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Effects on poverty and equity of the decline in collective tank
irrigation management in Tamil Nadu, India
Kei Kajisa*
International Rice Research Institute (IRRI) and Foundation for
Advanced Studies on International Development (FASID)
K. Palanisami Tamil Nadu Agricultural University
Takeshi Sakurai Policy Research Institute, Ministry of
Agriculture, Forestry and Fisheries of Japan
November, 2006
Abstract
This paper investigates the factors influencing the decline in
collective management of local commons and the impact of this
decline on agricultural production and household consumption. The
analysis is based on a village and household data set collected in
1999 in Tamil Nadu, India, where tank irrigation systems are
managed collectively for rice cultivation by informal water users’
organizations. Our statistical analyses find that one major reason
for the decline in collective tank irrigation management is the
dissemination of private well irrigation systems. Once the decline
has occurred, our analyses predict that the gap in rice yields
between farmers who have access to private wells and those who must
rely solely on tanks will widen, with only the latter group
suffering lower yields. Our analyses also find that the same
pattern holds for levels of income and consumption because the
affected farmers cannot sufficiently compensate for the loss of
their rice income by diversifying their income sources to
agricultural labor or non-agricultural work. In this way, the
decline in collective management results in greater inequality and
poverty. JEL classifications: D31, D71, O33, Q25 Key words: common
property, irrigation, income distribution, poverty, India. *
Corresponding author: Address: IRRI DAPO Box 7777, Metro Manila,
Philippines; E-mail: [email protected]
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1. Introduction
Tank irrigation management is at a crossroads. Tank irrigation
systems
collectively operated and managed by informal local bodies have
been a dominant
source of irrigation in southern India since time immemorial.
However, the agricultural
area under tank irrigation has been in retreat, especially since
the 1990s, due to the
decline in collective management. Concurrent with this is the
rapid dissemination of
private well irrigation systems. The percentage share of
tank-irrigated agricultural area
in southern India fell from 37 in the 1960s to 29 in the 1970s,
22 in the 1980s, and 18 in
the 1990s. Meanwhile, the percentage share of private wells rose
from 20 to 26 to 31
and to 40 in the corresponding periods (Fertilizer Association
of India, various issues).
The shift in relative share reflects not only a preference for
private wells when new
systems are installed but also the actual replacement of tank
systems by well systems.
This process has been associated with significant increases in
the average yield of rice, a
staple crop in the area, and in the average income level of
farmers.
Despite such positive effects on average, there is concern that
the diffusion of
private wells is also associated with increased poverty and
inequality. Access to water
for irrigation from tanks is available to all farmers in the
system command area in
principle. Access to irrigation water from private wells,
however, is limited to the
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owners and to those who can buy from the owners. Private wells
provide freedom in
irrigation water control, and thus those who have access can
increase their yields and
income. When farmers who can access private wells exit from the
collective
management of their tank system — out of disinterest or loss of
incentive — the quality
of tank management declines and the tanks deteriorate. When this
happens, farmers who
are dependent solely on tanks suffer a reduction in rice yields,
while farmers who have
recourse to private wells can still achieve high yields. Whether
this leads to a significant
increase in poverty and inequality is an empirical question. If
farmers with greater water
availability due to access to private wells increase their
demand for agricultural labor
and then hire farmers without access to wells, the income of the
farmers without access
to wells may not decline significantly. Besides, farmers without
access to wells may be
able to diversify their income source to non-agricultural
occupations. In this way,
farmers without access to wells may be able to compensate to
some extent for their
income loss caused by the yield reduction. On the other hand, if
opportunities to
participate in agricultural labor and non-agricultural work are
limited, the decline in
collective management results in a reduction in income of
farmers without access to
wells to the extent that it increases poverty and inequality
significantly.
Numerous attempts have been made to examine the determinants of
the decline
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in collective management (Wade, 1988; Ostrom, 1990; Ostrom et
al., 1994; Singh,
1994; Baland and Platteau, 1996; Palanisami and Easter, 2000;
Dayton-Johnson, 2000;
Bardhan, 2000; Bardhan and Dayton-Johnson, 2001; Fujiie et al.,
2005). However, few
quantitative analyses have been made of the impact of the
decline on the livelihoods of
rural households.1 This paper aims to both investigate the
determinants of the decline
and examine whether this decline results in lower yields and
income among farmers
without access to private wells. As this was found to be true,
this paper seeks to answer
the question of whether a revitalization of tank management
would reduce poverty and
inequality. The analysis is based on a set of data collected
from sample surveys at both
the village and household levels in Tamil Nadu, India.
The organization of this paper is as follows: the next section,
Section 2, explains
the characteristics of tanks and wells in Tamil Nadu and
advances the hypotheses.
Section 3 describes the study site and data collection. Sections
4 and 5 present the
regression analyses on the determinants of collective management
and on yield and
income effects. Finally, Section 6 summarizes the discussion and
presents policy
implications.
1 One exception is the benefit-cost analysis conducted by
Kikuchi et al. (2001). Some significant descriptive studies are
Jodha (1986), Singh (1994), Palanisami (2000), and Meinzen-Dick et
al. (2002a).
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2. Tanks and wells in Tamil Nadu
The tank irrigation system
The tank irrigation system consists of a water storage area,
sluices, and water
supply channels. The water storage area, or tank, is a small
reservoir constructed across
the slope of a valley to catch and store water. Water is
controlled by sluices that are
attached to the tank bank and is delivered to paddy fields
through the channels.
Traditionally, these tank systems are managed collectively by
informal local bodies,
called water users’ organizations (WUO), which mobilize
community labor to perform
the maintenance and management tasks. These tasks include (1)
de-silting the water
storage area, (2) cleaning the water supply channels, and (3)
arranging water
distribution among users. De-silting work for tanks with a
minimum command area of
40 hectares is supposed to be arranged and implemented by the
Public Works
Department (PWD) using state government funds. However, due to
the limitation of
such funds, de-silting work is occasionally done at the village
level based on WUO
initiatives. For command areas of less than 40 hectares, this
work must be done by the
village-level administration and the WUO. Channel cleaning and
water distribution are
arranged and implemented through collective action by the WUO,
regardless of system
size.
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The tank irrigation system has the attributes of a common pool
resource (CPR)
in that the exclusion of farmers within the command area is
difficult, but, if use exceeds
supply capacity, the resource will become exhausted. Hence, in
arranging collective
action, the system is doomed to face two types of CPR management
problems:
provision and appropriation (Ostrom et al., 1994). The former
problem arises in
arranging the maintenance of the resource stock, while the
latter occurs in arranging the
distribution of output flow from the resource. In the context of
tank management, the
former corresponds to de-silting and cleaning arrangements,
while the latter corresponds
to water distribution arrangements.
The central issue in CPR management of tank irrigation is the
resolution of the
provision problem because two technological features specific to
the tank irrigation
system make resolution difficult. First, tank irrigation
technology is indivisible to some
extent in terms of its supply capacity; hence, it is difficult
to reduce the supply capacity
precisely in accordance with a decline in the number of users.
Therefore, as long as the
tank irrigation system is used, it must maintain a certain
supply capacity level and, for
that purpose, a certain amount of labor input must be mobilized.
Second, the
maintenance work must be completed within a short period of time
at the outset of the
monsoon season. This means that all the participants in
maintenance work must be
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mobilized at once. However, this period corresponds to the
preparation period of rice
nurseries for those who are able to start preparation earlier
with water from wells. For
them, the opportunity cost of participation is high.2 Hence,
when the number of private
well users increases, it becomes difficult to mobilize a certain
amount of labor input
within a particularly short period of time. In this way, the
cost of maintaining the tank
system becomes too high to irrigate for the small number of
remaining users. While
Olson (1965) argues that a smaller group may facilitate the
arrangement of collective
management, due to the tank’s technological features, once the
number of formal users
declines, it becomes difficult to provide a sufficient level of
tank maintenance work.
Replacement of tank irrigation by private well irrigation
Even when tanks are properly maintained, having access to
private well
irrigation systems substantially increases rice yields because
those systems serve as a
supplementary source of irrigation and thus improve water
control, especially during the
critical stages of rice cultivation (Palanisami, 2000). Hence,
as long as the costs of
private wells are relatively low, farmers are willing to incur
the costs of installing
private wells even if they can still obtain water from
tanks.
