Computer simulation techniques permit considerable freedom in designing and elaborating models of socio-economic systems, as the form of the model is not restricted by the need to find explicit analytical solutions to a set of equations. Perhaps the greatest limitation to the scope
of such models is posed by the limits of the information concerning the behaviour of such systems. In most cases, any available data will have been collected for quite
different purposes and are seldom in the form required for the simulation model to be constructed.
The first step of the procedure used to quantify the simulation model of Papua New Guinea was to establish base data for the size and composition of the population
and the pattern of production in terms of the classifications that have been described. 1966 was selected as the initial year for the projections to be generated as this coincided with the first census of the indigenous population.
Census data were used to establish the distribution of the population by age, sex, educational qualifications and location, as shown by Table 2.6. Labour force participation rates were also derived from census tabulations, but the census data were not sufficient to describe the cross
classification of the population by the various characteristics needed for the model. The additional sources of data used
in carrying out a complicated sequence of estimates to derive the full cross-classification are presented in Appendix 1.
The two main sources used to estimate the volume and pattern of production for 1966 were the Papua New
Guinea National Accounts Statistics (1974) and the
inter-industry analysis of the economy by Parker (1973). Data from the national accounts were used for aggregates while figures derived from the intput-output table
facilitated sectoral breakdowns. A summary of the steps used to estimate initial values for these economic variables
The method for projecting changes in terms of these classifications is based on the interrelations summarised in Diagram 3.1, often following general
hypotheses derived from observations from other developing countries. A further set of problems met in quantifying the model was the matching of projections generated by the model to observed changes in Papua New Guinea since 1966.
The exact forms of the relationships incorporated in the model and the values assigned to parameters were themselves developed using a simulation technique. A preliminary
working version of each submodel was designed and tentative values assigned to every parameter. Then, using observed time paths of variables exogenous to the
submodel being tested,^ a first set of projections of the endogenous variables was generated. Parameter values were then selectively altered and the effects of these changes were simulated. This process was repeated in order to
improve the consistency of time paths generated by the model and observed time paths available from independent sources, such as census data for 1971 and time series from national accounts.
In every case, it was found that the correspondence of simulated and observed time paths could be improved by adapting the form of the relationships in each submodel as well as changing parameter values. The form of the model described and used in this study was reached through a
long sequence of simulations to obtain what was considered to be a satisfactory 'fit' to available observations.
These may include time paths to be projected by other submodels. The modular design of the overall model allowed each component, or submodel, to be tested independently.
The progressive adaptation of parameters and relationships in the process of arriving at the present form of the model simultaneously constituted a sensitivity analysis of the simulation model. This procedure was
useful in identifying the parameters whose values have an important influence on the behaviour of the model. This will allow the setting up of priorities for further research in order to improve the estimates used in this
study. The sensitivity testing also made it possible to reject alternative models which fitted available data, but were highly sensitive to small changes in parameters whose values could not be determined with any precision,
or could not be expected to remain constant over extended periods. For example, the need to design a model that could simulate the turning points associated with the
short-lived construction phase of the Bougainville project led to the rejection of many alternative models.
The detailed descriptions of the submodels will indicate that there are a very large number of parameters to be estimated. Moreover, when the population is
classified into over a thousand subgroups, there is a very large number of time paths to be fitted. It was found
to be beyond the scope of this study to use sophisticated statistical techniques to judge the 'goodness of fit' in every case, and to use criteria such as 'maximum likelihood estimates' to select parameter values. Wherever possible, the parameter estimates were based on direct observations from Papua New Guinea. In other cases, observations from other countries were used to help select preliminary
estimates for parameter values which were then revised through the simulation procedure just described. While studies of other countries were of considerable assistance,
it was felt to be inappropriate simply to accept parameter values derived by regression studies based on cross-sectional data. The special situation of Papua New Guinea, with
the importance of both foreign aid and natural resource based projects in its past and future development, means
that the pattern of economic and demographic change cannot be expected to follow closely the pattern shown by other developing countries.
Owing to the lack of accurate, independently observed time series for many of the variables generated by the model, particular attention was paid to a
relatively small number of variables. The pattern of fertility and mortality rates was established from an analysis of changes between the 1966 and 1971 census of population. The parameters of the submodel to derive rural-urban and return migration were chosen so that the volume and composition of net migrants predicted by the model was consistent with the relative growth of urban
and village populations over the intercensal period. The choice of parameters for the sub-model of the education system posed the least difficult problem, since data on enrolments by sex at each stage of every institution considered in the model were available from the Papua New Guinea Department of Education and Office of Higher Education.
There are no satisfactory estimates for the time path of total wage employment in Papua New Guinea over recent years; the only source of annual data,^ does not
Papua New Guinea, Department of Labour and
Industry, Labour Information Bulletins, Nos 1-9, Port Moresby.
cover all employers. After establishing an initial estimate for 1966 (as described in Appendix 1), the parameters of the submodel which derives the growth of employment from the growth of production were adjusted to give an estimate for total indigenous employment that was within 3% of the corresponding estimate by Parker (1973)
for 1978.'*' It was also checked that the time path of wage employment registered the upturn associated with
the start of the construction phase of the Bougainville copper project, and the slump following the end of
construction, exacerbated by rapid increases in indigenous wage rates. The projections of wage employment by skill
level and the proportion of expatriates employed at each skill level were matched to the cross-classification of the monetary sector workforce by industry and occupation,
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