tag:blogger.com,1999:blog-40524837949695518152024-01-13T03:12:23.853-08:00DEVECONDATADatasets for Development Economists.
Post a comment if you know any information relevant to each dataset.Unknownnoreply@blogger.comBlogger376125tag:blogger.com,1999:blog-4052483794969551815.post-69020363034793586202019-02-04T21:57:00.000-08:002019-02-04T22:00:02.627-08:00Penn World Table (PWT)The data source for real GDP per capita across countries over time. Available at <a href="http://www.rug.nl/ggdc/productivity/pwt/">www.rug.nl/ggdc/productivity/pwt/</a><br />
<br />
A good overview of how different versions of Penn World Table correspond to each other is given by Section 2.1 of <a href="https://www.nber.org/papers/w22216">Pinkovskiy and Sala-i-Marin (2016)</a>.<br />
<br />
Its Section 2.2 is also useful for whether you should use PWT or World Development Indicators for real GDP per capita.<br />
<br />
The major change from Version 7 to 8 was, if I understand correctly, a response to the critique by <a href="http://papers.nber.org/papers/w15455">Johnson et al. (2009)</a>.<br />
<br />
Below is my own rough summary of how real GDP per capita is computed up to Version 7: For more detailed accounts, see <a href="http://papers.nber.org/papers/w15455">Johnson et al. (2009)</a>.<br />
<br />
1. Collect data of prices of hundreds of identically specified goods and services prevailing in each "benchmark" country (this is done by the United Nations International Comparison Program, or ICP).<br />
<blockquote>
The PWT version 6 uses the 1993 ICP data. As the 2005 ICP data is <a href="http://go.worldbank.org/UI22NH9ME0">now released</a>, GDP figures in international dollars are likely to change. See <a href="http://rodrik.typepad.com/dani_rodriks_weblog/2008/01/fact-check-real.html">Arvind Subramanian's article on Dani Rodrik's blog</a>.</blockquote>
<br />
2. Obtain PPPs for the benchmark countries by comparing the prices of each good and service.<br />
<br />
3. Use capital city price surveys by United Nations International City Service Commission, Employment Conditions Abroad (a British firm), and the US State Department, to estimate PPPs for a wider range of countries.<br />
<br />
4. By regressing PPPs obtained in step 2 on PPPs obtained in step 3 for the sample of benchmark countries, PPPs for non-benchmark countries are estimated based on their PPP estimates obtained in step 3.<br />
<br />
5. Use PPPs to convert the countries' national currency expenditures (from national accounts) to a common currency unit.<br />
<br />
Steps 1-5 were carried out for the base year (1985 for PWT version 5; 1996 for PWT version 6.1).<br />
<br />
6. Real GDP per capita in PPP for other years is obtained by applying the growth rates from the constant-price national accounts series to the base-year real GDP per capita.<br />
<br />
See pages 329, 341-4 of Robert Summers and Alan Heston (1991) "The Penn World Table (Mark 5): An Expanded Set of International Comparisons, 1950-1988" <i>Quarterly Journal of Economics</i>, 106, pp.327-368.Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-4052483794969551815.post-67256207264605742052019-02-03T23:51:00.000-08:002019-02-03T23:54:51.384-08:00DMSP-OLS Nighttime Lights Time Series Version 4Downloadable <a href="http://ngdc.noaa.gov/eog/dmsp/downloadV4composites.html">here</a>. The spatial resolution is 30x30 arc-second (about 1x1 km) across the globe between 75 degrees north and 65 degrees south. Available annually since 1992 (and up to 2013, as of August 2016). The nighttime light intensity in each cell is represented by the "digital number", an integer from 0 to 63.<br />
<br />
For a quick summary of the dataset, see Section I of <a href="http://dx.doi.org/10.1257/aer.102.2.994">Henderson et al. (2012)</a>. For detailed discussion on the data, see <a href="http://sedac.ciesin.columbia.edu/binaries/web/sedac/thematic-guides/ciesin_nl_tg.pdf">Doll (2008)</a>.<br />
<br />
The data is becoming popular among economists.<br />
<br />
<a href="http://dx.doi.org/10.1257/aer.102.2.994">Henderson et al. (2012)</a> and <a href="http://dx.doi.org/10.1093/qje/qjw003">Pinkovskiy and Sala-i-Martin (2016</a>) use nighttime light to improve the data on national accounts GDP.<br />
<br />
Michalopoulos and Papaioannou (<a href="http://dx.doi.org/10.3982/ECTA9613">2013</a>, <a href="http://dx.doi.org/10.1093/qje/qjt029">2014</a>), and <a href="http://dx.doi.org/10.1086/685300">Alesina et al. (2016)</a> use nighttime light as a measure of living standards across African ethnic groups.<br />
<br />
<a href="http://dx.doi.org/10.1093/qje/qju004">Hodler and Raschky (2014)</a> exploit the annual panel nature of the data to find that the birth place of a new national leader becomes brighter after he assumes power.<br />
<br />
<a href="http://dx.doi.org/10.1016/j.jpubeco.2015.03.011">Baskaran et al (2015)</a> relate nighttime light to electoral cycles in India.<br />
<br />
<a href="http://dx.doi.org/10.1093/restud/rdw020">Storeygard (2016)</a> uses light as a measure of city-level income across cities in Africa.<br />
<br />
<a href="http://dx.doi.org/10.1093/qje/qjs011">Bleakey and Lin (2012)</a> use nighttime light as a measure of spatial distribution of contemporary economic activity, to see whether portage sites still predict where economic activities are concentrated today, long after their original advantage became obsolete.<br />
<br />
<b>Data construction</b><br />
<br />
To understand how this dataset is constructed from the original satellite images and the potential data issues, see <a href="http://dx.doi.org/10.1016/S0924-2716%2801%2900040-5">Elvidge et al. (2001)</a> and <a href="http://www.ngdc.noaa.gov/dmsp/pubs/Elvidge_WINTD_20091022.pdf">Elvidge et al. (2010)</a>. <a href="http://dx.doi.org/10.1186/1478-7954-6-5">Noor et al. (2008)</a> is also useful to understand this data. See also <a href="https://blogs.worldbank.org/impactevaluations/improving-granularity-nighttime-lights-satellite-imagery-guest-post-alexei-abrahams">Alexei Abrahams's guest post for Development Impact Blog</a>.<br />
<br />
<b>Data issues</b><br />
<br />
<u>Digital number</u>: it's "not exactly proportional to the physical amount of light received (called true radiance)," quoted from p. 999 of <a href="http://dx.doi.org/10.1257/aer.102.2.994">Henderson et al. (2012)</a>.<br />
<br />
<u>Top-coding</u>: The maximum value of light intensity is 63. This issue shouldn't matter much for poor and middle-income countries. <a href="http://dx.doi.org/10.1257/aer.102.2.994">Henderson et al. (2012)</a> remove Singapore and Bahrain from their cross-country analysis for this concern (see footnote 16)<br />
<br />
