I’m an economics PhD student. I’ve spent some time thinking about a) how to do good and b) how to succeed in academia.
The holy grail is a project that does good and results in a paper publishable in a peer reviewed journal. But I have struggled with this and I’ve noticed a few other EAs in early stages of academia doing so too.
I’ve come to think the reason is that we are problem-solvers. We want to change people’s lives for the better. When we try to think of a research project, we think about the problems with the world, and how to fix them.
What makes a good academic research paper is fundamentally different. I am far from being an experienced academic, but I understand that research is about pushing forward the frontier of knowledge and understanding. Examples are finding a better way to explain observed phenomena, measuring something previously unmeasured, documenting a previously undocumented connection or relationship, inventing a new technique or using an old technique in a new way, or showing that supposedly separate things are just different examples of a general case.
It’s tempting to say “research is solving the problem of not understanding” or “problem solving is increasing your understanding of how to solve the problem”. But that shoe-horning only de-emphasises their considerable differences. I would rather think of them as two different stages of a process. Research creates the knowledge, and problem-solving applies the knowledge.
A trap for EAs in early-stage academia is to do work that makes the world better, without fully realising its academic potential. For some projects, a small adjustment could unlock huge academic value.
For example, think of trying to answer the donor or policy question “How much money should I allocate to x vs. y?”. I believe that is usually a problem-solving question, even if no-one has tried to answer it before. A more academic question may be “Under what conditions is [general case of x]’s impact potential dominated by [general case of y]’s impact potential?” Then, after making your academic contribution, you can also throw in your x vs. y calculation, as an example, or policy implication.