Kiat-kiat Belajar Bahasa Inggris: Data Appendix of Dams

Data Appendix of Dams

Dams

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.

Geography

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

http://edcdaac.usgs.gov/gtopo30/gtopo30.html), and ’dnnet’ (river drainage network data,

available at http://ortelius.maproom.psu.edu/dcw/). 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 diﬀerent 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 http://chd.ucla.edu/dev-data ), 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 suﬃciently 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

(http://www.bsos.umd.edu/socy/vanneman/districts/index.html)). District colonial land

tenure system data is from Banerjee and Iyer (2005)

Rainfall

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

| 3 comments | Labels: Dams, More Articles

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December 12, 2011 at 10:00 AM

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