I've devoted a non-trivial chunk of this space to talking about how events and shocks that influence the nutritional intake and disease status in utero and during the early childhood years may play a large role in determining the health, education, earnings, and general well-being later in life. Research on the early life origins of disease and poverty has become popular in economics, as evidenced by the number of studies on this topic presented at the ASHE conference.
While all of these papers were interesting, four stood out:
1) Marten Lindeboom and colleagues looked at the impact of the 1846-47 Dutch Potato Famine on subsequent mortality risk. Their research strategy was to use the discontinuous timing of the famine, to compare cohorts in utero during the famine to those born just before and just after this period. They find strong links between famine exposure and mortality risk for men aged 50 and above, but not for women. These results are consistent with biological theories that posit that males are more vulnerable to shocks early in life than females.
2) Martin Salm, my discussant at the conference, presented some interesting work on the extent to which health contributes to the link between socioeconomic status across generations. Martin and his coauthor found that 20-30% of the correlation between parental education and child cognitive skills can be explained by childhood health conditions.
3) Tania Barham examined the effect of health interventions in Bangladesh, in particular the staggered introduction of child vaccination programs in the 1980s, on cognitive development. Using this variation, along with mother fixed effects, she finds strong links between early life exposure to these public health programs and the child's mini-mental state exam scores later in life.
4) Daniel Rees and Joseph Sabia studied the causal effect of breastfeeding on academic achievement. To get at causal inference, they utilize the variation in breastfeeding across the children of a given mother, with the assumption being that, conditional on the mother and a rich set of control for other child-specific prenatal and postnatal behaviors, this variation is essentially exogenous with respect to outcomes of interest. They find strong effects of breastfeeding on later outcomes.
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Behavioral economics, also an oft discussed subject in this blog, was also on display at the ASHE conference. Jody Sindelar, who happens to be my dissertation supervisor, gave a fantastic talk on the importance of behavioral economics to health economics. Mainstream microeconomics rests on the neoclassical model, where individuals rationally calculate all (discounted) costs and benefits before making a decision. The feeling among many economists is that the workhorse neoclassical models do pretty well in explaining most phenomena, and that behavioral economics is a fun curiousity with limited policy interest.
Jody's talk served as a reminder that, especially in health care, behavioral economics may offer important policy relevant insights. Of the series of interesting examples she presented, my favorite was from international organ donation rates: countries with presumed consent legislation (i.e., you have to opt-out to get out of donating your organs) have higher rates of donation that those with opt-in laws (i.e., you have to give someone permission to take your organs). Neoclassical economics doesn't quite explain this; behavioral economics does. The policy significance of some extra organs for extremely thin organ markets is quite large.
Brian Elbel, a graduate from my program at Yale and now a professor at NYU, gave an interesting talk on his experimental work studying the behavioral economics of choosing a Medicare managed care plan. Briefly, Brian found that consumers tend make suboptimal choices regarding their health care plans, or defer these choices altogether, when presented with a larger number of options (which is antithetical to neoclassical theories predicting increased efficiency with greater choice) and when bad options are presented along with good ones. His experimental evidence, along with his econometric evidence from another paper, suggest the power of behavioral economics in explaining choices related to a very important government program.
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The final plenary session at the conference was given by Gary Becker, a Nobel laureate who revolutionized economics by extending the field to the analysis of phenomena like childbearing, child investments, polygamy, crime, discrimination, etc. (He's also a fine blogger: check out the Becker-Posner blog in the links sidebar). Becker's talk was based on his latest research, looking at the total returns to education. The basic idea was that economists have focused greatly on the wage returns to education. However, the value of education likely goes well beyond wages: a slew of studies have shown strong correlations between an individual's education and his/her health, happiness, the probability of marriage and the "quality" of spouse, and the educational attainment and health of his/her children.
Becker's talk focused on an interesting puzzle: the recent boom in college education. More people in the U.S., especially women, are attending and completing college as opposed to even 15-20 years ago. Becker suggested that such a boom is likely driven by changes in the returns to education. As such, Becker investigated where the growth in the returns to college might be coming from. My favorite slide in the presentation showed the marriage probabilities of women as a function of high school, college, and post-graduate education. In the 1960s, women with graduate degrees were much less likely to get married their those women who finished high school or college. Today, the relationship between marriage probabilities and the woman's level of education is monotone increasing across all levels of education!
