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Revealing the Relationship Between Ship Crowding and Slave Mortality

Published online by Cambridge University Press:  16 May 2014

Nicolas J. Duquette
Nicolas J. Duquette*
Affiliation:
Assistant Professor, Sol Price School of Public Policy, University of Southern California, Lewis Hall 312, Los Angeles, California 90089-0626. E-mail: nduquett@umich.edu.
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The Journal of Economic History , Volume 74 , Issue 2 , June 2014, pp. 535 - 552
Copyright
Copyright © The Economic History Association 2014 

The Middle Passage of the transatlantic slave trade was crowded and often deadly. Many historical accounts have linked the overcrowded conditions on the voyages to slaves’ ill health and high mortality rates. For example, Alexander Falconbridge (Reference Falconbridge1788) describes a voyage where “the slaves were so crowded… that, without meeting with unusual bad weather, or having a longer voyage than common, nearly one-half of them died…” (p. 26).Footnote 1 Surprisingly three decades of studies by economic historians (see Table 1) have failed to find that more crowded voyages suffered higher slave mortality. Across a variety of datasets and empirical specifications, regressions of mortality on measures of crowding show no association, controlling for the duration of the passage, the size of the vessel,Footnote 2 and other covariates (Cohn and Jensen Reference Cohn and Jensen1982; Steckel and Jensen Reference Steckel and Jensen1986; Garland and Klein Reference Garland and Klein1985; Haines, McDonald, and Shlomowitz Reference Haines, McDonald and Shlomowitz2001; Hogerzeil and Richardson Reference Hogerzeil and Richardson2007). To date, no published study has found a positive, statistically significant relationship between crowding and slave mortality.

This purported fact has so intrigued scholars that the more recent literature hasproposed economic explanations for it. Herbert Klein (Reference Klein2010) argues that vessel overcrowding was exaggerated by European abolitionists, and that the conditions endured by slaves were comparable to those tolerated by European immigrants to the Americas (though worsened by tropical disease). Other scholars argue that there must be some omitted variable which mitigates mortality and is correlated with crowding, obscuring the effect—for instance, conditions in coastal slave forts (Haines, McDonald, and Shlomowitz Reference Haines, McDonald and Shlomowitz2001; Hogerzeil and Richardson Reference Hogerzeil and Richardson2007), or health improvements to shipping practices (Haines and Shlomowitz Reference Haines and Shlomowitz2000; Klein et al. Reference Klein, Engerman, Haines and Shlomowitz2001).

Table 1

QUANTITATIVE LITERATURE ON CROWDING AND SLAVE MORTALITY

Sources:

Author's tabulations.

This article revisits this question and demonstrates that studies finding no positive relationship between vessel crowding and Middle Passage mortality relied onunrepresentative samples of slave voyages. The true relationship is obscured by missing observations on voyage duration—a covariate almost universally included in studies of slave mortality.Footnote 3 If the “missingness” of voyage duration is related to the correlations between variables of interest, dropping the incomplete observations will bias estimates of the relationship between crowding and mortality.

Studies seek to control for voyage duration in regressions so that they can understand the effect of vessel crowding, holding voyage duration constant. The longer a vessel was at sea, the greater risk of death from starvation, drowning, or infectious disease (Galenson Reference Galenson1986, chap. 2). Within the Trans-Atlantic Slave Trade Database, 89 percent of all slave voyages lack information on the duration of the Middle Passage; among voyages with information on slave mortality, slave crowding, and ship size, 65 percent of voyages lack information on duration of the Middle Passage.Footnote 4 This implies that quantitative studies which control for voyage duration must omit more than half of all voyages with otherwise complete information. Using simple methods to correct for missing data on voyage duration, my analysis of the Trans-Atlantic Slave Trade Database shows a strong and robust association between crowded slave voyages and slave mortality, consistent with historical accounts.

Table 2 shows that information on voyage duration is not missing at random. Specifically, the relationship between ship crowding and slave mortality depends on whether duration of the Middle Passage is observed. The top panel shows very similar means and standard deviations for key variables across three subsamples—voyages recording all variables of interest, voyages recording all variables except for Middle Passage duration, and finally all observations recording any of the variables—with slightly greater variance in crowding among the voyages with missing data.Footnote 5 The bottom panel shows that the correlation coefficient between crowding and mortality is negative (–0.10) for voyages with a recorded duration, but positive when duration is not observed (0.11), and positive in the combined sample (0.04).

