6 Raster data taken from the following sources Ag Constraints

6 raster data taken from the following sources ag

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6 Raster data taken from the following sources: Ag. Constraints, LUC/SAEZ/index.html , plate 28; Elevation, ; Malaria, . mara.org.za/lite/download.htm ; Precipitation, . html , plate 1; Temperature, , plate 6; Rugged- ness, .
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8 JAMES FENSKE T ABLE 3. Summary statistics [Table 3 here] Date observed : This is the rough date at which the information on the society was recorded, according to the Ethnographic Atlas . Dates of observation are missing for the Bomvana and Betsileo. I recode the Bomvana to 1850, to match the date of observation for the other Xhosa. I recode the Betsileo to 1900, the modal date for the other Malagasy societies in the data. Dist. Atlantic ST : This is the minimum distance between the ethnic group’s centroid and the nearest major source of new world demand for slaves (Virginia, Havana, Haiti, Kingston, Dominica, Martinique, Guyana, Salvador, or Rio), computed using the globdist function in Stata. The choice of ports here follows Nunn (2008). Dist. Indian ST : This is, similarly, the distance to the nearest of Mauritius and Mus- cat. Dist Saharan ST : This is the minimum distance to Algiers, Tunis, Tripoli, Benghazi, or Cairo. Dist Red ST : This is the minimum distance to Mussawa, Suakin, or Djibouti. Crop type : I construct dummy variables out of the major crop types recorded in the Ethnographic Atlas . I treat these as exogenous characteristics of the natural envi- ronment, not as choices. Summary statistics for these controls and any other variables used in the later analysis are given in Table 3. It is clear that the greatest difficulty with these data are that they are anachronistic – the institutional variables are recorded at an earlier date than the geographic controls and the measure of ecological diversity. Insofar as broad differences across regions in their capacities for rainfed agriculture, terrain ruggedness, ecological regions and similar variables do not change much, this should only add measurement error to the analysis. It is possible, however, that states transform their environments in ways that non-state societies cannot. I use both instrumental variables and non- anthropogenic predictors of ecological types in Section 4 to address this concern. 3. R ESULTS Before undertaking more sophisticated econometric tests, it is worth investigating whether there is a visible unconditional relationship between the ecological measure of gains from trade and state centralization. Because centralization is a discrete variable, a scatter plot will not present the data clearly. Instead, in Figure 3, I cut the sample into two – societies above and below the median in terms of ecological diversity. For each, I show a histogram of the relative frequencies of states of each level of centralization. It is clear that, below the median (the lighter bars), it is more common for societies to have no levels jurisdiction above the local, or one level. Above the median, there is a greater
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  • Spring '17
  • JAMES FENSKE

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