In my novice attempts to understand higher education choices during my PhD the last few years, I have always wondered what factors matter the most. Of course, there are models of the orthodox or the newer types. I have mostly relied on the orthodox ones that take a general pool of variables and then predict the probabilities. I am using "orthodox" to indicate the pool of models that are accepted but are old and honestly have less empirical relevance. The fact that these are old doesn't reduce their logical validity; it is just that they are too general to learn from and imply something without relying on stronger assumptions. These are mainly the discrete choice family models that have existed for decades. They give us statements like, "Having the per-capita income increased by X amount, the Pr(Choosing Enrollment in Higher Education) increases (or decreases in case of the circumstances) by Y%". They are more of confirmation (not useless research findings) than novel discoveries.
Then, there are applied studies that use natural experiments, RCTs and others using causality econometric tools like the DiD, RDD and their other siblings. These are certainly more focused and nuanced, and definitely, when framing a policy, these are the evidence on your face. I am not interested in highlighting the flaws in these methods. It is just that, these causal methods came into place, addressing the issue of implications being too general in the so-called "orthodox" methods. However, these again are too specific that the policy maker has to worry about "what if something else changes", and the literature attempting to convince the reader that "we did actually control for everything else" goes on overburdening the researchers themselves. It is more like claiming to do something that can be done, but there are numerous attempts to make it happen, and everyone wishes to have a piece of the pie in this flowing river. There is literally so much evidence of causality that there are separate journals dedicated to authors reviewing the results of the particular strand.
As I see both the not-so-rigid distinctions in methodologies, I see several differences. First, another write-up would suffice about the concentration of the usage of these methods around the world. At least in India, it is unequal. It is not that a student in a rural state university will not or doesn't want to understand the simplicities of complexities of the causal methods and get the paper published in a good place, it is more that s/he doesn't know about it. Lemme give you a task: Where are the people publishing causal papers in India? Where are they from? How mobile are their carriers after even casually publishing a causal paper? With no offence, they have rightfully earned it. However, this inequality in the choice of method in higher education research (on or apart from education or higher education subjects) is immense. Second, after the emergence of the causal methods, orthodox relevant analyses have lost their relevance in the top or at least good-quality journals and thus lost the limelight. The findings of the orthodox models have become more intuitive and have lost their relevance in the elite academic circle.
A major criticism both strata of methods miss is the role of aspirations. I have seen professors hurrying to dive into the data-crunching part or rub their heads over the theoretical specifications of the same using mathematical formulations. All that is great, mostly given that they are the tools (established tools) for establishing logic in academia. However, the role of aspirations is a variable that can nullify the role of all other variables; this statement is based on empirical personal experience, so it lacks all sorts of statistical property confirmations but doesn't need any of that.
Consider this question,
"How do you account for the fact that a kid who never had a good house over his head didn't actually aspire for the higher education he would have had there been one over his head?"
The orthodox method might say "Okay.. those with pucca houses are by X% more likely to enroll in higher education than those without". The causal guy might say, "Oh come on, how do you factor-in her/is gender there? My findings confirm that while controlling for gender and all other factors that can impact her/is higher education attainment, people with pucca houses are Y times more likely to enroll than those with people without it. I can even show similar disaggregated results across gender groups. Huh..."
Me: "Well, any of that didn't quite answer my question. Did it?"
Being from a rural household, lemme keep this straight, I have never come across any model or finding or specification that could actually mildly satisfactorily explain why and how, instead and irrespective of everything, I am doing a PhD while others are not. And obviously, I am not expecting you to say, "Of course, it is obvious your aspiration was high." In academia, we need a lot more than just a tautology.
Obviously, don't go and search for a bunch of keywords on GS and mail me papers on the role of aspirations; I have read much of that. I might be writing one as well. But honestly, to no avail. Increasingly, the definition of "higher education" will change in the AI age. Many are already saying that it's not the years of schooling that matters (though the name of your school goes a long way.. haha..); higher quality and competence matter. I leave the debate for you to ruin your nice family time over dinner, but what is more important is as the age is arriving without higher education, how big will be the gap between humans? what will the repercussions of those gaps be? Nobody knows anything other than "Huge, Humongous, Gigantic" and much
more.
Jokes apart, it is important for the governments to invest in the role of aspirations among the kids to learn and enskill more. It is imperative for the researchers to ACTUALLY find out ways to visibly have logical explanations around aspirational ineffectiveness in the country and means to up aspirations towards prospects of education. You can scold me about discussing all the ineffectiveness of methods but not suggesting any; well, I will discuss if I come across any.
Till then.. Happy Reading..
Comments
Post a Comment