2 Non-participants are asked to contribute money for the
maintenance. However, the collection of money is often incompletely
implemented.
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In the 1990s, the costs of installing and operating private
wells declined
dramatically because of changes in several exogenous conditions.
First, the price of
pumps declined significantly due to the development of the
domestic pump industry.
Figure 1 shows the decline in the relative price of electric
motor pumps to paddy price
throughout the 1990s in Tamil Nadu. Second, the introduction of
the hydraulic drilling
method in the 1980s reduced the cost of drilling by 30%. Because
the nominal cost of
drilling did not change much since then and the paddy price
increased 135% in the
1990s, the relative price of drilling to paddy price declined.
Third, starting in 1989, the
government policy of free electricity supply to farmers made the
adoption of electric
pumps more attractive. Fourth, policies to facilitate
groundwater development, such as
the provision of credit by agricultural cooperatives for pump
and well investment,
accelerated the process. These exogenous changes in the 1990s
promoted the
dissemination of private wells.
One could argue that there is another reason: the deterioration
of tank systems
induced farmers to install private wells. We encountered such
opinions during our field
interviews and thus do not deny this direction of causality. At
the same time, however, it
is important to note that the sharp decline in the investment
and operation costs of
private well systems is also a strong driving force for the
dissemination of private wells.
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This paper examines some of the consequences of this phenomenon;
specifically,
it attempts to determine whether the diffusion of private well
irrigation leads to a
deterioration in tank management and tank quality, and
ultimately to greater poverty and
inequality. To understand how this process proceeds, we advance
our hypotheses in the
following subsection.
Hypotheses
The literature on collective action argues that if farmers have
ready access to
earning activities that are not reliant on local commons, their
incentive to devote
themselves to the collective management of such local commons
will be reduced
(Agrawal, 2001; Kikuchi, et al., 2001). Because of the tanks’
technological features
explained previously, once such farmers exit from collective
management, it becomes
difficult to provide a sufficient level of tank maintenance
activities. Thus, we
hypothesize the following:
Hypothesis 1
As the number of farmers with access to private well irrigation
systems grows, it
becomes more difficult to provide a sufficient level of
collective management of tank
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irrigation systems.
What will happen to farmers’ livelihoods if Hypothesis 1 holds?
When tanks are
well maintained, private wells are usually used for
supplementary irrigation at the
critical stages of rice cultivation. As explained before, by
improving water control at
these stages, the availability of well irrigation increases rice
yields (Palanisami, 2000).
Even if the tanks become less well maintained, yields for these
farmers are protected
because they have recourse to wells. The situation is quite
different for farmers who
cannot use private wells. While they do not suffer the complete
loss of their rice harvest,
they face lower yields because they are totally dependent on the
insufficiently
maintained tanks. This leads us to hypothesize the
following:
Hypothesis 2
The yields of farmers who use only tank irrigation because they
cannot access
private wells are lower than the yields of farmers who can
access wells. The decline in
tank management results in much lower rice yields only among
farmers without access
to private wells.
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Whether this leads to greater poverty and inequality is an
empirical question.
One income compensation strategy usually adopted by farmers
without access to wells
is to work as agricultural laborers for more productive farmers,
who are usually farmers
using private wells. If the labor demand increases appreciably
with the greater water
availability from private wells, the farmers without access may
be able to compensate
partially for their income loss caused by the yield decline.
Another possible strategy is
to diversify their income sources into non-agricultural
activities. If these strategies are
available for the farmers without access to wells, their incomes
may not decline
drastically. On the other hand, if these strategies are not
available and their income is
generated predominantly from their own rice plots that receive
less and less water from
the silted tanks, the lower yields will translate into lower
income. Thus, our third
hypothesis follows:
Hypothesis 3
Unless the farmers without access to private wells can
sufficiently diversify
their income sources to agricultural labor or non-agricultural
work, the decline in tank
irrigation management results in lower income for these
farmers.
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We test Hypothesis 1 in Section 4 and Hypotheses 2 and 3 in
Section 5.
3. Study site and data collection
This study is based on our survey of 79 tank-irrigated villages
randomly selected
from four contiguous districts (Madurai, Ramnad, Virudunagar,
and Sivaganga) in
southern Tamil Nadu State, India. In these districts, rice is
the dominant crop, irrigated
mainly by tanks supplemented by wells. Other crops such as
sorghum, millet, groundnut,
cotton, chili, and sugarcane are cultivated with or without
irrigation. In each village, we
conducted a group interview to collect information on the
management of tank irrigation
as well as on village characteristics. When a village uses
several tanks, we identified the
most important one through the group interview and collected
information on that
particular tank. We also interviewed 450 rice-farming
households, randomly selected
with a sample of 5 or 6 households from each village. Due to the
focus of this study, we
excluded non-farmers from the sample. The authors collected the
data in 1999. Normal
rainfall was recorded at the study sites in the survey year. We
observed no case wherein
collective tank management was not conducted due simply to
insufficient rainfall.
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4. Determinants of collective management
We test the first hypothesis postulated in Section 2 by means of
a cross-section
regression analysis based on sample village observations. For
this purpose, we must
specify the variables to measure the changes in the quality of
collective management as
well as the factors underlying the changes.
Measuring collective management
In order to measure the decline in collective management, we
must evaluate the
overall status of the collective actions devoted to tank
management in the survey year as
compared with the past. There are two approaches: (1) physical
performance indicators,
which measure the outcomes of collective action; and (2) more
direct measures of
cooperation, which evaluate the degree of cooperation.3 The
former approach is not
appropriate for our case due to difficulties in isolating the
current status of irrigation
from the influence of exogenous environmental conditions and
from accumulated past
successes or failures in collective management. Therefore, we
use the latter approach,
3 Examples of the former approach include Bardhan (2000), who
uses the index of the quality of the maintenance of distributaries
and channels, and Dayton-Johnson (2000), who uses the conditions of
canals as the proxy for the existence of collective action. Bardhan
(2000) also uses the latter approach when he uses the number of
conflicts and the frequency of rule violations among beneficiaries.
Another example of the latter approach is Fujiie et al. (2005), who
measure cooperation in terms of the success or failure in
organizing several water management–related activities.
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although this approach also presents difficulties. Past studies
generate a dichotomous
variable because of the difficulty in objectively ranking the
degree of cooperation. We
were able to generate a dummy for each of the three tasks of
collective management
explained in Section 2. However, evaluating each separately will
not necessarily provide
useful information regarding the overall status because the
activities may be mutually
substitutable, that is, the lack of one activity does not
necessarily indicate an overall
decline (Fujiie et al., 2005). Moreover, the lack of de-silting
and channel cleaning does
not necessarily indicate that the management level declined in
the survey year because
such activities are carried out according to need.4 In order for
the second approach to
produce appropriate measurements, we must be able to measure the
overall status in the
survey year.
The variable that we chose to implement in the second approach
to measuring
collective management is the dichotomous response of key village
informants to the
question of whether the informal water users’ organization (WUO)
is active or inactive
in the survey year; the dummy takes the value one if inactive.
We consider this dummy
variable to be an appropriate proxy for measuring tank
management activity because,
4 The need for channel cleaning varies across villages depending
on the structure of the irrigation systems and their environment.
Some villages require channel cleaning annually, while others may
need it only biennially, or even less frequently. The need for
de-silting work arises much less frequently, usually once or twice
in ten years.
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first, it evaluates the overall status of collective management,
and, second, it evaluates
the status in the survey year. Although this variable is
somewhat arbitrary as it is based
on subjective judgment, it is the best available proxy that we
were able to identify,
performing better in the regression analysis than other proxy
variables.5
Using this dummy as our measure, we classified the villages into
those with and
those without active collective management, and we found that
the number of “inactive
villages” (that is, without active collective management) is 31
(39%) and the number of
“active villages” is 48 (61%).