Another interesting puzzle from the Becker talk: if college education has such high total returns, why don't more people take the meal ticket? Becker commented that this is a question he is currently struggling with and that perhaps those who end up finding an answer to this would themselves win a Nobel prize. In a room with nearly a thousand health economists, I saw at least half instinctively lurch for their pens and notebooks as soon as he said this!
Saturday, June 28, 2008
Thursday, June 26, 2008
ASHE Conference Recap - Part I
I just got back from the 2008 ASHE conference (see previous post) at Duke University. I had an absolutely stellar time. In fact, I'd say I got a lot more out of this conference than from those in the past. I think this has a lot to do with the fact that:
1) Having been to all those previous conferences, I actually knew people at this one. Not only is it fun to catch up with old friends, but having old friends makes it easier to make new ones.
2) I have a better sense of what I am interested in, so I can self-select into sessions that will better satisfy my curiousity.
3) The average presentation at ASHE was high quality and the conference was well-attended by the field's superstars.
4) My presentation went really well and was well received.
5) The conference was at Duke, my alma mater. I ended up taking my first economics class, taught by Allen Kelley, on a lark, which, along with two other Allen Kelley classes, planted the seeds for my decision to take up a PhD a few years later.
---------------------
Some broad themes from the conference. The sessions started with a keynote address by Mark McClellan, an MD/PhD economist with strong academic and policy credentials (he was in charge of the Centers for Medicare and Medicaid Services and the FDA), along with a now (in)famous brother. The basic theme of the talk was how health economics can help with policymaking, especially with an impending new administration. While it was exciting to hear that policy-makers are begging for policy-relevant evidence from health economists, it was depressing to hear that, despite nearly forty years of health economics, officials at the CBO and OMB are still forced to write sentences like "Evidence on the effectiveness of this intervention is inconsistent and limited" when trying to score new public health and health care policies.
Indeed, I saw this with my own eyes when contemplating the sum evidence from the numerous sessions on obesity. The papers therein covered a wide range of topics: the effects of food and drink prices, fast foods, law changes, and education on obesity; the impact of obesity on academic achievement and labor market earnings; and the economic costs of obesity. Each of these topics requires some heavy statistical lifting to get around the obvious causal inference issues. All of the conference papers came armed with the latest techniques. However, the sum knowledge from the slew of studies, in terms of policy relevance, appears to be limited. For example, the papers collectively suggested that food prices, fast foods, taxes and law changes appear to have only modest effects on obesity prevalence. As such policies centered around these factors, such as the popular "fast food tax" may lack heft as useful public health instruments.
--------------------
Since, I mentioned methods, I should point out that it was heartening to see such a high level of scientific rigor at these meetings. There was a healthy obsession with causal inference, and nearly all of the papers used fixed effects or instrumental variables. For those who are not into the jargon, fixed effects is best explained by an example: imagine you have data following the same individuals over time. You are interested in learning the effect of some X on Y, but are worried about bias from some omitted variable U, a characteristic about the individual. Fixed effects allow you to essentially subtract of the individual level mean from X and Y. If U doesn't vary across individuals, fixed effects basically washes this confounder out of the estimation procedure. Now you can get at the causal effect X on Y.
Sibling or twin studies are a good example of fixed effects. Let's say you want to learn the causal effects of birthweight on later wages. The worry is that birthweight is correlated with genetic factors or socioeconomic status early in life, which is correlated with outcomes later in adulthood. Using twin fixed effects, you can control for (or difference out) for genes and shared early life socieconomic status.
Of course, if the omitted variables that you worry about change over time for an individual or vary across a set of siblings, fixed effects estimates will not recover causal effects. To get around this researchers often use instrumental variable (IV) methods (either alone or in conjunction with fixed effects), where a factor that affects the regressor of interest, but not the outcome directly or indirectly through some other factor, is used to identify causal impacts. A famous example is the use of the quarter of birth or school cutoff dates to predict attained schooling (individuals born later and near the cutoff date are younger when they finish schooling or a full grade behind when they drop out).
The validity and theoretical basis for instruments are hotly debated, and researchers now must meet a pretty heavy burden of proof to get people interested and convinced. Also, it is now well-known that instruments must predict the regressor of interest strongly enough so as to yield valid estimates: "weak" instruments lead to biases of their own. It was nice to see that researchers at the conference went into great detail when defending their assumptions underlying the use of fixed effects and instruments. It was also nice to see the discussants call these assumptions into question and pointing out the additional implicit assumptions that needed to be made to achieve validity. This sort of discussion forces people to write down an explicit model and all the required identifying assumptions.
