Thanks for your response! I still have some confusion, but this is somewhat tangentially related. In your CBA, you use an NPV figure of $3752bn as the output gain from growth. This is apparently derived from India’s 1993 and 2002 growth episodes.
The CBA calculation calculates the EV of the GDP increase therefore as 0.5*0.1*3572 = $178.56 bn. You acknowledge elsewhere in your writeup that efforts to increase GDP entail some risk of harm (and likewise with the randomista approach) so my confusion lies with the elision of this possible harm from the EV calculation.
Even if the probability that a think tank induces a growth episode—e.g. the probability that a think tank influences economic policy in country X according to its own recommendations—is 10%, then there is still obviously a probability distribution over the possible influence that successfully implemented think tank recommendations would have. This should include possible harms and their attendant likelihoods, right?
I recognize that the $3,572bn figure comes directly from Pritchett as part of an assessment of the Indian experience, but it’s not obvious to me that the number encapsulates the range of possibilities for a successful (in the sense of being implemented) intervention. I may be missing something, but it seems to me that a (perhaps only slightly) more rigorous CBA would have to itself include an expected value of success that incorporates possible benefits and harms for both Growth and Randomista approaches in the line of your spreadsheet model reading “NPV (@ 5%) of output loss from growth deceleration relative to counter-factual growth.”
I understand that what you’re envisioning is a sort of high-confidence approach to growth advocacy: target only countries where improvements are mostly obvious, and then only with the most robustly accepted recommendations. I still think there is a risk of harm and that the CBA may not capture a meaningful qualitative difference between the growth and randomista approaches. In principle, at least, the use of localized, small-scale RCTs to test development programs before they are deployed avoids large-scale harm and (in my view) pushes the mass of the distribution of possible outcomes largely above 0. No such obstacle to large harms exists, or indeed is even possible, in the case of growth recommendations. Pro-growth recommendations by economists have not been uniformly productive in the past and (I think) are unlikely to be so in the future.
I still favor this approach you suggest but, given the state of the field of growth economics—and the failure of GDP/capita to capture many welfare-relevant variables that you cite at the end of the writeup—I’d be keen to see more highly quantified conversation around possible harms.
Thanks for your response! I still have some confusion, but this is somewhat tangentially related. In your CBA, you use an NPV figure of $3752bn as the output gain from growth. This is apparently derived from India’s 1993 and 2002 growth episodes.
The CBA calculation calculates the EV of the GDP increase therefore as 0.5*0.1*3572 = $178.56 bn. You acknowledge elsewhere in your writeup that efforts to increase GDP entail some risk of harm (and likewise with the randomista approach) so my confusion lies with the elision of this possible harm from the EV calculation.
Even if the probability that a think tank induces a growth episode—e.g. the probability that a think tank influences economic policy in country X according to its own recommendations—is 10%, then there is still obviously a probability distribution over the possible influence that successfully implemented think tank recommendations would have. This should include possible harms and their attendant likelihoods, right?
I recognize that the $3,572bn figure comes directly from Pritchett as part of an assessment of the Indian experience, but it’s not obvious to me that the number encapsulates the range of possibilities for a successful (in the sense of being implemented) intervention. I may be missing something, but it seems to me that a (perhaps only slightly) more rigorous CBA would have to itself include an expected value of success that incorporates possible benefits and harms for both Growth and Randomista approaches in the line of your spreadsheet model reading “NPV (@ 5%) of output loss from growth deceleration relative to counter-factual growth.”
I understand that what you’re envisioning is a sort of high-confidence approach to growth advocacy: target only countries where improvements are mostly obvious, and then only with the most robustly accepted recommendations. I still think there is a risk of harm and that the CBA may not capture a meaningful qualitative difference between the growth and randomista approaches. In principle, at least, the use of localized, small-scale RCTs to test development programs before they are deployed avoids large-scale harm and (in my view) pushes the mass of the distribution of possible outcomes largely above 0. No such obstacle to large harms exists, or indeed is even possible, in the case of growth recommendations. Pro-growth recommendations by economists have not been uniformly productive in the past and (I think) are unlikely to be so in the future.
I still favor this approach you suggest but, given the state of the field of growth economics—and the failure of GDP/capita to capture many welfare-relevant variables that you cite at the end of the writeup—I’d be keen to see more highly quantified conversation around possible harms.