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Refugees integrate less successfully than other immigrants. Pre-migration stress, mental disorder and lack of human capital are the most popular explanations, but these propositions have received little empirical testing. The current study of Sri Lankan Tamils in Toronto, Canada, examines the respective contributions of pre-migration adversity, human capital, mental health and social resources in predicting integration. The paper aims to discuss these issues.

Participants are a probability sample of 1,603 Sri Lankan Tamils living in Toronto, Canada. The team, with a community advisory council, developed structured interviews containing information about pre- and post-migration stressors, coping strategies, and family, community, and institutional support. The questionnaire included the World Health Organization Composite International Diagnostic Interview module for post-traumatic stress disorder (PTSD). Interviews were translated, back-translated and administered by bilingual interviewers.

Two dimensions of integration emerged from a factor analysis of integration-related items: economic and psychosocial. Hierarchical multiple regression analyses revealed that PTSD militated against refugee economic integration, whereas pre-migration adversity (but not PTSD) compromised psychosocial integration. On both measures, increasing length of residence in Canada, and gender (male) were predictors of good integration, whereas age at arrival had an inverse relationship with integration. Religiosity had a positive effect on psychosocial integration but a negative effect on economic. Favourable perceptions of the health care system predicted economic integration and non-family support predicted psychosocial integration.

Results underline the importance of studying integration as a multifaceted phenomenon, help explain why refugees integrate less successfully than other immigrants, and highlight the importance of including mental health and mental health-related issues in integration discourse.

This study aims to draw on a less explored predictor – the average correlation of pairwise returns on industry portfolios – to predict stock market returns (SMRs) in the USA.

This study uses the average correlation approach of Pollet and Wilson (2010) and predicts the SMRs in the USA. The model is estimated using monthly data for a long time horizon, from July 1963 to December 2018, for the portfolios comprising 48 Fama-French industries. The model is extended to examine the effects of a longer lag structure of one-month to four-month lags and to control for the effects of a number of variables – average variance (AV), cyclically adjusted price-to-earnings ratio (CAPE), term spread (TS), default spread (DS), risk-free rate returns (R_f) and lagged excess market returns (R_s).

The study finds that the two-month lagged average correlation of returns on individual industry portfolios, used individually and collectively with financial predictors and economic factors, predicts excess returns on the stock market in an effective manner.

The methodology and results are of interest to academics as they could further explore the use of average correlation to improve the predictive powers of their models.

Market practitioners could include the average correlation in their asset pricing models to improve the predictions for the future trend in stock market returns. Investors could consider including average correlation in their forecasting models, along with the traditional financial ratios and economic indicators. They could adjust their expected returns to a lower level when the average correlation increases during a recession.

The finding that recession periods have effects on the SMRs would be useful for the policymakers. The understanding of the co-movement of returns on industry portfolios during a recession would be useful for the formulation of policies aimed at ensuring the stability of the financial markets.

The study contributes to the literature on three counts. First, the study uses industry portfolio returns – as compared to individual stock returns used in Pollet and Wilson (2010) – in constructing average correlation. When stock market becomes more volatile on returns, the individual stocks are more diverse on their performance; the comovement between individual stock returns might be dominated by the idiosyncratic component, which may not have any implications for future SMRs. Using the industry portfolio returns can potentially reduce such an effect by a large extent, and thus, can provide more reliable estimates. Second, the effects of business cycles could be better identified in a long sample period and through several sub-sample tests. This study uses a data set, which spans the period from July 1963 to December 2018. This long sample period covers multiple phases of business cycles. The daily data are used to compute the monthly and equally-weighted average correlation of returns on 48 Fama-French industry portfolios. Third, previous studies have often ignored the use of investors’ sentiments in their prediction models, while investors’ irrational decisions could have an important impact on expected returns (Huang et al., 2015). This study extends the analysis and incorporates investors’ sentiments in the model.

Financial threat is defined as fearful-anxious uncertainty regarding one’s current and future financial situation. The purpose of this paper is to examine predictors and outcomes of financial threat in two samples of students who completed an online questionnaire for course credit. The theoretical model the authors proposed tested the association between personal debt, anxiety, and economic hardship with financial threat, and in turn, financial threat’s relationship with willingness to change financial behavior (e.g. increase income, cut expenses, and reduce debt), job search activity, and psychological distress. Consistent across samples, structural equation modeling (SEM) revealed that the data fit the model and supported all four hypotheses. Debt, economic hardship, and anxiety were all related positively to financial threat, which itself related positively to willingness to change, job search, and psychological distress. Importantly, financial threat mediated the relationship between these economic-situational predictors and affective-behavioral outcomes of financial stain. Theoretical and practical implications of the findings are discussed.

