Agricultural Outcomes
We examine the impact of dam construction on gross and net measures of irrigated and
cultivated area. The net measures account for the relevant area at a single point in the
year, while the gross variables account for each separate use of the same area during a year.
Multi-cropping is measured as the ratio of gross to net cultivated area.
    Panel A of Table 5 provides OLS estimates (equations (3) and (4)), and Panel B 2SLS
estimates. Both sets of estimates suggest no significant impact of dams on gross or net
irrigated area in the districts where they are built (columns (1)-(4)). The 2SLS estimate
for own district irrigated area is positive but insignificant. The absence of a clear eect
in the district where the dam is built suggests that the submergence and degradation of
land around the reservoir has limited irrigation gains in the vicinity of the dam. The large
standard errors potentially reflect variation in the extent of submergence associated with
dierent sizes of dam.
    Dams significantly increase gross and net irrigated area in districts located downstream.19
The 2SLS estimates exceed the OLS estimates, but both are significant and statistically
indistinguishable from each other. The point estimate suggests that an additional dam
increases irrigated area (gross or net) in the downstream district by roughly 1.1%.
    In columns (5)-(8) we examine gross and net cultivated area. The 2SLS estimates of the
eect of a dam on both measures is negative and significant at the 10% level for downstream
districts, and in columns (6) and (8) for own district. This suggests the causal eect of a dam
is to reduce cultivable area, perhaps due to the submergence associated with construction of
reservoir and canals. In addition, some land may be lost due to waterlogging and salination.
Consistent with the idea that these eects are greater around the reservoir, we observe that
the own district eect is five times as large as the eect downstream. One explanation for
the insignificant OLS estimates is that more dams were allocated to districts where land
availability was otherwise expanding (say, due to higher returns to agriculture).
    Columns (9)-(10) examine the extent of multi-cropping and find a positive, but insignifi-
cant, increase in multi-cropping in the downstream districts. Finally in columns (9)-(10) we
observe that, as expected, area under water-intensive High Yielding Varieties (HYV) crops
increases downstream.

Table 6 uses annual data for 1971-1987 to examine agricultural production and yield for
nineteen crops, and fertilizer use. Columns (1) and (2) consider total production. Both
the OLS and 2SLS estimates suggest that dam construction led to an insignificant decline
in overall production in the district where they were built, and a significant increase in
production in the downstream districts. Similarly, agricultural yield showed an insignificant
decline in the district where dams were constructed, but a significant rise downstream (see
columns (3) and (4)). The own district results, again, suggest that the land around dams is
degraded by dam construction. This degradation is, in part, compensated for by increased
productivity elsewhere in the district. The downstream districts that do not bear any of
the environmental costs associated with dam construction enjoy positive productivity gains.
Finally, fertilizer use increases in downstream districts, (see column (6)).
Dam irrigation increases area devoted to water-intensive HYV crops. In Table 8 we
examine crop-wise outcomes for six major crops, three of which are water-intensive.20
    Columns (1)-(4) consider relatively less water-intensive crops, and columns (5)-(8) water-
intensive crops. We observe no impact on area devoted to dierent crops in the dam’s
own district. In downstream districts we observe a weak positive increase in total area
devoted to water-intensive and non water-intensive crops, and a very significant increase in
the area devoted to wheat, sugar and rice. Wheat and rice saw a sharp increase in water-
intensive HYVs over our sample period. Sugarcane is a water-intensive cash crop which is
very important in Western India (where most dam construction is concentrated).
    The impact of dams on crop yield is modest, even for highly water intensive crops (Panel
B). The increase in area devoted to water intensive crops, combined with modest yield in-
creases, leads to a significant increase in the production of water intensive crops in the
downstream district. An additional dam increases production of water intensive crops down-
stream by 0.6%. This is mainly attributable to a large increase in sugar and rice production.
However, millet and wheat production also increases significantly in the downstream district.
   These results are significant given that a major claim of dam critics is that dams cause
farmers to substitute towards water intensive crops which, while more profitable in the short
run, accentuate water shortages in the long run. While we find evidence that the area and
production of crops using more water, HYV crops and sugarcane, increase in downstream
districts, we do not observe any significant substitution away from major non water-intensive

    Moreover, the area and production of non-water intensive crops shows an insignificant
decline in the district where the dam is placed whereas production of water-intensive crops
increases. This pattern of coecients is sensible given that irrigated area shows some increase
while overall cultivated area in these districts declines. However, these results must be
interpreted with caution, since none of the individual coecients are significant.
Our results provide a consistent picture of the impact of dams on agricultural outcomes.
In the districts where they are built dams do not significantly alter overall agricultural
production. In downstream districts, they enhance overall agricultural production, and
production of some water-intensive cash crops (sugar) and staples which have seen the advent
of HYV (wheat and rice).