At some point though, I couldn't help but feel like there was some overkill and overpolicing, especially with the weak instruments issue (one researcher with a good IV strategy commented that she did not utilize the approach because her instruments were slightly weaker than what is considered kosher). Some new research on IV points suggests the use of estimation techniques that are robust to weak instruments: I was surprised that more conference-goers had not adopted these (indeed, mine was the only paper I saw that used these techniques). Second, some recent research suggests ways to proceed with instruments that might be "slightly" correlated with unobserved factors that influence the outcome and to test the sensitivity of IV results to varying degrees of instrument "badness." I would have liked to have seen a higher rate of adoption of these new techniques.
---------------------------------
In the next installment, I'll speak more about my ASHE experiences by summarizing some research on the early life origins of disease, behavioral economics, and the many benefits of education.
1) Having been to all those previous conferences, I actually knew people at this one. Not only is it fun to catch up with old friends, but having old friends makes it easier to make new ones.
2) I have a better sense of what I am interested in, so I can self-select into sessions that will better satisfy my curiousity.
3) The average presentation at ASHE was high quality and the conference was well-attended by the field's superstars.
4) My presentation went really well and was well received.
5) The conference was at Duke, my alma mater. I ended up taking my first economics class, taught by Allen Kelley, on a lark, which, along with two other Allen Kelley classes, planted the seeds for my decision to take up a PhD a few years later.
---------------------
Some broad themes from the conference. The sessions started with a keynote address by Mark McClellan, an MD/PhD economist with strong academic and policy credentials (he was in charge of the Centers for Medicare and Medicaid Services and the FDA), along with a now (in)famous brother. The basic theme of the talk was how health economics can help with policymaking, especially with an impending new administration. While it was exciting to hear that policy-makers are begging for policy-relevant evidence from health economists, it was depressing to hear that, despite nearly forty years of health economics, officials at the CBO and OMB are still forced to write sentences like "Evidence on the effectiveness of this intervention is inconsistent and limited" when trying to score new public health and health care policies.
Indeed, I saw this with my own eyes when contemplating the sum evidence from the numerous sessions on obesity. The papers therein covered a wide range of topics: the effects of food and drink prices, fast foods, law changes, and education on obesity; the impact of obesity on academic achievement and labor market earnings; and the economic costs of obesity. Each of these topics requires some heavy statistical lifting to get around the obvious causal inference issues. All of the conference papers came armed with the latest techniques. However, the sum knowledge from the slew of studies, in terms of policy relevance, appears to be limited. For example, the papers collectively suggested that food prices, fast foods, taxes and law changes appear to have only modest effects on obesity prevalence. As such policies centered around these factors, such as the popular "fast food tax" may lack heft as useful public health instruments.
--------------------
Since, I mentioned methods, I should point out that it was heartening to see such a high level of scientific rigor at these meetings. There was a healthy obsession with causal inference, and nearly all of the papers used fixed effects or instrumental variables. For those who are not into the jargon, fixed effects is best explained by an example: imagine you have data following the same individuals over time. You are interested in learning the effect of some X on Y, but are worried about bias from some omitted variable U, a characteristic about the individual. Fixed effects allow you to essentially subtract of the individual level mean from X and Y. If U doesn't vary across individuals, fixed effects basically washes this confounder out of the estimation procedure. Now you can get at the causal effect X on Y.
Sibling or twin studies are a good example of fixed effects. Let's say you want to learn the causal effects of birthweight on later wages. The worry is that birthweight is correlated with genetic factors or socioeconomic status early in life, which is correlated with outcomes later in adulthood. Using twin fixed effects, you can control for (or difference out) for genes and shared early life socieconomic status.
Of course, if the omitted variables that you worry about change over time for an individual or vary across a set of siblings, fixed effects estimates will not recover causal effects. To get around this researchers often use instrumental variable (IV) methods (either alone or in conjunction with fixed effects), where a factor that affects the regressor of interest, but not the outcome directly or indirectly through some other factor, is used to identify causal impacts. A famous example is the use of the quarter of birth or school cutoff dates to predict attained schooling (individuals born later and near the cutoff date are younger when they finish schooling or a full grade behind when they drop out).
