Essays in Household Finance

In recent years, the analysis of household financial decision making has become the main focus for both policymakers and academics. Hence this thesis first sets out to investigate the role of household financial literacy and psychological characteristics in household financial decisions. The results suggest that financial literacy is significantly associated with household financial management and practices such as credit management, cash flow management, retirement saving and investment. Further, while exploring the importance of stock market literacy on household decision to participate in the stock market, it is found that stock market literacy and trust distinctly influence the probability of household participation in the stock market. Furthermore, stock market literacy not only increases the likelihood of participation but also influences the share of wealth invested in the stock market. Also, economic shocks and future expectations are the key psychological characteristics that explain household decision to invest in stocks. However, upon participation, a larger set of psychological characteristics such as, past economic shock, future expectations, self-confidence, and time preference influence a household decision on how much to invest in stocks. Finally, the thesis examines the unwise financial decisions of households in unsecured debt management, credit card debt, mortgage debt management and investment diversification. The results show that financial distress and poverty increase the likelihood of households making unwise financial decisions. However, financial distress is found to outperform poverty in explaining the unwise financial decision of the households. Thus, the thesis brings to light the importance of financial literacy, psychological characteristics and financial distress for understanding household financial decision making

When It Rains It Drains: Psychological Distress and Household Net Worth

This paper establishes a sizeable negative effect of poor mental health on individuals’ net worth. In a representative panel of U.S. households, we find that a one standard deviation (or four unit) increase in Kessler’s K6 psychological distress level decreases net worth by 13.2 percent and increases by 5 percent the baseline risk of being in deficit net worth, where levels of debt outstrip the value of assets. Survival analyses further show that psychological distress accelerates the entry into and prolongs the stay in deficit net worth states, as well as increasing the probability of re-entry into deficit. Using a Blinder-Oaxaca decomposition, we find that differences in level of savings, medical debt and labor income predominantly explain the lower net worth and higher likelihood of deficit net worth of individuals with high psychological distress. Our findings highlight the significant longer-term implications of mental health on the net worth of individuals

An algorithm for the automatic detection and quantification of athletes’ change of direction incidents using IMU sensor data

Orientation tracking of a moving object has a wide variety of applications, including but not limited to military, surgical aid, navigation systems, mobile robots, gaming, virtual reality, and gesture recognition. In this paper, a novel algorithm is presented to automatically track and quantify change of direction (COD) incident angles or heading angles (i.e., turning angles) of a moving athlete using the inertial sensor signals from a microtechnology unit [an inertia measurement unit (IMU)] commonly used in elite sport. The algorithm is capable of automatically classifying a COD incident according to the degree of the turn and the direction of the turn (left or right). The system involves 1) the accurate determination of the heading angle using IMU sensor fusion and 2) the use of an algorithm to detect and categorize all changes in angle using various signal computation processing techniques. This paper presents the algorithm to detect changes in angle and subsequent categorization. The algorithm is intended to accurately quantify changes in mechanical loading (angle) during COD incidents, which may present a new perspective in the monitoring of athletes for performance enhancement and injury prevention purposes