity (Nolan, 1988).
12
While this undoubtedly represented an important cause of the decline, conflicts over water use had
allegedly resulted in damaged installations and sabotage of existing facilities (see Li, Ding and Yang,
1983).
7
Hypothesis 1: The effect of weather adversity intensifies the effect of the severity of
famine, in that poor weather and a severe famine experience combined to accelerate the rate of de-collectivization. This implies that, in
provinces where the Great Leap Famine was severe, poor weather at
the time of de-collectivization would accelerate the rate of decollectivization.
It also follows from the above discussions that, even though collective institutions can mobilize the construction and maintenance of irrigation projects more effectively, the marginal effects of such new constructions are conditional upon the prevailing levels of irrigation facilities henceforth accumulated. Where the EIA was already high by 1978 (we call this initial irrigation condition), the benefits to be expected from the continuing effort of the collectives would be small; for these villages,
they would have more to gain by switching to household farming. This brings us to
our second hypothesis.
Hypothesis 2: The effect of weather adversity intensifies the effect of public goods or
specifically irrigation conditions, in that poor weather and an effective
irrigation combined to accelerate the rate of de-collectivization. This
implies that, in provinces with effective irrigation, poor weather at the
time of de-collectivization would accelerate the rate of decollectivization.
In setting up our hypotheses, it needs to be emphasized that bad weather at
the time of de-collectivization likely has the possible effect of strengthening rather
than dismantling the collective institutions of agricultural production, mainly because collectives should be better able to facilitate mutual aid cooperation among
farmers in fighting natural disasters. It is only when it interacts with the incentive
8
(famine) and public goods (irrigation) variables that we expect a reversal in the sign
of its coefficient.
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.
Our 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.13 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
13
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.
9
province i covered by various climatic disasters in a given year t ,14 adverse weather
can be expressed as AWit = (d it −
1
1
dit ) /( ∑ dit ) . Since we normalize AWit by the
∑
T t
T t
average percentage of 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
Our second key independent variable is the relative severity of the famine,
and 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 . 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 third key explanatory variable is provincial variations in initial irrigated
condition (at the outset of de-collectivization in 1978), given that it is likely correlated
with collectivization (or the degree of de-collectivization). In order to estimate this
effect, we add an interaction term between adverse weather and the percentage of
(
)
irrigated acreage in 1978 in our estimation.15 Denoted as I i78 − I 78 , I i78 stands for
the percentage of irrigated acreage of province i in 1978, whereas I 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
14
15
This is the sown acreage covered by climate disasters (shouzai mianji) divided by total sown acreage.
We obtain this variable from Comprehensive statistical data and materials on 50 years of new China.
10
of weather adversity at average levels of per capita grain availability than when I i78
equals to zero.
4.2 Econometric Method
We begin with a baseline model in which the effect of adverse climatic variations (and its interactions with famine severity and initial irrigation acreage) on the
gradual variations in the degree of de-collectivization from the benchmark year 1978
through 1984 is estimated, using the pertinent panel data which includes the yearspecific effects:
yit = X it β + λt + vit
t = 1, 2," , T
(1)
where yit measures the percentage of households adopting household responsibility
during year t , X it are the key explanatory variables including weather adversity
( AWit ), famine death rate ( ri max ), initial irrigation condition ( I i1978 ), and the two pertinent interaction terms AWit * ri max amd AWit * I i1978 , by province, and λt is the timespecific effect for year t and vit is the residual error term. We firstly use OLS to estimate equation (1), and then control for the unobserved province-specific effect α i as
follows:
yit = X it β + α i + λt + vit
t = 1, 2," , T
(2)
5. Empirical Results
5.1 Baseline Results
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 six regressions. In estimations that include both the year of maximum excess death rate ( ri max )
during the Great Leap Famine and its interaction with weather adversity (columns (1)
and (2)), 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. In both estimations, weather adversity correlates negatively
with the percentage of households adopting the Household Responsibility System.
11
For instance, if the area affected/struck by natural disasters (comprehensively defined) is twice the average (i.e., AWit equals 1), the fraction of farm households
adopting the Household Responsibility System is approximately 18.497 percent
lower (column 1) than if there were no famine during 1959-61, i.e., ri max = 0 . 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). These evidences lend support to our first hypothesis (H1).
