Tag Archives: formal demography

Three Questions About Norms

Well, it certainly has been a while since I’ve written anything here. Life has gotten busy with new projects, new responsibilities, etc. Yesterday, I participated in a workshop on campus sponsored by the Woods Institute for the Environment, the Young Environmental Scholars Conference. I was asked to stand-in for a faculty member who had to cancel at the last minute. I threw together some rather hastily-written notes and figured I’d share them here (especially since I spoke quite a bit of the importance for public communication!).

The theme of the conference was “Environmental Policy, Behavior, and Norms” and we were asked to answer three questions: (1) What does doing normative research mean to you? (2) How do your own norms and values influence your research? (3) What room and role do you see for normative research in your field? So, in order, here are my answers.

What does doing normative research mean to you?

I actually don’t particularly like the term “normative research” because it sounds a little too much like imposing one’s values on other people. I am skeptical of the imposition of norms that have more to do with (often unrecognized) ideology and less about empirical truth – an idea that was later reinforced by a terrific concluding talk by Debra Satz. If I can define “normative” to mean with the intent to improve people’s lives, then OK.  Otherwise, I prefer to do “positive” research.

For me, normative research is about doing good science. As a biosocial scientist with broad interests, I wear a lot of hats. I have always been interested in questions about the natural world, and (deep) human history in particular. However, I find that the types of questions that really hold my interest these days are more and more engaged in the substantial challenges we face in the world with inequality and sustainability. In keeping with my deep pragmatist sympathies, I increasingly identify with Charles Sanders Pierce‘s idea that given the “great ocean of truth” that can potentially be uncovered by science, there is a moral burden to do things that have social value. (As an aside, I think that there is social value in understanding the natural world, so I don’t mean to imply a crude instrumentalism here.) In effect, there is a lot of cool science to be done; one may as well do something of relevance.  I personally have little patience for people who pursue racist or otherwise socially divisive agendas and cloak their work in a veil of  free scientific inquiry.  This said, I worry when advocacy interferes with intellectual fairness or an unwillingness to accept that one’s position is not actually true.

I think that we are fooling ourselves if we believe that our norms somehow don’t have an effect on our research.  Recognizing what these norms that shape your research – whether implicitly or explicitly – helps you manage your bias. Yes, I said manage. I’m not sure we can ever completely eliminate it. I see this as more of a management of a necessary trade-off, drawing an analogy between the practice of science and a classic problem in statistics, between bias and variance. The more biased one is, the less variance there is in the outcome of one’s investigation. The less bias, the greater the likelihood that results will differ from one’s expectations (or wishes). Recognizing how norms shape our research also deals with that murky area of pre-science: where do our ideas for what to study come from?

How do your own norms and values influence your research?

Some of the the norms that shape my own research and teaching include:

transparency: science works best when it is open. This places a premium on sharing data, methods, and communicating results in a manner that maximizes access to information. As a simple example, this norm shapes my belief that we should not train students from poor countries in the use of proprietary software (and other technologies) that they won’t be able to afford when they return to their home countries when there are free or otherwise open-source alternatives.

fairness: this naturally includes a sense of social justice or people playing on an equal playing field, but it also includes fairness to different ideas, alternative hypotheses, the possibility that one is wrong. This type of fairness is essential for one’s credibility as a public intellectual in science (particularly supporting policy), as noted eloquently in this interview with Dick Lewontin.

respect for people’s ultimate rationality: Trying to understand the social, ecological, and economic context of people’s decision-making, even if it violates our own normative – particularly market-based economic – expectations.

flexibility: solving real problems means that we need to be flexible in our approach, willing to go where the solutions lead us, learning new tools and collaborating. Flexibility also means a willingness to give up on a research program that is doing harm.

good-faith communication: I believe that there is no room for obscurantism in the academy of the 21st century. This includes public communication. There are, of course, complexities here with regard to the professional development of young scholars.  One of the key trade-offs for young scholars is the need for professional advancement (which comes from academic production) and activism, policy, and public communication. Within the elite universities, the reality is that neither public communication nor activism count much for tenure. However, as Jon Krosnick noted, tenure is a remarkable privilege and, while it may seem impossibly far away for a student just finishing a Ph.D., it’s not really. Once you prove that you have the requisite disciplinary chops, you have plenty of time to to use tenure for what it is designed for (i.e., protecting intellectual freedom) and engaging in critical public debate and communication.

humility: solving problems (in science and society) means caring more about the answer to a problem than one’s own pet theory. Humility is intimately related to respect for others’ rationality.  It also means recognizing the inherently collaborative nature of contemporary science: giving credit where it is due, seeking help when one is in over one’s head, etc. John DeGioia, President of Georgetown University, quoted St. Augustine in his letter of support for Georgetown Law Student, Sandra Fluke against the crude attacks by radio personality Rush Limbaugh and I think those words are quite applicable here as well.  Augustine implored his interlocutors to “lay aside arrogance” and to “let neither of us assert that he has found the truth; let us seek it as if it were unknown to both.” This is not a bad description of the way that science really should work.

