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Published in final edited form as: Int J Intercult Relat. 2023 Mar 15;94:101790. doi: 10.1016/j.ijintrel.2023.101790

Perceived Negative Political Climate Among Hispanic/Latino Adolescents Before and After the 2020 U.S. Presidential Election: Associations with Internalizing Symptoms and Substance Use

Pablo Montero-Zamora †,*, Saskia R Vos ‡, Jennifer B Unger ^, Ingrid Zeledon ^, Ryan Lee ^, Daniel W Soto ║, Eric C Brown ‡, Maria Duque †, Maria Fernanda Garcia §, Carolina Scaramutti ‡, Beyhan Ertanir , Seth J Schwartz †
PMCID: PMC10121197  NIHMSID: NIHMS1884047  PMID: 37091741

Abstract

The political climate often changes following the installment of a new president. This volatility presents opportunities for examining how elections might affect vulnerable subgroups such as Hispanic/Latino (HL) adolescents. The present study explored the perception of negative political climate among HL adolescents before and after the 2020 U.S. presidential election and its association with internalizing symptoms and substance use. We conducted the study in Los Angeles and Miami between 2020–2021, with a sample of 304 HL adolescents (Females = 60.8%), aged 15.3 years on average. Participants completed measures of negative political climate (pre- post-election) and measures of depressive symptoms, anxiety, substance misuse, and substance use intentions after the election. We used paired tests and linear mixed-effects modeling to explore changes in perceived negative climate before and after the election. Structural equation modeling was used to determine predictors of negative political climate and its associations with internalizing symptoms and substance use. Results indicated that following the election negative political climate increased significantly in Miami and among Cuban-origin adolescents but not in Los Angeles or among Mexican-origin adolescents. Pre-election perceived negative political climate was significantly predicted by gender, study site, and mother’s nativity. Pre-election negative political climate predicted post-election internalizing symptoms and substance use intentions indirectly through post-election negative political climate. HL youth’s perceived political climate is a complex construct that might vary across different sociopolitical contexts and populational sub-groups. Exploring variations in politically-based cultural stressors and their role as mental health and substance use risk factors is crucial to addressing HL disparities. KEY WORDS: Adolescence, Hispanic/Latino, political climate, presidential election, internalizing symptoms, substance use.

Introduction

During the last decade, the Hispanic/Latino (HL) community in the U.S. has significantly expanded. The percentage of people identifying as HL increased by 23% during the time period between 2010 (47.73 million; United States Census Bureau, 2010) to 2020 (62.1 million; United States Census Bureau, 2020). This increase represents 51.5% of the country’s total population growth (United States Census Bureau, 2010, 2020). Notably, in 2020, individuals 10 to 19 years accounted for 10.3 million (~18%) of the HL population, representing an increase of 4 million people since 2000 (when there were 6.3 million HL adolescents in the U.S.; United States Census Bureau, 2010; United States Census Bureau, 2020). Studying the health of HL adolescents is therefore a national priority given the dramatic expansion of this population.

Along with the increasing size of the HL population, disparities in mental health and substance use among HL youth are essential for researchers, practitioners, and policy makers to consider. Previous research has suggested that, despite having equal rates of major depressive episodes, HL youth are less likely to obtain mental health services compared to their non-HL White peers (Center for Behavioral Health Statistics and Quality, 2021), likely placing them at risk for mental health problems and unmet needs (Alegria et al., 2010; Ho et al., 2007). Further, HL youth with high levels of internalizing symptoms, such as depression and anxiety, may be less likely to access mental health services compared to HL youth with externalizing symptoms (e.g., conduct disorder), who are often more readily identified by parents and caregivers (Gudino et al., 2008). Potential barriers to health care among immigrants described in the literature, and that could explain HL youth’s lack of mental health services, include (a) worries related to being reported to authorities by health providers (e.g., Jacquez et al., 2016), and (b) concerns that health providers may not be able to relate to or speak to their experiences because the provider is an outgroup member or because there is a language barrier (e.g., Hacker et al., 2015; Jacquez et al., 2016). Understanding the mental health status of HL youth in the political environment surrounding them–and their families–might serve as a first step to clarifying the existing healthcare access inequities in this population in future research.

In addition, compared to non-HL Whites and African Americans, before age 13, more HL youth: (1) initiate drinking, (2) consume more illicit drugs, and (3) binge drink (Centers for Disease Control and Prevention, 2019; Johnston et al., 2020). Research has indicated that, compared to non-HL Whites with substance use disorders (SUD), HL youth with these disorders access formal SUD services at a considerably lower rate (Alegria et al., 2011). These mental health and substance use inequities, along with the growing representation of HL and other ethnic minority youth in the U.S., might have contributed to the U.S. ranking among the world’s top five countries for alcohol and drug misuse-related injury mortality since 1990 (e.g., interpersonal violence and road traffic accidents; Institute for Health Metrics and Evaluation, 2020). Although these statistics are not the “fault” of minority individuals, they underscore that persistent inequities in these outcomes are alarming and need to be addressed if the prevalence of alcohol and drug related injury mortality is to be reduced in the United States. It should be noted that these inequities are, at least to some extent, rooted in politically charged laws, policies, and practices that have marginalized HL communities (Feagin & Bennefield, 2014).

