AREC 345: Global Poverty & Economic Development
Lecture 6: The African Slave Trade
Professor: Pamela Jakiela Department of Agricultural and Resource Economics University of Maryland, College Park
The Long-Run Impacts of the Slave Trade
Exposure to the Slave Trade & Income in 2000 10
Scatter plot of GDP per capita by slave exports
Log GDP per capita in 2000 6 7 8 9
Mauritius Equatorial Guinea Seychelles Tunisia Botswana Namibia Morocco Swaziland
South Africa Egypt
Gabon Algeria
Libya
Cape Verde Islands Lesotho
Congo
Senegal Mozambique Ivory Coast BeninGhana Nigeria Cameroon Kenya Mauritania Sudan Gambia Somalia Burkina Liberia Mali Faso Angola Uganda Madagascar Guinea-Bissau Malawi Central African Republic Zambia Ethiopia Burundi Togo Guinea Tanzania Niger Chad Sierra Leone
Zimbabwe
Sao Tome & Principe Djibouti Rwanda Comoros
5
Democratic Republic of Congo
-2
0 2 4 6 8 Log total slave exports normalized by land area
10
Data from Nunn (2008)
Dep. Var. = GDP
E (GDP) = 7.517 − 0.117 · slave exports
↔
Slave exports Constant
OLS (1) −0.117∗∗∗ (0.025) 7.517∗∗∗ (0.126)
AREC 345: Global Poverty & Economic Development
Lecture 6: The African Slave Trade, Slide 5
Exposure to the Slave Trade & Income in 2000 Use regression results to calculate t-statistic: Regression results:
The formula for the t-statistic:
Dep. Var. = GDP
Slave exports Constant
OLS (1) −0.117∗∗∗ (0.025) 7.517∗∗∗ (0.126)
t-stat=
bˆ standard error of bˆ
= =
Absolute value of t-statistic is greater than 2.58, |t-stat| > 2.58 ⇒ statistically significant at the 99 percent level |t-stat| > 1.96 ⇒ statistically significant at the 95 percent level |t-stat| > 1.64 ⇒ statistically significant at the 90 percent level
AREC 345: Global Poverty & Economic Development
Lecture 6: The African Slave Trade, Slide 6
Exposure to the Slave Trade & Income in 2000
E (log GDP per capita) = 7.517 − 0.117 · log slave exports per square km Interpretation: Country South Africa Uganda Malawi Nigeria
Slave Exports 1.67 19.30 1062.97 2188.16
Log of Slave Exports 0.51 2.96 6.97 7.69
Predicted Log GDP 7.46 7.17 6.70 6.61
Predicted GDP $1,732.62 $1,300.75 $813.74 $747.82
Actual GDP $4,139 $788 $ 679 $1,156
Clearly, exposure to the slave trade isn’t the whole story!
AREC 345: Global Poverty & Economic Development
Lecture 6: The African Slave Trade, Slide 7
How Do We Interpret Our Regression Results? What linear regression tells us: • Is there a relationship between GDP and exposure to the slave trade? • Is the association statistically significant?
This doesn’t tell us whether the relationship is causal Other reasons for a significant association: • Reverse causality: changes in the dependent variable cause
changes in the independent variable • Omitted variable bias: some other factor is causing changes in
both the dependent and the independent variables
Selection bias is a form of omitted variable bias
AREC 345: Global Poverty & Economic Development
Lecture 6: The African Slave Trade, Slide 8
Adding Controls to a Regression Specification “Many of the countries that have the lowest slave exports are either small islands or North African countries, both of which tend to be richer than other countries in Africa.”
