Working for a better life : longitudinal evidence on the predictors of employment among recently arrived refugee migrant men living in Australia

Although a number of studies have investigated the predictors of employment among refugee migrants, there is a dearth of evidence from longitudinal data. This study investigated the cross-sectional and longitudinal predictors of employment among 233 adult refugee men living in South-East Queensland, Australia. Participants were interviewed four times at six-month intervals between 2008 and 2010. Using a conceptual model developed from the literature, Generalised Estimating Equations were used to model the predictors of employment. Over time, the employment rate increased from 44 percent to 56 percent. Region of birth, length of time in Australia, seeking employment through job service providers and informal networks, and owning a car were significant predictors of employment. Contrary to previous research, English language proficiency was not a significant predictor when other variables were controlled for. Recognition of overseas skills and qualifications decreased the chances of finding employment. The policy and program implications are discussed.

Longitudinal Evidence on Educational and Occupational Outcomes Amongst South Sudanese Men from Refugee Backgrounds Living in Urban and Regional Southeast Queensland

Australia has continued to benefit from the human, social and economic capital contributed by immigrant resettlement over many years. Humanitarian entrants have also made significant economic, social and civic contributions to the Australian society. Since 2000, approximately 160,000 people have entered Australia under the refugee and humanitarian resettlement program; around 15% have come from South Sudan and one third of these are adult males. In response to the 2003 evaluation of the Integrated Humanitarian Settlement Strategy (IHSS), which recommended to seek further opportunities to settle humanitarian entrants in regional Australia, the Department of Immigration and Citizenship (DIAC) has since encouraged regional settlement to “address the demand for less skilled labour in regional economies and to assist humanitarian entrants to achieve early employment”. There is evidence, however, of the many challenges faced by humanitarian arrivals living in regional areas. This chapter focuses on the educational and occupational outcomes among 117 South Sudanese adult men from refugee backgrounds. In particular, the chapter uses both cross-sectional (at first interview) and longitudinal data (four interviews with each participant at six-month intervals) to compares outcomes between men living in Brisbane and those living in the Toowoomba–Gatton region in Southeast Queensland.

Shrinkage empirical likelihood estimator in longitudinal analysis with time-dependent covariates-application to modeling the health of Filipino children

The method of generalized estimating equations (GEE) is a popular tool for analysing longitudinal (panel) data. Often, the covariates collected are time-dependent in nature, for example, age, relapse status, monthly income. When using GEE to analyse longitudinal data with time-dependent covariates, crucial assumptions about the covariates are necessary for valid inferences to be drawn. When those assumptions do not hold or cannot be verified, Pepe and Anderson (1994, Communications in Statistics, Simulations and Computation 23, 939–951) advocated using an independence working correlation assumption in the GEE model as a robust approach. However, using GEE with the independence correlation assumption may lead to significant efficiency loss (Fitzmaurice, 1995, Biometrics 51, 309–317). In this article, we propose a method that extracts additional information from the estimating equations that are excluded by the independence assumption. The method always includes the estimating equations under the independence assumption and the contribution from the remaining estimating equations is weighted according to the likelihood of each equation being a consistent estimating equation and the information it carries. We apply the method to a longitudinal study of the health of a group of Filipino children.

Four years on: Are the gazelles still running? A longitudinal study of firm performance after a period of rapid growth

This article examines variations in performance between fast-growth – the so-called gazelle – firms. Specifically, we investigate how the level of growth affects future profitability and how this relationship is moderated by firm strategy. Hypotheses are developed regarding the moderated growth–profitability relationship and are tested using longitudinal data from a sample of 964 Danish gazelle firms. We find a positive relationship between growth and profitability among gazelle firms. This relationship is moderated, however, by market strategy; it is stronger for firms pursuing a broad market strategy rather than a niche strategy. This study contributes to the current literature by providing a more nuanced view of the growth–profitability relationship and investigating the potential for the future performance of gazelle firms.

Diabetes mellitus and the risk of depressive and anxiety disorders in Australian women: A longitudinal study

