a) Producer Price Index b) Leader Coethnicity

Note: Density plot of point estimates from 500 permutation tests analogous to Table 2.2 column (1).

I apply a further robustness test, where the basic idea is to assume uncertainty about the actual treatment allocation and running a permutation test analogous to R.A.

Fisher’s approach of statistical inference (Young, 2017). The randomization would set the hypothesized causation de facto inactive. Figure B.2 shows a density distribution of coefficients for 𝑃 𝑃 𝐼_t × 𝐿𝑒𝑎𝑑𝑒𝑡ℎ_it(1) based on 500 permutation tests, which either randomly allocate the PPI or leader ethnicity. Randomly allocating producer prices, Figure B.2b indicates that the coefficient from column (6) of Table 2.2 would be among the top 1% of most negative coefficients. In the case of randomly allocated leader coethnicity, the picture is even clearer, where none of the 500 estimated coefficients would be as small as the original estimate from column (6). Hence, I do not find the previous pattern of differential effects for coethnic respondents if the hypothesized mechanism is not active.

Leave-one-(commodity)-out Analysis The literature has indicated strong hetero-geneities across crops – e.g., some ethnicities might specialize in farming specific

com-The exclusion of cocoa and coffee, which are main cash crops in Cameroon, Ethiopia, Ghana, Kenya, Ivory Coast, Nigeria, Tanzania, and Uganda, has some effect on results.

Notably, the coefficient for 𝑃 𝑃 𝐼_t × 𝐿𝑒𝑎𝑑𝑒𝑟_it(1) becomes statistically significant and positive, when cocoa is excluded. Moreover, the interaction for 𝑃 𝑃 𝐼_t ×𝐿𝑒𝑎𝑑𝑒𝑟_it(1)× 𝐿𝑒𝑎𝑑𝑒𝑡ℎ_it(1) and 𝑃 𝑃 𝐼_t × 𝐿𝑒𝑎𝑑𝑒𝑡ℎ_it(1) become insignificant in columns (1) and (2) respectively. Yet, the main pattern of disproportionally stronger gains for coethnic respondents is robust. Coefficients point in the expected direction and either the coeffi-cient for general ethnic biases or for ethnic biases in the leader birth region is statistically significant.

Table B.12 Robustness – Leave One Commodity Out Dep. Variable: Poverty Index of individual 𝑖in region 𝑟 in country𝑐

(1) (2) (3) (4) (5)

Note: All regressions include country-period, survey round and regional (province) level fixed effects as well as control variables from column (6) in Table 2.2. The commodity in the column head was left out for the estimation of the producer price treatment. Standard errors clustered by region and by country-period in parentheses. ^* 𝑝 <0.10,^** 𝑝 <0.05,^*** 𝑝 <0.01.

Price Makers Readers might be concerned that global commodity prices cannot be treated as exogenous for the main producers of certain crops. For instance, if farmers in Ivory Coast – the main exporter of cocoa – are affected by an unrelated poverty shock, they will be less able to invest in their cocoa plantations. This lowers the harvest in the subsequent year, which will then again contribute to poverty. Table B.13 addresses this concern by excluding crops for the construction of the PPI if the country exports

≥ 1% of the global trade volume. Given that African countries are among the main exporters for several cash crops, this is a fairly conservative robustness check. While

(0.263) (0.274) (0.296)

𝑃 𝑃 𝐼×𝐿𝑒𝑎𝑑𝑒𝑟(1) -0.095 -0.020

(0.183) (0.172)

𝑃 𝑃 𝐼×𝐿𝑒𝑎𝑑𝑒𝑡ℎ(1) -0.183^*** -0.170^***

(0.036) (0.040)

𝑃 𝑃 𝐼×𝐿𝑒𝑎𝑑𝑒𝑟(1)×𝐿𝑒𝑎𝑑𝑒𝑡ℎ(1) -0.137

(0.126)

𝑁 171872 124320 124320

Note: All regressions include country-period, survey round and regional (province) level fixed effects and are structured analogously to Table 2.2 columns (1) to (3). Standard errors clustered by region and by country-period in parentheses. ^*𝑝 <0.10,^** 𝑝 <0.05,^*** 𝑝 <0.01.

