Fisher, B.; Ellis, A. M.; Adams, D. K.; Fox, H. E.; Selig, E. R. Health, wealth, and education: the socioeconomic backdrop for marine conservation in the developing world. Marine Ecology Progress Series 530: 233-242, 2015. doi: 10.3354/meps11232
Why we care
This is a paper strictly about human population characteristics. But it’s in a marine science journal and we’re discussing it on a marine science site. What gives- why the focus on humans?
Conservation is an anthropocentric (human-centered) endeavor. Pandas aren’t conserving themselves, for example; we humans are trying to do that. Socioeconomic factors hugely influence how people see and interact with the environment, and therefore impact both their willingness to embrace environmentalism and how they might act for the environment if they do.
Let’s consider two fictional people to illustrate these ideas. Person 1 lives on the coast, depends on welfare for income, did not complete a high school-level education, and is most stressed about providing food for his kids. Person 2 is a lawyer with a vacation home on the coast and is most stressed about his closing arguments for an upcoming case. Which person do you think would invest more time and energy in thinking about the environment? What if I told you that Person 1’s primary source of food is a fish whose population is rebounding after local environmental management policies were put into place?
The authors of this article profile the health, wealth, and educational status of a number of communities in developing countries. This characterization contributes to our quantitative understanding of how human and marine health are intertwined, allowing us to better craft and manage marine conservation plans in addition to improving human quality of life.
Data were collected by ICF International (originally Inner City Fund International) in 38 developing countries from 2000-2012. Over 38,000 communities were surveyed and those within 20 km of the coast were designated as coastal. To describe “health”, the authors analyzed height-for-age standard deviations to identify communities with stunted children. “Wealth” was determined using a relative standard of living metric for communities within a single country. Thus, community wealth could be directly compared within but not between countries. The highest level of education attained by the adult responding to the survey comprised the “education” data used in this study.
The authors answered various questions about these data using linear mixed effects models generated using the statistical program R. I will not delve into the details here- the authors provide a clear description of their models in the paper if you’re interested.
There were significant differences in health, wealth, and education between coastal and non-coastal communities as well as between urban and rural communities. The greatest differences in these three metrics were seen between urban and rural communities, regardless of their proximity to the coast.
This difference between rural and urban communities was seen within just coastal communities as well, but the magnitude of the difference varied widely by country. These discrepancies are sometimes difficult to interpret, however. The authors point out that in Bangladesh, for example, the average child is nearly stunted, so there is no significant difference between child stunting in urban v. rural coastal communities.
When these urban v. rural trends were controlled for, coastal communities had better health, more wealth, and higher education levels than non-coastal communities on average.
The authors hypothesize that coastal communities may fare better than their non-coastal counterparts because of the low cost barrier to fishing, which can provide both food and income. They may also benefit from their proximity to ports and the exchange of goods that occurs there. Or coastal residents may be able to shift their livelihood when necessary, which might not be possible inland.
However, the data considered here is largely relative. Although surveyed coastal communities have a higher quality of life on average than non-coastal communities, the three metrics used in this study indicate that human poverty is severe in these developing countries, especially in rural areas.
Around 3 billion people, most of whom live in developing countries, critically depend on fish for protein. Improving marine resource management will simultaneously conserve the environment and improve the wellbeing of communities like those studied here. Knowing where the most impoverished households are will help managers understand resource use and craft effective conservation strategies. This study indicates that policymakers must reach beyond existing infrastructure in order to impact the rural communities that most need help.
Have your say
Have you encountered poverty or wealth along the coast? Let’s leave the theoretical language behind: what was it like in real life? Let me know in the comments!