Thursday, May 24, 2018


See also the Terrain Ruggedness Index.

The best elevation data as of 2016 seems to be WorldDEM, although I haven't seen any application in economics research. It's also not for free of charge. Below is the list of other elevation datasets (available for free of charge) that have been used by economists in the past.


Developed by the U.S. Geological Survey's Center for Earth Resources Observation and Science (EROS) in 1996, GTOPO30 provides elevations at the 30 arc seconds (roughly 1km) grid level. See the USGS/EROS website for detail.

GTOPO30 was used by Deininger and Minten (2002), Nunn and Puga (2007) to measure the degree of ruggedness of the earth surface of each country, and Duflo and Pande (2007) to calculate river gradient in India.

GTOPO30 is now superseded by Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010).


SRTM3 is an updated version of GTOPO30 (I suppose) at a higher spatial resolution of 3 arc-seconds (roughly 100m). SRTM30 is a version that aggregates SRTM3 to the 30 arc second resolution. SRTM30 is supposed to be better than GTOPO30. See Farr et al. (2007) for detail.

For SRTM3 (version 2.1), the data is available here and the documentation is available here. For SRTM30 (version 2.1), both the data and the documentation is available here. For a graphical interface to download the data, visit here.

SRTM30 has been widely used by economists: Taryn Dinkelman's working paper (now published in American Economic Review) "The Effects of Rural Electrification on Employment: New Evidence from South Africa" (to create an instrument for electricity grid placements); Melissa Dell's working paper (now published in Econometrica) "The Persistent Effects of Peru's Mining Mita" (to create control variables); Acemoglu and Dell's paper forthcoming in AEJ Macro "Productivity Differences Within and Between Countries" (to calculate the distance to paved roads that takes into account elevation); Olken (2009) (to obtain the strength of TV signals in each sub-district of Indonesia); and Yanagizawa (2009) "Propaganda and Conflict: Theory and Evidence from the Rwandan Genocide".

How to use GTOPO30 / SRTM30 in ArcGIS

Here is the tip for "How to import GTOPO30 or SRTM30 data into ArcMap (for ArcGIS 9.x)". Step 7 should be skipped because the SRTM30 version 2 uses the value 0 (instead of -9999) for the ocean (see section 1.2 of the documentation).

ASTER Global Digital Elevation Model Version 2

An alternative elevation data to SRTM. Rexer and Hirt (2014) validate SRTM and ASTER against elevation data in Australia, concluding that SRTM is superior in general, with ASTER better for mountainous areas.

Downloadable here.

Used by Mariaflavia Hariri's working paper entitled "Cities in Bad Shape: Urban Geometry in India".

Elevation data is also available by TerrainBase, constructed by National Oceanic and Atmospheric Administration (NOAA) and U.S. National Geophysical Data Center (downloadable at the Atlas of Biosphere). This one is used by Michalopoulos (2008). It is not clear if this is the same as, better or worse than, GTOPO30 and SRTM30. However, if the study area is the whole globe, this data is easier to use because it comes in one file. (GTOPO30 and SRTM30 are provided in several files each of which covers a part of the whole globe.)

World Port Index

World Port Index provides "the location and physical characteristics of, and the facilities and services offered by major ports and terminals world-wide (approximately 3700 entries)"

Used by

River network

Natural Earth provides the global river network polyline shape file as "Rivers + lake centerline".

This data is supposed to supersede the DNNET (River Drainage Network Data), part of the Digital Chart of the World, which was used by Duflo and Pande (2007) to locate rivers in India and to calculate the average river gradient in each district of India to predict the number of dams constructed. Penn State University's Library, which used to host the Digital Chart of the World, now directs data users to Natural Earth.

CIA World DataBank II offers the spatial data on navigable rivers. See this post on CIA World DataBank II.

Wednesday, May 23, 2018

Water bodies (lakes etc.)

There are several data sources for lakes and other water bodies

The Global Lakes and Wetlands Database (GLWD)

Developed by the World Wildlife Fund (WWF) in partnership with the Center for Environmental Systems Research, University of Kassel, Germany. Downloadable at the WWF's website.

SRTM Water Body Data (SWBD)

Created by the National Geospatial-Intelligence Agency (NGA) as a by-product of SRTM elevation data (see this post). The data is available here as thousands of shapefiles, each of which contains 1 by 1 degree area.

Natural Earth

Natural and artificial lake polygons can be obtained from the Lakes + Reservoirs data of Natural Earth.

GSHHG (Global Self-consistent, Hierarchical, High-resolution Geography Database)

This database also contains "islands within lakes" and even "ponds within islands within lakes".


There are several data sources for coastline across the globe. The coastline data is useful to calculate the distance to the nearest coastline (with the Near tool in ArcGIS), which can be used as a control variable in a cross-sectional regression.

Natural Earth (the resolution: 1 to 10 meters)

GSHHG (Global Self-consistent, Hierarchical, High-resolution Geography Database)

Terrestrial Ecoregions of the World

Compiled by Olson et al. (2001). Downloadable from a WWF webpage.

To browse the data, visit Data Basin.

Used by Henderson et al. (2016).

Tuesday, May 22, 2018

World Input-Output Database (WIOD)

Input-output tables for 40 countries, including Brazil, Mexico, China, India, Turkey, Indonesia, Russia, and former East European countries in EU, annually from 1995 to 2011.

The tables also include bilateral trade flows by sector.

Below is the excerpt from Timmer et al. (2015).

"The WIOTs are built up from pub- lished and publicly available statistics from national statistical institutes around the world, plus various international statistical sources such as OECD and UN National Accounts." (p. 578)

"[The datasets] provide details for 35 industries mostly at the two-digit ISIC rev. 3 level or groups thereof, covering the overall economy. These include agriculture, mining, construction, utilities, 14 manufacturing industries, telecom, finance, business services, personal services, eight trade and transport services industries and three public services industries." (p. 578)

"In addition to a national input–output table, imports are broken down according to the country and industry of origin in a WIOT. This allows one, for example, to trace the country of origin of the chemicals used in the food industry of country A." (p. 577)

"The values in WIOTs are expressed in millions of US dollars and market exchange rates were used for currency conversion. All transaction values are in basic prices reflecting all costs borne by the producer, which is the appropriate price concept for most applications. International trade flows are accordingly expressed in “free on board” (fob) prices through estimation of international trade and transport margins." (p.578)

Comparison to other international input-output tables

For the list of other international input-output tables, see here.

According to Timmer et al. (2015), the advantages of the WIOD include:
  1. Providing time-series data
  2. Ensuring a high level of data quality by being based on official and publicly available data from statistical institutes
  3. Based on national supply and use tables (from which national intput-output tables are derived by each country's statistical institute)
  4. Providing underlying data and statistics (provided as socio-economic accounts)
  5. Publicly available for free of charge
Data limitations include (see section 4 of  Timmer et al. (2015)):
  1. The country of origin of inputs
  2. Input-output table for the Rest of the World
  3. Trades in services
  4. Intra-firm trades
Visit for the list of academic papers using this database.