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Dams
Esther Duflo and Rohini Pande∗
Preliminary and incomplete
Abstract
Credible evidence on the returns to public investment in infrastructure in developing
countries remains limited. This paper examines this question in the context of large
dam construction in India. We use Indian district panel data to examine how increases
in the number of dams in own district, upstream to the district and downstream to the
district affect agricultural and poverty outcomes. We exploit geographic variation in
the suitability of districts for dam construction to construct instruments for the number
of dams placed in a district. A district in which a dam is placed sees no increases in
agricultural productivity and a rise in poverty. In contrast, districts downstream to such
a district witness a significant increase in agricultural productivity, and substitution in
favor of water intensive crops.
1 Introduction
In 2000, on average 9% of public spending in developing countries was on infrastructure (i.e.
roughly 1.4% of GDP, (all figures from IMF Finance Statistics)). Despite the magnitude
of infrastructure spending in developing countries, credible evidence on how increases in
physical infrastructure affect productivity and individual well-being remains limited. This
paper examines these questions in the context of large dam construction.∗The authors are from MIT and Yale University respectively. Pande thanks NSF for financial support
for this project under grant
1
Worldwide, over 45,000 large dams have been built and nearly half of the world’s rivers
are obstructed by at least one large dam. The reservoirs formed by these dams store roughly
3,600 cubic kms of water, generate 19% of the world’s electricity supply and provide irriga-
tion for between 30-40% of the 271 million hectares irrigated worldwide (World Commission
on Dams (2000), WCD).
The economic and social benefits of dams remain, however the subject of intense contro-
versy. Some argue that dam construction was essential for the observed increases in water
availability for irrigated agriculture and domestic or industrial use, hydropower generation
and flood control.XX referencesXX
Others argue that large dams are associated with very limited increases in agricultural
productivity, as they cause a loss of agricultural and forest land via submergence and
waterlogging and salinity in the command area of project. In addition, water provided
via dams is typically priced below that needed to recover the costs of dam construction
and maintenance. This, it is argued, has led farmers to change cropping patterns towards
water-intensive crops like sugarcane and cotton. As a result, dam construction may have
enhanced the very water shortage problem in agriculture it was intended to solve.
A different concern relates to the regional distribution of dam costs and benefits. Specifi-
cally, irrigation benefits go to those living downstream from the dam while the displacement
costs are borne by those living near the dam. According to WCD, global estimates sug-
gest that 40-80 million people have been displaced by reservoirs. In addition, large-scale
impounding of water is believed to cause public health problems in the vicinity of the dam
reservoir. This, it is suggested, implies that dam construction is likely to increase economic
inequality across regions.
Despite the intensity of this controversy, evaluations of the large-scale impact of dam
construction on poverty and agricultural outcomes remain limited. Most evaluations are
case studies, often limited to the largest dam projects. There is no evaluation of the im-
pact of the average dam on agricultural production, economic outcomes, and poverty and
inequality.
2
Part of the the reason for the lack of overall assessment of the impact of dams is the
difficulty of convincingly estimating the economic impact of dams: dams are constructed
in places that are suitable for them and have a need for water storage. In addition, the
location of dams is the result of often complicated political processes between regions with
differing economic and political clout. As a results, comparing outcomes in regions with
and without dams is unlikely to provide a causal estimate of the effects of dam construction.
A good example is provided by the Indian experience. Gujarat and Maharashtra, are the
two Indian states with the highest dam concentration. They also happen to be among the
richest states in the country, with respect to both levels and growth rates. It is clear that
the growth experience of these states cannot be entirely attributed to the dams. Further,
it is very likely that their success in attracting dams was, at least in part, related to their
economic performance.
The problem of convincingly estimating the impact of large infrastructure projects ex-
tends beyond dams: the placement of all large public capital projects, such as roads and
railroads, reflect regional need and a complicated decision-making process, which makes
estimating their impact particularly difficult.
In this paper we implement an empirical strategy for identifying the poverty and agri-
cultural productivity impact of dam construction in Indian districts which accounts for the
endogeneity of dam placement. Specifically, we exploit geographic differences in the suit-
ability of Indian districts for dam construction to construct instruments for number of dams
per district.
A number of reasons make India a suitable country for this study. India, with over
4,000 large dams, is the world’s third most prolific dam builder (after China and the USA).
Irrigation is the stated objective of over 96 percent of India’s dams.1 Moreover, it is possible
to construct a relatively long district-level data-set on agricultural and poverty outcomes
for Indian districts. Our poverty data span the period 1973-1999, and agriculture data1Large dam construction remains the main form of investment in irrigation potential in India. Almost
all of India’s dams are reservoir type storage projects which impound water behind the dam for seasonal,
annual and, sometimes, multi-annual storage and regulation of the river.
3
1973-1987. The decades of the 1970s and 1980s witnessed the most dam construction in
India. Finally, the extent of dam construction shows significant variation across Indian
districts. Today, roughly half of India’s districts have at least one dam. The maximum
density of dams is in Western India – nearly three-quarters of all dams are in the three
states of Maharashtra, Gujarat and Madhya Pradesh. In contrast, there were very limited
dam building in North India.
Part of the regional difference in dam construction reflects the differential capacity of
Indian states to finance the projects or obtain financing from India’s central government.
However, the difference is also, in part, due to differences in the suitability of environment.
Foremost, the construction of a dam requires a river. Second, however the river must
flow sufficiently rapidly. XX REFERENCES AND EXPLANATION FOR THISXX This
explains why the Gangetic plain has no dams, despite the presence of the Ganges.
The basic idea of our identification strategy is to use district geographic features to
predict the distribution of dams constructed in a specific state in a given year across district
in the state. We use GIS data to construct our measures of district geography. These include
the fraction of a district in different categories of elevation and inclination, the kilometers
of river in the district, and the fraction of river falling in different incline categories. We
predict the number of dams in a district in a given year by the interaction of the number
of dams in the State where the district is located with these geographical variables. Our
outcome regressions control for district fixed effects, a full set of state year interactions, and
the interactions of most district geography variables with the number of dams in the State
in that year. Only the interaction between the slope along the rivers and the number of
dams in the State in that year is assumed exogenous.
