F.1.2 Soil fertility definition

In this subsection, we estimate the sensitivity of our baseline estimates to alternative definitions of
soil fertility. Column (1), Table A.13 replicates our baseline estimates for an ease of comparison.
In column (2), we use our baseline measure of nutrient availability, but include the range of
nutrient availability values (maximum-minimum value) as measure of soil fertility heterogeneity,
instead of the variance. From column (3) to column (5), we estimate the sensitivity of our results
to the use of alternative data sources. First we use information from the European Commission
on the quality of 31 soil types in Africa that we rank in three different categories according to the
level of natural soil fertility that are coded on the basis of the description of the specific properties
of the 31 soil types (see section A for more details). The variable *Concentration Fertility* is the
Herfindahl index of this variable (column 3). In column (4), we make use of information on the
nitrogen density in the soil and in column (5) on the percentage of irrigated land in the cell. Soil
quality heterogeneity is found to magnify the effect of fertilizer price variations in all columns,
the only insignificant coefficient being the one in column (4) where Nitrogen density is used as a
proxy (the p-value is 0.2 in this case).

As mentioned in the body of the paper, soil fertility and fertilizer use are substitutes (an
assumption we make in the theoretical model) as long as natural soil fertility does not fall below
a certain level. This suggests that we should remove low fertility cells from the empirical analysis
in order to be consistent with the assumption of the theoretical model. In Table A.14, we
estimate the sensitivity of our baseline estimate (with the nutrient availability data source from
the *Harmonized World Soil Database* - see section A for more details). We alternately remove
i) cells for which the *average* constraints is “severe” or more (columns (1) and (2)), ii) cells for
which the *maximum* constraints is “severe” or more (columns (3) and (4)). Our results are robust
to these changes in the sample.
<table><caption>Table A.13: Alternative fertility measure</caption><thead><tr><th>Dep. var.<br>Fertility measure</th><th>(1)<br>Nutrient availability<br>(baseline)</th><th>(2)<br>(range)</th><th>(3)<br>Conflict incidence<br>Rich soils<br>(EU commission)</th><th>(4)<br>N density<br>(ORNL-DAAC)</th><th>(5)<br>Irrigated land<br>(PRIO-GRID)</th></tr></thead><tbody><tr><td>ln fertilizer price</td><td>0.156<sup>a</sup><br>(0.036)</td><td>0.145<sup>a</sup><br>(0.036)</td><td>0.138<sup>a</sup><br>(0.038)</td><td>0.207<sup>a</sup><br>(0.055)</td><td>0.117<sup>a</sup><br>(0.034)</td></tr><tr><td>× V(Fertility)</td><td>0.058<sup>a</sup><br>(0.008)</td><td>0.018<sup>a</sup><br>(0.003)</td><td></td><td>0.003<br>(0.002)</td><td>0.171<sup>b</sup><br>(0.069)</td></tr><tr><td>× Concentration Fertility</td><td></td><td></td><td>-0.028<sup>b</sup><br>(0.014)</td><td></td><td></td></tr><tr><td>× Fertility</td><td>0.021<sup>a</sup><br>(0.003)</td><td>0.020<sup>a</sup><br>(0.003)</td><td>0.010<sup>c</sup><br>(0.006)</td><td>-0.015<sup>a</sup><br>(0.005)</td><td>-0.001<br>(0.002)</td></tr><tr><td>Cell and Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>111588</td><td>111588</td><td>111061</td><td>111571</td><td>111605</td></tr></tbody></table>

LPM estimations. <sup>c</sup> significant at 10%; <sup>b</sup> significant at 5%; <sup>a</sup> significant at 1%. Conley (1999) standard errors in parentheses, allowing for spatial correlation within a 500km radius and for infinite serial correlation. In col. 1 we replicate our baseline estimate. In column 2, we use our baseline measure of nutrient availability, but include the range (maximum-minimum value) as measure of land heterogeneity, instead of the variance. In col. 3, concentration fertility is the Herfindahl index of types of soils computed from EU Commission data. In col. 4, V(Fertility) is the log of the standard deviation of the Nitrogen Density variable from ORNL-DAAC (http://webmap.ornl.gov/). In col. 5, V(Fertility) is the standard deviation of the share of irrigated land from PRIO-GRID. Fertility denote: in cols. 1-2, the average level of nutrient availability; in col. (3), the share of the cell covered by “rich soils”; in col. (4): the log of the level of Nitrogen Density; in col. (5): the average share irrigated.