Among the multiple causes driving HL youth mental health and substance use inequities in the U.S., the political climate (i.e., aggregate mood and opinions of society towards politics at a particular time) might play a significant role (Eskenazi et al., 2019). In 2018, during President Donald Trump’s administration, 47% of adult HLs reported that their mental health condition in the U.S. had worsened in the past year, 49% expressed concern about their place in U.S. society, and 55% were afraid of potential deportation (Pew Research Center, 2018). Nevertheless, the proportion of HL adults who reported that they were angry about the country’s political condition dropped by 52.3% between June and November 2020 (i.e., in the weeks following the 2020 presidential election). Similarly, optimism among HL adults increased by 28% during this same time period, with 67% of HL adults reporting being hopeful about the country’s future by the end of 2020 (Noe-Bustamante, 2020). In terms of political affiliation (membership or close association with a political party or organization), shifts in feelings about the country’s state were divergent after the election – hope increased by 53.2% among HL adults who identified as Democrats but decreased by 25.4% among those who identified as Republicans (Noe-Bustamante, 2020). These immediate changes in feelings and perceptions related to the U.S. political climate suggest that: (1) HLs are highly sensitive to the sociopolitical atmosphere and to shifts in this climate across time, and (2) HLs’ feelings about political issues are closely tied to their party affiliation. Most existing research on HL individuals’ perceptions of the U.S. political climate have focused on adults. Surprisingly, there is a lack of similar analyses conducted on youth populations, even though there is evidence of the association between caregivers’ party affiliation and children’s anxiety about political news (Caporino et al., 2020).

More research–especially among adolescents–is needed to understand how perceptions of negative political climate can impact internalizing symptoms and substance use among HL communities residing in liberal and conservative U.S. states. Especially for more liberal HLs and for those who are undocumented (or who have undocumented family members), the Trump administration’s immigration policies were likely perceived as threatening or stressful (Benavides et al., 2021). We refer to this type of environmental threat/stress as negative political climate. Consistent with social stress theory (Pearlin, 1989, 1999), in which socially disadvantaged individuals are more likely to be exposed to stressors that lead to a higher risk of mental illness (Mossakowski, 2014), among HL youth, cultural stress associated with a negative political climate has been linked with adverse health outcomes. For example, having an undocumented parent has been associated with poor emotional well-being among HL children (Brabeck & Xu, 2010). Parental deportation and a negative context of reception (i.e., feeling unwelcome and shut out of opportunities) have been associated with symptoms of depression among HL adolescents (Gulbas et al., 2016; Schwartz et al., 2014). Additionally, the effects of negative political climate among HL youth may be most deleterious for young women (Stafford et al., 2019), for youth whose parents are foreign-born (Roche et al., 2018; Suárez-Orozco & López Hernández, 2020), among youth who are further along in adolescence, for youth with less highly educated parents, from lower-income households, and for youth from larger families (Capps et al., 2020; Chavez et al., 2019).

The political climate often changes following an election and the installment of a new president. This volatility increases the challenges involved in understanding how political positions might affect vulnerable subgroups such as HL adolescents – but it also presents opportunities for examining how elections might affect HL adolescents’ cultural stress, internalizing symptoms, and substance use and misuse. In addition, given the importance of differences in perceptions of the political climate between Democrats and Republicans, it may be useful to compare more versus less liberal cities such as Los Angeles and Miami. Specifically, whereas Los Angeles voted strongly Democratic (64%) in both 2016 and 2020, Miami voted less strongly Democratic in 2020 (a 7-point margin in 2020 versus a 29-point margin in 2016).

The Present Study

In the present study, we aimed to explore the perception of negative political climate among HL adolescents before and after the 2020 U.S. presidential election in a strongly liberal city (Los Angeles) and a less strongly liberal city (Miami). We also examined the links of changes in perceived political climate with internalizing symptoms (depression and anxiety), and subsequently with substance use and misuse. Our overall objective was to shed light on politically based cultural stress as a risk factor for mental health and substance use problems among HL adolescents. Despite the critical role that youth–and parental–political party affiliation may have on the perception of how HL youth interpret the political climate in their community and therefore affect their behavior, we did not assess this information at the individual level. The results we report here should be interpreted in light of this omission.

In terms of hypotheses, we expected that, prior to the 2020 election, in Los Angeles–where most HLs are of Mexican or Central American descent – HLs would perceive a more negative political climate than in Miami, where many Cuban Americans vote Republican. We hypothesized this result given existing immigration laws, such as the Cuban Adjustment Act, which has facilitated the entry and integration of Cuban citizens into the US since 1966 (Nackerud et al., 1999), as well as the negative attention to immigration across the US-Mexico border during the election. Additionally, we examined other potential sociodemographic predictors of perceived negative political climate among HL youth before and after the election, such as age, gender, study site, mother’s nativity, and mother’s education (see the „Participants’ section for detailed information). Controlling for these other predictors allowed us to draw stronger conclusions regarding the predictive role of negative political climate vis-à-vis adolescent outcomes. Our primary analyses therefore examined the predictive associations of perceived pre- and post-election negative political climate with internalizing symptoms (i.e., depression and anxiety), substance use intentions, and substance misuse following the election.

Methods

Participants

Study participants were 304 self-identified Hispanic adolescents from Miami (36%) and Los Angeles (64%). Participants were primarily female (61%), aged 15.3 years on average (SD = .76, range 14–17 years), US-born (79%), and had foreign-born mothers (79%). The sample was diverse in terms of country of familial origin (58% Mexican, 25% Cuban, and 12% Central American [Guatemalan, Honduran, Nicaraguan, and Salvadoran], and 5% South American [Argentinean, Chilean, Colombian, Peruvian, Uruguayan, Venezuelan]), and year in school (9th = 18%, 10th = 51%, 11th = 30%, and 12th = 1%). Students reported an average household size of 5 members (SD = 1.8, range 2–12 household members). In terms of mothers’ education, 36% of participants’ mothers did not graduate from high school, 22% graduated from high school only, 23% attended college but did not graduate, and 18% graduated from college. The Miami sample was 66% Cuban or Cuban American (as evidenced by adolescents’ and parents’ places of birth), and the Los Angeles sample was 91% Mexican or Mexican American (see Appendix A for detailed participants characteristics by study site).