What room and role do you see for normative research in your field?

I believe that there is actually an enormous amount of room for normative research, if by “normative research,” we mean research that has the potential to have a positive effect on people’s lives. If instead we mean imposing values on people, then I am less sure of its role.

Anthropology is often criticized from outside the field, and to a lesser extent, from within it for being overly politicized. You can see this in Nicholas Wade’s critical pieces in the New York Times Science Times section following the American Anthropological Association’s executive committee excising of the word “science” from the field’s long-range planning document. Wade writes,

The decision [to remove the word ‘science’ from the long-range planning document] has reopened a long-simmering tension between researchers in science-based anthropological disciplines — including archaeologists, physical anthropologists and some cultural anthropologists — and members of the profession who study race, ethnicity and gender and see themselves as advocates for native peoples or human rights.

This is a common sentiment. And it is a complete misunderstanding. It suggests that scientists can’t be advocates for native peoples or human rights.  It also suggests that one can’t study race, ethnicity, or gender from a scientific perspective.  Both these ideas are complete nonsense.  For all the leftist rhetoric, I am not impressed with the actual political practice of what I see in contemporary anthropology. There is plenty of posturing about power asymmetries and identity politics but it is always done in such a mind-numbingly opaque language and with no apparent practical tie-in to policies that make people’s lives better. And, of course, there is the outright disdain for “applied” work one sees in elite anthropology departments.

Writing specifically about Foucault, Chomsky captured my take on this whole mode of intellectual production:

The only way to understand [the mode of scholarship] is if you are a graduate student or you are attending a university and have been trained in this particular style of discourse. That’s a way of guaranteeing…that intellectuals will have power, prestige and influence. If something can be said simply, say it simply, so that the carpenter next door can understand you. Anything that is at all well understood about human affairs is pretty simple.

Ultimately, the simple truths about human affairs that I find anyone can relate to are subsistence, health, and the well-being of one’s children. These are the themes at the core of my own research and I hope that the work I do ultimately can effect some good in these areas.

New Grant, Post-Doc Opportunity

Biological and Human Dimensions of Primate Retroviral Transmission
One of the great enduring mysteries in disease ecology is the timing of the AIDS pandemic. AIDS emerged as a clinical entity in the late 1970s, but HIV-1, the retrovirus that causes pandemic AIDS, entered the human population from wild primates many decades earlier, probably near the turn of the 20th century. Where was HIV during this long interval? We propose a novel ecological model for the delayed emergence of AIDS. Conceptually, in a metapopulation consisting of multiple, loosely interconnected sub-populations, a pathogen could persist at low levels indefinitely through a dynamic balance between localized transmission, localized extinction, and long-distance migration between sub-populations. This situation might accurately describe a network of villages in which population sizes are small and rates of migration are low, as would have been the case in Sub-Saharan Africa over a century ago.
We will test our model in a highly relevant non-human primate system. In 2009, we documented three simian retroviruses co-circulating in a metapopulation of wild red colobus monkeys (Procolobus rufomitratus) in Kibale National Park, Uganda, where we have conducted research for over two decades. We will collect detailed data on social interactions, demography, health, and infection from animals in a core social group.
We will also study a series of 20 red colobus sub-populations, each inhabiting a separate, isolated forest fragment. We will determine the historical connectivity of these sub-populations using a time series of remotely sensed images of forest cover going back to 1955, as well as using population genetic analyses of hypervariable nuclear DNA markers. We will assess the infection status of each animal over time and use viral molecular data to reconstruct transmission pathways.
Our transmission models will define the necessary conditions for a retrovirus to persist, but they will not be sufficient to explain why a retrovirus might emerge. This is because human social factors ultimately create the conditions that allow zoonotic diseases to be transmitted from animal reservoirs and to spread. We will therefore conduct an integrated analysis of the root eco-social drivers of human-primate contact and zoonotic transmission in this system. We will study social networks to understand how social resources structure key activities relevant to human-primate contact and zoonotic transmission risk, and we will explore knowledge, beliefs, and perceptions of human-primate contact and disease transmission for a broad sample of the population. We will reconcile perceived risk with actual risk through a linked human health survey and diagnostic testing for zoonotic primate retroviruses.
The ultimate product of our research will a data-driven set of transmission models to explain the long-term persistence of retroviruses within a metapopulation of hosts, as well as a linked analysis of how human social factors contribute to zoonotic infection risk in a relevant Sub-Saharan African population. Our study will elucidate not only the origins of HIV/AIDS, but also how early-stage zoonoses in general progress from “smoldering” subclinical infections to full-fledged pandemics.

