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The evolution of happiness pre and peri-COVID-19: A Markov Switching Dynamic Regression Model.

ABSTRACT: Happiness levels often fluctuate from one day to the next, and an exogenous shock such as a pandemic can likely disrupt pre-existing happiness dynamics. This paper fits a Marko Switching Dynamic Regression Model (MSDR) to better understand the dynamic patterns of happiness levels before and during a pandemic. The estimated parameters from the MSDR model include each state's mean and duration, volatility and transition probabilities. Once these parameters have been estimated, we use the one-step method to predict the unobserved states' evolution over time. This gives us unique insights into the evolution of happiness. Furthermore, as maximising happiness is a policy priority, we determine the factors that can contribute to the probability of increasing happiness levels. We empirically test these models using New Zealand's daily happiness data for May 2019 -November 2020. The results show that New Zealand seems to have two regimes, an unhappy and happy regime. In 2019 the happy regime dominated; thus, the probability of being unhappy in the next time period (day) occurred less frequently, whereas the opposite is true for 2020. The higher frequency of time periods with a probability of being unhappy in 2020 mostly correspond to pandemic events. Lastly, we find the factors positively and significantly related to the probability of being happy after lockdown to be jobseeker support payments and international travel. On the other hand, lack of mobility is significantly and negatively related to the probability of being happy.

SUBMITTER: Rossouw S

PROVIDER: S-EPMC8664229 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundHappiness may help to prevent negative physiological outcomes in response to life events; however, factors contributing to happiness during the COVID-19 pandemic have not been longitudinally investigated. This study explored the predictors of happiness in mothers of young children in Japan using comparable data that were obtained before and during the COVID-19 pandemic.MethodsWe conducted the baseline survey in February 2020, and 4 months later, we also conducted the follow-up survey. Throughout all 47 prefectures in Japan, 4,700 (100 respondents/prefecture) mothers of infants and/or preschoolers (0-6 years) participated in the baseline online survey; 2,489 of these also participated in the follow-up survey.ResultsWe performed hierarchical multiple regression analysis and our final model indicated that maternal happiness during COVID-19 pandemic was positively related to employment status (homemaker, β = 0.052, p = 0.014), levels of available social support (average, β = 0.052, p = 0.012, high, β = 0.055, p = 0.010) and happiness score before the pandemic (β = 0.467, p < 0.001), and satisfaction toward the measures against the COVID-19 at partners' workplace (average, β = 0.129, p < 0.001; high, β = 0.279, p < 0.001), preventive behavior against COVID-19 (average, β = 0.055, p = 0.002; high, β = 0.045, p = 0.015) and positive attitudes/thinking (β = 0.087, p < 0.001) during the pandemic. In contrast, poor mental health (K6 ≥5, β = -0.042, p = 0.011) before the pandemic and negative changes during the pandemic (≥3, β = -0.085, p < 0.001) were negatively related to maternal happiness during the pandemic. Our final model explained 44.9% of the variance in mothers' happiness during the COVID-19 pandemic.ConclusionsSatisfaction toward the measures against the COVID-19 at partners' workplace, preventive behavior, and positive attitudes/thinking were especially important for maternal happiness during the COVID-19 pandemic. Future study is needed to consider measures against infectious diseases in the workplace that are desirable for the well-being of parents with young children, taking into account the gender perspective.

Project description:BackgroundThe COVID-19 pandemic has initiated several initiatives to better understand its behavior, and some projects are monitoring its evolution across countries, which naturally leads to comparisons made by those using the data. However, most "at a glance" comparisons may be misleading because the curve that should explain the evolution of COVID-19 is different across countries, as a result of the underlying geopolitical or socio-economic characteristics. Therefore, this paper contributes to the scientific endeavour by creating a new evaluation framework to help stakeholders adequately monitor and assess the evolution of COVID-19 in countries, considering the occurrence of spikes, "secondary waves" and structural breaks in the time series.MethodsGeneralized Additive Models were used to model cumulative and daily curves for confirmed cases and deaths. The Root Relative Squared Error and the Percentage Deviance Explained measured how well the models fit the data. A local min-max function was used to identify all local maxima in the fitted values. The pure Markov-Switching and the family of Markov-Switching GARCH models were used to identify structural breaks in the COVID-19 time series. Finally, a quadrants system to identify countries that are more/less efficient in the short/long term in controlling the spread of the virus and the number of deaths was developed. Such methods were applied in the time series of 189 countries, collected from the Centre for Systems Science and Engineering at Johns Hopkins University.ResultsOur methodology proves more effective in explaining the evolution of COVID-19 than growth functions worldwide, in addition to standardizing the entire estimation process in a single type of function. Besides, it highlights several inflection points and regime-switching moments, as a consequence of people's diminished commitment to fighting the pandemic. Although Europe is the most developed continent in the world, it is home to most countries with an upward trend and considered inefficient, for confirmed cases and deaths.ConclusionsThe new outcomes presented in this research will allow key stakeholders to check whether or not public policies and interventions in the fight against COVID-19 are having an effect, easily identifying examples of best practices and promote such policies more widely around the world.

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The evolution of happiness pre and peri-COVID-19: A Markov Switching Dynamic Regression Model.

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