This pattern also holds in the typical multivariate regressions used in this literature. Following David Galenson (Reference Galenson1986, pp. 42–47),Footnote 6 I use ordinary least squares to estimate

\[ \begin{array} \[Mortalit{y_i} = {\beta _0} + {\beta _1}Crowdin{g_i} + {\beta _2}Tonnag{e_i} + {\beta _3}Duratio{n_i} + {\psi _{route\left(i \right)}} + {\phi _{decade\left(i \right)}} + {\varepsilon _i},\] \end{array} \
Table 2

SUMMARY STATISTICS AND CORRELATIONS BY SUBSAMPLES

Notes:

All panels have observed values for vessel name, shipping route, and shipping decade. Voyages which do not successfully complete a voyage on an Atlantic trading route are dropped. “All candidate observations” includes voyages that do not have observed values for vessel capacity, mortality rate, slave crowding, or middle passage duration; the other five panels only include the 2,167 observations that are not missing mortality rate, ship tonnage, or crowding. See Appendix Table 7 for construction of individual variables.

where Mortality is the mortality rate for voyage i, Crowding is the number of embarked slaves per shipping ton, Tonnage is the carrying capacity of the ship in English shipping tons, and Duration is the number of days on the Middle Passage.Footnote 7

Unobserved differences in the slave trade over time and across regions are controlled for using decade and trade route fixed effects. The Atlantic economy and trading practices both changed over the centuries of the transatlantic slave trade's existence (Carlos Reference Carlos1991, Reference Carlos1994; Steckel and Jensen Reference Steckel and Jensen1986; Klein and Engerman Reference Klein, Engerman, Eltis and Richardson1997; Haines and Shlomowitz Reference Haines and Shlomowitz2000; Whatley and Gillezeau Reference Whatley, Gillezeau, Rhode, Rosenbloom and Weiman2011). The mortality effects of these and other unobserved events are captured by decade fixed effects (ϕ decade(i) ). The literature has also established substantial variation in mortality across regions of embarkment and disembarkment (Haines, McDonald, and Shlomowitz Reference Haines, McDonald and Shlomowitz2001). The regression includes dummy variables to control for unobserved effects of the trade route (ψ route(i) ).Footnote 8 Standard errors are clustered by ship name to correct for nonindependence of voyages on the same vessel.

Table 3

MULTIVARIATE REGRESSION OF SLAVES’ MIDDLE PASSAGE MORTALITY RATE ON VOYAGE CHARACTERISTICS

*** p < 0.01.

** p < 0.05.

* p < 0.1.

Notes:

Standard errors in parentheses corrected for an arbitrary covariance structure by ship. In columns 6 and 7, estimates and errors are corrected for imputation bias using multiple random imputation (50 iterations). Crowding p-value is for Crowding variable alone in columns 1–4 and 6, and for a joint test of Crowding and interaction terms in columns 5 and 7. The R-squared presented in columns 6 and 7 is for an ordinary regression using the 50th imputation of Duration.

Column 1 of Table 3 reports regression coefficients for the subsample of the data with recorded values for Duration.Footnote 9 Consistent with the literature, the relationshipbetween crowding and mortality is statistically indistinguishable from zero. But this result is driven by the sample: columns 2 and 3 repeat the regression without the voyage duration variable. In column 2, which uses only voyages with unobserved duration, the relationship between crowding and mortality is positive and statistically significant. All else equal, within this sample we would expect a one-standard-deviation increase in crowding (0.714 slaves per shipping ton) to be associated with an increase in mortality of 1.46 percentage points, or 0.11 standard deviations. Column 3 repeats this regression for the sample with observed duration, and obtains an even larger negative pointestimate on Crowding, so controlling for voyage duration is not the reason for not finding a positive coefficient on Crowding in column 1.

