Economic insecurity and income inequality in the wellbeing debate

This thesis explores two key measures of economic wellbeing - economic insecurity and income inequality. Chapters 2 and 3 present an Economic Security Index (ESI) for New Zealand, a new measure of economic wellbeing. An ESI is a measure of economic insecurity that identifies which subgroups of the population are susceptible to financial loss due to events such as an unanticipated rise in medical expenses or a fall in income. An ESI also measures if these subgroups have sufficient financial resources to act as a buffer when economic shocks occur. The main index is constructed in Chapter 2 using micro-level data from New Zealand’s Household Labour Force Survey (HLFS) and Household Economic Survey (HES). An alternative index is constructed in Chapter 3 using longitudinal data from the Survey of Family, Income and Employment (SoFIE) to test the robustness of the main index’s findings. After controlling for various demographic and socioeconomic factors, the findings of both versions of the ESI predict similar patterns. The main findings suggest that economic insecurity is highly cyclical, tracking closely to GDP growth and the unemployment rate. This result suggests that insecurity in New Zealand is largely an involuntary phenomenon. It is also observed that insecurity varies by subgroups of the population. Groups that are more likely to experience economic insecurity are ethnic minorities, young adults, people with no educational qualifications, single-adult households, persons whose relationships ended over the ESI year, high-income households and persons on temporary employment contracts. In Chapter 4, both versions of the ESI and longitudinal health data from SoFIE are used to estimate the causal impact of economic insecurity on the mental wellbeing of New Zealanders. The study uses fixed effects models to test the hypothesis that being economically insecure increases the likelihood of poor mental wellbeing. To proxy for mental wellbeing, I use each respondent’s Kessler-10 (K10) score as well as the Short Form 36 (SF-36) health survey, two common indicators of mental disorders. For the SF-36 health survey, both the Mental and Physical Component Summary (MCS and PCS) Measures are used. To proxy for insecurity, the exogenously assigned ESI values from HLFS (‘predicted economic insecurity’) are used, as well as ESI values from SoFIE (representing ‘lived insecurity event’, such as if an individual loses their job or gets divorced). Consistent with the literature on this topic, the results suggest that both measures of insecurity lead to worse mental wellbeing; however, the results are not statistically significant. Further, the analysis produced mixed results when looking at the relationship between insecurity and physical health. Experiencing a ‘lived insecurity event’ is found to improve physical wellbeing, albeit the results are not statistically significant. ‘Predicted economic insecurity’ is found to have a strong, statistically significant negative relationship with each respondent’s standardised PCS scores, which suggests that insecurity worsens physical wellbeing. These associations remain after adjusting for several demographic and socioeconomic characteristics. In Chapter 5, the theme shifts to income inequality. This chapter uses ordinary least squares (OLS) regression analysis to determine the deep determinants of income inequality, a topic that has received very little attention in the literature. Specifically, the study explores whether the deep determinants of comparative development - which explains differences across countries - are also important in explaining within-country income inequality. The three main hypotheses that explain comparative development are the geography, institutions and genetics/fractionalization hypotheses. The results of the preferred model show that there are some elements from each of the three hypotheses that contribute to explaining within-country income inequality. Whether a country is landlocked or not, distance from the technological frontier, birthplace diversity and the time that has elapsed since the Neolithic Revolution are all found to be deep determinants of income inequality. The results also show that the deep determinants of income inequality vary depending on a country’s level of economic development. Explanations for each finding are discussed