Background Longitudinal studies examining the risk of depressive and anxiety disorders associated with diabetes are limited. This study examined the association between diabetes and the risk of depressive and anxiety disorders in Australian women using longitudinal data. Methods Datawere froma sample of women who were part of anAustralian pregnancy and birth cohort study. Data comprised self-reported diabetes mellitus and the subsequent reporting of depressive and anxiety disorders. Mood disorders were assessed according to the Diagnostic and Statistical Manual of Mental Disorders, fourth edition, obtained from participants using Composite International Diagnostic Interview (CIDI)-Auto (WHO WMH-CIDI CAPI, version 21.1.3). Multiple regression models with adjustment for important covariates were used. Results Women with diabetes had a higher lifetime prevalence of any depressive and/or anxiety disorder than women without diabetes. About 3 in 10 women with diabetes experienced a lifetime event of any depressive disorder, while 1 in 2 women with diabetes experienced a lifetime event of any anxiety disorder. In prospective analyses, diabetes was only significantly associated with a 30-day episode of any anxiety disorder (odds ratio [OR] 1.53, 95% confidence interval [CI] 1.09–2.15). In the case of lifetime disorders, diabetes was significantly associated with any depressive disorder (OR 1.37, 95% CI 1.03–1.84), major depressive disorder (OR 1.36, 95% CI 1.01–1.85), and posttraumatic stress disorder (OR 1.42, 95% CI 1.01–2.02). Conclusions The findings suggest that the presence of diabetes is a significant risk factor for women experiencing current anxiety disorders. However, in the case of depression, the association with diabetes only held for women who had experienced past episodes, there was no association with current depression. This suggests that the evidence is not strong enough to support a direct effect of diabetes as a cause of mood disorders.

Efficient estimation for rank-based regression with clustered data

Rank-based inference is widely used because of its robustness. This article provides optimal rank-based estimating functions in analysis of clustered data with random cluster effects. The extensive simulation studies carried out to evaluate the performance of the proposed method demonstrate that it is robust to outliers and is highly efficient given the existence of strong cluster correlations. The performance of the proposed method is satisfactory even when the correlation structure is misspecified, or when heteroscedasticity in variance is present. Finally, a real dataset is analyzed for illustration.

Rank regression for analysis of clustered data: A natural induced smoothing approach

We consider rank regression for clustered data analysis and investigate the induced smoothing method for obtaining the asymptotic covariance matrices of the parameter estimators. We prove that the induced estimating functions are asymptotically unbiased and the resulting estimators are strongly consistent and asymptotically normal. The induced smoothing approach provides an effective way for obtaining asymptotic covariance matrices for between- and within-cluster estimators and for a combined estimator to take account of within-cluster correlations. We also carry out extensive simulation studies to assess the performance of different estimators. The proposed methodology is substantially Much faster in computation and more stable in numerical results than the existing methods. We apply the proposed methodology to a dataset from a randomized clinical trial.

Weighted rank regression for clustered data analysis

We consider ranked-based regression models for clustered data analysis. A weighted Wilcoxon rank method is proposed to take account of within-cluster correlations and varying cluster sizes. The asymptotic normality of the resulting estimators is established. A method to estimate covariance of the estimators is also given, which can bypass estimation of the density function. Simulation studies are carried out to compare different estimators for a number of scenarios on the correlation structure, presence/absence of outliers and different correlation values. The proposed methods appear to perform well, in particular, the one incorporating the correlation in the weighting achieves the highest efficiency and robustness against misspecification of correlation structure and outliers. A real example is provided for illustration.

Estimation of growth parameters from multiple-recapture data

This article develops a method for analysis of growth data with multiple recaptures when the initial ages for all individuals are unknown. The existing approaches either impute the initial ages or model them as random effects. Assumptions about the initial age are not verifiable because all the initial ages are unknown. We present an alternative approach that treats all the lengths including the length at first capture as correlated repeated measures for each individual. Optimal estimating equations are developed using the generalized estimating equations approach that only requires the first two moment assumptions. Explicit expressions for estimation of both mean growth parameters and variance components are given to minimize the computational complexity. Simulation studies indicate that the proposed method works well. Two real data sets are analyzed for illustration, one from whelks (Dicathais aegaota) and the other from southern rock lobster (Jasus edwardsii) in South Australia.

Analysing commercial catch and effort data from a penaeid trawl fishery: A comparison of linear models, mixed models, and generalised estimating equations approaches

Statistical methods are often used to analyse commercial catch and effort data to provide standardised fishing effort and/or a relative index of fish abundance for input into stock assessment models. Achieving reliable results has proved difficult in Australia's Northern Prawn Fishery (NPF), due to a combination of such factors as the biological characteristics of the animals, some aspects of the fleet dynamics, and the changes in fishing technology. For this set of data, we compared four modelling approaches (linear models, mixed models, generalised estimating equations, and generalised linear models) with respect to the outcomes of the standardised fishing effort or the relative index of abundance. We also varied the number and form of vessel covariates in the models. Within a subset of data from this fishery, modelling correlation structures did not alter the conclusions from simpler statistical models. The random-effects models also yielded similar results. This is because the estimators are all consistent even if the correlation structure is mis-specified, and the data set is very large. However, the standard errors from different models differed, suggesting that different methods have different statistical efficiency. We suggest that there is value in modelling the variance function and the correlation structure, to make valid and efficient statistical inferences and gain insight into the data. We found that fishing power was separable from the indices of prawn abundance only when we offset the impact of vessel characteristics at assumed values from external sources. This may be due to the large degree of confounding within the data, and the extreme temporal changes in certain aspects of individual vessels, the fleet and the fleet dynamics.