The validity and theoretical basis for instruments are hotly debated, and researchers now must meet a pretty heavy burden of proof to get people interested and convinced. Also, it is now well-known that instruments must predict the regressor of interest strongly enough so as to yield valid estimates: "weak" instruments lead to biases of their own. It was nice to see that researchers at the conference went into great detail when defending their assumptions underlying the use of fixed effects and instruments. It was also nice to see the discussants call these assumptions into question and pointing out the additional implicit assumptions that needed to be made to achieve validity. This sort of discussion forces people to write down an explicit model and all the required identifying assumptions.
At some point though, I couldn't help but feel like there was some overkill and overpolicing, especially with the weak instruments issue (one researcher with a good IV strategy commented that she did not utilize the approach because her instruments were slightly weaker than what is considered kosher). Some new research on IV points suggests the use of estimation techniques that are robust to weak instruments: I was surprised that more conference-goers had not adopted these (indeed, mine was the only paper I saw that used these techniques). Second, some recent research suggests ways to proceed with instruments that might be "slightly" correlated with unobserved factors that influence the outcome and to test the sensitivity of IV results to varying degrees of instrument "badness." I would have liked to have seen a higher rate of adoption of these new techniques.
---------------------------------
In the next installment, I'll speak more about my ASHE experiences by summarizing some research on the early life origins of disease, behavioral economics, and the many benefits of education.
Friday, June 20, 2008
ASHE Conference
I will be attending the American Society for Health Economists (ASHE) Conference this weekend through early next week. Follow the link to check out the conference bill: it looks phenomenal! I will make a concerted attempted to blog about the proceedings in semi-real time, so definitely stay tuned.
Also, I will be presenting and discussing a paper at the conference. The abstract for my paper is provided below (let me know if you are interested in the topic and want to learn more or collaborate). Regarding the latter, I read a long time back in some economist's autobiographical account that being a good discussant and reviewer helps you develop street cred as a researcher. Nothing like a little self-promotion before I disappear from the research world to get plundered by medical school...
Paper Title: The Intergenerational Transmission of Height: Evidence from Mexico
A growing body of work suggests that health may be transferred across generations. This paper has two aims: to quantify intergenerational associations in height, a summary measure of long-run health and nutritional status, and to explore different factors and mechanisms that may generate these correlations. Using data from a rich household survey in Mexico, I use a variety of econometric methods to explore the nature of intergenerational correlations in the heights of parents and their children (aged 0-6 years). In particular, I use conditions faced by mothers and fathers during their childhood years as instruments for their heights, arguing that this strategy focuses attention on the portion of parental height sensitive to environmental conditions and public investment. I also explore gender differences in parent-child associations in height. I find that IV estimates on maternal height are generally twice as large as the corresponding OLS estimates, with the IV coefficient on paternal height near zero. These findings are highly robust to inclusion of a rich set of parental and household characteristics. Furthermore, the association between instrumented maternal height and child height is much larger for boys than for girls across all specifications, which is in contrast to the coefficients on predicted paternal height and measures of household resources. The results suggest that the portion of maternal height sensitive to environmental conditions faced during childhood is an important determinant of child health and, consequently, that early life conditions faced by girls may have intergenerational consequences. More speculatively, the results also suggest biological explanations beyond simple genetic transfers may explain an important portion of mother-child associations in height.
Also, I will be presenting and discussing a paper at the conference. The abstract for my paper is provided below (let me know if you are interested in the topic and want to learn more or collaborate). Regarding the latter, I read a long time back in some economist's autobiographical account that being a good discussant and reviewer helps you develop street cred as a researcher. Nothing like a little self-promotion before I disappear from the research world to get plundered by medical school...
Paper Title: The Intergenerational Transmission of Height: Evidence from Mexico
A growing body of work suggests that health may be transferred across generations. This paper has two aims: to quantify intergenerational associations in height, a summary measure of long-run health and nutritional status, and to explore different factors and mechanisms that may generate these correlations. Using data from a rich household survey in Mexico, I use a variety of econometric methods to explore the nature of intergenerational correlations in the heights of parents and their children (aged 0-6 years). In particular, I use conditions faced by mothers and fathers during their childhood years as instruments for their heights, arguing that this strategy focuses attention on the portion of parental height sensitive to environmental conditions and public investment. I also explore gender differences in parent-child associations in height. I find that IV estimates on maternal height are generally twice as large as the corresponding OLS estimates, with the IV coefficient on paternal height near zero. These findings are highly robust to inclusion of a rich set of parental and household characteristics. Furthermore, the association between instrumented maternal height and child height is much larger for boys than for girls across all specifications, which is in contrast to the coefficients on predicted paternal height and measures of household resources. The results suggest that the portion of maternal height sensitive to environmental conditions faced during childhood is an important determinant of child health and, consequently, that early life conditions faced by girls may have intergenerational consequences. More speculatively, the results also suggest biological explanations beyond simple genetic transfers may explain an important portion of mother-child associations in height.