Determinants of collective management
To test Hypothesis 1, the explanatory variable we use is the
density of private
wells in the tank command area. However, there may be reason for
concern that the
current number of private wells may be endogenously determined
in the model in that
5 This inactive WUO dummy has a high correlation (correlation
coefficient 0.75) with a dummy that becomes one when channel
cleaning had not been conducted in the last three years, indicating
the consistency of villagers’ cleaning behavior and the subjective
evaluation of the overall status. However, the channel cleaning
dummy was not used because it failed to produce reasonable
econometric results in the following section. The inactive WUO
dummy is consistent also with water supply conditions. Among the
inactive villages according to the classification of our variable,
48% of these villages claimed that the availability of tank water
had worsened, whereas the corresponding percentage goes down to 29%
in the active villages. Moreover, in the active villages, even in
those that claimed that the situation had worsened, the majority
claimed that the reason was bad rainfall rather than the poor
management of irrigation facilities, whereas this was reversed in
the inactive villages. The regression analysis with the dependent
dummy of water availability did not produce reasonable results
either. The low performance of these two alternative dummies stems
from the shortcomings explained in the first paragraph of this
section.
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inactive management may cause farmers to switch to private
wells. To circumvent this
problem, we apply the instrumental variable (IV) method, using
the relative cost of
pump sets and the relative cost of well digging to paddy price
as the identifying
instruments.6 Note that if we obtain a significant coefficient
of this well density
variable, this simply indicates causality from private well
dissemination to inactive
management without denying a possible reverse causality. In
other words, what we can
confirm from our regression analyses is that although such a
reverse causality possibly
exists, the direction from private well dissemination to
inactive management can also be
a strong driving force behind failure in collective
management.
We also control the other determinants suggested by the
literature on collective
action. Relying on a synthesis by Agrawal (2001), and also on an
empirical study of
tank irrigation management in Tamil Nadu by Bardhan (2000), we
divide the other
factors into six categories: exit options, heterogeneity, group
size, government
interventions, destructive shocks, and weather.7 The descriptive
statistics of the
variables are presented in Appendix Table A1.
6 The cost of pump set consists of the cost of pump with a
particular horsepower and the cost of equipments typical in a
survey village. The cost of digging is the cost required to dig a
typical depth of well in a village. These costs vary across
villages as the depth of well and the required pump set differ
across villages, depending on geographical conditions and the
locations of aquifer. 7 Agrawal’s (2001) synthesis relies on three
major contributors in this area, that is, Wade (1988), Ostrom
(1990), and Baland and Platteau (1996), supplemented by other
studies.
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To capture the accessibility to exit options, four variables are
employed in our
regression analysis: (1) the percentage of high-school graduates
in a village, (2) the
percentage of college graduates in a village, (3) male wage
rate, and (4) agricultural area
physically non-feasible for tank irrigation per agricultural
household. All these variables
are expected to have positive coefficients as they increase the
attractiveness of activities
other than rice cultivation.
Among the variables of the second category, heterogeneity
variables, we include
the two variables that measure wealth heterogeneity: (1) the
Gini coefficient and its
squared term of animal holdings and (2) of tank-irrigable plot
size. The squared terms
are included to capture the inverted U-shape relationship
between heterogeneity and
inactive management (Bardhan, 2000; Dayton-Johnson and Bardhan,
2002).
The third category, group size, is measured by the number of
households having
agricultural land in the tank command area. Along the theme of
Olson (1965), the
positive relationship between group size and inactive collective
actions is expected.
However, in the context of the tank management, a large group is
less likely to suffer
labor scarcity that would arise when a certain amount of labor
input is required to
maintain the indivisible tank technology. Hence, the effect of
this variable is an
empirical question, as empirically observed by Bardhan
(2000).
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Regarding the fourth category, government interventions, one
relevant policy
variable is the percentage length of the water supply channel
lined by blocks and cement.
This construction work is done by the Public Works Department
(PWD) with funds
from the government or from international aid programs.
According to a specialist at
Tamil Nadu Agricultural University, the selection of villages by
the PWD was more or
less at random. Channel lining reduces water loss in delivery
and also eases cleaning,
making tank irrigation more profitable with less labor. Hence,
even if private well users
exit from collective management, the introduction of this
labor-saving technology
makes management feasible by the remaining formal users. Thus,
we postulate that tank
management is less likely to be inactive when the channels are
lined (negative
coefficient). Another variable is a PWD tank dummy, which occurs
if the PWD is
supposed to perform tank de-silting. Although if this happens
villagers may come to
rely on the government even for those activities that are
supposed to be the
responsibility of the local water users’ organization, this may
also enhance collective
management by reducing the burden on the local organization; the
direction of the effect
is an empirical question.
The fifth category, destructive shocks, includes (1) a dummy of
at least one
ethnic or religious conflict within a village in the last ten
years and (2) the number of
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droughts. As the literature argues, after experiencing such
destructive shocks, collective
management tends to disappear (positive coefficients of these
variables) (Baland and
Platteau, 1996; Sethi and Somanthan, 1996). The last category is
weather and it is
measured by block-level annual rainfall in the survey year.
Regression results
The results of the regression analyses are presented in Table 1.
In addition to the
probit model results, we report the linear probability model
results, first, because the
probit model cannot include one policy variable, the percentage
of channel lining, due
to the perfect prediction problem, and, second, because the
linear model can provide
diagnostic statistics that confirm the validity of our IV
specification.8 The test statistics
for the linear IV analysis presented in the lower part of Table
1 indicate that the current
density is an endogenous variable (F test for endogeneity) but
it is significantly
predicted by the identifying instrumental variables (first-stage
F test) that can be
considered as exogenous to the model (χ2 test for over
identification), providing
confidence in the validity of the model specification
(Wooldridge, 2002). The first-stage
regression of the well density function (not reported here)
indicates that the coefficient
8 To circumvent the heteroskedasticity problem in the
application of OLS to the dichotomous dependent variable, we report
the robust standard errors in our OLS results.
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of the relative price of pump sets is negative and highly
significant, which is consistent
with our observation of the dissemination of private wells with
a reduction in pump
costs. The qualitative results of our IV probit model are the
same as those of our linear
model. A key finding from these IV results is that the density
of wells is statistically
significant with a positive sign. Our supplementary analyses
also find two supportive
empirical results which are presented in Appendix. Hence, we
conclude that the results
support Hypothesis 1. This finding is consistent with Palanisami
and Easter’s (2000)
study on tank irrigation in other regions in Tamil Nadu.
Turning to the other explanatory variables, the exit options are
not statistically
significant except for the male wage rate in the probit model.
The educational variables
become insignificant presumably because these variables capture
not only educated
people’s likelihood of exit (a positive coefficient) but also
the effect of their leadership
for collective management as local elites (a negative
coefficient) (Meinzen-Dick et al.,
2002b). Regarding wealth heterogeneity, the Gini of plot size
and its squared term are
strongly significant. Their signs are consistent with the
inverted U-shape relationship in
all four models, and thus are consistent with Bardhan’s (2000)
empirical study.9 This
result confirms the importance of socioeconomic factors for
collective management. As
9 For example, turning point value of Gini is 0.322 for linear
IV or 0.314 for Probit IV which is 0.549 or 0.452 standard
deviation higher than the mean value (=0.275).
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for the two kinds of destructive shocks, the number of droughts
is significantly positive.
This result suggests that even in villages where collective
management provides
positive economic returns to the participants, this collective
management may disappear
due to shocks. Turning to the policy variable, as we have
postulated, the percentage of
channel lining in the linear probability models is highly
significant with a negative sign.
Thus, this policy could be an effective intervention tool for
the revitalization of
collective management.
5. Impact on rice yields and income
Binary comparison
To develop an understanding of the impact of the decline in
collective
management, we compare rice yields, monthly income, monthly
consumption value,
and a subjective poverty assessment among 171 households in 31
inactive villages and
279 households in 48 active villages. These data appear in Table
2. The difference in
rice yields is clear: row (1) shows that rice yield in kg per
hectare is lower in the
inactive villages than in the active villages, and the
difference is statistically significant.
Note, however, that the difference is merely 7.6%, which seems
rather small. The lower
yield due to inactive management is likely largely compensated
for by the existence of a
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larger number of private well users in the inactive villages
than in the active villages.