<u>Bottom-censoring</u>: <a href="http://dx.doi.org/10.1257/aer.102.2.994">Henderson et al. (2012)</a> notes that there are "remarkably few pixels with digital numbers of 1 or 2" (p. 1000). <a href="http://dx.doi.org/10.1093/restud/rdw020">Storeygard (2016)</a> describes how the data processing algorithm causes bottom-censoring (see Appendix section A.8).<br />
<br />
<u>Compatibility across years and satellites</u>: Satellite sensors age over time and are replaced periodically. Thus, the same digital number does not necessarily mean the same level of light intensity across years and satellites. <a href="http://dx.doi.org/10.1257/aer.102.2.994">Henderson et al. (2012)</a> deal with this concern by controlling for year fixed effects in a regression of log GDP on log light per area.<br />
<ul>
<li>Alternatively, the following book chapter attempts to calibrate values from different satellites to account for inter-satellite differences and inter-annual sensor decay:</li>
<ul>
<li>Elvidge, Christopher D., Feng-Chi Hsu, Kimberly E. Baugh and Tilottama Ghosh (2014). "National Trends in Satellite Observed Lighting: 1992-2012." Global Urban Monitoring and Assessment Through Earth Observation. Ed. Qihao Weng. CRC Press. (The working paper version is <a href="http://ngdc.noaa.gov/eog/pubs_new.html">available here</a>.)</li>
<li>The calibrated version aggregated to the 0.5x0.5 degree cell level is available as part of <a href="http://grid.prio.org/">the PRIO-GRID data</a>.</li>
</ul>
</ul>
<u>Gas flare</u>: The digital number picks up gas flare caused by oil production. <a href="http://dx.doi.org/10.1257/aer.102.2.994">Henderson et al. (2012)</a> drops Equatorial Guinea from their cross-country analysis for this reason (footnote 16). In one of their robustness checks, <a href="http://dx.doi.org/10.1257/aer.102.2.994">Henderson et al. (2012)</a> also drop pixels within <a href="http://ngdc.noaa.gov/eog/interest/gas_flares.html">gas flare polygons</a>, so does <a href="http://dx.doi.org/10.1093/restud/rdw020">Storeygard (2016)</a>.<br />
<br />
<u>Blooming</u>: Light tends to be magnified over certain terrain types such as water and snow cover.<br />
<br />
<u>Blurring</u>: A single point source of light would be recorded in several neighbouring cells due to the way the satellite sensor captures the light emission. See <a href="https://blogs.worldbank.org/impactevaluations/improving-granularity-nighttime-lights-satellite-imagery-guest-post-alexei-abrahams">Alexei Abrahams's guest post for Development Impact Blog</a> for more detail.<br />
<br />
<ul>
<li><span style="color: red;">To deblur the data with Abrahams's Matlab code, you need the pct_lights.tif files. Unfortunately, this file for 2011 is missing on <a href="https://ngdc.noaa.gov/eog/dmsp/downloadV4composites.html">the website</a>. If you have downloaded and kept this file somewhere in your computer, let NOAA people know about it.</span></li>
</ul>
<br />
<br />
<u>High latitude locations</u>: Due to long daytime length, nighttime light cannot be observed in summer for high latitude locations (the raw satellite images are taken between 8:30 and 10:00 pm local time). For this reason, <a href="http://dx.doi.org/10.1257/aer.102.2.994">Henderson et al. (2012)</a> exclude observations north of the Arctic Circle.<br />
<br />
<br />
<b>Validation as a measure of income/wealth</b><br />
<br />
Logarithm of light intensity <u>per area</u> (and its long-run change over the 15-year period) is known to be linearly correlated with<br />
<ul>
<li>Logarithm of total GDP (and its change over the 15-year period) at the country-level (<a href="http://dx.doi.org/10.1257/aer.102.2.994">Henderson et al. (2012)</a>), at the sub-national region level (<a href="http://dx.doi.org/10.1093/qje/qju004">Hodler and Raschky (2014)</a>) and at the Chinese city/prefecture level (<a href="http://dx.doi.org/10.1093/restud/rdw020">Storeygard (2016)</a>, Table 1 columns 4-5). Estimated elasticity is around 0.3 in all these studies.</li>
<li>Average DHS wealth index (see <a href="http://devecondata.blogspot.se/2015/12/regional-development-data.html">this post</a>) across households at the enumeration area level in Africa (<a href="http://dx.doi.org/10.3982/ECTA9613">Michalopoulos and </a><a href="http://dx.doi.org/10.3982/ECTA9613">Papaioannou</a> 2013)</li>
</ul>
Logarithm of light intensity <u>per capita</u> is known to be linearly correlated with<br />
<ul>
<li>Logarithm of per capita GDP (and household-survey mean income/expenditure) across the world for 1992-2010 (<a href="http://dx.doi.org/10.1093/qje/qjw003">Pinkovskiy and Sala-i-Martin 2016</a>).</li>
</ul>
<a href="http://dx.doi.org/10.1093/qje/qjw003">Pinkovskiy and Sala-i-Martin (2016</a>) (p. 609) calibrate the exponent on the digital number to match the average income of the states in Mexico (obtained from <a href="http://devecondata.blogspot.se/2010/04/luxembourg-income-study.html">Luxembourg Income Study</a>). They note (fn. 20), "We allow the calibrated exponent to differ across years, but in no year is it smaller than 5/2, and in some years it is as large as 9. Therefore, it is likely that the specification that is prevalent in the literature (setting the exponent equal to unity) is incorrect."<br />
<br />
<b>Validation as a measure of public goods provision</b><br />
<div>
<b><br /></b></div>
<div>
<a href="http://dx.doi.org/10.1093/qje/qjt029">Michalopoulos and </a><a href="http://dx.doi.org/10.1093/qje/qjt029">Papaioannou</a><a href="http://dx.doi.org/10.1093/qje/qjt029"> (2014)</a> shows that logarithm of light intensity per area is correlated with access to electrification, presence of a sewage system, access to piped water, and education (averaged across households in each enumeration area) from Afrobarometer Surveys in 17 African countries.</div>
<div>
<b><br /></b></div>
<a href="http://dx.doi.org/10.1080/01431161.2013.833358">Min et al (2013)</a> validate this measure against survey-based electricity access measure in rural Senegal and Mali in 2011. Their conclusions (quoted from <a href="http://dx.doi.org/10.3390/rs6109511">Min and Gaba 2014</a>, p. 9512) are:<br />
<ul>
<li>Electrified villages are consistently brighter than unelectrified villages across a variety of nighttime satellite images</li>
<li>Electrified villages appear brighter in satellite imagery because of the presence of streetlights, and brightness increases with the number of streetlights.</li>
<li>The correlation between light output recorded by the satellite with household electricity use and access is low.</li>
</ul>
<a href="http://dx.doi.org/10.3390/rs6109511">Min and Gaba (2014</a>) conduct the same validation exercise for villages in Vietnam in 2013. They reach the same conclusions except for the last point: in Vietnam, household-level access to electricity is also correlated with nighttime light satellite images.<br />
