Discussion and Conclusion

Agriculture could theoretically contribute strongly to inclusive growth due to its large employment level and Africa’s rich endowment with soils suitable for cash crops. How-ever, research stresses that structural change and productivity growth are still fairly limited (McMillan et al., 2014; Barrett et al., 2017). The domestic political economy in African countries imposes strong distortions on agricultural markets (Lipton, 1977;

Anderson et al., 2013). Predictions in the literature of the outcomes are, however, am-biguous. Bates and Block (2009) argue that coethnics are favored if a policy maker from a rural region comes to power. Kasara (2007) suggests a contradicting pattern, where coethnic farmers would be taxed more heavily due to limited political competi-tion and higher monitoring capacities of coethnics. However, those former studies could

I find that coethnics disproportionally benefit from higher prices for commodities that can be produced in their region, while other groups in the same region do not benefit significantly. This suggests individual targeting rather than more general pub-lic goods provision. Indeed, controlling for infrastructure provision does not alter the findings. A placebo test supports the notion that this is indeed driven by political factors and not by some spurious correlation of more agriculturally skilled groups being also more likely to come to power. I provide some suggestive evidence that discrimina-tory tax regimes might drive results. In line with previous arguments that institutional change would improve agricultural productivity (Bates and Block, 2013), further econo-metric analysis suggests that democratic institutions can reduce, though not completely offset, these political biases. The example of discretionary fertilizer voucher distribution under president Bingu wa Mutharika of Malawi stresses that these political biases might persist even in democracies. Persisting clientelism can be attributed to the challenge of making credible commitments in young democracies (Keefer, 2007).

As success stories of African agricultural growth can be found in not fully demo-cratic, hybrid regimes (e.g., the coffee industry in Rwanda), further research could shed some light on the question which specific economic and political institutions are effec-tive in curbing ethnic biases in gains from trade. What is more, an interesting route for research is provided by the linkages of distinct ethnic groups, as Dickens (2018) indicates that patronage networks can include several affiliated ethnicities.

Finally, one main caveat of this study remains. The main outcomes rely on self-reported poverty assessments and perceptions due to the scarcity of data that provide better quantifiable metrics (e.g., expenditures, tax burden) along with information on individual ethnic affiliation. Future research could contribute to a more thorough un-derstanding of the suggested pattern, once more data become available.

Table B.1 Afrobarometer – Sampled Countries and Years Survey

Round

Years Sampled Countries

Round 1: 1999-2000 Botswana, Ghana, Lesotho, Malawi, Mali, Namibia, Nige-ria, South Africa, Tanzania, Uganda, Zambia, Zimbabwe Round 2: 2002-2004 Botswana, Ghana, Lesotho, Mali, Mozambique, Namibia,

Nigeria, Senegal, South Africa, Tanzania, Zambia, Zim-babwe

Round 3: 2005-2006 Benin, Botswana, Ghana, Lesotho, Madagascar, Malawi, Mali, Mozambique, Namibia, Nigeria, Senegal, South Africa, Tanzania, Uganda, Zambia, Zimbabwe

Round 4: 2008-2009 Benin, Botswana, Burkina Faso, Ghana, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mozambique, Namibia, Nigeria, Senegal, South Africa, Tanzania, Uganda, Zambia, Zimbabwe

Round 5: 2011-2013 Algeria, Benin, Botswana, Burkina Faso, Cameroon, Ghana, Guinea, Ivory Coast, Lesotho, Liberia, Madagas-car, Mali, Mauritius, Morocco, Mozambique, Namibia, Nigeria, Senegal, Sierra Leone, South Africa, Sudan, Swazi-land, Tanzania, Togo, Tunisia, Uganda, Zambia, Zimbabwe Round 6: 2014-2015 Algeria, Benin, Botswana, Burkina Faso, Cameroon, Egypt, Gabon, Ghana, Guinea, Ivory Coast, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritius, Morocco, Mozambique, Namibia, Niger, Senegal, Sierra Leone, South Africa, Sudan, Swaziland, Tanzania, Togo, Tunisia, Uganda, Zambia, Zimbabwe

Table B.2 Data Sources

Variable Name Description Years

Available

Source

Tax Support Support for tax collection: (1) “Strongly

Disagree.” to (5) “Strongly Agree.” 2002-2015 Afrobarometer (2018)

Poverty Aggregate of five individual poverty assess-ments ranging each from 1 “Never” to 5

“Always.”