Inequality measured by the Gini coefficient is higher among
households in the inactive
villages than in the active villages, indicating that yield
variation is larger in the former
than in the latter. Likewise, although the levels are very
small, the average of the
village-level Gini coefficients for the inactive villages is
higher than that for the active
villages, and the difference is statistically significant.
A similar structure is observed in row (2) in the adult
equivalent per capita
monthly income: that is, lower income and higher inequality in
the inactive villages.10
Comparison of the poverty indexes shows that both the incidence
of poverty and the
poverty gap are higher in the inactive villages.11
Comparison of the consumption value shows a slightly different
picture, though
the conclusions are essentially the same. The mean is lower in
the inactive villages but
the difference is not statistically significant, presumably
because of the existence of
consumption smoothing mechanisms to some extent. The magnitudes
of the Gini are
moderately higher for the inactive villages and significantly
different from those of the 10 The weights used for computation of
adult equivalent household size are 0.5 for a child of age below 5
years, 0.73 for a child aged 6-10, 0.83 for a child aged 11-14,
0.83 for a female above age 14, and 1.0 for males above age 14
(Rao, 1983). Household members living outside of the household
because of work are excluded but members living outside because of
educational pursuit are included as members of the household, on
the assumption that students receive financial support from the
household. 11 Use of the national poverty line of Rs. 324 monthly
per capita for 1993-4, instead of US$ 1, does not change the
qualitative results. The same applies to the comparison of the
consumption value.
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active villages.12 Moreover, poverty is more severe in the
inactive villages in terms of
both the ratio and the gap.13 These observations are consistent
with the villagers’
subjective assessment of their poverty conditions presented in
row (4): a larger
percentage of villagers in the inactive villages judged that
they were in serious poverty
than villagers in the active villages.
In summary, the binary comparison suggests that inactive
collective
management is associated with greater poverty and inequality
presumably because of
insufficient income compensation from agricultural labor and
non-agricultural work
among farmers without access to private wells in the inactive
villages. The regression
analyses in the following subsection examine whether and to what
extent this
consequence is explained by inactive collective management and
access to private wells,
controlling for other covariates.
Variables for regression analyses
We estimate the regression functions explaining rice yield,
agricultural labor
income, non-agricultural income, total income, and consumption
value. To test 12 The national-level Gini coefficient based on
expenditure data in 1999-2000 was 32.5, which is very close to our
Gini coefficients (all households in each category), indicating the
appropriateness of our data (World Bank, 2004). 13 The
national-level head count ratio of poverty and poverty gap are 34.7
and 8.2, respectively, in the 1999-2000 data (World Bank, 2004).
Our figures show higher magnitudes, reflecting the high incidence
of poverty in rural areas.
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24
Hypotheses 2 and 3, we include the village-level inactive
management dummy as one of
the explanatory variables. To confirm that the decline in
village-level collective
management is exogenous to each individual in the villages, we
apply the F-test for
endogeneity and confirm that it is not necessary for this
variable to be instrumented.14
Also important is the household-level variable that measures
inaccessibility to
private wells. This is a dichotomous variable: non-well-owners
who did not buy water
from well-owners are classified into the no-access group
(dummy=1), whereas owners
and non-owners with water transaction records are classified
into the access group
(dummy=0). The differential impacts of no access to wells with
and without collective
management are captured by considering four different cases.
Using the case of farmers
having access to wells at an active village as the base
(Access&Active), we construct
three irrigation status dummies: (1) no access at an active
village (No Access&Active),
(2) no access at an inactive village (No Access&Inactive),
and (3) access at an inactive
village (Access&Inactive).
14 The identifying instrumental variables used here are the
village-level variable used in our analyses of the determinants of
inactive collective management. These instruments are jointly
significant in the first-stage F-test. The variable measuring
inaccessibility to wells, which might be an endogenous variable, as
we discuss later, is excluded from the model in order to keep all
the variables other than the inactive collective management dummy
exogenous. The F-values and the corresponding p-values on the
residuals from the first-stage prediction of inactive collective
management (F-test for endogeneity) are F=0.02 and p=0.88 for the
rice yield regression, F=2.20 and p=0.14 for the consumption
regression, and F=0.48 and p=0.50 for the income regression,
indicating that the inactive management dummy is exogenous and use
of the OLS model will not cause serious problems.
-
25
In Table 3, we summarize the expected signs on these irrigation
status dummies.
Note that the base (Access&Active) is the most
irrigation-water-sufficient case. The
coefficients of the dummies capture the differences from this
base. Hence, in the rice
yield function, if Hypothesis 2 holds, we observe negative
coefficients on the first two
dummies, No access&Active and No access&Inactive, at a
larger magnitude for the
second one. As long as private wells provide sufficient
irrigation water, farmers achieve
a high yield even in an inactive village; thus, the coefficient
of the Access&Inactive
dummy is expected to have no significant difference from the
base. Turning to the
agricultural labor income function, if income is supplemented by
an increase in
agricultural labor income, we should observe positive
coefficients on the first two
dummies at a larger magnitude for the second one. The same
structure can be observed
in the non-agricultural income function. However, since
accessibility to non-agricultural
occupations is largely determined by level of education rather
than by irrigation status,
we may not observe significant coefficients on the irrigation
status dummies. Finally, if
supplemental income is large enough, in the total income and
consumption value
functions, we should not observe any significant coefficients on
the irrigation status
dummies. On the other hand, if supplemental income is
insufficient, we should observe
negative coefficients again on the first two dummies at a larger
magnitude for the
-
26
second one.
To measure irrigation status, we must also control for the
distance from the
irrigation source to farmers’ fields because, in gravity
irrigation systems like tank
systems, this distance affects water availability. Moreover, the
effect of distance may
vary depending on the status of collective management and well
access. In our
explanatory variables, we include not only the distance from the
tank but also the
interaction of distance with our three irrigation status
dummies.
Consequently, our regression equations include six explanatory
variables
containing information on the status of accessibility to wells.
Accessibility to wells,
however, potentially entails endogeneity and measurement
biases.15 These potential
biases are controlled by the instrumental variable method.16
The other determinants included in the rice yield regression are
a set of
household human and physical asset variables, socioeconomic
characteristics, and
15 There are two kinds of potential bias. First, under our
definition of this dummy variable, the no-access group would
include non-owner farmers who actually had access to well
irrigation but chose not to use it because they had enough water
from tanks or rainfall. These farmers presumably achieve yields and
income as high as those of the farmers who use wells. The incorrect
inclusion of them in the no-access group would result in an
underestimation of the negative impact of non-access. Second, if
high-income farmers selectively became well-owners, the impact of
non-access would be overestimated. 16 The identifying instrumental
variables that explain the six potentially endogenous variables
include not only those that explain non-access to wells—such as the
number of water sellers in a village, the number of water buyers in
a village, the cost of well digging and its squared term, and the
value of house building—but also the interactions of those
variables with the inactive management dummy and with distance from
the tank (Wooldridge, 2002).
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27
village-level characteristics. The descriptive statistics are
presented in Appendix Table
A4. The household asset variables include (1) the proportion of
working members with
primary, secondary, and college-level schooling, respectively;
(2) the average age of a
working member and its squared term; (3) the size of the rice
plot and its squared term;
(4) the value of animal holdings per working member and its
squared term; (5) the
number of working members; and (6) a tractor owner dummy. The
socioeconomic
variable is the gender of the household head (male=1).
Village-level differences are
controlled by the male wage rate, block-level annual rainfall,
the dummy measuring
accessibility to the village by a vehicle, and the percentage of
households wired for
electricity. In the regressions other than the rice yield
function, instead of rice plot size,
we use (1) the size of tank-irrigable plots per working member
and its squared term and
(2) the size of tank-non-irrigable plots per working member and
its squared term. Since
the consumption value is affected by permanent household income
instead of income
for a particular year, average rainfall over the last ten years
is used rather than annual
rainfall.
Regression results
The regression results for yield, agricultural labor income, and
non-agricultural
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28
income are reported in Table 4, and the results for total income
and consumption value
in Table 5. In the functions in Table 4, endogeneity is not
detected, thus we show the
OLS results alone. Meanwhile, Table 5 shows both the OLS and IV
results, since the
F-tests indicate the possibility of endogeneity. In Table 5, the
F-tests on the instruments
in the first-stage regression are highly significant for all six
endogenous variables,
indicating high predictive power. The over-identification tests
show the difficulty of
rejection of the exogeneity of the instruments at the 5%
significance level, adding
confidence to the validity of our IV specifications.