Table 2 about here
Also reported in Table 2, the estimation results show that differences in irrigated acreage do have the expected effect of dismantling the collective fabric of
China’s agricultural institutions in the presence of an exogenous weather shock (columns (3) and (4)). In these estimations, the interaction term between weather adversity and irrigated acreage is highly significant. This result implies that provinces with
proportionately greater irrigation acreage in 1978 tended to dismantle their communes when struck by bad weather. For instance, if the area affected/struck by natural disasters (comprehensively defined) is twice the average (i.e., AWit equals 1), the
fraction of farm households adopting the Household Responsibility System is approximately 1.294 percent higher (column 4) if the irrigation acreage is 1 percent
above the sample average. In Columns (5) and (6), we include both famine severity
and initial irrigation condition. Our baseline results do not change: weather adversity
is negatively correlated with the percentage of households adopting the Household
Responsibility System when excess mortality is equal to zero and irrigation acreage is
equal to the sample mean. Moreover, the two pertinent interaction terms have significant effects on the dependent variable.
5.2 Dynamic Panel Model
12
Since the percentage of households adopting household responsibility in a
given year might be affected by the degree of de-collectivization ( Yi t −1 ) in the previous year, it is necessary to include the lagged dependent variables in the regressions.
Thus we employ a dynamic panel model as follows:
L
yi t = ∑ ρlYi t −l + X i t β + α i + λt + vi t
(3)
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
(4)
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 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). To present
the empirical results accurately, we will report 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.16 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
16
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).
13
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
N
is supposed to randomly distribute around zero. 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. Estimation
results of the dynamic panel models are presented in Table 3. 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 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 3: 29.447/1.315), or where the average yearly
excess death rate is higher than 9.885 thousandths (column 2: 27.074/2.739), respectively. These results lend further support to H1. Similarly, the interaction term between initial irrigation condition and adverse weather is positive and significant,
lending support to H2.
Table 3 about here
14
We employ this alternative specification because we do not know a priori
whether or not we should include the lagged dependent variables in our estimations.
Given that estimates based on Fixed Effects and Dynamic Panel models have a useful
bracketing property, we can employ both in our estimations and allow them to help
us check the robustness of our findings. Consider, for instance, the interaction between weather adversity and famine severity ( AWit * ri max ), which has a positive coefficient. If we estimate with a Fixed Effects model in the instance where the lagged
dependent variables should have been included, we would have overestimated the
positive effect of the interaction term. Conversely, if we employ a Dynamic Model as
the estimator when the lagged dependent variables should not have been included,
the resulting coefficients would be too small (Angrist and Pischke, 2009). In our estimations of AWit * ri max , the pertinent coefficient resulting from employing the Fixed
Effects model is 1.305 (Column 6 in Table 2), whereas that of the Dynamic Panel
model is 1.300 (Column 3 in Table 3). The narrow range of the results between the
two estimators are thus solid evidence that our estimates of the interaction term between weather adversity and famine severity are sufficiently robust. The same is true
for our second dependent variable—the interaction between weather adversity and
irrigation. Although the range of the estimation results between the two methods is
(
)
much larger—the upper bound, AWit * I i78 − I 78 , is 1.523 (column 6 in Table 2), the
lower bound, AWit * ri max , is 1.323 (column 3 in Table 3)—they both significantly reject
the null hypothesis ( β = 0 ).
5.3 Robustness Check – Outliers
Given that the results might be driven by a few provinces with extremely
high famine mortality, we address the potential problem of possible outliers. To ensure that our empirical results are robust, we drop the three provinces with the most
severe mortality rates; they are: Anhui, Sichuan and Guizhou, whose maximum excess death rate is: 56.7 thousandths, 38.1 thousandths and 35.4 thousandths, respectively. In addition, these three provinces also differ from the others in that they all
adopted the Household Responsibility System before it was sanctioned by the central
15
government for its nationwide implementation in April 1982.17 To check that our
empirical results are not driven by these three provinces, we drop them from our
sample and repeat the same regressions again based on the specifications of Table 2.