Monday, June 16, 2008
The P-Value Contest
Many of you have likely come across one of the following sentences while perusing empirical work (doesn't matter what discipline or field):
"...the effect was statistically significant (p = 0.0401)..."
"...as per convention, we define statistical significance as a p-value of 0.05..."
"...income was significant at standard levels of confidence (p = 0.049)..."
0.05, or 5%, is the magic p-value (loosely, the probability that the given estimate or test statistic is due to random chance) that denotes the threshold separating statistical significance from insignificance. You don't need to be a Bayesian to realize that this designation is completely arbitrary: why not 0.04 (4%) or 0.06 (6%)?
I spent some time today (read: procrastinating) trying to find the story behind the origin of this convention. The most I could find was that R.A. Fischer, the founding father of statistics, decreed this as an "acceptable value" and everyone started to copy it.
I'm not satisfied: I'm guessing there is a fascinating story here. Why did Fischer choose 0.05 in the first place? How did the convention spread? Was it ever challenged? How do conventions develop in general?
This brings us to this summer's contest: whoever finds the best story (link/paper/book) behind the 5% convention will receive a copy of a recent popular press economics book of their choice. The prize is a small price for me to pay to have someone else do the work and bring the story to me - everyone wins.
"...the effect was statistically significant (p = 0.0401)..."
"...as per convention, we define statistical significance as a p-value of 0.05..."
"...income was significant at standard levels of confidence (p = 0.049)..."
0.05, or 5%, is the magic p-value (loosely, the probability that the given estimate or test statistic is due to random chance) that denotes the threshold separating statistical significance from insignificance. You don't need to be a Bayesian to realize that this designation is completely arbitrary: why not 0.04 (4%) or 0.06 (6%)?
I spent some time today (read: procrastinating) trying to find the story behind the origin of this convention. The most I could find was that R.A. Fischer, the founding father of statistics, decreed this as an "acceptable value" and everyone started to copy it.
I'm not satisfied: I'm guessing there is a fascinating story here. Why did Fischer choose 0.05 in the first place? How did the convention spread? Was it ever challenged? How do conventions develop in general?
This brings us to this summer's contest: whoever finds the best story (link/paper/book) behind the 5% convention will receive a copy of a recent popular press economics book of their choice. The prize is a small price for me to pay to have someone else do the work and bring the story to me - everyone wins.
Friday, June 13, 2008
Randomized Experiments
Great piece in this week's Economist about the increasing use of randomized experiments in the field of development economics and the methodology's potential usefulness in informing policy. (Here is an earlier post in this space on the same topic.)
I did have one minor issue with the following passage:
Go back to the bednets once more. You might conclude that the trial showed that they should always be given away. Yet it turns out that millions of nets were already in use in the part of Kenya where the field trial took place, so their value was known. The experiment guaranteed supplies, so it did not test the assertion that you need to charge something to encourage reliable suppliers. And the recipients were pregnant women, whereas the point of giving bednets away is to provide anti-malaria treatment universally. The evidence from western Kenya was clear. But it hardly settled the question of whether the government should give bednets away across the country. Questions like that may still have to be made on the basis of the soft evidence that randomistas turn up their noses at.
In my eyes, this point really gets at the value of replication. An well designed experiment carried out at a single point in time and space offers a way to get around statistical bias. However, the generalizability of such results is never clear. Reasoned projection to other contexts is one way to apply the results of one experiment to another context. However, I would think the ideal way to get at this would be, if possible, to replicate the experiment in different regions and time-points. This is has been long recognized in the medical world: everyone seems to realize that you can't rely on a single drug trial on a group of middle-aged males to project treatment effects across age and gender (though actual practice has not quite caught up to this belief).
Replication is likely undervalued among researchers and the money-men alike. Re-doing someone's experiment won't get you published in the American Economic Review (unless you find the opposite result or something) and once a large experiment for one purpose has been funded, I can see the loss of interest among donors to do it again. Hopefully, this incentive structure changes so that we can derive true context-specific treatment effects that, combined with reasoned projections (we do want to stop conducting the same study whence reasonable), can give us a better sense of policy directions, as opposed to projecting wildly based on single studies alone.