Procedures

In the current study we used two different waves of data, gathered before and after the 2020 U.S. presidential election, as part of a larger multi-phase sequential mixed-methods project (Creswell & Clark, 2017). Two research teams (1) recruited participants virtually from three high schools – one team recruited from one school located in Miami and the other team recruited from two schools in Los Angeles and (2) described study details to parents and their adolescent children via phone, text, and email. After providing electronic informed parental consent and youth assent, students completed the same online survey twice–through Qualtrics and REDCap survey platforms–in the Spring and Summer of 2020 (Wave 1) and the Spring of 2021 (Wave 2). A total of 29% of participants were lost to follow-up. No significant differences were found –on sociodemographic variables and baseline study outcomes–between participants who dropped out and those who stayed in the study, suggesting a low risk of selection bias due to differential attrition. The survey consisted of a series of items asking about demographics, cultural stress, acculturation, mental health, and substance use. We used a two-step translation process (Sireci et al., 2006) to generate a Spanish version of the original English survey. As part of the first step, a bilingual research team member translated the instrument from English to Spanish. As part of the second step, a second bilingual research team member translated the survey from Spanish to English. Approximately, 90% of participants chose to complete the survey in English. Finally, the two translators evaluated and resolved discrepancies. All participants received a $15 gift card as an incentive. The Institutional Review Boards from the two participating universities approved all procedures.

Measures

Demographics.

We incorporated demographic characteristics measured at wave 1 (i.e., before the election). Participants reported on their age in years, gender (male = 1, female = 0), study site (Miami = 1, Los Angeles = 0), mother’s nativity (U.S. born mother = 1, foreign-born mother = 0), household size (total number of household members residing), and mother’s educational background (e.g., did not graduate from high school = 1, and graduated from college = 4). Because of the low socioeconomic status of the sample, mother’s education was recoded as 1 = high school graduate vs. 0 = did not graduate from high school.

Negative political climate.

Negative political climate was measured using six self-reported items derived from the Multidimensional Inventory of Cultural Stress (Meca et al., 2023). The MICS items were generated based on focus groups conducted with 34 Hispanic adolescents in Miami and Los Angeles (Meca et al., 2023). The Negative Political Climate items assess the current political rhetoric surrounding immigration as perceived by study participants (e.g., With the current political situation I have felt more fearful). Items were rated on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Higher scores indicated greater perception of a negative political climate. Responses to the six items were summed to calculate a total score for each participant (before the election [M = 23.6, SD = 8.16, α = .88]; after the election [M = 24.4, SD = 6.66, α = .85]).

Internalizing symptoms.

We assessed adolescents’ internalizing symptoms at both pre- and post-election in terms of depression and anxiety. We measured depressive symptoms using the 10-item Boston Form of the Centers for Epidemiologic Studies Depression Scale (CES-D; Grzywacz et al., 2006). This short version of the CES-D (CESD-B) assesses depressive symptomatology during the week prior to assessment using specific symptoms, such as (1) I felt depressed, (2) I felt lonely, and (3) I felt that people disliked me. Items were rated on a 4-point scale, ranging from 1 (rarely or none of the time) to 4 (almost all the time). We calculated CESD-B scores by summing the scores for all ten items (before the election [M = 22.2, SD = 5.32, α = .73]; after the election [M = 22.9, SD = 5.31, α = .73]).

We assessed anxiety using the 7-item Generalized Anxiety Disorder scale (GAD-7; Spitzer et al., 2006). The GAD-7 assesses symptoms of anxiety during the two weeks to prior assessment, such as (1) nervousness, (2) inability to stop worrying, (3) excessive worry, (4) restlessness, (5) difficulty in relaxing, (6) easily irritated, and (7) fear of something awful happening. Participants answered each item using a 4-point Likert scale ranging from 1 (not at all) to 4 (nearly every day). We calculated the total GAD-7 score by summing the responses across the seven items (before the election [M = 13.53, SD = 5.58, α = .92]; after the election [M = 14.7, SD = 6.57, α = .95]).

Substance use.

We assessed both substance misuse and intentions to use. To assess substance misuse, we used the 6-item CRAFFT (Car; Relax; Alone; Forget; Friends; Trouble) questionnaire developed by Knight et al. (1999). The CRAFFT is a brief tool intended to screen for substance-related risks and problems among adolescents. Participants responded to each of the six CRAFFT questions (e.g., Have you ever ridden in a car driven by someone [including yourself] who was “high” or had been using alcohol or drugs?) using a yes/no response scale. We calculated the CRAFFT score as the number of “yes” responses (M = .367, SD = 1.01, α = .80). Intentions to use cigarettes, marijuana, and alcohol were measured by asking adolescents about their intentions to use these substances (tobacco products, marijuana products, and alcohol) in the next year. Participants rated items using a 4-point Likert scale ranging from 1 (definitely not) to 4 (definitely yes). We calculated a total score for intentions to use substances by summing the responses to all items (M = 10.5, SD = 3.31, α = .89).