As shown in Table 4, weather adversity and its interactions with famine severity and
initial irrigation condition remain highly significant, which reaffirms both of our hypotheses.
Table 4 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.18 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 de-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).19 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
17
For instance, roughly 43 percent, 22.4 percent, and 61.8 percent of the households in, respectively,
Anhui, Province, Sichuan Province, and Guizhou Province already switched to farm on an individualized basis by the end of 1980.
18
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).
19
Specifically, data limitations require us to use yearend 1981 as the cut-off point.
16
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 5, 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 5 about here
5.5 Robustness Check – Role of Food Availability, Grain Procurement and “Urban Bias”
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, the standard of living might be correlated
with famine severity and 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 decollectivization. Moreover, existing studies on the Great Leap Famine suggest that its
severity was affected also by the provincial variations in grain procurement (Kung
and Chen, 2011; Kung and Lin, 2003; Meng, Qian and Yared, 2010). Hence we also
control for variations in per capita grain procurement in 1978 in this robustness
check.
To proxy for the standard of living (or “the distance to minimum subsistence”), we employ per capita food availability, which is estimated using data on
provincial grain output and procurement.20 Denoted as
(f
78
i
− f 78 ) , f i 78 stands for
20
We obtain this variable from Materials of Agricultural Economy [Nongye Jingji Ziliao] (Ministry of
Agriculture, Animal Husbandry And Fisheries, 1983).
17
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 f i 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.
Similarly, we use net procurement per capita, namely total grain procurement per
capita minus return selling per capita, to proxy for the effect of government control.21
(
)
Denoted as pi78 − p 78 , pi78 stands for per capita net grain procurement of province
i in 1978, whereas p 78 stands for the mean per capita net grain procurement of all
provinces in the same year.
We also control for what Lin and Yang (2000) term “urban bias”, as it is found
to be correlated with famine severity.22 We do so by controlling for the initial share of
(
)
urban population in a province, denoted as ui78 − u 78 , where ui78 stands for the
share of urban population of province i in 1978, whereas u 78 stands for the mean
share of urban population of all Chinese provinces in the same year.
Reported in Table 6, the results of these estimations show that differences in
“distance to minimum subsistence” do not have the expected effect of sustaining the
collective fabric of China’s agricultural institutions in the presence of an exogenous
weather shock. However, the interaction between grain procurement per capita with
weather adversity is significantly and negatively correlated with the adoption of
Household Responsibility System. This result suggests that provinces with higher
per capita grain procurement in 1978 were more likely to be controlled more tightly
by the provincial government, and thus tended to postpone dismantling their communes when struck by bad weather. However, “urban bias” has no significant effect
21
The underlying idea is that for ease of grain procurement governments in provinces with larger procurement quotas were more likely to hold on to collective agriculture.
22
For these authors, “urban bias” essentially implies systemic differences in grain entitlement based on
one’s origin of residence (primarily between urban and rural)—with the urban population guaranteed a
fixed amount of grain consumption whereas the rural counterparts only the “residual”—hence the
“bias”.
18
on de-collectivization in the presence of adverse weather shocks. More importantly,
our key explanatory variables remain significant after these additional controls.
Table 6 about here
5.6 Robustness Check – Role of Other Public Goods
To ensure that the positive relationship between the interaction term and the
adoption of the Household Responsibility System is not spurious, we control for
other measures of public goods. If a significant relationship between these alternative
measures of public goods and de-collectivization is found, then it is possible that the
significant relationship between irrigation and de-collectivization is caused by some
omitted variables. Conversely, a lack of a significant relationship between these two
variables will lend support to our finding that the relationship between irrigation
and de-collectivization is not spurious.
In this connection our first control variable is machinery, denoted as
(m
78
i
− m78 ) , where mi78 stands for per hectare machinery power of province i in 1978,
and m78 the mean per hectare machinery power of all Chinese provinces in the same
year. The second pertinent control variable is per capita electricity usage, denoted
(
)
as ei78 − e 78 , where ei78 refers to per hectare electricity usage of province i in 1978,
and e 78 the mean per capita electricity usage of all Chinese provinces in the same
year.
