"Female-Headed Families: Why Are They So Poor?"

Over the last few decades in the United States, the poverty rate for female-headed families (with no husband present) has been about three times the poverty rate for male-headed families (with no wife present) and about six times the poverty rate for married- couple families. This paper addresses the question of why, in general, female-headed families are so much poorer than other families. A decomposition of poverty rates and a set of probit models are used to identify the factors which determine the poverty rates for the three family types. The following control variables are found to be important determinants of poverty for all three family types: education of family members; age, race, disability, and unemployment of the family head; geographical location, size and age composition of the family. Both married-couple families and male-headed families are found to be less poor than female-headed families mainly because additional units of those control variables which reduce (increase) poverty have a larger (smaller) impact in the case of the former two family types than in the case of female-headed families. Of lesser importance is the fact that female-headed families, on average, have less (more) of those control variables which reduce (increase) poverty.

"Poverty and Choice of Marital Status: A Self-Selection Model"

Over the last few decades in the United States, the poverty rate for female-headed families has been about five times the poverty rate for other family types. This paper addresses the question of why, in general, female-headed families are so much poorer than other families. Recognizing that individuals choose their own marital status, a self-selection model is used to identify the factors which determine the poverty rates for married- couple families, families headed by females with no husband present, and families headed by males with no wife present. The following control variables are found to be important determinants of poverty for all three family types: education of family members; age, race, disability, and unemployment of the family head; geographical location, size and composition of the family. Both married-couple families and male-headed families are found to be less poor than female-headed families mainly because the marginal effects of the control variables, and to a lesser extent the mean levels of the control variables, favor the former two types of families over female-headed families.

"Poverty and Household Composition"

This paper has investigated the relationship between poverty and family type, as reflected in the marital status and gender of the head of the family number of factors have been identified as important determinants of poverty for all family types: education and work experience of family members, race, disability, and unemployment of the family head, geographical location, size and composition of the family.

"The Measurement of Chronic and Transitory Poverty: with Application to the United States"

This paper proposes a method of measuring chronic and transitory poverty based on any additively-decomposable index of aggregate poverty. Chronic poverty and transitory poverty in the United States are measured using data from the Panel Study of Income Dynamics (1987 interviewing year). In an attempt to identify the most impoverished subpopulations, poverty indices are decomposed according to race, type of household and educational qualifications of the head of the household.

Ranking of Australian Economics Departments Based on Their Total and Per Academic Staff Research Output

This study uses cluster analysis to classify twenty-seven Australian economics teaching departments into groups that have similar quantities of research output, measured by two different publication counts, and similar quality of research output, measured by a citation count. Three distinct groups of departments are identified and factor analysis is used to rank the groups. Whether research output is measured in total or on a per staff basis, Melbourne is in the group that ranks first, the remaining members of the "group of eight" are in one or other of the top two groups, and at least fifteen other departments are always in the third-ranked group.Economics Departments, Australia, Ranking

Travail to No Avail? Working Poverty in Australia

During the last decade or so Australia has experienced high rates of economic growth and low levels of unemployment, conditions that are expected to impact favourably on working people at the lower end of the income distribution. But similar conditions in other countries have been accompanied by unexpectedly high rates of poverty among working people and their dependents. This paper investigates the extent and nature of working poverty in Australia. Its aim is to determine whether or not working poverty is the “new face of poverty in post-industrial Australia”.Working Poverty, working poor

Hourly Wages of Full-Time and Part-Time Employees in Australia

This study investigates some aspects of part-time and full-time employment in Australia. The main objective is to analyze whether part-time workers receive lower hourly wages than full-time workers who have similar levels of human capital and perform similar jobs. The study is based on unit-record data from Wave I of the Household, Income and Labour Dynamics in Australia (HILDA) Survey. The results indicate that unadjusted part-time wage penalties of 21 per cent for men and seven per cent for women can be explained by selection into full-time or part-time employment and controls for human capital and type of job. There are no statistically significant adjusted wage differentials after controlling for selection into type of employment and worker- and job-specific characteristics

The Effect of Geographic Mobility on Male Labour-Force Particpants in the United States

We use both fixed-effects and random-effects regression models to measure the effect of geographic mobility on earnings of labor-force participants in the United States. The results support the human-capital hypothesis: six years after moving, real earnings of male labor-force participants are about 20 percent higher than they would have been had the move not occurred. Men younger than 40, and men with family-unit incomes no more than five times the poverty line, experience even larger benefits from moving. The geographic mobility that is characteristic of the United States’ flexible labor market, in general, is beneficial to the movers

Eagles and turkeys: Human capital externalities, departmental co-authorship and research productivity

Lucas (1988) hypothesised that human capital externalities explain persistent productivity growth and become manifest via interactions between workplace colleagues. Consistent with the first part of this hypothesis, Fox and Milbourne (2006) concluded that an increase in the average level of human capital in Australian economics departments raised the research productivity of departmental members. This paper tests the robustness of this finding by using a direct, rather than a proxy, measure of human capital and confirms the existence of human capital externalities within Australian economics departments. But we extend the analysis in two important dimensions. Firstly, we investigate the second part of Lucas\u27 hypothesis by testing whether the externality becomes manifest via co-authoring. We find no evidence that this type of interaction is associated with higher research productivity, especially for higher quality outputs. Secondly, we control for the likely endogeneity of one\u27s peer group via instrumental variables estimation. In this case, we find that the peer group effect disappears completely for the highest quality outputs but remains for research output more broadly defined

A Multi-Dimensional Ranking of Australian Economics Departments

This study uses cluster analysis to classify Australian economics departments into groups that have similar quantities of research output, measured by two publication counts, and similar quality of research output, measured by a citation count. Three groups of departments are identified and factor analysis is used to rank the groups. Whether research output is measured in total or on a per staff basis, Melbourne is in the group that ranks first, the remaining members of the ‘group of eight’ are in one or other of the top two groups, and at least 15 other departments are in the third-ranked group