Log GDP per capita in 2000 6 7 8 9
10
Scatter plot of GDP per capita by slave exports Mauritius Equatorial Guinea Seychelles Tunisia Botswana Namibia
South Africa Egypt
Morocco Swaziland
Gabon Algeria
Libya
Cape Verde Islands Lesotho
Congo
SenegalBenin Mozambique Ivory Coast Ghana Nigeria Cameroon Kenya Mauritania Sudan Gambia Somalia Burkina Liberia Mali Faso Angola Uganda Madagascar Guinea-Bissau Malawi Central African Republic Zambia Ethiopia Burundi Togo Guinea Tanzania Niger Chad Sierra Leone
Zimbabwe
Sao Tome & Principe Djibouti Rwanda Comoros
5
Democratic Republic of Congo
-2
0 2 4 6 8 Log total slave exports normalized by land area North Africa
Island nation
10
Other
Data from Nunn (2008)
AREC 345: Global Poverty & Economic Development
Lecture 6: The African Slave Trade, Slide 9
Adding Controls to a Regression Specification A multivariate regression including a control for North Africa: E (GDP) = a + b · slave exports + c · north africa
Log GDP per capita in 2000 6 7 8 9
10
Scatter plot of GDP per capita by slave exports Mauritius Equatorial Guinea Seychelles Tunisia Botswana Namibia Morocco Swaziland
South Africa Egypt
Cape Verde Islands Lesotho
Gabon Algeria
Libya
Congo
SenegalBenin Mozambique Ivory Coast Ghana Nigeria Cameroon Kenya Mauritania Sudan Gambia Somalia Burkina Liberia Mali Faso Angola Uganda Madagascar Guinea-Bissau Malawi Central African Republic Zambia Ethiopia Burundi Togo Guinea Tanzania Niger Chad Sierra Leone
Zimbabwe
Sao Tome & Principe Djibouti Rwanda Comoros
5
Democratic Republic of Congo
-2
0 2 4 6 8 Log total slave exports normalized by land area North Africa
10
Sub-Saharan Africa
Data from Nunn (2008)
AREC 345: Global Poverty & Economic Development
Lecture 6: The African Slave Trade, Slide 10
Adding Controls to a Regression Specification
10
Scatter plot of GDP per capita by slave exports Log GDP per capita in 2000 6 7 8 9
Mauritius Equatorial Guinea Seychelles Botswana Namibia
South Africa
Gabon
Swaziland Congo
Tunisia
Cape Verde Islands Lesotho
SenegalBenin Mozambique Ivory Coast Ghana Algeria Nigeria Cameroon Libya Kenya Mauritania Sudan Gambia Somalia Burkina Liberia Mali Faso Angola Uganda Madagascar Guinea-Bissau Malawi Central African Republic Zambia Ethiopia Burundi Togo Guinea Tanzania Niger Djibouti Chad Sierra Leone
Zimbabwe Morocco
Sao Tome & Principe Rwanda Comoros
Egypt
5
Democratic Republic of Congo
-2
0
2
4 altexports
North Africa
6
8
10
Sub-Saharan Africa
Data from Nunn (2008)
Including a control is equivalent to subtracting off the average levels of x and y from N. African nations, so that the means equal the rest of sample AREC 345: Global Poverty & Economic Development
Lecture 6: The African Slave Trade, Slide 11
Adding Controls to a Regression Specification A multivariate regression including a control for North Africa: E (GDP) = a + b · slave exports + c · north africa
Mauritius Equatorial Guinea Seychelles Tunisia Botswana Namibia
South Africa Egypt
Morocco Swaziland
Gabon Algeria
Libya
Cape Verde Islands Lesotho
Congo
SenegalBenin Mozambique Ivory Coast Ghana Nigeria Cameroon Kenya Mauritania Sudan Gambia Somalia Burkina Liberia Mali Faso Angola Uganda Madagascar Guinea-Bissau Malawi Central African Republic Zambia Ethiopia Burundi Togo Guinea Tanzania Niger Chad Sierra Leone
Zimbabwe
Sao Tome & Principe Djibouti Rwanda Comoros
Log GDP per capita in 2000 6 8 9 7
Log GDP per capita in 2000 9 6 7 8
10
Scatter plot of GDP per capita by slave exports
10
Scatter plot of GDP per capita by slave exports
Mauritius Equatorial Guinea Seychelles Botswana Namibia
South Africa
Gabon
Swaziland Congo
Tunisia
Cape Verde Islands Lesotho
Zimbabwe Morocco
Sao Tome & Principe
Egypt
Somalia Liberia Uganda Central African Republic Zambia Burundi Niger Djibouti
Rwanda Comoros
5
Democratic Republic of Congo
5
Democratic Republic of Congo
SenegalBenin Mozambique Ivory Coast Ghana Algeria Nigeria Cameroon Libya Kenya Mauritania Sudan Gambia Burkina Mali Faso Angola Madagascar Guinea-Bissau Malawi Ethiopia Togo Guinea Tanzania Chad Sierra Leone
-2
0 2 4 6 8 Log total slave exports normalized by land area North Africa
Sub-Saharan Africa
10
Fitted values
Data from Nunn (2008)
AREC 345: Global Poverty & Economic Development
-2
0 North Africa
2
4 altexports
6
Sub-Saharan Africa
Data from Nunn (2008)
Lecture 6: The African Slave Trade, Slide 12
8 Fitted values
10
Adding Controls to a Regression Specification A multivariate regression including control for North Africa, islands E (GDP) = a + b · slave exports + c · north africa + d · island nation
Dep. Var.: Log GDP per Capita
Slave exports
OLS (1) −0.117∗∗∗ (0.025)
North Africa Island nation Constant
7.517∗∗∗ (0.126)
OLS (2) −0.100∗∗∗ (0.031) 0.415 (0.337) 0.169 (0.392) 7.397∗∗∗ (0.177)
Interpretation:
AREC 345: Global Poverty & Economic Development
Lecture 6: The African Slave Trade, Slide 13
Did Underdevelopment Cause Slave Exports?