Other inputs
The change in crop mix and the increase in HYV seed and fertilizer use in downstream areas
is consistent with the predictions of a simple agricultural production function.
    The two other inputs that the agricultural production function suggests should be af-
fected by increased dam irrigation are the use of alternative forms of water infrastructure
(this should decline), and the use of electricity (this should increase because pumping water
through the canals associated with dams requires electricity).21
    Panel A of Table 8 examines the impact of dams on the incidence of dierent forms of vil-
lage infrastructure (for brevity we only report 2SLS estimate). The results are as expected;
in downstream districts electrification increases and non dam-related water infrastructure de-
creases (the number of canals shows an insignificant increase). There are no other significant
implications of dam construction for public good provision in own district or downstream,
suggesting no crowding in (or out) of other government inputs.

Cost Benefit Analysis of Dams
We use our above results to provide a cost-benefit analysis of dams. Our analysis is tentative
since it requires several clearly contestable assumptions and is based on somewhat noisy point
estimates. In order to obtain an upper bound on this cost benefit analysis we choose the
assumptions which are the most favorable to dams.

We start with estimating the extent to which farmers substitute dam irrigation for other
forms of irrigation. Our estimates suggest that a dam increased net irrigated area by 0.7%
in its own district, and 1% downstream. Combined with the size of irrigated area in the
average district, these point estimates imply a dam-induced net increase in irrigated area of
6,300 hectares in 1985 (row B5 in table 9). Using the Indian agricultural census the Planning
Commission estimated that, in 1985, 23.6 millions hectares were irrigated by dams, or 8,758
hectares per dam (cited in Thakkar (2000)). These estimates suggest a modest crowding-out
of other investment in irrigation (of the order of 30%: see row B6 in Table 9).
    The cost of dam construction is generally expressed in terms of the cost of irrigating
an additional hectare by dam. We therefore base the cost-benefit analysis on a comparison
of the value of additional production per additional hectare irrigated (from our estimates)
with the capital and recurrent cost of an additional hectare irrigated by a dam (Planning
Commission estimates, cited in Thakkar (2000)). Using 1985 means and our estimates, we
calculate the increase in production due to a dam in a district downstream to be Rs. 2.99
million annually, or Rs. 60 million in present discounted value (assuming a 5% discount
rate, and an infinite life span for the dam). This is due to an increase in irrigated area of
3,864 hectares. The present discounted value of the net increase in production per irrigated
hectare is Rs. 13,686. In addition, farmers face a lower irrigation cost (some farmers who
would have used ground water irrigation now use dam irrigation). Since a farmer always had
the option of not using irrigation, an upper bound on reduction in irrigation cost is the value
of increased production on the land. As discussed earlier, our estimates suggest that dams
substituted for other forms of irrigation in 30% of dam irrigated land. Therefore, we divide
our benefit estimate by 70% to obtain the net benefit of the dam on agricultural profits (Rs.
19,011 in row C7).
   The Planning Commission estimated the development cost per hectare of dam irrigation
at Rs. 16,129 in 1985 (inclusive of capital cost, and an annual maintenance cost of Rs. 300
per hectare). Adding the fertilizer cost we obtain a total cost of Rs. 18,807 per hectare.
   Without accounting for the deadweight loss of taxation, these estimates suggest a barely
positive net present value of dam construction (1%). This turns negative if we assume
a conventional 15% figure for the deadweight loss associated with raising funds through
taxation. Our calculation overestimates the economic value of a dam in so far as it does
not account for the production decline in the dam’s own district (while imprecise and not
significantly dierent from zero, the point estimate is large and negative), and additional
labor and environmental costs. It underestimates the economic value of a dam in that it does