Data Analysis Plan

We used STATA version 17 (Statacorp, 2021) to examine the bivariate correlations among hypothesized baseline predictors of perceived negative political climate, as well as correlations of these baseline predictors with changes in negative political climate across time. We calculated Pearson r between pairs of continuous variables, polychoric correlations between binary and continuous variables, and tetrachoric correlations between pairs of binary variables. We explored frequencies (percentage agreement) before and after the election for each response option within each of the six negative political climate scale items. The McNemar’s test for paired nominal data was used to determine whether there was a significant change in item response options at the two time points. Then, mean differences between pre- and post-election individual item scores were analyzed using paired t-tests. Furthermore, changes in the overall adjusted total score for perceived negative climate before and after the election were estimated using linear mixed-effects (LME) modeling (Laird & Ware, 1982). We treated all LME intercept coefficients as random. The Time variable (i.e., data collection time point) was modeled as continuous and coded as 0 (pre-election) and 1 (post-election). Model building proceeded by examining a conditional model including demographic variables. We calculated model-adjusted mean differences for negative political climate, controlling for demographics, before and after the election. Analyzing response options and item-level differences between pre- and post-election time points allows us to specify the dimensions or components of negative political climate that may have been responsible for increases or decreases in the total political climate score. Controlling for demographic variables, and using maximum likelihood estimation, in our comparisons of total scale scores between pre-and post-election time points allows us to statistically equate youth living in Miami and in Los Angeles, from different national origins, across sex and age, and between youth whose mothers were U.S.-born versus foreign-born. Additionally, as an exploratory analysis, we conducted sub-group item-level analyses and a comparison of adjusted means across time, between sites, and within the largest national groups in Miami and Los Angeles (i.e., Cubans/Cuban Americans and Mexicans/Mexican Americans).

We next estimated a structural model (see Figure 1) using Mplus Version 7 (Muthén & Muthén, 2011) to test the research questions and hypotheses guiding the study. For this model, we created a latent variable for internalizing symptoms as part of this model because anxiety and depressive symptoms were extremely highly correlated (r = .70, p < .001). The two substance use indicators, substance misuse and intentions to use, were less strongly correlated (r = .57, p < .001), so we did not create a latent variable for substance use. Finally, as an additional exploratory analysis, we used the MODEL INDIRECT command in Mplus to test all potential paths from pre-election perceived negative political climate to: (1) internalizing symptoms, (2) substance misuse, and (3) substance intentions to use indirectly through post-election negative political climate. We evaluated the fit of the structural model to the data using the comparative fit index (CFI; Bentler, 1990), the Tucker-Lewis fit index (TLI; Tucker & Lewis, 1973), and the root mean square error of approximation (RMSEA; Browne et al., 1993). We indexed good model fit as CFI/TLI ≥ .95 and RMSEA ≤ .05, following Kline (2015) methodological recommendations. Regarding baseline parental covariates, we focused solely on the mother’s nativity and education to: (1) accommodate youth from single-parent families, (2) prevent multicollinearity problems as mothers’ and fathers’ demographic variables were relatively strongly (r > .4) correlated, and (3) obtain the best fitting and most parsimonious model. For all models we used a robust maximum likelihood estimator to include cases with missing data in analysis, and to adjust parameter estimates and fit statistics for the effects of non-normality.

Figure 1.

Figure 1

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Path Model for Perceived Negative Political Climate, Internalizing Symptoms, and Substance Use Before and After the 2020 US Presidential Elections

Note. All predictor variables were allowed to correlate with each other. Residual variances are not depicted

Results

Bivariate correlations

Table 1 presents the bivariate correlations among the baseline predictors of negative political climate. According to Cohen’s guidelines (Cohen, 1988), most correlation coefficients among predictors were of medium magnitude (.30); however, some small (.10) and large (.50) correlations also were observed in our sample. The two mother-related predictors (i.e., nativity and education) and household size at baseline differed significantly across the two study sites. Specifically, there were more U.S.-born mothers with higher education levels, and households with fewer members, in the Miami sample. Additionally, higher maternal education levels were significantly associated with older youth age, having U.S.-born mothers, and smaller household sizes.

Table 1.

Bivariate Correlations Among Study Variables Predicting Perceived Negative Political Climate

Study variable 1 2 3 4 5 6 7
1. Male 1.00 – – – – – –
2. Age .143 1.00 – – – – –
3. Site (Miami) .051 .183* 1.00 – – – –
4. U.S born mother −.128 −.002 −.277* 1.00 – – –
5. Household size −.098 −.112t −.495*** −.001 1.00 – –
6. Mother education level .037 .124* .656*** .322** −.209** 1.00 –
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t

p < .10

*

p < .05

**

p < .01

***

p < .001

Perceived negative political climate before and after the elections

Item-level change.

Table 2 summarizes item response option frequencies, means, and relative pre-post-election changes (along with statistical tests) for perceived negative political climate. Of the six items on the negative political climate subscale, responses to items 1 to 3 significantly declined after the election, whereas responses to items 4 to 6 decreased (the decrease was significant only for item 6). After the election, 35% more adolescents somewhat disagreed with having felt more fearful due to the current political situation, 45% fewer youth agreed, and 74% fewer strongly agreed, with this statement (item 1). This tendency led us to observe an overall significant reduction of almost 14% in responses to item 1. Similarly, we observed a significant 21% reduction in the number of students who strongly agreed that tougher immigration laws made them afraid for their own or their family’s future in the United States (item 2). In addition, after the election, we observed a significant 29% reduction in the number of adolescents who strongly agreed that, with the current political situation, students are more afraid about what may happen to them or their families (item 3). The mean score for item 3 also decreased significantly, by 15.5%, after the election. Concerning items 4, 5, and 6, we found significant reductions of 56.5%, 59.1%, and 20.4%, respectively, in the number of students who–following the election–strongly disagreed with being worried about: (1) what the end of the Deferred Action for Childhood Arrivals (DACA) and other protections would do to their family or friends, (2) government deportations of people they love, and (3) attacks against Hispanic/Latinos. All of these differences were statistically significant. Finally, we observed a significant 64.8% increase in the proportion of students who strongly agreed that they were worried about attacks against Hispanic/Latino populations. This increase in worries related to potential attacks on Latin-American communities was reflected in an overall significant 15.5% positive change in the mean score for this item. Similar patterns of change in item 6–related to potential attacks on HLs–were found in students from Miami (Mdiff = 1.14; t (68) = 5.10, p < .001) and those of Cuban descent (Mdiff = 1.03; t (33) = 3.30, p =.002). Responses to this item also increased, albeit to a lesser extent, among adolescents from Los Angeles (Mdiff = .404; t (145) = 4.17, p < .001) and among those of Mexican origin (Mdiff = 0.42; t (107) = 3.70, p <.001).