The strategy is thus robust to a range of omitted variable and possible endogeneity
concerns. First, all comparison are within state and year cells, and thus control for any
differential trends across states. Second, even if, within States, districts with more river
or districts with more slopes have, over time, evolved differently in a way correlated with
overall dam construction in the State, this is controlled for by the interaction between the
4
number of dams in the State in that year and these variables.
Since a key aspect of the controversy surroundings dams is that the unequal distribution
of the cost and benefits of dam construction, both across and within, districts, we identified
for each district the districts which are upstream and downstream to it. The predicted
number of dams for each upstream and downstream district are used to instrument for the
actual number of dams located in the districts upstream and downstream to a given district.
Our results reconcile the seemingly irreconcilable claims of the proponents and the ad-
versaries of dams. We find that dam construction does not improve agricultural production
or productivity in the districts where they are built. Wages do not improve in these districts,
and poverty increases. However, dams do increase agricultural production and yield in the
districts located downstream. In those district, irrigated area, agricultural production and
yield, and wages increase significantly, and poverty appear to be reduced (though the effect
is not significant).
Overall, however, the positive effects on poverty in the districts that are downstream
from a dam are too small to compensate for the negative effects in the dam’s district,
even though the overall effect on agricultural production are indeed positive. Dams ap-
pear to increase agricultural productivity at the expense of increasing poverty. [XX CAL-
CULATE: ARE THE BENEFITS TOO SMALL OR HAVE THEY BEEN UNEQUALLY
SHAREDXX]
The results reported here are important in their own right, and the strategy we employ
could potentially be used in the case of other infrastructure, where construction is in part
influenced by geographical characteristics. This may make it possible to provide convincing
estimates of the causal effects of large infrastructure projects.2
The remainder of the paper proceeds as follows: XX TO COMPLETEXX2Two studies in progress, on railroad in China (Banerjee, Duflo and Qian) and highways in the US
(Michaels) use a related approach, where they try to predict railroad or highway construction using the
pattern that the grid would have had if it had connected all cities and treaty port (for China) or all big
cities on a North-South and East-West axis (for the US).
5
2 Background
TBA
3 Data and Descriptive Statistics
3.1 Dams
The data on dams was obtained from the world registry of large dams, maintained by the
International commission of large dams (ICOLD). The registry lists all large dams completed
or under construction in India until year 2001.3, with information on the height, year of
completion, river it is built on, purpose (irrigation, electricity or both), and incomplete
information about reservoir capacity etc... The registry also gives the dam’s address. Using
this information, we manually obtained district information for India’s over 4,000 dams. We
then constructed the number of dams completed in each district in each year, and summed
this over the year to obtained our main regressor of interest, the number of dam present in
a district in a given year.
Figure 1 show the evolution of the number of dams in India. The main years for dam
construction were the the 70s and 80s decades. The number of dams was multiplied by
6 between 1965 and 1995. Figures 2 and 3 show that dams were far from being equally
distributed across India. In 1965, most districts had no dams, and the existing dams are
located in the Northwest regions (Gujrat and Maharashtra). By 1995, no dams had been
built in the Gangetic plain and the Northeast. A majority of the districts in the rest of the
country had at least one dam built, but the increases were, once again, highly concentrated
in the Western region. The median district in India had no dams by 1995. 10 States or
Union Territories (out of XX) had no dams.4 In what follows, since our strategy is to3A dam with a height of 15m or more from the foundation is defined as a large dam. Dams between
5-15m high with a reservoir volume of more than 3 million cubic metres are also classified as large dams.4These are Arunachal Pradesh, Meghalaya, Mizuram, Nagaland, Punjab, Sikkim, Dadra and Nagar
Haveli, Daman and Diu, Delhi, Pondicherry. The only big State among them in Punjab: Indian Punjab
has do dams in India due to an agreement with Pakistan forbidding the construction of dams on any river
6
compare districts with and without dams within the same State, we are excluding all States
and Union Territory which had no dams by 1999.
Excluding these states, the median district had one dam, 46% of the districts had no
dams, and the average number of dams in a district was 8.35, and the maximum number of
dam built was 118. The median district in Maharashtra, Gujrat and Madhya Pradesh had
39, 18 and 15 dams, respectively.
3.2 GIS data
We use GIS data for India to collate district-wise geographical information. These include
total area, river kilometers, district elevation and the inclination of the district, but overall,
and along river.5
These data exist polygon-wise, with each Indian district comprising multiple polygons.
For each district the percent of the district’s land area (summed across all polygons in a
district) in different elevation/slope categories was computed. To compute the share of
the river area falling in different inclination categories, we followed the same method and
restricted attention to polygons through which the river flowed.
Figure 4 show a map of India’s main river basins. It is apparent that major rivers flows
through area where there are no dams. The most obvious example is the Gangetic plain,
where there are essentially no dams despite the presence of the Ganges. Figure 5 shows
that this may be in part due to how flat this region is: most of the Gangetic plain is at an
elevation below zero. Figure 6 shows the map of the slope of the river along the district.
The Western regions, where most of the dams are located, appear to have a relatively large
fraction of river length with moderate elevation. However, other states (such as Kerala and
Karnataka, in South India), which also have rivers that are on a moderate incline, have
flowing towards Pakistan.5The data set used was the GTOPO30 (Elevation Data) downloaded from
http://edcdaac.usgs.gov/gtopo30/gtopo30.html. Slope calculated from GTOPO30. The river map
(Drainage-network) downloaded form http://ortelius.maproom.psu.edu/dcw/ File name used ’dnnet’. It
was processed by the CIESIN at the Earth Institute of the University of Columbia
7
fewer dams than the western regions, suggesting that geographic potential was the only
determinant of dam construction.
3.3 Agriculture Data and rural wages
The agricultural data are from the World Bank India Agriculture and Climate data-set
(www-esd.worldbank.org/indian). The data-set covers 271 Indian districts within thirteen
states of India, defined by 1961 boundaries and cover the years 1957-58 to 1986-87 for
production and crop by crop outcomes, and 1994 for wages, net irrigated areas, and net
cultivated area. .6
The agricultural wages series is an annual measure of male agricultural wages, con-
structed from monthly wage data collected by the Directorate of Economics and Statistics
(Ministry of Agriculture, India). In constructing the annual measure, June and August
were weighted more heavily to account for high intensity of field work during these months.