<table><caption>Table A.14: Removing low fertility cells</caption><thead><tr><th>Dep. var.<br>Robustness</th><th>(1)<br>Drop if <i>average</i> constraints<br>are “severe” or more</th><th>(2)<br>Drop if <i>maximum</i> constraints<br>are “severe” or more</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>ln fertilizer price</td><td>0.105<sup>a</sup><br>(0.032)</td><td>0.140<sup>a</sup><br>(0.034)</td><td>0.109<sup>a</sup><br>(0.033)</td><td>0.141<sup>a</sup><br>(0.035)</td></tr><tr><td>× V(Fertility)</td><td></td><td>0.058<sup>a</sup><br>(0.009)</td><td></td><td>0.079<sup>a</sup><br>(0.013)</td></tr><tr><td>× Fertility</td><td></td><td>0.020<sup>a</sup><br>(0.004)</td><td></td><td>0.020<sup>a</sup><br>(0.004)</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Cell FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>103326</td><td>103326</td><td>96679</td><td>96679</td></tr></tbody></table>

LPM estimations. <sup>c</sup> significant at 10%; <sup>b</sup> significant at 5%; <sup>a</sup> significant at 1%. Conley (1999) standard errors in parentheses, allowing for spatial correlation within a 500km radius and for infinite serial correlation. Cols. 1 and 2: we drop all cells for which the average nutrient availability constraints are measured as “severe” or more. Cols. 3 and 4: we drop all cells for which the maximum nutrient availability constraint is measured as “severe” or more.
### F.1.3 Fertilizer prices computation

Table A.15 displays some sensitivity estimates of our baseline results using alternative fertilizer price shocks. First, we use an alternative dataset on the required nutrient mix by crop from the US Department of Agriculture (columns (1) and (2)). In our baseline estimates, we consider the most suitable crop in each cell. In columns (3) and (4), we replicate our baseline estimates using the five main suitable crops in each cell. More precisely, we compute a fertilizer price index weighted by the relative suitability of each crop within cell. Last, in columns (5) and (6), we use an alternative measure of crop specialization (M3-CROP) built on the actual harvested area (year 2000). Our results are unaffected.

<table><caption>Table A.15: Alternative fertilizer price shocks</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr><tr><th>Dep. var.</th><th colspan="5">Conflict incidence</th></tr><tr><th>Robustness</th><th>Alternative nutrient mix</th><th>Multiple crops</th><th colspan="3">M3-crop data</th></tr></thead><tbody><tr><th scope="row">ln fertilizer price</th><td>0.099<sup>a</sup><br>(0.030)</td><td>0.121<sup>a</sup><br>(0.031)</td><td>0.167<sup>a</sup><br>(0.051)</td><td>0.189<sup>a</sup><br>(0.050)</td><td>0.072<sup>c</sup><br>(0.038)</td><td>0.116<sup>a</sup><br>(0.039)</td></tr><tr><th scope="row">× V(Fertility)</th><td></td><td>0.053<sup>a</sup><br>(0.008)</td><td></td><td>0.054<sup>a</sup><br>(0.008)</td><td></td><td>0.064<sup>a</sup><br>(0.009)</td></tr><tr><th scope="row">× Fertility</th><td></td><td>0.014<sup>a</sup><br>(0.003)</td><td></td><td>0.015<sup>a</sup><br>(0.003)</td><td></td><td>0.029<sup>a</sup><br>(0.004)</td></tr><tr><th scope="row">Cell and Year FE</th><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><th scope="row">Observations</th><td>111605</td><td>111588</td><td>111605</td><td>111588</td><td>92038</td><td>92004</td></tr></tbody></table>

<footnote>LPM estimations. <sup>c</sup> significant at 10%; <sup>b</sup> significant at 5%; <sup>a</sup> significant at 1%. Conley (1999) standard errors in parentheses, allowing for spatial correlation within a 500km radius and for infinite serial correlation. Cols. 1 and 2: alternative optimal nutrient mix from USDA used in the computation of local fertilizer prices. Cols. 3 and 4: we include the 5 main crops (weighted by their suitability) instead of the main crop in the computation of local fertilizer prices. Cols. 5-6: main crop computed from M3-CROP data instead of GAEZ.</footnote>
## F.2 Robustness: Estimation