<br />
See also <a href="http://dx.doi.org/10.1073%2Fpnas.1017031108">Chen and Nordhaus (2011)</a>.<br />
<br />
<b><br /></b>
<b>Aggregation methods</b><br />
<br />
The raw data ranges from 0 to 63 at the 30x30 arc-second cells. To be used in regression analysis, there are several ways to aggregate the raw data.<br />
<ul>
<li><a href="http://dx.doi.org/10.1257/aer.102.2.994">Henderson et al. (2012)</a> (see footnote 7) obtain the weighted average across pixels within a country, where the weight is the land area of each 30x30 arc-second pixel, obtained from <a href="http://sedac.ciesin.columbia.edu/data/set/grump-v1-land-geographic-unit-area">CIESIN/IFPRI/CIAT (2004)</a>.</li>
<li>Michalopoulos and Papaioannou (<a href="http://dx.doi.org/10.3982/ECTA9613">2013</a>, <a href="http://dx.doi.org/10.1093/qje/qjt029">2014</a>) and <a href="http://dx.doi.org/10.1093/qje/qju004">Hodler and Raschky (2014)</a> use the logarithm of light intensity per area within each spatial unit of analysis.</li>
<ul>
<li>Logarithmic transformation is used because the distribution of nighttime light intensity is right-skewed with around 10% of observations being zero.</li>
<li>0.01 is added to the average before taking log, to use the 10% of the observations without light.</li>
</ul>
<li><a href="http://dx.doi.org/10.1086/685300">Alesina et al. (2016)</a> and <a href="http://dx.doi.org/10.1016/j.jpubeco.2015.03.011">Baskaran et al (2015)</a> use the average or sum of light values from all pixels within each spatial unit of analysis divided by population.</li>
<li><a href="http://dx.doi.org/10.1016/j.jpubeco.2015.03.011">Baskaran et al (2015)</a> also measure the proportion of villages with the positive value of nighttime light at the village centroid. </li>
<li><a href="http://dx.doi.org/10.1093/restud/rdw020">Storeygard (2016)</a> measure the city-level light intensity as follows: first convert the original data "into one binary grid encoding whether a pixel was lit in at least one satellite-year. These ever-lit areas were then converted to polygons; contiguous ever-lit pixels were aggregated, and their DNs were summed within each satellite-year." (p. 1268)</li>
<ul>
</ul>
</ul>
<br />
<blockquote class="tr_bq">
</blockquote>
kdmtzhttp://www.blogger.com/profile/06718149677163689651noreply@blogger.com3tag:blogger.com,1999:blog-4052483794969551815.post-50401477518737687022018-12-17T01:18:00.002-08:002018-12-17T01:18:45.140-08:00Botswana 1946 census<a href="http://ghdx.healthdata.org/record/bechuanaland-population-and-housing-census-1946">Bechuanaland Population and Housing Census of 1946</a><br />
<br />
<a href="https://books.google.co.jp/books?id=yIV_NMDDIvYC">Acemoglu and Robinson (2012)</a>, p. 412, cite it as the last census of Botswana asking questions about ethnicity. "In the Ngwato reserve, for example, only 20 percent of the population identified themselves as pure Ngwato; though there were other Tswana tribes present, there were also many non-Tswana groups whose first language was not Setswana."kdmtzhttp://www.blogger.com/profile/06718149677163689651noreply@blogger.com0tag:blogger.com,1999:blog-4052483794969551815.post-59213325390001681472018-11-12T00:34:00.000-08:002018-11-12T00:36:40.494-08:00Doing Business surveysAnnual cross-country data on regulations, conducted by the World Bank, since 2004. As <a href="http://doi.org/10.1257/jep.30.1.247">Djankov (2016)</a> explains, it originated in <a href="http://www.doingbusiness.org/en/methodology">academic papers written by Andrei Shleifer and his coauthors</a>.<br />
<br />
The data is available for free at <a href="http://www.doingbusiness.org/en/data">the World Bank's website</a>.<br />
<br />
<a href="http://doi.org/10.1257/jep.29.3.99">Besley (2015)</a> discusses pros and cons of this dataset, including his own finding that the correlation between the Doing Business data and firm survey data is not always as expected (Table 2).<br />
<div>
<br /></div>
kdmtzhttp://www.blogger.com/profile/06718149677163689651noreply@blogger.com0tag:blogger.com,1999:blog-4052483794969551815.post-68148209079613950462018-11-05T22:15:00.000-08:002018-11-05T22:15:34.085-08:00Real GDP per capita<strong>World Development Indicators</strong><b> (WDI)</b> - in current/constant local currency unit and in current/constant US dollars since 1960<br />
<br />
---<br />
<br />
<strong><a href="http://pwt.econ.upenn.edu/php_site/pwt_index.php">Penn World Table</a></strong> (PWT) - in purchasing power parity since 1950<br />
<br />
<a href="http://devecondata.blogspot.com/2007/03/penn-world-table.html">See here</a> for my rough summary of data construction.<br />
<br />
See <a href="http://ideas.repec.org/a/aea/aecrev/v84y1994i5p1423-36.html">Nuxoll (1994)</a> for the validity of using economic growth rates from Penn World Table.<br />
<br />
See also <a href="http://www.nber.org/papers/w10866">Feenstra et al. (2004)</a><br />
<br />
For version 5.6, there is an augmented version constructed by <a href="http://www.stanford.edu/group/ethnic/workingpapers/addtabs.pdf">Fearon and Laitin (2003)</a>. Which is used by <a href="http://www.journals.uchicago.edu/JPE/journal/issues/v112n4/112408/112408.web.pdf">Miguel et al. (2004)</a>, hence contained in <a href="http://emlab.berkeley.edu/users/emiguel/data.shtml">their dataset</a>.<br />
<br />
---<br />
<b>Comparison of WDI vs PWT</b><br />
<br />
Discussing PWT version 6, <a href="http://dx.doi.org/10.1016/j.jmoneco.2012.10.022">Johnson et al. (2013)</a> argue that while PWT is good at cross-country comparison, economic growth is better measured by WDI. See also <a href="http://dx.doi.org/10.1257/mac.2.4.222">Ciccone and Jarocinski (2010)</a>.<br />
<br />
See Pinkovskiy and Sala-i-Martin's working paper "<a href="https://www.newyorkfed.org/research/staff_reports/sr778.html">Newer Need Not Be Better: Evaluating the Penn World Tables and the World Development Indicators Using Nighttime Lights</a>" for how much PWT versions 7 and 8 do any better.<br />
<br />
----<br />
<br />
<strong>Angus Maddison (2003) The World Economy: Historical Statistics (Paris: OECD)</strong> <br />
<br />
Annual data entries, wherever possible, from 1820 until 2001.<br />
<br />
Data for 1500, 1600, and 1700 is also available, used by <a href="http://www.atypon-link.com/doi/abs/10.1257/0002828054201305">Acemoglu, Johnson, and Robinson (2005)'s "The Rise of Europe" paper</a>.<br />
<br />
Downloadable from <a href="http://www.theworldeconomy.org/publications/worldeconomy/#1">the book's website</a> (you need username and password written at the end of Table of Contents in the book)<br />
<br />
Used by <a href="http://papers.nber.org/papers/w12269">Acemoglu and Johnson (2006)</a> for their analysis on the effect of life expectancy on economic growth between 1940 and 1980.<br />
