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Moral injury (MI) is a form of traumatic stress induced by perpetrating actions that transgress a person's beliefs and values. Existing research on MI has been mostly confined to military veterans, however there is reason to believe that the risk of MI among child soldiers is higher due to their age and history of abduction. This study examined the risk of MI in former child soldiers in Liberia and tested whether age and history of abduction moderate the relationship between perpetrating violence and MI based on a sample of 459 former child soldiers. Results from regression analysis confirmed that perpetrators had a higher risk of MI. However, while younger perpetrators were more vulnerable to MI, abduction history had no statistically significant moderation effect on the risk of MI. Further analysis also revealed that the moderation effects are primarily on anxiety, avoidance and negative feelings but not re-experiencing. These findings suggest that new tests and treatment models may be required for future disarmament, demobilization, rehabilitation and reintegration (DDRR) policy.

As a fundamental concept in peace research, trust, or the lack of it, has shown to be associated with the onset of violent conflict, the instability of negotiated settlement, and the sustainability of peace. Despite its proven importance, the question of how political trust can be built after civil conflicts has only received limited attention and remains unanswered. While previous studies demonstrated that improved provision of public services plays a significant role in a trust-building process, the present article shows a more nuanced picture, namely that service enhancement only works if it reflects the needs of people. Projects that do not properly mirror the needs of people, however, have no direct effect on building political trust. Using micro-level data from Sierra Leone, the article finds that people are more likely to trust governments that are willing to listen and respond to their needs and demands. Though government performance carries the previously hypothesized effect, its explanatory power reduces substantively once responsiveness is introduced into the analysis. This finding also holds when potential biases due to endogeneity and sample selection are considered. Results from a mediation analysis also indicate that if government performance has any effect, it is transmitted through the responsiveness mechanism. Overall, this article contributes to the literature by clarifying the mechanism of trust-building in post-conflict societies.

As a fundamental concept in peace research, trust, or the lack of it, has shown to be associated with the onset of violent conflict, the instability of negotiated settlement, and the sustainability of peace. Despite its proven importance, the question of how political trust can be built after civil conflicts has only received limited attention and remains unanswered. While previous studies demonstrated that improved provision of public services plays a significant role in a trust-building process, the present article shows a more nuanced picture, namely that service enhancement only works if it reflects the needs of people. Projects that do not properly mirror the needs of people, however, have no direct effect on building political trust. Using micro-level data from Sierra Leone, the article finds that people are more likely to trust governments that are willing to listen and respond to their needs and demands. Though government performance carries the previously hypothesized effect, its explanatory power reduces substantively once responsiveness is introduced into the analysis. This finding also holds when potential biases due to endogeneity and sample selection are considered. Results from a mediation analysis also indicate that if government performance has any effect, it is transmitted through the responsiveness mechanism. Overall, this article contributes to the literature by clarifying the mechanism of trust-building in post-conflict societies.

Recent research shows a link between higher rates of political participation of women and less corruption, yet little is known about the link between attitudes towards female political leadership and the acceptance of corruption. In order to fill this knowledge gap, this study employs data from the sixth wave (2010–2014) of the world values surveys for ghana, nigeria, rwanda, south africa and zimbabwe. The study finds that people who agree that men are better political leaders than women, regardless of their gender, are more likely to state that corruption is justifiable. The findings corroborate the argument that corruption is deeply rooted in patriarchal structures, which need to be addressed to achieve both higher political participation of women and lower levels of corruption. This chapter is relevant to sustainable development goal (sdg) 5, target 5.5, which focuses on the political participation of women and sdg 16, target 16.5, which focuses on the reduction of corruption.

AbstractThis study contributes to the literature of migration studies by addressing the question: why does international migration persist despite welfare improvements in migrant‐sending countries? We propose that the human rights condition of the origin countries is an important determinant of global migration. Although the human rights issue is not new to researchers in migration studies, the concern is primarily about the rights of migrants, refugees, asylum seekers or migrant workers in a host country. We undertake a bilateral panel data analysis to examine the pattern of global bilateral migration between 1995 and 2010. We find that international migration is positively associated with human rights conditions and income. Similar results are also obtained when we control for multilateral resistance and possible sample selection biases in a panel context. Our study implies that efforts to promote human rights may also be assessed in relation to their contribution to migration flows.

AbstractWe identify vulnerable groups through the examination of their employment status in the face of the initial coronavirus disease 2019 (COVID‐19) shock through the application of tree‐based ensemble machine learning algorithms on a sample of individuals over 50 years old. The present study elaborates on the findings through various interpretable machine learning techniques, namely Shapley values, individual conditional expectations, partial dependences, and variable importance scores. The structure of the data obtained from the Survey of Health, Aging and Retirement in Europe (SHARE) dataset enables us to specifically observe the before versus the after effects of the pandemic shock on individual job status in spatial labor markets. We identify small but distinct subgroups that may require particular policy interventions. We find that the persons in these groups are prone to pandemic‐related job loss owing to different sets of individual‐level factors such as employment type and sector, age, education, and prepandemic health status in addition to location‐specific factors such as drops in mobility and stringency policies affecting particular regions or countries.

This paper seeks to study work-related and geographical conditions under which innovativeness is stimulated through the analysis of individual and regional data dating from just prior to the smartphone age. As a result, by using the ISSP 2005 Work Orientations Survey, we are able to examine the role of work flexibility, among other work-related conditions, in a relatively more traditional context that mostly excludes modern, smartphone-driven, remote-working practices. Our study confirms that individual freedom in the work place, flexible work hours, job security, living in suburban areas, low stress, private business activity, and the ability to take free time off work are important drivers of innovation. In particular, through a spatial econometric model, we identified an optimum level for weekly work time of about 36 h, which is supported by our findings from tree-based ensemble models. The originality of the present study is particularly due to its examination of innovative output rather than general productivity through the integration of person-level data on individual work conditions, in addition to its novel methodological approach which combines machine learning and spatial econometric findings.