### F.2.1 Standard errors

<table><caption>Table A.16: Alternative levels of clustering</caption><thead><tr><th>Dep. var.</th><th>(1)</th><th>(2)</th></tr><tr><th></th><th colspan="2">Conflict incidence</th></tr></thead><tbody><tr><td>ln fertilizer price</td><td>0.119</td><td>0.156</td></tr><tr><td>distance: 100</td><td>(0.024)<sup>a</sup></td><td>(0.025)<sup>a</sup></td></tr><tr><td>distance: 250</td><td>(0.029)<sup>a</sup></td><td>(0.031)<sup>a</sup></td></tr><tr><td>distance: 750</td><td>(0.037)<sup>a</sup></td><td>(0.039)<sup>a</sup></td></tr><tr><td>distance: 1000</td><td>(0.037)<sup>a</sup></td><td>(0.040)<sup>a</sup></td></tr><tr><td>× V(Fertility)</td><td></td><td>0.058</td></tr><tr><td>distance: 100</td><td></td><td>(0.007)<sup>a</sup></td></tr><tr><td>distance: 250</td><td></td><td>(0.007)<sup>a</sup></td></tr><tr><td>distance: 750</td><td></td><td>(0.008)<sup>a</sup></td></tr><tr><td>distance: 1000</td><td></td><td>(0.009)<sup>a</sup></td></tr><tr><td>× Fertility</td><td></td><td>0.021</td></tr><tr><td>distance: 100</td><td></td><td>(0.002)<sup>a</sup></td></tr><tr><td>distance: 250</td><td></td><td>(0.003)<sup>a</sup></td></tr><tr><td>distance: 750</td><td></td><td>(0.004)<sup>a</sup></td></tr><tr><td>distance: 1000</td><td></td><td>(0.004)<sup>a</sup></td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td></tr><tr><td>Cell FE</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>111605</td><td>111588</td></tr><tr><td>Countries</td><td>43</td><td>43</td></tr><tr><td>Period</td><td>1997-2013</td><td>1997-2013</td></tr></tbody></table>

<footnote>LPM estimations. <sup>c</sup> significant at 10%; <sup>b</sup> significant at 5%; <sup>a</sup> significant at 1%. Conley (1999) standard errors in parentheses, allowing for spatial correlation within a 500km radius and for infinite serial correlation. In fertilizer price is our baseline fertilizer price shock, computed from using the optimal NPK mix (from IPNI) for the main crop produced by the cell (from GAEZ). Level fertility is the average level of nutrient availability of the cell, ranging from 1 to 5 (from HWSD). V(Fertility) is the variance of the nutrient availability level within the cell (from HWSD).</footnote>
## F.2.2 Alternative estimators

<table><caption>Table A.17: Nonlinear estimators</caption><thead><tr><th>Dep. var.</th><th>(1)<br>Conflict incidence</th><th>(2)</th><th>(3)<br># events</th><th>(4)</th></tr></thead><tbody><tr><td>ln fertilizer price</td><td>1.730<sup>c</sup><br>(0.935)</td><td>2.553<sup>c</sup><br>(1.324)</td><td>1.443<br>(1.155)</td><td>2.559<sup>c</sup><br>(1.452)</td></tr><tr><td>× V(Fertility)</td><td></td><td>1.202<sup>a</sup><br>(0.332)</td><td></td><td>1.293<sup>b</sup><br>(0.591)</td></tr><tr><td>× Fertility</td><td></td><td>0.482<sup>c</sup><br>(0.258)</td><td></td><td>0.714<sup>a</sup><br>(0.217)</td></tr><tr><td>Cell and Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Estimator</td><td>FE Logit</td><td>FE Logit</td><td>PPML</td><td>PPML</td></tr><tr><td>Observations</td><td>42772</td><td>42772</td><td>42908</td><td>42908</td></tr></tbody></table>

<footnote><sup>c</sup> significant at 10%; <sup>b</sup> significant at 5%; <sup>a</sup> significant at 1%. Standard errors clustered by country in parentheses. In fertilizer price is our baseline fertilizer price shock, computed from using the required NPK mix (from IPNI) for the main crop produced by the cell (from GAEZ). Level fertility is the average level of nutrient availability of the cell, ranging from 1 to 5 (from HWSD). V(Fertility) is the variance of the nutrient availability level within the cell (from HWSD). In columns (1) and (2) the dependent variable is a dummy taking the value 1 if at least a conflict event is observed in the cell during the year, 0 otherwise. In columns (3) and (4), the dependent variable is the number of conflict events observed in the cell during the year.</footnote>
## F.3 Robustness: Omitted variables