We can analyze the relationship between crowding and mortality in the whole sample while controlling for duration using two different missing data methods: the missing-indicator method and imputation analysis. Both approaches find a positive and statistically significant relationship between crowding and mortality, of similar and meaningful magnitudes.

First, I add two separate duration controls for observed and unobserved cases to the regression: a continuous variable equal to Duration when passage time is observed and zero when it is not, and a dummy variable equal to one if Duration is missing and zero if it is observed.Footnote 10 More precisely, I estimate

\[ \begin{array} \[Mortalit{y_i} = {\beta _0} + {\beta _1}Crowdin{g_i} + {\beta _2}Tonnag{e_i} + {\beta _3}Duration_i^* + {\beta _4}1\left\{{Duratio{n_i}\,{\rm{missing}}} \right\} + {\beta _5}\left\{{Crowdin{g_i}*Duration_i^*} \right\} + {\beta _6}\left({Crowdin{g_i}*1\left\{{Duratio{n_i}\,{\rm{missing}}} \right\}} \right) + {\psi _{route\left(i \right)}} + {\phi _{decade\left(i \right)}} + {\varepsilon _i}\] \end{array} \

where 1{Durationi missing} is equal to 0 if Duration is observed and 1 if Duration is missing, and Duration i * is defined

\[ \begin{array} \[Duration_i^*\left\{{\begin{array}{*{20}{c}} {Duratio{n_i},}&{if\,Duratio{n_i}\,is\,observed}\ {0,}&{if\,Duratio{n_i}\,is\,{\rm{missing}}} \end{array}} \right.\] \end{array} \

The coefficient β3 captures the correlation of duration and mortality when duration is observed, while β4 captures the relationship with unobserved duration.

Much of the literature argues that slave mortality was affected by crowding and voyage duration in a nonlinear manner—more crowded voyages may have exhausted food supplies sooner, experiencing rising mortality late in the voyage (Cohn and Jensen Reference Cohn and Jensen1982; Galenson Reference Galenson1986), or more crowded voyages may have spent longer embarking slaves at the African coast, increasing the risk of a disease outbreak in the early part of the voyage (Steckel and Jensen Reference Steckel and Jensen1986; Haines, McDonald, and Shlomowitz Reference Haines, McDonald and Shlomowitz2001). I add interaction terms between crowding and the two duration variables to capture these relationships. β5 estimates the association between mortality and the interaction of Crowding with Duration i *; β6 estimates the association between mortality and the interaction of Crowding with 1{Durationi missing}.

The regression is tabulated without interactions in Table 4, column 4, while column 5 includes the interaction terms. In column 4, the coefficient on Crowding is positive and statistically significant at the 5 percent level; an increase of one standard deviation (0.78 slaves per shipping ton) is associated with an increase of 1.1 percentage points in mortality rate in the pooled sample. When interaction terms are added, the Crowding coefficient remains large and positive, though the implied change in mortality of a marginal increase in crowding, for a voyage with a typical observed duration, becomes small.Footnote 11 A joint test in column 5 finds that the coefficients on Crowding and both interaction terms are jointly different from zero at the 5 percent significance level. The negative coefficients on the interaction terms are consistent with the literature finding nonlinear mortality rates driven by preboarding conditions.

I examine the robustness of these findings using imputation analysis. Where Duration is unobserved, I impute a value by regressing Duration all the other variables in equation 1 as regressors.Footnote 12 I use imputed values to estimate

\[ \begin{array} \[Mortalit{y_i} = {\beta _0} + {\beta _1}Crowdin{g_i} + {\beta _2}Tonnag{e_i} + {\beta _3}Duratio{n_i} + {\beta _5}\left\{{Crowdin{g_i}*Duratio{n_i}} \right\} + {\psi _{route\left(i \right)}} + {\phi _{decade\left(i \right)}} + {\varepsilon _i}\] \end{array} \

Columns 6 and 7 of Table 4 present results from this estimation. Consistent with the missing-indicator results from columns 4 and 5, the coefficient on Crowding is statistically significant at the 5 percent level in both specifications, and point estimates are similar across specifications in columns 4 and 6 (1.489 using imputation compared to 1.378), and columns 5 and 7 (3.206 compared to 2.514). Like the missing-indicator results, imputation methods also estimate a negative coefficient on the interaction of Crowding and Duration. The coefficients on Crowding and on the interaction term are jointly significant at the 5 percent level in column 7.

