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Education, Economic Development, and Technology Transfer: A Colonial Test

Published online by Cambridge University Press:  03 March 2009

John R. Hanson II
John R. Hanson II
Affiliation:
Professor of Economics, Texas A and M University, College Station, TX 77843–4228.
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Abstract

I test the hypothesis advanced by Richard Easterlin and others that the importation of modern technology and prospects for economic development in the Third World are principally a function of the local population's formal schooling. According to orthodoxy, manufacturing more than any other sector should repay investment in human capital. Yet the correlation of schooling with the manufacturing sector is much lower than with the mineral sector, an enclave in colonial economies and a symbol of underdevelopment.

Type
Articles
Information
The Journal of Economic History , Volume 49 , Issue 4 , December 1989 , pp. 939 - 957
Copyright
Copyright © The Economic History Association 1989

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References

1 Easterlin, Richard A., “Why Isn't the Whole World Developed?” this JOURNAL, 41 (03 1981), pp. 1–17.Google Scholar

2 Headrick, Daniel R., The Tentacles of Progress (New York, 1988), p. 384.Google Scholar

3 See, for example, Hopkins, Anthony G., An Economic History of West Africa (New York, 1973)Google Scholar and Fieldhouse, David, Colonialism, 1875–1940 (New York, 1981).Google ScholarFieldhouse's most recent book, Black Africa, 1945–1980 (London, 1986), tacitly conveys the familiar impression that the educational situation in Africa was so deplorable at independence that its previous effects were inconsequential.Google Scholar

4 Todaro, Michael P., Economic Development in the Third World (4th edn., New York, 1989), p. 430.Google Scholar

5 See, for example, Bowman, Mary Jean and Anderson, C.Arnold, “Concerning the Role of Education in Development”, in Geertz, Clifford, ed., Old Societies and New States (New York, 1963), pp. 247–79.Google Scholar

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7 Woytinsky, W. S. and Woytinsky, E. S., World Population and Production (New York, 1953), p. 767.Google Scholar

8 Ibid.

9 Ibid., p. 763.

10 For details on the low educational expenditures in colonial Africa, see Gifford, Prosser and Weiskel, Timothy, “African Education in a Colonial Context: French and British Styles”, in Gifford, Prosser and Louis, W.Roger, eds., France and Britain in Africa (New Haven, 1971), pp. 688–89.Google Scholar

11 Carnoy, Martin, Education as Cultural Imperialism (New York, 1974), p. 138. Italics added, based on the rest of Carnoy's discussion. Kilby's work on Nigeria echoes Carnoy.Google ScholarSee Kilby, Peter, Industrialization in an Open Economy: Nigeria 1945–1966 (Cambridge, 1969), p. 259.Google Scholar

12 LOGAGE is the logarithm of the difference (AGE) between the postwar date of independence (with a 1960 cutoff date for colonies whose independence came later) and the founding date of the colony, according to Encyclopaedia Brittanica, Inc., The New Encyclopaedia Britannica (15th edn., Chicago, 1988). The results presented here are not sensitive to the use of other possible founding dates in cases in which there is doubt or to other reasonable adjustments reflecting the checkered history of colonialism.Google ScholarThe source of the data on religion is Barrett, David, ed., World Christian Encyclopedia (Oxford, 1982). The fruit of a decade-long project beginning in 1968, this remarkably comprehensive and rigorous work not only surveys all branches of Christianity worldwide but also breaks national populations down according to religious affiliation. Standard categories and a standard format are used for every country. Historical data drawn from censuses of religion are presented. This excellent book makes it clear that national populations are not always easily categorized by religious affiliation, one reason why, though tempting, it is not necessarily apt to use a dummy variable to represent a country's religious character.Google Scholar

13 In Muslim northern Nigeria, for example, Christian mission schools were proscribed. See Kilby, Industrialization, pp. 236–37.Google Scholar

14 For example, compare the OLS equation explaining manufacturing as a share of national product (MANPR) with the analogous equation in Table 1. The OLS version is

where LIT is the adult literacy rate in 1960 and POP is population size.

15 See Chenery, Hollis and Syrquin, Moises, Patterns of Development, 1950–1970 (Oxford, 1975), for extensive discussion of the various technical issues involved. Their general conclusion is that for countries at a low level of economic development and in the face of serious data constraints more sophisticated or complicated econometric approaches yield little additional information.Google Scholar

16 International Bank for Reconstruction and Development, World Development Report, 1980 (New York, 1980), Annex, World Development Indicators;Google Scholarand World Tables, 1976 (Washington, 1976). The per capita income estimates for 1960 are the most recent ones provided by Summers and Heston, members of the United Nations International Comparison Project and the recognized leaders in estimating national incomes in poor countries. The results are not very sensitive, however, to the use of these estimates or some of their earlier ones.Google ScholarSee Summers, Robert and Heston, Alan, “A New Set of International Comparisons of Real Product and Price Levels Estimates for 130 Countries, 1950–1985”, The Review of Income and Wealth, series 34, no.1 (03 1988), pp. 1–27.CrossRefGoogle Scholar

17 David, Wilfred L., The Economic Development of Guyana, 1953–1964 (Oxford, 1969), p. 227.Google Scholar

18 Another potentially important effect is the well-known high correlation of higher literacy for females with lower infant mortality and better nutrition, which can raise labor force productivity in addition to other benefits.

