Thanks, this is relevant for researchers and people funding research and prioritizing/evaluating it. This includes Unjournal.org; we are looking to prioritize the evaluation of research relevant to animal welfare, and we have built/are building a ‘field specialist’ team focusing on this.
Some expansion on the theory of change/paths to impact/logic model for some of the leading cases could be particularly helpful. (You mention we should reach out to Martin Gould on this—I plan to do so.)
While some of these might be amenable to simpler ‘desk research’ (literature reviews, simple BOTECs, coalescing information), I think many of the more interesting ones could require some heavier lifting in terms of research depth and methodological expertise. I’m not saying the ’80% of the 80⁄20 research’ would not necessarily have value here, but it is my sense is that more in=depth rigorous work may be warranted, and potentially worth funding.
For example:
By how many years do animal welfare corporate commitmets speed up reforms that might eventually happen anyway due to factors like government policy, individual consumer choices, or broad moral change?
This is a challenging causal inference question; while case studies and ‘plausible inference’ from qualitative approaches could have value. It could also potentially be addressed quantitatively with methods like difference-in-differences, synthetic panels, and natural experiments. This might also require some expertise in dynamic/time-series models. It seems high-value, given that funding corporate campaigns have been claimed to be among the highest-impact animal welfare opportunities.
What percentage of people will be veg*n in 20, 50, or 100 years?
I guess the ToC here is that this informs the priority one should give to interventions to improve farmed-animal welfare conditions … because ’if most people are vegan anyways, it matters less. Or perhaps this is about the cost side of increasing global prosperity (meat-eaters dilemma) or supporting pro vs anti-natal policies?
I suspect predicting this out 100 years will be extremely difficult, in the sense that quantitative models and expertise might have little value. But for 10 or 20 years out, I think social science modeling expertise could be meaningful.
But some of the other questions seem more straightforwardly amenable to quantitative social science work, including careful and feasible RCTs, and sometimes non-RCT causal inference methods, and Bayesian statistical inference and presentation. E.g.,
How impactful would it be to get already sympathetic celebrities to speak up more on animal welfare?
Looking at simple before/after comparisons, or aggregating casual media reporting on this could be misleading. An RCT here seems feasible.
...What is the impact on sales of labeling laws that restrict the terms that can be used to describe/sell PBMAs and other plant-based products?
This seems like something mainstream academic/professional economists might consider or already be considering, perhaps in conjunction with legislative testimony or court cases. It seems amenable to formal empirical analysis, perhaps in the ‘empirical industrial organization/quantitative marketing’ literatures. But it is by no means simple… to estimate impact on quantities sold, considering competitive price responses, responses of substitute products, time trends and seasonality, etc.
Thanks, this is relevant for researchers and people funding research and prioritizing/evaluating it. This includes Unjournal.org; we are looking to prioritize the evaluation of research relevant to animal welfare, and we have built/are building a ‘field specialist’ team focusing on this.
Some expansion on the theory of change/paths to impact/logic model for some of the leading cases could be particularly helpful. (You mention we should reach out to Martin Gould on this—I plan to do so.)
While some of these might be amenable to simpler ‘desk research’ (literature reviews, simple BOTECs, coalescing information), I think many of the more interesting ones could require some heavier lifting in terms of research depth and methodological expertise. I’m not saying the ’80% of the 80⁄20 research’ would not necessarily have value here, but it is my sense is that more in=depth rigorous work may be warranted, and potentially worth funding.
For example:
This is a challenging causal inference question; while case studies and ‘plausible inference’ from qualitative approaches could have value. It could also potentially be addressed quantitatively with methods like difference-in-differences, synthetic panels, and natural experiments. This might also require some expertise in dynamic/time-series models. It seems high-value, given that funding corporate campaigns have been claimed to be among the highest-impact animal welfare opportunities.
I guess the ToC here is that this informs the priority one should give to interventions to improve farmed-animal welfare conditions … because ’if most people are vegan anyways, it matters less. Or perhaps this is about the cost side of increasing global prosperity (meat-eaters dilemma) or supporting pro vs anti-natal policies?
I suspect predicting this out 100 years will be extremely difficult, in the sense that quantitative models and expertise might have little value. But for 10 or 20 years out, I think social science modeling expertise could be meaningful.
But some of the other questions seem more straightforwardly amenable to quantitative social science work, including careful and feasible RCTs, and sometimes non-RCT causal inference methods, and Bayesian statistical inference and presentation. E.g.,
Looking at simple before/after comparisons, or aggregating casual media reporting on this could be misleading. An RCT here seems feasible.
This seems like something mainstream academic/professional economists might consider or already be considering, perhaps in conjunction with legislative testimony or court cases. It seems amenable to formal empirical analysis, perhaps in the ‘empirical industrial organization/quantitative marketing’ literatures. But it is by no means simple… to estimate impact on quantities sold, considering competitive price responses, responses of substitute products, time trends and seasonality, etc.