Table 2.

Perceived negative political climate among Hispanic adolescents before and after the 2020 U.S. Presidential elections

Before After Difference p-value
n % n % Δ % Change
Item 1. With the current political situation, I have felt more fearful
 Strongly Disagree 30 9.87 20 9.26 −0.61 −6.18 .847
 Disagree 54 17.8 50 23.2 5.39 30.3 .140
 Somewhat disagree 51 16.8 49 22.7 5.91 35.2 .036*
 Somewhat agree 67 22.0 64 29.6 7.59 34.4 .063
 Agree 69 22.7 27 12.5 −10.2 −44.9 .003*
 Strongly Agree 33 10.9 6 2.78 −8.08 −74.4 <.001*
 Mean (SD) 3.73 1.54 3.21 1.27 −.521 −13.9 <.001*
Item 2. Tougher immigration laws make me afraid for my or my family’s future in this country
 Strongly Disagree 54 17.8 25 11.6 −6.19 −34.9 .056
 Disagree 44 14.5 39 18.1 3.59 24.8 .114
 Somewhat disagree 31 10.2 23 10.7 .450 4.41 .758
 Somewhat agree 45 14.8 44 20.4 5.57 37.6 .216
 Agree 66 21.7 49 22.7 .980 4.51 .414
 Strongly Agree 64 21.1 36 16.7 −4.38 −20.8 .011*
 Mean (SD) 3.82 1.81 3.75 1.65 −.070 −1.84 .520
Item 3. With the current political situation, I think students are more afraid about what may happen to them or their families
 Strongly Disagree 16 5.25 7 3.23 −2.02 −38.5 1.00
 Disagree 18 5.90 17 7.83 1.93 32.7 .317
 Somewhat disagree 23 7.54 23 10.6 3.06 40.6 .106
 Somewhat agree 65 21.3 55 25.4 4.04 19.0 .497
 Agree 92 30.2 69 31.8 1.64 5.44 .915
 Strongly Agree 91 29.8 46 21.2 −8.64 −29.0 .006*
 Mean (SD) 4.66 1.31 4.38 1.32 −.278 −5.96 .012*
Item 4. I am worried what the end of DACA and other protections would do to my family or friends
 Strongly Disagree 42 13.8 13 5.99 −7.78 −56.5 .004*
 Disagree 45 14.8 26 12.0 −2.77 −18.8 .746
 Somewhat disagree 39 12.8 37 17.1 4.26 33.3 .258
 Somewhat agree 56 18.4 40 18.4 .070 .381 .900
 Agree 67 22.0 57 26.3 4.30 19.6 .204
 Strongly Agree 56 18.4 44 20.3 1.92 10.5 .505
 Mean (SD) 3.88 1.70 4.07 1.51 .190 4.89 .056
Item 5. I am worried that people I love will be deported
 Strongly Disagree 55 18.0 16 7.37 −10.7 −59.1 .004*
 Disagree 32 10.5 31 14.3 3.80 36.2 .746
 Somewhat disagree 16 5.25 23 10.6 5.35 102 .258
 Somewhat agree 40 13.1 36 16.6 3.48 26.5 .900
 Agree 68 22.3 50 23.0 .740 3.32 .204
 Strongly Agree 94 30.8 61 28.1 −2.71 −8.79 .505
 Mean (SD) 4.10 1.85 4.18 1.64 .079 1.92 .447
Item 6. I worry about attacks against Hispanic/Latinos
 Strongly Disagree 42 13.8 1 11 −2.82 −20.4 .019*
 Disagree 34 11.2 7 3.24 −7.94 −71.0 .001*
 Somewhat disagree 21 6.91 13 6.02 −.890 −12.9 .695
 Somewhat agree 59 19.4 28 13.0 −6.45 −33.2 .116
 Agree 78 25.7 75 34.7 9.06 35.3 .042*
 Strongly Agree 70 23.0 82 38.0 14.9 64.8 <.001*
 Mean (SD) 4.18 1.67 4.83 1.35 .647 15.5 <.001*
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Note. The McNemar’s test for paired nominal data was used to determine whether there was a significant change in item response options. Paired T-tests were used to define whether the mean difference between two observations (before vs. after) were equal to zero.

*

= p < .05 or lower

Overall total score change.

Results of the overall mean total score for perceived negative climate before and after the election are presented in Table 3. When comparing the overall mean total score in the full sample, we did not find any significant difference (Mdiff = 0.18, p = .657). However, a significant increase in perceived negative political climate was observed among students in Miami (Mdiff = 1.62, p = .020) but not in Los Angeles (Mdiff = −0.56, p = .241). When we examined these patterns among Cubans/Cuban Americans and Mexicans/Mexican Americans, we found that negative political climate increased significantly between pre- and post-election among individuals of Cuban descent (Mdiff = 1.41, p = .045). Negative political climate decreased among individuals of Mexican descent, but this difference was not statistically significant (Mdiff = −.782, p = .110).