Other data available in this data set include net cultivated area, net irrigated area, area
cultivated under each of the 15 major crops of India, and production and yield for the 15
major crops.
The agricultural wage, production yield variables are deflated by the state-specific Con-
sumer Price Index for Agricultural laborers provided in ?.7
India’s irrigation potential increased fourfold from 22.6 million hectares in 1951 to about
89.6 million hectares by 1997 ?, but it was only in part due to dam construction. Corre-
spondingly, the average share of cultivated area under irrigation in a district increased from
26% to 45% between 1973 and 1995 (the net cultivated area remained roughly constant over
the period). The increase in the availability of irrigation happened in all States with suffi-
cient water resources (the alternative to dams is ground water irrigation). The three states6Kerala and Assam are the major agricultural states absent from the data set. Also absent, but less
important agriculturally, are the minor states and Union Territories in the Northeastern part of India, as
well as the far-northern states of Himachal Pradesh and Jammu-Kashmir.7These are drawn from multiple sources – Indian Labor Handbook, Indian Labor Journal, Indian Labor
Gazette and Reserve Bank of India Report on Currency and finance
8
were most dams were built started from a very low share of irrigated area, and increased
rapidly (for example it went from 9% to 31% in Madhya Pradesh).
3.4 Poverty, Consumption, and inequality
Household survey data were obtained from the 1983-84, 1987-88, 1993-94 and 1999-2000
(“thick”) rounds of the Indian National Sample Survey (NSS). The NSS provide household
level information on expenditure patterns. In general, the surveys cover all Indian states
and collect information on about 75,000 rural and 45,000 urban households. Households are
sample randomly within districts, which makes it possible to use it to construct district-level
averages even though the NSS Organization does not report them.8 Data for the year 1973
where obtained from XX Srinivasan REFERENCEXX. District identifiers are available for
every year since 1987.9 For the years 1973 and 1983, the data can only be aggregated at the
NSS region level (a region is a group of district sharing common characteristics, for which
the sample is large enough that the NSSO considers that the data is “representative” of
the region). Dreze and Murthy (XXreferenceXX) provide a matching between the 1973 and
1983 NSS regions and the 1981 census district definition, and a matching between the 1981
census definition and the 1991 census definition.10 We matched the later data at the level
of the 1981 district definition, using census maps as well as other geographical indicators.
The aggregate statistics for each districts were computed by Topalova (2004). She follows
Deaton (2003a, 2003b) to compute adjusted poverty estimate. First, she uses the poverty
lines proposed by Deaton as opposed to the ones used by the Indian Planning Commission,
which are based on defective price indices over time, across states and between the urban
and rural sector. 11 In addition, the 1999-2000 round is not directly comparable to the8The NSSO considers that there are not enough observations at the district level to obtain reliable estimate
of the poverty in each district. This does not affect us, since we are reporting results from regressions using
a larger number of districts and do not make any inference about a particular distrit.9They had to be recovered from hard copies for the year 1993.
10India’s districts boundaries changed several time between 1961 and 1991, mostly due to the splitting of
districts into two parts.11Poverty lines were not available for some of the smaller states and union territories, namely: Arunachal
9
1993-1994 round. The 1999-2000 round introduced a new recall period (7 days) along with
the usual 30-day recall questions for the household expenditures on food, pan and tobacco.
The recall period also changed for durable goods. Due to the way the questionnaire was
administered, there are reasons to believe that this methodology led to an overestimate of
the expenditures based on the 30-day recall period, which in turn may affect the poverty
and inequality estimates. To achieve comparability with earlier rounds, she follows Deaton
and impute the correct distribution of total per capita expenditure for each district from
the households. expenditures on a subset of goods for which the new recall period questions
were not introduced. The poverty and inequality, and mean PCE measures were derived
from this distribution.
4 Empirical Strategy
The following two equations relate the outcome of interest (i.e. per capita consumption,
agricultural production to the number of dams in the district) in district i, which is part
of state s, in year t, to the number of dams present in this district in this year, and to the
number of dams in neighboring upstream and downstream district.12
yist = β1 + β2Dist + νi + μst + ωist, (1)
yist = β3 + β4Dist + β5DUist + β6DD
istνi + μst + ωist, (2)
Pradesh, Goa, Daman and Diu, Jammu and Kashmir, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim,
Tripura, Andaman and Nicobar Islands, Chandigarh, Pondicherry, Lakshwadweep, Dadra Nagar and Haveli.
The results are not sensitive to the inclusion of these states, with poverty lines assumed to be the same as
those of the neighboring states. Most of these are not included in our analysis because they have no dams
or we have no other data for them. For the ones who are included, we used the neighboring states’ poverty
line.12In what follows, we will refer to the “neighboring upstream districts” as “upstream districts”, for short
(and likewise for downstreawm).
10
where νi is a district fixed effect, μst is a state*year effect. The district fixed effect account
of any specificity of the districts that got more dams that are fixed over time. The state-year
effects account for any yearly shock common to all districts in a state: the regressions only
exploit differences in dam construction across district within a State. ωist a district-year
specific error term.13
The identification assumption underlying these regressions is that that the variations
in the dam construction across districts of the same state within a year is uncorrelated
with other shocks affecting these district. The assumption might be violated, for example
if dams where built in district with rapid agricultural growth (leading to a higher demand
for irrigation water).
To address this, we exploit the fact that dams are built along rivers which where there
is a sufficient water flow: to build dams, one needs a river with a sufficient incline. At the
same time, too steep inclines make dam construction impossible.
We therefore run the following regression to predict the average number of dams in
district i in year t:
Dist = α1+5∑
k=2
α2k(RSlki∗Dst)+4∑
k=2
α3k(Elki∗Dst)+5∑
k=2
α4k(Slki∗Dst)+(Xi∗Dst)α5+νi+μst+ωist
(3)
We run this regression for all districts, and we compute for each district its predicted
number of dams D̂ist (as the predicted value from equation 3, the predicted number of
dams that have been constructed in neighboring upstream districts D̂Uist (as the sum of
the predicted value from equation 3 for all the upstream districts, or 0 if the district has
no upstream district), and the predicted number of dams that have been constructed in
neighboring downstream districts, D̂Dist.