Reported in Table 7, the estimation results show that differences in machinery
power do not have any significant effect on agricultural de-collectivization in the
presence of an exogenous weather shock (Columns (1) and (2)). The same results apply to electricity usage; its interaction with weather adversity is not significant (Columns (3) and (4)). Together, these results suggest that provinces better “endowed”
with public goods other than irrigation tend not to dismantle their communes immediately when struck by bad weather, hence ruling out the possibility that the signifi-
19
cant relationship found between irrigation and de-collectivization is caused by omitted variables.
Table 7 about here
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. The
de-collectivization of agriculture in China, in particular the varying pace at which
provinces in China switched to the new institution of household farming, provides
us with an excellent social laboratory to empirically examine the tradeoffs between
the pros and cons associated with the two farming institutions from which farmers
and officials alike were allowed to choose. Our study is premised on the reasoning
that collective farm institutions, while efficacious in providing the “lumpy” public
goods such as irrigation, fail to provide sufficiently strong work incentives; in particular when faced with negative weather shocks. Our pertinent research question is
thus how actors—provincial officials and the people in this instance—make the decision on how soon they want to switch over to the new farming institution.
By employing provincial variations in mortality to proxy incentives, and differences in initial irrigation conditions to proxy public goods provision, we found
that provinces having suffered from poor incentives but benefited from good irrigation conditions made the same unwitting choice to de-collectivize earlier when struck
by bad weather. In short, adverse weather, when interacted with either poor incentives or good irrigation, has a reversed, accelerating effect on agricultural decollectivization. Part of our finding helps to explain why, for instance, 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. Our main results, as we have shown, remain robust to a non-trivial number of checks and controls and should therefore be taken as having demonstrated a statistical relationship
that goes beyond that of a mere spurious nature.
20
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. Ironically, the more successful a province developed its irrigation facilities under collective agriculture the more ready it was to abandon it.
21
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26
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
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 De-collectivization; 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).
27
%
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.
28
Table 1: Definition of Variables and Data Sources
Variables
Degree of decollectivization
Notations
Mean
S.D.
46.95
(44.37)
(2)
Weather adversity
AWit
0.00
(0.46)
(3)
Severity of Great Leap
Famine
Initial irrigation condition
ri max
12.53
(13.65)
I i78
32.74
(15.79)
fi 78
830.42
(485.17)
(6)
Initial distance to minimum subsistence
Net grain procurement
pi78
100.47
(103.50)
(7)
Machinery
mi78
1.27
(0.77)
(8)
Electricity
ei78
6.59
(3.00)
(1)
(4)
(5)
Yit
Proxies
Share of production teams with implementing
the Household Responsibility System between
1978 and 1984.
Based on drought, floods, gusts and hailstorms,
and frost and cold.
Maximum excess death rate for the period 19591961.
Percentage of irrigated acreage in total arable
acreage in 1978
Food availability per capita in 1978 (kg)
Data sources
A, B
C
D
E
F
Net grain procurement per capita in 1978 (kg)
F
Power of Agricultural Machinery per hectare in
1978 (kw/hectare)
Electricity Consumed in Rural Area per hectare
in 1978 (10000 kwh/hectare)
E
E
A. 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).
B. 1978 – 1982: Jae Ho Chung, 2000. Central Control and Local Discretion in China: Leadership and Implementation During Post-Mao Decollectivization.
C. Ministry of Civil Affairs (1995), People’s Republic of China. Report on China’s Natural Disasters [Zhongguo Zaiqing Baogao].
D. 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.”
E. 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
F. Department of Planning, Ministry of Agriculture, Animal Husbandry and Fisheries, 1983. Materials of Agricultural Economy [Nongye Jingji Ziliao].
