Root Causes of African Underdevelopment

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Table 4. Malaria and National SavingsDependent VariableMalaria Risk (MAL i )OLS Estimateobs = 45-15.21***(3.674)The Model⎛S⎞⎜ ⎟ = ς + ϑMALi+ ρlogyi+ ζ i⎝Y⎠i⎛Gross Savings as percentage of GDP in 2000S ⎞⎜ ⎟⎝Y⎠2SLS Estimate OLS Estimateobs = 45obs = 42-11.86***-14.85***(5.208)(3.869)2SLS Estimateobs = 42-12.25**(5.48)Log per Capita GDPin 2000 ( lo y )g i2.81(2.032)3.17(1.966)R 20.29890.3318F-Stat17.145.597.853.72P-value0.00020.02780.00140.033Instruments ME MENotes: ***, ** and * indicates significance level of 1%, 5% and 10% respectively against a two sided alternative.Figures in the parentheses are the respective standard errors. The instruments used is ME for Malaria Risk. Thestandard errors are heteroskedasticity robust. All the regressions reported above are carried out with an intercept.28
Data AppendixVariable name Description SourceLog Per Capita GDPin 2000 ( log y i)Natural log of real GDP per capita in2000. Real GDP figures are measuredin US $ in current prices and thefigures are PPP converted. ForBotswana, Mauritania and Namibiawe use 1999 values as anapproximation. For Central AfricanRepublic I use 1998 values. ForAngola, Zaire, and Sudan I use logper capita GDP (PPP adjusted andmeasured in current US$) from WorldDevelopment Indicator (WDI).Growth in Per CapitaGDP in (1960-2000)Executive Constraintsin 2000 (INS i )Malaria Risk (MAL i )Log total slaveexports per square km(SLVX i )Log Settler Mortality(LSM i )Log populationDensity in 1500(LPD i )Calculated using the formula1yˆ i2000 ≡ ( yi2000 − yi1960)40for 46African countries.A seven category scale, 1 to 7, with ahigher score indicating moreconstraint.Percentage of the population at risk ofmalaria transmission in 1994.This is natural log of the normalisedestimate of total number of slavesexported from Africa during theperiod 1400-1899. The number ofslave exports are normalised by thecountry size. The figure includes theAtlantic slave exports, the Indianocean slave exports, the TransSaharan slave exports, and the Redsea slave exports. I use Nunn (2004)’smethodology outlined in its dataappendix to back out the numbers.The other sources that I have usedfollowing Nunn (2004) is Elbl (1997);Eltis et al. (1999); Austen (1979);Austen (1988); and Austen (1992). Ihave divided each of these numbersby the corresponding country sizemeasured in km 2 . The country sizedata is from CID geography dataset.Natural log of estimated EuropeanSettler Mortality Rate in colonies andsettlementsLog of total population divided bytotal arable land in 1500 A.D. Source:McEvedy and Jones (1978) as cited inAcemoglu, et al. (2002).Penn World Table (PWT) 6.1Heston et al. (2002)Penn World Table (PWT) 6.1Heston et al. (2002)Polity IV datasetCentre for International Development(CID), Geography Datasets, HarvardUniversityNunn (2004)Acemoglu et al. (2001)McEvedy and Jones (1978)29
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 and 26: Nunn, N. (2004). “Slavery, Instit
Page 27 and 28: Table 2. Malaria and African Develo
Page 29: Table 3A. Additional Robustness Che
Page 33: Variable name Description SourceLin

Root Causes of African Underdevelopment

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