Denote Zist the vector of all the right hand side variables in equation 3, except for the
interactions RSlki ∗ Dst. Denote ZUi st the corresponding variables for upstream districts,
13It is likely to be autocorrelated over time, and we will control for that by clustering the equation at the
district level.
11
and ZDi st the corresponding variable for downstream districts.14
We then augment equations 1 and 2:
yist = γ1 + γ2Dist + Zistγ7 + νi + μst + ωist (4)
and:
yist = δ1 + δ2Dist + δ3DUist + δ4D
Dist + Zistδ5 + ZU
istδ6 + ZDistδ7 + νi + μst + ωist (5)
We estimate equation refstrutful1 with 2SLS, using D̂ist and Zist as instruments, and ref-
strutful2 using D̂ist, D̂Uist D̂D
ist, ZUist and ZD
ist as instruments.
The first stage equations are:
Dist = π1 + π2D̂ist + Zistπ7 + νi + μst + ωist (6)
and:
Δist = φ1 + φ2D̂ist + φ3D̂Uist + φ4D̂D
ist + Zistφ5 + ZUistφ6 + ZD
istφ7 + νi + μst + ωist (7)
where Δist represent Dist, DUist or DD
ist.
For equation 4, this 2-step procedure is identical to running a 2SLS using the interactions
RSlki ∗ Dst and Zist as instruments. For equation 5, this procedure uses the entire set
of districts to predict the relationship between the district geographical features and the
number of dams (rather than the set of districts which are upstream), and avoid averaging
the features when there are more than one upstream district.15
14If there is more than one upstream or dowstream district, the length of the rivers and the district area are
summed across all the upstream and downstream districts, while the other variables, which are proportions,
are averaged across districts.15XX Explain that the first stage is not significant otherwise.XX
12
5 Results
The estimates of equation 3 are presented in table 2, for two samples: the 5 years for which
we have data on poverty, inequality and mean per capital expenditure, and the 21 years for
which we have data on wages and some agricultural outcomes.16
The equations control for district fixed effects and state year effects. The estimate are
coefficients of interactions of the sum of the number of dams present in the state in a given
year, and district characteristics. They thus indicate which district within a state tend to
get more dams, as the number of dams in a state increases.
The pattern explaining the allocation of dams across districts within a state appear to
be sensible: dams tend to be built in districts which, compared to other districts in the
same states, are larger districts, have more rivers, where a larger fraction of the area is of
moderate elevation (250 to 500 meters), and of moderate slope (1.5% to 3%). Important for
our purpose, there are more dams built in districts where the a larger fraction of the river
have a moderate slope (1.5% to 3%). Surprisingly, there are also more dams built when a
larger fraction of the slope along the river is very steep (more than 10%). These are likely
to be hydroelectric dams XX CHECK HOW MANY DISTRICTS HAVE ANY SLOPES
LIKE THIS AND WHERE THEY AREXX. Together, the four interactions between the
slope along river and the number of dams present in the year in a particular year are
significant (the F-statistics are 2.37 and 3.17, respectively).
Table 3 present the actual first stage equations (equation ?? and ??. The number of
dams in a district is regressed on the predicted number of dams, the predicted number
of upstream dams and the predicted number of downstream dams (both calculated using
the predicted number of dams in all the upstream district for a given year). Likewise,
the number of dams in upstream and downstream districts are regressed on the predicted
number of upstream and downstream dams. All the control variables are included in the
regressions (the only excluded variables are the interaction of slope along river and the16At the moment, we do not have crop by crop production for the last 7 years in the sample. The first
stage is virtually identical in this sample.
13
number of dams present in the State in that year). Nor surprisingly, the coefficient of the
predicted dams is close to 1 columns 1 and 2, with a T statistics of over 5. The coefficients
of the predicted number of dams un upstreams (downstream) districts are also close to 1
and highly significant in the upstream (downstream) regressions.
Table 4 shows the OLS estimates of equations 1 and 2 (panel A) and the two stage least
squares estimation of equations 4 and 5, for the main agricultural outcomes. Both the OLS
and the IV suggest that dams lead to no significant gains in net irrigated area in the districts
where they are built (column 1), but significant gains in the districts located downstream.17.
The IV estimate is larger than the OLS estimate. The point estimate suggests that one
more dam increase the irrigated area in the downstream district by 2 hectares. This finding
is in line with the claim by the opponents of dams that the degradation of the land around
the reservoir and the amount of land taken up by irrigation canals more than compensate
the potential gains in irrigation due to the dams in the vicinity of the dam itself. Columns
3 and 4 show that there is no significant gains or loss in net cultivated area, although the
point estimate for the dam’s own districts are negative both in the OLS and the IV panels.
Column 7 and 8 show the impact of the dams on production. Both the OLS and the IV
suggest that dams are associated with a small and insignificant decline in overall production
in the district where they are built, and an increase in the downstream districts (row 2, in
both panels). In the IV panel, the estimate is significant at the 10% level. Yields (column 9
and 10) tells the same story, with insignificant decline in yield in the dam own districts, and
gain in the downstream districts (significant at the 5% level in the IV panel). These results
are also suggestive of a degradation of the land around the dam that is in part compensated
by the increase in productivity elsewhere in the district. The downstream districts, that do
not bear any of the environmental costs of the dams, enjoy positive productivity gains.
Tables 5 and 6 show the OLS and IV estimates of the impact of the dams on the area
cultivated, yield, and production, separately for the major crops or groups of crops. The17That is, districts that have more dams upstream have a larger irrigated area: see column 2, row 2 in
both panels
14
IV and OLS results are slightly different in this case, so we focus on the IV results in this
discussion (table 6). A criticism of dams is that the inadequate pricing of the water led
farmers to devote larger areas to extremely water intensive crop, notably sugar. Indeed, we
do find a net increase in the area devoted to sugar in the district downstream from a dam
(the coefficient suggest that one more dam in an upstream district increase the area devoted
by cotton by 1.7%, which is a large increase). The area devoted to rice and cotton increases
as well in downstream districts (the coefficient is significant only in the case of rice). There
is also a large (8% for each dam built) and significant increase in the area devoted to cotton
in the district where the dams are built (this is the only impact of dams we can detect on
agricultural variables in the dams’ own district.