not account for production gains in non-agricultural sectors due to electricity generation by
multipurpose dams.
    Our estimates for the production impact of each dam, combined with the annual increase
in the number of dams over the period (about 0.4 per year), indicates that dam construction
was responsible for about 9% of the growth in agricultural production between 1971 and 1987.
The World Commission on Dams (2000b), using very dierent methods, attributed 10% of
the growth in India’s agricultural production since 1950 to dams, and concluded that the
average dam’s net present value was slightly negative. Although this estimate was made by
a supposedly independent, non-partisan international body, dam proponents criticized these
estimates as overly conservative. The International Commission on Large Dams (ICOLD),
an international body for dam builders, claimed in its response that the contribution of
large dams to the growth in agricultural production was closer to 80% (Gopalakrishnan
2000). Our estimates suggest that World Commission on Dams (2000b) estimates are closer
to the truth.

Rural welfare
In this section we examine whether dams have created population groups who have not
received adequate compensation for losses suered, or whether the productivity eects of
dam construction, combined with appropriate redistributive policies, prevented the creation
of such groups.
    We use a district panel data-set on rural consumption, poverty and wage outcomes.
Clearly, our results would be biased if dam construction induces either the relatively rich
or the relatively poor populations to migrate across district boundaries. Panel B of Table 8
estimates the impact of dam construction on district census rural population outcomes. Dam
construction has a small insignificant positive eect on overall population and in-migrants
in both the dam’s own district and downstream district. This suggests that dam-induced
population movements across the district boundaries are not a serious concern, and we can
use the district panel to study the impact of dam building on rural welfare.22 However, to
assess the extent of any possible bias due to migration we compute bounds on the influence
of migration on the estimated impact of dams on poverty. The results are in Table 10; Panel
A provides OLS estimates and Panel B 2SLS estimates.
In columns (1) and (2) we consider mean per-capita expenditure. In column (1), we

find that an additional dam causes a statsitically significant decrease of 0.3% in per-capita
expenditure in the OLS specification, and an insignificant decline of 0.35% in the 2SLS spec-
ification. Column (2) includes dams built in upstream districts as an additional explanatory
variable. The coecient on the expenditure in a dam’s own district remains negative, and
is now significant at the 10% level in the 2SLS regression (and at the 5% level in the OLS
regression). Dams have a modest positive impact on per-capita expenditure in downstream
districts. However, both the OLS and 2SLS estimates are insignificant. The 2SLS estimate
is almost twice as large as, but statistically indistinguishable from, the OLS estimate.
    In columns (3) and (4) we observe that the decline in per-capita expenditure translates
into an increase in poverty. Columns (3) and (4) consider the head-count ratio (this is
the fraction of population with expenditure levels that place them below the poverty line).
Dams significantly increase poverty in their own district, and lead to a decline in poverty in
downstream districts. The downstream eect is significant at 5% in the 2SLS estimates. In
column (3), the OLS estimate for the own district eect is positive and significant, but the
2SLS estimate, while positive, is insignificant: this parallels our reduced form estimate.
    Columns (5) and (6) provide bounds that account for migration. We take the point
estimate of the eect of dams on in-migrants and make alternative assumptions about their
poverty status. We then recompute the head-count ratio (we follow the idea of “Manski
bounds” (Manski 1990)). In column (5), we compute the head-count ratio assuming that all
in-migrants are poor. This reduces the head-count ratio more in districts with more dams.
The OLS estimate of the own district eect changes sign and is insignificant. However,
the confidence intervals in columns (5) and (4) overlap. The 2SLS estimate is positive
but insignificant. In column (6), we recompute the head-count ratio assuming that all in-
migrants are rich. Not surprisingly, both the OLS and 2SLS estimates in column (6) exceed
the original estimates. As usual, as the Manski bounds are not tight, and the results have to
be interpreted with caution. However, they do not suggest severe bias with our 2SLS results.
    The head-count ratio is a relatively crude measure of the extent of poverty. The poverty
gap measures the depth of poverty – specifically, how much income would be needed to bring
the poor to a consumption level equal to the poverty line. Columns (7) and (8) consider
poverty gap as the dependent measure, and find similar eects: dams significantly increase
the poverty gap in their own district, and significantly reduce it downstream. The point
estimate for the poverty reduction associated with dam construction upstream varies from
one-fourth (in the OLS) to one-eighth (in the 2SLS) of the poverty increase in the dams’ own
district. In our sample there are, on average, 1.75 districts downstream of each dam. This

