Knowledge that Transforms

An attempt is made to estimate the effects of income and other variables on the pattern of interregional labor migration in Data for the study are contained in the 1960 Population Census of Ghana. The best information regarding migration is the distribution of population partitioned by age and sex by region of birth and region of residence in 1960. Ghana is divided into 7 administrative regions for which the government publishes population data. All data and analyses in this study are based on these 7 regions. The average income per African laborer was estimated for each of the regions. Measures of urbanization and education were derived from census data. Variables which are important in individual decisions were postulated but the form of individual decision functions was not specified. 2 variables are introduced which may account for systematic differences in individual responses: education as measured by the percentage of adult males in the region who have attended school; and urbanization measured by the percentage of regional population residing in towns of 5000 or more. Density of population was used in place of urbanization in 1 instance. A high proportion of the variation in interregional migration rates was explained by each of the functions. Since there were 7 regions in Ghana and 6 destinations corresponding to each origin the regressions were based on 42 observations. All of the coefficients except those for education were of the right or expected sign. Distance was a strong deterrent to migration. The highest elasticities related to income and migrants clearly tended to move to regions with high wage levels. When urbanization was included migration appeared to be more responsive to the level of home income than to income in other regions. If density was used income in other regions becomes more impoortant. Both origin and destination population were significant variables. Migration increased considerably more than in proportion to population of the home region. Migrants were attracted to regions of large population but the effect was less than proportionate. In this empirical analysis the simple dissatisfaction hypothesis concerning the effects of education was refuted. A larger percentage of educated than uneducated people do migrate especially to cities but the results were inconsistent with this observation. In sum it was demonstrated that migration in Ghana is responsive to income differentials. Distance is a strong deterrent to migration and is most likley to surrogate for differences in culture social organizations langage and transport cost. Education was negatively related to migration.

A BASIC proposition of economic theory is that, under competition, profit rates will tend toward equality. Baumol, on the other hand, has put forward the hypothesis that increased money will not only increase the total profits of the firm, but because it puts the firm in a higher echelon of imperfectly competing groups, it may very well also increase its earnings per dollar of investment [5, p. 33]. The logic of Baumol's proposition is that large firms have all of the options of small firms, and, in addition, they can invest in lines requiring such scale that small firms are excluded. It follows that . . so long as any industries are peculiarly well suited to large investments, and so yield disproportionate returns to sizeable funds, then, provided is prepared to move in response to profit differences, this will tend to be true of all other industries in which large firms operate [5, p. 37]. If this hypothesis is correct, we should find higher rates of return in large enterprises even in the long run and even in the absence of barriers to entry other than those directly associated with availability of capital. An empirical test of this proposition should give a basis for judging the height of the capital requirements barrier [2, p. 156], a potentially important element in the explanation of industry performance. The primary goal of this paper is to test Baumol's hypothesis. It is divided into three parts. In section I we discuss our data and our model, in section II we present and analyze our results, and in section III we draw a few tentative conclusions. I