Table 3.

Adjusted Mean Predictions and Change of Negative Political Climate Before and After the 2020 U.S. Presidential Election

Before After Before vs. After p-value
M SE M SE Mdiff SE
Full sample 23.9 .385 24.0 7.45 .176 .395 .657
Site
Miami 19.4 .649 21.1 .748 1.62 .696 .020
Los Angeles 26.1 .477 25.6 .528 −.562 .480 .241
Largest national group
Cuban/Cuban American 19.8 .658 21.2 .762 1.41 .706 .045
Mexican/Mexican American 26.6 .486 25.9 .538 −.782 .489 .110
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Note. Structural model-implied estimates controlled by age, gender, site, mother’s nativity, household size, and mother’s education. Mdiff = adjusted mean difference. SE = standard error.

Structural model

Direct effects.

The structural equation model provided a good fit to the data, χ2 (27) = 37.9, p = .08; CFI =.98; TLI = .95; and RMSEA =.037. As shown in Figure 1, on the internalizing symptoms factor, anxiety and depressive symptoms were associated with standardized factor loadings of .762 and .889, respectively. Perceived negative political climate before the election was negatively related to being male (β = −.290, p < .001), residing in Miami (β = −.451, p < .001), and having a U.S.-born mother (β = −.258, p < .001). In turn, perceived negative political climate before the election significantly and positively predicted perceived negative political climate following the election (β = .642, p < .001). Internalizing symptoms following the election were negatively related to being male (β = −.214, p < .01), and positively associated with perceived negative political climate measured after the election (β = .217, p < .01). Finally, post-election internalizing symptoms were significantly related to post-election substance misuse (β = .217, p < .001), and with intentions to use substances (β = .245, p < .001).

Indirect effects.

Asymmetric distribution of products tests indicated significant indirect paths from pre-election negative political climate to post-election internalizing symptoms (β = .139, p < .05); and substance intentions to use (β = .042, p < .05) (See Table 3). We also found significant indirect paths from post-election negative political climate to post-election substance intentions to use (β = .067, p < .05) through post-election internalizing symptoms. Indirect effects from pre- and post-election negative political climate to post-election substance misuse were both marginally significant.

Discussion

We conducted the present study to explore the perceived negative political climate before and after the 2020 U.S. Presidential election among a sample of HL adolescents from Los Angeles and Miami. We also assessed potential demographic predictors of negative political climate and of its associations with (1) internalizing symptoms, (2) substance misuse, and (3) substance use intentions after the election. Our results indicated that, across the two sites, overall perceived negative political climate did not change significantly before versus after the election; however, specific components of negative political climate did change, with decreases in fear occurring alongside increases in worry. We also observed significant changes in perceived negative political climate in Miami, and among Cuban/Cuban American students in particular. Pre-election perceived negative political climate was significantly predicted by gender (higher among girls), study site (higher in Los Angeles), and mother’s nativity. In addition, our findings suggest that adolescents’ internalizing symptoms (i.e., depression and anxiety), and substance intentions to use after the election were predicted not only by post-election negative political climate perceptions but also by pre-election levels of this same cultural stressor.

Findings suggest that total negative political climate scores did not change before versus after the 2020 U.S. presidential election. This result might be explained by different patterns of findings for items assessing adolescents’ fear (items 1–3, which decreased) versus worry (items 4–6, which increased) about the U.S. political situation and immigration policies. Overall, we observed that, prior to the election, more affective anxiety components, such as fear, were more predominant than cognitive anxiety components, such as worrying. However, this pattern was reversed after the elections – for example, worry about what might happen (e.g., verbal or physical attacks against HLs) increased. Furthermore, we observed an overall increase in perceived negative political climate among adolescents residing in Miami, and particularly among those who were of Cuban origin. This increase appeared to be attributable to worries about attacks against HL individuals and communities. However, no significant changes in the total negative political climate score emerged among adolescents from Los Angeles or who were of Mexican origin and increases in responses to the item referring to worries about attacks against HLs were quite small.

Given the predominance of Republican voters (~61%) in high-density Latino Miami-Dade County precincts (80% Latino or more) in 2020 (Dominguez-Villegas et al., 2021), and the fact that more than half of Latinos in Miami-Dade County are Cuban Americans who have traditionally supported the Republican Party (Dominguez-Villegas et al., 2021), we believe that our findings align with previous evidence suggesting that (1) caregivers’ party affiliation might influence children’s party identification and anxieties about politics (Caporino et al., 2020); and (2) conservativism could be associated with intolerance of uncertainty and fearfulness (Jost et al., 2003; Lilienfeld & Latzman, 2014). Our findings might be interpreted within a context where, by December 2020, more than 70% of Republicans believed there was widespread fraud in the presidential election and indicated that they did not trust the accuracy of the results (Keating, 2020; Montanaro, 2020). Although we did directly assess political party affiliation in the present study, the voting patterns among Hispanic residents in Miami suggest a very likely predominant Republican representation in our Miami sample. Such a conservative context would likely be characterized by a combination of low tolerance for uncertainty with high levels of mistrust in government. In turn, such low tolerance for uncertainty and high mistrust in government could have prompted significant doubts not only about the election results but also about potential attacks against those HL who did not vote for President Biden–as the majority of HL individuals did nationwide. Nonetheless, we recognize that more research is needed to further advance our understanding of how different types of anxiety, national origins, and party affiliations might influence perceived negative political climate changes among HL youth.

