key: cord-0789349-uxlmiihh authors: Akay, Alpaslan title: The Local and Global Mental Health Effects of the Covid-19 Pandemic date: 2022-01-11 journal: Econ Hum Biol DOI: 10.1016/j.ehb.2021.101095 sha: d590c5d745b72359816d637b13cc3373f13a3832 doc_id: 789349 cord_uid: uxlmiihh This paper investigates the mental health effects of the local and global level Covid-19 pandemic among the UK population. To identify the effect, we use a high-quality dataset and an original strategy where we match the previous day’s confirmed pandemic cases to a four-month panel of individual mental health information observed during the interview next day. The approach suggested in this paper aims to identify the average mental health effect on the overall population for the first and second waves of the pandemic. Using a linear fixed-effects model specification, we report robust findings that the average mental health in the UK is substantially reduced by the local and global pandemic. The total reduction in the average mental health of the UK population during our sampling period (April - June, 2020) is about 1.5% for the local and 2.4% for the global cases, which sum up to a 3.9% reduction. Extrapolating the total reduction in average mental health during the first wave of the pandemic (February - September, 2020) sums up to 2.8% while the effect is as large as 9.6% for the first and second waves together, which covers roughly a year since the start. An extensive robustness check suggests that the findings are stable with respect to alternative pandemic datasets, measures, estimators, functional forms, and time functions. The characteristics of the most vulnerable individuals (e.g., elderly, chronic illness, and job security concerns) and their household conditions (e.g., living alone and no private space) are explored. The paper discusses on the implications of the results. The previous experience with global traumatic events (e.g., previous widespread pandemics such as SARS-COV-1 and the Ebola outbreak or globally impactful terror events such as 9/11) suggests that one of the domains expected to be quickly aected by the current coronavirus (Covid-19 hereafter) pandemic is mental health (e.g., Mak Jones, 2021). To our knowledge, this paper is the rst to oer a methodology to identify the average marginal eects of the daily conrmed Covid-19 cases on the mental health outcomes, which can be extrapolated to calculate the total mental eect of whole rst and second waves of the pandemic. The strategy also allows us to undertake an extensive range of alternative analyses. First, we make a distinction by considering that mental health is not only aected by the immediate threats and disturbances due to the local pandemic but also by the overall global prevalence of the pandemic. Second, we conduct an extensive investigation on how individual constraints (e.g., old age, underlying chronic diseases, job security, unemployment, poverty, and future nancial concerns) and household circumstances (e.g., living alone, household composition, dependent kids, household size, and presence of a garden or a private space) correlate with the average mental health eect of the local and global pandemic. To reach these aims, the paper uses a high-quality dataset and a highly original strategy to identify the average marginal mental health eect of the local and global pandemic cases in the UK. 1 Our micro-dataset includes four waves of a monthly panel from April 2020 to July 2020, which is a part of a long yearly and highly representative panel dataset (the UK Household Longitudinal Survey -UKHLS). The dataset includes the General Health Questionnaire (GHQ), which is a well-known inventory allowing us to develop a solid mental health measure widely used in the literature (e.g., Clark and Oswald, 1994; Akay et al., 2012; Banks and Xu, 2020; Proto and Quintana-Domeque, 2021). The paper 1 We mainly focus on the conrmed cases as a measure of the pandemic instead of using conrmed pandemic deaths or cases and deaths together. One important issue to point out is that the pandemic cases and deaths might involve dierent degrees of measurement errors. This might be the situation when the pandemic cases and deaths are registered on dierent dates due to the choices made by governments. For instance, while the number of pandemic related cases supplied by the UK government reects the exact conrmed cases for a specic day, the pandemic dataset covers deaths within 28 days after being tested positive. 2 J o u r n a l P r e -p r o o f oers an identication strategy which diers from the existing studies (cf., Proto and Quintana-Domeque, 2021). Instead of measuring the change in mental health compared to previous years, we match the daily local and global cases with the individual panel using the next day interview dates (Akay et al., 2020) . Because the panel in the dataset is observed four times, this strategy of using the data can be interpreted as a series of random experiments on the same individuals conducted by nature four times for the dierent pandemic circumstances. The eect of the pandemic cases come from the daily uctuations of the local and global pandemic cases, which are assumed to be random with respect to the date of interview. Thus, our strategy identies the temporal average marginal eect of the pandemic cases on the mental health of the overall population. Exploiting the panel aspect of our data and four months of several time-variant individual and household characteristics, we use panel data xed-eects specications to estimate the eect of the previous day's local and global cases