Data on dams is from the World Registry of Large Dams, maintained by the International
Commission on Large Dams (ICOLD). The registry lists all large dams in India, completed
or under construction, together with the nearest city to the dam and date of completion.
We use city information to assign dams to districts in the year of completion.
Data on district area, river kilometers, district elevation and gradient and river gradient are
collated from two GIS files: GT OP O30 (elevation data, available at, and ’dnnet’ (river drainage network data,
available at The files were processed by CIESIN,
Earth Institute Columbia University using ARCGIS software. Polygon-wise GIS data exists
for every district. District gradient and elevation was computed as % district land area
in dierent elevation/gradient categories (summed across polygons in district). For river
gradient we used the same process but restricted attention to polygons through which the
river flowed. We identified neighboring districts, and within them upstream and downstream
districts, from District Census Maps.
Agriculture data
These data are from the Evenson and McKinsey India Agriculture and Climate data-set
(available at ), with an update. The data-set covers 271 In-
dian districts within 13 Indian states, defined by 1961 boundaries. Kerala and Assam are
the major excluded agricultural states. Also absent, but less important agriculturally, are
the minor states and Union Territories in Northeastern India, and the Northern states of
Himachal Pradesh and Jammu-Kashmir. Data on volume produced, fertilizer used and area
cropped are from the original data-set (1971-1987). We use the average 1960-65 crop prices
to obtain monetary production and yield values. Data on irrigated and total cultivated
area and male agricultural wages span 1971-1994. All monetary variables are deflated by the
state-specific Consumer Price Index for Agricultural laborers in Ozler and Ravallion. (1996),
base year 1973-74.
Rural Welfare data
We use household expenditure survey data collected by Indian National Sample Survey
(NSS). These are All India surveys with a sample size of about 75,000 rural and 45,000
urban households. Households are sampled randomly within districts.27 Only NSS for 1973
regional averages were obtained from Jain, K.Sundaram, and S.D.Tendulkar (1988). For
the 1983-84, 1987-88, 1993-94 and 1999-2000 (“thick”) rounds, Topalova (2004) computed
district-wise statistics using the poverty lines proposed by Deaton (rather than those of the
Indian Planning Commission, which are based on defective price indices over time, across

1999-2000 round introduced a new 7-day recall period, along with the usual 30-day recall
period, for household expenditures on most goods. This methodology is believed to have
led to an overestimate of the expenditures based on the 30-day recall period, making the
poverty and inequality estimates non-comparable to estimates for earlier years. To achieve
comparability across surveys she follows Deaton and imputes, for 1999, the correct district
per capita expenditure distribution from households expenditures on a subset of goods for
which the new recall period questions were not introduced. The poverty, inequality, and
mean per capita expenditure measures were derived from this distribution.
    District identifiers are available 1987 onwards (in hard copy for 1993). For 1973 and
1983, we have NSS region estimates (a region is a group of neighboring districts for which
the sample is suciently large for the NSS to deem the data “representative” of the region).
We use the district matching across censuses, and region to district matching, provided
in Murthi, P.V.Srinivasan, and S.V.Subramanian (2001) and in Indian censuses to match
regions to districts and account for district boundary changes.
Population, Public Goods and Landlord data
Population and Public Goods data are from the Decennial Census of India for the years
1971,1981 and 1991. The public goods data are referred to as village directory data, and
have been aggregated at the district level to generate the fraction of villages in the district
that have a particular public good (obtained from Banerjee and Somanathan (2005)). The
population data are in logs (obtained from the Maryland Indian District Database
( District colonial land
tenure system data is from Banerjee and Iyer (2005)
We use the rainfall data set, Terrestrial Air Temperature and Precipitation: Monthly and
Annual Time Series (1950-99), Version 1.02, was constructed by Cord J. Willows and Kanji
Maturate at the Center for Climatic Research, University of Delaware. The rainfall measure
for a latitude-longitude node combines data from 20 nearby weather stations using an inter-
polation algorithm based on the spherical version of Shepards distance-weighting method.
We define a rainfall shock as the fractional deviation of the district’s rainfall from the district
mean (computed over 1971-1999)
by Esther Duflo and Rohini Pande

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    12 Desember 2011 10.00 Reply To This Comment

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