Dams. Esther Duflo and Rohini Pande. Preliminary and incomplete. Abstract

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1 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

2 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 irrigation 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 controversy. 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. Specifically, 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 suggest that 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 impact of the average dam on agricultural production, economic outcomes, and poverty and inequality. 2

3 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 extends 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 agricultural productivity impact of dam construction in Indian districts which accounts for the endogeneity of dam placement. Specifically, we exploit geographic differences in the suitability 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 , and agriculture data 1 Large 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

4 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

5 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 adversaries 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 appear 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 COMPLETEXX 2 Two 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

6 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 , 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 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 States or Union Territories (out of XX) had no dams. 4 In what follows, since our strategy is to 3 A 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. 4 These 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

7 compare districts with and without dams within the same State, we are excluding all States and Union Territory which had no dams by 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. 5 The data set used was the GTOPO30 (Elevation Data) downloaded from Slope calculated from GTOPO30. The river map (Drainage-network) downloaded form File name used dnnet. It was processed by the CIESIN at the Earth Institute of the University of Columbia 7

8 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 to 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, constructed 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 Consumer 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. Correspondingly, 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 sufficient water resources (the alternative to dams is ground water irrigation). The three states 6 Kerala 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. 7 These 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

9 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 , , and ( 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 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 round is not directly comparable to the 8 The 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. 9 They had to be recovered from hard copies for the year India s districts boundaries changed several time between 1961 and 1991, mostly due to the splitting of districts into two parts. 11 Poverty lines were not available for some of the smaller states and union territories, namely: Arunachal 9

10 round. The 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, inyeart, 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 y ist = β 1 + β 2 D ist + ν i + μ st + ω ist, (1) y ist = β 3 + β 4 D ist + β 5 D U ist + β6d D istν i + μ st + ω ist, (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. 12 In what follows, we will refer to the neighboring upstream districts as upstream districts, for short (and likewise for downstreawm). 10

11 where ν i is a district fixed effect, μ st 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. ω ist 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: D ist = α 1 + α 2k (RSl ki D st )+ α 3k (El ki D st )+ α 4k (Sl ki D st )+(X i D st )α 5 +ν i +μ st +ω ist k=2 k=2 k=2 (3) We run this regression for all districts, and we compute for each district its predicted number of dams ˆ D ist (as the predicted value from equation 3, the predicted number of dams that have been constructed in neighboring upstream districts D ˆ ist U (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, ˆ D D ist. Denote Z i st the vector of all the right hand side variables in equation 3, except for the interactions RSl ki D st. Denote Z U i st the corresponding variables for upstream districts, 13 It is likely to be autocorrelated over time, and we will control for that by clustering the equation at the district level. 11

12 and Zi D st the corresponding variable for downstream districts.14 We then augment equations 1 and 2: and: y ist = γ 1 + γ 2 D ist + Z ist γ 7 + ν i + μ st + ω ist (4) y ist = δ 1 + δ 2 D ist + δ 3 D U ist + δ 4 D D ist + Z ist δ 5 + Z U istδ 6 + Z D istδ 7 + ν i + μ st + ω ist (5) We estimate equation refstrutful1 with 2SLS, using ˆ D ist and Z ist as instruments, and refstrutful2 using ˆ D ist, ˆ D U ist The first stage equations are: D ˆ ist D, ZU ist and ZD ist as instruments. D ist = π 1 + π 2 Dˆ ist + Z ist π 7 + ν i + μ st + ω ist (6) and: Δ ist = φ 1 + φ 2 Dˆ ist + φ ˆ 3 Dist U + φ ˆ 4Dist D + Z istφ 5 + Zistφ U 6 + Zistφ D 7 + ν i + μ st + ω ist (7) where Δ ist represent D ist, D U ist or DD ist. For equation 4, this 2-step procedure is identical to running a 2SLS using the interactions RSl ki D st and Z ist 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 If 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. 15 XX Explain that the first stage is not significant otherwise.xx 12

13 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 the 16 At 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

14 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. The 17 That is, districts that have more dams upstream have a larger irrigated area: see column 2, row 2 in both panels 14

15 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 millets 18, 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 on 18 Millets include Maize, jowar, Bajra, Ragi and Bari, and are cheap cereals that are not very water intensive. 15

16 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

17 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

18 Table 1: Descriptive Statistics A. Dams Number of dams in district Number of dams upstream to district Number of dams downstream to district B. Welfare log(pce) Headcount ratio Poverty gap Gini C. Agriculture log (agricultural wage) Share of cultivated area is irrigated Log(total production) Log (total yield)