on mental health. The results are highly consistent with the earlier studies using alternative identication strategies (cf., Banks and Xu, 2020; Proto and Quintana-Domeque, 2021). Our results suggest a signicant and negative mental health eect of the previous day's cases on the overall population in the UK. To put the eects of the local and global pandemic cases into context and summarise, we calculate the total eect of local and global cases the average eect of the mean number of cases for a particular pandemic period relative to average mental health in the UK. The results suggest that the total reduction in the average mental health during the sampling period (May -June, 2020) is about 1.5% for the local cases and 2.4% for the global cases, which sum up to a 3.9% reduction in the average mental health in the UK. The identication strategy also allows for extrapolating the eect for the rst wave (February -September, 2020) and for a period covering the rst and second waves of the pandemic (February, 2020 -February, 2021). The total reduction in the average mental health during the rst wave sums up to 2.8% while the total eect is as large as 9.6% during the rst and second waves of the pandemic. The results are also highly robust with respect to functional forms of Covid-19 measures, time functions, alternative estimators, and sources of alternative local and global Covid-19 datasets. The paper presents a discussion using a heterogeneity analysis with respect to individual and household constraints to determine the vulnerable and non-vulnerable individuals. We nd that older people with health constraints (e.g., chronic health conditions), people 3 J o u r n a l P r e -p r o o f who are living alone, those who are experiencing job insecurity and economic concerns, and households with constraints in their immediate living environment (e.g., no private space or garden) are the most vulnerable. The rest of the paper is organised as follows: Section 2 presents the micro dataset, the daily Covid-19 dataset, and the empirical design. Section 3 gives the econometric methods, stochastic specications, and our identication strategy. Section 4 presents the results split by main results and a robustness analysis. Section 5 presents an extensive discussion using a comprehensive heterogeneity analysis with respect to individual and household circumstances. Finally, Section 6 concludes the paper. The full set of questions appearing in the inventory is given in Appendix C, Table C.1. The inventory involves twelve domains of an individual's mental well-being, including sleep problems, depression, enjoyment of day-to-day activities, self-worth, and happiness. Each question is answered on an ordinal scale from (1) to (4) . An easy composite measure is formed by summing up the scores obtained from each question (e.g., Akay et al., 2012; Clark and Oswald, 1994) . The nal measure is normalised to range between 0 and 36. We reverse the scale so that the higher scores indicate higher mental health and use it as the main output variable (GHQ36 with 36-points scale). We also use each item in the inventory as an output variable representing a particular domain of mental health. WHO's Covid-19 online dashboard, which is also followed by many people via internet worldwide. 4 Simple statistics suggest that the datasets supplied by WHO, JHU and ECDC are highly similar to each other, as they mostly use the same ocial sources. Yet, because these datasets are collected by dierent institutions, methodologies and countries, they might lead to dierent estimates, especially for the global sums. We present a series of results in our robustness analyses using alternative Covid-19 datasets to check the robustness of our results. The Covid-19 data are in daily time series format including pandemic related conrmed number of daily cases and deaths by several countries all around the world. First, we dene global Covid-19 cases by summing up all cases worldwide (except the UK) for each day. Second, we dene the daily pandemic related conrmed cases in the UK. One important remark is that local and global cases and deaths are highly correlated over time. The correlation between global cases and deaths is 0.89 while the correlation in the UK is 0.92. It is also not possible to judge a priori which measure people follow from media or internet sources and which measure generates more fear or anxiety. As there are very high correlations and there might be large measurement error in the measures of the pandemic deaths, we prefer focusing on the local and global pandemic cases. Yet, to check the consistency, we are going to present results from the local and global pandemic deaths. In Figure 1 , Panels A and B, we present the time series patterns of the UK and global Covid-19 cases for the dates surrounding the sampling period from April, 20 to August 2. The overall pattern of the UK cases are decreasing while global cases are Table A .1. For Panels C and D, data points are obtained by averaging GHQ36 scores by the residuals of the UK and Global cases. We obtain the residuals from regressions which are conditioned on the linear trend, the day of the week, and wave dummies. The lines represent the linear regressions which use the underlying 29 data points. The grey area represents the 95% condence intervals. increasing during the sampling period. Yet, there is a substantial time variation in both pandemic measures, which is the key to this study. Empirical Design and Raw Relationships. The strategy of estimating the relationship is based on the exogenous variation in the daily pandemic