INTERNAL MIGRATION IN TURKEY, 1965–1985: TEST OF CONFLICTING FINDINGS IN THE LITERATURE
The purpose of this paper is to bring new insights to some of the conflicting findings in the migration literature about developing countries by using detailed analyses of Turkish migration data. The possible reasons behind inconsistencies in the findings are explained in terms of spatial and temporal characteristics of the data, and by national macro factors (mainly the level of urbanization and the characteristics of the urban systems). Tested were rural outâ€ migration and push factors; changes in the size of ruralâ€ toâ€ urban and urbanâ€ toâ€ rural return migration; the effect of distance; and the share and effect of ruralâ€ toâ€ urban migration in the urban growth and in the unbalanced population distribution in developing countries.
The effects of in- and out-migration on urban growth in Turkey (1965–85) and a comparison with the developed countries
Lowry's (1966) study about in- and out-migration rates started a series of discussions on pull and push effects, net migration, and urban growth. This paper presents the results of a similar study, done in Turkey, that tests whether Lowry's hypothesis is valid for developing countries. The results of this study indicate that, for the Turkish case, the effects of migration on urban growth had been contrary to those found in the developed countries, but that, as Turkish development increased over the time period 1965–85, the effects of migration increasingly came to resemble those of developed countries.
Complexity, Network-Decisions And Alternative Risk Premia: Some New Theories Of Structural Change And Portfolio Decisions.
Structural Change is the basis for many decisions such as government fiscal and monetary policies, government interventions, corporate-expenditure decisions, corporate strategic decisions, portfolio management decisions in finance; household allocation decisions, etc.. Structural change in industries and national economies is increasingly more difficult to detect partly because of the growth and dominance of Complex Systems and “Networks” in modern industries and economies – such Networks and Complex Systems include GVCs (global value chains), retail store networks; franchising networks; internet networks; services networks; credit-chains in industries; online social networks; etc.. The relationship between Complexity and Structural Change is well established in the physics, applied math and economic dynamics literatures. Traditional definitions and modeling of structural change have erroneously focused on changes in labor, technological progress, market share, and or capital – which are grossly inadequate in the new economy and post- global financial crisis (as confirmed by changes in the global retailing and housing industries and other industries). Also the modeling of structural change seems to be constrained by the availability of data, and often omits the relationship between structural change on one hand, and globalization, financial stability and international financial contagion. This article: i) introduces the Operations Strategy Model of structural change, explains how it differs from the Lewis Model of Structural Change; and explains the often omitted relationship between operations strategy (of companies and government agencies) and structural change; ii) introduces other new Complexity-based models/theories of Structural Change in industries; iii) reviews the relationships among Operations Research, Complexity and Structural Change; iv) explains the symbiotic relationship between structural change on one hand, and economic growth, financial stability, financial contagion and globalization; v) discusses the significant gap between academic research and practice and explains some issues and factors that should be considered in the development and testing of structural change models; vi) explains how alternative risk premia can be generated from new models of Structural Change.