“An alternative explanation for the relationship is that societies that were initially underdeveloped may have been more likely to engage in the slave trades, and these same societies are still relatively underdeveloped today.” How can we explore this possibility? • No data on GDP per capita is available from the 1400s • In primitive societies, population density is a proxy for income
Why is this the case?
• Nunn uses data on population density in 1400 to test whether
the least developed areas were the most impacted by slave trades
AREC 345: Global Poverty & Economic Development
Lecture 6: The African Slave Trade, Slide 14
Did Underdevelopment Cause Slave Exports?
Slave exports by land area 0 2 4 6 8
10
Scatter plot of slave exports by historical population density Ghana Togo Guinea-Bissau Benin
Angola
Nigeria Senegal Gambia Guinea Ethiopia Malawi Sierra Leone Mozambique Mali Burkina Faso Tanzania
Chad Sudan Democratic Republic of Congo Congo Madagascar Ivory Coast Kenya Cameroon Gabon Liberia Somalia Zambia Algeria Uganda Niger
Mauritania
Libya Central African Republic Zimbabwe South Africa
Burundi Egypt
Equatorial Guinea
-2
Namibia
Djibouti
Cape Comoros Mauritius Sao Seychelles Botswana Tome Verde& Islands Principe
-3
-2
Swaziland Lesotho
Morocco Tunisia
-1 0 1 2 Estimated population density in 1400
Rwanda
3
4
Data from Nunn (2008)
AREC 345: Global Poverty & Economic Development
Lecture 6: The African Slave Trade, Slide 15
Consequences of the Slave Trade Demographic consequences: • Loss of working age population, increased dependency ratio • Slower population growth, delayed urbanization (?)
Political consequences: • Undermined pre-existing political structures (e.g. Kongo Kingdom) • Empowered those willing to enslave others • Laid foundations of extractive (rather than productive) institutions
Social consequences: • Erosion of trust, decreased trade
AREC 345: Global Poverty & Economic Development
Lecture 6: The African Slave Trade, Slide 16
Impacts on Trust, Ethnic Fractionalization
.25
Ethnic diversity .5 .75
1
Scatter plot of ethnic diversity by slave exports Uganda Liberia Madagascar Congo Democratic Republic of Congo Cameroon Chad Kenya Nigeria Central African Republic Ivory Coast Sierra Leone Somalia Guinea-Bissau Libya Benin Angola Gambia Zambia Gabon South Africa Guinea BurkinaEthiopia Faso Tanzania Sudan Togo Senegal Mozambique Mali Malawi Ghana Niger Mauritania
Djibouti
Namibia
Morocco Mauritius Cape Verde Islands Botswana Rwanda
Zimbabwe
Equatorial Guinea
Seychelles
0
Algeria Burundi
Lesotho Egypt
Swaziland Tunisia Comoros
-2
0
2 4 6 Slave exports by land area
8
10
Data from Nunn (2008)
AREC 345: Global Poverty & Economic Development
Lecture 6: The African Slave Trade, Slide 17
Impacts on Trust, Ethnic Fractionalization
Atlantic Slave Trade
AREC 345: Global Poverty & Economic Development
Indian Ocean Slave Trade
Lecture 6: The African Slave Trade, Slide 18
Impacts on Trust, Ethnic Fractionalization
Nunn and Wantchekon (2011) estimate the relationship between exposure to the slave trade, how much people trust others E (Trust) = a + b · slave exports Unit of observation is the individual: • Data is from the Afrobarometer surveys: nationally-representative
surveys of African countries, conducted in local languages • Independent variable: how much an individual’s ethnic group was
exposed to the slave trade (throughout the course of history)
AREC 345: Global Poverty & Economic Development
Lecture 6: The African Slave Trade, Slide 19
Impacts on Trust, Ethnic Fractionalization
AREC 345: Global Poverty & Economic Development
Lecture 6: The African Slave Trade, Slide 20
Long-Term Consequences of the Slave Trade
AREC 345: Global Poverty & Economic Development
Lecture 6: The African Slave Trade, Slide 21
Study Guide: Key Terms
• Atlantic, Indian Ocean, Red Sea, and Trans-Saharan slave trades • ethnicity vs. shipping records • t-statistic • omitted variable bias • statistical significance • control (in a regression) • multivariate regression
AREC 345: Global Poverty & Economic Development
Lecture 6: The African Slave Trade, Slide 22
