Childhood Income Volatility and Adult Outcomes

Using data linked across generations in the Panel Study of Income Dynamics, I estimate the relationship between exposure to volatile income during childhood and a set of socioeconomic outcomes in adulthood. The empirical framework is an augmented intergenerational income mobility model that includes controls for income volatility. I measure income volatility at the family level in two ways. First, instability as measured by squared deviations around a family-specific mean, and then as percent changes of 25 percent or more. Volatility enters the model both separately and interacted with income level. I find that family income instability during childhood has a small, positive association with high school dropout– one which appears driven by volatility among children from lower income households. Evidence suggests that volatility exposure generally has a minimal impact on intergenerational outcomes relative to permanent income

ESSAYS ON INCOME VOLATILITY AND INDIVIDUAL WELL-BEING

My dissertation consists of three essays in which I document trends in earnings and income volatility, estimate potential causal mechanisms for changing volatility, and examine the long-term consequences of parental income volatility for children. In essay 2 I document trends in earnings and income volatility of individuals and families using matched data in the March Current Population survey from 1973 to 2009. Essay 3 advances the literature on volatility, using matched data from the CPS to identify demographic and labor market correlates of earnings volatility within education-birth year cohorts. This study collapses the cross-sectional CPS into a pseudo-panel and then estimates the association between earnings volatility and race, local economic activity, and industry, accounting for endogeneity and sample selection bias. In essay 4 I use data linked across generations in the Panel Study of Income Dynamics to estimate the relationship between exposure to volatile income during childhood and a set of socioeconomic outcomes in adulthood. The empirical framework is an augmented intergenerational income mobility model that includes controls for income volatility. I find that family income volatility rose by 38 percent over the past four decades, likely driven both by rising volatility of earnings and non means-tested non-labor income. Rising family income volatility occurs across race, education, and family structure. From essay 3, I find that individuals with lower mean earnings have higher earnings volatility. Earnings volatility is also weakly related to race, decreases when young and then rises while workers are still within prime working years. Industry and local economic conditions are significantly related to the occurrence of earnings volatility after accounting for education, though these links differ between men and women. Finally, when examining the intergenerational consequences of volatility, a weak negative association occurs between family income instability during childhood and adult educational outcomes in essay 4

Tackling the edge dynamic graph colouring problem with and without future adjacency information

Many real world operational research problems, such as frequency assignment and exam timetabling, can be reformulated as graph colouring problems (GCPs). Most algorithms for the GCP operate under the assumption that its constraints are fixed, allowing us to model the problem using a static graph. However, in many real-world cases this does not hold and it is more appropriate to model problems with constraints that change over time using an edge dynamic graph. Although exploring methods for colouring dynamic graphs has been identified as an area of interest with many real-world applications, to date, very little literature exists regarding such methods. In this paper we present several heuristic methods for modifying a feasible colouring at time-step t into an initial, but not necessarily feasible, colouring for a “similar” graph at time-step t+1t+1 . We will discuss two cases; (1) where changes occur at random, and (2) where probabilistic information about future changes is provided. Experimental results are also presented and the benefits of applying these particular modification methods are investigated

How Well Does SNAP Protect Families Against the Risk of Food Insecurity and Poor Health During Economic Downturns?

Our research project addressed the question of how well SNAP and the social safety net protects families against the risk of food insecurity and poor health during economic downturns. Previous research has documented the relationship between reductions in family incomes and food insufficiency and has examined the effects of resources that mitigate the effects of income volatility. The U.S. social safety net, including SNAP, exists to mitigate the deleterious effects of swings in family income, particularly among low- and moderate-income households. This work compares outcomes for lower income families and higher income families in response to economic downturns. To the extent that nutritional, food security and food-related health outcomes are unaffected by economic downturns, there is implicit evidence that the social safety net is working to protect economically disadvantaged families

Modifying colourings between time-steps to tackle changes in dynamic random graphs

Many real world operational research problems can be formulated as graph colouring problems. Algorithms for this problem usually operate under the assumption that the size and constraints of a problem are fixed, allowing us to model the problem using a static graph. For many problems however, this is not the case and it would be more appropriate to model such problems using dynamic graphs. In this paper we will explore whether feasible colourings for one graph at time-step t can be modified into a colouring for a similar graph at time-step t+1 in some beneficial manner

The Effect of the Earned Income Tax Credit in the District of Columbia on Poverty and Income Dynamics