Using an online questionnaire, participants completed measures of economic hardship, intolerance of uncertainty, job search behavior, financial threat, life satisfaction, general health, perceived stress, and willingness to change to financial behavior. The authors developed and tested a model that explores emotional and cognitive reactions to financial stressors following the recession.

Results of SEM revealed that the data fit the model and no modification indices were suggested. Examination of parameter estimates indicated that total debt, economic hardship, and anxiety were positively related to financial threat. Financial threat, in turn, positively related to willingness to change one’s financial behaviors, job search, and psychological distress. In addition, economic hardship and anxiety were positively related to psychological distress. That is, individuals who were feeling more threatened by their financial situation were more willing to change their financial situation and were more likely to engage in job search behavior. They were also more likely to report more psychological distress than individuals reporting lower levels of financial threat.

This study was cross-sectional and therefore precludes causal interpretations of the findings. Longitudinal data with repeated assessments of all measures would help determine the direction of causation. Also, the study relied on self-report data, which is prone to bias. For example, it is possible that some participants did not know their exact debt levels, which may have resulted in an under- or overestimation of debt levels. Future research should extend this line of research using objective measures. While the model tested in this study examined the impact of economic factors on perceived threat, behavior, and psychological distress, it did not include social and psychological resources. For example, the authors did not include measures of social support, coping, or personality, which may moderate the impact of economic variables and stress on psychological distress. Although financial knowledge/literacy was not studied here, future research could include it since it has been associated with a variety of financial behaviors such as cash-flow management, credit management, saving, and investing. There is some evidence that financial literacy can decrease emotional stress and anxiety (Vitt et al., 2000).

The current study can help researchers and practitioners understand the concept of financial threat among university students. For example, if students have incurred student loans and debt and begin displaying symptoms of distress, like anxiousness, worry, and irritability, they could be referred to a professional experienced in working with emotional and behavioral disorders related to financial issues. It can also help practitioners gain an understanding and insight into clients’ poor financial decision making. Government could initiate programs that help individuals cope with the negative effects of unemployment. Given that young people are experiencing disproportionately high unemployment that can have a lasting adverse effect on employment prospects and future earnings, the current post-secondary curriculum needs to prepare young people for the world of work, and gain a footing in the labor market. One way to achieve this is through high-quality work experiences (e.g. internships/apprenticeships). Identifying ways to mitigate the effects of debt and economic hardship is also imperative. For example, money and debt advice may improve one’s financial circumstances, which, in turn, may improve their physical and psychological well-being.

Future studies could focus on developing models predicting to financial stress using personality, psychological resources, and an objective measure of financial knowledge. Despite these limitations, this research demonstrates how emotional factors need to be included in economic models that also include debt and economic hardship. The study contributes to the economic and psychological literature by documenting how economic hardship and debt influence perceptions of threat, planned behavior, and psychological distress. The authors take a unique approach to describing economic hardship and financial threat as antecedents of distress, job search, and willingness to change. Future research could be directed toward employing the model for predicting behavior that would lessen economic stress and thereby leading to increased psychological well-being.

The study develops and tests an original theoretical model linking financial, emotional, and psychological variable in a comprehensive framework that is then tested empirically. This model is original with this paper.

This study is based on the earlier work of Iannaccone and Lutz, Kirkendall and Le Doux and Burlingame and uses socio‐economic data obtained from 77 school districts in Massachusetts through the period 1963–1972. The study examines a series of hypotheses related to the “gap” that may develop between community demand and school board response. Some of the findings are (i) the Iannaccone‐Lutz model of school board member incumbent defeat is valid and the “gap” between community demands and the school board's response is an important factor in the operational model; (ii) the school board's response to community's demands (tax rate) is an important variable in determining the “gap” resulting in incumbent defeat and in predicting the variance in this political phenomenon in school districts; (iii) socio‐economic‐political indicators of school district change selected by Kirkendall are related to school board member incumbent defeat.

