My friend, former tennis partner and former Yale MPHer Shaan Chatturvedi has just started blogging about his experiences in Guyana, where he is currently a working for the CDC Global AIDS Program. His most recent post, on the swine flu outbreak, is fantastic and promises of good things to come from his blog. Do check it out!
For more on the swine flu, check out this interesting article by Dr. Carlos del Rio, the chair of the Global Health Department of the Emory School of Public Health. There is a lot of interesting stuff in there about different control measures and the reasons why swine flu mortality might be higher in Mexico than in the US.
Wednesday, April 29, 2009
Tuesday, April 21, 2009
Long-Run and Intergenerational Effects of Early Childhood Environments
Great NBER working paper this week on the long-run and next generation returns to early life conditions. Specifically, Eric Gould and co-authors look at consequences driven by the airlift of Yemenite immigrants into Israel. In their own words:
This paper estimates the effect of the childhood environment on a large array of social and economic outcomes lasting almost 60 years, for both the affected cohorts and for their children. To do this, we exploit a natural experiment provided by the 1949 Magic Carpet operation, where over 50,000 Yemenite immigrants were airlifted to Israel. The Yemenites, who lacked any formal schooling or knowledge of a western-style culture or bureaucracy, believed that they were being "redeemed," and put their trust in the Israeli authorities to make decisions about where they should go and what they should do. As a result, they were scattered across the country in essentially a random fashion, and as we show, the environmental conditions faced by immigrant children were not correlated with other factors that affected the long-term outcomes of individuals. We construct three summary measures of the childhood environment: 1) whether the home had running water, sanitation and electricity; 2) whether the locality of residence was in an urban environment with a good economic infrastructure; and 3) whether the locality of residence was a Yemenite enclave. We find that children who were placed in a good environment (a home with good sanitary conditions, in a city, and outside of an ethnic enclave) were more likely to achieve positive long-term outcomes. They were more likely to obtain higher education, marry at an older age, have fewer children, work at age 55, be more assimilated into Israeli society, be less religious, and have more worldly tastes in music and food. These effects are much more pronounced for women than for men. We find weaker and somewhat mixed effects on health outcomes, and no effect on political views. We do find an effect on the next generation – children who lived in a better environment grew up to have children who achieved higher educational attainment.
I find this paper noteworthy for several reasons:
(1) The authors have a credible and interesting source of variation, and the actual early life exposures they look at have immediate policy implications
(2) The authors explore a wide variety of different outcomes, including behavioral aspects. In my dissertation, I argued that long-run returns need to be taken into account when making resource allocation decisions. However, this is difficult if only a subset of long-run returns are known. This paper really hits this gap.
(3) Finally, and more self-serving, the long-run effects of sanitation and clean water jive well with my thesis paper on the National Clean Water Program in Mexico (see the next post).
This paper estimates the effect of the childhood environment on a large array of social and economic outcomes lasting almost 60 years, for both the affected cohorts and for their children. To do this, we exploit a natural experiment provided by the 1949 Magic Carpet operation, where over 50,000 Yemenite immigrants were airlifted to Israel. The Yemenites, who lacked any formal schooling or knowledge of a western-style culture or bureaucracy, believed that they were being "redeemed," and put their trust in the Israeli authorities to make decisions about where they should go and what they should do. As a result, they were scattered across the country in essentially a random fashion, and as we show, the environmental conditions faced by immigrant children were not correlated with other factors that affected the long-term outcomes of individuals. We construct three summary measures of the childhood environment: 1) whether the home had running water, sanitation and electricity; 2) whether the locality of residence was in an urban environment with a good economic infrastructure; and 3) whether the locality of residence was a Yemenite enclave. We find that children who were placed in a good environment (a home with good sanitary conditions, in a city, and outside of an ethnic enclave) were more likely to achieve positive long-term outcomes. They were more likely to obtain higher education, marry at an older age, have fewer children, work at age 55, be more assimilated into Israeli society, be less religious, and have more worldly tastes in music and food. These effects are much more pronounced for women than for men. We find weaker and somewhat mixed effects on health outcomes, and no effect on political views. We do find an effect on the next generation – children who lived in a better environment grew up to have children who achieved higher educational attainment.