related local and global cases the day before the interview in which the mental health of individuals is observed. We rst identify the date on which the interviews are conducted. The exact dates are given in Figure 1 , Panels A and B, with lled points. We then match the pandemic cases the day before with the interview dates the next day in the US dataset. There are four dierent data collection episodes (Waves 1-4 as represented in Panels A and B of Figure 1 ) during which the mental health information is obtained. We interpret this strategy as a "natural experiment" conducted with the same group of individuals in four dierent time periods with dierent pandemic circumstances. There are 29 days on which people are assigned to be interviewed, i.e., "treated" with the previous day's pandemic cases. The exact number of interviews, the day of the week on which the interview is conducted, and some further descriptive statistics of the individuals' characteristics are given in Appendix At this point, we present two intuitive gures which show the initial raw relationship between the local and global pandemic cases and the average mental health obtained during the next day's interview. The results are presented in Panels C and D of Figure 1 . Each point in the gures represents the mean GHQ36 score corresponding to the local (Panel A) and global (Panel B) pandemic cases day before. To draw these gures, we obtain the residuals conditional on linear time trend, the day of the week dummies, and wave dummies. To describe the raw relationship, we also draw the linear regression line on the underlying data together with the 95% condence intervals of the raw predictions. Our rst observation is that there is a visible negative correlation between previous day local and global pandemic cases and the mental health outcomes next day. The slope of the linear regression is highly statistically signicant for both cases. In the following sections, we are going to present results from econometric specications where we allow for alternative sets of individual controls and time functions using our individual panel dataset. (1) and (2). The dependent variable, mental health GHQ i,d,w , is measured for each individual i on the day of interview d for four waves w. The Covid-19 related cases C that occurred in the U K and and globally GL the day before the interview d−1, are given in The key parameters to be estimated are β U K and β GL . The main reason that we prefer the previous day d − 1 is to ensure that people in the UK learned about the number of daily pandemic cases within the past 24 hours. To guarantee this for the global cases, we use 48 hours by considering the time zone dierences. In the econometric analysis below, we are going to present results where we also estimate alternative combinations of these measures in the same regression. In our baseline model specication, we prefer using the levels of the pandemic measures rescaled by 1,000 cases as in Figure 1 Stochastic Specications. The error specication in (2) include several components. First, we include wave specic eects W w . These are four wave dummies corresponding to the month of interview. Then, we introduce a series of further time functions to capture the time series properties of the daily pandemic cases. First, we control for the day of the week on which the interview is conducted dow s . Second, we add time trend specied in alternative functional forms f (t, W w ), including linear, quadratic, and wave-specic quadratic trends. To allow regional heterogeneity within the UK, e.g., to capture regional unobserved attitudes towards the pandemic or alternative lockdown rules, we control for the Government Ocial Regions (12 GOR dummies ρ r ). Finally, by exploiting the panel structure of the dataset, we allow for the time-invariant unobserved individual eects α i (e.g., personality or genetic predisposition). Because pandemic cases are time-variant, we can identify the parameters within a xed-eects framework where we allow for correlation between observed and unobserved variables. As we merge daily pandemic data with individuals' mental health observations and other characteristics, the model specication involves a`between' variation, which is distributed across 29 interview days, and a`within' individual variation, which is distributed across four months. The requirement for identifying the "causal" eect of the previous day's pandemic cases on mental health is that the characteristics of individuals should be similar across the interview days and that there is no sorting of individuals on a particular interview day by the previous day's local or global pandemic cases. Are these assumptions reasonable? We can imagine some possible threats relating to how the interviews are conducted, attrition, and sorting. First of all, during the pandemic, e.g., due to fear or lockdown restrictions, most people stay at home and interviews are held via digital sources, i.e., internet or phone. Second, there is a very little attrition in the data as the time between waves is very short and the participation rate in the survey is very high. As shown in Appendix A, Table A Our strategy of presenting our results is as follows. First, we obtain the baseline results and immediately present a stability analysis and results from the pandemic deaths. Second, interpretation and magnitudes of the results are provided. Third, we present detailed results by using each GHQ item in the inventory as a distinct output variable. Fourth, we conduct an extensive robustness analysis. Finally, an heterogeneity analysis in which