The Effect of Maternal Employment on the Likelihood of a Child Being Overweight

Childhood obesity has increased dramatically in the developed world. One cause of this trend, suggested by studies in the United States, is the increase in maternal employment. This paper explores if the causal relationship exists in Australia. Using recent data from the Longitudinal Survey of Australian Children (LSAC), a 2SLS procedure and a Full Information Maximum Likelihood (FIML) model that jointly estimates a multinomial treatment and binary outcome is used to control for endogeneity and self-selection bias, respectively. The results consistently show that maternal employment does have an impact on the likelihood of a child being overweight and that this impact is positive and statistically significant.Child obesity; Maternal employment; Regression analysis; 2SLS; FIML; Endogeneity; Self-selection bias

Fixed point action and topological charge for SU(2) gauge theory

We present a theoretically consistent definition of the topological charge operator based on renormalization group arguments. Results of the measurement of the topological susceptibility at zero and finite temperature for SU(2) gauge theory are presented.Comment: 3 pages, LaTeX file. Talk presented at LATTICE96(improvement

Scene Text Eraser

The character information in natural scene images contains various personal information, such as telephone numbers, home addresses, etc. It is a high risk of leakage the information if they are published. In this paper, we proposed a scene text erasing method to properly hide the information via an inpainting convolutional neural network (CNN) model. The input is a scene text image, and the output is expected to be text erased image with all the character regions filled up the colors of the surrounding background pixels. This work is accomplished by a CNN model through convolution to deconvolution with interconnection process. The training samples and the corresponding inpainting images are considered as teaching signals for training. To evaluate the text erasing performance, the output images are detected by a novel scene text detection method. Subsequently, the same measurement on text detection is utilized for testing the images in benchmark dataset ICDAR2013. Compared with direct text detection way, the scene text erasing process demonstrates a drastically decrease on the precision, recall and f-score. That proves the effectiveness of proposed method for erasing the text in natural scene images

Sibling health, schooling and longer-term developmental outcomes

We explore the extent to which starting primary school earlier by up to one year can help shield children from the detrimental, long-term developmental consequences of having an ill or disabled sibling. Using data from the Longitudinal Study of Australian Children, we employ a Regression Discontinuity Design based on birthday eligibility cut-offs. We find that Australian children who have a sibling in poor health persistently lag behind other children in their cognitive development - but only for the children who start school later. In contrast, for the children who commence school earlier, we do not find any cognitive developmental gaps. The results are strongest when the ill-health in the sibling is of a temporary rather than longer-term nature. We hypothesise that an early school start achieves this by lessening the importance of resource-access inequalities within the family home. However, we find mixed impacts on the gaps in non-cognitive development

The effect of changing financial incentives on repartnering

This paper examines how a reduction in the financial resources available to lone parents affects repartnering. We exploit an Australian natural experiment that reduced the financial resources available to a subset of separating parents. Using biweekly administrative data capturing separations occurring among low and middle income couples, we show that the policy reform significantly increased the repartnering hazard for affected separating mothers, especially those with low labour force attachment. Reconciliation with the woman's prior partner drives this result. Complementary analysis of an annual panel survey demonstrates that repartnering impacts are also present over the five years post-separation and that the impact on repartnering hazards is increasing in the extent of financial loss and the urgency of the impact. Together, these results demonstrate that one way that lone mothers respond to a reduction in financial resources available at the time of relationship breakdown is by repartnering more quickly

Using Machine Learning to Create an Early Warning System for Welfare Recipients

Using novel nation-wide social security data combined with machine learning tools, we develop predictive models of income support receipt intensities for any payment enrolee in the Australian social security system between 2014 and 2018. We show that off-the-shelf machine learning algorithms can significantly improve predictive accuracy compared to simpler heuristic models or early warning systems currently in use. Specifically, the former predicts the proportion of time individuals will be on income support in the next four years with greater accuracy, by a magnitude of at least 22% (14 percentage points increase in the R2), compared to the latter. This gain can be achieved at little extra cost to practitioners since it uses data currently available to them. Consequently, our machine learning algorithms can improve the detection of long-term income support recipients accruing a welfare cost nearly AUD 1 billion higher than individuals identified in the current system

Ownership Structure and Systematic Risk

This paper examines the relation between market risk, our measure for systematic risk, and ownership structure. For the overall sample ranging from 1983 to 2008, the correlation between market return and a firm\u27s specific return is related to the holdings by its institutional investors. Specifically, there is a significantly positive relationship between market risk and institutional ownership, and a significantly negative relationship between market risk and institutional concentration. The results are robust to the inclusion of other firm-specific variables such as size, leverage, and liquidity measures

Shaping Online Dialogue: Examining How Community Rules Affect Discussion Structures on Reddit

Community rules play a key part in enabling or constraining the behaviors of members in online communities. However, little is unknown regarding whether and to what degree changing rules actually affects community dynamics. In this paper, we seek to understand how these behavior-governing rules shape the interactions between users, as well as the structure of their discussion. Using the top communities on Reddit (i.e. subreddits), we first contribute a taxonomy of behavior-based rule categories across Reddit. Then, we use a network analysis perspective to discover how changing implementation of different rule categories affects subreddits' user interaction and discussion networks over a 1.5 year period. Our study find several significant effects, including greater clustering among users when subreddits increase rules focused on structural regulation and how restricting allowable content surprisingly leads to more interactions between users. Our findings contribute to research in proactive moderation through rule setting, as well as lend valuable insights for online community designers and moderators to achieve desired community dynamics