In the international businesses human resource elements acquired in different countries might have different values in varied industries due to the different quality of education and experiences in the original countries. Using selection models to evaluate expected values in earnings equation of human resource elements such as education and experiences etc. acquired in sending countries, system equations are expanded to examine also the values of science and engineering degrees in technology jobs with selectivity bias correction. This paper used the US census survey data of 2015 on earnings, academic degrees, occupations etc. The US has long maintained the policy of accepting more STEM workers than any other countries and helped maintaining own technological leadership. Assuming per capita GDP gap between the sending country and the US downgrades immigrant human resource quality, it rarely affects occupational selection but depresses earnings on average by two or more years’ worth of education. Immigrant quality index in the sense of GDP gap appears to be a valid tool to assess the expected earnings of the worker with. Engineering degrees increase significantly the probability of selecting not only engineering jobs but also general management jobs, as well as increasing the expected earning additionally over nine years’worth of education. Getting a technology job is additionally worth about four years of education. Economics and business degrees are worth additionally almost six years of education but humanities degrees depress expected earnings. Since years after immigration does not very fast enhance earnings capacity, education level and English language ability might be more useful criteria to expect better future earnings by.

In international businesses human resource elements acquired in different countries might have different values in varied industries due to different quality of education and experiences in original countries. Using existing models to evaluate expected values of human resource elements such as education and experience setc. acquired in sending countries they are expanded to examine also the values of science and engineering degrees in technology jobs with selectivity bias correction. This paper used the US census survey data of 2015 on earnings, academic degrees etc. to contrast qualitative effects with quantitative effects of human resource elements compared to those in the native and/or white group.