Table 4

MULTIVARIATE REGRESSION OF SLAVES’ MIDDLE PASSAGE MORTALITY RATE ON VOYAGE CHARACTERISTICS

*** p < 0.01.

** p < 0.05.

* p < 0.1.

Notes:

Standard errors in parentheses corrected for an arbitrary covariance structure by ship. In columns 6 and 7, estimates and errors are corrected for imputation bias using multiple random imputation (50 iterations). Crowding p-value is for Crowding variable alone in columns 1–4 and 6, and for a joint test of Crowding and interaction terms in columns 5 and 7. The R-squared presented in columns 6 and 7 is for an ordinary regression using the 50th imputation of Duration.

Figure 1

OBSERVED DURATION AND DISTRIBUTION OF CROWDING BY DECADE

Notes: Share Duration Observed is total observations with observed value of Duration divided by total observations in the sample, by decade. Lines chart medians and interquartile range of Crowding. Distribution statistics for Crowding are not presented for the decades before 1671 or after 1860, none of which have more than four voyages per decade. Appendix Table 2 presents voyage counts (total and observing Duration) and meanCrowding by decade.

Crowding, it seems, was associated with higher mortality—at least when voyage duration is not observed. Figure 1 charts the share of voyages with observed Duration and the distribution of Crowding by decade of departure from Europe. Of the 763 observations with observed Duration, 593 are from the period 1711–1780 when there were very few highly crowded voyages in the data set. In contrast, the periods before 1711 and after 1780 have more tightly packed voyages, yet a lower share of voyages with observed duration.Footnote 13 Almost all observations prior to 1710 are incomplete, as are a majority of voyages from 1751–1800 and after 1830.Footnote 14

Changes in national composition over time explain part of this pattern: France and Great Britain are the two largest contributors of complete observations to the data set;Footnote 15 during the 1781–1800 period, both experienced radical change in their slave trade industries, as the 1788 Dolben's Act imposed the first regulations on the British trade, while in 1789 the detailed French voyage data ended with the Revolution.Footnote 16 Omitting voyages without recorded duration means ignoring most of the voyages affected by Dolben's Act—of 389 British voyages in the sample departing after 1788, only nine record voyage duration—as well as almost all voyages departing before 1710.

An examination of a broader subset of voyages than previous studies finds a positive correlation between slave crowding and mortality in the Middle Passage, consistent with historical accounts. This result holds both for simple correlations and for incomplete data regressions. Using partially incomplete observations allows the analysis to consider not only a larger sample, but a sample that includes many more voyages during the earliest decades of the slave trade and after 1788, when slave voyages were least likely to record Middle Passage voyage duration and more likely to be highly crowded. Determining the economic motivations and other factors that would have led some voyages, but not others, to create and preserve records of their voyage duration is a question for future research.

Appendix

Appendix Table 1

SUMMARY STATISTICS FOR FULL SAMPLE

Appendix Table 2

OBSERVED VOYAGE DURATION BY DECADE

Appendix Table 3

OBSERVED VOYAGE DURATION BY NATION OF REGISTRATION

Appendix Table 4

OBSERVED VOYAGE DURATION BY EMBARKMENT REGION

Appendix Table 5

OBSERVED VOYAGE DURATION BY DISEMBARKMENT REGION

Appendix Table 6

LINEAR PROBABILITY MODEL: 1{DURATION MISSING} ON OTHER VARIABLES

Appendix Figure 1

PROPORTION OF VOYAGES WITH OBSERVED DURATION BY NATION

Notes: The solid lines mark the repeal of the Royal African Company's monopoly of the British slave trade (1698), the asiento granting the South Sea Company the right to trade to Spanish territories (1713), Dolben's Act (1788), and British prohibition on international slave trade (1807). The dashed line marks the beginning of the French Revolution (1789). Observation shares are plotted only for nations with at least five voyages in each decade. Dependent variable is share of 2,167 observations by decade with observed values for vessel tonnage and crowding, mortality rate, travel route, and year of departure, that also have observed values for voyage duration. Source: See Appendix Table 7.
Appendix Table 7