I am thrilled to report that our latest EID project proposal, Biological and Human Dimensions of Primate Retroviral Transmission, has now been funded (by NIAID nonetheless!).  I will briefly describe the project here and then shamelessly tack on the full text of our advertisement for a post-doc to work as the project manager with Tony Goldberg, PI for this grant, in the College of Veterinary Medicine, University of Wisconsin, Madison.  This project will complement the ongoing work of the Kibale EcoHealth Project. The research team includes: Tony, Colin Chapman (McGill), Bill Switzer (CDC), Nelson Ting (Iowa), Mhairi Gibson (Bristol), Simon Frost (Cambridge), Jennifer Mason (Manchester), and me. This is a pretty great line-up of interdisciplinary scholars and I am honored to be included in the list.

Biological and Human Dimensions of Primate Retroviral Transmission

One of the great enduring mysteries in disease ecology is the timing of the AIDS pandemic. AIDS emerged as a clinical entity in the late 1970s, but HIV-1, the retrovirus that causes pandemic AIDS, entered the human population from wild primates many decades earlier, probably near the turn of the 20th century. Where was HIV during this long interval? We propose a novel ecological model for the delayed emergence of AIDS. Conceptually, in a metapopulation consisting of multiple, loosely interconnected sub-populations, a pathogen could persist at low levels indefinitely through a dynamic balance between localized transmission, localized extinction, and long-distance migration between sub-populations. This situation might accurately describe a network of villages in which population sizes are small and rates of migration are low, as would have been the case in Sub-Saharan Africa over a century ago.

We will test our model in a highly relevant non-human primate system. In 2009, we documented three simian retroviruses co-circulating in a metapopulation of wild red colobus monkeys (Procolobus rufomitratus) in Kibale National Park, Uganda, where we have conducted research for over two decades. We will collect detailed data on social interactions, demography, health, and infection from animals in a core social group.

We will also study a series of 20 red colobus sub-populations, each inhabiting a separate, isolated forest fragment. We will determine the historical connectivity of these sub-populations using a time series of remotely sensed images of forest cover going back to 1955, as well as using population genetic analyses of hypervariable nuclear DNA markers. We will assess the infection status of each animal over time and use viral molecular data to reconstruct transmission pathways.

Our transmission models will define the necessary conditions for a retrovirus to persist, but they will not be sufficient to explain why a retrovirus might emerge. This is because human social factors ultimately create the conditions that allow zoonotic diseases to be transmitted from animal reservoirs and to spread. We will therefore conduct an integrated analysis of the root eco-social drivers of human-primate contact and zoonotic transmission in this system. We will study social networks to understand how social resources structure key activities relevant to human-primate contact and zoonotic transmission risk, and we will explore knowledge, beliefs, and perceptions of human-primate contact and disease transmission for a broad sample of the population. We will reconcile perceived risk with actual risk through a linked human health survey and diagnostic testing for zoonotic primate retroviruses.

The ultimate product of our research will a data-driven set of transmission models to explain the long-term persistence of retroviruses within a metapopulation of hosts, as well as a linked analysis of how human social factors contribute to zoonotic infection risk in a relevant Sub-Saharan African population. Our study will elucidate not only the origins of HIV/AIDS, but also how early-stage zoonoses in general progress from “smoldering” subclinical infections to full-fledged pandemics.

Post Doctoral Opportunity

The Goldberg Lab at the University of Wisconsin-Madison invites applications for a post-doctoral researcher to study human social drivers of zoonotic disease in Sub-Saharan Africa.   The post-doc will be an integral member of a new, international, NIH-funded project focused on the biological and human dimensions of primate infectious disease transmission in Uganda, including social drivers of human-primate contact and zoonotic transmission.  This is a unique opportunity for a post-doctoral scholar with training in the social sciences to study human-wildlife conflict/contact and health and disease in a highly relevant ecological setting.  The following criteria apply.

  1. Candidates must have completed or be near to completing a PhD in the social sciences, in a discipline such as anthropology, geography, sociology, behavioral epidemiology, or a relevant discipline within the public health fields.
  2. Candidates must have a demonstrated interest in health and infectious disease.
  3. Candidates must have prior field experience in Sub-Saharan Africa.
  4. Candidates must be willing to relocate to Madison, Wisconsin for three years.
  5. Candidates must be willing to spend substantial time abroad, including in Sub-Saharan Africa and at partner institutions in the United Kingdom.
  6. Candidates must have experience with collection and analysis of both quantitative and qualitative data.  Familiarity with methods such as social network analysis, GIS, participatory methods, and survey design would be advantageous.

The successful candidate will help lead a dynamic international team of students and other post-docs in a multi-institutional, multidisciplinary project.  Duties involve a flexible combination of fieldwork, analyses, and project coordination, in addition to helping to mentor students from North America, Europe, and Africa.  The successful applicant will be expected to explore new research directions of her/his choosing, assisted by a strong team of collaborators.

