Root Causes of African Underdevelopment

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Table 1. Descriptive StatisticsVariableNumber ofobs.Log GDP per capita in 462000 ( )log y iMean7.46StandardDeviation0.815Minimum6.19Maximum9.24Malaria Risk (MAL i )490.810.35301Executive Constraints in2000 (INS i )443.711.6417Log total slave exportsper square km (SLVX i )42-1.832.78-9.211.66Malaria Ecology (ME i )4910.598.28031.55Log settler mortality(LSM i )355.471.142.747.99Log population densityin 1500 (LPD i )450.891.30-1.974.61Distance from the coast(DC i )46 502.13 374.27 35.29 1319.58Notes: For a detailed discussion of the definition and source of these variables, see Data Appendix.24
Table 2. Malaria and African Development: Core ResultsDependent VariableMalaria Risk (MAL i )Executive Constraintsin 2000 (INS i )Panel A: The Modellog = λ + αMAL + βINS + γSLVX+ εy i i i i iOLS estimateobs= 37-1.28***(0.2832)-0.11(0.0714)Log per Capita GDP in 20002SLS estimateobs= 31-1.52***(0.5446)0.28(0.2231)Log total slave exportsper square km (SLVX i )R 2-0.05(0.0404)0.39100.02(0.0619)Endogeneity test (p)InstrumentsDependent Variables Malaria Risk (MAL i )obs= 31Malaria Ecology (ME i )Log Settler Mortality(LSM i )Panel B: The First Stage Regressions0.01**(0.0051)0.17***(0.0543)Executive Constraints in2000 (INS i )obs= 310.015(0.0410)-0.21***(0.0461)0.0335ME, LSM, LPD, DCLog total slave exports persquare km (SLVX i )obs= 310.19***(0.0482)0.27(0. 3813)Log Population Density in1500 (LPD i )-0.033(0.0443)-0.46*(0.2651)0.51*(0.3112)Distance from the coast(DC i )9e-05(0.0001)-0.001(0.0008)-0.004***(0.0009)0.50360.46360.6057F-stat6.602.279.99Notes: ***, ** and * indicates significance level of 1%, 5% and 10% respectively against a two sidedalternative. Figures in the parentheses are the respective standard errors. The standard errors reported in theregressions are heteroskedasticity robust. All the regressions reported above are carried out with an intercept.The endogeneity test p-value indicates that we can reject the null of exogeneity at 5% level of significance.R 225
Page 10 and 11: Rather than malaria influencing dev
Page 12 and 13: SLVX = ψ + ωDC + φME + πLSM +
Page 14 and 15: The result also holds when we chang
Page 16: periods. The young members work in
Page 19 and 20: strong correlation between malaria
Page 21 and 22: Therefore, the only feasible option
Page 25: Nunn, N. (2004). “Slavery, Instit
Page 29 and 30: Table 3A. Additional Robustness Che
Page 31 and 32: Data AppendixVariable name Descript
Page 33: Variable name Description SourceLin

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