<br />
Used also by <a href="http://papers.nber.org/papers/w12175">Persson and Tabellini (2006)</a>.<br />
<br />
For the latest updated data, see <a href="https://www.rug.nl/ggdc/historicaldevelopment/maddison/releases/maddison-project-database-2018">Maddison Project Database</a> (Bolt, Jutta, and Jan Luiten van Zanden, “The Maddison Project: Collaborative Research on Historical National Accounts,” Economic History Review, 67 (2014), 627–651.)<br />
<div>
<br /></div>
<br />
<br />
<b>----</b><br />
<b><br /></b>
<b>Barro-Ursua Macroeconomic Data</b><br />
<b><br /></b>
An attempt to correct Maddison's data. Used by Barro and Ursua "Rare Macroeconomic Disasters" and Barro "Convergence and Modernization Revisited".<br />
<br />
Downloadable from <a href="http://rbarro.com/data-sets/">Robert Barro's website</a>.<br />
<br />
<span class="Apple-style-span" style="color: #858383; font-family: "arial" , sans-serif;"><span class="Apple-style-span" style="font-size: 14px;"><b><br /></b></span></span>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-4052483794969551815.post-54263547370390618012018-11-05T18:21:00.001-08:002018-11-05T18:21:22.943-08:00Jones-Klenow well-being measure across countriesConstructed by <a href="https://www.aeaweb.org/articles?id=10.1257/aer.20110236">Jones and Klenow (2016)</a>. Quote from their abstract:<br />
<blockquote class="tr_bq">
We propose a summary statistic for the economic well-being of people in a country. Our measure incorporates consumption, leisure, mortality, and inequality, first for a narrow set of countries using detailed micro data, and then more broadly using multi-country datasets.</blockquote>
Data can be downloaded from <a href="https://www.aeaweb.org/articles?id=10.1257/aer.20110236">the AER website</a>.<br />
<br />kdmtzhttp://www.blogger.com/profile/06718149677163689651noreply@blogger.com0tag:blogger.com,1999:blog-4052483794969551815.post-40456824512291173172018-11-04T21:05:00.000-08:002018-11-04T21:06:31.508-08:00Global Preference Survey"an experimentally validated survey data set of time preference, risk preference, positive and negative reciprocity, altruism, and trust from 80,000 people in 76 countries" (<a href="http://doi.org/10.1093/qje/qjy013">Falk et al. (2018)</a>, abstract)<br />
<br />
Introduced by <a href="http://doi.org/10.1093/qje/qjy013">Falk et al. (2018)</a>.<br />
<br />kdmtzhttp://www.blogger.com/profile/06718149677163689651noreply@blogger.com0tag:blogger.com,1999:blog-4052483794969551815.post-88270952638818647582018-06-01T01:05:00.000-07:002018-06-01T01:05:41.372-07:00Regional ethnic diversity<a href="http://www.aeaweb.org/articles.php?doi=10.1257/aer.101.5.1872">Alesina and Zhuravskaya (2011)</a> construct ethnic diversity measures at the sub-national region level for 97 countries. The data is <a href="http://www.aeaweb.org/articles.php?doi=10.1257/aer.101.5.1872">available here</a> (click "Download Data Set").<br />
<br />
<a href="https://doi.org/10.1016/j.jdeveco.2018.01.003">Gershman and Rivera (2018)</a> construct alternative ethnic diversity measures at the sub-national region level for 36 African countries. The data is available as part of the replication files (see Appendix H on the journal webpage).kdmtzhttp://www.blogger.com/profile/06718149677163689651noreply@blogger.com0tag:blogger.com,1999:blog-4052483794969551815.post-80095755900661340412018-05-24T00:59:00.000-07:002018-05-24T00:59:15.889-07:00ElevationSee also <a href="http://devecondata.blogspot.se/2013/12/terrain-ruggedness-index.html">the Terrain Ruggedness Index</a>.<br />
<div>
<br />
The best elevation data as of 2016 seems to be <a href="http://www.geo-airbusds.com/worlddem/">WorldDEM</a>, 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.<br />
<br /></div>
<b>GTOPO30</b> <br />
<br />
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 <a href="http://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/gtopo30_info">the USGS/EROS website</a> for detail.<br />
<br />
GTOPO30 was used by <a href="http://ssrn.com/abstract=366499%20">Deininger and Minten (2002)</a>, <a href="http://diegopuga.org/papers/rugged.pdf">Nunn and Puga (2007)</a> to measure the degree of ruggedness of the earth surface of each country, and <a href="http://dx.doi.org/10.1162/qjec.122.2.601">Duflo and Pande (2007)</a> to calculate river gradient in India.<br />
<br />
GTOPO30 is now superseded by <a href="https://lta.cr.usgs.gov/GMTED2010">Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010)</a>.<br />
<br />
<br />
<b>SRTM3 / SRTM30</b><br />
<br />
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 <a href="http://doi.org/10.1029/2005RG000183">Farr et al. (2007)</a> for detail.<br />
<br />
For SRTM3 (version 2.1), the data is <a href="http://dds.cr.usgs.gov/srtm/version2_1/SRTM3/">available here</a> and the documentation is <a href="http://dds.cr.usgs.gov/srtm/version2_1/Documentation/">available here</a>. For SRTM30 (version 2.1), both the data and the documentation is available <a href="http://dds.cr.usgs.gov/srtm/version2_1/SRTM30/">here</a>. For a graphical interface to download the data, <a href="http://www.webgis.com/srtm30.html">visit here</a>.<br />
<br />
SRTM30 has been widely used by economists: Taryn Dinkelman's working paper (now published in American Economic Review) "<a href="http://www.princeton.edu/~tdinkelm">The Effects of Rural Electrification on Employment: New Evidence from South Africa</a>" (to create an instrument for electricity grid placements); <a href="http://econ-www.mit.edu/grad/mdell">Melissa Dell</a>'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); <a href="http://www.aeaweb.org/articles.php?doi=10.1257/app.1.4.1">Olken (2009)</a> (to obtain the strength of TV signals in each sub-district of Indonesia); and Yanagizawa (2009) "<a href="http://www.hks.harvard.edu/fs/dyanagi/Research/RwandaDYD.pdf">Propaganda and Conflict: Theory and Evidence from the Rwandan Genocide</a>".<br />
<br />
<b>How to use GTOPO30 / SRTM30 in ArcGIS </b><br />
<br />
Here is the tip for "<a href="http://www.geo.utexas.edu/courses/371c/Labs/Software_Tips/GTOPO30_import.htm">How to import GTOPO30 or SRTM30 data into ArcMap (for ArcGIS 9.x)</a>". 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 <a href="http://dds.cr.usgs.gov/srtm/version2_1/SRTM30/srtm30_documentation.pdf">the documentation</a>).<br />
<br />
<b>ASTER Global Digital Elevation Model Version 2</b><br />
<br />
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.<br />
<br />
<a href="https://asterweb.jpl.nasa.gov/gdem.asp">Downloadable here</a>.<br />
<br />
Used by <a href="http://real.wharton.upenn.edu/~harari/research.html">Mariaflavia Hariri</a>'s working paper entitled "Cities in Bad Shape: Urban Geometry in India".<br />