Results also indicated that perceived negative political climate before the election was significantly lower in boys residing in Miami and whose mothers were US-born. The links between being male and experiencing a reduced emotional response to electoral results (and to their effects on the political climate) are consistent with prior research with adolescents and young adults (DeJonckheere et al., 2018). Furthermore, as we hypothesized, before the election, adolescents living in Miami perceived a less hostile political climate than did youth from Los Angeles. The finding that Cuban adolescents in Miami felt less threatened before the election could be related to collecting our first wave of data during a Republican presidency with Republican control of the Senate. Moreover, as mentioned, historical laws such as the Cuban Adjustment Act (Nackerud et al., 1999) might potentiate this sense of security in those adolescents who, by virtue of being of Cuban origin, did have less fear about deportation. In terms of increases in negative political climate among Cuban-origin adolescents in Miami after the election, it should be noted that, just prior to leaving office in January 2017, President Barack Obama rescinded the “wet foot, dry foot” policy that allowed Cubans to stay in the United States as long as they touched U.S. soil before being arrested or detained by authorities. Cuban-origin youth may have been concerned that the inauguration of another Democratic Party president might have been detrimental to the interests of the Cuban American community. Further qualitative work is necessary to explore this potential explanation for our current findings.

In addition, we found that having a U.S.-born mother might predict lower levels of negative political climate before the elections. Prior studies on HL youth’s feelings during the 2016 election season have found a similar link between having foreign-born parents and feeling fearful and angry about anti-immigrant policies (Andrade, 2019; Suárez-Orozco et al., 2015; Suárez-Orozco & López Hernández, 2020; Teranishi et al., 2015). Youth whose parents were born in the U.S. are likely to worry considerably less about familial deportation policies than will youth whose parents are foreign-born – because U.S.-born parents cannot be deported.

In terms of associations between negative political climate and other study variables, findings suggested that perceived negative political climate is positively associated with internalizing symptoms and intentions to use substances. Our results are consistent with Eskenazi et al. (2019), who found that, in the first year after the 2016 presidential election, fear and worry about the personal consequences of U.S. immigration policy were associated with higher anxiety levels among a primarily Mexican-descent sample of HL adolescents in California. However, Eskenazi et al. (2019) did not find a significant relationship between youth perceived immigration policy and depressive symptoms. Two potential explanations for the difference in findings might center on (1) the inclusion of HL youth both Miami and Los Angeles in our sample versus only a Los Angeles sample in the Eskenazi et al. study and (2) the inclusion of only U.S.-born individuals within Eskenazi et al.’s sample. Our finding that the “political situation” impacts not only youth anxiety, but also depressive symptoms, is consistent with results reported by Caporino et al. (2020). However, our study is one of the first conducted exclusively with HL youth. Furthermore, our study was conducted during the COVID-19 pandemic (2020–2021), and the gender differences we observed in internalizing symptoms are consistent with other work conducted during this time (Racine et al., 2021). Specifically, both in the studies included in Racine et al.’s meta-analysis and in our results, girls scored significantly higher than boys on symptoms of anxiety and depression.

Lastly, we found that a perceived negative political climate before and after the 2020 elections predicted youth substance use. Although not all of the paths in our model are longitudinal, our findings suggest a multistage risk pathway wherein a perceived negative political climate before a presidential election might increase the likelihood of maintaining such negative perception after the election, which may in turn predict internalizing symptoms (i.e., depression and anxiety), which then may increase the risk of substance misuse and intentions to use. Although we did not find any specific direct effects between perceived political climate and substance use outcomes, we believe that our results increase the body of evidence about risk factors for risk-taking behaviors in U.S. HL youth. Thus, it is crucial to focus attention on early and ongoing exposure to socially negative environments as a foundational social factor vis-à-vis vulnerability to substance use and its consequences (Amaro et al., 2021). Finally, although more research is needed to establish a link between political climate and substance use, our findings are consistent with other studies reporting that some HL adolescents with elevated internalizing symptoms may turn to alcohol and drugs to cope with their symptoms (Gonzales et al., 2017; Hussong et al., 2011).

Limitations and Future Directions

We should consider the empirical results reported herein in light of some limitations. First, we recruited participants virtually during the COVID-19 pandemic. This situation presented several challenges that led our study to have a (1) reduced and less balanced sample across the two sites than we had anticipated and (2) limited statistical power (because our sample size was lower than we had planned) to detect significant links between perceived political climate and adolescents’ substance misuse. Additionally, the use of only Miami and Los Angeles as study sites may have yielded a sample of HL youth that might not fully represent the U.S. HL population. For example, only 5% of our participants were Puerto Rican, Dominican, or South American. Future studies could be conducted in other U.S. cities or states and populations so that our risk pathway involving political climate, internalizing symptoms, and substance use can be tested within a more representative sample. Further, it is important to contextualize the validation of our negative political climate scale within the specific time and setting. As data were collected towards the end of the Trump administration and during COVID-19, the stability of our inferences under different sociohistorical conditions is still unknown.

The use of only self-report measures is another important limitation. We do not know whether the findings would have been different had we used objective measures of political polarization. We also did not ask specifically about the election, meaning that we cannot be completely sure that the changes we observed were caused by the change of government rather than by other potential determinants. Similarly, we did not instruct the participants to think about their local or national political context. For example, in Miami, participants might be more hyperaware of their state’s anti-immigrant policies. In contrast, Los Angeles participants were in a state with more immigrant-friendly policies, which also represents the most prominent metropolitan home for DACA recipients–with more than seven times the number of beneficiaries living in Florida (U.S. Citizenship and Immigration Services, 2022). Given this limitation, we echo the recommendation forwarded by Morey (2018), who highlighted the need to conduct more research to understand the multilevel effects of immigration rhetoric at the individual, local (e.g., city, state), and national levels.