University of Wisconsin-Madison is a top-notch institution for research and training in the social and health sciences.  Madison, WI, is a vibrant city with outstanding culture and exceptional opportunities for outdoor recreation.

Applicants should send a current CV, a statement of research interests and qualifications (be sure to address the six criteria above), and a list of three people (names, addresses, e-mails) who can serve as references.

Materials and inquiries should be sent to Dr. Tony L. Goldberg (tgoldberg@vetmed.wisc.edu).  Application materials must be received by September 12, 2011 for full consideration; the position is available starting immediately and requires a three-year commitment.

Update on Stanford Workshop on Migration and Adaptation

Since my last update, we have added another faculty member to the workshop on Migration and Adaptation. Loren Landau, the Director of the African Centre for Migration and Society (ACMS) (formerly Forced Migration Studies Programme, FMSP) at Wits University in Johannesburg, South Africa will be joining us to discuss conceptual issues in understanding African migration as well as research opportunities through ACMS. This means that we have the following confirmed speakers:

  • James Holland Jones, Department of Anthropology and Woods Institute for the Environment, Stanford University (organizer): Formal Models of Migration; Population Projection
  • Shripad Tuljapurkar, Department of Biology, Stanford University (organizer): Stochastic Forecasting
  • Eric Lambin, Environmental and Earth Systems Science and Woods Institute for the Environment, Stanford University: Pixels to People Approaches to Studying Migration
  • David Lobell, Environmental and Earth Systems Science and Woods Institute for the Environment, Stanford University: Global Climate Change and Food Insecurity
  • William H. Durham, Department of Anthropology and Woods Institute for the Environment, Stanford University: Smallholder Responses to Risk and Uncertainty
  • Ronald Rindfuss, Carolina Population Center, University of North Carolina and The East-West Center: Population and Environment; Microsimulation
  • Amber Wutich, School of Human Evolution and Social Change, Arizona State University, Water Insecurity
  • Lori Hunter, Department of Sociology, University of Colorado: Migration and Health
  • David Lopez-Carr, Department of Geography, University of California Santa Barbara: Migration and Fertility on the Forest Frontier
  • Loren Landau, African Centre for Migration Studies, Witwatersrand, Conceptual and Empirical Issues in African Migration

This is a great line-up and I’m very excited about this (and there are still a couple invitations pending based on complicated field schedules). We will hold the workshop at the IRiSS facility at 30 Alta Rd., bordering the main campus. This is a lovely spot for a workshop.

Details on applying for the workshop are contained here. We will pay for approved travel expenses of accepted students, post-docs, and junior faculty associated with NICHD-funded population centers.

Stanford Migration and Adaptation Workshop

Information on our NICHD-funded April formal demography workshop on migration and adaptation is now posted on the website Stanford Center for Population Research (SCPR, pronounced “scooper”).  SCPR is itself hosted by Stanford’s Institute for Research in the Social Sciences (IRiSS), which is also the umbrella organization for the Methods of Analysis Program in the Social Sciences (MAPSS), a program that I currently direct. We will be having this little shindig at the new IRiSS facility on Alta Road, a lovely location on the hill behind Stanford’s main campus, quite near the Center for Advanced Study in the Behavioral Sciences. All of these workshops have been terrific, but I am particularly excited about this one because it brings together so many of the threads of work going on right here at Stanford on human ecology, demography, and the biophysical environment.  Much of this work is facilitated by the Woods Institute for the Environment, where I and a number of the other Stanford-based speakers sit.

As a quick teaser of the kind of work that we will discuss, I want to draw people’s attention to two papers by Stanford faculty participating in the workshop that are just out this week.  Eric Lambin has a paper (which also happens to be his inaugural paper in PNAS as a member of the NAS) on the interactions between globalization, land use, and future land scarcity. I saw a talk on this last week and it was terrific. Lambin and co-author Patrick Meyfroidt argue that there are four socio-economic mechanisms (displacement, rebound, cascade, and remittance effects) that are amplified by by the process of economic globalization and that can accelerate land conversion. David Lobell has a new paper out today in Nature Climate Change in which he and his co-authors capitalize on a treasure-trove of historical agricultural trials in Africa to measure the impact of warming on maize production.  They find that approximately 65% of areas will experience a decline in productivity with a one-degree rise in global temperature if rain patterns are optimal.  If rain is sub-optimal, as is likely to be the case, then every site would experience reduced productivity.  This supports David’s contention that the effects on agricultural productivity of temperature increase from global climate change can not be understood except in the context of changes in rainfall as well.

Potential students who are interested in studying these issues at Stanford have a number of options.  If anthropology is your thing, we have a Ph.D. focus area in Ecology and Environment within the Department of Anthropology.  Bill Durham, Lisa Curran, Rebecca Bird, Douglas Bird, and I all teach in this area. Another option, for the more interdisciplinarily inclined, is E-IPER.  This is a topic I will have to take up in more detail in a later post since I actually have to do some work organizing our workshop now!