19 Table 2: Geography and Dam Construction Dams Poverty sample Wage sample (1) (2) Dams in state*(fraction river slope of 1.5-3%) (4.07) (1.85) Dams in state*(fraction river slope of 3-5%) (6.31) (2.64) Dams in state*(fraction river slope of 6-10%) (7.91) (3.30) Dams in state*(fraction river slope above 10%) (5.63) (2.64) F-test for slope along river [0.050] [0.013] Dams in state*river length (0.0003) (0.0001) Dams in state*(fraction district slope of 1.5-3% slope) (4.84) (2.18) Dams in state*(fraction district slope of 3-5%) (9.32) (4.04) Dams in state*(fraction district slope of 6-10%) (17.43) (7.39) Dams in state*(fraction district slope above 10%) (15.63) (6.41) Dams in state*(elevation between metres) (0.95) (0.96) Dams in state*(elevation between metres) (0.89) (0.85) Dams in state*(elevation over metres) (24.29) (34.25) Dams in state*district area (square kilometers) ( ) ( ) Number of observations All regressions include district fixed effects and a full set of state*year interactions. Standard errors clustered by district are reported in parentheses. The poverty sample includes the years of 1973, 1983, 1987, 1993 and The wage sample includes years

20 Table 3: first stage regressions Poverty sample Whole sample own district own district Upstream Downstream Upstream Downstream Predicted dams (1) (2) (3) (4) (5) (6) (7) (8) Own district (0.200) (0.208) (0.308) (0.329) (0.267) (0.264) (0.479) (0.485) Upstream (0.027) (0.095) (0.037) (0.037) (0.114) (0.069) Downstream (0.042) (0.068) (0.119) (0.053) (0.124) (0.146) Number observations All regressions include the elevation, slope along district, river length and district area variables specified in Table 2 as additional controls. Regressions also include district fixed effects and a full set of state*year interactions. Standard errors clustered by district are reported in parentheses. The poverty sample includes the years of 1973, 1983, 1987, 1993 and The wage sample includes years

21 Table 4: Effect on overal agricultural outcomes net irrigated area net cultivated area log (area cultivated) Log (production ) Log (yield) 15 major crops) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) A. OLS Own district (0.2718) (0.2757) (0.2901) (0.3044) (0.0013) (0.0013) (0.0025) (0.0024) (0.0023) (0.0023) Upstream (0.2375) (0.1825) (0.0007) (0.0015) (0.0014) Downstream (0.1954) (0.1714) (0.0009) (0.0021) (0.0019) No. observations B. Two stage least squares Own district (1.0529) (1.7447) (1.0123) (1.4139) (0.0043) (0.0042) (0.0083) (0.0071) (0.0065) (0.0068) Upstream (0.4673) (0.4361) (0.0013) (0.0026) (0.0026) Downstream (0.7089) (0.5452) (0.0015) (0.0031) (0.0026) No. observations All regressions include elevation, slope along district, river length and district area variables specified in Table 2 as additional controls. Regressions also include district fixed effects and a full set of state*year interactions.

22 Table 5: OLS regression: Area cultivated, Yield, and Production by crops Millet Pulse Water intensive Sugar Cotton Rice Wheat (1) (2) (3) (4) (5) (6) (7) A. Area Cultivated Own district (0.0040) (0.0049) (0.0028) (0.0056) (0.0104) (0.0035) (0.0043) Upstream (0.0021) (0.0030) (0.0016) (0.0046) (0.0055) (0.0019) (0.0023) Downstream (0.0023) (0.0075) (0.0019) (0.0036) (0.0063) (0.0033) (0.0023) B. Yield Own district (0.0059) (0.0031) (0.0039) (0.0032) (0.0060) (0.0031) (0.0020) Upstream (0.0021) (0.0009) (0.0027) (0.0015) (0.0027) (0.0014) (0.0011) Downstream (0.0028) (0.0016) (0.0024) (0.0017) (0.0032) (0.0017) (0.0013) C. Production Own district (0.0057) (0.0044) (0.0047) (0.0068) (0.0126) (0.0042) (0.0046) Upstream (0.0027) (0.0032) (0.0027) (0.0045) (0.0063) (0.0020) (0.0026) Downstream (0.0036) (0.0076) (0.0028) (0.0043) (0.0088) (0.0030) (0.0027) All regressions include elevation, slope along district, river length and district area variables specified in Table 2 as additional controls. Regressions also include district fixed effects and a full set of state*year interactions.