implies that the poverty reduction in districts downstream from the district where a dam is
constructed is too small to compensate for the poverty increase in the dam’s own district.
    In columns (9) and (10) we find no significant eect of dam construction on inequality in
either own or downstream districts. Columns (11) and (12) consider male agricultural wages.
Annual data are available for 1971-1994, but for fewer states than in the poverty sample.23
One might expect higher land productivity (especially from the production of cash crops)
to translate into higher agricultural wages. Wages increase in districts located downstream
from a dam. The 2SLS and OLS estimates are similar and suggest that 10 dams located
upstream cause an increase in agricultural wages of roughly 0.02% (the 2SLS estimates are
insignificant). The point estimate for own district is positive but imprecise.
   Finally, columns (13) and (14) examine the claim that dams increase the incidence of
malaria and other waterborne diseases in neighboring areas. We use data on annual malaria
incidence from 1976 to 1995, but find no evidence of increased malaria incidence. This
suggests that the poverty increase is more likely related to the loss of agricultural land and
displacement than to negative health eects.
Dams and Rainfall Shocks
   A dierent channel through which dams may aect rural welfare is by improving water
security in the event of floods or droughts. If in years of bad rainfall dams provide insurance
within their own district, then they may increase welfare even if, on average, they reduce
consumption and increase poverty.
   In Table 11 we use annual rainfall data for Indian districts to examine the role of dams
in mediating the eect of rain shocks. Our rain shock measure is the fractional deviation of
annual rainfall from the district’s historical average.24
   The odd columns in Table 11 document the eect of rain shocks on agricultural and wel-
fare outcomes. Negative rain shocks decrease area irrigated, total production, and increase
poverty. In the even columns of Table 11 we examine whether dams mitigate or accentuate
the eect of rain shocks (for brevity, we only report 2SLS estimates, but the OLS estimates
are very similar). Having a dam upstream reduces the adverse eect of a negative rain shock:
the coecients on dams-rain shock interaction variable and rainshock variable have the op-
posite sign. In contrast, dams amplify the eect of a bad rain shock in their own district;
the coecients on dams-rain shock interaction variable and the rainfall variable now have

the same sign. The amplification eect is potentially due to restrictions on water use in the
dam’s catchment area.25
   These findings have significant implications for the dynamics of poverty in these districts.
Low level of migration and closed markets imply an amplification of negative shocks (Jay-
achandran 2004). The poor in India have limited access to insurance against risk in rural
India (Morduch (1995)), and faced with limited insurance options the poor make inecient
investments (Rosenzweig and Binswanger (1993), Rosenzweig and Wolpin (1993), Morduch
(1995)), which may further increase poverty.
Institutions and Poverty
    The inability or unwillingness of those who benefit from dams to compensate groups of
losers, or of the government to force them to do so, when both groups are clearly identifiable
ex-ante suggests poor institutions of redistribution. To explore this possibility we build upon
recent work by Banerjee and Iyer (2005).
    Banerjee and Iyer (2005) demonstrate significant dierences in the ability of the popu-
lation to organize, and obtain public goods, across Indian districts. They argue that these
dierences stem, in part, from dierent historical legacies. During the colonial period, the
British instituted dierent land revenue collection systems across districts. In some districts,
an intermediary (landlord) was given property rights for land and tax collection responsibil-
ities. In other districts, farmers were individually or collectively responsible for tax collec-
tion. ”Landlord” districts saw the emergence of a class of landed gentry, who had conflictual
relationships with the peasants. In these districts class relations remain tense, rendering
collective action more dicult. Districts under the landlord system continue to have lower
public good provision, lower agricultural productivity, and more infant mortality.26
    If the politically and economically disadvantaged are more able to demand redistribution
in non-landlord districts (and the elite feel more compelled to compensate losers), then the
poverty impact of dams should be smaller in those districts. Using Banerjee and Iyer’s data,
we interact dams with either a dummy for being a non-landlord district, or the fraction
of land under non-landlord rule in a district and include this interaction as an additional
explanatory variable (our instrument set is as before, plus their interaction with the landlord
variable). The results are presented in Table 12, and are striking. There is no systematic