<br />
<b>TerrainBase</b><br />
Elevation data is also available by TerrainBase, constructed by National Oceanic and Atmospheric Administration (NOAA) and U.S. National Geophysical Data Center (<a href="http://www.sage.wisc.edu/atlas/maps.php?datasetid=28&includerelatedlinks=1&dataset=28">downloadable at the Atlas of Biosphere</a>). This one is used by <a href="http://sites.google.com/site/steliosecon/ethnicdiversitytheoryandevidenceoct1.pdf?attredirects=0">Michalopoulos (2008)</a>. 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.)Unknownnoreply@blogger.com2tag:blogger.com,1999:blog-4052483794969551815.post-30488611219950044562018-05-24T00:00:00.001-07:002018-05-24T00:00:42.190-07:00World Port Index<a href="http://msi.nga.mil/NGAPortal/MSI.portal?_nfpb=true&_pageLabel=msi_portal_page_62&pubCode=0015">World Port Index</a> provides "the location and physical characteristics of, and the facilities and services offered by major ports and terminals world-wide (approximately 3700 entries)"<br />
<br />
Used by<br />
<br />
<ul>
<li><a href="http://www.cepr.org/active/publications/discussion_papers/dp.php?dpno=10704">Dreher et al (2015)</a> as one of the control variables in a regression of aid projects financed by China.</li>
<li><a href="http://doi.org/10.1093/qje/qjx030">Henderson et al. (2018)</a>, to predict where nighttime lights are observed.</li>
</ul>
kdmtzhttp://www.blogger.com/profile/06718149677163689651noreply@blogger.com0tag:blogger.com,1999:blog-4052483794969551815.post-15387164228531282532018-05-24T00:00:00.000-07:002018-05-24T00:00:20.222-07:00River network<a href="http://www.naturalearthdata.com/">Natural Earth</a> provides the global river network polyline shape file as "<a href="http://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-rivers-lake-centerlines/">Rivers + lake centerline</a>".<br />
<ul>
<li>Used by <a href="http://doi.org/10.1093/qje/qjx030">Henderson et al. (2018)</a>, to predict where nighttime lights are observed.</li>
</ul>
<br />
This data is supposed to supersede the DNNET (River Drainage Network Data), part of the Digital Chart of the World, which was used by <a href="http://dx.doi.org/10.1162/qjec.122.2.601">Duflo and Pande (2007)</a> 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 <a href="http://www.maproom.psu.edu/dcw/">directs data users to Natural Earth</a>.<br />
<br />
CIA World DataBank II offers the spatial data on navigable rivers. See <a href="http://devecondata.blogspot.com/2011/03/cia-world-databank-ii.html">this post on CIA World DataBank II</a>.Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-4052483794969551815.post-5795895939685778302018-05-23T22:40:00.000-07:002018-05-23T22:41:49.499-07:00Water bodies (lakes etc.)There are several data sources for lakes and other water bodies<br />
<br />
<b>The Global Lakes and Wetlands Database (GLWD)</b><br />
<br />
Developed by the World Wildlife Fund (WWF) in partnership with the Center for Environmental Systems Research, University of Kassel, Germany. Downloadable at <a href="https://www.worldwildlife.org/pages/global-lakes-and-wetlands-database">the WWF's website</a>.<br />
<br />
<ul>
<li>Used by <a href="http://doi.org/10.1093/qje/qjx030">Henderson et al. (2018)</a>, to predict where nighttime lights are observed.</li>
</ul>
<br />
<br />
<b>SRTM Water Body Data (SWBD)</b><br />
<br />
Created by the National Geospatial-Intelligence Agency (NGA) as a by-product of SRTM elevation data (<a href="http://devecondata.blogspot.com/2007/04/gtopo30.html">see this post</a>). The data is <a href="http://dds.cr.usgs.gov/srtm/version2_1/SWBD/">available here</a> as thousands of shapefiles, each of which contains 1 by 1 degree area.<br />
<br />
<b>Natural Earth</b><br />
<br />
Natural and artificial lake polygons can be obtained from <a href="http://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-lakes/">the Lakes + Reservoirs data of Natural Earth</a>.<br />
<br />
<b><a href="http://www.soest.hawaii.edu/pwessel/gshhg/">GSHHG</a> (Global Self-consistent, Hierarchical, High-resolution Geography Database)</b><br />
<br />
This database also contains "islands within lakes" and even "ponds within islands within lakes".kdmtzhttp://www.blogger.com/profile/06718149677163689651noreply@blogger.com0tag:blogger.com,1999:blog-4052483794969551815.post-14388285445370279842018-05-23T22:23:00.000-07:002018-05-28T17:15:36.749-07:00CoastlineThere 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.<br />
<br />
<a href="http://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-coastline/">Natural Earth</a> (the resolution: 1 to 10 meters)<br />
<br />
<a href="http://www.soest.hawaii.edu/pwessel/gshhg/">GSHHG</a> (Global Self-consistent, Hierarchical, High-resolution Geography Database)<br />
<ul>
<li>Very detailed. Based on the World Vector Shoreline project (Soluri and Woodson 1990). For Antarctica, it is based on Bohlander and Scambos (2007). See <a href="https://www.ngdc.noaa.gov/mgg/shorelines/data/gshhg/latest/readme.txt">GSHHG's readme file</a> for detail. </li>
<li>Used by <a href="http://doi.org/10.1093/qje/qjx030">Henderson et al. (2018)</a>, to predict where nighttime lights are observed.</li>
</ul>
kdmtzhttp://www.blogger.com/profile/06718149677163689651noreply@blogger.com0tag:blogger.com,1999:blog-4052483794969551815.post-28670547214237412252018-05-23T20:42:00.000-07:002018-05-23T20:42:26.319-07:00Terrestrial Ecoregions of the WorldCompiled by <a href="https://academic.oup.com/bioscience/article/51/11/933/227116/Terrestrial-Ecoregions-of-the-World-A-New-Map-of">Olson et al. (2001)</a>. Downloadable from <a href="https://www.worldwildlife.org/publications/terrestrial-ecoregions-of-the-world">a WWF webpage</a>.<br />
<br />
To browse the data, visit <a href="https://databasin.org/datasets/68635d7c77f1475f9b6c1d1dbe0a4c4c">Data Basin</a>.<br />
<br />
Used by <a href="http://www.nber.org/papers/w22145">Henderson et al. (2016)</a>.kdmtzhttp://www.blogger.com/profile/06718149677163689651noreply@blogger.com0tag:blogger.com,1999:blog-4052483794969551815.post-14667141565480016672018-05-22T22:39:00.000-07:002018-05-22T22:39:45.133-07:00World Input-Output Database (WIOD)<a href="http://www.wiod.org/">www.wiod.org</a><br />
<br />
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.<br />
<br />
The tables also include bilateral trade flows by sector.<br />
<br />
Below is the excerpt from <a href="http://dx.doi.org/10.1111/roie.12178">Timmer et al. (2015)</a>.<br />
<br />
"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)<br />
<br />
"[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)<br />
<br />
"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)<br />
<br />
"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)<br />