29
Table 2: Temporal Variations of De-collectivization across Provinces (Baseline Results)
(1)
AWit
ri max
AWit * ri max
-18.497***
(5.554)
0.015
(0.081)
0.950***
(0.280)
(2)
-23.696***
(6.809)
(3)
(4)
-3.428
(3.589)
-0.710
(4.061)
1.164***
(0.357)
I i78
0.023
(0.177)
0.985**
(0.479)
AWit * ( I i78 − I 78 )
1.294*
(0.689)
(5)
(6)
-16.462***
(4.935)
0.014
(0.080)
1.248***
(0.302)
0.028
(0.161)
1.420***
(0.425)
-16.749***
(6.053)
1.305***
(0.383)
1.523**
(0.587)
Provincial dummies
Year dummies
No
Yes
Yes
Yes
No
Yes
Yes
Yes
No
Yes
Yes
Yes
Observations
126
126
120
120
120
120
R-squared
0.53
0.56
0.50
0.53
0.56
0.59
Note. The dependent variable is the share of production teams adopting household farming each year during 1978-1984; Constant term is included in
the regressions but not reported. Standard errors in parentheses. * p<.1. ** p<.05. *** p<.01.
30
Table 3: Temporal Variations of De-collectivization across Provinces (Dynamic Panel
Model)
(1)
AWit
ri max
AWit * ri max
(2)
-29.447***
(7.298) +++
0.319***
(0.117) +++
1.315***
(0.342) +++
-5.998
(8.813)
I i78
(3)
-21.022**
(8.205) +++
0.344***
(0.121) +++
1.300***
(0.320) +++
0.209
(0.160)
1.323**
(0.666) ++
-0.513***
(0.172) +++
0.156
(0.124) ++
The first lag of the dependent
variable
The second lag of the dependent variable
-0.553***
(0.131) +++
0.171*
(0.098) +++
0.210
(0.174)
1.295
(0.924) ++
-0.675***
(0.162) +++
0.208**
(0.099) ++
Observations
Number of cross-sectional
units
86
23
81
22
81
22
m1
m2
0.034
0.018
0.028
0.578
0.425
0.550
Hansen test (Over-identifying
Restriction)
0.161
0.278
0.267
AWit * ( I i78 − I 78 )
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; Two-step corrected standard errors in parentheses. * indicates degree of
significance based upon two-step corrected standard errors; * significant at 10%, ** at 5%, and
*** significant at 1%, respectively; and + indicates degree of significance based upon one-step
robust 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 firstdifferenced residuals, asymptotically distributed N ( 0,1) ; Hansen test (P-value), which tests
the validity that the instruments are exogenous, is reported.
31
Table 4: Temporal Variations of De-collectivization across Provinces—Excluding the Outliers
AWit
ri max
AWit * ri max
(1)
(2)
-16.509**
(6.545)
-0.034
(0.142)
0.880**
(0.422)
-22.372**
(8.497)
(3)
-3.735
(4.043)
(4)
0.343
(4.447)
1.197**
(0.544)
I i78
0.037
(0.171)
1.373***
(0.475)
AWit * ( I i78 − I 78 )
1.876***
(0.621)
(5)
-14.189**
(5.869)
-0.070
(0.138)
1.127**
(0.450)
0.034
(0.160)
1.534***
(0.432)
(6)
-12.328*
(6.538)
1.093**
(0.519)
1.773***
(0.557)
Provincial dummies
Year dummies
No
Yes
Yes
Yes
No
Yes
Yes
Yes
No
Yes
Yes
Yes
Observations
110
110
104
104
104
104
R-squared
0.59
0.62
0.60
0.64
0.63
0.66
Note. The dependent variable is the share of production teams adopting household farming each year during 1978-1984; Constant term is included in
the regressions but not reported. Standard errors in parentheses. * p<.1. ** p<.05. *** p<.01.