The results of the rice yield function in Table 4 show the
expected signs and
magnitudes for the No access&Active and No
access&Inactive dummies. A key finding
from this is that compared with the yields of farmers with
access to wells in collective
management active villages (base case), the yields of farmers
without access to wells is
337 kg lower even in the same active villages, confirming the
importance of private
wells as a supplementary irrigation source. Another key finding,
this one related to
inactive villages, is that in those villages, the yield gap
between farmers with access and
those without is becoming wider (863 kg).17 This is larger by
far than the yield gap in
17 Note that, unexpectedly, the coefficient of the
Access&Inactive dummy is positive and significant (773.2 kg).
Note also that the distance has a negative effect only among
Access&Inactive farmers; at the average distance from the tank
(0.545 km), this variable has a negative effect of 664 kg. Hence,
the yield gap in inactive villages between the farmers without
access to wells and those with access to wells is computed as
754.4+(773.2 – 664.6)=863 kg.
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29
the active villages noted above. Based on this result, we
conclude that Hypothesis 2
holds. The results also indicate that all village
characteristics are not significant. This
feature holds for all the following regression functions except
for the agricultural labor
income function. Presumably, our key village variable,
collective management status,
representatively captures the village-level differences in our
tank-irrigated villages, and
thus its inclusion reduces the importance of the other
village-level variables.
The results for the agricultural labor income function in Table
4 also show the
expected signs and magnitudes for the irrigation status dummies,
indicating that lower
income is compensated for by agricultural labor. However, the
amount of compensation
is not large in either case: Rs. 10.4 or Rs. 16.8 per person per
month. In the
non-agricultural income function, the insignificant coefficients
for the first two
dummies indicate that this income source is not available to
either No access&Active
farmers or No access&Inactive farmers as a supplemental
income source. Rather,
non-agricultural income is determined by the proportion of
college-level graduates to
the number of working members in a household. These results
imply that opportunities
for income diversification are limited, and thus we expect lower
income and
consumption among farmers without access to private wells in
Table 5.
As expected, the coefficients of No access&Active and No
access&Inactive are
-
30
negative at larger magnitude for the second one. The IV results
on total income indicate
that in the active villages, the gap of total income between the
farmers with and those
without access to wells is Rs. 189.1, becoming wider (Rs. 269.5)
in the inactive
villages.18 The same qualitative pattern holds for the
consumption value function,
except for the insignificant coefficient of the distance
variable. Given an average
distance for all farmers of 0.4 km, the impact of distance is
not large, and thus it is
probably not captured as significant in the consumption value
function.
Counter-factual analysis
The regression analyses indicate that farmers without access to
wells suffer not
only lower yields but also a lower income and consumption value
when collective
management declines. Although the negative coefficients are
statistically significant, the
magnitudes of the impacts may be marginal. Hence, one may raise
a question as to
whether the lower income caused by the decline in collective
management is large
18 Two remarks are necessary here. First, comparison of the OLS
and IV results shows the OLS’s overestimation of the negative
effects, implying that the root of the bias is in the rich farmers’
self-selection of being well-owners. Since income level is
important to well ownership but yield, agricultural labor income,
and non-agricultural income alone may not be, the three functions
in Table 4 do not suffer an endogeneity problem. Second, the
coefficients of Access&Inactive are no longer significant.
Although the farmers with access to wells in the inactive villages
achieve high yields, they grow rice intensively, with the use of
wells as the primary source of irrigation. Sakurai and Palanisami
(2001) show that the intensive use of wells does not necessarily
result in high profit as it requires greater operation and
maintenance costs than does tank management. Hence, no significant
difference in profitability and thus in income is observed.
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31
enough to result in greater poverty and inequality in the
inactive villages. In other words,
will the revitalization of collective management help alleviate
poverty and inequality?
Using the regression results and assuming active collective
management in all the
villages, we predict yield, total income, and consumption value.
These descriptive
statistics, including the Gini and the poverty indexes computed
from the predicted
values, are added to Table 2 as column (A-2) and are shown as
Table 6.19 The last
column in Table 6 shows the differences between the inactive
villages under the
counter-factual assumption and the actual active villages.
The differences in the mean values are no longer significant for
either variable.
The Gini coefficients of income and consumption in the inactive
villages have declined,
resulting in insignificant differences in the comparison of
village-level averages.
Comparison of yields still indicates the existence of
significant differences, yet the
levels of the Gini coefficients themselves have become very
small. The changes in the
poverty indexes show that the poverty ratio and gap are reduced
to the levels of the
active villages. These results indicate that the negative
impacts from the decline in
collective management on farmers without access to wells are so
large that the
revitalization of collective management can contribute
significantly to reductions in 19 For comparability with the
figures in Table 2, the residuals from the actual data are added
back to the counter-factual predicted value. Otherwise, the Gini,
which measures a kind of variation, becomes incomparably small.
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32
poverty and inequality.
6. Conclusion and policy implications
Using village- and household-level data collected in Tamil Nadu,
India, we
examined the factors underlying the decline in collective
management of tank irrigation
systems and the impact of that decline on rural livelihoods,
especially in terms of
poverty and inequality. A key conclusion of our regression
analyses and counter-factual
analysis is that the diffusion of private wells is one of the
factors accelerating the
decline in collective management, and that this decline leads to
lower yields only
among farmers without access to private wells. Our analyses also
find that since these
without-access farmers cannot sufficiently compensate for their
lower income by
diversifying income sources to agricultural labor or to
non-agricultural work, lower
yields translate into lower income. Farmers with access to
private wells, by contrast, can
continue to obtain high yields and high levels of income. As a
result of this situation,
poverty and inequality increase; thus, through this process, the
decline in collective
management carries social costs. According to our field
observations in inactive villages,
this consequence has yet to reach the point where without-access
farmers sell their land
to with-access farmers. The former usually leave their land
fallow, relying on a
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33
subsidized rice quota from public ration shops for their
living.
One of the major contributing factors to the decline is the exit
behavior by some
farmers who have gained alternative irrigation sources such as
private wells. It is
difficult to deter their exodus from collective management
because they do not suffer
the consequences of the deterioration in tank performance,
unlike the remaining farmers.
The number of exiting farmers will inevitably tend to increase
in response to the
development of small or medium-size irrigation technologies that
are used by individual
farmers. Unless less labor-intensive systems are introduced or
some kind of support is
given, it will be difficult to maintain collective tank
management.
One promising possibility suggested by our analysis is the use
of lined channels
with tank systems. This technology significantly reduces the
labor required for
maintenance work and also increases water availability by
minimizing seepage.
Although thus far construction of lined channels has been
undertaken by the PWD, if
the return on investment is assured, WUOs may decide to take
over the task as a local
public goods initiative. It will be important to conduct
detailed cost-benefit analyses on
the impact of channel lining and, if warranted, design
appropriate support programs.
Charging an appropriate fee for electricity would also help
prevent a decline in
collective management. Under the present practice of free
electricity for agricultural
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34
purposes, the number of electric pumps tends to be more than the
social optimum,
resulting in both overexploitation of groundwater and a decline
in collective
management. A revision of the present policy must be seriously
considered.
Collective management may still decline even with the policies
mentioned
above because some socioeconomic factors such as village-level
heterogeneity and
destructive shocks can also cause a decline. Hence, an
alternative approach to reduce
poverty and inequality is to implement policies that increase
farmers’ prevention of
irrigation water shortages. The development and introduction of
a drought-tolerant rice
variety is one possibility. Educational development also
contributes by expanding
farmers’ income sources to non-agricultural activities. More
detailed policy implications
could be derived through further analyses on the impacts of new
rice technology and
educational development on water-constrained poor farmers, which
we leave for future
research.
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35
Acknowledgements
This study was funded by Global Environment Research Fund
through Ministry of
Environment, Japan. The authors acknowledge useful comments from
two anonymous
referees, Yujiro Hayami, Keijiro Otsuka, and Daiji Kawaguchi as
well as editorial
assistance from Suzanne Akiyama and Paul Kandasamy.