we explore the vulnerable and non-vulnerable individuals are given in the next section. Main Results. Table 1 provides the baseline estimates and initial checks. Our baseline specication (equations (1) and (2)) uses a linear xed-eects model, which includes the full set of individuals' personal and household characteristics as well as the pandemic measures. Column I of Table 1 presents the baseline estimates (see Table B Table 1 ). The estimated coecients are similar to those of the baseline given in Column I. As mentioned previously, pandemic cases and deaths are highly correlated, and they are expected to give similar information about the prevalence of the pandemic. Nevertheless, to check the consistency, in Columns IV, V, and VI, we present results from the local and global pandemic related deaths. The parameter estimates of the local and global deaths (rescaled by 1,000) are also negative and highly comparable with the local pandemic cases. Yet, the coecient of the local deaths on mental health is imprecisely estimated and only marginally signicant with p-value=0.115. Compared to the estimated parameter of global The model specications include the full set of individual, regional and time specic characteristics (see Appendix B, Table B .1). Clustered (at the day of the observation) standard errors are presented in the parentheses. *, **, and *** indicate signicance level at 10%, 5%, and 1% levels of signicance, respectively. cases, the relative magnitude of global deaths is more prominent on mental health, and the coecient is highly statistically signicant. One possible interpretation of this result is that people might fear more from the covid related deaths than cases. Yet, this result might also be related to measurement error or the lower level of pandemic related deaths in the UK during the sampling period. In the rest of the paper, we focus only on the local and global cases as the main pandemic measure. Average Marginal Eects. The results presented in Column I of Table 1 give the eect of pandemic measures on the mental health for 1,000 people increase in the local and global cases. In order to calculate the magnitudes for a more realistic local and global pandemic gures, we rst calculate the average marginal eect for a standard deviation increase in the pandemic cases. A standard deviation increase in the local (global) cases leads to a -0.049 (-0.042) standard deviation decrease in the GHQ36 scores. The eect of a standard Table 2 . Magnitudes, Extrapolation of the Eect, and Sensitivity Checks Note: Authors' own calculations from the Understanding Society (2020) Covid-19 module and WHO Covid-19 data (WHO, 2020). The model specications include the full set of individual, regional and time specic characteristics (see Appendix B, Table B .1). Clustered (at the day of the observation) standard errors are presented in the parentheses. *, **, and *** indicate signicance level at 10%, 5%, and 1% levels of signicance, respectively. deviation increase in the local cases is only slightly higher than global cases. 5 In order to obtain an easy measure to interpret the results, in Column I of Table 2 , we present the percentage change in the mean GHQ36 score (mean GHQ36 is 23.8 while s.d. Table 2 ). Total Eect of the Pandemic. To be able to obtain comparable measures across studies that use alternative identication strategies (e.g., Banks and Xu, 2020; Proto and Quintana-Domeque, 2021; Etheridge and Spanting, 2020) and to develop measures describing the "total eect" of the pandemic, we calculate the`non-marginal' mental 5 The eect of a standard deviation increase in the local (global) deaths on the mental health is -0.009 (-0.007). The relative magnitudes are highly comparable between local and global deaths. Yet their absolute sizes are lower and statistically imprecise compared to those of local and global cases. health eect for the overall mean pandemic cases relative to the mean GHQ36 score as Covid(C) U K β U K /GHQ36 and Covid(C) GL β GL /GHQ36. We calculate the total eect of the whole pandemic for i) the sampling period, ii) the rst wave of the pandemic, and iii) the rst and second waves of the pandemic. First, in Column II of Table 2 , we calculate the total eect for the sampling period using the mean pandemic cases for the 29 interview days (April 24, 2020 -July 31, 2020, see First and Second Waves of the Pandemic. In the remaining columns of Table 2 , we conduct a series of extrapolation exercises and sensitivity checks with respect to the sample used in the analysis. First, we extrapolate the total eect for the overall rst wave of the pandemic (covering the period about January 31, 2020 -September 1, 2020). Column III(A) suggests that the total eect sums up to 0.11 standard deviation of GHQ36, which is slightly lower, as the mean local and global cases are lower at the beginning and the end of the rst wave of the pandemic. At this point, we report two additional sets of checks to investigate the sensitivity of extrapolation with respect to the sample used. We exclude the rst sample period of data collected during April, 2020 (the sample size is 41,739) and nd that the estimated coecient for the local cases is about three times larger compared to the baseline specication (Column I, Table 1 ). The total eect without the rst sample period is found to be as large as 0.20 standard deviation of GHQ36 (Column III(B)). Next, we exclude the last wave collected during July, 2020 (the sample size is 44,758). The results are stable and the total eect of the pandemic (Column III(C)) is almost the same sizes as that reported in Column