### F.3.1 Alternative specifications

<table><caption>Table A.18: Alternative specifications</caption><thead><tr><th>Dep. var.</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th></th><th colspan="4">Conflict incidence</th></tr></thead><tbody><tr><td>ln fertilizer price</td><td>0.086<sup>a</sup><br>(0.029)</td><td>0.072<sup>b</sup><br>(0.030)</td><td>0.114<sup>a</sup><br>(0.031)</td><td>0.098<sup>a</sup><br>(0.031)</td></tr><tr><td>× V(Fertility)</td><td></td><td>0.025<sup>a</sup><br>(0.006)</td><td></td><td>0.024<sup>a</sup><br>(0.006)</td></tr><tr><td>× Fertility</td><td></td><td>-0.004<br>(0.003)</td><td></td><td>-0.005<br>(0.003)</td></tr><tr><td>Cell FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country×Year FE</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td></tr><tr><td>Country-specific time trends</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>111605</td><td>111588</td><td>111605</td><td>111588</td></tr></tbody></table>

<footnote><sup>c</sup> significant at 10%; <sup>b</sup> significant at 5%; <sup>a</sup> significant at 1%. Conley (1999) standard errors in parentheses, allowing for spatial correlation within a 500km radius and for infinite serial correlation.</footnote>
## F.3.2 Additional controls

<table><caption>Table A.19: Additional controls</caption><thead><tr><th>Dep. var.</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr><tr><td></td><td colspan="6" style="text-align: center;">Conflict incidence</td></tr></thead><tbody><tr><td>ln fertilizer price</td><td>0.106<sup>a</sup><br>(0.035)</td><td>0.133<sup>a</sup><br>(0.037)</td><td>0.077<sup>a</sup><br>(0.025)</td><td>0.100<sup>a</sup><br>(0.026)</td><td>0.119<sup>a</sup><br>(0.035)</td><td>0.149<sup>a</sup><br>(0.036)</td></tr><tr><td>× Fertility</td><td></td><td>0.016<sup>a</sup><br>(0.003)</td><td></td><td>0.012<sup>a</sup><br>(0.002)</td><td></td><td>0.017<sup>a</sup><br>(0.003)</td></tr><tr><td>× V(Fertility)</td><td></td><td>0.047<sup>a</sup><br>(0.008)</td><td></td><td>0.031<sup>a</sup><br>(0.006)</td><td></td><td>0.045<sup>a</sup><br>(0.014)</td></tr><tr><td>SPEI</td><td>-0.004<br>(0.005)</td><td>-0.005<br>(0.005)</td><td></td><td></td><td></td><td></td></tr><tr><td>× V(Fertility)</td><td></td><td>0.013<br>(0.009)</td><td></td><td></td><td></td><td></td></tr><tr><td># neighbouring cells in conflict</td><td></td><td></td><td>0.061<sup>a</sup><br>(0.002)</td><td>0.058<sup>a</sup><br>(0.002)</td><td></td><td></td></tr><tr><td>× V(Fertility)</td><td></td><td></td><td></td><td>0.010<sup>b</sup><br>(0.005)</td><td></td><td></td></tr><tr><td>ln agr. demand</td><td></td><td></td><td></td><td></td><td>-0.003<br>(0.008)</td><td>-0.002<br>(0.008)</td></tr><tr><td>× V(Fertility)</td><td></td><td></td><td></td><td></td><td></td><td>0.002<br>(0.017)</td></tr><tr><td>ln price mineral × mines > 0</td><td></td><td></td><td></td><td></td><td>0.122<sup>a</sup><br>(0.035)</td><td>0.124<sup>a</sup><br>(0.035)</td></tr><tr><td>× V(Fertility)</td><td></td><td></td><td></td><td></td><td></td><td>-0.004<br>(0.005)</td></tr><tr><td>Cell and Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>88278</td><td>88278</td><td>110160</td><td>110143</td><td>91618</td><td>91604</td></tr></tbody></table>

<footnote><sup>c</sup> significant at 10%; <sup>b</sup> significant at 5%; <sup>a</sup> significant at 1%. Conley (1999) standard errors in parentheses, allowing for spatial correlation within a 500km radius and for infinite serial correlation. SPEI is a measure of drought from PRIO-GRID. In agr. demand is a measure of the world demand for the main crop produced by the cell from Berman and Couttenier (2015). In price mineral × mines > 0 is price of the main mineral produced by the cell, and equals zero if the cell does not contain an active mine – as in Berman et al. (2017).</footnote>
## F.4 Robustness: Land inequality across and within ethnic groups