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36
Appendix Two supportive empirical results on the impact of well
dissemination on collective management
In order to confirm our claim, we conduct two regression
analyses. First, we
check the existence of the possible reverse causality from the
decline in tank
management to the installation of private wells. For this
purpose, we run a
household-level probit model explaining the determinants of
private well ownership.
The dependent variable is the dummy taking the value one if a
household owns at least
one private well. A key explanatory variable is the
village-level status of collective
management, which is exogenous to each individual household as
we will show in the
following section. Other important village-level variables
include the relative cost of
pump sets, the relative cost of well digging, and the percentage
of households with
electricity. Access to electricity is important as motor pumps
are the most popular type
in Tamil Nadu. Household-level characteristics such as wealth
and education are also
included. An important finding from the results reported in
Appendix Table A2 is that
the probability of well ownership increases significantly with
cheaper pump prices,
greater access to electricity, and larger physical and human
capital, but not significantly
with the status of collective management, indicating the
relative unimportance of the
reverse causality.
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37
Second, if the causality is as we claim it is, we would observe
an inverted
U-shape relationship between the productivity of farmers who
rely only on tanks and
the density of wells in the tank command area. Since the private
well users reduce the
use of tank water, tank water availability and thus the
productivity of the remaining tank
users would increase initially until the tanks became seriously
silted. Using a subsample
of farmers relying only on tanks, we regress the rice yield per
hectare on the density of
private wells and its squared term as well as on the other
relevant explanatory variables
which are the household and village characteristics used for the
yield regression in Table 4. The
results in Appendix Table A3 are consistent with our postulate,
indicating that yield
increases until the density reaches 0.6 standard deviations
higher than the mean value.
With these two pieces of supportive evidence, we claim that the
dissemination of private
wells, which has been accelerated by the recent reduction in
investment and operation
costs, has a dominant impact on the decline in collective
management.
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38
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42
Table 1: Results of the regression analyses explaining the
decline in collective management in villages
Dep. Var.: collective management (inactive=1)
Linear probability model Probit model OLS IV Probit IV probit
Private well Density of well in tank command areaa (# / ha) 0.58
1.22 0.78 2.11 (1.30) (2.34)** (0.83) (2.24)** Exit options
Percentage of secondary school graduates 0.01 0.01 0.02 0.02
(1.10) (1.47) (1.24) (1.43) Percentage of college graduates
-0.01 -0.02 -0.04 -0.01
(0.90) (1.10) (0.78) (0.25) Male wage rate (Rs./day) 0.01 0.01
0.04 0.03 (1.22) (1.37) (1.97)** (2.16)** Tank un-irrigable agric.
area/Ag HH (ha) -0.00 -0.00 -0.03 -0.03
(0.18) (0.23) (0.39) (0.52) Heterogeneity Gini coefficient of
value of animal holdings 1.22 1.28 1.86 -0.81
(0.93) (0.95) (0.32) (0.18) -0.20 -0.45 2.02 2.88 Gini
coefficient of value of animal holdings squared
(0.16) (0.35) (0.35) (0.56) Gini coefficient of tank-irrigable
plot size 7.61 7.47 24.66 14.08
(3.67)*** (3.40)*** (3.31)*** (2.10)** -12.02 -11.57 -39.79
-22.39 Gini coefficient of tank-irrigable plot size squared
(3.25)*** (2.95)*** (3.02)*** (1.97)** Size
-0.10 -0.13 -0.34 -0.33 Number of HH having agric. land in tank
command area (100HH) (1.04) (1.33) (0.98) (1.13)
Government intervention Channel lining (%) -0.02 -0.02
(2.65)** (2.38)** Tank type (PWD =1) -0.14 -0.24 -0.26 -0.59
(1.06) (1.79)* (0.59) (1.42) Destructive shocks
0.17 0.15 1.09 0.71 Ethnic or religious conflict in the last 10
years (yes=1) (0.94) (0.78) (2.01)** (1.47)
Number of droughts in the last 10 years 0.03 0.04 0.11 0.13
(1.71)* (2.25)** (1.96)* (2.62)** Rainfall Annual rainfall (mm)
0.00 0.00 0.00 0.00 (0.87) (0.88) (0.89) (0.51) Constant -1.82
-1.91 -8.16 -4.99 (2.92)*** (2.98)*** (3.55)*** (2.52)** F-stat for
endogeneity test (H0: no endogeneity) 3.44 [0.06] χ2-stat for
endogeneity test (H0: no endogeneity) 13.69 [0.00] Joint
significant test on instruments (First-stage F test) 3.26 [0.04] χ2
stat for over identification test 1.64 [0.20] F-stat (H0: all slope
coeff=0) 7.35 [0.00] 7.65 [0.00] χ2-stat (H0: all slope coeff=0)
36.55 [0.00] 41.38 [0.00]
Heteroskedasticity robust z-statistics in parentheses for probit
and IV probit analysis. Heteroskedasticity robust t-statistics in
parentheses for OLS and IV analyses. Numbers in brackets are
p-values. * significant at 10% level; **significant at 5% level;
*** significant at 1% level a Instrumented variable. Identifying
instruments are the cost of well digging and the cost of pump set
relative to rice price.
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43
Table 2: Comparison of rice yield, total income, consumption
value, and subjective poverty assessment of sample households
between collective management inactive villages and active
villages
(A) (B)
Households in
inactive villages
(villages: n=31)
(HH: n=171)
Households in
active villages
(villages: n=48)
(HH: n=279)
Difference
(A)-(B)
(|t-ratio|)
(1) Rice yield (kg/hectare)
Mean 3499 3786 -287 (2.59)***
Std. dev. 1275 1049
Gini (all households in each category) 20.7 15.4 5.3
Gini (average of village-level Gini) 9.8 6.7 3.1 (2.60)***
(2) Per capita monthly income (Rs./person) a
Mean 332 395 -63 (1.77)*
Std. dev. 370 363
Gini (all households in each category) 53.9 45.8 8.1
Gini (average of village-level Gini) 39.8 30.9 8.9 (1.98)**
Head count ratio of poverty (P0) b 58.5 48.3 10.2
Poverty gap (P1) b 33.7 21.6 12.1
(3) Per capita monthly consumption value (Rs./person) a
Mean 308 336 -28 (1.46)
Std. dev. 194 202
Gini (all households in each category) 34.1 31.8 2.3
Gini (average of village-level Gini) 23.2 20.4 2.8 (1.67)*
Head count ratio of poverty (P0) b 52.0 45.5 6.5
Poverty gap (P1) b 19.8 14.2 5.6
(4) Subjective poverty assessment Percentage of villages
assessing that the current condition of poverty is serious
61 29
* significant at 10% level; **significant at 5% level; ***
significant at 1% level. a The value is converted into a per capita
base using the adult equivalent number of present household
members. See footnote 9 for details of the conversion method. b
International poverty line of US$ 1 per day adjusted for purchasing
power parity is used.