However, this increase does not appear to be at the expenses of other major crops: there
is no significant decline in the downstream districts in the area devoted to various millets18,
pulse, and maize. Areas devoted to these crops do not appear to be affected in any way.
The impact of yield on the crop by crop basis appear to be modest, even for crops that
are heavily water intensive (panel B). None of the crop show significant increase in yield in
the downstream district, though the coefficient are positive for all crops except pulses and
rice.
The increase in area devoted to water intensive crop combined with moderate increase
in yield lead to a significant increase in the production of water intensive crops in the
downstream district (together, they increase by 0.9% for each dam built), due mostly to
a large increase in the production of sugar (2% for each dams), and in the production of
cotton in the dam’s district (the production increases by a staggering 8.2% for each dam
built, and this is due entirely to an increase in the area devoted to it). The only non-cash
crop that shows a significant increase is wheat, where the production increase by 0.8% for
each dam built in a district upstream.
Taken together, these results provide a consistent picture of the impact of dams on18Millets include Maize, jowar, Bajra, Ragi and Bari, and are cheap cereals that are not very water
intensive.
15
agricultural outcomes: dams have no positive impact on agricultural production in the
districts where they are built, except for the production of cotton. In downstream districts,
they improve agricultural production, both for some cash crop (sugar) and for an important
staple (wheat). These results suggest that the dam’s impact on welfare may be very different
in the dams’ own district and in neighboring districts.
Table 7 shows the OLS estimates of equations 1 and 2 (panel A) and the two stage least
squares estimation of equations 4 and 5, for the consumption and poverty measures.
Columns 1 and 2 shows the impact of dams on mean capita expenditure. In column
1, both the OLS and the IV suggest that more dams in a district leads to a decline in the
mean capita poverty expenditure (10 more dams would lead to a decrease of 3$ to 4%),
although only the OLS coefficient is significant (the OLS and IV point estimates are very
similar). In column 2, we include the number of dams built in upstream and downstream
districts. The impact on per capita expenditure in the dams’ district is not significant in
both the OLS and the IV regression (the point estimate is twice as large in the IV case,
although the two estimates are not statistically distinguishable). Dams may have a modest
positive impact on per capita expenditure in the dowstream districts, but the coefficient is
not significant. Columns 3 and 4 shyow the impact of dams on the headcount ratio, and
tell a very similar story: dams are associated with significant increase in poverty in their
own district, and with much smaller, insignificant declines in poverty in the downstream
districts (the point estimate is a tenth as large and the T statistics is just above 1 for the
coefficient of the number of dams in upstream districts). The head count ratio is a relatively
crude measure of the extent of poverty. The poverty gap, which is a measure of the depth
of poverty (this is a measure of how much income would be needed to bring all the poor to
a level of consumption equal to the poverty line), again tells a similar story: dams increase
the poverty gap in their own distict, and reduce it in the downstream district (the point
estimate is now significant at 5% level of confidence in the OLS case, and 15% in the 2SLS
case). The point estimate for the reduction of poverty associated to dams created upstream
is a fifth (in the OLS case) to a eighth (in the IV case) of that of the increase in poverty in
16
the dams’ own district. On average there are 1.75 district downstream of each dam in our
data. This implies that, on balance, the reduction of poverty in districts downstream to the
dams are too small to compensate for the increase in poverty in the dams’ own district. ’
Columns 7 and 8 show the impact of the dams on the gini coefficient. There is no
apparent pattern of an impact of dams on inequality either in their own district or in the
neigboring districts.
Finally, columns 9 and 10 show the estimates of the impact of the dams on the male
agricultural wages. The series is available for a longer time period (although for fewer
states), which explains the larger number of observations (the results are similar when we
restrict the year to the year for which we have NSS data). The results help drawing the
link between the agricultural results and the results on poverty and consumption. Higher
agricultural wages could have resulted from higher land productivity (especially from the
production of cash crops), and it has been shown that they are an important element for
reducing rural poverty (Dreze, XX). We find that wages do increase in districts located
downstream from a dam (the IV point estimate suggest that each dam located upstream
increase agricultural wages by 0.46%, with a point estimate of 0.27%; the OLS estimate is
smaller and insignificant). However, wages did not increase in the districts where the dams
were located. There appear to have been no economic force at play to compensate for the
cost occurred because of the dam construction.
6 Conclusion
TBA
17
1973 1999
A. Dams
Number of dams 2.645 7.697
in district
Number of dams 4.103 13.15
upstream to district
Number of dams 4.218 11.626
downstream to district
B. Welfare
log(pce) 3.7818 5.753
Headcount ratio 0.4701 0.239
Poverty gap 0.2677 0.0472
Gini 0.2838 0.257
C. Agriculture
log (agricultural wage) 1.226 1.618
Share of cultivated area 0.2552 0.4278
is irrigated
Log(total production) 10.8 10.613
Log (total yield) 4.807 4.73
Table 1: Descriptive Statistics
Poverty sample Wage sample
(1) (2)
Dams in state*(fraction river 14.92 18.44
slope of 1.5-3%) (4.07) (1.85)
Dams in state*(fraction river -23.84 -26.29
slope of 3-5%) (6.31) (2.64)
Dams in state*(fraction river 8.02 5.18
slope of 6-10%) (7.91) (3.30)
Dams in state*(fraction river 16.50 20.74
slope above 10%) (5.63) (2.64)
F-test for slope along river 2.37 3.17
[0.050] [0.013]
Dams in state*River length 0.0005 0.0006
(0.0003) (0.0001)
Dams in state*(fraction district 14.11 15.07
slope of 1.5-3% slope) (4.84) (2.18)
Dams in state*(fraction district 0.95 -3.37
slope of 3-5%) (9.32) (4.04)
Dams in state*(fraction district -16.44 -3.33
slope of 6-10%) (17.43) (7.39)
Dams in state*(fraction district 14.88 -0.80
slope above 10%) (15.63) (6.41)
Dams in state*(elevation between 1.11 0.67
250-500 metres) (0.95) (0.96)
Dams in state*(elevation between 2.44 2.06
500-1000 metres) (0.89) (0.85)
Dams in state*(elevation over -10.98 13.87
1000 metres) (24.29) (34.25)
Dams in state*district area 0.000001 0.000002
(square kilometers) (0.000003) (0.000001)
Number of observations 1785 5676
The poverty sample includes the years of 1973, 1983, 1987, 1993 and 1999. The wage
sample includes years 1973-1994.