I find this paper noteworthy for several reasons:
(1) The authors have a credible and interesting source of variation, and the actual early life exposures they look at have immediate policy implications
(2) The authors explore a wide variety of different outcomes, including behavioral aspects. In my dissertation, I argued that long-run returns need to be taken into account when making resource allocation decisions. However, this is difficult if only a subset of long-run returns are known. This paper really hits this gap.
(3) Finally, and more self-serving, the long-run effects of sanitation and clean water jive well with my thesis paper on the National Clean Water Program in Mexico (see the next post).
Friday, April 3, 2009
Correlation Does Not Imply Causation. And...?
Since I started grad school four years ago, I've noticed that the general public is much more attuned to idea of correlation not always implying causation. Of course, the indoctrination is not complete just yet, and there are plenty of instances where an association is mistaken for something more, but the fact that people are becoming better consumers of statistics is gratifying. I attribute this to the spate of popular press economics and statistics books/blogs in the last few years (though I might be in danger of confusing correlation and causation myself by saying this!)
The standards in empirical research reflect how seriously people are taking this motto: finding a clever instrumental variable or even experimental variation is no longer good enough. Papers without extensive "robustness" checks and falsification tests have less credibility now than they would have even five years ago. This, like the trend in the general public, is a good development.
However, with these positives come some more troubling tendencies. Specifically, I have a beef with the overuse of the causation-correlation dictum. Now, anybody can bring down a paper simply by saying "correlation does not imply causation" without having to provide a reason why this might be the case. For example, I am working on a paper looking at the long-run causal effects of birth year exposure to a clean water and sanitation efforts (I'll post a link to this paper in a month or so when a good draft is ready). I have a plausible identification strategy, and also include all sorts of controls, trends and falsification checks in my analysis to further establish causality. My results check out.
However, someone recently remarked told me that I should be concerned about omitted variables. When I pressed her on what these might be, she wasn't sure but commented that "there are always omitted factors."
Clearly, this isn't helpful. It's really easy to look/sound clever and point out that correlation does not imply causation: it is technically a true statement! But I think people who make this claim should talk about how it applies to the analysis at hand (i.e., have some kind of model or story that makes more explicit the nature of the potential biases and where they come from). Otherwise, the statement by itself is pretty uninformative and does little to advance our knowledge.
The standards in empirical research reflect how seriously people are taking this motto: finding a clever instrumental variable or even experimental variation is no longer good enough. Papers without extensive "robustness" checks and falsification tests have less credibility now than they would have even five years ago. This, like the trend in the general public, is a good development.
However, with these positives come some more troubling tendencies. Specifically, I have a beef with the overuse of the causation-correlation dictum. Now, anybody can bring down a paper simply by saying "correlation does not imply causation" without having to provide a reason why this might be the case. For example, I am working on a paper looking at the long-run causal effects of birth year exposure to a clean water and sanitation efforts (I'll post a link to this paper in a month or so when a good draft is ready). I have a plausible identification strategy, and also include all sorts of controls, trends and falsification checks in my analysis to further establish causality. My results check out.
However, someone recently remarked told me that I should be concerned about omitted variables. When I pressed her on what these might be, she wasn't sure but commented that "there are always omitted factors."
Clearly, this isn't helpful. It's really easy to look/sound clever and point out that correlation does not imply causation: it is technically a true statement! But I think people who make this claim should talk about how it applies to the analysis at hand (i.e., have some kind of model or story that makes more explicit the nature of the potential biases and where they come from). Otherwise, the statement by itself is pretty uninformative and does little to advance our knowledge.
Subscribe to:
Posts (Atom)