<br />
<b>Comparison to other international input-output tables</b><br />
<br />
For the list of other international input-output tables, <a href="http://www.wiod.org/otherdb">see here</a>.<br />
<br />
According to <a href="http://dx.doi.org/10.1111/roie.12178">Timmer et al. (2015)</a>, the advantages of the WIOD include:<br />
<ol>
<li>Providing time-series data</li>
<li>Ensuring a high level of data quality by being based on official and publicly available data from statistical institutes</li>
<li>Based on national supply and use tables (from which national intput-output tables are derived by each country's statistical institute)</li>
<li>Providing underlying data and statistics (provided as socio-economic accounts)</li>
<li>Publicly available for free of charge</li>
</ol>
<div>
Data limitations include (see section 4 of <a href="http://dx.doi.org/10.1111/roie.12178">Timmer et al. (2015)</a>):</div>
<div>
<ol>
<li>The country of origin of inputs</li>
<li>Input-output table for the Rest of the World</li>
<li>Trades in services</li>
<li>Intra-firm trades</li>
</ol>
</div>
Visit <a href="http://www.wiod.org/">www.wiod.org</a> for the list of academic papers using this database.<br />
<br />kdmtzhttp://www.blogger.com/profile/06718149677163689651noreply@blogger.com0tag:blogger.com,1999:blog-4052483794969551815.post-33181128537122088812018-05-22T22:35:00.001-07:002018-05-22T22:35:47.918-07:00Sovereign Debt Restructuring episodes<a href="https://doi.org/10.1111/jeea.12156">Asonuma and Trebesch (2016)</a> compile the dataset on sovereign debt restructuring episodes between <span style="background-color: white; color: #2a2a2a; font-family: Merriweather, serif; font-size: 16px;">1978 and 2010.</span>kdmtzhttp://www.blogger.com/profile/06718149677163689651noreply@blogger.com0tag:blogger.com,1999:blog-4052483794969551815.post-50828351281335004052018-05-21T22:35:00.000-07:002018-05-21T22:35:20.709-07:00Rainfall and Temperature datasets<b>Cross-section (climate) </b><br />
<br />
<span style="font-weight: bold;">WorldClim</span><br />
<ul>
<li><a href="http://www.worldclim.org/worldclim_IJC.pdf">Hijmans et al. (2005)</a> compile the climate data for the whole land surface on earth (excluding Antarctica) at a spatial resolution of 30 arc seconds (about 1km). Max and min temperature and monthly rainfall are available. </li>
<li>This is NOT the time-series weather data, but useful for learning the general climate at the very disaggregated level of areas.</li>
<li>Version 2 (<a href="https://doi.org/10.1002/joc.5086">Fick and Hijmans 2017</a>) offers monthly weather variables for 1970-2000. The variables include:</li>
<ul>
<li>Temperature (minimum, maximum, average)</li>
<li>Precipitation</li>
<li>Solar radiation</li>
<li>Wind speed</li>
<li>Water vapor pressure</li>
</ul>
<li>Downloadable at <a href="http://www.worldclim.org/">this website</a>.</li>
<li>Version 1 was used by <a href="http://www.nber.org/papers/w14680">Dell, Jones, and Olken (2009)</a> and <a href="https://academic.oup.com/qje/article/128/1/105/1840182/Human-Capital-and-Regional-Development">Gennaioli et al (2013)</a>.</li>
</ul>
<br />
<b>Time-series (weather)</b><br />
<br />
We follow the categorization of weather datasets by Section 2.2 of <a href="http://doi.org/10.1257/jel.52.3.740">Dell, Jones, and Olken (2014)</a>.<br />
<br />
<b><ground data="" station=""></ground>1. Ground station data</b><br />
<br />
<span style="font-weight: bold;">Global Historical Climatology Network</span><br />
<br />
<ul>
<li><a href="http://www.ncdc.noaa.gov/oa/climate/ghcn-monthly/index.php">The dataset</a> includes monthly records by rainfall stations and the location of such stations. Used by <a href="http://www-personal.umich.edu/~deanyang/papers/macciniyang_undertheweather.pdf">Maccini and Yang (2006)</a> for monthly precipitation data in Indonesia.</li>
</ul>
<br />
<a href="http://www.fao.org/nr/water/infores_databases_climwat.html">FAO Climwat</a><br />
<br />
<ul>
<li>"The CLIMWAT database includes data from a total of 3262 meteorological stations from 144 countries."</li>
</ul>
<br />
<a href="http://geonetwork3.fao.org/climpag/agroclimdb_en.php">FAOClim-NET</a><br />
<br />
<ul>
<li>Monthly data for 28,100 stations on evapotranspiration, precipitation, sunshine, temperature, vapour pressure, and wind speed.</li>
</ul>
<br />
<div>
For other station-based datasets, have a look at <a href="https://www.ncdc.noaa.gov/data-access/land-based-station-data/land-based-datasets">the National Climatic Data Center (NCDC)'s website</a>.</div>
<div>
<br />
<br />
<div>
</div>
<b>2. Gridded data</b></div>
<div style="-webkit-text-stroke-width: 0px; color: black; font-family: Times; font-size: medium; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;">
<gridded data=""></gridded></div>
<div style="-webkit-text-stroke-width: 0px; color: black; font-family: Times; font-size: medium; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;">
<b><br /></b></div>
<div style="orphans: 2; text-align: start; text-indent: 0px; widows: 2;">
<div style="-webkit-text-stroke-width: 0px; color: black; font-family: Times; font-size: medium; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; letter-spacing: normal; text-transform: none; white-space: normal; word-spacing: 0px;">
CRU TS 2.1</div>
<br />
<ul>
<li style="-webkit-text-stroke-width: 0px; color: black; font-family: Times; font-size: medium; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; letter-spacing: normal; text-transform: none; white-space: normal; word-spacing: 0px;">Monthly climate dataset by 0.5 degree grid for 1901-2002.</li>
<li>Variables available include precipitation, daily mean temperature, monthly average daily maximum temperature, monthly average daily minimum temperature, diurnal temperature range, vapour pressure, cloud cover, wet day frequency, frost day frequency.</li>
<li>To obtain other weather variables such as dew points from the CRU data, see <a href="http://www.cru.uea.ac.uk/~timm/grid/index-faq.html">its FAQ</a> no. 5.</li>
<li>See <a href="http://dx.doi.org/10.1002/joc.1181">Mitchell and Jones (2005)</a> for detail.</li>
<li>The CRU data is the best estimate of spatial distribution of weather at each point in time. To use the CRU data for time-series analysis, however do <a href="http://www.cru.uea.ac.uk/~timm/grid/ts-advice.html">read this</a>. Especially, bear in mind that if no station data is available, the average value for the month from 1960-1990 is imposed. Also, changes in weather over time may reflect not only actual weather changes but also changes in the availability of station data.</li>
<li>The original file is downloadable at <a href="http://www.cru.uea.ac.uk/~timm/grid/CRU_TS_2_1.html">Mitchell's website</a> (but you need to use Unix to browse the data file).</li>