Emotional intelligence development in radiation therapy students: A longitudinal study

Purpose: Emotional intelligence (EI) is an increasingly important aspect of a health professional’s skill set. It is strongly associated with empathy, reflection and resilience; all key aspects of radiotherapy practice. Previous work in other disciplines has formed contradictory conclusions concerning development of EI over time. This study aimed to determine the extent to which EI can develop during a radiotherapy undergraduate course and identify factors affecting this. Methods and materials: This study used anonymous coded Likert-style surveys to gather longitudinal data from radiotherapy students relating to a range of self-perceived EI traits during their 3-year degree. Data were gathered at various points throughout the course from the whole cohort. Results: A total of 26 students provided data with 14 completing the full series of datasets. There was a 17·2% increase in self-reported EI score with a p-value<0·0001. Social awareness and relationship skills exhibited the greatest increase in scores compared with self-awareness. Variance of scores decreased over time; there was a reduced change in EI for mature students who tended to have higher initial scores. EI increase was most evident immediately after clinical placements. Conclusions: Radiotherapy students increase their EI scores during a 3-year course. Students reported higher levels of EI immediately after their clinical placement; radiotherapy curricula should seek to maximise on these learning opportunities.

Socio-environmental factors and suicide in Queensland, Australia

Suicide has drawn much attention from both the scientific community and the public. Examining the impact of socio-environmental factors on suicide is essential in developing suicide prevention strategies and interventions, because it will provide health authorities with important information for their decision-making. However, previous studies did not examine the impact of socio-environmental factors on suicide using a spatial analysis approach. The purpose of this study was to identify the patterns of suicide and to examine how socio-environmental factors impact on suicide over time and space at the Local Governmental Area (LGA) level in Queensland. The suicide data between 1999 and 2003 were collected from the Australian Bureau of Statistics (ABS). Socio-environmental variables at the LGA level included climate (rainfall, maximum and minimum temperature), Socioeconomic Indexes for Areas (SEIFA) and demographic variables (proportion of Indigenous population, unemployment rate, proportion of population with low income and low education level). Climate data were obtained from Australian Bureau of Meteorology. SEIFA and demographic variables were acquired from ABS. A series of statistical and geographical information system (GIS) approaches were applied in the analysis. This study included two stages. The first stage used average annual data to view the spatial pattern of suicide and to examine the association between socio-environmental factors and suicide over space. The second stage examined the spatiotemporal pattern of suicide and assessed the socio-environmental determinants of suicide, using more detailed seasonal data. In this research, 2,445 suicide cases were included, with 1,957 males (80.0%) and 488 females (20.0%). In the first stage, we examined the spatial pattern and the determinants of suicide using 5-year aggregated data. Spearman correlations were used to assess associations between variables. Then a Poisson regression model was applied in the multivariable analysis, as the occurrence of suicide is a small probability event and this model fitted the data quite well. Suicide mortality varied across LGAs and was associated with a range of socio-environmental factors. The multivariable analysis showed that maximum temperature was significantly and positively associated with male suicide (relative risk [RR] = 1.03, 95% CI: 1.00 to 1.07). Higher proportion of Indigenous population was accompanied with more suicide in male population (male: RR = 1.02, 95% CI: 1.01 to 1.03). There was a positive association between unemployment rate and suicide in both genders (male: RR = 1.04, 95% CI: 1.02 to 1.06; female: RR = 1.07, 95% CI: 1.00 to 1.16). No significant association was observed for rainfall, minimum temperature, SEIFA, proportion of population with low individual income and low educational attainment. In the second stage of this study, we undertook a preliminary spatiotemporal analysis of suicide using seasonal data. Firstly, we assessed the interrelations between variables. Secondly, a generalised estimating equations (GEE) model was used to examine the socio-environmental impact on suicide over time and space, as this model is well suited to analyze repeated longitudinal data (e.g., seasonal suicide mortality in a certain LGA) and it fitted the data better than other models (e.g., Poisson model). The suicide pattern varied with season and LGA. The north of Queensland had the highest suicide mortality rate in all the seasons, while there was no suicide case occurred in the southwest. Northwest had consistently higher suicide mortality in spring, autumn and winter. In other areas, suicide mortality varied between seasons. This analysis showed that maximum temperature was positively associated with suicide among male population (RR = 1.24, 95% CI: 1.04 to 1.47) and total population (RR = 1.15, 95% CI: 1.00 to 1.32). Higher proportion of Indigenous population was accompanied with more suicide among total population (RR = 1.16, 95% CI: 1.13 to 1.19) and by gender (male: RR = 1.07, 95% CI: 1.01 to 1.13; female: RR = 1.23, 95% CI: 1.03 to 1.48). Unemployment rate was positively associated with total (RR = 1.40, 95% CI: 1.24 to 1.59) and female (RR=1.09, 95% CI: 1.01 to 1.18) suicide. There was also a positive association between proportion of population with low individual income and suicide in total (RR = 1.28, 95% CI: 1.10 to 1.48) and male (RR = 1.45, 95% CI: 1.23 to 1.72) population. Rainfall was only positively associated with suicide in total population (RR = 1.11, 95% CI: 1.04 to 1.19). There was no significant association for rainfall, minimum temperature, SEIFA, proportion of population with low educational attainment. The second stage is the extension of the first stage. Different spatial scales of dataset were used between the two stages (i.e., mean yearly data in the first stage, and seasonal data in the second stage), but the results are generally consistent with each other. Compared with other studies, this research explored the variety of the impact of a wide range of socio-environmental factors on suicide in different geographical units. Maximum temperature, proportion of Indigenous population, unemployment rate and proportion of population with low individual income were among the major determinants of suicide in Queensland. However, the influence from other factors (e.g. socio-culture background, alcohol and drug use) influencing suicide cannot be ignored. An in-depth understanding of these factors is vital in planning and implementing suicide prevention strategies. Five recommendations for future research are derived from this study: (1) It is vital to acquire detailed personal information on each suicide case and relevant information among the population in assessing the key socio-environmental determinants of suicide; (2) Bayesian model could be applied to compare mortality rates and their socio-environmental determinants across LGAs in future research; (3) In the LGAs with warm weather, high proportion of Indigenous population and/or unemployment rate, concerted efforts need to be made to control and prevent suicide and other mental health problems; (4) The current surveillance, forecasting and early warning system needs to be strengthened, to trace the climate and socioeconomic change over time and space and its impact on population health; (5) It is necessary to evaluate and improve the facilities of mental health care, psychological consultation, suicide prevention and control programs; especially in the areas with low socio-economic status, high unemployment rate, extreme weather events and natural disasters.

