AN EXPOSITION OF THE INFORMATION SECTOR APPROACH WITH SPECIAL REFERENCE TO AUSTRALIA

NOTES AND REFERENCES

1. Marc Porat, The Information Economy, OT Special Publication 77–12, US Department of Commerce, Washington DC, May 1977.

2. OECD, Information Activities, Electronics and Telecommunications Technologies, Paris, 1981.

3. See C. Jonscher, ‘Information resources and economic productivity’, Information Economics and Policy, 1, 1983, pp. 13–35.

4. Porat, op.cit.

5. The approach was developed by Porat, op.cit., and taken up by the OECD, op.cit.

6. See T. Mandeville and S. Macdonald, ‘Technological change and employment in the information economy: the example of Queensland’, Prometheus, 3, 1, 1985, p. 72.

7. D. M. Lamberton, ‘The theoretical implications of measuring the communication sector’ in M. Jussawalla and D.M. Lamberton (eds), Communication Economics and Development, Pergamon Press, 1982, pp. 36–59.

8. It should therefore be noted that primary, secondary and tertiary sectors referred to in studies using the information sector approach are net of PRIS items. Moreover, construction, normally counted as a tertiary industry, is classified as part of manufacturing, i.e. as part of the secondary sector.

9. This assumes the same level of productivity in PRIS and SIS. Differences in productivity would result in different amounts of resources necessary for performing the same task in the PRIS and SIS.

10. OECD, op.cit., pp. 34–5.

11. For a discussion of this short-cut method see S. Wall ‘The measurement of information activities’ in OECD, Information Activities, Electronics and Telecommunications Technologies, Vol. II: Background Reports, 1981, pp. 55–62.

12. OECD, op.cit., appendix.

13. This is just a brief outline of the approach. For a detailed exposition see Wall, op.cit. pp. 58–61. The occupation by industry matrix contains wage and salary earners, certain ‘informational’ proprietors and unpaid family members. The income of the latter two categories is imputed by applying wages observed for similar wage earners.

14. For example, it has been reported that Japan possessed 98,800 installed industrial robots in 1981, which by far exceeded the number of robots in Western European countries. However, if one applies the more restrictive definition used in these countries to Japan, that country‘s lead in robot population almost vanishes. See P. Otto, ‘How quickly will the robots arrive? Problems in forecasting technological development’, International Institute for Comparative Social Research, Science Center Berlin, Discussion Paper, No. 116, 1983, p. 7.

15. However, in the long run the increasing importance of robotisation of manufacturing and service industries will necessitate an improvement in the measurement of depreciation on information machines.

16. Porat, op.cit.

17. Seisuke Komatsuzaki and Taro Tanimitsu, ‘Japan's information industry: a structural analysis’, Economic Eye, March 1983, pp. 12–5.

18. See Wall, op.cit., p. 58.

19. This is pointed out in the OECD publication itself. See Wall, op.cit., p. 58.

20. See OECD, op.cit., Table 1.8, p. 35. The slow growth of the PRIS reported by Komatsuzaki and Tanimitsu seems to be due to their narrow definition of PRIS activities.

21. In a recent study, the author analysed the PRIS of the Republic of Korea in both 1975 and 1980. The existence of a large PRIS and its rapid growth were found. During the period which is known for its industrial development, the PRIS contribution to value added grew from 14.7 to 19.9 per cent (see H-J. Engelbrecht, ‘From newly industrialising to newly informatising country: the primary information sector of the Republic of Korea, 1975–1980’, paper presented to the 14th Conference of Economists, University of New South Wales, Sydney, May 1985). A structural analysis also confirmed the dominant position of the PRIS for generating potential growth in that economy. Substantial PRIS have also been found in Singapore (M. Jussawalla and C.W. Cheah, ‘Towards an information economy: the case of Singapore’, Information Economics and Policy, 1, 1983, pp. 161–76); Taiwan (H-J. Engelbrecht, ‘Measurement and structural analysis of the primary information sector of the Republic of China’, paper presented to the Workshop on Measurement of the Primary Information Sectors of Ten Pacific Region Countries, East-West Center, University of Haiwaii, November 1984); the ASEAN countries and Fiji (N. Karunaratne, ‘The information age and the larger ASEAN economies — Focuses on Indonesia, Malaysia and Thailand’, and ‘Pacific Islands and the information age — Focuses on Fiji and Papua New Guinea’, papers presented to the Workshop on Measurement of the Primary Information Sectors of Ten Pacific Region Countries, East-West Center, University of Hawaii, 1984) and Venezuela (Rubin, quoted in Latin American Economic Secretariat, ‘The information sector in the Latin American economy’, SP/LL/IX.O/DT No. 24, 1 August 1983).

22. The industry technology assumption implies that the information intensity of intermediate inputs is proportional to the information intensity of sectoral output. This and other assumptions which have to be made to reorganise the original input-output table are discussed in N. Karunaratne, ‘A methodology for the input-output analysis of the information economy’, paper presented at the Input-Output Workshop of the Regional Science Association of Australia and New Zealand, University of Melbourne, 1984 and Jussawalla and Cheah, op.cit. The technically minded reader is also referred to N. Karunaratne, ‘Planning for the Australian information economy’, Information Economics and Policy, 1, 1984, pp. 345–67, for a mathematical exposition of the methodology used to identify the PRIS.

