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The article discusses the unintended consequences of U.S. immigration policy on India's IT boom. In the 1990s, Indian college students and workers gained computer science skills with the hope of migrating to the U.S. IT industry. However, many ended up staying in India or returning after a short period due to restrictive immigration policies. These workers contributed to the development of India's IT sector, which experienced significant growth in the late 2000s. The article explores the impact of U.S. high-skill immigration policy on both India and the U.S., highlighting the potential benefits and drawbacks for each country. [Extracted from the article]

A measure of relative importance of variables is often desired by researchers when the explanatory aspects of econometric methods are of interest. To this end, the author briefly reviews the limitations of conventional econometrics in constructing a reliable measure of variable importance. The author highlights the relative stature of explanatory and predictive analysis in economics and the emergence of fruitful collaborations between econometrics and computer science. Learning lessons from both, the author proposes a hybrid approach based on conventional econometrics and advanced machine learning (ML) algorithms, which are otherwise, used in predictive analytics. The purpose of this article is two-fold: to propose a hybrid approach to assess relative importance and demonstrate its applicability in addressing policy priority issues with an example of food inflation in India, followed by a broader aim to introduce the possibility of conflation of ML and conventional econometrics to an audience of researchers in economics and social sciences, in general. [ABSTRACT FROM AUTHOR]