Peer-to-Peer (P2P) lending, which makes borrowers and investors meet directly through online platforms bypassing traditional financial institutions, is an emerging financing market after the traditional financial institutions crushed during the global financial crisis from 2007 to 2009. P2P lending platforms meet the credit demand more efficiently and play a vital role for the credit market and economic activity. This study sheds light on whether the credit spread of P2P lending is well predictive of economic activity compared to the bond credit spread which has been fully investigated in prior studies. Our findings show that the P2P credit spread performs similarly in predicting the economic activity as bond credit spread only during the financial crisis. However, the predictive power of P2P credit spread becomes inverse during the noncrisis periods since P2P lending platforms provide an alternative and easier financing channel to individuals who hardly borrow money for refinancing from traditional financial institutions. This study highlights the alternative role of P2P lending platform in financing and provides the evidence of different predictive powers of P2P credit spread on economic activity in different time periods.

Research on job precarity and job instability have largely neglected the labor market trajectories in which these employment and non-employment situations are experienced. This study addresses the mechanisms of volatility and precarity in observed work histories of labor market entrants using the National Longitudinal Survey of Youth of 1997. Several ideal-typical post-education pathways are modeled for respondents entering the labor force between 1997 and 2010, with varying indicators and degrees of precarity. A series of predictive models indicate that women, racial-ethnic minorities, and lower social class labor market entrants are significantly more likely to be exposed to the most precarious early careers. Moreover, leaving the educational system with a completed associate’s, bachelor’s, or post-graduate degree is protective of experiencing the most unstable types of career pattern. While adjusting for these individual-level background and education variables, the findings also reveal a form of “scarring” as regional unemployment level is a significant macro-economic predictor of experiencing a more hostile and turbulent early career. These pathways lead to considerable earnings penalties 5 years after labor market entry.

The purpose of this paper is to investigate the extent to which leader behaviors (authentic, directive, and transactional) predict subordinates' conceptualization of exchanges with their organization (i.e. social and economic exchanges).

Data were collected from 165 employees in various organizations within the USA using questionnaires.

Results showed that authentic leadership was positively related to social exchanges. Further, directive leadership was positively related to economic exchanges. Contrary to the authors' prediction that transactional leadership would be a positive predictor of economic exchanges, transactional leader behaviors predicted both social exchanges (positive relationship) and economic exchanges (negative relationship). Several of the relationships between leader behaviors and follower exchange relationships were mediated by employee attitudes (i.e. job satisfaction) and beliefs (i.e. exchange ideology).

Further research is necessary to elucidate the reasons why leader transactional behaviors drive social exchanges, and through what mechanisms.

Organizations and practitioners can use these finding to select leaders who foster desired employee behaviors. Coaching or training efforts to develop authentic leaders may also be beneficial. Organizations and practitioners may benefit by implementing leadership training initiatives that develop managers' authentic leadership.

The paper's results position authentic and directive leader behaviors as positive and negative predictors of social and economic exchanges, respectively. It also identifies mechanisms through which leader behaviors influence employees' perceptions of exchanges.

The purpose of this paper is threefold: first, to measure the health inequalities among malnourished children; second, to decompose the health inequalities to identify key socioeconomic predictors for child malnutrition; and third, to assess the change in the proportional contribution of key predictors over time.

The study has used data of National Family Health Survey (NFHS) conducted in 1992-1993, 1998-1999 and 2005-2006. The information on anthropometric indicators for children below three years of age is provided; however the study is restricted to “weight-for-age,” as it is considered to be a comprehensive indicator of child nutritional status. In the first stage of analysis, health inequalities are measured among malnourished children using concentration indices (CI) for each round of NFHS. In second stage, the inequalities are decomposed to estimate the proportional contribution of socioeconomic predictors. In the third stage, change in the relative contribution of socioeconomic predictors over three rounds is assessed to suggest target-specific policies and programs.

The results highlight a slow decline of only seven percentage points in the proportion of malnourished children in India during 1992-2006. The increasing values of CIs from −0.13 (1992) to −0.18 (2006) demonstrates the concomitant rise in economic inequalities among malnourished children. The results of decomposition analysis point toward household poor economic status and mother’s illiteracy as the major contributor of inequalities during 1992-2006. During the study period, the economic status explained 50, 65 and 59 percent of inequalities, whereas mother’s illiteracy explained 40, 30 and 29 percent of inequalities, respectively. Overall, the contributors to health inequalities remained the same over time with a change in their relative contribution.