An analysis of the determinants of job satisfaction when individuals' baseline satisfaction levels may differ.

A growing literature seeks to explain differences in individuals' self-reported satisfaction with their jobs. The evidence so far has mainly been based on cross-sectional data and when panel data have been used, individual unobserved heterogeneity has been modelled as an ordered probit model with random effects. This article makes use of longitudinal data for Denmark, taken from the waves 1995-1999 of the European Community Household Panel, and estimates fixed effects ordered logit models using the estimation methods proposed by Ferrer-i-Carbonel and Frijters (2004) and Das and van Soest (1999). For comparison and testing purposes a random effects ordered probit is also estimated. Estimations are carried out separately on the samples of men and women for individuals' overall satisfaction with the jobs they hold. We find that using the fixed effects approach (that clearly rejects the random effects specification), considerably reduces the number of key explanatory variables. The impact of central economic factors is the same as in previous studies, though. Moreover, the determinants of job satisfaction differ considerably between the genders, in particular once individual fixed effects are allowed for.

Do strategy choices matter for nascent firms? A study on effectuation and causation impacts on new ventures outcomes

Principal Topic : Nascent entrepreneurship has drawn the attention of scholars in the last few years (Davidsson, 2006, Wagner, 2004). However, most studies have asked why firms are created focussing on questions such as what are the characteristics (Delmar and Davidsson, 2000) and motivations (Carter, Gartner, Shaver & Reynolds, 2004) of nascent entrepreneurs, or what are the success factors in venture creation (Davidsson & Honig; 2003; Delmar and Shane, 2004). In contrast, the question of how companies emerge is still in its infancy. On a theoretical side, effectuation, developed by Sarasvathy (2001) offers one view of the strategies that may be at work during the venture creation process. Causation, the theorized inverse to effectuation, may be described as a rational reasoning method to create a company. After a comprehensive market analysis to discover opportunities, the entrepreneur will select the alternative with the higher expected return and implement it through the use of a business plan. In contrast, effectuation suggests that the future entrepreneur will develop her new venture in a more iterative way by selecting possibilities through flexibility and interaction with the market, affordability of loss of resources and time invested, development of pre-commitments and alliances from stakeholders. Another contrasting point is that causation is ''goal driven'' while an effectual approach is ''mean driven'' (Sarasvathy, 2001) One of the predictions of effectuation theory is effectuation is more likely to be used by entrepreneurs early in the venture creation process (Sarasvathy, 2001). However, this temporal aspect and the impact of the effectuation strategy on the venture outcomes has so far not been systematically and empirically tested on large samples. The reason behind this research gap is twofold. Firstly, few studies collect longitudinal data on emerging ventures at an early enough stage of development to avoid severe survivor bias. Second, the studies that collect such data have not included validated measures of effectuation. The research we are conducting attempts to partially fill this gap by combining an empirical investigation on a large sample of nascent and young firms with the effectuation/causation continuum as a basis (Sarasvathy, 2001). The objectives are to understand the strategies used by the firms during the creation process and measure their impacts on the firm outcomes. Methodology/Key Propositions : This study draws its data from the first wave of the CAUSEE project where 28,383 Australian households were randomly contacted by phone using a specific methodology to capture emerging firms (Davidsson, Steffens, Gordon, Reynolds, 2008). This screening led to the identification of 594 nascent ventures (i.e., firms that are not operating yet) and 514 young firms (i.e., firms that have