Dams
Table 2: Geography and Dam Construction
All regressions include district fixed effects and a full set of state*year interactions.
Standard errors clustered by district are reported in parentheses.
Up
str
ea
mD
ow
nstr
ea
mU
pstr
ea
mD
ow
nstr
ea
m
Pre
dic
ted
da
ms
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Ow
n d
istr
ict
1.1
01
.16
0.0
80
.24
0.8
30
.84
-0.2
40
.59
(0.2
00
)(0
.20
8)
(0.3
08
)(0
.32
9)
(0.2
67
)(0
.26
4)
(0.4
79
)(0
.48
5)
Up
str
ea
m-0
.04
0.8
50
.10
-0.0
02
0.6
90
.09
(0.0
27
)(0
.09
5)
(0.0
37
)(0
.03
7)
(0.1
14
)(0
.06
9)
Do
wn
str
ea
m-0
.05
0.2
30
.72
-0.0
20
.29
0.5
9
(0.0
42
)(0
.06
8)
(0.1
19
)(0
.05
3)
(0.1
24
)(0
.14
6)
Nu
mb
er
ob
se
rva
tio
ns
17
65
17
65
17
65
17
65
53
46
53
46
53
46
53
46
Ta
ble
3:
firs
t sta
ge
re
gre
ssio
ns
All
regre
ssio
ns inclu
de the e
levation, slo
pe a
long d
istr
ict, r
iver
length
and d
istr
ict are
a v
ariable
s s
pecifie
d in T
able
2 a
s a
dditio
nal contr
ols
. R
egre
ssio
ns a
lso inclu
de d
istr
ict fixed e
ffects
and a
full
set of sta
te*y
ear
inte
ractions.
Sta
ndard
err
ors
clu
ste
red b
y d
istr
ict are
report
ed in p
are
nth
eses.
The p
overt
y s
am
ple
inclu
des the y
ears
of 1973, 1983, 1987, 1993 a
nd 1
999. T
he w
age s
am
ple
inclu
des y
ears
1973-1
994.
ow
n d
istr
ict
ow
n d
istr
ict
Po
ve
rty s
am
ple
Wh
ole
sa
mp
le
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
A. O
LS
Ow
n d
istr
ict
0.1
196
0.2
189
0.1
714
-0.0
124
-0.0
006
-0.0
002
-0.0
035
-0.0
026
-0.0
030
-0.0
025
(0.2
718)
(0.2
757)
(0.2
901)
(0.3
044)
(0.0
013)
(0.0
013)
(0.0
025)
(0.0
024)
(0.0
023)
(0.0
023)
Upstr
eam
0.4
285
-0.0
710
0.0
008
0.0
022
0.0
014
(0.2
375)
(0.1
825)
(0.0
007)
(0.0
015)
(0.0
014)
Dow
nstr
eam
0.0
780
-0.1
982
-0.0
011
-0.0
019
-0.0
009
(0.1
954)
(0.1
714)
(0.0
009)
(0.0
021)
(0.0
019)
No. observ
ations
5346
5346
5346
5346
3855
3855
3854
3854
3855
3855
B. T
wo
sta
ge least
sq
uare
s
Ow
n d
istr
ict
0.4
178
2.0
912
-0.5
900
-1.2
819
0.0
042
0.0
039
0.0
012
-0.0
021
-0.0
032
-0.0
061
(1.0
529)
(1.7
447)
(1.0
123)
(1.4
139)
(0.0
043)
(0.0
042)
(0.0
083)
(0.0
071)
(0.0
065)
(0.0
068)
Upstr
eam
1.9
213
-0.6
330
-0.0
008
0.0
045
0.0
053
(0.4
673)
(0.4
361)
(0.0
013)
(0.0
026)
(0.0
026)
Dow
nstr
eam
-0.4
902
0.3
005
0.0
009
-0.0
023
-0.0
034
(0.7
089)
(0.5
452)
(0.0
015)
(0.0
031)
(0.0
026)
No. observ
ations
5346
5346
5346
5346
3855
3855
3854
3854
3855
3855
All
reg
ressio
ns in
clu
de
ele
va
tio
n,
slo
pe
alo
ng
dis
tric
t, r
ive
r le
ng
th a
nd
dis
tric
t a
rea
va
ria
ble
s s
pe
cifie
d in
Ta
ble
2 a
s a
dd
itio
na
l co
ntr
ols
. R
eg
ressio
ns a
lso
in
clu
de
dis
tric
t fixe
d e
ffe
cts
an
d a
fu
ll se
t o
f sta
te*y
ea
r in
tera
ctio
ns.