DATA SOURCES AND VARIABLE CONSTRUCTION

Footnotes

This research was generously supported by graduate fellowships and research funds from the National Bureau of Economic Research, the Economic History Association, and the University of Michigan. The author is grateful for the helpful comments of several thoughtful and generous readers and seminar participants, particularly to David J. Auerbach, Martha J. Bailey, D. James Baker, Benjamin A. Hicklin, George L. Hurrell III, Daniel Marcin, Edie Ostapik, Paul W. Rhode, Elyce J. Rotella, Warren C. Whatley, and two anonymous reviewers.

Included voyages have observed values for vessel tonnage and crowding, mortality rate, travel route, and year of departure. See Appendix Table 7.

Otherwise complete observations have observed values for vessel tonnage and crowding, mortality rate, travel route, and year of departure. See Appendix Table 7.

Otherwise complete observations have observed values for vessel tonnage and crowding, mortality rate, travel route, and year of departure. See Appendix Table 7.

Otherwise complete observations have observed values for vessel tonnage and crowding, mortality rate, travel route, and year of departure. See Appendix Table 7.

Otherwise complete observations have observed values for vessel tonnage and crowding, mortality rate, travel route, and year of departure. See Appendix Table 7 for a description of sample.

*** p < 0.01.

** p < 0.05.

* p < 0.1.

Great Britain is the omitted category for nation indicators. Standard errors in parentheses corrected for heteroskedasticity. Otherwise complete observations have observed values for vessel tonnage and crowding, mortality rate, travel route, and year of departure.

The table explains the sources and construction of variables used in this article, denoting the database's variable names in italics.

Slave trade variables are from the Trans-Atlantic Slave Trade Database, a machine-readable dataset gathered from archival records of individual voyages. The data and documentation are maintained by a consortium of scholars and academic institutions, and provided for public access and use at http://www.slavevoyages.org. My copy of the data was accessed in October, 2009.

1 For descriptions of the Middle Passage linking overcrowding to high mortality rates, see for example: Falconbridge (Reference Falconbridge1788, pp. 19–32); Riland (Reference Riland1828, pp. 50–66); Blake (Reference Blake1860, chap. 10); Tattersfield (Reference Tattersfield1991, pp. 144–54).

2 A control for vessel size is necessary to capture risk of disease. For the same degree of crowding, a larger vessel carried a larger number of slaves and crew, and took longer to procure and load the slave cargo, raising the absolute risk of a disease outbreak at sea.

3 The sole exception is Garland and Klein (Reference Garland and Klein1985), who focus on alternative measures of crowding, and do not use a multivariate regression approach.

4 Voyage duration is recorded for 3,798 observations of 34,957 in the complete database, or 763 voyages of 2,167 with largely complete information, as described in Appendix Table 7.

5 The sample includes voyages with years of departure spanning 1622 to 1864, though almost all observations come from the hundred-year period from 1711 to 1810. Summary statistics are comparable to the sample of Haines, McDonald, and Shlomowitz (Reference Haines, McDonald and Shlomowitz2001). Their sample has an average tonnage of 228.9 shipping tons (compared to 244 in the completely observed sample), an average mortality rate of 11 percent (11.9 percent), a slave/shipping ton ratio of 1.53 (1.48), and a Middle Passage duration of 64 days (73.6). Haines, McDonald, and Shlomowitz's sample differs by including voyages originating in East Africa, voyages seized mid-crossing, and voyages where ship tonnage is not observed.

6 These regressions differ from Galenson's by using effects for route rather than region of origin. This more flexible specification is feasible because the Trans-Atlantic Slave Trade Database includes many more observations than Galenson's 33. Appendix Tables 3, 5, and 6 tabulate properties of key variables by decade and by regions of embarkment and disembarkment and show significant variation across each.

7 The mortality rate is defined as slave deaths between the last port of embarkation and the first port of disembarkation, divided by number of slaves on board at departure from Africa. See Appendix Table 7.

