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«Edward Miguel National Bureau of Economic Research (NBER) and Department of Economics University of California, Berkeley (emiguel ...»

-- [ Page 1 ] --

Economic Shocks and Civil Conflict:

An Instrumental Variables Approach

Data Set

Edward Miguel

National Bureau of Economic Research (NBER) and

Department of Economics

University of California, Berkeley

(emiguel@econ.berkeley.edu)

Shanker Satyanath

Department of Politics

New York University

(shanker.satyanath@nyu.edu)

Ernest Sergenti

Department of Politics

New York University

(ejs210@nyu.edu)

1. OVERVIEW

There is a growing body of research that highlights the association between economic conditions and civil conflict (see Sambanis 2001 for a review).

The existing literature, however, does not adequately address the endogeneity of economic variables to civil war, and thus does not convincingly establish a causal relationship. In addition to endogeneity, omitted variables – for example, government institutional quality – may drive both economic outcomes and conflict, producing misleading cross-country estimates.

In “Economic Shocks and Civil Conflict: An Instrumental Variables Approach,” we use exogenous variation in rainfall as an instrumental variable for income growth in order to estimate the impact of economic growth on civil conflict. Weather shocks are plausible instruments for GDP growth in economies that largely rely on rain-fed agriculture, i.e., neither have extensive irrigation systems nor are heavily industrialized. The instrumental variable method makes it credible to assert that the association between economic conditions and civil war is a causal relationship, rather than simply a correlation.

Sub-Saharan Africa is the ideal region for this identification strategy: the World Development Indicator (WDI) database indicates that only one percent of crop land is irrigated in the median African country, and the agricultural sector remains large.

The data used in “Economic Shocks and Civil Conflict: An Instrumental Variables Approach,” are of four kinds: rainfall; conflict; economic, demographic, and development controls; and political institutional controls. The most original of these four is the rainfall data. Therefore, we devote an entire section of this manual, section 2, to a description of our four rainfall measures and an explanation of the methodology used to construct each. In section 3, we describe all of our data sources by category of data and we provide links to where the original information may be found on-line. A detailed description of each variable in the set is provided in section 4. In addition, this manual contains two appendices: appendix A lists all latitude and longitude points used to generate two of the four rainfall measures, the GPCP and NCEP measures, and appendix B presents the Stata codebook for all variables in the dataset.

2. RAINFALL DATA & CONSTRUCTION OF THE RAINFALL MEASURES

We employ four rainfall data sets:

A. Global Precipitation Climatology Project (GPCP)

–  –  –

C. U.N. Food and Agricultural Organization Climatic (FAOCLIM2) Data CD-ROM: World-Wide Agroclimatic Database.

FAO-Agrometeorology Group, Rome 2000.

D. Normalized Difference Vegetation Index (NDVI) http://edcw2ks21.cr.usgs.gov/adds/ A. Global Precipitation Climatology Project (GPCP) Data Set The Global Precipitation Climatology Project (GPCP) database of rainfall estimates stretches back to 1979. The GPCP data rely on a combination of actual weather station rainfall gauge measures and satellite information on the density of cold cloud cover, which is closely related to actual precipitation. The GPCP uses the Huffman et al. (1995, 1997) method of data selection and merging.

Estimates are made at 2.5 latitude and longitude degree intervals. The units of measurement are in millimeters of rainfall per day and are the average per month. We multiply each monthly average by the number of days in a given month, which gives us an estimate of total monthly rainfall.1 We then add up all of the total monthly estimates in a given year to generate an estimate of total yearly rainfall for each 2.5 latitude / longitude degree node. For example, the yearly rainfall estimate for any 2.5 latitude / longitude degree node in 1999 was

calculated as follows:

y1999 = a9901*31 + a9902*28 + a9903*31 + a9904*30 + a9905*31 + a9906*30 + a9907*31 + a9908*31 + a9909*30 + a9910*31 + a9911*30 + a9912*31 where aYYMM is the average daily rainfall in millimeters for month MM and year YY taken from the GPCP data set Next, each yearly rainfall estimate per 2.5 latitude / longitude degree node is averaged over all nodes in a given country to produce an estimate of total yearly rainfall per country. For example, our estimate of total yearly rainfall for Kenya is the average of the yearly rainfall estimates for the eight 2.5 latitude / longitude degree nodes in Kenya. See Appendix A for a listing of all nodes used in the calculation of the each country’s rainfall estimates.