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44
Table 3: Irrigation status dummy, irrigation water condition,
and expected sign and magnitude
Irrigation status dummy
Irrigation water condition Expected sign and magnitude in
Rice yield function
Agric. labor income function
Non-agric. income function
Total income/consumption function
No Access&Active Less sufficient (water from tanks is still
available) − + + / 0 − / 0
No Access&Inactive Severe − − + + + + / 0 − − / 0
Acess&Inactive Sufficient as long as private wells provide
water 0 0 0 0
Access&Active (base) Sufficient na na na na
Note: − : negative and significant (double negative indicates
larger magnitude) +: positive and significant (double positive
indicates larger magnitude) 0: insignificant +/0: sign and
significance depend on cases −/0: sign and significance depend on
cases
-
45
Table 4: Results of the regression analyses of the determinants
of rice yield, agricultural labor income, and non-agricultural
income
Dep. var. Rice yield Adult equivalent per cap. monthly agric.
labor
income
Adult equivalent per cap. monthly non-agric.
income OLS OLS OLS Variables related with irrigation status
No access&Active -337.0 10.4 10.9 (1.70)* (2.92)*** (0.59)
No access&Inactive -754.4 16.8 -7.6 (2.11)** (2.44)** (0.32)
Access&Inactive 773.2 -5.0 91.9 (2.37)** (1.00) (1.96)*
Distance from tank (km) 120.3 -1.6 -11.1 (0.67) (0.46) (0.58) No
access&Active*Distance -296.5 -4.6 5.9 (1.14) (0.96) (0.21) No
access&Inactive*Distance -155.2 -9.2 -28.8 (0.41) (1.47) (1.11)
Access&Inactive*Distance -1219.4 -0.3 -105.9 (3.51)*** (0.06)
(1.75)*Household characteristics Primary educ. proportion a 376.1
1.1 -26.5 (1.36) (0.17) (1.10) Secondary educ. proportion a 668.0
-8.4 -13.0 (2.59)** (1.14) (0.58) College educ. proportion a 1033.6
-25.3 521.5 (1.78)* (2.55)** (2.38)** Av. age b 11.1 0.2 2.4 (0.19)
(0.11) (0.34) Av. age sq. b -0.3 -0.0 -0.0 (0.37) (0.33) (0.48)
Rice plot size (ha) -502.9 (2.36)** Rice plot size sq. (ha)
81.5
(1.58) Av. tank-irrigable plots c (ha) -47.2 -137.0
(2.61)** (2.20)** Av. tank-irrigable plots sq. b 22.3 89.8
(1.81)* (2.76)*** Av. tank-un-irrigable plots b (ha) -4.8 -17.0
(1.19) (0.55) Av. tank-un-irrigable plots sq. b 0.5 1.8 (0.66)
(0.33) Av. value of animal holdings b (000 Rs.) 60.9 0.3 0.9
(3.16)*** (0.69) (0.41) Av. value of animal holdings sq. b -1.3
-0.0 -0.1 (2.32)** (0.45) (1.21) No. of working members 43.6 -1.1
-3.7 (1.50) (1.20) (0.82) Owner of tractor (owner=1) 37.3 44.2
-33.3 (0.08) (1.92)* (2.07)** Male HH head (male=1) 366.6 10.9 11.0
(1.26) (3.24)** (0.67)Village characteristics Male daily wage rate
(Rs./day) 10.6 0.1 0.0 (0.89) (0.29) (0.04) Annual rainfall (mm)
-0.1 -0.0 -0.0 (0.86) (0.39) (1.05)
Accessibility by vehicle (yes=1) -12.8 -2.2 -1.1 (0.11) (0.99)
(0.12) Percentage of HH with electricity (%) -7.1 0.2 0.2 (1.62)
(1.64)* (0.70)
Constant 3274.1 7.8 64.0 (2.49)** (0.19) (0.42)
F-stat for endogeneity test (H0: no endogeneity) 0.88 [0.51]
0.09 [0.99] 1.38 [0.22]F-stat (H0: all slope coeff=0) 6.35 [0.00]
2.86 [0.00] 2.91 [0.00]Village clustering robust t-statistics in
parentheses. Numbers in brackets are p-values. * significant at 10%
level; **significant at 5% level; *** significant at 1% level. a
The proportion to the number of working members. b Divided by the
number of working members.
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46
Table 5: Results of the regression analyses of the determinants
of total income and consumption value
Dep. var. Adult equivalent per cap. monthly income
Adult equivalent per cap. monthly consumption value
OLS IV OLS IVVariables related to irrigation status
No access&Active c -210.9 -189.9 -84.1 -75.1 (3.65)***
(3.40)*** (2.87)*** (2.54)** No access&Inactive c -286.8 -269.5
-124.6 -119.1 (3.79)*** (3.60)*** (3.05)*** (3.06)***
Access&Inactive c -5.8 17.3 83.9 101.7 (0.06) (0.16) (1.37)
(1.57) Distance from tank (km) -125.4 -119.1 -46.1 -50.7 (2.00)**
(1.70)* (1.32) (1.30) No access&Active*Distance c 37.3 33.7
15.2 26.4 (0.52) (0.36) (0.37) (0.49) No
access&Inactive*Distance c 41.2 29.9 23.5 26.5 (0.53) (0.35)
(0.52) (0.55) Access&Inactive*Distance c -55.7 -70.0 -95.6
-96.5 (0.55) (0.63) (1.49) (1.40)Household characteristics Primary
educ. proportion a -34.7 -30.5 105.0 107.7 (0.50) (0.44) (2.44)**
(2.50)** Secondary educ. proportion a -23.9 -23.0 61.4 60.6 (0.39)
(0.37) (1.65) (1.59) College educ. proportion a 684.3 685.3 476.6
475.9 (2.32)** (2.32)** (3.57)*** (3.55)*** Av. age b 9.8 9.3 -0.9
-1.4 (0.72) (0.69) (0.10) (0.14) Av. age sq. b -0.2 -0.2 -0.0 -0.0
(1.24) (1.22) (0.15) (0.10)
Av. tank-irrigable plots b (ha) 442.8 453.7 287.9 293.2
(2.96)*** (3.05)*** (3.65)*** (3.76)*** Av. tank-irrigable plots
sq. b 0.2 -2.0 -76.7 -76.9 (0.00) (0.02) (1.39) (1.41) Av.
tank-un-irrigable plots b (ha.) 440.7 440.5 74.3 71.9 (5.35)***
(5.32)*** (1.64) (1.61) Av. tank-un-irrigable plots sq. b -94.3
-94.8 -16.6 -16.6 (4.19)*** (4.22)*** (1.67)* (1.68)* Av. value of
animal holdings b (000 Rs.) 6.3 6.3 -0.9 -0.9 (1.10) (1.09) (0.23)
(0.25) Av. value of animal holdings sq. b -0.2 -0.2 0.1 0.1 (0.71)
(0.72) (0.66) (0.65) No. of working members 20.6 21.2 1.9 2.2
(2.26)** (2.33)** (0.39) (0.43) Owner of tractor (owner=1) 155.8
151.4 -0.5 -2.9 (2.24)** (2.10)** (0.01) (0.05) Male HH head
(male=1) 74.9 75.9 -14.1 -14.1 (0.76) (0.78) (0.24) (0.25)Village
characteristics Male daily wage rate (Rs./day) -0.7 -0.7 1.7 1.7
(0.30) (0.30) (0.83) (0.83) Average rainfall for 10 years (mm) 0.0
0.0 (0.11) (0.06) Annual rainfall (mm) -0.0 -0.0 (0.65) (0.64)
Accessibility by vehicle (yes=1) -11.7 -12.7 -12.4 -13.6 (0.53)
(0.57) (0.81) (0.87) Percentage of HH with electricity (%) 0.6 0.6
-0.1 -0.1 (0.77) (0.76) (0.13) (0.19)
Constant 180.1 170.7 238.4 246.1 (0.51) (0.50) (0.75) (0.77)
F-stat for endogeneity test (H0: no endogeneity) 2.10 [0.05]
2.41 [0.03] Joint significance test on instruments (First-stage F
test)
[0.00] for all six end. vars.
[0.00] for all six end. vars.
χ2 stat for over identification test 18.0 [0.11] 20.1 [0.07]
F-stat (H0: all slope coeff=0) 12.62 [0.00] 14.67 [0.00] 7.90
[0.00] 8.38 [0.00] Village clustering robust t-statistics in
parentheses. Numbers in brackets are p-values. * significant at 10%
level; **significant at 5% level; *** significant at 1% level. a
The proportion to the number of working members. b Divided by the
number of working members. c Instrumented variable. Identifying
instruments are the number of water sellers in a village, the
number of water buyers in a village, the cost of well digging and
its squared term, and the value of house building as well as the
interactions of these variables with the inactive management dummy
and with distance from the tank.