Stanford Workshop in Biodemography

On 29-31 October, we will be holding our next installment of the Stanford Workshops in Formal Demography and Biodemography, the result of an ongoing grant from NICHD to Shripad Tuljapurkar and myself.  This time around, we will venture onto the bleeding edge of biodemography.  Specific topics that we will cover include:

  • The use of genomic information on population samples
  • How demographers and biologists use longitudinal data
  • The use of quantitative genetic approaches to study demographic questions
  • How demographers and biologists model life histories

Information on the workshop, including information on how to apply for the workshop and a tentative schedule, can be found on the IRiSS website. We’ve got an incredible line-up of international scholars in demography, ecology, evolutionary biology, and genetics coming to give research presentations.

The workshop is intended for advanced graduate students (particularly students associated with NICHD-supported Population Centers), post-docs, and junior faculty who want to learn about the synergies between ecology, evolutionary biology, and demography. Get your applications in soon — these things fill up fast!

Some More Thoughts on Human Development and Fertility

I’m no longer on vacation which means that I have much less time to devote to blogging.  I just wanted to follow up on the last couple posts though before I jump back into the fray. I received some very stimulating comments from Edward Hugh and Aslak Berg, who are economists and contributers to the Demography Matters blog. They pointed to a recent blog post that Aslak wrote in response to my defense of the recent Nature paper by Myrskylä et al. Given how hysterical debate (ostensibly) over health care in the United States has been of late,  it is very refreshing to have a rational debate with intellectual give and take, arguments backed up by evidence, concern over truth, etc. You know, all those things that don’t seem to matter in contemporary American political discourse? So, my thanks to my interlocutors.

My basic reply is that I don’t disagree with much Ed and Aslak have said.  I nonetheless think that the Myrskylä et al. paper is of fundamental interest.  How can that be?  Well, I think that this turns on the question of causality. Does high HDI cause higher fertility? I think that this is unlikely in the strict sense.   We can use a handy graphical formalism called a directed acyclic graph (DAG) to illustrate causality (Judea Pearl, who pioneered the use of DAGs in causal analysis, has some very nice slides explaining both causal inference and the use of simple DAGs.  There is a whole group at Carnegie Mellon including Peter Spirtes, Richard Scheines, and Clark Glymour who work on the use of statistics and causal inference. Causal DAGs, as discussed in Pearl (1995), are a non-parametric generalization of path analysis and linear structural relations models first developed by Sewell Wright and familiar to geneticists, psychometricians, and econometricians).  The idea that HDI somehow causes fertility can be encapsulated in the following simple graph:

dag-simple

An arrow leads from HDI directly to fertility, indicating that HDI “causes” fertility. The thing is, I don’t believe this at all in the strictest sense.  HDI is a composite measure that includes six quantities (life expectancy at birth, log-per capita GDP at PPP in $US, adult literacy, and primary, secondary and tertiary school enrollment fractions).  This alone leads me to think that the results described by Myrskylä et al. are really (interesting) correlations and not causal relations. I suspect that Myrskylä and colleagues also think this.  In the discussion, the authors speculate on what it is about very high HDI that allows fertility to increase from its lowest levels generally seen at intermediate-high HDI. Their leading hypothesis relates to social structures that allow women to simultaneously be part of the workforce and have children: “analyses on Europe show that nowadays a positive relationship is observed between fertility and indicators of innovation in family behaviour or female labour-force participation.” They further suggest that the more conservative social mores of the rich East Asian countries may be why their fertility continues to plummet: “Failure to answer to the challenges of development with institutions that facilitate work–family balance and gender equality might explain the exceptional pattern for rich eastern Asian countries that continue to be characterized by a negative HDI–fertility relationship.”  The causal graph here might look like this:

dag-child

I’ve made the line between HDI and fertility dashed to indicate that the direct influence is reduced — it’s possible that its only influence is indirectly through childcare.  Now HDI causes changes in childcare structures and these are what have the major causal impact on fertility.  Really, I suspect it is more than that, of course.  One possibility is the existence of relatively high-fertility immigrants in many of these high-HDI countries. In the United States, the fertility of foreign- and native-born women (based on the most recent analysis of the Census Bureau’s Current Population Survey) was 2.1 and 1.8 respectively.   So foreign-born women in the United States have (period) TFRs that are nearly 20% higher than native-born women.  Similar results apply to European countries.  Is it possible that it’s not childcare arrangements but the fraction of foreign-born that is different between the high-HDI European and East Asian countries?  If that’s true, what’s going on with Canada? It’s not difficult to construct a story relating HDI to immigration: as development continues to increase and the skills of a workforce (and wages demanded by it) increase there are two forces increasing further immigration.  First of all, the country becomes a more attractive destination.  Secondly, as the skills/wages of the native labor force increase, there is need to find people who are willing to do the less highly skilled and lower paid labor.  The existence of high fertility migrants is an example of unmeasured heterogeneity, which is the bugaboo of demography and causal inference.  In this case, I think the heterogeneity might really be the object of interest and not simply a nuisance for causal inference.