8 Along with other unobservables, these decade and route effects control for expected voyage time. On longer trade routes, we would expect captains to manage the risks of the longer time at sea by crowding their vessels less. The effect of simply regressing slave mortality on crowding, then, would be ambiguous; it would be positive via the direct effects of more densely packing slave cargo, but negative through the negative correlation with time at sea.

9 All estimations in this article use 2,167 voyages from the Trans-Atlantic Slave Trade Database with observations on slave mortality, crowding, tonnage, trading route, and year of departure. Slave voyages that did not successfully complete a voyage on a trans-Atlantic trading route are omitted. Details on the sample and the construction of the variables are in Appendix Table 7.

10 This method for handling missing data, the missing-indicator method, has well-known statistical weaknesses which imply that my estimate of the association between crowding and mortality is likely too small. Jones (Reference Jones1996) demonstrates that the missing-indicator method leads to overstated standard errors and biased coefficients on both missing and non-missing explanatory variables. If the covariance between Duration and Crowding is negative in the sample where Duration is not observed—consistent with captains crowding ships more when a voyage is expected to be brief—then β1 should be understated and β3 overstated in Table 3, columns 4 and 5.

11 Within the sample with observed voyage duration, the mean voyage lasts 73.6 days. Within this sample, the marginal effect of crowding for the mean voyage would then be which is approximately equal to 2.51 −0.0365*73.6 = −0.18 in column 5 and 2.59 −0.0312 * 73.6 = +0.29 in column 6. For a one-standard-deviation increase in crowding (0.59 slaves / ton), these translate into modest mortality changes of −0.11 and +0.18 percentage points, respectively.

For voyages with missing duration, is 1.81 in column 5, implying that a one-standard- deviation increase in crowding (0.71 slaves/ton) on a voyage without observed voyage duration would have a mortality rate increase of about 1.3 percentage points,.

12 Specifically, the imputation step regresses Duration i = α0 + α1 Crowding i + α2 Tonnagei + ψ route(i) + ϕ decade(i) + εi. Imputed values of Duration are constructed from the predicted values estimated by this equation. Because imputing missing values will create an artificially high correlation between Duration and the other variables of interest, fifty imputations using randomized subsets of the data are used to estimate the robustness of the coefficients and fixed effects, as well as the distribution of the residual, εi. These estimates are used to correct estimates in table 3, columns 6 and 7, for imputation bias. Allison (Reference Allison2002) and Cameron and Trivedi (Reference Cameron and Trivedi2005, chap. 27) provide good overviews of the multiple random imputation method for unbiased analysis with missing data.

13 Rates of missing Duration data and mean mortality rate and crowding are tabulated in Appendix Table 2.

14 Appendix Table 7 reports estimates of regression 1{Durationi Missing} = δ0 + δ1 Mortalityi + δ3 Tonnagei + ηnation(i) + ψ route(i) + ϕ decade(i) + ε i , where η nation(i) are nation dummies and other variables are as previously described. Intriguingly, δ2 is negative and statistically significant at the 5 percent level, even after controlling for other influences on data observation.

15 Appendix Table 3 reports missing Duration data and mean mortality rate and crowding by nation of carrier. Of 763 complete observations in the data set, 549 are French and 67 British; only 6.6 percent of 1,018 British voyages report voyage duration. Though the French data constitutes a majority of the completely observed sample, the weak relationship between recorded duration and slave mortality is not a feature of the French data specifically. Omitting the French data or controlling for unobserved national influences on mortality with nation-fixed effects does not meaningfully change the regression results (estimates are suppressed for brevity).

16 Appendix Figure 1 displays decadal Duration reporting rates for voyages of the five countries with the most voyages in the data set (Britain, France, the United States, Portugal, and the Netherlands). While the Royal African Company is a large contributor of observations (161 in in the data set, 35 of them with observed duration), the British rate of Duration observation is highest following Parliamentary actions unfavorable to the company, including repealing its monopoly (1698), granting the asiento trade to the South Sea Company (1713), and the passage of Dolben's Act (1788).