23 Table 6: 2SLS regression: Area cultivated, Yield, and Production by crops (all variables in logarigthm) Water intensive Millet Pulse Wheat All Sugar Cotton Rice (1) (2) (3) (4) (5) (6) (7) A. Area Cultivated Own district (0.0109) (0.0137) (0.0107) (0.0102) (0.0278) (0.0395) (0.0114) Upstream (0.0037) (0.0047) (0.0036) (0.0033) (0.0076) (0.0090) (0.0037) Downstream (0.0047) (0.0069) (0.0043) (0.0037) (0.0076) (0.0142) (0.0056) B. Yield Own district (0.0102) (0.0058) (0.0071) (0.0129) (0.0092) (0.0167) (0.0093) Upstream (0.0044) (0.0019) (0.0020) (0.0042) (0.0029) (0.0053) (0.0026) Downstream (0.0044) (0.0021) (0.0024) (0.0043) (0.0035) (0.0065) (0.0034) C. Production Own district (0.0152) (0.0132) (0.0139) (0.0143) (0.0308) (0.0425) (0.0107) Upstream (0.0053) (0.0049) (0.0042) (0.0039) (0.0076) (0.0121) (0.0038) Downstream (0.0063) (0.0069) (0.0052) (0.0051) (0.0082) (0.0179) (0.0050) All regressions include elevation, slope along district, river length and district area variables specified in Table 2 as additional controls. Regressions also include district fixed effects and a full set of state*year interactions.

24 Table 7: Rural Poverty and Agricultural Wages Log(pce) Head count ratio Poverty gap Gini coefficient Log (wages) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) A. OLS Dams Own district (0.0012) (0.0011) (0.0008) (0.0008) (0.0003) (0.0003) (0.0003) (0.0003) (0.0023) (0.0029) Upstream (0.0005) (0.0004) (0.0001) (0.0001) (0.0012) Downstream (0.0007) (0.0007) (0.0002) (0.0002) (0.0014) No. observations B. 2SLS Dams Own district (0.0044) (0.0040) (0.0025) (0.0027) (0.0007) (0.0008) (0.0011) ( ) (0.0086) (0.0090) Upstream (0.0010) (0.0007) (0.0002) ( ) (0.0027) Downstream (0.0016) (0.0015) (0.0004) ( ) (0.0040) No. observations All regressions include the elevation, slope along district, river length and district area variables specified in Table 2 as additional controls. Regressions also include district fixed effects and a full set of state*year interactions. Standard errors clustered by 1973 NSS region*year are reported in parentheses. The poverty regressions include the years of 1973, 1983, 1987, 1993 and The wage regression includes years

25 FIGURE 1: Total Dams constructed in India, ICOLD Dam Register for India

26 Dams by District: 1965 Number of Dams

27 Dams by District: 1995 Number of Dams

28 River Basins MAP 1 of 1 11/13/ :44 AM Close Window

29 68 0'0"E 72 0'0"E 76 0'0"E 80 0'0"E 84 0'0"E 88 0'0"E 92 0'0"E 96 0'0"E 42 0'0"N Average Dam Slope by District 42 0'0"N 38 0'0"N 38 0'0"N 34 0'0"N 34 0'0"N 30 0'0"N 30 0'0"N 26 0'0"N 26 0'0"N 22 0'0"N 22 0'0"N 18 0'0"N 18 0'0"N 14 0'0"N 14 0'0"N 10 0'0"N 10 0'0"N 6 0'0"N Average Dam Slope AVDSLOP1 6 0'0"N 2 0'0"N '0"N 68 0'0"E 72 0'0"E 76 0'0"E 80 0'0"E 84 0'0"E 88 0'0"E 92 0'0"E 96 0'0"E

30 68 0'0"E 72 0'0"E 76 0'0"E 80 0'0"E 84 0'0"E 88 0'0"E 92 0'0"E 96 0'0"E 42 0'0"N Average River Slope by District 42 0'0"N 38 0'0"N 38 0'0"N 34 0'0"N 34 0'0"N 30 0'0"N 30 0'0"N 26 0'0"N 26 0'0"N 22 0'0"N 22 0'0"N 18 0'0"N 18 0'0"N 14 0'0"N 14 0'0"N 10 0'0"N 10 0'0"N 6 0'0"N Average Dam Slope AVRSLOP1 6 0'0"N 2 0'0"N '0"N 68 0'0"E 72 0'0"E 76 0'0"E 80 0'0"E 84 0'0"E 88 0'0"E 92 0'0"E 96 0'0"E