II. Finally, we extrapolate the total eect for a period covering the rst and second waves of the pandemic. The period roughly covers a year (January 31, 2020 -January 15, 2021). The results presented in Column IV suggest that the total eect of the pandemic is 0.38 standard deviation of Estimators. Table 4 presents an extensive robustness analysis. To compare the results, we replicated the baseline results in the rst row (Column I, Table 1 ). In Rows II-V, we investigate the robustness of the baseline xed-eects specication with various alternative specications. As mentioned above, the ordered probit model is the convenient model in our case as the GHQ36 measure is observed on an ordinal scale. We also note that, as the ordered probit is a non-linear model, the parameter estimates of this model specication cannot be directly compared with the baseline. The results from the cross-sectional ordered probit model are given in Row II. We obtain the same sign and signicance levels for both the local and global cases. To account for the ordinal nature of the dependent variable and xed-eects in a non-linear model, we estimate a "Blow and Cluster" ordered logit xed-eects model (Row III). The results follow the same signs and the parameter estimates are statistically signicant at the conventional levels. Finally, in Rows IV and V, we estimate the model specication with the cross-sectional pooled OLS and panel data linear random-eects model specication. We obtain highly comparable results with those of the baseline. Is the Relationship Spurious? Time Functions. There are dierent time patterns in the local and global pandemic cases during the sample period (see Figure 1 ). To Table 4 . Robustness Notes: Authors' own calculations from the Understanding Society (US, 2020) and World Health Organisation (WHO, 2020), and John Hopkins University Database (JHU, 2020), and European Centre for Disease Prevention and Control (ECDC, 2020). The local and global number of cases are rescaled by 1,000. The model specications include the full set of individual, regional and time specic characteristics (see Appendix B, Table B .1). Clustered (at the day of the observation) standard errors are presented in the parentheses. *, **, and *** indicate signicance level at 10%, 5%, and 1% levels of signicance, respectively. account for time related heterogeneity, our baseline specications have already used a linear trend term, the day of the week dummies, and wave-specic dummies. First, we introduce the quadratic trend into our baseline model specication (Row VI of Table 4 ). The estimated parameters are somehow larger in magnitude but they are all negative and the signicance levels hold. Second, we add the exponential trend term in the specication and obtain results which are highly similar to the baseline (Row VII (1) and (2) is rst estimated by combining the covid data supplied by the UK governments for the local and WHO data for the global cases. The results reported in Row X of Table 4 are highly similar to those of the baseline, as expected. Then, the baseline model is also estimated by using the JHU and ECDC Covid-19 datasets, and the results are reported in Rows XI and XII, respectively. While the results are highly similar to the JHU dataset, the estimated coecient for the local cases is somehow lower when we use the ECDC dataset. The preceding analysis documents heterogeneous eects for various dimensions. People who experience personal, social and household related constraints, and living environment and housing related factors might correlate with the better or worse mental health outcomes of the pandemic. We focus on dimensions relating to i) individuals' personal constraints (e.g., older people or chronic health conditions), ii) responses of individuals (e.g., exercises or prayer), iii) economic constraints (e.g., working from home or job security), iv) household circumstances (e.g., household composition), and v) constraints in living environment (e.g., no private garden or personal space). The methodology is based on an interaction specication where we interact pandemic cases with specic characteristics S k . The extended model specication replaces the coecients of pandemic cases β U K and β GL in equation (1) where S k is a binary variable dened for each variable k for which we investigate the heterogeneous eect. To be brief, we present only two coecients for S k = 1 and S k = 0 for each binary variable S k used in the heterogeneity analyses. The hypotheses that we aim to test are H 0 : β U K(S k =1) = β U K(S k =0) and The results are summarised in Table 5 and Table 6 below. Individual Characteristics. The statistics (WHO, 2020) suggest that the mortality risk of coronavirus is higher among older people, and thus these individuals might fear the pandemic more, leading to greater mental health problems. Indeed, Row I of Table 5 suggests that the mental health of older people (older than age 55) is aected more. Yet, the dierence in the estimated eects is statistically signicant only for the global cases. Another important constraint is the health status of individuals. The statistics also suggest that mortality risk due to coronavirus is higher among people with an underlying health condition, including coronary diseases, stroke, diabetes, and hypertension. The conjecture is that the pandemic creates greater fear and anxiety among individuals with an underlying health condition, which can trigger mental health problems. To investigate this, we generate a dummy variable indicating people who have long-term coronary disease, stroke, diabetes, or hypertension (the share is 7.4%). Row II of Table 5 stress and coping with adverse events is the religious belief and prayer (Aneshensel et al., 2013) . To investigate this, we use the frequency of praying. 