References

REFERENCES

Allison, Paul D. Missing Data. Sage University Paper 07-136. Thousand Oaks, CA: Sage Publications, 2002.Google Scholar
Blake, W. O. The History of Slavery and the Slave Trade, Ancient and Modern. Columbus, OH: H. Miller, 1860.Google Scholar
Cameron, A. Colin, and Trivedi, Pravin K.. Microeconometrics: Methods and Applications. Cambridge: Cambridge University Press, 2005.CrossRefGoogle Scholar
Carlos, Ann M. “Agent Opportunism and the Role of Company Culture: The Hudson's Bay and Royal African Companies Compared.” Business and Economic History 20 (1991): 142–57.Google Scholar
Carlos, Ann M. “Bonding and the Agency Problem: Evidence from the Royal African Company, 1672–1691.” Explorations in Economic History 31, no. 3 (July 1994): 313–35.Google Scholar
Cohn, Raymond L., and Jensen, Richard A.. “The Determinants of Slave Mortality Rates on the Middle Passage.” Explorations in Economic History 19, no. 3 (July 1982): 269–82.Google Scholar
Curtin, Philip D. The Atlantic Slave Trade: A Census. Madison: University of Wisconsin Press, 1969.Google Scholar
Eltis, David. “Mortality and Voyage Length in the Middle Passage: New Evidence from the Nineteenth Century.” The Journal of Economic History 44, no. 2 (1984): 301–08.CrossRefGoogle ScholarPubMed
Eltis, David, Lewis, Frank D., and McIntyre, Kimberly. “Accounting for the Traffic in Africans: Transport Costs on Slaving Voyages.” The Journal of Economic History 70, no. 4 (2010): 940–63.Google Scholar
Expanded data set. 2008. Voyages: The Trans-Atlantic Slave Trade Database. Available online at http://www.slavevoyages.org. (accessed October 2009).Google Scholar
Falconbridge, Alexander. Account of the Slave Trade on the Coast of Africa. London: J. Phillips, 1788.Google Scholar
Galenson, David W. Traders, Planters, and Slaves: Market Behavior in Early English America. Cambridge: Cambridge University Press, 1986.Google Scholar
Garland, Charles, and Klein, Herbert S.. “The Allotment of Space for Slaves Aboard Eighteenth-Century British Slave Ships.” The William and Mary Quarterly 42, no. 2 (April 1985): 238–48.Google Scholar
Haines, Robin, McDonald, John, and Shlomowitz, Ralph. “Mortality and Voyage Length in the Middle Passage Revisited.” Explorations in Economic History 38, no. 4 (2001): 503–33.CrossRefGoogle ScholarPubMed
Haines, Robin, and Shlomowitz, Ralph. “Explaining the Mortality Decline in the Eighteenth-Century British Slave Trade.” The Economic History Review 53, no. 2 (May 2000): 262–83.Google Scholar
Hogerzeil, Simon J., and Richardson, David. “Slave Purchasing Strategies and Shipboard Mortality: Day-to-Day Evidence from the Dutch African Trade, 1751–1797.” The Journal of Economic History 67, no. 1 (2007): 160–90.CrossRefGoogle Scholar
Jones, Michael P. “Indicator and Stratification Methods for Missing Explanatory Variables in Multiple Linear Regression.” Journal of the American Statistical Association 91, no. 433 (1996): 222–30.Google Scholar
Klein, Herbert S. The Atlantic Slave Trade. Cambridge: Cambridge University Press, 2010.Google Scholar
Klein, Herbert S., and Engerman, Stanley L.. “A Note on Mortality in the French Slave Trade in the Eighteenth Century.” In The Uncommon Market: Essays in the Economic History of the Atlantic Slave Trade, edited by Gemery, H.A and Hogendorn, Jan S, 239–60. New York: Academic Press Inc., 1979.Google Scholar
Klein, Herbert S., and Engerman, Stanley L. “Long-Term Trends in African Mortality in the Transatlantic Slave Trade.” In Routes to Slavery, edited by Eltis, David and Richardson, David, 36–48. London: Frank Cass & Co. Ltd., 1997.Google Scholar
Klein, Herbert S., Engerman, Stanley L., Haines, Robin, and Shlomowitz, Ralph. “Transoceanic Mortality: The Slave Trade in Comparative Perspective.” The William and Mary Quarterly 58, no. 1 (January 2001): 93–118.Google Scholar
Postma, Johannes. “Mortality in the Dutch Slave Trade, 1675–1795.” In The Uncommon Market: Essays in the Economic History of the Atlantic Slave Trade, edited by Gemery, H.A and Hogendorn, Jan S, 239–60. New York: Academic Press Inc., 1979.Google Scholar
Richardson, David. “The Costs of Survival: The Transport of Slaves in the Middle Passage and the Profitability of the Eighteenth-Century British Slave Trade.” Explorations in Economic History 24, no. 2 (1987): 178–96.Google Scholar
Riland, John. Memoirs of a West-India Planter. London: Milton, Adams & Co., 1828.Google Scholar
Steckel, Richard H., and Jensen, Richard A.. “New Evidence on the Causes of Slave and Crew Mortality in the Atlantic Slave Trade.” The Journal of Economic History 46, no. 1 (1986): 57–77.Google Scholar
Stein, Robert. “Mortality in the Eighteenth-Century French Slave Trade.” The Journal of African History 21, no. 1 (1980): 35–41.Google Scholar
Tattersfield, Nigel. The Forgotten Trade: Comprising the Log of the Daniel and Henry of 1700 and Accounts of the Slave Trade from the Minor Ports of England, 1698–1725. London: Jonathan Cape, 1991.Google Scholar
Whatley, Warren C., and Gillezeau, Rob. “The Fundamental Impact of the Slave Trade on African Economies.” In Economic Evolution and Revolution in Historical Time: edited by Rhode, Paul W, Rosenbloom, Joshua, and Weiman, David, 86–110. Palo Alto, CA: Stanford University Press, 2011.Google Scholar
Figure 0

