New Zealand's happiness and COVID-19: a Markov Switching Dynamic Regression Model

Happiness levels (states) are volatile and often fluctuate between a happy and unhappy state from one day to the next. The reasons for these shifts are mostly unobservable and not predictable. In this paper, we fit 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 predict the unobserved states' evolution over time using the one-step method. 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 frequencies of time periods with probabilities to be unhappy in 2020 mostly correspond to the 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, mobility is significantly and negatively related to the probability of being happy.