started operating from 2004) that were willing to participate in the study. Comprehensive phone interviews were conducted with these 1108 ventures. In a likewise comprehensive follow-up 12 months later, 80% of the eligible cases completed the interview. The questionnaire contains specific sections designed to distinguish effectual and causal processes, innovation, gestation activities, business idea changes and ventures outcomes. The effectuation questions are based on the components of effectuation strategy as described by Sarasvathy (2001) namely: flexibility, affordable loss and pre-commitment from stakeholders. Results from two rounds of pre-testing informed the design of the instrument included in the main survey. The first two waves of data have will be used to test and compare the use of effectuation in the venture creation process. To increase the robustness of the results, temporal use of effectuation will be tested both directly and indirectly. 1. By comparing the use of effectuation in nascent and young firms from wave 1 to 2, we will be able to find out how effectuation is affected by time over a 12-month duration and if the stage of venture development has an impact on its use. 2. By comparing nascent ventures early in the creation process versus nascent ventures late in the creation process. Early versus late can be determined with the help of time-stamped gestation activity questions included in the survey. This will help us to determine the change on a small time scale during the creation phase of the venture. 3. By comparing nascent firms to young (already operational) firms. 4. By comparing young firms becoming operational in 2006 with those first becoming operational in 2004. Results and Implications : Wave 1 and 2 data have been completed and wave 2 is currently being checked and 'cleaned'. Analysis work will commence in September, 2009. This paper is expected to contribute to the body of knowledge on effectuation by measuring quantitatively its use and impact on nascent and young firms activities at different stages of their development. In addition, this study will also increase the understanding of the venture creation process by comparing over time nascent and young firms from a large sample of randomly selected ventures. We acknowledge the results from this study will be preliminary and will have to be interpreted with caution as the changes identified may be due to several factors and may not only be attributed to the use/not use of effectuation. Meanwhile, we believe that this study is important to the field of entrepreneurship as it provides some much needed insights on the processes used by nascent and young firms during their creation and early operating stages.

Upper-body morbidity following breast cancer treatment is common, may persist longer-term and adversely influences quality of life

Background: Impairments in upper-body function (UBF) are common following breast cancer. However, the relationship between arm morbidity and quality of life (QoL) remains unclear. This investigation uses longitudinal data to describe UBF in a population-based sample of women with breast cancer and examines its relationship with QoL. ---------- Methods: Australian women (n = 287) with unilateral breast cancer were assessed at three-monthly intervals, from six- to 18-months post-surgery (PS). Strength, endurance and flexibility were used to assess objective UBF, while the Disability of the Arm, Shoulder and Hand questionnaire and the Functional Assessment of Cancer Therapy- Breast questionnaire were used to assess self-reported UBF and QoL, respectively. ---------- Results: Although mean UBF improved over time, up to 41% of women revealed declines in UBF between sixand 18-months PS. Older age, lower socioeconomic position, treatment on the dominant side, mastectomy, more extensive lymph node removal and having lymphoedema each increased odds of declines in UBF by at least twofold (p < 0.05). Lower baseline and declines in perceived UBF between six- and 18-months PS were each associated with poorer QoL at 18-months PS (p < 0.05). ---------- Conclusions: Significant upper-body morbidity is experienced by many following breast cancer treatment, persisting longer term, and adversely influencing the QoL of breast cancer survivors.