The present study is focussed on three rounds of NFHS data conducted at different time period and so it has certain limitations which should be kept in mind while interpreting the results. The study has revealed mother’s education and economic status of the household as the major contributory factors toward child health inequalities. However, one should not forget that the level and quality of education has undergone tremendous change from 1992 to 2006 which the authors could not capture considering the availability of data in the form of years of schooling. Second, since the NFHS-1 has collected the information about the caste groups in only three categories, i.e. schedule caste, schedule tribe and others; the authors have to pool the other backward caste groups with the general caste groups. Third, the authors have used the broad classification of place of residence, i.e. rural and urban area to analyze the inequalities assuming the uniform level of development across the urban regions; however there exists huge disparities within urban areas which leave scope for further research. Fourth, though, the authors have estimated the wealth based inequalities, but NFHS does not provide the absolute level of wealth and so the authors have computed the proxy measure for wealth based on the household assets which has been extensively used in the field of research. Despite these limitations, the authors however believe that the present research work has appropriately decomposed the inequalities among malnourished children and have revealed the changes in the proportional contribution of socioeconomic factors over time.

The decomposition analysis brought into light that average health indicators are insufficient for determining the right approach to health intervention programs. Health policy interventions have to focus ideally on both health averages and within and between group inequalities based on varying contributions of socioeconomic determinants.

Concentrated efforts along with the inter-sectoral concurrence, good nutrition governance, effective investment and unequal distribution of resources are pre-requisites to ameliorate the level and existing inequalities in child malnutrition in India.

The distinctiveness of this study can be primarily found in the use of all three rounds of NFHS data to estimate health inequalities among underweight children. The study has also decomposed the health inequalities to estimate and analyze the change in relative contribution of socioeconomic predictors for each round to facilitate the formulation of target-specific policies and programs.

Purpose – This chapter presents research on determinants of economic hardship and the effect of economic hardship on marital quality in two social contexts in Croatia: postwar recovery period (Study 1) and economic recession starting in 2009 to present (Study 2).

Methodology/approach – In Study 1 the sample consisted of 505 married couples (quota sample of Zagreb and neighboring villages). In Study 2 the sample consisted of 850 married couples (quota sample of Zagreb and 14 regions in Croatia). We have used the SPSS 18 Mixed Linear Model approach for data analysis. A number of variables representing individual characteristics of marital partners were entered as level 1. A number of variables representing marital dyad (duration of marriage, size of the family) were entered as level 2.

Findings – The variables of education, employment status, and size of the family turned out to be most predictive for economic hardship in both studies. Also, in both studies economic hardship turned out to be a very important predictor of marital quality.

Research limitations – The limitations of the studies are the absence of longitudinal approach and a probability sample.

Social implications – The studies carry important social implications showing that in the absence of government or community social support, partners’ social support could moderate negative effect of economic hardship on marital quality. We assume that this conclusion could be generalized to other social contexts as well.

Originality/value of chapter – The strength and originality of the studies was in multilevel approach in data analysis and treating marital partners as a dyad.

Trading on electricity markets occurs such that the price settlement takes place before delivery, often day-ahead. In practice, these prices are highly volatile as they largely depend upon a range of variables such as electricity demand and the feed-in from renewable energy sources. Hence, the purpose of this paper is to provide accurate forecasts..

This paper aims at comparing different predictors stemming from supply-side (solar and wind power generation), demand-side, fuel-related and economic influences. For this reason, this paper implements a broad range of non-linear models from machine learning and draw upon the information-fusion-based sensitivity analysis.

This study disentangles the respective relevance of each predictor. This study shows that external predictors altogether decrease root mean squared errors by up to 21.96%. A Diebold-Mariano test statistically proves that the forecasting accuracy of the proposed machine learning models is superior.

The performance gain from including more predictors might be larger than from a better model. Future research should place attention on expanding the data basis in electricity price forecasting.

When developing pricing models, practitioners can achieve reasonable performance with a simple model (e.g. seasonal-autoregressive moving-average) that is built upon a wide range of predictors.

The benefit of adding further predictors has only recently received traction; however, little is known about how the individual variables contribute to improving forecasts in machine learning.