Suchergebnisse

Cover -- Title Page -- Copyright Page -- Contents -- About the author -- How to use this book -- Introduction -- Part I You and your family -- 1 Marriage -- 2 Income splitting -- 3 Dependant (invalid and carer) tax offset -- 4 Children -- 5 Payments for new parents -- 6 Child care -- 7 Low-income earners -- 8 Senior and pensioner tax offset -- 9 Other government benefits -- Family Tax Benefit Part A -- Family Tax Benefit Part B -- Parenting payment -- JobSeeker Payment -- Youth Allowance -- National Disability Insurance Scheme -- Transition to work -- Home equity access scheme -- Seniors card -- Other benefits -- 10 Family breakdown -- Transfer of assets -- Transfer of superannuation -- Child support and spouse support payments -- 11 Death -- Date of death return -- Deceased estate returns -- Beneficiaries -- 12 Family trusts -- Part II Your employment -- 13 Car usage -- 14 Methods to claim car travel -- 15 Travel -- 16 Uniform -- 17 Home off ice -- 18 Other work-related deductions -- 19 Keeping those receipts -- 20 ATO hit lists -- 21 Redundancy -- Tax-free component of genuine redundancy -- Balance of redundancy -- Unused leave entitlements -- 22 Working a second job -- 23 Salary sacrifice -- 24 Fringe benefits -- 25 Living-away-from-home allowance -- Accommodation -- Food -- Part III Your education -- 26 Claiming self-education expenses -- Self-development courses -- 27 Student loans -- Higher Education Loan Program -- Student Financial Supplement Scheme -- Trade Support Loans -- FEE-HELP -- VET Student Loans -- OS-HELP -- 28 Austudy and ABSTUDY -- Austudy -- ABSTUDY -- 29 Scholarships -- 30 School building funds -- 31 Education savings plans -- Part IV Your investment property -- 32 Negative gearing -- 33 Interest -- 34 Depreciation and low-value pooling -- Low-value pooling -- 35 Repairs and maintenance -- 36 Borrowing and legal expenses.

AbstractEducation and family planning can both be influenced by policy and are thought to accelerate fertility decline. However, questions remain about the nature of these effects. Does the effect of education operate through increasing educational attainment of women or educational enrollment of children? At which educational level is the effect strongest? Does the effect of family planning operate through increasing contraceptive prevalence or reducing unmet need? Is education or family planning more important?We assessed the quantitative impact of education and family planning in high‐fertility settings using a regression framework inspired by Granger causality. We found that women's attainment of lower secondary education is key to accelerating fertility decline and found an accelerating effect of contraceptive prevalence for modern methods. We found the impact of contraceptive prevalence to be substantially larger than that of education. These accelerating effects hold in sub‐Saharan Africa, but with smaller effect sizes there than elsewhere.

This article describes an interesting application of Markov chain Monte Carlo (MCMC). MCMC is used to assess competing explanations of marital fertility decline. Data collected during the World Fertility Study in Iran are analyzed using methods developed to perform discrete time event history analyses in which unobserved heterogeneity is explicitly accounted for. The usual age-period-cohort identifiability problem is compounded by the presence of a fourth clock, duration since previous birth, and a fifth clocklike variable, mother's parity. The authors resolve this problem by modeling some of the clocks parametrically using codings suggested by alternating conditional expectation (ACE) and Bayes factors to decide which clocks are necessary. Compound Laplace-Metropolis estimates are used to compute Bayes factors for comparing alternative models. The new methods enable the authors to conclude that Iran's fertility decline was primarily a period effect and not a cohort effect, that it started before the Family Planning Program was initiated, that it was the same for women at all educational levels but varied depending on husband's education, and that it was greatest in the largest cities, particularly Tehran.

AbstractWe derive properties of latent variable models for networks, a broad class of models that includes the widely used latent position models. We characterize several features of interest, with particular focus on the degree distribution, clustering coefficient, average path length, and degree correlations. We introduce the Gaussian latent position model, and derive analytic expressions and asymptotic approximations for its network properties. We pay particular attention to one special case, the Gaussian latent position model with random effects, and show that it can represent the heavy-tailed degree distributions, positive asymptotic clustering coefficients, and small-world behaviors that often occur in observed social networks. Finally, we illustrate the ability of the models to capture important features of real networks through several well-known datasets.

The United Nations regularly publishes projections of the populations of all the world's countries broken down by age and sex. These projections are the de facto standard and are widely used by international organizations, governments and researchers. Like almost all other population projections, they are produced using the standard deterministic cohort-component projection method and do not yield statements of uncertainty. We describe a Bayesian method for producing probabilistic population projections for most countries that the United Nations could use. It has at its core Bayesian hierarchical models for the total fertility rate and life expectancy at birth. We illustrate the method and show how it can be extended to address concerns about the UN's current assumptions about the long-term distribution of fertility. The method is implemented in the R packages bayesTFR, bayesLife, bayesPop and bayesDem.