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Public Choice

20.04.2024

The political economy of climate action in Indian Country

verfasst von: Tessa Provins

Erschienen in: Public Choice

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Abstract

The public choice literature has long considered the political economy of environmental regulation and has examined a variety of national and subnational governments’ environmental policies aimed at adaption to climate change. However, there has been little attention paid to the determinants of environmental adaptive actions taken by indigenous governments. Given many indigenous peoples’ heightened vulnerability to issues caused by climate change, it is important to understand when and why they take action to adapt to climate change and what obstacles may stand in the way. I argue that natural resource abundance, informational resource access, population vulnerability, and reliance on natural resources will impact whether indigenous governments enact policies to respond to climate change. Using an original dataset of tribal actions addressing climate change for 574 federally recognized tribal governments, I find that the amount of land, educational attainment, broadband access, and proportion of tribal members working in the natural resource industries are positively associated with the tribal government’s responses to climate change.

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Fußnoten
1
Evans et al. (2020) examines climate change adaptation plans for tribes in all lower 48 states.
 
2
I have found no public reports that indigenous government leaders are among those who do not believe that climate poses a threat to their people and land. There is some variation in individuals’ beliefs in climate change and the potential effects on climate change policy adoption for other governments and groups of people (Fisher et al., 2013; Tjernström & Tietenberg, 2008).
 
3
An example of a mainstream news outlet is Indian Country Today which is a widely read indigenous news site.
 
4
One concern might be that there are not enough data points (links) collected for each tribe because the first 10 links are taken for each search. For each additional 10 links collected per tribe, it produces another 11,800 links to manually code, obviously requiring substantially more time and resources. However, the root of this concern is threats to inference because of undercounting tribal responses. If tribal responses were undercounted this would mean that my results are minimum effects, not maximum effects, and therefore, less problematic. However, to make sure that I was selecting an optimal number of links, I randomly selected 20 tribes and gathered 20 links for each tribe-search term pairing. After cross-referencing the links for those that matched my original search, the additional 20 links produced from the expanded search were coded for each tribe. The results showed almost no additional data was garnered from doubling the links for each of the tribes in my random sample.
 
5
Another concern might be that missing or broken links are non-random. This does not seem to be the case. I have included the distribution of missing or broken links in Appendix A Table 1.
 
6
Replication is of the utmost importance. To be clear this dataset is a snapshot in time and all links have been saved as PDFs that can be recoded for replication purposes. However, if the same search was run again, it might produce some different links. To deal with this, I have included an addendum to the original code in my replication files which identifies differences in links produced from searches run on a different date using the same parameters described for my dataset collection for future researchers.
 
7
To be clear, the measure produced by the IRT model is not a simple count of the number of actions that tribal government takes to adapt to climate change. A single-parameter IRT model creates an index accounting for the latent trait and the difficulty of each of the inputs. A count measure is a much rougher operationalization of the concept to a measure. However, the results of the count model may be of some use in the interpretation of the results. In Appendix E1, I include the negative binomial regression models with the count of tribal actions as the dependent variable. The results presented in the main body of the paper are by in large robust to this alternative specification.
 
8
This data is collected through the U.S. Census Burea and as a part of the American Community Survey (ACS).
 
9
This measure is included in the My Tribal Area Data and can not be disaggregated. While there could certainly be directional effects on the type and size of an industry by tribe, I can not measure this at this time. However, I do not expect that including these more nuanced measures would change the overall findings in this study.
 
10
This expectation is different than one might expect looking at national governments in other parts of the world. Explanations of these findings typically provide the rationale that poorer countries seem less interested in environmental preservation compared to rich countries. Some federally recognized tribes share similar characteristics of poor nations including lower household incomes, worse health outcomes, reliance on natural resource extraction to support the economy, and government capacity to deal with many of the other unique issues facing the respective people in each nation. However, this does not characterize all tribes or their people. Further, there are some important differences that lead me to the explanation and expectation presented here. First, is the size of tribes in terms of both land and population. Second, many tribes and their people have a different connection to the natural world for religious and spiritual reasons (not just economic) and some have a collective mindset and other cultural values that may differ from other poor nations across the globe.
 
11
Ideally, I would be able to measure the size and scope of government in the model. An ideal measure for the size and scope of tribal governments may include an operating budget or the number of bureaucratic agencies/departments. Unfortunately, there is no publicly disclosed or available data for these measures (especially for the number of tribes that I include in this analysis). The size and scope of tribal government are likely to be highly correlated with tribal population which I use for this reason in the models.
 
12
There is a decrease in the number of observations in Model 2 and Model 3 due to the coverage of variables for tribes from the American Community Survey.
 
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Metadaten
Titel
The political economy of climate action in Indian Country
verfasst von
Tessa Provins
Publikationsdatum
20.04.2024
Verlag
Springer US
Erschienen in
Public Choice
Print ISSN: 0048-5829
Elektronische ISSN: 1573-7101
DOI
https://doi.org/10.1007/s11127-024-01157-1

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