The Long-Term Effects of Africa's Slave Trades Dataset (2008)

Data source: Nathan Nunn

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Description:

To construct a measure of the total number of slaves taken from each country during the four slave trades between 1400 and 1900, Nunn collected data that report the total number of slaves exported from each port or region in Africa and data that reports the ethnic identity of slaves shipped from Africa.

There were a number of ways Nunn identified the ethnicity or nation of a slave:
The easiest was often by a slave's name. Slaves were often given a Christian first name and a surname that identified their ethnicity (e.g., Tardieu [2001]). As well, a slave's ethnicity could often be determined from ethnic markings, such as cuts, scars, hairstyles, or the filing of teeth (Karasch 1987, pp. 4-9). Information on the ethnicities of slaves shipped during the trans-Atlantic slave trade comes from 54 different samples, totalling 80,656 slaves, with 229 distinct ethnic designations reported.

The ethnicity data for the Indian Ocean slave trade come from six samples, with a total of 21,048 slaves and 80 different ethnicities reported. The data for the Red Sea slave trade are from two samples: one from Jedda, Saudi Arabia, and the other from Bombay, India. The samples provide information for 67 slaves, with 32 different reported ethnicities. For the trans-Saharan slave trade two samples are available: one from central Sudan and the other from western Sudan. The samples provide information on the origins of 5,385 slaves, with 23 different ethnicities recorded. The shipping data from Austen (1992) also provide additional information on which caravan slaves were shipped on, the city or town that the caravan originated in, the destination of the caravan, and in some cases the ethnic identity of the slaves being shipped.

Nunn combines the data in the following way:
Using the shipping data, Nunn first calculates the number of slaves shipped from each coastal country in Africa. In an example 100,000 slaves were shipped from Country A and 250,000 were shipped from Country C. The problem with relying on the shipping data alone is that many of slaves shipped from Country A may have come from Country B, which lies landlocked behind Country A. Then, using the ethnicity data, Nunn calculates the ratio of slaves from each coastal country relative to any landlocked countries located inland of the coastal country. This requires to map ethnicities to countries and aggregate up to the country level. In practice, this step relied on a great amount of past research by African historians, linguists, and ethnographers. The sources most heavily used are Koelle (1854), Murdock (1959), Curtin (1969), Higman (1984), and Hall (2005).

Last updated by source: 2012-04-25

Dataset type: Time-Series
Dataset level: Country

Citation:

When using this dataset, please cite as:
• Nunn, N. (2008). The long term effects of africa’s slave trades. Quarterly Journal of Economics, 123, 139–176.



Variables in this dataset:

Log Total Slave Export (Normalized by Land Area)
QoG Code: slavet_lnexparea

Total number of slaves taken from each country during the four slave trades between 1400 and 1900 normalized by land area.

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Log Total Slave Export (Normalized by Historic Population)
QoG Code: slavet_lnexppop

Total number of slaves taken from each country during the four slave trades between 1400 and 1900 normalized by average population.

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Minimum Atlantic distance (1,000 of kms)
QoG Code: slavet_mindistatl

Shortest sailing distances to the locations of demand in the trans-Atlantic slave trades.

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Minimum Indian distance (1,000 of kms)
QoG Code: slavet_mindistind

Shortest sailing distances to the locations of demand in the Indian Ocean slave trades.

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Minimum Red Sea distance (1,000 of kms)
QoG Code: slavet_mindistred

Shortest overland distances to the locations of demand in the Red Sea slave trades.

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Minimum Saharan distance (1,000 of kms)
QoG Code: slavet_mindistsah

Shortest overland distances to the locations of demand in the trans-Saharan slave trades.

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