1999-2015 Afrobarometer (2018)

Leader Ethnicity Information on leader’s ethnicity combined with information on individual ethnicity from Afrobarometer Round 3-5: “What is your tribe? You know, your ethnic or cul-tural group.” pro-ducer and consumer prices using prices and land use data.

1980-2015 IMF (2018), World

Bank (2018b),

Monfreda et al.

(2008) Democracy Binary variable if country has free & fair

elections with peaceful turnovers. 1980-2015 Based on Bjørnskov and Rode (2018)

Leader Binary indicator if administrative region

was the leader birth region. 1980-2015 Based on Dreher

et al. (2016) Total Road Length Length of all roads in the administrative

region measured in kilometers. 1992-2015 Data in Space

(2018) Precipitation Precipitation data based on observational

and satellite data. 1980-2013 Adler et al. (2003)

& Tollefsen et al.

(2012) Temperature Means of monthly global land surface

tem-peratures. 1980-2013 Fan and Van den

Dool (2008) &

Tollefsen et al.

(2012)

Distance to capital Distance to capital in kilometers. 1980-2014 Tollefsen et al.

(2012) Travel Time Travel time to most proximate urban

cen-ter. 1980-2014 Tollefsen et al.

McGuirk and Burke (2017). The Producer Price Index is based on price data from the IMF (2018) and the World Bank (2018b). As described in Section 2.3, prices are multiplied with regional weights on the harvested area and potential yield for the corresponding crop based on Monfreda et al. (2008).²⁸ For the consumer price index, regional weights are substituted with country-level caloric shares of crops from FAO’s

“Food Balance” sheets (FAO, 2018). The producer commodities include: cocoa, coffee, tea, tobacco, and cotton. For consumers, I consider prices of cocoa, coconuts, coffee, groundnuts, maize, palm oil, olives, oranges, rice, sorghum, soybeans, sugar, sunflowers, tea, and wheat.