log (
are
a c
ultiv
ate
d)
Log (
yie
ld)
Table
4: E
ffect on o
vera
l agricultura
l outc
om
es
Log (
pro
duction )
net irrigate
d a
rea
net cultiv
ate
d a
rea
15 m
ajo
r cro
ps)
Mill
et
Puls
eW
ate
r in
tensiv
eS
ugar
Cotton
Ric
eW
heat
(1)
(2)
(3)
(4)
(5)
(6)
(7)
A. A
rea C
ult
ivate
d
Ow
n d
istr
ict
-0.0
020
0.0
018
0.0
011
0.0
083
0.0
248
-0.0
053
0.0
009
(0.0
040)
(0.0
049)
(0.0
028)
(0.0
056)
(0.0
104)
(0.0
035)
(0.0
043)
Upstr
eam
0.0
043
0.0
066
-0.0
006
0.0
014
-0.0
021
0.0
017
0.0
025
(0.0
021)
(0.0
030)
(0.0
016)
(0.0
046)
(0.0
055)
(0.0
019)
(0.0
023)
Dow
nstr
eam
0.0
008
-0.0
100
-0.0
010
0.0
028
-0.0
003
0.0
022
-0.0
004
(0.0
023)
(0.0
075)
(0.0
019)
(0.0
036)
(0.0
063)
(0.0
033)
(0.0
023)
3800
3796
3815
3617
2367
3717
3474
B. Y
ield
Ow
n d
istr
ict
0.0
028
-0.0
002
0.0
026
-0.0
003
-0.0
054
-0.0
006
0.0
027
(0.0
059)
(0.0
031)
(0.0
039)
(0.0
032)
(0.0
060)
(0.0
031)
(0.0
020)
Upstr
eam
0.0
011
-0.0
026
0.0
041
0.0
003
-0.0
033
-0.0
017
0.0
002
(0.0
021)
(0.0
009)
(0.0
027)
(0.0
015)
(0.0
027)
(0.0
014)
(0.0
011)
Dow
nstr
eam
0.0
003
-0.0
012
0.0
002
0.0
000
0.0
007
-0.0
032
-0.0
007
(0.0
028)
(0.0
016)
(0.0
024)
(0.0
017)
(0.0
032)
(0.0
017)
(0.0
013)
3797
3789
3808
3605
2174
3706
3446
C. P
rod
ucti
on
Ow
n d
istr
ict
0.0
007
0.0
008
0.0
033
0.0
071
0.0
133
-0.0
062
0.0
042
(0.0
057)
(0.0
044)
(0.0
047)
(0.0
068)
(0.0
126)
(0.0
042)
(0.0
046)
Upstr
eam
0.0
054
0.0
039
0.0
031
0.0
014
-0.0
029
0.0
000
0.0
032
(0.0
027)
(0.0
032)
(0.0
027)
(0.0
045)
(0.0
063)
(0.0
020)
(0.0
026)
Dow
nstr
eam
0.0
010
-0.0
114
-0.0
003
-0.0
005
-0.0
040
-0.0
012
-0.0
014
(0.0
036)
(0.0
076)
(0.0
028)
(0.0
043)
(0.0
088)
(0.0
030)
(0.0
027)
3802
3789
3819
3699
2179
3708
3455
Table
5: O
LS
regre
ssio
n: A
rea c
ultiv
ate
d, Y
ield
, and P
roduction b
y c
rops
All
reg
ressio
ns in
clu
de
ele
va
tio
n,
slo
pe
alo
ng
dis
tric
t, r
ive
r le
ng
th a
nd
dis
tric
t a
rea
va
ria
ble
s s
pe
cifie
d in
Ta
ble
2 a
s a
dd
itio
na
l co
ntr
ols
. R
eg
ressio
ns
als
o in
clu
de
dis
tric
t fixe
d e
ffe
cts
an
d a
fu
ll se
t o
f sta
te*y
ea
r in
tera
ctio
ns.
Mill
et
Puls
eW
heat
All
Sugar
Cotton
Ric
e
(1)
(2)
(3)
(4)
(5)
(6)
(7)
A. A
rea C
ult
ivate
d
Ow
n d
istr
ict
0.0
012
-0.0
174
0.0
073
0.0
105
0.0
247
0.0
833
-0.0
025
(0.0
109)
(0.0
137)
(0.0
107)
(0.0
102)
(0.0
278)
(0.0
395)
(0.0
114)
Upstr
eam
0.0
023
0.0
009
0.0
049
0.0
035
0.0
168
0.0
081
0.0
061
(0.0
037)
(0.0
047)
(0.0
036)
(0.0
033)
(0.0
076)
(0.0
090)
(0.0
037)
Dow
nstr
eam
0.0
081
0.0
088
-0.0
012
-0.0
001
-0.0
036
-0.0
170
0.0
114
(0.0
047)
(0.0
069)
(0.0
043)
(0.0
037)
(0.0
076)
(0.0
142)
(0.0
056)
3800
3796
3474
3815
3617
2367
3717
B. Y
ield
Ow
n d
istr
ict
-0.0
038
0.0
059
0.0
055
-0.0
089
0.0
073
0.0
087
-0.0
116
(0.0
102)
(0.0
058)
(0.0
071)
(0.0
129)
(0.0
092)
(0.0
167)
(0.0
093)
Upstr
eam
0.0
053
-0.0
018
0.0
025
0.0
053
0.0
037
0.0
005
-0.0
031
(0.0
044)
(0.0
019)
(0.0
020)
(0.0
042)
(0.0
029)
(0.0
053)
(0.0
026)
Dow
nstr
eam
0.0
038
-0.0
050
-0.0
033
-0.0
086
0.0
007
-0.0
096
-0.0
091
(0.0
044)
(0.0
021)
(0.0
024)
(0.0
043)
(0.0
035)
(0.0
065)
(0.0
034)
3797
3789
3446
3808
3605
2174
3706
C. P
rod
ucti
on
Ow
n d
istr
ict
-0.0
024
-0.0
120
0.0
146
0.0
016
0.0
420
0.0
825
-0.0
137
(0.0
152)
(0.0
132)
(0.0
139)
(0.0
143)
(0.0
308)
(0.0
425)
(0.0
107)
Upstr
eam
0.0
075
-0.0
008
0.0
085
0.0
086
0.0
196
0.0
103
0.0
034
(0.0
053)
(0.0
049)
(0.0
042)
(0.0
039)
(0.0
076)
(0.0
121)
(0.0
038)
Dow
nstr
eam
0.0
120
0.0
031
-0.0
056
-0.0
092
-0.0
073
-0.0
265
0.0
020
(0.0
063)
(0.0
069)
(0.0
052)
(0.0
051)
(0.0
082)
(0.0
179)
(0.0
050)
3802
3789
3455
3819
3699
2179
3708
Table
6: 2S
LS
regre
ssio
n: A
rea c
ultiv
ate
d, Y
ield
, and P
roduction b
y c
rops (
all
variable
s in logarigth
m)
Wate
r in
tensiv
e
All
reg
ressio
ns in
clu
de
ele
va
tio
n,
slo
pe
alo
ng
dis
tric
t, r
ive
r le
ng
th a
nd
dis
tric
t a
rea
va
ria
ble
s s
pe
cifie
d in
Ta
ble
2 a
s a
dd
itio
na
l
co
ntr
ols
. R
eg
ressio
ns a
lso
in
clu
de
dis
tric
t fixe
d e
ffe
cts
an
d a
fu
ll se
t o
f sta
te*y
ea
r in
tera
ctio
ns.