23. All of the references given in footnote 21 and all of Karunaratne's studies quoted in this paper contain such an analysis.

24. Porat, op.cit., p. 188. Porat was the first to develop an input-output table incorporating the PRIS and SIS.

25. For a discussion of the difficulties involved in using macro-economic models for predictive purposes see J. Bessant, ‘Information technology and employment: some notes on the use of modelling techniques as a research tool’, Prometheus, 2, 2, 1984, pp. 176–89.

26. D. Lamberton, ‘Australia as an information society: who calls the shots?’, Search, 15, 3–4, 1984, pp. 101–2.

27. Mandeville and Macdonald, op.cit.

28. ibid.

29. The list of information occupations is based on the OECD Inventory of Information Occupations (OECD, op.cit., pp. 122–4). Readers interested in the informational or non-informational status of specific occupations are referred to the above publication. For a critique of the list of information occupations used in most information sector studies see J.R. Schement and L. Lievrouw, ‘A behavioural measure of information work’, Telecommunications Policy, December 1984, pp. 321–34.

30. We use the term ‘information intensity of the labour force’ in a macro-economic sense. It simply denotes the number of information workers as a percentage of the total number of workers either by industry or in the whole economy. The question of the degree of information intensity of occupations, i.e. what percentage of working time is devoted to informational tasks in a specific occupation, is an important but separate microeconomic issue which has to be addressed when determining the list of information occupations.

31. The 1971 figure is taken from Lamberton, op.cit., 1982, table 3.2, p. 45 and the 1981 figure from H-J. Engelbrecht, ‘Insights into the secondary information sector of Australia’, paper presented to the Workshop of Measurement of the Primary Information Sectors of Ten Pacific Region Countries, East-West Center, University of Hawaii, November 1984, table 1, p. 2. There are conflicting estimates of the information intensity of the Australian labour force which are most likely due to different criteria used in delineating information from non-information workers. The ABS, for example, reports an information intensity of 39.4 per cent for 1971 and 41.5 per cent for 1981.

32. Lamberton, op.cit, 1982, table 3.2.

33. The figure of about 36 per cent quoted in T. Mandeville and S. Macdonald, op.cit., has been adjusted to include the ‘inadequately described’ category in order to make it comparable to the 1981 figure reported for Australia.

34. N. Karunaratne and A. Cameron, ‘Input-output analysis and the Australian information economy’, Information & Management, 3, 1980, pp. 191–206.

35. N. Karunaratne, ‘Insights on the informatization of Australia and her developing neighbours’, paper presented to the 13th Conference of Economists, Western Australian Institute of Technology, Perth, 1984.

36. Australia did not supply data for the original report (i.e. for OECD, op.cit.).

37. OECD, op.cit., p. 37.

38. A study into the capabilities and opportunities of information technology in Australia commissioned by the Department of Science and Technology, for example, recommended a detailed plan of action for the promotion of selected information technology producing industries costing about $170 million over the next five years (see ‘Technology development in Australia’, Ascent, 5, November 1984, pp. 14–35). For a discussion of Australian government policies towards ‘sunrise’ industries see R. Joseph, ‘Recent trends in Australian government policies for technological innovation’, Prometheus, 2, 1, 1984, pp. 93–111.

39. Quoted in D.M. Lamberton, ‘Secondary sector analysis: methodology and data requirements’, paper presented to the Workshop on Measurement of the Primary Information Sectors of Ten Pacific Region Countries, East West Center, University of Hawaii, November 1984, p. 9.

40. Karunaratne, ‘A methodology for the input-output analysis of the information economy’, op.cit., 1984.

41. The only study known to the author explicitly employing the information sector approach (although at an aggregated level) and investigating the policy implications of the increasing informatisation of the Australian economy is a study by Karunaratne, ‘Planning for the Australian information economy’, op.cit., 1984. His analysis indicates that an accelerated growth rate of the PRIS would result in higher GDP than realisable under the present growth rate. However, the growth of the PRIS can only be achieved at the expense of fiscal restraint.

42. ORANI is a multisectoral general equilibrium model of the Australian economy which has been widely used for policy analysis. See P. Dixon, B. Parmenter, J. Sutton and D. Vincent, ORANI: A Multisectoral Model of the Australian Economy, North-Holland, Amsterdam, 1982.

43. T. Mandeville, S. Macdonald, B. Thompson and D.M. Lamberton, Technology, Employment and the Queensland Information Economy, Report to the Department of Employment and Labour Relations, Queensland, Brisbane, 1983.

44. OECD, op.cit.

45. Mandeville, et al., op.cit., 1983, pp. 66–8.

46. This view is based on an observed slow-down in PRIS growth and the fact that at least in one country, Norway, the PRIS seems to have contracted between 1975 and 1980 (OECD, Updating of the Data Base Contained in OECD Publication No. 6, Volume 1, Note by Secretariat, Paris, February 1984).