The objective of this paper is to analyse the impact of skill mismatch on labour turnover, unemployment, and informality, in the case of Colombia. We study Colombia because it is a developing country with one of the highest levels of unemployment and informality in Latin America, along with very restrictive institutions, such as the minimum wage. We found that skill mismatch can explain the high turnover of workers in the Colombian labour market, evident in the increase in hirings and also in separations. Additionally, we find a positive significant effect of skill mismatch on the levels of informality, but no significant effect on unemployment. This evidence remains even once we consider the role of labour market institutions such as minimum wage and non-wage labour costs.

This paper studies the impact of technological progress on unemployment in a search-matching model with heterogeneous multiworker firms. In the model, some firms continue to reap rewards from new technologies over time and contribute to job creation, while other firms obsolesce and reduce their employment. Thus, the model captures an endogenous change in the aggregate composition of firms (the firm-composition effect). Considering this effect along with the two canonical effects—the capitalization and creative-destruction effects—I examine the importance of each through a simulation. The results show that the firm-composition effect explains almost all the variation in unemployment in the model, mainly through shrinking the number of obsolescing firms relative to surviving firms and increasing the aggregate technology adoption rate when technology progresses rapidly.

This article explores the experiences of keyworkers within a third-sector employability programme targeted at people furthest from work. Using semi-structured qualitative interviews, the study investigated keyworker perspectives on effective elements of programme design, and what made the critical difference for those who did move into employment. Echoing previous research, findings confirmed the value of a holistic and personalised approach founded upon a consistent and long-term relationship of trust and support, through which programme participants developed the confidence and self-belief that was critical to progression towards employment. However, critical attention to the keyworker perspective offers novel insights, bringing to light challenges in defining role parameters and maintaining personal-professional boundaries in the interests of keyworkers and programme participants. We contribute to the body of research on employability practitioner perspectives, revealing that the strengths of person-centred support may simultaneously present tensions and wellbeing risks, where role parameters are very broadly defined.

The stigma faced by unemployed Americans places a toll on their wellbeing and decreases their life chances. While all unemployed Americans are subject to stigmatization, the stigma levied on Black Americans may be particularly potent due to racializing stereotypes that associate Blackness with the undeserving poor, including the inability to obtain employment. Given the social and economic challenges Black people face, research elucidating the racial complexities of unemployment stigma is needed. Through in-depth open format face-to-face interviews of unemployed individuals residing in urban and suburban areas, this study produces an alternative perspective on how impression management techniques are connected to both internalization and mitigation of unemployment stigma. This study contributes to employment, race, and stigma literature by providing a theoretical frame that synthesizes Du Boisian and dramaturgical concepts to conceptualize an “unemployed worker-self.” Through this framing, I find variations across race and community type in impression management techniques executed by unemployed people. I conclude with suggestions for future research and potential applications for the theoretical frame developed in this study.

Activation schemes are widely criticised, with the negative experiences of ‘the activated’ featuring prominently in the literature. This article presents the findings of a constructivist grounded theory study concerning the lived experience of long-term unemployment, welfare recipiency and community placement in activation schemes in Ireland, with a focus on the positive effects that participating in such schemes had on participants’ subjective well-being. For the participants in this research, community placement signified change, respite, and recovery that improved their subjective well-being by creating an experience that counteracted the draining experience of long-term unemployment and welfare recipiency. This study brings new elements to the discussion on the role of activation in promoting/diminishing the subjective well-being of the long-term unemployed.

In light of ongoing debates about income targeting in the welfare state, this article explores how the design and outcomes of income targeting policies are related to popular targeting preferences. Based on the unique combination of fine-grained opinion and policy indicators in a multilevel analysis, the results show that targeting preferences are indeed empirically related to targeting policies. However, whether these preferences are affected more by the de jure targeting design or the de facto targeting outcome seems to vary between two very different policy domains. In the case of unemployment benefits, the results suggest positive policy feedback: support for high-income targeting increases when unemployment benefits are designed to benefit those with previously higher incomes. For income taxation, by contrast, the results suggest negative policy feedback. In that case, it is not so much the de jure design but rather the de facto outcome that matters: the more taxes effectively work to the advantage of higher-income earners, the less support there is for a tax that levies the same amount on everyone, regardless of income.

Nigeria is home to a significant and growing “youth” population, over a third of it below 24 years of age. The demographic potential for productivity and growth this group represents occurs alongside large-scale unemployment among young people of working age. The “unemployment crisis” has deep historical roots and exists within a wider context of poor governance, insecurity, conflict, and poverty. Policy norms and practices to address youth unemployment in Nigeria largely centre on skill development and job creation, with complementary schemes selectively targeted to specific groups of vulnerable and poor individuals. Measures are typically ad hoc, detached from a coherent programme of sustainable reform. Pre-existing structural factors and capacity constraints also shape the effectiveness of these programmes. While a productivist focus within current policy is potentially empowering in giving young Nigerians “worker” status, it also detracts from attending to deep-rooted structural problems seen to affect young people in disproportionate ways. Securing meaningful choices and lives for Nigerian youth requires reframing youth unemployment beyond labour markets alone, and embedded in ideas of equality and collective action.

The 2022–2023 review of the Reserve Bank of Australia, published in March 2023, was a missed opportunity to reconsider the currently dominant framework for monetary policy. This framework, based on strong central bank independence and reliance on adjustments to central bank interest rates to achieve a 2–3% inflation target, has performed poorly at a global level and is no longer sustainable. A new framework, accepting a higher average rate of inflation and taking explicit account of the objectives of full employment and economic prosperity, is needed.

Statistical profiling of job seekers is an attractive option to guide the activities of public employment services. Many hope that algorithms will improve both efficiency and effectiveness of employment services’ activities that are so far often based on human judgment. Against this backdrop, we evaluate regression and machine-learning models for predicting job-seekers’ risk of becoming long-term unemployed using German administrative labor market data. While our models achieve competitive predictive performance, we show that training an accurate prediction model is just one element in a series of design and modeling decisions, each having notable effects that span beyond predictive accuracy. We observe considerable variation in the cases flagged as high risk across models, highlighting the need for systematic evaluation and transparency of the full prediction pipeline if statistical profiling techniques are to be implemented by employment agencies.