<li>The aggregate data at the country level is also available as "<a href="http://www.cru.uea.ac.uk/~timm/cty/obs/TYN_CY_1_1.html">TYN CY 1.1</a>".</li>
<li>The CGIAR Consortium for Spatial Information (CGIAR-CSI) provides <a href="http://cru.csi.cgiar.org/">the GIS version of CRU TS 2.1 data</a>. Even if you do not intend to use the GIS software, this dataset is useful because weather data files (in the comma delimited ascii format) are split into 6 20-year periods so that you can read these files in Excel, which does not allow you to read more than 256 columns.</li>
<li>These data files consist of columns entitled "value" (0.5 by 0.5 degree grid identifier) and "M<month>y<year>" (weather value for <month>of <year>). The mapping between "value" and geographic coordinates is available in "coordinates.txt". See CRU_21-readme.doc for details.</year></month></year></month></li>
</ul>
<br />
<div>
Terrestrial Air Temperature and Precipitation: Monthly and Annual Time Series (1950-1999)</div>
<div>
<br />
<ul>
<li>The spatial resolution is 0.5 x 0.5 degree cells.</li>
<li>Constructed by <a href="http://climate.geog.udel.edu/~climate/">Center for Climatic Research, University of Delaware</a>.</li>
<li>Only the average values during 1950-1999 are available.</li>
<li>Used by <a href="http://www.stanford.edu/~jayachan/indo_fires.pdf">Seema Jayachandran (2006) "Air Quality and Early-Life Mortality: Evidence from Indonesia's Wildfires"</a>.</li>
<li>For possible concerns to use this dataset for Africa, see footnote 7 of <a href="http://www.antoniociccone.eu/wp-content/uploads/2009/10/raindemocratization.pdf">Brückner and Ciccone (2009)</a>.</li>
</ul>
</div>
<span style="font-weight: bold;"><a href="http://daac.ornl.gov/CLIMATE/guides/cramer_leemans.html">Cramer and Leemans's CLIMATE data</a></span><br />
<br />
<ul>
<li>"Monthly averages of mean temperature, temperature range, precipitation, rain days and sunshine hours for the terrestrial surface of the globe, gridded at 0.5 degree longitude/latitude resolution" are available for the period 1930-1960.</li>
</ul>
<br />
<div style="-webkit-text-stroke-width: 0px; color: black; font-family: Times; font-size: medium; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; letter-spacing: normal; text-transform: none; white-space: normal; word-spacing: 0px;">
CRUTEM 3</div>
<div style="-webkit-text-stroke-width: 0px; color: black; font-family: Times; font-size: medium; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; letter-spacing: normal; text-transform: none; white-space: normal; word-spacing: 0px;">
</div>
<ul>
<li>Monthly temperature data at a 5-degree grid spatial resolution from 1850 to present. </li>
<li>Downloadable <a href="http://www.cru.uea.ac.uk/cru/data/temperature/">here</a>. </li>
<li>Used by <a href="http://www.cepr.org/pubs/dps/DP7572.asp">Bluedorn et al. (2009)</a>.</li>
</ul>
<br />
Global Six Century Temperature Patterns<br />
<div>
<br />
<ul>
<li>This dataset provides annual temperature at a 5 degree spatial resolution from 1730-1980. ASCII files are downloadable <a href="http://picasso.ngdc.noaa.gov/paleo/data/mann/">here</a>. For the documentation, see <a href="http://dx.doi.org/10.1038/33859">Mann et al. (1998)</a>. Used by <a href="http://www.cepr.org/pubs/dps/DP7572.asp">Bluedorn et al. (2009)</a>.</li>
</ul>
</div>
<div>
<b>3. Satellite Measurements</b></div>
<div style="-webkit-text-stroke-width: 0px; color: black; font-family: Times; font-size: medium; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; letter-spacing: normal; text-transform: none; white-space: normal; word-spacing: 0px;">
<b><satellite measurements=""></satellite></b></div>
<div style="-webkit-text-stroke-width: 0px; color: black; font-family: Times; font-size: medium; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; letter-spacing: normal; text-transform: none; white-space: normal; word-spacing: 0px;">
<br /></div>
<span style="font-weight: bold;"><a href="http://www.cpc.ncep.noaa.gov/products/global_precip/html/wpage.cmap.shtml">Climate Prediction Center Merged Analysis of Precipitation (CMAP)</a></span><br />
<br />
<ul>
<li>Daily average rainfall data at 2.5 by 2.5 degree grid level, available monthly since 1979.</li>
<li>Used by <a href="http://www.ajtmh.org/cgi/content/abstract/73/1/214">Thomson et al. (2005)</a> to estimate the impact of rainfall on malaria incidence in Botswana.</li>
</ul>
<br />
<span style="font-weight: bold;"><a href="http://www.gewex.org/gpcp.html">Global Precipitation Climatology Project (GPCP)</a></span><br />
<br />
<ul>
<li>Rainfall data similar to CMAP.</li>
<li>Used by <a href="http://elsa.berkeley.edu/~emiguel/pdfs/miguel_conflict.pdf">Miguel et al. (2004)</a> to instrument per capita income growth in relation to civil war incidence.</li>
<li>Rainfall data at the <span style="font-style: italic;">daily</span> frequency at <span style="font-style: italic;">1.0 by 1.0 degree</span> grid level is also available since October 1996.</li>
</ul>
<b><reanalysis data=""></reanalysis>4. Reanalysis Data</b><br />
<br />
See <a href="http://dx.doi.org/10.1093/reep/ret016">Auffhammer et al. (2013)</a> for detail.</div>
<br />
<br />
<br />Unknownnoreply@blogger.com1tag:blogger.com,1999:blog-4052483794969551815.post-52399150391378073712018-04-25T01:23:00.001-07:002018-04-25T01:23:21.007-07:00Public datasets on Sierra LeoneThe list is compiled by the Sierra Leone team of International Growth Centre. Visit <a href="https://www.theigc.org/country/sierra-leone/resources/">https://www.theigc.org/country/sierra-leone/resources/</a>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-4052483794969551815.post-4093852103420873952018-04-12T16:25:00.000-07:002018-04-12T16:31:36.333-07:00IPUMS International<a href="http://international.ipums.org/international/">Integrated Public Use Microdata Series (IPUMS) International</a> allow researchers to download censuses from various developing countries in the comparable format.<br />
<br />
See <a href="http://doi.org/10.1038/sdata.2018.7">Kugler and Fitch (2018)</a> for detail.<br />
<br />
See <a href="https://international.ipums.org/international-action/samples">this page</a> for the list of countries whose census is downloadable.<br />
<br />
<br />
<a href="http://sticerd.lse.ac.uk/seminarpapers/dg12032007.pdf">Bleakley (2006)</a> uses censuses from Brazil, Columbia, and Mexico downloaded from this website.<br />
<br />