My guess is that there are multiple causes.  Something like this seems likely to me:

dag-migration-childwith a number of other causes almost certainly contributing (either directly or indirectly) as well.

What I think is so valuable about the paper by Myrskylä and colleagues is that it makes us ask what the causal stories might be. What these scholars have done is initiate a chain of abductive reasoning.  Charles Sanders Pierce first identified abduction as a form of logical inference. Describing abduction, he wrote, “The surprising fact, C, is observed; But if A were true, C would be a matter of course, Hence, there is reason to suspect that A is true” (Collected papers: 5.189). Abduction is basically the process through which new hypotheses are created. Myrskylä have just revealed surprising fact C, namely, that fertility appears to increase with very high HDI.  We are surprised because all the previous literature on the relationship between economic development and fertility showed that the two were negatively related. Our goal now is to elucidate what A (almost certainly a multi-factorial quantity) is.  I like this paper because I see it as starting a new and productive area of research not because it identifies the cause of increased fertility in low-fertility countries.

The problematic correlations that Aslak notes (i.e., that the countries that show J-shaped HDI-TFR curves longitudinally are culturally related) may actually aid us in our quest to uncover the causal mechanism(s) that explains the HDI-TFR relation (more unmeasured heterogeneity). This, of course, would be a miserable situation if we thought that HDI was strictly causal since then HDI and whatever this latent cultural variable would be almost completely confounded.  But their very relationship may aid us in identifying what the actual causal mechanism is.

I look forward to more work in this exciting and important area of demographic research.  Maybe one of these days I’ll write more on causal directed acyclic graphs. It’s a pretty cool approach to science and one that I think merits much more attention in the social sciences

Follow-Up to the Reversal in Fertility Decline

In my last post, I wrote about a new paper by Myrskylä and colleagues in this past week’s issue of Nature.  Craig Hadley sent me a link to a criticism of this paper, and really more the science reporting of it in the Economist, written by Edward Hugh on the blog A Fist Full of Eruos within a couple hours of my writing.  Hugh levels three criticisms against the Myrskylä et al. (2009) paper:

  1. The authors use total fertility rate (TFR) as their measure of fertility, even though TFR has known defects.
  2. The reference year (2005) was a peculiar year and so results based on comparisons of other years to it are suspect.
  3. Even if fertility increases below its nadir in highly developed countries, median age of the population could increase.

The first two of these are criticisms of the Myrskylä et al. (2009) Nature paper and it is these that I will address here. The third is really a criticism of the Economist‘s coverage of the paper.

TFR is a measure of fertility and in demographic studies like these, what we care about is people’s fertility behavior.  In a seminal (1998) paper, John Bongaarts and Griffith Feeney pointed out that as a measure of fertility TFR actually confounds two distinct phenomena: (1) the quantum of reproduction (i.e., how many babies) and (2) the tempo of reproduction (i.e., when women have them).  Say we have two populations: A and B.  In both populations, women have the same number of children on average. However, in population B, women delay their reproduction until later ages perhaps by getting married at older ages.  In both populations, women have the same number of offspring but we would find that population A had the higher TFR. How is that possible? It is a result of the classic period-cohort problem in demography.   As social scientists, demographers care about what actual people actually do. The problem is that measuring what actual people actually do over their entire lifetimes introduces some onerous data burdens and when you actually manage to get data for individual lifetimes, it is typically horribly out-of-date. For example, if you want to look at completed fertility, you need to look at women who are 50 years old or older at the time.  This means that most of their childbearing happened between 20 and 30 years ago. Not necessarily that informative about current trends in fertility.

To overcome this problem, demographers frequently employ period measures of fertility, mortality, marriage, migration, etc.  A period measure is essentially a cross-sectional measure of the population taken at a particular point in time.  Rather than measuring the fertility of women throughout their lifetimes (i.e., looking at the fertility of a cohort of women where they are age 20, 30, 40, etc.), we measure the fertility of 20 year-olds, 30 year-olds, 40 year-olds, and so on at one particular point in time. We then deploy one of those demographers’ fictions.  We say that our cross-section of ages is a reflection of how people act over their life course.  TFR is a period measure.  We take the fertility rates measured for women ages 15-50 at a particular point in time (say, 2005) and sum them to yield the number of children ever born to a woman surviving to the end of her reproductive span if she reproduced at the average rate of the aggregate population.

