An annual publication is published by OECD named as ‘Trends in International Migration’. This also fails to disaggregate adequately beyond the highest qualification levels in which the categories are mainly primary, secondary and tertiary. Efforts have been made by others to infer from the data with OECD to offer details on migrant skills and qualifications into OECD nations and source countries. However, the data is highly limited and the methods used by them are highly unreliable. For example, in the United States, similar migration patterns are assumed for every OECD but still migration is largely shaped by multiple factors like linguistic ties, former colonial links and current networks making the assumptions quite misleading. Also, it has been noted by the authors that it is important to identify most of the migrants had been educated in the U.S. while making interpretation of their numbers on brain drain (Fan and Yatikta, 2011).
This would help in enhanced productivity. The Bank specifically argued about the 40% literacy rate is adequate for starting the process of technology transfer as FDI flows. It has been claimed by the bank that the decision of Intel in 1996 to locate somewhere in Costa Rica was mainly because of its investment environment and quality of the education system. Education is truly a factor responsible for unique benefits like reduced social cohesion and crime rates (Kaipeni et.al, 2012). Nevertheless, it must be noted that only skills are not sufficient to facilitate any social transformation important for higher productivity growth. Other measures need to be taken for permitting flexible adjustments to changes like constant learning and systems for social risk management for prevention of labor from returning to sustenance work when structural adjustments take place. Such contributions asserted that brain drain had neutral effects on the source countries and also focused on the advantages of free migration (Gibson, 2013).
Helgesen et.al (2013) has warned that supply of education in this model is not adequate for achieving the required structural transformations for successful liberalization and growth. The government will also have to make learning an appealing process by giving way to individual inequality and aspirations. Schooling productivity is quite low in countries where their government has not promoted a favorable environment for creating highly-paid jobs with most of their skilled workers working only in the public sector. There are policies which wad wage differentials artificially and minimize their returns towards post-schooling investment. It has been quite true in case of Middle East, North Africa and Sub-Saharan Africa and much less in Asia and Latin America (Helgesen et.al, 2013).
Thus, the trick is to learn the tips of shifts of facilitating growth in the service sector in developed nations and align the supply of skills and education to rising demand of skilled labor from social policies and FDI which permit some extent of wage inequality to appeal labor supply (Irina, 2013).
Changes taking place in technology, work organization, production process and international trade patterns explain the increase in earnings inequality. Changes taking place in the production process brought about changes in demand for particular labor types. Technological and organizational changes might have brought about the demand shift for dominating the supply shift resulting in increasing returns to schooling. It also increased the earnings inequality in developed countries and in few middle-income nations (Helgesen et.al, 2013).
The Bank had notified that higher level of education is mandatory for constant social adjustment which must be accessible by both men and women making it the most significant determinant for developing skills of succeeding generations. The World Development Report for 2006 had been outlined to emphasize on expenditure/income inequality, education and gender equality during the development process (Irina, 2013).