Using unique longitudinal administrative tax panel data for the District of Columbia (DC), we assess the combined effect of the DC supplemental earned income tax credit (EITC) and the federal EITC on poverty and income dynamics within Washington, DC, from 2001 to 2011. The EITC in DC merits investigation, as the DC supplement to the federal credit is the largest in the nation. The supplemental DC EITC was enacted in 2000, and has been expanded from 10 percent of the federal credit in 2001 to 40 percent as of 2009. To implement the study, we estimate least squares models with 0/1 dependent variables to estimate the likelihood of net-EITC income above poverty and near-poverty thresholds. We also estimate the likelihood of earnings growth and income stabilization from the EITC. To identify the effect of the EITC, we exploit variation in the EITC subsidy rate from 2008 to 2009, when an additional EITC bracket of 45 percent was added for workers with three or more dependent children, up from 40 percent in the previous year for workers with two or more children. We also estimate a model examining the impact of city-level changes to the EITC. The structure and richness of our data enable us to control for tax filer fixed effects, an important innovation from many previous EITC studies. Overall, we find that the combined EITC raises the likelihood of net-EITC income above poverty and near poverty by as much as 9 percent, with the largest consistent effects accruing to single-parent families

Earnings Volatility in America: Evidence from Matched CPS

We offer new evidence on earnings volatility of men and women in the United States over the past four decades by using matched data from the March Current Population Survey. We construct a measure of total volatility that encompasses both permanent and transitory instability, and that admits employment transitions and losses from self employment. We also present a detailed decomposition of earnings volatility to account for changing shares in employment probabilities, conditional variances of continuous workers, and conditional mean variances from labor-force entry and exit. Our results show that earnings volatility among men increased by 15 percent from the early 1970s to mid 1980s, while women’s volatility fell, and each stabilized thereafter. However, this pooled series masks important heterogeneity in volatility levels and trends across education groups and marital status. We find that men’s earnings volatility is increasingly accounted for by employment transitions, especially exits, while the share of women’s volatility accounted for by continuous workers rose, each of which highlights the importance of allowing for periods of non-work in volatility studies

Heuristic methods for colouring dynamic random graphs

Many real-world operational research problems can be reformulated into static graph colouring problems. However, such problems might be better represented as dynamic graphs if their size and/or constraints change over time. In this thesis, we explore heuristics approaches for colouring dynamic random graphs. We consider two different types of dynamic graph: edge dynamic and vertex dynamic. We also consider two different change scenarios for each of these dynamic graph types: without future change information (i. e. random change) and with probabilistic future change information. By considering a dynamic graph as a series of static graphs, we propose a modi fication approach which modifies a feasible colouring (or solution) for the static representation of a dynamic graph at one time-step into a colouring for the subsequent time-step. In almost all cases, this approach is beneficial with regards to either improving quality or reducing computational effort when compared against using a static graph colouring approach for each time-step independently. In fact, for test instances with small amounts of change between time-steps, this approach can be beneficial with regards to both quality and computational effort When probabilistic future change information is available, we propose a twostage approach which first attempts to identify a feasible colouring for the current time-step using our modification approach, and then attempts to increase the robustness of the colouring with regards to potential future changes. For both the edge and vertex dynamic cases, this approach was shown to decrease the problematic change introduced between time-steps. A clear trade-off can be observed between the quality of a colouring and its potential robustness, such that a colouring with more colours (i. e. reduced quality) can be made more robust

Is affect experiencing therapeutic in major depressive disorder? Examining associations between affect experiencing and changes to the alliance and outcome in intensive short-term dynamic psychotherapy.

Affect experiencing (AE), defined as the facilitation of client in-session bodily arousal and visceral experiencing of affect, is a distinct theoretical process presumed to contribute to therapeutic improvement. This study examined the role of AE in the treatment of major depressive disorder by exploring its association to client distress and therapeutic alliance on a session-by-session basis. A case series design was used to conduct an intensive analysis of the treatment process of 4 clients who received time-limited intensive short-term dynamic psychotherapy, 2 of whom were considered "recovered" and 2 who showed "no change" based upon posttreatment outcomes. Consistent with our hypothesis, we found that cross-correlations between AE and client distress discriminated between "recovered" and "no change" clients. In "recovered" clients, there was evidence that higher in-session peak affect experience was associated with reduced distress 7 days later. The results did not provide consistent evidence for a reverse effect, showing that lower distress during the preceding week predicted higher AE in that session. Finally, there was evidence that AE is an in-session activity that can promote the strengthening of the therapeutic alliance. These collective findings suggest that AE is an important treatment process that contributes to alliance formation and psychotherapeutic improvement. Clinical implications include further evidence that psychodynamic therapists can utilize AE as an active change ingredient for depression