Table 1QUANTITATIVE LITERATURE ON CROWDING AND SLAVE MORTALITY

Figure 1

Table 2SUMMARY STATISTICS AND CORRELATIONS BY SUBSAMPLES

Figure 2

Table 3MULTIVARIATE REGRESSION OF SLAVES’ MIDDLE PASSAGE MORTALITY RATE ON VOYAGE CHARACTERISTICS

Figure 3

Table 4MULTIVARIATE REGRESSION OF SLAVES’ MIDDLE PASSAGE MORTALITY RATE ON VOYAGE CHARACTERISTICS

Figure 4

Figure 1OBSERVED DURATION AND DISTRIBUTION OF CROWDING BY DECADE

Notes: Share Duration Observed is total observations with observed value of Duration divided by total observations in the sample, by decade. Lines chart medians and interquartile range of Crowding. Distribution statistics for Crowding are not presented for the decades before 1671 or after 1860, none of which have more than four voyages per decade. Appendix Table 2 presents voyage counts (total and observing Duration) and meanCrowding by decade.
Figure 5

Appendix Table 1SUMMARY STATISTICS FOR FULL SAMPLE

Figure 6

Appendix Table 2OBSERVED VOYAGE DURATION BY DECADE

Figure 7

Appendix Table 3OBSERVED VOYAGE DURATION BY NATION OF REGISTRATION

Figure 8

Appendix Table 4OBSERVED VOYAGE DURATION BY EMBARKMENT REGION

Figure 9

Appendix Table 5OBSERVED VOYAGE DURATION BY DISEMBARKMENT REGION

Figure 10

Appendix Table 6LINEAR PROBABILITY MODEL: 1{DURATION MISSING} ON OTHER VARIABLES

Figure 11

Appendix Figure 1PROPORTION OF VOYAGES WITH OBSERVED DURATION BY NATION

Notes: The solid lines mark the repeal of the Royal African Company's monopoly of the British slave trade (1698), the asiento granting the South Sea Company the right to trade to Spanish territories (1713), Dolben's Act (1788), and British prohibition on international slave trade (1807). The dashed line marks the beginning of the French Revolution (1789). Observation shares are plotted only for nations with at least five voyages in each decade. Dependent variable is share of 2,167 observations by decade with observed values for vessel tonnage and crowding, mortality rate, travel route, and year of departure, that also have observed values for voyage duration.Source: See Appendix Table 7.
Figure 12

Appendix Table 7DATA SOURCES AND VARIABLE CONSTRUCTION