pattern in the production and irrigation regressions, suggesting that technology drives the
dierential impact of dams on production and irrigation. However, the impact of dams on
poverty in own district is halved in non-landlord districts (the interaction coecient has a
t-statistic of 1.89 in the poverty gap regression), and we cannot reject the hypothesis that
dams do not increase poverty in non-landlord districts. There is no significant pattern in
the downstream districts where no losers are created.
   We conjecture that in non-landlord districts the population is either more eective in
organizing to demand compensation, or more equal in sharing among losers and winners
within the district. It is also possible that the absence of the landed gentry gives the displaced
more political power. More generally, these findings point to the relevance of the institutional
framework within which public policies, such as dam construction, are executed and suggest
‘weak institutions’ or social conflict may help explain why dam construction has particularly
strong distributional and poverty implications in developing countries.

Robustness Checks
We conclude this section with some alternative specifications and robustness checks
Leads and lags
    Columns (1), (4), (6) and (8) in Table 13 examine whether dams aect economic outcomes
prior to their construction. This specification check is particularly relevant for the poverty
regression since one could potentially attribute the poverty findings to a return to normal
poverty levels following a decline while the dam was being constructed. To examine this,
we include dams built up to 5 years in the future as an additional variable. Our faith in
our identification assumption is bolstered by the finding that in no case does future dam
construction aect current outcomes.
    Columns (2), (5), (7) and (9) examine whether the eect of dams persists 5 years after
dam construction. We find some evidence that the eect of dam is gradual; dams built 5
years ago, for example, aect poverty more than those built today.
    Column (3) investigates whether dams recharge the groundwater table. If they do, then,
relative to the short run, irrigation potential in a district should increase by more in the
long run. We estimate a level specification for 1994, where we regress gross irrigated area in
1994 on the number of dams built in 1974 and the number of dams in 1994 (see column (1),
Table (3) for the corresponding first stage). We cannot reject the hypothesis that 1974 dams
do not aect 1994 irrigated area and are, therefore, unable to find evidence of groundwater

Functional form
   Table 14 examines whether the eect of dams varies with dam size. Our instruments are
too weak for us to estimate multiple parameters. But since the 2SLS and OLS estimates are
similar for most specifications, we feel confident about focusing on OLS estimates.
    Our regressions include as separate regressors the number of small (below 16 meters),
medium (16 to 30 meters) and large (above 30 meters) dams. The eect of dams on poverty
is driven by large dams, while the impact of dams on productivity is driven by medium
dams. This is in line with the case-study literature which suggests the negative impact of
dams is the most pronounced for very large dams. It is also possible that, for small dams,
the positive and negative eects on poverty occur within the same district (since both the
catchment area and command area are smaller) and averaging these eects over the district
implies no aggregate eect.
   We also estimated, but do not report, a specification which includes as separate regressors
a dummy for whether there is at least one dam in a district, and the number of dams. The
dummy for ”at least one dam” is insignificant, and the coecient on the number of dams
remained unchanged (in magnitude or size) suggesting that the eect of the number of dams
on economic outcomes is linear in the number of dams.
Alternative neighborhood measures
    Table 15 examines whether the impact of dam construction in a district extends beyond
its neighboring downstream districts. For brevity, we only report 2SLS estimates.
    In Panel A we examine all neighboring districts. The eect of dams constructed down-
stream to a district is negative but insignificant, and there is no eect in neighboring districts
which are neither upstream or downstream. In Panel B we examine whether a district ben-
efits from dam construction in districts which are upstream to its upstream neighbors and
find no eect. Clearly, some large dams may help control floods several hundred kilometers
downstream. But for the average dam, it is unlikely that any eect extends beyond the next
two districts, and these results suggest that it does not extend beyond the next district.
    This table suggests that our focus on the district in which the dam was built and the
adjacent downstream district is appropriate for capturing the eects of most dams, even if
some dams aect more than the immediate neighboring districts. It also suggests that our
estimate of the economic impact of dams in own and upstream districts on district outcomes
is a reasonable approximation of the overall eect of dams, save any general equilibrium
eect which aects all district equally.

The most likely general equilibrium eect is a price eect, due to the increase in pro-
duction. If the poor are net sellers of agricultural products (Deaton (1989)), a decrease in
food prices is likely to accentuate poverty. In this sense, the failure to account for general
equilibrium eects is likely to underestimate the impact of dams on poverty.

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