Here is a simple (highly artificial) example of how this works.  (Demographic purists will have to forgive me for reversing the axes of a Lexis diagram, as I think that having period along the rows of the table is more intuitive to the average person for this example.)  The cells contain annual age specific fertility rates for each period. We calculate the period TFR by multiplying these values by the number of years in the age-class (which I assume is 5 for classes 10 and 40 and 10 for the others).  In 1940, we see the beginning of trend in delayed fertility — no women 15-20 (i.e., the “10 year-old” age class) have children.  This foregone early fertility is made up for by greater fertility of 20-30 year-olds in 1940.  Eventually, overall fertility declines — at least in the periods for which we have full observations since the 1950, 1960, and 1970 cohorts have not completed their childbearing when the observations stop.

TFR-tempo-example

When we measure the TFR in 1930, we see that it is higher than the TFR in 1940 (3 vs. 2.5).  Nonetheless, when we follow the two cohorts through to the end of their childbearing years (in blue for 1930 and red for 1940), we see that they eventually have the same cohort TFRs. That is, women in both cohorts have the same total number of children on average; it’s just that the women in 1940 begin childbearing later.  The behavior change is in tempo and not quantum and the period measure of fertility — which is ostensibly a quantum measure since it is the total number of children born to a woman who survives to the end of her childbearing years — is consequently distorted.

Bongaarts and Feeney (1998) introduced a correction to TFR that uses measures of birth order to remove the distortions.  Myrskylä et al. (2009) were able to apply the Bongaarts/Feeney correction to a sub-sample (41) of their 2005 data.  Of these 41 countries, they were able to calculate the tempo-adjusted TFR for 28 of the 37 countries with an HDI of 0.85 or greater in 2005. The countries with adjusted TFRs are plotted in black in their online supplement figure S2, reproduced here with permission.

Myrskyla_etal-figS2As one can easily see, the general trend of increasing TFR with HDI remains when the corrected TFRs are used.  This graphical result is confirmed by a formal statistical test: Following the coincident TFR minimum/HDI in the 0.86-0.9 window, the slope of the best-fit line through the scatter is positive.

Hugh notes repeatedly that Myrskylä et al. (2009) anticipated various criticisms that he levels.  For example, he writes “And you don’t have to rely on me for the suggestion that the Tfr is hardly the most desireable [sic] measure for what they want to do, since the authors themselves point this very fact out in the supplementary information.” This seems like good honest social science research to me. I’m not entirely comfortable with the following paraphrasing, but here it goes.  We do science with the data we have, not the data we wish we had.  TFR is a widely available measure of fertility that allowed the authors to look at the relationship between fertility and human development over a large range of the HDI. Now, of course, having written a paper with the data that are available, we should endeavor to collect the data that we would ideally want.  The problem with demographic research though is that we are typically at the whim of the government and non-government (like the UN) organizations that collect official statistics.  It’s not like we can go out and perform a controlled experiment with fixed treatments of human development and observe the resulting fertility patterns. So this paper seems like a good-faith attempt to uncover a pattern between human development and fertility.  When Hugh writes “the only thing which surprises me is that nobody else who has reviewed the research seems to have twigged the implications of this” (i.e., the use of  TFR as a measure of fertility), I think he is being rather unfair.  I don’t know who reviewed this paper, but I’m certain that they had both a draft of the paper that eventually appeared in the print edition of Nature and the online Supplemental material in which Myrskylä and colleagues discuss the potential weaknesses of their measures and evaluate the robustness of their conclusions. That’s what happens when you submit a paper and it undergoes peer review.  The pages of Nature are highly over-subscribed (as Nature is happy to tell you whenever it sends you a rejection letter).  Space is at a premium and the type of careful sensitivity analysis that would be de rigeur in the main text of a specialist journal  such as Demography, Population Studies, or Demographic Research, end up in the online supplement in Nature, Science, or PNAS.

On a related note, Hugh complains that the reference year in which the curvilinear relationship between TFR and HDI is shown is a bad year to pick:

Also, it should be remembered, as I mention, we need to think about base years. 2005 was the mid point of a massive and unsustainable asset and construction boom. I think there is little doubt that if we took 2010 or 2011, the results would be rather different.

The problem with this is that the year is currently 2009, so we can’t use data from 2010 or 2011.  It seems entirely possible that the results would be different if we used 2011 data and I look forward to the paper in 2015 in which the Myrskylä hypothesis is re-evaluated using the latest demographic data.  This is sort of the nature of social science research.  There are very few Eureka! moments in social science.  As I note above, we can’t typically do the critical experiment that allows us to test a scientific hypothesis.  Sometimes we can get clever with historical accidents (known in the biz as “natural experiments”). Sometimes we can use fancy statistical methods to approximate experimental control (such as the fixed effects estimation Myrskylä et al. use or the propensity score stratification used by Felton Earls and colleagues in their study of firearm exposure and violent behavior).  If we waited until we had the perfect data to test a social science hypothesis, there would never be any social science.  Perhaps things will indeed be different in 2011.  If so, we may even get lucky and by comparing why things were different in 2005 and 2011, gain new insight into the relationships between human development and fertility. Until then, I am going to credit Myrskylä and colleagues for opening a new chapter on our understanding of fertility transitions.