I did have one minor issue with the following passage:
Go back to the bednets once more. You might conclude that the trial showed that they should always be given away. Yet it turns out that millions of nets were already in use in the part of Kenya where the field trial took place, so their value was known. The experiment guaranteed supplies, so it did not test the assertion that you need to charge something to encourage reliable suppliers. And the recipients were pregnant women, whereas the point of giving bednets away is to provide anti-malaria treatment universally. The evidence from western Kenya was clear. But it hardly settled the question of whether the government should give bednets away across the country. Questions like that may still have to be made on the basis of the soft evidence that randomistas turn up their noses at.
In my eyes, this point really gets at the value of replication. An well designed experiment carried out at a single point in time and space offers a way to get around statistical bias. However, the generalizability of such results is never clear. Reasoned projection to other contexts is one way to apply the results of one experiment to another context. However, I would think the ideal way to get at this would be, if possible, to replicate the experiment in different regions and time-points. This is has been long recognized in the medical world: everyone seems to realize that you can't rely on a single drug trial on a group of middle-aged males to project treatment effects across age and gender (though actual practice has not quite caught up to this belief).
Replication is likely undervalued among researchers and the money-men alike. Re-doing someone's experiment won't get you published in the American Economic Review (unless you find the opposite result or something) and once a large experiment for one purpose has been funded, I can see the loss of interest among donors to do it again. Hopefully, this incentive structure changes so that we can derive true context-specific treatment effects that, combined with reasoned projections (we do want to stop conducting the same study whence reasonable), can give us a better sense of policy directions, as opposed to projecting wildly based on single studies alone.
Thursday, June 12, 2008
Public Health Priorities
Chris Blattman has an interesting post about the campaign to address the AIDS crisis. Some observers believe that the prevalence of AIDS has been completely overblown and that public health dollars are being allocated to this cause without due cause from the "hard evidence." A more detailed discussion is available in last week's Economist. The key phrase from that piece:
"...because of the single issue-activism that AIDS inspires, it receives a quarter of global health aid even though it causes only 5% of the burden of disease in poor and middle income countries. [Observers] also claim that ear-marking money in this way makes it harder to strengthen the health systems of those countries."
A very similar argument can be (and has been) advanced against organizations like Partners for Health.
So why are public health issues dominated by certain issues and not others? There are likely several reasons. First, it's likely easier for the public to identify with a single cause or disease and, therefore, it is easier to raise money targeting that particular condition. Second, diseases in general appear to be sexier than more diffuse problems. For example, one could argue that hunger is the proximal cause of most death and disease in the developing world. Somehow, AIDS, Malaria and diseases that lend themselves to the horrors of the imagination and the (potential) wonders of science are more eye-catching and press-worthy than concepts such as caloric intake and exposure to indoor air pollution (the latter thought to be the second leading cause of death and disease among children in the developing world).
Indeed, I think a lot of the dynamics in attention paid to given public health issues and policy and spending priorities might be explained by theories from political science (Kingdon's issue attention cycle is an example). Does anyone out there know of good work on this subject? I'm sure there is something out there...
Finally, are there ways to take advantage of spillovers from a great deal of attention towards a single disease or issue? It seems natural to me that attention for AIDS among the poor should increase attention to the myriad of other problems facing these societies as well; this simply seems like a matter of advertising to me. Furthermore, I don't see why this couldn't extend to the financial side: if grants to countries are made fungible (within certain bounds and fairly stringent monitoring of usage), there is no reason why AIDS money can't be nutrition money. I'd definitely appreciate your thoughts on these issues.
"...because of the single issue-activism that AIDS inspires, it receives a quarter of global health aid even though it causes only 5% of the burden of disease in poor and middle income countries. [Observers] also claim that ear-marking money in this way makes it harder to strengthen the health systems of those countries."
A very similar argument can be (and has been) advanced against organizations like Partners for Health.
So why are public health issues dominated by certain issues and not others? There are likely several reasons. First, it's likely easier for the public to identify with a single cause or disease and, therefore, it is easier to raise money targeting that particular condition. Second, diseases in general appear to be sexier than more diffuse problems. For example, one could argue that hunger is the proximal cause of most death and disease in the developing world. Somehow, AIDS, Malaria and diseases that lend themselves to the horrors of the imagination and the (potential) wonders of science are more eye-catching and press-worthy than concepts such as caloric intake and exposure to indoor air pollution (the latter thought to be the second leading cause of death and disease among children in the developing world).