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
A. O
LS
Dam
s
Ow
n d
istr
ict
-0.0
030
-0.0
043
0.0
028
0.0
036
0.0
008
0.0
010
0.0
0002
-0.0
0006
0.0
005
-0.0
006
(0.0
012)
(0.0
011)
(0.0
008)
(0.0
008)
(0.0
003)
(0.0
003)
(0.0
003)
(0.0
003)
(0.0
023)
(0.0
029)
Upstr
eam
0.0
003
-0.0
005
-0.0
002
-0.0
0006
0.0
013
(0.0
005)
(0.0
004)
(0.0
001)
(0.0
001)
(0.0
012)
Dow
nstr
eam
0.0
002
-0.0
001
-0.0
001
-0.0
0002
0.0
003
(0.0
007)
(0.0
007)
(0.0
002)
(0.0
002)
(0.0
014)
No. observ
ations
1715
1715
1715
1715
1715
1715
1710
1710
5346
5346
B. 2S
LS
Dam
s
Ow
n d
istr
ict
-0.0
043
-0.0
089
0.0
059
0.0
087
0.0
017
0.0
025
-0.0
003
0.0
0002
-0.0
022
-0.0
006
(0.0
044)
(0.0
040)
(0.0
025)
(0.0
027)
(0.0
007)
(0.0
008)
(0.0
011)
(0.0
0103)
(0.0
086)
(0.0
090)
Upstr
eam
0.0
005
-0.0
008
-0.0
003
-0.0
0004
0.0
046
(0.0
010)
(0.0
007)
(0.0
002)
(0.0
0025)
(0.0
027)
Dow
nstr
eam
-0.0
008
0.0
001
0.0
001
-0.0
0074
-0.0
004
(0.0
016)
(0.0
015)
(0.0
004)
(0.0
0033)
(0.0
040)
No. observ
ations
1715
1715
1715
1715
1715
1715
1710
1710
5346
5346
Table
7: R
ura
l P
overt
y a
nd A
gricultura
l W
ages
All
reg
ressio
ns in
clu
de
th
e e
leva
tio
n,
slo
pe
alo
ng
dis
tric
t, r
ive
r le
ng
th a
nd
dis
tric
t a
rea
va
ria
ble
s s
pe
cifie
d in
Ta
ble
2 a
s a
dd
itio
na
l co
ntr
ols
. R
eg
ressio
ns a
lso
in
clu
de
dis
tric
t fixe
d e
ffe
cts
an
d a
fu
ll se
t o
f sta
te*y
ea
r in
tera
ctio
ns.
Sta
nd
ard
err
ors
clu
ste
red
by 1
97
3 N
SS
re
gio
n*y
ea
r a
re r
ep
ort
ed
in
pa
ren
the
se
s.
Th
e p
ove
rty r
eg
ressio
ns in
clu
de
th
e y
ea
rs o
f 1
97
3,
19
83
, 1
98
7,
19
93
an
d 1
99
9.
Th
e w
ag
e r
eg
ressio
n in
clu
de
s y
ea
rs 1
97
3-1
99
4.
Log (
wages)
Log(p
ce)
Gin
i coeffic
ient
Head c
ount ra
tio
Povert
y g
ap
0
600
1200
1800
2400
3000
3600 1
950
1955
1960
1965
197
01975
1980
1985
1990
1995
2000
FIG
UR
E 1
: T
ota
l D
ams
con
stru
cted
in
In
dia
, IC
OL
D D
am R
egis
ter
for
Ind
ia
Dams by District: 1965
Number of Dams0
1 - 4
5 - 9
10 - 33
Dams by District: 1995
Number of Dams0
1 - 4
5 - 9
10 - 33
34 - 45
46 - 77
78 - 99
River Basins MAP http://wrmin.nic.in/riverbasin/allindia.htm
1 of 1 11/13/2004 10:44 AM
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68°0'0"E
68°0'0"E
72°0'0"E
72°0'0"E
76°0'0"E
76°0'0"E
80°0'0"E
80°0'0"E
84°0'0"E
84°0'0"E
88°0'0"E
88°0'0"E
92°0'0"E
92°0'0"E
96°0'0"E
96°0'0"E
2°0'0"N 2°0'0"N
6°0'0"N 6°0'0"N
10°0'0"N 10°0'0"N
14°0'0"N 14°0'0"N
18°0'0"N 18°0'0"N
22°0'0"N 22°0'0"N
26°0'0"N 26°0'0"N
30°0'0"N 30°0'0"N
34°0'0"N 34°0'0"N
38°0'0"N 38°0'0"N
42°0'0"N 42°0'0"N
Average Dam Slope by District
Average Dam SlopeAVDSLOP1
0.00
0- 0.
810
0.81
1- 2.
053
2.05
4- 3.
943
3.94
4- 8.
732
8.73
3- 16
.214
16.2
15- 26
.268
68°0'0"E
68°0'0"E
72°0'0"E
72°0'0"E
76°0'0"E
76°0'0"E
80°0'0"E
80°0'0"E
84°0'0"E
84°0'0"E
88°0'0"E
88°0'0"E
92°0'0"E
92°0'0"E
96°0'0"E
96°0'0"E
2°0'0"N 2°0'0"N
6°0'0"N 6°0'0"N
10°0'0"N 10°0'0"N
14°0'0"N 14°0'0"N
18°0'0"N 18°0'0"N
22°0'0"N 22°0'0"N
26°0'0"N 26°0'0"N
30°0'0"N 30°0'0"N
34°0'0"N 34°0'0"N
38°0'0"N 38°0'0"N
42°0'0"N 42°0'0"N
Average River Slope by District
Average Dam SlopeAVRSLOP1
0.00
0- 0.
923
0.92
4- 2.
070
2.07
1- 3.
800
3.80
1- 7.
104
7.10
5- 14
.747
14.7
48- 24
.739