Regarding baseline covariates, our analyses included only maternal–and not paternal–sociodemographic data (i.e., education, nativity). Although this analytical decision might represent a potential limitation, we are confident about our analytical approach as similar findings–and worsened model fit– emerged when we included fathers’ demographic information. We also did not ask youth about the political party that they or their parents supported – such a question is critical to ask in future work to understand the role that nation of familial origin and party affiliation might play in the perception of the political environment and its effects on youth behavior.

Finally, although our study included two time points, it was not fully longitudinal. The predictive sequence we tested consisted of four steps (pre-election political climate, post-election political climate, internalizing symptoms, and substance use), such that four time points would have been needed to test our model using a fully longitudinal design. The availability of only two time points meant that the paths from post-election political climate to internalizing symptoms and to substance use could only be specified as cross-sectional, and future work should use fully longitudinal designs. If our results are replicated in the context of future elections, they may find use in policy initiatives, such as those aimed toward helping HL youth to integrate successfully into U.S. receiving contexts and their associated political landscapes.

Conclusion

Despite these and other limitations, our study is one of the first to examine pre- to post-election differences in political climate perceptions among HL youth in two different U.S. cities. Our study is certainly among the first to do so in the context of the end of the Trump presidency. The use of two culturally and politically different cities in different parts of the U.S. suggests that HL youth, like their adult counterparts, are not a monolithic cultural or political group. Rather, some HL youth, especially those in Miami and those of Cuban descent, may be more politically conservative and may not have been pleased by the results of the 2020 election (see Galbraith & Callister, 2020). Our finding that some components of negative political climate increased after the election, whereas others decreased, suggests that HL youth’s perceived political climate is a complex construct. We hope that the present study inspires more work in this direction and paves the way for a body of evidence on politically-based cultural stress as a risk factor for mental health and substance use among HL adolescents. We also hope that this work will help researchers to design or enhance positive youth development interventions to teach HL youth strategies to cope with an adverse and changing political climate in childhood and adolescence.

Table 4.

Specific Indirect Effects Between Perceived Negative Political Climate, Internalizing Symptoms, and Substance Use

Specific Indirect Effect β SE p-value
Perceived Negative Political Climate → Internalizing Symptoms
NPC 1→NPC 2→INT 2 .139 .056 .012
Perceived Negative Political Climate → Internalizing Symptoms → Substance Misuse
NPC 1→NPC 2→INT 2→SMU 2 .030 .017 .066
NPC 2→INT 2→SMU 2 .047 .025 .062
Perceived Negative Political Climate → Internalizing Symptoms → Substance Intentions to Use
NPC 1→NPC 2→INT 2→SIU 2 .034 .180 .050
NPC 2→INT 2→SIU 2 .053 .027 .050
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Note. Structural model-implied estimates controlled by age, gender, site, mother nativity, household size, and mother’s education. β = standardized correlation coefficient. SE = standard error. NPC 1= perceived negative political climate before the elections. NPC 2= perceived negative political climate after the elections. INT 2= internalizing symptoms after the elections. SMU 2 = substance misuse after the elections. SIU 2 = substance intentions to use after the elections.

Acknowledgements.

We wish to thank the study participants for their collaboration and the research project leaders for reviewing the results to prevent any potential biases or misinterpretations.

Funding statement.

This study was supported by the National Institute of Child Health and Human Development of the National Institutes of Health (HD095636).

APPENDIX

Appendix

Appendix A

Baseline program sample characteristics for the full sample and across study sites

Characteristic Full sample Miami Los Angeles
Sample Size 304 108 (36%) 196 (64%)
Sex
 Female 185 (61%) 64 (59%) 121 (62%)
 Male 118 (39%) 44 (41%) 74 (38%)
Age (Mean, SD) 15.3 (0.76) 15.4 (0.75) 15.2 (0.76)
Nativity
 Non-US born 63 (21%) 11 (6%) 52 (48%)
 US-born 241 (79%) 185 (94%) 56 (52%)
Mother’s nativity
 Non-US born mother 239 (79%) 146 (74%) 93 (87%)
 US-born mother 64 (21%) 50 (26%) 14 (13%)
Familial nativity
 Mexico 146 (58%) 2 (2%) 144 (91%)
 Cuba 62 (25%) 62 (66%) 0 (0%)
 Central American 31 (12%) 16 (17%) 15 (9%)
 South American 12 (5%) 12 (13%) 0 (0%)
School year
 9th 56 (18%) 27 (25%) 29 (15%)
 10th 154 (51%) 58 (54%) 96 (49%)
 11th 92 (30%) 21 (19%) 71 (36%)
 12th 2 (1%) 2 (2%) 0 (0%)
Household size 5.0 (1.8) 4.1 (1.4) 5.4 (1.8)
Mother education level
 Less than high school 110 (36%) 99 (51%) 11 (10%)
 Graduated from high school 67 (22%) 46 (24%) 21 (19%)
 Some college 68 (23%) 37 (19%) 31 (29%)
 Graduated from college 55 (18%) 12 (6%) 43 (39%)
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Footnotes

Compliance with Ethical Standards. Institutional review board approval was obtained from study institutions. Electronic informed consent was provided by the participants’ legal guardians and electronic assent was obtained from all participants.

Conflict of interest. The authors have no conflicts to disclose.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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