TableB.3AfricanLeadersintheSample

CountryLeadernameEnteredofficeLeftofficeADM1regionEthnicityAlgeriaAbdelazizBouteflika05.05.2013ongoingBorninMoroccoArabBeninMathieuKerekou04.04.199606.04.2006SombaBeninThomasYayiBoni06.04.200606.04.2016BorgouYorubaBotswanaFestusMogae31.03.199801.04.2008CentralKalangaBotswanaIanKhama01.08.200801.08.2018BorninUKMongwatoBurkinaFasoBlaiseCompaore15.10.198731.10.2014Plateau-CentralMossiBurkinaFasoMichelKafando18.11.201417.09.2015CentreMossiBurundiPierreNkurunziza26.08.2006ongoingBujumburaMairieHutuCôted’IvoireAlassaneOuattara11.04.2011ongoingN’zi-ComoéDioulaCameroonPaulBiya06.11.1982ongoingSudBetiEgyptMohamedMorsi30.06.201203.07.2013AshSharqiyahN.A.EgyptAbdelFattahel-Sisi16.07.201326.04.2014AlQahirahN.A.EgyptMohammedHusseinTantawi11.02.2011ongoingAlQahirahN.A.EgyptHosniMubarak14.10.198111.02.2011AlMinufiyahN.A.GabonAliBongoOndimba16.10.2009ongoingBorninCongo-BrazzavilleTekeGhanaJohnEvansAtta-Mills07.01.200924.07.2012WesternFantiGhanaJohnMahama24.07.201207.01.2017NorthernGonjaGhanaJohnAgyekumKufuor08.01.200107.01.2009AshantiAsanteGhanaJerryRawlings31.12.198107.01.2001GreaterAccraEweGuineaAlphaConde21.12.2010ongoingBok´eMandinkaKenyaUhuruKenyatta09.04.2013ongoingNairobiKikuyuKenyaMwaiKibaki31.12.200209.04.2013CentralKikuyuLesothoPakalithalMosisili29.05.199816.06.2017Mohale’sHoekBasothoLesothoTomThabana16.06.2017ongoingMaseruBasothoLiberiaEllenJohnsonSirleaf16.01.200622.01.2018MontserradoGolaMadagascarMarcRavalomanana06.07.200217.03.2009AntananarivoMerinaMadagascarHeryRajaonarimampianina25.01.201407.09.2018AntananarivoMerinaMadagascarAndryRajoelina17.03.200925.01.2014AntananarivoMerinaMalawiBakiliMuluzi21.05.199424.05.2004MachingaYaoMalawiBinguwaMutharika24.05.200405.04.2012ThyoloLhomweMalawiJoyceBanda07.04.201231.05.2014ZombaYaoMalawiPeterMutharika31.05.2014ongoingThyoloLhomweMaliDioncoundaTraoré12.04.201204.09.2013KoulikoroBambaraMaliIbrahimBoubacarKeïta04.09.2013ongoingSikassoBambaraMaliAlphaOumarKonare08.06.199208.06.2002KayesBambara/FulaMaliAmadouToumaniToure08.06.200222.03.2012MoptiFula

Source:BasedonDreheretal.(2016)andowndatacollection.NodatafromAfrobarometeravailableforEgypt.

TableB.4AfricanLeadersintheSample(continued) LeadernameEnteredofficeLeftofficeADM1regionEthnicit NavinchandraRamgoolam05.07.2005ongoingPortLouisHindu MohammedVIofMorocco23.07.1999ongoingRabat-Salé-Zemmour-ZaerArab ArmandoEmilioGuebuza02.02.200515.01.2015NampulaTsonga JoaquimAlbertoChissano06.11.198602.02.2005GazaTsonga HifikepunyePohamba21.03.200521.03.2015OhangwenaOvam HageGeingob21.03.2015ongoingOtjozondjupaDamara MahamadouIssoufou07.04.2011ongoingTahouaHausa MuhamaduBuhari29.05.2015ongoingKatsinaIjaw GoodluckJonathan09.02.201029.05.2015BayelsaIjaw OlusegunObasanjo29.05.199929.05.2007OgunYoruba UmaruMusaYar’Adua29.05.200709.02.2010KatsinaFulani AbdoulayeWade02.04.200002.04.2012LougaWolof MackySall02.04.2012ongoingFatickPulaar/T ErnestBaiKoroma17.09.200708.04.2018NorthernTemne JacobZuma09.05.200914.02.2018KwaZulu-NatalZulu ThaboMbeki16.06.199924.09.2008EasternCapeXhosa UmarHassanAhmadal-Bashir30.06.1989ongoingNorthernJa’alin MswatiIIIofSwaziland25.04.1986ongoingManziniSwazi JakayaKikwete21.12.200505.11.2015PwaniKwere BenjaminMkapa23.11.199521.12.2005MtwaraNgoni FaureGnassingbe04.05.2005ongoingMaritimeKabre MoncefMarzouki13.12.201131.12.2014NabeulTunisia BejiCaidEssebsi31.12.2014ongoingSousseSardinian YoweriMuseveni26.01.1986ongoingNtungamoBanyank FrederickChiluba02.11.199102.01.2002CopperbeltBemba LevyMwanawasa03.01.200219.08.2008CopperbeltLenje MichaelSata23.09.201128.10.2014NorthernBemba RobertMugabe04.03.198019.11.2017HarareShona Source:BasedonDreheretal.(2016)andowndatacollection.