Oh, and I plan to cite the paper, as I’m sure many other demographers will too…

Reversal of Fertility Decline

In a terrific paper in the latest issue of Nature, Myrskylä and colleagues (including my sometime collaborator Hans-Peter Kohler) demonstrate that total fertility rate (TFR) — which we typically think of as declining with economic development — actually increases at very high levels of development.  One of the fundamental challenges of social science remains explaining the unprecedented decline in fertility witnessed in the twentieth century.  This fertility decline has gone hand-in-hand with economic development.  As Myrskylä et al. write, “The negative association of fertility with economic and social development has therefore become one of the most solidly established and generally accepted empirical regularities in the social sciences.”

For those social scientists with an evolutionary bent, this observation has been particularly vexing since it appears to violate our expectations regarding resource-holding and reproductive success.  In a great many traditional societies, researchers have documented a positive relationship between wealth and reproductive success.  However, as soon as people are embedded within (and actually integrated with) the structures of a state-level society, this relationship apparently changes: rich people in states appear to have fewer children than poor people.  And as the overall level of wealth of a state increases, the aggregate pattern of fertility also decreases.  Now there are plenty of caveats here.  Many scholars have committed the ecological fallacy in attributing causal explanations at the individual level based on aggregate (“ecological”) data. There is some evidence that the wealthy and well educated actually have marginally higher fertility in certain contexts, but the overwhelming weight of evidence shows that — at least at the aggregate level — increased wealth leads to decreased fertility. Until now.

The authors use the Human Development Index (HDI), a widely used measure of progress in human development.  The HDI combines three dimensions: (1) health, as measured by life expectancy at birth, (2) standard of living, as measured by the logarithm of per capita gross domestic product at purchasing power parity in US dollars, and (3) human capital as measured by adult literacy and the enrollment fraction in primary, secondary, and tertiary school.  HDI is now standardized so that it varies between zero and one.  This makes it easy to compare HDI across countries and through time.  The measure of fertility that Myrskylä and colleagues use is total fertility rate.  This is also probably the most commonly used measure of fertility.  It is the sum of a population’s age-specific fertility rates across a woman’s reproductive years, assuming that the woman survives this span.  It is a demographic fiction, but it is a useful fiction.

What Myrskylä et al. (2009) show (in their figure 1) is that TFR largely declines with HDI in 1975, as expected. The cool, unexpected finding that their paper reports is that in 2005, TFR declines with HDI to a point. When the HDI exceeds 0.9 though, fertility again increases. This plot is cross-sectional: it is a scatter plot of all countries’ HDI-TFR pairs for a particular time period. One reason why we don’t see this upward turn at the highest levels of human development in 1975 is that no countries had achieved this apparent threshold of HDI=0.9. Of course, from this plot we can’t rule out the existence of some “period effect.” That is, maybe there was just something different in society or the economy in 2005 compared to 1975.

back half template
Myrskylä et al. (2009) figure 1 (used with permission of the authors).

In figure 2, the authors plot longitudinal data for individual countries. They show that once HDI enters a window between 0.86-0.9 and TFR bottoms out, further increases in HDI lead to increases in TFR.

Myrskylä et al. (2009) figure 2 (used with permission of the authors)
Myrskylä et al. (2009) figure 2 (used with permission of the authors)

This greatly increases our confidence that there is, in fact, a causal relationship between increased human development and fertility.  The really cool thing about this plot, however, is the exceptions to the general trend that it shows. In particular, Japan, South Korea, and Canada (and to a lesser extent Austria, Australia, and Switzerland) do not show this pattern.  For these countries, further increases in HDI are associated with further declines in TFR. A distinct possibility is that for some countries, increasing human welfare also leads to institutions that permit people (particularly women) to have children and be educationally and economically successful at the same time — that is, not just people who were lucky enough to be born rich.  It’s a shocking idea. The authors write:

[A]n improved understanding of how improved labour-market flexibility, social security and individual welfare, gender and economic equality, human capital and social/family policies can facilitate relatively high levels of fertility in advanced societies is needed. For instance, analyses on Europe show that nowadays a positive relationship is observed between fertility and indicators of innovation in family behaviour or female labour-force participation. Also, at advanced levels of development, governments might explicitly address fertility decline by implementing policies that improve gender equality or the compatibility between economic success, including labour force participation, and family life. Failure to answer to the challenges of development with institutions that facilitate work–family balance and gender equality might explain the exceptional pattern for rich eastern Asian countries that continue to be characterized by a negative HDI–fertility relationship.

These are important problems and this is a fundamental contribution to our understanding of the relationships between economic development, human welfare, and reproductive behavior.