Despite growing interest in the impact of immigration on U.S. society, research has rarely examined the effects of immigration flows on the natural environment. The current study addresses this gap in research using data on 183 Metropolitan Statistical Areas drawn from the Environmental Protection Agency, the U.S. Census Bureau, and the National Oceanic and Atmospheric Administration to empirically assess the relationships between contemporary immigration and seven measures of air pollution. In doing so, we seek to (1) broaden knowledge about the social consequences of immigration to include its potential effects on the environment, (2) address competing theoretical perspectives about immigration-environment relationships (i.e., population pressure/social disorganization versus ecological footprint/community resource perspectives), and (3) extend knowledge about the predictors and sources of environmental harm within local communities. In contrast to popular opinion and population pressure positions, our research indicates that immigration does not contribute to local air pollution levels across any of the seven pollution measures examined.
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The IPAT or I = PAT equation proposed by Ehrlich and Holdren (1971) has been widely used among environmental and population social scientists to assess the impact of humans on the environment (or I). This impact is the product of the number of people (P), the amount of goods consumed per person (A), and the pollution generated by technology per good consumed (T). The STIRPAT model, built on the IPAT model, adds more sophistication and allows social scientists the ability to test hypotheses.
MSAs offer several advantages as study units for our analysis. First, they provide an alternative and relatively untapped spatial unit for assessing the robustness of immigration-pollution findings from prior research. Second, they provide larger sample sizes and greater statistical power for identifying potential immigration-pollution links compared to state-level analyses (see Squalli 2010). Last, MSAs provide greater internal uniformity or “spatial homogeneity” than more-highly aggregated study units (counties, states). As a result, they are less prone to statistical noise from internal heterogeneity and are more likely to capture community-level processes described in social disorganization and community resource positions (see Feldmeyer 2009; Feldmeyer and Steffensmeier 2009; Peterson and Krivo 2005).
Following standard procedures of social science research, we used principal components analysis to combine information from the six specific pollutants into our air pollution index with regression based factor scores. All six pollutants were loaded onto one factor (Eigenvalue = 1.671). Factor loadings for the specific pollutants are as follows: CO (0.310), NO2 (0.409), O3 (0.732), SO2 (0.307), PM10 (0.501), PM2.5 (0.725). Findings were nearly identical when pollutants were loaded onto two separate latent factors in supplemental analyses.
Our sample excludes MSAs in Puerto Rico. Several MSAs that recorded/reported no air quality information were also excluded from all analyses. It is unknown whether these MSAs (or MSAs that record only one or a few types of pollution) differ discernibly from those that report multiple air quality measures. Although we cannot fully address this caveat with the available data, we conducted several supplemental analyses to adjust for missing data using multiple imputation techniques and using dummy variables to control for the number of pollutants reported for each MSA. Results from the supplemental analysis were substantively similar to those reported here.
Although we controlled for percent workers in manufacturing, we did not control for overall economic growth of metropolitan areas. Economic growth is likely connected to increased air pollution (through an increased volume of cars and factories) as well as increased domestic and international migration (from job seekers). However, this omission seems unlikely to bias our main finding that immigration has null effects on air pollution. First, economic growth is closely associated with domestic migration levels, which we control for. Thus, adding measures of general economic growth in addition to our controls for employment sector, domestic migration, and total population growth is not likely to provide much further explanatory power and creates a substantially higher risk of multicollinearity. Second, given that our immigration effects on air pollution are null, it seems unlikely that an additional control for economic growth would drive them to significance (omitted variables pose a greater threat for Type 1 error, but are less likely to produce Type 2 errors). However, further attention to the potential connections between these concepts is warranted in future research.
In addition to collinearity tests, we conducted an extensive series of regression diagnostics and supplemental analyses to account for potential violations of Gauss–Markov assumptions. First, we looked at Cook’s D and DFFIT values to identify potential outliers. After identifying and removing outliers, we replicated all analyses. Findings from this analysis were nearly identical to those presented here. Second, we conducted Breusch-Pagan/Cook-Weisberg tests to assess heteroskedasticity. Based on these results, we performed several supplemental analyses using alternative estimation procedures commonly used in prior research (quantile regression and multivariate linear models with robust standard errors) to account for potential bias from outliers and heteroskedasticity (see Squalli 2009, 2010). Results of these models were substantively similar to those described here. We return to the results of these models in further detail in our discussion of findings.
Although ozone levels were greater than other pollutants, several MSAs also had PM2.5 and PM10 levels that exceeded EPA standards. Carbon monoxide, sulfur dioxide, and nitrogen dioxide were below EPA standards in all MSAs examined. It is also important to note that while MSAs with low air pollution levels tended to be low for all pollutants examined, MSAs often had high pollution levels for one or two pollutants but not all seven measures.
It is worth noting that immigration effects on nitrogen dioxide were negative but non-significant in reduced models and only reached significance in the full model with all controls.
Our presentation and discussion of findings focuses on the cross-sectional results over the time-lagged models for several reasons. First, the findings are remarkably similar using both methods. Second, as we noted earlier, the time-lagged models have much higher thresholds for finding significance compared to the cross-sectional models. Thus, the fact that immigration had consistent null effects on air pollution—even in the cross-sectional models where significance is easier to obtain—more clearly illustrates the overwhelming absence or “nullness” of immigration-pollution relationships.
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The authors would like to thank R. Scott Frey, Stephanie A. Bohon, and Meghan E. Conley for their useful comments to earlier versions of this manuscript.
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Price, C.E., Feldmeyer, B. The Environmental Impact of Immigration: An Analysis of the Effects of Immigrant Concentration on Air Pollution Levels. Popul Res Policy Rev 31, 119–140 (2012). https://doi.org/10.1007/s11113-011-9216-3
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