(Note: No degree grid node fell within the national boundaries for five small African countries – Burundi, Djibouti, Gambia, Guinea-Bissau, and Rwanda. In these cases, we assigned the rainfall measures from the nearest node(s) to their borders. See Appendix A.) Note that the following years contained a leap year: 1980, 1984, 1988, 1992, 1996, and 2000. For these years, we multiplied the average daily rainfall for February by 29 days instead of 28 days.





B. National Centers for Environment Prediction (NCEP) Data Set This data set is essentially similar to the GPCP data set presented above.

It differs in that it uses the Xie and Arkin (1997) method of data selection and merging. The construction of the total yearly estimates per country is identical to the one used with the GPCP.

C. U.N. Food and Agricultural Organization Climatic (FAOCLIM2) Data The FAOCLIM2 data set relies solely on gauge measures. Data are available starting in the early 1800’s for some countries. Unfortunately, rain gauge coverage becomes increasingly limited after 1990, and especially after 1996, leading to missing observations.

The units of measurement are in millimeters of rainfall per month per gauge station. We first calculate the average rainfall per month for the entire country by taking the average of the rainfall per month measurements across gauge stations. We then add up all of the country monthly averages in a given year to generate our measure of total yearly rainfall per country.

(Note: It is often the case that data are not available for many gauge stations.

That is, the total number of gauge stations used to calculate the average rainfall per month is not constant. Therefore, we include an additional variable in our data set, sm_obs, the total number of station-month FAOCLIM2 observations per year per country, to provide an estimate of the degree of precision of the total yearly rainfall per country measure.) D. Normalized Difference Vegetation Index (NDVI) Data Set The Normalized Difference Vegetation Index (NDVI) database of rainfall estimates also stretches back to 1979. NDVI provides a measure of the living green plant biomass on the surface of the Earth. It differs, therefore, from the other measures that employ satellite imaging, GPCP and NCEP, in that it estimates vegetation on the Earth and not the density of cold cloud cover.

Nevertheless, NDVI is closely related to rainfall, with a correlation of 0.9. We do not actively employ this measure in our paper, however, as vegetation levels may be a function of crop choices made in response to civil conflict, and thus could be endogenous to the conflict.

The methodology used to construct the total yearly estimates per country is similar to the one used with GPCP and NCEP. The major difference is, with NDVI, we use latitude and longitude intervals of 1 degree and estimates are made per dekad (roughly 10 days), whereas, with GPCP and NCEP, we use latitude and longitude intervals of 2.5 degrees and estimates are made per month.

Like before, first, we add up all of the total dekad estimates in a given year to generate an estimate of total yearly rainfall for each 1 degree latitude / longitude node. And, next, each yearly rainfall estimate per 1 degree latitude / longitude node is averaged over all nodes in a given country to produce an estimate of total yearly rainfall per country.

Finally, note, in our paper we focus on the GPCP dataset over the other four. First, as mentioned above, NDVI may suffer from endogenity. Second, of

the three remaining sources, GPCP is the only one that at the same time:

includes both gauge and satellite data; corrects for systematic errors in gauge measures; and rejects gauge measures thought to be unreliable (Rudolf 2000).

3. THE MSS Civil War Data Set - Overview The MSS Civil War Data Set combines data from several sources. The data sets used are listed below, by category of data: rainfall; civil conflict;

economic, demographic and development controls; and political institutional controls.

(Note: some sets fall into several categories. When this occurs, the set is listed only once under the first category on our list. For example, from the Fearon and Laitin set, we obtained civil war, economic, and political institutional data. This set is listed under the civil war category.)

1. Rainfall Data Sets A. Global Precipitation Climatology Project (GPCP)

–  –  –

D. Normalized Difference Vegetation Index (NDVI) http://edcw2ks21.cr.usgs.gov/adds/

2. Civil Conflict Data Sets A. Armed Conflict Data – International Peace Research Institute of Oslo, Norway and the University of Uppsala, Sweden (PRIO/Uppsala) http://www.prio.no/cwp/ArmedConflict

–  –  –

(from Sambanis - Journal of Conflict Resolution vol. 45, no. 3) http://www.yale.edu/unsy/civilwars/data.htm

3. Economic, Demographic, and Development Controls A. Global Development Network Growth Database (GDNGD) http://www.nyu.edu/fas/institute/dri/index.html B. World Development Indicators (WDI) CD-ROM: World Development Indicators 2002 Database.