32
Table 5: Temporal Variations of De-collectivization across Provinces—Controlling for the Role of Government
(1)
AWit
ri max
AWit * ri max
-18.773***
(5.490)
-0.014
(0.075)
0.778***
(0.267)
(2)
-23.970***
(6.658)
AWit * ( I i78 − I 78 )
AWit * Si
1.988
(3.264)
11.137
(7.595)
-8.128*
(4.422)
(4)
-5.687
(5.232)
11.638
(8.828)
0.051
(0.168)
1.351***
(0.450)
1.309
(3.460)
30.974***
(9.800)
(5)
(6)
-16.353***
(5.519)
31.376***
(11.401)
-16.622***
(4.664)
-0.019
(0.071)
0.992***
(0.276)
0.056
(0.160)
1.552***
(0.429)
2.318
(3.293)
18.367**
(8.134)
0.986***
(0.333)
I i78
Si
(3)
1.651***
(0.626)
1.030***
(0.329)
1.689***
(0.593)
18.861**
(8.875)
Provincial dummies
Year dummies
No
Yes
Yes
Yes
No
Yes
Yes
Yes
No
Yes
Yes
Yes
Observations
126
126
120
120
120
120
R-squared
0.54
0.57
0.54
0.57
0.58
0.60
Note. The dependent variable is the share of production teams adopting household farming each year during 1978-1984; Constant term is included in
the regressions but not reported. Standard errors in parentheses. * p<.1. ** p<.05. *** p<.01.
33
Table 6: Temporal Variations of De-collectivization across Provinces—Controlling for the Factors Correlated with the Great Leap Famine
AWit
ri max
AWit * ri max
I i78
AWit * ( I i78 − I 78 )
fi 78
AWit * ( fi 78 − f 78 )
(1)
-15.661***
(4.551)
0.068
(0.073)
1.206***
(0.296)
0.045
(0.162)
1.514***
(0.447)
6.776
(4.171)
-8.078
(13.741)
(2)
-16.574***
(5.982)
1.250***
(0.361)
1.521**
(0.594)
(3)
-15.262***
(4.192)
0.040
(0.064)
0.677**
(0.275)
0.147
(0.202)
0.386
(0.423)
(4)
-17.735***
(5.610)
0.688**
(0.318)
0.189
(0.605)
0.018
(0.020)
-0.147**
(0.057)
AWit * ( pi78 − p 78 )
1.204***
(0.368)
1.498**
(0.585)
-0.162**
(0.068)
ui78
AWit * ( ui78 − u 78 )
No
Yes
120
0.57
(6)
-16.541***
(5.968)
-6.634
(16.884)
pi78
Provincial dummies
Year dummies
Observations
R-squared
(5)
-14.316***
(4.849)
0.078
(0.078)
1.189***
(0.307)
0.047
(0.164)
1.610***
(0.502)
Yes
Yes
120
0.59
No
Yes
120
0.59
Yes
Yes
120
0.62
0.143*
(0.086)
-0.128
(0.195)
No
Yes
120
0.58
-0.270
(0.328)
Yes
Yes
120
0.59
Note. The dependent variable is the share of production teams adopting household farming each year during 1978-1984; Constant term is included in
the regressions but not reported. Standard errors in parentheses. * p<.1. ** p<.05. *** p<.01.
34
Table 7: Temporal Variations of De-collectivization across Provinces—Controlling for the
Factors Correlated with Public Goods Provision
(1)
AWit
ri max
AWit * ri max
I i78
AWit * ( I i78 − I 78 )
mi78
AWit * ( mi78 − m78 )
-14.733***
(4.975)
0.069
(0.076)
1.211***
(0.314)
-0.127
(0.223)
1.645***
(0.597)
5.957
(4.999)
-4.224
(8.268)
(2)
-16.049***
(6.003)
1.259***
(0.370)
1.638**
(0.698)
(3)
-15.374***
(4.721)
0.072
(0.082)
1.196***
(0.325)
-0.033
(0.176)
1.602***
(0.518)
(4)
-17.465***
(6.305)
1.245***
(0.388)
1.575**
(0.618)
-4.658
(11.613)
ei78
3.363
(2.688)
-2.218
(4.335)
AWit * ( ei78 − e 78 )
-3.392
(5.884)
Provincial dummies
Year dummies
No
Yes
Yes
Yes
No
Yes
Yes
Yes
Observations
120
120
120
120
R-squared
0.57
0.59
0.57
0.59
Note. The dependent variable is the share of production teams adopting household farming each year
during 1978-1984; Constant term is included in the regressions but not reported. Standard errors in
parentheses. * p<.1. ** p<.05. *** p<.01.
35