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47
Table 6: Comparison of rice yield, income, and consumption value
between collective management inactive and active villages under
the assumption of the revitalization of collective management in
inactive villages
(A-1) (A-2) (B)
Households ininactive villages
(villages: n=31)
(HH: n=171)
Predicted value
if active
Households in active villages
(villages: n=48)
(HH: n=279)
Difference (A-2)-(B) (|t-ratio|)
(1) Yield (kg/hectare)
Mean 3499 3673 3786 -112.5 (1.05)
Std. dev. 1275 1174 1049
Gini (all households in each category) 20.7 18.0 15.4 2.6
Gini (average of village-level Gini) 9.8 9.3 6.7 2.7
(2.63)***
(2) Per capita monthly income (Rs./person) a
Mean 332 400 395 5.2 (0.14)
Std. dev. 370 360 363
Gini (all households in each category) 53.9 43.0 45.8 -2.8
Gini (average of village-level Gini) 39.8 25.9 30.9 -5.0
(0.09)
Head count ratio of poverty (P0) b 58.5 48.0 48.3 -0.3
Poverty gap (P1) b 33.7 17.8 21.6 -3.8
(3) Per capita monthly consumption value
Mean 308 325 336 -11 (0.58)
Std. dev. 194 177 202
Gini (all households in each category) 34.1 29.7 31.8 -2.1
Gini (average of village-level Gini) 23.2 20.5 20.4 0.1
(0.05)
Head count ratio of poverty (P0) b 52.0 47.4 45.5 1.9
Poverty gap (P1) b 19.8 13.9 14.2 -0.3
*** significant at 1% level. a The value is converted into a per
capita base using the adult equivalent number of present household
members. See footnote 9 for details of the conversion method. b
International poverty line of US$ 1 per day adjusted for purchasing
power parity is used.
-
48
Appendix Tables Table A1: Descriptive statistics for the
regression analyses explaining the decline in
active collective management in villages Mean Standard
deviation
Dependent variable
Collective management (inactive=1) 0.41
Explanatory variables
Private well
Density of wells in tank command area (#/ha) 0.102 0.145
Exit options
Percentage of high-school graduates (%) 17.77 12.64
Percentage of college graduates (%) 3.83 4.11
Male wage rate (Rs./day) 56.27 9.59
Tank un-irrigable agric. area per agric. HH (ha) 2.91 2.70
Heterogeneity
Gini coefficient of value of animal holdings 0.461 0.165
Gini coefficient of value of animal holdings squared 0.240
0.165
Gini coefficient of tank-irrigable plot size 0.275 0.087
Gini coefficient of tank-irrigable plot size squared 0.083
0.050
Size
Number of HH having agric. land in tank command area (100HH)
0.757 0.567
Government intervention
Channel lining (%) 2.29 8.16
Tank type (PWD =1) 0.367 0.485
Destructive shocks
Ethnic or religious conflict in the last 10 years (yes=1)
0.114
Number of droughts in the last 10 years 3.53 2.74
Rainfall
Annual rainfall (mm) 1019 381
Identifying instrumental variables
Relative cost of well digging (100 kg. of rice) 265.32 86.39
Relative cost of pump set (100 kg. of rice) 62.80 19.29
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49
Table A2: Results of the probit analysis explaining the
determinants of private well ownership by individual farmers
Dep var.: ownership of private well (owning at least one private
well =1) Collective management (inactive=1) -0.221 (0.89)
Tank-irrigable plot size (ha) 0.121 (2.56)*** Tank-unirrigable plot
size (ha) -0.038 (2.00)*** Animal value (000 Rs.) 0.003 (0.81)
Distance from tank (km) 0.000 (1.66)* No. of working members 0.048
(1.32) Average schooling (years) 0.077 (2.23)*** Owner of tractor
(owner=1) 0.126 (1.68)* Male HH head (male=1) 0.518 (1.20) Average
rainfall for 10 years (mm) -0.001 (0.77) Percentage of HH with
electricity (%) 0.011 (1.83)* Cost of pump set relative to rice
price -0.010 (2.09)*** Cost of digging relative to rice price 0.001
(1.05) Constant -2.517 (2.50)*** χ2-stat (H0: all slope coeff=0)
33.28 [0.00]
Village clustering robust t-statistics in parentheses. Numbers
in brackets are p-values. * significant at 10% level; **significant
at 5% level; *** significant at 1% level.
-
50
Table A3: Results of the regression analysis explaining the
relationship between rice yield and the density of wells in the
tank command area among farmers relying only on tank systems
Dep. var.: rice yield (kg/ha)
Density of wells in tank command area Density of private wells
in tank command area (#/ha) 20,277.5
(4.56)*** Density of private wells in tank command area sq.
-55,524.1
(3.03)*** HH characteristics Distance from tank (km) 105.8
(0.59) Primary educ. proportion a -123.8
(0.42) Secondary educ. proportion a 394.1
(1.66)* College educ. proportion a 1,754.8
(2.17)** Av. age b 59.2
(0.80) Av. age sq. b -0.8
(0.84) No. of working members 40.7
(1.39) Rice plot size (ha) -372.5
(1.68)* Rice plot size sq. 57.4
(1.21) Av. value of animal holdings b (000 Rs.) 93.3
(3.65)*** Av. value of animal holdings sq. b -3.2
(2.93)*** Owner of tractor (owner=1) -21.3
(0.06) Male HH head (male=1) 367.0
(1.37) Village characteristics Male daily wage rate (Rs./day)
13.6
(1.01) Annual rainfall (mm) 0.1
(0.15) Accessibility by vehicle (yes=1) -112.2
(0.95) Percentage of HH with electricity (%) -7.7
(1.78)* Constant 1,173.5 (0.65) F-stat (H0: all slope coeff=0)
7.73 [0.00]
Village clustering robust t-statistics in parentheses. Numbers
in brackets are p-values. * significant at 10% level; **significant
at 5% level; *** significant at 1% level. a The proportion to the
number of working members. b Divided by the number of working
members.
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51
Table A4: Descriptive statistics for the regression analyses of
the determinants of yield, household consumption, and income
Mean Standard deviation
Dependent variables
Yield (kg/hectare) 3676.8 1147.7
Per capita monthly agric. labor income 13.9 36.0
Per capita monthly non-agric. income 53.1 145.4
Per capita monthly income (Rs.) 371.0 366.8
Per capita monthly consumption (Rs.) 325.4 199.1
Explanatory variables
Variable related to collective management
Inactive collective management (inactive=1) 0.38
No access to well (no access=1) 0.72
Distance from tank (km) 0.418 .464
Household characteristics
Primary educ. proportion 0.325 0.264
Secondary educ. proportion 0.410 0.285
College educ. proportion 0.024 0.086
Average age 37.56 6.46
Average age squared 1452.5 514.5
No. of working members 4.13 1.99
Rice plot size (ha) 0.927 0.710
Rice plot size sq. 1.362 2.767
Tank-irrigable plots per working member (ha) 0.295 0.270
Tank-irrigable plots per working member sq. 0.160 0.358
Tank-un-irrigable plots per working member (ha) 0.232 0.454
Tank-un-irrigable plots per working member sq. 0.260 1.588
Value of animal holdings per working member (000 Rs.) 3.995
5.184
Value of animal holdings per working member sq. 42.77 132.81
Owner of tractor (yes=1) 0.0267
Male HH head (male=1) 0.971
Village characteristics Male wage (Rs./day) 56.03 9.57
Annual rainfall (mm) 1009.7 351.8
Average rainfall for 10 years (mm) 832.2 129.0
Accessibility by vehicle (yes=1) 1.942 0.910
Percentage of HH with electricity in home (%) 68.13 22.49
-
52
Sources: Southern India Engineering Manufacturers’ Association,
various issues, Recommended List Prices
(Coimbatore, India: Southern India Engineering Manufacturers’
Association). Director of Statistics, various issues, Season and
Crop Report of Tamil Nadu (Tamil Nadu, India: Director
of Statistics, Government of Tamil Nadu). Note: Relative prices
are not reported in the years that pump price data are not
available.
Figure 1: Relative price of electric motor pumps to paddy
price(price of pump set/price of 1 kg of paddy)
1000
1500
2000
2500
3000
3500
4000
4500
1990 1995 2000Year
Kg o
f pad
dy 7.5 HP foropen well
7.5 HP forbore well
Figure 1: Relative price of electric motor pumps to paddy
price(price of pump set/price of 1 kg of paddy)
1000
1500
2000
2500
3000
3500
4000
4500
1990 1995 2000Year
Kg o
f pad
dy 7.5 HP foropen well
7.5 HP forbore well