<table><caption>Table A.20: Land inequality within and between ethnic groups: robustness</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr><tr><th>Dep. var.</th><td></td><td></td><td colspan="2">Conflict incidence</td><td></td><td></td></tr><tr><th>Ethnic groups data</th><td></td><td>Murdock</td><td></td><td>GREG</td><td></td><td></td></tr></thead><tbody><tr><td>ln fertilizer price</td><td>0.147<sup>a</sup><br>(0.035)</td><td>0.063<sup>b</sup><br>(0.030)</td><td>0.053<sup>c</sup><br>(0.030)</td><td>0.151<sup>a</sup><br>(0.035)</td><td>0.059<sup>b</sup><br>(0.030)</td><td>0.051<sup>c</sup><br>(0.030)</td></tr><tr><td>× V<sub>W</sub>(Fertility)</td><td>0.046<sup>a</sup><br>(0.007)</td><td>0.016<sup>a</sup><br>(0.006)</td><td>0.009<br>(0.006)</td><td>0.063<sup>a</sup><br>(0.009)</td><td>0.024<sup>a</sup><br>(0.007)</td><td>0.022<sup>b</sup><br>(0.009)</td></tr><tr><td>× V<sub>B</sub>(Fertility)</td><td>0.072<sup>b</sup><br>(0.031)</td><td>0.058<sup>b</sup><br>(0.027)</td><td>0.006<br>(0.029)</td><td>0.020<br>(0.039)</td><td>0.048<br>(0.037)</td><td>0.020<br>(0.049)</td></tr><tr><td>× Ethn. Frac.</td><td>0.016<br>(0.010)</td><td>0.025<sup>a</sup><br>(0.009)</td><td>0.021<sup>b</sup><br>(0.009)</td><td>0.020<sup>c</sup><br>(0.012)</td><td>0.048<sup>a</sup><br>(0.010)</td><td>0.040<sup>a</sup><br>(0.011)</td></tr><tr><td>× Density pop.</td><td></td><td></td><td>-0.001<br>(0.001)</td><td></td><td></td><td>-0.000<br>(0.001)</td></tr><tr><td>× V<sub>B</sub>(Fertility) × Density pop.</td><td></td><td></td><td>0.023<sup>b</sup><br>(0.011)</td><td></td><td></td><td>0.016<br>(0.017)</td></tr><tr><td>× V<sub>W</sub>(Fertility) × Density pop.</td><td></td><td></td><td>0.009<sup>a</sup><br>(0.002)</td><td></td><td></td><td>0.010<sup>a</sup><br>(0.004)</td></tr><tr><td>Cell FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>No</td></tr><tr><td>Country × Year FE</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>111316</td><td>111316</td><td>97580</td><td>110755</td><td>110755</td><td>97410</td></tr></tbody></table>

<footnote>LPM estimations. c significant at 10%; b significant at 5%; a significant at 1%. Conley (1999) standard errors in parentheses, allowing for spatial correlation within a 500km radius and for infinite serial correlation. ln fertilizer price × Fertility is included as a control variable. V<sub>W</sub>(Fertility) is the variance of soil fertility (nutrient availability) within ethnic groups, computed by cell. V<sub>B</sub>(Fertility) is the variance of soil fertility between ethnic groups, computed by cell. Density pop. is the log of population density in 1990. Ethn. frac. is an index of ethnic fractionalization in the cell. Data on ethnic groups come from Murdock (1959) in cols 1-3, and from the Geo-referencing of Ethnic Groups (Weidmann and Cederman (2010)) in cols. 4-6.</footnote>
G Additional references

- Meiyappan, P. and Jain, A. K. (2012), “Three distinct global estimates of historical land-cover change and land-use conversions for over 200 years”, *Frontiers of Earth Science*, vol. 6 no 2: pp. 122-139.

- Nunn, N. and Qian, N. (2011), “The Potato’s Contribution to Population and Urbanization: Evidence From A Historical Experiment”, *Quarterly Journal of Economics*, vol. 126 no 2: pp. 593-650.

- Portmann, F. T., Siebert, S. and Doll, P. (2010), “MIRCA2000-Global monthly irrigated and rainfed crop areas around the year 2000: A new high-resolution data set for agricultural and hydrological modeling”, *Global Biogeochemical Cycles*, vol. 24 no 1.

- Reynal-Querol, M. (2014), “Data on ethnic and religious fractionalization and polarization”, dataset, (http://www.econ.upf.edu/en/people/onefaculty.php?id=p1330).

- Vicente-Serrano, S. M., Begueria, S. and Lopez-Moreno, J. I. (2010), “A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index”, *Journal of Climate*, vol. 23 no 7: pp. 1696-1718.
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