Indeed, I think a lot of the dynamics in attention paid to given public health issues and policy and spending priorities might be explained by theories from political science (Kingdon's issue attention cycle is an example). Does anyone out there know of good work on this subject? I'm sure there is something out there...
Finally, are there ways to take advantage of spillovers from a great deal of attention towards a single disease or issue? It seems natural to me that attention for AIDS among the poor should increase attention to the myriad of other problems facing these societies as well; this simply seems like a matter of advertising to me. Furthermore, I don't see why this couldn't extend to the financial side: if grants to countries are made fungible (within certain bounds and fairly stringent monitoring of usage), there is no reason why AIDS money can't be nutrition money. I'd definitely appreciate your thoughts on these issues.
Sunday, June 8, 2008
My Prospectus Abstract
I will be handing in my prospectus, which is basically a glorified dissertation proposal, to the powers in charge (the Wizengamut?) this coming Monday. The abstract is provided below. If you are interested in this area of research and would like to hear more and/or collaborate, drop me a line. (I anticipate I'll be working on this broad topic for at least several years, so there is plenty of time to get in touch with me.)
A growing body of evidence from the biomedical sciences, epidemiology, and economics has shown that shocks to health and nutrition in utero and in early childhood are strongly associated with health and socioeconomic outcomes later in life. Furthermore, there is evidence that these correlations may extend to the next generation, as well. However, less is known about the causal nature of these associations, the relative importance of early life conditions in explaining broad trends in population health, how later life investments and experiences interact with endowments formed early in life to produce health outcomes in adulthood, and the exact mechanisms underlying these long-run and intergenerational associations. This thesis attempts to address these gaps in the context of two issues of policy interest in developing countries: obesity and intergenerational transfers of health status. The first paper will explore the recent and rapid rise in overweight, obesity, and associated morbidities among low and middle income countries. Using data from Mexico, I will empirically examine the effects of early childhood experiences, economic factors that ostensibly drive changes in diet and physical exertion, and the interaction between the two, on the prevalence of high body mass, type II diabetes and hypertension. The second and third papers will consider intergenerational correlations in height, a measure of long-run health and nutritional status. In the second analysis, using data from ruralVietnam Mexico
A growing body of evidence from the biomedical sciences, epidemiology, and economics has shown that shocks to health and nutrition in utero and in early childhood are strongly associated with health and socioeconomic outcomes later in life. Furthermore, there is evidence that these correlations may extend to the next generation, as well. However, less is known about the causal nature of these associations, the relative importance of early life conditions in explaining broad trends in population health, how later life investments and experiences interact with endowments formed early in life to produce health outcomes in adulthood, and the exact mechanisms underlying these long-run and intergenerational associations. This thesis attempts to address these gaps in the context of two issues of policy interest in developing countries: obesity and intergenerational transfers of health status. The first paper will explore the recent and rapid rise in overweight, obesity, and associated morbidities among low and middle income countries. Using data from Mexico, I will empirically examine the effects of early childhood experiences, economic factors that ostensibly drive changes in diet and physical exertion, and the interaction between the two, on the prevalence of high body mass, type II diabetes and hypertension. The second and third papers will consider intergenerational correlations in height, a measure of long-run health and nutritional status. In the second analysis, using data from rural
Friday, June 6, 2008
Obesity and Fast Foods Revisited
A recent post on the Marginal Revolution blog cites a study by economists Michael Anderson and David Matsa suggesting that greater access to fast food restaurants has little effect of body weight. This conclusion is drawn from two pieces of evidence. First, eating out increased total caloric consumption by only 24 calories a day. Second, the authors use the presence of highways as instruments for the local density of restaurants (to get around the fact that the placement of fast food restaurants may be correlated with demand for increased body weight and fatty foods, etc), with the resulting estimates showing little to no effect of an extra neighborhood restaurant.
Interestingly, another study, by economist Richard Dunn, finds a much larger effect of fast food restaurants on body weight despite using a very similar instrumental variable strategy. The differential results might be due to the time period under consideration (the former studies the mid 1990s while the later examines 2005) or, more intriguingly, the way fast food restaurant intensity is defined and coded in the data. The Anderson and Matsa study groups all full and limited services restaurants together while Dunn focuses specifically on McDonald's, Burger Kings, KFCs, and other establishments similar in spirit. Indeed, Dunn's results suggest that the type of restaurant matters in terms of the treatment effect estimates.
