| Literature DB >> 32952388 |
Abstract
Following the advent of the COVID-19 pandemic, analysts have noted a global rise of nationalism as countries have engaged in a number of nationalist moves in response to the pandemic. However, the implication of policy changes at the individual-level remains unclear: do citizens support those nationalist government responses? More importantly, do people tend to be more nationalistic following the outbreak? Building on terror management theory (TMT), this article examines whether and how ideological beliefs affect individuals' support for nationalist policies during the COVID-19 pandemic. According to TMT, to cope with death anxiety, people are predisposed to ideological defense, resulting in cohesion with individuals who validate their beliefs and hostility toward those who threaten them. Thus, we argue that when states' nationalist policies are aligned with their ideology, people tend to support them; yet, when states' nationalist policies contradict their ideology, people tend to withdraw their support. Specifically, this study found that as non-conservatives (compared to conservatives) are more concerned with the virus, they are more likely to show an inclination of ideological validation. Given that their ideology advocates more tolerance, non-conservatives are less likely to support nationalistic policies. To test the hypotheses, we applied structural equation modeling to a March 2020 CNN Poll (nationally representative US data about COVID-19). The statistical analysis demonstrated strong support for our arguments. © Journal of Chinese Political Science/Association of Chinese Political Studies 2020.Entities:
Keywords: Ideological defense; Nationalism; Political ideology; Terror management theory; The COVID-19 pandemic
Year: 2020 PMID: 32952388 PMCID: PMC7486972 DOI: 10.1007/s11366-020-09696-2
Source DB: PubMed Journal: J Chin Polit Sci ISSN: 1080-6954
Fig. 1Causal mechanism linking the pandemic to individuals’ attitudes toward nationalist policies
Descriptive Statistics of Variables Used in Models
| Mean | SD/Proportion | Min | Max | Observations | |
|---|---|---|---|---|---|
| Support for Travel Ban | 1.256 | 0.526 | 0 | 2 | 1211 |
| Degree of COVID-19 Threat | 2.620 | 1.039 | 1 | 4 | 1190 |
| Political Ideology | 2.803 | 1.174 | 1 | 5 | 1176 |
Conservative (1 = Conservative, 0 = Non-conservative) | 0.349 | 0 | 1 | 1211 | |
| Gender (1 = Male, 0 = Female) | 0.548 | 0 | 1 | 1211 | |
| Age | 54.194 | 19.124 | 18 | 96 | 1192 |
| Race (1 = White, 0 = Other) | 0.704 | 0 | 1 | 1211 | |
Household Annual Income (1 = $50 K or above, 0 = below$50 K) | 0.664 | 0 | 1 | 1211 | |
Education (1 = Graduate Degree, 0 = No Graduate Degree) | 0.156 | 0 | 1 | 1202 | |
| Being Employed | 0.522 | 0 | 1 | 1211 | |
| Being Retired | 0.340 | 0 | 1 | 1211 | |
Maximum Likelihood Estimates for the Relationship between the Degree of the COVID-19 Threat and Political Ideology (Model 1)
| Parameter | Unstandardized Coefficients | |
|---|---|---|
| Conservatives | Non-conservatives | |
| Political Ideology | ||
| ← Degree of COVID-19 Threat | 0.019 (0.023) | 0.066* (0.028) |
| ← Gender (1 = Male, 0 = Female) | −0.005 (0.052) | −0.142** (0.054) |
| ← Age | −0.001 (0.002) | −0.008*** (0.001) |
| ← Race (1 = White, 0 = Nonwhite) | −0.114† (0.060) | 0.090 (0.059) |
← Household Annual Income (1 = $50 K or above, 0 = below$50 K) | −0.052 (0.056) | −0.077 (0.059) |
← Education (1 = Graduate Degree, 0 = No Graduate Degree) | −0.043 (0.083) | 0.205** (0.072) |
| ← Being Employed | −0.039 (0.082) | −0.050 (0.082) |
| ← Being Retired | −0.098 (0.093) | 0.052 (0.105) |
| Observations | 423 | 788 |
Note: Goodness of fit test statistics are not reported since the model is a saturated model
Standard errors in parentheses.