Susceptibility to outliers Although the pattern is not driven by one specific com-modity, the possibility remains that one period, country or even region is driving the results. As there is no formal statistical test to pick outliers, I address this concern by a graphical analysis in Figure B.3, which plots the PPI against the regional mean of the outcome variable. Moreover, I consider the statistical leverage in Table B.14.³²

Figure B.3 Partial Regression Plots

a) Countries b) Years

Figure B.3a indicates that Nigerian regions are subject to outliers of PPI and poverty at both ends of the distribution. What is more, Figure B.3b reveals that there could be potential outliers in the years 2000, 2005 and 2014. This is potentially problematic as these observations are at the beginning and end of the time series and could, thus, induce a correlation with a time trend. Although Nigeria does not feature that frequently in the top 30 leverage observations, Table B.14 indicates that the observations at the end of the panel (2014 and 2015) have a high leverage. Those are the sampling years of the sixth Afrobarometer round and it might be problematic if one survey round would drive results (though survey round fixed effects partly account for this issue). For this reason, Table B.15 tests robustness excluding Nigerian respondents in column (1) and the years 2000, 2005, 2014 and 2015 in column (2). Although the triple interaction in the fourth row loses statistical significance in column (1), the pattern remains qualitatively unchanged and the main interaction points to significantly higher poverty reducing effects for leaders’ coethnics. The robustness test in column (2) corresponds to the main pattern and indicates that high leverage years do not drive the main results.

Table B.14 Leverage – Top 20 Observations

(1) (2) (3)

(1.118) (1.052)

Note: All regressions include country-period, survey round and regional (province) level fixed effects as well as control variables from column (6) in Table 2.2. Column (1) excludes Nigerian respondents, column (2) excludes the years 2000, 2005, 2014 and 2015. Standard errors clustered by region and by country-period in parentheses.

* 𝑝 <0.10,^** 𝑝 <0.05,^*** 𝑝 <0.01.

Miscellaneous

Table B.16 Heterogeneous Effects across Regime Types – Sample Split Dep. Variable: Poverty Index of individual 𝑖in region𝑟 in country𝑐

(1) (2)

Note: Sample split analogous to column (6) of Table 2.2. Standard errors clustered by region and by country-period in parentheses.

* 𝑝 <0.10,^** 𝑝 <0.05,^*** 𝑝 <0.01.

Table B.17 Pre Trends of Power Status and Producer Prices Dep. Variable: Binary power status of

individual 𝑖in region 𝑟 in country𝑐

(1) (2) to column (6) in Table 2.2. Standard errors clustered by region and by country-period in parentheses.

* 𝑝 <0.10,^** 𝑝 <0.05, ^*** 𝑝 <0.01.

Table B.18 Correlation – Lights & Producer Prices Log of night light emission in

Note: All regressions include period and regional (province) level fixed effects. Standard errors clus-tered by region and by country-period in parenthe-ses. ^* 𝑝 <0.10,^** 𝑝 <0.05, ^*** 𝑝 <0.01.

𝐸𝑥𝑝𝑒𝑛𝑑𝑖𝑡𝑢𝑟𝑒 𝑝.𝑐.c,r,t -0.0021^*** -0.0017^**

(0.0000) (0.0001)

𝑁 75 75

Country FE: No Yes

Year FE: No Yes

Note: Expenditure data are based on Living Standard Mea-surement Surveys. Standard errors clustered by country in parentheses. ^* 𝑝 <0.10,^** 𝑝 <0.05,^*** 𝑝 <0.01.

Table B.20 Robustness – Controlling for Conflict Dep. Variable: Poverty Index of individual 𝑖in region 𝑟 in country𝑐

(1) (2) (3)

All regressions include country-period, survey round and regional

Aid and conflict at the subnational