A short digression: I'm surprised that people think 24 extra calories a day is not a big deal. I recall a Stan Misler renal pathophysiology lecture where he commented that one extra can of soda a day, without concomitant increases in energy expenditure, over several months would greatly facilitate a transition from normal to high BMIs. The extra can of soda is about 100 calories. Given this, its not hard to imagine an extra 24 calories adding up (though over a longer period of time), as well. And this is saying nothing about the differential distribution of nutrients across home produced foods and fast foods.
Anyway, the results from both papers suggests that the jury on whether fast foods are a culprit in driving the obesity epidemic is still out. Furthermore, from the perspective of a graduate student, this debate provides two further take-home points. First, changes in measurement can drastically alter the substantive conclusions gleaned from any study. Second, it is important to follow up on seemingly closed research areas and/or definite studies, both to scrutinizing methodology and to revisit strong conclusions that may have broad policy impacts. Related to this latter notion, here is an interesting working paper by Daniel Hamermesh on replication in economics.
Interestingly, another study, by economist Richard Dunn, finds a much larger effect of fast food restaurants on body weight despite using a very similar instrumental variable strategy. The differential results might be due to the time period under consideration (the former studies the mid 1990s while the later examines 2005) or, more intriguingly, the way fast food restaurant intensity is defined and coded in the data. The Anderson and Matsa study groups all full and limited services restaurants together while Dunn focuses specifically on McDonald's, Burger Kings, KFCs, and other establishments similar in spirit. Indeed, Dunn's results suggest that the type of restaurant matters in terms of the treatment effect estimates.
A short digression: I'm surprised that people think 24 extra calories a day is not a big deal. I recall a Stan Misler renal pathophysiology lecture where he commented that one extra can of soda a day, without concomitant increases in energy expenditure, over several months would greatly facilitate a transition from normal to high BMIs. The extra can of soda is about 100 calories. Given this, its not hard to imagine an extra 24 calories adding up (though over a longer period of time), as well. And this is saying nothing about the differential distribution of nutrients across home produced foods and fast foods.
Anyway, the results from both papers suggests that the jury on whether fast foods are a culprit in driving the obesity epidemic is still out. Furthermore, from the perspective of a graduate student, this debate provides two further take-home points. First, changes in measurement can drastically alter the substantive conclusions gleaned from any study. Second, it is important to follow up on seemingly closed research areas and/or definite studies, both to scrutinizing methodology and to revisit strong conclusions that may have broad policy impacts. Related to this latter notion, here is an interesting working paper by Daniel Hamermesh on replication in economics.
Wednesday, June 4, 2008
New Link and Some Random Notes
1. Fellow Yalie Paula Chatterjee has just started blogging from Uganda, where she is working on a summer project educating and empowering commercial sex workers in Kampala. Paula is thoughtful, articulate and intelligent, and her first post marks the start of what should be an interesting blog. Indeed, the writing reminds me of James Hudspeth's travel blog, which has since expanded to a blog about all things (congrats to James, by the way, for his well-deserved acceptance at the Brigham and Womens Internal Medicine Residency Program). Both blogs are linked in the sidebar.
2. Great NYT article on Dark Matter and the search for the Theory of Everything. I've recently gotten into popular science writing, and I am working through James Gleick's biography on Richard Feynman right now. It's great stuff, and I am planning on reading the latest Einstein biography as well as a book on the history and significance of E=MC^2.
3. Speaking of science writing, I am trying to find a good book on the periodic table of elements. I've been intrigued by this ever since 8th grade, when we learned about how scientists converged on the periodic table and predicted the existence of many hitherto undiscovered elements. I found this book: any thoughts?
4. Is there an economics theory as powerful as the periodic structure of elements in making predictions about the future and/or unknowns?
2. Great NYT article on Dark Matter and the search for the Theory of Everything. I've recently gotten into popular science writing, and I am working through James Gleick's biography on Richard Feynman right now. It's great stuff, and I am planning on reading the latest Einstein biography as well as a book on the history and significance of E=MC^2.
3. Speaking of science writing, I am trying to find a good book on the periodic table of elements. I've been intrigued by this ever since 8th grade, when we learned about how scientists converged on the periodic table and predicted the existence of many hitherto undiscovered elements. I found this book: any thoughts?
4. Is there an economics theory as powerful as the periodic structure of elements in making predictions about the future and/or unknowns?
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