Thank you for the comment, I learned a lot from it. Would appreciate to hear what you think about my responses.
I think the first point about consumption smoothing is critical. My reading of the literature is almost the opposite—that although the poor find ingenious ways to save, their ability to smooth consumption is very limited. I wonder why that is. Maybe it’s because Portfolios of the Poor focuses on Bangladesh, India and South Africa which are much more developer financially than some other countries. But we will need to think about this some more.
The point about debt is very important and well taken. We will need to consider not just income but wealth more generally. I’ve corrected the question.
Regarding idiosyncratic shocks, our thought was that family level income/consumption/wealth data allows us to measure the frequency of idiosyncratic shocks. In addition, an Organization like GiveDirectly must in any case have direct communication with potential recipients to be able to transfer the money, which implies family illness may be observed.
Regarding systematic shocks, out thought was that it is easier for the donor to convert cash to goods than for the affected people. So if what they need in a drought is grain and not cash, the donor could accommodate this quite easily.
Hi Niki, glad to hear it helped. Here’s some more thoughts. Can’t promise they’re any good.
Yes, I agree the consumption smoothing point is critical. I could have worded my answer a bit better. What I meant to say is that rural households are good at trying to smooth consumption given their situation. That can still be a low overall ability given how sporadic income can be. The crux, I suppose, is whether we trust the households to smooth their own consumption or if we should make the decision for them. If we think the households are better able to make the decision, we send the transfer now. If we think they’re worse (perhaps for lack of willpower reasons), then we delay.
From the dataset I linked you, it’s tough to decompose the idiosyncratic shock from just family-level income / consumption / wealth data. You’d want to correct for village-level shocks and seasonality and so on. I believe it’s do-able but noisy. And it’s usually cleaner to simply ask the families whether they suffered from X adverse event in the past 12 months (which the India Human Development Survey does ask).
To do this more cleanly, you’d want a dataset that interviews each family multiple times in a single year. Then you could see the variability in income / consumption / wealth over time per family.
I’m not familiar with how GiveDirectly does their operations, but I’d be surprised if the communication was frequent enough that GiveDirectly staff could observe and react to every family illness in real-time.
On systemic shocks, you might be right. I was envisioning a bad natural disaster where the logistics of shipping more food out to villages is temporarily clogged up. But perhaps NGOs are more crafty that I think.
Thank you for the comment, I learned a lot from it. Would appreciate to hear what you think about my responses.
I think the first point about consumption smoothing is critical. My reading of the literature is almost the opposite—that although the poor find ingenious ways to save, their ability to smooth consumption is very limited. I wonder why that is. Maybe it’s because Portfolios of the Poor focuses on Bangladesh, India and South Africa which are much more developer financially than some other countries. But we will need to think about this some more.
The point about debt is very important and well taken. We will need to consider not just income but wealth more generally. I’ve corrected the question.
Regarding idiosyncratic shocks, our thought was that family level income/consumption/wealth data allows us to measure the frequency of idiosyncratic shocks. In addition, an Organization like GiveDirectly must in any case have direct communication with potential recipients to be able to transfer the money, which implies family illness may be observed.
Regarding systematic shocks, out thought was that it is easier for the donor to convert cash to goods than for the affected people. So if what they need in a drought is grain and not cash, the donor could accommodate this quite easily.
Hi Niki, glad to hear it helped. Here’s some more thoughts. Can’t promise they’re any good.
Yes, I agree the consumption smoothing point is critical. I could have worded my answer a bit better. What I meant to say is that rural households are good at trying to smooth consumption given their situation. That can still be a low overall ability given how sporadic income can be. The crux, I suppose, is whether we trust the households to smooth their own consumption or if we should make the decision for them. If we think the households are better able to make the decision, we send the transfer now. If we think they’re worse (perhaps for lack of willpower reasons), then we delay.
From the dataset I linked you, it’s tough to decompose the idiosyncratic shock from just family-level income / consumption / wealth data. You’d want to correct for village-level shocks and seasonality and so on. I believe it’s do-able but noisy. And it’s usually cleaner to simply ask the families whether they suffered from X adverse event in the past 12 months (which the India Human Development Survey does ask).
To do this more cleanly, you’d want a dataset that interviews each family multiple times in a single year. Then you could see the variability in income / consumption / wealth over time per family.
I’m not familiar with how GiveDirectly does their operations, but I’d be surprised if the communication was frequent enough that GiveDirectly staff could observe and react to every family illness in real-time.
On systemic shocks, you might be right. I was envisioning a bad natural disaster where the logistics of shipping more food out to villages is temporarily clogged up. But perhaps NGOs are more crafty that I think.