†<0.1, * < 0.05, ** < 0.01, *** < 0.001
Maximum Likelihood Estimates for the Relationship between Conservative Ideology and Support for the International Travel Ban (Model 2)
| Parameter | Unstandardized Coefficient |
|---|---|
| Support for Travel Ban | |
| ← Conservative | 0.177*** (0.032) |
| ← Degree of COVID-19 Threat | 0.005 (0.015) |
| ← Gender (1 = Male, 0 = Female) | 0.003 (0.031) |
| ← Age | 0.002 (0.001) |
| ← Race (1 = White, 0 = Nonwhite) | −0.040 (0.034) |
← Household Annual Income (1 = $50 K or above, 0 = below$50 K) | −0.115*** (0.033) |
← Education (1 = Graduate Degree, 0 = No Graduate Degree) | −0.065 (0.043) |
| ← Being Employed | 0.040 (0.047) |
| ← Being Retired | −0.014 (0.058) |
| Observations | 1211 |
Note: Goodness of fit test statistics are not reported since the model is a saturated model
Standard errors in parentheses.
†<0.1, * < 0.05, ** < 0.01, *** < 0.001
Maximum Likelihood Estimates for the Mediating Effect of Political Ideology on the Relationship between Degree of COVID-19 Threat and Support for Travel Ban (Model 3)
| Parameter | Unstandardized Coefficients | |
|---|---|---|
| Conservatives | Non-conservatives | |
| Support for Travel Ban | ||
| ← Political Ideology | −0.117* (0.053) | −0.091*** (0.025) |
| ← Degree of COVID-19 Threat | 0.019 (0.025) | 0.005 (0.019) |
| ← Gender (1 = Male, 0 = Female) | 0.080 (0.056) | −0.049 (0.036) |
| ← Age | 0.003 (0.002) | −0.000 (0.001) |
| ← Race (1 = White, 0 = Nonwhite) | 0.029 (0.066) | −0.064 (0.039) |
← Household Annual Income (1 = $50 K or above, 0 = below$50 K) | −0.205*** (0.061) | −0.083* (0.039) |
← Education (1 = Graduate Degree, 0 = No Graduate Degree) | −0.025 (0.090) | −0.064 (0.049) |
| ← Being Employed | −0.004 (0.089) | 0.060 (0.054) |
| ← Being Retired | −0.145 (0.101) | 0.061 (0.069) |
| Political Ideology | ||
| ← Degree of COVID-19 Threat | 0.019 (0.023) | 0.066* (0.028) |
| ← Gender (1 = Male, 0 = Female) | −0.005 (0.052) | −0.139* (0.054) |
| ← Age | −0.001 (0.002) | −0.007*** (0.001) |
| ← Race (1 = White, 0 = Nonwhite) | −0.114† (0.060) | 0.088 (0.059) |
← Household Annual Income (1 = $50 K or above, 0 = below$50 K) | −0.051 (0.056) | −0.075 (0.059) |
← Education (1 = Graduate Degree, 0 = No Graduate Degree) | −0.043 (0.083) | 0.206** (0.072) |
| ← Being Employed | −0.039 (0.082) | −0.051 (0.082) |
| ← Being Retired | −0.098 (0.093) | 0.052 (0.105) |
Support for Travel Ban ← Political Ideology ← Degree of COVID-19 Threat | −0.006* (0.003) | −0.002 (0.003) |
| Observations | 423 | 788 |
Note: Goodness of fit test statistics are not reported since the model is a saturated model
Standard errors in parentheses.
† < 0.1, * < 0.05, ** < 0.01, *** < 0.001.
Robustness Check
| Unexponentiated Coefficients | ||
|---|---|---|
| Conservatives | Non-conservatives | |
Robustness Check for Model 1 Ordered Logit Model of Predicting Political Ideology | ||
| Degree of COVID-19 Threat | 0.080 (0.096) | 0.216** (0.080) |
| Control Variables | Included | Included |
| Observations | 423 | 788 |
Robustness Check for Model 2 Predicting Support for Travel Ban | ||
| Conservative | 0.721*** (0.132) | |
| Control Variables | Included | |
| Observations | 1211 | |
| Conservative | 0.749*** (0.139) | |
| Control Variables | Included | |
| Observations | 1211 | |
| Robustness Check for Model 3 | ||
| KHB Method of Estimating the Indirect Effect of Degree of COVID-19 Threat on Support for Travel Ban Through Political Ideology | ||
| Indirect Effect | −0.009 (0.012) | −0.029* (0.015) |
| Control Variables | Included | Included |
| Observations | 423 | 788 |
| Indirect Effect | −0.011 (0.014) | −0.031* (0.016) |
| Control Variables | Included | Included |
| Observations | 423 | 788 |
Note: Multiple imputation (200 imputations) is used to handle missing data
Standard errors in parentheses.
†<0.1, * < 0.05, ** < 0.01, *** < 0.001