6 The indicator variable is dened among people who never pray and zero if they sometimes pray (the share is 26.7%). People who pray experience signicantly smaller mental health eects from the local and global pandemic cases (Row IV of Table 5 ). Table 6 suggest that people who live without a partner experience signicantly higher mental health eects for both the local and global cases. As reported in a recent study by Brodeur et al. (2021b) , loneliness appears to be one of the prominent factors relating to the mental health eects of the pandemic. However, living in a large household (number of household members is greater than 3) does not generate signicant dierences in the mental health eects of the pandemic (Row II, Table 6 ). Even though people in larger households might enjoy their time more during restrictions, the risk of coronavirus infection among the family members might also generate anxiety. Next, we investigate the mental health eects of individuals in households with a dependent child (the share is 9.5%), which can be another source of anxiety. Indeed, these individuals experience signicantly higher mental health eects for the local cases (Row III, Table 6 ). A partially similar result is also obtained among the people who spend time (higher than median hours 0) caring for a dependent person (the share is 23.4%). Yet, the dierences are not statistically signicant in any of the pandemic measures (Row IV, Table 6 ). Living Environment and Housing. The characteristics of individuals' physical living arrangements and constraints on where individuals reside might be correlated with the mental health eect of the pandemic. To investigate this, we rst focus on home ownership (the share is 75.9%). The results presented in Row V of Table 6 suggest that people who do not own the residential house experience a signicantly larger mental health eect for both the local and global cases. 21.6% of the house owners pay a mortgage and the nancial concerns during the pandemic might trigger further anxiety among these individuals. However, the results in Row VI suggest that the mental health eect is not signicantly dierent among these people. In the last three rows of Table 6 , we focus on the physical arrangement of the residential house during the pandemic. People who live in a small house (the number of rooms is less than 3) during the pandemic experience signicantly larger mental health problems (Rows VII, Table 6 ). Finally, we focus on individuals who do not have a private garden (the share is 12.3%) or a private space (room or desk) at their residential place (the share is 67.6%). These individuals experience signicantly higher mental health eects for an increase in the local and global cases (Rows VIII and IX, Table 6 ). Using an original empirical strategy, we investigate the eect of the local and global Covid- There are several important additional results reported in this paper. The paper investigates who is more vulnerable to the adverse mental health eects of the pandemic. We nd that older people with a chronic health condition, those who are unemployed or have job security concerns, and people who have to commute to working place experience greater mental health eects of the pandemic. People who are doing exercises and praying often experience lower mental health disturbances. During the pandemic period, the household composition, living environment, and housing play important roles on mental health. We nd that lonely people experience very high mental health problems due to the pandemic. Moreover, people with a dependent kid or those living in smaller houses, with no garden, and private space are also found to be more vulnerable for the adverse mental health eects of the Covid-19 pandemic. These results have important policy implications for the current and future pandemics. First, our detailed heterogeneity results can allow the policymaker to make specic policies for the ecient management of the pandemic, particularly in designing psychological interventions. Second, as one of the tools to reach individuals, the UK government (also WHO) have published online guidelines to raise awareness and to give support on potential adverse mental health eects. 7 The information supplied in this paper can help address psychological factors stemming from individual and household constraints, which can help prevent excessive use of mental health care institutions. Finally, one of the limitations of this study is that it does not include an investigation for the role of the vaccination and policy responses of the governments on the mental health outcomes of the individuals. These issues are left for future research as they require further data and identication strategies. -The mental health effects of the local and global level Covid-19 pandemic are investigated among the UK population. -The identification is based on matching the previous day's confirmed pandemic cases to a four-month panel of individual. -The total reduction in the average mental health of the UK population is 3.9%. -The total reduction in the first wave of pandemic is 2.8% while it is 9.6% for the first and second waves. -The paper explores the characteristics of the vulnerable and non-vulnerable individuals. Global Terror and Well-Being Relative Concerns of Ruralto-Urban Migrants in China Religion and Mental health". 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