–  –  –

http://apps.fao.org/default.jsp D. Fractionalization Data Alesina, Alberto, Arnaud Devleeschauwer, William Easterly, Sergio Kurlat, and Romain Wacziarg - Journal of Economic Growth, vol. 8, no.

2, 155-194.

http://www.stanford.edu/~wacziarg/papersum.html

4. Political Institutional Controls

–  –  –

http://www.cidcm.umd.edu/inscr/polity/ B. Database of Political Institutions (DPI) http://www.worldbank.org/research/bios/pkeefer.htm

–  –  –

http://www.freedomhouse.org/ratings/index.htm D. The Logic of Political Survival Data Set Bueno de Mesquita, Bruce, Alastair Smith, Randolph M. Siverson and

–  –  –

(from Levine and Renelt – American Economic Review LXXXII (1992), 942-963.) http://www.worldbank.org/research/growth/ddlevren.htm

4. THE MSS Civil War Data Set – Variable Descriptions

0. Identification Variables CCODE Correlates of War (COW) Country Code YEAR_ACTUAL From 1981 or the first year of independence of the country

COUNTRY_NAME

Country Name

COUNTRY_CODE

Secondary Country Code. Often but not always the same as the World Bank Country Code

1. Rainfall Variables A. Global Precipitation Climatology Project (GPCP) and Derived GPCP Global Precipitation Climatology Project estimate of average precipitation in millimeters per year. The exact source was NASA GPCP V2. It uses the Huffman et al. (1995, 1997) method of data selection and merging. See section 2 for an explanation of the methodology used to construct this measure.

Source: Global Precipitation Climatology Project (GPCP) GPCP_L GPCP lagged one year GPCP_L2 GPCP lagged two years GPCP_G GPCP growth: (GPCP - GPCP_l) / (GPCP_l) GPCP_G_L GPCP growth lagged one year: (GPCP_l - GPCP_l2) / (GPCP_l2) GPCP_G_FL

GPCP growth lagged forward one year:

GPCP_g_fl = GPCP_g[_n+1] if ccode==ccode[_n+1] GPCP_D GPCP first difference: (GPCP – GPCP_l) GPCP_D_L GPCP first difference lagged one year: (GPCP_l – GPCP_l2) / (GPCP_l2) GPCP_DF_MEAN GPCP difference from the mean (of the 1979 to 2001 observations) GPCP_DF_MEAN_1 GPCP_df_mean lagged one year GPCP_DF_MEAN_2 GPCP_df_mean lagged two years B. National Centers for Environment Prediction (NCEP) and Derived NCEP National Centers for Environment Prediction (NCEP) estimate of average precipitation in millimeters per year. The exact source was NOAA NCEP CPC Merged Analysis. It uses the Xie and Arkin (1997) method of data selection and merging. See section 2 for an explanation of the methodology used to construct this measure.

Source: National Centers for Environment Prediction (NCEP) NCEP_L NCEP lagged one year NCEP_L2 NCEP lagged two years NCEP_G NCEP growth: (NCEP - NCEP_l) / (NCEP_l) NCEP_G_L NCEP growth lagged one year: (NCEP_l - NCEP_l2) / (NCEP_l2) NCEP_G_FL

NCEP growth lagged forward one year:

NCEP_g_fl = NCEP_g[_n+1] if ccode==ccode[_n+1] NCEP_D NCEP first difference: (NCEP – NCEP_l) NCEP_D_L NCEP first difference lagged one year: (NCEP_l – NCEP_l2) / (NCEP_l2) NCEP_DF_MEAN NCEP difference from the mean (of the 1979 to 2001 observations) NCEP_DF_MEAN_1 NCEP_df_mean lagged one year NCEP_DF_MEAN_2 NCEP_df_mean lagged two years C. U.N. FAO Climatic (FAOCLIM2) Database and Derived SM_OBS Number of Station-Month observations used in the calculation of the FAO average precipitation in millimeters per year index.

Source: FAOCLIM2 FAO FAO Climatic (FAOCLIM2) Database estimate of average precipitation in millimeters per year. See section 2 for an explanation of the methodology used to construct this measure.

Source: FAOCLIM2 FAO_L FAO lagged one year FAO_L2 FAO lagged two years FAO_G FAO growth: (FAO - FAO_l) / (FAO_l) FAO_G_L FAO growth lagged one year: (FAO_l - FAO_l2) / (FAO_l2) FAO_G_FL

FAO growth lagged forward one year:



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