19 Headrick, Tentacles, p. 15.Google Scholar

20 Harrison, Lawrence E., Underdevelopment is a State of Mind (Lanham, 1985).Google Scholar

21 The simple correlation between population size and the literacy rate is nearly zero. This presumably explains why no one has noticed the connection between the spread of literacy and the size of population previously has been neglected and explains the focus on economies of scale and the like.

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23 EXPT was calculated from population data in Mitchell, B. R., International Historical Statistics: Asia and Africa (New York, 1982),CrossRefGoogle Scholarfor 1913 or a nearby year and export data in U.S.dollars in Stover, C., “Tropical Exports”, in Lewis, W. A., ed., Tropical Development, 1880–1913 (Evanston, 1970),Google Scholarsupplemented by Lewis, W. A., “The Growth Rate of World Trade, 1870–1973”, in Grassman, S. and Lundberg, E., eds., The World Economic Order: Past and Prospects (New York, 1973);Google ScholarYates, P. L., Forty Years of Foreign Trade (New York, 1959);Google Scholarand Issawi, C., An Economic History of the Middle East and North Africa (New York, 1982). Colonial records, which were well and assiduously kept, are the original source of the data, so these sources are mutually consistent. No attempt was made to estimate population when data were not available for 1913 or a close year.Google Scholar

24 The source of data is Hanson, John R. II, Trade in Transition (New York, 1980), pp. 26–27.Google Scholar

25 Data from Ibid, and Hanson, John R. II, “Export Shares in the European Periphery and the Third World before World War 1: Questionable Data, Facile Analogies”, Explorations in Economic History, 23 (01 1986), pp. 85–99.CrossRefGoogle Scholar

26 See, for example, Kmenta, Jan, Elements of Econometrics (2nd edn., New York, 1986), pp. 443–46.Google Scholar

27

28 Garnaut, Ross, “Resource Trade and the Development Process in Developing Countries”, in Krause, Lawrence B. and Patrick, Hugh, eds. Mineral Resources in the Pacific Area (San Francisco, 1978), p. 139.Google Scholar

29 Statistical tests of the type used earlier for EXPT corroborated this argument.

30 The share of minerals in national product was computed by multiplying MINEX by the ratio of exports to national product. Virtually all mineral production was exported.

31 The source is UNESCO, World Survey of Education (Geneva, 1958), vol. 2.Google Scholar

32 The countries are Algeria, Morocco, Tunisia, Ghana, Nigeria, Kenya, Zaire, Cameroon, Togo, India, Sri Lanka, Indonesia, Jamaica, Guyana, Uganda, Trinidad and Tobago, Burma, and Malaysia.

33 Bohr, Kenneth, “Investment Criteria for Manufacturing Industries”, Review of Economics and Statistics (05 1954), pp. 157–66.CrossRefGoogle Scholar

34 It should be mentioned, however, that in the early twentieth century complaints about the harm done by illiteracy to the productivity of factory workers were heard in India. See Carnoy, Education as Cultural Imperialism, p. 111.Google Scholar

35 World Resources Institute, World Resources, 1986 (New York, 1986), pp. 289–300.Google Scholar

36 David, Economic Development, chap. 4. By the 1950s mining and mineral processing represented about 3 percent of the labor force and over 11 percent of GDP, while manufacturing represented over 12 percent of the labor force and 3 percent of GDP, according to David. This implies much greater productivity in mining. For numbers, see Ibid., chap. 2, p. 77.

37 In Jamaica, as a matter of fact, the mining companies also are engaged in agriculture. See Ibid., p. 190.

38 Headrick, Tentacles, p. 275.Google Scholar

39 Bohr, “Investment Criteria”, p. 162.Google Scholar

40 Headrick, Tentacles, pp. 275–76.Google ScholarSee also David, Economic Development, p. 91, for references and comment on training in Guyana and Jamaica.Google Scholar

41 Bohr, “Investment Criteria”, p. 162.Google Scholar

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43 The criticized countries were Algeria, Angola, Burma, Cambodia, Sri Lanka, Ethiopia, Ghana, India, Indonesia, Iran, Laos, Malaysia, Morocco, Pakistan, Philippines, Sudan, Tanzania, Vietnam. The others were Guinea, Guyana, Zaire, Haiti, Iraq, Jamaica, Kenya, Korea, Liberia, Libya, Malawi, Mali, Nigeria, Syria, Taiwan, Thailand, Trinidad and Tobago, Tunisia, United Arab Republic, Zambia.Google Scholar

44 National Industrial Conference Board, Obstacles and Incentives, p. 84.Google Scholar

45 The omitted countries are Malawi, United Arab Republic, Vietnam, Laos, Angola, Taiwan, and Guinea.Google Scholar

46 The omitted countries are Vietnam, Malawi, and United Arab Republic.Google Scholar

47 The omitted countries are Laos, Vietnam, Cambodia, Guinea, United Arab Republic, Guyana, Haiti, Liberia, Libya, and Syria.Google Scholar

48 The classic account is Clower, Robert et al. , Growth Without Development (Evanston, 1966).Google Scholar

49 Mason, Edward S., “Raw Materials, Rearmament, and Economic Development”, Quarterly Journal of Economics, 66 (08 1952), pp. 332–33.CrossRefGoogle Scholar

50 Ibid., p. 336.