Tarozzi (2011) uses <a href="http://dx.doi.org/10.1016/j.jdeveco.2010.05.003">the 2000 Mexico census</a> downloaded from this website.Unknownnoreply@blogger.com1tag:blogger.com,1999:blog-4052483794969551815.post-28213498396325027582018-04-12T16:13:00.000-07:002018-04-12T16:13:28.886-07:00An agricultural survey for more than 9,500 African households"<span style="background-color: white; color: #222222; font-family: Lora, Palatino, Times, "Times New Roman", serif; font-size: 17px; letter-spacing: 0.17px;">conducted in the growing seasons 2002/2003 or 2003/2004 in eleven African countries: Burkina Faso, Cameroon, Ghana, Niger and Senegal in western Africa; Egypt in northern Africa; Ethiopia and Kenya in eastern Africa; South Africa, Zambia and Zimbabwe in southern Africa." (<a href="http://doi.org/10.1038/sdata.2016.20">Waha et al. 2016</a>)</span><br />
<br />kdmtzhttp://www.blogger.com/profile/06718149677163689651noreply@blogger.com0tag:blogger.com,1999:blog-4052483794969551815.post-11356723591747683572018-04-12T16:09:00.000-07:002018-04-12T16:09:18.033-07:00Roads and cities of 18th century FranceConstructed by <a href="https://www.nature.com/articles/sdata201548">Perret et al. (2015)</a> from scanned maps.kdmtzhttp://www.blogger.com/profile/06718149677163689651noreply@blogger.com0tag:blogger.com,1999:blog-4052483794969551815.post-82880977739211065032018-03-18T19:04:00.001-07:002018-03-18T19:04:27.749-07:00Bilateral trade data for 1850-1900Compiled by <a href="http://doi.org/10.1257/aer.20140832">Pascali (2017)</a> from primary sources. See its section II.C for detail and alternative datasets.Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-4052483794969551815.post-58129671986490234992018-03-13T05:10:00.000-07:002018-03-13T05:10:31.882-07:00Climatological Database for the World's Oceans (CLIWOC) 1750-1850http://webs.ucm.es/info/cliwoc/<br />
<br />
Used by <a href="http://doi.org/10.1257/aer.20140832">Pascali (2017)</a>.<br />
<br />Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-4052483794969551815.post-22506100137528738452018-01-29T17:45:00.000-08:002018-01-29T17:55:22.541-08:00Cross-country "years of education" datasets<b>Penn World Tables 9.0</b><br />
<b><br /></b>
See <a href="http://www.rug.nl/ggdc/docs/human_capital_in_pwt_90.pdf">this document</a>, which reviews the academic debate on the quality of Barro-Lee dataset.<br />
<br />
<a href="http://doi.org/10.1007/s10887-007-9011-5"><b>Cohen and Soto (2007)</b></a><br />
provide an alternative data to Barro and Lee (see below)<br />
<b><br /></b>
<b>Barro-Lee dataset</b><br />
<br />
A well-known dataset on average years of schooling (i.e. stock of human capital) by 5-year age group for 146 countries from 1950 to 2010. See <a href="https://doi.org/10.1016/j.jdeveco.2012.10.001">Barro and Lee (2013)</a> for detail. To download the data, visit<i> </i><a href="http://www.barrolee.com/">www.barrolee.com</a>. For data sources, see <a href="http://www.barrolee.com/aboutset/Appendix%20Notes_V.1.0.pdf">Appendix Notes</a>.<br />
<br />
For details on the data construction, read Robert J. Barro and Jong-Wha Lee, "<a href="http://www.cid.harvard.edu/cidwp/042.htm">International Data on Educational Attainment: Updates and Implications</a>" (CID Working Paper No. 42, April 2000). This 2000 paper is an updated version of <a href="http://dx.doi.org/10.1016/0304-3932%2893%2990023-9">Barro and Lee (1993)</a>. Both papers compare various measures of human capital.<br />
<br />
The average years of schooling is available for the six sets of the population: male over 25, female over 25, all over 25, male over 15, female over 15, all over 15. <br />
<br />
Population over the age of 15 "corresponds better to the labor force for many developing countries." (Barro and Lee 2000, p.2)<br />
<br />
Percentages of those who attained/completed each level of school in the total/male/female population are also available. Note that the sum of variables LU, LP, LS, and LH is 100; Lx-LxC, where x is either P, S, or H, is the percentage of those dropping out before completing primary, secondary, or higher school, respectively. In other words, the percentage of ".... school attained" contains the percentage of "... school complete". <br />
<br />
Downloadable at <a href="http://www.cid.harvard.edu/ciddata/ciddata.html">this page</a> by Center for International Development at Harvard University (CID).<br />
<br />
The data file in the panel dataset format is best avoided because it excludes countries not in Penn World Table 5.0 (e.g. former socialist countries). <br />
<br />
Note that variable SHCODE (numerical country code in Penn World Table 5.0) is different from the one in Penn World Table 5.6. <br />
<br />
A very minor point, but the data entries for USSR/Russia in 1990 seem unreliable. Population seems to refer to USSR while educational attainment figures seem to refer to Russia.<br />
<br />
Papers using this dataset include <a href="http://dx.doi.org/10.1257/000282805774669916">Acemoglu et al. (2005)</a> and <a href="http://dx.doi.org/10.1007/s10887-007-9015-1">Glaeser et al. (2007)</a>.<br />
<br />
For other datasets on average schooling years, see <a href="http://www.econ.nyu.edu/cvstarr/working/1991/RR91-26.pdf">Kyriacou (1991)</a>, which is used by <a href="http://dx.doi.org/10.1016/0304-3932%2894%2990047-7">Benhabib and Spiegel (1994, JME)</a>, and <a href="http://dx.doi.org/10.1016/0304-3878%2894%2900054-G">Nehru et al. (1995)</a>, which is used by <a href="http://wber.oxfordjournals.org/cgi/content/abstract/15/3/367">Pritchett (2000)</a>.<br />
<br />
See Krueger and Lindahl (2001, JEL) for critical reviews on average schooling year data.<br />
<br />
<br />kdmtzhttp://www.blogger.com/profile/06718149677163689651noreply@blogger.com5tag:blogger.com,1999:blog-4052483794969551815.post-26185763615226515002018-01-29T17:29:00.000-08:002018-01-29T17:52:00.728-08:00Cross-country school enrollment ratio data<span style="background-color: white; font-family: "times new roman";"><a href="https://doi.org/10.1016/j.jdeveco.2016.05.006">Lee and Lee (2016)</a> compile historical school enrollment ratios by gender since 1820 for 111 countries.</span><br />
<ul>
<li><span style="font-family: "times new roman";"><a href="http://www.barrolee.com/Lee_Lee_LRdata.htm">Downloadable here</a></span></li>
<li>This is an updated version of the dataset compiled by Barro, Robert J. and Jong-Wha Lee (2015) <i>Education Matters: Global Schooling Gains from the 19th to the 21st Century</i> (Oxford University Press) </li>
</ul>
<div>
<br /></div>
<br />
<a href="http://www.jstor.org/stable/2112627">Aaron Benavot and Phyllis Riddle (1988)</a> compiled cross-country data on the primary school enrollment ratio in the late 19th century and the early 20th century.<br />
<br />
<ul>
<li>Used by <a href="http://dx.doi.org/10.1257/aer.100.5.2060">Algan and Cahuc (2010)</a> to check the robustness of their finding that trust boosts economic growth.</li>
</ul>
kdmtzhttp://www.blogger.com/profile/06718149677163689651noreply@blogger.com0